Out-of-pocket patient payments

and vulnerable population groups

in Serbia

Jelena Arsenijevic

Out-of-pocket patient payments and vulnerable population groups in Serbia

© Jelena Arsenijevic, 2015

Cover page designed by Marko Milojevic

Printed by Ipskamp Drukkers, Enschede

Design by Legatron electronic Publishing, Rotterdam

All rights reserved. No part of this publication may be reproduced, stored in a

retrieval system, or transmitted in any form or by any means, electronic, mechanical,

photocopying, recording or otherwise, without the written permission from the author.

ISBN: 978-94-6259-617-7

Out-of-pocket Patient Payments

and Vulnerable Population Groups in Serbia

Dissertation

to obtain the degree of Doctor at Maastricht University,

on the authority of the Rector Magnificus, Prof. Dr. L.L.G. Soete in

accordance with the decision of the Board of the Deans,

to be defended in public

on Wednesday 1 April 2015, at 14.00 hours

by

Jelena Arsenijevic

SupervisorProf. dr. Wim Groot

Co-supervisorDr. Milena Pavlova

Assessment CommitteeProf. dr. S.M.A.A. Evers (chair), University Maastricht

Prof. dr. H. Maassen van den Brink, University Maastricht

Prof. dr. E.K.A. van Doorslaer, Rotterdam University

Prof. dr. G.G. van Merode, University Maastricht

Prof. dr. S. Simic, Belgrade University

Acknowledgement of funding

The study is financed by the European Commission under the 7 th Framework P r o g r a m ,

Theme 8 Socio-economic Sciences and Humanities, Project ASSPRO CEE 2007 (Grant

Agreement no. 217431). The content of the publication is the sole responsibility of the

author and it in no way represents the views of the Commission or its services.

Contents

Chapter 1 General Introduction 7

Chapter 2 Measuring the Catastrophic and Impoverishing Effect of Household 27

Health Care Spending in Serbia

Chapter 3 Different Types of Out-of-pocket Payments for Health Care: 49

How do they Contribute to Impoverishing and Catastrophic Effects

among Serbian Households?

Chapter 4 Out-of-Pocket Payments for Public Health Care Services by Selected 73

Exempted Groups in Serbia During the Period of the Post-war

Health Care Reforms

Chapter 5 The Effects of Chronic Diseases on Poverty 103

Chapter 6 Shortcomings of maternity care in Serbia 125

Chapter 7 General Discussion 151

References 175

Appendix 187

Summary 191

Acknowledgements 199

Curriculum Vitae 205

CHAPTER 1

General Introduction

8

Chapter 1

1.1 The scope of dissertation

During the period 1991-2000, Serbia faced a civil war that was accompanied with a severe

economic crisis. The infrastructure in the country was ruined, the government institutions

responsible for providing social protection, did no longer function adequately, while

the population was impoverished (Bajec et al., 2008). However, it was not possible to

estimate the extent of the impoverishment, since valid data were not available (Bogićević

et. al., 2002). After the civil war, in 2003, the Serbian government, in cooperation

with the World Bank, developed the first strategy to monitor and reduce poverty in the

country (World Bank, 2002). Related to this, the World Bank conducted the first Living

Standard Measurement Survey (LSMS) in Serbia in 2002, and subsequently in 2003 and

2007 (World Bank, 2011). The methodology for estimating the poverty level was also

provided by the World Bank (World Bank, 2003). The main goal was to measure and

decrease the level of poverty, and to assure social protection of vulnerable groups (Bajec

et al., 2008; Vukovic & Perisic, 2010). The social protection system was designed to

include not only financial assistance but also the provision of education and health care

services (Bajec et al., 2008; World Bank, 2003).

At the same time, in 2002, the Serbian government started reforms in the health care

system (Gajic-Stevanovic et al., 2010). In order to assure the financial sustainability of

the health care system, the government introduced a system of official co-payments in

the public health care sector (World Bank, 2003). Since compulsory health care insurance

already existed, the introduction of co-payments increased household spending on health

care (Bajec et al., 2008). In accordance with the strategy for poverty reduction and

social protection, the introduction of co-payments was accompanied with an exemption

mechanism (Gajic-Stevanovic, 2010). Nevertheless, the co-payments still present a risk

of financial burden not only for vulnerable groups but also for all health care users (Xu et

al., 2010). For example, frequent health care users, like chronically sick, are only partially

exempted from co-payments in Serbia and can experience high costs.

Impoverishment was not the only consequence of the civil war. Like in many other

post-conflict and transitional societies, corruption became a modus vivendi in the public

sector in Serbia (van Duyne, 2010). Since 2000, several policy documents were written in

order to propose a solution how to decrease corruption in the public sector (Bajec et al.,

2008; van Duyne, 2010; World Bank, 2003). Despite the pressure from the international

community and civil sector organizations, none of the Serbian governments applied any

restrictive policy measures towards corruption behavior in the public sector.

Corruptive behavior had a direct effect on health care consumers as well. In 2002,

when the official co-payments were introduced, different types of informal (under-the-

table) patient payments were already common practice in the health care system (CESID,

2011). Although anecdotal evidence suggests a high financial burden provoked by

1

Introduction

9

informal patient payments (Blic, 2013), hitherto, there is no empirical study reporting

on their existence and their impact in the health care sector. Corruptive behavior in

health care, such as informal payments, has negative effects on health care users. The

vulnerable population groups are also affected by those negative effects. For example,

pregnant women are officially exempted from official co-payments but they might still

be asked to pay informally for maternity care (Stamenkovic, 2011). For pregnant women

from vulnerable groups, such extra payments might have catastrophic effects on their

household budget. In contrast to official co-payments, the government has no ability to

mitigate the negative effects of informal payments because they are not registered and

thus, outside of government control.

Using data collected by the World Bank, previous studies have reported on the

reduction of the poverty level among the general population, praising the effects of the

applied strategy mentioned above (Bajec et al., 2008; Krstic, 2008). However, only few

of these studies have addressed the financial burden provoked by patient payments in

health care and their possible effects on poverty (Bredenkamp et al., 2011; Bredenkamp

et al., 2008). Furthermore, the studies focused on the period from 2002 to2003. Also,

they did not address the type and effects of out-of-pocket patient payments on vulnerable

groups (Bredenkamp et al., 2011; Bredenkamp et al., 2008).

This dissertation is motivated by the fact that although anecdotal evidence indicates

a high financial burden provoked by out-of-pocket spending in Serbia, the empirical

evidence is limited. Moreover, there is no evidence how different types of payments

(formal and informal) contribute to this burden. Therefore, the dissertation focusses

on these issues. Previous studies that address financial burden were not focused on

vulnerable groups. The dissertation also outlines to what extent vulnerable groups are

protected by the current exemption mechanism, with a special focus on pregnant women

and chronically sick.

1.2 Social protection – beyond the theory

Social protection is traditionally defined as a set of policy measures to protect individuals

from income loss (Esping-Andersen, 1990). Defined in this way, social protection is a

concept mainly focused on income protection related to a certain period of life, like being

unemployed or becoming poor due to certain life events (becoming a widow, orphan, or

living in a large families) (Esping-Andersen 1990; Tanzi, 2000). Three mechanisms are

mostly used to achieve this type of social protection: social insurance, social assistance

and labor market regulations (Barrientos, 2011).

In welfare states, the concept of social protection has a broader definition (Tanzi,

2002). Social protection is defined there as a set of policy measures to protect individuals,

10

Chapter 1

especially the critically poor, from financial losses due to high risk events (Holzman &

Jorgsen, 2001). Risk or hazard events include environmental risks like natural disasters,

social risks like unemployment, war or unexpected health shocks (illness), and political

risks like discrimination of minorities in conflict zones (Barrientos, 2010; Estevez-

Abe, Iversen & Soskice, 1999; Holzman & Jorgsen, 2001). Different stakeholders like

governments, public organizations but also non-governmental organizations (NGO) and

international organizations (UNSD), provide interventions related to social protection

(Holzman & Jorgsen, 2001; World Bank, 2003). In this way, social protection represents

a set of interventions that help individuals, households and communities to manage

different risks factors, but also to prevent the risk of financial losses and to anticipate

their possible consequences (Holzman & Jorgsen, 2001).

For a successful implementation of a social protection policy, it is necessary to identify

the population groups that are most vulnerable to certain risks (Holzman & Jorgsen,

2001). Population groups that are most often exposed to certain risks and/or do not have

enough resources to cope with the consequences provoked by risks events are usually

described as vulnerable groups (Fiszbein, Kanbur & Yemtsov, 2013; Holzman, 2001;

Philip & Ryanan, 2004). However, the risk factors that make certain population groups

vulnerable and eligible for social protection differ between developing and developed

countries (de Haan & Sturm, 2000). Moreover, the targeting of eligible population

groups not only depends on the type of risk but also on social, economic and political

circumstances in the country (Slater, 2008). In times of natural disasters, like earthquake,

children and elderly are perceived as vulnerable groups, but in times of war, minorities can

be perceived as the more vulnerable groups (UNRISD, 2010). Some population groups

can receive social protection assistance on political grounds (e.g. free higher education for

all ethnical minorities) (UNRISD, 2010). However, often population groups eligible for

social protection are not necessary the vulnerable groups (Fraser and Gordon 1994, Slater,

2008; UNRISD, 2010). For example not all children younger than 15 are eligible to be

exempted from official co-payments or not all elderly people should be exempted from

official co-payments.

According to the definitions above, poor population groups are always perceived as

eligible for social protection benefits. Although poverty is perceived as a clear vulnerability,

it is sometimes difficult to determine who is poor (Alkire & Foster, 2011). In order to

identify the poor, traditional poverty measures first identify a suitable indicator of wealth

(such as income or consumption) and then set a cut-off point, i.e. a poverty line defined

as a minimum level below which one is consider to be poor (Ravallion, 1989; Sen, 1997).

Recent studies have shown that poverty is a multidimensional concept that includes

not only income deprivation, but also psychological and sociological deprivation

(Atkinson, 2003; Bourgouignon & Charkavatry, 2003; Kakwani & Silber, 2008).

Following the multidimensional concept of poverty, social policy goes beyond protection

1

Introduction

11

towards social inclusion. Social inclusion emphasizes the satisfaction of cultural needs,

active participation in social life and subjective well-being as important dimensions not

only for individuals but also for the population wealth (Coates et al., 2001). A well-

designed social policy that provides equal inclusion of different population groups in

society does not only reduce poverty but also increases economic progress (Fiszbein,

Kanbur & Yemtsov, 2013).

In terms of health, social protection includes protection against health risks (like

epidemiological risks), patient protection that is related to quality of care and financial

protection that aims to protect people from unexpected health care shocks (Knaul et

al., 2012). One of the ways to assure financial protection in the health care sector is to

introduce universal social health insurance (Li et al., 2012). However, when universal

health insurance cannot provide financial sustainability of the health care system, patient

charges are necessary. In this case, social protection can be achieved by the implementation

of an exemption mechanism (Bitran & Giedon, 2002). The successful implementation

of an exemption mechanism also requires identification of eligible groups. Previous

literature has described four main methods to identify the population groups eligible for

exemption namely individual identification, identification based on group characteristics,

self-identification and self-selection based on types of services (Bitron & Giedan, 2002).

Population groups that are usually exempted include children, elderly, unemployed, and

poor (Bitron & Giedan, 2002; Tambor et al, 2010). However, when social protection in

health care fails, different population groups can experience a financial burden provoked

by health care spending (Xu et al., 2010). The level of the financial burden can be

measured by three different approaches namely the impoverishing effects of health care

spending (the proportion of people who go below the poverty line after the health care

costs are subtracted), catastrophic health care expenditure (pre-defined proportion of

household income or consumption that is spend on health care is perceived as catastrophic

if it exceeds certain threshold) and subjective poverty (individuals’ perception of being

poor) (Wagstaff, 2008; Xu et al., 2010). While the first two approaches are based on real

expenditure and strongly depend on welfare indicators, the third approach represents the

subjective perception of the possible burden.

1.3 Social protection in Serbia

Social protection in Serbia has a long tradition (Lakicevic, 1995). The first Red Cross

was founded in 1786. The formal funds for social protection of orphans, people with

mental illness, poor and people injured in wars were also established in 1786. Anecdotal

evidence shows that although social protection provided by the state existed officially

since 1786, it did not work in reality (Lazarevic, 1882). During the period of the Balkan

12

Chapter 1

wars (1912-1913), several funds were established to protect orphans, single mothers and

people injured during these wars (Stojanovic, 2010). Although some of those funds were

supported by the Serbian government, most of them were established by wealthy citizens

and had a more informal character (Stojanovic, 2010).

The first formal system of social protection was established after the Second World

War. The new socialistic government emphasized at that time the importance of

solidarity with vulnerable groups. Every local community had a special unit (center)

for social protection called Centar za socialni rad (Golubovic, 1997). The centers were

authorized to provide financial assistance, accommodation but also social inclusion for

vulnerable groups (such as special arts activities for disabled children). The centers

employed different types of professionals like lawyers, health care workers, social workers

and psychologists. Disabled people, children with cognitive disabilities, orphans, elderly,

single mothers, pensioners but also people with life-treating injuries from the Second

World War were perceived as vulnerable groups. The centers were funded directly by the

government (Bajec et al., 2008).

During the period of Yugoslavia, the poverty level was not monitored. The common

attitude of the socialistic government was that there were no poor people, only people

with certain life events who may face temporary financial difficulties (Palairet, 2001).

However, in reality, poverty existed but it was hidden (Bajec et al., 2008).

In 1991, the system of social protection was abolished by the Constitution of the

Republic of Serbia (Bajec et al., 2008). Many of the existing centers were closed. The

reason was the lack of financial resources. After the major political changes in 2000, the

new government re-established the system of social protection. The current system of

social protection consists of: a social insurance scheme (pension and invalidity insurance,

unemployment insurance and health insurance), family and social benefits and services,

and education. The system of social protection is funded by compulsory contributions

and by the government budget. It is jointly governed by the Ministry of Labor and Social

Policy, the Ministry of Health, the Health Insurance Fund and the Pension and Invalidity

Institute (Government of Republic of Serbia, 2014). The current social protection system

consists of 12 different programs aimed to protect different vulnerable groups (Bajec et

al., 2008). The services are provided by local municipalities and the re-established Centar

za socijalni rad within each municipality. Several groups are defined as vulnerable, namely:

people with disabilities, children without parental care, the Roma population, refugees

and internally displaced people, victims of family abuse, unemployed, homeless persons,

war veterans, discriminated groups (people who are HIV positive). Social protection in

health care in Serbia is based on an exemption mechanism (Bajec et al., 2008). A large

number of population groups is exempted (Gajic-Stevanovic, 2010). As mentioned before,

as part of the social protection system, a strategy for monitoring and reducing poverty

was established in 2003 (World Bank, 2003). Since then, people with a monthly income

1

Introduction

13

below the national poverty line are also considered as a vulnerable group. Although

the normative legalization of social protection exists, the system is characterized by a

low level of efficiency (e.g. they do not provide support for all vulnerable groups, very

often all vulnerable groups are not targeted well). The inefficiency and limited financial

resources are the main reason for the difficulties in the provision of social services.

1.4 Health care system in Serbia

1.4.1 Historical overview of health care system in SerbiaThe first organized provision of health care in Serbia was reported at the beginning

of twenty century (Stojanovic, 2006). The health care system was organized through

two levels: the first level consisted of general practitioners (GPs), and the second

level consisted of several general hospitals. GPs were obliged to obtain a work permit

from the Ministry of Internal Affairs, Department of Health (Stojanovic, 2006). Local

municipalities gave them apartments and a small monthly allowance in order to keep

them in their municipalities. Their services were paid by out-of-pocket payments by

patients and were perceived as hardly affordable for the average citizen. Patients who

could not afford direct out-of-pocket payments paid by goods (eggs, sugar, etc.). Some of

them were using services of pseudo-doctors.

The first Serbian medical society was founded in 1878. In 1914, at the beginning of

the First World War, 534 doctors were registered in Serbia (Milicevic-Santric, 2009).

After the war, official state statistics reported one physician per 2716 citizens in the

Kingdom of Yugoslavia. Physicians were also acting as medical researchers. Organized

in a medical society, they annually published a statistical report with epidemiological

indicators.

The second level of the health care system included a network of general hospitals

situated mostly in big cities (Beograd, Sabac, Kragujevac, Pozarevac, Sombor, Subotica)

(Durlevic, 2012). They were founded and paid by local municipalities. Patients were

referred to hospitals by local physicians (Durlevic, 2012). There were no reported out-

of-pocket patient payments, but citizens were sent to hospitals only if they had a severe

illness or some of the contagious diseases.

In 1918, infant mortality during delivery in the Belgrade district was 5.5 per 1000

deliveries (Joksimovic, 1911), while the average life expectancy of men was 50.2 years

and for women 41.2 (Joksimovic, Statisticki Godisnjak, 1911). Those indicators were

similar to other countries in the Balkan Peninsula but much less favorable than those

in France for example (Stojanovic, 2006). The Serbian medical society also emphasized

problems of alcoholism, especially among young men in high school (26.2% of them were

drinking regularly every night) (Narodno Zdravlje, 1909). The Serbian state encouraged

14

Chapter 1

physicians to report and publish the data, but did not use the data to design actual policy

measures (Stojanovic, 2006).

After the Second World War the new state was founded, the Federal People’s Republic

of Yugoslavia and in 1963 the name is changed to Socialistic Federative Republic of

Yugoslavia (SFRJ). The state consisted of 6 federal units named republics (Slovenia,

Croatia, Bosnia and Herzegovina, Serbia, Montenegro and Macedonia) with the legal

right to act as independent units within the federation (National Constitution Act,

1943). The SFRJ was a country characterized by a unique type of socialism compared to

other socialist countries, so called “samoupravljanje” or self-managed society (Markovic,

1968). Officially, the country was “self-managed” by the citizens, but in reality, the

country was controlled by the communist elite organized in local, republic and federal

party levels (Perovic, 2013). The citizens of the SFRJ indicated that the master piece of

this unique type of socialism was the health care system.

According to officials, the health care system was a combination of the experience

from the UK and the USSR. The health care system was organized through primary,

secondary and tertiary care. Similarly to the UK, primary care included GPs who were

supposed to act as gate keepers towards secondary and tertiary care (Vukomanovic, 1972).

Also, similarly to the USSR model, primary care centers included specialists for maternal

care, occupational medicine and children care. In this way, citizens were able to visit those

specialists without any referral (Saric & Rodwin, 1993). Secondary care included general

hospitals, while tertiary care included state clinical centers and specialized hospitals.

The health care institutions were owned by the society (Saric & Rodwin, 1993). Private

practices were forbidden.

Contrary to the USSR health care model (the so called Semashko model implemented

in other socialist countries), the health care system in the SFRJ was funded by compulsory

health insurance contributions. Health insurance contributions were paid by all employed

citizens at 8% of their gross salaries. Non-employed citizens were insured by employed

family members or by the national insurance body (‘self-managing communities of

interest‘, so called the SIZ). Children, students and pensioners were exempted from paying

contributions but were still covered by the health insurance scheme. Individual farmers

were not allowed to join the compulsory health insurance until 1958, while after 1958,

they were contributing through general taxes. The allocation of collected funds and their

control was done by the SIZ. The SIZ was established as a quasi-insurance company. The

SIZ included two groups of elected members: the group of consumers (citizens, workers,

teachers, local party leaders) and the group of providers (physicians, hospital managers

some of them also local party leaders etc.). The two groups negotiated what should be

included in the compulsory health care package (Saric & Rodwin, 1993). The negotiation

process was facilitated by an administrative body, i.e. the general management of the

SIZ. The SIZ existed as a federal, republic and local body. The allocation of funds was

1

Introduction

15

done by the federal SIZ to the republics, and the republics’ government body allocated

money either directly to providers (a fixed annual budget for hospitals was negotiated

with the republics SIZ) or to the local SIZ (primary care was funded by the local SIZ)

(Letica, 1984). More developed republics like Slovenia, Croatia and Serbia allocated more

money and included more services in the compulsory health care package (for example in

those three republics spa treatment for 3 weeks was included in the compulsory package)

(Mastilica, 1990). If citizens from Slovenia decided to use any health service in Croatia,

the SIZ of Slovenia was obliged to reimburse the SIZ of Croatia for this particular service.

Similar regulations were applied to different municipalities within the same republics

(Saric & Rodwin, 1993). The SIZ of a particular republic also allocated funds for provider’s

salaries, but the decision who should be employed was done by the health care managers

in each institution.

During the 1970s, the system received a lot of attention from researchers from the

UK and the US (Himmelstein et al., 1984; Parmelee, 1979; Ward, 1973). They were

describing the health care system of the SFRJ as a “Swedish model in the Balkan”.

Although highly prized by citizens, authorities and foreign researchers, this system also

had several limitations (Letica, 1982; Saric & Rodwin, 1993).

1.4.2 Limitations of the health care system in the SFRJ Although, described as an “idealistic system”, the SFRJ health care system in practice

had several drawbacks (Letica, 1984). GPs were never acting as true gate keepers. The

majority of GPs were young physicians waiting for additional education. Therefore, they

had a tendency to refer citizens to the secondary level (Saric & Rodwin, 1993). The

geographical accessibility to secondary and tertiary care was not equal for the whole

country (Vukomanovic, 1972). In order to promote the constantly ongoing growth in

health care, the local the SIZ were stimulating an increase in the number of beds per

capita in secondary care and an increase in the number of physicians per capita (Saric

& Rodwin, 1993). Moreover, the highly developed republics allocated more money to

their SIZ provoking social and health inequalities in the country (Mastilica, 1990) (see

Figure 1.1). The differences in the allocated funds among different republics were the

first precursor for the occurrence of social and health inequalities. Social inequalities were

also reported within the same republic. For example, the infant mortality rate in 1981 in

the well-developed parts in Serbia, like Central Serbia and Vojvodina, was respectively

23.8 infant deaths per birth and 17.1infant deaths per birth while in Kosovo, it was 62.9

infant death per birth (Mastilica, 1990). The working urban class was favored within

the health care system, as they were using health care services more often and they were

better informed about the available services (Mastilica, 1990; Parmelee, 1979). Managers

and party leaders lived on average longer than non-educated workers or farmers. They

also reported fewer chronic diseases (Mastilica, 1990).

16

Chapter 1

Although officially funded by the SIZ, in practice, secondary and tertiary care was funded

directly by the republic governments. Very often, when the fixed annual budget was

exceeded before the end of the year, state companies gave so called “donations” (Palairet,

2001). Donations gave some extra rights to their workers (preventive use of diagnostic

services or spa resorts). The SIZ included consumers in the decision making process,

but in practice consumer representatives were loyal members of the communistic party

(Saric & Rodwin, 1993). In this way, the communist party controlled all the decisions

of the SIZ. Often, the SIZ management was asked to “borrow” money to ministry of

education or to some companies for education or some other service. Thus, the door to

“state corruption” was open.

In the beginning of 1980, it was clear that the shortage in resources could not be

overcome by the state and the SIZ introduced the first official co-payments (participacija)

for services like plastic surgery or alternative medicine. The decision had never been

approved by the central committee of the communist party and was highly criticized in

public (Palariet, 2001; Saric & Rodwin, 1993)

Figure 1.1: Infant mortality rates in SFRJ per republic (1954-1981), source: Mastilica, 1990.

1

Introduction

17

1.4.3 Post-socialistic period and health care reforms in SerbiaIn 1992, two republics (Croatia and Slovenia) used their constitutional right to declare

independence from the SFRJ, provoking the beginning of a civil war (Black, 1993). The

civil war had a great impact on the health care system of the SFRJ (Mastilica, 2001;

Palairet, 2001). Although the republics had their own SIZs and independence in the

decision-making process, the pharmaceutical industry and the industry for disposable

materials were spread in different geographical areas (e.g. a part of the infusion systems

was produced in Croatia, and another part in Serbia) (Palairet, 2001). During the civil

war, the former cooperation between these areas ceased to exist. More than 40% of the

society-owned companies in Serbia were not able to function anymore because they could

not cooperate with their partners in other republic (Madzar, 1998). Beside the civil war,

Serbia was facing economic sanctions imposed by the United Nation Security Council

(UNSC). At the same time, monetary inflation reached its highest level (Nelson, 2003).

These developments had a devastating impact on the health care sector. Companies

that did not function, could not provide payments for compulsory health care insurance

and those that were functioning provided payments on an irregular basis (Palairet, 2001).

The UN sanctions were banning imports of goods including pharmaceuticals, medical

supplies, and disposable materials. The high monetary inflation depreciated the already

law salaries of physicians (Black, 1993).

At the same time, in 1992, Serbia introduced the new Law of Health Care, with the

objective to centralize all financial resources and management decisions related to the

Serbian SIZ, which later led to the creation of the Health Insurance Fund (HIF) (Bajec

et al., 2008). The health care system maintained the same structure, i.e. it was divided

into primary, secondary and tertiary care. The main change was related to primary care.

The local municipalities were no longer responsible for primary care (Bajec et al., 2008).

Contrary to the SFRJ period, where primary health care centers were praised, the new law

shifted the focus and financial resources towards secondary and tertiary care (Bajec et al.,

2008). Around 20 of the 150 small primary health care centers were closed (World Bank,

2009). All managers in the health care sector were then employed by the government.

The newly established HIF could not always assure basic medical supplies and

adequate healthcare services. This resulted in a lack of basic medical materials, supplies

and pharmaceuticals and led to irregular payment of salaries to health care professionals

(Black, 1993). Moreover, it was not possible to maintain the already existing infrastructure.

Often, physicians were asking patients and their families to bring to the hospital food

and necessary pharmaceuticals for their operations (Palairet, 2001). Also, some physicians

were asking patients to pay them extra directly otherwise they would not provide any

services (Black, 1993). Corruption was prevalent not only at the level of health care

provision but also at the state level. For example, it became known that during the period

18

Chapter 1

1990-1998, two ministers of health were transferring money from the HIF to outside the

country (Pieterel, 2001; van Duyne et al., 2010)

The civil war ended by NATO bombing of Serbia and Montenegro in 1999. According

to the NGO sector and international communities, in 1999, more than 47% of Serbian

population was living below the absolute poverty line (UNDP, 2006). The infrastructure

of the health care facilities was damaged and the system was collapsing. During the

period 1991-2000, many international organizations (UN, WHO, UNHCR, Red Cross)

were providing aid to Serbian hospitals to prevent a humanitarian crisis (Black, 1993;

WHO, 2005). Nevertheless, the level of contagious diseases was increasing as well as the

incidence of mental diseases (UNHCR, 1999). The high number of refugees and people

injured during the civil war increased the pressure on the already collapsing health care

system.

After the major political change in 2000, the Serbian government introduced health

care reforms. The main objective was to improve efficiency, service quality and equity in

health care (World Bank, 2009). As part of the health care reforms, in 2002, the Serbian

government introduced official co-payments for services covered by the compulsory health

insurance to improve the financial situation of the public health care system. The first co-

payments were with amounts ranging from almost symbolic 0.59 US dollars up to 30 US

dollars (World Bank, 2009). The introduction of official co-payments was accompanied

with an exemption mechanism. In 2003, the World Bank approved approximately 20

million US dollars to support the health care reforms. The initial planning included

the introduction of IT technologies in public primary health care centers and hospitals,

better prevention programs and financial reforms. In 2005, the Law of Health Care was

revised aiming to facilitate the financial health care reforms taking into account the

organization of the health care system. The same law allowed physicians who are full

time employed in the public sector, to work also in the private sector (Ministry of Health,

2005). In 2007, the additional Law of Health Care was introduced. The main goal was to

decentralize the health care system. According to this law, local municipalities were made

responsible for primary care and regional hospitals (Ministry of Health, 2007). Several

legal acts were introduced from 2008-2010, mostly defining the level of out-of-pocket

patient payments. Specifically, co-payments for physician services, laboratory diagnostic

radiography and ultrasound examination have increased twice during the period 2008-

2010.

1.4.4 The current funding of the health care system Today, the public health care system in Serbia is funded by resources collected through

compulsory health care insurance (controlled by the HIF and through resources provided

by the government (controlled by the Ministry of Health), as well as by out-of-pocket

payments. The compulsory health insurance covers 96.2% of the total population (71%

1

Introduction

19

are contributions from employees, 21% contributions from the Retirement Fund, and 1%

contribution from farmers) (Bajec et al., 2008; World Bank, 2009). For the unemployed

and those who are exempted, the government is paying for compulsory health care

insurance (12.3% of the minimum net salary in the country). The HIF also uses the

collected payments to reimburse the costs of sick leave and to reimburse the travel costs

for health care utilization. The fund is also responsible for the positive pharmaceutical

lists. The Ministry of Health is directly responsible for capital investments. Since 1992,

private health care providers exist in Serbia but they are not included in the compulsory

insurance system. Moreover private health care is not monitored by the Ministry of

Health.

As mentioned earlier, health care is provided through primary care facilities (outpatient

services) as well as secondary and tertiary care facilities (inpatient care in hospitals and

clinical centers). Regarding the payment mechanism in primary care facilities, a capitation

based payment system has been introduced (World Bank, 2009). Every medical doctor

is reimbursed at a standard rate for each patient. In order to encourage physicians to

provide services to their patients, additional fee for services are introduced. It should

be mentioned however that hitherto the capitation payments system is only partially

introduced. The reimbursement in secondary and tertiary care has remained unchanged

(through annual fixed budgets and salaries) although there are plans for the introduction

of a DRG-based system (diagnostic related groups or output related system) (World

Bank, 2009).

Out-of-pocket patient payments include official co-payments for public health care

services as well as payments for private health care. Official co-payments are paid for

diagnostic procedures, physician’s visits, pharmaceuticals and for so called-non-standard

procedures. The amount of the official co-payment varies between 1 USD (e.g. for a

referral) and 551 USD (e.g. for disposable materials). The maximum annual co-payment

by a patient (excluding payments for disposable materials and pharmaceuticals) may not

be higher than one third of a patient’s salary or patient’s average net income.

Next to the formal types of patient payments, there are also informal patient payments

(cash and gifts in kind given to the physician), as well as payments for “bought & brought

goods” (i.e. payments for goods brought by the patient to the health care facility such

as disposable materials and medicine). Informal patient payments are defined as every

direct patient’s contribution (in cash or in kind) for publicly funded services .This

includes informal payments to physicians and envelope payments to other medical staff.

They have spread in Serbia since the economic crisis in 1992. During the period of the

SFRJ, informal patient payments were present only in the form of gratitude payments or

gifts. Moreover, recent research shows an increasing problem of institutional corruption

in Serbia due to informal patient payments (CESID, 2011). Payments for “bought &

brought goods” have their roots in the times when the health care settings lacked medical

20

Chapter 1

materials, supplies, and pharmaceuticals due to the financial crisis (CESID, 2011). In

such circumstances, medical staff asks the patients and/or relatives to bring supplies and

pharmaceuticals that are necessary for the treatment that the hospital cannot provide due

to poor funding (Palairet, 2001).

In 2007, households’ out-of-pocket payments were estimated to be 34.8% of

total health expenditure, total health expenditure was 10.4% of GDP (WHO, 2012),

although these shares are slightly different in other sources (e.g. Bajec et al., 2008; Gajic-

Stevanovic, 2010; Markovic 2011; World Bank, 2009; WHO, 2012). The share of out-

of-pocket payments as a percentage of total health expenditure is similar to that in other

Western Balkan countries but much higher than in other EU countries (Bredenkamp et

al., 2008; Tomini et al., 2011). It should be mentioned however that this estimate of total

out-of-pocket payments does not include informal patient payments as well as payments

for “bought & brought goods”. Although official co-payments were introduced in 2002,

informal patient payments and payments for “bought & brought goods” still exist. There

is still no official strategy for their reduction. The existence of different types of out-of-

pocket patient payments can decrease the utilization of health care. They can also provoke

impoverishment among users with a low-social economic status.

In order to prevent the negative effects of official co-payments, an exemption

mechanism was introduced with the objective to assure equity in access to healthcare. It

concerns both outpatient and inpatient services. The Serbian Law on Health Insurance

defines several population groups that are exempted from patient fees: children younger

than 15 years, pregnant women, persons older than 65 years, disabled persons, HIV-

infected persons, monks, people with low family income, unemployed, chronically ill

people, military service servants, people registered as refuges and the Roma population

(HIF, 2010). According to the Serbian law, groups that are exempted from patient fees

should not be charged at all when they use healthcare services (Bajec et al., 2008).

1.5 Research gap

During the last twenty years, a lot of scientific research has been conducted on social

protection in health care systems in CEE countries (Alam et al., 2005). Most attention

was paid to health care systems that were based on the so called Semashko model like

Bulgaria, Hungary, Romania (Baji et al., 2012; Delceva, Balabanova & McKee, 1997;

Lewis, 2000) and the former Soviet republics (Georgia, Moldova, Russia, Tajikistan

and Ukraine) (Ensor & Savelyeva , 1998; Falkingham, 2004; Ferrer-i-Carbonell &van

Praag, 2001; Gotsadze et al., 2009; Stepurko et al.,2010). Health care systems based on

the Semashko model were characterized by free of charge services funded through tax-

based funding mechanisms (Ensor, 2004). However, those systems were not financially

1

Introduction

21

sustainable (Lewis, 2000). After the collapse of Soviet Union, the main change within

most of those health care systems was the development of the private health care sector

and the introduction of insurance-based system funding. In some of those systems, official

co-payments were also introduced (e.g. Bulgaria). Beside, most of those systems were also

characterized by the existence of informal patient payments (Lewis, 2000). The existence

of different types of out-of-pocket patient payments (formal and informal) provoked a

financial burden in CEE countries, reduced the accessibility of health care services and

influenced the quality of provided services. Previous studies related to CEE countries

have addressed those issues. Particular attention is paid to informal patient payments

(Lewis, 2000).

However, the ex-Yugoslavia health care system was financed by compulsory health

insurance already during the communist period. Nevertheless, the system was still not

sustainable. After the collapse of the communism, all ex-Yugoslavia republics inherited

a compulsory health insurance system, but followed different patterns regarding the

introduction of patient payments (Albreht & Klazinga, 2009). Slovenia, an EU member

since 2002, introduced additional private insurance instead of pure co-payments, while

other republics including Serbia introduced official patient payments accompanied by

exemption mechanisms (Albreht & Klazinga, 2009). Except Slovenia, in all ex-Yugoslavia

republics informal patient payments are widely spread. In Serbia, a newly designed social

protection system in health care was officially introduced in 2002 (Bajec et al., 2008).

Nevertheless, the scientific evidence about the effectiveness of the social protection

system in health care in Serbia is still lacking. Moreover, there is still no evidence about

the effectiveness of the social protection system in health care in Serbia in comparison

with other CEE countries. This dissertation intends to fill this gap by exploring the

extent and intensity of the financial burden provoked by different types of out-of-pocket

patient payments and their impact on vulnerable population groups. Furthermore,

this dissertation examines the effectiveness of the current exemption mechanism in the

Serbian health care system.

1.6 Research aim, objectives and data used in the dissertation

In the previous paragraphs we gave a broad overview of the historical development of the

social protection system in Serbia. We also gave an extended overview of the historical

development of the health care system in Serbia focusing on public health care services.

In this dissertation, we explore one aspect of social protection in the public health

care system in Serbia, namely the out-of-pocket payments. As mentioned earlier, the

out-of-pocket patient payments are a well-known trigger for economic burden in low

and middle income countries. In Serbia, the burden of out-of-pocket payments is only

22

Chapter 1

scantily studied. Therefore, the main aim of this dissertation is to examine the effects of

out-of-pocket patient payments on vulnerable population groups in Serbia. Serbia represents an interesting case where different forms of out-of-pocket patient

payments have co-existed with compulsory health insurance for more than a decade. In

this dissertation, attention is paid to the relation between different types of payments

and poverty. Moreover, the dissertation explores to what extent those payments affect

vulnerable groups like the chronically sick and pregnant women. Following the main

research aim, several research questions are addressed:

What is the financial burden provoked by out-of-pocket patient payments?

Out-of-pocket patient payments are well-known financial burden (Xu et al., 2010).

Several studies have examined the effects of out-of-pocket patient payments in developing

countries (Xu et al., 2010; van Doorslaer, 2007; Wagstaff, 2008). However, current

research still does not provide consensus on how the financial burden provoked by out-

of-pocket patient payments should be assessed. This research question focuses on the

measurement of the financial burden provoked by out-of-pocket patient payments in

Serbia. The main objective is to give an overview of the methodology used to measure the

financial burden provoked by out-of-pocket payments. We also estimate the level of the

financial burden provoked by out-of-pocket patient payments in Serbia using different

methodological approaches (e.g. Xu et al., 2010; Wagstaff, 2008).

How do different types of out-of-pocket patient payments contribute to the financial burden?

Like in other Central and Eastern European countries (CEE), in Serbia, the practice

of giving money and gifts in kind to health care professionals is common. Besides the

informal patient payments, previous studies have described different forms of payments

in CEE countries like quasi-formal and quasi-informal patient payments (Stepruko et

al., 2014). However, in Serbia scientific evidence on the different types of out-of-pocket

patient payments is lacking. Moreover it is not clear how different types of payments

contribute to the financial burden. In order to address this research question, we examine

the level of different types of out-of-pocket patient payments in Serbia. We provide an

overview of different types of payments: formal payments, informal payments and goods

that are brought to health care facilities. We also analyze the possible financial burden

provoked by each type of payments using different approaches (e.g. Xu et al., 2010;

Wagstaff, 2008).

1

Introduction

23

To what extent does the exemption mechanism protect vulnerable groups?

In health care systems that rely on out-of-pocket patient payments, exemption mechanisms

are often used to protect most vulnerable groups. However, previous literature suggests

that not all exemption mechanisms are effective (Lagarde & Palmer, 2008; Perkins et al.,

2009) .To assess this question, we provide an overview of possible pitfalls of the design

and implementation of an exemption mechanism (Lagarde & Palmer, 2008; Perkins et

al., 2009; Witter et al., 2010). We also describe how the exemption mechanism in Serbia

is designed and implemented. More precisely, we examine whether selected exempted

groups (older than 65 years, younger than 15 years, disabled, unemployed and people

with low family income status) pay official copayments when they are supposed to be

exempted from such fees.

What is the relation between chronic diseases and poverty?

Previous literature reports on the growing prevalence of chronic diseases in Serbia

(Jankovic, Simic & Marinkovic, 2010). Patients diagnosed with a chronic disease are

frequent users of health care. In a situation where different types of out-of-pocket patient

payments exist, patients can easily experience the financial burden and become poor.

Moreover, poverty can also be a trigger for developing a chronic disease (Alleyene et al.,

2013). Therefore, the relation between chronic diseases and poverty is characterized by

a complex joint causality (Bonu et al., 2005; Engelgaou, Karan & Mahal, 2012; Xu et

al., 2003). To assess this joint causality in Serbia, we apply an instrumental variable (IV)

approach (Geneau et al., 2010; Khandker, Koolwal & Samad, 2010).

To what extent are pregnant women protected within the health care system in Serbia?

Maternity care is usually one of the most sensitive parts in the health care systems over

the world. During the transition period in CEE countries, informal patient payments for

maternity care and for surgery were the highest when compared with informal payments

for other services (Stepurko et al., 2013). However, previous research that has examined

maternity care in CEE countries mostly focused on macro indicators like the mortality

rate during the birth or the presence of skilled persons during the birth. Data related

to the financial and other aspects of social protection in maternity wards specifically in

Serbia, are limited. In order to assess the question above, we do not only examine the

financial protection mechanisms, but also several other aspects of maternity care like

accessibility to maternity care, quality of care and policy regulations (Ronsmans, 2001).

We combine data from three different sources: online collected

24

Chapter 1

questionnaires, literature review and data obtained from official and hospital guidelines

and institutional websites (Jick, 1979).

1.6 Data used in the analyses

For this dissertation, we use different data. To provide an answer to the first four research

questions, we use data from the LSMS carried out in 2002, 2003 and 2007 by The World

Bank group. The Serbian LSMS data for 2002 consists of 19725 participants living in

6386 households, for 2003 the total number of participants is 8027 living in 2548

households. Data for 2007 consists of 17,375 participants living in 5,557 households.

Although, the intention was to have panel data for year 2002 and 2003, current datas are

cross-sectional.

The data consists of nine different modules including a health module. The health

module includes variables regarding various types of health care spending of household

members for hospitalization, pharmaceuticals, and diagnostics in public inpatient units

during the last 12 months and variables on household spending on physician visits,

pharmaceuticals, and diagnostics in outpatient public health care units during the

preceding month. The data also contain information about the demographic structure,

household conditions, agriculture, health care, education, labor activities and social

welfare programs in Serbia. A standardized questionnaire is used during the survey. The

survey and the questionnaire were designed by the World Bank, and the collection of data

was conducted by the Statistical Office of the Republic of Serbia. The data were collected

directly from the respondents by trained interviewers. For children under the 15 years,

parents were giving the answers (World Bank, 2011). Detailed information about the

LSMS data for Serbia can be found on the World Bank website (http://go.worldbank.

org/8XI2AXPP00.

To provide an answer to the last research question, we use data collected through

online questionnaire by the Serbian NGO “Mother Courage”, as well as data obtained

from the World Health Organization (WHO), World Bank and United Nations High

Commissioner for Refugees (UNHCR).

1.7 General structure of the dissertation

After this general introduction chapter, in Chapter 2, the dissertation describes different

approaches to measure the financial burden provoked by out-of pocket patient payments

and analyzes the burden of

1

Introduction

25

these payments in Serbia. Then, in Chapter 3, we examine the financial burden of different

types of out-of-pocket patient payments. In Chapter 4, we also describe the effects of the

exemption policy on several vulnerable groups: older than 65 years, younger than 15

years, disabled, unemployed and people with low family income status. Also, we examine

the effects of out-of pocket patient payments on chronically sick in Chapter 5. In Chapter

6, we outline how the pregnant women are protected in the health care system in Serbia.

A general discussion of the key research findings is provided in Chapter 7.

CHAPTER 2

Measuring the Catastrophic and Impoverishing Effect of Household Health Care Spending in Serbia

Published as:

Arsenijevic, J., Pavlova, M., & Groot, W. (2013). Measuring the catastrophic and impoverishing effect of household health care spending in Serbia. Social Science & Medicine, 78, 17-25.

28

Chapter 2

Abstract

Introduction: Out-of-pocket patient payments can impose a catastrophic burden on

households. This problem may not only affect poor but also wealthy households who

need to use health care frequently. The available literature offers no consensus on how to

measure poverty and how to measure the effects of out-of-pocket payments on household

budgets. The objective of this chapter is to contribute to current research in this area

by comparing results across different approaches. In particular, the chapter examines

the catastrophic and impoverishing effects of health care spending in Serbia applying

different types of thresholds used in previous research. The application of various

approaches allows us to analyze the robustness and convergent validity of the results. We

also include the subjective poverty approach in our examination.

Method: We use household data collected in LSMS in 2007. The Serbian LSMS data

offers data for 2007 and consists of 17,375 participants living in 5,557 households.

Results: Our results indicate that irrespective of the approach applied, out-of-pocket

patient payments have a catastrophic effect on poor households in Serbia. Moreover, some

households that are above the absolute, relative and subjective poverty lines respectively,

after the subtraction of out-of-pocket payments fall below these poverty lines. The

probability of catastrophic out-of-pocket patient payments is higher in rural areas, and

among chronically sick household members (namely, people with diabetes and mental

diseases, as well cardiology diseases in some instances). Perceived health status also

appears to be a significant indicator.

Conclusions: Policy in Serbia should aim to protect vulnerable groups, especially

chronically sick patients and people from rural areas.

2

Financial burden and out-of-pocket patient payments

29

2.1 Introduction

The catastrophic impact of out-of-pocket patient payments on the household budget is

a frequent problem in low- and middle-income countries in Asia (Falkingham, 2004;

Limwattananon et al., 2007; Yardim et al., 2010), Africa (Ekman, 2007; Xu et al., 2006;

van Doorslaer et al., 2006), South America (Baeza & Packard, 2006) and South-Eastern

Europe (Bredenkamp et al., 2010; Gotsadze et al., 2009; Habicht et al., 2006) but also in

OECD countries, e.g. Portugal, Greece, the UK and the US (Xu et al., 2003;Wagstaff &

van Doorslaer, 2003). In low- and middle-income countries, even small medical costs can

become a considerable burden for poor households and may discourage the use of health

care services (Falkingham et al., 2010; Habicht et al., 2006; Skarbinski et al., 2002;

Szende & Culyer, 2006; Roberts et al., 2004; Xu et al., 2003; Vian et al., 2006). When

out-of-pocket patient payments are a major source of health care financing, they can

push even wealthy households into poverty (Bredenkamp et al., 2010; Xu et al., 2006).

Most households find it difficult to recover from such financial shock, especially if they

are exposed to health costs over a longer period of time, for example in case of chronic

diseases (Abegunde & Stanciole, 2008; Hwang et al., 2001; McIntyre et al., 2006).

A number of studies have examined the problem of poverty caused by out-of pocket

patient payments (Flores at al., 2008; Russel, 2004; Xu et al., 2003; van Doorslaer et al.,

2007; Wagstaff & van Doorslaer, 2003). However, studies apply a variety of methods.

In particular, some studies estimate the burden of health expenditure on households

(catastrophic expenditure approach) while other studies estimate the impact of this

expenditure on poverty levels (impoverishing effects approach).Within each approach,

different indicators of household wealth (income and consumption) and different poverty

thresholds based on these indicators are applied (Xu et al., 2010; van Doorslaer et al.,

2006), see Figure 2.1. Overall, there is no clear consensus about the poverty measures

that should be used in empirical analyses (Xu et al., 2003; Xu et al., 2010).

The aim of this chapter is to contribute to the current research in this area by comparing

the effects of out-of-pocket patient payments on individual’s budget estimated by the

application of different approaches, different wealth indicators and different poverty

thresholds. Based on Wagstaff and van Doorslaer (2003) and Xu et al. (2003), we first

provide an overview of the different wealth indicators, different approaches and different

poverty thresholds within each approach. Then, we apply these approaches to an identical

data for Serbia. This allows us to analyze the robustness and convergent validity of the

results. In addition to earlier studies, we apply a third approach based on subjective

poverty measures (Marks, 2005). The data for the analysis are taken from the LSMS in

2007 (see Chapter 1, Section 1.6).

Our analysis is relevant not only to research but also to future health care reforms.

LSMS data for several Western Balkan’s countries, namely Albania, Bosnia and

30

Chapter 2

Herzegovina, Montenegro, Kosovo and Serbia have already been used in a comparative

study on the measurement of the poverty effects of out-of-pocket payments (Bredenkamp

et al., 2010). The results confirm the growing incidence and intensity of catastrophic

health expenditures in this region. However, the characteristics of potentially vulnerable

households and household members have not been identified before. Since these countries

are going through continuous health care reforms, our research can contribute to a more

evidence-based policy debate on this issue.

What poverty effects: catastrophic or impoverishing effects?

What wealth measure: expenditure/consumption orincome (objective or subjective)?

What level: household or individual level?

Poverty measure selected

What sample: those who used health care or total sample?

Figure 2.1: Selection of approaches related to financial burden

2

Financial burden and out-of-pocket patient payments

31

2.2 Background

2.2.1 Indicators of household wealthPoverty measures are related to direct indicators of household wealth such as income (total

amount of money received by the households), or consumption and expenditure (the total

amount of money that households spend). Although consumption and expenditure are

not identical by definition, they are used interchangeably (also in this chapter) because

in practice they do not considerably differ (O’Donnell et al., 2008). However, household

income can be significantly different from household consumption (and expenditure).

For example, some households can achieve a higher consumption level than their

income allows, by mobilizing additional resources (e.g. borrowing money and/or selling

assets) to pay for various goods/services (not only health care). In this case, income is

on average less than consumption. When income is used as a wealth indicator, these

additional resources are ignored (Flores et al., 2008; Kim & Jang, 2010; van Doorslaer et

al., 2006). However, when consumption is taken as a wealth indicator, wealthy households

who do not have to borrow money or sell assets and poor households that rely on such

coping strategies to survive, are treated similarly.

Also, consumption includes the consumption value of long-term assets (such as a

house), which is difficult to measure. If average values for the country are used as proxies,

the consumption level of individuals outside expensive cities is frequently artificially

increased. These assets cannot be easily sold and transferred into money (Haughton &

Khandker, 2009; O’Donnell et al, 2008) especially in times of an economic crisis. Thus,

the fact that individuals have a high consumption level does not necessarily mean that

they have a high ability to pay. In contrast, income represents direct household command

over resources, and is a better indicator of the household’s ability to pay for health care.

However, the accurate measurement of income can be questioned in case of a large

informal economy where a large share of income remains unregistered/unreported and

where goods and services are directly exchanged (e.g. exchanging life stock and agricultural

products for services). This can also be a reason that household income measured is lower

than household consumption. In cases where a large part of the households’ income is

generated by informal sources, the use of household consumption and expenditure data

is advised (see e.g. Deaton, 1997; Xu et al., 2003; Wagstaff & van Doorslaer, 2003;

Wagstaff, 2008; World Bank, 2008).

Overall, the literature (Haughton & Khandker, 2009; O’Donnell et al. 2008)

concludes that all indicators of wealth have their pros and cons, and there is no “best”

indicator of wealth. Based on this insight, we use both wealth indicators in our study.

32

Chapter 2

2.2.2 Approaches to measure the poverty effects of out-of-pocket patient paymentsThree main approaches are described in the literature to measure the poverty effects of

out-of-pocket payments for health care: catastrophic health expenditure, impoverishing

effects, and subjective poverty. Since these approaches all have their limitations, we

employ all three of them and compare their results. An outline of these three approaches

is provided below.

Catastrophic health care expenditure

Catastrophic health care expenditure defines out-of-pocket patient spending as

catastrophic if it exceeds a certain threshold in a given period (Wagstaff, 2008). The

threshold represents a pre-defined proportion of household income or consumption.

The threshold is arbitrary and can vary from 5 up to 40% of total income/consumption

(Habicht et al., 2006; Gotsazde et al., 2009; Knaul et al., 2006; Limwattananon et al.,

2007; Shakarishvili, 2006; Xu et al., 2003; Yardim et al., 2010). The threshold of 40%

has received wide application in empirical research (Xu et al., 2003; Xu et al., 2007). Xu

et al. (2003) propose to subtract the food expenditures from total household consumption

when applying the catastrophic expenditure approach. This requires the assumption that

food and health care expenditure are not substitutes (Wagstaff, 2008). Since we consider

both food and health expenditures as necessities, we do not subtract food expenditure

from total consumption. The catastrophic expenditure approach has been widely used in

previous research to examine the incidence of catastrophic health care expenditure, the

influence of policy institutions and insurance on it, as well as the characteristics of the

most vulnerable households (Wagstaff, 2008; Wagstaff & Pradhan, 2005). Studies suggest

that households in lower income groups are more likely to experience catastrophic health

care expenditure, but if the threshold is set lower, catastrophic health care expenditures

are also observed within richer households (Wagstaff & van Doorslaer, 2003). Also,

households with elderly, unemployed and chronically sick members are more likely to

experience a catastrophic financial burden (Kawabata et al., 2002).

Impoverishment due to health care spending

The impoverishing effect of health care spending is measured by the proportion of

households that goes below the poverty line after health care spending is subtracted from

total income or consumption (Wagstaff, 2008). It is based on the comparison between

the incidence of poverty before and after the subtraction of health care spending by the

household. For the calculation of the impoverishing effect of out-of-pocket payments,

absolute (Ravallion, 1998) and relative (Foster, 1998) poverty lines are used as thresholds

(Wagstaff & van Doorslaer, 2003). In low- and middle-income countries, poverty

measures are mostly based on consumption (Xu et al., 2006). However, it is also possible

2

Financial burden and out-of-pocket patient payments

33

to define poverty lines on household income such as a specific minimum level household

income or by using average household income. Previous research has reported a higher

incidence of poverty after subtracting health care costs among the chronically sick, in

particular among people on who are on chemotherapy and renal dialysis (Knaul et al.,

2006; Wagstaff & van Doorslaer, 2003).

Subjective poverty

Subjective poverty is a measure based on the respondents’ perception of being poor

(Ferrer-i-Cabonell & van Praag, 2001; Marks, 2005). Although, there are several ways of

measuring subjective poverty, they are all based on an assessment of personal wellbeing.

Most of the measures are constructed as a one item scale, which makes them easy to

apply. Subjective poverty is the only measure that includes the psychological component

of poverty (Ferrer-i-Cabonell & van Praag, 2001; Marks, 2005). Previous research has

shown that men are more likely to perceive themselves as subjectively poor. Subjective

poverty is higher among single parents’ households and among single people. In contrast

to the other two approaches, subjective poverty is not necessarily related to age, education

or health status. However, a higher income is related to a less frequent perception of

being poor. The analysis of the relation between subjective poverty and out-of-pocket

payments for health care can show whether increased out-of-pocket spending leads to a

more frequent perception of being poor. Since subjective poverty is informative in this

respect, we have also incorporated it in our research.

2.2.3 A framework to compare the performance of different approachesThe two approaches – catastrophic health care expenditure and impoverishing effects –

identify the share of households/individuals who experience an economic burden due to

out-of-pocket patient payments. We use this as a framework to compare the performance

of these approaches. To avoid overestimation of poverty levels, we focus our comparison

on the absolute poverty line for the impoverishing effects approach and the 40% level

for the catastrophic expenditure approach. We compare the incidence of the economic

burden caused by out-of-pocket patient payments indicated by the two approaches using

the two poverty thresholds mentioned above and the two wealth indicators – income

and consumption. Thus, we compare not only the approaches, but also the wealth

indicators. We do not directly compare the subjective poverty approach with the other

two approaches. We only use the subjective poverty approach for further explanation of

the catastrophic and impoverishing effects. However, we compare factors associated with

impoverishing and catastrophic effects with factors associated with subjective poverty.

34

Chapter 2

2.3 Methods

In this chapter we use LSMS data collected for 2007 by World Bank. A detailed

description of the data is provided in Chapter 1.The data contains several poverty

measures, including household consumption per household member as well as absolute

and relative poverty lines (for detailed technical information see World Bank, 2011). The

absolute poverty line is based on the monetary value of the minimum food basket plus

other goods that households with a minimum basket food consumption, are supposed to

spend. The absolute poverty line for 2007 is set to be 8883 CSD (≈ 100 euro) per adult

per month. Based on this poverty line, all respondents in the Serbian LSMS data for 2007

are classified into two consumption-based categories: poor and non-poor. The relative

poverty line is calculated as 60% of the median of total household consumption (11283

CSD per person per month).

To measure the impoverishing effect of health care spending based on income, we also

create income-based poverty categories. For this purpose, we first create a variable that

indicates total household income, which includes an extensive list of income categories

included in the Serbian LSMS data (for details see World Bank, 2011). Total income per

household per year is then divided by the number of household’s members in order to

obtain income per household member. We did not use any adjustment for economies of

scale related to household size since such adjustments are based on scales that are fairly

arbitrary, developed mostly for high income countries (e.g. the Oxford scale) and not very

well applicable to former-socialist countries (Ferrer-i-Carbonell & van Praag, 2001) like

Serbia. Haughton and Khandker (2009) show that there are no significant differences

in results when there is some correction for potential economies of scales compared to a

straightforward use of income per capita.

Based on this, we created an additional categorical variable, which indicates whether

income per household member per month is:

– less than the absolute poverty line of 8883 CSD,

– between the absolute poverty line of 8883 CSD and relative poverty line of 11283

CSD,

– between the relative poverty line of 11283 CSD and average net income of 36610

CSD,

– more than the average net income of 36610 CSD.

All poverty lines listed above, are multiplied by 12 to obtain an annual estimation.

To calculate the catastrophic effects of health care spending, we use the variable

indicating household consumption, available in the Serbian LSMS for 2007. We also

use data about household health care expenditure provided in the data, which includes

both formal and informal payments for out-patient and hospital care. Since dental care

is predominantly provided in the private sector and is not included in the compulsory

2

Financial burden and out-of-pocket patient payments

35

insurance package, it is not included in our analysis. Data related to out-patient care are

based on a re-call period of one month and the data for hospital care use a re-call period

of 12 months. Therefore, we have multiplied the payments for out-patient care by 12

to obtain an annual estimate comparable to the data for hospital care (possible over- or

under-estimation might have occurred). We then divide health care expenditure first by

total household expenditure and then by total household income. Thus, we obtain two

new variables that indicate the percentage of total health care expenditure spent on health

care and the percentage of total household income spent on health care respectively.

We use two additional items from the LSMS data in order to measure subjective

poverty. The first item uses the following question to measure satisfaction with the

household’s financial status: “How would you rate the current financial status of your

household?” The answer can range from 1 (bad) to 5 (very good). We use this answer

as an indicator of subjective poverty. The second item asks for the minimum amount

that would satisfy household needs. We compare the answer to this question with

actual household income and divide respondents into two categories: subjectively poor

(when the income perceived as necessary is higher than actual household income), and

subjectively non-poor (when this condition does not apply).

To assess the impoverishing effect of health care spending, we calculate the percentage

of respondents (health care users and total sample) who are poor based on three types

of thresholds: income, consumption and subjective poverty respectively. We use

descriptive statistics. We repeat the analysis after subtracting health care expenditures.

We also calculate pre-payment and post-payment poverty headcounts and poverty index

using different types of poverty lines. We also calculate the catastrophic health care

expenditure ratio. As indicators of wealth, we use both income and consumption (based

on expenditure). We present different poverty thresholds for this ratio ranging from 10%

to 40% of household income and consumption respectively.

We perform regression analysis to explore the variations within the social-demographic

groups. Ordered probit regression is performed using the following dependent variables:

pre-payment income category (from 0 = below the absolute poverty line to 3 = above the

average net income), change in income category after subtracting health care spending

(from 0 = no change to -2 = two categories lower), catastrophic health care expenditure

based on consumption (from 1 = up to 10% burden to 5 = up to 40% burden) and

perceived-income categories (from 1= bad to 3 = good). We also perform binary logistic

regression with the following dependent variables: pre-payment consumption category

(0 = non-poor, 1 = poor) and subjective poverty category (0 = non-poor, 1 = poor). In all

regressions, we include two groups of independent variables: socio-demographic variables

(gender, age, marital status, education, residence place, nationality, employments and

household size) and health status (perceived health status, existence of chronic diseases

36

Chapter 2

like asthma and bronchial diseases, cardiology diseases, cancer, diabetes). All data are

analyzed using statistical package SPSS (version 16).

2.4 Results

Table 2.1: presents the consumption and income characteristics of all households included

in our study.

Table 2.1: Household income, consumption and expenditure per income- and consumption-based

quintiles (all households)

Income per household per

month[CSD] a

Consumption per household per

month[CSD] a

Households reporting lower

income than consumptionN (% within

quintile)

Consumption of food per

household per month[CSD] a

Health care expenditure per household per

month[CSD] a

Consumption-based quintiles

Poorest 17075.36 22998.80 950(17.1) 4344.39 359.41

2 24359.02 35862.78 865(15.6) 6011.35 609.60

3 29398.51 47021.51 870(15.6) 7128.60 802.53

4 34867.76 60453.09 857(15.4) 8751.54 1049.87

Richest 45450.23 62686.31 900(16.2) 11675.28 1970.48

Income-based quintiles

Poorest 3227.76 36174.08 1105(19.9) 6684.57 804.97

2 13058.12 33612.13 1041(18.7) 6774.46 838.93

3 22882.12 45020.53 901(16.21) 7214.83 927.88

4 36798.31 57465.34 792 (14.2) 7795.11 990.35

Richest 71718.41 80308.61 603(10.8) 8623.43 1069.35a 1 CSD = 0.0125 Euro

2

Financial burden and out-of-pocket patient payments

37

Tab

le 2

.2: C

atas

trop

hic

heal

th c

are

expe

ndit

ure

per

inco

me-

and

con

sum

ptio

n-ba

sed

quin

tile

s (a

ll r

espo

nden

ts)

Shar

e of

hou

seh

old

exp

end

itu

re o

n h

ealt

h

care

exp

ress

ed p

er c

onsu

mp

tion

-bas

ed q

uin

tile

Poo

rest

23

4R

ich

est

Tot

al

N (%

wit

hin

q

uin

tile

)N

(% w

ith

in

qu

inti

le)

N (%

wit

hin

q

uin

tile

)N

(% w

ith

in

qu

inti

le)

N (%

wit

hin

q

uin

tile

)N

(% w

hol

e sa

mp

le)

No

heal

th c

are

expe

ndit

ure

1717

(49.

4)13

89 (4

0.0)

1308

(37.

7)14

66 (4

2.2)

1568

(45.

1)74

48(4

2.9)

Mor

e th

an 0

% u

p to

10%

1492

(42.

9)17

95 (5

1.7)

1861

(53.

6)17

96 (5

1.6)

1677

(48.

3)86

21(4

9.6)

Mor

e th

an 1

0% u

p to

20%

212

(6.1

)19

9 (5

.7)

183

(5.3

)12

3 (3

.5)

158(

4.5)

875(

5.0)

Mor

e th

an 2

0% u

p to

30%

27 (0

.8)

44 (1

.3)

69 (2

.0)

34 (1

.0)

42(1

.3)

216(

1.2)

Mor

e th

an 3

0% u

p to

40%

5 (0

.1)

16(0

.5)

11(0

.3)

30(0

.9)

17(0

.5)

79(0

.5)

Mor

e th

an 4

0%23

(0.7

)31

(0.9

)42

(1.2

)29

(0.8

)11

(0.3

)13

6(0.

8)

Shar

e of

hou

seh

old

inco

me

spen

t on

hea

lth

ca

re e

xpre

ssed

per

inco

me-

bas

ed q

uin

tile

Poo

rest

23

4R

ich

est

Tot

al

N (%

wit

hin

q

uin

tile

)N

(% w

ith

in

qu

inti

le)

N (%

wit

hin

q

uin

tile

)N

(%

qu

inti

le)

N (%

q

uin

tile

)N

(% w

hol

e sa

mp

le)

No

heal

th c

are

expe

ndit

ure

2373

(69.

0)13

89(3

9.6)

1403

(40.

5)14

09 (4

0.8)

1556

(44.

6)81

59(4

7.0)

Mor

e th

an 0

% u

p to

10%

542

(15.

8)14

09 (4

0.2)

1585

(45.

8)16

79 (4

8.6)

1690

(48.

5)69

05(3

9.7)

Mor

e th

an 1

0% u

p to

20%

220

(6.4

)40

2 (1

1.5)

253

(7.3

)25

0(7.

2)17

2(4.

9)12

97(7

.5)

Mor

e th

an 2

0% u

p to

30%

61 (1

.8)

127(

3.6)

100

(2.9

)48

(1.4

)33

(0.9

)36

9(2.

1)

Mor

e th

an 3

0% u

p to

40%

87 (2

.5)

66 (1

.9)

37(1

.1)

31(0

.9)

22(0

.6)

243(

1.4)

Mor

e th

an 4

0%15

8 (5

.6)

111

(3.2

)85

(2.5

)35

(1.0

)13

(0.4

)40

2(2.

3)

38

Chapter 2T

able

2.3

: Inc

ome

grou

ps, c

onsu

mpt

ion

grou

ps, a

nd s

ubje

ctiv

e in

com

e gr

oups

bef

ore

and

afte

r th

e su

btra

ctio

n of

hea

lth

care

spe

ndin

g

Var

iab

leIn

com

e ca

tego

ries

a, b

Con

sum

pti

on-b

ased

cat

egor

ies

(hea

lth

car

e u

sers

)C

ateg

orie

s b

ased

on

su

bje

ctiv

e in

com

e(h

ealt

h c

are

use

rs) c

Tot

al

sam

ple

d

Poo

r e

Non

poo

r e

Tot

al

use

rsB

adN

eith

er b

ad

nei

ther

goo

dG

ood

Tot

al

use

rs

Average income perhousehold member

≤ ab

solu

te p

over

ty li

ne22

3(4.

5)22

77(4

5.9)

2500

(50.

3)11

90(2

4.2)

938(

19.1

)34

7(7.

1)24

75(5

0.4)

8255

(47.

6)

> a

bsol

ute

pove

rty

line

≤

rela

tive

pov

erty

line

12(0

.2)

479(

9.6)

491(

9.9)

231(

4.7)

174(

3.5)

77(1

.6)

482(

9.8)

1607

(9.3

)

> r

elat

ive

pove

rty

line

≤

aver

age

net

inco

me

40(0

.8)

1336

(26.

9)13

76(2

7.7)

561(

11.4

)53

2(10

.8)

269(

5.5)

1362

(27.

7)50

81(2

9.3)

> a

vera

ge n

et in

com

e9(

0.2)

590(

11.9

)59

9(12

.1)

285(

5.8)

185(

3.8)

125(

2.5)

595(

12.1

)24

03(1

3.9)

Var

iab

leIn

com

e ca

tego

ries

a, b

Con

sum

pti

on-b

ased

cat

egor

ies

(hea

lth

car

e u

sers

)C

ateg

orie

s b

ased

on

su

bje

ctiv

e in

com

e (h

ealt

h c

are

use

rs) c

Tot

al

sam

ple

d

Poo

r e

Non

poo

rT

otal

u

sers

Bad

Nei

ther

bad

n

eith

er g

ood

Goo

dT

otal

u

sers

Average income per household member

after subtracting the household health care

spending

≤ ab

solu

te p

over

ty li

ne22

4(4.

5)25

68(5

1.7)

2792

(56.

2)13

29(2

7.0)

1035

(21.

0)40

1(8.

2)27

65(5

6.3)

8587

(49.

4)

> a

bsol

ute

pove

rty

line

≤

rela

tive

pov

erty

line

11(0

.2)

366(

7.4)

377(

7.6)

171(

3.5)

132(

2.7)

66(1

.3)

369(

7.5)

1486

(8.6

)

> r

elat

ive

pove

rty

line

≤

aver

age

net

inco

me

40(0

.8)

1199

(24.

1)12

39(2

4.9)

496(

10.1

)50

1(10

.2)

229(

4.7)

1226

(24.

9)49

19(2

8.3)

> a

vera

ge n

et in

com

e9(

0.2)

549(

11.1

)55

8(11

.2)

271(

5.5)

161(

3.3)

122(

2.5)

554(

11.3

)23

54(1

3.5)

a A

bsol

ute p

over

ty li

ne o

f 888

3 C

SD p

er h

ouse

hold

mem

ber

per

mon

th, 1

CSD

= 0

.012

5 E

uro

b R

elat

ive p

over

ty li

ne o

f 112

83 C

SD p

er h

ouse

hold

mem

ber

per

mon

thc N

umbe

rs in

bra

cket

s pre

sent

the p

erce

ntag

e of h

ealt

h ca

re u

sers

d Num

bers

in b

rack

ets p

rese

nt th

e per

cent

age o

f tot

al sa

mpl

ee B

ased

on

cons

umpt

ion-

base

d po

vert

y li

ne

2

Financial burden and out-of-pocket patient payments

39

As suggested by the table 2.1, about 80% of the households report a higher consumption

level than income. The share of households where consumption exceeds income is the

highest in the poorest quintiles (based on both income and consumption).

Table 2.2 presents the incidence of catastrophic health care expenditure by income

quintiles and consumption quintiles calculated for the entire sample (per household

member). When we compare the results related to the 40% threshold, based on

income as a wealth indicator, 2.3% respondents experience the burden, while based on

consumption, this share is 0.8%. When catastrophic health care expenditure is calculated

using income, the poorest quintiles seem most affected, even when we look at the 40%

threshold, but when the calculation is based on consumption, the 40% burden is heaviest

among the middle quintiles.

As indicated in Table 2.3, within the group of health care users, 50.3% of the

respondents are below the absolute poverty line based on income before the subtraction

of household health care spending, and 56.2% are below that line after the subtraction

of health care spending. This indicates an increase of 5.9 percentage points (including

individuals who are non-poor based on consumption, and mostly subjectively poor based

on subjective income). For the total sample, these percentages are 47.6% and 49.4%

respectively, which indicates an increase of about 2 percentage points.

We use both income and consumption, to calculate the pre-payment and post-payment

headcount and poverty index (see Table 2.4). We provide results for the entire sample as

well as for health care users. For health care users, the differences between pre-payment

and post-payment headcounts for the absolute poverty line is 5.9 percentage points for

income-based measures and 2.0 percentage points for consumption-based measures. For

the total sample, these differences are 2.4 percentage points and 1.1 percentage points

respectively.

Table 2.5 presents our regression results (see methods section). The results suggest

that respondents who are married, live in big cities, work and have higher education

are less likely to be classified as poor. Respondents with a mental health disease and

from larger households have a higher probability to be classified as poor. Impoverishing

effects of health care spending is more likely for respondents who report chronic diseases

(like diabetes) and some progressive illnesses (like cancer), as well as for respondents

who perceive their health as bad. Household size also appears significant. Being married,

having a job and living in rural areas are protective characteristics of poverty caused by

health care.

40

Chapter 2T

able

2.4

: Pov

erty

hea

dcou

nt a

nd p

over

ty g

ap w

ith

diff

eren

t de

finit

ion

of p

over

ty li

ne a

nd d

iffe

rent

mea

sure

of w

ealt

h

Pov

erty

mea

sure

s u

sin

g co

nsu

mp

tion

as

a m

easu

re o

f w

ellb

ein

gP

over

ty m

easu

res

usi

ng

inco

me

as a

mea

sure

of

wel

lbei

ng

Ab

solu

te p

over

ty li

ne a

Rel

ativ

e p

over

ty li

ne

bA

bso

lute

pov

erty

lin

e a

Rel

ativ

e p

over

ty li

ne

b

Tot

alsa

mp

leH

ealt

h c

are

use

rsT

otal

sam

ple

Hea

lth

car

e u

sers

Tot

alsa

mp

leH

ealt

h c

are

use

rsT

otal

sam

ple

Hea

lth

car

e u

sers

Pre

-pay

men

t po

vert

y he

adco

unt

7.5%

7.0%

16.5

%15

.1%

47%

50.3

%57

.0%

60.0

%

Pos

t-pa

ymen

t po

vert

y he

adco

unt

8.6%

9.0%

18.6

%18

.7%

49.4

%56

.2%

58.0

%63

.0%

Per

cent

age

poin

ts c

hang

e1.

1 2

.02.

13.

62.

45.

91.

03.

0

Pre

-pay

men

t po

vert

y di

ffer

ence

s 21

260.

2417

375.

6329

833.

6325

908.

7559

188.

8058

152.

9476

101.

5675

160.

07

Pos

t-pa

ymen

t po

vert

y di

ffer

ence

s22

457.

5120

234.

4230

811.

3728

495.

1563

761.

5175

181.

1181

093.

5989

703.

58

Pre

-pay

men

t po

vert

y ga

p in

dex

0.01

50.

009

0.03

60.

029

0.26

0.28

0.32

0.33

Pos

t-pa

ymen

t po

vert

y ga

p in

dex

0.02

30.

015

0.04

30.

039

0.30

0.39

0.35

0.42

Per

cent

age

poin

ts c

hang

e0.

80.

60.

70.

14.

011

.03.

09.

0a A

bsol

ute p

over

ty li

ne o

f 888

3 C

SD p

er h

ouse

hold

mem

ber

per

mon

th, 1

CSD

= 0

.012

5 E

uro

b R

elat

ive p

over

ty li

ne o

f 112

83C

SD p

er h

ouse

hold

mem

ber

per

mon

th

2

Financial burden and out-of-pocket patient payments

41

Tab

le 2

.5: R

esul

ts o

f the

reg

ress

ion

anal

ysis

Exp

lan

ator

y va

riab

les

Dep

end

ent

vari

able

s

Pov

erty

cat

egor

y(f

rom

0 =

bel

ow a

bso

lute

pov

erty

li

ne

to 3

= a

bov

e av

erag

e n

et in

com

e)

Ch

ange

in p

over

ty

cate

gory

aft

er

sub

trac

tin

g h

ealt

h

care

sp

end

ing

(fro

m

0 to

-2

cate

gori

es)

Bel

ow a

bso

lute

p

over

ty li

ne

afte

r su

btr

acti

ng

hea

lth

ca

re s

pen

din

g (0

= a

bov

e;

1 =

bel

ow)

Cat

astr

oph

ic h

ealt

h

care

exp

end

itu

re

cate

gory

(fro

m 1

= u

p t

o 10

%

to 5

= m

ore

than

40

%)

Ind

icat

or o

f su

bje

ctiv

e p

over

ty(0

= s

ub

ject

ivel

y n

on-p

oor;

1 =

su

bje

ctiv

ely

poo

r)

Per

ceiv

ed in

com

e(f

rom

1 =

bad

to

3 =

goo

d)

coef

fici

ent

SEco

effi

cien

tSE

coef

fici

ent

SEco

effi

cien

tSE

coef

fici

ent

SEco

effi

cien

tSE

Thr

esho

ld 0

-0.9

30.

15-2

.54

0.34

-3.4

70.

56-0

.37

0.15

2.16

0.37

0.45

0.22

Thr

esho

ld 1

-0.5

80.

15-1

.56

0.34

--

0.09

0.15

--

1.54

0.22

Thr

esho

ld 2

0.66

0.15

--

--

0.37

0.15

--

--

Thr

esho

ld 3

--

--

--

0.60

0.15

--

--

Gen

der

2 =

fem

ale

/ 1 =

mal

e0.

050.

040.

380.

63-0

.13

0.14

-0.0

20.

04-0

.00

0.09

0.05

0.06

Mar

ital

sta

tus

1 =

mar

ried

/ 0

= n

ot m

arri

ed-0

.09*

*0.

010.

81**

0.21

-.01

*0.

44-0

.03*

0.01

0.05

0.0.

30.

030.

02

Age

age

in y

ears

0.00

0.00

0.00

-0.0

0-0

.00

0.00

0.00

0.01

-0.0

1*0.

00-0

.00

0.02

Edu

cati

on le

vel

from

1 =

bas

ic t

o 9

= h

ighe

st le

vel

0.12

**0.

00-0

.07*

*0.

010.

060.

030.

010.

00-0

.08*

0.02

0.05

**0.

02

Wor

k st

atus

1 =

wor

king

/ 0

= n

ot w

orki

ng0.

11**

0.04

0.07

0.23

-0.0

10.

150.

010.

04-0

.28*

0.09

-0.0

60.

06

Nat

iona

lity

1= S

erbi

an /

0 =

oth

er0.

31**

0.06

-0.0

8-0

.09

0.12

0.20

-0.0

90.

05-0

.06

0.13

-0.0

10.

09

Hou

seho

ld s

ize

num

ber

of h

ouse

hold

mem

bers

-0.4

9**

0.01

0.07

**0.

020.

020.

04-0

.01

0.06

-0.0

20.

270.

010.

02

Urb

an1

= c

ity

/ 0 =

rur

al0.

27**

0.04

-0.1

7**

0.07

0.21

0.14

0.04

0.04

-0

.06

0.09

0.19

**0.

06

Per

ceiv

ed h

ealt

hfr

om 1

= v

ery

good

to

4 =

ver

y ba

d-0

.11*

*0.

02-0

.09*

-0.0

40.

29*

0.09

0.22

*0.

020.

17*

0.06

-0.0

1*0.

04

* p

< 0

.05;

**

p <

0.0

1

42

Chapter 2T

able

2.5

: Res

ults

of t

he r

egre

ssio

n an

alys

is (

cont

inue

d)

Exp

lan

ator

y va

riab

les

Dep

end

ent

vari

able

s

Pov

erty

cat

egor

y(f

rom

0 =

bel

ow a

bso

lute

pov

erty

li

ne

to 3

= a

bov

e av

erag

e n

et in

com

e)

Ch

ange

in p

over

ty

cate

gory

aft

er

sub

trac

tin

g h

ealt

h

care

sp

end

ing

(fro

m

0 to

-2

cate

gori

es)

Bel

ow a

bso

lute

p

over

ty li

ne

afte

r su

btr

acti

ng

hea

lth

ca

re s

pen

din

g (0

= a

bov

e;

1 =

bel

ow)

Cat

astr

oph

ic h

ealt

h

care

exp

end

itu

re

cate

gory

(fro

m 1

= u

p t

o 10

% t

o 5

= m

ore

than

40%

)

Ind

icat

or o

f su

bje

ctiv

e p

over

ty(0

= s

ub

ject

ivel

y n

on-p

oor;

1 =

su

bje

ctiv

ely

poo

r)

Per

ceiv

ed in

com

e(f

rom

1 =

bad

to

3 =

goo

d)

coef

fici

ent

SEco

effi

cien

tSE

coef

fici

ent

SEco

effi

cien

tSE

coef

fici

ent

SEco

effi

cien

tSE

Ast

hma

bron

chos

pasm

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed0.

000.

07-0

.00

-0.1

9-0

.45

0.26

0.08

0.07

-0.0

50.

17-0

.15

0.10

Car

diov

ascu

lar

dise

ase

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed0.

010.

04-0

.03

0.06

0.00

0.14

0.04

0.03

-0.0

40.

090.

18**

0.06

Abd

omen

dis

ease

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed-0

.04

0.05

-0.1

4*-0

.07

0.30

0.16

0.13

0.05

0.03

0.13

-0.1

80.

06

Dia

bete

s1

= d

iagn

osed

/ 0

=no

t di

agno

sed

0.13

*0.

06-0

.20*

*0.

080.

37*

0.18

0.07

0.06

0.00

0.14

0.18

*0.

09

Epi

leps

y1

= d

iagn

osed

/ 0

= n

ot d

iagn

osed

0.22

0.19

0.18

-0.3

0-1

.14

1.02

-0.1

20.

19-0

.45

0.46

-0.0

00.

03

Pro

gres

sive

dis

ease

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed0.

090.

09-0

.27*

*0.

120.

58*

0.27

0.12

0.

10-0

.12

0.23

0.14

0.15

Rhe

umat

olog

y di

seas

e1

= d

iagn

osed

/ 0

= n

ot d

iagn

osed

-0.0

80.

06-0

.02

0.07

0.12

0.17

0.03

0.

350.

190.

140.

050.

08

Legs

or

feet

dis

ease

1 =

dia

gnos

ed /

0= n

ot d

iagn

osed

0.03

0.06

-0.0

60.

080.

090.

190.

10

0.06

-0.1

80.

150.

010.

09

Bec

k an

d ne

ck

dise

ase

1 =

dia

gnos

ed /

0 =

not

diag

nose

d-0

.06

0.06

-0.1

7*0.

080.

230.

190.

04

0.06

0.03

0.14

0.07

0.09

Oph

thal

mol

ogy

dise

ase

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed

0.01

0.05

-0.2

2**

0.07

0.64

*0.

16-0

.43

0.06

0.19

0.14

0.02

0.08

All

ergi

es1

= d

iagn

osed

/ 0

= n

ot d

iagn

osed

0.04

0.12

-0.1

90.

150.

190.

350.

41

0.13

-0.3

10.

270.

080.

18

Men

tal i

llne

ss1

= d

iagn

osed

/ 0

= n

ot d

iagn

osed

-0.1

7**

0.07

0.18

*0.

10-0

.32

0.24

-0.0

1 0.

060.

.20

0.18

0.14

0.10

* p<

0.0

5;

** p

< 0

.01

2

Financial burden and out-of-pocket patient payments

43

Exp

lan

ator

y va

riab

les

Dep

end

ent

vari

able

s

Pov

erty

cat

egor

y(f

rom

0 =

bel

ow a

bso

lute

pov

erty

li

ne

to 3

= a

bov

e av

erag

e n

et in

com

e)

Ch

ange

in p

over

ty

cate

gory

aft

er

sub

trac

tin

g h

ealt

h

care

sp

end

ing

(fro

m

0 to

-2

cate

gori

es)

Bel

ow a

bso

lute

p

over

ty li

ne

afte

r su

btr

acti

ng

hea

lth

ca

re s

pen

din

g (0

= a

bov

e;

1 =

bel

ow)

Cat

astr

oph

ic h

ealt

h

care

exp

end

itu

re

cate

gory

(fro

m 1

= u

p t

o 10

% t

o 5

= m

ore

than

40%

)

Ind

icat

or o

f su

bje

ctiv

e p

over

ty(0

= s

ub

ject

ivel

y n

on-p

oor;

1 =

su

bje

ctiv

ely

poo

r)

Per

ceiv

ed in

com

e(f

rom

1 =

bad

to

3 =

goo

d)

coef

fici

ent

SEco

effi

cien

tSE

coef

fici

ent

SEco

effi

cien

tSE

coef

fici

ent

SEco

effi

cien

tSE

Hea

ring

-spe

ech

prob

lem

s1

= d

iagn

osed

/ 0

–not

dia

gnos

ed-0

.13

0.09

0.08

0.12

-0.2

70.

270.

16

0.09

-0.0

10.

210.

050.

13

Oth

er d

isea

ses

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed-0

.07

0.05

-0.1

5*0.

070.

160.

170.

20 *

0.

05-0

.06

0.13

-0.1

50.

08

N49

7649

7646

7546

7546

7549

76

-2LL

(chi

-squ

are)

8639

.14

(202

0.24

*)24

50.6

(137

.85*

)19

27.9

(79.

39)

1088

2(10

.852

.17)

3500

.4(6

8.02

)89

93.2

8 (1

11.8

*)

Nag

el. R

²0.

400

0.07

00.

050.

060.

030.

031

* p

< 0

.05;

**

p <

0.0

1

Tab

le 2

.5: R

esul

ts o

f the

reg

ress

ion

anal

ysis

(co

ntin

ued)

44

Chapter 2

Participants, who live in urban areas and have a higher education, are more satisfied with

their financial status (less likely to report subjective poverty) than other groups. Having

diabetes or heart diseases is also a significant indicator of satisfaction with financial status.

In all regression models, being married and perceiving one’s own health as good are

associated with a lower financial burden of out-of-pocket payments.

In addition to those who paid for health care and suffer impoverishing or catastrophic

effects, there were also individuals who forwent the use of health care services due to

payments. Our data show that 12.2 % of the respondents needed health care but could

not afford it because it was too expensive.

2.5 Discussions and conclusions

According to our results, 47.6% of all respondents can be classified as poor using income

as the indicator of wealth and absolute poverty line as the poverty threshold (47% pre-

payment poverty headcount). The official Serbian statistics suggest a poverty level of

7.5% of total population (Statistical Office of the Republic of Serbia, 2011) but based on

consumption (World Bank, 2009). When we use consumption as a measure of wealth, our

results confirm the official statistics on poverty (7.5% pre-payment poverty headcount),

as well as previous studies (Bajec et al., 2008).

This huge discrepancy between the pre-payment poverty levels using different

indicators of wealth is related to the nature of the wealth indicators and the way how

the wealth indicators are measured (see background section). As observed in our analysis,

about 80% of the households report a higher consumption level than income, which

results in lower poverty levels based on consumption than those based on income. This

discrepancy can be attributed to additional resources mobilized by the households by

borrowing money and/or selling assets. Another reason for this discrepancy can be that

in the Serbian LSMS, the consumption value of houses is calculated as a regional average

(World Bank, 2009). Thus, the consumption of persons outside expensive cities (such

as Belgrade), is artificially increased. The existence of an informal economy in Serbia

is another reason for the differences between consumption-based and income-based

estimations in our study.

Despite the huge difference in the pre-payment poverty levels discussed above, the

poverty effects of the health care spending are less diverse across the wealth indicators:

2-2.4% of individuals experiencing catastrophic and/or impoverishing effects based on

income-based poverty measures versus 0.8-1.1% based on consumption-based poverty

measures. Nevertheless, in relative terms, this difference means a two times higher

catastrophic and impoverishing effect based on income than based on consumption.

The discrepancy between the poverty measures based on income and those based on

2

Financial burden and out-of-pocket patient payments

45

consumption confirms that the use of multiple wealth indicators provides a better insight

in the prevalence of poverty.

When we compare the different approaches while keeping the wealth indicators

constant (e.g. using the income-based poverty measures indicated by the different

approaches), we observe a similarity: 2% of the respondents go below the absolute

poverty line (impoverishing effects) and 2.3% of the respondents spend more than 40%

of their total income on health care (catastrophic effects). The comparability between

these findings is an indication of the robustness of the two approaches.

However, when we apply the impoverishing effects approach, the burden is higher

for families who are already poor or close to the absolute poverty line. This is consistent

with the idea that the impoverishing effects reflect the poverty situation in society and

emphasized the poverty effects among vulnerable groups (Adhikiri et al., 2009). On

the other hand, the catastrophic health care expenditure approach shows a burden of

health care spending among the poorest quintiles but also among middle quintiles.

This phenomenon is well described in the literature (Adhikiri et al, 2009; Wagstaff &

van Doorslaer, 2003) and we also observe it in our results when we use consumption-

based poverty measures. However, in case of income-based poverty measures, the poorest

quintiles are most affected by catastrophic health care spending. This implies that the

choice of the wealth indicator (see background section) might be the reason for the

differences across catastrophic and impoverishing effects reported in previous studies.

Both approaches – the impoverishing effects and catastrophic health care expenditure

– are widely used in country comparison studies (van Doorslaer et al., 2006; Xu et al.,

2010). However, recent studies show that they both have limitations in capturing the

financial burden provoked by out-of-pocket patient payments (Flores & O’Donnell, 2013).

Both approaches focus on those people who actually experience the financial burden,

while those who forgo the use of health care services because they cannot afford it are

not observed by those approaches (O’Donnell et al., 2008; . Flores & O’Donnell, 2013).

One way to overcome those limitations is to examine the different coping strategies

that households use to overcome the financial burden provoked by out-of-pocket patient

payments (Flores et al., 2008). Such strategies include cutting in short term consumption

of other goods or selling assets and borrowing money, or just do not use health care

services (Flores et al., 2008). Households that forgo the use of health care services can

face higher medical expenditure later in time or income loss and wealth deprivation (if

one of the household members do not work in order to provide care to other member)

(Flores & O’Donnell, 2013). The use of longitudinal data that measures different coping

strategies is considered as a desirable way to overcome the limitations of those approaches

(Wagstaff, 2006). Another approach is based on measuring the downside risks provoked

by medical expenditure. This approach estimates the level of risk that households can

bear conditional on their preferences. Moreover, this approach estimates the potential risk

46

Chapter 2

before the household uses the health care services and is based on an ex-ante perspective

(Flores & O’Donnell, 2013).

In this study, we use regression analysis to examine socio-demographic factors

associated with catastrophic health care expenditure, impoverishments effects and

subjective poverty. When we use variables related to impoverishment effects as dependent

variables, we identify a higher number of independent variables that are statistically

significant than when we use variables related to catastrophic health expenditure.

One of the reasons can be that in calculating dependent variables, we use income for

impoverishment, while when we calculate categories related to catastrophic health care

expenditure we use consumption. Results related to subjective poverty are consistent

with results on the impoverishing effects based on income. Previous research results show

a high correlation between income and subjective poverty (Deaton, 2008).

Also, our regression results indicate several population groups at risk of poverty based

on both real and subjective income, as well as based on the change in the income category

due to health care spending.

This includes groups with higher education, living in urban areas, having poor health

and diagnosed with diabetes. Respondents, who are not-married, and have certain chronic

diseases are at risk of impoverishing (i.e. changing their poverty group) due to health

care spending. Our results are consistent with previous research, although it should be

mentioned that only few studies (Falkingham, 2004) recognize perceived health as an

important indicator. In contrast to previous research however, our results do not indicate

statistically significant differences by age and employment status.

There are several possible reasons in the current health care system in Serbia that can

trigger the poverty effect of health care spending measured in our study. These include

the monopoly position of the HIF, the existence of informal patient payments together

with official co-payments, the complex and still not fully applied exemption mechanism,

as well as the lack of an adequate provider payment mechanism (such as DRG system in

hospitals and/or capitation based reimbursement mechanism), which is a reason for poor

monitoring of the money flow within the health care system.

Special attention should be paid to our findings for chronically sick persons. These

persons are recognized as a vulnerable group when it comes to health care spending. This

group needs to use health care frequently and the accumulated out-of-pocket payments

can easily become unbearable. Our findings suggest that people with diabetes and

progressive chronicle diseases are especially vulnerable to out-of-pocket payments. From

equity perspective, reduced or no patient charges should be considered for these groups.

In addition to the poverty effects of out-of-pocket payments, the problem of forgoing

health care due to payments (reported by 12% of our sample) also requires the policy

attention in Serbia.

2

Financial burden and out-of-pocket patient payments

47

Our cross-sectional data do not allow us to estimate the long-term poverty effects of

out-of-pocket patient payments. Since the out-of-pocket patient payments are enforced,

and usually non-discretionary shocks (Wagstaff & van Doorslaer, 2003) and even small

but frequent health care payments can produce persistent financial burden (Gertler &

Gruber, 2002), panel data would suit better in exploring their long term effects and the

variation of these effects across approaches and wealth indicators. Panel data can also help

to examine the mechanisms that are used in financing out-of-pocket patient payments

(O’Donnell et al., 2005). Furthermore, future research should apply different approaches

such as catastrophic medical expenditure risks to give a more comprehensive picture

regarding the burden provoked by out-of-pocket patient payments in Serbia.

CHAPTER 3

Different Types of Out-of-pocket Payments for Health Care: How do they Contribute to Impoverishing and Catastrophic Effects among Serbian Households?

Submitted as:

Arsenijevic, J., Pavlova, M., & Groot, W. (2015). Out-of-pocket payments for health care in Serbia.

50

Chapter 3

Abstract

Introduction: This study focuses on the impoverishing and catastrophic effects of

different types of out-of-pocket payments for health care. In contrast to previous poverty

studies, we distinguish three types of out-of-pocket patient payments: official co-

payments, informal (under-the-table) payments and payments for “bought & brought

goods” (i.e. payments for health care goods brought by the patient to the health care

facility).

Methods: We examine the impoverishing and catastrophic effects of each type of out-

of-pocket payments on household budgets in Serbia. For this purpose, we use data from

the LSMS data carried out in 2007. Out-of-pocket patients payments for both outpatient

and inpatient health care are included. Consumption-based indicators to measure and

compare the impoverishing and catastrophic effects of the three types of out-of-pocket

payments are used. We also explore the socio-demographic determinants of different

types of payments.

Results: Our results show that total out-of-pocket patient payments in Serbia create

a substantial burden on households. All three types of out-of-pocket patient payments

may provoke impoverishing and catastrophic effects for Serbian households. Regarding

the regression results, users with an income below the poverty line, those from rural

areas and who are not married are more likely to report payments for “bought & brought

goods, while young and more educated users are more likely to report informal patient

payments.

Conclusions: The distinction between different types of out-of-pocket payments is

essential in assessing impoverishing and catastrophic effects. Serbian policymakers need

to consider different strategies to deal with informal payments and eliminate the practice

of “bought & brought goods”. These will be important to decrease the overall burden of

out-of-pocket payments in Serbia.

3

Different types of out-of-pocket payments

51

3.1. Introduction

Most of previous studies have looked at out-of-pocket payments as a univalent concept,

without making a distinction between different types of payments. In particular, patient

payments in some countries include not only official co-payments regulated by official

policy arrangements but also informal (under-the-table) payments (Falkingham, 2004;

Kutzin et al., 2009; Lewis, 2000). Recent evidence suggests that the presence of informal

patient payments is not unique to low income countries but is also observed in some

high-income countries (Greece, Italy, France, Austria) (Stepurko et al., 2010; Tambor et

al., 2014). The distinction between official co-payments and informal patient payments

is important since the strategies for dealing with their catastrophic and impoverishing

effects differ. In case of official co-payments, these negative effects can be diminished by

an adequately designed exemption mechanism (Xu et al., 2010) while in case of informal

payments; these effects are better mitigated by applying strategies for their elimination.

In CEE countries, out-of-pocket patient payments became an important part of health

care financing during the 1990s (Moreno-Serra & Wagstaff, 2010; Thompson & Witter,

2000). Their catastrophic and impoverishing effects on households have been clearly

shown in recent studies (Bredenkamp et al., 2011; Gotsadze, Zoidze, & Rukhadze,

2009; Habicht et al., 2006). A significant number of studies also confirm the existence

of informal patient payments in this region (Balabanova & McKee, 2002; Falkingham,

2004; Kutzin et al., 2009; Lewis, 2000, Shishkin, 2003; Szende & Culyer, 2006; Vian et

al., 2006). However, studies have so far exclusively focused on the description of informal

payments and the reasons for their existence. Hitherto, there is no research on whether

these payments have significant catastrophic and impoverishing effects on households.

This chapter aims to analyze this issue from the perspective of the Serbian public

health care system. This system presents an interesting case due to the parallel existence

of official co-payments, informal patient payments (cash and gifts in kind given to the

physician), as well as payments for “bought & brought goods” (i.e. payments for goods

brought by the patient or their families to the health care facility such as disposable

materials and pharmaceuticals) (Hubrecht & Najman, 2005). The latter type of out-of-

pocket payments has rarely been studied before. Thus, their scope and scale are largely

unknown, as well as their effects on poverty. We study the variation in catastrophic and

impoverishing effects of these types of payments across socio-demographic groups in

Serbia. We focus on the public health care system excluding payments in the private

health care sector. Detailed information about the Serbian public health care system can

be found in Chapter 1.

Similarly, to Chapter 2, to study the catastrophic and impoverishing effects of the

tree types of out-of-pocket payments in the Serbian public health care system, we use

household-level data from the LSMS for Serbia carried out in 2007. The data are analyzed

52

Chapter 3

using quantitative methods (statistical packages STATA 8). After this introductory

section, we first outline the operational definition of out-of-pocket payments applied in

our analysis. In the next step, we present the method section describing the analytical

framework of the analyses and data that are used. Based on this, results are presented. The

chapter concludes by a discussion and conclusions for policy and research.

3.2. Background – different types of out-of-pocket patient payments

Informal patient payments are widely studied (Lewis, 2007; Stepurko et al., 2010) but

there is still no consensus about the definition of informal payments (Gaal et al., 2006).

For example, the definition given by Lewis, states that any payment given in kind or in

cash to public health providers outside the official channels as well as any purchase that

should be covered by the health care system is an informal payment. This definition

emphasizes the unregistered and unaudited nature of informal patient payments.

However, Gaal et al.(2006) point out that not all types of informal patient payments

are unregistered and unaudited. They use the example of medical pharmaceuticals that

are bought by patients for their treatment. In order to provide a more comprehensive

definition that includes all types of informal payments, Gaal et al. (2006) define informal

patient payments as every direct patient’s contribution (in cash or in kind) for the services

that should be provided free of charge. This includes informal payments to physicians,

envelope payments and gifts but also payments for “bought & brought goods”. In

hospital care, “bought & brought goods” payments can include payments to purchase

pharmaceuticals, medical materials, even hospital equipment or meals that patients are

required to bring to the hospital although patients are entitled to get these for free (Gaal

et al. 2006). In outpatient care, “bought & brought goods” can include pharmaceuticals

that are on the positive lists but are not available in state pharmacies so patients have to

pay for them in a private pharmacy.

A drawback of the above definitions is that they do not separate payments for goods

brought by patients to the health care facilities (i.e. the “bought & brought goods”

payments) from pure informal payments. We find it essential to make a distinction

between these two types of patient payments because they differ in nature (see Figure 3.1).

While informal patient payments (such as gifts to the physicians) remain unregistered,

payments for “bought & brought goods” (e.g. for pharmaceuticals bought in pharmacy

and brought to the hospital) are officially registered at the point of purchase but usually

not visible in the financial flows of the institution that provides the services. Also,

payments for “bought & brought goods” are usually essential for the treatment, while

informal payments (gifts and money given to physicians) are not necessary related to

patient treatment. The motivation for informal patient payments can also vary from

3

Different types of out-of-pocket payments

53

a request by the medical staff to voluntary patient payments to obtain better care or

to express pure gratitude (Balabanova & McKee, 2002; Balabanova et al., 2004;

Bredenkamp et al., 2011; HIF, 2011; Stepurko et al., 2010). In contrast, the payments

for “bought & brought goods” always result from a request of the provider and they

are frequently necessary in the curative process (World Bank, 2005). Informal patient

payments are provided before, during or after the curative process, while payments for

“bought & brought goods” occur during the curative process and outside the health care

setting where the service is provided, sometimes within a particularly short period of

time (Lewis, 2007).

Given their registered but shadow nature, patient payments for “bought & brought

goods” represent an important problem in the health care system. Policy makers may

easily overlook this type of out-of-pocket payments as long as the purchase of goods

by patients outside the health care setting does not breach any laws and regulations.

Patients may also underreport their out-of-pocket expenditures related to a treatment if

they are not specifically asked about goods that they brought to the health care setting

(TNS Media Gallup, 2010). Nevertheless, if payments for “bought & brought goods” are

frequent, they might substantially increase the burden of out-of-pocket payments to the

patient and their household.

Although some poverty studies (Flores et al., 2008; Habicht et al., 2006; Thompson

& Witter, 2000) have taken the complex nature of out-of-pocket payments into account

by making a distinction between payments for pharmaceuticals and for health care

services, or between payments by different groups of health care users (e.g. chronically

sick patients), hitherto, no distinction has been made between the catastrophic and

impoverishing effects of informal payments and official co-payments. Needless to say,

there is no study that has specifically focused on the catastrophic and impoverishing

effects of patient payments for “bought & brought goods” distinct from the pure informal

payments (as defined above). Therefore, in this chapter, we study the catastrophic and

impoverishing effects of these three different types of out-of-pocket patient payments

separately.

54

Chapter 3

Figure 3.1: Types of out-of-pocket payments is Serbia

3.3 Methods and data description

3.3.1 Data descriptionAs in Chapter 2, we use the LSMS data for Serbia collected under the supervision of

the World Bank in 2007. Detailed information about the LSMS data for Serbia can be

found in Chapter 1. In this chapter, we use data related to official co-payments and

informal payments for both outpatient and inpatient care, and out-of-pocket payments

for “bought & brought goods” in case of hospitalization only. Data for payments for

“bought & brought goods” for outpatient care are not available. Informal and “bought &

brought goods” payments of a household member for other individuals are also registered.

Specifically, some users of outpatient services in our data who were not hospitalized also

report informal payments and payments for “bought & brought goods” for inpatient care.

Since Serbian patients are not supposed to bring goods for their hospitalization, we

assume that payments for “bought & brought goods” have a quasi-informal nature as

defined by Stepurko (2013). This means that the goods are officially purchased by the

patients or their families but the fact that they are brought to the hospital is against

official regulation. Direct payments to health care providers for goods that should be

provided for free, are treated as informal payments.

The data does not provide any indication of which particular pharmaceutical or

diagnostic procedure has been paid for. The data includes information regarding the

private health care sector, which we do not analyze. Based on variables in the health

module of the data, we have created four variables that specify respectively the total

3

Different types of out-of-pocket payments

55

out-of-pocket patient payments, total official co-payments, total informal payments and

total payments for “bought & brought goods” per household member during the last

12 months. The data for outpatient care are based on a re-call period of one month and

the data for inpatient care use are based on a re-call period of 12 months. Therefore,

we have multiplied the payments for out-patient care by 12 to obtain an annual

estimate comparable to the data for inpatient care. We do not exclude the possibility

that the method used for annualizing the costs can lead to over- or under-estimation.

However, this is still a widely advised and applied method to make figures comparable

(Linden & Samuels, 2013). Although annualized costs can lead to overestimation, they

are still widely advised and applied (Linden & Samuels, 2013). Total out-of-pocket

patient payments include both direct (official, informal and “bought & brought goods”

payments) and indirect (transport costs and extra accommodation costs) medical costs

for health care. Official co-payments include the payments related to physician visits,

nurse intervention, laboratory tests, ultrasounds, referrals, hospital services and other

services. Informal payments include the amounts that are given to physicians and/or

nurses on their request or as a gift. The payments for “bought & brought goods” include

the payments for pharmaceuticals and/or disposable and orthopedic materials that the

patient brought to the health care institution and that should be provided for free by the

institution.

3.3.2. Analytical framework for assessing the catastrophic and impoverishing effects We estimate the effects of different types of out-of-pocket patient payments on households’

wealth using two approaches: catastrophic health care expenditure and impoverishing

effects of out-of-pocket payments. In Chapter 2 of this dissertation, both approaches have

been described in detail. Furthermore, we also described the disadvantages related to

both approaches (Chapter 2). Here, we outline how we calculate the indicators related to

impoverishment and catastrophic effects. We first choose a wealth indicator. Although,

there is no consensus about the “best” indicator of wealth, consumption is the most often

used indicator of wealth in low-and middle-income countries (such as Serbia). Since these

countries frequently have a low tax morale and a large informal economy, it is difficult

to measure individual or household income accurately (Knaul et al., 2006). Therefore,

in this chapter, we take consumption as an indicator of wealth. The LSMS data contains

consumption per household as well as per adult equivalent. We have also calculated

consumption per household member. However, there is no statistically significant

difference between consumption per adult equivalent and per household member.

Following previous studies, we use consumption per household member.

We calculate the impoverishment due to a given type of out-of-pocket payments as

the proportion of households that end up below the poverty line after the annual amount

56

Chapter 3

of that type of out-of-pocket payments is subtracted from their total annual consumption

(Knaul et al., 2006). In our analysis, we included the absolute and relative poverty lines

(for detailed technical information see Chapter 2). The absolute poverty line is equal to

8883 CSD per adult equivalent per-month, which is about 5.40 US per person per day or

approximately 150 US dollars per adult equivalent per-month. The relative poverty line

is defined as 60% of median consumption per adult. This amount was 11283 CSD (about

207.40 US dollars) per adult equivalent per-month in 2007 (Gajic-Stevanovic et al.,

2010). Both poverty lines have been explained in detail in Chapter 2 of this dissertation.

Using the absolute poverty line as a threshold, we calculate the poverty headcount (the

incidence of people who go below the poverty line after health care spending is subtracted

from their total consumption) and the poverty gap index (the mean distance separating

the population from the poverty line, where the non-poor are considered to have the

distance zero). We repeat the calculations using the relative poverty line as a threshold.

We use descriptive statistics to present the above impoverishing indicators and thus, to

summarize the impoverishing effect of the three types of out-of-pocket payments as well

as of the total out-of-pocket payments.

As in Chapter 2, we assume that a given type of out-of-pocket payments has

catastrophic effects on household budgets when the annual amount of that type of

out-of-pocket payments exceeds a certain share of the annual household consumption

(catastrophic threshold). Since there is no clear consensus regarding the threshold, in this

study we use a threshold of 10% especially because we only look at one component of the

out-of-pocket payments (Xu et al., 2010). In order to examine the catastrophic effects of

the three types of out-of-pocket payments, we divide the annual amount of each type of

payment per person per year by the total annual household expenditure per household

member. As explained above, we assume that a situation is catastrophic for the household

budget when the type of payment exceeds 10% of household expenditure. The percentage

of respondents for whom the threshold is exceed, is known as the catastrophic payment

headcount. We also calculate the catastrophic payment gap (which is analogue to the

poverty gap index) and the concentration index (i.e. the variation of the catastrophic

effects across the consumption quintiles). The catastrophic payment gap shows the depth

of poverty, while the concentration index shows how the proportion of those who exceeds

the thresholds varies across different consumption quintiles (van Doorslaer et al., 2007).

We calculate the catastrophic and impoverishing effects of different types of payments

and total out-of-pocket patient payments for the total sample and health care users in our

sample.

3

Different types of out-of-pocket payments

57

3.3.3. The analytical framework for analyzing the probability of different types of out-of-pocket patient paymentsWe examine the probability of payment and the amounts paid for official co-payments,

payments for “bought & brought goods” and informal patient payments. For this purpose,

we run three sample selection models for the three separate types of payments: official

co-payments, payments for “bought & brought goods” and informal patient payments.

The first part of the model also known as the selection equation, uses a binary outcome

variable (e.g. official co-payments yes = 1; no = 0), while the second part uses a linear

regression to model the amount paid officially, if the binary outcome variable is higher

than 0. The selection equation assumes that the probability to pay is determined by a

latent variable. We use the variable living in an urban/rural area as identifying variables

for all three types of out-of-pocket patient payments. We apply analogous sample-

selection models for informal patient payments and payments for “bought & brought

goods”. As independent variables, based on previous studies (Ensor, 2004; Stepurko

et al., 2010; Stepurko et al., 2013), we include socio-demographic variables (gender,

education, marital status, settlement, work status and household size) as well as binary

variables regarding the presence of different chronic diseases (asthma, cardio-vascular

diseases, diabetes mellitus, hear and speech disorders etc.). We expect that people with

chronic diseases (more frequent users of health care services) report more often all three

types of payments (Gordeev et al., 2014). Also, we expect that people living in urban

areas, women and people with good perceived health have a lower probability to report

any types of out-of-pocket patient payments (Tambor et al., 2014).

In order to solve the problem of a skewed data distribution, we use a logarithmic

transformation for variables related to paid amounts for all three types of payments.

We also present results from OLS regression related to the amount paid for all three

types of out-of-pocket patient payments.

3.4 Results

In this study among the 17,375 respondents in the sample, we identify 22.8% who

report official co-payments, 16.4% who report “bought & brought “payments and 2.9%

report informal patient payments. If we look from the perspective of health care users,

we identify 4,976 (28.6% of the household members, i.e. the sample) health care users

of public health care services (those in our sample who report using outpatient and/

or inpatient health care services during the last 12 months). As indicated in Table 3.1,

among the health care users, 93.9% respondents report some type of payments for public

health care services (i.e. official co-payments, informal payments and/or “bought &

brought goods” payments) during the last 12 months.

58

Chapter 3

The majority of health care users 84.7% report official co-payments. However, a

considerable number of health care users 61.1% also report “bought & brought goods”

payments, whereas only 5.7% health care users declare that they have paid informally.

Also, 191 (3.8%) of health care users reported all three types of payments. Among payers,

the average amount that is paid for “bought & brought goods” per year is higher than the

official payments and informal payments. As mentioned above, only a small number of

respondents report informal payments.

Table 3.1 also separately presents data for payments for outpatient care and payments

for inpatient care. As suggested by the table 3.1, informal payments are more frequent

in case of inpatient services (10.9% of all payers for inpatient care reported informal

payments) than in case of payments for outpatient care (≈2% of all payers for outpatient

care reported informal payments). Payments for “bought & brought goods” for outpatient

services are not measured in the Serbian LSMS 2007.

The results regarding the catastrophic effects of each type of payment for public

health care services are presented in Table 3.2. We present the catastrophic effects of

each type of payments for different consumption quintiles. Using the 10% threshold, we

observe catastrophic effects for all types of out-of-pocket payments. However, this effect is

stronger for payments for “bought & brought goods” payments and official co-payments

than for informal payments. Also, we observe that the burden of official co-payments for

the 10% threshold is highest among the third quintile, while for “bought & brought

goods” payments the burden is highest among the second quintile. The concentration

index shows that the catastrophic effects of official co-payments are stronger among the

poorest quintiles, while informal patient payments impose a higher burden for non-poor

respondents.

3

Different types of out-of-pocket payments

59

Tab

le 3

.1: D

escr

ipti

ve s

tati

stic

s pe

r ty

pes

of p

aym

ents

a

Typ

es o

f p

aym

ents

% o

f u

sers

re

por

tin

g th

e p

aym

ent

typ

e

Pay

men

t si

ze

bas

ed o

n u

sers

% o

f p

ayer

s re

por

tin

g th

e p

aym

ent

typ

e

Pay

men

t si

ze

bas

ed o

n p

ayer

s

Mea

n (S

D)

Min

.M

ax.

Mea

n (S

D)

Min

.M

ax.

Pay

men

ts f

or o

utp

atie

nt

and

inp

atie

nt

care

Offi

cial

co-

paym

ents

84

.7%

4394

.8(1

0913

.1)

014

4720

(90.

1%)

5188

.8(1

1683

.8)

614

4720

Pay

men

ts fo

r “b

ough

t &

bro

ught

goo

ds”

61.1

%80

42.3

(185

69.3

)0

2916

00(6

5.0%

)11

739.

1(21

446.

6)20

2916

00

Info

rmal

pat

ient

pay

men

ts5.

7%28

1.5(

3980

.1)

012

8000

(6.1

)49

14.4

(159

57.7

)25

1280

00

Pay

men

ts f

or o

utp

atie

nt

care

Offi

cial

co-

paym

ents

85.3

%45

15.7

(111

40.9

)0

1447

20(9

0.5%

)52

93.7

(118

90.6

)24

1447

20

Offi

cial

co-

paym

ents

for

phys

icia

ns v

isit

59.7

%42

.3(1

47.8

)0

3000

(63.

3%)

42.2

(147

.8)

2030

00

Offi

cial

co-

paym

ents

for

phar

mac

euti

cals

45

.2%

105.

1(31

7.9)

050

00(4

7.9%

)23

2.6(

440.

5)10

5000

Offi

cial

co-

paym

ents

for

labo

rato

ry a

naly

ses

16.4

%17

1.6(

805.

5)0

2000

0(1

7.4%

)10

44.7

(174

3.9)

20

2000

0

Offi

cial

co-

paym

ents

for

disp

osab

le m

ater

ials

19.8

%10

4.3(

499.

4)0

9000

(11.

5%)

961.

7(12

15.7

)20

9000

Offi

cial

co-

paym

ents

for

tran

spor

t23

.7%

121.

9(42

2.7)

012

000

(25.

1%)

515.

0(74

3.3)

2012

000

Info

rmal

pat

ient

pay

men

ts1.

7%18

.8(5

06.7

)0

3200

0(1

.8%

)10

60.1

(367

6.7)

2532

000

Mon

ey r

eque

sted

by

med

ical

sta

ff0.

23%

2.96

(106

.4)

050

00(0

.24%

)12

56.8

(188

7.5)

2550

00

Gif

ts g

iven

to

med

ical

sta

ff1.

55%

15.9

(495

.5)

032

000

(1.6

%)

1030

.1(3

886.

4)50

3200

0

a Mea

sure

d in

200

7 in

CSD

, 1 C

SD =

0.0

125

Eur

o or

0.0

1 U

SD

60

Chapter 3 T

able

3.1

: Des

crip

tive

sta

tist

ics

per

type

s of

pay

men

ts a (

cont

inue

d)

Typ

es o

f p

aym

ents

% o

f u

sers

re

por

tin

g th

e p

aym

ent

typ

e

Pay

men

t si

ze

bas

ed o

n u

sers

% o

f p

ayer

s re

por

tin

g th

e p

aym

ent

typ

e

Pay

men

t si

ze

bas

ed o

n p

ayer

s

Mea

n (S

D)

Min

.M

ax.

Mea

n (S

D)

Min

.M

ax.

Pay

men

ts f

or in

pat

ien

t ca

re

Offi

cial

co-p

aym

ents

72.8

%37

08.9

(807

09.2

)0

1020

00(7

7.1%

)50

93.7

(985

6.2)

6.

1020

00

Offi

cial

co-

paym

ents

for

hosp

ital

izat

ion

48.2

%18

88.6

(640

5.5)

010

0000

(51.

0%)

3918

.8(8

789.

3)15

1000

00

Offi

cial

co-

paym

ents

for

phar

mac

euti

cals

22.1

%52

8.9(

2434

.3)

050

000

(23.

3%)

2398

.7(4

738.

9)50

5000

0

Offi

cial

co-

paym

ents

for

labo

rato

ry s

ervi

ces

12.1

%33

5.8(

1851

.6)

040

000

(12.

8%)

2781

.8(4

662.

7)25

4000

0

Offi

cial

co-

paym

ents

for

disp

osab

le m

ater

ials

6.8%

584.

1(36

74.8

)0

5000

0(7

.2%

)85

34.9

(114

49.3

)40

5000

0

Offi

cial

co-

paym

ents

for

tran

spor

t38

.2%

371.

4(10

95.7

)0

2000

0(4

0.4%

)97

1.8(

1600

.5)

2020

000

Pay

men

ts fo

r “b

ough

t & b

roug

ht”

good

s 22

.7%

526.

3(26

99.8

)0

5500

0(2

4.1%

)23

16.5

(529

3.8)

6055

000

Pay

men

ts fo

r ph

arm

aceu

tica

ls b

roug

ht b

y pa

tien

t19

.932

2.1(

1451

.6)

030

000

(21.

0%)

1621

.1(2

920.

9)60

3000

0

Pay

men

ts fo

r di

spos

al m

ater

ials

br

ough

t by

pat

ient

1.9%

90.6

(118

5.9)

025

000

(2.1

1%)

4540

.5(7

259.

1)15

025

000

Pay

men

ts fo

r or

thop

aedi

c m

ater

ials

br

ough

t by

pat

ient

2.

8%11

3.6(

1425

.9)

030

000

(2.9

)41

20.3

(769

5.3)

100

3000

0

Info

rmal

pat

ient

pay

men

ts10

.4%

525.

4(36

63.7

)0

8000

0(1

0.9%

)50

71.1

(103

61.5

)50

8000

0

Mon

ey r

eque

sted

by

med

ical

sta

ff0.

4%72

.2(1

463.

3)0

4000

0(0

.4%

)19

000(

1645

2)40

0040

000

Gif

ts t

o m

edic

al s

taff

10.1

%45

3.2(

3313

.9)

080

000

(10.

7%)

4497

.6(9

568.

7)50

8000

0

a M

easu

red

in 2

007

in C

SD, 1

CSD

= 0

.012

5 E

uro

or 0

.01

USD

3

Different types of out-of-pocket payments

61

Tab

le 3

.2: S

hare

of h

ouse

hold

hea

lth

care

exp

endi

ture

per

con

sum

ptio

n-ba

sed

quin

tile

s fo

r to

tal s

ampl

e an

d he

alth

car

e us

ers

Poo

rest

qu

inti

le2

34

Ric

hes

t q

uin

tile

Tot

al

sam

ple

Hea

lth

ca

re u

sers

Tot

al

sam

ple

Hea

lth

ca

re u

sers

Tot

al

sam

ple

Hea

lth

ca

re u

sers

Tot

al

sam

ple

Hea

lth

ca

re u

sers

Tot

al

sam

ple

Hea

lth

ca

re u

sers

Tot

al

sam

ple

Hea

lth

ca

re u

sers

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

Ave

rage

con

sum

ptio

n pe

r qu

inti

le

1114

09.6

111

4794

.616

7159

.21

1670

89.4

2157

72.8

021

5902

.128

1617

.63

2824

14.5

4789

72.9

148

2530

.425

0964

.325

5221

.8

For

tot

al p

aym

ents

:

Ave

rage

am

ount

pai

d (m

ean)

a30

16.3

859

66.8

850

94.6

482

12.9

469

62.4

511

982.

7177

44.0

314

945.

4810

910.

6121

922.

7667

39.5

1271

7.9

No

heal

th c

are

expe

ndit

ure

1717

(49.

4)76

(8.4

)13

89 (4

0.0)

58(5

.7)

1308

(37.

7)49

(4.7

)14

66 (4

2.2)

61(6

.1)

1573

(45.

3)58

(5.7

)74

53(4

2.9)

302(

6.1)

Mor

e th

an 0

% u

p to

10

%14

92 (4

2.9)

696(

76.7

)17

95 (5

1.7)

813(

80.3

)18

61 (5

3.6)

832(

80.0

)17

96 (5

1.6)

810(

80.8

)16

72 (4

8.1)

814(

80.4

)86

16(4

9.6)

3965

(79.

7)

Mor

e th

an 1

0% (p

over

ty

head

coun

t)26

7(7.

7)13

5(14

.9)

290(

8.4)

142(

14.0

)30

5(8.

8)15

9(15

.3)

216(

6.2)

132(

13.2

)22

8(6.

5)14

1(13

.9)

1306

(7.5

)70

9(14

.2)

Pos

t-pa

ymen

t po

vert

y ga

p in

dex

*0.

070.

07

Con

cent

rati

on in

dex*

*

For

offic

ial c

o-pa

ymen

ts:

Ave

rage

am

ount

pai

d (m

ean)

a22

97.4

226

43.6

730

32.3

131

25.4

142

59.6

546

96.2

256

16.7

262

56.2

174

61.4

189

14.7

922

97.9

4394

.2

No

heal

th c

are

expe

ndit

ure

1882

(54.

1)15

6(17

.2)

1661

(47.

8)16

2(16

.0)

1549

(44.

6)14

5(13

.9)

1686

(48.

5)14

4(14

.4)

1804

(51.

9)15

9(15

.7)

8582

(49.

4)76

6(15

.4)

Mor

e th

an 0

% u

p to

10%

1533

(44.

1)71

7(79

.1)

1755

(50.

5)82

4(81

.3)

1840

(53.

0)84

8(81

.5)

1729

(49.

7)81

9(81

.7)

1623

(46.

7)82

1(81

.0)

8480

(48.

8)40

29(8

1.0)

Mor

e th

an 1

0% (p

over

ty

head

coun

t)61

(1.7

)34

(3.7

)58

(1.7

)27

(2.7

)85

(2.4

)47

(4.5

)63

(1.9

)40

(4.0

)46

(1.3

)33

(3.3

)61

(1.8

)18

1(3.

6)

Pov

erty

gap

inde

x0.

060.

06

Con

cent

rati

on in

dex

-0.0

02a M

easu

red

in C

SD, 1

CSD

= 0

.012

5 E

uro

62

Chapter 3T

able

3.2

: Sha

re o

f hou

seho

ld h

ealt

h ca

re e

xpen

ditu

re p

er c

onsu

mpt

ion-

base

d qu

inti

les

for

tota

l sam

ple

and

heal

th c

are

user

s (c

onti

nued

)

Poo

rest

qu

inti

le2

34

Ric

hes

t q

uin

tile

Tot

al

sam

ple

Hea

lth

ca

re u

sers

Tot

al

sam

ple

Hea

lth

ca

re u

sers

Tot

al

sam

ple

Hea

lth

ca

re u

sers

Tot

al

sam

ple

Hea

lth

ca

re u

sers

Tot

al

sam

ple

Hea

lth

ca

re u

sers

Tot

al s

amp

leH

ealt

h

care

use

rs

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

N

(% w

ith

in

qu

inti

le)

For

info

rmal

pay

men

ts:

Ave

rage

am

ount

pai

d (m

ean)

a

4171

.11

2458

.00

1700

.67

1814

.78

1962

.74

1941

.46

1369

.99

1431

.88

8698

.47

1050

8.38

145.

328

1.5

No

heal

th c

are

expe

ndit

ure

3428

(98.

6)88

2(97

.2)

3377

(97.

2)96

7(95

.5)

3358

(96.

7)99

4(95

.6)

3331

(95.

8)93

7(93

.4)

3279

(94.

4)91

2(90

.0)

1677

3(96

.5)

4692

(94.

3)

Mor

e th

an 0

% u

p to

10%

44(1

.3)

24(2

.6)

97(2

.8)

46(4

.5)

116(

3.3)

46(4

.4)

147(

4.2)

66(6

.6)

188(

5.4)

97(9

.6)

592(

3.4)

279(

5.6)

Mor

e th

an 1

0% (p

over

ty

head

coun

t)4(

0.1)

1(0.

1)0(

0.0)

0(0.

0)0

(0.0

)0(

0.0)

0(0.

0)0(

0.0)

6(0.

2)4(

0.1)

10(0

.1)

5(0.

1)

Pov

erty

gap

inde

x0.

170.

14

Con

cent

rati

on in

dex

0.2

For

“b

rou

ght

& b

ough

t” p

aym

ents

:

Ave

rage

am

ount

pai

d (m

ean)

a

3701

.26

3701

.26

5431

.94

5431

.94

6793

.22

6793

.22

8132

.65

8132

.65

1321

5.70

1321

5.70

4296

.380

42.3

No

heal

th c

are

expe

ndit

ure

2948

(84.

8)37

9(41

.8)

2846

(81.

9)38

5(38

.0)

2820

(81.

2)38

6(37

.1)

2877

(82.

7)40

2(40

.1)

2856

(82.

2)39

6(39

.1)

1434

7(82

.6)

1948

(39.

1)

Mor

e th

an 0

% u

p to

10%

501(

14.4

)50

1(55

.2)

581(

16.7

)58

1(57

.4)

612(

17.6

)61

2(58

.8)

571(

16.4

)57

1(56

.9)

576(

16.6

)57

6(56

.9)

2841

(16.

4)28

41(5

7.1)

Mor

e th

an 1

0%

(pov

erty

hea

dcou

nt)

27(0

.8)

27(3

.0)

47(1

.4)

47(4

.6)

42(1

.4)

42(4

.3)

30(0

.9)

30(3

.0)

41(1

.1)

41(4

.1)

188(

1.1)

188(

3.7)

Pos

t-pa

ymen

t po

vert

y ga

p in

dex

0.07

00.

07

Con

cent

rati

on in

dex

0.00

4a M

easu

red

in C

SD, 1

CSD

= 0

.012

5 E

uro.

3

Different types of out-of-pocket payments

63

Table 3.3 presents the descriptive results regarding the poverty groups before and after

the subtraction of different types of payments for public health care services. Payments

for “bought & brought goods” provoke a higher burden in terms of impoverishing

effects than the other two types of payments. If we only consider users of health care, the

percentage difference between the post-payment and pre-payment poverty headcount

for “bought & brought goods” payments is similar to those for official co-payments.

Since informal patient payments are not frequently reported in our sample, the poverty

headcount and poverty gap index for those payments is close to zero.

Table 3.4 presents the results of the selection model (known as the Heckman model)

for three types of out-of-pocket patient payments. The probability to report official co-

payments and payments for “bought & brought goods” is higher among participants

with a chronic disease (asthma, cardiovascular diseases, diabetes and progressive diseases).

Participants with lower perceived health report more often all three types of payments.

On the other side, participants living in urban areas (0.19; p≤ 0.05) have a higher

probability to report official co-payments while participants living in rural areas (-0.08;

p≤ 0.05) have a higher probability to report payments for “bought & brought goods”.

The probability to report informal patient payments is higher among those who are

better educated, younger, living in urban areas and working. Results from the second

stage regression show that among those who paid officially, higher amounts are reported

by those who are unemployed. In case of participants who report payments for “bought

&brought goods”, higher amounts are paid by patients diagnosed with progressive

diseases and higher educated. Amounts paid informally are lower in larger households,

while higher amounts are reported among those diagnosed with asthma. Respondents

from non-poor households more frequently report all three types of payments.

Table 3.5 presents the results of the three OLS models related to the amounts paid

for three types of payments. The regression analysis includes participants who paid for

certain type of services. Our results show that payers with lower perceived health, report

higher amounts for official co-payments and payments for “bought & brought goods”.

Higher amounts for informal patient payments are reported by payers with diagnosed

asthma. Results from OLS are similar to those of the sample selection models.

.

64

Chapter 3T

able

3.3

: Pov

erty

hea

dcou

nt a

nd p

over

ty g

ap w

ith

diff

eren

t de

finit

ion

of p

over

ty li

ne a

nd d

iffe

rent

typ

es o

f pay

men

ts

Pov

erty

mea

sure

s re

late

d

to t

otal

pay

men

tsP

over

ty m

easu

res

rela

ted

to

offi

cial

co-

pay

men

tsP

over

ty m

easu

res

rela

ted

to

info

rmal

pay

men

tsP

over

ty m

easu

res

rela

ted

to

“ b

ough

t &

bro

ugh

t go

ods”

p

aym

ents

Ab

solu

te

pov

erty

lin

e a

Rel

ativ

e

pov

erty

lin

e b

Ab

solu

te

pov

erty

lin

e a

Rel

ativ

e

pov

erty

lin

e b

Ab

solu

te

pov

erty

lin

e a

Rel

ativ

e

pov

erty

lin

e b

Ab

solu

te

pov

erty

lin

e a

Rel

ativ

e

pov

erty

lin

e b

Tot

al s

amp

le

Pre

-pay

men

t po

vert

y he

adco

unt

7.5%

16.5

%2.

9%7.

5%0.

00%

0.00

2%0.

8%2.

4%

Pos

t-pa

ymen

t po

vert

y he

adco

unt

8.6%

18.6

%3.

2%8.

2%0.

00%

0.00

2%1.

0%3.

0%

Per

cent

age

poin

ts c

hang

e1.

1%2.

1%0.

3%0.

7%0.

00%

0.00

%0.

2%0.

6%

Pre

-pay

men

t po

vert

y di

ffer

ence

s 21

260.

2429

833.

6315

395.

5525

071.

3748

18.9

113

766.

3816

267.

7724

620.

05

Pos

t-pa

ymen

t po

vert

y di

ffer

ence

s22

457.

5130

811.

3716

735.

0025

420.

5714

605.

2318

092.

7617

060.

6925

791.

22

Pre

-pay

men

t po

vert

y ga

p in

dex

0.01

50.

036

0.00

40.

014

0.00

0.00

0.00

10.

004

Pos

t-pa

ymen

t po

vert

y ga

p in

dex

0.02

30.

043

0.00

50.

015

0.00

0.00

0.00

10.

006

Hea

lth

car

e u

sers

Pre

-pay

men

t po

vert

y he

adco

unt

7.0%

15.1

%4.

68%

12.2

7%0.

00%

0.40

%3.

05%

8.68

%

Pos

t-pa

ymen

t po

vert

y he

adco

unt

9.0%

18.7

%5.

00%

13.4

0%0.

00%

0.40

%3.

78%

10.0

0%

Per

cent

age

poin

ts c

hang

e2.

0%3.

6%0.

32%

1.13

%0.

00%

0.00

0.73

%1.

32%

Pre

-pay

men

t po

vert

y di

ffer

ence

s17

375.

6325

908.

7516

896.

1925

529.

0948

17.9

112

326.

5516

267.

7724

620.

05

Pos

t-pa

ymen

t po

vert

y di

ffer

ence

s20

234.

4228

495.

1518

223.

0726

217.

8311

028.

0514

946.

1117

060.

3125

791.

22

Pre

-pay

men

t po

vert

y ga

p in

dex

0.00

90.

029

0.00

70.

029

0.00

0.00

0.00

10.

014

Pos

t-pa

ymen

t po

vert

y ga

p in

dex

0.01

50.

039

0.01

00.

026

0.00

0.00

0.00

50.

019

a A

bsol

ute p

over

ty li

ne o

f 888

3 C

SD p

er h

ouse

hold

mem

ber

per

mon

th, 1

CSD

= 0

.012

5 E

uro

b R

elat

ive p

over

ty li

ne o

f 112

83C

SD p

er h

ouse

hold

mem

ber

per

mon

th

3

Different types of out-of-pocket payments

65

Tab

le 3

.4: R

esul

ts o

f the

Hec

kman

sel

ecti

on m

odel

, pay

ers

Exp

lan

ator

y va

riab

les

Offi

cial

co

-pay

men

tsP

aym

ents

for

“b

ough

t &

bro

ugh

t go

ods”

Info

rmal

pat

ien

t

pay

men

ts

Sele

ctio

n

mod

elA

mou

nt

1 p

aid

(Ln

)Se

lect

ion

m

odel

Am

oun

t 1

pai

d (L

n)

Sele

ctio

n

mod

elA

mou

nt

1 p

aid

(Ln

)

BSE

BSE

BSE

BSE

BSE

BSE

Gen

der

2 =

fem

ale

/ 1 =

mal

e-0

.04*

0.21

0.0

30.

050.

14*

0.03

-0.0

20.

06 0

.14

0.04

0.0

20.

15

Mar

ital

sta

tus

1 =

mar

ried

/ 0

= n

ot m

arri

ed 0

.09

0.23

0.0

30.

060.

040.

030.

060.

05 0

.14*

0.04

0.3

30.

18

Age

age

in y

ears

-0.0

20.

01-0

.01

0.01

0.01

0.01

-0.0

020.

002

-0.0

1*0.

001

-0.0

3*0.

01

Ed

uca

tion

al le

vel

Uni

vers

ity

degr

ee1=

yes

/ 0=

no-0

.07

0.06

0.2

5*0.

11 0

.03

0.06

0.5

6*0.

12 0

.14*

0.08

0.4

6**

0.33

Up

to h

igh

scho

ol1=

yes

/ 0=

no-0

.06

0.03

-0.2

60.

06-0

.02

0.03

-0.2

20.

06-0

.17*

0.05

-0.3

2**

0.19

Wor

k st

atus

1 =

wor

king

/ 0

= n

ot w

orki

ng-0

.02

0.03

-0.1

6*0.

06-0

.04

0.03

-0.0

80.

06 0

.08*

0.05

0.5

7*0.

20

Nat

iona

lity

1= S

erbi

an /

0 =

oth

er 0

.03

0.04

0.0

20.

07 0

.03

0.04

-0.0

60.

07 0

.07

0.06

0.3

10.

23

Hou

seho

ld s

ize

num

ber

of h

ouse

hold

mem

bers

-0.0

3*0.

01-0

.01

0.02

-0.0

10.

08 0

.01

0.02

0.0

20.

01-0

.25*

0.05

Urb

an1

= c

ity

/ 0 =

rur

al 0

.19*

0.02

-0.0

8*0.

03 0

.11*

0.03

Per

ceiv

ed h

ealt

h go

od-0

.91*

0.04

0.0

30.

15-0

.81*

0.04

-0.1

50.

19-0

.18*

0.06

-0.7

6*0.

25

Per

ceiv

ed h

ealt

h ba

d 0

.40*

0.03

0.3

6*0.

07 0

.40*

0.03

0.2

9*0.

09 0

.12*

*0.

05 0

.78*

0.27

Exe

mpt

ed g

roup

s1

= e

xem

pted

/ 0

= n

ot-e

xem

pted

-0.0

10.

04 0

.18*

0.06

-0.0

40.

03 0

.01

0.06

-0.0

10.

06 0

.34

0.25

Con

sum

ptio

n pe

r pe

rson

1= m

ore

than

abs

olut

e po

vert

y li

ne 0

.54*

0.05

0.6

60.

12 0

.53*

0.06

0.6

8*0.

14 0

.82*

0.18

3.3

0*0.

84

1 All

am

ount

s are

pre

sent

ed a

s nat

ural

loga

rith

m tr

ansf

orm

atio

ns

*p≤

0.05

** p

≤ 0.

10

66

Chapter 3T

able

3.4

: Res

ults

of t

he H

eckm

an s

elec

tion

mod

el, p

ayer

s (c

onti

nued

)

Exp

lan

ator

y va

riab

les

Offi

cial

co

-pay

men

tsP

aym

ents

for

“b

ough

t &

bro

ugh

t go

ods”

Info

rmal

pat

ien

t

pay

men

ts

Sele

ctio

n

mod

elA

mou

nt

1 p

aid

(Ln

)Se

lect

ion

m

odel

Am

oun

t 1

pai

d (L

n)

Sele

ctio

n

mod

elA

mou

nt

1 p

aid

(Ln

)

BSE

BSE

BSE

BSE

BSE

BSE

Ast

hma

& b

ronc

hosp

asm

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed 0

.27*

0.07

0.0

80.

09 0

.26*

0.06

0.0

30.

09 0

.09

0.10

0.9

3*0.

04

Car

diov

ascu

lar

dise

ase

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed 0

.56*

0.04

-0.0

10.

07 0

.49*

0.03

0.2

70.

09 0

.05

0.06

-0.3

40.

21

Abd

omen

dis

ease

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed 0

.32*

0.05

0.1

3*0.

07 0

.22*

0.04

0.0

50.

07 0

.04

0.08

0.1

90.

28

Dia

bete

s1

= d

iagn

osed

/ 0

=no

t di

agno

sed

0.3

6*0.

06 0

.18*

0.08

0.3

9*0.

05-0

.04

0.09

0.1

20.

09 0

.56

0.39

Epi

leps

y1

= d

iagn

osed

/ 0

= n

ot d

iagn

osed

0.2

50.

02.

-0.2

50.

29 0

.34*

0.18

-0.0

70.

29 0

.23

0.28

-0.7

30.

90

Pro

gres

sive

dis

ease

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed 0

.32*

0.05

0.1

4*0.

08 0

.37*

0.04

0.2

5*0.

09 0

.07

0.08

0.1

90.

33

Rhe

umat

olog

y di

seas

e1

= d

iagn

osed

/ 0

= n

ot d

iagn

osed

0.0

70.

05 0

.05

0.07

0.0

60.

04 0

.05

0.07

0.0

20.

08 0

.41

0.34

Legs

or

feet

dis

ease

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed 0

.00

0.06

-0.0

10.

08 0

.06

0.05

0.0

90.

08 0

.07

0.09

0.2

10.

39

Bec

k an

d ne

ck d

isea

se1

= d

iagn

osed

/ 0

= n

ot d

iagn

osed

0.0

70.

05 0

.06

0.08

0.0

40.

05 0

.04

0.08

0.0

40.

08 0

.21

0.31

Oph

thal

mol

ogy

dise

ase

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed

-0.0

50.

04 0

.06*

0.07

0.1

5*0.

04 0

.05

0.07

0.1

20.

08 0

.38

0.36

Men

tal i

llne

ss1

= d

iagn

osed

/ 0

= n

ot d

iagn

osed

0.0

20.

06 0

.11

0.09

-0.0

40.

05-0

.23*

0.09

-0.1

60.

11-0

.64

0.05

Hea

ring

-spe

ech

diffi

cult

ies

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed-0

.27*

0.08

0.1

40.

11-0

.03

0.06

-0.0

20.

11-0

.02

0.12

0.3

50.

45

Con

stan

t-3

.16*

0.15

9.0

4*0.

03-3

.7*

0.14

7.3

10.

89-3

.60.

36 6

.07*

2.1

N s

elec

ted

3690

2845

504

Ath

rho

-0.0

7-0

.01

1.7*

LR t

est

of in

dep.

eqn

s. (c

hi2 )

0.39

0.10

18.2

7*

Wal

d ch

i2 (w

hole

mod

el)

130.

24*

129.

0890

.23

1 All

am

ount

s are

pre

sent

ed a

s nat

ural

loga

rith

m tr

ansf

orm

atio

ns

*p≤

0.05

** p

≤ 0.

10

3

Different types of out-of-pocket payments

67

Tab

le 3

.5: R

esul

ts o

f the

thr

ee li

near

reg

ress

ions

, pay

ers

Exp

lan

ator

y va

riab

les

Dep

end

ent

vari

able

s (L

n t

ran

sfor

mat

ion

)

Offi

cial

co

-pay

men

tsP

aym

ents

for

“b

ough

t an

d b

rou

ght

good

s”In

form

al

pat

ien

t p

aym

ents

BSE

BSE

BSE

Gen

der

2 =

fem

ale

/ 1 =

mal

e 0

.04

0.05

0.0

20.

05-0

.11

0.15

Mar

ital

sta

tus

1 =

mar

ried

/ 0

= n

ot m

arri

ed 0

.06

0.05

0.0

80.

05-0

.09

0.17

Age

age

in y

ears

-0.0

1*0.

02-0

.007

*0.

002

-0.0

060.

007

Ed

uca

tion

leve

l

Uni

vers

ity

degr

ee1=

yes

/ 0=

no 0

.22

0.11

0.6

0*0.

12 0

.52*

*0.

28

Up

to h

igh

scho

ol1=

yes

/ 0=

no-0

.22*

0.06

-0.2

2*0.

06 0

.05

0.02

Wor

k st

atus

1 =

wor

king

/ 0

= n

ot w

orki

ng 0

.18

0.30

0.6

4*0.

15-0

.65

0.78

Nat

iona

lity

1= S

erbi

an /

0 =

oth

er 0

. 04

0.08

-0.0

60.

07 0

.18

0.23

Hou

seho

ld s

ize

num

ber

of h

ouse

hold

mem

bers

-0.0

10.

02 0

.01

0.02

-0.2

60.

05

Urb

an1

= c

ity

/ 0 =

rur

al 0

.16

0.05

0.0

60.

05-0

.03

0.06

Per

ceiv

ed h

ealt

hR

efer

ence

cat

egor

y=fa

ir

Per

ceiv

ed h

ealt

h1=

good

/ 0=

othe

rs-0

.11

0.08

-0.1

50.

09-0

.19

0.18

Per

ceiv

ed h

ealt

h1=

bad/

0=ot

her

0.4

1*0.

06 0

.30*

0.06

0.5

5*0.

22

Exe

mpt

ed g

roup

s1

= e

xem

pted

/ 0

= n

ot-e

xem

pted

0.2

2*0.

07 0

.10

0.07

0.1

40.

26

Con

sum

ptio

n pe

r pe

rson

1=

mor

e th

an a

bsol

ute

pove

rty

line

0.6

7*0.

11 0

.65*

*0.

11 1

.27

0.81

*p≤

0.05

** p

≤ 0.

10

68

Chapter 3T

able

3.5

: Res

ults

of t

he t

hree

line

ar r

egre

ssio

ns, p

ayer

s (c

onti

nued

)

Exp

lan

ator

y va

riab

les

Dep

end

ent

vari

able

s (L

n t

ran

sfor

mat

ion

)

Offi

cial

co

-pay

men

tsP

aym

ents

for

“b

ough

t an

d b

rou

ght

good

s”In

form

al

pat

ien

t p

aym

ents

BSE

BSE

BSE

Ast

hma

& b

ronc

hosp

asm

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed 0

.08

0.09

0.0

20.

08 0

.73*

*0.

35

Car

diov

ascu

lar

dise

ase

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed 0

.01

0.05

-0.0

30.

05-0

.29

0.21

Abd

omen

dis

ease

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed 0

.14*

0.07

0.0

60.

07 0

.26

0.21

Dia

bete

s1

= d

iagn

osed

/ 0

= n

ot d

iagn

osed

0.0

18*

0.08

0.1

20.

07 0

.22

0.32

Epi

leps

y1

= d

iagn

osed

/ 0

= n

ot d

iagn

osed

-0.2

60.

29-0

.02

0.27

0.0

70.

97

Pro

gres

sive

dis

ease

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed 0

.15*

0.07

0.2

50.

07-0

.46

0.27

Rhe

umat

olog

y di

seas

e1

= d

iagn

osed

/ 0

= n

ot d

iagn

osed

0.0

60.

07 0

.08

0.07

0.1

90.

29

Legs

or

feet

dis

ease

1 =

dia

gnos

ed /

0= n

ot d

iagn

osed

0.0

20.

08 0

.12

0.08

0.0

40.

33

Bec

k an

d ne

ck d

isea

se1

= d

iagn

osed

/ 0

= n

ot d

iagn

osed

0.0

60.

08-0

.04

0.08

0.2

60.

31

Oph

thal

mol

ogy

dise

ase

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed

0.0

70.

08 0

.05

0.07

-0.3

20.

29

Men

tal i

llne

ss1

= d

iagn

osed

/ 0

= n

ot d

iagn

osed

0.1

00.

09-0

.22

0.09

-0.3

10.

42

Hea

ring

-spe

ech

diffi

cult

ies

1 =

dia

gnos

ed /

0 =

not

dia

gnos

ed 0

.13

0.11

-0.0

20.

10 0

.32

0.45

Con

st 5

.50.

49 5

.32*

6.3

3*2.

03

N39

6028

4550

4

R²

0.06

0.07

0.14

*p≤

0.05

** p

≤ 0.

10

3

Different types of out-of-pocket payments

69

3.5 Discussion and conclusions

In this study, we distinguish three types of out-of-pocket patient payments in the Serbian

public health care sector: official co-payments, payments for “bought & brought goods”

and informal payments. We analyse the level of these types of payments among different

socio-demographic groups in Serbia. Given the available data, we operationalise patient

payments for “bought & brought goods” as the costs for pharmaceuticals and/or disposable

materials and nonmedical goods that the patient or the relatives brought to the hospital.

Our results show that official co-payments and payments for “bought & brought

goods” present a relatively significant proportion of out-of-pocket payments in Serbia

while pure informal payments are somewhat less frequent (only 5.7% of all health care

users). Furthermore, the share of payments for “bought & brought goods“ are higher than

the share of informal and official co-payments in the annual household consumption.

The explanation for the high share of patient payments for “bought & brought goods” in

Serbia can be found in the nature of these payments. Goods that are requested by medical

doctors are very often necessary for medical treatment, and it is less possible to ignore

bringing these goods than to pay the informal patient payments. Also, both patients

and providers might perceive payments for “bought & brought goods” differently from

informal payments, for example view them as less problematic from an ethical point

of view. One of the reason that patient do not perceive “bought & brought goods”

payments problematic from ethical point of you, is that the payments for “bought &

brought goods” have their roots in the times when the health care settings lacked medical

materials, supplies, and pharmaceuticals due to a financial crisis (Gaal et al., 2010). In

such circumstances, medical staff would ask the patient and/or relatives to bring supplies

and pharmaceuticals that are necessary for the treatment but the hospital cannot provide

due to poor funding (Kutzin et al., 2009). From the perspective of patients and their

families, this request is perceived as an act of cooperation and extra attention on the

side of the health care provider rather than corruption. Furthermore, patients and their

families might expect that by buying and bringing goods like medicine that are not

available in the hospital, they will secure better quality of care (Garfield, 2001; Stepurko

et al., 2013;Stepurko et al., 2015). At first glance, such request is not interwoven with

benefits to the health care providers and at the same time it is important for the curative

process. In practice however, “bought & brought goods” can generate additional benefits

for health care providers in several manners. For example, hospital staff may still declare

the use of supplies and pharmaceuticals (even though these are brought by the patient)

and can sell the “saved” medical goods on the black market, or use the “saved” goods in

their private practices, or simply divide the money claimed for these goods in the form of

an extra bonus. Such situations have been mentioned in research articles but the evidence

for their existence is sparse and therefore, anecdotal (CESID, 2011).Additionally, our

70

Chapter 3

regression results show that participants with a higher education have a higher probability

of paying informally. Apparently, these health care users are more aware of the common

practice of paying additionally for health services that should be provided for free. They

apply the so called “do-it-yourself” approach described in the literature as alternative

politics (Cohen, 2012). These alternative politics refer to situation when people are

dissatisfied with current government policy, they take unilateral initiatives. This means

that they obtain the desired services but in a way that is different from the way defined

by government policy, i.e. in a semi-private way (Cohen, 2012). Thus, better educated

people pay informally because they anticipate that government policy does not work and

if they do not pay informally, they will not get adequate service.

The limitations of this study are mostly related to the data. We use an existing dataset

collected by others, which provides no information about the type of treatment or type of

pharmaceuticals that patients pay for. Also, we do not have information about the obstacles

related to the utilisation of health care. More precisely, we do not know if patients forego

using services that they need because they cannot afford them. Furthermore, a recall

period of 12 months is rather long and may lead to recall bias. Information regarding

“bought & brought goods” payments is only available for inpatient care and we do not

know which particular pharmaceuticals or type of disposable material has been brought

and bought into hospital. Moreover, payments for “bought & brought goods” are reported

not only by inpatient health care users, but also by household members. The latter group

made payments for “bought & brought goods “for others. Despite these limitations, the

dataset provides a representative sample and information on official co-payments and

informal payments for health care, as well as on payments for “bought & brought goods”,

which makes it particularly useful for our study.

Our results show that there are respondents who report all three types of payments.

These findings indicate that the current health care policy regarding official co-payments

is not efficient. Health care users who pay officially do not have a guarantee that they will

receive the services which they officially pay for. In order to obtain adequate health care

service, health care users are often forced to bring necessary goods and pay informally.

Official co-payments were introduced to provide better financial sustainability in addition

to premiums from the Republic Health Insurance Fund (RHIF)(Bajec et al., 2008;Gajic-

Stevanovic, 2010; Vukovic &Perisic, 2011). Recent studies (Gavrilovic & Trmcic, 2013;

Stosic et al., 2014) emphasize that official co-payments are very low in nominal amounts

and therefore do not contribute to the financial sustainability of the health care system.

Since they are part of the official health policy, we do not question their existence here.

However, Serbian policymakers should better regulate the system of patient charges. In

particular, policymakers need to consider strategies to deal with informal payments and

eliminate the practice of “bought & brought goods”. These will be important policy

measures to decrease the overall burden of out-of-pocket payments in Serbia.

3

Different types of out-of-pocket payments

71

Despite the high share of payments for “bought & brought goods” (as indicated by

our results), they are often neglected by both researchers and policy-makers (Garfield,

2001; McCarthy, 2007). Moreover, recent results in other CEE countries show that

even when the probability of informal patient payments has decreased, the purchase of

medical supplies and pharmaceuticals that should be provided for free, continuous to

exist (Stepurko et al., 2015). The reason can be seen in the fact that in many countries

pure informal patient payments are strictly forbidden, while payments for “bought &

brought goods” are not fully regulated (Stepurko et al., 2015). Since the public health

care services in those countries are still poorly funded, payments for “bought & brought

goods” can be a valuable source for additional funding. Thus, the distinction between

the two types of unofficial payments in empirical research is important. Future research

should use longitudinal data to analyse the evolution and dynamics of these payments at

different time points. Furthermore, using qualitative data can give better insight in the

determinants of these payments. In some countries, health care users are more willing to

pay informally for public health care services than officially in private facilities (Stepurko

et al., 2015). It would be useful to examine willingness to buy and bring foods in public

services instead of paying for them in private facilities.

CHAPTER 4

Out-of-Pocket Payments for Public Health Care Services by Selected Exempted Groups in Serbia During the Period of the Post-war Health Care Reforms

Published as:

Arsenijevic, J., Pavlova, M., & Groot, W. (2013). Out-of-pocket payments for public healthcare services by selected exempted groups in Serbia during the period of post-war healthcare reforms. The International Journal of Health Planning and Management. DOI: 10.1002/hpm.2188

74

Chapter 4

Abstract

Background: This chapter focuses on the exemption mechanism that accompanies

patient co-payments for outpatient and inpatient hospital care in Serbia. The objective

was to investigate the level and dynamics of out-of-pocket payments for this type of

services by exempted groups (older than 65 years, younger than 15 years, unemployed,

disabled and individuals with low family income) compared with that by other groups.

Methods: For this purpose, we use household data collected in the LSMS carried out in

2002, 2003 and 2007. These years correspond to the start of the recent reforms in the

Serbian healthcare sector and 1 and 5 years after the start of the reform.

Results: Our results show that people who belong to exempted groups were paying

for healthcare in 2002, 2003 and 2007. They report different types of out-of-pocket

payments for outpatient and inpatient hospital care.

Conclusions: Thus, despite the ambition of the Ministry of Health in Serbia to promote

equity in healthcare as a leading aim of the reforms, the implementation of the exemption

mechanism fails to protect the targeted groups. Future exemption mechanisms should be

pro-poor oriented but should also exempt those whose health status requires a frequent

healthcare use.

4

Exemption mechanism

75

4.1 Introduction

Systems of patient co-payments are often accompanied by exemptions in order to protect

equity in health care financing and access (Blas & Limbabala, 2001; Gilson, 1997; van

Doorslaer & Wagstaff, 1992). Children, elderly, unemployed and disabled people are often

eligible for exemptions from paying co-payments because these groups are thought to be

unable to pay for health care or use health care frequently (Chapter 1). If these groups are

not exempted from co-payments, they incur high health care expenses (taken as a share

of their household income), which might have catastrophic effects on their household

budgets (Bitran & Giedion, 2003; James et al., 2006; Kruk et al., 2008; Perkins et

al., 2009; Sepehri & Chernomas, 2001). Alternatively, vulnerable groups might forgo

or delay the use of essential health care services if co-payments are high (Deninger &

Mpunga, 2005; Kruk et al., 2008; Nynator & Kutzin, 1999; Perkins et al., 2009).

As we mentioned in Chapter 1, two main reasons are put forward for the failure

of exemption mechanisms: an inadequate design of the exemption mechanism and the

inability of policy-makers to implement it in practice (Gilson et al., 1995; Masiye et

al., 2010; Russell, 2004; Sepehri & Chernomas, 2001). For example, if the design of

the exemption mechanism is based on individual identification (e.g. identifying persons

who are poor), the assessment of individual-specific eligibility criteria (e.g. being poor)

may be difficult in practice. Alternatively, group targeting that identifies eligible

groups (e.g. elderly, children, monks, civil servants etc.) may neglect the importance of

health status and ability to pay (Hanson et al., 2007; Mkandawire, 2005; Quayyum et

al., 2009; van Adams & Harnett, 1996). Individuals belonging to such groups do not

necessarily use health care frequently and are not necessarily unable to pay for health

care. At the same time, groups who actually need the exemption may be easily left out

of the exemption scheme (Quayyum et al., 2009; Tambor et al., 2010). Instead, self-

targeting gives exemption rights to everyone, but makes those rights more attractive

for a target population (e.g. allowing patients who are able to pay to skip the queue

while providing free-of-charge care for those who cannot pay and have to wait). However,

self-targeting can impact on egalitarian issues (Hanson et al., 2007; Mkandawire, 2005).

The legislation may also fail to clarify who will compensate health care providers for

the revenue loss due to the exemptions – the users who are able to pay, donors, the

government, or others (Bitran & Giedion, 2003). If the exemption policy is designed

to directly include health care providers in the process of co-payments collection, the

actual exemption of eligible patients depends on the providers’ willingness to grant the

exemption. Also, when the exemption policy only addresses direct patient payments

for medical costs (hospitalization, laboratory analysis, etc.), vulnerable groups although

exempted, may forego health care use because of high indirect costs like costs of transport

and accommodation (Kruk et al., 2008; Perkins et al., 2009; Quayyum et al., 2010;

76

Chapter 4

Russell et al., 2004). Those indirect costs are sometimes even a higher burden than the

co-payments themselves (Kruk et al., 2008; Perkins et al., 2009; Sepehri & Chernomas,

2001).

Even when the exemption mechanism is adequately designed, its implementation

might still fail in practice (James et al., 2006). The failure of the exemption mechanism

can be caused by a lack of appropriate dissemination of information about its existence

(Deininger & Mpuga, 2005; Gilson et al., 2001) or the social stigma associated with

exemptions (Messen et al., 2006). Providers’ reluctance to grant the exemption and a

negative attitude among medical staff towards exempted groups may also play a role

(Nyonator & Katzin, 1999; Perkins et al., 2009). As a result, individuals who are entitled

to an exemption, might still be asked to pay the formal co-payment or even to pay

informally either in cash or as a gift in kind (Blas & Limbabala, 2001; Borgi et al.,2003;

Nyonator & Kutzin, 1999; Perkins et al., 2009; Witter et al., 2007). The existence of

informal patient payments should be addressed as part of an overall anti-corruption policy,

but also during the implementation of exemptions from official co-payments (Kruk et al.,

2008). The adequate implementation of the exemption policy should include additional

strategies like improving the quality of care in public health care facilities and an equal

distribution of skilled health care providers in different geographical areas (Kruk et al.,

2008; Lagarde & Palmer, 2008; Perkins et al., 2009; Witter et al., 2010). If the quality

of health care provided for free is low, exempted groups may need to pay for better service

or to use alternative non-medical care (Honda et al., 2011; Kruk et al., 2008; Lagarde &

Palmer, 2006; Mkandawire, 2005; Perkins et al., 2009; Quayyum et al., 2010; Witter et

al., 2010).

The sparse evidence on the implementation of exemption mechanisms pertains only

to African and Asian countries (Jacobs, Price & Oeun, 2007; Kruk et al., 2008; Lagarde &

Palmer, 2006; Lagarde & Palmer, 2008; Messen et al., 2006; Mkandawire, 2005; Sepehri

& Chernomas, 2005). As suggested by these studies, due to the reasons described above,

only in a few cases, an adequate exemption policy is successfully implemented. Still,

there are many middle-income countries in Central and Eastern Europe with ongoing

health care reforms. These reforms include, among other measures, the implementation

of patient co-payments accompanied by exemption mechanisms. Hitherto, there is

no evidence from these countries on the effectiveness of their exemption mechanisms

(Balabanova, 2007; Polleti et al., 2007).

This chapter focuses on the exemption mechanism that accompanies patient co-

payments in Serbia, one of the Eastern European countries. Our aim is to review the

problems with the exemption mechanism in Serbia reported in the literature, and to

investigate the level and dynamics of out-of-pocket payments for outpatient and inpatient

hospital care by selected exempted groups. We are specifically interested in whether

exempted groups pay official co-payments when they are supposed to be exempted from

4

Exemption mechanism

77

such fees. For this purpose, we perform quantitative analyses using the Serbian Living

Standard Measurement Study (see World Bank, 2011) carried out in 2002, 2003 and

2007 (i.e. when co-payments were implemented in Serbia, and 1 and 5 years after their

implementation).

4.2 Background

4.2.1 The context of the exemption mechanism in Serbia As we outlined in Chapter 1, during 2002, the first post-war health care reforms were

launched in Serbia. The health care reforms, launched by the Ministry of Health, have

focused on the renovation of medical equipment and the improvement of physicians’ skills

(World Bank, 2009). The health care financial mechanism based on compulsory health

insurance, also required changes since it was unable to assure resources for an adequate

service provision (Gajic-Stevanovic, 2010). The reforms of the health care financial

mechanism were coordinated by the HIF. The introduction of patient co-payments in

2002 was one of the first measures to improve the financial sustainability of the already

“poor” health care system.

At present, the Ministry of Health emphasizes the importance of technical equipment

innovation, re-building of the current infrastructure and staff education HIF, on the

other side, focuses on financial reforms, more particularly on reforms of the official co-

payments. Thus, the two main stakeholders have a different focus regarding the reform

path. However, neither of these stakeholders directly focuses on the barriers to access,

such as indirect and informal payments, but also the long waiting times in the Serbian

health care system. Although these factors are marked as a problem to equity in health

care access (Bajec et al., 2008), there is still no systematic approach to solve it.

4.2.2. The design of the exemption mechanism in Serbia and design-related problems The system of patient co-payments in Serbia (introduced in 2002) is accompanied by

an exemption mechanism with the objective to assure equity in access to health care. It

concerns both outpatient and inpatient services. The Serbian Law on Health Insurance

defines several population groups that are exempted from patient co-payments: children

younger than 15 years, pregnant women, persons older than 65 years, disabled persons,

HIV infected persons, monks, people with low family income, unemployed, chronically

ill people, military service servants, monks people registered as refuges and the Roma

population (Chapter 1). According to the Serbian law, groups that are exempted from

patient co-payments should not be charged at all when they use health care services (Bajec

et al., 2008). A detailed definition of those groups is available in guidelines (Official

78

Chapter 4

Gazette of RS, n. 1/2007, 52/2007, 99/2007, 14/2008, 20/2008, 7/2009, 82/2009 &

23/2010). About 1.2 million people (16% of the total population) have the status of

exempted individuals, i.e. belonging to at least one of the exempted groups (Institute

for health Insurance of Serbia, 2010). The share of the officially exempted population

is similar to that in many developing countries (Bitran & Giedion, 2003; Palmer et

al., 2004; Poletti et al., 2007) but relatively low compared to some European countries

(Tambor et al., 2011). For example, patient co-payments apply only to 60% of the

population in Italy and to 50% in Portugal (Tanner, 2008).

It should be noted however that not all groups included in the exemption list are

unable to pay or need health care frequently (e.g. monks, children and elderly as mentioned

above). At the same time, some of the groups that cannot afford to pay co-payments are

not clearly defined, even though from the point of view of the legislation, they have in

principle the right to an exemption. The reason for this is the fact that the description

of exemptions in guidelines are not clear and that they can change considerably over a

short period of time. This is the case of people with low family income. In Serbia, people

with an income below the minimum net income, should be exempted from regular

patient co-payments (Official Gazette of RS, 11/2010). The threshold of the minimum

net income is calculated by the Serbian Statistical Office and it is officially used by the

Serbian government (Statistical Office of the Republic of Serbia, 2010). However, that

income threshold has changed almost every year (even twice a year) and so has the right to

an exemption for people with low family income. The HIF adjusts the threshold every six

months. This creates confusion among patients and health care providers. Although the

definition of low family income status has been regulated for a certain period of time by

special policy documents (Official Gazette of RS, n. 1/2007, 52/2007, 99/2007, 14/2008,

20/2008, 7/2009, 82/2009 & 23/2010), this information is not readily available for the

individual patient.

Also, guidelines are not clear enough for the majority of the population. They have

been written in strict law-centered language. In conclusion, the procedures described in

guidelines need to be simplified and made available for the most of the users. Moreover,

private outpatient and inpatient hospital services in Serbia are not included in the system

of compulsory health insurance (Gajic-Stevanovic, 2010). Therefore, private health care

providers are not obliged to follow the exemption policy and may still charge patients

belonging to exempted groups. The lack of an exemption mechanism in private health

care institutions can also lead to the failure of the exemption mechanisms especially if

the patient needs quick access to health care for which he/she has to wait at the public

hospitals (Lagarde & Palmer, 2008). For example, due to access-related problems (e.g.

long waiting times), Serbian patients often use private health care services even when

they have a referral to specialized care in the public health care sector.

4

Exemption mechanism

79

4.2.3 The implementation of the exemption policy and current implementation problemsBesides the design, the successful implementation of exemption policy requires clear

agreements among the main stakeholders. According to the Serbian legislation related

to exemptions, the health care costs of the exempted groups are covered by the Ministry

of Health. For this purpose, the ministry provides 12.3% of the annual net revenue of

HIF. However, the amount that the institute spends on exempted groups is much lower

than that percentage and moreover, it is decreasing (Chapter 1). In 2004, the institute

spent 3.14% of the total revenue for exempted groups, and in 2009, this percentage was

only 1.88% (Bajec et al., 2008). What is more, in 2007, the Ministry of Health did not

transfer the necessary amount for the exempted groups to HIF (World Bank, 2009). One

of the reasons that the Ministry of Health sends money irregularly is the lack of resources

from the central budget. The other reason is the lack of clear responsibility regarding the

implementation of the exemption mechanism between HIF and Ministry of Health. In

particular, there is no policy document that defines the role of the main stakeholders and

providers regarding the exempted groups (Bajec et al., 2008). Thus, there is no single

institution responsible for the application of the entire exemption mechanism. Also, the

increased level of corruption among the main stakeholders can be an additional reason

for irregular money transfers (Chapter 1). Anecdotal evidence describes “unwritten”

agreements between HIF and Ministry of Health for not-paying the costs for exempted

groups to the providers or choosing the providers that will be paid (Stamenkovic, 2011).

During the period 2007-2010, new documents were provided by the Ministry of

Health as “guidelines” for health institutions how to apply the legislation concerning

exemptions (Official Gazette of Republic Serbia, 2007, 2008, 2009, 2010). Despite

these new guidelines, the definition of some groups that are eligible for exemptions (e.g.

people with low family income) is still unclear. Moreover, the procedure of obtaining

an exemption status is rather complicated. For example, if a person with low family

income applies for the exemption, he has to provide several different documents (e.g.

a confirmation of the person’s individual income, confirmation of property), and bring

them to the nearest department of HIF.

Then, HIF decides whether to grant the exemption and in case of a positive decision,

issues an official confirmation (so called Obrazac UP-2) to the person. This confirmation

is valid only with the person’s health card (the substitution for the health insurance

card in Serbia). For some of the exempted groups (such as people with low income), the

exemption status is granted by HIF, for others (such as people diagnosed by HIV), it

needs to be confirmed by a GP. When the person visits the GP in primary health care,

the administrative staff puts a stamp in the health care card that confirms the status of

the exempted person. Overall, it is hard and time-consuming for a patient to obtain

all administrative documents and to establish whether one is entitled to an exemption.

80

Chapter 4

Recently, with the aim to enhance the implementation of capitation in primary health

care, HIF started introducing electronic health cards. Since each insured person will have

such health card, this can be used for a more rational targeting of exempted groups.

However, the new health care cards can be a possible solution for avoiding visible

notification of being exempted/not exempted directly on the card.

In addition to this, the issues concerning the social stigma associated with the

exemptions and possible providers’ reluctance to grant the exemption or to induce

informal payments (the latter is commonly reported in Eastern European countries; see

Stepurko et al., 2010), raises the question whether the exemption mechanism in Serbia is

actually effective. In other words: did exempted groups pay for health care services that

should be provided free-of-charge to them?

Hitherto, there are no official data about the effectiveness of the exemption mechanism

that was introduced in 2002. It is only known (Bajec et al., 2008) that the exemption

mechanism has never been fully implemented in Serbia due to reasons described above

(mainly the lack of clear responsibility of the main stakeholders, and the lack of necessary

financial resources). To examine the effectiveness of the Serbian exemption mechanism,

in this study, we investigate the out-of-pocket payments for outpatient and inpatient

hospital services by selected exempted groups compared to the out-of-pocket payments

by other groups.

4.3 Methods

As in Chapter 2 and Chapter 3, for our analysis, we use the LSMS data for Serbia. For

the purpose of this study, we use the data collected at three time points: 2002, 2003

and 2007 among 19725, 8027 and 17375 respondent respectively (The World Bank

group, 2011). The three samples are representative for Serbia (World Bank, 2011). As we

mentioned in Chapter 1, the attempt was to have panel data for 2002 and 2003 but this

was not achieved. As a result, the number of hospital users who participated in both 2002

and 2003 survey is very low (less than 2%) and prohibits any separate panel analysis.

The data collected for 2007 are not connected to the previous two years. Therefore, we

consider the data as cross-sectional.

As explained above, we focus on both outpatient and inpatient hospital care. We

examine five exempted groups: older than 65 years, younger than 15 years, disabled,

unemployed and people with low family income status. These are the exempted groups

that we could identify based on the data. Moreover, the ability to pay of these exempted

groups is often questioned in Serbia and their utilization of health care services is often

intensive. The questionnaire that was used in the LSMS survey, does not allow us to

identify respondents who belong to other exempted categories, like pregnant women

4

Exemption mechanism

81

and HIV infected persons. The identification of another relevant group – the group of

individuals with low family income – is also not straightforward because (as mentioned

earlier) the poverty line is continuously changing even during one year. Nevertheless,

considering the importance of this group for our analysis (they have been identified as

vulnerable by Serbian guidelines), we took the lowest poverty line for each year (2002,

2005 and 2007) to identify individuals belonging to this exempted group.

Based on the LSMS data, we compare data on the probability of paying and the amount

of out-of-pocket payments for outpatient and inpatient hospital services across the five

exempted groups that we identified. We also compare the out-of-pocket payments for

outpatient and inpatient hospital services paid by the five exempted groups with that of

other population groups (non-exempted and exempted group that we could not identify).

We present data for all five exempted groups together, for each of the exempted groups

separately, and for other population groups.

We divide the data on out-of pocket payments for inpatient hospital care available in

the LSMS data into four payment categories:

– Official co-payments that should not apply to the exempted groups. This group

incorporates official co-payments for physician visits, hospitalizations, pharmaceuticals

prescribed on behalf of HIF, laboratory analyses, disposal material (like surgical).

– Indirect patient payments like transport costs that are not included in the exemption

scheme. Those data are reported for health care users but also for accompanying

households’ members.

– Payments for goods bought & brought by the patient in case of hospitalization. This

group consists of payments for medical goods (including pharmaceuticals, disposal

materials and orthopedic materials) that should be provided by the hospital to any

hospitalized patient for free, but the patient is required to bring to the hospital.

Data on payments for goods bought & brought by patients in outpatient were not

collected.

– Informal payments. This group incorporates one part of the informal payments that

were requested by the medical staff either in cash or in kind, and payments that were

given as a gift.

Two types of analyses are performed (SPSS 17.00) to analyze the variation in the out-

of-pocket payments – comparative descriptive analysis and regression analysis. First,

we compare the five exempted groups and the rest of the respondents with regard to

the propensity to pay (I’ve paid, I didn’t pay) officially, by buying and bringing goods,

informally and indirectly. We use χ²-tests for the comparison of this first set of dependent

variables given their binary nature. Then, we compare the amounts reported by the

five exempted groups and the rest of the respondents. We compare this second set of

dependent variables using ANOVA, more precisely Bonferroni correction as post hoc

82

Chapter 4

test. Furthermore, we explore the differences between the selected exempted groups and

other population groups at different time points, namely in 2002 (the introduction of the

exemption mechanism), in 2003 (one year after) and in 2007 (5 years after).

Second, for both outpatient and inpatient hospital services, we perform logistic and

linear regressions. We perform a set of logistic regression analyses with the first set of

dependent variables indicating the propensity to pay (I’ve paid, I didn’t pay) for different

types of payments. Then, we run a set of linear regressions with the amounts paid (the

second set of dependent variables), using only the subset of respondents who paid for

hospital services. As a basic year, we take 2002, providing two dummy variables for

2003 and 2007. The dummy variables are entered in the same block. An indicator for

the exempted groups (taken together) is also included as a dummy variable. Interactions

between the dummy for exempted groups and each dummy for the year are also included

in the model. We also include basic social demographic characteristics such as gender

and marital status. We do not include age and income because they are already used to

define the respective exempted groups. Thus, we obtain two sets of regression models

(related to the propensity and the amounts respectively) with the same predictors (social

demographic variables, two dummy variables for the two years – 2003 and 2007 and an

indicator for exempted groups). We also include relevant interactions between variables.

All amounts are expressed in Serbian dinars (Serbian national currency; 1 USD ≈ 88.3

CSD) and they have been corrected by the Consumer Price Index (CPI index) for the

inflation rate (for a definition of CPI index see OECD, 2010). Since the analysis is on a

micro level, weighting is not used.

4.4 Results

4.4.1 Descriptive statistics and comparisonsIn 2002, 2003 and 2007, the five exempted groups analyzed in our study, reported

payments for outpatient and inpatient hospital services (see Table 4.1) while these persons

should not have paid any official co-payments according to the Serbian exemption policy.

The payments by the five exempted groups include payments of official fees, indirect

payments, payments for “bought & brought goods”, and informal payments.

Our results for outpatient care (see Table 4.2) show that from the five exempted

groups, outpatient care was most frequently used by elderly people. In outpatient care,

exempted groups most often paid official fees for visiting a physician (representing 8.9%,

6.8% and 29.6% of all users in 2002, 2003 and 2007 respectively, (see Table4.1). Official

fees for pharmaceuticals were also relatively often paid by the five exempted groups.

However, the highest amounts for outpatient care were paid in 2007 for disposable

materials by persons with two or more exemption criteria (see Table 4.2).

4

Exemption mechanism

83

For all types of outpatient payments (official and informal), ANOVA models are not

statistically significant. Thus, for outpatient payments, we do not observe statistically

significant differences across the five exempted groups and when we compare the five

exempted groups to other population groups (i.e. non-exempted and exempted groups

that we could not identify based on our data).

Inpatient hospital care was also most frequently used by elderly people compared to the

other four exempted groups (see Table 4.3). If we look at all five exempted groups together

(see Table 4.1), in inpatient care, they most often paid for transport and pharmaceuticals

(both official fees for pharmaceuticals and payments for pharmaceuticals brought to the

hospital). The payment of official fees for hospitalization by the five exempted groups was

also frequent (representing 9.6%, 9.8% and 26.5% of all users in 2002, 2003 and 2007

respectively, see Table 4.1). If we look through different exempted groups, the amount

paid for hospitalizations varies from 808.00 CSD in 2003 (9.1 USD) to 5255.00 CSD

(59.4 USD) in 2007 (see Table 4.3). The highest amounts for hospitalization were paid in

2003 by the group of older than 65 and the lowest amounts were reported by the group

younger than 15 (also in 2003).

84

Chapter 4T

able

4.1

: The

dis

trib

utio

n of

out

-of-

pock

et p

atie

nt p

aym

ents

am

ong

the

sele

cted

exe

mpt

ed g

roup

s an

d ot

her

popu

lati

on g

roup

s

Ou

t-of

-poc

ket

pat

ien

t p

aym

ents

rep

orte

d in

20

02O

ut-

of-p

ock

et p

atie

nt

pay

men

ts r

epor

ted

in

2003

Ou

t-of

-poc

ket

pat

ien

t p

aym

ents

rep

orte

d in

20

07

Sele

cted

exe

mp

ted

gr

oup

sO

ther

pop

ula

tion

gr

oup

sSe

lect

ed e

xem

pte

d

grou

ps

Oth

er p

opu

lati

on

grou

ps

Sele

cted

exe

mp

ted

gr

oup

sO

ther

pop

ula

tion

gr

oup

s

Rep

orte

d

pay

men

ts *

No

pay

men

ts

rep

orte

d *

Rep

orte

d

pay

men

ts *

No

pay

men

ts

rep

orte

d *

Rep

orte

d

pay

men

ts *

No

pay

men

ts

rep

orte

d *

Rep

orte

d

pay

men

ts *

No

pay

men

ts

rep

orte

d *

Rep

orte

d

pay

men

ts *

No

pay

men

ts

rep

orte

d *

Rep

orte

d

pay

men

ts *

No

pay

men

ts

rep

orte

d *

Ou

tpat

ien

t ca

reN

use

rs=

456

2N

use

rs=

170

3N

use

rs=

467

5

For

mal

pat

ien

t fe

es:

Phy

sici

an v

isit

s40

8(8.

9)19

45(4

2.6)

1179

(25.

8)91

0(19

.9)

115(

6.8)

792(

46.5

)43

5(25

.5)

361(

21.2

)13

86(2

9.6)

1157

(24.

7)14

12(3

0.2)

720(

15.4

)

Pha

rmac

euti

cals

286(

6.3)

1777

(38.

9)69

4(15

.2)

1279

(28.

1)10

5(6.

2)80

2(47

.1)

297(

17.4

)49

9(29

.3)

1179

(25.

2)13

64(2

9.2)

933(

20.0

)11

99(2

5.6)

Labo

rato

ry a

naly

ses

180(

3.9)

1866

(40.

9)41

2(9.

0)15

52(3

4.0)

72(4

.2)

835(

49.0

)16

2(9.

5)63

4(37

.2)

357(

7.6)

2186

(46.

8)41

1(8.

8)17

21(3

6.8)

Dis

posa

ble

mat

eria

ls30

(0.7

)19

91(4

3.6)

34(0

.7)

1860

(40.

7)10

(0.6

)89

7(52

.7)

10(0

.6)

786(

46.2

)58

(1.2

)24

85(5

3.2)

30(0

.6)

2102

(45.

0)

Info

rmal

pay

men

ts:

Mon

ey r

eque

sted

by

staf

f8(

0.2)

2124

(46.

6)9(

0.2)

2043

(44.

7)4(

0.2)

903(

53.0

)3(

0.2)

793(

46.6

)5(

0.1)

2538

(54.

3)6(

0.1)

2126

(45.

5)

Gif

ts t

o st

aff

51(1

.1)

2073

(45.

4)65

(1.4

)19

44(4

2.6)

11(0

.6)

896(

52.6

)10

(0.6

)78

6(46

.2)

33(0

.7)

2510

(53.

7)39

(0.8

)20

93(4

4.8)

Ind

irec

t p

aym

ents

:

Tran

spor

t76

4(16

.7)

1366

(29.

9)79

0(17

.3)

1202

(26.

3)31

5(18

.5)

592(

34.8

)28

0(16

.4)

516(

30.3

)59

9(12

.8)

1944

(41.

6)50

8(10

.9)

1624

(34.

7)

*Num

bers

in b

rack

ets p

rese

nt th

e per

cent

age o

f N u

sers

Not

e: D

ata

rela

ted

to o

utpa

tien

t car

e hav

e re-

call

per

iod

of o

ne m

onth

and

dat

a re

late

d to

inpa

tien

t car

e hav

e re-

call

per

iod

for

one y

ear;

pay

men

ts fo

r br

ough

t and

bou

ght g

oods

are

not

ava

ilab

le fo

r ou

tpat

ient

pa

ymen

ts.

4

Exemption mechanism

85

Tab

le 4

.1: T

he d

istr

ibut

ion

of o

ut-o

f-po

cket

pat

ient

pay

men

ts a

mon

g th

e se

lect

ed e

xem

pted

gro

ups

and

othe

r po

pula

tion

gro

ups

(con

tinu

ed)

Ou

t-of

-poc

ket

pat

ien

t p

aym

ents

rep

orte

d in

20

02O

ut-

of-p

ock

et p

atie

nt

pay

men

ts r

epor

ted

in

2003

Ou

t-of

-poc

ket

pat

ien

t p

aym

ents

rep

orte

d in

20

07

Sele

cted

exe

mp

ted

gr

oup

sO

ther

pop

ula

tion

gr

oup

sSe

lect

ed e

xem

pte

d

grou

ps

Oth

er p

opu

lati

on

grou

ps

Sele

cted

exe

mp

ted

gr

oup

sO

ther

pop

ula

tion

gr

oup

s

Rep

orte

d

pay

men

ts *

No

pay

men

ts

rep

orte

d *

Rep

orte

d

pay

men

ts *

No

pay

men

ts

rep

orte

d *

Rep

orte

d

pay

men

ts *

No

pay

men

ts

rep

orte

d *

Rep

orte

d

pay

men

ts *

No

pay

men

ts

rep

orte

d *

Rep

orte

d

pay

men

ts *

No

pay

men

ts

rep

orte

d *

Rep

orte

d

pay

men

ts *

No

pay

men

ts

rep

orte

d *

N u

sers

= 1

002

N u

sers

= 3

89N

use

rs=

1052

For

mal

pat

ien

t fe

es:

Hos

pita

liza

tion

96 (9

.6)

433(

43.2

)18

6(18

.6)

287(

28.6

)38

(9.8

)19

1(49

.5)

61(1

5.8)

99(2

5.4)

279(

26.5

)28

4(26

.9)

228(

21.7

)26

1(24

.8)

Pha

rmac

euti

cals

116

(11.

6)36

0(36

.0)

136(

13.6

)30

1(30

.1)

61(1

5.8)

168(

43.4

)43

(11.

1)11

4(29

.5)

130(

12.4

)37

2(35

.4)

102(

9.7)

248(

23.6

)

Labo

rato

ry a

naly

ses

27 (2

.7)

440(

43.9

)49

(4.9

)37

2(37

.2)

11(2

.8)

218(

56.5

)19

(4.9

)13

8(35

.8)

62(5

.9)

351(

33.4

)65

(6.2

)24

3(23

.1)

Dis

posa

ble

mat

eria

ls43

(4.3

)42

9(42

.8)

60(6

.0)

365(

36.5

)14

(3.6

)21

5(55

.7)

16(4

.1)

141(

36.5

)41

(3.9

)26

5(25

.2)

31(2

.9)

204(

19.4

)

Bou

ght

& b

roug

ht g

oods

:

Pha

rmac

euti

cals

137(

13.7

)34

2(34

.2)

143(

14.2

)28

8(28

.8)

47(1

2.2)

182(

47.2

)40

(10.

4)11

7(30

.3)

136(

12.9

)10

2(9.

7)73

(6.9

)94

(8.9

)

Dis

posa

ble

mat

eria

ls38

(3.8

)43

5(43

.5)

38(3

.8)

382(

38.1

)9(

2.3)

220(

57.0

)11

(2.8

)14

6(37

.8)

8(0.

7)55

6(52

.8)

14(1

.3)

475(

45.2

)

Ort

hope

dics

16 (

1.6)

457(

14.6

)11

(1.1

)40

3(40

.3)

10(2

.6)

219(

56.7

)6(

1.6)

151(

39.1

)14

(1.3

)54

9(52

.2)

15(1

.4)

474(

45.1

)

Info

rmal

pay

men

ts:

Mon

ey r

eque

sted

by

staf

f8

(0.8

)48

0(48

.0)

14(1

.4)

420(

42.0

)3(

0.8)

226(

58.5

)1(

0.3)

156(

40.4

)1(

0.0)

75(7

.1)

3(0.

3)68

(6.5

)

Gif

ts t

o st

aff

71(7

.1)

408(

40.8

)79

(7.8

)35

6(35

.6)

30(7

.8)

199(

51.6

)18

(4.7

)13

9(36

.0)

32(3

.0)

75(7

.1)

59(5

.6)

68(6

.5)

Indi

rect

pay

men

ts:

Tran

spor

t21

4 (2

1.4)

271(

27.1

)21

2(21

.1)

224(

22.4

)91

(23.

6)13

8(35

.8)

62(1

6.1)

95(2

4.6)

234(

22.2

)16

9(16

.1)

168(

15.9

)10

5(9.

9)

*Num

bers

in b

rack

ets p

rese

nt th

e per

cent

age o

f N u

sers

Not

e: D

ata

rela

ted

to o

utpa

tien

t car

e hav

e re-

call

per

iod

of o

ne m

onth

and

dat

a re

late

d to

inpa

tien

t car

e hav

e re-

call

per

iod

for

one y

ear;

pay

men

ts fo

r br

ough

t and

bou

ght g

oods

are

not

ava

ilab

le fo

r ou

tpat

ient

pa

ymen

ts.

86

Chapter 4Ta

ble

4.2:

Out

-of-

pock

et p

aym

ents

for

outp

atie

nt s

ervi

ces

duri

ng t

he p

revi

ous

mon

th, a

mou

nts

in d

inar

s; 1

USD

≈ 8

8.3

dina

rs

For

mal

pat

ien

t fe

es f

or

ph

ysic

ian

vis

its

For

mal

pat

ien

t fe

es f

or

ph

arm

aceu

tica

lsF

orm

al p

atie

nt

fees

for

la

bor

ator

y an

alys

esF

orm

al p

atie

nt

fees

for

d

isp

osab

le m

ater

ials

2002

2003

2007

2002

2003

2007

2002

2003

2007

2002

2003

2007

Tot

al

sam

ple

(use

rs)

4562

(100

.0)

1703

(100

.0)

4675

(100

.0)

4562

(100

.0)

1703

(100

.0)

4675

(100

.0)

4562

(100

.0)

1703

(100

.0)

4675

(100

.0)

4562

(100

.0)

1703

(100

.0)

4675

(100

.0)

N u

sed

(%to

tal)

4562

(100

.0)

1703

(100

.0)

4675

(100

.0)

4036

(88.

5)17

03(1

00.0

)46

75(1

00.0

)40

30(8

8.3)

1687

(99.

00)

4675

(100

.0)

3915

(85.

8)16

67(9

7.9)

4675

(100

.0)

N p

aid

(%to

tal)

1587

(34.

8)55

0(3

2.3)

2798

(59.

9)98

0(2

1.4)

402

(23.

6)21

12(4

5.2)

592

(12.

9)23

4(1

3.7)

768

(16.

4)64

(1.4

)20

(1.2

)88

(1.9

)

Mea

n pa

id72

.477

.942

.34

127.

713

9.1

2398

.715

80.0

719.

710

44.7

5146

.219

05.5

3900

.6

SD19

8.95

220.

914

7.8

284.

842

0.9

4738

.925

07.3

2289

.317

43.9

1489

4.6

3490

.755

43.0

Low

-in

com

e an

d u

nem

plo

yed

N u

sed

(%to

tal)

284

(6.2

)94

(5.5

)15

4(3

.3)

242

(5.3

)94

(5.5

)15

4(3

.3)

247

(5.4

)94

(5.5

)15

4(3

.3)

242

(5.3

)94

(5.5

)15

4(3

.3)

N p

aid

(%to

tal)

119

(2.6

)29

(1.7

)81

(1.7

)45

(0.9

)16

(0.9

)72

(1.5

)50

(1.1

)15

(0.9

)23

(0.5

)3

(0.0

)1

(1.9

)3

(0.1

)

Mea

n pa

id67

.31

120.

051

.458

.949

7.2

216.

243

7.0

2032

.018

11.3

943.

313

800.

071

33.3

SD14

2.58

347.

282

.497

.517

35.6

402.

798

9.7

3299

.321

61.2

1275

.0-

1114

6.9\

Dis

able

d in

div

idu

als

N u

sed

(%to

tal)

60(1

.3)

20(1

.2)

386

(8.3

)49

(1.1

)20

(1.2

)38

6(8

.3)

49(1

.1)

20(1

.2)

386

(8.3

)43

(0.9

)-

386

(8.3

)

N p

aid

(%to

tal)

25(0

.5)

9(0

.5)

223

(4.8

)17

(0.4

)7

(0.4

)21

1(4

.5)

8(0

.2)

4(0

.2)

68(1

.5)

1(0

.0)

Mea

n pa

id27

2.0

28.9

66.2

91.2

68.6

316.

939

5.6

107.

513

96.6

9000

.0-

6545

.7

SD10

91.0

10.5

112.

112

0.1

50.1

589.

650

8.1

129.

919

27.1

--

8515

.3

Old

er t

han

65

year

s

N u

sed

(%to

tal)

1401

(30.

7)60

0(3

5.2)

1385

(29.

6)12

30(2

6.9)

600

(35.

2)13

85(2

9.6)

1231

(26.

9)60

0(3

5.2)

1385

(29.

6)12

03(2

6.4)

600

(35.

2)13

85(2

9.6)

N p

aid

(%to

tal)

227

(4.9

)74

(4.3

)92

9(1

9.9)

182

(3.9

)77

(4.5

)74

5(1

5.9)

111

(2.4

)52

(3.1

)21

4(4

.6)

19(0

.4)

8(0

.5)

Mea

n pa

id71

.075

.34

59.2

214.

516

9.5

238.

339

8.4

320.

078

5.7

3894

.716

17.5

1685

.1

SD13

2.0

158.

815

0.8

385.

329

6.5

418.

961

5.4

469.

811

04.7

6605

.731

96.7

1973

.1

4

Exemption mechanism

87

Tab

le 4

.2: O

ut-o

f-po

cket

pay

men

ts fo

r ou

tpat

ient

ser

vice

s du

ring

the

pre

viou

s m

onth

, am

ount

s in

din

ars;

1 U

SD ≈

88.

3 di

nars

(co

ntin

ued)

For

mal

pat

ien

t fe

es f

or

ph

ysic

ian

vis

its

For

mal

pat

ien

t fe

es f

or

ph

arm

aceu

tica

lsF

orm

al p

atie

nt

fees

for

la

bor

ator

y an

alys

esF

orm

al p

atie

nt

fees

for

d

isp

osab

le m

ater

ials

2002

2003

2007

2002

2003

2007

2002

2003

2007

2002

2003

2007

Tota

l sa

mpl

e(us

ers)

4562

(1

00.0

)17

03

(100

.0)

4675

(1

00.0

)45

62

(100

.0)

1703

(1

00.0

)46

75

(100

.0)

4562

(1

00.0

)17

03

(100

.0)

4675

(1

00.0

)45

62

(100

.0)

1703

(100

.0)

4675

(1

00.0

)

You

nge

r th

an 1

5 ye

ars

N u

sed

(%to

tal)

564

(12.

3)17

7 (1

0.4)

401

(8.6

)50

7 (1

1.1)

177

(10.

4)40

1 (8

.6)

500

(10.

9)17

7 (1

0.3)

401

(8.6

)49

7 (1

0.9)

177

(10.

4)40

1 (8

.6)

N p

aid

(%to

tal)

30

(0.6

)2

(0.1

)34

(0

.7)

37

(0.9

)4

(0.2

)34

(0

.7)

6 (0

.1)

1 (0

.1)

19

(0.4

)7

(0.1

)1

(0.1

)4

(0.1

)

Mea

n pa

id57

.340

.020

0.9

115.

635

.026

3.9

128.

350

0.0

1617

.417

07.1

300.

037

25.0

SD48

.528

.28

460.

917

9.8

19.2

399.

396

.01

-22

67.2

1132

..3-

4262

.5

Two

or m

ore

of t

he

abov

e ex

emp

tion

cri

teri

a

N u

sed

(%to

tal)

44

(0.9

)16

(0

.9)

217

(4.6

)35

(0

.8)

16

(0.9

)21

7 (4

.6)

39

(0.8

)-

217

(4.6

)-

-21

7 (4

.6)

N p

aid

(%to

tal)

7 (0

.1)

1 (0

.0)

119

(2.5

)5

(0.1

)1

(0.1

)11

7 (2

.5)

5 (0

.1)

-33

(0

.7)

--

10

(0.2

)

Mea

n pa

id37

.140

.068

.473

.060

.028

2.9

80.0

-30

9.7

--

6993

.0

SD24

.3-

99.1

34.5

-40

6.1

74.8

-40

5.2

--

8581

.7

Oth

er p

opu

lati

on g

rou

ps

N u

sed

(%to

tal)

2209

(4

8.4)

796

(46.

7)21

32

(45.

6)19

73

(43.

2)79

6 (4

6.7)

2132

(4

5.6)

1964

(4

3.0)

796

(46.

7)21

32

(45.

6)18

94

(41.

5)79

6 (4

6.7)

2132

(4

5.6)

N p

aid

(%to

tal)

1179

(2

5.8)

435

(25.

5)14

12

(30.

2)69

4 (1

5.2)

297

(17.

4)93

3 (2

0.0)

412

(9.0

)16

2 (9

.5)

411

(8.8

)34

(0

.7)

10

(0.6

)30

(0

.6)

Mea

n pa

id69

.276

.77

77.4

111.

311

5.3

202.

854

4.5

743.

011

11.0

4428

.82

1107

.144

63.7

SD15

0.7

222.

5621

1.4

265.

623

8.3

423.

919

26.3

2519

.119

51.3

1325

3.9

993.

652

23.4

88

Chapter 4 T

able

4.2

: Out

-of-

pock

et p

aym

ents

for

outp

atie

nt s

ervi

ces

duri

ng t

he p

revi

ous

mon

th, a

mou

nts

in d

inar

s; 1

USD

≈ 8

8.3

dina

rs (

cont

inue

d)

Pay

men

ts f

or t

ran

spor

tM

oney

req

ues

ted

by

med

ical

sta

ffG

ifts

to

med

ical

sta

ff

2002

2003

2007

2002

2003

2007

2002

2003

2007

Tota

l sa

mpl

e(us

ers)

4562

(100

.0)

1703

(100

.0)

4675

(100

.0)

4562

(100

.0)

1703

(100

.0)

4675

(100

.0)

4562

(100

.0)

1703

(100

.0)

4675

(100

.0)

N u

sed

(%to

tal)

4122

(90.

3)17

03 (1

00.0

)46

75 (1

00.0

)41

84 (9

1.7)

1083

(63.

6)46

75 (1

00.0

)40

95 (8

9.8)

1667

(97.

9)46

75 (1

00.0

)

N p

aid

(%to

tal)

1554

(34.

1)59

5 (3

4.9)

1107

(23.

7)17

(0.4

)7

(0.4

)11

(0.2

)11

6 (2

.5)

21 (1

.2)

72 (1

.5)

Mea

n pa

id33

1.1

398.

051

4.9

2965

.925

25.7

1256

.861

1.9

729.

510

30.1

SD64

1.1

829.

974

3.3

7823

.655

24.1

1887

.516

70.0

1489

.338

86.4

Low

-in

com

e an

d u

nem

plo

yed

N u

sed

(%to

tal)

253

(5.5

)94

(5.5

)15

4 (3

.3)

257

(5.6

)94

(5.5

)15

4 (3

.3)

251

(5.5

)94

(5.5

)15

4 (6

.8)

N p

aid

(%to

tal)

98 (2

.1)

31 (1

.8)

41 (0

.9)

1 (0

.0)

1 (0

.1)

1 (0

.0)

7 (0

.1)

3 (0

.2)

2 (0

.0)

Mea

n pa

id34

0.3

563.

743

2.4

150.

015

00.0

200.

081

7.1

133.

330

0.0

SD47

0.4

946.

161

8.2

--

-10

48.5

144.

328

2.8

Dis

able

d in

div

idu

als

N u

sed

(%to

tal)

51 (1

.1)

20 (1

.2)

386

(8.3

)50

(1.1

)-

386

(8.3

)50

(1.1

)-

386

(8.3

)

N p

aid

(%to

tal)

21 (0

.5)

7 (0

.4)

102

(2.2

)1

(0.0

)-

1 (0

.0)

2 (0

.0)

-8

(0.2

)

Mea

n pa

id66

0.0

1814

.363

1.0

900.

0-

200.

027

5.0

-16

6.3

SD12

67.9

3627

.185

7.0

--

-

154.

9

Old

er t

han

65

year

s

N u

sed

(%to

tal)

1267

(27.

8)60

0 (3

5.2)

1385

(29.

6)12

98 (2

8.5)

-13

85 (2

9.6)

1271

(27.

8)60

0 (3

5.2)

1385

(29.

6)

N p

aid

(%to

tal)

484

(11.

7)21

9 (1

2.9)

347

(7.4

)4

(0.0

)-

1(0.

0)39

(0.9

)6

(0.4

)15

(0.3

)

Mea

n pa

id35

7.4

301.

545

3.9

362.

5-

5000

.026

9.5

511.

761

6.7

SD86

6.2

340.

181

2.2

170.

2-

541.

997

7.2

1303

.4

4

Exemption mechanism

89

Tab

le 4

.2: O

ut-o

f-po

cket

pay

men

ts fo

r ou

tpat

ient

ser

vice

s du

ring

the

pre

viou

s m

onth

, am

ount

s in

din

ars;

1 U

SD ≈

88.

3 di

nars

(co

ntin

ued)

Pay

men

ts f

or t

ran

spor

tM

oney

req

ues

ted

by

med

ical

sta

ffG

ifts

to

med

ical

sta

ff

2002

2003

2007

2002

2003

2007

2002

2003

2007

Tota

l sa

mpl

e(us

ers)

4562

(100

.0)

1703

(100

.0)

4675

(100

.0)

4562

(100

.0)

1703

(100

.0)

4675

(100

.0)

4562

(100

.0)

1703

(100

.0)

4675

(100

.0)

You

nge

r th

an 1

5 ye

ars

N u

sed

(%to

tal)

519

(11.

4)17

7 (1

0.4)

401

(8.6

)52

7 (1

1.6)

177

(10.

4)40

1 (8

.6)

5 14

(11.

3)17

7 (1

0.4)

401(

8.6)

N p

aid

(%to

tal)

149

(3.6

)54

(3.2

)54

(1.2

)2

(0.0

)2

(0.1

)1

(0.0

)3

(0.0

)2

(0.1

)2

(0.0

)

Mea

n pa

id 3

20.4

293.

1544

7.4

925.

076

50.0

020

0.0

275.

030

50.0

527.

5

SD47

2.9

342.

649

9.8

601.

110

394.

5-

238.

541

71.9

668.

2

Two

or m

ore

of t

he

abov

e ex

emp

tion

cri

teri

a

N u

sed

(%to

tal)

40 (0

.9)

16 (0

.9)

217

(4.6

)-

16 (0

.9)

217

(4.6

)-

-21

7 (4

.6)

N p

aid

(%to

tal)

12 (0

.3)

4 (0

.2)

55 (1

.2)

-1

(0.1

)1

(0.0

)-

-6(

0.1)

Mea

n pa

id20

00.0

227.

559

9.0

-60

0.0

4800

--

251.

2

SD-

190.

761

8.1

--

--

-18

5.9

Oth

er p

opu

lati

on g

rou

ps

N u

sed

(%to

tal)

1992

(43.

6)79

6 (4

6.7)

2132

(45.

6)20

52 (4

4.9)

796

(46.

7)21

32 (4

5.6)

2009

(44.

0)79

6 (4

6.7)

2132

(45.

6)

N p

aid

(%to

tal)

790

(17.

3)28

0 (1

6.4)

508

(10.

9)9

(0.2

)3(

0.2)

6 (0

.1)

65 (1

.4)

10 (3

5.4)

39 (0

.8)

Mea

n pa

id30

8.9

442.

453

8.2

5118

.993

.357

0.8

821.

157

5.0

1549

.4

SD48

5.0

953.

571

0.1

1054

4.7

94.5

759.

721

45.3

1042

.551

89.8

90

Chapter 4

Tab

le 4

.3: O

ut-o

f-po

cket

pay

men

ts fo

r in

pati

ent

hosp

ital

ser

vice

s du

ring

the

last

12

mon

ths,

am

ount

s in

din

ars;

1 U

SD ≈

88.

3 di

nars

For

mal

pat

ien

t fe

es f

or

hos

pit

aliz

atio

nF

orm

al p

atie

nt

fees

for

p

har

mac

euti

cals

For

mal

pat

ien

t fe

es f

or

lab

orat

ory

serv

ices

For

mal

pat

ien

t fe

es f

or

dis

pos

able

mat

eria

lsP

aym

ents

for

tran

spor

t

2002

2003

2007

2002

2003

2007

2002

2003

2007

2002

2003

2007

2002

2003

2007

Tota

l sa

mpl

e(us

ers)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

N u

sed

(%to

tal)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

823

(82.

1)38

6 (9

9.9)

852

(80.

9)88

8 (8

8.6)

380

(97.

7)72

1 (6

8.5)

897

(89.

5)37

8 (9

7.2)

541

(51.

4)92

1 (9

1.9)

386

(99.

9)67

6 (6

4.3)

N p

aid

(%to

tal)

282

(28.

1)99

(2

5.4)

507

(48.

2)25

2 (2

5.2)

104

(26.

9)23

2 (2

2.0)

76

(7.6

)30

(7

.7)

127

(12.

1)10

3 (1

0.3)

30

(7.7

)72

(6

.8)

426

(42.

5)15

3 (3

9.3)

402

(38.

2)

Mea

n pa

id44

83.5

3275

.939

18.8

3073

.420

59.2

2398

.715

80.0

2050

.027

81.8

3603

.152

58.0

8534

.910

78.1

800.

197

1.8

SD28

558.

512

003.

487

89.3

7336

.129

01.6

4738

.925

07.3

2427

.946

62.7

5682

.211

685.

911

449.

317

69.9

1470

.516

00.5

Low

-in

com

e an

d u

nem

plo

yed

N u

sed

(%to

tal)

55 (5

.5)

36 (9

.3)

33 (3

.1)

71 (7

.1)

36 (9

.3)

75 (7

.1)

75 (7

.5)

35 (8

.9)

57 (5

.4)

74 (7

.4)

36 (3

4.2)

37 (3

.5)

75 (7

.5)

36 (9

.8)

63 (5

.9)

N p

aid

(%to

tal)

34 (3

.4)

3 (0

.8)

16 (1

.5)

21 (2

.1)

5 (1

.3)

24 (2

.3)

9 (1

.0)

1 (0

.3)

14 (1

.3)

9 (1

.0)

4 (1

.0)

7 (0

.6)

35 (3

.8)

13 (3

.4)

42 (3

.9)

Mea

n pa

id13

706.

021

48.9

2452

.926

04.8

1916

.917

91.7

758.

232

6.7

2059

.347

07.7

2575

.015

42.9

*90

9.7

685.

945

1.7*

SD72

784.

048

29.5

5491

.131

97.1

2633

.221

36.1

668.

326

1.0

3538

.540

42.4

2335

.782

4.3

1493

.877

3.1

389.

3

Dis

able

d in

div

idu

als

N u

sed

(%to

tal)

10 (0

.9)

-10

3 (9

.8)

16 (1

.5)

5 (1

.3)

75 (7

.1)

16 (1

.6)

-67

(6.4

)16

(1.5

)-

44 (4

.2)

16 (1

.5)

5 (1

.3)

62 (5

.9)

N p

aid

(%to

tal)

4 (0

.4)

-55

(5.2

)4

(0.4

)1

(0.3

)19

(1.8

)2

(0.2

)-

10 (0

.9)

3 (0

.3)

-10

(0.9

)3

(0.3

)1

(0.3

)33

(3.1

)

Mea

n pa

id37

75.0

-33

37.5

1400

.021

66.7

5868

.4*

2250

.0-

3380

.050

0.0

-17

700.

0*66

6.7

1400

1805

.1*

SD27

88.5

-71

92.9

955.

711

93.0

1124

8.2

1767

.7-

3737

.143

5.9

-15

304.

728

8.7

-36

25.9

Old

er t

han

65

year

s

N u

sed

(%to

tal)

145

(14.

5)15

2 (3

9.1)

286

(27.

2)28

9 (2

8.9)

152

(39.

1)21

6 (2

0.5)

279

(27.

9)15

2 (3

9.1)

181

(17.

2)28

5 (2

8.5)

152

(39.

1)13

7 (1

3.2)

292

(29.

2)15

2 (3

9.1)

175

(16.

6)

N p

aid

(%to

tal)

41 (4

.1)

28 (7

.3)

161

(15.

3)68

(6.8

)44

(11.

4)61

(5.8

)13

(1.3

)9

(2.3

)29

(2.7

)25

(2.5

)10

(2.6

)19

(1.8

)12

8 (1

2.7)

62 (6

.1)

98 (9

.3)

Mea

n pa

id35

95.1

5950

.142

96.6

1721

.522

43.1

1727

.0*

1203

.811

40.0

1628

.28

4904

.044

75.0

6944

.791

9.1

405.

173

0.8*

SD59

26.8

2280

2.5

1059

6.9

2651

.931

40.6

2678

.615

03.9

2185

.919

22.6

7110

.642

44.2

9969

.916

08.3

456.

984

6.1

p<

0.0

5

4

Exemption mechanism

91

Tab

le 4

.3: O

ut-o

f-po

cket

pay

men

ts fo

r in

pati

ent

hosp

ital

ser

vice

s du

ring

the

last

12

mon

ths,

am

ount

s in

din

ars;

1 U

SD ≈

88.

3 di

nars

(co

ntin

ued)

For

mal

pat

ien

t fe

es f

or

hos

pit

aliz

atio

nF

orm

al p

atie

nt

fees

for

p

har

mac

euti

cals

For

mal

pat

ien

t fe

es f

or

lab

orat

ory

serv

ices

For

mal

pat

ien

t fe

es f

or

dis

pos

able

mat

eria

lsP

aym

ents

for

tran

spor

t

2002

2003

2007

2002

2003

2007

2002

2003

2007

2002

2003

2007

2002

2003

2007

Tota

l sa

mpl

e(us

ers)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

You

nge

r th

an 1

5 ye

ars

N u

sed

(%to

tal)

39 (3

.9)

33 (8

.5)

80 (7

.6)

85 (8

.5)

33 (8

.5)

66 (6

.3)

82 (8

.2)

33 (8

.5)

51 (4

.8)

82 (8

.1)

-42

(3.9

)87

(8.7

)33

(8.5

)46

(4.4

)

N p

aid

(%to

tal)

15 (1

.4)

7 (1

.8)

11 (1

.0)

18 (1

.7)

7 (1

.8)

4 (0

.4)

2 (0

.2)

1 (0

.3)

3 (0

.3)

4 (0

.4)

-3

(0.3

)42

(4.1

)14

(3.6

)24

(2.3

)

Mea

n pa

id18

87.1

808.

052

55.4

533

73.5

1194

.084

5.0

1600

.018

00.0

2333

.365

5.0

-67

46.6

1695

.769

1.2

788.

33

SD41

01.5

627.

249

31.0

5731

.188

7.2

588.

919

70.9

-14

43.4

427.

5-

1147

8.0

2994

.095

9.3

983.

1

Two

or m

ore

of t

he

abov

e ex

emp

tion

cri

teri

a

N u

sed

(%to

tal)

8 (0

.9)

3 (0

.8)

61 (5

.8)

15 (1

.5)

3 (0

.8)

70 (6

.6)

15 (1

.5)

3 (0

.8)

57 (5

.4)

15 (1

.4)

3 (0

.8)

46 (4

.4)

15 (1

.4)

3 (0

.8)

57 (5

.4)

N p

aid

(%to

tal)

2 (0

.2)

3 (0

.8)

28 (2

.7)

5 (0

.5)

1 (0

.3)

22 (2

.0)

1 (0

.1)

1 (0

.3)

6 (0

.6)

2 (0

.2)

3 (0

.8)

2 (0

.2)

6 (0

.6)

1 (0

.3)

37 (3

.5)

Mea

n pa

id14

25.0

4772

.435

49.2

3230

.034

58.3

1175

.0*

400.

030

0.0

1458

.310

00.0

4666

.760

50.0

752.

512

86.5

988.

6

SD95

4.6

4955

.168

69.9

4452

.952

59.5

1330

.6-

-13

13.9

707.

128

86.8

8414

.096

2.9

2539

.112

62.9

Oth

er p

opu

lati

on g

rou

ps

N u

sed

(%to

tal)

275

(27.

5)15

7 (4

0.3)

489

(46.

5)34

7 (3

4.7)

157

(40.

3)35

0 (3

3.3)

421

(42.

1)15

7 (4

0.3)

308

(29.

3)42

5 (4

2.5)

157

(40.

3)23

5 (2

2.3)

436

(4

3.5)

157

(40.

3)27

3 (2

5.9)

N p

aid

(%to

tal)

186

(18.

6)53

(14.

6)22

8 (2

1.7)

134

(13.

4)35

(8.9

)10

2 (9

.7)

49 (4

.8)

17 (4

.4)

65 (6

.2)

60 (6

.0)

16 (4

.1)

31 (2

.9)

212

(21.

2)62

(16.

1)16

8 (1

5.9)

Mea

n pa

id30

11.9

2241

.041

27.7

3844

.815

57.6

2621

.6*

1848

.929

00.0

3502

.932

35.7

6509

.384

65.2

1102

.510

05.7

1101

.3

SD12

374.

144

12.9

8741

.994

84.1

1635

.144

76.5

2985

.525

79.0

5916

.356

23.7

1635

0.9

1128

3.0

1605

.516

41.6

1562

.2

p<0.

05

92

Chapter 4T

able

4.3

: Out

-of-

pock

et p

aym

ents

for

inpa

tien

t ho

spit

al s

ervi

ces

duri

ng t

he la

st 1

2 m

onth

s, a

mou

nts

in d

inar

s; 1

USD

≈ 8

8.3

dina

rs (

cont

inue

d)

For

mal

pat

ien

t fe

es f

or

hos

pit

aliz

atio

nF

orm

al p

atie

nt

fees

for

p

har

mac

euti

cals

For

mal

pat

ien

t fe

es f

or

lab

orat

ory

serv

ices

For

mal

pat

ien

t fe

es f

or

dis

pos

able

mat

eria

lsP

aym

ents

for

tran

spor

t

2002

2003

2007

2002

2003

2007

2002

2003

2007

2002

2003

2007

2002

2003

2007

Tota

l sa

mpl

e(us

ers)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

N u

sed

(%to

tal)

910

(90.

81)

241

(61.

9)40

5 (3

8.5)

893

(89.

1)15

9 (4

0.9)

1052

(1

00.0

)88

7 (8

8.5)

106

(27.

2)10

52

(100

.0)

922

(92.

1)74

(19.

1)14

7 (1

3.9)

902

(90.

0)48

(12.

3)23

4 (2

2.2)

N p

aid

(%to

tal)

280

(27.

9)87

(22.

4)20

9 (1

9.8)

76 (7

.5)

14 (3

.6)

21 (1

.9)

27 (2

.7)

16 (4

.1)

29 (2

.8)

22 (2

.2)

4 (1

.0)

4 (0

.4)

150

(14.

8)48

(12.

3)91

(8.7

)

Mea

n pa

id26

53.5

4191

.716

21.0

3461

.826

90.0

4540

.576

22.6

3091

.941

20.3

1232

3.6

3700

.019

000.

022

21.5

1857

.344

97.5

SD98

15.1

2566

4.4

2920

.983

89.2

2999

.172

59.1

2082

9.1

6329

.476

95.3

3174

6.5

3897

.916

451.

949

51.0

2564

.095

68.7

Low

-in

com

e an

d u

nem

plo

yed

N u

sed

(%to

tal)

74 (7

.4)

27 (6

.9)

32 (3

.0)

76 (7

.5)

22 (5

.7)

33 (3

.1)

76 (7

.5)

15 (3

.8)

33 (3

.1)

76 (7

.5)

12 (3

.1)

11 (1

.0)

76 (1

.6)

5 (1

.3)

16 (1

.5)

N p

aid

(%to

tal)

17 (1

.7)

10 (2

.6)

17 (1

.6)

8 (0

.8)

5 (1

.3)

3 (0

.3)

4 (0

.4)

3 (0

.8)

1 (0

.1)

1 (0

.1)

1 (0

.3)

-13

(1.5

)5

(1.3

)5

(0.5

)

Mea

n pa

id14

84.2

2597

0.0*

2431

.712

00.0

1366

.728

3.3

4270

.047

00.0

300.

015

0000

.060

00.0

1406

.725

40.0

825.

0

SD12

16.1

7526

0.5

3511

.289

9.4

1001

.318

9.3

6533

.863

92.9

--

-15

97.4

1804

.968

3.9

Dis

able

d in

div

idu

als

N u

sed

(%to

tal)

16 (1

.5)

1 (0

.3)

39 (3

.7)

16 (1

.6)

1 (0

.3)

103

(9.8

)16

(1.6

)-

103

(9.8

)16

(1.6

)-

15 (1

.4)

16 (0

.3)

1 (0

.3)

21 (1

.9)

N p

aid

(%to

tal)

6 (0

.6)

-22

(2.1

)1

(0.1

)-

2 (0

.2)

--

2 (0

.2)

--

-3

(0.1

)1

(0.3

)6

(0.6

)

Mea

n pa

id28

66.7

-23

07.7

1500

.0-

7000

.0-

-32

50.0

--

-17

100.

020

00.0

3128

.3

SD36

61.5

-42

50.4

--

7071

.1-

-24

74.9

--

-24

329.

6-

3333

.7

Old

er t

han

65

year

s

N u

sed

(%to

tal)

288

(28.

6)89

(22.

5)10

9 (1

0.4)

283

(28.

3)61

(15.

7)28

6 (2

7.2)

284

(28.

4)41

(10.

5)28

6 (2

7.2)

293

(29.

2)28

(7.2

)29

(2.8

)28

7 (3

.9)

14 (3

.6)

42 (1

7.9)

N p

aid

(%to

tal)

93 (9

.3)

30 (2

.9)

67 (6

.4)

26 (2

.5)

2 (0

.5)

2 (0

.2)

8 (0

.8)

9 (2

.3)

10 (1

.0)

5 (0

.5)

1 (0

.3)

1 (0

.1)

40 (3

.9)

14 (3

.6)

14 (1

.3)

Mea

n pa

id18

64.4

11

01.8

1115

.852

98.1

1375

.022

50.0

8335

.037

57.8

5183

.326

40.0

300.

040

000

785.

612

67.9

3542

.9

SD35

22.2

1112

.311

43.8

1109

3.2

1108

.735

3.6

1701

4.7

7730

.982

46.3

2246

.8-

-99

5.4

2044

.557

41.7

p<0.

05

4

Exemption mechanism

93

Tab

le 4

.3: O

ut-o

f-po

cket

pay

men

ts fo

r in

pati

ent

hosp

ital

ser

vice

s du

ring

the

last

12

mon

ths,

am

ount

s in

din

ars;

1 U

SD ≈

88.

3 di

nars

(co

ntin

ued)

For

mal

pat

ien

t fe

es f

or

hos

pit

aliz

atio

nF

orm

al p

atie

nt

fees

for

p

har

mac

euti

cals

For

mal

pat

ien

t fe

es f

or

lab

orat

ory

serv

ices

For

mal

pat

ien

t fe

es f

or

dis

pos

able

mat

eria

lsP

aym

ents

for

tran

spor

t

2002

2003

2007

2002

2003

2007

2002

2003

2007

2002

2003

2007

2002

2003

2007

Tota

l sa

mpl

e(us

ers)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

1002

(1

00.0

)38

9 (1

00.0

)10

52

(100

.0)

You

nge

r th

an 1

5 ye

ars

N u

sed

(%to

tal)

86 (8

.6)

23 (5

.9)

21 (1

.9)

83 (8

.3)

11 (2

.8)

80 (7

.6)

82 (8

.2)

6 (1

.5)

80 (7

.6)

88 (8

.7)

4 (1

.0)

13 (1

.2)

87 (

1.5)

4 (1

.0)

17 (1

.6)

N p

aid

(%to

tal)

18 (1

.7)

4 (1

.0)

6 (0

.6)

3 (0

.3)

1 (0

.3)

1 (0

.1)

3 (0

.3)

-1

(0.1

)2

(0.2

)-

-15

(1.4

)4

(1.0

)4

(0.4

)

Mea

n pa

id11

90.0

90

0.0

2300

.010

50.0

400.

015

00.0

2700

.0-

1000

.030

00.0

--

2166

.121

75.0

3945

.7

SD10

42.9

496.

721

20.4

697.

8-

-30

11.6

--

2121

.3-

-22

84.8

1090

.571

91.2

Two

or m

ore

of t

he

abov

e ex

emp

tion

cri

teri

a

N u

sed

(%to

tal)

15(1

.4)

28(7

.2)

37(3

.5)

15(1

.5)

19(4

.9)

61(5

.8)

15(1

.5)

11(2

.8)

61(5

.8)

15(1

.5)

6(1.

5)8(

0.8)

1(0.

1)7(

1.8)

11(1

.0)

N p

aid

(%to

tal)

3(0.

3)10

(2.6

)24

(2.3

)-

1(0.

3)-

1(0.

1)1(

0.3)

2(0.

2)-

1(0.

3)-

1(0.

1)7(

1.8)

3(0.

3)

Mea

n pa

id77

2.5

1163

.013

92.9

-40

00.0

-20

00.0

500.

020

0.0

-80

00.0

-50

0.0

1614

.338

3.3

SD72

4.2

1502

.018

64.7

-35

35.5

--

-14

1.4

--

--

2457

.329

2.9

Oth

er p

opu

lati

on g

rou

ps

N u

sed

(%to

tal)

431

(43.

0)73

(18.

7)16

7 (1

5.8)

420

(42.

0)45

(11.

6)48

9 (4

6.5)

414

(41.

4)33

(8.5

)48

9 (4

6.5)

434

(43.

4)24

(6.2

)71

(6.7

)43

5 (7

.7)

17 (4

.4)

127

(12.

1)

N p

aid

(%to

tal)

143

(14.

2)33

(8.4

)73

(6.9

)38

( 3.

8)5

(1.3

)14

(1.2

)11

(1.1

)3

(0.7

)15

(1.4

)14

(1.4

)1

(0.3

)3

(0.3

)77

(7.7

)17

(4.4

)59

(5.6

)

Mea

n pa

id35

52.2

17

17.9

*17

08.2

3019

.445

28.6

5730

.810

177.

335

0.0

4617

.172

80.0

500.

012

000.

025

78.9

2158

.853

52.1

SD13

463

.027

62.8

3662

.976

09.2

4081

.186

55.8

2980

3.1

86.6

8934

.595

52.8

-10

583.

045

26.1

3466

.311

169.

2

p< 0

.05

94

Chapter 4

For 2002, the ANOVA analysis shows no statistically significant difference between the

official co-payments, indirect payments (transport), payments for ”bought & brought

goods” and informal payments reported by the five exempted groups, as well as between

these groups and other population groups. For 2003, the results are similar to those

for 2002. There are no statistically significant differences between the five exempted

groups for different types of payments except for “bought & brought goods” payments for

pharmaceuticals. The unemployed and people with low family income reported higher

payments (than other exempted groups examined in our study) for pharmaceuticals that

they brought to the hospital. This difference is also statistically significant when these

exempted groups are compared to other population groups.

For 2007, the ANOVA analysis shows an overall trend of increased payments. Both

exempted and others (non-exempted and exempted that we could not identify within

the data) paid more for officially co-payments, “bought & brought goods” payments

and informal payments for inpatient care compared to previous years (see Table 4.3).

Although the absolute amount is higher, there is no statistically significant difference

between the five exempted groups or compared to other population groups. It should

be noted however that the disabled paid a higher amount in official co-payments for

pharmaceuticals than other exempted groups examined in our study. They also paid more

in official co-payments for disposable materials. Both differences are significant. Overall,

the five exempted groups report more often “bought & brought goods” payments than

informal payments for inpatient care. For example, individuals in the five exempted

groups who report payments for pharmaceuticals brought and bought to the hospital

represent 12% of all users, while for informal payments in inpatient care this share is only

3%.

4.4.2 Regression results As we mentioned above, we perform logistic and linear regression for both outpatient

and inpatient services. The regression models include social demographic characteristics,

three dummy variables (indicators for belonging to one of the five exempted groups, for

2003 and for 2007 respectively) and two interaction variables (between the indicator

for belonging to one of the five exempted groups and the two year indicators). We use

the same set of independent variables for both outpatient and inpatient hospital care.

The regression models are not significant for any type of payment regarding outpatient

services. Those results are compatible with results of the ANOVA analyses. Therefore, we

do not present regression results for outpatient services.

Regarding inpatient services, both logistic and linear regression models are significant.

The results of the logistic regression are presented in Table 4.4. They indicate factors that

are associated with the propensity of paying out-of-pocket for inpatient care across the

five exempted groups and other population groups, as well as the differences across the

4

Exemption mechanism

95

years. This way, we examine if the exemption policy is implemented in practice. The

statistically significant results are described below.

Regarding official co-payments for hospitalization, the indicators for 2003 and 2007

are statistically significant. Overall, all respondents have a lower propensity to pay for

hospitalization in 2003 and 2007 than in 2002. The indicator for belonging to one of the

five exempted group is also a significant predictor indicating that these exempted groups

paid less often for hospitalization than other population groups. However, the interaction

between year 2007 and the five exempted groups show that exempted persons in these

groups pay more frequently for hospitalization in 2007. Being married and/or having

chronicle diseases also indicate lower payments for a hospitalization in 2002 than in 2003

and 2007.

With regard to official co-payments, for pharmaceuticals, disposable materials and

laboratory analyses for inpatient care significant predictors are the year 2007 and the

indicator of belonging to one of the five exempted groups. The probability to pay for

these services was less in 2007 and for the five exempted groups. For transport costs, the

propensity to pay in 2003 was significantly less than in 2002 for all respondents. Also,

respondents from the five exempted groups paid less frequently for these services than

other groups. Among the social demographic variables, significant predictors are being

married, presence of chronicle diseases and/or type of settlement.

For pharmaceuticals that respondents brought to the hospital, significant predictors

are the indicator for the five exempted groups, the interaction between year 2007, and

the indicator of belonging to one of the five exempted groups and the presence of chronic

diseases. Belonging to one of the five exempted groups itself means a lower propensity

of paying but a higher propensity of paying in 2007. For disposable materials that were

brought by patients, the only significant predictor is year 2007.

To study the size of reported payments for inpatient care, we carry out linear regression.

We apply linear regression analysis only for respondents who reported that they have

paid for different types of inpatient hospital services (see Table 4.5). We use the specific

amount paid as a dependent variable.

The linear models are significant for disposable materials that were paid officially. For

disposable materials that are paid officially, significant predictors are year 2007, being

married and type of settlement. For pharmaceuticals that were paid officially, significant

predictors were an indicator for the year 2003, as well as belonging to one of the five

exempted groups. For transport costs, a significant predictor is year 2007.

96

Chapter 4T

able

4.4

: Res

ults

of l

ogis

tic

regr

essi

on; d

epen

dent

var

iabl

e –

repo

rted

pay

men

ts (y

es/n

o) fo

r in

pati

ent

hosp

ital

ser

vice

s du

ring

the

last

12

mon

ths

(for

tho

se

who

wer

e ho

spit

alis

ed)

Exp

lan

ator

y va

riab

les

incl

ud

ed in

th

e an

alys

is

Hos

pit

alis

atio

n

(for

mal

fe

es)

Ph

arm

a-ce

uti

cals

(f

orm

al f

ees)

Dis

pos

able

m

ater

ials

(f

orm

al f

ees)

Lab

orat

ory

serv

ices

(f

orm

al f

ees)

Tra

nsp

ort

(for

mal

fe

es)

Ph

arm

a-ce

uti

cals

b

rou

ght

by

the

pat

ien

t

Dis

pos

able

m

ater

ials

b

rou

ght

by

the

pat

ien

t

Ort

hop

aed

ic

mat

eria

ls

bro

ugh

t b

y th

e p

atie

nt

Mon

ey

req

ues

ted

by

med

ical

sta

ff

Gif

ts t

o m

edic

al

staf

f

BSE

BSE

BSE

BSE

BSE

BSE

BSE

BSE

BSE

BSE

Con

stan

t 0

.78*

0.34

-0.4

60.

34-2

.16*

0.52

-1.5

8*0.

49-0

.19

0.30

-0.1

80.

35-2

.64*

0.67

-2.4

8*0.

85-2

.45*

1.28

-3.5

8*0.

45

2003

-1.1

3**

0.22

-0.1

70.

10-0

.12

0.32

0.1

50.

31-0

.34

0.24

-0.3

60.

23-0

.50

0.44

-0.1

40.

66-1

.43

1.04

-0.3

70.

29

2007

-0.8

9**

0.19

-0.1

80.

18-0

.78*

*0.

28 0

.60*

0.40

-0.3

00.

16-0

.71*

*0.

19-1

.25*

0.38

-0.1

30.

47-1

.37

0.74

-0.4

60.

54

sele

cted

exe

mpt

ed

grou

ps-1

.19*

*0.

19-0

.36*

*0.

11-0

.12

0.22

-0.6

8*0.

26-0

.17

0.14

-0.2

10.

16 0

.16

0.25

0.1

40.

42-0

.90

0.51

0.0

70.

58

2003

x s

elec

ted

exem

pted

gro

ups

0.0

30.

30 0

.28

0.29

-0.5

50.

44-0

.42

0.46

0.1

50.

26-0

.08

0.30

-0.1

70.

54-0

.64

0.77

1.2

11.

26-0

.25

1.26

2007

x s

elec

ted

exem

pted

gro

ups

1.3

7**

0.23

0.0

90.

22 0

.04

0.34

0.1

60.

32-0

.12

0.19

0.4

4**

0.22

-0.9

70.

52-0

.49

0.56

-0.6

31.

26-0

.93

0.28

gend

er-0

.96

0.09

-0.1

80.

10-0

.05

0.15

-0.0

90.

14-0

.07

0.08

-0.0

10.

10-0

.02

0.20

-0.1

80.

25-0

.60

0.40

0.6

3**

0.14

mar

riag

e 0

.47*

*1.

05-0

.08

0.11

0.5

8*0.

18 0

.08

0.16

0.1

80.

09 0

.16

0.11

0.3

00.

22-0

.08

0.26

-0.2

00.

43 0

.30*

0.15

wor

k st

atus

0.9

80.

61 0

.09

0.06

0.0

90.

09 0

.00

0.09

-0.0

90.

05 0

.08

0.06

-0.0

20.

13-0

.01

0.15

0.0

10.

26-0

.01

0.08

urba

n 0

.11

0.09

0.1

50.

10-0

.21

0.15

0.3

1*0.

14 0

.05

0.08

0.1

90.

10-0

.18

0.20

-0.3

00.

25 0

.22

0.40

0.1

60.

13

Edu

cati

on le

vel

0.0

00.

00 0

.00

0.00

0.0

00.

00 0

.00

0.00

0.0

00.

00 0

.00

0.00

0.0

00.

00 0

.00

0.00

0.0

00.

00 0

.00

0.00

Hou

seho

ld s

ize

0.0

20.

03 0

.05*

0.02

0.0

60.

04-0

.05

0.04

0.0

7**

0.02

-0.0

10.

03 0

.02

0.06

-0.0

80.

07-0

.02

0.11

0.0

50.

04

Chr

onic

le d

isea

se-0

.43*

*0.

01-0

.40*

*0.

12-0

.09

0.18

-0.5

4*0.

17-0

.21*

*0.

01-0

.72*

*0.

13 0

.21

0.22

-0.0

40.

30-0

.14

0.44

0.2

70.

14

-2LL

(Nag

el. R

²)24

51.3

1 (0

.14)

2515

.26

(0.0

3)13

16.0

5 (0

.04)

1442

.95

(0.0

4)30

46.2

0 (0

.02)

2435

.43

(0.0

6)84

6.06

(0.0

7)60

7.99

(0.0

2)27

7.44

(0.0

9)16

45.1

7 (0

.07)

N18

5719

3812

7116

6916

5012

3296

751

338

623

52

p< 0

.05

** p

< 0

.01

4

Exemption mechanism

97

Tabl

e 4.

5: R

esul

ts o

f lin

ear

regr

essi

on; d

epen

dent

var

iabl

e –

amou

nt p

aid

for

inpa

tien

t ho

spit

al s

ervi

ces

duri

ng t

he la

st 1

2 m

onth

s, in

din

ars;

1 U

SD ≈

88.

3 di

nars

(for

tho

se w

ho p

aid

for

hosp

ital

isat

ion)

Exp

lan

ator

y

vari

able

s in

clu

ded

in t

he

anal

ysis

Hos

pit

alis

atio

n

(for

mal

fee

s)P

har

mac

euti

cals

(f

orm

al f

ees)

Dis

pos

able

m

ater

ials

(f

orm

al f

ees)

Lab

orat

ory

serv

ices

(for

mal

fe

es)

Tra

nsp

ort

(for

mal

fee

s)P

har

mac

euti

cals

b

rou

ght

by

the

pat

ien

t

Dis

pos

able

m

ater

als

bro

ugh

t b

y th

e p

atie

nt

Ort

hop

aed

ic

mat

eria

ls

bro

ugh

t b

y th

e p

atie

nt

Mon

ey r

equ

este

d b

y m

edic

al s

taff

Gif

ts t

o m

edic

al

staf

f

BSE

BSE

BSE

BSE

BSE

BSE

BSE

BSE

BSE

BSE

Con

stan

t 30

11.9

1255

.838

44.8

450.

632

35.7

934.

518

48.9

371.

511

02.5

97.9

3552

.295

8.3

3019

.411

96.8

1000

0.1

4100

7280

.077

70.0

2578

.955

8.6

2003

-137

8.60

2816

.20

-214

6.97

*11

02.8

931

34.6

426

67.7

286

3.53

1747

.42

-100

.71

244.

05-2

119.

9823

72.0

520

41.4

531

82.6

5-7

695.

0710

967.

67-1

5270

.21

3053

1.74

-616

.39

1313

.53

2007

-147

4.92

2261

.16

-160

8.92

685.

4277

60.8

1*23

74.7

815

48.7

188

5.50

-96.

65*

210.

68-3

658.

3720

20.0

435

1.59

2323

.63

-552

2.98

7806

.69

-241

66.5

921

905.

5656

9.35

809.

49

Sele

cted

exe

mpt

ed

grou

ps40

11.7

423

96.7

8-1

801.

62*

782.

1917

50.0

018

71.0

2-1

007.

.40

958.

88-1

88.3

216

4.49

-189

3.85

1572

.98

840.

5516

49.9

5-4

004.

7668

60. 7

523

895.

3921

597.

80-7

34.4

480

0.73

2003

x s

elec

ted

exem

pted

gro

ups

-150

2.60

4336

.68

2526

.95

1426

.61

-431

5.37

3837

. 05

-782

2717

27.0

5-1

06.1

031

2.92

6087

.28*

3112

..38

-341

1.98

3749

.05

3190

.19

1241

7.06

-461

91.7

241

022.

9630

9.99

1682

.13

2007

x s

elec

ted

exem

pted

gro

ups

-375

8.00

2955

.96

1243

.05

1096

.80

-953

.83

2882

.48

-107

.52

1205

.03

-38.

3022

7..8

416

47.5

323

63.6

6-2

678.

5036

24.1

638

42.3

693

87.4

445

595.

3842

582.

19-7

45.7

412

85.2

3

Gen

der

-118

5.32

1253

.47

-698

.34

498.

88-5

74.1

113

37.3

741

0.80

516.

18-8

4.87

106.

3654

5. 3

110

94.9

326

0.71

1640

.52

424.

5143

20.5

4-9

496.

1013

755.

1746

.76

883.

97

Mar

riag

e22

45.7

814

03.4

032

6.05

529.

7739

52.4

5*15

86.7

151

4.39

572.

0425

.34

114.

01-2

504.

37*

1189

.19

-847

.67

1646

.12

3025

.83

3956

.85

-907

4.11

1607

8.25

426.

6092

4.63

Wor

k st

atus

-147

0.80

812.

42-1

46.5

631

3.26

686.

8881

9.03

-355

. 47

333.

46-8

0.58

64.0

7-1

443.

21*

651.

9618

00.8

710

64.5

112

47.3

623

64.9

7-1

8933

.796

51.6

6-1

104.

60*

529.

77

Urb

an-1

304.

8612

22.1

5-4

56.8

249

1.92

2599

.21*

1316

.43

278.

3651

6.88

-88.

4110

8.59

-156

6.22

1043

.51

-134

3.24

1608

.83

972.

7742

41.3

3-1

5930

.12

1616

9.57

-118

7.78

822.

26

Edu

cati

onal

leve

l64

3.97

*29

4.34

-2.3

60.

000.

000.

0015

0.25

112.

270.

000.

000.

000.

000.

000.

0011

15.7

510

20.3

326

43.5

827

73.1

70.

000.

00

Hou

seho

ld s

ize

10.0

336

8.41

87.5

013

8.73

-131

.60

390.

6810

8.40

163.

0938

.12

28.5

685

6.63

*30

7.14

906.

7946

4.64

468.

5513

32.0

1-1

5930

.12

1616

9.57

477.

02*

227.

69

Chr

onic

le d

isea

se-2

484.

4114

92.0

7-1

052.

6859

7.28

581.

7215

00.6

0-1

062.

5065

5.43

-85.

8012

1.32

-303

8.27

*14

12.4

0-3

030.

7716

63.9

1-4

494.

1445

90.6

2-2

532.

4915

200.

37-1

582.

3789

2.58

R²

00.0

300

.03

00.0

600

.09

00.0

100

.05

00.1

000

.09

00.5

100

.08

N88

858

820

523

398

157

611

172

2930

4

p< 0

.05

98

Chapter 4

4.5 Discussions and conclusions

Our results confirm that the selected exempted groups included in our study, pay for both

outpatient services as well as for inpatient care. However, payments are less frequently

reported for outpatient care than for inpatient services, except for the year 2007. This

difference between the services is expected since the official co-payments for inpatient

health care in Serbia are much higher than those for outpatient care. For example, one

day in hospital is two times more expensive than an examination by a GP (Sl. glasnik

RS”, br. 1/2007, 52/2007 i 99/2007). Moreover, the absence of statistically significant

regression models for outpatient payments implies a low propensity of paying by the five

exempted groups. Therefore, these exempted groups can still benefit from outpatient

care. However, some exempted users (even though it is a small percentage) reported

payments for outpatient care. Thus, we cannot perceive the exemption mechanism in

outpatient care as effective.

Regarding inpatient hospital care during 2002, 2003 and 2007, the five exempted

groups reported formal, “bought & brought goods”, informal and indirect payments. The

highest amounts for hospitalization were paid in 2003 by the group older than 65 and

the lowest amounts were reported by the group younger than 15 also in 2003. The five

exempted groups also report a high frequency of paying for pharmaceuticals that they

brought to the hospital. Respondents classified as older than 65 as well as respondents

classified as unemployed and with low family income, emerged as the most vulnerable

groups (since they cannot pay for health care services). It needs to be pointed out that

respondents, who met two exemption criteria, and also reported that they paid formally

and informally. They also reported that they bought & brought medical goods (e.g.

pharmaceuticals, disposable material) to the hospital. Thus, the exemption mechanism

in inpatient hospital care is also not effective.

We recognize that our study has certain limitations since we only explore the out-

of-pocket payments by some of the exempted groups, and only those types of payments

that are included in the LSMS data. Also, the information on informal patient payments

is sensitive and to some extent, informal patient payments might be underreported.

Nevertheless, our analysis shows that the implementation of the exemption mechanism

in Serbia, has failed, in particular for the elderly people (older than 65 years) and the poor

(low family income and unemployed).

Although some elderly are capable to pay the official co-payments, they are among

the most frequent users of both outpatient and inpatient health care (as shown by our

results as well). The evidence for other countries also indicates that the elderly have

more hospital admissions and a longer length of stay than other groups (Heinrich et

al., 2008). The accumulated patient co-payments might present a significant financial

burden for this group if the exemption mechanism is not effective. Moreover, the

4

Exemption mechanism

99

increased utilization of hospital care by elderly people can affect the implementation of

the exemption mechanism. The accumulated costs of hospital services used by the elderly

(when exempted from official co-payments) can increase the need for public funds for

health care used by this group. If the required funds are not transferred to the providers of

hospital care, or are transferred but with a delay, health care providers might be reluctant

to grant exemptions to elderly patients (as well as to other exempted groups).

As we have mentioned earlier, patients with low income have to make additional

efforts to achieve their exempted status. This includes additional documents, time and

money. Our results also show that this group pays more for pharmaceuticals, disposable

materials and orthopedic devices brought by patients to the hospital than other population

group. This complies with the evidence from other low- and middle-income countries

(McIntyrea et al., 2006; Tatar et al., 2007). People, who are perceived as poor, are not

asked directly for money, but they are asked to bring pharmaceuticals to the hospital.

This indirectly saves money for providers. At the same time, the shadow nature of these

“bought & brought goods” payments makes them an even more significant burden on

the patients’ household budget (Chapter 3). Raising awareness among patients regarding

goods that they are entitled to receive free-of-charge during their hospitalization, could

empower the patient and decrease this type of patient payments.

With regard to informal payments, all five exempted groups as well as other population

groups report such payments. Therefore, we cannot claim that informal payments are a

substitute for official co-payments. The inability of official co-payments to substitute

the informal ones is discussed by Ensor (2004) for Eastern European countries. The

experience from developing countries also confirms that official fees for health services,

do not necessarily replace the informal payments (Belli et al., 2004; Ensor, 2004). This

indicates that the attention of Serbian policy-makers should focus on finding suitable

strategies for dealing with informal payments in the health care sector and with the

corruption in general.

When we consider the time-perspective, we observe that in 2003, immediately after

the reforms started, the situation became more favorable for the five exempted groups

regarding the inpatient care. In 2007, the situation was more similar to 2002 when

the reforms started. A possible explanation is the political situation in Serbia in 2004

when a new conservative government took power (Bilic & Georgaca, 2007). The policy

of this government affected the health care reforms in terms that they stopped all reforms

during 2004-2008.

Although previous studies have addressed the catastrophic and impoverishing effects

of out-of-pocket patient in Serbia (Chapter 2; Bredenkamp et al., 2010), our results

suggest that further research should also explore the economic implications of out-

of-pocket patient payments for specific exempted groups. Future research should also

100

Chapter 4

explore the issue of leakage and under coverage, which we were not able to identify using

the LSMS data.

This chapter has analyzed the out-of-pocket payments for outpatient and inpatient

hospital care by five exempted groups in Serbia during the period of the post-war health

care reforms. The empirical results suggest that individuals eligible for an exemption

still report payments for hospital services. These payments include official co-payments

but also “bought & brought goods”, indirect and informal payments.

Thus, despite the wish of the Ministry of Health to promote equity as a leading

goal of the health care reforms, the implementation of the exemption mechanism, both

in outpatient and inpatient hospital care, is failing. The failures are visible in terms

of content (as described in the background section) as well as in terms of application

process (as shown by our results). Policy-makers should pay attention to the transparency

of legalization and supporting regulations, the effective targeting of exempted groups

and providing better access to public health services. The existence of clear guidelines

regarding the exemption mechanism and their availability to patients can decrease

confusion and resistance among providers, but may also increase the awareness of

potential recipients (Bitran & Giedion, 2003). Research from low and middle income

countries shows that low awareness, lack of information and fear that health care will not

be provided adequately are leading causes of failure of the exemption mechanism (Jacobs

et al., 2007; Nyonator & Katzin, 1999).

Moreover, instead of asking eligible people to apply for exemption, a targeting

mechanism through the insurance system itself may be more effective and less costly

for users. In particular, our results are based on experiences in a health care system that

requires direct formal payments from the users of health care services to the providers of

these services. The existence of a formal payment channel between users and providers

may well contribute to the existence of informal payments. Giving the national insurance

company the possibility to collect the official co-payments instead of the health care

providers could eliminate the formal monetary aspect in the patient-provider relation

and could contribute to the elimination of informal payments. This could be a policy

consideration also in other countries where user co-payments are paid directly to providers

and where informal payments are widely spread. Such strategy could also help to avoid

the payment of official co-payments by exempted groups.

The Ministry of Health in Serbia should improve on the design and the adequate

implementation of the exemptions from patient co-payments. The exemption policy in

Serbia should be pro-poor oriented exempting primarily those who cannot pay or use

health care frequently. Besides, the exemption policy should be based on the health status

of the users to avoid catastrophic effects of accumulated patient payments by frequent

users, such as chronic patients (Chapter 2; Bredenkamp et al., 2010). The exemption

mechanism should include clearly defined criteria on who is eligible for an exception

4

Exemption mechanism

101

and disseminate information about this patients’ right among exempted groups (Bitran

& Giedion, 2003). Awareness among the exempted groups that they have this right

(with no consequences for the quality of care received) could diminish the problem of

stigmatization and may encourage exempted groups to use this right.

There should be also an adequate financial plan to assure that public funds are

transferred directly from insurance company to health care providers for health care

provided to exempted groups. The monitoring of the implementation process should

be more transparent (Balabanova, 2007). The possibility of stigmatization, a negative

providers’ attitude and resistance towards the exemption mechanism can be solved by

changing attitudes of both providers and health care consumers towards exemptions.

Our results can be useful for other countries that are in the process of ongoing health

care reforms and are introducing or have introduced formal user co-payments for health

care services. As suggested by our results and discussion, policy-makers in these countries

should not limit themselves to having an exemption mechanism that accompanies the

system of user co-payments. They also need to pay attention to both the design and the

implementation of this exemption mechanism, in order to be able to effectively protect

all vulnerable population groups.

CHAPTER 5

The Effects of Chronic Diseases on Poverty

Submitted as:

Arsenijevic, J., Pavlova, M., & Groot, W. (2014). The effects of chronic diseases on poverty

104

Chapter 5

Abstract

Background: Chronic diseases are more likely to occur among poor individuals, and at

the same time, patients with chronic diseases have a higher probability of becoming poor.

This implies a double-sided relation between chronic diseases and poverty. The existence

of a joint causality can lead to biased estimates of the poverty effects provoked by chronic

diseases. The aim of this chapter is to model the casual effects of chronic diseases on

poverty when other common factors are controlled for.

Methods: To explore this joint causality, we use LSMS data for Serbia for 2007. We

apply an instrumental variable approach. We also present results of OLS regressions. As

outcome variables, we use indicators of pre-payment poverty and catastrophic effects

of out-of-pocket patient payments for different types of chronic diseases. Instrumented

variables are indicators of chronic diseases: cardiovascular diseases, diabetes mellitus

and cancer within the household. We use two groups of instruments: The first group is

related to health-related lifestyle behavior (e.g. smoking behavior and eating habits). The

second group of instrument variables consists of environmental variables like living in

an area affected by uranium during the NATO bombing and being a refugee during the

period 1999-2007.

Results: Our results show that all three chronic diseases can impose an economic burden

on households when other relevant factors are controlled for. Different risk factors are

related to different chronic diseases. Also, some chronic diseases (diabetes mellitus) can

cause both pre-payment poverty and catastrophic effects.

Conclusions: In order to decrease the poverty effects caused by chronic diseases, policy

makers in Serbia should provide more effective exemption mechanisms to patient

payments. Improvements in the organization of care for patients with a chronic disease

can also enhance the efficiency of service utilization and thus, decrease the level of out-of-

pocket patient payments.

5

The effects of chronic diseases on poverty

105

5.1 Introduction

Chronic non-communicable diseases are a major cause of financial hardship for patients

and their households (Abegunde & Stanciole, 2008; Adeyi, Smith, & Robles, 2007;

Bloom & Finlay, 2009; Russell, 2004). Once diagnosed, chronic diseases frequently

require continuous use of health care. When the health care system heavily relies on

out-of-pocket patient payments, increased utilization of health care due to a chronic

disease imposes a high financial burden on the patient and the household. In this way,

a chronic disease can push even non-poor households into poverty and can drive already

poor households deeper into poverty (Sachs, 2001; Xu et al., 2010) 2001; Xu et al., 2010.

Therefore, chronic diseases present not only a health problem but also a social problem

(Sachs, 2001) 2001.

At the same time, poverty is a cause of getting a chronic disease (Beaglehole et al.,

2011; Tunstall-Pedoe, 2006). Poverty is associated with several factors that contribute

to developing a chronic disease, like material deprivation, unhealthy living conditions

and bad nutrition (Tunstall-Pedoe, 2006). Besides poverty, other risks factors for

developing a chronic disease include aging, heredity, an unhealthy life style (smoking

habits, alcohol consumption, unhealthy diet and physical inactivity) and environmental

factors (pollution, stress)(Alleyne et al., 2013). While some of these risk factors are non-

modifiable like ageing and heredity, others like an unhealthy life style factors can be

changed. However, some of those risk factors, particularly health-related life style risk

factors, are associated with income.

As outlined above, the relation between chronic diseases and poverty is double-sided,

and while chronic diseases can provoke poverty, poverty can also be a trigger for developing

a chronic disease. Taking in account this joint causality between chronic diseases and

poverty, the increased prevalence of chronic diseases and their poverty effects can result

in prolonged social inequalities when patients are required to pay out-of-pocket for the

services they use (Tagoe, 2012).

Studies that examine the poverty effects of out-of-pocket patient payments for chronic

diseases, have not taken in account this complex joint relation between chronic diseases

and poverty (Bonu, Rani, Peters, Jha, & Nguyen, 2005; Engelgau, Karan, & Mahal,

2012; Xu et al., 2003). Those studies implicitly assume that the effect of chronic diseases

on poverty is one-sided, and indicators of chronic diseases are treated as exogenous

predictors (for example in regression equations). The findings of these studies suggest

that when chronic diseases are present, the poverty effects of out-of-pocket patient

payments are statistically significant. The existence of a joint causality implies that poor

people are more likely to develop a chronic disease(Alleyne, et al., 2013) while patients

with a chronic disease have a higher probability to become poor(Wagstaff, 2002; Xu, et

al., 2003). This can lead to biased estimates of the poverty effects provoked by chronic

106

Chapter 5

diseases if the joint causality is not taken into account. Only few studies have examined the

joint relation between poverty and general health status, but without paying particular

attention to chronic diseases(Buddelmeyer & Cai, 2009). Addressing the joint relation

between chronic diseases and their poverty effects can provide better insight into the role

of other risk factors for developing a chronic disease (e.g. eating habits, environmental

factors, smoking behavior) (Tunstall-Pedoe, 2006).

The aim of this chapter is to model the causal effects of chronic diseases on poverty

effects of out-of-pocket patient payments when other common factors are controlled for.

To explore this joint causality, we use LSMS data for Serbia for 2007(Chapter 1). We apply

an instrumental variable (IV) approach (Khandker, Koolwal, & Samad, 2010; Wooldridge,

2012). We also present the results obtained from ordinary least squares (OLS) regressions.

We include in the analysis the three most common chronic conditions classified as leading

chronic diseases (WHO, 2005), namely diabetes mellitus, cardiovascular diseases and

progressive diseases. These diseases are also identified as an economic burden for patients

and their households in various countries (Adeyi, et al., 2007; Alleyne, et al., 2013;

Gordon, Scuffham, Hayes, & Newman, 2007; Rasekaba, Lim, & Hutchinson, 2012).

In Serbia, estimated total costs of diabetes mellitus are 6% of total health expenditure

(Bjegovic et al., 2007). Diabetes mellitus, cardiovascular diseases and cancer are three

diseases with highest prevalence in Serbia since 2000. They are also leading causes for

mortality (Janković, Simić, & Marinković, 2010). Since the total out-of-pocket patient

payments in Serbia are estimated as 35% of total health expenditure, which is a rather

high rate compared to other European countries(Vuković & Perišić, 2011), although

people with diagnosed chronic diseases in Serbia, are partially exempted from official

co-payments, we expect that they also have a high probability to experience poverty.

5.2 Methods: data and statistical analysis

In this study we use the Serbian LSMS data for 2007. As we outlined in Chapter 1,

the data contain variables that can be used as indicators of household wealth, like total

household consumption. Also, information on out-of-pocket patient payments for both

inpatient and outpatient care are available. The data also contain information about the

presence of chronic diseases among household members.

We first describe the variables included in the estimation of models. Then, we outline

the statistical methods that we apply.

5

The effects of chronic diseases on poverty

107

5.2.1 Outcome variablesWe use two outcome variables: pre-payment poverty and the catastrophic effects of out-

of-pocket payments. We first create a binary indicator of pre-payment poverty. As in

previous studies (Wagstaff, 2008), households with total household consumption lower

than the absolute poverty line are classified as poor. As we mentioned in Chapter 2, the

absolute poverty line is calculated by the Republic Statistics Office of Serbia. It is defined

as minimum food basket plus other goods that households with minimum basket food

consumption are supposed to spend, and it is estimated as 8,883 CSD (≈ 100 Euro) per

person per month. Based on this, we have calculated the poverty line at the household

level by multiplying the absolute poverty line per person by the number of household

members. Thus, if the total household consumption is less than the absolute poverty

line for that household, the household is classified as poor and the indicator is coded as 1

(poor), otherwise it is coded as 0 (non-poor). We use this indicator as an outcome variable.

To assess the financial burden of out-of-pocket patient payments for different types of

chronic diseases, we apply catastrophic health care expenditure approaches.

Catastrophic health care expenditures occur when the total out-of-pocket patient

payments exceed a certain threshold (Xu, et al., 2010). The threshold is defined as a

proportion of the total household consumption and can vary from 5 to 40% (Chapter 2).

We use the threshold of 40% because it is the most conservative measure of catastrophic

effects (Xu et al., 2006). To identify the households that experience catastrophic health

care expenditure, we create a binary indicator of catastrophic health care expenditure

(including all households). If the total out-of-pocket payments of a household exceed

40% of the total household consumption, this binary indicator is coded as 1 (catastrophic

expenditure), otherwise it is coded as 0 (no catastrophic expenditure). This is also an

outcome variable in our analysis.

5.2.2 Indicators of chronic diseases Data regarding chronic diseases of household members are collected using questions

about the diagnosis of diseases. For example: “Are you diagnosed with diabetes mellitus

by a medical doctor?” Similar questions are asked for other chronicle diseases including

cardiovascular diseases (hypertension, myocardial infarction, etc.) and progressive diseases

(cancer). For the purpose of our analysis, we create three binary indicators for diabetes

mellitus, cardiovascular diseases and progressive diseases respectively.

If at least one household member is diagnosed with a given disease, the respective

diseases-specific indicator is coded as 1 (household members diagnosed with such

disease), otherwise it is coded as 0 (no household member diagnosed with such disease).

All respondents who did not give a yes-answer to the questions on the three chronic

diseases, are considered as non-diagnosed by a medical doctor as having these chronic

diseases.

108

Chapter 5

5.2.3 Covariates We use variables presenting household characteristics as covariates in all our models.

More precisely, we use age of the head of the household, type of settlement, gender of the

head of the household, level of education of the head of the household, nationality of the

head of the household, household size and income percentiles. Since health care insurance

is compulsory and virtually everyone is insured, we do not include this as covariate.

5.2.4 Instrumental variable approachAs we have outlined before, in this study we apply an IV approach (Wooldridge, 2012).

The IV approach is a regression model that is applied when at least one of the predictors

is endogenous (Khandker, et al., 2010). If endogenous predictors are used in a linear

(OLS) or non-linear (probit, logistic) regression, they can give inconsistent parameter

estimates. One way to overcome the problems with an endogenous predictor is to find

instrumental variable(s), i.e. exogenous variables that are related to the endogenous

predictor but not related to the outcome variable. The most known form of IV approach

is the two stage least square method (2SLS) (Wooldridge, 2012). In the first stage

regression, the instrumental variable is used as a regressor for the endogenous predictor.

Predicted values of the endogenous predictor are used in a second stage regression, with

the outcome variable as a dependent variable. In order to apply 2SLS approach, both

endogenous predictor/predictors and outcome variables should be continues. However, in

our study both endogenous predictor and outcome variables are of a binary nature. Since

the previous literature shows that conventional 2SLS models are appropriate when both

the outcome and endogenous variable are discrete once valid instruments are found, we

apply the most commonly used 2SLS model in our analyses (Angrist & Krueger, 2001).

The 2SLS model for binary predictors is based on a two-stage regression. In the first stage

regression of our analysis, we regress the binary disease-specific indicators described above

(diabetes mellitus, cardiovascular diseases and cancer) on the instrumental variables and

covariates. In the second stage regression, we regress the binary indicators of poverty,

and catastrophic health care expenditure on the predicted values of the disease-specific

indicators (based on the first stage regression) and the same set of covariates. The

regression model is defined below by equations 1 and 2:

where yi is one of the outcome variables (i.e. the indicator of poverty or catastrophic

health care expenditure); z is one of the instrumented variables (i.e. one of the disease-

specific indicators that define whether the household has a member diagnosed with

5

The effects of chronic diseases on poverty

109

diabetes mellitus, cardiovascular chronic diseases or progressive diseases), and is the

predicted value of z obtained in the first stage regression. Covariates common for equation

1 and equation 2, are denoted as x1, x

2, etc., while instrumental variables are denoted as

instrument1, instrument

2, etc. and they are included in equation 2 only. The error terms

are denoted with δ and ε, for equation 1 and equation 2, respectively.

First equation 2 is estimated (first stage regression) and then equation 1 (second stage

regression). We use the software package Stata 9 (command ivregress). Since we have two

outcome variables and three instrumented variables, we have six different models.

5.2.5 Instrumental variables One of the challenges in performing the IV approach is to identify relevant and valid

instruments. Overall, good instruments should satisfy two main criteria (Cawley &

Meyerhoefer, 2012; Wooldridge, 2012). First, they should be correlated with the

instrumented variable (in our study having a household member diagnosed with diabetes,

cardiovascular diseases or progressive diseases), which is known as the relevance criterion.

Second, they should not be correlated with the error term in the model of the outcome

variable, which is known as the validity criterion.

In order to identify valid instruments, we have first searched the existing literature

(Abegunde & Stanciole, 2008; Adeyi, et al., 2007; Alleyne, et al., 2013; Basu, Stuckler,

McKee, & Galea, 2013; Beaglehole, et al., 2011; Geneau et al., 2010; Ludwig et al., 2013;

Mayer-Foulkes & Pescetto-Villouta, 2012; Murphy, Mahal, Richardson, & Moran, 2013;

Russell, 2004). After we identified possible instruments, we have applied statistical tests

to test for the relevance and validity of the instruments.

According to the literature, good instruments cover a wide range of characteristics

from genetic characteristics, number of relatives to environmental factors. We have

identified potential instruments and classified them in two groups of variables.

The first group is related to health-related life style behavior (e.g. smoking behavior

and eating habits such as sweet food consumption or alcohol consumption). In the previous

literature, health related life style behavior such as smoking and alcohol consumption

have been considered as endogenous variables (Eisenberg & Quinn, 2006; Fletcher,

2011;Mullahy, 1997). It is also known that an unhealthy life style is more often observed

among poor population groups (Cutler et al., 2011), while recent studies show that rich

people consume alcohol more moderately than the poor (van Kiippersluis & Galama,

2013). The relation between poverty status and engagement in health related life style

behavior can question the suitability of smoking behavior and alcohol consumption as

suitable instruments. However, recent studies for Serbia show that there is no difference

in wealth status among smokers (Djikanovic et al., 2011). Similar findings are observed

for alcohol consumption (Jankovic et al., 2010). Therefore, we have used alcohol

consumption and smoking as potential instruments. Moreover, these variables show no

110

Chapter 5

strong correlation with our indicators of poverty and catastrophic expenditure, which is

in line with previous studies in Serbia mentioned above.

Smoking behavior is measured in the LSMS data as the number of cigarettes consumed

per month per household. We use this to create an indicator of smoking behavior (1 for

households with smokers and 0 otherwise). We also create an indicator of the eating habits

of the household. For this purpose, we calculate the total food consumption, fat food

consumption, sweet food consumption and alcohol consumption from data available in

LSMS. Based on Basu et al. (2013), we have grouped different food items in different food

categories (fat, sweet etc.). Also, we have summed up the quantity of different food items

for each food category and expressed them as kilojoules per household per month(Basu,

et al., 2013). We use the share of fat food as a percentage of total food consumption as our

first indicator related to eating habits, the share of sweet food as a percentage of total food

consumption as a second indicator and the share of alcohol on total food consumption as

a third indicator of eating habits.

The second group of instrumental variables consists of environmental variables like

living in an area affected by uranium during the North Atlantic Treaty Organization

(NATO) bombing and being a refugee during the period 1999-2007 (both coded as 1 in

case of an yes-answer and coded as 0 in case of a no-answer). We expect that people living

in areas affected by uranium bombs will develop more often cancer (Egawa et al., 2012)

and cardio-vascular diseases (Douple et al., 2011). Exposure to the war situation and

the necessity to live the home, can lead to the development of many diseases, including

cardiovascular diseases and diabetes, among refugees (Spiegel & Salama , 2000). Among

the refugees in ex-Yugoslavia, cardiovascular diseases and diabetes mellitus occurred as

long-term consequences of prolonged stress and the use of inadequate coping mechanisms

(such as denying) (Vlajkovic, 2000). Although being a refugee can be associated with

lower income, this was for the majority of refugees in Serbia only a short-term effect.

Recent studies show that in the period 2005-2008, the majority of refugees in Serbia

did not differ in their wealth status compared with the general population (Bajec, 2008;

UNDP, 2006).

To check for the relevance of the instruments, we run the first stage regression as a

OLS model using potential instrument variables as predictors of each of the indicators

of chronic diseases (Wooldridge, 2012). Data are presented in Appendix 1 (Table A.1).

When diabetes mellitus is used as a dependent variable, non-significant predictors are the

share of fat food in the total food consumption and living in an area affected by uranium

during the NATO bombing. For cardiovascular diseases, the non-significant predictor

are the share of fat food in total households’ consumption and being a refugee during

the period 1999-2007, while for the progressive diseases, the only significant predictors

are alcohol and smoking consumption. Therefore, based on this relevance test, non-

significant predictors for a chronic disease are excluded as potential instrument variables.

5

The effects of chronic diseases on poverty

111

With regard to the validity of the instruments, we should analyze the correlation between

the instruments and the error term in the model of the outcome variable. This correlation

is not possible to check upon directly since we cannot observe the error term before

the model is estimated (Wooldridge, 2012). Therefore, we first test if the potential

instruments are correlated with one of three outcome variables. If a potential instrument

is correlated with an outcome variable, it should not be used in the final model (Hausman

& Taylor, 1981). Our results of this initial validity test are presented in Appendix of this

dissertation (Table A.2). We have considered that instruments are correlated with an

outcome variable if the correlation coefficient is significant and higher than ±0.2.

Based on the relevance test and the initial validity tests described above, for the pre-

payment poverty model, we include the presence of a refugee in the household and alcohol

consumptions as instruments. We use same instruments for all three chronic diseases.

Regarding catastrophic health care expenditures, for diabetes mellitus we choose the

share of sweet food consumption in total household consumption, alcohol consumption,

number of cigarettes consumed per households, and presence of the refugees in the

households as instrument variables. The same set of variables is chosen for cardiovascular

diseases with the only difference that the presence of refugees in the household is replaced

with municipalities affected by uranium rich-bombs. The only instrument for progressive

diseases is the number of cigarettes per household. We have also checked whether the

instruments are correlated with each other or with other covariates. We did not find

significant correlations with the chosen instruments.

With regard to the validity of the final models, we use the endogeneity test known

as the Hausman test. The Hausman test examines whether the endogenous predictor is

truly endogenous. In other words, the Hausman test checks the hypothesis if there is any

correlation between the error term in the first stage regression and the error term in the

second stage regression (H0: cov(εi, δi)=0). If the H

0 is true both OLS and instrumental

variable estimators are consistent and therefore it is not necessary to use 2SLS. If the null

hypothesis is rejected, 2SLS is required (Hausman, 1978).The Hausman test is provided

in STATA as a post estimation test (command: estat endogenous). We report this test

for each of our 6 models. Another test is performed to examine whether the instruments

are correlated with the error term from the second stage regression. The test is known

as the over identification test or Sargan test. The Sargan test is also provided in STATA

(command estat overid). The results of the two tests are presented for each of the 6

models separately in Table 5.2 and Table 5.3 respectively. Also, we independently run

the second stage regression using a ordinary least square regression (OLS) model as a

“naïve” model that does not capture the endogeneity of the instrumented variable. We

compare the estimators obtained from the OLS regression with those obtained from the

IV model in order to establish the validity of obtained estimators. The details regarding

this comparison are presented in the result section.

112

Chapter 5

5.3 Results

In table 5.1, we present the results of the descriptive statistics for each of the variables

used in our analyses. Our results show that the average consumption of fat food is higher

than the average consumption of sweet food. The consumption of cigarettes is quite

common in Serbia: 53.8% of households have a smoker residing. Of the three chronic

diseases, cardiovascular diseases are most prevalent within the household (39.8%). The

prevalence of diabetes mellitus is 10.1% and prevalence of progressive diseases is 16.3%.

Regarding the outcome variables, the incidence of catastrophic effects is distributed

among all income quintiles, while pre-payment poverty based on consumption is present

only among the lower income quintiles (cross tabulation data that are not presented).

Table 5.2 presents the results of the first three IV models where we use the binary

indicator of pre-payment poverty as a second stage dependent variable and each of

the three chronic conditions as first stage dependent variables. In these models, we

use only two instrumental variables (namely alcohol consumption and the presence of

refugees in the household), since the other instrumental variables are correlated with the

binary indicator of pre-payment poverty. We estimate these 2SLS models including all

households in the sample (N=5,557).

The results of the first stage regressions in Table 5.2 suggest that being diagnosed

with diabetes mellitus is more probable to happen in households with refugees. A

higher consumption of alcohol is negatively related with having a household member

with diabetes or cardiovascular diseases. Larger households and households with a lower

income have a higher chance of a progressive disease.

The results of the second stage regressions in Table 5.2 related to diabetes mellitus

show that households where the head of the household is a man, households in rural areas,

households that have children younger than 18, and households with a lower level of

income, have a higher chance of being poor. Similar results are observed for cardiovascular

diseases: households with a head with lower level of income and households with children

older than 7 have a lower chance to be poor.

5

The effects of chronic diseases on poverty

113

Tab

le 5

.1: D

escr

ipti

ve s

tati

stic

s fo

r th

e to

tal s

ampl

e of

hou

seho

lds

(N=

5557

)

Fre

qu

enci

esM

edia

nM

inM

ax

Ou

tcom

e va

riab

les

Pre

-pay

men

t po

or (b

ased

on

hous

ehol

d co

nsum

ptio

n)1

= p

oor

(7.3

%);

0 =

non

-poo

r (9

2.7%

)0.

000

1

Impo

veri

shin

g ef

fect

s of

hea

lth

care

exp

endi

ture

1 =

impo

veri

shin

g ef

fect

- ye

s (3

.3%

) 0 =

impo

veri

shin

g ef

fect

-no

(96.

7%)

0.00

01

Cat

astr

ophi

c ef

fect

s of

hea

lth

care

exp

endi

ture

1 =

cat

astr

ophi

c ef

fect

-ye

s (2

.4%

) 0 =

cat

astr

ophi

c ef

fect

-no

(97.

6%)

0.00

01

Inst

rum

ente

d va

riab

les

Dia

gnos

ed d

iabe

tes

wit

hin

the

hous

ehol

ds1

= y

es (1

0.1%

) 0 =

no

(89.

9%)

0.00

01

Dia

gnos

ed c

ardi

o-va

scul

ar d

isea

se w

ithi

n th

e ho

useh

olds

1 =

yes

(39.

8%) 0

= n

o (6

0.2%

)0.

000

1

Dia

gnos

ed p

rogr

essi

ve d

isea

se in

abd

omen

wit

hin

the

hous

ehol

d1

= y

es (1

6.3%

) 0 =

no

(83.

7%)

0.00

01

Inst

rum

enta

l var

iab

les

Shar

e of

fat

food

in t

otal

food

con

sum

ptio

nP

erce

ntag

e of

fat

food

in t

otal

food

con

sum

ptio

n12

.00

0.1

87.8

8

Shar

e of

sw

eet

food

in t

otal

food

con

sum

ptio

nP

erce

ntag

e of

sw

eet

food

in t

otal

food

con

sum

ptio

n33

.00

0.1

98.7

0

Num

ber

of c

igar

ette

s co

nsum

ed p

er h

ouse

hold

Con

tinu

ous

from

0 t

o m

axim

um28

0.00

044

80.0

0

Shar

e of

alc

ohol

in fo

od c

onsu

mpt

ion

Per

cent

age

of a

lcoh

ol in

tot

al fo

od c

onsu

mpt

ion

1.21

080

.73

Mun

icip

alit

ies

affe

cted

by

uran

ium

–ric

h bo

mbs

1

= y

es (4

8.3%

); 0

= n

o (5

1.7%

)0.

480

1

Pre

senc

e of

ref

ugee

s in

the

hou

seho

ld

1 =

yes

(4.1

%);

0 =

no

(95.

9%)

0.00

01

Cov

aria

tes

Hou

seho

ld t

he s

ize

From

1 u

p to

12

3.00

112

Gen

der

of t

he h

ead

of t

he h

ouse

hold

1 =

mal

e (7

2.6%

); 0

= fe

mal

e (2

7.4%

)1.

000

1

Nat

iona

lity

of t

he h

ead

of t

he h

ouse

hold

1 =

Ser

bian

(85.

9%);

0 =

oth

ers

(14.

1%)

1.00

01

Age

of t

he h

ead

of t

he h

ouse

hold

From

15

up t

o 98

57.0

015

98

Num

ber

of k

ids

youn

ger

than

7 y

ears

wit

hin

the

hous

ehol

dFr

om 0

up

to 4

0.00

04

Num

ber

of k

ids

olde

r th

an 7

and

you

nger

tha

n 18

yea

rs

From

0 u

p to

60.

000

6

Inco

me

perc

enti

les

Per

cent

iles

(1-5

)3.

000

5

Edu

cati

on o

f the

hea

d of

the

hou

seho

ld0

= u

p to

pri

mar

y sc

hool

; 2 =

up

to s

econ

dary

sch

ool;

3

= u

p to

hig

h sc

hool

3.79

13

114

Chapter 5T

able

5.2

: Res

ults

of t

he 2

SLS

mod

els;

dep

ende

nt v

aria

ble:

pre

-pay

men

t po

or a (

N=

5557

)

Exp

lan

ator

y va

riab

les

n

clu

ded

in t

he

anal

ysis

Poo

r (1

= y

es; 0

= n

o)In

stru

men

ted

= d

iab

etes

mel

litu

s (1

= y

es; 0

= n

o)

Poo

r(1

= y

es; n

o =

0)

Inst

rum

ente

d c

ard

iova

scu

lar

dis

ease

s (1

= y

es; 0

= n

o)

Poo

r(1

= y

es; n

o =

0)

Inst

rum

ente

d p

rogr

essi

ve d

isea

ses

(1 =

yes

; 0 =

no)

Coe

ffici

ent

SEC

oeffi

cien

tSE

Coe

ffici

ent

SE

Seco

nd

sta

ge r

egre

ssio

n r

esu

lts

Hou

seho

ld s

ize

-0.0

110.

317

-0.0

24*

0.01

2-0

.017

0.01

1

Type

of s

ettl

emen

t-0

.026

*0.

008

-0.0

230.

013

-0.0

070.

014

Gen

der

of t

he h

ouse

hold

hea

d *0

.017

0.00

9 0

.031

0.01

5 0

.037

*0.

017

Nat

iona

lity

of t

he h

ouse

hold

hea

d-0

.009

0.01

1-0

.016

0.01

7-0

.012

0.01

8

Age

of t

he h

ouse

hold

hea

d-0

.008

0.00

03-0

.005

*0.

002

-0.0

010.

001

Edu

cati

onal

leve

l of t

he h

ouse

hold

hea

d-0

.009

0.00

3-0

.006

0.00

4

Num

ber

of k

ids

youn

ger

than

7 y

ears

0.0

54*

0.01

0 0

.089

*0.

002

0.0

530.

019

Num

ber

of k

ids

olde

r th

an 7

yea

rs 0

.040

*0.

007

0.0

73*

0.02

0 0

.056

0.01

7

Inco

me

perc

enti

les

-0.0

356*

0.03

3-0

.031

*0.

005

-0.0

29*

0.00

5

Dia

bete

s m

elli

tus

0.9

52*

0.31

7

Car

dio-

vasc

ular

dis

ease

s 0

.074

*0.

246

Pro

gres

sive

dis

ease

s 1

.04

0.38

5

Con

stan

t 0

.187

*0.

045

0.2

11*

0.05

4 0

.079

*0.

041

a Con

sum

ptio

n be

low

the p

over

ty li

ne o

f 888

3 C

SD p

er m

onth

;*

p< 0

.01

** p

<0.

05

***

p< 0

.10

5

The effects of chronic diseases on poverty

115

Tab

le 5

.2: R

esul

ts o

f the

2SL

S m

odel

s; d

epen

dent

var

iabl

e: p

re-p

aym

ent

poor

a (N

=55

57) (

cont

inue

d)

Exp

lan

ator

y va

riab

les

in

clu

ded

in t

he

anal

ysis

Poo

r (1

= y

es; 0

= n

o)In

stru

men

ted

= d

iab

etes

mel

litu

s (1

= y

es; 0

= n

o)

Poo

r(1

= y

es; n

o =

0)

Inst

rum

ente

d c

ard

iova

scu

lar

dis

ease

s (1

= y

es; 0

= n

o)

Poo

r(1

= y

es; n

o =

0)

Inst

rum

ente

d p

rogr

essi

ve d

isea

ses

(1 =

yes

; 0 =

no)

Coe

ffici

ent

SEC

oeffi

cien

tSE

Coe

ffici

ent

SE

Firs

t st

age

regr

essi

on r

esul

ts

Shar

e of

alc

ohol

in t

otal

food

con

sum

ptio

n-0

.009

*0.

003

-0.0

02*

0.00

1-0

.005

0.02

4

Pre

senc

e of

ref

ugee

s in

hou

seho

ld 0

.057

*0.

020

0.0

320.

031

-0.0

010.

001

Hou

seho

ld s

ize

0.0

20*

0.00

3 0

.043

*0.

006

0.0

25*

0.00

5

Type

of s

ettl

emen

t 0

.012

0.00

8 0

.009

0.01

4-0

.009

0.01

0

Gen

der

of t

he h

ouse

hold

hea

d 0

.002

0.00

8-0

.015

0.01

5-0

.017

0.01

2

Nat

iona

lity

of t

he h

ouse

hold

hea

d-0

.009

0.01

1-0

.001

0.01

8-0

.004

0.01

4

Age

of t

he h

ouse

hold

hea

d 0

.003

*0.

0003

0.0

09*

0.00

1 0

.002

0.00

0

Edu

cati

onal

leve

l of t

he h

ouse

hold

hea

d-0

.002

20.

002

-0.0

08*

0.00

4-0

.003

0.00

2

Num

ber

of k

ids

youn

ger

than

7 y

ears

-0.0

35*

0.01

0-0

.091

*0.

015

-0.0

31*

0.01

2

Num

ber

of k

ids

olde

r th

an 7

yea

rs-0

.023

*0.

0069

-0.0

750.

010

-0.0

36*

0.00

8

Inco

me

perc

enti

les

-0.0

010.

0034

-0.0

070.

005

-0.0

29*

0.00

5

Con

stan

t 0

.090

*0.

026

0.1

40*

0.04

1 0

.027

*0.

032

Hau

sman

tes

tF(

1,55

45)=

22.5

386

(p =

0.0

000)

F(1,

5545

) = 2

9.63

79 (p

= 0

.000

0)F(

1,55

45) =

25.

2162

(p =

0.0

0)

Sarg

an t

est

chi2

(1) =

2.6

9289

(p =

0.1

008)

chi2

(1) =

.000

013

(p =

0.9

971)

chi2

(1) =

.867

216

(p =

0.3

517)

a Con

sum

ptio

n be

low

the p

over

ty li

ne o

f 888

3 C

SD p

er m

onth

;*

p< 0

.01

**

p<

0.0

5

***

p< 0

.10

116

Chapter 5T

able

5.3

: Res

ults

of t

he 2

SLS

mod

els;

dep

ende

nt v

aria

ble:

cat

astr

ophi

c ef

fect

s of

hea

lth

care

exp

endi

ture

(N=

5557

)

Exp

lan

ator

y va

riab

les

incl

ud

ed in

th

e an

alys

isC

atas

trop

hic

eff

ects

(1 =

yes

; 0 =

no)

Inst

rum

ente

d =

dia

bet

es m

elli

tus

(1 =

yes

; 0 =

no)

Cat

astr

oph

ic e

ffec

ts(1

= y

es; n

o =

0)

Inst

rum

ente

d c

ard

iova

scu

lar

dis

ease

s(1

= y

es; 0

= n

o)

Cat

astr

oph

ic e

ffec

ts(1

= y

es; n

o =

0)

Inst

rum

ente

d p

rogr

essi

ve d

isea

ses

(1 =

yes

; 0 =

no)

Coe

ffici

ent

SEC

oeffi

cien

tSE

Coe

ffici

ent

SE

Seco

nd

sta

ge r

egre

ssio

n r

esu

lts

Hou

seho

ld s

ize

0.0

08*

0.00

2 0

.045

0.00

3 0

.017

*0.

004

Type

of s

ettl

emen

t 0

.003

0.00

4 0

.004

0.00

5 0

.005

0.00

5

Gen

der

of t

he h

ouse

hold

hea

d 0

.001

0.00

5 0

.003

0.00

5-0

.005

0.00

7

Nat

iona

lity

of t

he h

ouse

hold

hea

d 0

.002

0.00

6 0

.001

0.00

6 0

.001

0.00

7

Age

of t

he h

ouse

hold

hea

d 0

.000

0.00

0-0

.000

70.

0000

5 0

.001

**0.

000

Edu

cati

onal

leve

l of t

he h

ouse

hold

hea

d 0

.001

0.00

1 0

.002

0.00

1-0

.003

0.00

1

Num

ber

of k

ids

youn

ger

than

7 y

ears

-0.0

050.

006

0.0

030.

007

-0.0

17*

0.00

5

Num

ber

of k

ids

olde

r th

an 7

yea

rs-0

.001

0.00

4 0

.006

0.00

5-0

.013

0.00

4

Inco

me

perc

enti

les

-0.0

03**

0.00

1-0

.003

0.00

2-0

.006

**0.

002

Dia

bete

s m

elli

tus

0.1

66*

0.07

5

Car

dio-

vasc

ular

dis

ease

s 0

.158

*0.

059

Pro

gres

sive

dis

ease

s 0

.035

0.01

6

Con

stan

t-0

.023

0.01

5-0

.093

0.44

-0.0

150.

017

* p<

0.01

**

p<

0.05

**

* p<

0.1

0

5

The effects of chronic diseases on poverty

117

Tab

le 5

.3: R

esul

ts o

f the

2SL

S m

odel

s; d

epen

dent

var

iabl

e: c

atas

trop

hic

effe

cts

of h

ealt

h ca

re e

xpen

ditu

re (N

=55

57) (

cont

inue

d)

Exp

lan

ator

y va

riab

les

incl

ud

ed in

th

e an

alys

isC

atas

trop

hic

eff

ects

(1=

yes;

0=

no)

Inst

rum

ente

d=

dia

bet

es m

elli

tus

(1=

yes;

0=

no)

Cat

astr

oph

ic e

ffec

ts(1

=ye

s; n

o=0)

Inst

rum

ente

d c

ard

iova

scu

lar

dis

ease

s(1=

yes;

0=

no)

Cat

astr

oph

ic e

ffec

ts(1

=ye

s; n

o=0)

Inst

rum

ente

d p

rogr

essi

ve d

isea

ses

(1=

yes;

0=

no)

Coe

ffici

ent

SEC

oeffi

cien

tSE

Coe

ffici

ent

SE

Fir

st s

tage

reg

ress

ion

res

ult

s

Shar

e of

sw

eet

food

in t

otal

food

con

sum

ptio

n-0

.001

3*0.

0002

-0.0

012*

0.00

3-

-

Shar

e of

alc

ohol

con

sum

ptio

n in

tot

al fo

od c

onsu

mpt

ion

-0.0

01*

0.00

03-0

.002

*0.

005

--

Num

ber

of c

igar

ette

s co

nsum

ed p

er h

ouse

hold

-0.0

000.

000

-0.0

001

0.00

0 0

.001

*0.

000

Mun

icip

alit

ies

affe

cted

by

uran

ium

-ri

ch b

ombs

0.0

44*

0.01

2-

-

Pre

senc

e of

ref

ugee

s in

hou

seho

ld 0

.055

*0.

020

--

--

Hou

seho

ld s

ize

0.0

23*

0.00

4 0

.473

**0.

006

0.0

20*

0.00

5

Type

of s

ettl

emen

t 0

.014

**0.

008

0.0

020.

014

-0.0

060.

010

Gen

der

of t

he h

ouse

hold

hea

d 0

.000

20.

0009

-0.0

140.

015

-0.0

21*

0.01

1

Nat

iona

lity

of t

he h

ouse

hold

hea

d -0

.009

60.

012

-0.0

050.

018

-0.0

030.

014

Age

of t

he h

ouse

hold

hea

d 0

.003

*0.

002

0.0

09*

0.00

1-0

.027

*0.

002

Edu

cati

onal

leve

l of t

he h

ouse

hold

hea

d -0

.003

0.00

2-0

.010

*0.

003

-0.0

030.

002

Num

ber

of k

ids

youn

ger

than

7 y

ears

-0.3

69*

0.01

0-0

.094

*0.

015

-0.0

25*

0.01

2

Num

ber

of k

ids

olde

r th

an 7

yea

rs-0

.234

*0.

007

-0.0

76*

0.01

0-0

.031

*0.

009

Inco

me

perc

enti

les

-0.0

002

0.00

3-0

.007

0.00

5-0

.007

**0.

004

Con

stan

t-0

.323

0.02

8 0

.093

*0.

044

0.0

040.

032

Hau

sman

tes

tF(

1,55

45) =

3.3

5723

(p =

0.0

670)

F(1,

5545

) = 6

.226

37 (p

= 0

.012

6)F(

1,55

45) =

3.2

0389

(p =

0.0

735)

Sarg

an t

est

chi2

(3) =

4.6

7877

(p =

0.1

969)

chi2

(3) =

1.8

9878

(p =

0.5

937)

-

* p<

0.0

1 **

p<

0.0

5 **

* p<

0.1

0

118

Chapter 5

Regarding progressive diseases, households where the head of the household is a man

and with a lower level of income, have a higher chance to be poor. With regard to the

instrumented variables in the second stage regression, having a household member with

diabetes mellitus, cardiovascular diseases or progressive diseases significantly predict

being poor. However, as indicated in Appendix 1, Table A.3, when we apply ordinary least

square regression without instrumental variables, diabetes mellitus and cardiovascular

diseases are significantly related to pre-payment poverty, while progressive disease is not

significant predictor.

Table 5.3 presents the results of the tree 2SLS models using catastrophic health care

expenditures as the second stage dependent variable and the three indicators of chronic

conditions as first stage dependent variables. Results refer to the whole sample of

households (N = 5,557).

The first stage regressions in table 5.3 indicate that level of sweet food intake as a

share of total food consumption, alcohol consumption and the presence of a refugee in

the household are significant predictors of having a member with diabetes mellitus. Also,

households with a member diagnosed with a cardiovascular disease are more likely to live

in municipalities affected by uranium bombs, with a lower number of children and with

a head of the household with a lower education.

Based on the second stage regression results in table 5.3, instrumented diabetes

mellitus and cardio-vascular diseases are the significant predictors of catastrophic health

care expenditure. The same holds for a lower household income. Contrary to diabetes

mellitus and cardiovascular diseases, instrumented progressive disease is not a significant

predictor of catastrophic health care expenditures. The results regarding catastrophic

health care expenditures and progressive diseases show that households without children,

households from low income groups and households that consume cigarettes have a

higher probability of having one member diagnosed with a progressive disease. However,

when we apply OLS regression without using instrumental variables (see Appendix Table

A.3), all three indicators of chronic diseases are significant predictors of catastrophic

health care expenditure.

5.4 Discussion and conclusions

In this study, we have examined the pre-payment poverty and catastrophic effects of

out-of-pocket patient payments on households with at least one member diagnosed with

a chronic disease. For that purpose, we have applied a 2SLS model using instrumental

variables. The results from the OLS regression without instruments (comparable with the

second stage regression) show lower absolute coefficients for all three chronic diseases,

while results obtained from the IV models show higher values for all coefficients related to

5

The effects of chronic diseases on poverty

119

three chronic diseases. Also, when the IV approach is used, results regarding pre-payment

poverty show that having a household member with diabetes mellitus or cardiovascular

diseases is a significant predictor of poverty in Serbia. On the other side, being diagnosed

with progressive diseases is not a significant predictor of poverty. However, the results

from the OLS regression (without instrumented variables) show that the presence of

any of the three chronic diseases is a significant predictor of pre-payment poverty. For

catastrophic expenditure models, both IV regression and OLS regression consistently

indicate that that having a household member with diabetes mellitus or cardiovascular

diseases is a significant predictor of catastrophic payments but not progressive diseases.

Another diversity that should be mentioned is the negative coefficients of the diseases

indicators in OLS when the outcome variable is pre-payment poverty. This questions

whether the instruments used are the most adequate indicators. But a possible reason

can be also found in the fact that the estimations of the coefficients provided by the IV

models are more accurate (since we captured the endogeneity) than estimation of the

coefficients provided by OLS.

Although, validity tests for all models show that instruments are strong, we found

some differences when comparing the coefficients of corresponding covariates in the IV

models and OLS models. In the models where the pre-payment poverty is used as an

outcome variable, the coefficients of the corresponding covariates in the OLS models and

IV models are more diverse (considerably changing the absolute value and direction)

when compared with models where the outcome variable is catastrophic health care

expenditure. The possible reasons can be found in the nature of the instrumental variables

used, namely being a refugee or alcohol consumption. For example, there might be a

relation between these instrumental variables and poverty. But as we mentioned before,

in our sample we did not find significant correlation between those two variables and pre-

payment poverty. Furthermore, results from the literature for Serbia also show that there

is no relation between a healthy life style and poverty (Djikanovic et al., 2011). Similar

findings are observed regarding refugee status and poverty (UNDP, 2006). Another

reason can be that there is a strong joint relation between the catastrophic payments and

chronic diseases, and a less strong joint relation between poverty and chronic diseases.

5.4.1 Chronic diseases, out-of-pocket patient payments and economic burden The results from the second stage regression when instruments are used, show that all

three chronic diseases can impose an economic burden on households, but some of the

predictors can be overestimated if we do not account for the joint relation between a

chronic disease and poverty (as discussed above). Our results also show that there are some

differences between the three chronic diseases. While diabetes mellitus and cardiovascular

diseases are significant predictors of the catastrophic effects, progressive diseases are not.

120

Chapter 5

This means that households with diabetes and cardiovascular diseases are spending a

significant part of their annual budget on health care. Reasons for the high economic

burden provoked by diabetes mellitus and cardiovascular diseases can be found in the

nature of the disease but also in the organization of the health care system in Serbia.

Diabetes mellitus is known as an expensive disease (Bjegovic, et al., 2007). The treatment

of diabetes mellitus includes polymorph pharmacotherapy and several types of disposable

materials. Although pharmacotherapy and disposable materials (needle, glucose meter,

test strips etc.) should be on “the positive list” and covered by the health insurance in

Serbia, very often physicians in public health institution prescribe brand names that are

not covered by compulsory health insurance (Biorac, Jakovljević, Stefanović, Perović, &

Janković, 2009). The organization of the health services related to treatment of diabetes

mellitus is another obstacle. The lack of counselling services within primary health

care also increases the probability of an economic burden related to diabetes mellitus.

Without receiving proper counselling regarding the behavioral risks factors, patients

are forced to rely on medical treatment applied in primary and secondary health care.

Health services related to diabetes mellitus are organized through primary secondary and

tertiary care. In all of these three levels, official co-payments are charged, sometimes for

the same services at two different levels (results of laboratory analyses from primary care

are usually not used in hospitals and therefore a new laboratory analysis is performed by

the hospital). Additional and not always necessary, out-of-pocket patient payments can

increase the economic burden not only to patients but also to their families. Moreover,

patient diagnosed with diabetes mellitus are partially exempted from some types of

out-of-pocket patient payments (Official Gazette Republic of Serbia, 2009). However,

the types of services where the exemption mechanism should be applied are not clearly

defined (Chapter 4). This means that very often exemption mechanism towards patients

diagnosed with diabetes mellitus is not applied because of complicated administration

regarding the partial exemption.

Previous studies that have estimated the direct and indirect costs for 99 persons

diagnosed with diabetes mellitus in a small city in Central Serbia have shown that

real patient’s costs are 2.28 times higher than the national estimates (Biorac, et al.,

2009). Although those studies do not examine the economic burden from a households’

perspective, they show the same trend as the results in our study. Moreover, previous

epidemiological studies (Bjegovic, et al., 2007; Matejić, Kesić, Marković, & Topić,

2008; Tepavcevic, Matejic, Gazibara, & Pekmezovic, 2011; Dejana Vuković, Bjegović,

& Vuković, 2008) have shown that the majority of the cases diagnosed with diabetes

mellitus are properly registered by the national statistical office, since the diagnosis

includes few variations: Diabetes Mellitus Type I and Diabetes Mellitus type II and

Gestational Diabetes. Since in our analyses we include only officially diagnosed cases,

5

The effects of chronic diseases on poverty

121

accurate registration can also be an explanation for the high catastrophic effects of out-of-

pocket patient payments related to diabetes mellitus.

However, instrumented cardiovascular disease is also a significant predictor

of catastrophic health care expenditure. The reasons can be found in the complex

organization of health care services (both at the primary, secondary and tertiary level).

Also, the network of private health care providers that are related to cardiovascular

diseases is quite spread in Serbia (which is not the case with endocrinologists) (Institute of

Public Health of Serbia, 2013). In this chapter we only address the out-of-pocket patient

payments in public health care services. This implies that the real burden provoked by

both diabetes mellitus and cardiovascular diseases can be even higher for households and

their members.

The model regarding the catastrophic effects of out-of-pocket patient payments for

progressive diseases is not significant. The OLS estimators show that the presence of

a progressive disease is not a significant predictor of catastrophic effects. In Serbia, a

progressive disease can be diagnosed and treated only in public hospitals. The exemption

mechanism is usually applied there. However, previous studies have shown that there

are a high number of premature deaths related to progressive diseases. This implies that

many of patients that are diagnosed with one of progressive diseases never receive proper

treatments (Matejic, Vukovic, Pekmezovic, Kesic, & Markovic, 2011).

5.4.2 Factors associated with the presence of chronic diseasesAlthough it is not the main goal of our study, using an IV approach allows us to assess the

impact of some life-style and environmental factors on the incidence of chronic diseases.

The results from the first stage regressions show that different risk factors are associated

with different chronic diseases. Significant instrumental variables for having diabetes

mellitus are having a refugee in the household. The share of alcohol consumption and

of sweet food in total household consumption is negatively associated with diagnosed

diabetes mellitus. Other eating habits like smoking habits are not significant risk factors

for households that already have at least one member diagnosed with diabetes mellitus.

This may be because households with a member diagnosed with diabetes mellitus have

already changed some of their eating habits in order to adjust to the needs of the ill

member (Basu, et al., 2013). A significant instrumental variable for a cardiovascular

disease is living in municipalities that have been exposed to uranium NATO bombs.

These results can be biased by the fact that the NATO bombing is related to stress

which is one of the risk factors specific for cardiovascular diseases. However, living in

municipalities affected by uranium bombs, is not a significant predictor for a progressive

chronic disease like cancer. One of the reasons is that the effects of the NATO bombing

cannot yet be observed (data were collected seven years after the NATO bombing). On the

other side, a significant predictor of cancer is the consumption of cigarettes. In a nutshell,

122

Chapter 5

our results have shown that environmental factors play an important role in developing

diabetes mellitus and cardiovascular diseases, while life style variables are more related to

progressive diseases. Those results are consistent with previous epidemiological studies

in Serbia (Bjegovic, et al., 2007; Janković, et al., 2010).

5.4.3 LimitationsThe limitations of this study are related to the data. We use cross-sectional data. The

LSMS data for Serbia are collected for 2002, 2003 and 2007, but they are not panel data.

Therefore, we only use data for 2007 as the most recent data. The complex nature of the

economic burden should include a depletion of assets and coping mechanisms (using

savings, borrowing the money) that households apply during a longer period of time.

Moreover, patients and their households change their consumption and spending over

time. Also, the treatment of chronic diseases is characterized by fluctuation over time

(Russell, 2004). This fluctuation also affects medical spending. Longitudinal data may

provide a better insight into those problems. One possibility to overcome the limitations

of cross-sectional data and the fact that chronic diseases have long-term effects is to

use a catastrophic medical expenditure risk approach (Flores & O’ Donnell, 2013). This

approach is based on an ex-ante perspective and uses the measures of downside risks

rising from unexpected health shocks. Furthermore, contrary to catastrophic health care

expenditure that is based on actual medical expenditure (ex-post measures), this risk

approach is based on the estimation of the risk. In this way, it would also be possible to

estimate the risks of catastrophic health expenditures in cases of new chronic diseases

but also in case of comorbidities (Flores & O’ Donnell, 2013) Another limitation is

related to the use of consumption as an indicator of wealth. Although, we are aware that

other indicators of wealth like income and expenditure can be also used, we perform our

analyses using only consumption. Since Serbia is a middle-income country with a widely

spread informal economy, we consider consumption as the most appropriate indicator

of wealth. The advantages and disadvantages of using consumption as the measurement

of wealth have been discussed in Chapter 2. We also have limitations related to the

instruments. However, our choice of instruments is limited by the data available in

the LSMS data we used. Furthermore, all estimations for instrumental variables are

calculated on a household level. This means that we do not know if the person diagnosed

with certain chronic diseases is engaged in unhealthy life style behavior. However, recent

studies show that life style behavior is similar among the family members living in the

same households (Khader & Alsadi, 2008).

5

The effects of chronic diseases on poverty

123

5.4.4 Policy implications and conclusionsIn 2008, the Serbian government signed the Stabilization and Association agreement

with EU. As a part of this agreement, the first national poverty reduction strategy has

been published. The document is addressing the possible policy implications for poverty

reduction in Serbia. The policy implications are related to health care and poverty

reduction mostly addressing problems with long-term care, preventive programs and

exemption mechanisms for vulnerable groups (unemployed, minorities etc.) (Vukovic &

Perisic, 2011). However, our results show that people diagnosed with one of the three

leading chronic diseases are also a vulnerable group. As we described above, out-of-

pocket patient payments related to chronic diseases can provoke a high economic burden

for households. Although some patient with chronic diseases should be exempted from

official co-payments, exemption mechanisms usually does not work in practice. As we

outlined in Chapter 4, there is a complicated administration procedure to obtain the

exempted status. Since people diagnosed with chronic diseases usually use health care

more often, policy makers should facilitate administrative procedures regarding the

exemption mechanism.

As part of the poverty reduction strategy, the Serbian Ministry of Health has started

two prevention programs: one is related to alcohol consumption and other is designed to

decrease the level of smoking among chronically sick. Although preventive programs can

decrease the burden of disease in the future, they do not protect the households with a

sick member from the economic burden of out-of-pocket patient payments. For a proper

strategy of poverty reduction, policy makers in Serbia should pay attention not only to

the prevention of chronic diseases but also to the protection of those who are diagnosed

with a chronic disease.

The possible reason for the economic burden provoked by chronic diseases can also be

the organization of the health care system. There is no clear structure in the treatment

of chronic diseases. Patients diagnosed with a chronic disease are treated in primary,

secondary and tertiary care. Usually there is no direct communication between the

different levels, and same diagnostic procedures are repeated several times on different

levels, as mentioned earlier. Each procedure has to be paid for separately, which means

that repeating procedures exposes the patient and their household to even higher burden

(Gavrilovic & Trmcic, 2013). The existence of informal patient payments makes their

burden even higher.

Another reason for the high economic burden is the long waiting lists in public

health care facilities. Patients diagnosed with a chronic disease require more frequent

diagnostic procedure and more frequent physician’s check-ups. Since waiting lists are

long, patients are usually referred to an outpatient private clinic (Chapter 6). However,

the costs of those private clinics are not covered by the official insurance system and the

private clinics do not apply the exemption mechanism. In our study, we estimate the

124

Chapter 5

economic burden provoked by public health services only. However, the total economic

burden that households with a member diagnosed with a chronic disease experience can

be much higher. The inclusion of private health institution in the compulsory insurance

system may therefore be considered.

CHAPTER 6

Shortcomings of maternity care in Serbia

Published as:

Arsenijevic, J., Pavlova, M., & Groot, W. (2014). Shortcomings of Maternity Care in Serbia. Birth, 41(1), 14-25.

126

Chapter 6

Abstract:

Background: This chapter examines process-related quality and access indicators and

patient payments for maternity care in Serbia.

Methods: We apply a method of triangulation using data collected through three sources:

online questionnaires filled in by mothers who delivered in one of the maternity wards in

Serbia in the period 2000-2008, research publications and official guidelines.

Results: Our results show that many women who delivered in a maternity wards in Serbia

indicate problems with the treatment they receive. The existence of informal patient

payments as well as so called “special connections” make the position of Serbian women

in maternity wards vulnerable, especially when they have neither connections nor the

ability to pay. Problems in the communication with medical staff (obstetricians, other

physicians, midwifes and nurses) during the process of birth are also frequently reported.

Conclusions: Actions should be taken to improve bedside manners of medical staff. In

addition, the government should consider the involvement of private practitioners paid

by the national insurance fund to create competition and decrease the need for informal

payments and “connections.”

6

Shortcomings of maternity care in Serbia

127

6.1 Introduction

Previous research in the field of maternity care in CEE countries has focused on macro-

level access indicators (i.e. indicators at a system level). These macro indicators do not

capture the quality of the services provided but only measure clinical outcomes (e.g.

maternal mortality ratio, rate of cesarean sections) and service provision in general (e.g.

presence of a skilled attendant at birth, availability of emergency obstetric care). Another

problem with the system-level indicators in CEE is the reliability of the data, such as gaps

in the registration and incorrect or false registrations (UNFPA, 2009). Underreporting

of maternal deaths, unattended home births and induced abortions are suspected to bias

statistics in some of these countries. For example, many cases of maternal mortality are

counted as death caused by other reasons (Stamenkovic, 2011). Aside from the clinical

outcomes, the process indicators of maternity care (quality of care, patient payments for

maternity care, accessibility and policy regulation) are rarely examined, and especially not

from the perspective of the users of these services (Stepurko et al., 2013). For example,

few studies that describe the system of maternity care in CEE countries have emphasize

problems with patient payments in maternity wards (Danilovich, 2010; Stepurko et

al., 2013; Szende & Culyer, 2006; Vian et al., 2006). During the communist period,

health care services in these countries, including maternity care, were provided for free.

During the transition period however, in some CEE countries, official co-payments and

other formal patient charges, regulated by official policy arrangements were introduced.

Quasi-formal payments (not strictly regulated by the government) also appeared. At the

same time, informal patient payments (cash and in-kind gifts) become widely spread

(Danilovich, 2010; Stepurko et al., 2013). Informal payments are payments that are

either initiated by the patient/patient’s relatives to obtain better quality of care or quicker

access, or requested by medical staff (physicians, nurses) to provide needed care. In some

CEE countries, informal payments exist both on top of and independently from formal

patient charges.

The objective of this chapter is to examine process-related indicators regarding the

accessibility of maternity care, the quality of care received, patient payments and policy

regulations for maternity care in Serbia (one of CEE countries) (Ronsmans, 2001). We

examine those indicators using data from different sources: data collected by an online

questionnaire among women who used maternity care, as well as data from published

studies and official guidelines. We analyze the data combining qualitative and quantitative

techniques. Using three different sources of information with possible counteracting

biases to examine the same phenomenon, we apply triangulation as a research strategy

(Denzin, 1978, Tashakkori & Teddlie, 2003). This helps us to overcome biases in our

analysis and to provide a comprehensive picture of maternity care in Serbia.

128

Chapter 6

6.2 Background

As we outlined in Chapter 1, health care reforms in Serbia started in 2002, with the main

focus on the improvement of the technical infrastructure and financial sustainability. As

in some other CEE countries, official co-payments were introduced in order to strengthen

the efficiency and sustainability of the health care system. Since the country had passed

through a difficult civil war and the rates of new born babies had been decreasing

since 1991, the government wanted to increase the fertility rate by exempting child

delivery from the official co-payments. This exemption policy was combined with a set

of regulations that allow fully paid parental leave of up to 12 months for mothers. The

regulations include some other benefits like free public transport for pregnant women

and new-born children (Chapet 4).

Maternity care in Serbia is an integrated part of the Serbian health care system

inherited from the former Yugoslavia. Similar to the other CEE countries, the system

is centralized and administratively regulated by the Ministry of Health. Primary and

secondary health care units are involved in the provision of maternity care. Prenatal

care is organized through primary health care centers so called “domovi zdravlja”. The

services provided at these centers include regular follow-ups like laboratory analyses,

ultrasounds examination but also administrative measures, like confirming pregnancy,

determining pregnancy leave and the referral for the maternity ward. Some anecdotal

evidence (Blic, 2011) shows that prenatal care is mostly provided in the private sector

because of increased demand and the lack of supply in the public facilities. When

delivery is approaching, however, pregnant women are obliged to go to the public sector

maternity ward. According to the Serbian law, it is obligatory to deliver in one of the

state owned maternity wards. Currently, 76 maternity wards are operating in different

cities in Serbia (Institute of Mother and Child Health Care of Serbia, 2009). The network

of maternity wards is equally spread in the different geographical areas, which is an

important advantage. Every pregnant woman who has compulsory health care insurance

and a referral from a primary health care physician should have access to a maternity

ward.

During the civil war (1991-2000), maternity wards like all others hospitals in Serbia

were lacking basic supplies (oxygen, pharmaceuticals, blood for transfusion) and medical

staff received their salaries with delay (Chapter 1). However, in 2000, after the political

changes, the WHO and the World Bank provided humanitarian aid to Serbian hospitals

with special attention to the maternity wards (Jeffery, 2003). It was expected that the

government and humanitarian aid would improve the situation in the maternity wards.

Previous literature regarding the organization of maternity care in Serbia is limited.

However, the few existing studies (Andrejic, 2010, Shiffmana et al., 2002) show some

patterns of behavior within the current system of maternity care. The care process is still

6

Shortcomings of maternity care in Serbia

129

operating within a hierarchical model with the obstetrician at the top of the pyramid

while the interests of users are mostly neglected. Obstetricians usually have obtained a

master degree in medicine accompanied with five years of practical education so called

“specijalizacija”. Since the midwives are educated in vocational school, their position

within the hierarchical model is lower compared to obstetricians and other physicians

(Stambolovic, 1996). During childbirth, the role of the midwife is undermined

(Stambolovic, 1996). The technocratic model of childbirth is followed. The delivery is

guided by the obstetrician using an active approach, which assumes that all women,

even those identified as “low-risk”, need intensive monitoring in the hospital, often need

medication and often or always need episiotomy and artificial delivery (vacuum, forceps

etc.) (Kloosterman &Thiery, 1977). In this way, the Serbian system resembles systems in

some high-income countries where the delivery is also not perceived as a physiological

process, but rather as a medical event (Chalmers, 2012).

Regulations within the Serbian system are not transparent and change very often. The

care process is “closed”, i.e. the presence of the family is not allowed and the presence of

the father is “discouraged”. Consequently, future mothers become even more dependent

on the health care providers. The obligation to deliver in a hospital (it is not allowed to

deliver at home) in an isolated environment is defended by obstetricians using arguments

like it is not hygienic to deliver at home and there are no facilities to allow the father to be

present in the hospital. Health care users keep silent about this - there is an “unwritten”

rule that experiences from maternity wards are not spoken about in public (Andrejic,

2010).

But, in 2008, in a very short period of time (2-3 weeks), several babies and/or their

mothers died in maternity wards in Belgrade. Their families were complaining about

inadequate care and corruption in maternity wards. Inspired by this event, Branka

Stamenkovic, an ordinary women who occasionally wrote a blog on a popular website,

described in one of her blogs the experiences of her own delivery (Stamenkovic, 2011).

Many women commented on her blog sharing similar experiences. Women, involved in

the discussion in the blog, decided to raise this in public and the unwritten rule of silence

was broken. The Ministry of Health put out a very strong announcement that they wanted

to explore the information published on the blog. However, Branka Stamenkovic founded

a civil initiative “Mother Courage” and invited the Minister of Health and a few highly

regarded obstetricians to take part in a public discussion. During the public debate on

the national TV channel, Branka Stamenkovic invited all women who delivered in the

last ten years to visit the website of “Mother Courage” and to fill in the questionnaire

about their own experiences during delivery.

The voluntaries of “Mother Courage” checked the identity of the women (e-mail

address, personal data, day of delivery etc.) that were responding to the questionnaire.

130

Chapter 6

The existence of an anonymous on-line questionnaire has been the first public civil

action to improve the conditions in the Serbian maternity wards. The civil initiative

was supported by many public figures (actors, politicians, etc.) but also by some

well-known obstetricians. After 657 questionnaires were collected on the website

of “Mother Courage”, they named three main problems in Serbian maternity care:

poor communication, corruption and outdated medical protocols that are still in use

(Stamenkovic, 2011). With the aim to solve the shortcomings, the Ministry of Health

translated in Serbian the WHO guidelines for communication: “General principles of

communication with pregnant women and their families” and “General principles of

treatment and care in the maternity ward” (Institute of Mother and Child Health Care

of Serbia, 2009). The aim was to improve the communication between medical staff

and pregnant women, and to change the dominant active approach to childbirth and

replace it with a natural physiological delivery approach (Ministry of Health, 2010).

Although, the Ministry of Health has promised to implement the WHO communication

guidelines, these guidelines are not fully implemented in practice but are only adjusted

to fit the current organization of maternity care in Serbia (Ministry of Health, 2010). The

maternity ward in Pancevo was an exceptional case in that it actually implemented some

of these guidelines (Andrejic, 2009). In addition to the unsuccessful implementation of

the guidelines, problems caused by corruption exist (Andrejic, 2009). Among others,

this refers to informal patient payments. Also, the absence of patient-centered care still

continues in the maternity wards in Serbia.

The “Mother Courage” initiative ended in 2011 as it was not possible to come to an

agreement among the main stakeholders about the problems in the maternity wards.

The Ministry of Health has published the results of their own research (Banjac et al.,

2010) that paint an idealistic picture of maternity wards in Serbia. However, anecdotal

evidence confirms the continued existence of corruption, outdated protocols and poor

communication in maternity wards in Serbia. In this chapter, we use the data collected

by the Mother Courage initiative to describe and analyze the process related indicators

in maternity wards in Serbia. We compare the results to data from other sources, namely

published studies and guidelines.

6.3 Methods

In order to study the process-related indicators of accessibility, quality, payments and

regulations in maternity care in Serbia, we apply a mixed-method approach, more

precisely a fully mixed sequential approach (Leech & Onwuegbuzie, 2009) using both

qualitative and quantitative data. We use different sources of information on indicators in

Serbian maternity care: the data collected through the online portal of Mother Courage,

6

Shortcomings of maternity care in Serbia

131

a literature review of published studies as well as official documents and guidelines.

Thus, by combining data from different sources, we apply the method of triangulation

as a research strategy. Triangulation combines different data and methods to counteract

possible biases in the data sources. Also, triangulation allows us to observe variations

within the same data sources but also among different data sources (Happ et al., 2006;

Jick, 1979), which helps us to verify our findings. The different sources of data and the

analytical techniques applied to these data (i.e. our research phases), are subsequently

described.

6.3.1 Online questionnaireThe questionnaire is designed by the public civil initiative “Mother Courage”. The

questionnaire is posted on an online portal (website) and respondents can register through

that portal to fill in the questionnaire but remain anonymous to the wider public. The user

verification is done by “Mother Courage” based on personal data provided by respondents

and matching them with hospital records.

The questionnaire consists of 22 open-ended questions. Since the questions are open-

ended, the data are qualitative. The sample of respondents is biased: only women who

are able to use internet and are motivated to answer, are included in the sample (see also

background section). In total, 657 women participated till 2011 (when our analysis was

carried out). Some of these respondents (415 in total) did not follow the format of the

questionnaire and instead reported their experience as a story. Data in this format are

difficult to compare to data collected by the questionnaires and we have excluded those

cases from the analyses. Also, some of the respondents (144 in total) were reporting

experiences from the period 1991-2000 (the war period) and we have also excluded them.

We use data collected between 2000 (the year when main political changes have occurred

in Serbia) and 2008 (the year when the last interview that followed the format of the

questionnaire was provided). Only few questionnaires (3 in total) were filled in by fathers,

and moreover, two of them were not present at the moment of childbirth. Therefore, we

present data of only one father who was present during the delivery. Finally, 95 semi-

structured questionnaires filled in by mothers and one questionnaire filled in by father,

are included in the analyses.

We use framework analysis (Srivastava, 2009) to analyze the qualitative data collected

through the questionnaire. Framework analysis is a technique used to analyze qualitative

data for social policy research. A framework analysis does not require collection of data

during certain time-scale, but allow researchers to quantify qualitative data (Richie &

Spencer, 2002). In framework analyses, five steps are applied: 1) familiarization with

data; 2) identification of a thematic framework, 3) indexing the data following the

framework; 4) charting the data in accordance with the identified theme; and 5) mapping

and interpretation (Ritchie & Spencer, 1994).

132

Chapter 6

Since our study is focused on process-related indicators in maternity care, we identify

several themes: accessibility to maternity care, quality of care, patient payments, and

policy regulations. We also add the themes: innovations in maternity care as well as health

and system indicators at a macro level applied by the UN and the WHO (Hulton et al.,

2000; Ronsmans, 2001). For further analysis, we operationalize accessibility through

three dimensions: spatial, temporal and psychological using the approach of Berchi &

Achart (1980). The spatial dimension refers to geographical access to health care facilities.

The temporal dimension refers to waiting time, while the psychological dimension

considers social distance between providers and health care users (communication skills

of the staff). Quality of care is operationalized as clinical quality (obstetric care and

procedures, quality of equipment and level of physician skills) and social quality of care

(e.g. facility maintenance) (Berchi & Achart, 1980). We operationalize patient payments

through several dimensions: type of payment, receiver of payment, frequency of payment,

magnitude of payment, purpose of payment and attitude towards payments (Stepurko

et al., 2013). Policy regulations are operationalized as regulations regarding payments,

equity, and governmental/hospital protocols (Hulton et al., 2000). Innovations in

maternity care in Serbia are operationalized by the presence of the partner during delivery

and innovations regarding immediate breastfeeding and rooming-in (the practice of

keeping a newborn infant in a crib near the mother’s bed instead of in a nursery during the

hospital stay ) (Stepurko et al., 2013). Health system indicators are represented through

maternal and infant mortality, rate of caesarean sections and rate of emergency delivery,

as well as the presence of a skilled care provider (accredited health care professional). We

present the results along these themes.

Furthermore, we quantify the responses to the questions to analyze the information

collected by the open-ended questions quantitatively. A detailed description of the

questions and quantified variables are presented in Table 6 1. The quantitative data are

analyzed using descriptive statistics (statistic package SPSS 17.00) and are presented

through the same set of themes identified in the qualitative analysis. Data on social

demographic characteristics are not collected in the questionnaires, which precludes

further analysis on variations across population groups.

6.3.2 Literature reviewIn our literature search, we systematically identify publications on Serbian maternity

care (both in English and Serbian) and screen them for their relevance. We use the

following key words: maternity care, child delivery, child birth and obstetric care in

combination with the words Serbia, empirical study, access, equity, quality, reforms. All

possible combinations of keywords are used. The following bases are searched: PubMed,

Picarta, EBSCO (which also includes Psychinfo, Psychoarticles, Socindex and Medline),

Informahealth, Cochrane and Google Scholar. We consider a publication to be relevant

6

Shortcomings of maternity care in Serbia

133

if it examines the characteristics of the Serbian maternity care system particularly when

it focuses on the process-related indicators that we have mentioned above. We divide

the publications selected for the review in two groups. The first group includes articles

that are written by independent researchers and we label them as “non-government”

publications. The second group of articles includes articles that are written by researchers

appointed to the Serbian public sector as a part of their regular working tasks. We label

them as “pro-government” publications. The data from the literature review are analyzed

using the framework analysis described above, i.e. the same sets of themes and indicators

are applied.

6.3.3 Official and hospitals guidelines and institutions’ websitesWe also review data from official guidelines that report on process-related indicators

(rate of delivery with complications like hemorrhaging, rate of caesarean section, but also

maternal mortality ratio). Also, we search the websites of international organizations (e.g.

UN, WHO, WB), as well as those of Serbian hospitals and Ministry of Health to identify

and compare the values of health indicators as well as other indicators to those identified

in research publications. Besides the official guidelines, we include hospital guidelines

as well. Each maternity ward in Serbia has its own official guideline and few of them are

available online. We analyze the data using the framework analysis and the set of themes

and indicators described above.

The results from the three research phases are presented descriptively as well as in the

form of tables. The focus is on the comparison across of the different sources of data as

well as on the identification of gaps in research and policy.

6.4 Results

We present our results per theme (described in the methods section) referring to all

source of data: questionnaires, literature reviews and review of guidelines. Qualitative

data obtained from the questionnaires, are summarized and supported by quotations (see

Box 6.1). Quantitative data are presented in Table 6.1 and Table 6.2. The majority of

the respondents in our sample (68.5%) describe childbirths that occurred in one of the

maternity wards in Belgrade. Only two questionnaires are from the south of Serbia, while

the questionnaires from Vojvodina (15.8%) and Central Serbia (14.0%) are almost equally

represented. For a comparison across different sources of data that we use, qualitative

findings from all sources are presented in Table 6.3.

134

Chapter 6

6.4.1 Accessibility Results from all three sources (qualitative and quantitative data from online questionnaire,

both groups of publications -“non-government” and “pro-government” and the official

guidelines) show that spatial access to maternity care in Serbia is adequate. In particular,

official guidelines in Serbia indicate that the networks of maternity wards are equally

spread over different geographical areas (Institute of Mother and Child Health Care

of Serbia, 2009). The analyses of the qualitative data collected through the on-line

questionnaire show that even small cities (like Gornji Milanovac, Vrsac, Pancevo, Cuprija)

have their own maternity wards.

The main problem reported in the qualitative data regarding spatial access to hospital

care is related to “outdated referral”. As the women explain, according to Serbian

regulations, every woman has to have a referral from the state provider that is not older

than 3 weeks. Since the date of a childbirth cannot be exactly predicted, 12 (12.63%) of

women from our sample report that they faced problems because of an “outdated referral”.

Before these women could be officially admitted, they had to wait at the reception for a

“new referral” from the primary care physician to be brought to them by their relatives.

Hospital guidelines also confirm that the requirement for the pregnant women to have a

referral for hospital admission complicates access to care in some cases.

Regarding temporal access, the qualitative data from the questionnaires show

problems with the waiting time for certain procedures. This includes procedure such as

epidural analgesia, additional ultrasound etc. The main obstacle regarding psychological

accessibility relates to communication problems between users and providers. As many

as 65 (68.4%) of the women in our sample describe medical staff as very distant and

non-approachable (see Box 6.1, Quotation 1).

6

Shortcomings of maternity care in Serbia

135

Box 6.1: Selected quotations from the on-line interviews

Quotation 1:

“I had very good connections, so everything went well with my delivery. But

anyway, the way they communicated with me made me to feel like I am in prison.”

Quotation 2:

“They did not bring my baby for regular breastfeeding. I’ve asked them-what

is wrong? They replied –he is sick, ask the pediatrician, on the floor above us. I

went upstairs with a urinary catheter and an infusion bottle in my hands. I saw

a woman in a white coat and I asked her where I can find the pediatrician, she

replied: you have just found one, and are you lucky. Do you like how I look?

Probably you have never seen a pediatrician in your village that is why you all

come and look for me!”

Quotation 3:

“I did not have any influence on the course of the delivery neither doctor has ever

informed me what is going to happen.”

Quotation 4:

“This hospital was terribly dirty-they had not painted the walls since the hospital

was opened in 1800. Everywhere you could see a lot of rats-they were the only

ones that could eat the hospital food. I’ve seen much better conditions in the

stables in our village-at least stables have heating and warm water.”

Quotation 5:

“The worst thing after delivery is that you cannot have any visitors. In the state a

woman is after delivery it is most important to have the support from your family.

I really had the impression that they do not allow visits just because they want to

punish us – I could not find any rational reason.”

Quotation 6:

“Yeah, visits are allowed neither during nor after delivery because of hygienic

reasons, of course if you are the cousin of the nurse on night shift-then anyone

without infection can come and visit you.”

136

Chapter 6

Quotation 7:

“I was a witness- I was sharing a room with her. She was very young, 18. She could

not deliver naturally, they said to her you need a Caesarian but you have to pay.

Her husband is a farmer, they do not have money. So he had to sell the cow (one of

two) and to give the money to the doctor. After that they put her on the program

for a Caesarian section.”

Quotation 8:

“I did not give money to anyone that was the main problem.”

Quotation 9:

“There is no necessary Caesarian, there is only paid one!”

Quotation 10:

“I was placed in a so-called baby friendly ward-there is nothing friendly

about it! The nurse would come only during the night at the same

time to wake up us and our sleeping babies, yelling on us what kind of

mothers you are-you are sleeping and I am supposed to feed your babies.”

Several patterns of communication failure can be identified. For example, the

quantification of the data shows that 51 (53.7%) of the women in the sample report

derogative communication. These include addressing pregnant women by inappropriate

cynical or derogative nicknames (e.g. that the women is lazy or fat), shouting at patients,

and showing no respect towards patients (see Table 6.1). Women also describe the

incapability of staff to interpret women’s behavior and to address their needs (see Box 6.1,

Quotation 2). For example, very often, future mothers describe that they did not receive

any answer to the questions they asked (see Box 6.1, Quotation 3). In one interview

provided by a father, he describes that the communication with health professionals was

adequate, but that they have communicated mostly with him, not with his wife. He was

not involved in the decision making process, but he was informed by the health care

providers what is going to happen.

Also, he stated that he was the main support to his wife and that this was making him

to feel that he is an active part of the process.

Data from “non-government” publications are consistent with data obtained from

the questionnaires regarding the problems with temporal and psychological access to

maternity care (Andrejic, 2010; Janevic et al., 2011; Shiffmana et al., 2002). In contrast

to this, “pro-government “publications (Banjac et al., 2010) do not report problems with

temporal and psychological access (see Table 6.3).

6

Shortcomings of maternity care in Serbia

137

6.4.2 Quality of careRegarding clinical quality, the data from the on-line questionnaires suggest problems

with the obligatory procedures. For example, admission procedures, which also involve

obligatory shaving and enema, are described by the majority of interviewed women (70.4%

of the sample) as very bad and humiliating (see Box 6.1, Quotation 6). Many women in

our sample (72.6%, see Table 6.1) experienced inconveniences, felt humiliated and were

treated with low respect during the medical procedures. As many as 19 (20.0%) of the

women in our sample reported that they had manual vaginal examination in a situation

that they perceived as inconvenient. For example, when a group of students visited the

maternity ward, manual vaginal examinations were performed for instructional purposes.

Half of these women were not asked for permission (10 out of 20). Some of them were not

informed about what was going to happen (16 of 20).

Medical equipment is described as not working or not properly used by 11 (11.57%)

women. For example, beds that can be adjusted to sitting position were not connected

with the electrical supplier so they did not work, or every woman was connected to a

ctg (machine that measure the heart rates of the baby) during the birthing process but

the needle of the ctg did not work. However, problems with equipment are more often

reported during the period 2000-2005, compared with the period 2005-2008.

Problems with the accommodation (social quality) are also often reported (62 women,

i.e. 65.7% of the sample). For example, women report problems due to a lack of hot

water, rooms with insects, lack of clean linen in the room, non-cleaned toilets and the

very poor quality of the food served (see Box 6.1, Quotation 4). Problems with room are

most often described (55 women, 57.89%) than problems with bathroom (41 women,

43.15%) and problems with food (10 women, 10.5%). Contrary, in one interview filled

in by father, he emphasizes that because of his presence, his wife was in a single patient

room that was very well equipped. She did not need to share the room or bathroom with

other women.

Despite these quality problems, the level of physician skills is generally rated as good

by the mothers interviewed. This finding is confirmed by the quantitative results where

39 women (41.1% of the sample) perceive the physicians’ skills as adequate (see Table

6.1). However, the physicians’ motivation is frequently questioned by the women in our

sample.

Qualitative data from the questionnaires also show that quality improvements in

maternity services leave much to be desired. Among others, this relates to the visits

of family members that are not officially allowed after delivery. Women emphases that

without family members, they felt alone, sad and isolated after the delivery (see Box 6.1,

Quotation 5). Some hospitals allow visits - usually only one hour during the afternoon. In

our sample, women report that informal connections play an important role to be allowed

visitors (see Box 6.1, Quotation 6).

138

Chapter 6

The other sources of data that we used confirm the above findings. Regarding the

clinical quality of maternity care, qualitative data from “non-government “publications

and official guidelines (Andrejic, 2010; Janevic et al.,2011; Shiffmana et al., 2002)

are consistent with results obtained from the questionnaire. The “pro-government”

publications do not examine the quality of care.

6.4.3 Patient paymentsWomen in our sample frequently report that they have to make quasi-formal payments

and informal payments for maternity services. Quasi-formal payments are charged for so

called standard services (such as the presence of skilled persons during the delivery, for

the delivery itself, for any type of analgesia etc.) that should be provided free of charge.

Overall, quasi-formal hospital payments are most often reported for epidural analgesia.

The reported amount for an epidural varies from hospital to hospital and is about

100-200 euro. This amount has to be paid in advance by the pregnant women. In case

the epidural is not used (e.g. in case of a caesarean section), the patient does not get any

money back, as stated by some respondents in our sample.

In addition to the official charges for standard services, women also report informal

patient payments. The existence of informal patient payments is reported by all three

sources. However, their magnitude varies from 2% in the “pro-government” publications

to 22.1% in the online questionnaires (Banjac et al., 2010; Janevic et al., 2011). Data

reported in “non-government” publications show frequency similar to the data reported

in the online questionnaires (10-14% of the maternity care users). Data reported in “pro-

government” publications are related to 2009, while data related to “non-government”

publications are for the period 2000-2010, and data from questionnaires are related to

the period 2000-2008. Only women, who have reported informal patient payments are

asked whom they bribed. As reported by our respondents, receivers of informal patient

payments are obstetrician, anesthesiologists, and midwives-nurses.

6

Shortcomings of maternity care in Serbia

139

Tab

le 6

.1: Q

uest

ions

and

res

pons

e re

late

d to

pro

cess

-rel

ated

indi

cato

rs (N

=95

)

Yes

No

Mis

sing

val

ues

Did

you

hav

e an

y in

conv

enie

nce

duri

ng y

our

stay

in h

ospi

tal?

69(7

2.6%

)26

(27.

4%)

none

Nam

eN

ickn

ame

Mis

sing

val

ues

How

did

the

y ca

ll y

ou in

the

hos

pita

l? B

y yo

ur n

ame

and

surn

ame

or b

y de

roga

tive

nic

knam

es?

44(4

6.3%

)51

(53.

7%)

none

Yes

No

Mis

sing

val

ues

Did

you

nee

d to

ask

for

perm

issi

on t

o se

e yo

ur c

hild

or

coul

d yo

u do

tha

t at

any

tim

e?65

(68.

4%)

28 (2

9.5%

)2(

2.1%

)

Yes

No

Mis

sing

val

ues

Did

you

hav

e an

y pr

oble

ms

wit

h br

east

feed

ing?

56(5

8.9)

%34

(35.

8%)

5(5.

3%)

good

bad

Mis

sing

val

ues

How

wou

ld y

ou r

ate

the

prof

essi

onal

kno

wle

dge

of o

bste

tric

ians

?39

(41.

0%)

48(5

0.5%

)8(

8.4%

)

Yes

No

Mis

sing

val

ues

Hav

e yo

u ev

er d

oubt

the

val

idit

y of

cur

rent

med

ical

pro

toco

ls a

nd r

ules

in d

eliv

ery

unit

s?

76(8

0.0%

)18

(18.

9%)

1(1.

1%)

Yes

No

Mis

sing

val

ues

Did

you

nee

d to

bri

be s

omeo

ne?

21 (2

2.1)

%69

(72.

7%)

5(5.

3%)

Yes

N

o M

issi

ng v

alue

s

Did

you

use

con

nect

ions

? 26

(27.

4%)

64(4

5.2%

)5(

5.3%

)

Vag

inal

C

esar

ean

Mis

sing

val

ues

Wha

t w

as t

he t

ype

of c

hild

birt

h-va

gina

l or

Ces

area

n?56

(58.

9%)

36(3

7.9%

)3(

3.2%

)

Fem

ale

Mal

eM

issi

ng v

alue

s

Wha

t w

as t

he g

ende

r of

the

obs

tetr

icia

n w

ho w

as a

ssis

ting

the

bir

th?

27(2

8.4%

)35

(36.

8%)

33(3

4.7%

)

Yes

No

Mis

sing

val

ues

Did

you

hav

e an

y pr

oble

ms

wit

h eq

uipm

ent

62(6

5.7%

)33

(34.

3)%

none

140

Chapter 6T

able

6.2

: Inf

orm

al p

atie

nt p

aym

ents

and

som

e qu

alit

y of

car

e in

dica

tors

a

Rep

orte

d b

rib

es

Rep

orte

d c

onn

ecti

ons

Tot

al s

amp

le

No

Yes

Mis

sin

gN

oY

esM

issi

ng

Per

ceiv

ed le

vel o

f ob

stet

rici

an’s

sk

ills

:

Bad

M

oder

ate

G

ood

M

issi

ng v

alue

s

21(2

2.1%

)

14(1

4.7%

)

33(3

4.1%

)

1(1.

1%)

9(9.

5%)

7(7.

4%)

5(5.

3%)

none

1(1.

1%)

3(3.

2%)

1(1.

1%)

none

26(2

7.4%

)

15(1

5.8%

)

22(2

3.2%

)

1(1.

1%)

4(4.

2%)

6(6.

3%)

16(1

6.8%

)

none

1(1.

1%)

3(3.

2%)

1(1.

1%)

none

31(3

2.6%

)

24(2

5.3%

)

39(4

1.1%

)

1(1.

1%)

Ob

stet

rici

an’s

gen

der

:

Mal

e

Fe

mal

e

M

issi

ng v

alue

s

27(2

8.4%

)

17(1

8.9%

)

25(2

6.3%

)

7(7.

4%)

9(9.

5%)

5(5.

3%)

1(1.

1%)

1(1.

1%)

3(3.

2%)

19(2

0.0%

)

23(2

4.2%

)

22(2

3.2%

)

15(1

5.8%

)

3(3.

2%)

8(8.

4%)

1(1.

1%)

1(1.

1%)

3(3.

2%)

35(3

6.8%

)

27(2

8.4%

)

33(3

4.7%

)

Typ

e of

ch

ild

bir

th:

V

agin

al

C

esar

ean

M

issi

ng v

alue

s

38(4

0.0%

)

30(3

1.6%

)

1(1.

1%)

14(1

4.7%

)

6(6.

3%)

1(1.

1%)

4(4.

2%)

none

1(1

.1%

)

43(4

5.3%

)

19(2

0.0%

)

2(2.

2%)

9(9.

5%)

17(1

7.9%

)

none

4(4.

2%)

none

1(1.

1%)

56(5

8.9%

)

36(3

7.9%

)

3(3.

2%)

Per

cep

tion

of

hos

pit

al p

roto

cols

:

Bad

G

ood

M

issi

ng v

alue

s

51(5

3.7%

)

17(1

7.9%

)

1(1.

1%)

21(2

2.1%

)

none

none

4(4.

2%)

1(1.

1%)

none

52(5

4.7%

)

11(1

1.6%

)

1(1.

1%)

20(2

1.1%

)

6(6.

3%)

none

4(4.

2%)

1(1.

1%)

none

76(8

0.0%

)

18(1

8.9%

)

1(1.

1%)

Exp

erie

nce

of

inco

nve

nie

nce

:

No

Y

es

33(3

4.7%

)

46(4

8.4%

)

2(2.

1%)

19(2

0.0%

)

1(1.

1%)

4(4.

2%)

13(1

3.7%

)

51(5

3.7%

)

12(1

2.6%

)

14(1

4.7%

)

1(1.

1%)

4(4.

2%)

26(2

7.4%

)

69(7

2.6%

)

Tot

al s

amp

le

69(7

2.6%

)21

(22.

1%)

5(5.

3%)

64(6

7.4%

)26

(27.

4%)

5(5.

3%)

95(1

00%

)a P

erce

ntag

es in

dica

te %

of t

he to

tal s

ampl

e (N

= 9

5).

6

Shortcomings of maternity care in Serbia

141

The most frequently bribed health providers according to our sample, are obstetricians

(12 women report that they have bribed them, while only 4 women report that they have

bribed a midwife/nurse). Women, who report informal payments, describe that they gave

money to the obstetrician to secure his/her presence during the delivery and to secure

better quality of care. Additionally, to secure the application of epidural analgesia in

time, money is also given to the anesthesiologist (thus, in addition to the quasi-formal

payments described above). The highest amount of informal payment that is reported in

our sample is 500 euro. Women who have reported informal patient payments still report

problems with quality of care. For example, 21 women (22.1% of those who bribed

someone) report bad hospital protocols, and 19 women (20.0% of the sample) report that

they have experienced inconveniences during their stay in the hospital. Furthermore,

they also perceive hospital protocols as bad (22.1%) and only 5.3% of them perceive

obstetrician skills as good.

Asked if they have bribed someone, women that have filled in the online questionnaire

answered with yes or no, but some of them answered with “I had connections”. In our

sample, 26 women (27.4%) reported that they had connections. They state that they

could avoid informal payments by using “personal connections”, e.g. friends, colleagues

or relatives, who worked in hospitals. Thus, “personal connections” helped them to assure

special treatment and adequate care. They consider those “connections” as a means to

secure help. Having “connections” means the presence of someone whom they can trust.

The number of respondents who reported “personal connections” is increasing during

the period 2005-2008, while at the same time, number of respondents who reported

informal patient payments is decreasing. Moreover, women with “connections” report

fewer inconvenience (14 women or 14.7%) than those who have paid informally (19

women or 20.0%) (See Box 6. 1, Quotation 7-9). Attitudes regarding informal patient

payments are also examined through the qualitative data collected in the questionnaire.

Women, who bribed physicians, generally do not approve of informal patient payments,

but they state that they paid informally for the safety of their child. Among women,

who did not bribe anyone and did not have “connections”, 14 (14.73%) state that they

regretted this fact and they would do so next time.

“Non-government” publications also confirm the existence of quasi-formal payments

(Andrejic, 2010). The presence of quasi-formal payments is caused by the discrepancy

between official and hospital guidelines. Although the former claim that maternity care

is free-of-charge, the latter show the actual charges by the hospitals.

6.4.4 Policy regulations and innovation in maternity careWe observe a mismatch between official guidelines and hospital regulations. In

particular, hospital protocols define obligatory procedures that are inconsistent with

official government protocols such as obligatory shaving and enema. Also, visits of family

142

Chapter 6

members are usually not allowed by hospital regulations, while the official protocols do

not forbid these visits.

Regarding innovations in maternity care, according to the official guidelines (see

Table 6.3), the presence of the father is allowed. However, fathers are obliged to bring the

results of laboratory tests to prove that they do not have an infectious disease. Test should

not be older than seven days. They also have to pay an official entrance fee in advance.

The amount of this fee varies from hospital to hospital (Narodni Front, 2012). Data from

the questionnaire show that the fee ranges from 50 euro to 150 euro. Since the time of a

childbirth cannot be exactly predicted, it is clear that not many fathers are able to meet

these requirements.

Problems with other innovations such as immediate breastfeeding are usually

explained by a lack of skills of nurses to help and encourage mothers to start breastfeeding

immediately after birth (Becker &, Zisovska, 2008). Special cases are the “baby-friendly”

wards that are designed to allow mothers and their babies to be constantly together, i.e.

rooming-in (Becker &, Zisovska, 2008).

The quantitative data from the on-line questionnaires show that, 61.5% of women

in our sample report problems with breastfeeding. For example, the maternity wards are

usually characterized as poorly organized since patients are often left without any help

from medical staff regarding breastfeeding (see Box 6. 1, Quotation 10).

Data from “non-government” publications confirm the problems related to

immediate breastfeeding described above (Banjac et al., 2010). However, some of the

official guidelines report the opposite and describe “baby-friendly” maternity wards or a

rooming-in approach as highly regarded by mothers (Janevic et al., 2011).

6.4.5 Health system indicatorsQualitative data from the questionnaires show that a skilled person is present during

the delivery but only some of the time. Usually a midwife and/or a physician are present

during the admission procedure and when the birth is approaching. During the course of

the childbirth, women are usually left alone. In exceptional cases, for those that have paid

informally and/or had “connections”, medical staff was present during the whole course of

delivery. Data from other sources namely from the websites of international organizations

(e.g. UN, WHO, WB) report that 98% of all deliveries in Serbia are attended by skilled

persons (Jeffery, 2003; UNFPA, 2009). To a certain extent, this macro indicator does not

correspond to the data from the questionnaire since it does not refer to the presence of a

skilled person during the entire birth process.

Qualitative data obtained from the sample show that a cesarean section is perceived

by medical staff as a desirable and safe way of delivery. In our sample, 36 women

(37.9%) report a Caesarian section, mostly women who had “connections”. Women with

“connections” report that by opting for a caesarean section planned in advance, they could

6

Shortcomings of maternity care in Serbia

143

secure the presence and involvement of the obstetrician of their own choice. Data obtained

from the WHO suggest that official rate for Caesarian sections in Serbia for 2008 was

19.3%, which is higher than in some EU countries but lower than in some other EU

countries (see Table 6.3). Also, some women in our sample express a preference for having

a normal physiological delivery 6 (6.31%), instead of induced labor or a Cesarean section.

Data regarding maternal mortality obtained from the UN and the WHO show a

higher value of maternal mortality for 2010 compared with 2007. This is explained by

poor reporting procedures (Jeffery, 2003; UNFPA, 2009).

6.5 Discussion and conclusions

In our study, we examine several different micro-indicators of the accessibility, service

quality and patient payments in maternity wards in Serbian hospitals, as well as policy

regulations and innovations in maternity care (see Table 6.3) to present a comprehensive

picture of the care delivery process in Serbia and its shortcomings. Our focus is on the

users’ perspective. We apply a mix-methods approach, which provides more insight into

the problems of maternity care in Serbia.

We recognize that the online data that we use, represents a non-representative

purposive sample (i.e. only women who have delivered in a certain period of time are

included and also, the questionnaire is available only to women who are capable of using

the internet, mostly from urban areas, and women motivated to participate). This is a

limitation of our study. To overcome this limitation, we use other sources of information

(research publications and official guidelines), applying triangulation as a research

strategy. The method of triangulation indicates similarities across the different sources

of data that we used, but also some discrepancies. We also use our qualitative data to

examine the possible trend changes during different periods of time.

Results on the spatial accessibility are consistent through all types of resources.

Results regarding the psychological accessibility (communications) are similar within

two sources: online questionnaires and non-government publications. Communication

characterized by a derogative style accompanied with disrespect for the pregnant women

is reported by 53.7% of women. However, the “pro-government group” of publication

does not report those problems. Previous literature has reported that Ministry of Health

has recognized this problem (Ministry of Health, 2010). In order to improve the

communication skills of physicians with management positions, the Ministry of Health

has provided training for them (Supic et al., 2010). Evaluation of the training has shown

that problems with communication skills still exist. Results regarding the hospital

protocols also show similarities between online questionnaires and non-government

publication. Both sources describe the existence of procedures like obligatory shaving and

144

Chapter 6

enema during every delivery, which is reported in other CEE countries as well (Chalmers,

1997; Danishevski et al., 2006; Szende &, Culyer, 2010). However, there is no evidence

that this procedure has any positive effects on the childbirth. Results from all sources are

consistent regarding the obstetrician’s skills that are perceived as very good. However,

the magnitude of reported problems with different types of equipment is decreasing in

the period 2005-2008, in comparison with the period 2000-2005.

Informal patient payments are reported in the online questionnaires but also in the

publications found in our literature review. However, their magnitude is varying through

different sources (from 2% in “pro-government group” of publications to 22.1% in

online collected questionnaires). According to the online questionnaires, obstetricians

are most often bribed, while midwives are rarely bribed. This might be related to their

lower position in the hierarchical system. A similar situation is observed in other CEE

countries (Danishevski et al., 2006; Stepurko et al., 2013).

Data collected through the online questionnaire allow us to combine results

regarding informal patient payments with some aspects of quality of care. Women who

have reported informal patient payments, still experience inconveniences and only 5.3%

of them rate their obstetrician skills as good. On the other side, women who did not

bribe anyone (including women with “connections”) more frequently (17.9%) perceive

obstetrician skills as good.

Our results show that women with “connections” are most likely to be satisfied with

obstetrician skills (see Table 6.2) and experience fewer inconveniences. It is not clear

whether the “personal connections” represent an exchange of favors or a more secure

way of informal payments – bribing with trust. According to our results, the position of

women with “connections” is most favorable compared with those who paid informally

or those who did not bribe anyone. On the other side, they also experience inconveniences

in the communication with medical staff and problems in the application of medical

procedures (see Box 6. 1, Quotation 1).

Our data do not show, whether women who did not bribe anyone, did not have money

to do so or were satisfied with their obstetricians. However, 14 (14.73%) of them reported

that they would use informal patient payments next time in order to get better care.

We have also observed in the online questionnaires and “non-government” publications

that magnitude of informal patient payments is decreasing for the period 2005-2008.

Since the data from “pro-government” publications are collected during 2009, this is the

possible explanation for the low rate of informal patient payments reported there.

The high magnitude of informal patient payments in maternity wards has been

described in other European countries (Danishevski et al., 2006; Stepurko et al., 2013;

Szende &Culyer, 2006; Vian et al., 2006) as well. Moreover, in Ukraine, Hungary,

Greece and Albania, informal patient payments are most often reported in maternity care

(Kaitelidou et al., 2013; Stepurko et al., 2013; Szende & Culyer, 2006; Vian et al., 2006).

6

Shortcomings of maternity care in Serbia

145

The patterns are different in different countries. While in Ukraine, informal patient

payments are a part of the open negotiations with the obstetricians about the childbirth,

in Greece informal patient payments are usually paid for elective Cesarean sections, as

a most secure way of delivery (Kaitelidou et al., 2013; Stepurko et al., 2013). While

in Ukraine informal patient payments are given to obstetrician as an act of solidarity

since their income is low, in Albania, informal payments are usually given as an act of

gratitude (Stepurko et al., 2013; Szende & Culyer, 2006). In Greece, the main reason

for informal payments is an extra service, e.g. elective Caesarian section (Kaitelidou et

al., 2013). Also, legal regulations regarding informal patient payments are different

in different countries. Informal patient payments were legalized in Hungary, while in

Albania and Greece although wide spread, they are considered illegal. In Ukraine, quasi-

formal payments in the form of “donations” by the patient to medical institutions exist

and are also expected by the providers (Stepurko et al., 2013). Also, similar to our study,

the importance of “personal connections” to receive adequate maternity care is reported

in other countries as well (Stepurko et al., 2013).

Although informal patient payments are spread in public maternity wards in Serbia

and a large group of women report problems with the care they receive, there is no

alternative channel to receive maternity care during childbirth. Childbirth in private

maternity hospitals are still impossible due to the strict laws and regulations that forbid

giving birth outside the official institutions.

Also, we observe a slow diffusion of innovations in Serbian maternity hospitals. One of

the reasons is the education of medical staff that is still based on the “active approach“(see

Background section) during childbirth. Medical staffs are usually reluctant to accept

innovations. Especially obstetricians consider their role as most important during the

process of childbirth and they perceive any innovation that increases the role of the

patients as a decrease of their own power (Stambolovic, 1996).

Another problem is related to the high rate of Caesarian sections, reported also in

different sources.

An elective Caesarian section is sometimes considered as the safest way of delivery

(Kaitelidou et al., 2013). Women with “connections” most often reported Caesarian

section in the online questionnaires.

Despite the problems in Serbian maternity wards, users of maternity care are silent

about this in public. This silence among the health care users can be explained by two

paradigms that prevail in Serbia. One is the “patriarchal paradigm” (Bracewell, 1996)

and the other one is described as the “doctor-centered paradigm” (Chalmers, 1997).

The first paradigm defines the role of women as a mother and housewife. The

experience of motherhood is highly important for every woman.

The suffering during the childbirth will be overcome by the happiness of motherhood.

The childbirth is performed outside or at the backside of the house in the presence of a

146

Chapter 6

midwife. The presence of the father is considered as a shame. This paradigm was renewed

in Serbia as a part of the nationalistic ideology during the civil war in ex-Yugoslavia

(the early 1990s). Even, the Serbian Academy of Art and Science published an official

manifest in 1990, appealing on Serbian women to leave their jobs and fight against the

“white plague” – a new disease for the Serbian people. The term “white plague” refers to

the fact that the number of new born babies in Serbia is lower than during the previous

communistic period.

The second paradigm describes the childbirth as a technological intervention

with a high use of medical techniques (the technocratic approach). The childbirth is

not considered as a natural physiological process but as an actively conducted medical

event. The woman’s body is a machine that produces a child as a final product and

individual differences and preferences are not considered important. The obstetrician’s

role is perceived as the most important and the most responsible during the birth. This

paradigm prevailed during the period of the Socialistic Federative Republic of Yugoslavia

(1945 – 1990). During the period of the economic crises and the civil war in the 1990s,

the “doctor-centered” paradigm was combined with the pro-nationalistic “patriarchal

paradigm” shaping the current system of maternity wards. The emergence of informal

patient payments, the increased lack of resources and the halting health care reforms

further framed the system of maternity care in Serbia, while hiding and neglecting major

problems within the system.

The civil initiative “Mother Courage”, who collected the on-line data for our study,

raised the issue of the bad conditions in maternity wards in Serbia for the first time in

public. However, from raising awareness of this issue to actually changing the system

requires some additional steps. Further research should focus among other issues, on

the preferences of women regarding the childbirth. Increasing the knowledge of health

care users about their rights is important but has to be accompanied with a government

initiative to implement reforms within the current system. Also, future research should

examine the attitude and perceptions of different health care professionals, i.e. midwives

and obstetricians. The role of the midwife should be enhanced as well as the consideration

of a child delivery as a normal physiological event. The objective should be that equal

access to medical procedures and equal treatment are provided for every pregnant woman.

We have examined process related indicators of maternity care in Serbia. However,

most of the data that we found are qualitative. The validity of qualitative data is enhanced

by an analysis of their credibility, transferability and dependability (Baji et al., 2011).

In order to ensure the validity of our data and to combine them with quantitative

findings (Baji et al., 2011), we have applied a mixed-method approach with triangulation

as a research strategy. Triangulation allows us to compare different sources of data

(credibility) and to build comprehensive picture of maternity care in Serbia. Also,

triangulation allows us to compare our findings with those form other CEE countries.

6

Shortcomings of maternity care in Serbia

147

In order to present comprehensive picture of maternity wards in Serbia, we present our

findings using both triangulation and a qualitative time trends perspective. All three

sources of our data indicate that spatial accessibility and obstetricians skills are the most

positive indicators of maternity care in Serbia. Combining different aspects of maternity

care (quality, accessibility, patient payments we could draw some conclusions based on

our findings. Physicians’ skills are mostly observed as good, but their communication

with patients and the way they treat pregnant women are described as poor. Although

some efforts are made by the Ministry of Health to improve the level of communication

among the health care professionals, problem still exists. Besides the problems regarding

the communication, our data also show problems with equipment, different types of

patient payments and slow diffusion of innovations. Maternity wards in Serbian hospitals

usually have the necessary equipment but very often this equipment is not used or it is

overused. Informal patient payments and “connections” are used by the pregnant women

as a way to secure better care.

However, some improvements are observed during the period 2005-2008, mostly

regarding the affordability of equipment but also regarding hygienic improvements.

At the same time, the intensity (but not the magnitude) of informal patient payments

is decreasing, while so called “connections” are more often reported. Women with

“connections” are more likely to replace the “active way of delivery” with the more

technocratic model, i.e. elective Caesarian section, increasing the cost of delivery (Varjacic,

2005). Our results do not provide the data about women’s preferences regarding the child

birth. Future research should focus on the examination of preferences of health care users

towards maternity care, but also on examination of attitude of health care professionals.

In order to provide conditions for childbirth in accordance with women’s preferences

the government should regulate policies regarding the childbirth process. Positive

aspects like the broad network of maternity wards and good obstetrician skills are good

starting points. Enhancing the position of midwifes can also improve the communication

between the patient and providers and secure more respect for different preferences. The

government may include private providers, paid by the Republic Institute of Health

Insurance (HIF). Including private providers can decrease the need of informal patient

payments and “connections”.

148

Chapter 6T

able

6.3

: Ind

icat

ors

of m

ater

nity

car

e in

Ser

bia

base

d on

dif

fere

nt d

ata

sour

ces

(que

stio

nnai

res

coll

ecte

d on

line

, gui

deli

nes,

lite

ratu

re r

evie

w)a

Cha

ract

eris

tics

of m

ater

nity

ca

re d

urin

g th

e ch

ildb

irth

Lite

ratu

re r

evie

wG

uide

line

sQ

uest

ionn

aire

s co

llec

ted

onli

ne

“Non

-gov

ernm

ent

grou

p”“P

ro-g

over

nmen

t gr

oup”

Offi

cial

gui

deli

nes

Hos

pita

l gui

deli

nes

Acc

essi

bili

ty

Spat

ial

Sati

sfied

Sati

sfied

Acc

ess

to m

ater

nity

war

d is

a r

ight

of e

very

pre

gnan

t w

omen

Onl

y w

ith

prop

er r

efer

ral

Pro

blem

s w

ith

refe

rral

Tem

pora

lLo

ng w

aiti

ng t

ime

for

cert

ain

proc

edur

es li

ke u

ltra

soun

dsSa

tisfi

edN

o w

aiti

ng li

sts

Long

wai

ting

tim

e fo

r ce

rtai

n pr

oced

ures

like

ult

raso

unds

etc

.

Psy

chol

ogic

alLo

w le

vel o

f com

mun

icat

ion

Goo

d le

vel o

f com

mun

icat

ion

Pat

ient

-pro

vide

r co

mm

unic

atio

n pr

oble

ms

Qua

lity

of c

are

Cli

nica

l qua

lity

of c

are

Old

pro

toco

lsH

igh

leve

l of p

hysi

cian

ski

llsO

ld p

roto

cols

Old

pro

toco

lsO

ld p

roto

cols

Hig

h le

vel o

f phy

sici

ans

skil

ls, b

ut

low

leve

l of m

otiv

atio

n

Soci

al q

uali

ty o

f car

eN

o fa

mil

y vi

sits

Sati

sfied

No

visi

tors

No

visi

tors

Pat

ient

pay

men

tsla

ck o

f mot

ivat

ion

Type

of p

aym

ents

Info

rmal

pay

men

tsQ

uasi

-for

mal

Low

leve

l of i

nfor

mal

pa

ymen

tsFr

ee o

f cha

rge

Qua

si-o

ffici

alIn

form

al p

aym

ents

Qua

si-f

orm

al“s

peci

al c

onne

ctio

ns”

Rec

eive

r of

pay

men

ts-

--

-P

hysi

cian

s an

d nu

rses

Freq

uenc

y of

pay

men

ts14

.7 u

p to

39.

2% in

form

al

paym

ents

1.9%

info

rmal

pay

men

tsM

ostl

y in

form

al p

aym

ents

(22.

1%)

Mag

nitu

de o

f pay

men

ts-

-50

0 eu

ro

Pur

pose

of p

aym

ents

Bet

ter

qual

ity

of c

are

Gra

titu

deB

ette

r qu

alit

y of

car

eSa

fety

Att

itud

e to

war

ds p

aym

ents

Neg

ativ

e-

Neg

ativ

ea T

rian

gula

tion

ref

ers t

o re

sear

ch st

rate

gy th

at a

llow

s us t

o co

mbi

ne b

oth

qual

itat

ive a

nd q

uant

itat

ive d

ata

from

dif

fere

nt so

urce

s in

orde

r to

ove

rcom

e pos

sibl

e bia

ses

6

Shortcomings of maternity care in Serbia

149

Tab

le 6

.3: I

ndic

ator

s of

mat

erni

ty c

are

in S

erbi

a ba

sed

on d

iffe

rent

dat

a so

urce

s (q

uest

ionn

aire

s co

llec

ted

onli

ne, g

uide

line

s, li

tera

ture

rev

iew

) (co

ntin

ued)

a

Ch

arac

teri

stic

s of

mat

ern

ity

care

du

rin

g th

e ch

ild

bir

th

Lit

erat

ure

rev

iew

Gu

idel

ines

Qu

esti

onn

aire

s co

llec

ted

on

lin

e

“Non

-gov

ern

men

t gr

oup

”“P

ro-g

over

nm

ent

grou

pO

ffici

al g

uid

elin

esH

osp

ital

gu

idel

ines

Pol

icy

regu

lati

on

Reg

ulat

ion

rega

rdin

g th

e pa

ymen

tsFr

ee o

f cha

rge

Free

of c

harg

eFr

ee o

f cha

rge

Qua

si-o

ffici

al p

aym

ents

Reg

ulat

ion

rega

rdin

g th

e ho

spit

al p

roto

cols

Old

pro

toco

lsSa

tisfi

edO

ld a

nd u

nnec

essa

ry p

roto

cols

Inno

vati

on in

mat

erni

ty c

are

Pre

senc

e of

fam

ily

mem

bers

Not

all

owed

-N

ot a

llow

edN

ot a

llow

ed, e

xcep

t fo

r sp

ecia

l co

nnec

tion

s

“roo

min

g in

”W

itho

ut h

elp

Sati

sfied

Wit

hout

any

hel

p

Hea

lth

ind

icat

ors

Mat

erna

l mor

tali

ty14

.48(

2007

); 19

.91(

2009

);17.

57 (2

010)

Infa

nt m

orta

lity

dur

ing

birt

hbin

fant

dea

th p

er 1

000l

ive

birt

hs=

7(20

09)

Rat

e of

Cae

sare

an s

ecti

on19

.3%

(cal

cula

ted

by W

HO

(201

1) fo

r a

peri

od 2

000-

2010

, nom

inat

or is

the

num

ber

of c

aesa

rean

sec

tion

and

de

nom

inat

or is

the

num

ber

of t

otal

bir

ths

in d

efine

d po

pula

tion

169.

3 pe

r 10

00 b

irth

s fo

r 20

07 a

nd 2

38.5

4 pe

r 10

00

birt

hs fo

r 20

10 (

calc

ulat

ed b

y In

stit

ute

of P

ubli

c H

ealt

h Se

rbia

)

36(3

7.9%

) of 9

5 ca

ses

Rat

e of

em

erge

ncy

ch

ild

bir

th

Pre

senc

e of

ski

lled

per

son

Occ

asio

nall

yO

blig

ator

yO

blig

ator

yO

blig

ator

yO

ccas

iona

lly

a Tri

angu

lati

on r

efer

s to

rese

arch

stra

tegy

that

all

ows u

s to

com

bine

bot

h qu

alit

ativ

e and

qua

ntit

ativ

e dat

a fr

om d

iffe

rent

sour

ces i

n or

der

to o

verc

ome p

ossi

ble b

iase

sb R

efer

s to

peri

nata

l mor

tali

ty r

ates

-num

ber

of d

eath

s occ

urri

ng in

the p

erio

d fr

om a

bout

thre

e mon

ths b

efor

e to

one m

onth

aft

er b

irth

CHAPTER 7

General Discussion

152

Chapter 7

7.1 Introduction

The main goal of this dissertation was to examine the effects of out-of-pocket patient

payments on vulnerable population groups in the public health care system in Serbia.

The social protection policy in the health care system in Serbia emphasizes the financial

protection of vulnerable population groups, which includes mechanisms like compulsory

health care insurance and protection from high out-of-pocket patient payments (Chapter1).

In this dissertation, we specifically focus on the effects of out-of-pocket payments

for public health care services and goods on vulnerable population groups. We first

examined the effects of out-of-pocket patient payments on household budgets (Chapter

2). Furthermore, we examined the financial burden provoked by different types of out-of-

pocket patient payments, namely official co-payments, informal payments and payments

for “bought & brought goods” (Chapter 3). The exemption from official co-payments is

used by policy makers in Serbia as a mechanism to protect vulnerable population groups.

In Chapter 4, we outlined the design and implementation of the current exemption

mechanism in Serbia, and we examined the effectiveness of this mechanism. In addition

to that, we also examined to which extent certain population groups that are considered

as vulnerable, namely chronically sick and pregnant women, experience the effects of the

social protection policy (Chapter 5 & 6). In Chapter 5, we examined the financial burden

of three leading chronic diseases in Serbia (diabetes mellitus, cardiovascular diseases and

cancer) on Serbian households. In Chapter 6, we explored the financial protection of

pregnant women but also the accessibility to maternity care, non-medical quality of care

and policy regulations.

For the analyses presented in this dissertation, we combined two methods. Quantitative

data analyses, based on a representative survey data collected by the World Bank in 2002,

2003 and 2007, are used in Chapter 2 to 5. Mixed-methods, combining qualitative and

quantitative data analyses are used in Chapter 6 (data collected by literature review, on-

line semi-structured questionnaires and data collected from official guidelines). The main

findings of this dissertation are presented in this chapter by six statements. Each statement

is discussed and finalized suggestions for further research and policy recommendations.

7.2 Discussion of main findings

Although out-of-pocket patient payments are a higher burden for the poor, their catastrophic

effects are experienced by all socio-demographic groups.

Out-of-pocket patient payments can impose a high financial burden on the poor and

more specifically on the lowest consumption-based quintile (Habicht et al., 2006;

7

General discussion

153

Limwattananon et al., 2007; Xu et al., 2006). Even if the introduction of out-of-pocket

patient payments in the public health care system is accompanied with social protection

mechanisms, like exemption mechanisms or compulsory health insurance, as it is in

Serbia, the financial burden for poor population groups remains high. However, non-poor

population groups may also experience the financial burden if they are exposed to health

care costs over a longer period of time like in case of a chronic disease.

In this dissertation, we use two different approaches - namely the impoverishing effects

and catastrophic health care expenditure approach - to estimate the burden created by

out-of-pocket patient payments in the public health care system. Within each approach,

we use different indicators of wealth (income and consumption) and different thresholds

as cut-off points. Irrespective of the approach we apply, the results indicate that out-of-

pocket patient payments for public health care services and goods create a burden for

poor population groups (Chapter 2). When we calculate the impoverishing effects using

income as an indicator of wealth, the share of poor people (the lowest consumption-based

quintile) who go below the absolute poverty line is 20.5%, while among the rich, it is

1.2%. Since the impoverishment approach uses a poverty line as a cut-off point, this

result is to be expected. People who are close to the poverty line are more likely to go

below poverty line after the subtraction of health care costs. However, when we estimate

the effects of out-of-pocket patient payments using catastrophic health care expenditure

(based on both indicators of wealth), the burden is nearly equally experienced by all

income groups in Serbia.

Not only the chosen approach plays a role in estimating the burden of out-of-pocket

patient payments, but also the choice of an indicator of wealth and the cut-off point

play a role. When we calculate the catastrophic health care expenditures, the cut-off

point is a predefined threshold presenting a given proportion of income/consumption

(the indicator of wealth), while when we calculate the impoverishing effects the cut-off

point is an absolute number (a certain amount of money).

This means that based on catastrophic health expenditures, even rich people can

spend a high proportion of their income on health care services, but they do not necessary

become poor because of it. Furthermore, when the threshold is set lower, a high burden

is also experienced by the richest population groups in Serbia. For example, the incidence

of individuals who experience catastrophic health expenditure when the 10% threshold

is used, amounts to 42.9% among the poor (the poorest consumption percentile) and

48.3% among the rich (the highest consumption percentiles) (Chapter 2).

The application of different indicators of wealth within the same approach also

leads to different estimates of the burden created by out-of-pocket patient payments.

Catastrophic effects estimated using income as an indicator of wealth, are highest among

the low-income quintiles, while when consumption is used as an indicator of wealth,

the burden is heaviest among the middle-income quintiles. This means that although

154

Chapter 7

people from middle-income quintiles do not spend a significant proportion of their

income on health care, they might reduce the consumption of some other goods and/or

services (education) in order to be able to pay for health care (van Doorslaer et al., 2007).

Moreover, this result shows that in countries like Serbia, with a large informal economy,

consumption is perceived as a more accurate indicator of wealth than income (Haughton

& Khandker, 2009). For example, in order to avoid taxes, many companies in Serbia do

not register their employees (Krstic, 2008).

As outlined in Chapter 1, the LSMS data have been used in Serbia since 2002 to

identify the poverty rate (Bajec et al., 2008). The poverty rate is calculated based on the

absolute poverty line as a threshold and consumption as an indicator of wealth (World

Bank, 2011). People who go below this poverty line are considered poor. The official

poverty rate in Serbia for 2002 was 14.5%, and poverty was more prevalent among people

with a low level of education, among larger households and in rural environments (Bajec

et al., 2008). In 2007, the official poverty rate had decreased to 7.5%, while poverty was

still more prevalent in rural areas, larger households and among people with a low level of

education. Thus, although the absolute poverty rate decreased between 2002 and 2007,

the same groups were at risk of being poor.

In this dissertation, we have examined the association between poverty (being poor or

not) and different socio-demographic groups. We find that the same socio-demographic

characteristics as those mentioned above are associated with poverty in Serbia in 2007

(Chapter 5). Furthermore, we also examined the association between different socio-

demographic groups and impoverishment or catastrophic health care expenditure.

Our results show that catastrophic health expenditure is mostly associated with socio-

demographic characteristics like being single, having a poor perceived health and having

a chronic disease (Chapter 2).

The impoverishment effects are associated with being single, having a poor perceived

health and different types of chronic diseases. Socio-demographic characteristics like living

in rural areas or in larger households are not significantly associated with impoverishing

or catastrophic effects. This suggests that poor people also more often forgo the use of

health care when confronted with high payments (Banerjee & Duflo, 2011). Even though

some of the care foregone might have been unnecessary, postponing the use of health care

that is necessary can be detrimental for one’s health status and can lead to higher health

care costs later on.

Our results show that each of the approaches applied to examine the burden provoked

by out-of pocket patient payments addresses only a specific aspect of this problem.

Therefore, for the purpose of a broader overview, the effects on out-of-pocket patient

payments should be examined using all available approaches.

7

General discussion

155

Further research: Health care users and their families use different coping mechanisms to

prevent the financial burden of health care expenditure. They can borrow money or sell

assets. Although those coping mechanisms can reduce the financial burden for a short-

term period, very often, they have a negative impact on household wealth in the long

run (van Doorslaer, 2007). In this dissertation, we do not have data to examine the use of

coping mechanisms in Serbia. Future studies should pay attention to those mechanisms

since they give a better insight in the extent of the financial burden of out-of-pocket

payments. Since out-of-pocket patient payments are usually non-discretionary shocks,

panel data can clarify the distribution of catastrophic health care expenditure among

different income groups. Also, further studies on the impoverishing and catastrophic

effects of out-of-pocket payments in Serbia can include household expenditures at the

private health care sector, which was outside the scope of this dissertation.

Policy implications: The social protection policy in the Serbian public health care sector

is designed to address a broad range of population groups. However, current policy

makers in Serbia use an absolute poverty line to identify poor population groups. Our

results show that out-of-pocket patient payments are a financial burden not only for

poor population groups but also for non-poor groups. Policy makers should use different

approaches, different thresholds and different indicators of wealth in order to identify

vulnerable groups.

Even if non-poor population groups do not go below the absolute poverty line after

health care spending, they still experience a financial burden. In case of chronic diseases,

when this financial burden is prolonged, non-poor population groups can be pushed into

poverty. Policy makers should not neglect the impact that informal payments (as well as

other forms of patient payments) have on the financial burden.

Payments for “bought & brought goods” are perceived as positive among health care

users, but their catastrophic effects are higher than those provoked by informal patient

payments.

In Chapter 3, we outline different types of out-of-pocket patient payments that exist

in the Serbian public health care sector, namely official co-payments, informal patient

payments and payments for “bought & brought goods”. We also analyze the financial

burden that each type of payments imposes on the household budget.

As described in Chapter 3, the distinction between the official co-payments and

informal patient payments is well-described in the literature (Ensor, 2004; Lewis, 2002;

Stepurko et al., 2010). However, previous studies do not emphasize the difference

between the pure informal patient payments and payments for “bought & brought

goods” (Gaal et al., 2006; Lewis, 2002). In Chapter 3, we make a distinction between

the pure informal payments (such as cash and/or in-kind gifts given on staff’s request

156

Chapter 7

or in a form of gratitude), and payments for “bought & brought goods” (i.e. payments

for goods brought by the patients or their families to the health care facility, such as

disposable materials and pharmaceuticals). We found that this distinction is important

since those two types of out-of-pocket patient payments can affect different population

groups, including those exempted from official co-payments. Moreover, since those two

types of payments are not planned by policy makers, often their scope and effects are not

monitored and they remain unknown.

Payments for “bought & brought goods” are not unique for Serbia. They are also

described in other CEE countries (Stepurko et al., 2013). Previous studies called them

quasi-informal payments (Stepurko et al., 2013). While pure informal payments are

unregistered, payments for “bought & brought goods” are registered at the moment

of purchasing but not visible in the financial flows of the institution that provides the

services. Informal patient payments can be requested or given out of gratitude, payments

for “bought & brought goods” are always requested by health care providers and these

goods are necessary for the patient in treatment.

In Serbia, policy makers neglect informal patient payments, but they do recognize the

existence of payments for “bought & brought goods” (Adzic & Adzic, 2011; Matejic et al.,

2010; Stosic & Karanovic, 2014; Sorensen, 2007; Stanic, 2002). Payments for “bought

& brought goods” are usually described as payments for goods (such as pharmaceuticals)

necessary for the curative process but not available in hospitals (Palairet, 2001). Although

recognized by policy makers, those payments are also rarely examined in Serbia (Adzic &

Adzic, 2011; Stanic, 2002).

Informal patient payments and payments for “bought & brought goods” differ not

only by their nature. As shown by previous research, the attitudes of health care users

towards informal patient payments can be both negative and positive (Stepurko et al,

2010). In Bulgaria, the public attitudes towards informal patient payments are more

negative in case of cash informal payments but more positive in case of in-kind gifts

(Atanasova et al., 2013). Previous evidence from Serbia shows that informal patient

payments are perceived as a negative phenomenon (CESID, 2011). They were usually

related to the time of economic crisis when the salaries of physicians were low (CESID,

2011). Nowadays, when physicians request the informal patient payments, the health

care users’ perception is negative (Agencija za Borbu Protiv Korupcije, 2013). Although

perceived as a negative phenomenon, informal patient payments still exist in Serbia.

Health care users still feel the obligation to give informal patient payments to obtain

good care, have better access to medical services and also as a form of gratitude (Agencija

za Borbu Protiv Korupcije, 2013).

Payments for “bought & brought goods” are always requested by medical staff,

but the perception of Serbian health care users towards them is almost always positive

(Gavrilovic & Trmcic, 2013; TNS Media Gallup, 2011). As we mentioned in Chapter 3,

7

General discussion

157

payments for “bought & brought goods” in Serbia have their roots in the time of the civil

war, when hospitals lacked medical materials, supplies, and pharmaceuticals (Palairet,

2001). In such situation, physicians would ask the patient’s family to bring the necessary

goods. Families have perceived this as an act of cooperation from the side of health care

providers (Garfield, 2001; Palairet, 2001). Moreover, from the perspective of health care

users, payments for “bought & brought goods” do not bring any benefit to health care

providers but they are very necessary for the curative process (TNS Media Gallup, 2011).

However, today, although hospitals do not lack basic goods, payments for “bought &

brought goods” continue to exist (Chapter 3). One of the reasons is that when health care

providers ask for “bought & brought goods”, patients and their families do not perceive

this as an act of corruption (CESID, 2011).

At the first glance such requests are not interlinked with benefits for health care

providers. However, health care providers can still benefit from those payments. Hospital

can still declare the use of goods brought by patients as goods provided by the hospital.

Also, it is possible to use the “saved” goods in providers’ private practices, or simply

divide the money claimed for these goods in the form of an extra bonus (TNS Media

Gallup, 2011). The other reason for the existence of payments for “bought & brought

goods” is that those payments are not subject to the Serbian law (Chapter 3) while for

example, informal patient payments are described in the Serbian law as an act of “la

petite” corruption (Krivicni Zakonik, clan, 368). This means, that both patients and

providers do not breach any law in case of payments for “bought & brought goods”

(Gavrilovic & Trmcic, 2013).

Considering the differences between informal patient payments and payments

for “bought & brought goods” mentioned above, we examine the catastrophic and

impoverishing effects provoked by each type of payments. For this purpose, we use

consumption as an indicator of wealth. We consider that payments have catastrophic

effects on the household’s budget if they exceed 10% of the annual household consumption.

As shown in Chapter 3, both informal payments and payments for “bought & brought

goods” provoke catastrophic effects on household budgets. Catastrophic effects provoked

by payments for “bought & brought goods” are highest among health care users from the

lower income quintiles, while informal patient payments provoke the highest burden

among the health care users from the highest income quintiles. Moreover, payments

for “bought & brought goods” are more frequent than pure informal patient payments.

In total, 61.7% of health care users (N = 4976) reported payments for “bought &

brought goods”. At the same time, only 5.7% of health care users reported informal

patient payments. However, the magnitude of informal patient payments is higher

than for payments for “bought & brought goods”. The maximum nominal amount paid

informally (80 000 CSD ≈ 911 USD) for inpatient care is almost two times higher than

the maximum nominal amount reported for payments for “bought & brought goods”

158

Chapter 7

(50 000 CSD ≈ 570 USD). This suggests that payments for “bought & brought goods”

provoke the heaviest burden among low social economic groups. Besides that, their

catastrophic effects are higher, than the catastrophic effects provoked by informal patient

payments. While 3.7% of health care users in our sample spend more than 10% of their

annual consumption on payments for “bought & brought goods”, only 0.7% exceeds the

same threshold because of informal patient payments. Possible reasons can be found in

the nature of payments for “bought & brought goods”.

Those payments occur in a short period of time, the paid amount is high and the share

of payments for “bought & brought goods“ are higher among health care users from the

lower income groups.

Although payments for “bought & brought goods” do not breach any laws in Serbia

and although they are positively perceived by health care users (TNS Media Gallup,

2013), as suggested by our results, their shadow nature and catastrophic effects impose

an economic burden for health care users (Chapter 3). Payments for “bought & brought

goods” are more frequent and have the more intensive catastrophic effects, compared

to pure informal payments, but are not recognized by both health care users and policy

makers in Serbia as a negative practice. Furthermore, the catastrophic effects provoked by

payments for “bought & brought goods” are experienced by vulnerable groups like the

low-income population groups and population groups diagnosed with chronic diseases

(Chapter 3). According to current social policy in Serbia, poor population groups in

Serbia are exempted from official co-payments (Official Gazette of Republic of Serbia,

2007). The aim is to protect the poor population groups from a possible economic burden

(Gajic-Stevanovic, 2010). However, the catastrophic effects provoked by payments for

“bought & brought goods”, obstruct this intention of policy makers. In this way, poor

population groups are not adequately protected. It seems that payments for “bought &

brought goods” represent a new more sophisticated type of corruption than pure informal

patient payments. While pure informal patient payments more often provoke a higher

burden on the high-income population groups, among those who experience catastrophic

health care expenditure provoked by informal patient payments. 5.4% is from richest

quintile, while 1.3% is from poorest quintile, the burden of payments for “bought &

brought goods” is experienced by 84.8% respondents from the low-income groups which

is slightly lower in comparison with burden experienced by respondents from richest

quintile (Chapter 3, Table 3.2). This implies that even if the poor do not pay informally,

they find a way to bring the pharmaceuticals and/or goods that are necessary for curative

process. As long as social policy in Serbia do not adequately address the payments for

“bought & brought goods” (as mentioned above, payments for “bought & brought goods”

are not illegal), the poor population groups are at risk of economic shock when frequent

health care services are needed.

7

General discussion

159

Further research: Our results show that payments for “bought & brought goods” can

provoke an economic burden for health care users. However, our data provide information

about the payments for “bought & brought goods” only for inpatient services.

Also, the data provides no information about the type of treatment or type of

pharmaceuticals that patients pay for. Future research that will include such information

can provide a better ground for developing a new policy tool to detect and decrease

not only informal, but also these quasi-informal patient payments. In particular, studies

should focus on identifying the scope and magnitude of those payments at all levels of

the health care system. Also, identifying the way how those payments are used from

the perspective of health care system will contribute to the development of new more

efficient policy towards their elimination.

Policy implications: Informal patient payments in Serbia, although reported in anecdotal

evidence, are still not reported in empirical studies (Matejic et al., 2011). As we mentioned

above, informal patient payments are overall neglected by policy makers in Serbia (Stosic

& Krstic, 2014). Moreover, our results show that informal patient payments are also

accompanied with payments for “bought & brought goods” (quasi-informal payments).

Payments for “bought & brought goods” are even more frequent than informal patient

payments (Chapter 3). Since the payments for “bought & brought goods” are not illegal,

they are more accepted from patients and their families than informal patient payments.

Nevertheless, they can still provide additional benefits for health care providers. For

example, health care providers can use brought goods in their private practice (Stanic,

2002). Policy makers should design effective strategies to decrease the level of those

payments and to eliminate the reasons for their existence. One way is to increase the

awareness of health care providers about the negative effects that can be provoked by

payments for “bought & brought goods”. Also, the Serbian legislation should recognize

the informal nature of payments for “bought & brought goods”. This will be essential

to protect the vulnerable population groups including the poor. For example, the

government can officially reimburse the costs of goods brought by the patients and their

families that are not covered by the health care providers. This will also help to shed more

light on the exact scope and level of these payments.

The large number of population groups that are exempted from official co-payments in Serbia

is seen as an indicator of a strong social policy, but they are also a tool (means) to buy social

peace.

Official co-payments were introduced in Serbia in 2002, as part of the financial health

care reforms (Bajec et al., 2008). They are accompanied by an exemption mechanism

(Gajic-Stevanovic, 2010). As outlined in Chapter 4, the aim of the exemption mechanism

160

Chapter 7

is to protect vulnerable population groups from the economic burden provoked by official

co-payments.

Various population groups are exempted: children younger than 15 years, pregnant

women, persons older than 65 years, disabled persons, HIV-infected persons, monks,

people with low family income, unemployed, chronically ill people, military service

servants, people registered as refugees and the Roma population (Chapter 4). The

government explained that the high number of exempted population groups is a reflection

of a long tradition of solidarity and equity in Serbia (Stosic & Krstic, 2014). Despite the

fact the Serbian government promotes equity in the provision of health care, in Chapter

4, we show that the exemption mechanism in Serbia is not effective since individuals

belonging to exempted groups report paying official co-payments. We discuss two main

reasons for the failure of the exemption mechanism in Serbia: the design of the exemption

mechanism and the implementation of the exemption mechanism.

As described in Chapter 4, the design of the exemption mechanism is described in

detail in several legal documents known as guidelines (Official Gazette of RS, n. 1/2007,

52/2007, 99/2007, 14/2008, 20/2008, 7/2009, 82/2009 and 23/2010). Although

the amounts for official co-payments in Serbia are low (Vukovic & Perisic, 2011), the

exemption mechanism is designed to include broad population groups like children

younger than 15 or people older than 65, military servants and monks. Not all members

of those population groups are unable to pay for health care services. For example, people

older than 65 and younger than 15 are the most frequent users of both outpatient and

inpatient health care services during the period 2002-2007 (Chapter 4). However, the

payments for health care services for children are paid by their parents. Not all parents are

unable to pay for health care services. A better policy approach would be to use parents’

income as an indicator for exemption, instead of exempting all children younger than 15.

Regarding people older than 65, they also report official patient payments. However, the

amounts that they report are lower than the amounts reported by other exempted groups.

For example, the lowest official amount for 2007 for pharmaceuticals is paid by persons

older than 65. However, people older than 65 report high amounts for gifts given to

medical staff.

On the other side, people with low income, although exempted, often pay high

amounts. For example, the highest amount in 2002 is paid for hospitalization by

unemployed persons while the highest official amount for 2007 is paid for pharmaceuticals

by disabled persons (Chapter 4).Some population groups with chronic conditions are not

exempted or just partially exempted and they experience the burden provoked by patient

payments.

According to the current law, people diagnosed with diabetes mellitus in Serbia are

partially exempted. In Chapter 5 of this dissertation, we show that people diagnosed

7

General discussion

161

with diabetes mellitus experience an economic burden provoked by out-of-pocket patient

payments.

The implementation of the exemption mechanism also has weaknesses. As outlined in

Chapter 4, since the guidelines are not clear and written in law-centered language, it is very

confusing for patients and health care providers to understand for which services, (partial)

exemption mechanisms should be applied (Biorac et al., 2009). Even when the exemption

mechanism should be applied for all health care services (including pharmaceuticals and

disposable materials), in practice, some population groups face the difficulty to use their

right to free-of-charge health care use. For example, according to the guidelines, people

with a family income lower than the minimum net income in Serbia should be exempted

from official co-payments (Official Gazette of RS, 11/2010). As we explained in Chapter

4, the minimum net income is calculated by the Serbian statistic office and it changes

every 6 months. This means that the right to an exemption can also change every 6

months. In this way, patients might be unaware of the adjustments in official guidelines

that take place at the beginning of every year. Another obstacle in the implementation of

the exemption mechanism is related to the procedure to obtain the exempted status. The

procedure is administratively difficult and time consuming (Vukovic & Perisic, 2011).

Even, when the exempted status is obtained, for some exempted groups, health care

providers have to confirm the status. However, there is no document that defines the

responsibility of health care providers towards the implementation of the exemption

mechanism (World Bank, 2005). In Chapter 6, we show that although pregnant women

are exempted from official co-payments during childbirth, in order to obtain the presence

of obstetrician some of them pay informally. This suggests that the quality of care that

is provided for exempted groups is not always adequate. Moreover, because of difficult

administrative procedures (necessary documents), pregnant women are often pushed to

pay quasi-official fees as well.

Two main stakeholders, the Ministry of Health and HIF, are involved in the

implementation of the exemption mechanism. The Ministry of Health is responsible

to cover the costs of exempted groups for health care services. The ministry provides

the money for exempted groups to HIF, and HIF allocates the resources to health care

providers. However, the percentage of total revenue that HIF allocates to health care

providers for exempted groups has decreased since 2007 (Bajec et al., 2008).

Following the practice established during the civil war, the Ministry of Health sends

the money to HIF irregularly (Chapter 1 & 4). One of the reasons is the lack of resources;

the other reason is the selective allocation of resources (Chapter 4). Moreover, there is not

a particular policy document that defines the responsibility of each stakeholder.

The Ministry of Health emphasizes that despite the economic difficulties, health

care in Serbia is still provided for free for most vulnerable groups. This attitude of the

Serbian government is preserved from the time of civil war. During that period many

162

Chapter 7

companies did not pay the contributions to the national insurance fund since they were

lacking financial resources (Bajec et al., 2008). On the other side, the government at that

time used the special policy decision to exempt certain population groups from paying

the contributions for complementary health insurance (Stanic, 2002). For example,

military civil servants and monks were not exempted from paying the contributions for

complementary health insurance during the period of the SFRJ (Stanic, 2002). However,

in 1992 those two groups were exempted from contributions. Moreover, in 1997, farmers

were allowed by the special policy act to contribute to compulsory health insurance

with 12 CSD ≈ 0.14 USD per year (Stanic, 2002). During this period, the health care

system was centralized and all managers in hospitals and national insurance fund were

directly appointed by the government (Bajec et al., 2008; Saric & Rodwin, 1993). In

this way, by controlling the management and exempting the “special” population groups

from compulsory health insurance, the political establishment of Slobodan Milosevic

managed to use the health care system as a political tool to buy social peace (Stankovic,

2002). Although the political establishment changed in 2000, the tradition of special

exemptions from compulsory health insurance was transformed into special exemptions

from official co-payments.

However, it was clear that the high number of exempted groups will influence the

financial sustainability of health care system in Serbia. In the period 2003-2008, there

were some attempts to reduce the number of health services that were included in the

insurance package, but there was no attempt to decrease the number of exempted groups

(Adzic & Adzic, 2011). In 2003, the World Bank requested the Serbian government to

decrease the number of exempted groups (World Bank, 2005). The government decided

that retired people will not be exempted from official co-payments (World Bank, 2005).

However, members of this population group use the right to be exempted as people older

than 65 or disabled (Adzic & Adzic, 2011). In this way, the number of exempted persons

did not decrease in total. Since 2003, no government has questioned this type of social

protection and it has never been a topic in the health care reforms (Stosic & Krstic 2014).

During the parliamentary elections in 2004, 2008 and 2012, the main political

parties emphasizes that health care should remain free of charge for vulnerable population

groups (Adzic & Adzic, 2014; B92, 2014). Recent attempts of the previous Minister of

Economy to simplify the exemption procedure and to decrease the number of exempted

individuals led to his resignation (Radulovic, 2013).

Overall, our results show that the most vulnerable groups, namely those who cannot

afford to pay, are not protected. Moreover, the propensity to pay officially for exempted

groups was high in 2002 (the beginning of health care reforms) and in 2007 (5 years

after the health care reforms) (Chapter 4). During this period of 5 years, the exemption

mechanism was never fully implemented. Even when the exemption mechanism is

implemented, like in maternity care, the quality of provided services is low (Chapter

7

General discussion

163

6). Although the current exemption mechanism is promoted as a strong tool to achieve

social protection in health care, it is still just a political tool to buy social peace.

Further research: As we mentioned above, some exempted groups experience the economic

burden provoked by out-of-pocket patient payments. However, we do not examine how

each of the exempted population groups is affected by different types of out-of-pocket

patient payments. This can be a relevant topic for future study. Future research should

also explore the issue of leakage and under-coverage for different exempted groups.

Policy implications: Policy makers in Serbia should assure a less complicated implementation

of the exemption mechanism. The guidelines that describe the exemption mechanism

should be made more clear and available for patients. Instead of including a large number

of population groups, future policy should pay attention to the adequate targeting of

eligible groups (for example using the already existing insurance system) and adequate

provision of health care services for those who are exempted. The exemption policy in

Serbia should be pro-poor oriented and based on health care status.

While poverty can be a trigger to develop a chronic disease, chronic diseases can also provoke

poverty.

Poverty is one of the main risk factors for developing chronic diseases (Bonu et al; 2005;

Engelagau et al., 2012; WHO, 2005). Also, poverty is associated with poor access to

health care services. Poor people often forgo the use of health care services in order to

prevent high financial burden (Banerjee & Duflo, 2012).

In case of chronic diseases, forgoing medical treatment usually leads to more serious

conditions that require even more expensive medical care (Banerjee & Duflo, 2012).

Although poverty is a main trigger to develop a chronic disease, chronic diseases keep

the poor population group in a vicious cycle of poverty (Chapter 5).

Additionally, the prevalence of chronic diseases is growing all over the world (Adeyi,

Smith & Roberts, 2007; Abegunde & Stanciole, 2008; Bloom & Canning, 2008; Russel,

2004). Once diagnosed, chronic diseases lead to a frequent use of health care services.

Frequent use of health care services and the life-long duration of chronic diseases can push

even wealthy households into poverty (Alleyne et al., 2013). In this way, chronic diseases

are not only provoked by poverty, but they can also generate poverty. This implies a joint

relation between the poverty and chronic diseases.

In Serbia, poverty is systematically assessed since 2002/3 (Chapter 1). The first policy

document that assesses the effects of poverty on population health and health care system

was published in 2003 (World Bank, 2003). The document reports the increased burden

of chronic diseases, more precisely cancer, cardiovascular diseases, diabetes mellitus

164

Chapter 7

and mental diseases. Socio-economic factors related to poverty (unemployment) and

environmental factors (NATO bombing and stress) are seen as the main reasons for the

increased burden of chronic diseases (World Bank, 2003). Individual characteristics like

alcohol consumption, smoking and inactive behavior also contribute to the burden of

chronic diseases (Bjegovic et al., 2007). However, the document also state that patients

with diagnosed chronic diseases and disabled individuals report higher out-of-pocket

patient payments than other population groups (World Bank, 2003). This means that

people with diagnosed chronic diseases are more likely to be pushed into poverty because

of the increased health expenditure (Xu et al., 2007). Although the document reveals

the double relation between the chronic diseases and poverty, this relation has not been

explored. However, the Serbian government used the results from this report to design

the current social protection policy. People diagnosed with certain chronic diseases

are exempted from official co-payments (Chapter 4). The full exemption mechanism

is applied for people diagnosed with cancer, while a partial exemption mechanism is

applied for people with diagnosed diabetes mellitus and cardiovascular diseases (Official

Gazette of Republic of Serbia, 2010).

As mentioned above, the aim of the social protection policy is to prevent a potential

financial burden among those groups (Holzman & Jorgsen, 2001). However, even a well-

designed social policy does not always achieve its goal in practice.

Therefore, policy effects should be evaluated after certain period of time. Nevertheless,

5 years (2007) after this social protection measure (the exemption from official co-

payments) was introduced in Serbia, the effects of three main chronic diseases on poverty

were not explored (Chapter 5). Such analysis is however needed to show to which extent

those population groups are protected in reality. Beside the effectiveness of the current

social policy, this analysis can also show which aspects of current policy need to be

improved.

Therefore, in this dissertation, we explore the relation of the three leading chronic

diseases in Serbia with poverty and the financial burden provoked by out-of-pocket

payments when these diseases are present. For this purpose, we use two-stage least square

instrumental variable (IV) approach known as 2SLS (Chapter 5). To assess the financial

burden caused by out-of-pocket patient payments for different types of chronic diseases,

we use one of the approaches mentioned before in Chapter 2, namely the catastrophic

health care expenditure approach (Xu et al., 2007; Wagstaff, 2008). We first identify

households with at least one member with a diagnosed chronic disease, specifically diabetes

mellitus, cardiovascular diseases and progressive diseases. To control for endogenity in

the disease indicators, we use two groups of instruments. The first group is related to

health-related life style behavior (e.g. smoking behavior and eating habits). The second

group of instrumental variables consists of environmental variables like living in an area

7

General discussion

165

affected by uranium during the NATO bombing and being a refugee during the period

1999-2007.

Our results show that all three diseases are significant predictors for pre-payment

poverty when other factors are controlled for. People diagnosed with one of the three

chronic diseases are more likely to be poor. Out-of-pocket patient payments can push

those households even deeper into poverty, which is described as a vicious cycle of poverty

(Banerjee & Duflo, 2012). A family member who is ill cannot work. In this way, health

expenditure is increasing while the available family income is decreasing (Banerjee &

Duflo, 2012). Households are pushed to use coping mechanisms, including borrowing

money, selling assets, and decreasing other expenditures like education (Flores et al.,

2008). However, those mechanisms have only short-term effects, and in case of prolonged

chronic diseases, households are not able to cope with long-term effects of poverty.

Furthermore, our results show significant catastrophic effects of out-of-pocket

payments for diabetes mellitus and cardiovascular diseases (Chapter 5). Diabetes mellitus

and cardiovascular diseases are described as expensive diseases in Serbia (Biorac et al.,

2009; Ivanova et al., 2009).

The medical treatment of diabetes mellitus requires a special food regime, use of

many disposable materials and frequent visits to the doctor. The medical treatment of

cardiovascular diseases, in general, also requires frequent use of medical services (Chapter

5). In Serbia, the organization of health care system contributes to the increased financial

burden. People diagnosed with diabetes and cardiovascular diseases have to use health

services from primary to tertiary level (Official Gazette, 2010). Moreover, they are only

partially exempted from official co-payments (Official Gazette, 2010). According to

our results, diagnosed progressive diseases are not associated with catastrophic health

expenditure. The main therapy protocols for chemotherapy and radiotherapy are free

of charge for all population groups (Official Gazette of RS, 2010). This can prevent a

financial burden for people diagnosed with cancer. Also, people diagnosed with cancer

are usually retired based on their disability to work (Official Gazette of RS, 2010).

In this way, they receive a regular monthly income, irrespectively of their previous

working status. This can also be a protective mechanism for financial burden. As we have

mentioned above, poverty is one of the main reasons to develop chronic diseases (WHO,

2005). However, our results have shown that environmental factors can contribute to

certain chronic diseases (Chapter 5). People who are refugees are more likely to experience

diabetes mellitus, while people who were exposed to uranium bombs are more likely to

experience cardio-vascular diseases (Chapter 5). Although previous literature has reported

on the relation between the exposition to uranium bombs and cancer (Bjegovic et al.,

2007) our data do not confirm this.

However, our data show that different chronic diseases are also a reason for poverty.

The organization of health care system can contribute to the financial burden caused by

166

Chapter 7

chronic diseases (Biorac et al., 2009). As discussed in Chapter 5, the provision of health

care services for chronically sick is a complex process and requires the involvement of

different levels of the health care system. In case when those levels are not coordinated,

patients face problems like the provision of redundant services and additional costs for

them. Additionally, although, social protection measures exist, they do not adequately

target the most vulnerable groups (Chapter 4). As outlined in Chapter 4 disabled

population groups and people diagnosed with certain chronic diseases, such as cancer,

and people with hearing and speaking problems report official co-payments for health

care services even though they should be exempted.

Further research: In our data, we lack data on the way how chronic diseases are diagnosed.

For example, cardiovascular diseases include a broad range of medical diagnosis from

high blood pressure to stroke. Another limitation is related the treatment of chronic

diseases.

The treatment is characterised by fluctuation over time (Russel, 2008) and the real

financial burden can be observed only with longitudinal data. Further research needs to

focus on these issues.

Policy implications: The current health policy in Serbia is dominantly focused on the

prevention of chronic diseases. However, future policy should pay attention to people

who are already diagnosed with a chronic disease. As we outlined in Chapter 5, better

financial protection of vulnerable groups requires effective exemption mechanisms. In

Serbia, policy makers should design less complicated exemption mechanisms. Moreover,

the organization of the health care system should be changed in order to facilitate both

the availability and affordability of health care services for chronically sick. Specifically, in

Serbia, the care for the chronically sick should be provided as much as possible within the

same location. Since health care management of chronically sick individuals requires the

involvement of both outpatient and inpatient services, they need to actively cooperate.

This can be achieved by using electronic medical records for which technical support was

provided in 2008 by the European Agency for Reconstruction (EAR, 2008). In this way,

it is possible to decrease the provision of redundant services at primary and secondary

level. Regarding affordability, the current exemption mechanism should be extended

for all services related to diabetes mellitus and cardiovascular diseases. In case when all

services are included in the exemption mechanism, the implementation of exemption

policy would be easier for both patients’ and providers.

7

General discussion

167

The main problem in maternity care in Serbia is not the low level of obstetricians and

midwifes skills, but the absence of compassion on the side of medical staff.

Maternity care is often used as one of the main indicators in evaluating health care systems

(Bonu et al., 2005; Hulton et al., 2000). If maternity care is well organized to provide

affordable services with good quality, this is seen as a sign that the health care system as

a whole is functioning well (Kanya et al.,; 2014Ronsmans, 2001). The specific position

of maternity care within the health care system is related to the outcome of maternity

care in comparison with other health care units (Ronsmans, 2001). While all other of

the units within the health care system work with ill people, maternity care is oriented

towards patients (i.e. pregnant women) of whom the majority is healthy. Only a small

percentage (on average 5%) of all pregnancies in Serbia are diagnosed as pathological

and they are treated within specialized units (Guyton & Hall, 2000; Stambolovic 1996).

Although, giving birth is a natural physiological process, during the process of

delivery many women are perceived as vulnerable due to the value that a new born child

has for the parents and the society (Chalmers, 2012; Moris, 2007). A new born child is

often perceived as an investment in the future and the society acts in a manner to protect

future investments (the new born child) but also to satisfy the needs of the pregnant

women (Moris, 2007). In some countries, where women do not earn their own income

or where the decisions regarding household expenditure are made by other household

members, pregnant women are also perceived as financially vulnerable (Borghi et al.,

2003). This means that maternity care should be organized not only to provide good

physical care but also to provide affordable services with good quality and adequate access

that are also compatible with women’s needs.

In Serbia, there is an additional reason for the specific position of maternity care

(Chapter 6). In particular, the value that the new born child has for the society increased

in the post conflict society (Andrejic, 2010). During the civil war, the total number

of new born children decreased in comparison with the period of the SFRJ (Andrejic,

2010). The government encouraged women to have more children (Andrejic, 2010). At

the same, the difficult economic situation left many women outside their work (Krstic,

2008). The first social protection measure related to this was a full exemption of pregnant

women in maternity wards from official co-payments (Official Gazette of Republic of

Serbia, 2010). Moreover, both prenatal and postnatal care were provided for free (Janevic

et al., 2011). Other social protection measures included one year of paid maternity leave

and free use of public transport for new mothers (Bajec et al., 2008). The government

explained those measures as a consequence of long-term tradition of social protection

within the health care system in Serbia (Adzic & Adzic, 2011). In this way, the Serbian

government wanted to emphasizes the importance of financial protection for expecting

mothers. In terms of maternity care, social protection is also related to quality of care

168

Chapter 7

(Cameron et al., 2013). Specifically, women should receive care that is in accordance with

evidence-based data but also in accordance with their preferences.

The first evaluative study about maternity wards in Serbia was conducted in 2005

by UNICEF (UNICEF, 2005). The study was focused on indicators at the system level

like maternal mortality or the presence of skilled person during the childbirth. However,

service-related indicators like access to maternity care, patient payments, quality of

care received and policy regulation were not evaluated. In this dissertation, we examine

service related indicators in maternity wards in Serbia. For this purpose, we combine

data collected among women through the online portal of Mother Courage, results of

a literature review of published studies as well as a review of official documents and

guidelines.

We apply mixed methods using both qualitative and quantitative data (Chapter

6). Our results from all three sources show that the main problems in maternity wards

in Serbia are related to the low psychological accessibility (poor bedside manners and

derogative communication) as well as to the various forms of patient payments.

The three reports published by the Serbian government and the World Bank in 2008,

2010 and 2011 that have described the social protection measures in the health care sector

in Serbia, do not report evidence on the implementation of the exemption mechanism

in the maternity wards (Bajec et al., 2008; Government of the Republic of Serbia, 2010;

Vukovic &Perisic, 2011). However, as shown by the results in this dissertation (Chapter

6), although exempted from official co-payments, many women report informal patient

payments and quasi-formal patient payments (official charges set by the facility but

not regulated by the government). Quasi-formal payments are charged by hospitals for

services that should be provided for free (e.g. epidural analgesia). The main reason for

paying informally is to obtain better quality of care and safety for the new born child

(Stepurko et al., 2013). However, our results show that informal patient payments do not

guarantee better quality of care (Chapter 6). Even though some women report informal

patient payments they still experience some inconveniences related to quality of care,

namely problems with equipment and obligatory but non necessary procedures during

the admission. They also report poor bedside manners and derogative communications.

Despite the problems stated above, the level of physician skills is rated well. This

result is consistent in all three sources that we have used. Moreover, data obtained from

the online collected questionnaires show that physician skills are rated as good also

by women who did not pay informally (Chapter 6). However, the majority of women

from this sample (including those who have reported informal patient payments) report

poor bedside manners, derogative communication and lack of compassion on the side

of health care providers (Chapter 6). They often state that they have not been informed

about the medical procedures or that health care providers address them by protocol

number not by name (Chapter 6). Our results also identify a group of pregnant women

7

General discussion

169

who have reported “special connections” instead of informal patient payments. “Special

connections” are described as friends or relatives who work in the hospital and who can

ensure a special treatment and adequate care. “Special connections” represent someone

whom the pregnant women can trust. Our results also show that women with “special

connections” report fewer inconvenience than those who pay informally (Chapter 6).

In this way, special connections represent a type of informal social protection. It means

that pregnant women in Serbia are aware that formal social protection will not ensure

adequate care in maternity wards. The existence of special connections also emphasizes

that formal financial protection in Serbia is a necessary but not sufficient way to ensure

adequate care.

Further research: In Chapter 6, we use data obtained from online collected questionnaire.

The data that we use represent a non-representative purposive sample. This means that we

only include women who have delivered in a certain period of time. Also, we only include

women who are capable of using the Internet, mostly women from urban areas, and

women motivated to participate. In order to overcome this limitation, we use data from

two more sources, namely the literature review as well as a review of official documents

and guidelines. However, a future study that uses a more representative sample can give

a more comprehensive picture about the service indicators in maternity wards in Serbia.

Policy implications: In Serbia, state maternity wards still have a monopoly position. The

medical technocratic approach is dominant during childbirth. The choice for a certain

type of delivery (like normal physiological childbirth, or childbirth with analgesia) is

still limited. The monopoly position is supported by derogative communication of health

care providers (Chapter 6). Although the Serbian government in cooperation with WHO

has organized training for health care providers in order to improve their communication

skills, problems of derogative communication are still present (Chapter 6). Moreover,

training for obstetricians should be accompanied with better management and control in

maternity wards. In order to provide good services in the maternity wards, the Serbian

government should take in account women’s preferences. The government should also

educate physicians to respect those preferences. This means that good physicians’ skills

are a necessary but not a sufficient condition for providing good quality of care. The

current system of medical education in Serbia is focusing on technical skills of future

physicians. The patients’ needs are not recognized as important for the curative process.

The physicians should become aware that the satisfaction of patient needs can lead to

more effective curative outcomes.

170

Chapter 7

Social protection should be more than preventing monetary poverty, it should also refer to

social inclusion.

Social protection represents a set of policy measures that help vulnerable population

groups to deal with risk factors (Holzman & Jorgsen, 2001; also in Chapter1). The main

goal of those policy measures is to prevent poverty (Holzman & Jorgsen, 2001; Chapter

1).

Most often, those measures are focusing on financial protection (Holzman & Jorgsen,

2001). However, as outlined in Chapter 1, poverty is a multidimensional concept that

includes both monetary and non-monetary aspects (Atkinson, 2003; Bourgouignon &

Charkavatry, 2003; Kakwani & Silber, 2008). According to the multidimensional nature

of poverty, the new definition of social protection moves towards a broader concept of

social inclusion (WHO, 2005). Social inclusion is the process that prevents people from

being excluded from society because of poverty, lack of life long education, illness or

as a result of discrimination (European Commission, 2004). This definition assumes

that the precursor for social inclusion is compassion (Standing, 2013). Compassion or

compassionate empathy is defined as a recognition of other people needs, feeling with

them and be ready to help when is needed (Goleman, 2007). Following the previous

definition, in terms of health care, financial accessibility of health care services is only

one aspect of social protection (Chapter 1). Social protection also includes good quality of

provided services and patient-oriented care (Cameron et al., 2013).

In this dissertation, the main focus is to examine the effects of out-of-pocket payments

for public health care services and goods on the financial protection of vulnerable

population groups. As discussed above, our results suggest that 1.1% of all respondents

experience impoverishing effects and 0.8% of all respondents experience catastrophic

health care expenditure based on consumption. However, both impoverishing effects and

catastrophic health care expenditure are higher among health care users (Chapter 2 &

3). Furthermore, we have also shown that certain exempted groups still report official

co-payments (Chapter 4). This implies that the current exemption mechanism is not well

designed and not fully implemented (Chapter 4). We also, show that some population

groups perceived as vulnerable like pregnant women pay for their care and some others

like chronically sick experience catastrophic health care expenditure (Chapter 5 & 6).

Those findings suggest that in some cases, financial protection is not fully achieved in the

Serbian public health care sector.

In this dissertation we do not examine other financial aspects of social exclusion related

to health care, like not being able to use health care services because of discrimination,

lack of social insurance coverage or patients’ attitude towards the quality of provided care.

However, in some parts of our study, our results allow us to go further than pure financial

protection. They show that financial aspects are not the only reason for the failure of the

7

General discussion

171

social protection mechanism. We observe the importance of other than financial aspects

examining the financial protection but also quality of care and policy regulations in the

maternity wards in Serbia (Chapter 6).

In Serbia, maternity care is formally free of charge (Chapter 6). Due to the existence

of informal and quasi-official patient payments, this universal financial protection in

maternal care is limited although it still formally exists (Chapter 6). Moreover, some

women in our study stated that the quality of the provided care does not always satisfy

their needs (Chapter 6). Even if women needs are recognized, adequate help is not always

provided (Chapter 6). This is in accordance with previous studies that have examined

the satisfaction with care provided in hospitals in Serbia. This study was based on a

representative sample, and participants report the lowest level of satisfaction with care

provided in maternity wards (Institute of Public Health, 2011). As we mentioned before,

in our study, many women rated the physicians’ skills as good (Chapter 6), but they

report problems in communication with health care providers. In most cases, women in

our study report derogative communication and disrespectful manners (Chapter 6). Good

quality of care does not include only physicians’ skills and good technical knowledge, but

also compassion with patient needs (Cameron et al., 2013).Those results are in accordance

with previous research on health care in Serbia. For example, research conducted in 2005

by the World Bank measured the opinions of Serbian citizens about the health care system

(World Bank, 2005). This study showed that citizens in Serbia perceived the behaviour of

the staff related to patients as the main problem in the health care system (91.7% report

this as an aspect of the highest importance for improving health care) (World Bank,

2005).

Our results regarding the implementation of the exemption mechanism also enlighten

some factors that can lead towards social inclusion. For example, vulnerable population

groups in Serbia often face difficult administrative procedures that are required to obtain

the exemption status (Chapter 4). IHIS and Ministry of Health, as the main stakeholders

responsible for the exemption mechanism, are still organized as purely bureaucratic

systems. Their decisions are usually based on rigid administrative procedures that do

not reflect evidence on the real needs of vulnerable population groups to be exempted

(Chapter 4).

Further research: In this study, we examine the link between out-of-pocket patient payments

in the public health care system and the financial protection of vulnerable groups in

Serbia. As we mentioned above, social protection goes beyond financial protection. It

includes also social inclusion (Chapter 1). In case when social inclusion is not achieved,

some vulnerable population groups might experience discrimination within the health

care system. We do not examine this issue, which can be the focus of further research.

172

Chapter 7

Future studies should also address the possible stigmatization of vulnerable population

groups within health care system and possible financial effects of the stigmatization.

Policy implications: Our results show that the financial aspects of social protection are not

always effectively applied in the Serbian public health care sector. Moreover, the need

for compassion as an additional aspect of social protection is not recognized by health

care providers and policy makers. As we mentioned above, social inclusion is a complex

concept that includes not only financial protection but also acceptance of vulnerable

population groups within society and respect of their needs (WHO, 2005). Policy makers

should do more to increase awareness towards this type of social protection. Recognizing

the need of patients can also facilitate the implementation for financial protection.

7.3 Concluding remarks

The public health care system in Serbia is jointly funded by compulsory health

insurance collected by HIF and the Ministry of Health, as well as by out-of-pocket

patient payments. Health expenditure from state entities (Ministry of Health & HIF)

expressed as a percentage of GDP varied from 6.6%-6.7% in the period 2001-2007 and

it slightly decreased in 2010 to 5.4% (Government of the Republic of Serbia, 2010),

but it is still comparable to some EU countries (Bulgaria, Hungary, Latvia, Lithuania)

(Gavrilovic & Trmcic, 2014). This level of public health care funding is expected to be

a preventive factor for the financial burden (Xu et al., 2007). The nominal amount of

out-of-pocket patient payments is low and very often described as symbolic (Vukovic &

Perisic, 2011). This is also in accordance with the government social protection policy

which aims to prevent the financial burden provoked by the health care expenditure.

However, our results show some weaknesses of this policy, namely impoverishing effects

and catastrophic health expenditure provoked by official co-payments. These effects are

similar as those in other Western-Balkan countries (Bredenkamp et al., 2010). However,

impoverishing effects and catastrophic health care expenditure provoked by payments

for “bought & brought goods” are much higher. This means that the government should

not neglect those types of payments. A future policy should assure protection from those

types of payments. Although the government spends a significant percentage of GDP on

health care, the allocation of this money is not always monitored (Lecic-Tosevski, 2010).

For example, hospitals are still financed through annual fixed budget. This opens the

door for the existence of payments for “bought & brought goods” but also for existence

of other aspect of corruption.

Furthermore, the current level of the financial burden can be decreased by a more

effective exemption mechanism. Better targeting of vulnerable groups is the precursor

7

General discussion

173

for a better implementation of the exemption mechanism (Palmer, 2000). Policy makers

should pay attention to possible obstacles related to the exemption mechanism, like

difficult administrative procedures, stigmatization and discrimination of disadvantage

groups.

A future policy should be more pro-poor oriented but also take in account the health

status. For example, the current policy emphasizes the importance of prevention of

chronic diseases. However, people who are already diagnosed with a chronic disease face

not only a financial burden but also the poorly organized public health care system.

The difficulties in the organization of health care are also observed in maternity wards,

namely old protocols, forbidden visits of family members are some of the indicators of

poor organization. This dissertation provides evidence on the quality of care offered

in maternity wards. Problems with different types of patient payments, social and

psychological accessibility are reported. The inclusion of private health care providers in

the system of compulsory health insurance can decrease the problems related to patient

payments as well as those related to psychological and social accessibility.

Health care reforms in Serbia started in 2002 and they are still ongoing. During

this period some aspects are improved (better technical equipment, better availability

of pharmaceuticals), some others are worsened (waiting lists, existence of different types

of out-of-pocket patient payments). However, many policy drafts have changed since

2002. The main reasons were political. Every time when the government is changed, a

new policy is introduced. This prevents the continuity of health care reforms. In order to

assure positive outcome of the health care reforms, the Serbian government should assure

more transparency within the system and better recognition of patients’ rights.

References

175

References

Abegunde, D.O., & Stanciole, A. (2008). The economic impact of chronic diseases: How do households respond to shocks? Evidence from Russia. Social Science & Medicine, 66, 2296-2307.

Adhikari, S., Maskay, N., & Sharma, B. (2009). Paying for hospital-based care of Kala-azar in Nepal: assessing catastrophic, impoverishment and economic consequences. Health Policy and Planning, 24, 129-139.

Adeyi, O., Smith, O., & Robles, S. (2007). Public policy and the challenge of chronic noncommunicable diseases: World Bank Publications.

Adzic, S., & Adzic, J. (2009). The Public Healthcare System in the Transition Countries the Case Study of Serbia. Interdisciplinary Management Research, 5, 515-540.

Agencija za borbu protiv korupcije republike Srbije. (2013). Verifikacija rizika iz planova integriteta i analiza uspesnosti samoprocene organa javne vlast: sistem zdravstva. Beograd.

Alam, A., Murthi, M., Yemtsov, R., Murrugarra, E., Dudwick, N., Hamilton, E., & Tiongson, E. (2005).  Growth, poverty and inequality: Eastern Europe and the former Soviet Union. Washington, DC: World Bank.

Albreht, T., & Klazinga, N. (2009). Privatisation of health care in Slovenia in the period 1992–2008. Health policy, 90(2), 262-269.

Alleyne, G., Binagwaho, A., Haines, A., Jahan, S., Nugent, R., Rojhani, A., & Stuckler, D. (2013). Embedding non-communicable diseases in the post-2015 development agenda. The Lancet, 381(9866), 566-574.

Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics, 95(7), 476-487.

Angrist, J., & Krueger, A. B. (2001). Instrumental variables and the search for identification: From supply and demand to natural experiments: National Bureau of Economic Research.

Andrejic A. The politics of experience: the discursive contestation of medical management of childbirth in Serbia. Open University 2010, Budapest.

Atanasova, E., Pavlova, M., Velickovski, R., Nikov, B., Moutafova, E., & Groot, W. (2011). What have 10 years of health insurance reforms brought about in Bulgaria? Re-appraising the Health Insurance Act of 1998. Health Policy, 102(2), 263-269.

Atkinson, A. B. (2003). Multidimensional deprivation: contrasting social welfare and counting approaches. The Journal of Economic Inequality, 1(1), 51-65.

Baji P,Pavlova M, Gulácsi L, Groot W. User fees for public health care services in Hungary: Expectations, experience, and acceptability from the perspectives of different stakeholders. Health Policy 2011, 102: 255-262

Barrientos, A. (2011). Social protection and poverty. International Journal of Social Welfare, 20(3), 240-249.

Basu, S., Stuckler, D., McKee, M., & Galea, G. (2013). Nutritional determinants of worldwide diabetes: an econometric study of food markets and diabetes prevalence in 173 countries. Public Health Nutrition, 16(01), 179-186.

Baeza, C., & Packard, T. (2006). Beyond survival: protecting households from health shocks in Latin America. World Bank, Washington DC.

Bajec, J., Krstic, G., Pejin-Stokic, L.J., & Penev, G. (2008). Social protection and social inclusion in the Republic of Serbia. Brussels: European Commission.

Balabanova, D., & McKee, M. (2002). Understanding informal payments for health care: the example of Bulgaria. Health Policy, 62(3), 243-273.

176

References

Balabanova, D., McKee, M., Pomerleau, J., Rose, R., & Haerpfer, C. (2004). Health service utilization in the former Soviet Union: evidence from eight countries. Health Services Research, 39(6p2), 1927-1950.

Balabanova, D. (2007). Health sector reform and equity in transition.

Banjac Lj, Tendjera Milicevic D, Krstic, V. Задовољство породиља пруженом здравственом заштитомна територији Рашког округа. Zdravstvo i zastita 2010; 6:7-15.

Barrientos, A. (2011). Social protection and poverty. International Journal of Social Welfare, 20(3), 240-249.

Belli, P., Gotsadze, G., & Shahriari, H. (2004). Out-of-pocket and informal payments in health sector: evidence from Georgia. Health Policy, 70(1), 109-123.

Berki SE, Ashcraft MLF. HMO enrolment who joints what and why: a review of literature. Milbank Memorial Fund Quarterly/Health and Society 1980; 58: 588-632.

Beaglehole, R., Bonita, R., Horton, R., Adams, C., Alleyne, G., Asaria, P., Casswell, S. (2011). Priority actions for the non-communicable disease crisis. The Lancet, 377(9775), 1438-1447

Becker G, Zisovska E. Evaluation of the baby-friendly hospital initiative in Serbia for the period 1995-2008. WHO 2009

Bilić, B., & Georgaca, E. (2007). Representations of “mental illness” in Serbian newspapers: A critical discourse analysis. Qualitative Research in Psychology,4(1-2), 167-186.

Bitrán, R., & Giedion, U. (2002). Waivers and exemptions for health services in developing countries. Final draft. World Bank, 89.

Bjegovic, V., Terzic, Z., Marinkovic, J., Lalic, N., Sipetic, S., & Laaser, U. (2007). The burden of type 2 diabetes in Serbia and the cost-effectiveness of its management. The European Journal of Health Economics, 8(2), 97-103.

Black, M. (1993). Collapsing health care in Serbia and Montenegro. BMJ, 307, 1135-1137.

Blas, E., & Limbambala, M. E. (2001). User-payment, decentralization and health service utilization in Zambia. Health Policy and Planning, 16(suppl 2), 19-28.

Biorac, N., Jakovljević, M. B., Stefanović, D., Perović, S., & Janković, S. (2009). Assessment of diabetes mellitus type 2 treatment costs in the Republic of Serbia. Vojnosanitetski Pregled, 66(4), 271-276.

Bjegovic, V., Terzic, Z., Marinkovic, J., Lalic, N., Sipetic, S., & Laaser, U. (2007). The burden of type 2 diabetes in Serbia and the cost-effectiveness of its management. The European Journal of Health Economics, 8(2), 97-103.

Blic zena. Koliko kosta porodjaj, Blic 2013; accessed online in June, 2013 http://www.bebac.com/vesti/koliko-kosta-porodjaj.

Bloom, D. E., & Finlay, J. E. (2009). Demographic change and economic growth in Asia. Asian Economic Policy Review, 4(1), 45-64.

Bracewell W. Women, motherhood and contemporary Serbian nationalism. Women’s Studies International Forum 1996; 19(1/2): 25-33.

Bredenkamp, C., Mendola, M., & Gragnolati, M. (2011).Catastrophic and impoverishing effects of health expenditure: new evidence from the Western Balkans. Health Policy and Planning, article in press: doi: 10.1093/heapol/czq070

Bredenkamp, C., Gragnolati, M., & Ramljak, V. (2008). Enhancing efficiency and equity: Challenges and reform opportunities facing health and pension systems in the Western Balkans. The International Bank for Reconstruction and Development / The World Bank.

Bratani B, Fidler N, Felc Z, Truden Dobrin P. Breastfeeding and Baby Friendly Hospital Initiative in Slovenia. Advances in Experimental Medicine and Biology 2005; 569: 205-206.

Bogicevic, B., Krstic, G., & Mijatovic, B. (2002). Poverty in Serbia and reform of governmental support for the poor.

References

177

Borghi, J., Hanson, K., Acquah, C. A., Ekanmian, G., Filippi, V., Ronsmans, C., & Alihonou, E. (2003). Costs of near-miss obstetric complications for women and their families in Benin and Ghana. Health Policy and Planning, 18(4), 383-390.

Bourguignon, F., & Chakravarty, S. R. (2003). The measurement of multidimensional poverty. The Journal of Economic Inequality, 1(1), 25-49.

Bonu, S., Rani, M., Peters, D. H., Jha, P., & Nguyen, S. N. (2005). Does use of tobacco or alcohol contribute to impoverishment from hospitalization costs in India? Health Policy and Planning, 20(1), 41-49.

Brueckner G. 2006. NHA Final Report in Serbia. Geneva: World Health Organization

Buddelmeyer, H., & Cai, L. (2009). Interrelated dynamics of health and poverty in Australia: IZA Discussion Papers.

Cameron, R. A., Mazer, B. L., DeLuca, J. M., Mohile, S. G., & Epstein, R. M. (2013). In search of compassion: a new taxonomy of compassionate physician behaviours. Health Expectations.

Cawley, J., & Meyerhoefer, C. (2012). The medical care costs of obesity: an instrumental variables approach. Journal of Health Economics, 31(1), 219-230.

Chalmers B. Changing childbirth in Eastern Europe. Which systems of authoritative knowledge should prevail? Berkeley 1997. University of California Press.

Chalmers B. Childbirth Across Cultures: Research and Practice. Birth 2012; 39: 276-289.

Cherecheş, R. M., Ungureanu, M. I., Sandu, P., & Rus, I. A. (2013). Defining informal payments in healthcare: A systematic review. Health Policy, 110(2), 105-114.

CESID. (2011). Corruption in health. Source: http://www.cesid.org/lt/articles/programi/borba-protiv-korupcije/zavrseni-projekti/ (accessed on 12/09/2011).

Coates, A., Farnsworth, K., & Zulauf, M. (2001). Social exclusion and inclusion: partnerships for neighbourhood regeneration in London. South Bank University, Faculty of Humanities and Social Science.

Cohen, N. (2012). Informal payments for health care–the phenomenon and its context. Health Economics, Policy and Law, 7(03), 285-308.

Cutler, D., Lleras-Muney, A., Vogl, T., Glied, S., & Smith, P. (2011). Oxford handbook of health economics: Oxford University Press Oxford, UK.

Danilovich N. Growing inequalities and reproductive health in transitional countries: Kazakhstan and Belarus. Journal of Public Health Policy 2010; 31:30–50.

Danishevski K, Balabanova D, McKee M, et al. Delivering babies in the time of transition in Tula, Russa. Health Policy and Planning. 2006;21(3):195–205

Deaton, A. (1997). The analysis of household surveys: A microeconometric approach to development policy. Baltimore, MD: Johns Hopkins University Press.

Deaton, A. (2008). Income, health, and well-being around the world: Evidence from the Gallup World Poll. Journal of Economic Perspectives, 22 (2), 53–72.

De Haan, J., & Sturm, J. E. (2000). On the relationship between economic freedom and economic growth. European Journal of Political Economy, 16(2), 215-241.

Deininger, K., & Mpuga, P. (2005). Economic and welfare impact of the abolition of health user fees: Evidence from Uganda. Journal of African Economies, 14(1), 55-91.

Delcheva, E., Balabanova, D., & McKee, M. (1997). Under-the-counter payments for health care: evidence from Bulgaria. Health policy, 42(2), 89-100.

Djikanovic, B., Marinkovic, J., Jankovic, J., Vujanac, V., & Simic, S. (2011). Gender differences in smoking experience and cessation: do wealth and education matter equally for women and men in Serbia? Journal of Public Health, 33(1), 31-38.

178

References

Douple, E. B., Mabuchi, K., Cullings, H. M., Preston, D. L., Kodama, K., Shimizu, Y., . . . Shore, R. E. (2011). Long-term radiation-related health effects in a unique human population: lessons learned from the atomic bomb survivors of Hiroshima and Nagasaki. Disaster medicine and public health preparedness, 5(S1), S122-S133.

Durlević, Z. (2012). Razvoj zdravstvenih delatnosti u obnovljenoj Srbiji nakon drugog srpskog ustanka. Naucni casopis urgentne medicine, 35.

Ekman, B. (2007). The impact of health insurance on outpatient utilization and expenditure: evidence from one middle-income country using national household survey data. Health Research Policy and Systems, 5, 1-15.

Egawa, H., Furukawa, K., Preston, D., Funamoto, S., Yonehara, S., Matsuo, T., . . . Kodama, K. (2012). Radiation and smoking effects on lung cancer incidence by histological types among atomic bomb survivors. Radiation research, 178(3), 191-201.

Eisenberg, D., & Quinn, B. C. (2006). Estimating the effect of smoking cessation on weight gain: an instrumental variable approach. Health services research, 41(6), 2255-2266.

Ensor, T. (2004). Informal payments for health care in transition economies. Social Science & Medicine, 58, 237–246.

Ensor, T., & Savelyeva, L. (1998). Informal payments for health care in the Former Soviet Union: some evidence from Kazakstan. Health Policy and Planning, 13(1), 41-49.

Engelgau, M. M., Karan, A., & Mahal, A. (2012). The economic impact of non-communicable diseases on households in India. Global Health, 8(9).

Esping-Andersen, G. (1990). sta. 1990. The three worlds of welfare capitalism.

Estevez-Abe, M., Iversen, T., & Soskice, D. (2001). Social protection and the formation of skills: a reinterpretation of the welfare state. Varieties of capitalism. The institutional foundations of comparative advantage, Oxford, 145.

Falkingham, J. (2004). Poverty, out-of-pocket payments and access to health care: evidence from Tajikistan. Social Science & Medicine, 58, 247-258.

Falkingham, J., Akkazieva, B. & Baschieri, A. (2010). Trends in out-of-pocket payments for healthcare in Kyrgyzstan, 2001–2007. Health Policy and Planning, 25, 427-436.

Ferrer-i-Carbonell, A., & van Praag, B. (2001). Poverty in Russia Federation. The Institute for the Study of Labor, Bonn.

Fiszbein, A., Kanbur, R., & Yemtsov, R. (2013). Social Protection, Poverty and the Post-2015 Agenda. World Bank Policy Research Working Paper, (6469).

Flores, G., Krishnakumar, J., O’Donnell, O., & van Doorslaer, E. (2008). Coping with health-care costs: implications for the measurement of catastrophic expenditures and poverty. Health Economics, 17 (12), 1393 -1412.

Fletcher, J. M. (2012). Peer influences on adolescent alcohol consumption: evidence using an instrumental variables/fixed effect approach. Journal of Population Economics, 25(4), 1265-1286.

Flores, G., & O’Donnell, O. A. (2013). Catastrophic medical expenditure risk.

Foster, J.E. (1998). Absolute versus relative poverty. American Economic Review, 88 (2), 335–341.

Fraser, N., & Gordon, L. (1994). A genealogy of dependency: Tracing a keyword of the US welfare state. Signs, 309-336.

Gaal, P., Belli, P. C., McKee, M., & Szócska, M. (2006). Informal payments for health care: definitions, distinctions, and dilemmas. Journal of Health Politics, Policy and Law, 31(2), 251-293.

Gaál, P., Jakab, M., Shishkin, S., Kutzin, J., Cashin, C., & Jakab, M. (2010). Strategies to address informal payments for health care. Implementing health financing reform: lessons from countries in transition. Copenhagen: World Health Organization, 327-60.

References

179

Gajic-Stevanovic, M., Dimitrijevic, S., Vuksa, A., & Jovanovic, D. (2010). Health care system and expenditure in Serbia from 2004 to 2008 (in Serbian). Belgrade: Institute of Public Health of Serbia.

Garfield, R. (2001). Economic Sanctions, Health, and Welfare in the Federal Republic of Yugoslavia: 1990-htpp:www.humanitarnopravo.org/sanctions/handbook/docsRhandbook/GarfieldROcha’Yug. pdf>(accessed July 12, 2010).

Gavrilović, A., & Trmčić, S. (2013). Health insurance system in Serbia-quality, reform, financial sustainability

Geneau, R., Stuckler, D., Stachenko, S., McKee, M., Ebrahim, S., Basu, S., Alwan, A. (2010). Raising the priority of preventing chronic diseases: a political process. The Lancet, 376(9753), 1689-1698.

Gertler, P., & Gruber, J. (2002). Insuring consumption against illness. The American Economic Review, 92, 51

Gilson, L., Russell, S., & Buse, K. (1995). The political economy of user fees with targeting: developing equitable health financing policy. Journal of International Development, 7(3), 369-401.

Gilson, L., Kalyalya, D., Kuchler, F., Lake, S., Oranga, H., & Ouendo, M. (2000). The equity impacts of community financing activities in three African countries. The International journal of health planning and management, 15(4), 291-317.

Golubović, Z. (1997). Antropologija u personalističkom ključu. Gutenbergova Galaksija -70.

Goleman, D., & Ekman, P. (2007). Three Kinds of Empathy: Cognitive, Emotional, Compassionate. 2007b. http://www. danielgoleman. info/blog/2007/06/12/three-kindsof-empathy-cognitive-emotional-compassionate.

Gotsadze, G., Zoidze, A., & Rukhadze, N. (2009). Household catastrophic health expenditure: evidence from Georgia and its policy implications. BMC Health Services Research, 9(1), 1-9.

Gotsadze, G., Bennet, S., Ranson, K., & Gzirishvili, D. (2005). Health care-seeking behavior and out-of-pocket payments in Tbilisi, Georgia. Health Policy and Planning, 20(4), 232-242.

Gordon, L., Scuffham, P., Hayes, S., & Newman, B. (2007). Exploring the economic impact of breast cancers during the 18 months following diagnosis. Psycho-Oncology, 16(12), 1130-1139.

Institute of Mother and Child Health Care of Serbia. Opsti principi za negu I lecenje u porodilistu. Beograd, 2009

Habicht, J., Xu, K., Couffinhal, A., & Kutzin, J. (2006). Detecting changes in financial protection: creating evidence for policy in Estonia. Health Policy and Planning, 21(6), 421–431.

Hanson, K., Worrall, E., & Wiseman, V. (2007). Targeting services towards the poor: a review of targeting mechanisms and their effectiveness. Health, economic development and household poverty: from understanding to action, 134-154.

Happ, M., Dabbs, A., Tate, J., Hricik, A., & Erlen, J. 2006. Exemplars of mixed methods data combination and analysis. Nursing research, 55 (2): S43.

Haughton, J., & Khandker, S. (2009). Handbook on poverty and inequality. World Bank, Washington, DC.

Hausman, J. A. (1978). Specification tests in econometrics. Econometrica: Journal of the Econometric Society, 1251-1271.

Hausman, J. A., & Taylor, W. E. (1981). A generalized specification test. Economics Letters, 8(3), 239-245.

Heinrich, S., Luppa, M., Matschinger, H., Angermeyer, M. C., Riedel-Heller, S. G., & König, H. H. (2008). Service Utilization and Health-Care Costs in the Advanced Elderly. Value in Health, 11(4), 611-620.

Himmelstein, D. U., Lang, S., & Woolhandler, S. (1984). The Yugoslav Health System: Public Ownership and Local Control. Journal of Public Health Policy, 423-431.

Holzmann, R., & Jorgensen, S. (2001). Risk and vulnerability: the forward looking role of social protection in a globalizing world. Washington, DC: World Bank.

180

References

Holzmann, R., & Jørgensen, S. (2001). Social Risk Management: A new conceptual framework for Social Protection, and beyond. International Tax and Public Finance, 8(4), 529-556.

Hubrecht J, Najman B. Serbia : discrimination and corruption, the flaws in the health care systems. International federation for human rights, Report N.416; 2005.

Hulton L, Matthews Z, Stone R. A framework for the evaluation of quality of care in maternity services. University of Southempthon, 2000.Hwang, W., Weller, W., Ireys, W., & Anderson, G. (2001) Out-of-pocket medical spending for care of chronic conditions. Health Affairs (Millwood), 20 (6), 267–278.

Jacobs, B., Price, N. L., & Oeun, S. (2007). Do exemptions from user fees mean free access to health services? A case study from a rural Cambodian hospital. Tropical Medicine & International Health, 12(11), 1391-1401.

Janevic T, Pooja S, Bradley E, Dimitrievska V. “There’s no kind of respect here” A qualitative study of racism and access to maternal health care among Romani women in the Balkans. International Journal for Equity in Health 2011, 10:53

Jankovic, J., Simic, S., & Marinkovic, J. (2010). Inequalities that hurt: demographic, socio-economic and health status inequalities in the utilization of health services in Serbia. European Journal of Public Health, 20(4), 389-396.

Jeffery H. Evaluation of integrated maternal and childhood program in Serbia. UNICEF 2003.

James, C. D., Hanson, K., McPake, B., Balabanova, D., Gwatkin, D., Hopwood, I., & Xu, K. (2006). To Retain or Remove User Fees?. Applied health economics and health policy, 5(3), 137-153.

Jick, T. 1979. Mixing qualitative and quantitative methods: Triangulation in action. Administrative science quarterly: 602-611.

Kaitelidou D, Tsirona C, Galanis P, Siskou O, Mladovsky P, Kouli E, Prezerakos P, Theodorou M, Sourtzi P, Liaropoulos L. Informal payments for maternity health services in public hospitals in Greece. Health Policy 2013, 109: 23-30Khandker, S. R., Koolwal, G. B., & Samad, H. A. (2010). Handbook on impact evaluation: quantitative methods and practices: World Bank Publications.

Kakwani, N., & Silber, J. (Eds.). (2008). Quantitative approaches to multidimensional poverty measurement. New York: Palgrave Macmillan

Kawabata, K., Xu, K., & Carrin, G. (2002). Preventing impoverishment through protection against catastrophic health expenditure. Bulletin of World Health Organization, 80, 612.

Kim, Y., & Jang, B. (2010). Relationship between catastrophic health expenditures and household incomes and expenditure patterns in South Korea. Health Policy, 100(2), 239-246.

Kloosterman G.J, Thiery M. (1977). Begeleiden tijdens de baring. Haarlem.

Knaul, F.M., Arreola-Ornelas, H., Mendez-Carniado, O., Bryson-Cahn, C., Barofsky, J., et al. (2006). Health System Reform in Mexico 4 - Evidence is good for your health system: policy reform to remedy catastrophic and impoverishing health spending in Mexico. Lancet, 368, 1828-1841.

Kutzin, J., Ibraimova, A., Jakab, M., & O’Dougherty, S. (2009). Bismarck meets Beveridge on the Silk Road: coordinating funding sources to create a universal health financing system in Kyrgyzstan. Bulletin of the World Health Organization, 87(7), 549-554.

Krstić, G. (2008). Poverty profile in Serbia in the period from 2002-2007. Living Standard Measurement Survey: Serbia 2002–2007 (LSMS).

Kruk, M. E., Mbaruku, G., Rockers, P. C., & Galea, S. (2008). User fee exemptions are not enough: out-of-pocket payments for ‘free’delivery services in rural Tanzania. Tropical medicine & international health, 13(12), 1442-1451.

Kraljevine Srbija (1911). Statisticki godisnjak

Lagarde, M., & Palmer, N. (2008). The impact of user fees on health service utilization in low-and middle-income countries: how strong is the evidence?.Bulletin of the World Health Organization, 86(11), 839-848C.

References

181

Lazarević, L. K. (1899). Pripovetke (Vol. 2). Srpska Štamparija.

Lakićević, S. (1995). The influence of the positive law relations on the system of social care.  Socijalna Politika i Socijalni Rad, 31(4), 25-38.

Leech N,Onwuegbuzie A. A typology of mixed methods research designs. Quality & Quantity 2009; 43(2): 265-275

Letica, S. (1984, September). Primary health care–ideology/illusion/and reality–case study of Yugoslavia. In International conference on primary health care: it’s relevance for national health care and social movements.

Lewis, M. A. (2000).  Who is paying for health care in Eastern Europe and Central Asia?. World Bank Publications.

Lewis M. Informal payments and the financing of health care in developing and transition countries. Health Affairs, 2007; 26(4): 984–997.

Linden, A., & Samuels, S. J. (2013). Estimating measurement error when annualizing health care costs. Journal of Evaluation in Clinical Practice, 19(5), 933-937.

Limwattananon, S., Tangcharoensathien, V., & Prakongsai, P. (2007). Catastrophic and poverty impacts of health payments: results from national household surveys in Thailand. Bullten World Health Organization, 85(8), 600-606.

LSMS Project 2002-2003: life in Serbia through survey data. Strategic Marketing, 2007.

Ludwig, J., Duncan, G. J., Gennetian, L. A., Katz, L. F., Kessler, R. C., Kling, J. R., & Sanbonmatsu, L. (2013). Long-term neighborhood effects on low-income families: Evidence from Moving to Opportunity: National Bureau of Economic Research.

Madzar, L. (1998). The roots and implications of Yugoslav economic adversity. Forschungsberichte-Wiener Institut fur Internationale Wirtschaftsvergleiche, 70-82.

Matejić, B., Kesić, V., Marković, M., & Topić, L. (2008). Communications about cervical cancer between women and gynecologists in Serbia. International Journal of Public Health, 53(5), 245-251.

Matejic, B., Vukovic, D., Pekmezovic, T., Kesic, V., & Markovic, M. (2011). Determinants of preventive health behavior in relation to cervical cancer screening among the female population of Belgrade. Health Education Research, 26(2), 201-211.

Markovic, M. (2010). The right to health in Serbia. University of Aberdeen.

Marks, G. (2005). Income poverty, subjective poverty and financial stress. Department of Family and Community Services. Melbourne Institute of Applied Economic and Social Research.

Mayer-Foulkes, D. A., & Pescetto-Villouta, C. (2012). Economic Development and Non-Communicable Chronic Diseases. Global Economy Journal, 12(4).

Markovic, M. (1965). Socijalizam i samoupravljanje. Smisao i perspektive socijalizma. Zagreb, 54-71.

Mastilica, M. (1990). Health and social inequities in Yugoslavia. Social Science & Medicine, 31(3), 405-412.

Masiye, F., Chitah, B. M., & McIntyre, D. (2010). From targeted exemptions to user fee abolition in health care: experience from rural Zambia. Social Science & Medicine, 71(4), 743-750.

McIntyre, D., Thiede, M., Dahlgren, G., & Whitehead, M. (2006). What are the economic consequences for households of illness and of paying for health care in low- and middle-income country contexts? Social Science & Medicine, 62(4), 858-865.

Meessen, B., Van Damme, W., Tashobya, C. K., & Tibouti, A. (2006). Poverty and user fees for public health care in low-income countries: lessons from Uganda and Cambodia.  The Lancet,  368(9554), 2253-2257.

Milicevic, M. S., Bjegovic, V., Terzic, Z., Vukovic, D., Kocev, N., Marinkovic, J., & Vasic, V. (2009). Serbia within the European context: An analysis of premature mortality. Population Health Metrics, 7(1), 12.

Mkandawire, T., & United Nations Research Institute for Social Development. (2005).  Targeting and universalism in poverty reduction. Geneva: United Nations Research Institute for Social Development.

182

References

Moreno-Serra, R., & Wagstaff, A. (2010). System-wide impacts of hospital payment reforms: evidence from Central and Eastern Europe and Central Asia. Journal of Health Economics, 29(4), 585-602

Mullahy, J. (1997). Instrumental-variable estimation of count data models: Applications to models of cigarette smoking behavior. Review of Economics and Statistics, 79(4), 586-593.

Murphy, A., Mahal, A., Richardson, E., & Moran, A. E. (2013). The economic burden of chronic disease care faced by households in Ukraine: a cross-sectional matching study of angina patients. Int J Equity Health, 12(1), 38.

Narodni front. Sta treba poneti u porodiliste. Narodni Front 2012, accessed on 12/8/2012 on : http://www.gakfront.org/.

Nyonator, F., & Kutzin, J. (1999). Health for some? The effects of user fees in the Volta Region of Ghana. Health policy and planning, 14(4), 329-341.

O’Donnell, O., van Doorslaer, E., Rannan-Eliya, R., et al. (2008). Who pays for health care in Asia? Journal of Health Economics, 27, 460-475.

O’Donnell, O., van Doorslaer, E., Rannan-Eliya, R., et al. (2005). Who pays for health care in Asia? EQUITAP, Working Paper # 1. Erasmus University, Rotterdam and IPS, Colombo.

Palairet, M. (2001). The Economic Consequences of Slobodan Milo w evi’.Europe-Asia Studies, 53(6), 903-919.

Palmer, N., Mueller, D. H., Gilson, L., Mills, A., & Haines, A. (2004). Health financing to promote access in low income settings—how much do we know?.The Lancet, 364(9442), 1365-1370.

Parmelee, D. E., Burns, T. R., Krleža-Jerić, K., Sekulić, D., Skupnjak, B., & Svalander, P. (1979). User Influence in Health Care: Some Observations on the Yugoslav Experience. Scandinavian Institutes for Administrative Research, Lund.

Philip, D., & Rayhan, M. I. (2004). Vulnerability and Poverty: What are the causes and how are they related?. ZEF Bonn, center for Development Research, University of Bonn.

Perić, T. (2006). At Risk: the social vulnerability of Roma, refugees and internally displaced persons in Serbia. United Nations Development Program Serbia.

Perović, L. (2013). Istorija Jugoslavije kao otvoren proces. Helsinška povelja, (179-180), 37-49.

Perkins, M., Brazier, E., Themmen, E., Bassane, B., Diallo, D., Mutunga, A.& Ngobola, O. (2009). Out-of-pocket costs for facility-based maternity care in three African countries.  Health policy and planning, 24(4), 289-300.

Poletti, T., Balabanova, D., Ghazaryan, O., Kocharyan, H., Hakobyan, M., Arakelyan, K., & Normand, C. (2007). The desirability and feasibility of scaling up community health insurance in low-income settings—Lessons from Armenia. Social Science & Medicine, 64(3), 509-520.

Public Information Law, “Official Gazete of the Republic of Serbia”, no. 1/2007; 52/2007; 99/2007;14/2008;20/2008;7/2009; 82/2009 & 23/2010.

Quayyum, Z., Nadjib, M., Ensor, T., & Sucahya, P. K. (2009). Expenditure on obstetric care and the protective effect of insurance on the poor: lessons from two Indonesian districts.  Health policy and planning, czp060.

Radulovic, S. (2013). Tekst ostavke. http://blog.b92.net/blog/308/Sasa-Radulovic/

Rasekaba, T. M., Lim, W. K., & Hutchinson, A. F. (2012). Effect of a chronic disease management service for patients with diabetes on hospitalisation and acute care costs. Australian Health Review, 36(2), 205-212.

Ravallion, M. (1998). Poverty lines in theory and practice. LSMS working paper no. 133. Washington, DC: World Bank.

Ritchie J, Spencer L. Qualitative data analysis for applied policy research by Jane Ritchie and Liz Spencer in A.Bryman and R. G. Burgess [eds.] “Analyzing qualitative data”,1994;173-194.

References

183

HIF (Health Insurance Fund) doi: http://www.eng.rfzo.rs/index.php/numberofinsurance, assessed on 2010.-

Russell, S. (2004). The economic burden of illness for households in developing countries: a review of studies focusing on malaria, tuberculosis, and human immunodeficiency virus/acquired immunodeficiency syndrome. American Journal of Tropical Medicine and Hygiene, 71(Suppl. 2), 147–155.

Roberts, M., Hsiao, C., Berman, P., & Reich, R. (2004). Getting health reform right: a guide to improving performance and equity. New York: Oxford University Press.

Ronsmans C. How can we monitor progress towards improved maternal health? London School of Hygiene and Tropical Medicine, London, 2001

Sachs, J. D. (2001). The strategic significance of global inequality. Washington Quarterly, 24(3), 185-198.

Saric, M., & Rodwin, V. G. (1993). The once and future health system in the former Yugoslavia: myths and realities. Journal of Public Health Policy, 220-237.

Sen, A. K. (1997). From income inequality to economic inequality. Southern Economic Journal, 384-401.

Sepehri, A., & Chernomas, R. (2001). Are user charges efficiency-and equity-enhancing? A critical review of economic literature with particular reference to experience from developing countries. Journal of International Development,13(2), 183-209.

Sepehri, A., Chernomas, R., & Akram-Lodhi, H. (2005). Penalizing patients and rewarding providers: user charges and health care utilization in Vietnam. Health Policy and Planning, 20(2), 90-99.

Shakarishvili, G. (2006). Poverty and equity in healthcare finance: Analyzing post-Soviet healthcare reform. Open Society Institute, Budapest.

Shishkin, S. V. (2003).  Informal out-of-pocket payments for health care in Russia. Moscow Public Science Foundation.

Shiffmana J, Skrabalo M, Subotic, J. Reproductive rights and the state in Serbia and Croatia. Social Science and Medicine 2002; 54:625-642

Slater, R., Farrington, J., & Holmes, R. (2008).  A conceptual framework for understanding the role of cash transfers in social protection. Overseas Development Institute.

Skarbinski, J., Walker, K., Baker, L.C., Kobaladze, A., Kirtava, Z., & Raffin T.A. (2002). The burden of out-of-pocket payments for health care in Tbilisi, Republic of Georgia. JAMA, 287, 1043–1049.

Sörensen, J. S. (2006). The shadow economy, war and state building: social transformation and re-stratification in an illiberal economy (Serbia and Kosovo).Journal of Contemporary European Studies, 14(3), 317-351.

Spiegel, P. B., & Salama, P. (2000). War and mortality in Kosovo, 1998–99: an epidemiological testimony. The Lancet, 355(9222), 2204-2209.

Srivastava A, Thomson S. Framework analysis: a qualitative methodology for applied policy research. Journal of Administration and Governance 2009; 4:2

Statistical Office of the Republic of Serbia. (2011). Online at: http://webrzs.stat.gov.rs/WebSite/

Stanic, J. (2002). Zdravstvo u vremenu tranzicije. Republika, 296.

Stambolovic V. .Porodjaj. Matica srpska 1996, Belgrade.

Stamenkovic B. (2011). Price iz bolnice: Nema leka ni za lek. B92. Online at : http://blog.b92.net/text/19273/Price-iz-bolnice%3A-Nema-leka-ni-za-lek/ (accessed on 12/09/2011).

Stošić, S., Karanović, N., Nedeljković, U., Dubljanin-Raspopović, E., Ilić, N., Dačković, J., ... & Đurić-Jovičić, M. D. (2014). Health care economics in Serbia: Current problems and changes. Vojnosanitetski pregled, (00), 2-2.

Stepurko, T., Pavlova, M., Gryga, I., & Groot, W. (2010). Empirical studies on informal patient payments for health care services: a systematic and critical review of research methods and instruments. BMC health services research,10(1), 273.

184

References

Stepurko, T., Pavlova, M., Levenets, O., Gryga, I., & Groot, W. (2013). Informal patient payments in maternity hospitals in Kiev, Ukraine. The International journal of health planning and management, 28(2), e169-e187.

Stojanović, D. (2006). U senci” velikog narativa”: Stanje zdravlja žena i dece u Srbiji početkom XX veka, Žene i deca 4. U Srbija u modernizacijskim procesima XIX i XX veka, Beograd: Helsinški odbor za ljudska prava u Srbiji.

Stojanović, D. (2010). U ogledalu ‘drugih’. Novosti iz prošlosti. Znanje, neznanje, upotreba i zloupotreba istorije, Beograd: Beogradski centar za ljudska prava.

Supic ZT, Bjegovic V, Marinkovic J, Milicevic MS, Vasic V. Hospital management training and improvement in managerial skills: Serbian experience. Health Policy.2010; 96(1):80-89.

Szende, A., & Culyer, A. (2006). The inequity of informal payments for health care: the case of Hungary. Health Policy, 75, 262–271.

Tagoe, H. (2012). Household burden of chronic diseases in Ghana. Ghana medical journal, 46(2 Suppl), 54.

Tanner, M. (2008). The grass is not always greener: a look at national health care systems around the world. Cato Policy Analysis Paper, (613).

Tanzi, V. (2002). Globalization and the future of social protection. Scottish Journal of Political Economy, 49(1), 116-127.

Tambor, M., Pavlova, M., Woch, P., & Groot, W. (2011). Diversity and dynamics of patient cost-sharing for physicians’ and hospital services in the 27 European Union countries. The European Journal of Public Health, 21(5), 585-590.

Tambor, M., Pavlova, M., Golinowska, S., Sowada, C., & Groot, W. (2012). Towards a stakeholders’ consensus on patient payment policy: the views of health-care consumers, providers, insurers and policy makers in six Central and Eastern European countries. Health Expectations.

Tambor, M., Pavlova, M., Rechel, B., Golinowska, S., Sowada, C., & Groot, W. (2014). The inability to pay for health services in Central and Eastern Europe: evidence from six countries. The European Journal of Public Health, 24(3), 378-385.

Tatar, M., Özgen, H., Sahin, B., Belli, P., & Berman, P. (2007). Informal payments in the health sector: a case study from Turkey. Health Affairs, 26(4), 1029-1039.

Tepavcevic, D. K., Matejic, B., Gazibara, T., & Pekmezovic, T. (2011). Trends and patterns of ovarian cancer mortality in belgrade, serbia: a joinpoint regression analysis. International Journal of Gynecological Cancer, 21(6), 1018-1023

Tosevski, D. L., Milovancevic, M. P., & Deusic, S. P. (2007). Reform of mental health care in Serbia: ten steps plus one. World Psychiatry, 6(2), 115.

Tomini, S., Groot, W., & Pavlova, M. (2012). Informal payments and intra-household allocation of resources for health care in Albania. BMC Health Care Research, 12, 17. doi:10.1186/1472-6963-12-17

Thompson, R., & Witter, S. (2000). Informal payments in transitional economies: implications for health sector reform. The International journal of health planning and management, 15(3), 169-187.

TNS Media Gallup. (2010). Ispitivanje javnog mnjenja o korupciji u Srbiji: analiticki izvestaj. Beograd.

Tunstall-Pedoe, H. (2006). Preventing Chronic Diseases. A Vital Investment: WHO Global Report. Geneva: World Health Organization, 2005. pp 200. CHF 30.00. ISBN 92 4 1563001. Also published on http://www.who.int/chp/chronic_disease_report/en. International Journal of Epidemiology, 35(4), 1107-1107.

van Adams, A; Hartnett, T. (1996). Cost sharing in the social sectors of sub-Saharan Africa : impact on the poor. World Bank Discussion papers ; no. WDP 338. Africa Technical Department series. Washington, D.C. The World Bank. 

van Doorslaer, E., O’Donnell,O., Rannan-Eliya, R.P., et al. (2006). Effect of payments for healthcare on poverty estimates in 11 countries in Asia: an analysis of household survey data. Lancet, 368, 1357-1364.

References

185

van Doorslaer, E., O’Donnell,O., Rannan-Eliya, R., Somanathan, A., Adhikar,SR., Akkazieva, B., Harbianto,D., et al. (2007). Catastrophic payments for healthcare in Asia. Health Economics, 16(11), 1159–1184.

van Duyne, P. C., Stocco, E., Bajovic, V., Milenovic, M., & Lojpur, E. E. (2010). Searching for corruption in Serbia. Journal of Financial Crime, 17(1), 22-46.

Vlajković, J. ur.(1997) Psihologija izbeglištva. Beograd: Nauka.

Vian, T., Grybosk, K., Sinoimeri, Z., & Hall, R. (2006) Informal payments in government health facilities in Albania: results of qualitative study. Social Science & Medicine, 62, 877–87.

Vuković, D., Bjegović, V., & Vuković, G. (2008). Prevalence of chronic diseases according to socioeconomic status measured by wealth index: health survey in Serbia. Croatian medical journal, 49(6), 832.

Vuković, D., & Perišić, N. (2011). Annual National Report 2011.

Vukomanović, M. (1972). Radnička klasa Srbije u drugoj polovini XIX veka (Vol. 4). Rad.

Wagstaff, A. (2002). Poverty and health sector inequalities. Bulletin of the world health organization, 80(2), 97-105.

Wagstaff, A. & Pradhan, M. (2005). Health insurance impacts on health and nonmedical consumption in a developing country. Policy Research Working Paper Series 3563. The World Bank.

Wagstaff, A., & Van Doorslaer, E. (1992). Equity in the finance of health care: some international comparisons. Journal of health economics, 11(4), 361-387.

Wagstaff, A., & van Doorslaer, E. (2003). Catastrophe and impoverishment in paying for health care: with applications to Vietnam 1993-98. Health Economics, 12, 921-934.

Wagstaff, A. (2008). Measuring financial protection in health. Policy Research Working Paper Series 4554. The World Bank.

Ward, W. D. (1973). Proceedings of the International Congress on Noise as a Public Health Problem (Dubrovnik, Yugoslavia, May 13-18, 1973).

Witter, S., Arhinful, D. K., Kusi, A., & Zakariah-Akoto, S. (2007). The experience of Ghana in implementing a user fee exemption policy to provide free delivery care. Reproductive health matters, 15(30), 61-71.

Witter, S., Dieng, T., Mbengue, D., Moreira, I., & De Brouwere, V. (2010). The national free delivery and caesarean policy in Senegal: evaluating process and outcomes. Health Policy and Planning, czq013.

WHO. (2012). NHA. Retrived from : http://apps.who.int/nha/database/DataExplorerRegime.aspx

Wooldridge, J. (2012). Introductory econometrics: A modern approach: Cengage Learning.

World Bank. (2005). Serbia and Montenegro - Social Sector Adjustment Credit Project. Washington, DC:

World Bank. (2008). World development indicators 2008. Washington, DC: International Bank for Reconstruction and Development / The World Bank.

World Bank. (2009). Serbia: Doing more with a less. Addressing the fiscal crisis by increasing public sector productivity. Report No. 48620-YF.

World Bank. (2011). Technical report for Serbia LSMS 2007. Online at: http://go.worldbank.org/8XI2AXPP00 (permanent page).

UN. The Millennium Development Goals Report. United Nations 2012, New York.

UNDP. At risk: the Social Vulnerability of Roma, refugees and internally Displaced Persons in Serbia. 2006. Belgrade,Serbia

UNFPA. A Review of progress in maternal health in Eastern Europe and Central Asia. United Nations Population Fund 2009, New York.

UNIRSD. Combiting poverty and inequality. 2010. Geneva.

Xu, K., Evans, D., Kawabata,K., Zeramdini, R., Klavus,J., & Muray, CHL. (2003). Household catastrophic health expenditure: A multicountry analysis. Lancet, 362, 111– 117.

186

References

Xu K, Evans, D.B., Kadama, P., Nabyonga, J., Ogwal, P.O., et al. (2006). Understanding the impact of eliminating user fees: Utilization and catastrophic health expenditures in Uganda. Social Science & Medicine, 62(4), 866-876.

Xu, K., Evans, D.B., Carrin, G., Aguilar-Rivera, AM ., Musgrove, P., & Evans, T. (2007). Protecting households from catastrophic health spending, Health Affairs, 26, 972–983.

Xu, K., Saksena, P., Jowett, M., Indikadahena, C., Kutzin, J., & Evans, D. (2010). Exploring the thresholds of health expenditure for protection against - financial risk. World Health Report, Background Paper, 19.

Yardim, M.S., Cilingiroglu, N., & Yardim, N. (2010). Catastrophic health expenditure and impoverishment in Turkey. Health Policy, 94, 26-33.

Appendix

188

Appendix

Table A.1: Ordinary least square regression (OLS) for three outcome variables and possible instruments (N=5557) (Chapter 5)

Diagnosed diabetes within the households

Diagnosed cardio-vascular disease within the

households

Diagnosed progressive disease in abdomen

within the household

B SE B SE B SE

Share of fat food in total food consumption

0.0004 0.0005 0.001 0.0007 0.0001 0.0005

Share of sweet food in total food consumption

-0.0014* 0.0002 -0.001* 0.0004 -0.0002 0.0003

Number of cigarettes consumed per household

0.000 0.000 0.001** 0.000 0.001* 0.000

Share of alcohol in food consumption

-0.013* 0.0003 -0.002* 0.0005 -0.015* 0.0004

Municipalities affected by uranium–rich bombs

-0.011 0.008 0.024* 0.013 -0.012 0.009

Presence of refuges in household

0.0437* 0.201 -0.014 0.033 -0.168 0.0250

R-squared ( R² ) 0.0095 0.0094 0.0041

*p< 0.05;**p 0.1

Table A.2: Correlation coefficient (Spearman’s rho) between potential instruments and outcome variables (N=5557) (Chapter 5)

Catastrophic effects of health care expenditure

Pre-payment poor (based on consumption)

Share of fat food in total food consumption 0.021 -0.034**

Share of sweet food in total food consumption -0.26 0.017

Number of cigarettes consumed per household -0.003 -0.71*

Share of alcohol in food consumption 0.002 -0.11

Municipalities affected by uranium –rich bombs 0.012 -0.33*

Presence of refuges in household -0.015 0.012

*p< 0.05; **p 0.1

Appendix

189

Table A3: Second stage OLS regression (Chapter 5)

Pre-payment poverty

B SE B SE B SE

Diagnosed progressive disease in abdomen within the household

-0.007* 0.009

Diagnosed cardio-vascular disease within the households

-0.023* 0.007

Diagnosed diabetes within the households

-0.024* 0.011

Household size 0.084* 0.003 0.009* 0.003 0.008** 0.003

Type of settlement -0.013** 0.007 -0.012** 0.007 -0.014** 0.008

Gender of head of the household 0.014** 0.008 v0.014** 0.008 0.183 0.069

Nationality of the head of the household

-0.017** 0.009 -0.017** 0.009 -0.017** 0.009

Age of the head of the household 0.002* 0.0002 0.002* 0.001 0.002* 0.001

Number of kids younger than 7 years within the household

0.023* 0.008 0.021* 0.008 0.022* 0.008

Number of kids older than 7 and younger than 18 years

0.019* 0.006 0.017* 0.006 0.018* 0.005

Income percentiles -0.036* 0.003 -0.040* 0.028 -0.037* 0.003

Education of head of the household -0.012 0.002 -0.012* 0.002 -0.012* 0.002

Constant 0.097 0.22 0.094* 0.022 0.095* 0.022

Catastrophic effects of health care costs

B SE B SE B SE

Diagnosed progressive disease in abdomen within the household

0.032* 0.005

Diagnosed cardio-vascular disease within the households

0.022* 0.004

Diagnosed diabetes within the households

0.032 0.006

Household the size 0.010* 0.002 0.010* 0.001 0.010* 0.002

Type of settlement 0.001 0.004 0.006 0.004 0.0054 0.0045

Gender of head of the household 0.001 0.006 0.0003 0.005 -0.0001 0.004

Nationality of the head of the household

0.001 0.000 0.001 0.005 0.0014 0.006

Age of the head of the household 0.001* 0.000 0.0004* 0.0002 0.001* 0.000

Number of kids younger than 7 years within the household

-0.009* 0.005 -0.008** 0.005 -0.009** 0.005

Number of kids older than 7 and younger than 18 years

0.004 0.003 -0.003 0.042 0.004 0.003

Income percentiles -0.004 0.001 -0.004* 0.002 -0.004* 0.002

Education of head of the household

0.001 0.0001 -0.005 0.001 0.0004 0.0011

Constant -0.039* 0.135 -0.036* 0.013 -0.036* 0.013

Summary

192

Summary

Out-of-pocket patient payments and vulnerable population groups in Serbia

Summary Since 2002, the public health care system in Serbia is funded by compulsory health care

insurance contributions, government spending and official co-payments. Similar to other

CEE countries, the official co-payments are accompanied by informal patient payments

and payments for “bought & brought goods” (i.e. payments for goods that should be

provided for free). The co-existence of the three types of out-of-pocket patient payments

can provoke a significant financial burden. To enhance social protection, following the

introduction of official co-payments, the Serbian government introduced exemption

mechanisms. The existence of compulsory health insurance represents another measure of

social protection. Moreover, the Serbian government spent on average 6.7% of GDP in

the period 2001-2007 on direct financing of public health care. This level of government

expenditure is in accordance with EU standards and provides an additional way of social

protection.

Despite the existence of social protection measures, the financial burden produced by

the three types of out-of-pocket patient payments is still largely unknown. The scientific

evidence on this topic is limited. Moreover, the social protection measures address only

the official co-payments, while the informal patient payments and payments for “bought

& brought goods” are unregistered by those measures.

The aim of this dissertation is to examine the financial burden provoked by out-of-

pocket patient payments on vulnerable population groups in Serbia. The dissertation

consists of seven chapters. Here we present the main findings from each chapter.

Chapter 1 presents a broad overview of the development of the public health care

system in Serbia, as well as its current status. The chapter also outlines the concept of

social protection within the health care system in Serbia. We focus mainly on financial

aspects of social protection. We provide this information as background information to

facilitate the understanding of the results from the dissertation. In this chapter, we also

outline the main goal, objectives and methodology that is used in the dissertation. The

main goal of the dissertation is to examine the effects of out-of-pocket patient payments

on vulnerable population groups in Serbia. Following the main goal, we first examine

the effects of out-of-pocket patient payments on the household budget using different

approaches outlined in the literature (Chapter 2). Furthermore, we examine the financial

burden of different types of out-of-pocket patient payments (Chapter 3). We further

examine whether the exemption mechanism effectively protects vulnerable groups

(Chapter 4). In Chapter 5 and 6, we examine to what extent two vulnerable population

groups (namely chronically sick and pregnant women) are protected by the current

social policy. In Chapter 5, we examine the effects of out-of-pocket patient payments on

Summary

193

households with chronically sick members, specifically household members diagnosed

with one of the three leading chronic disease: diabetes mellitus, cardiovascular diseases

and cancer. Chapter 6 addresses financial protection in maternity wards in Serbia, but also

some other indicators like the accessibility of maternity care, non-medical quality of care

and policy regulations. Chapter 7 presents the main findings of the dissertation followed

by suggestions for future research and policy recommendations.

In this dissertation we use two research approaches. In Chapters 2 to 5, we use

quantitative data analyses based on representative data collected by the World Bank

in 2002, 2003 and 2007. In Chapter 6, we apply a mixed–method design combining

qualitative and quantitative data analyses. Data for the analyses in Chapter 6 are

obtained from three different sources, namely literature review, on-line semi-structured

questionnaires and official guidelines.

In Chapter 2, we examine the effects of out-of-pocket patient payments on the

household’s budget. The literature does not provide consensus how to measure the

financial burden provoked by out-of-pocket patient payments. Therefore, we describe

three different approaches that have been used in previous studies to assess the financial

burden provoked by out-of-pocket patient payments, namely catastrophic health care

expenditure, impoverishing effects and subjective poverty. The objective is to compare

the results across different approaches. Within each approach, different indicators

of household wealth (income, expenditure and consumption) and different poverty

thresholds based on these indicators are applied. Catastrophic health care expenditure

defines out-of-pocket patient spending as catastrophic if it exceeds a certain threshold in

a given period. The threshold represents a pre-defined proportion of household income

or consumption. The threshold is arbitrary and can vary from 5 up to 40% of total

income/consumption. The impoverishing effect of health care spending is measured by

the proportion of households that goes below the poverty line after health care spending

is subtracted from total income or consumption. It is based on a comparison between

the incidence of poverty before and after the subtraction of health care spending by the

household. For the calculation of the impoverishing effects of out-of-pocket payments,

absolute and relative poverty lines are used as thresholds. The two approaches –

catastrophic health care expenditure and impoverishing effects – identify the share of

households/individuals who experience an economic burden due to out-of-pocket patient

payments. Subjective poverty captures the personal perception of being poor. It is usually

measured as the individual evaluation of being poor. Our results indicate that irrespective

of the approach applied, out-of-pocket patient payments have a catastrophic effect on

poor households in Serbia. Moreover, households that are above the absolute, relative and

subjective poverty lines, after the subtraction of out-of-pocket payments, fall below these

poverty lines. The probability of catastrophic out-of-pocket patient payments is higher

among chronically sick household members (namely, people with diabetes and mental

194

Summary

diseases, as well as cardiology diseases in some instances). Perceived health status also

appears to be a significant indicator.

In Chapter 3, we examine the level of different types of out-of-pocket patient

payments in Serbia as well as their effects on household budgets. First we outline the

distinction between the three types of out-of-pocket patient payments in Serbia. We

make a distinction not only between formal and informal patient payments (cash and gifts

in kind given to the physician), but we also analyze the effects of payments for “bought &

brought goods” (i.e. payments for goods brought by the patient to the health care facility

such as disposable materials and pharmaceuticals). The previous literature has considered

payments for “bought & brought goods” as part of informal patient payments. We find

it essential to make a distinction between these two types of patient payments because

they differ in nature. While informal patient payments (such as gifts to the physicians)

remain unregistered, payments for “bought & brought goods” (e.g. for pharmaceuticals

bought in a pharmacy and brought to the hospital) are officially registered at the point

of purchase but not visible in the financial flows of the institution that provides the

services. Thus, in Chapter 3, we estimate the burden of different types of out-of-pocket

patient payments (official co-payments, informal payments and payments for “bought

& brought goods”) using two approaches: catastrophic health care expenditure and the

impoverishing effects of out-of-pocket payments. To assess the catastrophic effects of

different types of payments, we use a threshold of 10%. This means that health care

expenditure has catastrophic effects for individuals/households if it exceeds 10% of their

total consumption. For the assessment of the impoverishing effects, we use the absolute

poverty line as a threshold. Within both approaches - catastrophic health care expenditure

and impoverishing effects - we use consumption as an indicator of wealth. Our results

show that all three types of out-of-pocket patient payments may push Serbian households

into poverty. Thus, out-of-pocket patient payments in Serbia impose a substantial burden

on households. The catastrophic effects of “bought & brought goods” payments are higher

than those of pure informal payments, and comparable to those of official copayments.

In Chapter 4, we focus on the exemption mechanism that accompanies official co-

payments in Serbia. We examine whether selected exempted groups (older than 65 years,

younger than 15 years, disabled, unemployed and people with low household income) pay

official co-payments when they are supposed to be exempted from such fees. We compare

the effects of the exemption mechanism when co-payments were implemented in Serbia,

and 1 and 5 years after their implementation. We compare data on the probability of

paying and the amount of out-of-pocket payments for outpatient and inpatient hospital

services across the five exempted groups that we identified. We also compare the out-of-

pocket payments for outpatient and inpatient hospital services paid by the five exempted

groups with that of other population groups (non-exempted and exempted group that

we could not identify). Our results confirm that the selected exempted groups included

Summary

195

in our study, pay for both outpatient services as well as for inpatient care. However,

payments are overall less frequently reported for outpatient services than for inpatient

services. This difference between the services is expected since the official copayments for

inpatient health care in Serbia are much higher than those for outpatient care. Regarding

inpatient hospital care, the five exempted groups reported formal, “bought & brought

goods”, informal and indirect payments. Our results show that the implementation of

the exemption mechanism, both in outpatient and inpatient hospital care, is failing. The

failures are also visible in terms of the design of the exemption mechanisms.

Chapter 5 of this dissertation examines the relation between the presence of chronic

diseases and poverty. Chronic diseases are a major cause of financial hardship for patients

and their households. Diagnosed chronic diseases usually require a higher utilization of

health care services. When the health care system heavily relies on out-of-pocket patient

payments, increased utilization also increases direct spending by households. In this way,

chronic diseases become a trigger for financial hardship. Moreover, chronic diseases are

more likely to occur among poor individuals, and at the same time, patients with chronic

diseases have a higher probability of becoming poor. This implies a double-sided relation

between the chronic diseases and poverty. The existence of a joint causality can lead to

biased estimates of the poverty effects provoked by chronic diseases. In Chapter 5, we

examine the joint causality between the chronic diseases and poverty using an instrumental

variable approach. As outcome variables, we use indicators of pre-payment poverty and

the catastrophic effects of out-of-pocket patient payments for different types of chronic

diseases. Instrumented variables are indicators of chronic diseases: cardiovascular diseases,

diabetes mellitus and cancer within the household. We use two groups of instruments: The

first group includes indicators of health-related lifestyle behavior (e.g. smoking behavior

and eating habits). The second group of instrumental variables consists of environmental

variables like living in an area affected by uranium during the NATO bombing and

being a refugee during the period 1999-2007. Our results show that all three chronic

diseases can impose an economic burden on households when other relevant factors are

controlled for. However, diabetes mellitus and cardiovascular diseases are significant

predictors of catastrophic health care expenditure, while cancer is not. This result can be

explained by the design of the exemption mechanism in Serbia. Patients diagnosed with

diabetes mellitus and cardiovascular diseases are only partially exempted, while patients

diagnosed with progressive diseases are fully exempted from official co-payments. Our

results also show that different risk factors like life-style behavior and environmental

factors are associated with different chronic diseases. Diabetes mellitus is significantly

associated with having a refugee in the household, while living in municipalities affected

by uranium NATO bombs is significantly associated with cardiovascular diseases.

In Chapter 6 of this dissertation, we provide evidence on the out-of-pocket payments

for maternity care. In Serbia, health care services related to maternity care are officially

196

Summary

free of charge. The social protection policy related to future mothers also includes free

maternity leave up to one year and free public transport. In Chapter 6 of this dissertation,

we examine whether the health care services related to maternity care are provided for

free as defined by the Serbian regulation. Since the modern concept of social protection

does not only include financial protection, we also examine accessibility to maternity

wards in Serbia (geographical, psychological and social accessibility), clinical quality

(obstetric care/procedures, quality of equipment and level of physician skills) and social

quality of care (e.g., facility maintenance), patient payments (type of payment, receiver

of payment, frequency of payment, magnitude of payment, purpose of payment and

attitude toward payments ),policy regulations (regulations with respect to payments,

equity and governmental/hospital protocols), innovations in maternity care (the presence

of the partner during childbirth and innovations about immediate breastfeeding and

rooming-in), and health indicators (health system indicators such as maternal and infant

mortality, cesarean rate, presence of a skilled care practitioner ). For this purpose, we apply

a mixed-method. We use data collected through three sources: online questionnaires

filled in by mothers who delivered in one of the maternity wards in Serbia in the period

2000–2008, research publications, and official guidelines. By combining the data from

different sources we use the method of triangulation. To compare the qualitative data

from all three sources, we apply framework analysis. The results show a good network of

maternity wards in Serbia. On the other hand, many women who gave birth in maternity

wards in Serbia indicate problems with the treatment they received. The existence of

informal patient payments and so-called “special connections” make the position of

Serbian women in maternity wards vulnerable, especially when they have neither

connections nor the ability to pay. Poor communication and lack of sympathetic bedside

manners of medical staff (obstetricians, other physicians, midwives, and nurses) during

the birth process are also frequently reported.

Chapter 7 provides the discussion of the main results, policy implications and

implication for future research. Overall, the results reported in this dissertation show

that out-of-pocket patient payments provoke a financial burden in Serbia. The extent of

the financial burden and the affected population groups are conditioned by the approach

that is used. When we apply impoverishing effects to assess the burden, poor population

groups are mostly affected. When we use the catastrophic health care expenditure

approach, the burden is higher for middle-income groups. Our results also show that

not only the chosen approach, but also indicators of wealth and cut-off points play a role.

Different types of out-of-pocket patient payments also influence the extent of financial

burden. Payments for “bought & brought goods” have most intensive catastrophic

effects. Moreover, catastrophic effects provoked by payments for “bought & brought

goods” are experienced by some vulnerable population groups like low income groups

and population groups diagnosed with chronic diseases. Informal patient payments are

Summary

197

more prevalent among wealthier population groups. The shadow nature of payments for

“bought & brought goods” and their high catastrophic effects require additional attention

of policy makers in Serbia. Although the exemption mechanisms in Serbia include a large

number of population groups, the impoverishing effects of official co-payments are still

high. One of the reasons is that some vulnerable groups like people diagnosed with

diabetes mellitus are only partially exempted. Our results show that the failure of the

exemption mechanism is not only related to the design, but also to its implementation.

Even when the exemption mechanism is fully applied, like in case of maternity care,

vulnerable population groups still report informal patient payments. Those findings

suggest that financial protection is still not achieved. The results from this dissertation

suggest that social protection policy in Serbia related to health care should focus on

population groups that are frequent health care users (chronically sick) and those from

low income groups. Moreover, as our results related to maternity care suggests, social

protection should go beyond financial protection and include also some aspect of social

inclusion like the respect of patients’ rights and transparent communication.

198

Acknowledgements

Acknowledgements

199

Sta sve placaju pacijenti u okviru sistema javnog zdravlja Republike Srbije-osvrt na vulnerabilne grupe

Od 2002 , sistem javnog zdravlja Republike Srbiji se finansira kroz budzetsko davanje

(sredstva koja obezbedjuje vlada Republike Srbije kroz porez gradjana), putem obaveznog

zdravstvenog osiguranja i kroz participaciju pacijenata-korisnika usluga. Slicno kao

i u drugim zemljama Centralne i Istocne Evrope, sistem javnog zdravlja u Srbiji nije

ostao imum na tzv. placanja ispod ruke (mito ili korupcija“, u stranoj literaturi poznato

kao:„informal patient payments“). Pored klasicnog davanja mita u vidu poklona ili

novca zdravstvenom osoblju, u sistemu javnog zdravlja Republike Srbije postoji i tzv.

dodatno davanje u vidu lekova, materijala ili hrane (payments for “bought and brought

goods”). Radi se o sredstvima koja bi trebalo da budu obezbedjena osiguranim licima

u okviru sistema javne zdravstvene zastite ali ih zdravstvene ustanove nemaju ili imaju

u nedovoljnoj i neadekvatnoj kolicini pa porodica pacijenata mora da ih kupi i donese.

Primeri takvog placanja su: donosenje lekova koji su neophodni za lecenje a bolnica ih

nema, donosnje hrane, spavacice itd. Postojanje tri razlicite vrste placanja (zvanicna

participacija, mito /korupcija i davanja u vidu dodatnih sredstava) u okviru sistema javnog

zdravlja Republike Srbije, stavlja korisnike zdravstvenih usluga u nezavidan finansijski

polozaj. Korisnici zdravstenih usuga su na taj nacin izlozeni povecanom finansijskom

riziku. U zelji da zastiti vulnerabilne socijalne grupe, vlada Republike Srbije je donela

zakon po kome su ove grupe izuzete od placanja zvanicne participacije. To je samo jedna

mera socijalne zastite koju je vlada Republike Srbije sprovela u okviru sistema javnog

zdravlja. Samo postojanje zdravstvenog osiguranja takodje predstavlja meru socijalne

zastite. Davanja republicke vlade za zdravstvo koja su tokom perioda 2001-2007 iznosila

u proseku 6.7% republickog BDP, takodje predstavljaju meru socijalne zastite i u skladu

sa standardim EU. Bezobzira na postojanje mera socijalne zastite, korisnici zdravstvenih

usluga u Srbiji se suocavaju sa finansijskim teskocama. Finansijske teskoce su posebno

uocljive medju tzv. vulnerabilnim grupama i prouzrokvane su direktnim i neocekivanim

placanjem iz dzepa.

Glavni cilj ove disertacije je da ispita obim i intezitet finansijskih teskoca prouzrokavnih

placanjem iz dzepa a sa kojima se susrecu vulnerabilne grupe.

Disertacija je organizovana kroz sedam poglavlja. Prvo poglavlje predstavlja uvod u

disertaciju. Ovo poglavlje opisuje danasnji zdravstveni sistem u Srbiji i nacine njegovog

finansiranja. Takodje ovo poglavlje oslikava i aktuelne probleme vezane za finansiranje

zdravstvenog sistema u Srbiji. Poglavlje se takodje odnosi i na probleeme sa kojima se

200

Acknowledgements

suocavaju pacijenti prilikom placanja zdravstvenih usulga. Iako su reforme u javnom

zdravstvu u Srbiji, zapocele jos 2002, do danas nema empirijskih studija koje su se bavile

ovim problemima. Jedan od najupecatljivijih primera sigurno je, postojanje placanja

putem mita. Iako se anegdotski izvori poput dnevnih novina ili televizije redovno

bave ovim problemom, do danas nema empirijskih istrazivanja koja govore o obimu

i intezitetu placanja putem mita u srpskom zdravstvu. Takodje nema ni podataka, ni

koliki je obim i intezitet finansijskog tereta sa kojim se suocavaju korisnici zdravstvenih

usluga a koji moze biti izazvan upravo postojanjem razlicitih vrsta placanja. Cilj ove

disertacije je da odgovori na ova pitanja koristeci empirijske metode istrazivanja. Za

potrebe ove disertacije korisceni su podaci prikljupeni u studiji o zivotnom standardu koja

je sprovedena u periodu 2002-2007 od strane Svetske Banke.Podaci su reprezentativni za

teritoriju Republike Srbije, bez Kosova i Metohije.

Drugo poglavlje ove teze prezentuje ustaljene empirijske metode kojima se ispituje

obim finansijskog tereta prouzrokovanog placanjem zdravstvenih troskova. Koristeci dve

ustaljene metode i podatke o zivotnom standardu, u drugom poglavlju prezentujemo

rezultate koji pokazuju u kom obimu se pojedinci u Srbiji suocavaju sa osiromasenjem ili

katastrofalnim troskovima usled placanja zdravstvenih usluga. Nasi rezultati pokazuju

da procena osiromasenja ili katastrofalnih troskova u mnogome zavisi ne samo od metode

koju koristimo, vec i od izabranih indikatora nivoa zivotnog standarda. Tako, npr. u Srbiji

obim osiromasenja prouzrokovan troskovima za zdravstvene usluge zavisi i od toga da li se

u proceni kao indikator nivoa zivotnog standarda koriste ukupna primanja domacinstva

na mesecnom nivou ili njihova mesecna potrosnja. Poput drugih zemalja sa sivom

ekonomijom i u Srbiji su mesecna primanja domacinstva manja od mesecne potrosnje,

sto govori u prilog postojanja neprijavljenih primanja ili posedovanje dodatnih resursa za

ostvarenje prihoda. Nasi rezultati pokazuju da osobe obolele od hronicnih bolesti, osobe

koje zive u ruralnim podrucjima i oni koji zive sami imaju vecu verovatnocu da se suoce

sa osiromasenjem usled postojanja zdravstvenih troskova.

Trece poglavlje teze ispituje u kojoj meri razlicite vrste placanja-zvanicna participacija

i placanje ispod ruke (mito,korupcija) doprinose osiromasenju gradjana Srbije. Nasi

resultati pokazuju da su efekti placanja u vidu davanja hrane lekova i odstalih sredstva

pogubniji za budzet korisnika zdravstvenih usluga nego placanje ispod ruke. Zvanicna

participacija takodje doprinosi osiromasenju gradjana, posebno tzv.vulnerabilnih grupa

kao sto su stari, deca, oboleli od hronicnih bolesti itd.

Cetvrto poglavlje teze bavi se upravo ovim vulnerabilnim grupama koje su zakonima

Republike Srbije oslobodjene od placanja zvanicne participacije. Iako zakon kao

vulnerabilne grupe navodi 16 kategorija, ukljucujuci i svestenike, u ovom poglavlju

mi identifikujemo cetiri grupe: osobe mladje od 15 godina starosti, osobe starije od 65

godina, osobe sa primanjima ispod republickog proseka i osobe onesposobljene za rad.

Nasi rezultati pokazuju da iako zvanicno oslobodjeni participacije, ove grupe placaju

zdravstvene usluge. Takodje nasi rezultati pokazuju da trenutna polisa izuzimanja od

Acknowledgements

201

participacije ne samo da se ne primenjuje adekvatno, vec nije ni adekvatno dizajnirana.

Tako npr. Nisu sve osobe starije od 65 godina finansijski vulnerabilne. Buduca polisa bi

trebalo da obezbedi bolju identifikaciju osoba koje su zaista finansijski vulnerabilne.

Peto poglavlje ove teze se bavi jos jednom vulnerabilnom grupom-osobama kod

kojih je dijagnostifikovana jedna od tri hronicne bolesti-diabtes mellitus, neki oblik

maligne bolesti ili neko od oboljenja iz grupe kardio-vaskularne bolesti. U ovom

poglavlju ispitujemo efekte placanja iz dzepa na ove subpopulacijske grupe u Srbiji i

na budzete njihovih porodica. Nasi rezultati pokazuju da osobe obolele od dijabetsa ili

nekog od oboljenja iz kardio-vaskularne grupe imaju vecu verovatnocu da budu izlozeni

osiromasenju usled placanja iz dzepa, dok su osobe sa dijagnozom nekog iz grupe od

malignih oboljenja manje izlozene tom riziku. Jedan od razloga je i trenutna organizacija

zdravstvenog sistema u Srbiji-vecina dijagnostickih i terapijskih procedura vezana za

maligna oboljenja se obavlja u tzv.“drzavnim“ustanovama, dok se procedure evzane za

druge dve grupe hronicnih oboljenja vrlo cesto moraju obaviti u nekoj od privatnih

ustanova usled dugackih lista cekanja.

Sest poglavlje ove teze se bavi placanjima i kvalitetom pruzene usluge u porodilistima

u Srbiji. Trudnice se u Srbiji takodje oslobodjene placanja zvanicne participacije i imaju

status vulnerabilne grupe. Cilj ove mere je da se poboljsa stopa radjanja u Srbiji koja je

u stalnom padu od 1991. Nasi rezultati pokazuju da trudnice kao i druge vulnerabilne

grupe placaju tzv. „pre-natalnu“negu a i sam porodjaj. Vrste placanja sa kojima se one

susrecu su razlicita: od mita lekaru do donosenja lekova i spavacica. Takodje nasi rezultati

pokazuju da davanje mita lekaru u porodilistima nije garant kvalitetne usluge. Rezultati

takodje svedoce o postojanju jos jednog nematerijalnog oblika placanja-a to su „veze“.

Trudnice koje su bile zadovoljne kvalitetom pruzene usluge su uglavnom poznavale lekara

ili su do njega dosle preporukom. U takvom slucaju one nisu davale mito u vidu novca,

ali ostaje nejasno da li se ovakva usluga uzvraca nekom drugom uslugom. Istrazivanje

u porodilistima je pokazalo i da finansijska zastita nije jedini vid socijalne zastite koji

nedostaje zdravstvenom sistemu Srbije. Naime vecina trudnica navodi los odnos prema

njima, omalovazavanje i negiranje njihovih potreba kao glavni problem tokom boravka u

porodilistu.

Sedmo poglavlje ove teze prezentuje zakljucke koji su proizisli iz svih prethodnih

analiza.Takodje ovo poglavlje daje i predloge za bolje regulative u sistemu javnog

zdravlja Republike Srbije. Iako Ministarstvo zdravlja Republike Srbije sa ponosom istice

poboljsanja koja su nasrupila od 2002 i pcetka reformi u zdravstvu kao su npr. bolja

dostupnost lekova, ili bolja tehnicka opremljenost bolnica, rezultati naseg istrazivanja

pokazuju i neka pogorsanja-poput veceg obima placanja korisnika zdravstvenih usuga i

nedovoljnog obima zastite tzv. vulnerabilnih grupa. Obzirom da je cilj vlade Republike

Srbije pridruzivanje clanstvu Evropske Unije, pitanja mita i korupcije u zdravstvu ce

predstavljati zasigurno jedan od najvecih izazova u narednom periodu.

Acknowledgements

203

Thank you words

At the end of my PhD, I also felt the personal need to say thank you to some people who

meant a lot to me during the last few years.

To start this PhD would not have been possible without my co-promoter, Milena.

Milena, thank you for believing in me when no one did (including myself). Besides

giving me the chance to pursue my PhD research, you also provided a nice empathic

work-environment. It was nice to come to work and to know that there is always someone

with whom you can share all your doubts and who always has a time for one more coffee!

Thank you for long night readings of all the terrible first drafts. Thank you for sharing

your knowledge with me even when I was resistant to hear you. I will always remember

our discussions about “utility” and “altruism”. You thought me how patience and

consistency are important and that they do not necessary “kill” the creativity. Somehow

you always were there when I needed you, always listening and understanding. For all

these years, thank you for being a great friend and tough criticizer!

To my promoter, Wim - I always admired you capability to find the most rational

solution for most irrational problems. It was a pleasure to work with someone who is not

only a great researcher but also a true “eruditus” with whom I could always discuss literally

everything. Thank you for always being willing to read “just one more draft”, usually in

the train due to my one night deadlines, for helping me to transfer my “dissident ideas”

into nice scientific papers, for finding just one right word that I was always missing, for

putting “the” and “a” in the places where one native speaking Slavic person would never

assume they are needed. I also thank you for endless academic discussions, for inspiring

me to learn how to read Dutch, for being patient with Balkan temperament, and for all

good jokes (usually on my behalf).

Thank you both for “infecting” me with one of the “most addictive contagious disease”

-research. Also, thank you for providing me the opportunity to continue my research here

in Maastricht. Since our previous meetings were full of challenging discussions, good

ideas and laughs, I am looking forward to more of it to come!

I also want to thank some special friends that made this PhD experience unique.

To Katarina, Aca i Relja for being my second family here and for giving me a feeling of

warm Serbian home outside Serbia. Aca, thank you for wonderful party weekends full of

good Serbian food! Relja, hvala ti za sve divne osmehe, divno vreme provedeno u igranju

i za najlepsi kurs holandskog! bedankt voor het spelen en nederlands praten! Draga, tebi

hvala za svu bezrezervnu podrsku svih ovih godina, za sve duge telefonske razgovore,

i za sve neplanirane sastanke u Zondagu. (Dear Katarina, thank you for unconditional

support, long talks and all unplanned parties in Zondag).

204

Acknowledgements

To Fede for wonderful South European jokes so necessary to survive the Dutch weather

and for last minutes arrangements-for going out or for a trip!

Lieve Nora, thank you for all hedonistic Maastricht nights-starting with good dinners

and wine, continue with dancing in Muziekgieterij and finishing with sitting on the

window of Zondag and discussing philosophy, politics and Dutch guys, of course!

To Eva, for making my master year here unforgettable, for all beautiful trips together and

for more others to come! To Marija and Bojan, for great Stockholm evenings.

To Luisa, for being always present, for nice support and for Italian lessons! To Ghislaine,

for sharing an office during last two years, but not only the office-thank you for sharing the

lunches together, experiences and important private moments. I will always remember

our walk on Irish cliffs! To Vera, for wonderful Krakow trips and Maastricht movie

nights! To Eveline for great Monday evenings with jazz music. To Adrienne –for nice

conference in Crete and for tango evenings. To Marla, for early morning coffee walks-so

I can be at work in time ;).

To all my ASSPRO CEE colleagues-being a part of ASSPRO CEE family does not

mean only having nice project meetings in beautiful cities like Budapest or Vilnius, it

also means working with nice people. Special thanks go to my PhD fellows –Petra, Elka,

Marzena and Tania for wonderful ladies nights in Ginger.

To my Serbian friends, who despite all geographical distance and different life

circumstances stay with me all these years. To Misa Matijasevic, my “friend in crime” for

his unconditional support during my application for MTEC scholarship and for the fact

that he was my first visitor here. To Marko for designing the cover page of this thesis. To

all dear people for wonderful summer nights “pod tremom”.

To my parents - who supported me in my choices, although they were different from

convenient Serbian standards. Thank you for raising me to believe in my dreams.

Last, but not least, to my sister Ana, for being my best adviser and my faithful supporter.

For all these years, thank you for being my emotional shelter in every storm and my best

companion in all great moments. Thank you and Moca together, for giving me a nice

felling of home whenever I am in Belgrade. To my nephew Kosta-dragi Kosta, hvala ti

za sve osmehe , za sve zagrljaje, za sve divne razgovore i za sve angry birds igrice koje

sam naucila zahvaljujuci tebi .

Maastricht, 2015

205

Curriculum Vitae

Curriculum Vitae

Jelena Arsenijevic was born in 1978 in Kragujevac, Serbia. She obtained a degree in

clinical psychology in Belgrade Serbia, completing the studies as the top student of her

generation. She worked for a number of years in practice (oncology, neuropsychology).

In 2008, she obtained a full scholarship from the Dutch Ministry of Foreign Affairs to

pursue a Master’s degree in Public Health at Maastricht University. Upon graduation,

she worked as a research assistant on the topic of ankyloses spondylitis at the Department

of Health Organization Policy and Economics, Maastricht University.

In 2010, Jelena started her PhD within the FP7 project ASSPRO CEE 2007 focused

on out-of-pocket payments for health care services in Central and Eastern European

countries. In 2012, she obtained a CAPHRI travel grant that allowed her to spend 2

months at the London School of Economics in 2013 when she carried out a study on the

relation between poverty and chronic diseases among elderly in EU countries. Also, in

2013, she was a rapporteur for Slovenia within the Study on Corruption in the Health Care

Sector, commissioned by European Commission. During her PhD she also participated in

exchange programs with University of Krakow and University of Tbilisi, organized by

CAPHRI. At present, she is working as post-doctoral researcher in the CHAFEA project

ProHealth 65+ focused on financing and organization of health promotion activities for

elderly.