social and structural determinants of prevalence and treatment of sexually transmitted infections in...

Social and Structural Determinants ofPrevalence and Treatment of Sexually Transmitted Infections in

Southwestern Uganda

by

Benjamin West Bellows

B.A. (University of Michigan) 1996M.P.H. (University of California, Berkeley) 2004

A dissertation submitted in partial satisfaction of the requirements for the degree of

Doctor of Philosophy

in

Epidemiology

in the

GRADUATE DIVISION

of the

UNIVERSITY OF CALIFORNIA, BERKELEY

Committee in charge:

Professor Arthur Reingold, ChairProfessor Malcolm PottsProfessor Alan Hubbard

Spring 2009

Social and Structural Determinants of Prevalence and Treatment of Sexually Transmitted Infections in Southwestern Uganda

Copyright 2009

by


Abstract

Evaluating Output-Based Aid for the Treatment of Sexually Transmitted Infections in Southwestern Uganda

by


Doctor of Philosophy in EpidemiologyUniversity of California, BerkeleyProfessor Arthur Reingold, Chair

One approach to delivering health assistance in developing countries is output-

based aid (OBA), which reimburse health care providers for treating voucher-bearing

patients. In 2006, an OBA program was established in southwestern Uganda to increase

treatment for sexually transmitted infections (STIs), particularly among the poor. The

objectives of this research were to assess the appropriateness of the OBA strategy, to

evaluate whether the intervention was successful, and to measure social capital within

this population and examine its role in STI treatment.

The data for this research were generated in two cross-sectional surveys

conducted in southwestern Uganda in 2006 and 2007. The findings are described in three

manuscripts. The first manuscript measured the prevalence of STI symptoms and the

utilization of private and public healthcare in the region. Poor respondents were more

likely to have STI symptoms and were less likely to seek treatment for symptoms.

Respondents with symptoms expressed a preference for seeking treatment at private

health providers. Overall, the findings indicated that an OBA program for treatment in

private facilities was an appropriate strategy in the region.

The second manuscript measured changes in knowledge of STI symptoms,

1

utilization of STI treatment services, and the prevalence of syphilis. Between 2006 and

2007, knowledge of STI symptoms increased and the prevalence of syphilis decreased.

Among respondents with any STI symptoms, those close to OBA clinics had a larger

increase in the proportion using STI services. The prevalence of syphilis decreased more

for respondents living closer to OBA clinics. The findings indicated that the OBA

program was successful in achieving several of its goals.

In the third manuscript, two measures of social capital (cognitive and structural)

were developed and validated. There were significant associations between cognitive

social capital and health behaviors, including increased disclosure of STI test results

among respondents with high cognitive social capital. Disclosure is an important factor in

STI treatment. Social capital can also be used to draw economic resources to pay for

transport and medical services.

These findings contribute to understanding of economic and social barriers to

healthcare in southwestern Uganda and have implications for similar low-income regions.

________________________________________

Arthur Reingold, M.D., Chair

2

Dedication

For my wife, Nicole

i

Table of Contents

Chapter I: Introduction to dissertation 1 – 17

A. Background on OBA voucher programs 1

B. Uganda vouchers for STI treatment 6

C. Survey design 7

Chapter II: Factors predicting utilization of treatment services for sexually transmitted infections in southwestern Uganda

18-49

A. Abstract 18

B. Background 20

C. Research objectives 23

D. Methods 24

E. Results 31

F. Discussion 33

Chapter III: Impact of an output-based aid voucher program on knowledge of STI symptoms, utilization of STI treatment services and prevalence of syphilis in southwestern Uganda

50-83

A. Abstract 50

B. Background 53

C. Research objectives 60

D. Methods 61

E. Results 68

F. Discussion 72

Chapter IV: Social capital and health - testing the reliability and validity of a social capital instrument in southwestern Uganda using item response theory

84-133

A. Abstract 84

B. Background 86

C. Methods 94

ii

Table of Contents

D. Results 108

E. Discussion 114

Chapter V: Summary of findings and conclusions 134-140

A. Main findings 134

B. Policy Implications 135

C. Future directions 138

References 141

iii

List of Tables and Figures

Tables

Table I-1 Range of indicators for evaluating an OBA program 11

Table I-2 Selected Parishes for Survey (30 Mbarara, 11 Bushenyi) 12

Table I-3 Selection of 82 Villages 14

Table I-4 Summary of sampling frame 17

Table II-1Comparison of sex and age in 2002 Uganda Census population and the survey population in 2002, 2006 and 2007

44

Table II-2 Description of respondents in 2006 and 2007 by sex 45

Table II-3Relationship between four dimensions of poverty and three evaluation outcomes among respondents on the 2006 and 2007 surveys in the Mbarara region.

47

Table II-4

Utilization of STI treatment in previous six months by four different measures of poverty at public and private facilities, including drug shops and traditional healers, among respondents in the 2006 and 2007 surveys in the Mbarara region

49

Table III-1Distance between village of residence and contracted clinics for patients using vouchers in Mbarara region 2006-2008

78

Table III-2GenMatch balance on the means of matched variable after matching

79

Table III-3Relationship between four dimensions of poverty and three evaluation outcomes among respondents on the 2006 and 2007 surveys in the Mbarara region

80

Table III-4

Among poor respondents in four alternate definitions of poverty, knowledge of STI symptoms, use of STI treatment and the prevalence of syphilis in the Mbarara study population 2006 and 2007

81

Table III-5 Prevalence of syphilis by distance to a contracted clinic 82

Table III-6Distributions of matching variables in the unmatched and matched datasets

83

iv


Table IV-1 Social capital items 120

Table IV-2Description of respondents’ demographic factors, economic status, and household characteristics by survey year

124

Table IV-3 Percent missing values for each of the social capital items 125

Table IV-4Average logits for respondent ability at each response category

126

Table IV-5 Correlation matrix between explanatory variables 127

Table IV-6 Cognitive social capital and health-related behaviors 128

Table IV-7 Structural social capital and health-related behaviors 129

Figures

Figure II-1Directed acyclic graph of the proposed causal factors in utilization of STI treatment

38

Figure II-2Directed acyclic graph of the proposed causal factors in STI symptoms

39

Figure II-3Distribution of education levels among 5,198 respondents, Mbarara region surveys in 2006 and 2007

40

Figure II-4Distribution of the number of common household assets (0-7 assets) among 5,088 respondents in Mbarara surveys in 2006 and 2007

41

Figure II-5Distribution of household food insecurity scale (0-27) among 5,098 respondents in Mbarara region surveys in 2006 and 2007. Higher values indicate greater food insecurity

42

Figure II-6Distribution of monthly household expenditure among 5,137 respondents in Mbarara region surveys in 2006 and 2007 ($1 = 2000 UgSh)

43

Figure III-1Direction of financial flows under supply-side and demand-side strategies

76

Figure III-2A directed acyclic graph of the effect of distance to contracted STI clinics on 1) knowledge of STI symptoms, 2) any STI treatment seeking, and 3) the prevalence of syphilis

77

v


Figure IV-1 Item response curve for “trust of pharmacies” in 2006 survey 130

Figure IV-2Information curve for the item ‘trust of pharmacies’ from the 2006 survey

131

Figure IV-3 Item response curve for “trust of pharmacies” in 2007 132

Figure IV-4Wright Map of respondents on cognitive social capital items from 2006 survey

133

Figure V-1Percent of households below poverty line by subcounty (National Census 2002)

140

vi

Acknowledgements

I want to recognize the many efforts of many folks who made it possible for me to

submit this dissertation. The German Development Bank (KFW) funded the surveys and

evaluation of the output-based aid program in southwestern Uganda. In particular, I

would like to thank Claus Janisch and Martin Schmid for entrusting a graduate student

with the evaluation of the program.

Several individuals were instrumental in the completion of the surveys in Uganda.

My colleagues at Mbarara University of Science and Technology in Uganda, Drs Fred

Bagenda and Edgar Mulogo, were exceedingly patient as we worked out the sampling

frame, the logistics, and the myriad details needed to launch two large surveys. The data

collection teams were sensitive and knowledgeable during each of the surveys. Christine

Namayanja and the staff at Marie Stopes International Uganda were very accommodating

during my time in Kampala and inspiring in the good work in reproductive health

services they provide in the country.

In the United States, Martha Campbell, Melodie Holden and everyone at Venture

Strategies were essential in coordinating the research and budgets. Additionally,

individuals at the Bixby Program were supportive in this endeavor, particularly by

funding graduate student researchers Richard Lowe and Matt Hamilton to help conduct

aspects of the evaluation.

I am very grateful for the assistance of my dissertation committee. This research

would not have happened without Professor Malcolm Potts who graciously introduced

me to Claus Janisch and the opportunity to work on this project. Alan Hubbard and Art

Reingold provided valuable feedback on my manuscripts and offered many helpful ideas

vii

for improvements. In addition to my dissertation committee, other faculty at Berkeley

have been essential in inspiring me and mentoring me, especially Ray Catalano, Ann

Swidler, and Bill Satariano.

I would like to thank my family and friends for their support during my time in

graduate school. I have dedicated my dissertation to my wife Nicole, who has listened to

countless hours of discussion on output-based aid and accompanied me to Uganda for

four months with our darling daughter Ani. As our family expands, I look forward to

facing our future adventures together.

viii

Chapter I: Introduction to dissertation

This dissertation is an examination of two surveys in southwestern Uganda that

were conducted to evaluate an output-based aid (OBA) voucher program for treatment of

sexually transmitted infections (STIs). From these surveys, three manuscripts were

developed to document several salient economic and social factors in sexual healthcare

utilization in the region.

Background on OBA voucher programs

The combined use of vouchers and output-based contracting is generally known

as output-based aid (OBA) (Gorter et al. 2003; Janisch and Potts 2005; Sandiford, Gorter

and Salvetto 2002). In traditional salaried positions in the health sector, staff may have

little incentive to raise their productivity or to be concerned with patient perceptions of

health care quality (Robinson 2001). OBA contracts, however, create incentives to

improve the quality of healthcare and increase the utilization of important health services.

OBA vouchers stimulate patient demand for healthcare and give the patient the

purchasing power to seek care from the full range of available providers.

OBA programs have the potential to improve healthcare and health outcomes at

facility-level and in the general population (see Table I-1). Improvements are grouped

into four broad categories of measures: knowledge, behavior (including utilization),

costs, and disease status (prevalence, incidence, patient disease stage).

1

Knowledge is measured among patients, providers, and general population.

Common metrics include knowledge of disease signs, program characteristics (i.e. where

to find a voucher or clinic location), and provider recall of treatment guidelines.

Improvements in behaviors in the context of a health systems intervention largely

encompass health-seeking behaviors. There may be other barriers to care, such as

distance, that would keep patients from seeking care; however, if cost is the principal

reason for poor service uptake among the ill, we expect to see an increase in utilization at

contracted facilities. If the burden of untreated disease is high in the general population, it

may be possible to detect a change in the utilization patterns of the general population as

well.

Cost metrics are another important area to monitor in OBA programs. To evaluate

OBA programs, monitoring patient out-of-pocket spending, facility revenue and costs,

and related expenses give insight into whether the facility contracts and patient subsidies

are making improvements in healthcare delivery and health outcomes.

The final area to monitor is population disease burdens. Populations can include

patients and general populations. Monitoring disease burden can be as complex or simple

as dictated by need to determine the impact, how ever that may be defined. Risk of new

disease in a population served by clinics newly contracted may be one measure. Odds of

exposure in clinic-based cases and controls may be another approach. Change in

prevalence in a before-after design may be yet another metric that indcates to the

administrators, funders, and other interested parties whether the program was a success.

Several countries have employed OBA strategies to deliver health services to low-

income populations. In addition to the Uganda program, four specific programs are

2

discussed here to detail the history of OBA program development and the various

locations where OBA programs have been implemented.

Taiwan voucher program for contraception

The first use of output-based vouchers for healthcare in low-income countries was

done on a large scale in Taiwan in the 1960s and 1970s. The Taiwan Ministry of Health

offered male and female sterilization services at a range of government and private

facilities (Cernada and Chow 1969). The system was set up to subsidize the cost for low

income couples, targeting the service to couples with two or more children. The program

also wanted to be efficient so as to save funds and increase the number of qualified

couples who could use the program. The voucher subsidized a routine health service in

which all couples seeking sterilization participated, regardless of their income level.

Couples that did not qualify for the subsidy purchased their voucher and selected their

provider. Low-income couples did not pay the fee but received the same voucher, chose

from the same network of providers and received the same level of care (Lin and Huang

1981).

Nicaragua voucher program treatment of STIs in the 1990s

After the Taiwan program, there is no record of other OBA programs until 20

years later when Nicaragua implemented two voucher programs to treat STIs in 1995.

One program targeted vouchers to commercial sex workers for treatment of sexually

transmitted infections (STIs) as an HIV intervention. One important rationale for STI

treatment as it relates to HIV prevention. Although there is debate on the magnitude, it is

3

generally recognized that some STIs can increase the probability of viral shedding from

HIV positive persons as well as increase the susceptibility of HIV-negative persons to

infection (Buchacz et al. 2004; Grosskurth et al. 2000; Holmes et al. 1999; O'Farrell

2002)1. Additionally, there is evidence to suggest that it is easier to persuade individuals

to make use of improved STI treatment services which are accessible, effective and free

of charge than to achieve substantial and lasting changes in promoting sexual behavior

change (Hayes et al. 1995) as cited in (Borghi et al. 2005).

The sex worker vouchers were distributed around Managua and used in 22,082

visits between 1995 and mid 2008. The adolescent vouchers were used 15,134 times in

the same period. Sandiford and colleagues noted that the introduction of the sex worker

voucher was accompanied by annual declines in the prevalence of syphilis (8.6%) and

gonorrhea (9.4%) among the poorest sex workers (2002). Utilization of adolescent

reproductive health services and use of contraceptives were higher in the voucher group

compared to a control group (OR 3.1, 95% CI 2.5-3.8) (Meuwissen, Gorter and

Knottnerus 2006b).

Gujarat safe delivery vouchers in 2005

In recent years, there has been increasing interest in OBA programs for

reproductive health services. There have been many voucher pilots in south Asia recently,

although often launched with a limited scope of work and poorly documented. One

1 Using data from two studies in East Africa, Grosskurth and colleagues estimated the attributable fraction

of HIV infection due to STIs at 10-43% depending on the magnitude of HIV epidemic and risk behaviors

of the population. Grosskurth, H., R. Gray, R. Hayes, D. Mabey, and M. Wawer. 2000. “Control of

sexually transmitted diseases for HIV-1 prevention: Understanding the implications of the Mwanza and

Rakai trials.” Lancet 355:1981-7.

4

exception is the program in Gujarat state in India with its drive to use vouchers for

maternal delivery, “Chiranjeevi Yojana” (‘eternal life scheme’), launched in December

2005. The objective of the program was to improve the institutional delivery rate by

subsidizing access to private medical providers for pregnant women living below the

official poverty line (BPL) in remote areas with the highest infant and maternal mortality.

The scheme was launched as a single year pilot project in five districts: Banaskantha,

Dahod, Kutch, Panchmahal, and Sabarkantha (Bhat et al. 2009).

Voucher holders were provided a transport stipend and private contracted

providers were reimbursed on a capitation payment basis. The payments were made for a

batch of 100 deliveries to take care of case-mix differences (i.e., normal or complicated

deliveries). The costs for normal and complicated deliveries were based on market

prevailing rates and using locally relevant probabilities of complicated and normal cases,

an average cost per delivery was worked out. The scheme used a voucher system to target

the people living below poverty line (Bhat et al. 2006).

An evaluation survey was conducted among 262 voucher-using mothers and 394

similar non-voucher-using mothers. A vast majority (97%) of the voucher beneficiaries

delivered in private facilities, 2.7% deliveries were conducted in government facilities,

and one voucher-purchaser had a home delivery. In the non-voucher group, 21% of

women delivered at home, 1% in government facilities and 77% in private institutions

(Bhat et al. 2009).

5

Bangladesh Safe Delivery Vouchers

Bangladesh vouchers are currently being distributed to poor pregnant women for

antenatal, delivery, and post-natal care. With funding from World Bank donors, poor

women are offered free transport and clinic service fees. The poor are identified in

several ways. In 11 of the targeted districts (upazila) household asset scoring was used to

select poor from non-poor. In the remaining nine districts, all women qualified. Although

the targeted number of vouchers was not stated in the report, there were an estimated

174,000 deliveries annually in the 21 districts. There is a lengthy reimbursement schedule

for specific services - from transport to food supplements following delivery. However,

on average reimbursements are 1648 taka (USD 24) (Koehlmoos et al. 2008; Ministry of

Health and Family Welfare 2007).

Uganda vouchers for STI treatment

The focus of this dissertation is the OBA voucher program for STI treatment.

With funding from the German Development Bank (KFW), an OBA pilot in Uganda was

launched in 2006 to provide an improved standard of care for laboratory-based STI

diagnosis and treatment. The non-profit Marie Stopes International-Uganda (MSIU)

managed the pilot program’s operations, service provider contracts, claims and payment,

and fraud control. Sixteen clinics were contracted by the program launch with the goal to

sell more than 10,000 vouchers a year. At the request of the donor German Development

Bank (KFW) and the Uganda Ministry of Health, a population evaluation of the pilot

program was conducted by the OBA technical adviser at the Berkeley-based NGO,

6

Venture Strategies for Health and Development, researchers from the University of

California at Berkeley, and faculty at the Mbarara University of Science and Technology.

This dissertation uses data from two population-based surveys in which

respondents were interviewed about selected STI outcomes and health-seeking behaviors.

Study participants were sampled from three groups: communities near contracted private

clinics in Mbarara, Kiruhura, Ibanda and Isingiro districts; communities near comparable

non-contracted private clinics in a comparison district, Bushenyi; and communities in

Mbarara without nearby private facilities.

The first manuscript in this dissertation explores the patterns of STI symptoms

and STI treatment utilization at the appropriateness of an OBA voucher strategy; the

second manuscript evaluates the impact of the program between year 1 and year 2; and

the third manuscript develops two measures of social capital and explores the

relationships between social capital and health-related behaviors and outcomes.

Survey design

All three manuscripts are based on two population-based surveys collected in

southwestern Uganda. The paired population surveys shared a common design. Both the

baseline and follow-up survey were intended to select a representative sample of men and

women between 15 and 49 years of age from 82 villages in Mbarara, Kiruhura, Ibanda,

Isingiro and Bushenyi districts. In both surveys, a sample of respondents was selected

from a four-stage design using population weights from the 2002 Uganda census.

7

Sample size

Each survey drew a stratified sample selected using a multi-stage cluster design.

In the four stage design, the first stage sample of parishes was selected by probability

proportional to population size, followed by a second stage sample of the villages from

selected parishes, again by probability proportional to population size. The third stage

sample consisted of households enumerated from selected villages. Economic

information at the household level was measured in the survey. The final stage sample

was comprised of one household resident interviewed at each selected household.

First stage selection

The Ugandan Bureau of Statistics (UBOS) has census data freely available down

to parish level. There are 240 parishes in the old Mbarara district, ranging in size from

438 to 22,032 inhabitants (old Mbarara district had 1,088,356 persons in the 2002

census). There are 170 parishes in Bushenyi district, ranging in size from 622 to 8,608

inhabitants (Bushenyi district had 731,392 persons in the 2002 census). In 2006, the

Mbarara district was split into four new administrative districts. However, for the

purposes of the sampling frame, the previous administrative boundaries were used.

Parishes (and the analogous “municipal ward”) constituted the first-stage sampling units

for sample selection. For the first selection stratum, parishes were stratified by whether

they had either no private clinics or one or more private clinics. By including the 15

Mbarara parishes with OBA clinics in the first stage, respondents were oversampled from

parishes with an OBA clinic (see Tables I-2 and I-3). Eleven parishes from Bushenyi

containing one or more private clinics were also included (Table I-2). Fifteen Mbarara

8

parishes without an OBA clinic were sampled from the remainder of Mbarara parishes

(225) using probability proportional to size (PPS) systematic sampling without

replacement, where size was defined by the total parish population (Tables I-2 and I-4).

If is the population in parish , then the probability of including the parish in

the sample is given by:

where is the number of parishes selected in the sample in that district and is the

total number of persons in the 225 non-OBA parishes of Mbarara district.

Second stage selection

At the second stage, two enumeration areas (EAs) or “villages” from each parish

were selected with probability proportional to parish size without replacement (Table I-

3). If is the population in enumeration area (EA) , then the probability of including

the EA in the sample is given by:

where is the number of EAs selected in the sample in that parish and is the total

number of persons in the parish (all potential EAs).

Third stage selection

At the third stage, survey teams took a sample of households from each selected

village and municipal cell. Teams were to select a sample of 36 individuals from each of

82 villages and cells (total=2,952) at random households in the village. This was done by

9

first randomly selecting a household from an enumerated list of all households in the

village or cell. At each selected household, all potential respondents were enumerated.

Then, one individual between 15-49 years of age was selected at random to complete the

survey.

The original selection of controls was made so as to measure health status in

parishes without contracted OBA clinics (selected from Mbarara, Ibanda, Isingiro, and

Kiruhura districts) and areas that had non-contracted private clinics (selected parishes in

neighboring Bushenyi district). No other factors were taken into consideration in parish

selection.

All survey data were double entered into an EpiData (version 3.1) database

containing a range of logic checks. Item response models were fit using ConstructMap

(version 4.2 University of California at Berkeley). All statistical analyses for the

multivariable modeling were done in STATA (version 10.1).

Human subjects approval was granted for both surveys by the institutional review

boards at the University of California, Berkeley (#2005-8-24) and the Mbarara University

of Science and Technology.

10

Table I-1: Range of indicators for evaluating an OBA program that treats STIs

LEVEL Knowledge Behavior Costs Disease burden

Healthcare Facility

Provider knowledge of treatment or reporting protocols

Clinic utilization before and after program launch

Fraction of patients at clinic who used voucher

Claims-based reimbursements

Claims-based diagnosis & treatment

General population

Voucher recognition

Heard marketing messages

Recognize need for service

Self-reported healthcare use

Percent of voucher used by targeted population

Self-reported patient out-of-pocket

Lab confirmed syphilis

Self-reported STI symptoms

11

Table I-2: Selected Parishes for Survey (30 Mbarara, 11 Bushenyi)

District Sub-county Parish Parish Population

1 MBARARA IBANDA BUFUNDA 13,937

2 MBARARA KASHARI KABARE 4,713

3 MBARARA IBANDA KAGONGO 8,791

4 MBARARA MBARARA MUN. KAKOBA 22,032

5 MBARARA KASHARI KAKYERERE 5,186

6 MBARARA MBARARA MUN. KAMUKUZI 15,676



9 MBARARA KAZO KAZO 7,195

10 MBARARA RWAMPARA NYEIHANGA 3,030

11 MBARARA MBARARA MUN. RUHARO 7,794

12 MBARARA NYABUSHOZI RUSHERE 4,988

13 MBARARA NYABUSHOZI RUSHERE 4,988

14 MBARARA MBARARA MUN. RUTI 4,824

15 MBARARA ISINGIRO MABONA 4,619

16 MBARARA KASHUMBA KASHUMBA 8,338

17 MBARARA KASHUMBA KIGARAGARA 5,927

18 MBARARA BISHESHE NYAKATOKYE 5,805

19 MBARARA KICUZI KANYWAMBOGO 2,532

20 MBARARA KIKYENKYE KEIHANGARA 7,896

21 MBARARA NYAMAREBE KYENGANDO 6,976

22 MBARARA NYAKITUNDA NYAKARAMBI 4,358

23 MBARARA BUBAARE RUGARAMA 3,390

24 MBARARA BUREMBA KIJOOHA 4,838

25 MBARARA KANONI ENGARI 5,427

26 MBARARA KAZO RWAMURANGA 2,533

27 MBARARA SANGA RWABARATA 2,673

12

District Sub-county Parish Parish Population

28 MBARARA BUGAMBA KIBINGO 3,787

29 MBARARA RUGANDO MIRAMA 3,638

30 MBARARA RUGANDO NYABIKUNGU 4,988

31BUSHENYI KABWOHE-

ITENDERO T.CKABWOHE 4,628

32 BUSHENYI SHUUKU KISHABYA 5,901

33 BUSHENYI KYEIZOBA KITWE 4,506

34 BUSHENYI KIGARAMA MABARE 6,166

35 BUSHENYI KYAMUHUNGA MASHONGA 8,170

36 BUSHENYI RYERU NDEKYE 4,619

37BUSHENYI KABWOHE-

ITENDERO T.CNYANGA 4,332

38 BUSHENYI MITOOMA RUSHOROZA 3,684

39 BUSHENYI BUSHENYI TC WARD I 6,028

40 BUSHENYI BUSHENYI TC WARD III 7,592

41 BUSHENYI BUSHENYI TC WARD IV 3,899

13

Table I-3: Selection of 82 Villages

District Parish Name Village NameVillage Households

Village Population

1 MBARARA KASHUMBA BURAMA 115 474

2 MBARARA KASHUMBA KASHUMBA 184 759

3 MBARARA KIGARAGARA KAMISHWA 157 799

4 MBARARA KIGARAGARA RWAMACUMU 66 336

5 MBARARA NYAKATOKYE RWEBIYENJE I 44 218

6 MBARARA NYAKATOKYE BIGYERA 47 233

7 MBARARA KANYWAMBOGO KABUHWEJU 67 308

8 MBARARA KANYWAMBOGO KISABO I 126 579

9 MBARARA KEIHANGARA NGANGO I 108 521

10 MBARARA KEIHANGARA KANYEGANYEGYE 72 347

11 MBARARA KYENGANDO RWENKUREJU I 89 388

12 MBARARA KYENGANDO KOBUHURA A. 76 332

13 MBARARA NYAKARAMBI OMUBUSHAMI 122 548

14 MBARARA NYAKARAMBI OMUKINIKA 140 629

15 MBARARA RUGARAMA NKAAKA 153 792

16 MBARARA RUGARAMA RUGARAMA I 187 968

17 MBARARA KIJOOHA MUSHAMBYA 136 690

18 MBARARA KIJOOHA BUREMBA 185 938

19 MBARARA ENGARI RUSHANGO 113 603

20 MBARARA ENGARI NYABUBARE II 75 400

21 MBARARA RWAMURANGA MIRAMA 152 852

22 MBARARA RWAMURANGA RWAMURANGA 126 706

23 MBARARA RWABARATA RWAMUHUKU 192 774

24 MBARARA RWABARATA RWONYO 139 560

25 MBARARA NGUGO/KIBINGO NTSINGWA I 65 327

26 MBARARA NGUGO/KIBINGO RUSHANJE 101 509

27 MBARARA MIRAMA RWEMIYENJE 72 355

28 MBARARA MIRAMA MIRAMA II 49 242

29 MBARARA NYABIKUNGU MIKAMBA 69 367

14


Village Population

30 MBARARA NYABIKUNGU KABOBO 68 361

31 MBARARA BUFUNDA MPIIRA STREET 194 853

32 MBARARA BUFUNDA NYAKATEETE II 92 405

33 MBARARA KAGONGO KAFUNDA 73 357

34 MBARARA KAGONGO KASHAKA II 133 650

35 MBARARA MABONA MABONA 71 334

36 MBARARA MABONA KYAMUDIMA 84 390

37 MBARARA KAMUSHOKO RWEMPOGO 146 794

38 MBARARA KAMUSHOKO RWAMBABANA 98 533

39 MBARARA RWENSHANKU RWENTURAGARA 169 841

40 MBARARA RWENSHANKU RWENSHANKU 106 528

41 MBARARA KABARE NSHOZI 52 198

42 MBARARA KABARE KARUHAMA 99 376

43 MBARARA KAKYERERE BWIZIBWERA TR. A 124 575

44 MBARARA KAKYERERE RWANYAMAHEMBE 131 607

45 MBARARA KAZO KAZO II 228 1161

46 MBARARA KAZO KAZO I 195 993

47 MBARARA KAKOBA KISENYI 'B' 353 1405

48 MBARARA KAKOBA LUGAZI ‘A’ 549 2186

49 MBARARA KAMUKUZI KAKIIKA 'B' 603 2315

50 MBARARA KAMUKUZI KASHANYALAZI 286 1098

51 MBARARA RUHARO NKOKONJERU 'A' 309 1453

52 MBARARA RUHARO KIYANJA 396 1862

53 MBARARA KATETE KATETE CENTRAL 'A' 251 1106

54 MBARARA KATETE NYAMITANGA 'A' 165 727

55 MBARARA RUTI KAFUNDA 99 402

56 MBARARA RUTI KATEERA 'A' 147 596

57 MBARARA RUSHERE RUSHERE T/C 'A' 252 1254

58 MBARARA RUSHERE RUSHERE T/C 'B' 172 856

59 MBARARA NYEIHANGA NYEIHANGA 40 183

15


Village Population

60 MBARARA NYEIHANGA RWABAJOJO 59 270

61 BUSHENYI NDEKYE RYERU I 142 638

62 BUSHENYI NDEKYE RYERU II 117 561

63 BUSHENYI WARD I CENTRAL CELL 'A' 281 1348

64 BUSHENYI WARD IV CELL C 372 1756

65 BUSHENYI WARD III CELL B 'A' 217 982

66 BUSHENYI KITWE KITWE 99 488

67 BUSHENYI MASHONGA NYAKATEMBE 123 569

68 BUSHENYI WARD I CENTRAL CELL 'B' 253 1213

69 BUSHENYI KITWE RWENTUHA TC 215 1060

70 BUSHENYI WARD III CELL B 'B' 484 2190

71 BUSHENYI MASHONGA KAYANGA 118 546

72 BUSHENYI WARD IV CELL D 338 1595

73 BUSHENYI RUSHOROZA NYAKASHOJWA 67 359

74 BUSHENYI RUSHOROZA MITOOMA TOWN 205 1099

75 BUSHENYI MABARE NYAKAMBU 143 696

76 BUSHENYI KISHABYA KISHABYA 90 465

77 BUSHENYI KISHABYA KYENJOJO 75 388

78 BUSHENYI NYANGA KIGIMBI 146 612

79 BUSHENYI NYANGA KABWOHE TOWN B 451 1889

80 BUSHENYI MABARE KATWE 59 287

81 BUSHENYI KABWOHE KABWOHE TOWN A 406 1729

82 BUSHENYI KABWOHE KAMWEZI 44 187

16

Table I-4: Summary of sampling frame

Mbarara OBA parishes

Mbarara Non-OBA parishes

Bushenyi parishes

Total

Population 119,824 968,532 731,392 1,819,748

Number of parishes 15 225 170 410

Number of parishes in sample 15* 15 11* 41

Number of selected villages in parishes

30 30 22 82

Number of households 5,107 3,473 4,341 12,921

Number of selected households^

1,080 1,080 792 2,952

Total village population 22,922 16,184 20,181 59,287

* purposively sampled (probability of selection =1) ^ 36 households per EA village were planned in the survey

17

Chapter II: Factors predicting utilization of treatment services for

sexually transmitted infections in southwestern Uganda

Abstract

Rationale: There is growing interest in the potential for private sector healthcare

to meet public health needs in low-income countries. Policymakers have a number of

options to choose when considering how to utilize private sector healthcare for public

health goals.

One strategy for extending public health planning into private facilities is through

the use of output-based contracts for sexual and reproductive health services. The Uganda

Ministry of Health and the German Development Bank (KfW) launched a project in July

2006 using output-based aid (OBA) contracts to subsidize treatment of sexually

transmitted infections (STIs) at eighteen private clinics in four districts of southwestern

Uganda.

Objectives: Using population survey data from 2006 and 2007, this study aimed

to examine four independent measures of poverty and determine the association of

poverty measures with STI outcomes and risk behaviors, in order to better understand the

population most in need of STI services. An additional objective was to determine

whether individuals prefer private or public providers for STI treatment.

Methods: Data from two cross sectional surveys of approximately 2,600

respondents in 82 villages were used to fit logistic models of general healthcare

utilization and STI treatment services at public and private healthcare providers.

Explanatory variables were household asset score, food insecurity score, education level,

respondent age, respondent sex, and respondent’s partnership status. Respondents were

18

also asked whether certain structural factors (i.e. ability to pay, transport, and provider

availability) were significant problems when accessing medical advice. The three

outcomes of interest were whether the respondent reported STI symptoms in the previous

six months, whether the respondent sought any care for STI symptoms, and respondent’s

choice of public or private health facility when service was sought.

Results: The poverty scores were not highly correlated with each other (highest

pairwise Pearson coefficient r= 0.28), suggesting the scores measured different

dimensions of poverty. Overall, those with higher poverty scores were more likely than

those with lower poverty scores to report having had one or more STI symptoms in the

past six months. Among respondents who reported having any STI symptom in the past

six months, those with higher poverty scores were less likely to have used STI treatment

than those with lower poverty scores. There was a clear preference among all respondents

for using private facilities for STI treatment. Private clinics account for a large proportion

of STI treatment visits. Depending on the poverty measure used, 48-54% of poor

respondents went to private facilities for STI treatment.

Conclusions: There is evidence that the poor have a high STI burden and that the

private sector is a significant source of STI treatment. There is also evidence that the poor

use private facilities as much, if not more, than they use public facilities. Interventions to

improve STI treatment services in the private sector could reach a large proportion of the

population given the current utilization pattern.

19

Background

Sexually transmitted infections (STIs) constitute a large health and economic

burden. Seventy-five to 85 percent of the estimated 340 million annual new cases of the

four most common curable STIs (gonorrhea, syphilis, trichomoniasis, and chlamydia)

occur in low-income countries, and STIs, excluding HIV, account for 17 percent of

economic losses due to illness in 15-44 year old women (Mayaud and Mabey 2004). STIs

also facilitate the sexual transmission of HIV, thereby indirectly imposing additional

morbidity and mortality burdens on developing countries (Grosskurth et al. 2000;

Grosskurth et al. 1995).

Private sector plays a large role in healthcare delivery

In a study of Demographic and Health Survey (DHS) data from 22 African

countries, Prata and colleagues (2005) found that the poorest quintile of children had the

highest burden of diarrhea and acute respiratory infection and the lowest use of treatment

services for those conditions. However, among children who were seen by a medical

provider for diarrhea and ARI, most of those (77% for diarrhea and 74% for ARI) from

the poorest quintile who received care went to private providers (Prata et al. 2005). A

study from 2008 found that in Nigeria and Uganda, people in the lowest economic

quintile received more than 60 percent of their healthcare in the private for-profit sector

(Ghatak, Hazlewood and Lee 2008).

The proportion of a national population treated by private providers differs across

sub-Saharan Africa (Ghatak et al. 2008), but in most countries a large proportion of STI

treatment occurs in private sector healthcare facilities (Adu-Sarkodie 1997; Brugha and

20

Zwi 1998; Jacobs et al. 2004; Voeten et al. 2001; Wawer et al. 1999; WHO 2001).

Although there is vigorous debate about the role of the public and private sectors in

meeting population healthcare needs (Mayor 2009; Over 2009), policymakers recognize

that private providers have a significant role in healthcare delivery and can serve as a

complement to public sector healthcare in sub-Saharan Africa (Bennett et al. 2005;

Ghatak et al. 2008; Hanson et al. 2008; Mills et al. 2002).

What is the “private sector”?

Definitions of “private health sector” vary; however, there is agreement that it

represents non-state healthcare providers. In the widest definition, private sector

providers include traditional healers; unregulated drug shops; registered for-profit,

independent clinics run by senior nurses or medical doctors; secular non-profit clinics

(NGOs); mission or faith-based facilities; and large networks of for-profit clinics and

hospitals (Mills et al. 2002). A 2009 report by Oxfam International underscored the

political challenges when discussing private sector healthcare (Mayor 2009). The report

expressed a legitimate concern with including informal providers, like drug shops,

chemists, traditional healers, and small clinics in a broad “private sector” category. The

report stressed that informal providers generally lack capacity to treat many complicated

conditions, unlike large, better equipped private and public facilities. However, informal

providers are similar to larger private facilities in that they are responsive to adjustments

in incentives and can be considered potential participants in policy interventions to

improve population health (Mills et al. 2002; Patouillard et al. 2007; Peters, Mirchandani

and Hansen 2004).

21

Various types of private providers make up significant segments of the market for

sexual and reproductive health (SRH) services in different African countries. For

instance, among 291 respondents in Nairobi, Kenya who reported STI symptoms, most

men and women had sought care in private independently owned clinics (72 percent and

57 percent, respectively) (Voeten et al. 2001). Of men who sought treatment for STIs in

Ghana and Cameroon, 75 percent in Ghana and 50 percent in Cameroon sought treatment

through the informal private sector, generally using traditional healers and drug shops,

prior to visiting a government health center (Adu-Sarkodie 1997; WHO 2001). Self-

medication and purchase of over the counter (OTC) treatment from pharmacies and other

private sector sources accounted for an estimated 90 percent of antimicrobial STI

treatment in Ghana (Brugha and Zwi 1999). Dartnell and colleagues reported that in

South Africa, traditional healers were the first source of STI care for 80 percent of

patients who attended a formal health sector provider (1997). In Mwanza, Tanzania, 30

percent of men seeking STI treatment sought treatment in the informal private sector or

traditional treatment (Jacobs et al. 2004). In the Rakai district in Uganda, Wawer and

colleagues reported that fewer than 20 percent of adults with symptomatic STIs attended

government clinics (1999).

Is the private sector a significant source of STI treatment in western Uganda?

To increase the utilization of STI treatment services and improve the quality of

care, the Uganda Ministry of Health began in July 2006 to subsidize STI treatment at

accredited private clinics in four southwestern districts. At the program’s launch, it was

not well known whether the demand-side program, with its economic subsidy, would

reach a large portion of the population with STI symptoms in need of treatment services.

22

To measure the program’s impact, an evaluation was designed based on surveys

conducted before and 16 months after program launch, among communities identified as

control and intervention areas.

Before measuring the program’s impact, it is important to know what share of the

population seeks care at private facilities. This study set out to explore poverty-related

differences in the prevalence of reported STI symptoms, proportion of those with STI

symptoms who sought STI treatment, and the proportion of those who sought STI

treatment who did so at private and public facilities.

Research Objectives

Using data from a combined dataset of two cross sectional surveys, the following

three questions and hypotheses were explored:

1. Are the poor more likely to report STI symptoms compared to the non-poor?

Hypothesis: Individuals with higher poverty scores are more likely to report

having one or more STI symptoms in the six months prior to a survey

compared to individuals with lower poverty scores.

2. Among those with one or more STI symptoms, are the poor less likely to seek

treatment for STI symptoms?

Hypothesis: Individuals with STI symptoms who have higher poverty scores

are less likely to seek treatment for STI symptoms compared to individuals

with lower poverty scores.

23

3. Among those seeking treatment for STI symptoms, are the poor less likely to seek

treatment in the private sector?

Hypothesis: Individuals with higher poverty scores who have sought treatment

for STI symptoms are less likely to report having sought treatment in the

private sector compared to individuals with lower poverty scores.

The proposed mechanism for utilization of STI treatment is presented as a

directed acyclic graph (DAG) in Figure II-1. Three general types of STI risk factors are

present: economic, demographic and sexual behavior and knowledge. Figure II-2 presents

a similar DAG for self-reported STI symptoms as the outcome.

Methods

Sampling frame

The sampling frame was designed to select a representative sample of 2952 men

and women between 15 to 49 years of age from five districts: Mbarara, Kiruhura, Ibanda,

Isingiro and Bushenyi. The sample was selected in a four-stage design using the 2002

Uganda census. The first stage consisted of a sample of parishes (local administrative unit

of 5,000-20,000 population). 15 parishes were selected by probability proportional to

population size (PPS) and 26 parishes were purposively selected because of the presence

of a clinic. In the second stage, villages were sampled from parishes by probability

proportional to village population size. The third stage sample randomly drew households

enumerated within selected villages. The final stage of the survey selected one household

resident for interview at each selected household.

24

Primary explanatory variables

The primary explanatory variables of interest were four measures of poverty. In

order to assess which individuals had high levels of poverty, we used four common

measures of socioeconomic status: education, household assets index, household monthly

expenditure, and household food insecurity access scale.

Poverty measure 1: Education

The amount of education an individual attained can be used to estimate the level

of economic deprivation during the respondent’s youth. Although schooling has often

been completed years before the interview, there are long term social, economic and

health sequelae associated with the amount of completed education. In this study,

education levels were grouped initially into five categories: no primary school, some

primary school, completed primary school, some secondary school, and completed

secondary school. The five category ordinal education variable had a bimodal

distribution, with peaks for completing primary and completing secondary (see Figure II-

3). For this study, a dichotomous variable (completed primary versus some secondary and

above) was created from the five category ordinal variable.

Poverty measure 2: Household assets index

A household assets index was based on seven questions about respondents’

household assets and living conditions: having electricity, a radio, a TV, a telephone, a

refrigerator, a lantern, and a cupboard. Using household assets indices to measure relative

poverty is common in the absence of income data (Filmer and Pritchett 1998). In Filmer

25

and Pritchett’s method, which has been used extensively by the World Bank, an asset

index is weighted by a scoring factor that is assigned to each variable in the linear

combination of the variables that constitute the first principal component. This principal

component, developed from classical test theory, is conceptualized as the unobservable

latent poverty variable. Each household asset variable is normalized by its mean and

standard deviation, and the weights are the standardized first principal component of the

observed household assets (Expert Group on Poverty Statistics 2006; Falkingham and

Namazie 2002). The Filmer and Pritchett index includes additional questions about

housing materials, water access, and household disease control measures, such as use of

bed nets. For this study, the household asset score (see Figure II-4) was restricted to

seven binary questions on durable household objects and the presence of electricity, all of

which are indicative of longterm economic status.

The distribution was left-skewed, as seen in Figure II-4. For this study, the

variable was made dichotomous, with all values above the median grouped against all

values at and below the median.

Poverty Measure 3: Household food insecurity access scale

The Household Food Insecurity Access Scale (HFIAS) is calculated from nine

items measuring food availability, food access and food utilization in the 30 days prior to

the interview (Coates, Swindale and Bilinsky 2006). The HFIAS questions relate to three

different domains of food insecurity found to be common to cultures examined in a cross-

country literature review: anxiety and uncertainty about the household food supply,

insufficient food intake and its physical consequences, and insufficient quality of food

26

(Coates 2004; Coates et al. 2006; Food and Nutrition Technical Assistance (FANTA)

Project 2004). The HFIAS is a continuous variable bound by 0 and 27 and with a left-

skewed distribution, as seen in Figure II-5. For this study, the food insecurity variable

was made into a dichotomous variable with all values above the median grouped against

all values at and below the median.

Poverty Measure 4: Household monthly expenditures

Respondents were asked for the gross monthly expenditures in their household.

Amounts are reported in Uganda shillings. The distribution was highly left-skewed, as

demonstrated in Figure II-6. For this study, a dichotomous variable was created dividing

values above the median from those at and below the median.

Control variables

Data were also collected on respondents’ demographic characteristics (age, sex,

and marital status), whether an individual lived in an urban or rural village, and

respondents’ STI behavioral and knowledge risk factors (number of sex partners in the

previous six months, receiving money for sex in the previous six months, and knowledge

of STI symptoms). Respondent-level characteristics, behaviors, and knowledge were

determined by self-report. In assessing respondents’ knowledge of STI symptoms,

respondents were asked to name symptoms that could be the result of a sexually

transmitted infection. No prompts were given; interviewers checked responses against a

list of nine common possible symptoms.

27

Outcomes variables

Reporting of one or more STI symptoms

Respondents were asked whether in the previous six months they had experienced

foul smelling penile or vaginal discharge, burning irritation during urination, or non-

traumatic sores on the genitalia. A single additive index was created from the three

responses for male respondents. The same index was modified for female respondents to

include only burning irritation during urination and non-traumatic sores on the genitalia.

Foul smelling discharge was excluded as it was considered not specific. A new binary

variable was created to contrast respondents having one or more symptoms from those

without any symptoms.

STI treatment utilization

Among those who reported having one or more STI symptoms, respondents were

asked the number of times they sought treatment for STI symptoms in the past six

months.

Type of facility visited for STI treatment

Respondents who sought treatment were asked about the type of facility they

visited for their most recent STI symptom(s). Responses were limited to self-treatment,

traditional healer, drug shop, private clinic, private-not-for-profit (NGO) clinic, private

hospital, government clinic, government hospital, or mission hospital. These were then

grouped into modern private providers (drug shop, NGO clinic, private clinic, and private

hospital), and modern public providers (government clinic, government hospital and

28

mission hospital). Mission facilities coordinate with government and often have staff on

government salary and so are included in the public sector category. Excluded were

traditional healers, who, by definition, do not practice modern medicine, and self-

medication responses.

Analysis/Model building methods

In this study, poverty was alternatively defined by four constructs: household

assets, household monthly expenditures, household food insecurity, and respondent

education level. Each was made into a dichotomous exposure variable as explained

above. For the first three research questions, a logistic model was fit for each

combination of outcome and poverty measure, resulting in four models for each of the

first three questions.

The first question was whether respondent poverty was associated with self-

reporting of one or more STI symptoms in the previous six months. Separate logistic

models were fit for each poverty construct: household assets, household monthly

expenditures, household food insecurity, and respondent education level.

The second question asked whether a higher poverty score predicted greater odds

of STI treatment utilization, among respondents reporting any STI symptoms in the

previous six months. Separate logistic models were fit for each poverty construct:

household assets, household monthly expenditures, household food insecurity, and

respondent education level.

The third question was whether individuals with higher poverty scores (as

determined by the four alternative constructs) used STI care at private or public

29

providers. Private providers were defined as any drug shop, private-for-profit clinic,

private-for-profit hospital and NGOs. Public providers included government clinics,

government hospitals and mission hospitals. Traditional healers and self-medication were

excluded from this question. Separate logistic models were fit for each poverty construct:

household assets, household monthly expenditures, household food insecurity, and

respondent education level.

Categorical variables were reported as proportions and continuous variables were

reported as means with standard deviations (SD) or medians with interquartile range

(IQR). Bivariate and multivariate associations were reported using odds ratios and 95

percent confidence intervals. All statistical tests were two-sided and considered

significant at = 0.05. Statistical analyses were done using STATA version 10.1

(College Station, TX). Bivariate odds ratios were estimated to test for associations

between the independent variables and the primary outcomes. Multivariate (adjusted)

odds ratios are reported as tests for association between each poverty score and outcome

controlling for potential confounders.

Human subjects approval was granted by the institutional review boards at the

University of California, Berkeley (#2005-08-24) and the Mbarara University of Science

and Technology.

30

Results

Recruitment and description of the sample

Table II-1 describes the sample population. The sampling frame was designed to

reach 2,952 15-49 year old respondents in 82 villages. The survey response rate was 88

percent (2639 / 2952) in 2006 and 93 percent (2757 / 2952) in 2007. Compared to the

general population, there was a disporportionately high number of older respondents and

of female respondents.

Characteristics of surveyed population

Table II-2 describes the demographic, economic, and behavioral characteristics of

the men and women in the study. Men were more likely to be older and single than

women, although the difference was not statistically significant. Men also reported higher

monthly household expenditures. It is not clear if the women were coming from

households with lower monthly expenditures or if, perhaps, there were gender differences

in recall, knowledge, or reporting of household expenditures. It is also not known if

female respondents were in female headed households.

Based on reported behaviors, men had higher STI risk. Men reported a higher

frequency of unprotected sex (X2= 10.8, df=1, p=0.001) and were more likely to have

given money for sex (X2= 243.0, df=1, p<0.001) during the prior six months. However,

more men knew two or more STI symptoms compared to women (53 percent versus 50

percent, Χ2=5.8, df=1, p=0.016). Pairwise Pearson’s correlation tests between

independent variables demonstrated no collinearity.

31

In a pairwise Pearson’s test, the four measures of poverty were all significantly

associated (p<0.001) but not highly correlated. The largest correlation coefficient

(r=0.28) was between median household expenditures and median number of household

assets.

Finding 1: The poor have greater STI burden

Poverty was defined independently by four different dichotomous measures:

respondent education completed primary or not; household monthly expenditure above or

below the median; household asset score above or below the median; and household food

insecurity above or below the median. As shown in Table II-3, in two of the four

measures of poverty the “not poor” groups were less likely to have one or more STI

symptoms in the previous six months compared to the “poorer” groups, even after

controlling for demographic and behavioral factors (household median assets adjusted

OR=0.76 [95% CI=0.66-0.87], respondent education adjusted OR=0.68 [95% CI=0.59-

0.77]). Household median monthly expenditures and median food insecurity index did

not have a significant association with having one or more STI symptoms.

Finding 2: The poor use fewer STI treatment services

In this study, among respondents who reported having any STI symptoms in the

previous six months, the “not poor” were significantly more likely to use any type of STI

treatment service or product2 compared to the “poor” (household median monthly

expenditures adjusted OR=1.31 [95% CI=1.05-1.63], respondent education adjusted

OR=1.26 [95% CI=1.01-1.57]). Two of the four poverty measures were not significantly

associated with use of STI treatment (household median asset score adjusted OR=1.25

2 including self-medication, traditional healers, drug shops and full range of private and public facilities

32

[95% CI=0.99-1.57] and household median food insecurity index adjusted OR=0.88

[95% CI=0.71-1.10]).

Sixty-four percent of respondents with one or more STI symptoms failed to seek

any treatment. When asked why they did not seek any treatment for their STI complaint,

55% of respondents cited “lack of money”. “Not a serious condition” was the second

most common reason.

Finding 3: Private clinics account for a large proportion of STI treatment visits

Among the respondents who sought STI treatment in the previous six months, the

“not poor” were significantly more likely than “the poor” to use private providers,

including drug shops. Three of the four poverty measures had significant bivariate

associations with use of private providers (education crude OR=1.39 [95% CI=1.02-

1.89], median household assets crude OR=1.65 [95% CI=1.20-2.28], and food insecurity

index crude OR=1.43 [95% CI=1.04-1.96]).

Depending on the poverty measure used, 48-54% of poor respondents went to

private facilities, including drug shops, while 42-44% went to government facilities and

the remainder (7-9%) either self-medicated or saw a traditional healer (see Table II-4).

Discussion

Main findings

This study sheds light on the burden of STI symptoms in southwestern Uganda

and the need for appropriate health care interventions. There are several important

findings from this study. First, the data indicate that STI symptoms are common among

the population in southwestern Uganda, with 40% of individuals reporting at least one

33

STI symptom in the past six months. Moreover, the majority (63%) of these individuals

did not seek any form of treatment for their STI symptoms. The poor were more likely to

report having at least one STI symptom in the previous six months and were more likely

than the “not poor” to not use any treatment for their STI symptoms.

These findings reveal a need for health care interventions that enable individuals

to seek treatment for STI symptoms, as an increase in prompt treatment would not only

lessen the pain and suffering and possible sequelae among those who are experiencing

STIs, but could also decrease the transmission of STIs to partners and reduce the

incidence of STIs in a particular region. Among the reasons respondents gave for not

seeking treatment for STI symptoms, “lack of money” was the most frequently stated

reason (data not shown). This finding further underscores the importance of aiming

health policy interventions towards those with fewer economic resources.

Measuring poverty in the developing world is more challenging than the in

developed world, where poverty thresholds have been established through commonly

available income data and then tested and used for public policy purposes. The four

poverty measures used here represent significantly different concepts of poverty, as

confirmed by the relatively low correlation coefficients between the measures (r=0.28). A

below median number of household assets indicates long term deprivation. Having a low

education level represents deprivation experienced during childhood, although the effects

of low educational achievement typically carry forward to adulthood. The food insecurity

index measures consumption within the previous 30 days, as does the 30 day household

expenditures measure. Each of these poverty measures is an imperfect measure of overall

34

level of poverty; however, by examining all four, it is hoped that the latent concept of

poverty was more fully captured than by any poverty measure alone.

While this study found that those with higher poverty scores had a greater need

for STI services, the third research question explores the odds of using private versus

public facilities for STI treatment. Some might argue that health care interventions

directed at the poor should focus on bolstering government facilities in Uganda, where

services are nominally free, although informal charges are common. However, among

those who did visit a provider for STI treatment, respondents with high and low poverty

scores showed a preference for private facilities, including drug shops. The potential

reasons for this preference include perceived better quality of care at private facilities;

greater access to private facilities in terms of provider’s hours and proximity; and broader

level of treatment options available at private facilities.

While both the poor and “not poor” used private facilities more than

governmental facilities, the poor used private providers in lower proportions compared to

the “not poor”. These findings indicate that another viable option for improving access to

STI treatment for the poor is to enable them to obtain treatment in the private sector

through an output-based aid subsidy.

Limitations

It is important to acknowledge this study’s limitations. The first limitation is that

it is unknown whether any respondents from the first survey were interviewed in the

second survey. Non-independence of clustered observations within each survey year was

accounted for in the multivariable models; however, it is not known, and it is not possible

to control for, interviewing the same person in both surveys.

35

Additionally, although the survey had high response rates (88% in 2006 and 93%

in 2007), the survey population over-sampled older and female respondents and the study

did not reweight for the regional population structure. Sampling weights were not applied

not to the study data and quantified results are not generalized to the regional population.

Within the sample, the odds of having one or more STI symptoms did not differ by age or

gender; however, older individuals with STI symptoms were more likely to seek

treatment for their symptoms compared to younger individuals.

Another possible limitation might result from the stigma of reporting use of

traditional healers. Anecdotally, there is shame in reporting use of traditional healer

services to a modern medical professional. Our survey was carried out by medical

teaching faculty at Mbarara University and it is possible that respondents were reluctant

to mention their use of traditional medicine, leading us to underestimate the proportion of

the survey population using traditional healers. A greater use of traditional medicine than

indicated in this study would indicate, given the poor quality of the care provided by

traditional healers, a continued need to educate the population about higher quality

modern healthcare. If further investigations determine that use of traditional medicine is

more common than our study found, health policy interventions to increase modern STI

treatment will need to include elements of social marketing to convince a subset of the

population to shift away from traditional medicine towards modern treatment of STI

symptoms or traditional healers could be brought into the voucher program to refer

patients with STI symptoms to accredited clinics.

36

Conclusions

In spite of these limitations, this study has demonstrated that there is a substantial

need for STI treatment in southwestern Uganda, particularly among the poor. One

proposed strategy for providing STI services to the poor is through an output-based aid

intervention that subsidizes STI care in the private health facilities. Because those with

higher poverty scores indicated a preference for using private facilities and a pattern of

using private facilities for the treatment of STI symptoms, an output-based aid

intervention has the potential to be successful in reaching large numbers of poor patients

with STI symptoms. Future work should focus on evaluating output-based aid voucher

programs in this context.

37

Figures and Tables

Figure II-1: Directed acyclic graph of the proposed causal factors in utilization of STI treatment

*Facilities are defined as public hospitals, private hospitals, public clinics, private clinics,

W2: SPATIAL RISKRural versus urban location

Health outcome

1. Utilization of private and govt clinics in previous 6 months

2. Use of any facilities* for STI treatment in previous 6 months

W3. KNOWLEDGE & BEHAVORIAL RISKS: Unprotected sex previous 6

months Knowledge of STI symptoms Having more than 1 partner in

previous 6 months

A. ECONOMIC RISK: Food insecurity Household assets Education Household expenditure

W1. DEMOGRAPHIC FACTORS: Age Sex Marital status

38

Figure II-2: Directed acyclic graph of the proposed causal factors in STI symptoms

W2: SPATIAL RISKRural versus urban location

Health outcome

Having at least 1 STI symptom in past 6 months

W3. KNOWLEDGE & BEHAVORIAL RISKS: Unprotected sex in previous 6

months Knowledge of STI symptoms Having two or more partners in

previous 6 months

A. ECONOMIC RISK: Food insecurity Household assets Education Household Expenditure


39

Figure II-3: Distribution of education levels among 5,198 respondents, Mbarara region surveys in 2006 and 2007

40

Figure II-4: Distribution of the number of common household assets (0-7 assets) among 5,088 respondents in Mbarara surveys in 2006 and 2007.

41

Figure II-5: Distribution of household food insecurity scale (0-27) among 5,098 respondents in Mbarara region surveys in 2006 and 2007. Higher values indicate greater food insecurity.

42

Figure II-6: Distribution of monthly household expenditure among 5,137 respondents in Mbarara region surveys in 2006 and 2007 ($1 = 2000 UgSh)

43

Table II-1: Comparison of sex and age in 2002 Uganda Census population and the survey population in 2006 and 2007

2002 Census 2006 Survey 2007 Survey

Age Malen=379,164

Femalen=414,099

Male n=1,044

Female n=1,546

Male n=1,355

Female n=1,279

15-24 years

47% 47% 21% (217) 28% (425) 29% (387) 33% (426)

25-34 years

30% 30% 40% (419) 40% (621) 40% (545) 39% (500)

35-49 years

23% 23% 40% (408) 32% (500) 31% (423) 28% (353)

44

Table II-2: Description of respondents in 2006 and 2007 by sex

Women (n=2639) Men (n=2757) Chi-Square

Socio-demographic factors

Respondent age (mean in years)

29.9 years (n=2808)

SD= 8.8 years

31.1 years (n=2420)

SD= 9.1 years

Marital status

Single/widowed/divorced

Married or cohabitating

28% (n=786)

72% (n=2001)

32% (n=763)

68% (n=1636)

X2=7.9, df=1, p=0.005

Poverty

Monthly household expenditure (Uganda shillings)

Mean: 84000

(SD: 201000)

Median: 50000

(IQR: 30000-100000)

n=2737

Mean: 101,800

(SD 201,600)

Median:60,000

(IQR: 30,000- 100,000)

n=2399

Above vs below median

X2=24.9, df=1, p<0.001

Household food insecurity score (0-27)

Mean: 8 (SD: 5.5)

Median: 7

(IQR: 4-11)

n= 2738

Mean: 7 (SD: 5.5)

Median: 7

(IQR: 4-11)

n= 2359


X2=0.16, df=1, p=0.690

Household assets (7 common goods)

Mean: 2 (SD 2)

Median: 2

(IQR: 1-3)

n= 2730

Mean: 2 (SD: 2)

Median: 2

(IQR: 1-3)

n= 2357


X2=4.4, df=1, p=0.035

Education

No formal (n=651)

Some primary (n=1789)

Completed primary (n=880)

Some secondary (n=1113)

Complete secondary (n=764)

440 (16%)

1016 (36%)

420 (15%)

565 (20%)

347 (13%)

n=2788

211 (9%)

773 (32%)

460 (19%)

548 (23%)

417 (17%)

n-2409

X2=94.9, df=1, p<0.001

Village characteristics

Commercial villages

Rural villages

1565 (55%)

1265 (45%)

1388 (57%)

1055 (43%)


X2=1.22, df=1,

45

Women (n=2639) Men (n=2757) Chi-Square

p=0.269

STI behavioral risks & knowledge in previous six months

Unprotected sex

Consistent condom

1945 (86%)

331 (14%)

1804 (89%)

228 (11%)

Gave money for sex 61 / 2609 (2%) 333 / 2307 (14%)

Knows 2+ STI symptoms

Knows only 0-1 symptoms

1338 (50%)

1347 (50%)

1250 (53%)

1098 (47%)

46

Table II-3: Relationship between four dimensions of poverty and three evaluation outcomes among respondents on the 2006 and 2007 surveys in the Mbarara region

One or more STI symptoms in previous six months among sexually active

Obtained any STI treament+ in previous six months among those reporting 1 or more STI symptoms

Obtained private STI treatment among those who sought any STI treatment at public or private facilities (incl drug shops)

Education

Total N (“poor” and not poor”) 4201 2058 653

No complete primary (“poor”) 971/ 1956(50%)

283/ 1011(28%)

164/ 309(53%)

Completed primary (“not poor”) 951/ 2245(42%)

341/ 1027(33%)

210/ 344(61%)

Bivariate OR 0.75*** 1.28** 1.39*

Bivariate 95% CI 0.66-0.84 1.06-1.55 1.02-1.89

Multivariate aOR^ 0.68*** 1.28* 1.32

Multivariate 95% CI 0.59-0.77 1.06-1.55 0.80-2.18

Household monthly expenditures


Median and below (“poor”) 1001/ 2139 (47%)

284/ 1011(28%)

178/ 328(54%)

Above median (“not poor”) 896/ 2032(44%)

333/ 989(34%)

194/ 324(60%)

Bivariate OR 0.90 1.30** 1.26

Bivariate 95% CI 0.79-1.01 1.08-1.56 0.92-1.71

Multivariate aOR^ 0.91 1.27* 1.05-1.54

Multivariate 95% CI 0.79-1.04 1.05-1.54 0.72-1.55

47

Table II-3: Continued

One or more STI symptoms in previous six months among sexually active

Obtained any STI treament+ in previous six months among those reporting 1 or more STI symptoms

Obtained private STI treatment among those who sought any STI treatment at public or private facilities (incl drug shops)

Household asset score


Median and below (“poor”) 1150/ 2382(48%)

256/ 1227(29%)

191/ 371(52%)


267/ 788(34%)

172/ 270(64%)

Bivariate OR 0.79*** 1.25* 1.65**

Bivariate 95% CI 0.71-0.91 1.02-1.55 1.20-2.28

Multivariate aOR^ 0.76*** 1.24* 1.41

Multivariate 95% CI 0.66-0.87 1.02-1.51 0.95-2.11

Household food insecurity in past 30 days


Median and below (“poor”) 899/ 1950(46%)

312/ 1096(29%)

167/ 317(53%)


301/ 984(34%)

197/ 321(61%)

Bivariate OR 0.98 1.27* 1.43*

Bivariate 95% CI 0.87-1.11 1.04-1.56 1.04-1.96

Multivariate aOR^ 0.96 1.26* 1.49*

Multivariate 95% CI 0.84-1.10 1.03-1.55 1.01-2.21

*p < 0.05, **p < 0.01, ***p < 0.001

^ multivariate models control for respondent demographic factors (age, sex and marital status), village level access to healthcare (as approximated by commercial status of respondent’s village), and risk behaviors (unprotected sex, having two or more partners, giving money for sex).

+ “any STI treatment” includes self-medication, traditional healers, and the full range of allopathic providers

48

Table II-4: Utilization of STI treatment in previous six months by four different measures of poverty at public and private facilities, including drug shops and traditional healers, among respondents in the 2006 and 2007 surveys in the Mbarara region

Utilization of STI treatment in previous 6 months by the poor

Low education (n=339)

Few household assets (n=405)

Low household expenditure (n=356)

Low household food security (n=342)

All respondents who sought tx(n=724)

Govt clinics and hospitals

145 (43%) 180 (44%) 150 (42%) 150 (44%) (284) 39%

Private, including drugs shops

164 (48%) 191 (49%) 178 (50%) 167 (54%) (377) 52%

Traditional healers & self-treated

30 (9%) 34 (7%) 28 (8%) 25 (7%) (63) 9%

49

Chapter III: Impact of an output-based aid voucher program on the prevalence of syphilis and utilization of treatment services for sexually transmitted infections in southwestern Uganda

Abstract

Rationale: A voucher program subsidizing access to healthcare services to treat

sexually transmitted infections (STIs) began after a baseline survey in 2006. Sixteen

clinics were contracted and thirteen clinics saw more than 10,000 patients (the other three

clinics combined saw fewer than 200 total patients) between the program launch in July

2006 and the follow-up survey in November 2007.

Objectives: The study sought to determine the impact of the voucher program in a

before-and-after design measuring three important outcomes in year one and year two of

the evaluation: the proportion of respondents who recognized two or more STI

symptoms; among respondents having one or more STI symptoms, the proportion who

sought STI treatment; and the prevalence of syphilis. The same analysis was conducted

on four subgroups of the poor, alternately defined by the following dimensions of

poverty: median household monthly expenditures, median household assets, completed

primary school, and median household food insecurity score. The study also sought to

determine whether distance was inversely correlated with STI treatment utilization.

Finally, the study aimed to measure in populations <11 kilometers and ≥11 kilometers

from contracted OBA clinics the change between 2006 and 2007 on the proportion who

sought STI treatment, among respondents having one or more STI symptoms, and the

prevalence of syphilis.

Methods: Three data sets were used: the claims management database, population

surveys conducted in 2006 and 2007, and a spatial dataset for the region indicating

50

administrative boundaries, clinic locations and roads. We compared distance between

each patient’s village to the nearest contracted clinic for the 14,961 patients who used

vouchers for STI treatment to determine whether use of STI voucher treatment services

decreased with greater distance. Observations from the first survey were matched to

observations from the second survey using a nonparametric matching package

(GenMatch) in R (version 2.8.1) to control for potential confounding by the matched

variables. Logistic models were fit using the matched dataset. The “distance to contracted

clinic” cutpoint was set at the median value of the continuous version of “distance to

contracted clinic” before fitting it in logistic models with STI treatment utilization and

the prevalence of syphilis as outcomes of interest.

Results: A majority of the patients using an STI voucher (54%) sought care ≤10

kilometers of their village of residence. Distance was inversely correlated with use of STI

treatment services at contracted clinics (r= -0.78). Knowledge of STI symptoms

increased 18% between the first and second years (aOR=1.43 95% CI=1.22-1.68). STI

treatment utilization among those reporting having had one or more STI symptoms in the

previous six months increased 15% between the first and the second year; however, the

increase was not statistically significant (aOR=1.14, 95% CI=0.89-1.47). The prevalence

of syphilis, as measured by the VDRL test, decreased 42% between the two surveys

(aOR=0.63, 95% CI=0.48-0.79). There was a greater reduction in the prevalence of

syphilis among respondents between 2006 and 2007 who lived <11 kilometers from a

contracted facility compared to respondents who lived ≥11 kilometers from a contracted

clinic (57% decrease versus 20% decrease).

51

Conclusions: The OBA voucher program appeared to improve knowledge of STI

symptoms and reduce the prevalence of syphilis. There was not a significant

improvement in the utilization of STI treatment in the full study population. However, the

distance from village of residence to contracted OBA clinic appeared to be a significant

barrier to utilization of STI treatment. Greater distances significantly attenuated a local

area effect (<11 kilometers) of the voucher program on the utilization of STI treatment

utilization and the prevalence of syphilis.

52

Background

There is abundant evidence from many low-income countries that the poor benefit

less than the wealthy from collectively funded health services and suffer a disease burden

as great or greater (Bustreo, Harding and Axelsson 2003; Castro-Leal et al. 2000;

Gwatkin, Bhuiya and Victora 2004; Palmer et al. 2002; Prata et al. 2005). In situations

where private providers are unable or unwilling to satisfy healthcare needs of the poor,

governments may decide to use public funding to subsidize access. When governments

decide to provide healthcare, they can either supply medical staff and facilities or

purchase health services from qualified professionals and institutions (Preker, Harding

and Travis 2000).

Low-income countries, with donor support, have largely engaged in supply side

healthcare production (i.e. building, stocking, and staffing health facilities). In some

cases, donor support of healthcare supply has resulted in measurable improvements in

health services management and health outcomes; however, there are many examples of

supply side donor-funded programs that were poorly planned, improperly managed and

unable to demonstrate links between expenditures and improvements in healthcare

management or health outcomes (Ensor and Ronoh 2005). Although the application of

private sector models to stimulate demand for public health goods and services has been

successfully piloted (Grant and Walford 2004, p 13), the majority of these models have

utilized supply side financing (Ensor and Ronoh 2005).

53

What finance strategies are available to donors and governments?

Governments seeking to improve efficiency in healthcare delivery or equity in

health may decide to increase access to health goods and services via one or more of the

following options: 1) unrestricted cash payments or vouchers (economic ‘gifts’), 2)

conditional demand subsidies to patients for health goods or services, 3) competitive

purchasing of goods or services on behalf of consumers, or 4) monopoly, often

government, production of health goods or services for consumers (Posner et al. 2000).

Conditional government payments can be channeled through demand-side

subsidies that the user, armed with a voucher, can use to shop for a provider among those

approved and willing to accept the voucher. Janssen and colleagues identified

competitive vouchers, flat-rate subsidies and direct cash subsidies as typical demand-side

tools (2004). Unlike direct cash subsidies, which do not restrict consumer choice on how

the cash is spent, a voucher limits the bearer to a specific set of goods and services at a

fixed reimbursement amount (Janssen et al. 2004; Steuerle 2000). The voucher is

essentially conditional cash paid before the service, while a conditional cash transfer is

paid only after the service has been provided or the condition is met.

Alternatively, conditional payments can fund the supply of health goods or

services (e.g. paying a performance bonus to health workers or organizations), which is

more common in the infrastructure sectors where natural monopoly characteristics may

exist (World Bank 2006). On the supply side, performance contracts are useful tools for

enforcing delivery standards and service quality (Eichler 2001; Logie, Rowson and

Ndagije 2008).

54

What is output-based aid (OBA)?

The use of vouchers in combination with results-based contracting can stimulate

consumer demand for and increase the supply of competitively contracted healthcare

goods and services. The use of vouchers and results-based contracting is generally known

as output-based aid (OBA) (Gorter et al. 2003; Janisch and Potts 2005; Sandiford et al.

2002). In salaried positions not linked to performance, staff may have little incentive to

raise their productivity or be concerned with patient perceptions of health care quality

(Robinson 2001). Under an OBA contract, however, incentives are created to increase the

number of patients seen. A voucher empowers the patient to choose his or her health

provider. Informed patient choice has the potential to induce providers to improve the

quality of their services.

Donors and governments are interested in a variety of reforms utilizing output

based models, including “quasi-contracts” between government agencies;

commercializing public agencies; contracting out specific services to the private sector;

transferring responsibility for providing services to the private sector through concessions

or outright privatization; and providing demand subsidies directly to consumers (Brook

and Petrie 2001). In contrast with more traditional supply side financing approaches,

these schemes seek to define objectives and specify expected performance in terms of

outputs rather than inputs (Brook and Petrie 2001). Under quasi-contracting, for instance,

public health staff may receive a regular salary, but bonuses are conditional on meeting

performance targets; an example of quasi-contracting is the Rwanda OBA program (Rusa

and Fritsche 2006). In Rwanda, there is no explicit competition between providers for

patients; however, there are competitive incentives for contracted facilities to improve the

55

quality of care. In this situation, results-based contracting emphasizes improvements to

an organization’s ability to deliver public services (Brook and Petrie 2001). The pay-for-

performance incentives are largely contained to institutional management, such as

staffing quality, supply chain, buildings and other inputs (Eichler 2001; Logie et al.

2008). This focus on incentives for better management is a useful mechanism for

improving service quality; however, it does not link patient utilization to performance

payments as is done with demand side subsidies.

Output-based models can also be used to finance demand side interventions. It is

possible to transfer power to the patient and remunerate providers according to the

number of patients they are able to attract, as reflected in Figure III-1 (Bhatia and Gorter

2007). The feasibility of using targeted vouchers in demand side finance has been

demonstrated in several regional projects (Bellows, Mulogo and Bagenda 2008; Ensor

2004; Gorter et al. 2003; Grant and Walford 2004). Vouchers deliver a conditional

economic subsidy to recipients, ideally giving the bearer the ability to choose from a

selected set of goods and services at approved providers, who compete for the voucher.

OBA links performance-based contracting and demand stimulation

Brook and Petrie (2001) identified a basic choice when deciding whether to

provide a service in a competitive market or through what might be called supply-side

results contracting, what they call “monopolistic supply arrangements”. Where healthcare

providers are many and easily accessed, vouchers targeted to consumers can give patients

a choice and create incentives for provider efficiency and attention to patient satisfaction.

Where there are few providers, the absence of a potential market or concern with weak

healthcare supply might lead program planners to focus on linking reimbursement to

56

improvements in provider service delivery, without any attempt to link subsidies to

patient choice. The decision whether to subsidize demand or supply requires

consideration of the contract’s length, how many facilities should be contracted, how to

monitor quality, and how best to ensure that the service provider has incentives to be

efficient. Competitive bidding for short term contracts can provide a useful incentive

structure. Programs linking provider performance with contractual payments have been

implemented in a number of settings (Logie et al. 2008; World Bank 2006). Other

demand-side programs, using cash transfers or vouchers, give purchasing power to the

consumer and pay providers according to the number of patients they are able to attract

(Bhatia and Gorter 2007). Demand side financing is increasingly being implemented in

an effort to improve access to reproductive and health services in low-income countries

(Behrman and Knowles 1998) (DfID 2006; Sandiford et al. 2005).

Why implement OBA?

OBA voucher schemes have four aims: to improve provider quality; stimulate

utilization of selected services; target services to high-priority populations; and contain

costs (Mushi et al. 2003; Steuerle 2000). Examples of high priority populations include

poor youth at high risk of HIV infection, women suffering from domestic violence,

pregnant women or mothers of very young children, and administrative districts with a

high incidence of STIs.

Voucher schemes may induce clinic-level improvements without any competitive

pressure placed on service providers, although it is commonly assumed that voucher

programs introduce greater competition (Gorter et al. 2003). Competition may occur

57

during the initial clinic accreditation and later as contracted clinics compete for voucher-

bearing patients.

OBA vouchers are combined with contracts in which service suppliers or provider

networks agree to service delivery standards (Grant and Walford 2004 p 32). It may not

be necessary to give a physical voucher to consumers. Providers can be contracted to

deliver healthcare to properly screened patients (e.g. poor or high risk patients) without

requiring the patients to bring a physical voucher for each visit (Bradford and Shaviro

2000).

Utilization

Some services that have weak incentives (i.e. male circumcisions for HIV

prevention) or high utilization costs (i.e. facility-based maternal delivery) are good

candidates for OBA voucher programs (Griffith, Bellows and Potts 2007; Janisch and

Potts 2005). Salaried staff providing these services have little incentive to raise their

productivity or to be concerned with patient perceptions of health care quality (Bhatia

and Gorter 2007; Robinson 2001). Linking payments to performance and giving patients

their choice of provider introduces incentives for providers to treat more patients and to

treat them well.

In a voucher program in Nicaragua, a voucher program for treatment of STIs

among youth in Managua was credited with a 10% increase in service utilization

compared to the routine care in the absence of the vouchers (Borghi et al. 2005). In a

voucher program for STI treatment in Uganda, utilization at seven contracted clinics

increased on average 200% in the first 12 months of the program, compared to the 12

months prior to the program launch (Lowe and Bellows 2007).

58

Quality

It is assumed that in a voucher program, providers will maintain high quality

medical services to keep patient satisfaction high. The assumption is that there are no

market failures; that consumers have multiple options when seeking care. In reality,

providers may be contracted in areas with few or no alternatives.

A study of providers in a voucher program in Nicaragua used simulated patients

to measure provider adherence to protocols (Meuwissen et al. 2006a). Nineteen clinics

were contracted to accept vouchers from youth seeking sexual and reproductive health

services, principally contraception. Sixteen clinics were visited by the simulated patients

before the study (three clinics were not visited) and only eight of the simulated patients

were provided treatment according to the guidelines. One month after the voucher

program began, the 16 providers treated all simulated patients according to guidelines

(Meuwissen et al. 2006a).

Distance to care

Voucher programs can be expected to improve population health outcomes and

healthcare utilization if healthcare is sufficiently high quality and the targeted population

can reach the service providers.

Although it has long been recognized that proximity to health services is

associated with increased utilization (Dear 1977; Jarvis 1850), there has been no evidence

that distance from home to clinic is a barrier to healthcare utilization in voucher

programs. The correlation between healthcare utilization and distance from a patient’s

home to clinic likely varies with the specific service. For some health services in which

59

anonymity might be highly valued, greater distance from home may be preferred as fewer

people would recognize the patient.

Studies from sub-Saharan Africa indicate that distance is one of several critical

factors in accessing reproductive and sexual healthcare (Mills et al. 2006; Molesworth

2007; Thaddeus and Maine 1994). For example, a study in South Africa found that

demand for private insurance decreased as the distance from home to service providers

increased (Söderlund and Hansl 2003). In several studies of adherence to HIV

antiretroviral therapy (ART) in Africa, distance was consistently recognized as an

important barrier to adherence to treatment (Rosen et al. 2007; Uzochukwu et al. 2009).

Research objectives

The primary objective of this study was to evaluate the impact of the OBA

program in southwestern Uganda. We hypothesized that the combination of social

marketing and the economic subsidy of the OBA program would result in the following

outcomes:

1. an increase in knowledge of STI symptoms as a result of extensive social

marketing and health education efforts from the OBA program across the entire

study area between 2006 and 2007;

2. an increase in utilization of STI treatment services among respondents with one or

more STI symptoms by subsidizing cost and increasing patient demand for care

among individuals living close to contracted clinics and less so among individuals

living farther away from the clinics;

3. a decrease in the prevalence of syphilis due to treating more patients and treating

patients more appropriately under the program’s quality service delivery

60

guidelines, with the decrease being greater among individuals living close to

contracted clinics than among individuals living farther away;

4. among the respondents who had one more STI symptoms, an increase in the

proportion who utilized STI treatment between 2006 and 2007

5. among all respondents who submitted to the VDRL test, a decrease in the

prevalence of syphilis between 2006 and 2007

6. among the poor, an increase in the knowledge of STI symptoms between 2006

and 2007;

7. among the poor with one or more STI symptoms, an increase in the proportion

who used STI treatment between 2006 and 2007;

8. among the poor, a decrease in the prevalence of syphilis between 2006 and 2007.

Figure III-2 presents a directed acyclic graph of the effect of distance from place

of residence to contracted STI clinics on the proportion of respondents who know two or

more STI symptoms, the proportion of respondents with one or more STI symptoms who

seek any STI treatment, and the prevalence of syphilis among all respondents.

Methods

Three data sets were used: two surveys of the population, conducted in 2006 and

2007; a voucher claims management database; and a spatial dataset for the region

indicating administrative boundaries, clinic locations and roads.

61

Population-based surveys dataset

The first dataset was from cross sectional surveys conducted by Mbarara

University of Science and Technology (MUST) in 2006 and 2007, in which respondents

in the same 82 villages were targeted in each survey. In each survey, study participants

were asked about household assets, food insecurity, alcohol use, general healthcare

utilization in the previous six months, knowledge of STI symptoms, STI behavioral risks

(i.e. number of partners and condom use), STI treatment utilization in the previous six

months, and respondent’s social capital.

Study participants were also screened for syphilis, gonorrhea and trichomoniasis

(women only). After informed consent was obtained and the interview completed, a

blood sample and vaginal or urethral swab were collected. The samples, stored in

transport media, were returned to Mbarara University on a daily basis for processing in

the laboratory.

Survey team leaders also took the coordinates of the approximate center of each

village using a handheld Garmin eTrex GPS unit. Village coordinates were then added to

the spatial dataset, described below, using ArcMap software (version 9.2 Build 1500,

Redlands, CA).

Voucher claims dataset

The second dataset came from an ongoing voucher program launched in July

2006 to subsidize treatment of STIs at contracted clinics in the region. Claims submitted

for reimbursement were entered into a database at the management agency. For the

period July 2006 to April 2008, there were 14,989 voucher records that documented

62

patient sex, age, home village, STI services received, and cost of STI treatment at the

clinic.

Local areas spatial dataset

The third dataset was a spatial dataset from the Uganda Bureau of Statistics. It

contained administrative areas down to local village areas, primary roads, and population

density. Spatial data were accessed using ArcMap software (version 9.2 Build 1500,

Redlands, CA). The coordinates for each contracted clinic were measured on site with a

handheld Garmin eTrex GPS unit and then added to the spatial dataset. We used the

spatial data to estimate distances to nearest clinics from our surveyed villages (e.g. first

dataset above) and from the voucher patients' home villages (e.g. second dataset above).

We then used the “distance to clinic” measurements for the voucher patients to

test whether utilization was inversely correlated with distance to contracted clinics. If

such a relationship was found, we could use distance as a “treatment” in the cross

sectional surveys and test whether living in a village near to contracted clinics was

associated with a greater change in the proportion of STI utilization and the prevalence of

syphilis between 2006 and 2007, compared to living in a village far from contracted

clinics.

Control of confounding

In this study, we calculated the prevalence of syphilis (VDRL results) and

proportion of individuals with one or more STI symptoms who used STI treatment in

both surveys. To control for potential confounding when making comparisons of

outcomes between surveys, we used a non-parametric search algorithm to match

63

respondents on ten variables likely to be associated with the three outcomes of interest

(knowledge of STI symptoms, utilization of STI treatment, and prevalence of syphilis)

and the exposures of interest (survey year, distance to care, and poverty).

Common analytic methods to adjust for potential confounding include

stratification and multivariable regression (Greenland and Morgenstern 2001).

Stratification is the simpler method to implement; however, when a sufficiently large

number of variables are stratified, it can result in cells with sparse data and lead to

imprecise estimates of association (i.e. “sparse-data problem”) (Greenland and

Morgenstern 2001; Greenland, Robins and Pearl 1999). The most common method to

avoid the sparse-data problem is multivariable regression, which examines the potential

effect of an exposure of interest, while simultaneously holding constant any statistical

association between other factors (confounders) and the outcome of interest (Grimes and

Schulz 2002).

Although there are advantages to multivariable regression analysis, there are

limitations in its ability to control confounding. Confounding covariates are controlled for

one regressor at a time; regression does not attempt to balance the joint distribution of the

confounders independent of the exposure of interest. The common regression analysis is

often inadequate to measure the full complexity of the interaction between confounding

covariates, treatment, and outcome. Given the constraints of standard regression, Sekhon

(2008) developed a non-parametric matching method to achieve balance of the joint

distribution across all levels of treatment to control for confounding.

64

GenMatch package in R

The Matching package in R (version 2.8.1 ) (abbreviated as GenMatch) provides

matching functions for propensity score, Mahalanobis, inverse variance and a genetic

search algorithm for optimal balance of the joint distribution of any variable set (Sekhon

2008). As a generalization of propensity score and Mahalanobis distance matching,

GenMatch optimizes the balance of the joint distribution of observed covariates between

treated and control groups (Mebane and Sekhon 1998; Sekhon and Mebane Jr. 1998).

Although a propensity score is not necessary, the nonparametric GenMatch algorithm is

improved when a propensity score is added. Sekhon demonstrated that GenMatch is able

to find good balance of the joint distribution of covariates in the treatment and control

populations and to reliably reproduce experimental outcomes from non-experimental

matching designs (Diamond and Sekhon 2006; Sekhon 2008). GenMatch optimizes the

joint distribution of observed variables by using a genetic search algorithm that

determines the best weight for each variable. By default GenMatch matches 1-to-1 with

replacement and estimates the average treatment effect among the treated (ATT).

For our analysis, observations were matched with replacement. Observations were

matched exactly on the following variables: parish of residence, sex, age in years, the

number of sex partners in the previous six months, the number of health facilities in the

respondent’s village, whether the respondent had any unprotected sex in the previous six

months, and binary variables to indicate missingness for respondent sex, age in years,

number of sex partners, and unprotected sex. Because the match was restricted to

respondents from the same parish, the universe of potential pairings with the same sex,

65

age in years, number of sex partners, unprotected sex and same pattern of missingness

was limited to a range of 50 to 72 respondents for each parish.

GenMatch was set to optimize the genetic algorithm in samples of 1000

observations each generation (pop.size=1000), the number of maximum generations was

set to 50 (max.generations=50) and the number of generations to continue calculating

after optimization was reached was set to four (wait.generations=4).

“Wait.generations=4” determines that if there is no improvement in optimization of

matches within four generations, the matching process stops.

Model selection

Matched observations from GenMatch were exported from R to Stata. Multilevel

mixed-effects logistic regression models were run in Stata (“xtmelogit” command in

version 10.1 for Windows) for the following dichotomous outcomes: knowledge of two

or more STI symptoms among all respondents who completed the survey interview, use

of STI treatment services among study participants who reported one or more STI

symptoms in the previous six months, and prevalence of syphilis among all study

participants who submitted samples for VDRL syphilis screening.

Different names are used in the literature: contextual models, hierarchical linear

model, hierarchical linear regression, random coefficient model, and hierarchical mixed

model, among others (Diprete and Forristal 1994), however, they share a common intent

to carry out a simultaneous multivariable analysis of effects at micro (e.g. respondent)

and macro (e.g. group) levels (Diprete and Forristal 1994; Duncan, Jones and Moon

1998; Krieger 2001).

66

Diez-Roux (2000) summarized the differences between multilevel analysis and

other modeling approaches in epidemiology. Multilevel analysis allows for the

simultaneous examination of the effects of group-level and individual-level predictors,

accounts for the non-independence of observations within groups, treats groups as

sampled from a larger unobserved population of groups, inter-individual and intergroup

variation can be examined (as well as the contributions of individual-level and group-

level variables to those variations) (Diez-Roux 2000).

In this study, we used multilevel mixed-effects logistic models to estimate effects

that we assumed had hierarchical variance structures at respondent level, at the level of

pairs across survey years, and at parish level. Under random effects we consider

individual differences as random disturbances drawn from a distribution specified in the

model. The random effects model has the advantage of using fewer degrees of freedom,

and that individual differences are considered random rather than fixed and estimable.

Poverty assessment

As explained elsewhere, poverty was a multidimensional concept measured

separately by four variables in the study. The four poverty variables were dichotomous:

median household monthly expenditures, median number of household assets,

educational level completed, and median food insecurity score. The change in

proportions of knowledge of STI symptoms, STI treatment utilization, and prevalence of

syphilis was compared between 2006 and 2007 among the four alternate definitions of

the poor.

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Results

Distance as a barrier to STI treatment

Distance to clinic from patient village was inversely correlated with use of STI

voucher treatment services. Using the voucher claims data and the spatial dataset, we

found that the majority of voucher patients (54%) traveled fewer than ten kilometers.

Generally, distance was inversely correlated with use of STI treatment services at

contracted clinics (r= -0.78).

When voucher patients had a choice of clinics, they chose the nearest clinic 87%

of the time. Table III-5 presents the distance, in five kilometer increments, from each

patient’s home village to the clinic they visited. In the left-hand column is the distance

from each patient’s home village to the nearest contracted clinic. Every row of the table

indicates, in five kilometer increments, the distance to the nearest clinic from each

patient’s village and the distance from each patient’s village to the clinic he or she

actually visited. For instance, the first row of the table shows that among the 5,741

patients who lived within five kilometers of the nearest clinic, 5,467 patients (95%)

visited a clinic within five kilometers of their village of residence. The remaining 274

patients (5%) of 5,741 patients ≤5 kilometers of a contracted clinic traveled farther than

the nearest clinic: 193 patients (3.3%) traveled to another clinic 5-10 kilometers from

home, 12 patients (<1%) traveled to another clinic 10-15 kilometers from home, and 35

(<1%) patients traveled to another clinic that was 20-25 kilometers from home.

Considering the choice of clinics made by patients, we see in the bottom row in

Table III-1 (i.e. the marginal values of “Total”) that as the actual distance from village of

residence to contracted clinic increased from five to 10 kilometers, and 10 to 15

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kilometers and so forth, the number of patients visiting clinics at greater distances

decreased. 40% of patients traveled five kilometers or fewer to visit a voucher clinic.

20% of patients traveled between 5 and 9.9 kilometers to visit a voucher clinic; 10% of

patients traveled between 10 and 14.9 kilometers and 11% traveled between 15 and 19.9

kilometers to visit a voucher clinic. The remaining 19% traveled more than 20 kilometers

to visit a clinic (see Table III-1).

GenMatch cohort is balanced on potential confounders

Matching on the joint distribution of ten potential confounders, the GenMatch

function in R generated a dataset of 3438 observations sampled from the original

unmatched dataset, with some observations matched multiple times. The match was done

without considering the outcomes of interest. The match criteria specified exact match on

the joint distribution of the ten variables: parish of residence, age in years, sex, number of

sex partners in the previous six months (0, 1, 2, or 3 or more), having unprotected sex in

the previous six months, having any health facility in the village and missingness

indicators for sex, age in years, number of sex partners, and having unprotected sex. As a

result, the after-match balance was perfect between respondents from the two survey

years (see Table III-6).

Knowledge of STI symptoms increased after the voucher program began

The proportion of respondents in the surveys who could recognize two or more

STI symptoms increased between July 2006 and November 2007. In this 16-month

period, the voucher program ran extensive social marketing and health education

programs on the radio and in community presentations throughout the region. When

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respondents were asked which medium they trusted the most for STI information, radio

was the highest ranked. In 2006, over 65 percent of respondents indicated “radio” as their

preferred means to learn about STIs and in 2007 more than 70 percent named “radio” as

their preferred source for STI information. In both surveys, “friends and peers” (10%)

and “government clinic staff” (7%) were distant second and third options as sources of

STI information.

In 2006, 49% of respondents (716/1470) could name two or more STI symptoms;

at the time of the second survey in 2007, 58% of respondents (892/1528) correctly named

two or more symptoms (aOR=1.43, 95% CI=1.22-1.68) (see Table III-3). Increases in

STI knowledge did not vary significantly by distance to a contracted clinic.

STI treatment utilization increased after the voucher program began

When analyzing utilization among those having had one or more STI symptoms

without considering distance from village of residence to clinics, the odds of respondents

having used any STI treatment service in the previous six months had a non-significant

increase between 2006 and 2007 (27% to 31% between 2006 and 2007, aOR=1.14, 95%

CI=0.89-1.47) (see Table III-7). Utilization of STI treatment did not distinguish between

types of clinics visited (public or private) or whether the respondent used a voucher.

When distance was taken into consideration, a much higher proportion of

respondents who lived <11 kilometers from the contracted clinics and who reported

having STI symptoms used STI treatment, compared to respondents who lived ≥11

kilometers from the contracted clinics. Respondents who reported STI symptoms and

lived <11 kilometers from a contracted clinic had a 48% increase in STI treatment

utilization (from 29% in 2006 to 43% in 2007) compared to a <1% increase among

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respondents with STI symptoms who lived ≥11 kilometers from a contracted clinic (23%

in 2006 and 24% in 2007, see Table III-8).

Prevalence of syphilis decreased after the voucher program began

The prevalence of syphilis (positive VDRL) was lower in 2007 than 2006

(aOR=0.62 95% CI=0.44-0.93) (see Table III-3). The decrease in the prevalence of a

positive VDRL test between 2006 and 2007 was greater among study participants who

lived <11 kilometers from a contracted clinic (57% decrease) than among participants

who lived ≥11 kilometers from a contracted clinic (20% decrease) (Table III-9).

Increase in knowledge of STI symptoms among the poor

The poverty measures were not highly correlated; the highest Pearson pairwise

correlation coefficient was 0.28. Regardless of the approach used to classify poverty, the

proportion of poor respondents who could name two or more STI symptoms significantly

increased between the launch of the voucher program in 2006 and the follow-up survey in

2007 (low monthly household expenditures 51% to 57%, aOR=1.37; few household

assets 46% to 57%, aOR=1.68, low food security score 48% to 61%, aOR=1.82, low

education level 47% to 54%, aOR=1.34; see Table III-10).

Increase in STI utilization among the poor

As mentioned previously, the four different poverty measures were not highly

correlated. In the four separate models that restricted the dataset to observations that met

alternate definitions of the poor, among those who reported having one or more STI

symptoms in the previous six months there was some evidence that utilization of STI

treatment services increased between 2006 and 2007. Among the poor defined by two

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alternate measures of poverty, there was a significant increase in the proportion of STI

treatment used between 2006 and 2007 for those reporting one or more STI symptoms

(low food security 25% to 38%, aOR=2.08; low education level 22% to 28%, aOR=1.70;

see Table III-6). In two other alternate measures of poverty (i.e. low household monthly

expenditure and few household assets), there was a non-significant increase in the

proportion of STI treatment utilized by the poor who reported one or more STI

symptoms.

Decrease in prevalence of syphilis among the poor

The prevalence of syphilis decreased among the poor after the launch of the

voucher program. Among the poor defined by three alternate measures of poverty, there

was a significant decrease in the prevalence of syphilis (VDRL) between 2006 and 2007

(low education level 6.1% to 1.8%, aOR=0.25; low food security score 5.6% to 1.9%,

aOR=0.23, few household assets 6.9% to 4.3%, aOR=0.62).

Discussion

As expected, we found an inverse correlation in clinic utilization with distance

from village of residence to contracted clinic. There were limitations in the way distance

was measured. We used a direct line measurement (i.e. “as the crow flies” distance),

when in reality, patients travel non-linear routes from home to clinic. Linear distance to

clinic very likely underestimates the true distance to clinic. We would expect, but have no

way to correct for in the current study, local differences in non-linear routes. For

example, patients in a certain village are relatively close to clinic but the only available

route has many curves and, as a result, represents a great distance to travel. In contrast,

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patients from another village may have a longer linear distance to clinic, but their route to

clinic is also straight. In our spatial table it would appear that patients from the more

distant village visited the clinic in greater numbers when in reality, the route followed by

patients from the near village had a greater distance to travel. This would reduce the

magnitude of the inverse correlation between distance and healthcare utilization by

misclassifying patients near to clinics as far and patient far from clinics as near.

In addition to the non-linearity of most travel routes, patients also have a varying

ability to pay out of pocket for transport, and the type of transport they can afford affects

the practical distance they can expect to travel. We expect that poverty modifies the

relationship between distance from home to clinic and utilization of that clinic. At higher

levels of income, patients can travel farther and can exercise more choice in selecting a

healthcare provider.

The STI treatment utilization observed in this study is not a direct measure of

utilization due to vouchers per se. The increase in STI utilization could have been driven

by the social marketing campaign carried out during this study period, by an increase in

incidence of an STI (likely not syphilis), by the economic incentive of the voucher

subsidy, or by some other unknown factor(s).

In the matched dataset, 27 of 214 respondents in 2007 reported using a voucher

yet we observed a 15% increase in utilization of STI treatment compared to 2006. The

social marketing of vouchers and health education on radio and in communities may have

had a role in increasing treatment seeking at non-voucher and voucher facilities alike.

Considered the importance that respondents placed on radio as a medium for health

information, it is possible that respondents were motivated to seek STI treatment

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regardless of whether they had a voucher. Additional research on the impact of marketing

on the purchase and utilization of vouchers, including longitudinal studies of marketing

events and utilization trends, ought to be considered.

The poverty measures were not highly correlated suggesting that the measures

reflected different dimensions of poverty. Regardless of which measure was used to

define poverty, knowledge of STI symptoms increased between 2006 and 2007 among

the poor. In three alternate measures of poverty, there was a significant decrease in the

prevalence of syphilis among the poor. Among the poor in two of the four poverty

measures, there was a significant increase in utilization of any STI treatment services

between 2006 and 2007. Regardless of which measure was used to define poverty, none

of the models indicated a significant decrease in utilization between 2006 and 2007 or a

significant increase in the prevalence of syphilis between 2006 and 2007. The evidence is

not strong; however, there is some evidence that, among the poor, there was an increase

in the proportion of respondents using STI treatment services and a decrease in the

prevalence of syphilis between 2006 and 2007.

Matching on the joint distribution of potential confounders did remove the

potential for confounding from the matched variables. However, there was a cost; the loss

of many unmatched observations (n=3676) represented a large loss of information. There

are limitations to creating a synthetic cohort, even a cohort with strict matching

requirements. It is not possible to claim that the matched observations are observations on

the same “synthetic individual”.

Matching on the joint distribution resulted in the inclusion of many observations

from 2007 with missing values in sex, age, number of sex partners in previous six

74

months, and having unprotected sex in previous six months. Observations were matched

on value of their joint distribution, not the values of each variable. As a result, it was

possible to match individuals with different values in some of their 10 matching variables

as long as the value of the joint distribution was the same. The matched dataset had a

greater proportion of missing values in the matched variables in 2007 (10-11% of

observations) compared to the unmatched 2007 dataset in which 2-5% of observations

were missing values for those matched variables (see Table III-6). The implications are

unknown; however, if missingness is correlated with the outcomes of interest,

missingness could act as a statistical confounder by differentially excluding observations

from 2007, as the statistical software drops observations with missing values from

multivariable models. The issue warrants further analysis.

Alternative analytic methods might control for confounding more efficiently than

GenMatch in this study of the impact of vouchers. These methods include machine model

selection using an algorithm like Deletion/Substitution/Addition (DSA) for counterfactual

causal estimation in g-computation (Petersen et al. 2006; Sinisi and van der Laan 2004).

Comparing the efficiency gains in g-computation to GenMatch would be a useful

investigation in future analysis.

We observed local area effects on utilization STI treatment and the prevalence of

syphilis; this was expected as we assumed that the new voucher services would attract

new patients and likely be of higher quality. Our study suggests that output-based aid

voucher programs, like the Uganda STI treatment program, can have multiple positive

health impacts in local populations.

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Figures and Tables

Figure III-1: Direction of financial flows under supply-side and demand-side strategies

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Figure III-2: A directed acyclic graph of the effect of distance to contracted STI clinics on 1) knowledge of STI symptoms, 2) any STI treatment seeking, and 3) the prevalence of syphilis

OUTCOMES

1. Knowledge of STI symptoms

2. Prevalence of syphilis

3. Utilization of STI treatment in previous six months

W3. SEXUAL RISK: Any unprotected

sex previous six months

Number of symptoms previous six months

Partner disclosure of past STIs

Knowledge of STI symptoms

V1. ECONOMIC RISK: Food insecurity Household assets Education Household

expenditure


A. Access to treatment

Distance to clinic from

village

UNSEEN: Voucher use in sex networks

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Table III-1: Distance between village of residence and contracted clinics for patients using vouchers in Mbarara region 2006-2008

Distance to nearest clinic

distance to clinic actually visited (km)

5 10 15 20 25 30 35 40 45 50 miss. Total

5km 5,467 193 12 4 35 30 0 0 0 0 0 5,741

10km - 2,380 99 58 59 648 0 0 0 0 0 3,244

15km - - 1,218 261 68 35 0 0 0 0 0 1,582

20km - - 0 1,192 37 264 0 0 0 0 0 1,493

25km - - - - 152 11 0 0 0 0 0 163

30km - - - - - 70 0 0 0 0 0 70

35km - - - - - - 229 0 0 0 0 229

40km - - - - - - - 208 0 0 0 208

45km - - - - - - - - 103 0 0 103

50km - - - - - - - - - 596 0 596

miss. - - - - - - - - - - 1532 1,532

Total 5,467 2,573 1,329 1,515 351 1,058 229 208 103 596 1532 14,961

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Table III-2: GenMatch balance on the means of matched variable after matching*

Parish Before Matching After Matching

mean treatment 20.841 21.161

mean control 21.043 21.161

std mean diff -1.7058 0

Sex Before Matching After Matching



std mean diff 18.544 0

Age Before Matching After Matching




Number of sex partners in 6 months Before Matching After Matching




Number of health facilities in village Before Matching After Matching


mean control 0.86055 0.71326


Any unprotected sex in 6 months (NA=2) Before Matching After Matching




*GenMatch also balanced on “missingness” for sex, age, number of sex partners, and unprotected sex.

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Table III-3: Knowledge of STI symptoms, use of STI treatment and the prevalence of syphilis in the Mbarara study population 2006 and 2007

Respondent knew two or more STI symptoms

Respondent sought any STI treatment+ in past 6 months

Respondent had a reactive VDRL result

Total N 2998 1642 3201

2006 participants 716/1470 (49%) 256/948 (27%) 95/1527 (6.2%)

2007 participants 892/1528 (58%) 214/694 (31%) 60/1674 (3.6%)

Bivariate OR 1.51*** 1.27 0.54***

Bivariate 95% CI 1.30-1.75 0.99-1.61 0.38-0.76

Multivariate aOR^ 1.43*** 1.14 0.63*

Multivariate 95% CI

1.22-1.68 0.89-1.47 0.44-0.93

*p<0.05, **p<0.01, ***p<0.001

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Table III-4: Among poor respondents in four alternate definitions of poverty, knowledge of STI symptoms, use of STI treatment and the prevalence of syphilis in the Mbarara study population 2006 and 2007

Respondent knows 2+ STI symptoms

Respondent seeks any STI treatment in previous 6 months

Respondent has a reactive VDRL result

Low household monthly expenditures

Total N 1611 859 1536

2006 participants 409/810 (51%) 123/497 (25%) 32/768 (4.2%)

2007 participants 458/801 (57%) 104/362 (29%) 44/768 (5.7%)

Bivariate OR 1.39** 1.25 1.52

Bivariate 95% CI 1.13-1.72 0.88-1.77 0.91-2.45

Multivariate aOR^ 1.37** 1.22 1.57

Multivariate 95% CI 1.10-1.70 0.84-1.77 0.93-2.65

Low household asset score

Total N 1800 962 1669

2006 participants 426/931 (46%) 145/560 (26%) 56/814 (6.9%)

2007 participants 498/869 (57%) 103/402 (26%) 37/855 (4.3%)

Bivariate OR 1.67*** 1.04 0.59*

Bivariate 95% CI 1.37-2.04 0.75-1.46 0.37-0.92

Multivariate aOR^ 1.68*** 1.04 0.62*

Multivariate 95% CI 1.36-2.06 0.73-1.47 0.39-0.99

Low household food insecurity in the past 30 days

Total N 1399 776 1336

2006 participants 208/655 (48%) 106/429 (25%) 31/559 (5.6%)

2007 participants 487/794 (61%) 132/347 (38%) 15/777 (1.9%)

Bivariate OR 1.83*** 2.10*** 0.26***

Bivariate 95% CI 1.46-2.29 1.47-3.01 0.13-0.51

Multivariate aOR^ 1.82*** 2.08*** 0.23***

Multivariate 95% CI 1.44-2.30 1.40-3.08 0.11-0.46

Low education score

Total N 1464 874 1416

2006 participants 366/779 (47%) 116/531 (22%) 45/743 (6.1%)

2007 participants 368/685 (54%) 95/343 (28%) 12/673 (1.8%)

Bivariate OR 1.33* 1.62** 0.24***

Bivariate 95% CI 1.07-1.66 1.14-2.32 0.12-0.48

Multivariate aOR^ 1.34* 1.70** 0.25***

Multivariate 95% CI 1.07-1.68 1.18-2.47 0.12-0.49

*p<0.05, **p<0.01, ***p<0.001

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Table III-5: Prevalence of syphilis by distance to a contracted clinic in the Mbarara study population

VDRL results 2006 survey 2007 survey Percent change

2 tail T-test

Near to clinic (<11km)

58/789 (7.3%)95% CI=5.5-9.2%

27/931 (2.9%)95% CI=1.8-3.9%

57% p<0.001

Far from clinic (≥11km)

37/738 (5.0%) 95% CI=3.4-6.6%

33/743 (4.4%)95% CI=2.9-5.9%

20% p=0.604

STI treatment utilizationNear to clinic (<11km)

143/472 (30%)95% CI=26-35%

127/325(39%)95% CI=33-44%

30% p=0.011

Far from clinic (≥11km)

113/476 (24%)95% CI =20-28%

87/369 (24%) 95% CI=19-28%

0% p<0.001

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Table III-6: Distributions of matching variables in the unmatched and matched datasetsPopulation Unmatched (n=5395) GenMatch (n= 3438)

Parishes 41 41

Sex Women =2,830 (53%)Men =2,443 (45%)Missing =122 (2%)

Women =2,288 (67%)Men =794 (23%)Missing =356 (10%)

Age Mean =31 (SD=8.9)Median =29 (IQR 24-37)Missing =166 (3%)

Mean =29 (SD=6.9)Median =28 (IQR 25-32)Missing =364 (11%)

Sex partners previous six months

0 =867 (16%)1 =3,439 (64%)2 =514 (10%)3 =387 (7%)Missing =188 (4%)

0 =156 (5%)1 =2,786 (81%)2 =88 (3%)3 =52 (2%)Missing =356 (10%)

Health facilities in respondent’s village

0 =3,182 (59%)1 =884 (16%)2 =652 (12%)3 =202 (4%)4 =291 (5%)5 =184 (3%)Missing =0

0 =2,248 (65%)1 =622 (18%)2 =212 (6%)3 =92 (3%)4 =134 (4%)5 =130 (4%)Missing =0

Unprotected sex 0 =1,425 (26%)1 =3,691 (68%)Missing =279 (5%)

0 =244 (7%)1 =2,834 (82%)Missing =360 (11%)

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Chapter IV: Social capital and health - testing the reliability and validity of a social capital instrument in southwestern Uganda using item response theory

Abstract

Rationale: There is emerging evidence that social capital, defined as the cognitive

and structural features of social organization such as networks, norms, and social trust,

has a significant beneficial association with general well-being, mental health, healthcare

utilization, and infectious and chronic disease outcomes in high-income countries.

However, limited work has been done on social capital in low-income countries. A better

understanding of how social capital affects health and health behaviors in developing

countries could have important implications for improving health care interventions.

Objectives: The primary objective of this survey is to develop two measures of

social capital (cognitive and structural) based on a population survey in southwestern

Uganda. Additional objectives are to test the reliability and validity of the social capital

measures and to determine whether social capital is related to important health behaviors

or health outcomes, particularly sexually transmitted infections.

Methods: An 18-item instrument measured cognitive and structural social capital

in two cross sectional population surveys conducted in 2006 and 2007. Using item

response models, 15 items that measured cognitive social capital (CSC) were tested for

item fit, reliability and validity. Three items that measured structural social capital (SSC)

were tested for reliability and validity.

Results: The 15 CSC items fit non-overlapping response curves and the mean

square fit statistic was normal. CSC items’ Cronbach’s α=0.81 and SSC items

Cronbach’s α=0.96. Items analysis found that in 14 of 15 CSC items, respondents

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exhibited incrementally greater cognitive social capital as response categories increased.

The social capital measures were examined for their relationships with health behaviors

and health outcomes in multivariable logistic models. There were significant associations

between increased cognitive social capital and decreased odds of male aggression,

decreased odds of having ≥2 sex partners, increased odds of unprotected sex, increased

odds of HIV+ disclosure to a partner, and increased odds of disclosure of a genital sore to

a partner. No multivariable associations were found between structural social capital and

selected health-related behaviors, including aggression, number of sex partners,

unprotected sex, and HIV+ disclosure.

Conclusions: Social capital in southwestern Uganda appears to be nested within

respondents’ psyches (cognitive social capital) rather than present as a community level

construct (structural social capital). Although the evidence is preliminary and additional

research is needed, the findings suggest that programs to improve social capital should

consider giving preference to cognitive interventions that build trust over interventions

that shape social structure.

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Background

Social capital is defined as the cognitive and structural features of social

organization, such as networks, norms, and social trust that make cooperation possible

within and between groups. Two schools of thought have developed from this general

definition. Putnam views social capital as an inherently relational property of the

‘collective’ whereas Bourdieu juxtaposes social capital against other forms of capital

(e.g. economic and human) and suggests it is something that can be conceptualized at the

individual level and accessed through their agency (Bourdieu 1986; Putnam 1993, 1995).

To put Putnam’s concept in the simplest terms, social capital is a resource that groups,

not individuals, can access. Acknowledging that social capital is inherently relational,

Bourdieu’s approach is more relevant to a discussion of individuals accessing resources

from social networks that can then be translated in material capital.

Social capital has both cognitive and contextual or structural dimensions

(Harpham, Grant and Thomas 2002; Islam et al. 2006; Portes 1998). The cognitive

component includes norms, values, attitudes and beliefs and refers to individuals’

perceptions of others’ trustworthiness, reciprocity, mutual obligation, and social

interaction (Bourdieu 1986; Islam et al. 2006; Narayan and Cassidy 2001; Putnam 1993,

1995).

The structural component of social capital refers to the observable aspects of

social organizations, such as savings groups, parent-teacher associations, and local

governance committees, which foster social exchange and reify trust. Structural social

capital is often manifest in the density and intensity of associational links and social

activity, the presence of social institutions and organizations or alternatively patterns of

86

engagement within civil society (Grootaert and van Bastelaer 2002; Harpham et al. 2002;

Islam et al. 2006; Narayan and Cassidy 2001).

Structural social capital can be categorized as horizontal, reflecting ties that exist

among social peers, or vertical, reflecting ties that exist between distinct socio-economic

classes or patron-client relationships with differences in power and resource bases

(Cullen 2000). Peer linkages, including intra-group trust and neighborliness, cement

group identities and foster collective action. Vertical ties coordinate action across

horizontal group identities. Vertical social capital in healthcare, for instance, is manifest

in the doctor-patient relationship and more broadly in a patient’s trust of health systems

(Gilson 2003). In some studies, “vertical social capital” is labeled “linking social capital”

and refers to communities’ ability to leverage resources, ideas, and information from

formal institutions, such as the health sector or village leadership structures (Szreter and

Woolcock 2004).

Horizontal structural social capital reflects ties among social or economic equals,

identified based on one or more common characteristics such as a profession, trade,

income level, social standing, or institutional membership. These ties between social

equals can be found within and between groups – leading to social capital ties that bind or

bridge groups. Bonding social capital refers to relationships within homogeneous groups,

whereas bridging social capital refers to ties that link groups who are unlike each other

(e.g. different ethnicity, educational level, occupation, or other characteristics) (Putnam

1995). In other words, different dimensions of social capital operate on different levels:

within groups (i.e. bonding), across groups (i.e. bridging or horizontal) and through ties

to public institutions or formal associations (i.e. linking or vertical) (Saegert, Thompson

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and Warren 2001; Szreter and Woolcock 2004). Present in different quantities in a

population, the relative concentrations of these three forms of social capital can lead to

strikingly different outcomes (Colletta and Cullen 2000).

Social capital shares elements with social control. Strong in-group cohesion

(bonding social capital) and weak between-group connections (bridging social capital)

can result in horrific social pathologies such as the Chinese Cultural Revolution and the

Rwanda and Cambodia genocides (Colletta and Cullen 2000). Less dramatically, strong

in-group patriarchal cohesion was the motivating power behind ‘witch killings’ in local

Ugandan communities of the late 1980s, as patriarchal traditions were reasserted in

response to the social disarray from a long-running civil war and an emergent HIV

epidemic (Allen and Heald 2004).

A sharp distinction can be made between social capital and social control. Social

capital may contain elements of social compulsion or control, but it also provides a safety

valve on social pressures by bridging across horizontal and vertically aligned groups.

Although debate continues about the nuances of the nature of social capital, an

extensive and rapidly growing literature has found social capital to be associated with

health behaviors and outcomes. Researchers have found that social capital is generally

associated with greater self-reported health (Kawachi, Kennedy and Glass 1999;

Subramanian, Kim and Kawachi 2002).

Berkman and Glass summarized these relationships through a theoretical

construct of the Social Network Theory. They identified multiple psychosocial

mechanisms through which social capital might influence health outcomes (Berkman and

Glass 2000; Bury and Gabe 2004). The major psychosocial mechanisms are:

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Social support: The structure of network ties influences health through the various

types of social support that network members can access. Emotional support

relates to the sharing of love, caring, and sympathy within a network.

Instrumental support refers to assistance with tangible needs – aid in kind, money

or labor. Appraisal support suggests help in decision-making, feedback, or in

deciding which course of action to take. Knowledge transfer within networks or

the provision of informational support is also presented as a form of social

support (Berkman and Glass 2000).

Social influence: “People obtain normative guidance by comparing their attitudes

with those of a reference group of similar others” (Berkman and Glass 2000)

Social engagement: This includes the opportunities provided by social networks

for companionship and sociability, which provide coherence and belonging, and

help to define and reinforce social roles and identity (Berkman and Glass 2000).

Person-to-person contact: Networks also influence disease by restricting or

promoting exposure to infectious disease agents. In this context, disease

transmission is not random but rather based on shared social networks (HIV being

one important example) (Berkman and Glass 2000).

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Access to material resource: Social networks and the ties between actors create

social and economic opportunities for improving access to material resources,

either directly or indirectly (Berkman and Glass 2000).

The relative size and efficiency of these mechanisms affect the rate of diffusion of

health information and the likelihood that health-related behaviors are adopted –

behaviors such as seeking healthcare at modern facilities and completing a full course of

tuberculosis therapy. Cohesive communities may be better advocates for improving the

conditions of all through collective support and stigma reduction. In addition, strong ties

within communities are a source of self-esteem and mutual respect, even among

marginalized groups, such as those living with HIV (Pronyk 2009).

To summarize, social capital is defined as the cognitive and structural features of

social organization such as networks, norms, and social trust. Structural features include

close bonding ties within a proximate group and more distal bridging links across

multiple groups and networks. There is emerging evidence that social capital has a

significant, and generally positive, association with well-being, mental health, healthcare

utilization, and infectious and chronic diseases in high-income countries. Social capital

measures such as civic engagement, organizational membership, and trust in others have

been associated with lower all-cause mortality, lower rates of self-reported poor health,

better mental health status, and decreased violence (Kawachi, Kim and Subramanian

2004; Putnam 1995). In a review of 42 studies from OECD countries in North America,

Europe and Australia, social capital has been most frequently measured in a combination

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of cognitive (mainly trust and reciprocity) and structural (participation and civic

engagement) dimensions (Islam et al. 2006).

Epidemiologists find social capital conceptually useful because it facilitates the

measurement of complex socio-economic processes in coherent ways that in turn help to

explain variations in the health status of individuals and communities (Kawachi et al.

1997). Theories of social capital come to public health by way of sociology and political

science and have been incorporated into social epidemiology only recently. Social capital

has increasingly been seen as a predictor of observed differences in population health

outcomes for individuals, communities, countries and even regions (Kawachi 1999;

McKenzie, Whitley and Weich 2002; Pilkington 2002).

Social capital in Africa

Little is currently known about whether social capital is a valid and meaningful

construct in the African region. Additionally, little research has explored how social

capital constructs may relate to health in resource scarce African settings. What is known

about social capital in Africa is based largely on qualitative research. An ethnographic

study in Mbarara, Uganda; Dar es Salaam, Tanzania; and Jos, Nigeria found that social

capital was a locally meaningful concept and had implications for health maintenance in

HIV-infected patient populations (Ware et al. 2009). The researchers found that among

individuals on HIV therapy, those with greater social networks could call on more

resources to support their ongoing HIV treatment and felt more socially obligated to take

drugs on schedule. The study generalized that with additional social capital, there were

greater resources; however, there were behavioral constraints placed on individuals with

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greater access to social capital. Whether these behavioral constraints were a health deficit

or asset depended on the behavior.

Another ethnographic study in Malawi found that the accrual of both greater

resources and greater constraints accruing to individuals with higher levels of social

capital was very important in low-income societies, in which informal ties were often the

exclusive source of support (Swidler and Watkins, 2007).

The World Bank’s Social Capital Initiative (SCI) conducted a thorough review of

measures in various case studies of social capital in low-income countries and concluded

that social capital could be measured by three types of proxy indicators: membership in

local associations and networks, trust and adherence to norms, and levels of collective

action (Grootaert and van Bastelaer 2002).

One survey conducted by the World Bank in Ghana and Uganda measured social

capital and how it varied by group structure and network size, subjective well-being (not

health status), political engagement, sociability, community activities, violence and

crime, and communications (Narayan and Cassidy 2001). The researchers found that

social capital was multi-dimensional and that certain dimensions were consistently salient

at different levels of group aggregation in both countries. The dimensions were: network

characteristics and membership frequency, generalized norms of reciprocity, togetherness

(community solidarity), everyday sociability, neighborhood connections, volunteerism

and trust (Narayan and Cassidy 2001). No measures of health status were collected.

The ability of social capital to both constrain individual choice and reward

individuals for group-normative behavior can help explain the wide range of HIV

prevalence and incidence in different social groups across southern Africa. The evidence

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suggests that social capital is neither good nor bad; it simply acts as a mechanism to

realize group norms.

In cross sectional studies from Zimbabwe and South Africa, membership in some

social groups was associated with low HIV prevalence while membership in other social

groups was associated with increases in the risk of HIV infection (Campbell and

MacPhail 2002; Gregson et al. 2004). In the South Africa study, Campbell and colleagues

measured civic participation, as one aspect of social capital, to understand community

influences on HIV infection. They found that participation in organizations like churches,

sports clubs, and youth groups was protective against HIV infection, while membership in

social groups with high levels of social drinking was associated with an increased risk of

HIV infection (Campbell, Williams and Gilgen 2002).

Pronyk et al. (2006; 2008b) conducted a randomized cluster intervention in South

Africa to test whether cognitive and structural social capital were associated with a lower

risk of acquiring HIV infection. Pronyk et al. distinguished cognitive social capital (CSC)

from structural social capital (SSC), defining CSC as respondents’ established norms and

psychological trust, and SSC as social membership and participation in social institutions.

They found that the male respondents in households with greater levels of CSC had a

lower prevalence of HIV infection and higher levels of reported condom use. Among

female respondents, similar relationships with CSC were observed. However, while

greater SSC was associated with protective psychosocial attributes and risk behaviors, it

was also associated with a higher prevalence of HIV infection among female respondents

(Pronyk et al. 2008b).

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In summary, the social capital literature is limited in the African context. More

research is needed to determine the extent to which social capital is a valid concept in

African communities and whether it is related to health outcomes in Africa. The limited

evidence to date suggests that not all social capital promotes health, and that identifying

and selectively encouraging the creation of social capital that maintains health is critical

to informing health interventions and health promotion efforts.

New Contribution

This study aspires to contribute to the understanding of social capital in Africa by

measuring social capital in a survey of individuals in southwestern Uganda. There are

three main objectives to this research. The first objective is to create two indexed

measures of social capital using data from the survey, a cognitive social capital measure

and a structural social capital measure. Next, it is important to evaluate the indices in

terms of their reliability and validity. The third objective is to examine how the social

capital indices are associated with health behaviors and health outcomes, particularly

those relevant to STIs.

Methods

Description of the item set

Twenty-six questions related to social capital were asked in the 2006 survey and

28 questions were asked in the 2007 survey. For the five category questions, respondents

were shown an image of five water glasses ordered from “completely full” to “empty”

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before answering these questions to demonstrate the concept of “degrees of agreement”

for a population potentially unaccustomed to Likert scale responses.

For this study, cognitive social capital was developed largely as a function of an

individual’s trust in other persons in their community. Fifteen questions common to both

surveys assessed cognitive social capital by asking about respondents’ trust of others and

whether they view themselves as a community member. Three questions assessed

respondents’ structural social capital as measured by engagement in community groups.

The first 14 questions (“Trust different groups”) common to 2006 and in 2007

asked respondents to rate their trust of professionals and institutions in their community

on a five category Likert scale. These questions were drawn from the instrument

developed by a World Bank project led by Narayan et al. in Ghana and Uganda (2001).

There were seven trust questions unique to the 2006 survey and nine trust

questions unique to the 2007 survey. Six of the questions asked only in 2006 concerned

the community’s shared values, as the respondent views them. These questions were also

drawn from Narayan and Cassidy (2001). The seventh question asked only in 2006

concerned whether the community’s level of trust varies from other communities in the

region. Four of the nine unique questions in the 2007 survey explored the community’s

shared values in slightly different ways from questions asked in the 2006 survey. The

remaining five questions from 2007 (“Informal Social Control”) asked about the level of

collective response to specific non-normative behaviors in the community. The final

section was drawn from the validated instrument in Sampson et al study of social capital

in Chicago (1997) and then modified in consultation with an anthropologist at Mbarara

University of Science and Technology in Uganda.

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Analysis and model building methods

Social epidemiology deals in the social distribution and social determinants of

health (Berkman and Kawachi 2000; Krieger 2001). When measuring latent

characteristics, it is common to use composite scores as an observable proxy. For

instance, a study of violence and neighborhood cohesion in Chicago scored local social

cohesion and the community’s ability to respond to threats like the loss of social services

and the perceived threat of youth on the street. The composite score became a measure of

the latent “collective efficacy” of the community (Sampson et al. 1997).

“Collective efficacy” is a group’s belief in its ability to act together and is

conceptualized as the combination of group cohesion and agency (the ability of a group

to come together and then work together). The concept is not directly observable; it is

hidden or latent, yet it was possible for Sampson and colleagues to measure collective

efficacy using ten questions about example behaviors with a theoretical foundation in

collective efficacy. In Sampson’s study the “collective efficacy” score helped to explain

why some Chicago neighborhoods had a lower incidence of violence.

When designing an instrument to measure a latent variable, there are two aspects

to consider: instrument reliability and validity. Reliability is the repeatability or

variability in measurement of a latent variable. A reliable instrument will consistently

produce similar results, with little variance, following repeated measurement of

individuals. Validity is the ability of an instrument to distinguish ‘truth’ from

measurement noise.

Two schools of measurement theory, classical test theory and item response

modeling, offer methods for estimating instrument reliability. Under classical test theory,

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psychometricans assume an instrument’s composite score (X) for any characteristic (e.g.

social cohesion, math skill, or socio-economic status) is the sum of the respondent’s

‘true’ value (T) plus random error (E), irrespective of respondent or item characteristics

(Wilson, Allen and Li 2006a).

X= T+Ε

In this method, each respondent’s score on the instrument is assumed to be drawn

from a probability distribution of responses for the true score. By definition, the

expectation of this probability distribution is the true value for the population sample.

To determine instrument reliability, the ratio of the mean true score variance to

the mean observed score variance is calculated in the sample population. It is impossible

under this framework to measure the reliability of a single respondent’s score on the

instrument, as it is assumed the respondent’s score (T) is true with zero variance, so a

ratio of the respondent’s true score variance to the observed score variance (i.e.

respondent’s reliability) is also zero. Researchers have proposed solutions, including the

use of repeated sampling of the respondent, or parallel testing, but the fundamental

problem remains in instances when respondents are sampled only once (Wilson et al.

2006a).

One weakness in classical test theory is that the relative contribution of a survey

instrument item’s difficulty and the respondent’s ability cannot be separated, which could

lead a researcher to incorrectly assume the result of the respondent’s math score or social

capital value is ‘true’, even when the score may be the result of the instrument design

(Wilson et al. 2006a).

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Item response models are built on a less stringent assumption that a respondent’s

answer to any question is a probability based on two unknowns: the respondent’s inherent

ability to agree with the instrument’s items (commonly designated as θ) and each item’s

inherent difficulty (commonly denoted as δ), regardless of the population of respondents

surveyed. For example, a respondent may believe an item that asks about ‘willingness to

lend’ is more difficult to agree with than an item that asks about ‘trust of community

leaders’. Item difficulty is expressed on the same scale as respondent ability. In the IRM

literature, both item difficulty and respondent ability are presented as logarithmic

transformations of odds of an event (logits). There are three potential relationships

between respondent ability and item difficulty (Wilson, Allen and Li 2006b):

1. Θ = δ. When respondent ability and item difficulty are the same, the probability of

response for any two choices (yes/no, 1 or 2, 3 or 4) is 0.5. For instance, if we are

measuring respondent’s trust of others as a yes/no option and the item asks about

trust of lending to others, a moderately trusting respondent has a 50% probability

of choosing “yes” (Wilson et al. 2006b).

2. Θ > δ. When respondent ability (in our example, a high level of trust) exceeds

item difficulty (asking about trust of lending to others), the probability of

responding “yes” is greater than 50% (Wilson et al. 2006b).

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3. Θ < δ. When respondent ability (in our example, a low level of trust) is lower than

item difficulty (asking about trust of lending to others), the probability of

responding “yes” is less than 50% (Wilson et al. 2006b).

The basic relationship between respondent ability (θ) and item difficulty (δi) is

expressed in the following model:

Pr(Xi =1| θ, δi) = f (θ - δi)

The general function can be arranged as a logistic Rasch model that forms the

basis for a family of item response models (Wilson et al. 2006a). Modeling both

respondents and the items necessitates a “multi-level” approach, as such models consider

the probability of any response a function of the survey item difficulty and a respondent’s

ability to answer the item. Because reliability and validity testing can be conducted at the

level of both the respondent and the item, item-response models have the ability to

identify item sets with optimal fit that reduce measurement error and improve the

potential for generalizability of findings (Wilson et al. 2006b).

Item fit

Item Response Function

The probability of response for all categories can be plotted on a curve commonly

called an item response function (IRF). For clarity’s sake, the item – “trust of

pharmacies” will be used as an example. In Figure IV-1, the locations for all respondents

on the continuum of the latent construct are plotted on the horizontal axis (imagine, for

example, all respondents arrayed along a continuum from low to high trust). The vertical

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axis displays the probability of answering any level for a given item (Wilson et al.

2006b).

This type of figure is customarily called an item response function, IRF, (other

common terms are item characteristic curve and cumulative probability curve). The IRF

depends on the respondents’ level of latent characteristic (θ) and item difficulty (δ). In

this example, respondents’ trust of pharmacies is plotted along four response boundaries:

the probability of responding either 0 or 1 (from “very unlikely” to “unlikely”), 1 or 2

(“unlikely” to “indifferent”), 2 or 3 (“indifferent” to “likely”), and 3 or 4 (“likely” to

“very likely”).

Figure IV-1 also provides the logit locations or “thresholds” where the item

response curves indicate a 50% probability of responding on each pair of responses. In

the example plot, the logit location of 50% probability response for 0 or 1 is -1.23 (see

list of 4 threshold values in lower left corner of Figure IV-1). The exponential of the log-

odds (-1.23) is an odds of 0.29. The odds of 0.29 (a ratio of the probability of respondent

ability over one minus the probability of respondent ability) indicates that the

probabilities are not equal and there is a great deal of error in the estimated location. In

contrast, the 50% probability of choosing either 2 or 3 (“indifferent” to “likely” trust

local pharmacies) is located at -0.13 logits (an odds of 0.87) and is a more reliable

estimated location. Items with “better fit” will have non-overlapping item response

functions with 50% probability thresholds located closer to 0 logits.

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Mean Square Fit Statistic

Item fit can also be determined from the residual between the estimated item

parameters (the four smooth curves in Figure IV-1) and the observed curves (indicated by

dashed lines and dots).

For any respondent, the difference between the observed and the expected

response on any item is estimated by the following:

Yin = Xin - Ein

Where Yin, Xin, and Ein are the residual, observed response, and expected response for

person n responding to item i.

Each respondent’s expected response (Ein) is characterized by the following

probability:

Where Ki is the number of response categories for the item (a measure of difficulty), δ is

a vector of the parameters for item i, and k is the probability of observing a response in

each category. For example, on a 5 point Likert scale asking a respondent’s trust of the

police, k is the probability of observing a response in each category given two unknowns:

the respondent’s inherent trust of others and the difficulty of the specific question about

police.

The measurement model is the researcher’s best estimate of the theoretical

construct’s functional form. To determine how well the data fit the measurement model,

it is possible to calculate the ratio of observed mean residuals and the expected mean

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residuals for any item i. This is called the mean square (MS) fit statistic, as described by

Wilson (2004).

For any respondent, the expected squared residual for any item i is the summation

of the squared difference between the observed response (k) and the expected response

(Ein).

The average of these expected squared residuals (expected variance) across all

respondents is:

The mean of the squares of the observed residuals (observed variance) across all

respondents is:

The mean square fit statistic, or simply the weighted mean square, is the ratio of

observed variance to expected variance:

It is generally agreed that a ‘good’ weighted mean square value is bounded at 0.75

and 1.33 (Adams and Khoo 1996) cited in (Wilson 2004). Wilson (2004) notes that

another fit index, the weighted t, can attempt to transform the weighted mean square into

a normal distribution and be used to apply Student’s t test of normality. The test,

however, is likely to be significant for many items with a large sample size. Combining

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the t-test and weighted mean square fit statistic to determine item fit is a more cautious

approach than either test alone (Wilson 2004).

If there is evidence of misfit, a decision must be made whether to remove the

item, collapse response categories, or try fitting a different model. If the instrument

testing is early enough to allow another round of interviews, it should be considered.

In summary, item fit is an important iterative diagnostic process in which item

response functions are plotted and the mean square fit statistic is used to determine

whether additional work is needed on item design. Careful attention to item fit is a useful

preliminary process before measuring the instrument for reliability and validity.

Reliability

Reliability refers to the consistency of measurements that describe a latent

characteristic of a population through a survey instrument. Item response theory

commonly uses two indicators to determine internal reliability: the standard error of

measurement and a ratio of variances similar to Cronbach’s alpha.

Standard error of measurement (SEM)

Item response theory and classical test theory alike are interested in consistently

scoring respondents’ latent characteristics. The portion of the measurement that is

inconsistent is the residual error. This measurement error is a function of the each

respondent’s ability to answer the items, the conditions of the interviews, the instrument

design, and the interviewer’s ability to properly score responses.

The standard error of measurement (SEM) is a function of the respondent location

on the construct continuum and the standard deviation of the raw scores. The more the

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respondent’s ability, θ, approaches equality with an item’s difficulty, δ, the more likely

his or her response reflects the unobserved truth. Respondents whose ability to answer

does not match the items’ difficulty will be placed on the construct continuum with

greater error than well matched respondents and items.

When item or full test scores are plotted against the standard error, a parabola is

formed. Figure IV-2 displays the information curve for the item ‘trust of pharmacies’.

The information curve is the reciprocal of the square of the SEM (Wilson 2004).

Reliability in measurement is usually assessed in a single instrument because one

instrument is what the researcher used in his or her study. However, it is possible to

consider measuring consistency between alternate forms of the instrument or repeated

tests of the same instrument. In the Uganda study we did not run these alternate forms of

reliability tests.

Ratio of variances (Cronbach’s alpha)

In classical test theory, the internal consistency or reliability coefficient

commonly used for polytomous data is Cronbach’s α (Cronbach 1990). In item response

theory, an equivalent coefficient is calculated as a ratio of variances from the marginal

maximum likelihood (MML) estimation algorithm (Daniel 1999; Wilson et al. 2006a).

Among other applications, this reliability value can be used to predict the effect on

reliability of reducing or increasing the number of items using the Spearman–Brown

formula (Cronbach 1990; Wilson et al. 2006a). It can also be used to compare the

reliability of any given item set against the conventional threshold for a reliable

instrument of α= 0.65 (Wilson et al. 2006b).

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Validity

In the item response modeling literature, the term “validity” covers three areas of

instrument consistency: internal or construct validity, content validity, and external

validity (Wilson et al. 2006b). Each of these types of validity refers to a body of evidence

based on appropriate statistical tests. Evidence is gathered from these areas to determine

how valid an instrument might be. Three common validity tests to measure an

instrument’s performance – internal structure, items analysis, and external correlation –

can address internal, content validity, and external validity (Wilson et al. 2006a).

Internal validity

Testing for validity based on internal structure (construct hierarchy) can

determine whether the observed data are correlated with a priori expectations based on

theory. Spearman’s rho is a common method to test the rank order of items in each

construct. However, in the case of the social capital measures, no such a priori

expectations for item order were developed (i.e. there was no belief that trust of

healthcare providers would have a higher mean score than trust of extended family).

Content validity

The second area of validity testing is content validity. Here, the interest is in the

structure of the data. Do they have a meaningful, conceptually valid structure? One

common content validity test in item response modeling is what Wilson (2004) terms an

“item analysis”, or a check of the relationship between respondents’ mean location at

each category in each item. In this test, the order of mean locations of the respondent

groups on each item is compared against the ordered response categories. Are

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respondents with a weaker or lower ability giving low responses as expected? If the item

categories are structured properly, there should be an incremental progression as

respondents with higher ability answer more difficult categories in each item. For

instance, we assumed that only respondents with high levels of unobserved cognitive

social capital (CSC) will, on average, score highly on items asking about trust.

External validity

External validity is evidence that the construct measurements are related as

expected with respondent characteristics, behaviors or outcomes. The relationships

usually are tested by means of bivariate correlations and multivariate models of

association.

For this study, the estimated a posteriori (EAP) values for the continuous

cognitive social capital score were imported into Stata (version 10.1 for Windows,

College Station, TX). The continuous cognitive social capital (CSC) score was grouped

into low, medium, and high tertiles to aid in understanding the score. It was hard to

understand what a change of 0.5 logits of CSC would mean, whereas a move from low to

medium CSC could be qualitatively understood. A dichotomous version of CSC was also

tested but considered not as conceptually useful, as the individuals with lowest CSC were

grouped with those of moderate social capital, and this study was most interested in

identifying health-related behaviors associated with low social capital.

For the structural social capital (SSC) construct, quartiles were estimated for each

of the three variables: number of groups the respondent belonged to, the amount of

money contributed to groups in an average month, and the number of days volunteered to

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each group in an average month. The quartiles were then summed and divided by three to

create a three-category variable of low, medium and high structural social capital.

The bivariate correlations were calculated using the social capital scores and ten

variables with a theoretical link to cognitive and structural social capital: men who hit

others in the previous 12 months, women hit by others in the previous 12 months, having

more than one sexual partner in the previous six months, having any unprotected sex in

the past six months, having sought healthcare when ill in the past six months, having

sought STI treatment in the previous six months, having ever disclosed a positive

gonorrhea test result to any partner, having ever disclosed a clinically confirmed genital

sore to any partner, having ever disclosed clinically confirmed genital discharge to any

partner, and having ever disclosed HIV positive status to any partner.

For multivariable modeling, the cognitive and structural social capital scores were

used as independent variables, and with other covariates of interest, tested for association

with the health-related behaviors. The covariates of interest included individual

demographic factors, household economic status, individual reproductive health

knowledge and behaviors, and community characteristics. The community characteristics

were commercial status of parish (whether it was rural or had a trading center) and the

distance from respondents’ village to the nearest contracted OBA clinic.

All of the explanatory covariates were categorical or binary: age group, sex,

marital status, education, above median monthly household expenditure, above median

household asset score, median household food security, and presence of a trading center

in the village. Age group was constructed from three categories: 15-24, 25-34, and 35-49

years of age. Sex was a dichotomous value of 0 for female and 1 for male respondents.

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Marital status was a dichotomous variable with single, widowed, or divorced set to 0 and

married or cohabitating set to 1. Education was a dichotomous variable with completed

primary school or fewer years of education set to 0, and some secondary education and

more years of education set to 1. Three economic variables were used: monthly

household expenditure, household asset index, and household food insecurity. A

dichotomous variable was created by dividing values above from those at or below the

median value. The variable for village with trading center was a dichotomous measure of

commercial activity in each survey village. Having a concentration of shops and

businesses likely introduced many unobserved differences in the local society compared

to rural areas with less commercial activity.

Results

Respondent characteristics

Of the 5,396 respondents from the 2006 and 2007 surveys, 214 respondents were

missing all values for the 18 social capital items and were excluded from the study.

Characteristics of 5,182 respondents (2558 from 2006, 2624 from 2007) with complete

values for social capital are presented in Table IV-2 by survey year.

There was a significant difference in proportions of the sexes between survey

years. There was also a significant difference in proportions of respondents by marital

status between survey years. The two surveys also differed significantly in terms of the

distribution of educational level of the respondents.Dichotomous measures of poverty

(e.g. median monthly household expenditure, median household asset index, and median

household food insecurity) were significiantly different between survey years. As the

same villages were surveyed both years, there was no significant difference between

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survey years in whether the village was a trading center or a rural community. Because of

differences in respondent characteristics, social capital scores were estimated by year and

then data from both years were combined and analyzed as a single cross-sectional survey.

Items selected for cognitive social capital

The 2006 and 2007 surveys shared 15 variables that measured cognitive social

capital by describing respondents’ trust of others and their own perceived trustworthiness.

Two variables (trust of NGO providers and trust of mission hospitals) were missing for

more than 10 percent of respondents (Table IV-3). Although there was concern about the

large number of missing values, those two variables were included in the cognitive social

capital (CSC) index. Three variables in both surveys measured structural social capital

(SSC) by asking respondents for the number of groups they belonged to, the number of

days they volunteered to work with those groups in an average month, and the amount of

money they contributed to those groups in an average month.

For the 18 variables, missing values were imputed using multiple imputation by

chain equation (MICE) under the assumption of missingness at random (the “ice” add-in

for Stata version 10.1).

Item fit results

There were two major operational steps to create a measure of the latent construct

using item response models. The first step was to choose and evaluate a measurement

model and the second step was to test the reliability and validity of the model results.

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As discussed above, the item response models were built from general

probabilistic measurement models in the family of polytomous Rasch models. Because of

its generality, a conditional maximum likelihood model was selected (referred to as a

“partial credit model” in the educational testing literature) and then evaluated for best fit.

As noted above, the item-response function can be plotted for each item. An

example of an IRF for the item “trust of pharmacies” is given in Figure IV-1. The

respondent locations, “Latent Trait (logits)”, were plotted on the horizontal axis, and the

probability of selecting response ‘1’ to a given item is shown on the vertical axis (Wilson

et al. 2006b).

On the original questionnaire, respondents were asked, on a scale of 0 (low) to 4

(high), to rank their trust of pharmacies in their community. In the IRF plotted in Figures

IV-1 and IV-3, the probability of moving from one level of social capital to another was

compared to respondents’ ability to answer. The evidence of item fit, in contrast to

respondent fit, was developed from information contained in the IRF plots like those in

Figures IV-1 and IV-3.

Reliability results

The Wright Map plots the logit location of each item-step. Each item has five

categories ranging from low cognitive social capital (0) to high cognitive social capital

(4). The item-step is the location where the probability of answering (0 or 1), (1 or 2), (2

or 3), (3 or 4) is balanced at 50 percent for each category in each item. The Wright Map

(Figure IV-4) indicates the item-step locations for each of the 15 cognitive social capital

items in the surveys. In each item, there are four steps between the five categories.

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The coefficient alpha (similar to Cronbach’s alpha) in the 2006 item set is 0.86

and in the 2007 item set is 0.72. Although there is no hard rule for evaluating the

coefficient alpha, the closer it approaches one, the better the items “hang together” and

likely represent a single latent variable. Generally, values above 0.7 are considered

acceptable and indicative of consistency (Wilson et al. 2006b). 0.86 is a better value and

is in keeping with generally better performance in the 2007 survey data.

Validity results

In the item response modeling literature two validity tests are commonly

recommended: internal content analysis of the items and external tests of correlation

(Wilson et al. 2006a). Testing for validity based on internal structure (construct

hierarchy) can determine whether the observed data are correlated with a priori

expectations based on theory. However, in the case of the social capital measures, no

such a priori expectations for item order were developed (i.e. there was no belief that

trust of healthcare providers would have a higher mean score than trust of extended

family).

Content validity

We expect at each categorical response level (in this case 0 to 4) to see the mean

location or mean respondent “ability” to increase. In a well designed instrument, more

difficult item categories are correlated with higher respondent ability. Table IV-4

contains the average logit value for respondents at each response category (0, 1, 2, 3, and

4) for each item of the instrument.

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Fourteen of 15 cognitive social capital items exhibited an increase in mean ability

across the response categories. In effect, respondents displayed incrementally greater

trust in 14 of the 15 items as response categories increased. This is evidence that the item

design was appropriate.

External validity: multivariable associations between cognitive social capital and

structural social capital and respondent characteristics

Before running separate multivariable models for cognitive social capital and

structural social capital and health-related behaviors, a pairwise Pearson’s test was

performed on all the explanatory variables. All correlations between explanatory

variables were well below the standard threshold of 0.8, indicating that multicollinearity

was not a serious concern (IV-5). The highest correlation was between median of

household goods and median monthly household expenditures (0.28).

In evaluating the external validity of the CSC and SSC, it was hypothesized that

these indices would be associated with positive health behaviors. Included in the surveys

were questions on HIV and other sexually transmitted infections, health care utilization,

and violence/aggression. A number of positive health behaviors were associated with

having higher CSC scores, including:

Among respondents who reported being HIV positive in 2007 (n=113, see Table

IV-6), those who had the highest CSC score had a significantly higher odds of

disclosing their HIV status to a partner (aOR=1.98, 95% CI 1.02-3.51),

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Among respondents who reported ever having a genital sore (n=107, Table IV-6),

those who had the highest CSC score had a significantly higher odds of telling a

partner about their genital sore (aOR=2.20, 95% CI=1.00-4.83),

Among respondents who reported their number of sex partners in the previous six

months (n=5142, Table IV-6), those with the highest CSC score had a lower odds

of having two or more partners (aOR=0.80 95% CI=0.69-0.93),

Among men who reported hitting others at least once since the age of 15 (n=769,

Table IV-6), men with the highest CSC had the lowest odds of having hit others in

the past year (aOR=0.65 95%, CI=0.53-0.81).

Among women who reported being hit by others at least once since the age of 15

(n=942) CSC was not associated with being hit in the previous 12 months;

however, the direction of effect was protective (aOR= 0.85, 95% CI=0.70-1.03).

One health behavior, seeking treatment for a STI symptom, had a statistically

significant result that was in the opposite direction of the original hypothesis. Among

respondents who reported having an STI symptom in the previous six months (n=769,

Table IV-6), those with highest CSC scores had a significantly lower odds of seeking STI

treatment (aOR=0.80, 95% CI=0.65-0.97). For this result, poverty acted an effect

modifier; among the poor, the relationship between high CSC and low odds of STI

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treatment was more pronounced (aOR=0.75, 95% CI=0.58-0.97). While among the not-

poor, high CSC and odds of STI treatment were not significantly associated.

The following health-related behaviors had no empirical relationship with CSC:

having unprotected sex in the previous six months; seeking any type of healthcare if sick

in previous six months; ever disclosing a positive test for syphilis; ever disclosing a

positive test for gonorrhea; and ever disclosing clinically confirmed genital discharge. No

health-related behaviors were associated with the SSC score (Table IV-7).

Discussion

Study limitations

There were several limitations to this study. The cognitive and structural social

capital scores were developed as measurements of latent social capital that, based on a

review of the literature, we posited was causally antecedent to observed health-related

behaviors. A strong case for causality could not be made, given the lack of an observed

temporal order between the proposed causes and effects. Longitudinal observation and in-

depth qualitative interviews are needed to establish whether the statistical associations

observed between variables hypothesized to be inter-related in our theoretical model

reflect the direction of causality implied by the model.

Data collection and entry was another study limitation. Data entry in 2006 was the

first time that the study coordinators and the lead investigator implemented the design.

Extensive data re-entry and cleaning were conducted, but there may have been error in

the data collection. The consistently higher reliability tests, including for example the

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coefficient alpha in 2007 (α=0.86) versus 2006 (α=0.72), could suggest that the 2007 data

may have been collected and entered in a more consistent manner than the 2006 data.

Another limitation was the surveyed population’s understanding of sexually

transmitted infections. In 1999, a study in the region noted a general misunderstanding of

STIs; study participants then named tuberculosis, leprosy, and skin fungal infections as

being sexually transmitted (Nuwaha et al. 1999). Anecdotally in and around Mbarara,

syphilis is a term used for a wide range of health complaints from rashes to backache.

Although interviewers were trained to use standard terms for STIs, trust, and other key

concepts in the survey, it is possible that the respondents failed to understand the medical

meaning of specific concepts like genital sores or syphilis.

Study significance

Social capital is little studied in the context of health outcomes in sub-Saharan

Africa. This study was one of the first to test, in a population-based sample, that cognitive

and structural social capital are: (1) valid constructs in rural and semi-urban Ugandan

settings and (2) determinants of several health-related behaviors, including partner-

disclosure of HIV status. Little prior work has been done in sub-Saharan Africa to

measure relationships between social capital and health-related behaviors.

Based on tests from item response theory, the 15 items on cognitive social capital

fit together and have strong reliability and validity. Interestingly, in the 2006 survey,

“trust of NGOs”, and in the 2007 survey, “feel like a full member of the community”, did

not demonstrate internal validity as the mean ability scores by category did not order

themselves by the category value; that is, for several higher categories the mean ability

scores were lower than the mean ability scores in lower categories. However, both

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problematic items were specific to a single survey year and the other measures of

reliability and validity indicated reasonably good performance, i.e. the coefficient

reliability was above 0.72 in both years.

Other studies on social capital and health have been validated using methods

drawn from classical test theory (Narayan and Cassidy 2001). As Wilson (2006a) noted,

there is a great deal of concordance in classical test theory and item response theory.

However, item response theory offers greater flexibility and utility in measuring latent

variables (Wilson et al. 2006a). One of the more helpful innovations in item response

modeling is the development of a common scale for respondents and items as

demonstrated on the Wright Map (Wilson et al. 2006a). Qualitative assessment of the

instrument is possible and allows for assessment of possible gaps in instrument coverage

of the latent variable space. As Wilson summarized “the item response modeling

approach can do all [his italics] that you can do in the classical approach when it comes

to assessing items and instruments, and it can do a great deal ‘more’.” (Wilson et al.

2006a)

One strength of this analysis was the careful attention given to the design and

validation of measures of cognitive and structural dimensions of social capital, including

their empirical relationship with health-related behaviors and the statistical effects of

potential confounders. Using the item response modeling approach, this study suggests

that in southwestern Uganda, CSC has an important relationship with several health-

related behaviors.

Higher CSC was associated with a higher odds of disclosing his or her HIV status

to a partner and higher odds of telling a partner about genital sores. Disclosure of HIV

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status and potential STIs can be a tool in prevention. Disclosure to sexual partners

accompanied by the choice to reduce risk by using condoms and reducing partners can be

effective in limiting STI infections, including HIV. Helen Epstein argued in her book,

Invisible Cure, that disclosure and frank discussion of HIV, mediated by a socially

cohesive population, was responsible for the dramatic reduction in HIV prevalence in

Uganda in the early 1990s (Epstein 2007).

Additionally, the current study found that respondents with two or more partners

are more likely to have lower CSC. Sexual networks with a high number of concurrent

partnerships is a well established risk factor for high incident HIV transmission.

Our cross sectional findings support the general thesis that greater CSC is

associated with lower violence. In our study, we found that among male respondents who

reported hitting others since 15 years of age, the odds of having hit someone in the

previous 12 months was lower among men with a higher cognitive social capital score.

However, unlike the findings of Pronyk and colleagues, our study found that among

women who reported being hit by others since 15 years of age, there was no association

between being hit in the previous 12 months and CSC. The comparability of our findings

to those of Pronyk et al. is limited. First, their cohort did not include men. Also they

measured incident intimate partner violence prospectively; we measured the prevalence

of a specific form of violence, physical hitting, in the previous 12 months, without

restricting it to intimate partnerships.

One surprising finding in our study was that respondents with high CSC and who

reported having one or more STIs were less likely to use STI treatment. Further research

should explore this question, to determine if this finding holds up in other surveys and

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contexts. Focus groups or open ended interviews could provide new insights into the

nature of trust and STI treatment. At this point, we only can surmise that there may have

been stigma associated with reporting or using STI treatment and that individuals with

high levels of CSC were more sensitive to community opinion. Alternatively, individuals

with high levels of CSC may have trusted their partners more and disregarded signs of

infection. Future research should explore the relationship between CSC and various types

of healthcare utilization.

Another surprising finding was that SSC was not significantly associated with any

health behavior. Previous research had suggested that structural interventions can

produce significant reductions in domestic partner and intimate partner violence (Merson,

Dayton and O’Reilly 2000; Sumartojo et al. 2000; Waldo and Coates 2000). Pronyk and

colleagues observed in their South African study that, while higher levels of SSC were

associated with protective psychosocial attributes and risk behavior, SSC was also

associated with higher rates of HIV infection (Pronyk et al. 2008b). Although the SSC

instrument in our study was reliable, the lack of any empirical relationship to external

health behaviors indicates a need to conduct new qualitative research on the meaning and

importance of group membership and consider new items for the instrument.

An important implication of our results is the need to explore the causal

relationship between social capital and health behaviors and health outcomes, through

further study with observation over time in longitudinal studies. If future research

indicates that higher CSC yields safer health behaviors, there are clear policy

implications. Those designing health interventions in Africa may want to consider adding

elements to their interventions that enhance social capital to individuals. How to build

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CSC effectively is not clear. One approach that was piloted in South Africa involved

participation in HIV education skills classes and a microfinance program (Pronyk et al.

2008a). Microfinance programs are appealing because, at their core, they are about

enabling exchange of funds, which requires trust and, as Pronyk demonstrated,

microfinance can foster social capital.

Another approach to building social capital in the context of STI treatment

services is social marketing. Previous studies have shown that trust plays a role in the

usefulness of information provided in social marketing (Thiede 2005). Simply put, health

information must be trusted to be effective. Building trust and social capital can improve

the effectiveness of social marketing, which in turn could increase utilization of STI

treatment services.

Future work needs to focus on exploring the relationship between CSC and STI

treatment seeking. If future studies confirm the finding that those with high CSC are less

likely to seek treatment, there will be a need to consider this a complicating factor in the

delivery of STI services.

Of course, future research should shed light on the appropriate courses that social

marketing campaigns can and should take. What the present study has shown, however, is

that CSC can be adequately measured within a population and that this measure is

associated with important health behaviors.

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Tables and Figures

Table IV-1: Social capital items

PART 1: TRUST DIFFERENT GROUPS

These questions are only to learn more about the community, not individuals’ opinions or gossip. Questions are about trust and we are interested to know how you see trust in the community. On a scale from 1 to 5, where 1 means ‘to a very small extent’ and 5 means ‘to a very large extent’, how much do you feel you can trust the people in each of the following groups? Please show the respondent the drawing of glasses of water to communicate the idea of a scale from 1 to 5, where a glass is drawn with no water (1), a little water (2), half full of water (3), mostly full (4), and completely full of water (5).

2006 2007

1. People in your tribe?

2. People of your religion?

3. People in other tribes?

4. People of other religions?

5. People in your village/neighborhood?

6. The business owners and traders you buy things from or do business with?

7. People in your extended family?

8. Local/municipal government?

9. Judges/courts/police?

10. Providers at for-profit private healthcare facilities?

11. Providers at not-for-profit or NGO healthcare facilities?

12. Providers at mission hospitals?

13. Sellers at drug shops and chemists?

14. Providers at government/public healthcare facilities?

1. People in your tribe/ ethnic or cultural group?

2. People of your religion?

3. People in other tribes / ethnic or cultural groups?

4. People of other religions?

5. ASKED IN #17 FOR 2007

6. The business owners and traders you buy things from or do business with?

7. People in your extended family?

8. Local/municipal government?

9. Judges/courts/police?

10. Providers at for-profit private healthcare facilities?

11. Providers at not-for-profit or NGO healthcare facilities?

12. Providers at mission hospitals?

13. Sellers at drug shops?

14. Providers at government healthcare facilities?

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Table IV-1: Social capital items, cont.

Part 2: SHARED VALUES

How likely do you agree with each of the following statements? Please phrase your agreement as very likely, likely, neither likely nor unlikely, unlikely, or very unlikely.

2006 2007

Think of a scale from 1 to 5. At number 1 you feel that people in the community cannot be trusted. At the number 5 you feel people in the community can generally be trusted.

15. On that scale from 1 to 5, where would you rank the trustworthiness of people in the community?

16. Would you say that most of the time people in the community are just looking out for themselves, or they are trying to be helpful? (1= looking out for self, 5= helpful)

17. Do you think that most people in the community would try to take advantage of you if they got the chance, or would they try to be fair? (1=take advantage, 5= be fair)

18. On a scale from 1 to 5, where 1 is very unlikely and 5 is very likely, how likely is it that you would ask your neighbors to take care of your children for a few hours if you were sick?

19. How likely is it that you would ask your neighbors for help if you were sick? (1= not likely, 5=very likely)

20. Do people in this community generally trust one another in matters of lending and borrowing? (4 point scale: 1=trust a great deal, 2=trust somewhat, 3=distrust somewhat, 4=distrust a great deal)

Think of a scale from 1 to 5. At number 1 you feel that people in the community cannot be trusted. At the number 5 you feel people in the community can generally be trusted.

15. People around here are willing to help their neighbors

16. This is a cohesive neighborhood or village, that is, it is a community with a great deal of togetherness

17. People in this neighborhood or village can be trusted

18. People in this neighborhood or village generally do not get al.ong with each other

19. People in this neighborhood or village do not share the same values

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Part 3: GROUP MEMBERSHIP

2006 2007

21. On a scale of 1 to 5, where 1 is disagree completely and 5 is agree completely, how do you feel about the following statement.

“I feel accepted as a full member of this village/neighborhood.”

22. How many groups or organizations do you belong to? These could be religious groups, sports teams, clan groups, or just groups of people who get together regularly to do an activity or tasks.

23. In an average month, how much money, if any, do you contribute to the groups to which you belong?

24. In an average month, how many days do you participate in the activities of the groups to which you belong?

20. On a scale of 1 to 5, where 1 is disagree completely and 5 is agree completely, how do you feel about the following statement.

“I feel accepted as a full member of this village/ neighborhood.”

21. How many groups or organizations do you belong to? These could be religious groups, sports teams, clan groups, or just groups of people who get together regularly to do an activity or tasks.

22. In an average month, how much money, if any, do you contribute to the groups to which you belong?

23. In an average month, how many days do you participate in the activities of the groups to which you belong?

Part 4: CHANGE IN TRUST ACROSS SPACE & TIME

2006 2007

In the last year, has the level of trust improved, worsened, or stayed the same?

Compared with other villages in the district, how much do people in this community trust each other in matters of lending and borrowing? (3 point scale: More than other communities, the same as other communities, or less than other communities)

In the last year, has the level of trust in the community improved, worsened, or stayed the same?

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Part 5: INFORMAL SOCIAL CONTROL

Would you say it is very likely, likely, neither likely nor unlikely, unlikely, or very unlikely that your neighbors and fellow community members would intervene in the following scenarios in some way

2006 2007

NONE

25. How likely would your neighbors and fellow community members intervene if children were skipping school and loitering in the towns

26. How likely would your neighbors and fellow community members intervene if children were damaging /dirtying houses, cars, property or people?

27. How likely would your neighbors and fellow community members intervene if children were showing disrespect to an adult in form of teasing and abusing?

28. How likely would your neighbors and fellow community members intervene if a fight broke out in front of their house?

29. How likely would your neighbors and fellow community members intervene if an institution/service center (i.e. health unit, community hall, sports ground) closest to their homes was threatened with closure?

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Table IV-2: Description of respondents’ demographic factors, economic status, and household characteristics by survey year

2006 Survey 2007 Survey Pearson X2

Sex Female Male

59% (n=1503)41% (n=1054)

48% (n=1272)52% (n=1352)

55.3***

Age Females’ mean age (years) Males’ mean age (years)

30.5 (n=1466)32.4 (n=1029)

(n=1261)30.6 (n=1338)

55.3***

Marital status Single, widowed, or divorced Married or cohabitating

26% (n=651)74% (n=1860)

34% (n=874)66% (n=1716)

37.2***

Monthly household expenditure (Ugandan shillings)

Mean: 84,800(SD 139,000)Median: 50,000(IQR 30,000 – 100,000)n=2508

Mean: 100,700(SD 250,000)Median: 50,000(IQR 30,000 – 100,000)n=2551

10.9***

Household food insecurity score (0-27)

Mean: 7 (SD 6)Median: 6 (IQR 3-10)n=2475

Mean 8 (SD 5)Median 8 (IQR 5-11)n=2564

3.9*

Household assets index (7 common goods)

Mean: 2 (SD 2)Median: 2 (IQR 1-3)n=2434

Mean: 3 (SD 2)Median 2 (IQR 1-3)n=2591

45.6***

Education level No formal education Some primary Completed primary Some secondary Completed secondary

14% (n=356)35% (n=888)9% (n=221)20% (n=502)22% (n=547)

11% (n=286)34% (n=871)25% (n=649)23% (n=589)8% (n=204)

380.6***

Urban vs. RuralVillage Rural Urban (w/ trading centers)

57% (n=1468)43% (n=1090)

55% (n=1437)45% (n=1187)

3.6+

+ p> 0.05* p≤0.05** p<0.01*** p<0.001

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Table IV-3: Percent missing values for each of the social capital items

Variable # Missing % Missing

Cognitive social capital

1 SC600_trust_ur_tribe 260 4.8

2 SC601_trust_ur_religion 272 5.0

3 SC613_trust_public_provider 303 5.6

4 SC604_trust_comm_2006 306 5.7

5 SC605_trust_business 316 5.9

6 SC612_trust_drug_sellers 325 6.0

7 SC617C_full_member 329 6.1

8 SC609_trust_private_provider 341 6.3

9 SC606_trust_extend_family 373 6.9

10 SC603_trust_other_rel 386 7.2

11 SC607_trust_local_govt 395 7.3

12 SC602_trust_other_tribe 424 7.9

13 SC608_trust_judges 474 8.8

14 SC610_trust_NGO_providers 851 15.8

15 SC611_trust_mission_hospital 1054 19.5

Structural social capital

1 SC618A_How_many_groups 180 3.5

2 SC618B_How_much_money 227 4.4

3 SC618C_How_many_days 254 4.9

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Table IV-4: Average logits for respondent ability at each response category

2006 2007

Item Mean ability Mean ability

0 1 2 3 4 0 1 2 3 4

1 Trust same ethnicity -0.14 -0.13 0.31 0.61 1.32 -0.08 0.10 0.36 0.72 0.95

2 Trust same religion -0.18 -0.18 0.19 0.60 1.24 -0.16 0.16 0.40 0.71 0.96

3 Trust other ethnicity 0.12 0.30 0.70 1.07 2.05 0.24 0.35 0.61 0.89 1.12

4 Trust other religions 0.13 0.38 0.70 1.10 2.10 0.25 0.36 0.63 0.89 1.18

5 Trust community -0.19 0.05 0.41 0.77 1.55 0.48 0.54 0.62 0.79 0.95

6 Trust business owners -0.05 0.20 0.57 0.91 1.70 0.22 0.44 0.61 0.81 1.13

7 Trust extended family -0.07 0.04 0.39 0.63 1.30 0.09 0.25 0.54 0.79 0.91

8 Trust local govt 0.06 0.11 0.56 0.77 1.44 0.30 0.38 0.62 0.80 0.97

9 Trust judges 0.42 0.47 0.72 0.95 1.73 0.47 0.60 0.69 0.87 1.17

10 Trust private providers 0.04 0.05 0.52 0.86 1.64 -0.03 0.27 0.56 0.77 1.05

11 Trust NGO providers -0.13 -0.02 0.45 0.80 1.50 -0.01 0.26 0.51 0.75 1.06

12 Trust mission providers -0.09 0.04 0.47 0.71 1.35 0.19 0.33 0.55 0.74 1.06

13 Trust drug sellers -0.09 0.18 0.57 0.90 1.69 0.24 0.42 0.63 0.83 1.19

14 Trust public clinics -0.17 -0.11 0.23 0.64 1.24 -0.05 0.23 0.56 0.74 0.93

15 Feel full member of community

-0.11 -0.07 0.30 0.64 0.98 0.56 0.00 0.45 0.72 0.74

Shaded cells indicate items with non-ordinal item-step means

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Table IV-5: Correlation matrix between explanatory variables

Age Sex Marital Educ Rural/

Urban

HH Exp HH Goods

Food Sec

Age 1.0000

Sex 0.0627 1.0000

Marital 0.2548 -0.0382 1.0000

Educ -0.1729 0.1123 -0.0524 1.0000

Rural/

Urban

-0.0940 -0.0198 -0.1187 0.1962 1.0000

HH Exp 0.0682 0.0667 0.0807 0.1529 0.1997 1.0000

HH Goods

0.0090 0.0282 0.0360 0.2651 0.2416 0.2842 1.0000

Food Sec

0.0268 -0.0032 0.0305 -0.1457 -0.0991 -0.1436 -0.195 1.0000

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Table IV-6: Cognitive social capital and health-related behaviors

Outcomes Low CSC

Mid. CSC

High CSC

Bivariate OR

Bivariate 95% CI

Multivariate

aOR

Multivariate 95% CI

Men who hit othersi

33% 22% 20% 0.69*** 0.57-0.85 0.65*** 0.53-0.81

Women hit by othersi

30% 18% 26% 0.85 0.71-1.03 0.85 0.70-1.03

2 or more sex partnersi

20% 16% 16% 0.88** 0.81-0.97 0.90* 0.82-0.98

Unprotected sex with any partneri

82% 89% 91% 1.00 0.84-1.19 0.96 0.80-1.15

Ever disclose HIV if result was positive++

51% 62% 76% 1.85* 1.17-2.92 1.89* 1.04-3.43

Ever disclose gonorrhea if result was reactive ++

64% 71% 78% 1.39 0.80-2.40 1.25 0.72-2.17

Ever disclose genital sore if provider confirmed STI++

78% 81% 93% 1.72* 1.05-2.83 2.20* 1.00-4.83

Ever disclose STI discharge if provider confirmed STI++

82% 60% 90% 1.22 0.67-2.23 1.14 0.52-2.48

Ever disclose syphilis if result was reactive++

83% 76% 84% 1.07 0.82-1.41 1.09 0.78-1.52

Seek any healthcare past six monthsi

84% 84% 88% 1.15 0.93-1.41 1.10 0.89-1.36

Seek STI care in past six monthsi

41% 32% 35% 0.76** 0.63-0.93 0.80* 0.65-0.97

i Multivariable model adjusted for age group, marital status, sex, education (completed primary or not), above median monthly HH expenditure, above median HH asset score, median HH food security, village commercial activity, year of survey, and the variance of respondents clustered by village * p<0.05** p<0.01*** p<0.001++ Mulitvariable model adjusted for age group, marital status, sex, education (completed primary or not), village commercial activity and the variance of respondents clustered by village

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Table IV-7: Structural social capital and health-related behaviors

Outcomes Low SSC

Mid. SSC

High SSC

Bivariate OR

Bivariate 95% CI

Multivariate (aOR)

Multivariate 95% CI

Men who hit othersi

22% 24% 28% 0.97 0.74-1.28 0.94 0.68-1.29

Women hit by othersi

26% 26% 24% 1.05 0.75-1.49 1.06 0.72-1.59

2 or more sex partnersi

18% 15% 18% 0.93 0.83-1.06 0.93 0.82-1.07

Unprotected sex with any partneri

90% 88% 85% 0.98 0.80-1.20 0.97 0.80-1.18

Ever disclose HIV if result was positive++

64% 67% 62% 1.01 0.64-1.60 0.70 0.39-1.26

Ever disclose gonorrhea if result was reactive ++

72% 81% 65% 1.06 0.64-1.75 0.67 0.34-1.30

Ever disclose genital sore if provider confirmed STI++

93% 84% 79% 1.67 0.84-3.34 1.54 0.72-3.26

Ever disclose STI discharge if provider confirmed STI++

90% 79% 74% 1.55 0.75-3.21 1.36 0.62-3.01

Ever disclose syphilis if result was reactive++

82% 89% 76% 1.40 0.97-2.03 1.15 0.81-1.64

Seek any healthcare past six monthsi

83% 87% 85% 0.91 0.74-1.13 0.97 0.79-1.20

Seek STI care in past six monthsi

40% 35% 35% 1.06 0.91-1.23 1.02 0.86-1.20

i Multivariable model adjusted for age group, marital status, sex, education (completed primary or not), above median monthly HH expenditure, above median HH asset score, median HH food security, village commercial activity, year of survey, and the variance of respondents clustered by village * p<0.05** p<0.01*** p<0.001++ Multivariable model adjusted for age group, marital status, sex, education (completed primary or not), village commercial activity and the variance of respondents clustered by village

129

Figure IV-1: Item response curve for “trust of pharmacies” in 2006 survey

130

Figure IV-2: Information curve for the item ‘trust of pharmacies’ from the 2006 survey

131

Figure IV-3: Item response curve for “trust of pharmacies” in 2007

132

Figure IV-4: Wright Map of respondents on cognitive social capital items from 2006 survey

Wright Map (EAP) Variable: Cognitive social capital IRT Categories Map of person estimates and response model parameter estimates ===================================================================== respondents Thurstonian Thresholds (Recoded) --------------------------------------------------------------------- | | 3 | | | |3.4 4.4 9.4 | XX| | X|6.4 13.4 2 | X|8.4 10.4 | XX|5.4 11.4 | X|4.3 7.4 9.3 12.4 | XX|1.4 3.3 14.4 1 | XXXXXXXXXXXX|2.4 6.3 13.3 | XXXXXXX|10.3 | ------------XXXXXXXX|4.2 5.3 8.3 9.2 11.3 0 | XXXXXXXXXXXXXXXXXXXX|3.2 7.3 12.3 | XXXXXXXXXXXXX|1.3 6.2 13.2 14.3 | XXX|2.3 10.2 | XXX|5.2 8.2 11.2 -1 | XXXXX|4.1 7.2 9.1 12.2 | X|1.2 3.1 14.2 | X|2.2 | X|6.1 10.1 13.1 -2 | |5.1 8.1 11.1 | X|7.1 12.1 | |1.1 14.1 | |2.1 -3 | | | | ===================================================================== Each X represents 35 respondents, each row is 0.255 logits Model Specifications:Measurement Model = Modified Rating ScaleProficiency Estimation Method = EAP Maximum Logit = 6.00Minimum Logit = -6.00Integration Method = QuadratureQuadrature Points = 15EM convergence criteria = 0.001

133

Chapter V: Summary and conclusions

The three studies included in this dissertation explored social and economic

antecedents to healthcare utilization in southwestern Uganda. This concluding chapter

summarizes the main findings of this dissertation, the potential policy implications, and

areas for future research.

Main findings

In Chapter II, regardless of the four binary poverty measures used, we found that

there was a greater prevalence of STI symptoms among the poor than among the non-

poor. We also found that the poor were also more likely to not use STI treatment.

Additionally, the poor and non-poor appeared to use STI treatment services more

frequently at private providers than at public providers.

In Chapter III, we found that between the baseline survey in 2006 and the 16-

month follow-up survey in 2007 that the level of knowledge of STI symptoms improved,

the use of any STI treatment increased, and the prevalence of syphilis decreased in the

surveyed population. One important factor in utilization of STI treatment was distance

from village of residence to healthcare provider. Distance from village of residence to

clinic was inversely correlated with utilization (r= -0.78). Among respondents <11

kilometers from contracted clinics, there was a significant increase in the proportion of

respondents using STI treatment services between 2006 and 2007 while there was no

significant increase in the proportion of respondents ≥11 kilometers (30% increase versus

0% increase). We also found that distance was associated with a greater reduction in the

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prevalence of syphilis among respondents who lived <11 kilometers of a contracted

facility between 2006 and 2007 compared to respondents who lived ≥11 kilometers from

a contracted clinic (57% decrease versus 20% decrease).

In Chapter IV, two indices for measuring social capital cognitive (CSC) and

structural (SSC) were determined to be reliable and valid. As part of the validity testing,

the social capital measures were examined for any association with health behaviors and

health outcomes. There were significant multivariable associations between increased

CSC and decreased odds of male aggression, decreased odds of having two or more sex

partners, increased odds of unprotected sex, increased odds of HIV+ disclosure to

partner, and increased odds of disclosure of a genital sore to partner.

Policy implications

Applications and limitations of OBA programs

The findings from this dissertation suggest that output-based aid voucher

programs, structured like the Uganda STI treatment program, are an appropriate strategy

that can have multiple positive health impacts in local populations. However, there are

limitations to the OBA approach. OBA voucher programs require a responsive

management agency, a credible accreditation process, a transparent claims process and a

pool of competent healthcare providers.

The OBA approach may not work as well in areas where there are few providers

available to contract and similarly in lightly populated rural areas where few people

would likely use the voucher services in sufficient numbers to generate significant

revenue for providers. Conversely, OBA does well when providers compete to enter the

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system on contract. OBA as implemented in Uganda was launched in a region where the

local economy was generally doing well compared to other parts of the country and there

were many private providers available to contract. We know from our study that the

private sector was a significant source of STI treatment and that the poor used private

facilities as much, if not more, than they used public facilities. Yet, if the purpose of

OBA is to serve the poor, were the poor in the Mbarara region Uganda’s truly destitute?

There were certainly destitute individuals in the rural areas, but compared to the northern

region, the Mbarara region overall was doing well. Using the 2002 census measure of

households in poverty, the Mbarara region (Ibanda, Isingiro, Kiruhura, and Mbarara

districts) was in the top quintile of wealth as measured by the proportion of poor

households among all households in each district; 7.0% of Mbarara households were poor

compared to the national median of 10.1% (IQR= 7.2%-17%) (See Figure V-1).

One of the limitations of OBA as practiced in Uganda is that it is a peri-urban

strategy; the available clinics are usually located within short distances to concentrated

populations in trading centers and towns. There are alternatives; in Kenya, a UNICEF

voucher program functions within public facilities and is able to serve disbursed rural

populations where there is little cash economy and no private providers. According to

UNICEF, the percentage of women seeking a skilled attendant for delivery in the

province rose from 8 percent to 25 percent after the new voucher program was launched

(Sittoni 2009).

Another limitation of OBA is the need for significant management capacity. The

management agency ought to have the knowledge, the flexibility and the authority to

identify quickly provider fraud, respond to provider incompetence, improve patient

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satisfaction, and conduct a transparent financial and medical review of all claims. At

successful facilities, healthcare providers are busy and have little time to devote to claims

entry. The management agency ought to balance a judicious review of financial and

medical components on each claim with a need to reimburse providers quickly. In the

Uganda STI program, several providers dropped out after extended delays in

reimbursement; providers grew to distrust the management team when payments failed to

be processed on time. In the Bangladesh voucher program, there were lengthy

reimbursement schedules for specific services - from transport to food supplements

following delivery (Koehlmoos et al. 2008). Already overworked district health staff

were delayed making reimbursements to beneficiaries (Begum et al. 2008).

When appropriate to the local healthcare context, OBA is an appealing and viable

financing strategy: it can target the poor in economically diverse populations; it can

engage and improve private and public sector services; and it create incentives for high

quality healthcare provision.

Social capital, OBA, and STIs

Social capital is an underutilized framework to better understand why health

interventions succeed or fail in low-income African countries. Social capital is a measure

of one’s ability to convert social contacts and networks into other resources. The social

milieu in which health programs are implemented can have an important effect on the

success of a health program. In a simple example, a poor patient with many friends is

better off than the same patient without many friends. Social contacts can be sources of

emotional support, economic security, and provide a sense of security and purpose. These

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social resources are not well understood and poorly measured in Africa yet may be

important factors in healthcare delivery in OBA strategies.

If the findings from this research on the relationship between cognitive social

capital and health behaviors are verified by future research, it has challenging policy

implications. Building stocks of social capital is a potentially difficult intervention and

the benefits to health are not easy to estimate. Trust is a significant element in cognitive

social capital and exposure to economic and health information exchange has been

demonstrated to increase CSC (Pronyk et al. 2008a). Do the health information

campaigns in the Uganda OBA program have the potential to not only increase

knowledge of the program, but also improve the levels of social capital by building trust

in the voucher?

Pronyk and colleagues (2008a) showed that a microfinance intervention could

contribute to greater levels of social capital. Microfinance can increase both CSC and

SSC as greater trust is earned from repeated economic interaction and associations form

as people benefit from proximity to functional microfinance entities. In such settings,

increases in economic exchange and levels of social capital could also lead to increases in

healthcare utilization.

Future directions

Looking forward, there are two primary areas for future research. First, more

research is needed on the meaning of social capital and how it relates to population health

and health services in Africa. In particular, there is significant uncertainty about the

magnitude of the relationship and how it may change over time in relation to health care

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interventions. There is significant uncertainty also about how to build social capital.

Second, more evaluation of OBA programs are needed to understand where they

can be successful geographically, which services ought to be promoted, whether the

targeted population is served, and how successful programs can be scaled. All OBA

voucher programs to date have been regionally discrete pilots at best serving tens of

thousands of patients per year. The OBA model, however, could potentially serve

national populations. However, to offer delivery services to all poor Ugandan women, for

instance, would require the Ministry of Health to make the strategy a top priority and

shift major financial resources to the program.

The OBA programs are a significant improvement in the management of

healthcare in low-income regions of the world. Healthcare delivery ought to be more

responsive to patient needs and the OBA strategy is an important step in the direction of

more accountable and cost-effective health systems.

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Figure V-1: Percent of households below poverty line by subcounty (National Census 2002)

Note: Subcounties in the Mbarara region (Ibanda, Isingiro, Kiruhura, and Mbarara districts) are outlined in black

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REFERENCES

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social and structural determinants of prevalence and treatment of sexually transmitted infections in...

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