improving the quality and use

8/10/2019 Improving the Quality and Use

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Strengthening health systems

in Asia and the Pacifc through

beer evidence and pracce

For the PDF version of

this paper and other

related documents, visit

www.uq.edu.au/hishub

Theme: Building health informaon systems

Working Paper Series Number 5 November 2009 WORKING PAPER

Improving the quality and useof health informaon systems:essenal strategic issues

Health Informaons System Knowledge Hub

Instute for Health Metrics and Evaluaon


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1Working Paper Series Number 5 November 2009

HealthInformaonSystemsKnowled

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Contents

Acronyms and abbreviaons ................................................................................................................... 2

Introducon............................................................................................................................................. 3

Recent developments in strengthening health informaon systems ..................................................... 4

The health informaon system as a stascal tool ................................................................................. 7

Health data sources ................................................................................................................................. 9

Conclusion .............................................................................................................................................. 14

References .............................................................................................................................................. 15


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HealthInformaonSystemsKnowledg

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Acronyms and abbreviaons

DHS demographic and health surveysHMN Health Metrics Network

ICD-10 Internaonal Stascal Classicaon of Diseases and Related Health Problems,10th revision

MDG Millennium Development Goal

RHINO Roune Health Informaon Systems

WHO World Health Organizaon


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The paper is intended to provide a succinct overview

of issues that countries and the donor community

might wish to consider when developing strategies

and pracces to improve the quality and use of

health informaon.

This paper sets out some of the crical issues that

countries and donors should consider when invesng in

the development of health informaon systems. These

range from incenves and pracces to improve the

quality and, especially, the use of health informaon by

those in policy who have the greatest need for reliable,

mely and relevant health informaon for planning, to

strategies to create a culture of informaon demand

and use. Experiences from countries such as Mexico and

Brazil have been analysed to draw aenon to pracces

that might be protably adopted elsewhere, parcularly

in the AsiaPacic region. The paper includes a cursory

overview of internaonal iniaves in health informaon

strengthening. It also reviews the crical role of various

health data sources in supporng indicator development

for the monitoring and evaluaon of health status and

health program eecveness. The paper is intended

to provide a succinct overview of issues that countries

and the donor community might wish to consider when

developing strategies and pracces to improve the

quality and use of health informaon.

Introducon


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to produce high quality primary data and to make this

data available for measurement purposes. Data are a

rst step, but quality data is the real aim. The glossary

for evidence based public health (Rychetnik et al. 2004)

denes data quality as, the degree to which data items

are accurate, complete, relevant, mely, suciently

detailed, appropriately represented (e.g. consistently

coded using a clinical coding system) and retain sucient

contextual informaon to support decision making.

These characteriscs that dene data quality should be

taken as principles for any health metrics eort, and at

the same me, serve as a challenge for HIS.

Incenvising quality and use

For a typical health informaon system in a developing

country, it is not easy to achieve data quality. Frequently,

health data in developing countries are incomplete

they either miss a poron of the populaon or do

not cover all relevant aspects of health. This is oen

through no fault of their own; they simply do not have

the resources needed to achieve a comprehensive

system instantaneously, but they can denitely work to

improve what they have. There is also somemes a lack

of support from the supply perspecve for improvingdata quality. There are few incenves to correct the

crude data gathered for the health informaon system

at a naonal or district level. This generates a perverse

cycle in which decision-makers reacng to the quality

problems in the data exclude those data from their

decision-making. In turn, providers of data choose not

to invest in improvements because nobody is consuming

their products to begin with (Ash et al 2004).

To break this cycle, it is necessary to create incenves,

both to use beer informaon at the local level and

for providers to deliver high-quality, mely data. Agood starng point would be for the central health

informaon system to require data to be disseminated

on a clear schedule. However, this would not be enough

to ensure that local providers are using the informaon.

In other words, ensuring high-quality data is a necessary

precondion for geng that informaon used by

decision-makers and praconers, but it is in itself,

not enough.

For a typical health informaon system in a

developing country, it is not easy to achieve data

quality. Ensuring high-quality data is a necessary

precondion for geng that informaon used by

decision-makers and praconers, but it is, in itself,

not enough.

Health metrics and health informaon

systems

Murray and Frenk put forth the term metrics, to refer

both to the science of measurement and to a specic set

of instruments and indicators that provide the empirical

basis to understand a parcular object of enquiry

and acon (Murray & Frenk 2008). According to this

denion, health metrics is related to measurement of,

and to the instruments used to measure the health of

the populaon. Measurement refers to esmang the

magnitude of some aribute of an object (health status)

and usually includes measuring tools, such as stascal

models, which are calibrated to compare the object

against an established standard.

It is important to think of health as the product of many

intertwined factors. A systemic approach to gathering

and assessing health informaon is therefore paramount

to good decision-making. This perspecve and approach

is quite common in medicine for clinical or individual

purposes. However when the problem concerns the

organizaon, community or general populaon, the

approach is oen fragmented. In the face of this

challenge, technical soluons may be perceived as the

cure-all, when in fact a more systemac diagnosis

and treatment is needed. The systemic approach forpublic health problems should consider simultaneously

human resources, the organizaonal environment and

processes, and technology - and how to align them. In

other words, besides the technical transformaon of

the health system, an important cultural change in its

organizaon needs to happen. It cannot burden the

informaon providers to a degree that it is resisted, or

be so cumbersome for an informaon consumer that its

results are ignored.

The work to be done on the health informaon

system side is inherently connected with the idea of

measurement. Any HIS should have as two of its goals

Recent developments in strengtheninghealth informaon systems


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good-quality data. There is also a scarcity of incenves to

use the informaon collected through health informaon

systems for decision-making and poor use of evidence

from any source for decision-making.

There is a very useful tool developed by the Roune

Health Informaon Systems (RHINO) to idenfy which

aributes of health informaon systems directly

contribute to developing poor-quality products (Aqil

et al 2009). The instrument used the Publishing

Requirements for Industry Standards Metadata (PRISM)

framework, which organises the determinants of the

health informaon system performance into three

blocks: technical, organisaonal and behavioural.The instrument was designed to be applied at a local

level, and the results obtained, unl now, have been

used more for local consumpon. Nevertheless, it is

possible, and important, to generalise some lessons

aer its applicaon in South Africa, Tanzania, Pakistan

and Mexico. In relaon to the organisaonal and

behavioural determinants, two main problems have

emerged as common in all countries: the lack of a culture

of informaon, and the immense need for in-country

capacity building.

Conversely, there are some experiences thatdemonstrate that a posive and supporve culture

can be constructed around the producon and use

of informaon using the products of the current

health informaon system. For example, since 2001,

the Ministry of Health in Mexico has been publishing

an annual accountability report called Salud-Mexico

(Secretaria de Salud 2002). The goal of this report is to

document the states performance benchmarking system.

The model adopted to present the informaon was

based on selecng mainly health outcome indicators.

The report uses the past year (or somemes the past two

years) as reference points to measure improvements in

the health system.

The Mexican Government created incenves for

informaon providers by mandang that the report

be released publicly in a cizens forum, which brings

together important federal and state decision-makers,

civil society leaders, academics and the media. The

government presets a date for the release of the report

to provide a clear deadlinethe beginning of the second

quarter of the year. Taking this approach created strong

incenves for informaon providers to complete data

collecon, data processing and data integraon withina short period. Because it was a naonal accountability

Creang a culture of informaonand building capacity

Building capacity in a country, like fostering a culture

of knowledge, requires a good understanding of the

operaonal environment. In many countries, building

capacity requires reorganising past methods for

informaon collecon. For example, for some naonal

surveys, if the sample process is not standardised, the

results are not comparable. This disconnuity allows

health informaon system managers and other decision-

makers to operate in dierent dimensions; they may

only focus on immediate results or specic areas andnot examine naonwide trends. A lack of harmonisaon

between naonal surveys also results in a weaker

informaon system because managers and decision-

makers have no uniform way to hold their systems

accountable at a larger scale. Also, the capabilies of

health informaon system workers are more dicult

to understand without a uniform test to apply. With

more rigorous programs to build worker capabilies

and program capacity, health informaon systems could

become stronger both within and across countries.

Another reason that capacity is somemes weak is that

health informaon systems do not span across a Ministryof Health or equivalent. Instead, there are somemes

only small units of informaon and informacs within

the health programs, but no organised systems to bring

them together. In order to scale up the abundance

of data across units, branches and departments, it is

necessary to highlight the importance of having a logical

and transparent structure; the importance of integraon

to serve all users; and the importance of keeping

autonomy regarding any kind of informaon (avoiding

conicts of interest) (Krikeberg 2007). If these principles

were applied to the informaon gathering systems by

ministries of health in a comprehensive and systemacmanner, the quality, use and usefulness of informaon

would increase markedly.

The lack of a culture of informaon is widespread. This

concept can be dened through several selected domains

by aempng to understand the degree to which a

country: uses numbers to describe problems and their

soluons; aempts to understand a problem through

the collecon of data and informaon; establishes a

connuous quality learning process; and empowers

people through imparng informaon and knowledge.

In summary, in developing countries, there is oen anabsence of a common commitment to, and support for,


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management demonstrates the various capacies in

which health informaon systems have been used.

The HMN developed a parcular health informaon

system assessment framework to help countries assess

how best to improve their systems. This framework

adapts key components arculated by the General

Theory of Systemsinputs, process, outputs and

boundaries. As such, it does not prescribe precise,

step-by-step requirements, but rather what the general

components are and why they are needed. The HMN

framework proposed a new structure in which health

informaon system resources, not data, are the input.

Specically, the structure denes input as the legislave,regulatory and planning framework required to ensure

fully funconing health informaon systems, and the

resources to ensure that each system is funconal

(personnel, nancing, logiscs support, and informaon

technology and communicaon).

The framework idenes three key components in

the process of strengthening the health informaon

system for a parcular country. The components are

idenfying indicators, data sources and methods for data

management. According to this approach, collecng,

storing, processing, compiling and analysing the data areall integral parts of well-funconing health informaon

system. The outputs include two key components:

informaon products, and disseminaon and use (ie the

transformaon of data into informaon that will become

the basis for evidence, and how to make the products of

the system available and accessible to decision-makers).

The important connecon between this framework and

how health informaon systems can be used by decision-

makers has been presented by Lozano et al (2007)

in the context of assessing eecve coverage for key

intervenons. Three specic lessons were arculated,which could be generalised to other types of health

informaon:

1. Naonal or local household health surveys are

good data sources to measure eecve coverage;

however, they must be supplemented by other

techniques to measure some aspects (eg quality).

2. Roune registries can also be very good data

sources; however, registries should be at the

individual level and have a high degree of accuracy.

In the coverage example, follow-up of people with

Health informaon systems are dened in many

dierent ways, depending on who is using them versus

who is implemenng them. Although these denions

vary, health informaon systems more generally

are the nexus of informaon, technology and the

accompanying processes to provide strategic access to

informaon for decision-makers. Health informaon

systems are comprised of resources, mechanisms

and methods that facilitate the acquision, storage,

retrieval and use of data in health and health decision-

making. The role of a health informaon system is to

determine what informaon needs to be collected and

tracked; to establish mechanisms for collecng the

informaon; to build and sustain an ongoing process of

adding value to the data collected; to ensure that the

data are understood and used; and to substanate the

need for data collecon so that funding is maintained.

Health informaon systems can have mulple

aributes. They can be: paent centred or public health

oriented; subject based (paents, doctors, etc) or task

based (hospital discharges registries); paper based or

computer based.

In any of its contexts, a health informaon system

should act as value chain with dierent components

that transform the original facts gathered into

knowledge that can be applied by policy-makers to

improve populaon health. While there are diering

opinions in the literature about the scope of health

informaon systems, there is general agreement that

they are complex, dynamic, context based and of great

social importance (Lenz et al 2002, Detmer 2003).

To capture this informaon in a more standardised

and comparable way, the use of health metrics has

been increasing. Most recently, exibility of health

informaon systems has been highlighted as an

important component. This exibility includes beingable to receive and store data from many dierent

sources and from mulple dimensionsfrom individual

paent informaon to populaon-level me trends of

morbidity; from storing only alphanumeric informaon

to storing medical diagnosc images. Other recent

changes include the heightened consideraon of health

informaon system users. This is evidenced by the

inclusion of paents and health consumers and through

diversifying the use of data beyond paent care and

administrave purposes. The shi from focusing mainly

on technical health informaon system problems to

those of change management and strategic informaon

The Health informaon system as astascal tool


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Network for Health Informaon (with the acronym RIPSA

in Portuguese). The consensus process was arduous and

lasted about six years. However, the product delivered

was a manual that included detailed analyses about the

quality of each database, key indicators to track, and

common denions and ways to measure them. The

agreement included a naonal mandate about the use

of crude and corrected stascs when reports are used

to describe subnaonal levels. It included descripons of

the data source to be used and the methods to correct

completeness problems. RIPSA connues to operate

today as a valuable process to ensure high-quality,

standardised indicators (RIPSA 2002).

Geng naonal agreements about the inclusion of

corrected stascs as an input for decision-makers is

crucial. In Brazil, it was possible because there was a

crical mass of experts that were able to employ these

stascal methods and to engage in nuanced and

informed arguments with the naonal group charged

with making informaon available and useful. They were

even able to strategically think about useful indicators for

measuring progress with MDG such as infant mortality

and maternal mortality. This opened the door to bridging

the gap between informaon and policy.

AbouZahr et al (2007) proposed a schema to smooth

the pathway from informaon to policy. Their

recommendaons for good pracce in the informaon

producon process should be considered seriously

because they apply to all health systems and to both

developing and developed countries. According to

the WHO framework for Health Systems Performance

Assessment (Murray and Frenk 2000, WHO 2000),

health informaon is one of the key components of

the stewardship funcon of health systems. To support

key acvies such as priority seng and performance

assessment, high-quality informaon is a fundamental

ingredient. Today, health informaon is becoming more

important and is idened as a fundamental building

block of the health system (WHO 2007). It is as important

as service delivery, the health workforce or nancing.

AbouZahr et al (2007) also raise the importance of the

tension that internaonal agencies usually generate for

countries when the local and internaonal indicators

do not match. In a country with a decentralised health

system, a similar problem also arises, though on a

smaller scale, between the federal/naonal and the

regional or local authories. Another obstacle can be amisunderstanding about ocial data. For many people,

chronic condions such as high blood pressure and

diabetes, and other acute condions, is necessary.

3. Fragmentaon of the health system generates a

fragmented health informaon system. Eecve

coverage is a metric that is useful in combining

informaon from both the public and private sectors

of the health system.

The use of informaon with sucient quality for

comparability purposes requires establishing a broad

(naonal or internaonal) and widely accepted

mechanism of standardisaon. Somemes, there is

tension amongst the involved groups that works against

such acceptance of standardisaon. As informaon

becomes more global, this problem will likely

receive more aenon. TheInternaonal Stascal

Classicaon of Diseases and Related Health Problems,

10th revision (ICD-10), is a good example of a stascal

tool that creates constant tension between universal

standardisaon and local circumstances. However, this

tension cannot be resolved by sheer force because the

problem is persistent. For example, whenever there

is an update to the ICD, to ensure the highest degree

of comparability between countries each individualcountry must comprehensively update their systems and

retrain their health workforce to code deaths using the

updates. The ICD creates updates because it wants to

incorporate latest advances in medical knowledge, but

the transional cost to individual countries can be high.

Health informaon systems must have standards, but,

at the same me, countries should have the exibility

to adapt to changes in their own health systems. The

need for both standardisaon and exibility must be

balanced in any health informaon system. In many

instances, these opposing aributes have helped

drive reform discussions. For example, in South Africa,

standardisaon of health data was a major element in

the process of changing the health informaon system.

In Brazil in 1995, an ad hoc group was formed to develop

and agree on a way to design databases at dierent

instuons to help store informaon to calculate a

core set of agreed indicators. Representaves from the

main areas of the Ministry of Health, key parcipants

from the informaon instuons of the country, and

representaves from universies and academia all played

a role. The Pan American Health Organizaon (PAHO)

was also an important player in this iniave (PAHO2001). The project became known as the Interagency


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Health data sources

At a more general level, health stascs can be classied

under two broad categories: primary microdata, and

combined (aggregated) secondary macro-datasets.

Several typologies exist that capture specic types of

health data within these main categories.

Primary microdata

This is the category of major importance for generang

health stascs and is the foundaon of most other

datasets.

Vital registraon

Vital registraon data collects birth and death

informaon and, given their importance, provides some

of the most advanced and standardised types of health

data across countries. The goal of any vital registraon

system is to accurately record allbirths and deaths in the

populaon as a whole. We can classify vital registraon

sources into four dierent types:

Complete vital registraon systems, including cause-

of-death cercaon and coding according to the

WHO ICD-10. For this system, the death cercate can

provide crical data, parcularly if the informaon

is digised along with the ICD-10 codes. These data

become even more useful if they include a naonal ID

number on the death cercate that allows matching

to other data collected in household surveys,

censuses or health service registries.

Incomplete vital registraon systems that collect

cause-of-death informaon according to the ICD-10,

but are incomplete and/or do not have reliable death

cercaon or naonal ID systems in place.

Sample registraon systems, where vital registraon

has been implemented in a sample of localies

that are intended to be representave of the enre

country. These systems have been successfully

introduced in very populous countries such as India

and China.

Demographic surveillance sites (DSS) are used in

many low-income countries to monitor vital events

in specic communies. DSS were oen inially

developed as research sites and have been used

for large-scale clinical trials. However, a number of

them have persisted and diversied the informaonthey collect. The largest network of such sites is the

ocial data by denion are those gures produced

by government agencies, regardless of the quality of

the data. If the informaon comes from the Oce of

Stascs of the Ministry of Health, it is automacally

considered to be ocial and ready to use. However,

informaon released by a government agency must sll

be scrunised for quality.

Despite this tension, from a country perspecve,

it is important that a health informaon system

responds primarily to country needs and secondarily

to internaonal needs, such as those of the WHO.

Even aer these priories have been sorted out, data

ownership is not a minor issue, and is likely to be one ofthe most common sources of misunderstandings and a

signicant obstacle to successful collaboraon between

naonal and internaonal agencies. When there is no

clear ownership of informaon, there is likely to be

tension. The problems arise when the calculaons from

internaonal agencies produce esmates for the same

indicator that dier from those of naonal agencies.

Somemes, it is dicult methodologically to explain

why the two might be dierent. However, even if there

is good reason for the two to dier methodologically,

it is even more dicult to explain polically, and to a

nontechnical audience, why a predicted value from an

internaonal agency is much higher or lower than the

ocial data of the country. The negave consequences

of these problems generate two dierent reacons.

Naonal stakeholders, believing that the internaonal

organisaons are not acng in their best interest, will

not share their data. Subsequently, the internaonal

organisaons end up using incomplete databases, which

forces them to model more esmates with limited data

and simply perpetuates the original problem.


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of health surveys in the United States. The objecve

of the BRFSS is to collect uniform, state-specic data

on health risk behaviours, clinical prevenve health

pracces and health care access that are associated

with the leading causes of morbidity and mortality.

Data are collected from a representave sample in each

state, and the sampling is designed to provide naonal

esmates when data from all states data are combined

(Mokdad et al 2003).

Fourth, disease-specic or condion-specic

standardised surveys, oen sponsored by WHO to

examine a parcular health problem, have been carried

out in many countries of the world. These surveys, whichare relavely standardised due to WHO sponsorship,

cover malnutrion, adolescent risk factors, oral health,

etc. A good example is WHOs Global Database on Child

Growth and Malnutrion, which comprises informaon

collected from populaon-based naonal and

subnaonal surveys that follow a standard procedure

to obtain comparable results from about 155 countries.

In Australia, the Naonal Survey of Mental Health and

Wellbeing (SMHWB) is designed to collect informaon

on the mental health and wellbeing of the Australian

populaon. The objecves of the survey are to provide

informaon, about Australians aged 18 years or more,

on the prevalence of selected major mental disorders,

the level of disability associated with these disorders,

and the health services used and the help needed

as a consequence of mental health problems. The

informaon is collected by personal interview from usual

residents from approximately 15 000 private households.

Fih, verbal autopsy is a highly specialised survey to

esmate cause-of-death paerns in populaons that

lack vital registraon. The survey focuses on collecng

informaon from families of the deceased about signs

and symptoms before the death. It can also be used by

trained health professionals to idenfy causes of death

in environments where most individuals die at home

or without any contact with medical establishments.

The verbal autopsy method has been developed to ask

relaves of the deceased a series of symptom-based

quesons about the events leading up to death, as well

as broader sociodemographic and risk factor informaon

that might yield clues as to the cause of death of the

deceased. Based on this informaon, a cause of death

can be assigned by a clinician. Although potenally very

useful, there is less standardisaon in verbal autopsy

instruments that have been used. However, there

have been eorts to improve their comparability. Also,

Internaonal Network of Field Sites with Connuous

Demographic Evaluaon of Populaons and Their Health

in Developing Countries (INDEPTH). Because they are

not designed to be naonally representave, these

sites are less useful to monitor naonal health levels.

Household interview surveys

Household interview surveys have followed standardised

instruments and protocols, beginning with the World

Ferlity Survey in the 1970s. It is useful to think of them

under several dierent headings according to their

main purpose.

First, there are a set of fairly standardised mulcountry

health interview surveys. These are not standardised

with one another, but each survey program is

implemented in approximately the same form in mulple

countries. These include the demographic and health

surveys (DHS), the World Health Surveys, the mulple

indicator cluster surveys (MICS), the Pan Arab Project

for Family Health (PAPFAM), the Pan Arab Project for

Child Development (PAPCHILD) and the United States

Centers for Disease Control and Prevenon (CDC)

Reproducve Health Surveys. These surveys provide

valuable informaon across and within countries,parcularly because each survey is fairly standard across

the countries in which it has been implemented. For

example, each DHS is fairly comparable to another,

regardless of the country or the year in which it was

implemented. The surveys are also useful because of

the amount of specic health details that can be elicited

from these interviews.

Second, there are standardised mulcountry surveys, the

primary purpose of which is collecng socioeconomic

data, but which also have health modules. Examples

include the Living Standards Measurement Surveys(LSMS) and the United Naons Household Surveys.

The LSMS includes detailed health quesons that are

helpful in measuring the ulisaon of health care and

health expenditures.

Third, naonal household health interview surveys are

somemes ulised by countries to track health issues

and levels within their own countries. These surveys

allow for standardisaon over me and are specically

tailored to a countrys health prole. In the United

States , the Naonal Health Interview Survey has been

conducted for over 30 years. The Behavioural Risk FactorSurveillance System (BRFSS) is a state-based system


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Budgets and expenditure reports

Expenditure and budgetary data provide valuable

informaon on nancial resources for health. These data

come from naonal budget documents, expenditure

reviews and audit reports. Most oen, this informaon

is available at the summary level. As part of the

management of health services, budget and expenditure

data are frequently provided by nancial management

informaon systems, which are somemes maintained

for the government as a whole, rather than just for the

health system. There is less informaon being collected

on individual expenditures on health, but these data are

certainly desirable to obtain when possible.

For policy development and strategic planning, nancial

data are oen compiled using the methodology for

naonal health accounts (NHA) (OECD 2000, World

Bank et al 2002, Poullier et al 2003). This system

provides informaon on the amount of nancial

resources available for health and their ows across

the health system. The breakdown of data into private

and public sector categories is an important aspect in

this regard. In addion, the disaggregaon of nancial

informaon by major disease or health program area is

possible. At subnaonal levels, budgetary informaonlinked to health system funcons and, in parcular,

health intervenons, is a minimum requirement for

performance budgeng.

Epidemiological observaonal studies

Epidemiological observaon studies follow a cohort of

individuals over a number of years and are useful to

provide informaon about disease progression and other

key factors for disease and survival. They are generally

completely researcher driven but, nonetheless, can be

useful for assessing populaon health. Studies in the

United States that have tracked individuals include the

Framingham cohort, the Nurses Health Study and the

American Cancer Societys Cancer Prevenon Study.

Health facility assessments

Health facility assessments are intended to capture the

resources and inputs of a specic health centre, be it

a primary care clinic, a community health centre or a

specialty clinic. Modules include facility infrastructure,

health centre budget reviews, pharmaceucal

inventories, secondary output review, and services

for specic condions such as tuberculosis treatment.

As a result, it is advisable to regularly assess the quality

of health service data and to help ensure some basic

standardisaon, to the extent possible, to beer serve

naonal and regional interests. Regular monitoring also

helps to beer understand the aggregate capacity of a

health system to provide care. Supervisory systems can

be used to collect standardised and systemac data and

to provide comparisons over me and between clinics

and regions. Addional data may be collected through a

health facility survey, which is usually based on a sample

of clinics.

Such a survey may consider dierent aspects of service

quality such as the availability of drugs, commodiesand trained sta. Special techniques such as record

review or observing clientprovider interacons can

add considerable value to the assessment, but they also

increase costs and complexity. Data collected from record

reviews and stang inventories can be used to validate

roune administrave stascs on the volume of services

delivered and on the availability and geographical

distribuon of human resources.

Census data

Where available, populaon-level census data can serve

as the primary informaon source for determining the

size of a populaon; its geographical distribuon; and

the social, demographic and economic characteriscs

of its people. Censuses have been undertaken in most

countries in recent decades and, in some places, for

more than a century. The Stascs Division of the United

Naons Department of Economic and Stascal Aairs

(UNDESA) has developed principles, recommendaons

and manuals for populaon and housing censuses

available from their website (UNDESA 2010).

From a health perspecve, informaon on populaonnumbers and distribuon by age, sex and other

characteriscs is essenal for naonal and local planning,

esmang target populaon sizes and trends, and

evaluang rates of service coverage and future needs.

Census data can also provide valuable informaon

on some key health outcomes, parcularly mortality.

Informaon on major health determinants and other

key factors such as poverty, housing condions, water

and sanitaon, can also be collected in a census. The

nature of the census allows for small-area esmaon and

disaggregaon by key straers such as socioeconomic

status. Censuses can also provide valuable informaonon the number of health professionals working in the

health sector.


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Health facility assessments are important in evaluang

not only the resources that are necessary to provide

health services to a populaon, but also to evaluate the

quality of the services being provided and factors related

to the regular provision of medicaments, such as stock-

out rates.

DHS Macro conducts service provision assessments in

selected countries on a quasi-regular basis, gathering

informaon from health facilies on the type and quality

of care that they provide. The informaon provided to

policy-makers includes data on health facilies in-country,

and their resources, and basic systems and specic

health services (eg basic child health services or maternalcare).

Secondary macro-datasets

Aggregate health indicators, such as those created by

internaonal organisaons, are generally based on

datasets that aggregate individual data. They are usually

a mixture of datasets, oen poorly documented. Two

major problems exist with these types of datasets.

First, some countries do not have data on the quanty

of interest. Therefore, although some aggregates can

be compared, they are not enrely comparable cross-

naonally, due to missing informaon from some

countries. Second, those who create these aggregates

(eg WHO, World Bank, United Naons Childrens Fund,

United Naons Development Programme and United

Naons Populaon Fund) do not always detail how the

aggregate was generated. What is worse, countries oen

report back to WHO the esmates that WHO provides as

being their naonal esmates. This pracce discourages

countries investment and interest in developing naonal

health informaon systems designed to meet theirhealth development needs.

Data that are generated through research are oen

coupled with results that are very important for policy.

These sources vary widely, depending on the aim of the

research, and therefore the data obtained are dicult to

categorise. However, they are useful as further sources

of informaon. Thus far, the availability of these datasets

has not been standardised, but there is increasing

pressure to ensure that such data be made available

by publicaon. The Instute for Health Metrics and

Evaluaon (IHME), for example, has included this as a

core principle of its operaon, and makes all data used in

publicaon available on its website (IHME 2010).


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Lozano R, Solis P, Gakidou E, et al (2006). Benchmarking

performance of Mexican states with eecve coverage.

Lancet 368(9548):17291741.

Mokdad AH, Stroup DF and Giles WH (2003). Public

health surveillance for behavioral risk factors in a

changing environment. Recommendaons from the

Behavioral Risk Factor Surveillance Team. Morbidity and

Mortality Weekly Report. Recommendaons and Reports

52(RR-9):112.

Murray CJL and Frenk J (2000). A framework for

assessing the performance of health systems. Bullen of

the World Health Organizaon79(6):717732.

Murray CLJ, Lopez A, Barofsky J et al (2007). Esmang

populaon cause-specic mortality fracons from in-

hospital mortality: Validaon of a new method. PloS

Medicine4(11):e326 0112.

OECD (Organisaon for Economic Co-operaon and

Development) (2000).A System of Health Accounts,

OECD, Paris.

PAHO (Pan American Health Organizaon) (2001).

Indicadores de Salud: Elementos bsicos para el anlisis

de la situacin de salud.Bolen Epidemiolgico4:15.

PARIS21 (Partnership in Stascs for Development in the

21st Century) Secretariat.A Guide to Designing a Naonal

Strategy for the Development of Stascs(NSDS).www.

paris21.org/sites/default/les/1401_0.pdf (Accessed

18 November 2011).

PHDSC (Public Health Data Standards Consorum)

(2007). Building a Roadmap for Health Informaon

Systems Interoperability for Public Health (Public

Health Uses of Electronic Health Record Data).www.

ihe.net/Technical_Framework/upload/IHE_PHDSC_Public_Health_White_Paper_2007_10_11.pdf (Accessed

1 March 2010)

Poullier J, Hernndez P and Kawabata K (2003).

Naonal Health Accounts: Concepts, Data Sources, and

Methodology. Health Systems Performance Assessment:

Debates, Methods and Empiricism, World Health

Organizaon, Geneva.

RIPSA (Interagency Network for Health Informaon)

(2002).Indicadores Bsicos Para a Sade No Brasil:

Cenceitos e Aplicacoes, RIPSA, Brasilia.

AbouZahr C, Adjei S and Kanchanachitra C (2007). From

data to policy: Good pracces and cauonary tales.

Lancet369:10391046.

Aqil A, Lippeveld T and Dozumi D (2009). PRISM

framework: a paradigm shi for designing, strengthening

and evaluang roune health informaon systems.

Health Policy and Planning 112.

Ash J, Berg M and Coiera E (2004). Some unintended

consequences of informaon technology in health care:

the nature of paent care informaon system related

errors.Journal of the American Medical Informaon

Associaon11:104112.

Detmer D (2003). Building the naonal health

informaon infrastructure for personal health, health

care services, public health and research. BMC Medical

Informacs3:112.

Evans T and Stanseld S (2003). Health informaon in

the new millennium: a gathering storm? Bullen of the

World Health Organizaon81(12):856.

Haux R (2006). Health informaon systemspast,

present, future.Internaonal Journal of Medical

Informacs75:268281.

HIS Hub (Health Informaon Systems Knowledge Hub).

www.uq.edu.au/hishub/about-the-his-hub (Accessed

March 1 2010).

HMN (Health Metrics Network) (2008).Assessing the

Naonal Health Informaon System: An Asssessment

Tool (Version 4.00), World Health Organizaon, Geneva.

IHME (Instute for Health Metrics and Evaluaon).

www.healthmetricsandevaluaon.org (Accessed

1 March 2010).

Krickeberg K (2007). Principles of health informaon

systems in developing countries.Health Informaon

Management Journal36(3):820.

Lenz R, Elnstner T, Siegele H et al (2002). A praccal

approach to process support in health informaon

systems.Journal of the American Medical Informaon

Associaon9:571585.

Lozano, R (2008). Es posible seguir mejorando los

registros de las defunciones en Mxico?Gaceta Medica

de Mxico144(6):525533.

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Secretara de Salud (2002).Informacin para la rendicin

de cuentas 2001, Federal Government, Salud, Mxico.

WHO (World Health Organizaon) (1993). Guidelines for

the Development of Health Management Informaon

Systems, WHO, Geneva. www.wpro.who.int/publicaons/

pub_9290611065.htm (Accessed 22 November 2011).

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Health Report 2000Health Systems: Improving

Performance, WHO, Geneva.

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Business: Strengthening Health Systems to ImproveHealth Outcomes, WHOs Framework for Acon,

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and USAID (United States Agency for Internaonal

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demographic/sources/cwp2010/docs.htm (Accessed1 March 2010).


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The Knowledge Hubs for Health Iniave

The Health Informaon Systems Knowledge

Hub is one of four hubs established by

AusAID in 2008 as part of the Australian

Governments commitment to meeng

the Millennium Development Goals and

improving health in the Asia and Pacic

regions. All four hubs share the common

goal of expanding the experse and

knowledge base to help inform and guide

health policy.

The Knowledge Hubs are funded by

AusAIDs Strategic Partnership for

Health Iniave.

Health Informaon Systems Knowledge Hub

The University of Queensland

Aims to facilitate the development and integraon of health

informaon systems into the broader health system strengthening

agenda, and increase local capacity to ensure that cost-eecve,

mely, reliable and relevant informaon is available. The Health

Informaon Systems Knowledge Hub also aims to beer inform

health informaon systems policies across Asia and the Pacic.

www.uq.edu.au/hishub

Human Resources for Health Knowledge Hub

The University of New South Wales

Aims to contribute to the quality and eecveness of Australias

engagement in the health sector in the AsiaPacic region by

developing innovave policy opons for strengthening human

resources for health systems. The hub supports regional, naonal

and internaonal partners to develop eecve evidence-informed

naonal policy-making in the eld of human resources for health.

www.hrhhub.unsw.edu.au

Health Finance and Health Policy Knowledge Hub

The Nossal Instute for Global Health(University of Melbourne)

Aims to support regional, naonal and internaonal partners

to develop eecve evidence-informed naonal policy-making,

parcularly in the eld of health nance and health systems. Key

themac areas for this hub include comparave analysis of health

nance intervenons and health system outcomes; the role of

non-state providers of health care; and health policy development

in the Pacic.

www.ni.unimelb.edu.au

Compass: Womens and Childrens Health Knowledge Hub

Compass is a partnership between the Centre for Internaonal

Child Health, The University of Melbourne, Menzies School

of Health Research and Burnet Instutes Centre for

Internaonal Health.

Aims to enhance the quality and eecveness of women's and

childrens health intervenons and focuses on supporng the

Millennium Development Goals 4 and 5improved maternal

and child health, and universal access to reproducve health. Key

themac areas for this hub include regional strategies for child

survival; strengthening health systems for maternal and newborn

health; adolescent reproducve health; and nutrion.

www.wchknowledgehub.com.au


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A strategic partnerships iniave funded by the Australian Agency for Internaonal Development

improving the quality and use

Documents