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Global Health ActionSupplement 2, 2010

CONTENTS

Forewords

INDEPTH WHO-SAGE studyOsman Sankoh 2

The INDEPTH WHO-SAGE collaboration � coming of age

Ties Boerma 3

Guest Editorial

The INDEPTH WHO-SAGE multicentre study on ageing, health, and well-being among people aged

50 years and over in eight countries in Africa and AsiaRichard Suzman 5

Participating Sites - List of Staff 8

Ageing and adult health status in eight lower-income countries: the INDEPTH WHO-SAGE collaboration

Paul Kowal, Kathleen Kahn, Nawi Ng, Nirmala Naidoo, Salim Abdullah, Ayaga Bawah, Fred Binka,

Nguyen T.K. Chuc, Cornelius Debpuur, Alex Ezeh, F. Xavier Gomez-Olive, Mohammad Hakimi,

Siddhivinayak Hirve, Abraham Hodgson, Sanjay Juvekar, Catherine Kyobutungi, Jane Menken,

Hoang Van Minh, Mathew A. Mwanyangala, Abdur Razzaque, Osman Sankoh, P. Kim Streatfield,

Stig Wall, Siswanto Wilopo, Peter Byass, Somnath Chatterji and Stephen M. Tollman 11

Assessing health and well-being among older people in rural South Africa

F. Xavier Gomez-Olive, Margaret Thorogood, Benjamin D. Clark, Kathleen Kahn and Stephen M. Tollman 23

Health status and quality of life among older adults in rural TanzaniaMathew A. Mwanyangala, Charles Mayombana, Honorathy Urassa, Jensen Charles, Chrizostom Mahutanga,

Salim Abdullah and Rose Nathan 36

The health and well-being of older people in Nairobi’s slums

Catherine Kyobutungi, Thaddaeus Egondi and Alex Ezeh 45

Self-reported health and functional limitations among older people in the Kassena-Nankana District,

GhanaCornelius Debpuur, Paul Welaga, George Wak and Abraham Hodgson 54

Patterns of health status and quality of life among older people in rural Viet Nam

Hoang Van Minh, Peter Byass, Nguyen Thi Kim Chuc and Stig Wall 64

Socio-demographic differentials of adult health indicators in Matlab, Bangladesh: self-rated health,

health state, quality of life and disability level

Abdur Razzaque, Lutfun Nahar, Masuma Akter Khanam and Peter Kim Streatfield 70

Health and quality of life among older rural people in Purworejo District, Indonesia

Nawi Ng, Mohammad Hakimi, Peter Byass, Siswanto Wilopo and Stig Wall 78

Social gradients in self-reported health and well-being among adults aged 50 and over in

Pune District, India

Siddhivinayak Hirve, Sanjay Juvekar, Pallavi Lele and Dhiraj Agarwal 88

Health inequalities among older men and women in Africa and Asia: evidence from eight Health

and Demographic Surveillance System sites in the INDEPTH WHO-SAGE study

Nawi Ng, Paul Kowal, Kathleen Kahn, Nirmala Naidoo, Salim Abdullah, Ayaga Bawah, Fred Binka,

Nguyen T.K. Chuc, Cornelius Debpuur, Thaddeus Egondi, F. Xavier Gomez-Olive, Mohammad Hakimi,

Siddhivinayak Hirve, Abraham Hodgson, Sanjay Juvekar, Catherine Kyobutungi, Hoang Van Minh,

Mathew A. Mwanyangala, Rose Nathan, Abdur Razzaque, Osman Sankoh, P. Kim Streatfield,

Margaret Thorogood, Stig Wall, Siswanto Wilopo, Peter Byass, Stephen M. Tollman and Somnath Chatterji 96

In addition to the mentorship and editing provided by the Supplement Editors, each paper has been subjected to regular peer review.

INDEPTH WHO-SAGE study

My foreword to the first INDEPTH supplement

published in GHA, which comprised a series of

papers by the INDEPTH NCD Surveillance in

Asia Working Group, stated that the work demonstrated

‘the increasing ability of the INDEPTH Network to

harness the collective potential in Health and Demo-

graphic Surveillance Systems in low- and middle-income

countries to provide a better, empirical understanding of

health issues of populations under continuous evaluation.’

In that foreword I also noted, ‘with that collaborative

research, we have seen some of the objectives of

INDEPTH being achieved: we have strengthened the

capability of several of our young scientists to conduct

and analyse longitudinal health and demographic studies;

and some of them have become first authors of scientific

papers for the first time’ (1).

The current supplement, by the INDEPTH Adult

Health and Ageing Working Group, is a compilation of a

series of excellent site-specific and cross-site papers,

which has reinforced the opinions I expressed previously.

I feel privileged to be writing these forewords at a time

when these studies are being completed and their results

are being disseminated in scientific publications. The

INDEPTH WHO-SAGE collaboration started several

years ago during the tenure of office of my predecessor,

Professor Fred Binka. It was he who provided the initial

support to the Adult Health and Ageing group, enabling

it to engage with WHO in this partnership. I therefore

wish to share with him the credit for this success.

I am delighted to have taken part in two key analysis

workshops graciously hosted by the Umea Centre for

Global Health Research, Umea University, Sweden in

2008, and by the Harvard Centre for Population and

Development Studies, Cambridge, MA, USA in 2010. I am

also well aware of those previously hosted by the University

of Witwatersrand’s School of Public Health as well as the

WHO, more recently in June 2010. I saw INDEPTH

scientists presenting their work and taking part in rigorous

data analysis, and witnessed exemplary collaboration

demonstrated by our partners in Umea and Boston.

They contributed expertise and resources to strengthen

the capacities of our scientists to take leading roles in this

work.

While in Umea and Boston, I saw our colleagues there

demonstrating expertise in data analysis and in how to

interrogate and make sense out of data that had been

collected thousands of miles away. That experience made

me feel that there was a great need for INDEPTH to

establish a training centre for health and demographic

surveillance systems so that many more scientists from

low- and middle-income countries could be trained in

complex longitudinal data analysis techniques.

On behalf of the INDEPTH Board and myself, I wish to

thank the World Health Organization who have been

exemplary partners in this collaboration, and also the key

funder, National Institute on Aging and National Institutes

of Health (NIA, NIH). I wish to highlight the pivotal role

played by Dr. Richard Suzman (NIA, NIH) who ‘was

always there’ as a funder and competent scientist during

this collaboration and is the Senior Editor of this Supple-

ment. I also want to acknowledge the Health and Popula-

tion Division, School of Public Health, University of the

Witwatersrand, South Africa, for its ongoing role as

satellite secretariat of the INDEPTH Adult Health and

Ageing Working Group. This multi-site and multi-country

INDEPTH project has succeeded because of the commit-

ment and scientific leadership of Professor Stephen Toll-

man, the leader of the INDEPTH Adult Health and Ageing

Working Group. Furthermore, I wish to appreciate the

advice provided by the INDEPTH Advisory Committee

through its member Professor Stig Wall at Umea University.

Through resources provided for core institutional

support to INDEPTH by the Wellcome Trust, Sida/

GLOBFORSK, Rockefeller Foundation, Gates Founda-

tion and Hewlett Foundation, we were able to contribute

financially to the Adult Health and Ageing Working

Group for the successful completion of this work. I was

happy to learn of WHO’s success in securing further

resources from NIA, NIH for a Phase II of these

INDEPTH WHO-SAGE studies and, in this regard,

look forward to our continuing collaboration.

The dataset generated by these studies is being made

freely available and INDEPTH will encourage wider use

of the data.

Congratulations!

Osman Sankoh

Executive Director, INDEPTH Network

Reference

1. Sankoh O. Foreword. Global Health Action Supplement 1, 2009.

DOI: 10.3402/gha.v2i0.2085

�FOREWORDINDEPTH WHO-SAGE Supplement

Global Health Action 2010. # 2010 Osman Sankoh. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproductionin any medium, provided the original work is properly cited.

2

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5441

The INDEPTH WHO-SAGEcollaboration � coming of age

It is no surprise that there is a lack of evidence on the

health of older populations in low- and middle-

income countries. Much current attention is focused

on the Millennium Development Goals, prioritising

maternal and child health and leading infectious diseases.

The epidemiological transition is relatively recent and

health researchers and policy-makers are still grappling

with the new data demands. And even in high-income

countries, which face increasingly large older populations

and predominance of chronic diseases, there are major

evidence gaps.

The set of papers in this Supplement represent a

significant step towards better evidence on the health

of older populations. The papers are based on studies

in four African and four Asian countries as part of a

collaboration between two multi-country networks. The

first network is the well-established International Network

for the Demographic Evaluation of Populations and Their

Health (INDEPTH) in developing countries. It is an

international platform of sentinel demographic sites that

provides health and demographic data and research to

enable developing countries to set health priorities and

policies based on longitudinal evidence and includes more

than 30 sites, mostly in Africa and Asia. It has an

outstanding record of collecting vital statistics and has

been a vehicle for the generation of information on a wide

range of health topics.

The second network is the World Health Organization

(WHO) Study on Global AGEing and Adult Health

(SAGE). SAGE is a multi-country study that addresses

health and health-related outcomes and their determinants

in populations around the world with a focus on low- and

middle-income countries. The emphasis is on common

methodological approaches to ensure cross-population

comparability. SAGE country studies aim for a long-

itudinal cohort design with the inclusion of populations

50 years and over along with a comparative cohort of

persons aged 18�49 years. The first round has recently been

completed in China, Ghana, India, Mexico, Russia and

South Africa.

The SAGE and INDEPTH networks have initiated a

collaboration to study adult health and ageing in low- and

middle-income settings. This collaboration offers several

unique features which will allow both the generation of

unique evidence and detailed methodological work to

validate self-reported morbidity and survey mortality

data. INDEPTH sites have relatively large populations

under surveillance with regular monitoring of vital events,

which allows the inclusion of a standard short module to

examine health and health-related outcomes in regular

surveillance rounds. In addition, innovative strategies can

be developed to link survey and surveillance data to inform

larger national estimates as well as developing and testing

strategies for robust small area estimates. In three of the

eight countries with sites � Ghana, India and South Africa

� reported in this volume of Global Health Action (GHA),

national SAGE studies are ongoing.

The collaboration will also draw upon the expertise

within INDEPTH sites to improve methods in data

collection in older populations in low- and middle-

income countries. This includes improved recording of

age, development of verbal autopsy tools to assess the

cause of death in the ageing population, the measurement

of health and health-related outcomes for ageing care

providers caring for HIV/AIDS orphans, and the care-

giving burden and its association with health. Other

routinely collected demographic data such as migration

and its relationship to health outcomes will also be

essential. Furthermore, some sites have data from other

studies on changing patterns in risk factors and can relate

that to the health status of older adults.

This Supplement to GHA brings together the first set of

papers from this collaboration. This set of papers focuses

on describing the current situation among older people

and identifies a number of consistent patterns. For

instance, the health of women among older adults is worse

than that of men; living alone jeopardises health and well-

being; and being poor is bad for health. There are,

however, important differences within and between sites

as well. For example, older adults in Vadu, India, who are

not in a partnership are not as badly off as in other study

sites, probably because of support from extended and

adjoined families; older adults with the poorest health in

Purworejo, Indonesia, are clustered in the semi-urban belt

of the district; and patterns of the older adult population

structure are changing as exemplified by the predominance

of older men in Agincourt, South Africa and of older

women in the slums of Nairobi, Kenya. The results also

reveal close relationships between declining health, in-

creasing disability and worsening of quality of life in the

ageing population.

�FOREWORDINDEPTH WHO-SAGE Supplement

Global Health Action 2010. # 2010 Ties Boerma. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproductionin any medium, provided the original work is properly cited.

3


These first results herald the coming of more substantive

analyses of the complex relationship between non-fatal

health status and subsequent mortality and the factors that

influence that relationship within and across SAGE�INDEPTH sites.

This unique collaboration between WHO�SAGE and

the INDEPTH Network will lead to ongoing efforts to

follow these populations over time, to look at longitudinal

changes in the key outcomes of interest and their predictors.

This kind of evidence will be increasingly essential to

shape policies and programmes for the health of older

populations in low- and middle-income countries.

Ties Boerma, Director

Health Statistics and Informatics

World Health Organization

Geneva, Switzerland

Ties Boerma

4 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5442

The INDEPTH WHO-SAGEmulticentre study on ageing, healthand well-being among people aged50 years and over in eight countries inAfrica and Asia

This supplement to Global Health Action presents

the first results from the INDEPTH WHO-SAGE

multicentre study, comprising background infor-

mation (1), site-specific results (2�9) and an overall

multicentre analysis (10). Reporting on one of the first

cross-national studies of ageing in Africa and Asia, this

supplement might be termed historic, especially when

coupled with the demographic circumstances of popula-

tion ageing, and the simultaneous public release of the

microdata from the eight sites. According to a UN

projection, the world is only a few years away from a

historic watershed � when for the first time in human

history those aged 65 and over will outnumber those

under age 5 (11). Awareness of population ageing and its

consequences is by now quite widespread in European

policy circles; but the issue is only just reaching the radar

screens of most low-income nations. What steps should

low-resource countries take (and when), in advance of the

demographic, epidemiologic, and economic transitions

associated with population ageing? Industrialised nations

experienced population ageing after they became wealthy;

most low-resource countries will have to cope with this

transition prior to becoming wealthy. Minimal attention

has been given to the dynamics of health and their

economic consequences in developing countries, which

are now among the fastest ageing nations. To date, the

attention of global institutions has been riveted almost

solely on children rather than the needed dual focus on

both groups of societies’ dependents: children and older

people. Unfortunately, no manual exists to guide the

preparations of nations at different levels of development

or stages of the demographic ageing transition, and

governments have to navigate without adequate maps or

GPS systems. While the demographic changes occur over

a timeline measured in decades, the development of new

institutions and systems, including sound pension and

insurance systems, need to be set up decades in advance of

any transition. The long-term costs of public sector

pensions in Africa are already giving rise to expressions

of anxiety in some financial circles. The results from the

standardised data for the four African and four Asian

country sites presented in this Supplement represent a

significant advance on previously available information

for charting the evolution of the demographic and

epidemiological transitions in low-income countries.

Two decades ago, there was a distressing paucity of

demographic, economic, and health data on adult health

and ageing for low-resource countries (12). Most of the

available data were cross-sectional. However, longitudinal

studies, most especially ones that combine health and

economic status data within the same study, are needed to

understand many of the dynamics of ageing. To remedy

the abysmal lack of information on older populations in

low-income countries, the U.S. National Institute on

Ageing (NIA), a component of the National Institutes of

Health (NIH), commissioned a series of reports

on ageing in developing countries from the U.S. Bureau

of the Census (13, 14), and the U.S. National Academy of

Sciences (15). Although as recently as 1990 almost all

industrialised societies also suffered from a lack of

adequate data (especially longitudinal), significant pro-

gress has since been made in establishing nationally

representative longitudinal studies, such as the Health

and Retirement Study USA (HRS), the English Long-

itudinal Study on Ageing (ELSA), and the Survey of

Health, Ageing and Retirement in Europe (SHARE).

These surveys, with their data on health and economic

status, cognitive functioning, and biological assessment

are transforming several areas of social and behavioural

science (16). Over the past several years, NIA has

encouraged efforts to develop nationally comparable

representative studies in low-resource countries. We are

now seeing successes in developing comparable and

coordinated national surveys in countries such as Mexico

(MHAS), China (CHARLS), and in earlier stages, India

(LASI). Additionally, the NIA, in concert with WHO,

seized the opportunity to develop a network of low-cost

adult health and ageing-related surveys that piggy-backed

on the World Health Survey. The network, known as the

Study on Global AGEing and Adult Health (SAGE), has

�GUEST EDITORIALINDEPTH WHO-SAGE Supplement

Global Health Action 2010. # 2010 Richard Suzman. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproductionin any medium, provided the original work is properly cited.

5


fielded studies in Ghana, South Africa, India, China,

Mexico, and Russia. INDEPTH WHO-SAGE resulted

from an opportunity to field a standardised set of surveys

of adult health and ageing in eight INDEPTH health and

demographic surveillance system (HDSS) sites, with the

survey content drawn heavily from the SAGE, SHARE,

and HRS surveys. While there have been a number of

cross-national surveys focusing on ageing in Asia, this is

the first involving sub-Saharan Africa.

From the beginning, the INDEPTH network of socio-

demographic surveillance sites offered significant poten-

tial for understanding health and demographic processes

within low-income countries, most especially within rural

areas. The addition of new survey data on adult health

and ageing to the data portfolio of the INDEPTH sites

significantly enhances the value of the surveillance sites

themselves, and adds value to the survey data through

linkage to the rich local epi-demographic history and

context created by the INDEPTH sites. The new survey

data also substantially enhance the capacity of the

Network to evaluate or assess the impact of policy

interventions, such as the establishment or major mod-

ification of pension or health systems. Further, the ability

to compare the results of three of the INDEPTH WHO-

SAGE sites [South Africa (2), India (9), and Ghana (5)]

with the nationally representative SAGE surveys for

those countries will provide the opportunity to assess

the generalisability of INDEPTH WHO-SAGE small-

area results for these three countries.

In 1996, the Global Burden of Disease project made

the remarkable projection that within a few decades, non-

communicable disease would outpace infectious diseases

as a cause of morbidity and mortality in all regions of the

globe (17). Although the projected epidemiological

transition was largely a function of population ageing,

the implications of these projections were largely ignored.

INDEPTH WHO-SAGE will become an important

observatory of the epidemiological transition in low-

income countries. The introductory article in this supple-

ment (1) clearly shows that at baseline, the four

INDEPTH WHO-SAGE Asian countries (Viet Nam,

Bangladesh, Indonesia, and India) have moved further

toward the relative predominance of non-communicable

disease than the African countries (South Africa, Tanza-

nia, Kenya, and Ghana). Based on the experience of

industrialised nations, the projected increase in degen-

erative non-communicable diseases that tracks increases

in adult life expectancy will be accompanied by an

increasing loss of physical and cognitive functioning

and growing levels of disability. The increase in disability

will result in reduced capacity for work among older

workers, loss of autonomy, and the need for substantial

care in old age, which is enormously costly in terms of

both economics and well-being. During the 1980s in the

United States, the prevalent view in epidemiological and

ageing circles was that while modern medicine could

delay death, it could not prevent or delay the onset of

degenerative diseases, which could not be treated effec-

tively. Most believed that increases in old age longevity

would lead to a pandemic of disability, with disabled life

expectancy increasing substantially. However, an impor-

tant finding was that in the United States, between 1982

and 2001, disability among those aged 65 and over

declined by 25%, demonstrating the substantial plasticity

of individual ageing (18). More recently, concern has

been rising that the epidemic of obesity will lead to

substantially increased disability, offsetting the gains. As

life expectancy increases in these middle and low-income

countries, no one knows yet whether disabled life

expectancy will outpace healthy life expectancy, or

whether there will be any compression of morbidity and

disability, especially if onset starts later in life.

The collection of data on the same individuals in later

waves of INDEPTH WHO-SAGE will allow researchers

to investigate a whole set of questions not amenable to

analysis within the current cross-sectional data. Long-

itudinal data are needed to tackle a variety of questions

posed by the authors of this supplement. Answering

questions such as how chronic disease-related disability

evolves, how long individuals with specific diseases

survive, whether self-reported health predicts survival

better than the health score, or how living arrangements

and widowhood affect health and well-being, require

panel data. Longitudinal data are also needed, for

example, to identify the mechanisms by which old age

pensions can improve the health and general welfare of

grandchildren if part of the pension is distributed to

those grandchildren. Similarly, in the absence of a

randomised trial, longitudinal data would be essential

to assess the impact of pensions on the health of

pensioners � do old age pensions that end when the

pensioner dies improve the health and well-being of the

pensioner? If so, is it by means of increasing pensioners’

ability to purchase food and health care, or is it because

they feel more needed by their family, or do their families

take better care of them to keep the pension income

flowing? It is therefore important that the current

samples are followed up regularly and that every effort

is made to track individuals during the interim periods �a strength of health and demographic surveillance � in

order to ensure a high response rate for these follow-ups.

The decision to release the microdata simultaneously

with this supplement, via the Global Health Action Web

site (http://www.globalhealthaction.net), is a noteworthy

milestone for INDEPTH and will be a great boon for

research on adult health and ageing in the respective

countries. Cross-national research in both developing and

developed countries has been seriously hampered by slow

release of microdata, sometimes more than a decade after

collection, and sometimes not ever as in the case of the

Guest Editorial


http://www.globalhealthaction.net

first WHO cross-national survey on ageing conducted

around 1979�1980. The tension between speedy data

release and the desire of the data collectors to hold onto

the data until they have had a chance to fully mine those

data laboriously collected from the study they had

designed, is perhaps greatest today in low-income coun-

tries of Africa and Asia. However, in order to justify the

very considerable expense of cross-national longitudinal

studies, costs of the data need to be amortised over as

many secondary data projects as possible, and the research

products must also become useful to policy makers as

soon as possible. Science requires replication, and the lack

of data sharing can slow down research and the produc-

tion of policy-relevant results. It has been the experience

of studies such as HRS, ELSA, and SHARE that such

longitudinal studies catalyse new fields of social and

behavioural science and coalesce whole groups of re-

searchers around the studies’ data, forming new scientific

communities. In this case, every effort should be made to

get these data as rapidly as possible to pre- and post-

doctoral students and junior faculty of at least the eight

countries involved in the study. At the same time appro-

priate efforts must be made to maintain the ethics of data

confidentiality, ensuring that respondent anonymity is not

breached, especially since these studies were all conducted

in specific and known geographic areas, which makes the

protection of anonymity more challenging.

The agreement by the INDEPTH WHO-SAGE prin-

cipal investigators to conduct the study with the under-

standing that the data would be speedily released is highly

commendable, and one can predict that the dividends to

the study will perhaps be greater than the INDEPTH

team imagines.

Commendations and acknowledgements are due to

several institutions and groups, including the INDEPTH

leadership, WHO staff, faculty at Umea and Harvard

who facilitated important data analysis workshops for

INDEPTH WHO-SAGE, and the many peer reviewers

involved in this supplement.

Richard Suzman

Division of Behavioral and Social Research

National Institute on Aging

National Institutes of Health

Bethesda, MD, USA

All views expressed in this editorial are entirely those of

the author, and do not necessarily reflect those of NIA or

NIH.

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Guest Editorial

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5480 7

The INDEPTH WHO-SAGE multicentre study was only possible because of the hard work of many staff at each

participating site, as well as the authors of papers in this Supplement:

Agincourt, South Africa

Hector Dhlamini

Victoria Dlamini

Regan Gumede

Simon Khosa

Glory Khoza

Thoko Khensani Machavi

Muziwakhe Solly Maluka

Olga Mambane

Nash Manzini

Sinah Manzini

Merriam Perseverence Maritze

Lawrence Pedney Mashale

Ishmael Mashigo Ishamel

Ntanga Moses Mathabela

Phanuel Mathebula

Council Mbetse

Warren Mdluli

Gordon Mkhabe

Obed Mokoena

Linneth Mthetho

Violet Ndlovu

Simon Delly Ndzimande

Sizzy Ngobeni

Vusi Ngwenyama David

Morris Sibuyi

Busisiwe Sibuyi

Morris Mdawu Sibuyi

Promise Sibuyi

Ellah Sihlangu Ellah

Bernard Silaule

Phamela Nombulelo Tibane

Nomsa Ubisi

Ifakara, Tanzania

Novatus Chagodola

Deogratias Chamanga

Yassin Chikoko

Timoth Chogo

Panga Husein

Godwin John

Lukresia Kadungula

Luitfrid Kaduvaga

Tukae Kapati

Gonzaga Kasanga

Sophia Kayera

Godfrey Kidege

John Killian

Athuman Kipembe

Celsius Kipinga

Charles Kuwonga

Amoses Kyovecho

Mary Lazaro

Nassoro Likumi

Silivanus Lisoadinge

Zuhura Lungombe

Emanuel Luvanda

Jacob Lyanga

Sauda Magubikira

Stephen Magwaja

Athuman Makanganya

Albert Masalu

Isaya Mashinga

Shabani Matengana

Madunda Mkalimoto

Ally Mpangile

Raphael Msabana

Edimund Msalabule

Mshamu Mshamu

Bernadi Mwambale

Elisha Mwandikile

Bonaventura Mwarabu

Simbani Mwikola

Abdala Mwinshehe

Joseph Mwonja

Honesta Mzyangizyangi

Mwanaid Ngagonja

Calstus Ngalanga

Msafiri Ngalisoni

Jonson Ngenga

Mwadawa Ngumbi

Joseph Njavike

Hadija Nyanga

Amina Salumu

Joyce Shayo

Athumani Utwakumwambu

Nairobi, Kenya

Mohammed Ali

Callen Bwari

Wekesah Murunga Frederick

Abduba Salesa Galgalo

Anthony Chomba Gathuita

Antony Kagiri Gichohi

Jane Wahake Gitonga

James Hotendo

David Ireri

David Otieno Juma

Gedion Kennedy Juma

Maureen Kadogo

Deborah Kagai

Adan Kalicha

Phanuel M. Kasuni

Joel Kasyoka

George Kidiga

Catherine Kimatu

Joshua Musila Kivonge

Esther Nyambura Macharia

Mary Marubu

Catherine Mbalu

Kennedy Mose Momanyi

Geoffrey Ndungu Mondia

David Karuga Muhika

Wanjiru Murigi

Stanley Murithi

Samuel Mutuma

Hawa Hassan Mwangangi

Damaris R Mwangi

Grace Mumbua Mwania

Booker Ndayhaya

Henry Ndungu

Deborah Nganga

Moses Mwithiga Ngugi

Esther Wanjiru Njeri

Jedidah Njeri

Melchizedek Nyakundi

Thomas Ondieki Nyandika

Peter Nyongesa

Audrey Achieng Ocholla

George Ochieng Oduor

Clement Oduor

David Ouma Ojuka

Mildred Adhiambo Onyango

Peter Onyango

Evaline Achieng Otteng

Meshack Odede Owino

Benson Mbithi Peter

Jacqueline Ratemo

Sarah Nabalayo Simiyu

Ruth Waithera Wairimu

Peter Agutu Waka

�Participating SitesINDEPTH WHO-SAGE Supplement

Global Health Action 2010. # 2010 List of participating staff This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproductionin any medium, provided the original work is properly cited.

8


Moses Wanyama

Philip Kibet Wendot

Abdikadir Adan Yarrow

Abdikadir Adan Yarrow

Navrongo, Ghana

Diana Abagale

Catherine Abakis

Irene Abase

Aboyinga Abokiya

Gana Abongbe

Anthony Achana

Bawotua Kwoyire Adda

Clare Addah

Sixtus Addah

Martin Adiga

Emefa Adiku

Ophelia Adjei

Charles Adongo

Immaculate Adongo

Beatrice Afobiku

Akwia Agangba

Charles Agangzua

Evelyn Agomah

Timothy Ajubala

Felicia Akanlassi

Emmanuel Akantosige

James Akayasi

Roger Akobga

Isaac Akumah

Jacob Anabia

Robert Kwotera Ane

Christopher Aniwe

Rufina Anoah

Scholastica A-Oho

Raphael Apana

Mathew Apatinga

Vida Apayire

Freda Apee

Thompson Apempale

Peter Asobayire

Gilbert Asuliwono

Rita Asumboya

Justina Asumboya

Martina Atenka

Ajentio Atulugu

Joana Awineboya

Francis Awineboya

Tamgomse Ayaam

Peter Ayangba

Denisia Ayibello

Akua Ayirewora

Raymond Azagisiya

Jesse Jackson Azambugi

Michael Banseh

Emma Chiratogo

Afia Damwura

Everest Dery

Atinganne Dominic

Theresa Fumjegeba

Yeji Godwin

Francis Gweliwo

Mohammed T Ibn-Salia

Memuna Issaka

Fatima Issaka

Dauda Ahmed Jadeed

Martin Kambonga

Joana Kampoe

Edmond Kanyomse

Joseph Katasuma

Mac Kolley

Felix Kondayire

Fati Kumangchira

Ferreol B. Lagejua

Richard Latinga

Jerry Atua Lucas

Christina Luguchura

Rose Mary Luguyimang

Rita Luguzuri

Ziblim Mahama

Luuse Matholomew

John Memang

Ayangba A. Mensah

William Minyila

Ismail M Mohammed

Abangba Moses

Anastasia Musah

Maxwell Naab

Vitus Nabengye

Andrews Opoku

Rose Parese

Lucy Pelabia

Boniface Pwadurah

Habibatu Salifu

Andriana Sumboh

Felicity Titigeyire

Patience Tito

Francis Yeji

Yahaya Zulhaq

Filabavi, Viet Nam

Dang Thi Minh Anh

Nguyen Thi Be

Nguyen Thi Ngoc Bich

Le Thi Thanh Binh

Quach Thi Thanh Binh

Phung Thi Chien

Nguyen Thi Dau

Phung Thi Dinh

Tran Thanh Do

Phuong Thuy Duyen

Nguyen Thi Ngoc Ha

Dinh Cong Ha

Phung Thi Hai

Do Thi Thanh Hien

Nguyen Phuong Hoa

Nguyen Thi Mai Huan

Hoang Thi Hue

Chu Phi Hung

Nguyen Quoc Hung

Do Manh Hung

Bui Thi Huong

Nguyen Thi Huong

Nguyen Thi Thanh Huong

Ngo Thi Huyen

Nguyen Thanh Huyen

Nghiem Thi Hy

Nguyen Van Lam

Ngo Thi Lien

Phan Thi Thanh Lieu

Giang Thi Tuyet Loan

Truong Hoang Long

Nguyen Thi Luyen

Nguyen Thi Ly

Nguyen Thi Nguyet Minh

Phung Thi Minh

Nguyen Binh Minh

Phung Thi My

Nguyen Thi Duy Na

Phan Thi Nang

Phung Thi Nga

Nguyen Thi Minh Nham

Tran Thi Nhan

Nguyen Thi Nhung

Dinh Thuy Nhung

Phuong Thi Nhung

Tran Thi Kim Oanh

Doan Thi Hoang Oanh

Phung Thi Thu Phuong

Tran Thi Mai Phuong

Dao Dinh Sang

Nguyen Thi Sinh

Nguyen Thi Thanh Tam

Nguyen Thi Tam

Tran Thi Tha

Bui Thi Thanh Thao

Phung Thi Thanh Thao

Nguyen Thi Thu

Dang Thi Hong Thuy

Nguyen Thi Thuy

Nguyen Thi Thuyet

Phung Thi Tinh

List of participating staff


Tran Khanh Toan

Phung Thi Toan

Nguyen Thanh Tu

Nguyen Lan Viet

Khuat Thi Xuan

Matlab, Bangladesh

Ali Ahmed

Halena Akhter

Abul Kalam Azad

Jiabonessa Begum

Nazma Khanam

Bahadur Mia

Shirajum Munira

Samira Akhter Sultana

Purworejo, Indonesia

Abdul Wahab

Agung Nugroho

Ami Rumhartinah

Ardiyanti

Arif

Bambang Sukma Widadi

Budi Hartiningsih

Devie Caroline

Didi Yudha Prastika

Didik Fery Kristianto

Djaswadi Dasuki

Dwi Lestari Priastuti

Dwi Rosmalawati

Eka Yuli Astuti

Eko Setianto

Eni E.

Erry Ariyanti

Fahruddin

Fatma Yunita

Feri Budiarto

Hafsah Tahir

Haryanto

Hendras Bintar

Hendro Budi

Irfan Cahyadi

Ita Saraswati

Joko S.

Juana Linda

Kartini

Khotib Subhan

Kusen

Lasmi

Ledjar

Lidya Hastuti

Lilik Dewanti

Mintorowati

Muhtadi

Murtiyah

Nur Wicaksono

Nurtiyah

Pitoyo

Purnawati

Puspita Handayani

Ratih Widayanti

Ratna

Retno Handayani

Robert Arian Datusanantyo

Rosyid Budiman

Rustiningsih

Ruwayda

Sendy

Siti Aminah

Siwi Rahmawati

Sri Purnaningsih

Sri Suryani

Sugeng

Sugeng

Sugun

Suharyani

Sujarwo

Sukarman

Sukirman

Sumarta

Supriyo Pratomo

Sutaryo

Teguh Imam

Teguh Rohaji

Tetra

Tetra Lintang

Titik Rahayu

Tri Atmi

Tri Wahyu

Tri Wantoro

Utari Marlinawati

Wahyu Fatmawati

Warsiyah

Winarti

Wisnu

Yekti Utami

Yudha Prastika

Yunardi

Yusmiyati

Yusuf

Vadu, India

Kalpana Agale

Jyoti Bhosure

Bharat Choudhari

Nilam Fadtare

Shilpa Fulaware

Prashant Gaikwad

Vijay Gaikwad

Tejashri Ghawte

Trupti Joshi

Deepak Mandekar

Anita Masalkar

Sayaji Pingale

Ratan Potdar

Somnath Sambhudas

Dinesh Shinde

Secretariat

Raymond Akparibo

Sixtus Apaliyah

Zubeida Bagus

Sadiya Ooni

Dereshni Ramnarain

Jackie Roseleur

Titus Tei

Birgitta Astrom

List of participating staff


Ageing and adult health status ineight lower-income countries: theINDEPTH WHO-SAGE collaborationPaul Kowal1,2*, Kathleen Kahn3,4,5#, Nawi Ng4,5,6#,Nirmala Naidoo1, Salim Abdullah5,7, Ayaga Bawah5,Fred Binka5, Nguyen T.K. Chuc5,8, Cornelius Debpuur5,9,Alex Ezeh5,10, F. Xavier Gomez-Olive3,5, Mohammad Hakimi5,6,Siddhivinayak Hirve5,11, Abraham Hodgson5,9, SanjayJuvekar5,11, Catherine Kyobutungi5,10, Jane Menken12,13,Hoang Van Minh5,8, Mathew A. Mwanyangala5,7,Abdur Razzaque5,13, Osman Sankoh5, P. Kim Streatfield5,13,Stig Wall4#, Siswanto Wilopo5,6, Peter Byass4#,Somnath Chatterji1 and Stephen M. Tollman3,4,5#

1Multi-Country Studies Unit, World Health Organization, Geneva, Switzerland; 2University ofNewcastle Research Centre on Gender, Health and Ageing, Newcastle, NSW, Australia; 3MRC/WitsRural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health,Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; 4Centre forGlobal Health Research, Epidemiology and Global Health, University of Umea, Umea, Sweden;5INDEPTH Network, Accra, Ghana; 6Purworejo HDSS, Faculty of Medicine, Gadjah Mada University,Yogyakarta, Indonesia; 7Ifakara Health Institute, Ifakara, Morogoro, Tanzania; 8FilaBavi HDSS,Faculty of Public Health, Hanoi Medical University, Hanoi, Viet Nam; 9Navrongo HDSS, Navrongo,Ghana; 10African Population & Health Research Center, Nairobi, Kenya; 11Vadu Rural HealthProgramme, KEM Hospital Research Centre, Pune, India; 12University of Colorado, Boulder, CO,USA; 13Matlab HDSS, ICDDR,B, Dhaka, Bangladesh

Background: Globally, ageing impacts all countries, with a majority of older persons residing in lower- and

middle-income countries now and into the future. An understanding of the health and well-being of these

ageing populations is important for policy and planning; however, research on ageing and adult health that

informs policy predominantly comes from higher-income countries. A collaboration between the WHO Study

on global AGEing and adult health (SAGE) and International Network for the Demographic Evaluation of

Populations and Their Health in developing countries (INDEPTH), with support from the US National

Institute on Aging (NIA) and the Swedish Council for Working Life and Social Research (FAS), has resulted

in valuable health, disability and well-being information through a first wave of data collection in 2006�2007

from field sites in South Africa, Tanzania, Kenya, Ghana, Viet Nam, Bangladesh, Indonesia and India.

Objective: To provide an overview of the demographic and health characteristics of participating countries,

describe the research collaboration and introduce the first dataset and outputs.

Methods: Data from two SAGE survey modules implemented in eight Health and Demographic Surveillance

Systems (HDSS) were merged with core HDSS data to produce a summary dataset for the site-specific and

cross-site analyses described in this supplement. Each participating HDSS site used standardised training

materials and survey instruments. Face-to-face interviews were conducted. Ethical clearance was obtained

from WHO and the local ethical authority for each participating HDSS site.

Results: People aged 50 years and over in the eight participating countries represent over 15% of the current

global older population, and is projected to reach 23% by 2030. The Asian HDSS sites have a larger

#Supplement Editor, Kathleen Kahn, Editor, Nawi Ng, Chief Editor, Stig Wall, Deputy Editor, Peter Byass, Supplement Editor, Stephen M.Tollman, have not participated in the review and decision process for this paper.

�INDEPTH WHO-SAGE Supplement

Global Health Action 2010. # 2010 Paul Kowal et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproductionin any medium, provided the original work is properly cited.

11


proportion of burden of disease from non-communicable diseases and injuries relative to their African

counterparts. A pooled sample of over 46,000 persons aged 50 and over from these eight HDSS sites was

produced. The SAGE modules resulted in self-reported health, health status, functioning (from the WHO

Disability Assessment Scale (WHODAS-II)) and well-being (from the WHO Quality of Life instrument

(WHOQoL) variables). The HDSS databases contributed age, sex, marital status, education, socio-economic

status and household size variables.

Conclusion: The INDEPTH WHO�SAGE collaboration demonstrates the value and future possibilities for

this type of research in informing policy and planning for a number of countries. This INDEPTH WHO�SAGE dataset will be placed in the public domain together with this open-access supplement and will be

available through the GHA website (www.globalhealthaction.net) and other repositories. An improved

dataset is being developed containing supplementary HDSS variables and vignette-adjusted health variables.

This living collaboration is now preparing for a next wave of data collection.

Keywords: ageing; survey methods; public health; burden of disease; demographic transition; disability; well-being; health

status; INDEPTH WHO-SAGE

Access the supplementary material to this article: INDEPTH WHO-SAGE questionnaire (including

variants of vignettes), a data dictionary and a password-protected dataset (see Supplementary files

under Reading Tools online). To obtain a password for the dataset, please send a request with ‘SAGE

data’ as its subject, detailing how you propose to use the data, to [email protected]

Received: 18 May 2010; Revised: 6 July 2010; Accepted: 8 July 2010; Published: 27 September 2010

The ageing of populations is often considered as a

global public health success, but results in many

ensuing challenges, particularly in lower- and

middle-income countries where societies did not grow

wealth before growing old, as in higher-income countries.

Societal ageing will affect economic and health systems in

all nations, including the ability of states and societies

to both maintain contributions from and also provide

resources for older population groups.

But will population ageing affect lower- and higher–

income countries in similar ways? The projected

macroeconomic and health impacts from longer life

expectancies have only recently become clearer for

higher-income nations (1�5); but few non-Organization

for Economic Cooperation and Development (OECD)

countries have the data to determine if extended longevity

coincides with healthier lives until older ages (that is, a

compression of morbidity). Unlike wealthier countries,

the existing formal social protection systems in most

lower-income countries cover only a small proportion of

the older population (6); however, if we believe in

demographic dividends, lower-income countries will

have a long lead period to collect data which can be

used to inform economic and health systems (7). Burden

of disease shifts from maternal/child health and acute

communicable diseases to chronic infectious and non-

communicable diseases in lower-income countries will

challenge health systems without the data necessary to

inform policy and planning (8�11).

Interest in the measurement and comparability of

adult health, the ageing process and well-being at

older ages across countries has been increasing in

recent years. The potential benefits of cross-national

studies of ageing that enable us to understand the nature

of demographic and epidemiological transitions have

been widely recognised (12, 13). The US Health and

Retirement Study (HRS) and other notable surveys,

such as the English Longitudinal Study on Ageing

(ELSA), have provided the necessary evidence base to

begin to address the needs and contributions of older

persons in higher-income countries. However, the ma-

jority of older persons now and into the future will

reside in lower-income countries where the evidence base

is very limited.

The HRS and ELSA studies, and more recently the

World Health Organization’s (WHO) multi-country

Study on global AGEing and adult health (SAGE),

have also been used as the basis for harmonisation with

other national studies and many cross-national compar-

isons. Longitudinal ageing studies are critical to develop

the evidence base to better understand ageing processes

and adult health dynamics, especially in countries with

limited mortality data due to poorly functioning or low

coverage of vital registration systems. They have parti-

cular advantages in their ability to examine multiple

exposures, determinants and outcomes, and to measure

relationships over time: all essential aspects for under-

standing ageing across different contexts. However, while

critical to research, policy and planning, longitudinal

studies are resource and time intensive.

The extent to which lower-income countries have

begun to generate and use critical evidence for an

Paul Kowal et al.


www.globalhealthaction.net

mailto:[email protected]

effective health response has been slow and suboptimal in

many countries (14). This lack of evidence is particularly

prominent in low- and middle-income countries, partly

because the demographic transitions have been relatively

recent and also because political will and financial

support have not been sufficient. Combining standar-

dised survey modules with existing surveillance infra-

structures, especially systems collecting vital registration

details, offers a unique opportunity to reduce research

costs and efficiently collect needed data in low- and

middle-income countries.

If populations in any country are to age well, an

improved understanding of ageing processes, of resilience

factors for well-being, and of the determinants of health

status (HS) across countries are needed. This knowledge

will in turn inform health care and social protection

policies and planning. Results from a collaboration

between the WHO-SAGE survey platform and the

International Network for the Demographic Evaluation

of Populations and Their Health in developing countries

(INDEPTH), involving Health and Demographic

Surveillance Sites (HDSS) in eight countries (four

African and four Asian) will provide HS, disability and

well-being results for ageing and adult health in South

Africa, Tanzania, Kenya, Ghana, Viet Nam, Bangladesh,

Indonesia and India. Data collection included methods

to improve cross-country comparability, thereby provid-

ing a basis for comparisons with data from higher-income

countries, such as the US Health and Retirement

Study and the ELSA. This article describes the back-

ground to the INDEPTH WHO-SAGE collaboration

and introduces the methods used to generate the first

wave of results � which includes site-specific analyses and

cross-site comparisons.

BackgroundThe WHO’s Multi-Country Studies unit, with the sup-

port of the US National Institute on Aging’s Behavioral

and Social Research Program (NIA BSR), has imple-

mented multi-country ageing and adult health studies to

fill data gaps in lower-income countries and has worked

to improve cross-national comparability with available

data. WHO’s SAGE conducts nationally representative

household health surveys in six countries, with direct

links to an additional 14 countries through various

collaborations. SAGE is guided by an international

expert Advisory Committee and coordinated from

WHO’s Multi-Country Studies unit. In addition, com-

parisons with ageing research in higher-income countries,

such as the US HRS, English ELSA and the pan-

European Survey of Health, Ageing and Retirement in

Europe (SHARE) are ongoing.

WHO’s collaboration with INDEPTH has generated

data from HDSS sites in eight countries (Africa: Agin-

court, South Africa; Ifakara, Tanzania; Nairobi, Kenya;

Navrongo, Ghana; Asia: Filabavi, Viet Nam; Matlab,

Bangladesh; Purworejo, Indonesia and Vadu, India) and

provides another valuable data collection platform for

cross-national comparisons of ageing. The NIA BSR was

instrumental in bringing the two groups together from

the outset and has provided technical guidance through-

out in combining survey and surveillance data collection

efforts to fill needed data gaps on ageing and adult

health. WHO SAGE, the INDEPTH Adult Health and

Ageing Working Group, the NIA and the eight partici-

pating INDEPTH HDSS sites have developed a colla-

boration built on these survey and surveillance data

collection platforms. This included health and well-being

survey data collected within or parallel to HDSS house-

hold (HH) census update rounds and linked socio-

demographic household data. While this initial dataset

is cross-sectional, there are plans to include longitudinal

HDSS data and further waves of data collection using an

adapted summary version of the SAGE instrument in the

HDSS sites. This will significantly enhance the value of

the collaboration and resulting datasets by tracking

changes over time in the same population samples and

relating them to health determinants, predictors and

outcomes, such as mortality in older adults. An introduc-

tion to the countries, HDSS sites and research methods

follows.

Setting the stage

Country characteristicsThe ongoing demographic shift provides concrete evi-

dence that most countries will be faced with an increas-

ingly old or ageing population � the challenge is for

national and international health communities to use

available data to best prepare for these changes. At

present, 62% of older persons reside in less developed

countries and this is projected to increase to almost 80%

by 2050 (15).

Table 1 includes the estimated and projected total

populations and proportions of older adults for the world

and participating INDEPTH countries in 2009 and 2030.

The World Bank income category is also included for

each country, with a mix of five low- and three middle-

income countries (16). In 2009, over 281 million people

aged 50 years and over resided in the eight nations

included in this collaboration, which constitutes 20% of

the global population in that age group (15). Similarly,

18% of the global population aged 60 and over lives in

these eight countries. These proportions will increase to

23% and 21%, respectively, by 2030. Over the same time

period, the percentage of the population aged 0�14 years

in these countries will drop from 29.9 to 28.5% and five of

the eight countries will have a larger proportion of

persons aged 60 and over than under 15 years by 2050

(the four Asian countries and South Africa). Overall, the

Ageing and adult health status in eight lower-income countries


percentage increase in population aged 60� will grow

more in the African than Asian countries.

With ageing populations and increasing life expectan-

cies, countries will inevitably see changing population

disease burdens. Burdens of disease, risk factors and

patterns of injury are changing through a complex

combination of evolving social, demographic, health,

political and economic processes. Diseases thought to

be the domain of higher-income countries are now

significant causes of morbidity and mortality in a number

of lower- and middle-income countries (17�19).

The most recent Global Burden of Disease (GBD)

2004 update includes distributions of mortality and

morbidity by three major groupings: (Group I) commu-

nicable diseases, maternal health and nutrition; (Group

II) non-communicable diseases; and (Group III) violence

and injuries. The 2004 update incorporates revisions and

new data working from the initial 1990 GBD (20). The

1990 GBD results estimated 44% of total burden was

Group I, 41% for Group II and 15% for Group III

worldwide (21). These data show that even in 1990, NCDs

were a significant contributor to mortality rates. Fig. 1

shows the distributions of fatal disease burden by geo-

graphic grouping and country for 2004. Preliminary results

indicate a substantial increase in the proportion of deaths

due to non-communicable diseases from 59% in 2002 to

69% in 2030 (19). All the participating Asian HDSS sites

had higher NCD rates than the 1990 estimates � and

Indonesia had a much higher Group III burden. Countries

that are at an earlier phase of the demographic transition

typically have a higher mortality burden from Group I

conditions, and this is more clearly the case for the African

countries participating in the INDEPTH WHO�SAGE

collaboration (Fig. 1). South Africa’s burden profile is

exceptional here because as an upper-middle income

country, a lower communicable disease burden is expected;

however, the massive HIV/AIDS burden clearly shifts the

burden distribution. Similarly, despite being a lower-

income country, Viet Nam has a comparatively lower

communicable disease burden.

Shifting to morbidity, the top three contributors to

morbidity burdens in middle-income countries in 2004

were unipolar depressive disorders, ischaemic heart dis-

ease and cerebrovascular disease (20). The top three for

lower-income countries were lower respiratory infections,

diarrhoeal diseases and HIV/AIDS. Fig. 2 illustrates the

burden of non-fatal health outcomes by major grouping

Table 1. Population totals and proportions of older adults for the world and by INDEPTH country, in 2009 and projected to

2030

2009 2030

Country

Country income

categorya Total, Nb 50�, N (%) 60�, N (%) Total, N 50�, N (%) 60�, N (%)

World 6,829 1,379 (20.2) 737 (10.8) 8,309 2,283 (27.5) 1,370 (16.5)

Sub-Saharan Africa 843 110 (10.9) 54 (5.3) 1,308 157 (12.0) 78 (5.9)

South Africa UMI 50 8 (15.0) 4 (7.1) 55 10 (19.1) 6 (11.1)Tanzania Low 44 4 (9.5) 2 (4.8) 75 8 (10.6) 4 (5.3)

Kenya Low 40 3 (8.8) 2 (4.1) 63 7 (11.5) 3 (5.5)

Ghana Low 24 2 (11.2) 1 (5.7) 35 5 (15.3) 3 (7.7)

Asia 4,121 785 (19.1) 400 (9.7) 4,917 1,398 (28.4) 821 (16.7)Viet Nam Low 88 15 (17.2) 6 (8.6) 105 32 (30.6) 19 (18.2)

Bangladesh Low 162 20 (12.9) 10 (6.0) 203 46 (22.9) 23 (11.3)

Indonesia LMI 230 40 (17.4) 20 (8.8) 271 79 (28.9) 43 (16.0)

India LMI 1,198 187 (15.6) 89 (7.4) 1,485 343 (23.1) 185 (12.4)Pooled INDEPTH

country (8) totals

1,836 281 (15.3) 135 (7.3) 2,293 531 (23.2) 286 (12.5)

aWorld Bank country income category: Low, low income; LMI, lower-middle income; UMI, upper-middle income.bN in millions (,000,000).

Sources: UN Population Division (15) and World Bank (16).

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Violence/injury

Non-communicable

Communicable

Africa Asia

Fig. 1. Mortality profiles (age-standardised death rates) by

major Burden of Disease grouping and country, 2004 (WHO

2008).

Paul Kowal et al.


and INDEPTH HDSS site country for 2004, indicating

those conditions which lead to longer years of life lived in

a state of less than full health (non-fatal health outcomes

or disability). The figure illustrates the mixture of disease

burden in the participating low- and middle-income

countries, with Group I burden featuring more promi-

nently in African countries and Group II in Asian

countries. Still, a majority of the main chronic conditions

predominate in older age groups in both regions (19).

From currently available data, the overall contribution of

disability from non-communicable diseases is projected to

grow substantially and ageing will be one of the major

drivers of the burden (22).

HDSS characteristicsINDEPTH (http://www.indepth-network.org) is a net-

work of 37 sites in 19 countries in Africa, Asia,

Central America and Oceania based on health and

socio-demographic surveillance within defined areas.

The network brings together virtually all of the world’s

HDSSs located in low- and middle-income settings, and

currently covers over 2 million individuals. Regular

household census updates at each HDSS site allow for

continuous, household-level monitoring of all vital events

(births, deaths and migrations) in the defined population.

INDEPTH provides an exceptional resource with which

to characterise the health, demographic and social

dynamics of some of the world’s most vulnerable

populations. The INDEPTH Adult Health and Ageing

Working Group has established INDEPTH’s capability

to contribute critical insights into the adult health, ageing

and disease transitions evolving in Africa and Asia, and

to use this understanding to inform policy and evaluate

interventions of potentially high impact.

SAGE characteristicsThe SAGE project (http://www.who.int/healthinfo/

systems/sage) has become a leading multi-country study

on ageing and adult health in lower- and middle-income

countries. Launched in 2003 as part of the WHO’s World

Health Survey (WHS), SAGE has implemented nation-

ally representative population surveys in six core coun-

tries: China, Ghana, India, Mexico, the Russian

Federation and South Africa. The specific aims of

SAGE are to:

. Obtain reliable, valid and comparable data on levels of

health on a range of key domains for older adult

populations.

. Examine patterns and dynamics of age-related

changes in health using longitudinal follow-up of

survey respondents as they age, and to investigate

socio-economic consequences of these health changes.

. Supplement and cross-validate self-reported measures

of health and the anchoring vignette approach to

improve comparability of self-reported measures,

through measured performance tests for selected

health domains.

. Collect data on health examinations and biomarkers

to improve reliability of data on morbidity, risk factors

and monitor effect of interventions.

The baseline data collection for SAGE (Wave 0) was

conducted as part of the 2002/2003 WHS with SAGE

Wave 1 data collected between 2007 and 2010. Biennial

longitudinal follow-up is planned with Wave 2 in 2011

and Wave 3 in 2013.

SAGE provides data on the levels and differences in

health and well-being across low- and middle-income

countries, and methodologies that improve health mea-

surement and cross-national comparability. SAGE covers

a broad range of topics, with a focus on health, disability,

risk factors, stress, happiness, social networks, economic

well-being, care-giving, health care utilisation and health

systems responsiveness. Furthermore, a host of biomar-

ker data was collected, including anthropometrics, phy-

sical performance tests and dried blood spots.

Another objective for SAGE is to develop working

relationships and linkages to other data collection plat-

forms, including surveys and surveillance sites, to better

understand changing health over the life course,

compression of morbidity and perceptions of health,

quality of life and economic well-being within and across

countries. SAGE has a history of collaborating with other

ageing research, like the US HRS; ELSA; SHARE;

China Health, Ageing, Retirement Longitudinal Study;

Longitudinal Ageing Study in India; and, now with

INDEPTH HDSS sites. The collaboration with IN-

DEPTH extends the possibilities of longitudinal house-

hold-based research through the combination of survey

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Violence/injury

Non-communicable

Communicable

Africa Asia

Fig. 2. Morbidity profiles (age-standardised DALYs) by

major Burden of Disease grouping and country, 2004

(WHO 2008).



http://www.indepth-network.org

http://www.who.int/healthinfo/systems/sage


and surveillance methods and provides opportunities to

apply new methodological techniques to cross-country

ageing research.

The collaborationThe collaboration between WHO-SAGE and INDEPTH

has pursued four main goals: (1) to develop and

implement a survey instrument that can be incorporated

into a regular census update round placing minimal

additional demands on existing research infrastructure;

(2) to implement the full SAGE survey in parallel to a

summary short survey round, but with separate infra-

structure and resources; (3) to determine key areas where

INDEPTH HDSS sites could be used as methodological

laboratories to pilot new methods and test hypotheses �so as to exploit the complementary strengths of both

survey and surveillance data; and (4) to derive more

integrated analytical plans to assess ageing and adult

health at national and sub-national levels.

For this article, we address goals 1 and 4 above using a

summary version of the full SAGE instrument which was

implemented in eight INDEPTH HDSS sites. This part

of the collaboration had two primary aims. The first was

to use survey and surveillance data to describe the

situation of ageing and adult health within and across

participating HDSS sites. This included the adaptation

and implementation of standardised SAGE survey mod-

ules on health and wellbeing in INDEPTH HDSS sites.

The HDSS sites identified overlapping content in their

respective surveillance data and the SAGE survey instru-

ments. HDSS sites then worked to enhance the compar-

ability of the socio-demographic data collected at each

site to be included in a cross-site dataset (for example,

comparing socio-economic status indicators and map-

ping education levels to an international standard). The

second aim was to determine the feasibility of collecting

longitudinal data through combining the two types of

data collection efforts as a means to establish ageing and

adult health trends in a range of countries. A first step

was to develop a survey instrument adapted from the full

SAGE questionnaire that could be inserted into a regular

census round without significant disruption to the infra-

structure and process. The belief was that the potential

increase in efficiency from adding modules to the regular

data collection rounds, coupled with new analytical

techniques, could provide data on changing health and

well-being at a reduced cost whilst retaining the strengths

of both surveillance and survey data. These data would

then be used to inform the design of interventions

addressing vital aspects of older adult health and

functioning and, importantly, have the potential to be

monitored more frequently within the HDSS sites than

with the national-level surveys.

MethodsThe initial step was to develop a health and well-being

module that could be nested within a typical census

update round in an INDEPTH HDSS site. This meant

that the interview needed to be approximately 15�20 min

in duration with minimal impact on interviewers and

respondents. A meeting between WHO and INDEPTH

at the University of the Witwatersrand, South Africa in

2006 was used to examine psychometric properties of the

health and quality of life sections of the SAGE survey

instrument based on results from the 2005 SAGE pilot

study (n�1,500) conducted in Ghana, India and Tanza-

nia, to determine priorities, to outline the scope of the

working relationship and to invite interested HDSS sites

to participate. During the meeting, the survey instru-

ments and results from the SAGE pilot were reviewed

with commentary from each INDEPTH HDSS site. The

group then worked together to create a shortened

summary version of the full SAGE questionnaire (the

INDEPTH WHO�SAGE instrument, available as a

supplementary file to this article, including variants of

vignettes) which consisted of questions on HS and

vignettes, functioning and subjective well-being. This

summary questionnaire was subsequently piloted in

each HDSS site in 2006/2007 before implementing the

full data collection. Pilot results and interview debriefings

were used to refine and finalise the standardised ques-

tionnaire to be used across all HDSS sites. This version

was then translated and back-translated in local lan-

guages using translation protocols from both the WHS

and INDEPTH HDSS sites.

Standard interview protocols, training curricula

(including a DVD with video clips of example interviews)

and quality assurance procedures were used across all

HDSS sites. Training sessions with experienced inter-

viewers were conducted for survey teams at each HDSS

site. These training sessions lasted an average of 4.5 days.

The interview teams had the added advantage of long-

standing relationships within the surveillance sites.

Face-to-face interviews with participants aged 50 and

over were conducted in the course of the regularly

scheduled census in three HDSS sites. Separate survey

activities were used in five HDSS sites, where in one site it

was part of a broader ageing survey (Nairobi). Feedback

from the survey teams indicated that it took about

three weeks to become maximally efficient at interviews

and data collection. Across all the sites, the mean

interview time, excluding vignettes, was 20 minutes

towards the end of the survey process. This was about

14 minutes less than the average time at the beginning of

the interview process. The vignettes took an average of

13 minutes of interview time, again, the time decreasing

from an average of 19 minutes at the beginning of the

process.

Paul Kowal et al.


Vignette methodologyCross-national comparative data analysis enhances un-

derstanding of HS differences, ageing dynamics and

cultural differences, but also facilitates the evaluation of

the performance of health, social and economic systems,

and policies to address ageing and health. Typically, the

measurement of HS relies on self-reported responses in

surveys and the self-response data take the form of

ordered categorical (ordinal) responses. Eight domains

of health were used, which account for up to 80% of the

variance in HS (23). As part of the WHO cross-country

health survey approach, anchoring vignettes have been

used to position self-reported responses onto a common

scale comparable across individuals. An anchoring vign-

ette is a description of a concrete level on a given health

domain that respondents are asked to evaluate with

the same questions and response scales applied to

self-assessments on that domain.

A concrete example of the HS questions and vignettes

for one health domain, mobility, follows:

Female respondent X is asked two questions about her

own level of mobility,

Q1 Overall in the last 30 days,

how much difficulty did youhave with moving around?

‘Was it none, mild, moderate,

severe, extreme or cannot dothis?’

Q2 In the last 30 days, how

much difficulty did you havein vigorous activities?


severe, extreme or cannot dothis?’

Next the respondent is asked to respond to questions

about the vignettes. Vignettes are brief stories that

describe a certain fixed level of health, with five vignettes

covering a range of mobility levels. The respondent is

instructed to put herself in the shoes of the person

described in the vignettes and answer the same question

as if she were that person:

[Someshni] has a lot of swelling in her legs due to her

health condition. She has to make an effort to walk

around her home as her legs feel heavy.

Q3 How much difficulty did

[Someshni] have with movingaround?


severe or extreme or cannot

do this?’

Q4 How much difficulty did

[Someshni] have in vigorousactivities?


severe or extreme or cannot

do this?’

By mapping responses to various questions on the same

health domain to a common comparable scale, anchoring

vignettes may provide a bridge between data collected

across cultures or population sub-groups [further detailed

information about anchoring vignettes and statistical

models is available elsewhere (24�27)].

Ethical clearance was obtained from research review

boards local to each participating HDSS site (several of

which are linked to universities), plus from the WHO

Ethical Review Committee as part of SAGE. Informed

consent was obtained from each respondent prior to

interview.

Sample: Six HDSS sites collected data from the entire

population aged 50� in their HDSS. Sampling in the two

remaining HDSS sites (Navrongo, Ghana and Matlab,

Bangladesh) was based on random selection of persons

aged 50 and over within the HDSS site. For comparison

purposes, a smaller sample of younger adults (aged

18�49, n�5,794) was interviewed in five HDSS sites

using similar methods.

Questionnaire: The abbreviated survey instrument

consisted of two modules adapted from the full SAGE

questionnaire: the HS and associated vignette questions

plus Activities of Daily Living (ADL)-type questions

(following the WHO Disability Assessment Scale version

II (WHODAS-II) model), and questions on subjective

well-being as measured by the 8-item version of the WHO

Quality of Life (WHOQoL) instrument (28). Some HDSS

sites chose to add additional modules and/or questions,

but the primary goal was a standardised questionnaire

that could be applied in all HDSS sites embedded within

existing HDSS census rounds.

Additional data targeted for inclusion into the final

dataset, and deriving directly from the HDSS, included

socio-demographic characteristics, such as age, sex,

education, marital status, socio-economic status and

household information, such as the number of household

members.

DatasetFollowing site-level data entry and cleaning, and after a

data-sharing agreement was reached between the partici-

pating INDEPTH HDSS sites and with WHO, data were

forwarded to a central location (Umea, Sweden) for

cleaning and imputation of missing data. Regular corre-

spondence between HDSS sites improved the efficiency of

the data checking and cleaning process. A working

meeting held in 2008 at Umea University, Sweden, was

used to harmonise data across the sites, finalise the

dataset and agree on initial outputs. A first dataset was

generated and included:

. Comprehensive HH information including roster of all

members (by age, sex, marital status, education,

location (urban or rural), HH head) and socio-

economic status.

. For each respondent: age and date of birth, sex,

marital status and education.

. From the adapted SAGE modules: overall general self-

rated health, HS from eight domains plus related

vignette information, functioning assessment from the



12-item WHODAS and subjective quality of life

results from the 8-item WHOQoL.

. Plans to archive the data at WHO, INDEPTH and the

University of Michigan’s National Archive of Com-

puterized Data on Aging (NACDA) to maximise

opportunities to share data and provide multiple

access portals.

The four main outcome variables derived from this data

and reported in the site-specific and cross-site articles in

this issue are self-rated general health (SRH), overall HS,

disability levels (WHODAS) and subjective quality of life

(WHOQoL).

Overall general self-reported health (SRH)Two overall general health questions were asked, each

with 5-point Likert-type response scales. The first is a

question asked very often in surveys: ‘In general, how

would you rate your health today? Would you say, very

good (1), good (2), moderate (3), bad (4) or very bad

(5)?’; and the other was a question related to general

difficulties in day-to-day tasks: ‘Overall in the last 30

days, how much difficulty did you have with work or

household activities? Was it, none (1), mild (2), moderate

(3), severe (4) or extreme/cannot do (5)?’ These types of

global measures of self-rated health are commonly used

in health surveys and as measures of population health.

At the individual level, the global self-rated health

question is a good predictor of many health and health-

related outcomes (29, 30). However, the true meaning of

responses to a single question for a multi-dimensional

construct and the reliability of this measure over time has

been questioned (31, 32).

Health status (HS)Health scores were calculated based on self-reported

health in eight health domains covering affect, cognition,

interpersonal activities and relationships, mobility, pain,

self-care, sleep/energy, and vision. Each domain included

at least two questions. Asking more than one question

about difficulties in a given domain provides more robust

assessments of individual health levels and reduces

measurement error for any single self-reported item.

Item response theory (IRT) was used to score the

responses to the self-reported health questions using a

partial credit model which served to generate a composite

HS score (33, 34). An item calibration was obtained for

each item. In order to determine how well each item

contributed to common global health measurement, chi-

square fit statistics were calculated. The calibration for

each of the health items was taken into account and the

raw scores were transformed through Rasch modelling

into a continuous cardinal scale where a score of

0 represents worst health and a maximum score of

100 represents best health.

Functional status (WHODAS)Self-reported functioning was assessed through the stan-

dardised 12-item WHO Disability Assessment Scale,

Version 2 (WHODAS) (35). It is a well-tested instrument,

with published psychometric properties and a good

predictor of global disability (36�38). The WHODAS is

compatible with the International Classification of Func-

tioning, Disability and Health (ICF) and contains many of

the most commonly asked ADL and Instrumental Activ-

ities of Daily Living (IADL) questions. The WHODAS

instrument also provides an assessment of severity of

disability (39). Results from the 12-items were summed to

get an overall WHODAS score, which was then trans-

formed to a 0�100 scale, with 0 as best functioning (no

disability) and 100 maximum disability.

Subjective well-being and quality of life (WHOQoL)An 8-item version of the World Health Organization

Quality of Life instrument (WHOQoL) was used to

assess perceived well-being (28). This is a cross-culturally

valid instrument for comprehensively assessing overall

subjective well-being, yet is also very brief. Knowing that

health and quality of life are strongly associated yet

distinct concepts, WHOQoL will help describe the

relationship in older persons across countries and over

time. Results from the 8-items were summed to get an

overall WHOQoL score which was then transformed to a

0�100 scale, similar to the health score.

Implementation resultsEight INDEPTH HDSS sites collected data using the

summary questionnaire (see Table 2). Sample sizes

ranged from almost 2,100 to over 12,000, with a total

combined sample of over 46,000 persons aged 50 and

over. Additionally, a random sample of persons aged 18�49 was included in five HDSS sites � as a comparison

population � but these were not included in the initial

dataset or analyses.

The survey took an average of 4.7 months to complete

with a range of 3�8 months. Five sites implemented the

survey as a stand-alone effort, with the three remaining

HDSS sites (Navrongo, Ifakara and Agincourt) imple-

menting the survey as part of a scheduled census update.

Two of these three HDSS sites finished on schedule, with

the one site requiring additional time and staff to

complete the census and survey.

Discussion

Platform for research on adult health and ageingIn light of the projected demographic and epidemiologic

transitions associated with an ageing world, a WHO and

INDEPTH collaboration has demonstrated the capacity

to generate data across African and Asian settings to

better understand health outcomes and their determi-

Paul Kowal et al.


nants in older adult populations. The initial results from

the collaboration between WHO-SAGE and INDEPTH

HDSS sites are a milestone for longitudinal research on

ageing and adult health and provide an exceptional

platform for multi-site and multi-country, longitudinal

research on ageing and adult health in lower-income

countries in Africa and Asia.

The data collection platform has the potential to

substantially enhance the applications of findings from

both survey (SAGE) and surveillance-based (INDEPTH)

data collection. The very nature of the HDSS sites, with

geographic boundaries defining their populations, along

with established infrastructure and human resources,

present a number of opportunities for methodological

development and hypothesis testing prior to scaling to a

national-level survey. A number of topics could be

explored, such as the relationships between morbidity,

well-being, social networks and mortality, because of the

documentation levels and frequency of contact. Similarly,

surveillance sites benefit from enhanced generalisability

of results, expansion of objectives and comparability to

other survey data, to name a few. Additionally, the

methodological and practical strengths of each are

accentuated, resulting in improved financial efficiencies

for conducting longitudinal ageing research.

The collaboration will also support data harmonisa-

tion, data management and analytic capacity develop-

ment, cross-validation and calibration of measures,

contextualisation of the detailed information from

HDSS within broader national patterns and trends, joint

efforts to disseminate results and consideration of their

policy implications.

The analysis of levels, trends and differentials in

leading health problems globally is needed to identify

persistent and emerging health challenges for older

populations, and to monitor and evaluate health and

social programmes to determine what works, assess how

specific programmes are performing and inform decisions

regarding programme design and implementation.

Limitations and difficultiesAs with any longitudinal study, problems were experi-

enced with locating respondents to be included �especially men, many of whom may be migrant labourers.

Interviewers found difficulty in questioning the oldest

old, even after training and increased awareness about the

potential issues with interviewing this population seg-

ment. In addition, difficulties were experienced with

explaining the vignettes, some of which included scenar-

ios possibly foreign to rural settings. As part of the

analysis of results, response patterns to the vignette

questions would clearly indicate if, in the end, a

respondent did not understand the vignettes.

Feasibility of longitudinal monitoring of adult healthand ageingAlthough we aimed to assess the feasibility of incorpor-

ating the INDEPTH WHO-SAGE short questionnaire

into routine HDSS activities, only three of the eight sites

attempted this, with the other five sites conducting the

survey as a separate field activity. Of the three HDSS sites

integrating the survey, one found need for additional time

and staff. Interviewers needed time to gain experience

interviewing older respondents and to develop strategies

for high-quality interviews: the average duration of

interviews, excluding vignettes, decreased on average by

14 minutes from about 34 minutes at the beginning of

Table 2. Selected features of participating HDSS sites: INDEPTH WHO-SAGE study, 2006�2007

Approximate HDSS

site populations Study population

HDSS site Country

Year

started

Periodicity of

census updates

Total

population

Total 50

years

and over

Anticipated study

population, all

ages

Final study

population

50 years and over

AfricaAgincourta South Africa 1992 Annually 70,000 8,400 6,500 4,085

Ifakaraa Tanzania 1996 Every 4 months 84,000 9,400 5,000 5,131

Nairobia Kenya 2000 Every 4 months 69,000 2,700 2,700 2,072

Navrongoa Ghana 1993 Every 4 months 144,000 22,900 5,000 4,584

AsiaFilabavib Viet Nam 1999 Every 3 months 50,000 8,500 8,500 8,535

Matlaba Bangladesh 1966 Every 2 months 212,000 33,800 5,000 4,037

Purworejob Indonesia 1990 Annually 53,000 14,200 14,200 12,395

Vadua India 2003 Every 6 months 68,000 8,000 8,000 5,430Totals 750,000 107,900 54,900 46,269

aSupport from the US National Institute on Aging.bSupport from Swedish Council for Working Life and Social Research.



the interviews to 20 minutes towards the end. The average

length ended up at about 28 minutes.

In general, sites found value in targeting the age group

of 50 and over, focusing on health rather than routine

HDSS questions, linking INDEPTH WHO-SAGE data

with existing HDSS variables and subsequent health

outcomes. Any further data collection efforts will seek

to shorten the questionnaire further; incorporate the

survey modules into the routine census round; provide

more training to implement the vignettes; and interview

the entire population under surveillance rather than using

a sample, where possible.

Future plans and possibilitiesThe next steps in the INDEPTH WHO-SAGE collabora-

tion include further work on improving the existing

dataset, incorporating additional existing HDSS vari-

ables and future rounds of data collection. Work will be

undertaken to further harmonise HDSS variables, across

INDEPTH HDSS sites, for example, re-examining the

education data and wealth quintiles from each site. This

will help to improve comparability across HDSS sites and

countries, and with the nationally representative full

SAGE studies implemented in three of the countries

(South Africa, Ghana and India).

Additional HDSS variables have already been identi-

fied and will be added to the current summary dataset to

produce an enhanced dataset. Planned additions include

longitudinal HDSS data such as in- and out-migration,

births, deaths, additional respondent characteristics

(mother tongue, ethnicity, religious denomination) or

changes in respondent and household characteristics over

time (education, marital status, walls, floors, water,

sanitation, fuel use for cooking, food security), and

relevant data about health (non-communicable disease

risk factors for example) and household composition

(members). We will also include historical HDSS data to

cover at least SAGE baseline years (back through 2002).

Three HDSS sites (Agincourt, Navrongo and Vadu)

collected data using both the summary and full versions

of the SAGE questionnaire. Examination of data from

respondents who completed both the short and full

survey will be undertaken and then compared with the

nationally representative SAGE survey in their respective

countries. These steps will allow examination of sub-

national variation in health levels, as well as variation in

the relationships between physical and mental function-

ing and other socio-demographic factors. The perfor-

mance of the SAGE health module and vignettes among

older adults in the surveillance sites can also be compared

to the performance in the community SAGE samples

from these countries. It will provide opportunities to

compare and correlate findings from African and Asian

countries participating in SAGE with INDEPTH sites in

the same � as well as contrasting � national settings.

Further exploration of results using small area analyses

and optimising the combination of survey and surveil-

lance data are needed.

Finally, another wave of data collection is planned, for

which funding was recently secured. Further hypothesis

testing can be undertaken to take advantage of the

unique panel data that the ongoing surveillance systems

provide. For example, differences in functioning at older

ages given different socio-economic and health transition

environments may be explored in cross-site comparisons.

The contrast, for instance, between the leading health

problems in Navrongo, Ghana, which remain dominated

by many persistent ‘pre-transition’ challenges (infectious

diseases, nutritional disorders, maternal and perinatal

conditions) and the emerging epidemics of non-commu-

nicable diseases in Agincourt, South Africa, provide a

detailed epidemiologic backdrop for analysis of variation

in levels on core health domains (40). Other hypotheses

that could be examined relate to functioning of older

adults in the context of evolving childcare contributions

(for example, due to AIDS mortality of household

members), levels of family and household support, and

associated economic activity. Health issues of mortality,

the compression of morbidity and social networks will

also be pursued. The ability to connect comparable data

on different dimensions of functioning to rich databases

on individual and household variables has the potential

to support important analyses for a wide range of

questions concerning shifting determinants of health in

older adults in settings undergoing dramatic socio-

demographic changes.

Archiving and sharingAppropriate metadata and the summary SAGE dataset

with selected HDSS variables included will be made

publicly available to researchers in concert with the

publication of this supplement (see Supplementary files

under Reading Tools online). The dataset will also

be archived in the University of Michigan’s National

Archive of Computerized Data on Aging (NACDA).

ConclusionThis collaboration provides both the practical tools and

infrastructure for collecting critical evidence needed by

researchers and policy-makers. Health, disability, living

conditions and social support are concerns for ageing

populations throughout the world. Considering the

dearth of health and well-being data for older people in

most lower- and middle-income countries (13, 41, 42),

this collaboration directly addresses this data gap now

and into the future. WHO and INDEPTH will work to

improve availability and use of reliable, valid and

comparable health information at the country and global

levels, developing and improving tools and methods for

collecting this information, and providing norms, stan-

Paul Kowal et al.


dards and technical guidance for data collection, re-

search, analysis and synthesis of knowledge. The articles

that follow in this supplement illustrate the value and

quality of the data collected as part of this collaboration.

Acknowledgements

WHO Multi-Country Studies unit contributed the SAGE survey

instruments, supporting materials and technical support. The Umea

Centre for Global Health Research provided technical support and

advice to the INDEPTH HDSS sites and hosted an analytic and

writing workshop in 2008. The Health and Population Division,

School of Public Health, University of the Witwatersrand, provides

co-leadership for this initiative and serves as a satellite secretariat for

the INDEPTH Adult Health and Ageing Working Group.

Conflict of interest and fundingFinancial support for six HDSS sites (four African sites

plus Matlab and Vadu) was provided by the US National

Institute on Aging through an interagency agreement

with the World Health Organization, and for two HDSS

sites (FilaBavi and Purworejo) from the Swedish Council

for Working Life and Social Research (FAS) through

Umea University.

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Assessing health and well-beingamong older people in rural SouthAfricaF. Xavier Gomez-Olive1,2*, Margaret Thorogood1,3,Benjamin D. Clark1,4, Kathleen Kahn1,2,5# andStephen M. Tollman1,2,5#

1MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of PublicHealth, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa;2INDEPTH Network Accra, Ghana; 3Warwick Medical School, University of Warwick, Coventry, UK;4Centre for Population Studies, London School of Hygiene and Tropical Medicine, London, UK;5Umea Centre for Global Health Research, Epidemiology and Global Health, Umea University, Umea,Sweden

Background: The population in developing countries is ageing, which is likely to increase the burden of non-

communicable diseases and disability.

Objective: To describe factors associated with self-reported health, disability and quality of life (QoL) of older

people in the rural northeast of South Africa.

Design: Cross-sectional survey of 6,206 individuals aged 50 and over. We used multivariate analysis to

examine relationships between demographic variables and measures of self-reported health (Health Status),

functional ability (WHODASi) and quality of life (WHOQoL).

Results: About 4,085 of 6,206 people eligible (65.8%) completed the interview. Women (Odds Ratio (OR)�1.30, 95% CI 1.09, 1.55), older age (OR�2.59, 95% CI 1.97, 3.40), lower education (OR�1.62, 95% CI 1.31,

2.00), single status (OR�1.18, 95% CI 1.01, 1.37) and not working at present (OR�1.29, 95% CI 1.06, 1.59)

were associated with a low health status. Women were also more likely to report a higher level of disability

(OR�1.38, 95% CI 1.14, 1.66), as were older people (OR�2.92, 95% CI 2.25, 3.78), those with no education

(OR�1.57, 95% CI 1.26, 1.97), with single status (OR�1.25, 95% CI 1.06, 1.46) and not working at present

(OR�1.33, 95% CI 1.06, 1.66). Older age (OR�1.35, 95% CI 1.06, 1.74), no education (OR�1.39, 95% CI

1.11, 1.73), single status (OR�1.28, 95% CI 1.10, 1.49), a low household asset score (OR�1.52, 95% CI 1.19,

1.94) and not working at present (OR�1.32; 95% CI 1.07, 1.64) were all associated with lower quality of life.

Conclusions: This study presents the first population-based data from South Africa on health status,

functional ability and quality of life among older people. Health and social services will need to be

restructured to provide effective care for older people living in rural South Africa with impaired functionality

and other health problems.

Keywords: adult health; ageing; self-reported health; disability; quality of life; South Africa; rural; INDEPTH WHO-SAGE





Received: 3 November 2009; Revised: 10 June 2010; Accepted: 8 July 2010; Published: 27 September 2010

The world’s population is ageing and projections

show that this increase will continue (1, 2). The

percentage of the world’s population aged 65 and

over is projected to increase steeply in coming years

#Supplement Editor, Kathleen Kahn, Supplement Editor, StephenM. Tollman, have not participated in the review and decision processfor this paper.


Global Health Action 2010. # 2010 F. Xavier Gomez-Olive et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction inany medium, provided the original work is properly cited.

23



(1�3). The growth in the world population aged 50 and

over is expected to increase from 21% in 2011 to 34% in

2050. This increase will affect not only developed

countries but also developing countries (1). In particular,

in developing countries demographers have predicted an

increase of 140% between 2006 and 2030 (4), from 35 to

more than 69 million (3). The health effects of this global

demographic change are, as yet, not fully known but

estimations predict that the change in age structure in

coming years will bring an increase in mortality due to

non-communicable diseases, changing the pattern of the

most common causes of death in the different regions of

the world and the world as a whole (2). In 2005 it was

estimated that a total of 37 million chronic disease deaths

occurred worldwide, and more than three-quarters (77%)

were in people aged above 60 (5, 6). Many of these deaths

were preventable and a call has already been made

for active interventions to decrease this death rate by

2015 (5). For most of the developing world, and

particularly for sub-Saharan Africa, this epidemic of

non-communicable diseases is appearing at a time when

countries are also experiencing a crippling HIV epidemic.

The recent availability of highly active anti-retroviral

therapy (HAART) means that, for those people with

access to treatment, AIDS is becoming a chronic disease

requiring long-term clinical management (7, 8).

The high HIV prevalence and recent access to

HAART, together with an ageing population and the

emerging epidemic of non-communicable diseases, will

put immense pressure on already weak health services as

well as on society as a whole, with important changes in

household structure (9) and in the roles and responsi-

bilities of older people (10).

In South Africa, the proportion of the population aged

50 and over has slightly increased from 14.8% in 2006

(11) to 15% in 2009 (12) and is predicted to be 19% in

2030 (1). This research is based in the Agincourt sub-

district of rural northeast South Africa, where the

proportion 50 years and over in the study population

was 9.9% in 1992, 10.7% in 2000 and 11.7% in 2007

(Fig. 1). In this area there are high labour migration rates

of around 60% in adult males 35�50 years old (13) and

high HIV-related mortality in young adults (14, 15).

Despite a falling life expectancy at birth (14), we have

seen an increase in the older population. Information

from annually updated health and socio-demographic

surveillance has shown an increase of 15% in non-

communicable diseases during the past 10 years, while

the number of chronic conditions overall requiring long-

term care has increased 2.6-fold (16). This may increase

the existing high burden on health services depending on

the proportion of older people seeking health care.

In addition, this may increase the demand for social

support for these individuals in their communities.

Changes in the social structure and roles and respon-

sibilities of older people, particularly women, have

already occurred (10). In this new reality, older women

face additional responsibilities such as nursing their sick

children and taking care of their grandchildren (17).

Older people have also become the main bread winners

through their social pension, which is sometimes

the family’s only source of income (18). In 2006, any

South African citizen (women 60 years or older and men

65 years or older) living in South Africa could apply for

the government monthly pension (the Old Age Grant).

This grant also depends on the person’s income, taking

into account the total amount in the family if the person

is married (19, 20).

For all the above reasons, the health and well-being of

older adults in rural South Africa has become a crucial

issue which may impact the well-being of the entire

population. However, the impact of the changing age

structure and the growth in chronic disease and disability

is poorly understood. We have therefore set out to

address this gap. In this article, we describe the findings

of a population survey of people aged 50 and over which

included information on their self-reported health, levels

of disability and overall quality of life (QoL), which is the

first time that such findings have been reported.

Methods

Study settingThe study site covers an area of 402 km2 of semi-arid

scrub land. It is situated in the rural northeast of South

Africa in the Bushbuckridge sub-district of Ehlanseni

District, Mpumalanga Province. In the 2006 census, there

was a population of 71,587 people living in 21 villages

and 11,734 households. Individuals aged 50 and over

constituted 12% of the population.

The MRC/WITS Rural Public Health and Health

Transitions Research Unit (Agincourt Unit) has been

monitoring causes of death, births and migration in a

population of around 70,000 people since 1992 (21). EachFig. 1. Trend in proportion of population 50 years and older

in Agincourt sub-district, South Africa, 1992�2007.

F. Xavier Gomez-Olive et al.


individual and household has a unique identifying

number. The information is updated annually by trained

fieldworkers through a household census. Each year,

additional modules focusing on specific research and

policy issues (for example, food security, household assets,

health care utilisation, labour participation and temporary

migration) are included. A verbal autopsy, to determine

probable cause of death, is conducted on every death.

Although there has been substantial development in

the area since democratic elections in 1994, and a

standpipe providing clean water and an electricity supply

to households is available in all villages, the infrastructure

remains poor. There is a high unemployment rate with

36% of the total adult population unemployed and

looking for work (29% of men and 46% of women �unpublished data, 2004). As is common in rural South

Africa and the region, reflecting the structure of the

regional economy, labour migration is high, especially in

men aged 35�50 years old of whom 60% live outside the

study area for more than 6 months per year (13).

There are six clinics and one health centre within the

study area; these are served by three hospitals situated 25

and 45 km away (22). The public health service staff are

heavily over-committed, staff training is limited, and

chronic disease management programmes are not yet

fully developed. Improvement of primary health care

services is a priority for the Province (16).

SampleUsing the 2005 Agincourt census update, all 6,206

individuals aged 50 and over and living permanently in

the study area were highlighted on the 2006 household

roster used by field workers to update census informa-

tion. In this manner, field workers knew which indivi-

duals should be invited to complete the additional

questionnaire described in the next section. If an

individual was not available for interview at the first

visit, the field worker made up to two further visits to

attempt to complete the interview. Before the 2006 census

update, a similar but more extensive questionnaire was

conducted in a sample of 575 individuals 50 years old or

more. Those individuals were excluded from this study.

Data collectionField workers employed in the annual census update were

trained to administer the questionnaire. We used a

questionnaire adapted from the World Health Organiza-

tion (WHO) Study on Global AGEing and Adult Health

(23) (the SAGE study). It included questions on self-

reported health, functionality (mobility, self-care, pain

and discomfort, cognition, interpersonal activities, sleep/

energy, affect, vision and general health conditions) and

well-being, as well as the eight questions which form the

WHO Quality of Life (WHOQoL) measure. Additional

demographic data were extracted from the Agincourt

HDSS database: data routinely collected every year were

extracted from the 2006 census, while Household Asset

Score and Employment Status data were extracted from

the most recent available data (2005 and 2004, respec-

tively).

Local staff translated the questionnaires forward and

backward into Shangaan, the local language. The final

version of the questionnaire included amendments fol-

lowing a pilot conducted in several households before the

start of data collection.

During the 4 months of field work, three stages of

quality control were implemented: (1) field workers cross-

checked each others’ forms on a weekly basis; (2) field

supervisors carried out daily supervision and weekly

quality control checks; and (3) two full-time workers

checked the completeness and quality of all census

questionnaires including the SAGE questionnaires prior

to data entry. Any identified errors were referred back to

the field worker who revisited the respondent to correct

the data.

VariablesWe considered factors that could be associated with levels

of QoL and disability in our population including: age,

education, marital status, household assets, nationality,

employment status and household conditions. We calcu-

lated age at interview from the recorded date of birth and

reported age in four age groups: 50�59 years, 60�69,

70�79 and 80�.

Education was categorised according to the WHO-

recommended levels of education: no formal education;

less than six years of formal education; and six years or

more of formal education. This information was obtained

from the census database, which is updated every 5 years

using a full questionnaire on education status (last

updated in 2006).

Since many unions are traditional rather than civic and

polygamy is practised by some people, we categorised

marital status into two groups: (1) currently married or

living as married; and (2) single, including anyone with-

out a current partner (i.e. those who had never married or

were separated, divorced or widowed).

To evaluate the potential role of socio-economic status

in our analyses, we used a household asset score. This

score was developed using principal component factor

analysis and 34 variables derived from the 2005 census

questionnaire � including information collected about the

type and size of dwelling, access to water and electricity,

appliances and livestock owned and transport available.

During and following the civil war in Mozambique, the

Agincourt area received many refugees; hence we re-

corded a variable ‘nationality of origin’ (South African/

Mozambican). The Mozambican group are separately

identified in the census data and it has been previously

observed that this group differs from the host South

Cross-sectional survey of older people in rural South Africa


African population in measures such as education,

household assets and child mortality (24). Many Mozam-

bicans have now taken South African nationality which

allows them to work legally and receive state pensions.

Employment status (currently working or not) is based

on Agincourt 2004 census data, when it was most recently

collected. The majority of those not working were not

looking for work, but had retired in the sense they had

concluded their working career.

In order to examine whether health and well-being

were affected by the age structure of the household, we

created a dichotomous variable for those living in

households with younger members and those living in

households with no one under the age of 50, using data

from the 2006 census.

Health Status, Disability and Quality of Life(QoL) scoresThese three measures progress from what may be seen as

a more basic health status assessment (Health Status)

through to more complex functioning of the person

(WHODAS) and then the person’s satisfaction with their

life (WHOQoL). WHODAS is a scale designed to

measure disability (with a high score indicating a severe

lack of physical functioning). Thus, for consistency

between the scores used in this study, an inverted score

designated WHODASi has been used, with the conse-

quence that all three scores are based on a 0�100 scale,

and in all cases a high score indicates a good outcome.

Table 1 shows the domains used to calculate the variables

and their scales.

Health Status is a composite score which includes

functionality and QoL domains. Health Status generally

refers to physical and occupational functions, psycholo-

gical states, social interaction and somatic sensations (25).

This general health score was derived using item response

theory (IRT) parameter estimates in Winsteps, a Rasch

measurement software package (http://www.winsteps.

com). IRT uses Maximum Likelihood Estimation, which

combines the pattern of responses as well as the char-

acteristics of each specific item for the multiple health

Table 2. Background characteristics by response for 6,206

adults 50 years and older living permanently in the Agin-

court sub-district, 2006

Variables

Respondents

(N�4,085)

Non-respondents

(N�2,121)

p-Value for

difference

respondentsvs. non-

respondents

Sex (%)

Men 1,012 (24.8) 926 (43.7) B0.001

Women 3,073 (75.2) 1,195 (56.3)Mean age (SD) 66.6 (10.6) 64.8 (11.3) B0.001

Age group (years)

50�59 1,297 (31.7) 923 (43.5) B0.001

60�69 1,221 (29.9) 546 (25.7)

70�79 1,077 (26.4) 413 (19.5)80� 490 (12.0) 238 (11.2)

Education level (%)

No formal education 2,601 (65.8) 1,038 (67.5) B0.001

Less than or equal

to 6 years

757 (19.2) 218 (14.1)

More than 6 years 594 (15.0) 292 (18.9)

Marital status (%)

Single 2,223 (54.4) 1,125 (53.0) �0.302

Current partnership 1,862 (45.6) 996 (47.0)

Household asset score (%)

First quintile 629 (15.9) 313 (18.5) �0.125Second quintile 753 (18.9) 312 (18.5)

Third quintile 766 (19.3) 330 (19.5)

Fourth quintile 841 (21.2) 329 (19.5)Fifth quintile 978 (24.6) 405 (24.0)

Mean number of

household

members (SD)

7.0 (4.1) 7.4 (4.6) �0.002

Household members

aged 50 years and

over (SD)

32.1 (25.9) 28.9 (25.9) B0.001

Nationality of origin

South African 2,972 (72.8) 1,399 (66.0) B0.001Mozambican 1,111 (27.2) 720 (34.0)

Occupational status in 2004

Working 503 (14.6) 481 (28.8) B0.001

Not working 2,930 (85.3) 1,189 (71.2)

Table 1. Domains and scales

Health status WHODASi WHOQoL

Domains Mobility Interpersonal activities Enough energy for daily lifeSelf-care Difficulties in daily living: Enough money to meet needs

Pain and discomfort � Standing Satisfaction with:

Cognition � Walking � Your healthInterpersonal activities � Household duties � Yourself

Sleep/energy � Learning � Ability to perform daily activities

Affect � Concentrating � Personal relationships

Vision � Self-care � Condition of your living placeRate your overall quality of life

Scale 0 (poor health) to 100 (good health) 0 (low ability) to 100 (high ability) 0 (low quality of life) to 100 (high quality of life)



questions (each with multiple response categories) to

produce the final health score. The health score is then

transformed to a scale of 0�100. IRT models the relation-

ship between a person’s reported Health Status and their

probability of responding to each question in a multi-item

scale. A key feature of IRT modelling is that item

parameter estimates should be invariant to group mem-

bership (i.e. each item functions similarly across groups of

people from different cultures) (26).

To measure disability levels we used the WHODAS II

(World Health Organization Disability Assessment Sche-

dule II) scale that assesses day-to-day functioning in six

activity domains. There are 10 questions with multiple

response options. Measurement of functionality was

calculated by asking participants about difficulty experi-

enced performing certain activities during the past 30

days, and transformed into the WHODASi score for

functional ability as described above.

QoL was measured using the Word Health Organisa-

tion Quality of Life (WHOQoL) scale. WHO defines QoL

as ‘the individual’s perception of their position in life in

the context of the culture and value systems in which they

live and in relation to their goals, expectations, standards

and concerns’ (27, 28). QoL domains include questions on

self-rated general health and questions on satisfaction.

The WHOQoL score is presented on a scale of 8�40

Table 3a. Demographic variables by sex [n, (%)] for 4,085 adults aged 50 and over in Agincourt sub-district, 2006

Variable Males Females Total

p-Value for

difference between

male and female

Sex (%) 1,012 (24.8) 3,073 (75.2) 4085 (100) pB0.001

Mean Age in years (95% CI) 67.8 (67.1, 68.5) 66.1 (65.7, 66.4)

Age group (years)

50�59 275 (27.2) 1,022 (33.3) 1,297 (31.7) df�3

60�69 321 (31.7) 900 (29.3) 1,221 (29.9) p�0.001

70�79 269 (26.6) 808 (26.3) 1,077 (26.4)

80� 147 (14.5) 343 (11.2) 490 (12.0)

Partnership status

In a partnership 771 (76.2) 1,091 (35.5) 1,862 (45.6) df�1

Currently single 241 (23.8) 1,982 (64.5) 2,223 (54.4) pB0.001

Education level

No education 549 (54.2) 2,052 (66.8) 2,601 (63.7) df�3

Less than 6 years 214 (21.1) 543 (17.1) 757 (18.5) pB0.001

Six years or more 209 (20.6) 385 (12.5) 594 (14.5)

Missing data 40 (4.0) 93 (3.0) 133 (3.3)

Household asset score (quintiles)

First (lowest) 159 (15.7) 470 (15.3) 629 (15.4) df�5

Second 167 (16.5) 586 (19.1) 753 (18.4) p�0.016

Third 171 (16.9) 595 (19.4) 766 (18.7)

Fourth 212 (20.9) 629 (20.5) 841 (20.6)

Fifth (highest) 279 (27.6) 699 (22.7) 978 (23.9)

Missing data 24 (2.4) 94 (3.1) 118 (2.9)

Household with and without people aged less than 50 years

With under 50 853 (84.3) 2841 (92.5) 3694 (90.4) df�1

Without under 50 159 (15.7) 232 (7.5) 391 (9.6) pB0.001


South African 767 (75.9) 2,205 (71.8) 2,972 (72.8) df�1

Mozambican 244 (24.1) 867 (28.2) 1,111 (27.2) p�0.011


Working 169 (19.7) 334 (13.0) 503 (14.7) df�1

Not working 690 (80.3) 2,240 (87.0) 2,930 (85.4) pB0.001



(where 8 is the best QoL) and transformed to a

0�100 scale corresponding to the other scores.

Data entry and analysisWe entered data using CSPro 3.1 data entry programme

(http://www.census.gov/ipc/www/cspro/index.html) which

includes validation checks, and data was then extracted to

Stata 10.1 (College Station, TX, USA) for analysis.

Logistic regression was performed to assess the relation

between potentially associated factors and confounders,

and the three outcomes, i.e. health score, functionality

(WHODASi) and quality of life (WHOQoL). We first

carried out a univariate analysis with each of the census

variables and then constructed a multivariate model based

on the results of the univariate analyses (Tables 5, 7 and 9).

Variables which were significantly related to the outcome

measures in a univariate analysis were introduced into the

model sequentially and then discarded if the effect was not

significant at the level of p�0.1.

Ethical clearanceEthical clearance for the MRC/WITS Rural Public

Health and Health Transitions Research Unit � Health

and Socio-Demographic Surveillance System (Agincourt)

� census and modules has been granted by the Committee

for Research on Human Subjects (Medical) of the

University of the Witwatersrand, Johannesburg, South

Africa (Ref No. M960720). Ethical clearance for the

Agincourt-INDEPTH Study on Global Ageing and

Adult Health was given by the Committee for Research

on Human Subjects (Medical) of the University of the

Witwatersrand, Johannesburg, South Africa (Ref No.

R14/49).

Table 3b. Demographic variables by age group for 4,085 adults aged 50 and over in Agincourt sub-district, 2006

Age groups 50�59, N (%) 60�69, N (%) 70�79, N (%) 80�, N (%) Total N (%) p-Value

Sample distribution 1,297 (31.8) 1,221 (29.9) 1,077 (26.4) 490 (12) 4,085 (100)

Mean (95% CI) 54.5 (54.4�54.7) 64.8 (64.6�64.9) 74.5 (74.3�74.7) 84.9 (84.6�85.3)

Sex

Male 275 (21.2) 321 (26.3) 269 (25.0) 147 (30.0) 1,012 (24.8) df�3

Female 1,022 (78.8) 900 (73.7) 808 (75.0) 343 (70.0) 3,073 (75.2) p�0.001

Marital status

In a partnership 732 (56.4) 615 (50.4) 374 (34.7) 141 (28.8) 1,862 (45.6) df�3

Currently single 565 (43.6) 606 (49.6) 703 (65.3) 349 (71.2) 2,223 (54.4) pB0.001

Education level

No formal education 630 (48.6) 736 (60.3) 844 (78.4) 391 (79.8) 2,601 (63.7) df�9

Primary or less than six years 304 (23.4) 253 (20.7) 144 (13.4) 56 (11.4) 757 (18.5) pB0.001

Six years or more 316 (24.4) 193 (15.8) 61 (5.7) 24 (4.9) 594 (14.5)

Missing 47 (3.6) 39 (3.2) 28 (2.6) 19 (3.9) 133 (3.3)

Socio-economic quintiles

First (lowest) 198 (15.3) 153 (12.5) 186 (17.3) 92 (18.8) 629 (15.4) df�15

Second 233 (18.0) 198 (16.2) 220 (20.4) 102 (20.8) 753 (18.4) p B0.001

Third 238 (18.4) 246 (20.2) 199 (18.5) 83 (16.9) 766 (18.8)

Fourth 258 (19.9) 258 (21.1) 231 (21.5) 94 (19.2) 841 (20.6)

Fifth (highest) 337 (26.0) 326 (26.7) 217 (20.2) 98 (20.0) 978 (23.9)

Missing 33 (2.5) 40 (3.3) 24 (2.2) 21 (4.3) 118 (2.9)

Adult in the household

Youth plus older 1,206 (93.0) 1,123 (92.0) 964 (89.5) 401 (81.8) 3,694 (90.4) df�3

Only older 91 (7.0) 98 (8.0) 113 (10.5) 89 (18.2) 391 (9.6) pB0.001

Nationality

South African 957 (73.8) 919 (75.3) 740 (68.7) 356 (72.7) 2,972 (72.8) df�3

Mozambican 339 (26.2) 301 (24.7) 337 (31.3) 134 (27.4) 1,111 (27.2) p�0.003

Occupational status

Working 284 (26.4) 160 (15.3) 44 (4.9) 15 (3.6) 503 (14.7) df�3

Not working 791 (73.6) 883 (84.7) 859 (95.1) 397 (96.4) 2,930 (85.4) pB0.001



ResultsFrom the 6,206 people aged 50 years and over selected

from the 2005 census, 4,085 (65.8%) responded to a

questionnaire. Of those that did not complete a ques-

tionnaire, 1,616 (26.0%) were absent at the time of the

interview, 218 (3.5%) had died, 47 (0.75%) declined to

take part and 240 (3.9%) were unable to answer the

questions (mainly due to different health conditions).

A comparison of respondents and non-respondents

(Table 2) shows that non-respondents were significantly

younger (mean age 64.8 vs. 66.6, pB0.001), included a

higher proportion of men (43.7% vs. 24.8%, pB0.001)

and were better educated. There were no differences in

marital status or socio-economic status, but respondents

included significantly more South Africans than Mozam-

bicans and proportionally more people who were cur-

rently not working (85.3% vs. 71.2%; pB0.001).

About 85% of respondents were ‘currently not work-

ing’, but the majority of these were not formally

‘unemployed’ (i.e. actively searching for work but not

finding it). The 5.7% of people who were formally

unemployed included 15% of those aged 50�59 and

4.3% of those aged 60�69 (data not shown).

Among the respondents, there were significant differen-

ces between men and women in all the variables (Table 3a).

Only a quarter of the respondents were men (24.8%), and

Table 4. Range of Health Status (quintiles) by demographic variables [n, (%)] for 4,085 adults aged 50 and over in

Agincourt sub-district, 2006

Health status quintile

Variable 1 (poorest) 2 3 4 5 (best) p-Value

Sex

Male 160 (15.8) 170 (16.8) 175 (17.3) 215 (21.2) 292 (28.8) df�4

Female 641 (20.9) 597 (19.4) 562 (18.3) 639 (20.8) 634 (20.6) pB0.001

Age group (years)

50�59 170 (13.1) 240 (18.5) 220 (17) 315 (24.3) 352 (27.1) df�12

60�69 183 (15) 209 (17.1) 239 (19.6) 283 (23.2) 307 (25.1) pB0.001

70�79 270 (25.1) 207 (19.2) 202 (18.8) 193 (17.9) 205 (19)

80 and over 178 (36.3) 111 (22.7) 76 (15.5) 63 (12.9) 62 (12.7)

Partnership

In a partnership 277 (14.9) 341 (18.3) 328 (17.6) 411 (22.1) 505 (27.1) df�4

Currently single 524 (23.6) 426 (19.2) 409 (18.4) 443 (19.9) 421 (18.9) pB0.001

Education level

No education 590 (22.7) 500 (19.2) 475 (18.3) 510 (19.6) 526 (20.2) df�8

Less than 6 years 120 (15.9) 140 (18.5) 147 (19.4) 166 (21.9) 184 (24.3) pB0.001

Six years or more 65 (10.9) 97 (16.3) 96 (16.2) 159 (26.8) 177 (29.8)


First (lowest) 126 (20.0) 120 (19.1) 111 (17.7) 131 (20.8) 141 (22.4) df�16

Second 159 (21.1) 148 (19.7) 138 (18.3) 155 (20.6) 153 (20.3) p�0.321

Third 145 (18.9) 135 (17.6) 147 (19.2) 163 (21.3) 176 (23.0)

Fourth 164 (19.5) 177 (21.1) 152 (18.1) 163 (19.4) 185 (22.0)

Fifth (highest) 179 (18.3) 165 (16.9) 160 (16.4) 219 (22.4) 255 (26.1)

Household with and without people aged less than 50

With under 50 696 (18.8) 702 (19) 671 (18.2) 787 (21.3) 838 (22.7) df�4

Without under 50 105 (26.9) 65 (16.6) 66 (16.9) 67 (17.1) 88 (22.5) p�0.003


South African 623 (21.0) 558 (18.8) 506 (17.0) 619 (20.8) 666 (22.4) df�4

Mozambican 178 (16.0) 209 (18.8) 229 (20.6) 235 (21.1) 260 (23.4) p�0.003


Working 59 (11.7) 74 (14.7) 93 (18.5) 119 (23.7) 158 (31.4) df�4

Not working 612 (20.9) 569 (19.4) 518 (17.7) 612 (20.9) 619 (21.1) pB0.001



the men were older (67.8 years vs. 66.1 years; pB0.001),

more likely to be in a current partnership (76.2% vs. 35.5%;

pB0.001) and more likely to be in paid employment.

Demographic variables presented by age group

(Table 3b) show that the proportion of males increased

with age (21.2% in 50�59 age group vs. 30% in the 80�age group; p�0.001); the younger age group was better

educated (24.4% in the 50�59 age group vs. 4.9% in 80�have 6 years or more of formal education; pB0.001); the

two younger age groups have higher socio-economic

status (26.0 and 26.7% in the younger groups vs. 20.2

and 20.0% in the older age groups; pB0.001).

Table 4 shows the range of Health Status responses by

each of the demographic variables, while Table 5 shows

the results of univariate and multivariate logistic regres-

sion analysis examining the odds of reporting a Health

Status in one of the bottom two quintiles. Household

asset score, household age structure and nationality of

origin did not show a significant association in univariate

analysis. In the final multivariate model, women had a

30% higher risk than men (odds ratio (OR)�1.30, 95%

confidence interval (CI) 1.09, 1.55) of reporting a low

Health Status. Older age (OR�2.59, 95% CI 1.97, 3.40),

lower education level (OR�1.62, 95% CI 1.31, 2.00),

single marital status (OR�1.18, 95% CI 1.01, 1.37) and

not working at present (OR�1.29, 95% CI 1.06, 1.59)

were also all related to a poorer Health Status. People of

Mozambican origin were 24% less likely to report a

Health Status in the bottom two quintiles (OR�0.76,

95% CI 0.64, 0.91).

The quintiles for self-reported ability (WHODASi

score) are shown in Table 6, while Table 7 shows the

results of univariate and multivariate logistic regression

analysis examining the odds of reporting a WHODASi

score in one of the bottom two quintiles (poorer self-

reported functioning). In multivariate analysis, women

were more likely to be in the bottom two quintiles of self-

reported functioning (OR�1.38, 95% CI 1.14, 1.66), as

were older people (OR�2.92, 95% CI 2.25, 3.78), those

with less education (OR�1.57, 95% CI 1.26,1.97), those

not in a current partnership (OR�1.25, 95% CI 1.06,

1.46) and those who were not working (OR�1.33, 95%

CI 1.06, 1.66).

Although women were significantly more likely than

men to be in the lowest two quintiles of self-reported QoL

� WHOQoL (Table 8), this effect disappeared after

adjusting for other variables, as did the effect of house-

hold age structure and nationality of origin (Table 9). In

the final multivariate model, older age (OR�1.35, 95%

CI 1.06, 1.74), lack of education (OR�1.39, 95% CI

1.11, 1.73), not being in a current partnership (OR�1.28,

95% CI 1.10, 1.49), having a low household asset score

(OR�1.52, 95% CI 1.19, 1.94) and not working at

present (OR�1.32; 95% CI 1.07, 1.64) were all asso-

ciated with a higher odds of being in one of the lower two

quintiles for WHOQoL (Table 9).

DiscussionIn this study we describe the well-being and functionality

of the population aged 50 and over in the Agincourt

Table 5. Factors associated with poor Health Statusa score

for 4,085 adults aged 50 and over in Agincourt sub-district,

2006

Variables

Univariate model

OR (95% CI)

Multivariate model

OR (95% CI)

Sex

Male 1 1

Female 1.42 (1.23, 1.64) 1.30 (1.09, 1.55)

Age group (years)

50�59 1 1

60�69 1.13 (0.97, 1.32) 1.05 (0.88, 1.26)

70�79 1.81 (1.53, 2.13) 1.46 (1.19, 1.78)

80� 3.09 (2.45, 3.89) 2.59 (1.97, 3.40)

Education level

No formal education 1.97 (1.64, 2.35) 1.62 (1.31, 2.00)

Less than 6 years 1.51 (1.22, 1.88) 1.42 (1.12, 1.79)

Six years or more 1 1

Marital status

Single 1.52 (1.34, 1.72) 1.18 (1.01, 1.37)

In current partnership 1 1


With under 50 1 Not included in the

final model

Without under 50 1.19 (0.97, 1.48)

Household asset score

First quintile (lowest) 1.23 (1.01, 1.51) Not included in the

final model

Second quintile 1.36 (1.12, 1.65)

Third quintile 1.18 (0.98, 1.43)

Fourth quintile 1.33 (1.11, 1.60)

Fifth quintile (highest) 1


South African 1 1

Mozambican 0.95 (0.82, 1.09) 0.76 (0.64, 0.91)


Working 1 1

Not working 1.69 (1.40, 2.05) 1.29 (1.06, 1.59)

aIRT (Item Response Theory) used when measuring health status.

The Health Status scale was divided in quintiles. The best Health

Status was defined as those in the two highest quintiles, while the

worst Health Status was defined as those in the three lower

quintiles.



Health and Socio-demographic Surveillance Site by

measuring three main variables (scores) that flow from

a more basic health status assessment (Health Status)

through to more complex functioning of the person

(WHODASi) and then to the person’s satisfaction with

their life (WHOQoL).

Women were 30% more likely than men to report a

poor state of health (low Health Status). Other factors

associated with a worse Health Status were aged above

70 years, lower levels of formal education, being single

and currently not working. On the other hand, being of

Mozambican origin is related to a better-reported Health

Status. As with the Health Status, women were more

likely to report poorer functionality (WHODASi) than

men. Age significantly affected functionality only from

70 years of age. People aged 80 and over had a threefold

increase in risk of reporting poorer functionality. Pro-

gressively lower levels of education related to a gradual

increase in functional problems. Being single or ‘not

working at present’ were also associated with worse

functionality. There was no gender difference in QoL.

However, our analysis showed the following factors

related to lower QoL: older age group, no formal

education, being single and currently not working.

Table 6. WHODASia by demographic variables [n, (%)] for 4,085 adults aged 50 and over in Agincourt sub-district, 2006

WHODASi quintile

Variable 1 (high ability) 2 3 4 5 (low ability) p-Value

Sex

Male 328 (32.4) 184 (18.2) 165 (16.3) 160 (15.8) 175 (17.3) df�4

Female 701 (22.8) 542 (17.6) 526 (17.1) 642 (20.9) 662 (21.5) pB0.001

Age group (years)

50�59 398 (30.7) 264 (20.4) 220 (17) 256 (19.7) 159 (12.3) df�12

60�69 364 (29.8) 238 (19.5) 210 (17.2) 217 (17.8) 192 (15.7) pB0.001

70�79 198 (18.4) 177 (16.4) 188 (17.5) 233 (21.6) 281 (26.1)

80 and over 69 (14.1) 47 (9.6) 73 (14.9) 96 (19.6) 205 (41.8)

Partnership


Currently single 484 (21.8) 357 (16.1) 368 (16.6) 459 (20.7) 555 (25.0) pB0.001

Education level

No education 583 (22.4) 419 (16.1) 443 (17) 539 (20.7) 617 (23.7) df�8


Six years or more 206 (34.7) 130 (21.9) 99 (16.7) 89 (15) 70 (11.8)


First (lowest) 168 (26.7) 98 (15.6) 105 (16.7) 127 (20.2) 131 (20.8) df�16

Second 181 (24) 139 (18.5) 129 (17.1) 153 (20.3) 151 (20.1) p�0.218

Third 184 (24) 157 (20.5) 123 (16.1) 136 (17.8) 166 (21.7)

Fourth 191 (22.7) 148 (17.6) 148 (17.6) 176 (20.9) 178 (21.2)

Fifth (highest) 281 (28.7) 166 (17) 170 (17.4) 179 (18.3) 182 (18.6)


With under 50 940 (25.5) 662 (17.9) 631 (17.1) 720 (19.5) 741 (20.1) df�4

Without under 50 89 (22.8) 64 (16.4) 60 (15.4) 82 (21) 96 (24.6) p�0.199


South African 719 (24.2) 535 (18) 522 (17.6) 560 (18.8) 636 (21.4) df�4

Mozambican 309 (27.8) 191 (17.2) 169 (15.2) 241 (21.7) 201 (18.1) p�0.005


Working 179 (35.6) 98 (19.5) 85 (16.9) 81 (16.1) 60 (11.9) df�4

Not working 686 (23.4) 523 (17.9) 502 (17.1) 574 (19.6) 645 (22.0) pB0.001

aWHODASi: Using the World Health Organization Disability Assessment Schedule II (WHODAS II) the variable scale was inverted and

divided into quintiles.



Finally there was a gradient in the expected direction in

the relationship between lower QoL and lower socio-

economic status measured by household asset score.

Our data show that women report significantly poorer

functionality for both Health Status and WHODASi, the

two measures that include variables of functionality,

although they do not report a lower QoL. There are

several possible explanations for this. Women may objec-

tively have poorer functionality but do not regard this as a

problem, or women may be more active in the home than

their retired partners and therefore more aware of a

change in functionality, or women may be more aware of

their own health and therefore report health problems in a

higher proportion than men. At present, the data are not

available to explore this issue further.

The oldest age group (people aged 70 and over)

reported worst QoL and functioning. However, the age

group 60�69 years presented no significant difference in

Health Status and functioning measures compared with

the 50�59 year age group. Moreover, they reported a

significantly better QoL than the younger 50�59 age

group. This may be related to the fact that women who

retire at 60 and men at 65 are still in good health. In

addition, they receive old-age grants (pensions) which

allows them a better life with higher food security and,

importantly, with greater capacity to help children in

their households who then enjoy higher food security and

better schooling (29). At older ages (70 and over), Health

Status and functioning had deteriorated and they re-

ported worse levels of both variables despite still receiving

pension grant.

The household asset score was created as a proxy for

household socio-economic status. The asset data used in

this study were collected in 2005, a year earlier than the

study was conducted. Our data did not show any relation

between this score and either the Health Status or the

WHODASi. However, the household asset score is

significantly related to the WHOQoL that measures

satisfaction with one’s life. This could mean that people’s

socio-economic status has no relation to being physically

and socially functional, but impacts on how satisfied

people are with their life and expectations (30).

Unemployment among Agincourt’s adult population

(including both permanent and temporary residents) is

36%, representing 29% of men and 46% of women

(Collinson, personal communication). In our study

sample, 85% of all respondents were ‘not currently

working’, but only 5.7% were formally unemployed.

There is a significant relationship between currently not

working and Health Status, WHODASi and WHOQoL

even after controlling for age group.

Other work in the Agincourt study site has shown

residents of Mozambican origin to be a vulnerable sub-

group (24, 31). We thus expected Mozambican nation-

ality to have a significant relationship with low Health

Status, low WHODASi and low WHOQoL. However, no

relationship with WHOQoL and WHODASi was found,

and being Mozambican was associated with less like-

lihood of reporting a lower Health Status, meaning that

those of Mozambican origin reported feeling in better

health than their South African counterparts. This may

be related to a healthy immigrant selectivity that may

decrease over coming years (32).

The Agincourt HDSS includes individuals living

permanently in the area and those that spend more

than 6 months per year outside the study area but remain

linked to their rural households. Some permanent

Table 7. Factors associated with poor self-reported functio-

ning (WHODASia) for 4,085 adults aged 50 and over in


Variables

Univariate model

OR (95% CI)

Multivariate model

OR (95% CI)

Sex

Male 1 1Female 1.49 (1.28, 1.73) 1.38 (1.14, 1.66)

Age group (years)

50�59 1 1

60�69 1.07 (0.90, 1.27) 1.00 (0.83, 1.21)

70�79 1.94 (1.64, 2.29) 1.62 (1.32, 1.99)80� 3.38 (2.73, 4.20) 2.92 (2.25, 3.78)

Education level

No formal education 2.19 (1.80, 2.67) 1.57 (1.26, 1.97)

Less than 6 years 1.49 (1.18, 1.88) 1.33 (1.03, 1.72)

Six years or more 1 1

Marital statusSingle 1.66 (1.46, 1.88) 1.25 (1.06, 1.46)


HH with and without people aged less than 50

With under 50 1 Not included in

the final modelWithout under 50 1.28 (1.03, 1.57)


First quintile (lowest) 1.24 (1.03, 1.50) Not included in

the final model

Second quintile 1.11 (0.91, 1.35)Third quintile 1.16 (0.95, 1.41)

Fourth quintile 1.19 (0.97, 1.46)

Fifth quintile (highest) 1

Nationality of originSouth African 1 Not included in

the final model

Mozambican 0.98 (0.85, 1.13)


Working 1 1Not working 1.83 (1.48, 2.25) 1.33 (1.06, 1.66)

aWHODASi: Using the World Health Organization Disability

Assessment Schedule II (WHODAS II) the variable scale was

inverted and divided into quintiles. ORs reflect odds for those in

the two lowest quintiles of functionality.



residents work in the surrounding area making it difficult

to find them at home. In this study, 76% of non-

respondents were not found at home for interview

despite three visits to the household. Men participate

in the labour force more than women, and the non-

respondents represented nearly 50% of all men and 30%

of all women expected to participate in the study. Table 2

shows that non-respondents included twice the propor-

tion of workers compared to respondents. Moreover,

69% of workers among the non-respondent group were

aged between 50 and 59 years (data not shown). Those

who out-migrate permanently from the study area

(around 3% of the total population per year) are not

followed up and so it is not possible to measure their

impact on the health status and functionality of the

remaining population. Thus, the study may have under-

estimated the reported health of the population given

that the results show the health status of those that live

most of the year in the study area.

This study presents the first population-based data

from South Africa on Health Status, functionality and

WHOQoL. Other studies have focused on specific

diseases (33, 34) or on defining the best domains with

which to evaluate QoL and Health Status (30).

Table 8. WHOQoLa by demographic variables [n (%)] for 4,085 adults aged 50 and over in Agincourt sub-district, 2006

WHOQoL quintile

Variable 1 (high) 2 3 4 5 (low) p-Value

Sex

Male 244 (24.2) 217 (21.5) 168 (16.6) 171 (16.9) 210 (20.8) df�4

Female 566 (18.4) 623 (20.3) 608 (19.8) 678 (22.1) 596 (19.4) pB0.001

Age group (years)

50�59 269 (20.8) 274 (21.1) 246 (19.0) 261 (20.1) 246 (19.0) df�12

60�69 279 (22.9) 281 (23.0) 238 (19.5) 257 (21.0) 165 (13.5) pB0.001

70�79 185 (17.2) 214 (19.9) 209 (19.4) 225 (20.9) 242 (22.5)

80 and over 77 (15.7) 71 (14.5) 83 (16.9) 106 (21.6) 153 (31.2)

Partnership


Single 378 (17.0) 446 (20.1) 405 (18.2) 478 (21.5) 514 (23.1) pB0.001

Education level

No education 454 (17.5) 508 (19.5) 513 (19.7) 565 (21.7) 558 (21.5) df�8


Six years or more 157 (26.4) 151 (25.4) 102 (17.2) 91 (15.3) 93 (15.7)


First (lowest) 94 (14.9) 128 (20.4) 117 (18.6) 135 (21.5) 155 (24.6) df�16

Second 119 (15.8) 158 (20.1) 144 (19.1) 168 (22.3) 164 (21.8) pB0.001

Third 162 (21.1) 155 (20.2) 141 (18.4) 177 (23.1) 131 (17.1)

Fourth 157 (18.7) 183 (21.8) 157 (18.7) 174 (20.7) 169 (20.1)

Fifth (highest) 269 (27.6) 200 (20.5) 187 (19.1) 165 (16.9) 155 (15.9)


With under 50 735 (19.9) 772 (20.9) 708 (19.2) 768 (20.8) 710 (19.2) df�4

Without under 50 78 (20.0) 68 (17.4) 68 (17.4) 81 (20.7) 96 (24.6) p�0.099


South African 624 (21) 617 (20.8) 559 (18.8) 587 (19.7) 585 (19.7) df�4

Mozambican 189 (17.0) 223 (20.1) 215 (19.4) 262 (23.6) 221 (19.9) p�0.014


Working 136 (27.0) 114 (22.7) 95 (18.9) 86 (17.1) 72 (14.3) df�4

Not working 568 (19.4) 603 (20.6) 566 (19.3) 614 (21.0) 579 (19.8) pB0.001

aWHOQoL: The World Health Organization Quality of Life score was calculated and then divided into quintiles.



Measuring health status, functionality and QoL at the

population level in older people is important to under-

stand the health, welfare and social support needs of this

growing proportion of the population. As the Agincourt

population continues to age, along with millions living in

similar rural settings, it will become increasingly important

for health and social services to adapt and improve in order

to provide effective care for a growing older population

with significantly impaired functionality and other health

problems. We plan to continue to monitor the health and

well-being of older people. This will provide information

on how societal changes are affecting their health and well-

being, assist policy makers to predict demand for health

services, and inform the development of appropriate and

cost-effective health and social services.

Acknowledgements

We thank the study participants, field team and local authorities.

Special thanks to Dr. Oscar Franco (Warwick University, UK) and

to Ms. Marguerite Schneider (Human Sciences Research Council,

RSA) for providing useful comments for the improvement of the

manuscript. This study was funded by the National Institute on

Aging of the National Institutes of Health, USA and by the

Wellcome Trust, UK (Grant Nos. 058893/Z/99/A and 069683/Z/02/

Z). It was carried out in collaboration with the World Health

Organization.

Conflict of interest and fundingThe authors have not received any funding or benefits

from industry to conduct this study.

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Age group (years)

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Occupational status in 2004Working 1 1

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http://www.odi.org.uk/resources/download/1067.pdf

http://www.capegateway.gov.za/eng/directories/services/11586/47491

http://www.capegateway.gov.za/eng/directories/services/11586/47491

http://www.who.int/healthinfo/systems/sage/en/index1.html


Health status and quality of lifeamong older adults in rural TanzaniaMathew A. Mwanyangala1,2*, Charles Mayombana1,2,3,Honorathy Urassa1,2, Jensen Charles1,2,Chrizostom Mahutanga1,2, Salim Abdullah1,2,3 andRose Nathan1,2,3

1Ifakara Site Health Institute, Ifakara, Morogoro, Tanzania; 2INDEPTH Network, Accra, Ghana;3Mikocheni Office, Ifakara Health Institute, Tanzania

Background: Increasingly, human populations throughout the world are living longer and this trend is

developing in sub-Saharan Africa. In developing African countries such as Tanzania, this demographic

phenomenon is taking place against a background of poverty and poor health conditions. There has been

limited research on how this process of ageing impacts upon the health of older people within such low-

income settings.

Objective: The objective of this study is to describe the impacts of ageing on the health status, quality of life

and well-being of older people in a rural population of Tanzania.

Design: A short version of the WHO Survey on Adult Health and Global Ageing questionnaire was used to

collect information on the health status, quality of life and well-being of older adults living in Ifakara Health

and Demographic Surveillance System, Tanzania, during early 2007. Questionnaires were administered

through this framework to 8,206 people aged 50 and over.

Results: Among people aged 50 and over, having good quality of life and health status was significantly

associated with being male, married and not being among the oldest old. Functional ability assessment was

associated with age, with people reporting more difficulty in performing routine activities as age increased,

particularly among women. Reports of good quality of life and well-being decreased with increasing age.

Women were significantly more likely to report poor quality of life (odds ratio 1.31; pB0.001, 95% CI 1.15�1.50).

Conclusions: Older people within this rural Tanzanian setting reported that the ageing process had significant

impacts on their health status, quality of life and physical ability. Poor quality of life and well-being, and poor

health status in older people were significantly associated with marital status, sex, age and level of education.

The process of ageing in this setting is challenging and raises public health concerns.

Keywords: health status; quality of life; older people; ageing; Health and Demographic Surveillance System; INDEPTH

WHO-SAGE





Received: 20 November 2009; Revised: 29 March 2010; Accepted: 8 July 2010; Published: 27 September 2010

Human populations throughout the world are

living longer than ever before � but this is a

relatively new phenomenon in developing coun-

tries. It is estimated that nearly 63% of the population

aged 60 and over are living in developing countries, and

further projected that by 2050 nearly 1.5 billion older

people will reside in developing countries (1). The

number of older people is growing rapidly in sub-Saharan

Africa (2). Changes in the ageing process within devel-

oping countries have been observed through shifts in

population age composition. This process is associated

with rapid declines in fertility and mortality (3). In the


Global Health Action 2010. # 2010 Mathew A. Mwanyangala et al. This is an Open Access article distributed under the terms of the Creative CommonsAttribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, andreproduction in any medium, provided the original work is properly cited.

36



near future, larger older populations will become ubiqui-

tous in Africa (1, 4, 5). Tanzania has a total population of

34 million of whom 4% are aged 50 and over. It is also

among the countries in sub-Saharan Africa with at least

1 million older people, and this proportion is projected to

rise to 10% of the total population by 2050 (6, 7).

Furthermore, the absolute number of people entering the

older cohort is increasing (7).

In developing African countries such as Tanzania,

many older people reach retirement age after a lifetime

of poverty and deprivation, poor access to health care

and poor diet. This situation can leave them with

insufficient personal savings as a consequence of a

fragile earning history (8, 9). In most developing

countries, formal social security systems have only

limited coverage and inadequate benefit payments (10,

11). As a result, the majority of older people depend

on family support networks, a reality that is well

appreciated in most parts of sub-Saharan Africa (12�14). Furthermore, it is recognised that traditional social

security systems are evolving, attenuating and rapidly

disappearing due to pressures from urbanisation, in-

dustrialisation and HIV/AIDS (15). At the same time it

is widely reported that older people have more sub-

stantial inter-individual variability in health related to

age than do younger people (16, 17). The health care

system spends a small fraction of the budget on

treating older adult illness and access to care is limited

and not a policy priority in most developing countries

(6, 18�20).

Within developing countries the demographic transi-

tion towards older populations is likely to constrain

future health care systems. The attitude of health care

providers towards older people makes their situation

even more difficult. It has been reported that older

people in Tanzania are frequently mistreated by health

care providers when they seek care (21). Although

provision of free health services to older people is

stipulated in the Tanzanian National Ageing policy,

many older people still do not access these services

due to inability to prove their age, aggravated by the

limited availability of health services, equipment and

expertise (6).

The economies of rural Tanzanian settings are pre-

dominantly supported by subsistence agriculture, which

provides little or no pension coverage and limited health

care services. The age structure of these settings is already

being impacted by the emigration of younger people to

urban areas and the return of older people to rural

environments from urban areas on retirement.

Current health challenges and existing policies act to

hide the situation of older people. A large body of

research has described the process of ageing using

contrasting perspectives: demographic characteristics,

physical health, cognitive impairment, disability and

self-perceived health of older people in developed coun-

tries (22�24). In the developing world, studies of popula-

tion ageing have been focused primarily on Asia and

Latin America. In Tanzania there has been limited

research on explaining process of societal ageing and

impact on the health of older people, especially in rural

settings where people are most beset by poverty and poor

health conditions. This study aims to describe the impact

of ageing on the health status and well-being of older

people in a rural Tanzanian population using data

collected by the Ifakara Health Institute’s Health and

Demographic Surveillance System (HDSS) in collabora-

tion with the INDEPTH Network and the WHO Survey

on Adult Health and Global Ageing (SAGE). Our aim

was to provide a better understanding of the health and

well-being of older people in developing countries. The

resulting information will provide a baseline for examin-

ing the relationship between ageing and other health

outcomes during demographic transition in these settings.

This will help to raise awareness about the predicament of

older people, support possible policy interventions and

stimulate further research.

Fig. 1. Maps of Africa, Tanzania and the Ifakara HDSS area.

Health status and quality of life among adults in Tanzania


Design

Geography of the HDSS areaThe Ifakara HDSS area is located in southern Tanzania

in parts of the Kilombero and Ulanga districts, both in

the Morogoro region (latitude 8.08�8.68 S and longitude

35.98�36.68 E), as shown in Fig. 1. The Ifakara DSS

covers an area of 2,400 km2 in the Kilombero Valley.

The HDSS site was initiated in September 1996. A

baseline census was conducted between September and

December 1996 in 25 villages covering a population of

about 93,000 people living in 19,000 households. Since

January 1997, each household has been visited once every

4 months to record births and pregnancies, deaths and

migrations. In order to document community-based

causes of death, the HDSS has conducted verbal

autopsies since 2002.

The area is predominately rural with scattered house-

holds. Many local houses have brick walls but only 34%

have a corrugated iron roof. The main ethnic groups are

Wapogoro, Wandamba, Wabena, Wahehe and Wam-

bunga, with several other smaller groups. Most of the

inhabitants are Christian or Muslim. All residents speak

the Kiswahili language. Subsistence farming of maize,

rice and cassava occupies the majority of the population.

Fishing is also common both for local consumption and

shipping to other towns within the country.

Data collectionIn January 2007, all households with people aged 50 and

over were identified from the Ifakara HDSS database.

These households were subsequently visited to interview

these older people. The questionnaires and the consent

forms were translated to Kiswahili. All field workers were

trained for 3 days prior to conducting the interviews,

including 1 day of tool piloting. Surveys started in the

middle of January 2007 and ended in April 2007. During

field work, interviewers were closely supervised by field

supervisors who accompanied them on interviews, per-

formed spot-checks and re-interviewed where appropri-

ate. Also, desk checks on the completed questionnaires

were done to identify errors before computer data entry.

All questionnaires that raised queries were returned to

interviewers for clarification in the field. Data entry was

conducted using a double entry system in CSPro. Verbal

informed consent was obtained from all older people who

participated in this study. All individuals were inter-

viewed using the WHO-abbreviated survey instrument

short module adapted from the full SAGE questionnaire:

the health status and associated vignette questions plus

Activities of Daily Living (ADL)-type questions (follow-

ing the WHO Disability Assessment Scale version II

[WHODAS-II] model), and questions on subjective

well-being as measured by the 8-item version of the

World Health Organization Quality of Life (WHOQoL)

instrument. A copy of the INDEPTH WHO�SAGE

summary questionnaire is available as a supplementary

file. Additional data targeted for inclusion into the final

data set, derived directly from the HDSS, included socio-

demographic characteristics, such as age, sex, education,

marital status, socio-economic status and household

information, such as the household size.

Health status informationHealth status scores were calculated based on health

responses in eight health domains covering affect, cogni-

tion, interpersonal activities and relationships, mobility,

pain, self-care, sleep/energy and vision. Each domain

included at least two questions. Asking more than one

question about difficulties in a given domain provides

more robust assessments of individual health levels and

reduces measurement error for any single response item.

Item Response Theory (IRT) was used to score the

responses to the health questions using a partial credit

model which served to generate a composite health status

score (25, 26). An item calibration was obtained for each

item. In order to determine how well each item con-

tributed to common global health measurement, chi-

squared fit statistics were calculated. The calibration for

each of the health items was taken into account and the

raw scores were transformed through Rasch modelling

into a continuous cardinal scale where a score of zero

represents worst health and a maximum score of 100

represents best health. More details on the application of

the IRT approach to computing patient-reported health

outcomes are described in Chang and Reeve, and

Kyobungi (27�31). The IRT has been judged as among

the most efficient, reliable and valid methods to evaluate

measures of health (32�37).

Quality of life and well-beingIn this study we define quality of life as individual

perceptions of life in the context of local culture and

value systems, as well as in relation to goals, expectations,

standards and concerns. An 8-item version of the

WHOQoL instrument was used to assess perceived

well-being (38). This is a cross-culturally valid instrument

for comprehensively assessing overall subjective well-

being, yet is also very brief. It recognises that health

and quality of life are strongly associated yet distinct

concepts. Results from the 8-items were summed to get an

overall WHOQoL score which was then transformed to a

0�100 scale, similar to the health status score. The

WHOQoL instruments have been used in other studies

of older people in Africa (39, 40).

Functional status assessmentPersonal functioning was assessed through the standar-

dised 12-item WHODAS-II. It is a well-tested instrument,

with published psychometric properties, and a good

Mathew A. Mwanyangala et al.


predictor of global disability (41�43). The WHODAS is

compatible with the International Classification of

Functioning, Disability and Health (ICF) and contains

many of the most commonly asked ADL and Instru-

mental Activities of Daily Living (IADL) questions. The

WHODAS instrument also provides an assessment of

severity of disability. Results from the 12-items were

summed to get an overall WHODAS score, which was

then transformed to a 0�100 scale, with zero represent-

ing no disability. Since this scale runs counterintuitively

to the WHOQoL and health status scores, it was

inverted to a scale designated here as WHODASi, in

which 100 represents the best situation, i.e. no disability,

and which thus represents a measure of functional

ability.

Socio-economic status of householdsThe socio-economic status of households was assessed by

constructing a household wealth index based on house-

hold asset ownership, level of education of the head of

household and household characteristics, as proposed

and validated by Filmer (44). Data on asset ownership

were collected within the HDSS framework.

Data analysisData were analysed using Stata version 10. Simple cross-

tabulations and multivariate analysis were done to

describe the situation of ageing, health status, physical

disability, quality of life and well-being of older people.

The median values for health status, WHOQoL and

WHODASi were computed, and used to define cut-off

points for assessing good or poor status. Mean scores

were calculated for different sex and age groups. In order

to investigate the factors associated with health and

quality of life, univariate and multivariate models were

run. In both models, social and demographic variables

were fitted as possible explanatory variables. Principal

component analysis (PCA) was conducted on household

characteristics and asset ownership data to investigate

associations between these variables at the household

level. Wealth index quartiles were constructed to investi-

gate associations between health status and household

wealth.

ResultsA total of 8,206 older people from 3,914 households were

identified from the Ifakara DHSS. In visits, 63% were

successfully interviewed (n�5,131). The majority of non-

responders were men (52%) in the 50�59 age group. The

reasons for non-response included hearing impairment,

out-migration, refusal, death and absence during the day

of the interview. Characteristics of responders and non-

responders are shown in Table 1.

Among those interviewed, the majority were women

(n�2,668). The mean age of respondents was 62.5 years

with a standard deviation of 9.2. The majority of people

in this study were within the 50�59 age group, and 67% of

the respondents were married, while 39% of respondents

had no formal education. In the majority of households

(54%), less than 25% of household members were

50 years old or above. The mean size of households

where older people lived was 10.4 (standard deviation

6.0). Only 2% of households were composed solely of

older people living on their own.

Functional status assessment and quality of lifeThe mean and median quality of life scores (WHOQoL)

were 68.2 and 68.8, respectively, with the proportion below

the median decreasing with increasing age (Table 2). The

mean and median functional ability scores (WHODASi)

Table 1. Background characteristics of study subjects

Variables Respondents

(n�5,131)

Non-respondentsa

(n�3,075)

Sex (%)

Men 47.8 52

Women 52.2 48

Mean age (years) (SD) 62.6 (9.2) 61.3 (7.8)

Age group (years)

50�59 43.7 48.5

60�69 32.8 33.2

70�79 18.2 17.9

80 and over 5.3 0.3

Education level (%)

No formal education 39.3 41.4

Less than or equal to

six years

56.6 45.2

More than six years 4.1 13.3

Marital status (%)

Currently single 33.3 29.0

In current partnership 66.7 71.0

Socio-economic quartile (%)

Lowest quartile 19.2 19.6

Second quartile 19.4 23.7

Third quartile 21.1 19.9

Highest quartile 40.3 36.7

Mean no. of household

members (sd)

10.4 (6.0)

Percentage of household

members aged 50 years

and over

22.9

aIncludes those listed in the HDSS database who had out-

migrated or died prior to interview visit, and those who did not

respond for other reasons.



were 84 and 90, respectively. Functional ability was lower

among women than men in all age groups.

Distributions of health statusThe median health status score of the surveyed popula-

tion was 68.4. Health status was associated with age and

gender (Table 3). Poor health status was associated with

increasing age and among women.

Factors associated with poor quality of life andhealth statusOdds ratios for below-median quality of life and health

status showed significant associations with being female,

older and unmarried (Tables 4 and 5). Women were more

likely to report poor health as well as being scored for

lower quality of life than men. Lower quality of life was

also significantly associated with the two lower socio-

economic quartiles. However, no association between

socio-economic status and self-reported health was

evident in multivariate analysis controlling for other

factors (Table 5). Age composition within households

and education were not appreciably associated with either

quality of life or health status in multivariate analyses.

DiscussionThis study observed that among older adults men

reported better health status than women, and that

health status, quality of life and physical ability

deteriorated markedly with increasing age. This is in

line with empirical knowledge of the physiological

processes of ageing and linked to disease and ill health.

These results underscore the reality of existing gender

biases in relation to economic power, which may be the

product of lower levels of education and savings, and

the poorer life-time earning histories many women have

(45). The results are consistent with those reported

recently by the Tanzanian Ministry of Health and

Social Welfare, which found that older people make

up around one-third of all disabled people in Tanzania

(46). Higher quality of life and good health status was

associated with being married, a high level of education

and higher socio-economic status of the household.

This reinforces the hypothesis that individual health is

improved by education, possibly due to having greater

access to information on health, better eating habits

and self-care (47, 48).

These results reveal sex differences in longevity, with

larger numbers of women than men aged 50 and over,

despite their poorer health outcomes. The mean house-

hold size of 10 observed for households containing

older people in this study area is broadly reflective of

socio-cultural practices in rural areas of most countries

in sub-Saharan Africa, where older people tend to

live in extended family households rather than inde-

pendently (49). This is reflective of the current Tanza-

nia Ageing policy which prioritises family as the basic

institution of care and support for older people (50).

Few studies have been conducted on adult health and

ageing in Tanzania. The approach of assessing individual

health status based on self-reported health status has

been criticised by various scholars, and it has been

suggested that self-reported health status should not be

used to estimate disease prevalence and identify indivi-

duals with disease (47, 51). Thus, although the current

Table 2. Distribution of quality of life (WHOQoL) and func-

tional ability (WHODASi) outcomes by age and sex

Variables Men (n�2,463) Women (n�2,668)

Mean WHOQoL score (SD)

50�59 years 69.3 (5.6) 68.8 (6.6)

60�69 years 68.4 (5.9) 67.6 (6.9)

70�79 years 67.0 (7.3) 67.2 (9.4)

80 years and over 64.3 (7.1) 66.1 (11.7)

Percentage of respondents with WHOQoL less than median

50�59 years 28.8 37.0

60�69 years 39.1 50.3

70�79 years 52.8 59.7

80 years and over 67.9 71.2

Mean WHODASi score (SD)

50�59 years 90.4 (13.4) 87.5 (14.4)

60�69 years 87.1 (14.9) 82.2 (16.2)

70�79 years 80.5 (18.1) 74.0 (21.3)

80 years and over 68.4 (22.1) 59.0 (24.9)

Percentage of respondents with WHODASi less than median

50�59 years 35.0 43.9

60�69 years 45.2 61.2

70�79 years 62.0 73.5


Table 3. Distribution of self-reported health status outcomes

by age and sex


Mean health status score (SD)

50�59 years 74.5 (13.0) 72.1(12.1)

60�69 years 71.5 (12.2) 68.4 (10.3)

70�79 years 67.1 (11.2) 64.5 (11.0)

80 years and over 61.3 (10.2) 58.5 (9.2)

Percentage of respondents with health status less than median

50�59 years 34.8 41.3

60�69 years 43.8 54.2

70�79 years 60.0 66.8




study indicates a clear association between older people’s

perception of age and health, further medically based

studies are required to confirm the health burden of older

people in rural Tanzania. Following up this sample over

time would be useful to see how these data relate to

subsequent health outcomes.

Several studies have shown socio-economic status to be

associated with older people’s health status, quality of life

and well-being (52�54). However, the current study also

detected an association between household socio-

economic status and quality of life, but not between

wealth and self-reported health description. Similar

observations have been documented elsewhere (55), and

may be due to the fact that household asset-based wealth

indices can be unrelated to individual health status,

depending on which member of the household is head

and who owns assets (56).

Although Tanzania is the second country in Africa

to have a national Ageing policy, after Mauritius, many

issues related to older people are not yet fully defined.

For example, even in the National Strategy for Poverty

Reduction (57), older people are not fully considered.

Older people are widely recognised as being a valuable

source of information, knowledge and experience.

Thus, attempts should be made to consider and

improve their health status and quality of life within

this and other rural settings in Tanzania and other

developing countries.

ConclusionThe health status and quality of life of older people in

rural Tanzania is reduced significantly during the

ageing process. Perceptions of physical disability also

increase with age in this population. Poor quality of life

and well-being, and health status in older people are

significantly related to marital status, sex and age.

Specifically, quality of life decreases with age, and

women experience poorer quality of life and a greater

burden of physical disability than men. Thus, the

process of ageing presents a clear public health

challenge in this setting.

Table 4. Factors associated with below-median quality of life (WHOQoL)

Variables Univariate model (OR and 95% CI) p-value Multivariate model (OR and 95% CI) p-value

Sex

Men 1

Women 1.37 (1.22�1.53) pB0.001 1.27 (1.11�1.45) pB0.001

Age group (years)

50�59 1 1

60�69 1.63 (1.43�1.86) pB0.001 1.57 (1.38�1.80) pB0.001

70�79 2.60 (2.22�3.04) pB0.001 2.37 (2.01�2.80) pB0.001

80� 4.52 (3.44�5.92) pB0.001 4.33 (3.26�5.75) pB0.001

Education level

No formal education 1.63 (1.22�2.19) p�0.001 1.17 (0.86�1.60) p�0.315

Less than or equal to six years 1.46 (1.30�1.64) pB0.001 1.03 (0.76�1.39) p�0.845

More than six years 1

Marital status

Now single 1.62 (1.44�1.82) pB0.001 1.19 (1.04�1.37) p�0.010


Proportion aged 50 years and over in the same household (%)

B25 0.79 (0.63�0.98) p�0.035 0.92 (0.69�1.23) p�0.575

25�49 0.80 (0.63�1.00) p�0.049 0.96 (0.75�1.23) p�0.749

50�74 0.86 (0.65�1.13) p�0.272 1.05 (0.83�1.33) p�0.697

575 1 1

Socio-economic quartile

Lowest quartile 0.71 (0.61�0.82) pB0.001 0.71 (0.69�0.99) p�0.042

Second quartile 0.61 (0.52�0.71) pB0.001 0.62 (0.63�0.87) pB0.001

Third quartile 0.81 (0.70�0.94) p�0.006 0.75 (0.75�1.03) p�0.118

Highest quartile 1 1



Acknowledgements

We would like to thank the Kilombero and Ulanga district councils

for their support to the Ifakara HDSS. We extend our gratitude to

the leadership of Mlabani village for allowing us to pilot test the

survey tools. We highly appreciate the hard work and commitment

of the HDSS field and data management teams. We are indebted to

the respondents who voluntarily offered their time for interviews and

shared the useful information without which the survey would not

have been possible. We are thankful to the INDEPTH Network and

WHO Survey on Adult Health and Global Ageing (SAGE).

Conflict of interest and fundingFunding support for the HDSS was provided by the

Swiss Development Corporation, Norvatis Foundation,

USAID and the Tanzanian Ministry of Health and Social

Welfare, which is highly appreciated.

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*Mathew A. MwanyangalaIfakara Site Health InstituteP.O. Box 53, Ifakara, Morogoro, TanzaniaEmail: [email protected]



http://www.siteresources.worldbank.org/INTPRS1/Resources/TanzaniaPRSP(June-2005).pdf





The health and well-being of olderpeople in Nairobi’s slumsCatherine Kyobutungi1,2*, Thaddaeus Egondi1,2 andAlex Ezeh1,2

1African Population & Health Research Centre, Nairobi, Kenya; 2INDEPTH Network, Accra, Ghana

Background: Globally, it is estimated that people aged 60 and over constitute more than 11% of the

population, with the corresponding proportion in developing countries being 8%. Rapid urbanisation in sub-

Saharan Africa (SSA), fuelled in part by rural�urban migration and a devastating HIV/AIDS epidemic, has

altered the status of older people in many SSA societies. Few studies have, however, looked at the health of

older people in SSA. This study aims to describe the health and well-being of older people in two Nairobi

slums.

Methods: Data were collected from residents of the areas covered by the Nairobi Urban Health and

Demographic Surveillance System (NUHDSS) aged 50 years and over by 1 October 2006. Health status was

assessed using the short SAGE (Study on Global AGEing and Adult Health) form. Mean WHO Quality of

Life (WHOQoL) and a composite health score were computed and binary variables generated using the

median as the cut-off. Logistic regression was used to determine factors associated with poor quality of life

(QoL) and poor health status.

Results: Out of 2,696 older people resident in the NUHDSS surveillance area during the study period, data

were collected on 2,072. The majority of respondents were male, aged 50�60 years. The mean WHOQoL score

was 71.3 (SD 6.7) and mean composite health score was 70.6 (SD 13.9). Males had significantly better QoL

and health status than females and older respondents had worse outcomes than younger ones. Sex, age,

education level and marital status were significantly associated with QoL, while slum of residence was

significantly associated with health status.

Conclusion: The study adds to the literature on health and well-being of older people in SSA, especially those

in urban informal settlements. Further studies are needed to validate the methods used for assessing health

status and to provide comparisons from other settings. Health and Demographic Surveillance Systems have

the potential to conduct such studies and to evaluate health and well-being over time.

Keywords: Nairobi; slum settlements; older people; ageing; well-being; quality of life; INDEPTH WHO-SAGE






The proportion of older people is increasing world-

wide. Globally, it is estimated that people aged 60

and over currently constitute more than 11% of

the population; over 20% in developed nations and about

8% in developing ones. The proportion of older people

globally is expected to double to 22% by 2050 (1). In

Africa, people aged 60 and over account for only 5% of

the population; this is projected to increase to 11% by

2050 (2). In this study setting, people aged 60 and over

constituted 1.6% of the population, and those aged 50

and over constituted 4.9% of the population under

surveillance. It is estimated that people aged 60 and

over in Kenya as a whole constituted 4.0% of the total

population in 2005 and this proportion is expected to

increase to 4.5% by 2015 and to 9.3% by 2050 (3). Older

people will therefore form an increasingly important sub-

group in numeric terms in developing nations.

Older people have traditionally been held in high

esteem in many African societies for their wisdom, role

as heads of families and roles in conflict resolution. More


Global Health Action 2010. # 2010 Catherine Kyobutungi et al. This is an Open Access article distributed under the terms of the Creative CommonsAttribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, andreproduction in any medium, provided the original work is properly cited.

45



recently, older people have been involved in the fight

against HIV/AIDS, especially in their role as caregivers

for HIV-infected family members and orphans left behind

by deceased relatives. On the other hand, older people

have not been spared by the direct effects of HIV/AIDS.

A recent AIDS indicator survey in Kenya shows that the

HIV prevalence among the 50�54 years age group is 8%

(similar for both males and females). The prevalence for

females is similar to that in the 45�49 age group while for

males, the prevalence is higher in the 50�54 age group.

The HIV prevalence in urban areas is also higher than in

rural areas (8.9% vs. 7.0%) (4) and even higher (11.4%) in

the study area according to a recent survey (APHRC,

unpublished data). Apart from HIV/AIDS, older people

are also most affected by chronic degenerative diseases.

This implies that in Kenya and many other countries in

sub-Saharan Africa (SSA), older people most probably

bear a dual burden of disease.

Population ageing is occurring in a context of rapid

urbanisation in SSA. Africa is urbanising at a rate faster

than any other region in the world and by 2030 more than

half of the SSA population will live in urban areas (5).

The pace of urbanisation in many SSA countries has not

been matched by economic growth. In fact, in countries

like Kenya, urbanisation has been rapid amid economic

stagnation. This has resulted in an increase in the number

and size of informal settlements or slums in many cities.

It is estimated that more than 70% of urban residents in

SSA live in slum or slum-like conditions. In Kenya, this

percentage is about 71% (6). The informal nature of these

settlements means that they are underserved by the public

sector in the provision of basic amenities and services

including health, education, water and sanitation, and

garbage collection services. Slums are also characterised

by high levels of unemployment, overcrowding, insecur-

ity, greater involvement in risky sexual practices, social

fragmentation, and high levels of mobility (7�9). Studies

from different SSA countries have shown that slum

residents have worse health outcomes than their rural

counterparts (10�13). For example, childhood mortality

in poor urban areas of Zambia and Malawi is higher than

in rural and peri-urban areas (11, 14). Desperate living

conditions and lack of livelihood opportunities could

predispose residents to risky health-related behaviours

such as high alcohol consumption, unsafe sex, smoking

and other substance abuse. All these factors have adverse

effects on health which may be compounded by poor

access to health services.

Ageing in an urban setting, especially a slum settle-

ment, poses its own challenges. These include weak social

networks, neglect and loss of respect and stature that are

often accorded older people in more stable communities.

It should be expected, therefore, that older people in slum

settlements have poor or even poorer health outcomes

just like other sub-populations therein.

As the HIV/AIDS pandemic rages in SSA and as slums

grow in a rapidly urbanising continent, it is important

that the impact of these processes on older people is

assessed and addressed. The intersection between the

HIV/AIDS pandemic, population ageing and uncon-

trolled urbanisation in SSA will have far-reaching con-

sequences on the social, economic and health spheres of

societies.

Despite the evident need to understand issues that

affect older people in SSA, relative to other demographic

trends, ageing in Africa has only recently started receiving

attention in research and policy-making. There is a near

absence of policies and programmes targeting older

people in most countries in SSA (15), and Kenya is no

exception. Health policies and programmes are geared

towards the traditional vulnerable groups of women of

reproductive age and children. The current National

Health Sector Strategic Plan however recognises that

older people have special needs that are different from

other adults and hence spells out specific interventions

for older people (16). In addition to regular curative and

preventive services, such interventions include annual

screening and provision of curative services for degen-

erative diseases, and counselling for lifestyle changes. It

remains to be seen whether these interventions have been

translated into real programmes that serve older people

in health facilities.

The fact that older people have been long neglected in

many policies and programmes in Kenya means that

there is a dearth of research on their health and well-

being. This study therefore aims to fill the gap in ageing

research in Africa by describing the health and well-being

of older people living in two Nairobi slums.

Methods

Study settingThe study was conducted in two slum communities where

the African Population and Health Research Centre

(APHRC) is implementing the longitudinal Nairobi

Urban Health and Demographic Surveillance System

(NUHDSS). The NUHDSS covers large parts of the two

slums of Korogocho and Viwandani in Nairobi City,

Kenya’s capital and commercial centre. Both commu-

nities are informal settlements located about 5�10 km

from the city centre. The population under surveillance as

of 1 January 2007 was 59,513 individuals living in 21,993

households.

The NUHDSS started after an initial census in August

2002. Since January 2003, data on core demographic

events (births, deaths, in- and out-migrations) have been

collected and updated every 4 months during routine

Health and Demographic Surveillance System (HDSS)

rounds.

Catherine Kyobutungi et al.


Data collectionData for this study was collected from all residents of

the NUHDSS who were aged 50 years and over as of

1 October 2006. Eligible participants (n�2,696) were

identified from the most up-to-date NUHDSS database

at the time. Data were collected on 2,072 respondents

who had complete interviews and only these were

included in the analysis. Out of the 624 who were not

interviewed, 102 refused to be interviewed, 27 had died,

213 had out-migrated and no contact was made with the

rest for various reasons including absence of a competent

respondent, entire household absent for prolonged peri-

ods and unknown whereabouts. The final response rate

was 84.4% after omitting the 240 older people later found

to have died or out-migrated.

Data were collected in the framework of a larger

study on the linkages between urbanisation, migration,

poverty and health over the life course. An interviewer-

administered questionnaire was used to collect data.

Interviewers had a minimum education level of Form 4

(12 years of schooling) and were residents in the NUHDSS

area. They were trained over a five-day period followed by

two days of field testing. Each group of five interviewers

was supervised by a team leader who manually edited all

completed forms, conducted random spot checks on at

least 5% of forms filled by each field worker under his/her

supervision, and offered additional training whenever

necessary.

Self-reported health status was assessed using the short

form of the individual SAGE (Study on Global Ageing

and Adult Health) questionnaire, available as a Supple-

mentary File to this paper. Details of how this tool was

developed, validated and adapted for use in this survey

are described elsewhere (17). In brief, this form has

sections on health status descriptions in eight domains of

health including mobility, self-care, affect, vision, pain

and discomfort, sleep/energy, interpersonal activities and

cognition. Typically, questions ask about how much

difficulty the respondent had had in the preceding

30 days with tasks or activities in the eight domains.

Responses range from no difficulty to extreme difficulty

on a five-item scale. In addition, the SAGE form has

questions on functioning assessment using items in the

Activities of Daily Living / Instrumental Activities of

Daily Living (ADL/IADL) tool as well as on Subjective

Well-being and Quality of Life (QoL).

This paper focuses on two measures of self-reported

health status: QoL and health status scores. The QoL was

assessed using the World Health Organization Quality of

Life tool (WHOQoL) score, on a scale from 0 to 100

where 100 is the best QoL. Details of how this is

computed are described elsewhere (17). Health status

scores were computed using Item Response Theory (IRT)

parameter estimates in Winsteps†, a Rasch measurement

software package (http://www.winsteps.com). More de-

tails on how scores for this study were derived are

provided elsewhere (17). In brief, IRT uses Maximum

Likelihood Estimation methods to model the relationship

between a person’s health status and their probability of

responding to each question in a multi-item scale. Each

item is modelled to have a set of parameters which

describe the relationship between the item and the

measured construct as well as how the item functions

within a population. The health score is then transformed

to a scale of 0 to 100 (where 100 is the best health status).

More details on the application of the IRT approach to

computing patient-reported health outcomes are avail-

able in the paper by Chang and Reeve (18).

Statistical analysisDescriptive analyses were conducted for both measures of

health. For WHOQoL, mean scores were computed for

different categories of respondents. The different cate-

gories include: sex (male, female), age (age groups: 50�59,

60�69, 70�79, 80�), educational level (no formal educa-

tion, up to 6 years of formal education, more than 6 years

of education), marital status (in current partnership,

never married, separated, divorced and widowed), wealth

index (quintiles), whether respondent stays alone (Yes,

No) and proportion of people aged 50 years and over in

the same household (B25%, 25�49%, 50�74%, 75%�). In

addition, the proportion of respondents in each category

with a WHOQoL score less than the median was

computed. For the health status score, mean scores

were also calculated and the proportion of respondents

falling below the overall median score was calculated for

each category of respondents.

Exploratory analyses were conducted to determine the

factors associated with poor QoL and poor health status.

For both measures of health, respondents who had scores

below the median were categorised as having poor QoL

or poor health, respectively.

In order to investigate the effect of non-response, we

fitted a logistic regression model using response status as

the outcome and key socioeconomic and demographic

characteristics as explanatory variables. A completed

interview was defined as response while an incomplete

interview for a participant determined to be resident in

the study area at any time during the survey was

considered non-response. Gender, education and wealth

index were found to be associated with non-response. The

predicted probability of responding was calculated for

every individual in the data using the fitted model. Once

the predicted probability was calculated, its inverse

became the weight for that observation. The computed

weights were re-adjusted to approximately add up to the

sample size. These weights were included in subsequent

univariate and multivariable logistic regressions using the

categorical health outcomes described above to adjust for

non-response. The variables found to be associated with



http://www.winsteps.com

non-response were also included in the model as pre-

dictors. Results are presented for the models adjusted for

non-response.

ResultsThe descriptive characteristics of the study participants

are shown in Table 1. The characteristics of non-

respondents are also shown. Demographic characteristics

for non-respondents were obtained from the existing

NUHDSS database. Marital status for non-respondents

could not be established since this variable is not

routinely collected and may change over time. There

were no major differences between respondents and non-

respondents except for wealth index, where a larger

proportion of non-respondents fell in the poorest wealth

quintile compared to respondents, and living arrange-

ments, whereby a quarter of the respondents were staying

alone compared to more than a third of non-respondents.

These differences were both statistically significant (pB

0.001). Among both respondents and non-respondents,

there were more males than females and the majority of

respondents were in the 50�59 year age group. A majority

of the respondents had at least six or more years of

schooling. The average number of household members

for the respondents was about four members per house-

hold compared to about three for non-respondents.

The distribution of WHOQoL and health statusscoresThe distribution of WHOQoL and health status scores is

shown in Table 2. The median values used as cut-offs

were 71.9 for WHOQoL and 67.5 for health status. The

higher the WHOQoL score, the better the QoL, and the

higher the health status scores, the better the health

status. The mean WHOQoL score was lower for older

Table 1. Background characteristics of study subject (re-

spondents and non-respondents)

Variables

Respondents

(N�2,072)

Non-respondents

(N�384)

Sex (%)

Men 1,327 (64.4%) 302 (79.1%)

Women 745 (36.0%) 80 (20.9%)

Mean age (SD) 59.2 (9.06) 57.1 (7.5)

Age group

50�59 years 1,358 (65.4%) 283(73.9%)

60�69 years 458 (22.1%) 69 (18.0%)

70�79 years 163 (7.9%) 23 (6.0%)

80 years and over 93 (4.5%) 8 (2.1%)

Education level (%)

No formal education 571 (28.7%) 77 (21.4%)

Less than or equal

to 6 years

562 (28.2%) 81 (22.5%)

More than 6 years 858 (43.2%) 202 (56.1%)

Marital status (%)

Now single 662 (32.0%) �

In current partnership 1,410 (68.1%) �

Wealth index (%)

First quintile (Poorest) 518 (25.0%) 177 (46.3%)

Second quintile 206 (10.0%) 6 (1.6%)

Third quintile 514 (24.8%) 16(4.2%)

Fourth quintile 453 (21.9%) 69 (18.1%)

Fifth quintile (Least poor) 380 (18.4%) 114 (29.8%)

Mean number of household

members (SD)

4.12 (3.19) 3.0 (2.5)

Proportion of household

members aged 50

years and over (SD)

0.52 (0.34) 0.62 (0.3)

Stays alone

Yes 496 (24.0%) 140 (36.5%)

No 1,576 (76.0%) 244 (63.5%)

Site of residence (%)

Korogocho 1,462 (70.6%) 214 (55.7%)

Viwandani 610 (29.4%) 170 (44.3%)

Table 2. Distribution of WHOQoL and Health Status Scores

by age and sex

Variables Men (n�1,331) Women (n�747)


50�59 years 73.1 (5.8) 70.9 (6.3)

60�69 years 71.9 (6.4) 68.3 (6.6)

70�79 years 71.1 (6.2) 65.7 (7.2)

80 years and over 67.3 (9.1) 63.8 (8.5)

Proportion of respondents with WHOQoL below the median

50�59 years 32.0% 45.8%

60�69 years 43.9% 64.6%

70�79 years 51.9% 79.8%

80 years and over 71.1% 78.2%


50�59 years 74.7 (13.9) 69.7 (12.5)

60�69 years 71.0 (12.9) 63.9 (10.6)

70�79 years 69.0 (13.3) 60.2 (10.6)

80 years and over 59.3 (15.9) 56.6 (10.9)

Proportion of respondents with health status score below the

median

50�59 years 33.3% 50.8%

60�69 years 49.1% 73.5%

70�79 years 46.8% 83.3%

80 years and over 79.0% 90.9%



respondents but with some sex differences. Female

respondents had, on average, appreciably lower WHO-

QoL scores than their male counterparts in the same age

group. Similar effects were observed when the propor-

tions of respondents with a WHOQoL score below the

median were considered, with poorer QoL associated

with women and older age groups.

A similar pattern to that observed for WHOQoL

scores was observed with the health status scores. The

average health status scores decreased with increasing age

and females have lower scores than males, indicating

worse health status. The proportion with health status

scores below the median increased with age, particularly

among females.

The results for the two measures of self-reported health

status consistently showed that health status and QoL

deteriorated in both sexes as people got older and that

females had significantly worse health outcomes than

males.

Factors associated with poor QoL and poorhealth statusBoth univariate and adjusted logistic regression results

using WHOQoL as the outcome are presented in Table 3.

Male respondents were significantly less likely to have

poor WHOQoL compared to females in the univariate

models. However in adjusted models, this effect was

attenuated and was of borderline statistical significance.

An age gradient, consistent with the descriptive results, is

observed in the logistic regression models. In adjusted

models, the oldest respondents (80�) had almost three

times the risk of having poor QoL as the youngest

respondents (50�59 years). An education gradient was

also observed whereby individuals with no education or

less than 6 years of education were more likely to report

poor QoL compared to those with more than 6 years of

education. This association was significant in both

univariate and adjusted models. Marital status was found

to be associated with QoL. Respondents who were in

some kind of partnership were least likely to report poor

QoL. Separated and widowed respondents had signifi-

cantly worse QoL than those in partnership. There was

no significant relationship between the proportion of

older people living in a household and QoL. Wealth

index had an inverted-V relationship with QoL. In

adjusted models, respondents in the poorest and least

poor quintiles had similar odds of reporting poor QoL

while those in the second quintile had higher odds of

poor QoL. Only the odds ratio for being in the second

quintile approached statistical significance.

The results on factors associated with poor self-

reported heath state are presented in Table 4. Poor health

status was associated with gender, age, educational level

and marital status among older people. As observed with

QoL, male respondents were less likely to report poor

health as compared to female counterparts (Adjusted

odds ratio: 0.69, 95% CI: 0.54�0.89) and the oldest

respondents were close to six times as likely to report

poor health as the youngest in adjusted models. Indivi-

duals with no formal education were more likely to

report poor health compared to those with more than

6 years of education. Individuals who were never married

were almost twice as likely to report poor health status

compared to those who were in partnership while

Table 3. Factors associated with poor quality of life

Variables

Univariate model

(OR and 95% CI)

Multivariate model

(OR and 95% CI)

Site

Viwandani 0.59 (0.49�0.72) 0.85 (0.68�1.07)

Korogocho 1.00 1.00

Sex

Men 0.44 (0.36�0.53) 0.78 (0.61�1.01)

Women (Ref) 1.00 1.00

Age group

50�59 years 1.00 1.00

60�69 years 1. 97 (1.59�2.45) 1.55 (1.22�1.96)

70�79 years 3.59 (2.48�4.95) 2.06(1.40�3.02)

80 years and over 5.42 (3.33�8.81) 2.94 (1.71�5.02)

Education level

No formal

education

3.07 (2.46�3.82) 1.68 (1.29�2.18)

Less than or equal

to 6 years

1.73 (1.39�2.16) 1.25 (0.98�1.60)

More than 6 years

(Ref)

1.00 1.00

Marital status

In current

partnership (Ref)

1.00 1.00

Never married 1.63 (1.04�2.54) 1.17 (0.71�1.92)

Separated 2.12 (1.47�3.04) 1.55 (1.04�2.31)

Divorced 2.31 (1.40�3.80) 1.52 (0.87�2.64)

Widowed 2.79 (2.20�3.52) 1.52 (1.12�2.07)

Proportion aged 50 years and over in the same household

B25% 0.96 (0.76�1.20) 1.03 (0.80�1.34)

25�49% 0.96 (0.76�1.21) 1.01 (0.78�1.31)

50�74% 0.68 (0.53�0.88) 0.72 (0.54�0.96)

]75% (Ref) 1.00 1.00

Wealth Index

First quintile 0.96 (0.73�1.26) 1.01 (0.74�1.37)

Second quintile 2.18 (1.61�2.95) 1.37 (0.98�1.91)

Third quintile 1.46 (1.10�1.93) 1.22 (0.90�1.65)

Fourth quintile 1.29 (0.98�1.71) 1.06 (0.78�1.44)

Fifth quintile (Ref) 1.00 1.00



widowed individuals were 1.6 times more likely. The

wealth index and proportion of people aged 50 years

and over in the household were not significantly asso-

ciated with reported health status.

DiscussionKenya, like many SSA countries, has been hard hit by the

HIV/AIDS epidemic. During the 1980s, Kenya’s child

mortality declined steadily until the 1990s, when a

reversal in the trend was observed (19). The reversal in

childhood mortality coincided with an economic crisis

and could have been exacerbated by the growth of the

HIV/AIDS epidemic. As a result, Kenya is a country still

in the early stages of the health transition. However, as

non-communicable diseases gain a foothold in SSA, it is

unlikely that the country will follow a uni-directional

path towards the second and third stages of the health

transition. While there is paucity of data on the

magnitude of the non-communicable disease burden in

the country, studies show that the prevalence of risk

factors for these illnesses is increasing (20). Within the

study setting, there is a high mortality burden from HIV/

AIDS (21) but in the absence of morbidity studies, it is

hard to quantify the extent to which the country could be

enduring a dual burden of disease characterised by high

mortality and morbidity from both infectious diseases

and non-communicable diseases as has been suggested.

The proportion of older people in the study area is

lower than the national estimate (3) and this is due to the

fact that more young people in the economically produc-

tive age groups migrate and stay in the city to find work

and economic opportunities. For similar reasons, in

all age groups except the population under 15 years, the

number of males is more than double that of females in

the study area. Since migrants into the NUHDSS

constitute a very large proportion of residents, sex

differences are even greater at older ages since older

females are less likely to migrate and historically, more

males migrated to cities. These reasons partly explain why

we have a high proportion of older people (25%) staying

alone. Other reasons for this observation may include

widowhood especially among females, divorce or separa-

tion or split households where other family members are

left in rural areas while the older person works in the city

(22). The study area has a sex and age distribution which

is unlike the national one but is similar to the distribution

for Nairobi city (Fig. 1). The population pyramids in

Fig. 1 (a) and (b) both show a predominance of the 20�29

year age groups among males and females and significant

narrowing of the pyramid after the age of 50 years which

is more pronounced among females.

The sex and age distribution is also different between

the two slums because Viwandani slum, being near the

industrial area, is mostly inhabited by migrant male

labourers seeking job opportunities in the surrounding

industries. Older people who are less likely to find

employment in the industries are therefore less likely to

reside in Viwandani and prefer Korogocho and other

slums where they are mostly engaged in informal

businesses.

Qualitative research in the Nairobi slums where the

study was conducted shows that older people play several

important roles in society. They are considered fair

arbitrators in disputes within families and in the com-

munity. They are also considered to have a wealth of

experience and wisdom and hence their advice is sought

Table 4. Factors associated with poor health status

Variables

Univariate model

(OR and 95% CI)

Multivariate model

(OR and 95% CI)

Site

Viwandani 0.38 (0.31�0.46) 0.50 (0.40�0.63)

Korogocho 1.00 1.00

Sex

Men 0.36 (0.30�0.43) 0.67 (0.52�0.86)

Women 1.00 1.00

Age group

50�59 years 1.00 1.00

60�69 years 2.32 (1.86�2.88) 1.83 (1.43�2.34)

70�79 years 3.06 (2.17�4.31) 1.73 (1.17�2.60)

80 years and over 9.47 (5.20�17.26) 5.66 (3.00�10.69)

Education level

No formal

education

3.27 (2.62�4.08) 1.50 (1.16�1.96)


6 years

1.77 (1.42�2.20) 1.19 (0.94�1.52)

More than 6 years 1.00 1.00

Marital status

In current partnership

(Ref)

1.00 1.00

Never married 2.86 (1.79�4.56) 1.88 (1.10�3.19)

Separated 1.91 (1.33�2.74) 1.24 (0.82�1.89)

Divorced 2.42 (1.46�4.01) 1.45 (0.83�2.53)

Widowed 3.48 (2.72�4.43) 1.59 (1.16�2.18)

Proportion aged 50 years and over in the same household

B25% 1.09 (0.86�1.37) 1.10 (0.80�1.43)

25�49% 1.08 (0.85�1.36) 1.11 (0.85�1.46)

50�74% 0.92 (0.71�1.18) 0.97 (0.72�1.29)

]75% 1.00 1.00

Wealth index

First quintile 0.78 (0.60�1.03) 1.02 (0.75�1.40)

Second quintile 1.78 (1.32�2.40) 1.12 (0.80�1.57)

Third quintile 1.31 (1.00�1.73) 1.05 (0.77�1.42)

Fourth quintile 1.16 (0.88�1.52) 0.88 (0.65�1.19)

Fifth quintile 1.00 1.00



on various issues. Older people are also perceived as

important in community development initiatives where

they provide leadership and counsel though they are also

perceived by some as gatekeepers and impediments to

development. During community crises, they play a

leading role in mobilising the community (22). These

roles are in addition to more traditional roles of heads of

household, breadwinners and care givers for grandchil-

dren. However, older people are also more vulnerable

in these settings due to altered family structures and

living arrangements. Almost 25% of the respondents live

alone and are therefore more likely to be deprived of

social support structures. The HIV/AIDS epidemic in

SSA has also led to an increased number of orphans,

most of whom are cared for by grandparents who are

likely to be older people (23). In the study area, 19.5%

of respondents were looking after children below the

age of 15 years. Out of these 1,019 children, 770 were

either orphans or their parents’ whereabouts were un-

known.

(a) Study site: Korogocho and Viwandani, 2002a (b) Nairobi City, 1999b

(c) National population pyramid for Kenya, 1999b

aSource: APHRC NUHDSS data.

bSource: Ref. (22).

0–4

10–14

20–24

30–34

40–44

50–54

60–64

70–74

80+

Percentage

Males Females

0–4

10–14

20–24

30–34

40–44

50–54

60–64

70–74

80+

Males Females

Percentage

Males Females

0–4

10–14

20–24

30–34

40–44

50–54

60–64

70–74

80+

Males Females

80+

Percentage

Males Females

10.0 8.0 6.0 4.0 2.0 0.0 2.0 4.0 6.0 8.0 10.010.0 8.0 6.0 4.0 2.0 0.0 2.0 4.0 6.0 8.0 10.0

10.0 8.0 6.0 4.0 2.0 0.0 2.0 4.0 6.0 8.0 10.010.0 8.0 6.0 4.0 2.0 0.0 2.0 4.0 6.0 8.0 10.0

Fig. 1. Population pyramids for the study area, Nairobi City and the whole of Kenya



Older people in many parts of SSA have been engaged

in efforts to mitigate the effects of HIV/AIDS due to the

increased mortality of people in the reproductive and

more economically productive age groups. The high HIV/

AIDS and tuberculosis burden in the study area (20)

means that the chronic ill-health associated with these

conditions has led to role reversal whereby older people

are providing care to their ill and dying family members.

About 7% of respondents were caring for someone with a

prolonged illness at the time of the interview while another

6% had cared for someone in the past 3 years. Such

responsibilities, coupled with economic adversity, may

negatively affect the health and well-being of older people.

With respect to the specific findings, in both univariate

and multivariate analysis for the measure of self-reported

health, females have worse outcomes than males at all age

groups; these deteriorate, as expected, with age. Older

female disadvantage in health status has been described

in industrialised country settings (24�26), and so our

findings add to the body of evidence supporting this

association.

Korogocho respondents have significantly worse health

outcomes than Viwandani residents. Other studies in the

NUHDSS have shown similar findings in other age

groups but it is unclear what the underlying reasons are

since both slums have poor environmental sanitation and

poor access to social services. Viwandani is however

inhabited by mostly labour migrants seeking employment

in the nearby industrial area and hence there are more

employment opportunities. In addition, a larger propor-

tion of residents in Viwandani stay for short periods and

then move on compared to Korogocho. It is possible that

residents do not stay long enough to be exposed to the

hazardous slum environment or that, in the Viwandani

cash-based economy, economically unsuccessful migrants,

who could potentially have worse outcomes, move else-

where and leave behind the more successful ones. This is

apparent in the characteristics of non-respondents, who

are more likely to be from Viwandani and also more

likely to be in the poorest wealth quintile. A migrant

tracking study that assesses reasons for migration out of

the slums and post-migration economic and health status,

while logistically extremely challenging, would be helpful

in clarifying these issues.

As expected, a clear age gradient is observed for both

measures of health status; however the gradient is steeper

for the self-reported health status than for QoL. Marital

status has a significant effect on health outcomes though

the pattern of the effect differs for the two health outcomes.

In both cases, married respondents or those in partnership

have better health outcomes than other respondents. The

relationship between being married and well-being has

long been established (26, 27), albeit with other health

outcomes, as has the association between poor health

outcomes and widowhood and never married status.

The association between wealth index and QoL is an

inverted V-shape but this variable had no significant

association with reported health status. This could be

explained by the lower response rates among the poorest

wealth quintiles compared to other quintiles. On the other

hand, in an environment with high levels of deprivation, it

is possible that differences in wealth are marginal in real

terms and have no tangible impact on health outcomes.

Self-reported measures of health status have not been

widely used in SSA in general nor in Kenya in particular.

Their validity as a measure of health has therefore not

been established, but the finding of steep age and

education gradients with worse female health scores

point to a good degree of internal validity.

It is known that the validity of self-reported measures

of health and their reliability are influenced by underlying

socio-cultural factors including basic and health literacy,

cultural perceptions of illness, disability and health status

among others (28, 29). Further studies including vign-

ettes should investigate the influence of such factors on

the validity of self-reported health in this population. On

the other hand, the longitudinal framework offered by

demographic surveillance sites offers a unique opportu-

nity to validate these measures by assessing their perfor-

mance against objective measures of health and in

predicting mortality.

The absence of similar studies in the country and in the

region makes it hard to interpret some of the findings.

However, comparison with findings from other HDSS

sites may shed more light. Other important research

questions include the coping strategies and factors

associated with resilience and healthy ageing among older

people in resource-deprived settings as well as coping

strategies in the absence of strong contributory national

social security funds.

The study adds to the limited body of literature

regarding health and well-being of older people in SSA

and especially those in urban informal settlements.

Further studies are needed to validate the methods used

for assessing health status and to provide comparisons on

which the health of the older urban poor can be judged.

Acknowledgements

This research uses data partly collected under the Urbanisation,

Poverty and Health Dynamics (UPHD) Research Programme in the

Nairobi Urban Health and Demographic Surveillance System

(NUHDSS). We are also grateful to the WHO-SAGE group for

availing the SAGE instrument which was used in data collection and

for their support in the analysis and interpretation of the data. We

also wish to acknowledge the contribution of the APHRC’s

dedicated field and data management teams, and the residents of

Korogocho and Viwandani for their continued participation in the

NUHDSS.



Conflict of interest and fundingThe UPHD Research Programme is funded by the

Wellcome Trust UK (grant number GR078530AIA).

Work in the NUHDSS has been supported by grants

from the William and Flora Hewlett Foundation and the

Rockefeller Foundation. We acknowledge funding from

the National Institutes of Health which enabled us to

collect the data on health status assessment.

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*Catherine KyobutungiAfrican Population & Health Research CenterLongonot Road, Upper HillP.O. Box 10787, GPO 00100, Nairobi, KenyaTel: �254 20 2720400Fax: �254 20 2720380Email: [email protected]



http://www.un.org/esa/population/publications/ageing/ageing2009.htm

http://www.un.org/esa/population/publications/ageing/ageing2009.htm

http://www.globalaging.org/elderrights/world/2008/africa.pdf

http://www.globalaging.org/elderrights/world/2008/africa.pdf

Self-reported health and functionallimitations among older people in theKassena-Nankana District, GhanaCornelius Debpuur1,2*, Paul Welaga1,2, George Wak1,2 andAbraham Hodgson1,2

1Navrongo Health and Demographic Surveillance System, Navrongo, Ghana; 2INDEPTH Network,Accra, Ghana

Background: Ghana is experiencing significant increases in its ageing population, yet research on the health

and quality of life of older people is limited. Lack of data on the health and well-being of older people in the

country makes it difficult to monitor trends in the health status of adults and the impact of social policies on

their health and welfare. Research on ageing is urgently required to provide essential data for policy

formulation and programme implementation.

Objective: To describe the health status and identify factors associated with self-rated health (SRH) among

older adults in a rural community in northern Ghana.

Methods: The data come from a survey on Adult Health and Ageing in the Kassena-Nankana District

involving 4,584 people aged 50 and over. Survey participants answered questions pertaining to their health

status, including self-rated overall health, perceptions of well-being and quality of life, and self-reported

assessment of functioning on a range of different health domains. Socio-demographic information such as

age, sex, marital status and education were obtained from a demographic surveillance database.

Results: The majority of older people rated their health status as good, with the oldest old reporting poorer

health. Multivariate regression analysis showed that functional ability and sex are significant factors in SRH

status. Adults with higher levels of functional limitations were much more likely to rate their health as being

poorer compared with those having lower disabilities. Household wealth was significantly associated with

SRH, with wealthier adults more likely to rate their health as good.

Conclusion: The depreciation in health and daily functioning with increasing age is likely to increase people’s

demand for health care and other services as they grow older. There is a need for regular monitoring of the

health status of older people to provide public health agencies with the data they need to assess, protect and

promote the health and well-being of older people.

Keywords: self-reported health status; functional limitations; older people; INDEPTH WHO-SAGE; adult health; Kassena-

Nankana District; Ghana



under Reading Tools online). To obtain a password for the dataset, please send a request with ‘‘SAGE

data’’ as its subject, detailing how you propose to use the data, to [email protected]


Although population ageing is often associated

with industrialised societies such as Europe,

America and Japan, the phenomenon is gradu-

ally gaining attention in the developing world. Advances

in public health and the associated improvement in life

expectancy has increased the proportion of the aged

population in the developing world. It is expected that the

proportion of older people will grow rapidly in many

parts of the developing world, including sub-Saharan

Africa (1, 2). The rapid growth of the aged population

poses various challenges. Chronic diseases and disability

are disproportionately high among older people. Thus, a


Global Health Action 2010. # 2010 Cornelius Debpuur et al. This is an Open Access article distributed under the terms of the Creative CommonsAttribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, andreproduction in any medium, provided the original work is properly cited.

54



growing elderly population will increase the demand for

health care and other social services. Due to their low

economic development, inadequate health infrastructure

and limited social security programmes, meeting the

needs of older people in the developing world and

especially in sub-Saharan Africa is and will be difficult.

This is likely to be compounded by the erosion of the

traditional family support systems for older people.

Policies and programmes that address the health and

other needs of the growing aged population are urgently

needed to ensure successful ageing and functional in-

dependence of the aged.

However, health research in developing countries

(including Ghana) has been and continues to be heavily

focused on younger population groups. As such, the

extent of ageing, the health needs of the ageing popula-

tion, as well as the implications of national policies for

the health and welfare of the aged are poorly understood

and yet to be well appreciated. Questions about changing

health over the life course and compression of morbidity

demand an empirical basis for analysis, particularly in the

context of planning and preparing social protection

mechanisms (health and pension systems) to meet the

demands of this growing population group.

The ongoing World Health Organization’s global Study

on Adult Health and Ageing (SAGE) provides an

important platform for generating empirical data on

ageing and health transition for policy formulation and

programme implementation, especially in sub-Saharan

Africa. Four African countries � Ghana, South Africa,

Tanzania and Kenya � are participating in this pro-

gramme of research, and have conducted various surveys

on ageing and adult health using comparable instruments.

This article draws on data from a survey conducted in the

Kassena-Nankana District of northern Ghana as part of

this global programme of research on ageing.

We describe the health status of older people based on

their own reports on various aspects of their health. We

then examine factors associated with self-rated health

(SRH) among older people. In particular, we examine

whether perceived disability in various activities of living

influences rating of one’s health status. The social,

ecological and economic circumstances of the district

are more representative of the northern ecological zone of

Ghana as well as other Sahelian populations to the north

of Ghana than of the southern and coastal zones of the

country (3). The results of this study therefore have

relevance for our understanding of the health of older

people in Ghana and beyond.

Methods

The settingThe Kassena-Nankana District1 (KND) in the Upper

East region of Ghana is located at the northern-most part

of the country and shares a boundary with Burkina Faso

to the north. Since 1993, the Navrongo Health Research

Centre (NHRC) has been operating a demographic

surveillance system in this area. The district lies between

latitudes 10.5 and 11.08 N and longitudes 1.0 and 1.58 W

(4). The land is relatively flat and covers an area of 1,675

km2, with altitude of between 200 and 400 m above mean

sea level. Located in the Guinea savanna belt, the ecology

of the study area is typically Sahelian, with a short rainy

season from April to September and a prolonged dry

season from October to March. The mean annual rainfall

is about 1,300 mm, with the heaviest rains occurring in

August. Monthly temperatures range from 20 to 408C,

with the mean annual minimum and maximum being 22.8

and 34.48C, respectively.

Data from demographic surveillance estimated the

population of the district as at end of June 2007 to be

147,536 with females constituting 53%, giving a M:F

ratio of 0.89. About 38% of the population is under

15 years old, while those aged 65 and over constitute

4.7%. This gives a dependency ratio of 74.5%. The district

is largely rural with dispersed settlements. There are two

main ethnic groups � the Kassenas and the Nankanas �with other ethnic groups forming about 5% of the

population. Although mortality and fertility are high,

there have been declines since the 1990s. For instance, the

crude death rate declined from 18.7 to 10.4 per 1,000

between 1997 and 2007, while the crude birth rate fell

from 29.4 to 26.2 per 1,000 and the total fertility rate

from 5.0 to 4.0 during the same period.

The economy of the district is largely agrarian with

about 90% of the population dependent on subsistence

agriculture. Major crops grown are cereals such as millet,

maize, sorghum and rice. The Tono irrigation dam as well

as several dug-out dams in various communities facilitate

irrigated farming and dry-season gardening. Rearing of

animals like cattle, goats, sheep and poultry form part of

the agricultural activities. Due to the dependence on

agriculture and declining agricultural yields, poverty is

endemic in the area. The district has a poor road network

and transportation in many parts is limited to bicycles

and occasional vehicles. Typically, movement within

communities is by foot and use of bicycles. Recently,

however, there has been an increase in the use of motor

bikes, especially in the urban part of the district.

Health facilities in the district include one hospital

(located in Navrongo), six health centres, three clinics and

several chemist’s shops. In addition to these static health

facilities, community health officers have been deployed

to several communities to offer door-to-door services to

the people. As part of recent efforts to promote access to

basic health services, a national health insurance scheme

has been instituted and district mutual health insurance

schemes are operational in all districts of the country.

The main causes of morbidity in the study area are

Self-reported health and functional limitations among older people in Ghana


malaria, gastroenteritis and acute respiratory infection.

Periodic outbreaks of epidemic meningococcal meningitis

have been recorded in the district. Service provision data

suggest an increasing prevalence of hypertension and

diabetes, and there is need for more systematic docu-

mentation of the type and prevalence of non-communic-

able diseases among adults. The Adult Health and

Ageing study being implemented in the district as part

of the INDEPTH WHO�SAGE initiative will contribute

towards highlighting the health situation of adults and

inform health care delivery in the district. The survey

reported here is the first district-wide population-based

survey of adults to collect information on self-reported

health status among persons aged 50 and over, and thus

provides baseline data for monitoring and evaluating

adult health.

DataThe data for this study come from the summary version

of the INDEPTH WHO-SAGE Adult Health and

Ageing Survey implemented by the NHRC. The Adult

Health and Ageing Survey is an INDEPTH Network

multi-site activity in collaboration with the World Health

Organization’s Study on global AGEing and Adult

Health (SAGE). The survey forms part of efforts by

the INDEPTH Network to establish a longitudinal

database on older people to inform policies related to

their well-being. Ethical approval for the study was

obtained from the ethics committee of the Ghana Health

Service as well as the institutional review board of the

NHRC. Community approval was obtained from the

chiefs and elders. Written consent from individuals was

obtained before interview.

The summary version of the SAGE study primarily

targeted older people (50 and over), although smaller

samples of adults 18�49 years were also included. A

single-stage simple random sample of 6,074 older people

(50 years and over) and 1,360 younger adults (18�49 years) in the Kassena-Nankana District was drawn

using the Health and Demographic Surveillance System

(HDSS) database as a sampling frame. The data collec-

tion was integrated into the routine HDSS data collection

round that took place between January and April 2007.

Trained HDSS interviewers visited households and con-

ducted face-to-face interviews with selected individuals.

The questionnaire was written in English although

the interviews were conducted in the local languages

of respondents. Translation of the questions in to Kassim

and Nankam � the two principal languages in the dis-

trict � (and back translation from the local languages into

English) as well as pre-testing of the questionnaire was

done as part of interviewer training.

The questions asked in the survey were grouped under

two sections � Health Status Descriptions, and Subjective

Well-being and Quality of Life. Items under Health

Status Descriptions included overall rating of health,

questions on eight domains of health (mobility, self-care,

pain and discomfort, cognition, interpersonal activities,

sleep/energy, affect and vision), as well as functional

assessment questions. Vignettes for health status descrip-

tions were included in the Full SAGE survey but not in

the Summary version. Under the Subjective Well-being

and Quality of Life section, respondents were asked

questions on their thoughts about their life situation.

Almost all the questions in the questionnaire had 5-point

scale response categories. Background information on

age, education, marital status of each respondent as well

as household information were obtained from the routine

HDSS data.

Standardised self-reported surveys of health have

contributed immensely to the understanding of the

health status of elderly people in the developed world

and Asia. However, such studies (particularly those

focusing on older people) are rare in sub-Saharan

Africa. The data reported in this article will contribute

towards bridging the knowledge gap on the health status

of older people in sub-Saharan Africa and the develop-

ing world at large.

Outcome variablesThe primary outcome of interest in this study is overall

SRH status. This is based on respondents’ assessment of

their current health status on a 5-point scale in response

to the question: ‘In general, how would you rate your

health today?’ Response categories were: very good,

good, moderate, bad and very bad. Barely 10% of

respondents rated their health as very good and few

rated their health either as ‘bad’ (4.8%) or ‘very bad’

(0.2%). Almost half (49.4%) reported their health as

‘good’, while 36.6% rated their health as moderate.

From this we created a dichotomous measure coded 0 if

response was ‘very good’ or ‘good’ and 1 if response was

‘moderate’, ‘bad’ or ‘very bad’. This simple measure of

health status has been used in population-based epide-

miological research, and has been identified as a powerful

predictor of morbidity and mortality (5�7). In dichot-

omising SRH in our analysis, we follow the lead

of previous researchers who adopted a similar approach

(5,7�9) and the observation by Manor et al. (10) that such

dichotomisation does not make any difference.

Other indicators of health status examined in this study

are overall health status and self-reported functional

limitations. The overall health status of individuals was

assessed based on responses to questions in eight

domains of health covering affect, cognition, interperso-

nal activities and relationships, mobility, pain, self-care,

sleep/energy, and vision. At least two questions were

asked in each domain, thus providing more robust

assessments of individual health levels and reducing

measurement error for any single self-reported item. An

Cornelius Debpuur et al.


overall health status score (HSS) for each respondent was

derived from responses to these various items using Item

Response Theory (IRT) parameter estimates in Winsteps,

a Rasch measurement software package (http://www.win

steps.com). The health score is then transformed to a

scale of 0�100 (where 0 represents the worst health and

100 the best health status).

Based on self-reports of difficulty in carrying out

various activities contained in the health status descrip-

tions section of the questionnaire, an index of overall

disability (WHO Disability Assessment Scale � WHO

DAS) was constructed. Self-reported functioning was

assessed through the standardised 12-item WHODAS,

Version 2 (11). On a 5-point scale, respondents rated their

level of difficulty in carrying out various activities. These

responses were used to create a score of overall disability;

the score was then transformed to a scale ranging from 0

(no disability) to 100 (greatest disability). In effect,

WHODAS is an overall summary of one’s perceived

difficulties in carrying out various functions of daily

living. A higher score indicates greater perceived diffi-

culty in carrying out daily functions, while a lower score

indicates lower perceived difficulty in functioning. In

order to make this score conceptually consistent with the

HSS, it was inverted to a score designated here as

WHODASi, so that a higher score (on a 0�100 scale)

represents better functioning. In the analyses we grouped

WHODASi into quintiles to represent levels of functional

ability.

Socio-demographic variablesSocio-demographic information on respondents was

obtained from routine demographic surveillance data

including sex, age, education, marital status, relationship

to head of household, number of older people in the

household and household economic status. Age was

categorised into three subgroups: 50�59, 60�69 and

70�. Marital status was categorised as married or

unmarried. Educational status was categorised as never

attended school or ever attended school. In the analysis

those who have never attended school are referred to as

having no formal education, while those who have ever

attended school are described as having some formal

education. The socioeconomic status of households was

assessed in terms of wealth quintiles based on possessions

and housing characteristics. The five quintiles represent

poorest, poorer, poor, less poor and least poor house-

holds. In terms of relationship to the head of household,

respondents were described as head, spouse of head,

parent of head or other relation to head of household.

The number of older people in the household was

expressed as a proportion of the total number of people

in the household and grouped into quartiles for the

analysis.

AnalysisThe analysis is in two parts. First, we describe the health

status of older people based on three indicators: overall

SRH, an index of self-reported functional ability (WHO-

DASi) and an overall HSS. In the second part of the

analysis, we explore factors related to poor SRH using

logistic regression. In this analysis we are particularly

interested in the influence of reported functional ability

(WHODASi) on self-related health status. Functional

ability is an important dimension of health and an

individual’s assessment of ability to perform basic daily

activities is likely to influence SRH. However, the

magnitude of the influence of functional limitations on

SRH may be mitigated by factors such as the cause and

duration of disability, awareness of co-morbidity and

access to assistive devices. Generally, we expect that adults

with greater functional disability will rate their health

poorer than those with lower disability. We controlled for

confounders such as age, ever attended school, marital

status, relationship to household head, socioeconomic

status and proportion of household members aged 50 or

over. These factors have been identified as significant

factors in self-reported health, as have age and gender

differences (5, 12). Similarly, marital status, education,

socioeconomic status and social support have been

identified as relevant factors in health status (13). We

include relationship to household head and proportion of

older people in household as crude indicators of social

support.

ResultsAlthough a sample of adults aged 18�49 years were

interviewed using the summary version of the SAGE

Adult Health Survey, our analysis in this article is limited

to older participants in the survey. Of the 6,074 older

people targeted for survey, 4,584 were successfully inter-

viewed (a response rate of 75.5%). A major reason for

non-participation was the inability of the interviewers to

meet the targeted respondent after at least three visits to

the household. Other reasons include migration, death

and inaccurate information. In Table 1 we compare

respondents and non-respondents in terms of back-

ground characteristics (sex, age, education, marital status,

relationship to household head, socioeconomic quintile

of household, average household size and proportion of

household members aged 50 years and over). The data

indicate that compared with respondents, non-respon-

dents were largely male, slightly younger, unmarried,

more educated and from relatively less poor households.

These are likely to be more active and mobile and hence

are more likely to be away from home during the survey.

To the extent that our respondents are not representative

of the older population of the district, our results may

have limited generalisability.



The majority of respondents (61%) were female, and

the average age was 62.5 years, with men (average age of

63.7 years) being older than the women (average age of

61.7 years). Nearly three-quarters of the respondents

were aged below 70, and less than 10% had ever attended

school. About half of the respondents were married,

while a similar proportion were heads of households.

Overall, less than half of the respondents (42%) rated

their overall health as poor, with slightly more women

(45%) than men (35%) reporting poor health (Table 2).

The percentage of older people reporting poor health

increased with age among both men and women. How-

ever, the greatest differentials in SRH were observed in

terms of levels of functional disability. The proportion of

respondents reporting poor health was substantially

higher among those also reporting low functional ability,

both in men and women. Whereas less than one in five

participants in the highest category of functional ability

reported poor health, more than three in four of those in

the lowest category of functional ability reported poor

Table 1. Background characteristics of 4,584 adult respon-

dents and 1,437 non-respondents aged 50 and over in

northern Ghana

Variables

Respondents

(n�4,584)

Non-respondents

(n�1,437)

Sex (%)

Men 39.0 44.9

Women 61.0 55.1

Mean age (SD) 62.5 (9.1) 61.4 (9.0)

Age group (%)

50�59 years 43.0 50.2

60�69 years 35.9 30.8

70�79 years 16.6 14.8


Education level (%)



6 years

3.9 3.5


Marital status (%)

Now single 46.3 50.5


Socioeconomic quintile (%)

First quintile 27.5 23.5

Second quintile 24.4 18.8

Third quintile 21.9 20.4

Fourth quintile 18.7 21.5


Relationship to household head (%)

Head 51.0 52.3

Spouse 21.1 15.4

Parent 13.8 10.9

Other relation 14.2 21.4

Mean number of house-

hold members (SD)

6.6 (4.6) 6.2 (5.1)

Mean proportion of

household members

aged 50 and over (SD)

0.4 (0.2) 0.4 (0.3)

Table 2. Proportions reporting poor self-rated health among

4,584 adults aged 50 and over in northern Ghana

Variables Men (%) Women (%) All (%)

Sex

Men � � 35.2

Women � � 45.2

Age group (years)

50�59 26.7 37.7 33.5

60�69 33.7 48.8 43.4

70 years and over 50.7 58.8 54.9

Education level

No formal education 37.1 46.3 42.9

Some formal education 25.5 36.4 29.8

Marital status

Now single 41.1 48.5 47.3

In current partnership 33.9 40.6 36.6

Relationship to household head

Head 34.7 44.9 38.2

Spouse 14.8 41.5 40.8

Parent 38.1 52.2 51.3

Other relation 43.5 47.0 46.1

Proportion of household members aged 50 and over (%)

B25 32.9 45.4 40.3

25�49 37.2 44.3 41.4

50�74 32.6 44.4 40.4

575 46.4 53.2 50.7

Socioeconomic quintile

Poorest quintile 36.7 45.7 41.8

Second quintile 36.8 52.0 45.7

Third quintile 37.6 46.3 43.1

Fourth quintile 30.1 41.3 37.5

Least poor quintile 29.1 34.8 32.7

WHODASi quintile

Highest ability quintile 13.8 20.0 16.8

Second quintile 23.3 28.3 26.4

Third quintile 35.4 44.1 40.8

Fourth quintile 54.9 57.6 56.7

Lowest ability quintile 75.6 77.6 76.9



health (Fig. 1). Other differentials were also observed in

terms of education, marital status and household socio-

economic status. The proportion of household popula-

tion aged 50 and over is included to indicate social

support within the household. Households with more

than half of members aged 50 or over have a greater

proportion of elderly dependents and possibly less social

support, hence SRH in such households could be poorer

than in less-dependent households.

Table 3 shows results of WHODASi and HSS for older

men and women in the Kassena-Nankana District by age

category. A higher WHODASi score indicates a higher

level of functional ability compared to a lower score. The

mean WHODASi for the sample is 70.9 (73.7 for men and

69.1 for women), with the mean score decreasing with age

such that the oldest respondents had lowest functional

ability. Thus, the reported level of functional ability

decreased with age. This pattern was observed among

both men and women (Fig. 2). Generally, the WHODASi

score was lower among women compared to men of

comparable age. The age�sex pattern in functional limita-

tions is evident in the proportion of older people whose

WHODASi scores were below the median for the overall

sample. Higher proportions of participants in the older

age groups had scores below the median than their

younger counterparts. Similarly, more women in each age

group had WHODASi scores below the median (72.2)

compared to men.

For the overall HSS a higher score indicates better

health than a lower score. The mean HSS score for the

sample was 64.0 with men scoring slightly higher (65.8)

than women (62.8) as shown in Table 3. In terms of age,

younger age groups tended to report better health (higher

mean HSS) than their older counterparts; while more

women than men in each age group reported HSS below

the median (63.5).

These three indicators measure different dimensions of

health, and although SRH, WHODASi and HSS are

related, none is completely determined by the others.

SRH and WHODASi are positively related with correla-

tion of 0.49, while SRH and HSS are similarly correla-

ted (0.50). The highest correlation is found between

WHODASi and HSS (0.84).

On the basis of SRH, WHODASi and HSS, reported

health status declined with age and was slightly worse

among women than men. We explored the association

between functional disability and SRH among older

people while controlling for selected socio-demographic

factors such as sex, age, education, marital status,

relationship to head of household, proportion of people

aged 50 and over in the household and socioeconomic

quintile of the household.

Table 4 presents logistic regression results with poor

SRH as the outcome variable. In the univariate model

most factors (except proportion of household members

aged 50 and over, and household socioeconomic status)

had a significant association with SRH status. Individuals

with lower functional ability levels were more likely to

report poor health than their colleagues with better

functional ability. Men appeared less likely to report

poor health than women. Other researchers have sug-

gested that women’s poorer rating of their health may be

indicative of greater sensitivity to health conditions rather

than a female health disadvantage (7). The oldest old

were much more likely to rate their health poorly than

those 50�59 years old. Similarly those with no education

were more likely to report poor health than those with

some education; being single was associated with reports

of poor health. In terms of relationship to the household

head, those who were parents of or otherwise related to

the head appeared more likely to report poor health

compared to the household heads themselves.

In the multivariate model, the effects of WHODASi

remained significant, with respondents in the higher

disability quintiles much more likely to report poor

health status than those in the lowest disability quintile.

In other words, adults with greater functional limitations

were more likely to rate their health as poor compared to

those with less functional limitations. The other factors

that had significant effect on SRH were sex and house-

hold wealth quintile. Women were more likely than men

to rate their health as poor, while older people in the two

higher wealth quintiles were less likely to rate their health

as poor compared to their counterparts in the least

wealthy quintile. The effects of age were barely significant

after allowing for WHODASi, although older adults

appeared more likely to report poor health than their

younger colleagues.

These results suggest that functional disability is the

primary factor associated with overall SRH among older

people in the Kassena-Nankana District. The influence

40

50

60

70

80

Mea

n W

HO

DA

Si f

unct

iona

l abi

lity

scor

e

50–54 55–59 60–64 65–69 70–74 75–79 80 +Age group

Poor self-rated health Good self-rated health

Fig. 1. Mean WHODASi functional ability score, by age

group and self-rated health, among 4,584 adults aged 50 and

over in northern Ghana.



of functional limitations on SRH observed in this study is

consistent with findings from other studies (9, 14, 15).

Other significant determinants of SRH were sex and

household wealth quintile. Although age, education and

marital status appeared to be significant in the univariate

analysis, their significance eroded when other variables

were controlled for in the multivariate analysis. Unlike

other studies, it appears that these factors are not

important determinants of SRH in this population.

DiscussionData on adult health status, particularly the health of

older people in sub-Saharan Africa, are required to

monitor trends in the health status of adults and the

extent to which social and health policies impact on older

people. One relatively easy way of generating such data is

through population surveys of self-reported health. The

implementation of such surveys has contributed immen-

sely to the understanding of ageing and transitions in

health with age in the developed world and Asia.

Although self-reported health is subjective, it has been

found to be a good predictor of future health care use and

mortality. In 2007, the NHRC conducted a survey on

ageing and adult health in the Kassena-Nankana District

of Ghana as part of the INDEPTH WHO-SAGE Adult

Health Study. The survey collected information on self-

reported health among adults in the district. Data from

this survey have been analysed to describe the health

status as well as identify factors associated with SRH

status among older people in this rural setting.

Our results indicate that the majority of older people

rated their overall health as good. However, women were

more likely than men to rate their health as poor.

A similar pattern was observed with regard to reported

Table 3. Distribution of WHODASi functional ability score and health status score by age and sex among 4,584 adults aged 50

and over in northern Ghana

Variables Men (n�1,789) Women (n�2,795) All (n�4,854)


50�59 years 79.7 (15.7) 74.8 (15.0) 76.6 (15.4)

60�69 years 74.5 (16.9) 67.6 (16.8) 70.1 (17.2)

70 years and over 63.6 (20.9) 58.1 (18.7) 60.7 (20.0)

All ages 73.7 (18.7) 69.1 (18.7) 70.9 (18.1)

Proportion of respondents with WHODASi less than median

50�59 years 29.4 40.1 36.2

60�69 years 41.8 57.7 51.9

70 years and over 63.7 75.2 69.7

All ages 42.5 53.0 48.9


50�59 years 68.4 (9.4) 65.2 (7.4) 66.4 (8.3)

60�69 years 65.9 (8.7) 62.1 (7.4) 63.5 (8.1)

70 years and over 61.7 (9.3) 58.3 (7.3) 59.9 (8.5)

All ages 65.8 (9.6) 62.8 (7.8) 64.0 (8.7)

Proportion with health status score less than median

50�59 years 27.2 39.1 34.8

60�69 years 39.6 57.5 51.0

70 years and over 59.9 77.2 68.8

All ages 39.9 52.8 47.8

Mea

n W

HO

DA

Si f

unct

iona

l abi

lity

scor

e

50–54 55–59 60–64 65–69 70–74 75–79 80+Age group

Men

40

50

60

70

80

Women

Fig. 2. Mean WHODASi functional ability score, by age

group and sex, among 4,584 adults aged 50 and over in

northern Ghana.



functional disability and overall health score. Functional

disability was higher among women compared to men.

Among both men and women, older adults were more

likely to report functional disability. Adults with higher

functional disability were more likely to rate their health

as poor compared to those with lower disability. Multi-

variate regression results showed that levels of functional

disability, sex and household wealth quintile had sig-

nificant influence on SRH status.

The findings in this study are comparable with the

results of previous studies in various parts of the

world. Earlier studies have noted the existence of socio-

demographic differentials in SRH. Research evidence

suggests that men generally report fewer diseases and

fewer limitations in activities of daily living at older ages

than their female counterparts. Women are more likely to

rate their health poorer and to report more functional

limitations and disability than men (16�20). Irrespective

of sex, however, older age is related to higher odds of

reporting health problems and various studies have

observed that older adults tend to rate their health

poorer than their younger colleagues (16, 21, 22). Lower

socioeconomic status is associated with worse morbidity,

mortality and self-reported health in older persons (23).

Other factors such as marital status, socioeconomic

status and education are also known to affect health

status (24, 25), although marital status and education did

not appear significant in our analysis.

Older people in this district face considerable health

challenges like their colleagues elsewhere. As our results

indicate, there is considerable increase in functional

limitations and poor health with age, with more women

tending to report health problems than their male

counterparts. Unfortunately however, older people in

the Kassena-Nankana District do not only have to deal

with functional limitations, but also have to deal with

infectious diseases such as malaria and gastroenteritis.

What is more, they grapple with these health challenges in

a context of inadequate health care and weak social

support systems. Public policy and health interventions

that promote healthier lifestyles and improve access to

health care are required to improve the health and quality

of life of older people. In spite of increasing urbanisation,

the majority of Ghana’s older people live in rural areas

where health and social services are inadequate. Educa-

tion and information on healthy living need to be made

available to the general population to enhance prevention

and control of chronic conditions. Programmes need to

focus attention on promoting healthy ageing. Bold policy

decisions are also needed to integrate ageing and adult

health issues into all aspects of national planning and

development. Some observers have noted that the con-

cerns of older people remain marginalised in Ghana’s

social and economic debates (21). There is the need to

marshal evidence on the health situation of older people

in the country and to use this evidence to advocate for

programmes and policies to address the health care and

other needs of older people. This study has highlighted

the situation of older people in one of the rural districts in

Ghana, and it is hoped that this will broaden the evidence

Table 4. Factors associated with poor self-rated health

among 4,584 adults aged 50 and over in northern Ghana

Variables

Univariate model

(OR and [95% CI])

Multivariate model

(OR and [95% CI])

WHODASi quintile

Highest ability

quintile

1.00 1.00

Second quintile 1.78 [1.43�2.22]** 1.65 [1.32�2.07]**

Third quintile 3.42 [2.72�4.23]** 3.18 [2.56�3.96]**

Fourth quintile 6.51 [5.18�8.17]** 5.76 [4.54�7.32]**

Lowest ability

quintile

16.56 [13.1�20.9]** 14.23 [11.1�18.3]**

Sex

Men 1.00 1.00

Women 1.54 [1.37�1.75]** 1.40 [1.38�1.73]**

Age group (year)

50�59 1 1

60�69 1.52 [1.32�1.74]** 1.12 [0.96�1.31]

70 years and over 2.41 [2.06�2.82]** 1.24 [1.02�1.51]*

Education level


Some formal

education

0.56 [0.46�0.69]** 0.92 [0.71�1.17]

Marital status

Now single 1.56 [1.32�1.75]** 0.94 [0.78�1.13]


Relationship to household head

Head 1 1

Spouse 1.11 [0.96�1.30] 0.94 [0.75�1.19]

Parent 1.70 [1.43�2.03]** 1.00 [0.80�1.26]

Other relation 1.38 [1.15�1.66]** 1.06 [0.85�1.33]

Proportion of household members aged 50 and over (%)

B25 1.00 1.00

25�49 1.04 [0.90�1.20] 1.00 [0.85�1.18]

50�74 1.00 [0.85�1.18] 0.96 [0.80�1.17]

575 1.52 [1.23�1.88]** 1.49 [1.16�1.92]**

Socioeconomic quintile

Poorest quintile 1.00 1.00

Second quintile 1.17 [0.99�1.38] 1.12 [0.93�1.35]

Third quintile 1.05 [0.88�1.25] 0.95 [0.78�1.15]

Fourth quintile 0.83 [0.70�1.00] 0.74 [0.60�0.90]**

Least poor quintile 0.67 [0.52�0.87]** 0.65 [0.48�0.88]**

*pB0.05; **pB0.001.



on the health status of older Ghanaians and contribute

towards effective policy formulation in the country.

Results from the national SAGE study conducted in the

country around the same time as our study will provide a

broader national picture on the health status of older

people. For the purposes of monitoring the health status

of older people, such studies need to be conducted

periodically and in a variety of settings.

This initial survey has demonstrated the feasibility of

conducting population-based health surveys of adults

in rural Ghana. The results of the analyses are

generally consistent with other studies and indicate

the scope for monitoring population health using self-

assessments of health. There is the opportunity for

follow-up and longitudinal analysis anchored on the

HDSS platform existing in the district. Future analyses

will explore the relationship between SRH and mor-

bidity and mortality in this population. The INDEPTH

WHO-SAGE Adult Health Research platform (of

which this study is a part) is uniquely placed to

contribute towards an understanding of the relationship

between SRH and subsequent morbidity and mortality

in the region. Subsequent analysis of SRH and

mortality from INDEPTH Network sites will contri-

bute to the literature on this topic, which is currently

under-researched in sub-Saharan Africa.

ConclusionAs in other developing countries, the population of

older people in Ghana is increasing steadily. Despite the

increasing number of older people in the country,

however, very little is known about their health status,

especially for those in rural areas. This lack of knowl-

edge impedes development and implementation of

policies and programmes as well as evaluation of the

impact of social and health policies on older people.

Ghana is participating in the WHO multi-country

SAGE. The data presented in this study form part of

this global study. Our results suggest that the ageing

process in this district is consistent with what has been

observed in other parts of the world. SRH declines with

age among both men and women. It appears that with

increasing age there is a decline in health which is

manifest in increasing functional disability. This depre-

ciation in health and daily functioning increases the

demand for health care and other services by older

people. Therefore, steps need to be taken to address the

health care and other needs of older people. Health

policies and programmes that improve functional capa-

city and well-being for older people are particularly

urgent. There is also the need for regular monitor-

ing and assessment of the health status of older people

to provide public health agencies with the data they

need to assess, protect, and promote the health and well-

being of older people. The present study will serve as a

baseline for monitoring trends in the health status of

older people in the Kassena-Nankana District.

Acknowledgements

The authors would like to thank the people of the Kassena-Nankana

District, especially all the men and women who agreed to be

interviewed, for their support and participation in the study. We

are grateful to the NHRC staff who collected and processed the data

for this study.

Conflict of interest and fundingThis project was supported by a grant from the

INDEPTH WHO-SAGE study and the INDEPTH

Network. The authors would like to acknowledge the

INDEPTH Network for their financial support.

Note1. In 2008 the Kassena-Nankana District was split

into two districts � Kassena-Nankana and Kassena-

Nankana West districts. In this article we use the

original name of the district to refer to the two

districts.

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Patterns of health status and qualityof life among older people in ruralViet NamHoang Van Minh1,2*, Peter Byass3#, Nguyen Thi Kim Chuc1,2

and Stig Wall3#

1Faculty of Public Health, Hanoi Medical University, Hanoi, Viet Nam; 2INDEPTH Network, Accra,Ghana; 3Department of Public Health and Clinical Medicine, Umea Centre for Global Health Research,Umea University, Umea, Sweden

Background: To effectively and efficiently respond to the growing health needs of older people, it is critical to

have an indepth understanding about their health status, quality of life (QoL) and related factors. This paper,

taking advantage of the INDEPTH WHO-SAGE study on global ageing and adult health, aims to describe

the pattern of health status and QoL among older adults in a rural community of Viet Nam, and examine

their associations with some socio-economic factors.

Methods: The study was carried out in the Bavi District, a rural community located 60 km west of Hanoi, the

capital, within the Epidemiological Field Laboratory of Bavi (FilaBavi). Face-to-face household interviews

were conducted with people aged 50 years and over who lived in the FilaBavi area. The interviews were

performed by trained surveyors from FilaBavi using a standard summary version SAGE questionnaire. Both

descriptive and analytical statistics were used to examine the patterns of health status and QoL, and

associations with socio-economic factors.

Results: Higher proportions of women reported both poor health status and poor QoL compared to men.

Age was shown to be a factor significantly associated with poor health status and poor QoL. Higher

educational level was a significant positive predictor of both health status and QoL among the study subjects.

Higher economic status was also associated with both health status and QoL. The respondents whose families

included more older people were significantly less likely to have poor QoL.

Conclusion: The findings reveal problems of inequality in health status and QoL among older adults in the

study setting by sex, age, education and socio-economic status. Given the findings, actions targeted towards

improving the health of disadvantaged people (women, older people and lower education and economic

status) are needed in this setting.

Keywords: older people; health status; quality of life; rural; Viet Nam; INDEPTH WHO-SAGE





Received: 5 March 2010; Revised: 13 May 2010; Accepted: 8 July 2010; Published: 27 September 2010

During the past few decades, under the forces

of a demographic transition characterised by

declining fertility rates and increasing life

expectancy, the proportion of people in the world

population who reach middle age and beyond is increas-

ing sharply (1�3). Developing countries are currently

ageing much faster than industrialised countries (3, 4).

In 2002, almost 400 million people aged 60 and over

lived in the developing world. By 2025, it may rise to

840 million representing 70% of all older people world-

wide (2, 3).#Deputy Editor, Peter Byass, Chief Editor, Stig Wall, have notparticipated in the review and decision process for this paper.


Global Health Action 2010. # 2010 Hoang Van Minh et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproductionin any medium, provided the original work is properly cited.

64



Population ageing in low- and middle-income countries

has special implications for many public services, especially

for health care, in these countries. The health care systems

of many developing countries are still focused on

childhood and infectious diseases as well as reproductive

health services. But the ageing population leads to

increasing demands for care that addresses chronic health

conditions (2). Nowadays, in all countries, and in low- and

middle-income countries in particular, measures to help

older people remain healthy and active are urgently needed

(2, 4). To effectively and efficiently respond to the growing

health needs of older populations, it is critical to have an

indepth understanding about their health conditions,

quality of life (QoL) and related socio-economic factors.

Viet Nam, a developing country in South-East Asia, is

undergoing demographic transition and experiencing

rapid population ageing. The proportion of people aged

50 years and over rose from 12.6% in 2000 to 14.1% in

2005 and will account for 18.9% of the total population in

2015 (3). In Viet Nam, the number of older people living

in rural areas is about 3.5 times higher than those living

in urban areas (5). About 44% of older Vietnamese are

working, but mostly in agricultural activities which

provide low and unstable incomes. Other sources of

income, including pension and social assistance benefits,

are significant factors to reduce risks for older people.

However, the coverage of the current social protection

system in Viet Nam is not adequate (6).

Life expectancy in Viet Nam reached 72.2 years in

2005, a relatively high level compared to the nation’s

economic conditions. However, the average healthy life

expectancy was far lower, at 58.2 years and ranked 116

among 174 countries in the world (7). Health care for the

older people in Viet Nam has been improved, but the

accessibility for vulnerable and low-income older people

is still low, and the poorer shoulder greater burdens of

health care costs in terms of percentage of household

expenditure (8).

As in other developing countries, little empirical

research has been conducted in Viet Nam on the health

status, QoL and related socio-economic status among

older people. This article, therefore, taking advantage of

the INDEPTH WHO-SAGE study on global ageing and

adult health (9), aims to describe the patterns of health

status and QoL among older adults in a rural community

of Viet Nam, and examine their associations with some

socio-economic factors.

Methods

Study design and settingThis was a population-based cross-sectional study, car-

ried out in Bavi District, a rural community located 60

km west of Hanoi, the capital, within the Epidemiological

Field Laboratory of Bavi (FilaBavi). The FilaBavi Health

and Demographic Surveillance System (HDSS), sup-

ported by Sida/SAREC, was established in 1999 with a

sample of around 50,000 individuals from the Bavi

District. People aged 50 and over accounted for about

17% of the total population under surveillance. The

surveyed population includes three distinct groups: those

in mountainous areas, highlands, and riverside or island

dwellers (10). The FilaBavi HDSS is a member of the

INDEPTH network (11).

Data collectionFace-to-face household interviews were planned for all

people aged 50 years and over who lived in the FilaBavi

area between the end of 2006 and the beginning of 2007.

The interviews were done by trained surveyors from

FilaBavi using a summary version of the SAGE ques-

tionnaire (available as a Supplementary File to this

paper). Further details of the study methodology are

available separately (9). The questionnaire was translated

into the local language and pre-tested before official use.

Spot-checks and re-checks on sample data were con-

ducted by supervisors for quality control.

MeasurementsOutcome variable

Self-reported health status and QoL among the study

subjects were outcome variables. Health status scores were

calculated based on self-reported health levels in eight

health domains covering: affect, cognition, interpersonal

activities and relationships, mobility, pain, self-care, sleep/

energy, and vision. Each domain included at least

two questions. Asking more than one question about

difficulties in a given domain provides more robust

assessments of individual health levels and reduces mea-

surement error for any single self-reported item. Health

status scores were computed by using Item Response

Theory (IRT) parameter estimates in Winsteps†, a Rasch

measurement software package (http://www.winsteps.

com). Higher health status scores within a 0�100 scale

imply better health status. Respondents who had health

status scores below the median were categorised as having

poor health status. QoL was assessed by using the eight-

item version of the World Health Organization Quality of

Life instrument (WHOQoL). Results from the eight items

were summed to get an overall WHOQoL score which was

then transformed into a 0�100 scale. The higher the

WHOQoL score, the better the QoL. Respondents

who had WHOQoL scores less than the median were

considered as having poor QoL. More details on how

scores for this study were derived are given elsewhere (9).

Explanatory variables

Explanatory variables included sex (male, female), age

(grouped as 50�59, 60�69, 70�79, 80� years), educational

level (no formal education, up to 6 years of formal

WHO-SAGE study on older adults in rural community of Viet Nam




education, more than 6 years of education), marital

status (in current partnership, never married, separated,

divorced and widowed), wealth index (quintiles), whether

respondent stays alone (yes or no) and proportion

of people aged 50 years and over in the same household

(B25%, 25�49%, 50�74%, �75%�). The wealth quintiles

were constructed by using principal component analysis

techniques (12). Variables used included household in-

come, the area of land owned, type of house, materials of

roof and floor, toilet facilities, electricity and water

supplies, and ownership of a range of durable assets for

each household.

Data analysisBoth descriptive and analytical statistics were performed.

The data analysis began with calculation of frequencies

and percentages of the variables of interest. Multivariable

logistic regressions were then carried out to examine

the association between health status and QoL with

the selected explanatory variables.

Ethical considerationsThe protocol of this study was approved by the Scientific

Board of FilaBavi. All subjects in the study were asked

for their written informed consent before collecting data,

and they had complete right to withdraw from the study

at any time without disadvantage.

Results

Characteristics of the study populationsOf the total 8,874 people aged 50 and over living in the

study setting at the time of the survey, there were 8,535

who participated in the study (amounting to 96%). Four

percent of subjects were unable to participate as they were

away (2.3%) or were not healthy enough to take part in

the survey (1.7%). The background characteristics of

potential study subjects (respondents and non-respon-

dents) are described in Table 1. There were no significant

differences in socio-economic characteristics between the

respondents and the non-respondents.

Distribution of health status and WHOQoL scoresTable 2 presents the distribution of health status scores of

the study population by age and sex. The overall mean

health status score was 66.2 and median 65.0. In both

sexes, the average health status scores decreased with age.

Men had higher health status scores than women of the

same age group. Overall, the proportion of respondents

with below-median health status among men and women

was 39.1 and 58.3%, respectively.

A similar pattern was observed for QoL. The overall

mean WHOQoL score was 61.2, median 62.5. In both

sexes, the average WHOQoL score decreased with age.

Women had lower WHOQoL scores than men of the

same age group. Overall, the proportion of respondents

with poor QoL among men and women was 38.4 and

52.4%, respectively (Table 3).

Fig. 1 shows the distribution of the study subjects by

health status and QoL. Women were shown to have

poorer health status than men. About 25.9% of men and

40% of women reported having both poor health status

and poor QoL.

Factors associated with poor health status and poorquality of life (QoL)Multivariate logistic regression analyses of the association

between poor health status and poor QoL, and socio-

economic status are shown in Table 4. Men were shown to

be significantly less likely to have poor health status

compared to women. Older respondents had poorer health

status than those younger. People with lower educational

levels had a significantly higher probability of having poor

health status than those with higher educational levels.

Table 1. Background characteristics of study subjects (res-

pondents and non-respondents)

Respondents

(n�8,535)

Non-respondents

(n�339)

Gender

Male (%) 3,469 (40.6) 140 (42.6)

Female (%) 5,066 (59.4) 189 (57.4)

Age (years)

50�59 (%) 3,221 (37.7) 148 (45)

60�69 (%) 2,258 (26.5) 87 (26.3)

70�79 (%) 2,086 (24.4) 45 (13.8)

80 and over (%) 970 (11.4) 49 (14.9)

Mean age (SD) 65.3 (10.7) 63.7 (19.2)

Education

No formal education (%) 878 (10.3) 85 (25.9)

Primary orB6 years (%) 4,190 (49.1) 112 (33.9)

More than 6 years (%) 3,467 (40.6) 132 (40.2)

Marital status

In current partnership (%) 5,895 (69.1) 215 (65.5)

Now single (%) 2,640 (30.9) 114 (34.5)

Economic status of household

Poorest quintile 1,209 (14.2) 41 (12.4)

Second quintile 1,548 (18.2) 52 (15.7)

Third quintile 1,787 (21) 65 (19.9)

Fourth quintile 1,996 (23.4) 81 (24.6)

Least poor quintile 1,976 (23.2) 90 (27.4)

Mean number of

household members (SD)

4.2 (2) 4.3 (1.9)

Proportion of household

members aged 50 and

over (SD)

50.7 (28.9) 49.5 (28.8)

Hoang Van Minh et al.


Study subjects in the lowest wealth quintile were more

likely to have poor health status than those belonging to

the highest wealth quintile. There was no significant

association between poor health status and marital status

or the proportion of older people living in a household.

Similar to the pattern of health status, poor QoL was

shown to be significantly associated with women, older

ages, lower educational levels and lower economic status.

Table 4 shows that the respondents whose families had

more older people (]75% people aged 50 years and over

in the same household) were significantly less likely to

have poor QoL. Women, older people, those with lower

educational level, without any marital partnership, with

fewer older people in the family and with lower economic

status were more likely to have both poor health status

and poor QoL.

DiscussionThis article describes the pattern of health status and

QoL among older adults in a rural community of Viet

Nam. It reveals socio-economic inequalities in health

status and QoL among older adults in the study setting.

We found that a higher proportion of women reported

both poor health status and poor QoL compared to men.

The findings are in line with recent studies on health

status from other Asian countries such as Pakistan (13),

Bangladesh (14) and Singapore (15). Gender inequality in

health has been well documented in the international

literature (16). The findings are also consistent with

results from previous studies on QoL that reported

female disadvantages in both emotional and subjective

well-being (17�20). One likely explanation could be that

women are more likely to suffer from conditions that are

debilitating but not fatal. The paradox is that women

report poorer health but live longer, and this is true in

almost every country in the world (21).

Age was shown to be a factor significantly associated

with poor health status and poor QoL. This has been

consistently shown in previous studies (13�15). In our

setting, chronic diseases were shown to be more prevalent

among women and older people (22).

We found that higher educational levels were signifi-

cant positive predictors of both health status and QoL

among the study subjects. Education is well known as an

important factor for health, both among men and

women, particularly in rural areas. The findings are

consistent with previous studies (13�15). Education is

assumed to have a positive effect on health status

since persons with more education are assumed to be

better informed about health matters, diet and disease

Table 2. Distribution of health status scores by age and sex

among 8,535 adults aged 50 years and over in northern rural

Viet Nam

Variables

Men

(n�3,469)

Women

(n�5,066)


50�59 years 72.5 (11.5) 68.8 (9.4)

60�69 years 68.8 (9.9) 64.8 (7.9)

70�79 years 65.3 (9.2) 61.7 (8.2)

80 years and over 60.1 (8.8) 57.6 (8.2)

All ages 60.1 (8.8) 57.6 (8.2)

Proportion of respondents

with health status score

below the median

68.9 (11.0) 64.4 (8.4)

50�59 years (%) 24.4 35.4

60�69 years (%) 33.1 51.7

70�79 years (%) 50.1 67.1

80 years and over (%) 70.0 81.5

All ages (%) 35.9 54.2

Table 3. Distribution of WHOQoL scores by age and sex

among 8,535 adults aged 50 years and over in northern rural

Viet Nam


Mean QoL score (SD)

50�59 years 65.7 (12.7) 62.2 (12.3)

60�69 years 64.1 (13.2) 60.9 (12.6)

70�79 years 61.9 (14.1) 57.7 (13.2)

80 years and over 56.6 (14.1) 53.8 (14.0)

All ages 63.7 (13.5) 59.5 (13.2)

Proportion of respondents with WHOQoL score below median

50�59 years (%) 32.0 45.1

60�69 years (%) 37.3 48.6

70�79 years (%) 44.5 58.3

80 years and over (%) 60.9 66.2

All ages (%) 38.4 52.4

25.9

13.2 12.5

48.3

40.0

18.3

12.4

29.3

–

10.0

20.0

30.0

40.0

50.0

60.0

Poor health and poorquality of life

Poor health and non-poor quality of life

Non- poor health andpoor quality of life

Non- poor health andnon- poor quality of

life

(%)

Men Women

Fig. 1. Distribution (%) of study subjects by health status

and quality of life, among 8,535 adults aged 50 years and

over in northern rural Viet Nam.



prevention measures leading to better health conditions,

consequently leading to higher QoL.

Similarly, improvements in economic status are also

likely to raise both health status and QoL. In addition to

providing means for purchasing health care, higher

economic status can provide better nutrition, housing

and recreational opportunities. The findings are consis-

tent with previous studies in Asia (13�15), in Europe (23)

and the Americas (24).

This study also revealed a positive effect of having more

older people living in the same family. This positive effect

may be the result of mutually practised health beliefs and

behaviours, shared physical environments and interperso-

nal relations between the older people in the same family.

We need to note some limitations of this study. Firstly,

the cross-sectional nature of the data limited our ability

to understand causal mechanisms that resulted in

particular heath status and QoL outcomes among the

study population. Secondly, low educational level and the

presence of impaired cognition in older people might

have led to inaccuracies in the self-reported data. Our

careful training and field supervision would have over-

come this problem to some extent.

In summary, this study provides cross-sectional evi-

dence on patterns of health status and QoL among older

adults in rural Viet Nam. The findings reveal problems of

inequality in health status and QoL among older adults

in the study setting by sex, age, education and economic

status. Given these findings, actions to enhance the health

of disadvantaged people (women, the elderly, less edu-

cated and lower economic status) are needed in this

setting.

Table 4. Factors associated with poor health status and poor quality of life among 8,535 adults aged 50 years and over in

northern rural Viet Nam

OR with 95% CI

Variables Poor health Poor QoL Poor health and poor QoL

Gender

Men 0.7 (0.6�0.8)a 0.8 (0.7�0.9)a 0.8 (0.7�0.9)a

Women 1 1 1

Age group

50�59 years 1 1 1

60�69 years 1.5 (1.3�1.7)a 1.0 (0.9�1.1) 1.3 (1.1�1.5)a

70�79 years 2.4 (2.1�2.8)a 1.2 (1.1�1.4)a 1.9 (1.6�2.2)a

80 years and over 4.6 (3.7�5.7)a 1.8 (1.5�2.1)a 3.0 (2.4�3.6)a

Educational level

No formal education 2.7 (2.2�3.3)a 2.1 (1.7�2.5)a 2.3 (1.9�2.9)a

Less than or equal to 6 years 1.6 (1.4�1.7)a 1.5 (1.4�1.7)a 1.6 (1.4�1.8)a

More than 6 years 1 1 1

Marital status

Now single 1 1 1

In current partnership 0.9 (0.8�1.0) 0.9 (0.8�1.0) 0.8 (0.7�0.9)a

Proportion of people aged 50 years and over in the same household

B25% 1.2 (1.0�1.4) 1.6 (1.4�1.9)a 1.4 (1.2�1.6)a

25�49% 1.1 (1.0�1.3) 1.6 (1.4�1.9)a 1.4 (1.2�1.6)a

50�74% 1.2 (1.0�1.4) 1.5 (1.3�1.7)a 1.4 (1.2�1.6)a

]75% 1 1 1

Socio-economic quintile

Poorest quintile 1.7 (1.4�2.0)a 3.2 (2.7�3.8)a 2.5 (2.1�3.0)a

Second quintile 1.2 (1.0�1.4) 2.0 (1.8�2.4)a 1.6 (1.4�1.9)a

Third quintile 1.2 (1.0�1.4) 1.7 (1.5�2.0)a 1.5 (1.3�1.8)a

Fourth quintile 1.1 (1.0�1.3) 1.6 (1.4�1.9)a 1.5 (1.3�1.7)a

Least poor quintile 1 1 1

aSignificant results (95% CI does not include 1).

Hoang Van Minh et al.


Acknowledgements

This research was supported by FAS, the Swedish Council for Social

and Work Life Research, Grant No. 2003-0075. We would like to

thank INDEPTH WHO-SAGE for support and contribution of the

SAGE instrument.



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*Hoang Van MinhFaculty of Public HealthHanoi Medical UniversityNo 1, Ton That Tung, Dong DaHanoi, Viet NamTel: �84 48523798Fax: �84 45745070Email: [email protected]



http://esaunorg/unpp

http://esaunorg/unpp

Socio-demographic differentialsof adult health indicators in Matlab,Bangladesh: self-rated health, healthstate, quality of life and disability levelAbdur Razzaque1,2*, Lutfun Nahar3, Masuma Akter Khanam4

and Peter Kim Streatfield1,2

1Matlab Health and Demographic Surveillance System, ICDDR,B, Mohakhali, Dhaka, Bangladesh;2INDEPTH Network, Accra, Ghana; 3Department of Social Science, East West University, Dhaka,Bangladesh; 4Chronic Disease Unit, ICDDR,B, Dhaka, Bangladesh

Background: Mortality has been declining in Bangladesh since the mid- twentieth century, while fertility has

been declining since the late 1970s, and the country is now passing through the third stage of demographic

transition. This type of demographic transition has produced a huge youthful population with a growing

number of older people. For assessing health among older people, this study examines self-rated health, health

state, quality of life and disability level in persons aged 50 and over.

Data and methods: This is a collaborative study between the World Health Organization Study on global

AGEing and adult health and the International Network for the Demographic Evaluation of Populations and

Their Health in developing countries which collected data from eight countries. Two sources of data from the

Matlab study area were used: health indicator data collected as a part of the study, together with the ongoing

Health and Demographic Surveillance System (HDSS) data. For the survey, a total of 4,000 randomly

selected people aged 50 and over (HDSS database) were interviewed. The four health indicators derived from

these data are self-rated health (five categories), health state (eight domains), quality of life (eight items) and

disability level (12 items). Self-rated health was coded as dummy while scores were calculated for the rest of

the three health indicators using WHO-tested instruments.

Results: After controlling for all the variables in the regression model, all four indicators of health (self-rated

health, health state, quality of life and disability level) documented that health was better for males than

females, and health deteriorates with increasing age. Those people who were in current partnerships had

generally better health than those who were single, and better health was associated with higher levels of

education and asset score.

Conclusions: To improve the health of the population it is important to know health conditions in advance

rather than just before death. This study finds that all four health indicators vary by socio-demographic

characteristics. Hence, health intervention programmes should be targeted to those who suffer and are in the

most need, the aged, female, single, uneducated and poor.

Keywords: adult health; self-rated health; health state; quality of life; disability; Matlab; Bangladesh; INDEPTH WHO-SAGE





Received: 10 December 2009; Revised: 3 June 2010; Accepted: 8 July 2010; Published: 27 September 2010

Mortality has been declining in Bangladesh since

the mid-twentieth century, while fertility has

been declining since the late 1970s, and the

country is now passing through the third stage of

demographic transition (1). This type of demographic

transition has produced a huge youthful population and


Global Health Action 2010. # 2010 Abdur Razzaque et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproductionin any medium, provided the original work is properly cited.

70



a growing number of older people. Due to such an age

structure, the population is now experiencing a double

disease burden; over 50% of deaths in Matlab are now

due to non-infectious diseases (2).

Bangladesh is one of the 20 developing countries with

the largest numbers of older people, and by 2025

Bangladesh, along with four other Asian countries, will

account for about half of the world’s older population (3).

In fact, population increase among those aged 65 and

over was negligible in Bangladesh during the first half

of the twentieth century, but it increased substantially

during the second half (2.4 million) and it is projected to

increase by 20.8 million during the first half of the

twenty-first century (4).

As social security is almost non-existent for older people

in Bangladesh (pension for government and semi-

government employees 5%, and governmental support

for elderly people 10%), older people usually live in

extended households and depend primarily on adult

children for economic support and personal care (5).

However, the traditional family support system for older

people is under pressure due to the increasing out-

migration of household members to cities, and women’s

labour force participation outside the home, causing

vulnerability for older people.

In Bangladesh about 50% of the population fall below

the poverty line, and so older people are likely to be in ill

health, in social isolation and in poverty (6). Moreover,

the majority of the older people live in rural areas where

there is no specialised care service for older people

in health facilities (Upazila Health Complex). Based

on Matlab data, it was documented earlier that the

prevalence of chronic morbidity was 75% among older

people (last 3 months) while it was about 50% (last

1 month) for acute morbidity (7); 2.1% of older males and

3.6% of females could not use a toilet without help.

As costs associated with assessing health status of a

population are high, there is a need for low-cost health

indicators, particularly for developing countries. Currently,

some low-cost health indicators are available for developed

countries that are good predictors of mortality and

functional ability (8�11), but such indicators are rare for

the developing countries. Based on the Matlab Health and

Socio-economic Status Survey of Bangladesh, (12) it was

reported that adults of this community can effectively

assess their own health even with poor education and low

levels of interaction with the modern health system.

The current study has collected data on four indicators

of health using a summary version (SAGE�INDEPTH) of

the full WHO-SAGE questionnaire: self-rated health,

health state, quality of life and disability level. The study

will examine these four health indicators for people aged

50 and over, and their relationship with various

socio-demographic characteristics as well as the inter-

relationship of these health indicators.

Methods

SettingData for this study come from Matlab Upazila (sub-

district) where the International Centre for Diarrhoeal

Disease Research, Bangladesh (ICDDR,B) has main-

tained a field station since 1963. Matlab is a rural area

located about 55 km south-east of Dhaka. The area is a

low-lying deltaic plain intersected by the tidal river Gumti

and its numerous canals. In the past, major modes of

transport within the area were walking, country boat and

in some cases small steamer or launch. However, in recent

years most of the villages have become accessible by

rickshaw. Farming is the dominant occupation, except in

a few villages where fishing is the means of livelihood (13).

Most of the farmers are in marginal situations with less

than a hectare of land and 40% of them are landless. For

many families, sharecropping and work on others’ land on

a daily wage basis have become the main sources of

livelihood. Some people work in mills and factories in

different towns and cities but their families live in the study

area. Rural�urban out-migration is about 5% in recent

years, while it is about 1% for international migration;

however, these rates were much lower in the 1980s (3.3% vs.

0.3%). Women are largely restricted to activities in the

home, with relatively little opportunity to venture outside

the home, although these restrictions have decreased in

recent years. Rice constitutes the staple food and is

harvested three times annually. Rates of illiteracy are

high and are higher among older people.

Since 1966 the ICDDR,B has maintained a Health and

Demographic Surveillance System (HDSS) in the Matlab

area covering about 225,000 people. The surveillance

system collects data on births, deaths, migrations, mar-

riages, divorces and household divisions (14), and also

collects cross-sectional socio-economic data which are

available for 1974, 1982, 1996 and 2005. The HDSS data

are of high quality because they have been collected

during regular household visits (every 2 weeks until 1997,

every month between 1998 and 2006 and every 2 months

since then) by the Community Health Research Workers

(CHRWs).

Since October 1977, half of the surveillance area has

been exposed to Maternal and Child Health and Family

Planning (MCH-FP/ICDDR,B service area) services while

the other half is a comparison area (15, 13). These two areas

are almost similar in socio-economic conditions but differ

in access to the MCH-FP programme. Beginning in

1996, the community-based maternity care service of

the ICDDR,B service area was gradually phased out

and replaced by a facility-based strategy of sub-centres.

Socio-demographic differentials of adult health indicators in Matlab


However, these health services are targeted mainly at

mother and child health and not to older population,

except for services for diarrhoeal diseases. In fact, treat-

ment for diarrhoea has been provided from the Matlab

field hospital since the beginning and such service is open

to all irrespective of place of residence.

The history of modern medicine is rather short in

Bangladesh, since it did not reach the rural population

until after World War II. During 1947�1970 the physical

infrastructure for delivering health services by the then

government was mainly urban-based, and such services

were more curative than preventive in nature. The

government accepted primary health care as a national

health objective in 1978, since when the health care

system has been reoriented to provide essential care to the

general mass of the population. Funding for the health

sector increased significantly from the early 1980s, with

new facilities including Maternal and Child Welfare

Centres in urban and sub-urban areas, Upazila Health

Complexes at Upazila level and Family Welfare Centres

at Union level (16). In Matlab town the government runs

a 31-bed free general hospital with nine doctors (Upazila

Health Complex) along with several Family Welfare

Centres, each with a sub-assistant Community Medical

Officer and a Family Welfare Visitor. Except for the

service from Upazila Health Complex, all other services

are targeted to maternal and child health. Finally, there

are across the country both private practitioners (quali-

fied and unqualified), private clinics (in big cities) and

traditional practitioners (Ayurvedic, Unani and Homoeo-

pathy); these services cover the population across all age

groups.

Data and methodsThis is a multi-country study between the World Health

Organization Study on global AGEing and adult health

(SAGE) and the International Network for the Demo-

graphic Evaluation of Populations and Their Health in

developing countries (INDEPTH), and collected data

from eight countries of Africa and Asia. Two sources of

data from the Matlab study area were used: survey data

collected as a part of the study and the ongoing HDSS

data. For the survey, questionnaires were received from

the SAGE�INDEPTH and piloted in the field after

translating into local languages. A total of 4,000 people

50 years and older, out of 31,400, were selected randomly

from the HDSS database (ICDDR,B-service area); a

sample from half of the HDSS area was selected to

minimise travel time to visit the sample households.

The survey was conducted by a team of college-graduate

females with data collection experience. Interviewers

received extensive training on data collection, particularly

about asking questions on sensitive topics and on the data

collection tools designed for the survey. The interviews

were conducted at the residence of the respondent by

face-to-face interview and contact with absentees was

attempted three times. As a quality check, about 2% of

samples were re-interviewed by an independent field

worker/supervisor and feedback was incorporated

accordingly.

Based on the survey data, four health indicators were

calculated: self-rated health, health state, quality of life

and disability level. Self-rated health was a categorical

variable (five categories), health state was measured

through eight domains (affect, cognition, interpersonal

activities and relationship, mobility, pain, self-care, sleep/

energy, and vision), quality of life was measured through

eight items and disability was assessed through 12 items

(17). Self-rated health was coded as a dummy while

scores were calculated using the WHO-tested instruments

for health status, quality of life and disability level. All

three of these scores were transformed into

0�100 scales on which higher scores indicate better

outcomes [better health status, better quality of life

(WHOQoL) and better functional ability (WHODASi)].

Analyses were undertaken using both bivariate

and multivariate methods. The dependent variable was

dichotomous for self-rated health and involved contin-

uous scores for health state, quality of life and disability

level. The independent variables were age of respondent,

sex, marital status, proportion of people aged above 50 in

the household, education level and asset quintiles.

Age was grouped into four (50�59, 60�69, 70�79 and 80

and over), completed years schooling into three (none, 1�5 and 6 years or more), marital status into two (now

single and in current partnership) and proportion of

people aged above 50 in household into four groups

(B0.25, 0.25�0.49, 0.50�0.74 and 0.75 or more). Asset

index was calculated based on a number of consumer

items (radio, watch, etc.), dwelling characteristics (wall

and roof material) and type of drinking water and toilet

facilities (18). For this study we have studied first to fifth

quintiles as poorest to richest.

For examining the interrelationship between two

variables, self-rated health was grouped into two categories

(very good, good, moderate�1 and bad/very bad�0);

health status (IRT health 555.2�0 and�55.2�1); quality

of life (WHOQoL 580.0�0 and�80.0�1); disability

level (WHODASi 581.0�0 and�81.0�1); x2-tests

were performed for significance level.

ResultsAbout two-fifths of the sample belonged to the age group

50�59 years while about one-fifth were aged 70 and over

(Table 1). Educational level was low, with about 55%

illiterate and only about 15% had six or more years of

schooling. About 25% of people were single, 30% of

household members were 50 years or older and mean

household size was slightly over 5. Sample households are

not equally distributed across quintiles, with more from

Abdur Razzaque et al.


the fourth and fifth quintiles, because the quintiles are

population-based. In fact, sample characteristics are

comparable to the population characteristics.

All four measures of health indicator (self-rated health,

health state, quality of life and disability level) indicated

that health was better for males than females irrespective

of age categories and health deteriorated gradually as age

increased (Table 2). For self-rated health, the proportion

with good health declined from 87.2% to 48.9% for males

and 77.4% to 24.2% for females between age groups 50�59 and 80 years and over; while for health status, the

mean score declined from 65.7 to 55.6 for males and 57.7

to 50.7 for females between these two age groups. For

quality of life, the mean score decreased from 80.3 to 76.4

for males and 77.3 to 71.4 for females between age groups

50�59 and 80 years and over; while for functional ability

level, the mean score decreased from 84.0 to 54.6 for

males and 62.1 to 42.0 for females between these two age

groups.

Table 3 shows multivariate relationships for self-

rated health and health status by socio-demographic

characteristics. After controlling for all other variables

in the regression model (logistic), males reported sig-

nificantly better health (2.19 times) than females; health

got significantly worse as age increased (7.70 times better

for age group 50�59 and reduced to 2.07 times for age

group 70�79 compared to age group 80 years and over);

educated people had significantly better health than

uneducated (0.74 times for those with no formal educa-

tion and 0.87 times for those less or equal to 6 years

compared to those with six or more years of education);

and health got significantly worse as socio-economic

status declined (0.74 times for first quintile to fifth

quintile).

For health status, after controlling for all other variables

in the regression model (linear regression), the score for

males increased by 7.07 per unit change in the female score;

for age group 50�59, the score increased by 8.76 per unit

change and 2.51 times per unit change for age group 70�79

compared to those in age group 80 years and over; for no

formal education the score declined by 1.22 per unit change

and by 0.74 per unit change for those with less or equal to

six years compared to those with more than six years of

schooling; for single persons the score declined by 0.08 per

unit change of those in a current partnership; and for first

Table 1. Background characteristics (%) of the study popu-

lation in Matlab, Bangladesh

Variables

Respondents

(N�3,990)

Non-respondents

(N�31,425)

Sex

Men 49.9 47.4

Women 50.1 52.6

Age group (years)

50�59 45.3 44.0

60�69 33.8 34.0

70�79 17.1 17.3

80 and over 3.8 4.7

Education level


Less than or equal to 6 years 28.7 28.7


Marital status

Now single 23.8 29.7


Socio-economic quintile

First quintile 15.2 13.6

Second quintile 16.6 16.8

Third quintile 17.5 20.3

Fourth quintile 23.2 23.5


Mean number of household

members

5.4 4.9

Percentage of household

members aged 50 years

and over

18.6 16.6

Table 2. Distribution of health indicators by age and sex for

4,037 adults aged 50 and over in Matlab, Bangladesh

Indicators

Men

(N�2,016)

Women

(N�2,021)

Self-rated health (Percentage of very good/good/moderate)

50�59 years 87.2 77.4

60�69 years 77.9 60.1

70�79 years 64.4 42.9


Mean health status (score)

50�59 years 65.7 57.7

60�69 years 62.2 55.4

70�79 years 59.2 51.3


Mean quality of life (score)

50�59 years 80.3 77.3

60�69 years 79.0 74.7

70�79 years 77.8 72.1


Mean functional ability level (score)

50�59 years 84.0 62.1

60�69 years 76.1 54.5

70�79 years 66.3 45.8




socio-economic quintile the score declined by 1.04 per unit

change compared to those in fifth quintile.

Table 4 shows the multivariate relationship of quality

of life (WHOQoL) and disability level (WHODASi) by

socio-demographic characteristics. After controlling for

all other variables in the regression model (linear regres-

sion), the WHOQoL score for males increased by 2.01

per unit change in female score; for age group 50�59 the

score increased by 3.42 per unit change and by 0.87 per

unit change for age group 70�79 compared to those in age

group 80 years or more; for single persons the score

decreased by 4.04 per unit change of those in a current

partnership; for no formal education the score decreased

by 0.81 per unit change, and by 0.31 per unit change for

those with less or equal to 6 years compared to those with

six years or more schooling; and for the first socio-

economic quintile the score decreased by 2.95 per unit

change and by 0.93 per unit change for those in the

fourth quintile compared to those in the fifth quintile.

For functional ability level, after controlling for all

other variables in the regression model (linear regression),

the score for males increased by 20.17 per unit change in

the female score; for age group 50�59 the score increased

by 25.49 per unit change and by 8.96 per unit change for

those in age group 70�79 compared to those 80 years or

more; for no formal education the score decreased by

4.31 per unit change and by 2.66 per unit change for

those with less or equal to 6 years compared to those with

six or more years of schooling; and for the first socio-

economic quintile the score decreased by 2.32 per unit

change compared to those in fifth quintile.

All four health indicators (self-rated health, health

state, quality of life and disability level) show that males,

those who were younger, educated and those in higher

socio-economic groups reported better health, compared

to females, older age groups, illiterates and those in lower

socio-economic groups.

Table 5 shows the interrelationship of different health

indicators. Results show that all four health indicators are

highly significantly related to each other.

DiscussionBangladesh is currently passing through the third stage

of demographic transition, where both fertility and

mortality rates are at relatively low levels. Such as

demographic transition has produced a huge youthful

population with a growing number of older people (4),

where disease patterns are changing from infectious to

Table 3. Multivariate models of factors associated with self-rated health (logistic regression) and health state (linear regression)

for 4,037 adults aged 50 and over in Matlab, Bangladesh

Variables Self-rated health (Exponent of b and 95% CI) Health status (b coefficient and 95% CI)

Sex (ref: women)

Men 2.19 (1.83, 2.62)** 7.07 (6.48, 7.66)**

Age group (ref: 80 years and over)

50�59 years 7.70 (5.34, 11.09)** 8.76 (7.44, 10.07)**

60�69 years 4.06 (2.84, 5.08)** 5.95 (4.64, 7.25)**

70�79 years 2.07 (1.43, 2.99)** 2.51 (1.15, 3.87)**

Education level (ref: more than 6 years)

No formal education 0.74 (0.57, 0.95)* �1.22 (�1.98, �0.46)**

Less or equal to 6 years 0.87 (0.67, 1.13) �0.74 (�1.51, 0.03)***

Marital status (ref: in current partnership)

Now single 0.97 (0.79, 1.18) �0.08 (�0.78, 0.63)

Proportion aged 50 years and over in the household (ref: ]0.75)

0.25 1.06 (0.82, 1.38) �0.03 (�0.93, 0.87)

0.25�0.49 0.97 (0.75, 1.25) �0.08 (�0.95, 0.79)

0.50�0.74 0.83 (0.63, 1.10) �0.55 (�1.51, 0.41)

Socio-economic quintile (ref: Fifth quintile)

First quintile 0.74 (0.58, 0.94)* �1.04 (�1.85, �0.23)*

Second quintile 0.81 (0.64, 1.02)*** �1.33 (�2.10, �0.57)**

Third quintile 0.78 (0.62, 0.98)* �0.90 (�1.64, �0.15)*

Fourth quintile 0.93 (0.76, 1.15) �0.56 (�1.24, 0.11)

*PB0.05; **PB0.01; ***PB0.10.



non-infectious (2). Traditionally, older people are viewed

in this society as an integral part of the family and used to

enjoy absolute authority over the younger generation;

however, the status of older people is under pressure due

to demographic, social and economic change (19).

As a result of mortality decline during the past few

decades, life span has increased significantly in Bangladesh

but it is not known whether health status has improved

during the increased life span. The study found that all

four health indicators (self-rated health, health state,

quality of life and ability level) deteriorated with

increasing age. The finding is in agreement with a recent

study from Matlab that the prevalence of chronic disease

increased with age (20). It is likely that this population will

need more support (physical/co-residence, social and

economic) as the number of older people is increasing

rapidly along with an increase in chronic diseases.

In Bangladesh, older females survive better than males

(2) but health indicators from the current study (self-

rated health, health state, quality of life and disability

Table 5. Inter-relationship of different health indicators in order persons, Matlab, Bangladesh

Quality of life Disability level Health state Self-rated health

Quality of life

Disability level x2�526.7

PB0.001

Health state x2�355.8 x2�645.6

PB0.001 PB0.001

Self-rated health x2�313.3 x2�303.8 x2�499.2

PB0.001 PB0.001 PB0.001

Note: Health indicators (categories): self-rated health (very good, good, moderate�1 and bad/very bad�0); Health status (IRT health

�55.2�1 and 555.2�0); Quality of life (WHOQoL 580.0�0 and�80.0�1); Disability level (WHODASi 581.0�0 and�81.0�1).

Table 4. Multivariate models (linear regression) of factors associated with quality of life and functional ability level for 4,037

adults aged 50 and over in Matlab, Bangladesh

Variables Quality of life (b coefficient and 95% CI) Functional ability level (b coefficient and 95% CI)

Sex (ref: women)

Men 2.01 (1.68, 2.34)** 20.17 (18.82, 21.52)**

Age group (ref: 80 years and over)

50�59 years 3.42 (2.69, 4.16)** 25.49 (22.50, 28.48)**

60�69 years 2.07 (1.34, 2.80)** 18.08 (15.00, 21.06)**

70�79 years 0.87 (0.11 1.63)* 8.96 (5.86, 12.06)**

Education level (ref: more than 6 years)

No formal education �0.81 (�1.23, �0.38)** �4.31 (�6.05, �2.57)**

Less or equal to 6 years �0.31 (�0.75, �0.11) �2.66 (�4.42, �0.89)**

Marital status

Now single (ref: in current partnership) �4.04 (�4.43, �3.64)** 0.19 (�1.42, 1.82)

Proportion aged 50 years and over in the household (ref: ]0.75)

0.25 �0.23 (�0.74, 0.26) 0.34 (�1.70, 2.40)

0.25�0.49 �0.04 (�0.53, 0.44) 0.80 (�1.19, 2.80)

0.50�0.74 �0.04 (�0.57, �0.50) �0.37 (�2.57, 1.83)

Socio-economic quintile (ref: least poor quintile)

Poorest quintile �2.95 (�3.41, �2.50)** �2.32 (�4.17, �0.48)*

Second quintile �2.29 (�2.71, �1.86)** �2.04 (�3.76, �0.30)*

Third quintile �1.40 (�1.82, �0.98)** �1.46 (�3.16, 0.23)

Fourth quintile �0.93 (�1.31, �0.55)** �0.72 (�2.27, 0.81)

*PB0.05; **PB0.01.



level) demonstrate that females are worse-off than males

during old age. However, it was reported that the health

disadvantage for women reflect their ‘greater sensitivity’

to health conditions (12). In this society, where women

continue to be valued less than men as documented in the

past (21), older women’s health reflects their lifelong

experience of discrimination, deprivation and neglect (6).

Traditionally, older women also own fewer assets and

have less control over family income, and a recent study

from Matlab reported that females experience more

chronic disease than their male counterparts (20).

All four health indicators documented that health

is better among educated/rich than uneducated/poor

people. The finding is also in agreement with mortality

patterns, in which educated/rich people had lower

mortality than uneducated/poor (2). Some years ago, it

was reported (22) that socio-economic differentials in

mortality indicate that a degree of success has been

achieved in one section of the community that has not

been achieved in others. In Matlab (20), it has been

documented that some chronic diseases (stroke, heart

disease, diabetes) increase with increased education while

others (joint pain, pulmonary, hypertension, cancer)

decrease.

All four health indicators were found to be interrelated

and these indicators also showed similar patterns

by socio-demographic characteristics. This indicates that

these health indicators, although measuring different

dimensions of health, had some common characteristics.

Preliminary analysis of the same dataset show that these

four health indicators are also predictors of subsequent

mortality (23).

To improve the health of the population, it is im-

portant to know their health status in advance rather

than just before death. The findings of this study have

policy implications in terms of assessing the overall

burden of diseases and effectiveness of health systems.

Moreover, the study indicates that health intervention

programmes should be targeted to those who suffer and

need most: the older, female and uneducated/poor people.



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*Abdur RazzaqueHDSUICDDR,BMohakhali, Dhaka-1212BangladeshEmail: [email protected]



Health and quality of life among olderrural people in Purworejo District,IndonesiaNawi Ng1,2,3*#, Mohammad Hakimi2,3, Peter Byass1#,Siswanto Wilopo2,3 and Stig Wall1#

1Department of Public Health and Clinical Medicine, Centre for Global Health Research,Epidemiology and Global Health, Umea University, Umea, Sweden; 2Purworejo Health andDemographic Surveillance System, Faculty of Medicine, Gadjah Mada University, Yogyakarta,Indonesia; 3INDEPTH Network, Accra, Ghana

Introduction: Increasing life expectancy and longevity for people in many highly populated low- and middle-

income countries has led to an increase in the number of older people. The population aged 60 years and over

in Indonesia is projected to increase from 8.4% in 2005 to 25% in 2050. Understanding the determinants of

healthy ageing is essential in targeting health-promotion programmes for older people in Indonesia.

Objective: To describe patterns of socio-economic and demographic factors associated with health status, and

to identify any spatial clustering of poor health among older people in Indonesia.

Methods: In 2007, the WHO Study on global AGEing and adult health (SAGE) was conducted among 14,958

people aged 50 years and over in Purworejo District, Central Java, Indonesia. Three outcome measures were

used in this analysis: self-reported quality of life (QoL), self-reported functioning and disability, and overall

health score calculated from self-reported health over eight health domains. The factors associated with each

health outcome were identified using multivariable logistic regression. Purely spatial analysis using Poisson

regression was conducted to identify clusters of households with poor health outcomes.

Results: Women, older age groups, people not in any marital relationship and low educational and socio-

economic levels were associated with poor health outcomes, regardless of the health indices used. Older

people with low educational and socio-economic status (SES) had 3.4 times higher odds of being in the worst

QoL quintile (OR�3.35; 95% CI�2.73�4.11) as compared to people with high education and high SES. This

disadvantaged group also had higher odds of being in the worst functioning and most disabled quintile

(OR�1.67; 95% CI�1.35�2.06) and the lowest overall health score quintile (OR�1.66; 95% CI�1.36�2.03).

Poor health and QoL are not randomly distributed among the population over 50 years old in Purworejo

District, Indonesia. Spatial analysis showed that clusters of households with at least one member being in the

worst quintiles of QoL, functioning and health score intersected in the central part of Purworejo District,

which is a semi-urban area with more developed economic activities compared with other areas in the district.

Conclusion: Being female, old, unmarried and having low educational and socio-economic levels were

significantly associated with poor self-reported QoL, health status and disability among older people in

Purworejo District. This study showed the existence of geographical pockets of vulnerable older people in

Purworejo District, and emphasized the need to take immediate action to address issues of older people’s

health and QoL.





Keywords: adult health; health status; clustering; quality of life; disability; ageing; Purworejo; Indonesia; INDEPTH

WHO-SAGE


#Editor, Nawi Ng, Deputy Editor, Peter Byass, Chief Editor, Stig Wall, have not participated in the review and decision process for this paper.


Global Health Action 2010. # 2010 Nawi Ng et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproductionin any medium, provided the original work is properly cited.

78



Advances in public health and medical technolo-

gies have driven population growth in the last

century. Increasing life expectancy and longevity

in many highly populated low- and middle-income

countries has led to an increased number of older people.

In 2006, about 500 million people (7.5%) of the 6.5 billion

world population were aged 65 years and over, and this

number is projected to double by 2030, to represent

12.5% of the global population (1, 2). During 2007�2050,

the population in low- and middle-income countries is

projected to increase by 61% (3). By 2030, the population

aged 65 years and over is projected to increase by 140% in

developing countries (1, 4). About 53% of this older

population lives in Asia, home to 61% of the world’s

population.

Indonesian population structure has shifted signifi-

cantly towards an ageing population since 1950. The total

fertility rate (TFR) has decreased from 5.5 in 1950�1955

to 2.4 children per woman in 2000�2005. Life expectancy

has increased from 37.5 to 68.6 years during the same

period. As a consequence, the population aged 60 years

and over increased from 6.2% in 1950 to 8.4% in 2005

and is projected to increase to 23.7% in 2050 (5). The

Indonesia National Socio-Economic Survey in 2004

showed variation in the proportion of older people across

the provinces in Indonesia ranging from 2% in Papua to

12.8% in Yogyakarta. The proportion of older people in

Central Java was about 9.5%. The survey also showed

that about one-third of those over 60 years reported an

illness during the month prior to the survey with no

differences between rural and urban areas (6).

The expected growth in the ageing population in

Indonesia poses significant challenges to the health

system and government. Currently, the health system

focuses more on battling infectious diseases such as

malaria, tuberculosis, diarrhoea and dengue fever. Re-

sources have not been allocated proportionally to the

larger and increasingly threatening burden of chronic

non-communicable diseases such as heart diseases, stroke,

diabetes, cancer and hypertension (7). Changing family

structure and patterns of work and retirement pose

immediate economic challenges, particularly to the social

insurance systems. The pensions and social insurance

system only cover a small percentage of the Indonesian

population who work in the formal sector, which excludes

most of the older population. Indonesian social insurance

schemes, which are limited to covering formal workers in

productive age groups and poor population sectors, are

not designed to anticipate an ageing population (8). The

lack of a social safety net increases the vulnerability of

older people to poor health and quality of life (QoL),

mostly due to the threat of chronic illness from non-

communicable diseases, and lack of financial support for

accessing health care.

Indonesian older people play an important role in their

families and their society. In traditional Javanese society,

older parents typically co-reside with one of their young-

est children, usually a daughter (extended family), who

accepts responsibility to take care of them until they die.

Well-off older persons provide key intergenerational

support for families (9), have high social status and are

respected in their communities. Javanese people highly

respect older people because of the value placed on

lineage. Though many of the older people in Indonesia,

particularly those who are widowed, live in poverty, they

also contribute significantly to the rural economy; many

engage actively in agricultural industries as non-skilled

labour. Most of them, particularly women, have low

education. Older people who are still working are less

economically dependent on their next-of-kin (10).

Elderly care and intergenerational relationships have

become an emerging issue, particularly for those who

live in urban areas, as societal values change from

extended family to nuclear family structures, and younger

generations become more mobile in search of better

career opportunities.

A significant amount of research and literature on

older people in Indonesia is available, mainly from

anthropological studies focusing on the socio-cultural

aspects of ageing, intergenerational relationships and

changes in family structure and support for older people

(10�13). However, studies on health status and QoL

among the older population are largely lacking, and very

little is known about morbidity among Indonesia’s older

population (6, 14).

Self-reported health has been identified as a strong

predictor of morbidity and subsequent mortality (15, 16).

While evidence has mainly come from developed coun-

tries, it can also be extended to low-income settings such

as Indonesia, as shown by Frankenberg and Jones in the

panel data analysis of the Indonesia Family Life Survey

(IFLS) in 1993, 1997 and 2000. The IFLS data shows that

individuals who perceived their health as poor are more

likely to die, and the association remains even after being

adjusted for physical function, physical illness and

depression, weight, height and indicators of high blood

pressure (17). An understanding of older people’s health

and well-being will provide important information on any

special health care needs and demand for services, and

this knowledge can be used to guide planning of health

interventions and programmes (18).

The primary objectives of this study are to describe

patterns of socio-economic and demographic factors that

determine the health status of older people in Indonesia.

The secondary objective is to identify the clustering

pattern of poor health among them. Knowledge on the

determinants of health status and spatial distribution of

poor health will help to improve our understanding of

older people’s health, thus providing evidence for the

Health of older people in Indonesia


district authorities in promoting better health status and

developing targeted interventions for disadvantaged po-

pulations in their specific geographical areas.

Methods

Study area and participantsThe WHO Study on global AGEing and adult health

(SAGE) (19) was conducted in a functioning Health and

Demographic Surveillance System (HDSS) site in Pur-

worejo District, Central Java, Indonesia. The Purworejo

HDSS is a member of the INDEPTH Network, which

consists of 38 HDSS sites in Africa, Asia and Oceania

(http://www.indepth-network.org). The district is located

between longitudes 1098 and 1108E and latitude 78S,

about 60 km from Yogyakarta City. It covers an area of

1,035 km2, spanning a diverse geographical area from the

coast in the south to the mountains in the north. The

district has 750,000 inhabitants (26% under 15 years old,

63% in the economically productive age group and 11%

over 65 years old). Eleven percent of those over six years

of age have had no formal education. About 89% of the

population live in rural areas. The Purworejo HDSS has

been running since 1994 covering a total of 600,000

person-years of observation (20). In 2006, the total

population under surveillance was 55,000 (13,443 house-

holds living in 128 enumeration areas). The study was

conducted between January and June 2007. We identified

and invited all adults aged 50 years and over to

participate in the study, a total of 14,958 people.

InstrumentsThis study used the modified and shortened version of

the INDEPTH WHO-SAGE questionnaire (19), con-

sisting of subjective well-being and QoL, function and

disability, and health status description modules. All the

questionnaires were translated into Bahasa Indonesia and

were pilot-tested during November�December 2006.

Data collection and managementHousehold visits were conducted by trained surveyors

who administrated the survey questionnaire. Supervisors

conducted spot-checks and revisits to 5% of the partici-

pants to ensure the quality of data obtained. All

questionnaires were checked and validated by field

supervisors and then sent to the central office in Gadjah

Mada University, Yogyakarta, for data entry. Data entry

was conducted in D-Entry software and the SAGE data

was linked to the surveillance database. Double entry was

also conducted on 5% of total questionnaires. Demo-

graphic variables (such as age, highest level of education

completed, marital status, household size and proportion

of person over 50 years old within household) and

geographic coordinates of each household were extracted

from the surveillance database. The SAGE dataset was

also linked to data from the household socio-economic

survey conducted in 2004. The socio-economic survey

collected data on household characteristics and owner-

ship of non-disposable and disposable goods, and socio-

economic status (SES) quintiles were derived through

principal component analysis (21). The final merged

dataset was converted into STATA data format for data

analysis.

Data analysisThree outcome measures were used in the analysis: self-

reported QoL, self-reported problems in functioning

and disability, and overall health status. Each of those

measures was developed as composite indices from series

of validated questions (22).

The composite index for self-reported QoL was

adapted from the WHO Quality of Life (WHOQoL)

tool (23). The index was derived from eight questions

assessing respondent’s thoughts about their life and life

situation, satisfaction with themselves and their health,

ability to perform daily living activities, personal relation-

ships, living conditions and overall life. Answers to the

Likert scale were summed up and later transformed to a

0�100 scale with 0 representing the worst QoL and 100

representing the best QoL.

Questions to assess problems in functioning and

disability were adapted from the WHO Disability Assess-

ment Schedule (WHODAS) 12-item instrument (24). The

series of questions assessed any difficulties faced by the

respondents in performing different daily life activities

due to their health conditions. The responses were

collected on the Likert scale and different weights were

assigned to responses from different questions. The total

score was then inverted to transform it to an index

between 0 and 100, with 0 representing extreme problems

or complete disability and 100 representing a total

absence of disability, termed WHODASi. The use of

WHODAS in the INDEPTH WHO-SAGE study has

been described elsewhere (22).

Overall health status was measured using self-reported

health derived from eight health domains, including

affect, cognition, interpersonal relationships, mobility,

pain, self-care, sleep/energy, and vision (19). Two ques-

tions in each domain, which measured the difficulties

faced by the respondents in performing activities, were

put to the respondents and responses were collected using

a five-response scale. Item response theory with partial

credit model was used to generate a composite health

status score. Following each item calibration using chi-

squared fit statistics to evaluate its contribution to the

composite health score, the raw composite score was

transformed through Rasch modelling into a continuous

cardinal scale, with 0 representing worst health and

100 representing best health (22). The psychometric

Nawi Ng et al.



properties of the health score have been evaluated else-

where (25).

All the three continuous indices (WHOQoL, WHO

DASi and overall health status) were later categorised

into quintiles, independently. The three outcome mea-

sures were defined as being in the worst quintiles for

QoL, functioning and disability, and overall health,

as defined by the three indices, respectively. Socio-

demographic and economic factors associated with being

in the worst quintile of each health outcome were

identified through multivariable logistic regression. The

SES quintiles were later regrouped into low (first and

second), middle (third) and high (fourth and fifth)

quintiles. Educational levels were defined as low (no

formal education), medium (less than 6 years of educa-

tion) and high (at least 6 years of education). As there

was moderate correlation between educational level and

socio-economic groups, we combined the educational and

socio-economic groups into five categories in the analysis.

The regression analysis was performed separately for each

outcome measure. All analyses were conducted using

STATA statistical software version 10.0.

The SAGE data containing individual observations on

the health outcomes was transformed into household

level data, by counting the number of individuals in each

household belonging to the worst quintile of each index.

This household level data was later merged with the

geographical coordinates in the surveillance area. The

purely spatial analysis using Poisson probability model-

ling was conducted to identify clusters of households with

at least one member being in the worst quintile of QoL,

disability and health score, independently. The total

number of people aged 50 years and over in each

household was used as the population in the analysis.

Monte Carlo hypothesis testing was used with 999

replications and a significance level of 0.05. The risk

estimates for each cluster were identified. The analysis

was conducted using SaTScanTM software, version 7.0

(26).

The Research Ethics Committee at Gadjah Mada

University and Purworejo District Health Offices ap-

proved the SAGE study in Purworejo District, Indonesia.

Documented informed consent was obtained from each

individual prior to the study.

ResultsA total of 14,958 individuals aged 50 years and over were

visited, with data obtained from 12,459 individuals

(83%). Cleaned and complete data from 11,753 indivi-

duals were available for analysis. The background char-

acteristics of the respondents and the non-respondents

(n�2,564) were presented in Table 1. Reasons for not

participating in the study included: could not be reached

after two visit attempts (81%), refusal (8.3%), died (5%)

and out-migration (5.7%).

Over half of the study participants were women (54%),

and the majority (84%) had less than 6 years of

education. Only 7.2% of the study participants were

aged 80 years and over. The data showed that 29% of the

participants were not in a marital relationship but most

of the participants did not live alone. The average number

of household members was 3.5. As the study covered

all older people in the surveillance area, the house-

hold socio-economic quintiles presented in this study

Table 1. Background characteristics of respondents and

non-respondents among adults aged 50 years and over in

Purworejo, Indonesia

Variables

Respondents

(N�11,753)

Non-respondents

(N�2,564)

Sex, n (%)

Men 5,420 (46.1) 1,285 (50.1)

Women 6,333 (53.9) 1,278 (49.9)

Age, mean (standard

deviation)

64.1 (9.4) 65.5 (11.5)

Age group, n (%)

50�59 years 4,344 (36.9) 928 (36.2)

60�69 years 4,045 (33.3) 709 (27.7)

70�79 years 2,644 (22.7) 595 (23.2)

80 years and over 720 (7.2) 331 (12.9)

Education level, n (%)

No formal education 3,440 (29.6) 659 (27.4)


6 years

6,459 (54.7) 1,257 (52.2)

More than 6 years 1,854 (15.7) 491 (20.4)

Marital status, n (%)

In current partnership 8,400 (71.0) 1,925 (77.6)

Being single 3,353 (29.0) 556 (22.4)

Socio-economic quintile, n (%)

First quintile 2,394 (20.4) 225 (17.1)

Second quintile 2,317 (19.8) 259 (19.6)

Third quintile 2,390 (20.3) 248 (18.8)

Fourth quintile 2,387 (20.3) 303 (23.0)

Fifth quintile 2,265 (19.2) 285 (21.6)

Number of household

member, mean

(standard deviation)

3.5 (1.7) 3.5 (1.8)

Proportion of household member aged 50 years and over, n (%)

B25% 995 (8.6) 324 (12.9)

25�49% 3,288 (28.0) 646 (25.6)

50�74% 3,853 (32.6) 733 (29.1)

]75% 3,617 (30.9) 818 (32.5)

Note: All figures were weighted to the Purworejo HDSS popula-

tion in 2007.



reflected the quintiles in the whole surveillance popula-

tion (Table 1).

Table 2 presents summary statistics of three different

health indices of WHOQoL, WHODASi and overall

health status scores across different age groups and sexes.

Overall, a higher proportion of women aged over 50 years

in Purworejo District were categorised in the worst

quintiles of health indices as compared to men. These

patterns were observed consistently in all age groups. A

larger discrepancy in functioning and disability and

health status was observed across age groups in men

and women. The differences of QoL index were, however,

less prominent across age groups in men and women. The

results showed that function, QoL and overall health

status decreased substantially among the oldest age-

group, with more than 50% of those over 80 belonging

to the worst function and disability and overall health

status quintiles.

Being in the older age group, having low education and

being in a low socio-economic group, and not being in a

marital relationship were significantly associated with

higher odds of being in the worst quintiles for QoL,

functioning and disability, and overall health, respec-

tively. The multivariable analysis showed that respon-

dents aged over 80 years were more than 3.3 times more

likely to be in the worst quintile of QoL compared to

those aged between 50 and 59 years. They were 12.6 and

10.6 times more likely to be in the worst functioning and

overall health score quintiles, respectively. The education

and socio-economic gradient was also prominent for QoL

reporting, with individuals in the low SES group who had

a low level of education being 3.4 times more likely be in

the worst quintile of QoL compared to those with high

education in the high SES group (Table 3 and Fig. 1). The

overall effects of low SES and education were less

prominent, though statistically significant, for being in

the worst disability and overall health status quintiles.

The spatial analysis revealed clusters of households

with at least one member being in the worst quintile of

QoL, functioning and disability, and overall health,

respectively (Fig. 2). Clusters of households with a

member being in the worst quintile of self-reported

QoL were identified in the northern part of the district,

which is a mainly hilly and mountainous area. This area

is less developed, less urbanised and contains many

households categorised in the poorest socio-economic

quintile. In contrast, the clusters of households with

at least one member being in the worst quintile of

overall health status were identified in the mid-southern

part of the district, mainly highly populated semi-urban

and coastal areas. This part of Purworejo District is

mainly low land covering four main sub-districts of

Bayan, Banyuurip, Kutoarjo and Purworejo. These are

the four most populated sub-districts in Purworejo

District with a population density ranging from 918 to

1,700 inhabitants per km2. Most households in these

areas fall within the richest socio-economic quintile with

the majority of people over 50 having had at least six

years of education.

DiscussionIn addition to risks for the oldest old, our study showed

that people with low levels of education and SES

had higher odds of having poorer self-reported QoL

and health. Economic instability during old age may

Table 2. Distribution of health indices by age-group and sex

among 11,753 adults aged 50 years and over in Purworejo

District, 2007

Indices Men Women

WHO Quality of Life (QoL) score

Mean score (95% CI)

50�59 years 75.5 (75.3�75.7) 75.1 (74.9�75.3)

60�69 years 74.6 (74.3�74.8) 73.9 (73.7�74.1)

70�79 years 73.3 (72.9�73.6) 72.6 (72.3�72.9)

80 years and over 71.7 (70.9�72.4) 71.5 (70.7�72.3)

Percentage in the worst quintile (95% CI)

50�59 years 11.8 (10.4�13.2) 14.7 (13.2�16.1)

60�69 years 17.3 (15.5�19.1) 22.0 (20.3�23.7)

70�79 years 25.9 (23.5�28.4) 32.0 (29.6�34.4)

80 years and over 37.4 (32.4�42.3) 42.9 (37.7�48.1)

WHO Disability Assessment Schedule (WHODASi) score

Mean score (95% CI)

50�59 years 93.2 (92.8�93.6) 91.2 (90.8�91.7)

60�69 years 88.4 (87.7�89.0) 84.2 (83.6�84.9)

70�79 years 81.0 (80.0�81.9) 76.2 (75.2�77.2)

80 years and over 70.9 (68.7�73.1) 66.4 (64.1�68.7)


50�59 years 5.5 (4.5�6.5) 8.8 (7.7�10.0)

60�69 years 14.3 (12.6�15.9) 23.4 (21.6�25.1)

70�79 years 28.0 (25.4�30.5) 40.1 (37.5�42.6)

80 years and over 52.0 (46.8�57.1) 59.2 (54.0�64.3)

Overall health score

Mean score (95% CI)

50�59 years 77.3 (76.9�77.8) 74.7 (74.3�75.1)

60�69 years 73.0 (72.5�73.5) 69.9 (69.5�70.3)

70�79 years 68.4 (67.9�69.0) 66.0 (65.6�66.5)

80 years and over 64.1 (63.2�65.1) 62.9 (61.9�63.8)


50�59 years 6.0 (5.0�7.0) 10.6 (9.4�11.9)

60�69 years 15.6 (13.9�17.2) 27.2 (25.4�29.1)

70�79 years 30.7 (28.1�33.3) 43.6 (41.0�46.1)

80 years and over 50.4 (45.3�55.5) 60.8 (55.7�65.9)

Note: All figures were weighted to the Purworejo HDSS popula-

tion in 2007.

Nawi Ng et al.


Table 3. Three different models in assessing factors associated with poor health indices among 11,753 adults aged 50 years and over in Purworejo District, 2007

Model 1: Being in the worst QoL

quintile as outcome

Model 2: Being in the worst WHODASi

quintile as outcome

Model 3: Being in the worst health status

score quintile as outcome

Variables

Unadjusted ORs

(95% CI)

Adjusted ORs

(95% CI)

Unadjusted ORs

(95% CI)

Adjusted ORs

(95% CI)

Unadjusted ORs

(95% CI)

Adjusted ORs

(95% CI)

Sex

Men 1 1 1 1 1 1

Women 1.30 (1.19�1.42) 1.13 (1.02�1.26) 1.57 (1.44�1.73) 1.39 (1.25�1.55) 1.69 (1.55�1.85) 1.50 (1.35�1.66)

Age group

50�59 years 1 1 1 1 1 1

60�69 years 1.62 (1.45�1.83) 1.41 (1.25�1.59) 3.09 (2.69�3.55) 2.75 (2.38�3.17) 3.09 (2.71�3.52) 2.73 (2.38�3.11)

70�79 years 2.69 (2.38�3.04) 2.09 (1.83�2.39) 6.74 (5.86�7.75) 5.54 (4.76�6.44) 6.55 (5.73�7.48) 5.34 (4.63�6.16)

80 years and over 4.38 (3.69�5.22) 3.32 (2.75�4.01) 16.1 (13.3�19.4) 12.6 (10.3�15.5) 13.6 (11.4�16.4) 10.6 (8.69�12.9)

Marital status

Being single 1.86 (1.7�2.05) 1.32 (1.16�1.49) 2.74 (2.5�3.01) 1.56 (1.38�1.77) 2.79 (2.55�3.06) 1.56 (1.38�1.76)

In current partnership 1 1 1 1 1 1

Percentage aged 50 years and over in the household

B25% 0.88 (0.74�1.05) 0.85 (0.64�1.13) 1.06 (0.9�1.25) 0.76 (0.57�1.02) 1.02 (0.87�1.19) 0.81 (0.60�1.07)

25%�49% 0.84 (0.75�0.94) 1.05 (0.87�1.27) 0.80 (0.71�0.89) 0.92 (0.75�1.13) 0.73 (0.65�0.82) 0.90 (0.73�1.09)

50%�74% 0.73 (0.65�0.82) 0.96 (0.84�1.10) 0.68 (0.61�0.76) 0.93 (0.81�1.07) 0.64 (0.57�0.71) 0.89 (0.78�1.02)

]75% 1 1 1 1 1 1

Family size 0.96 (0.93�0.99) 1.04 (0.99�1.09) 0.98 (0.95�1.01) 1.08 (1.02�1.13) 0.96 (0.93�0.98) 1.05 (1.00�1.10)

Education and SES

High SES, high education 1 1 1 1 1 1

High SES, low-middle

education

1.78 (1.46�2.16) 1.37 (1.12�1.68) 2.31 (1.92�2.79) 1.36 (1.12�1.66) 2.33 (1.95�2.79) 1.39 (1.15�1.68)

Middle SES, all education

levels

2.22 (1.82�2.71) 1.77 (1.44�2.16) 2.36 (1.95�2.87) 1.44 (1.18�1.77) 2.25 (1.87�2.71) 1.37 (1.12�1.66)

Low SES, middle-high

education

2.81 (2.32�3.41) 2.47 (2.03�3.01) 1.77 (1.46�2.15) 1.27 (1.04�1.57) 1.81 (1.51�2.18) 1.30 (1.07�1.58)

Low SES, low education 5.11 (4.21�6.21) 3.35 (2.73�4.11) 4.15 (3.42�5.03) 1.67 (1.35�2.06) 4.21 (3.50�5.06) 1.66 (1.36�2.03)

Note: WHOQoL, World Health Organization Quality of Life; WHODASi, World Health Organization Disability Assessment Schedule. All analyses were weighted to the Purworejo HDSS

population in 2007.

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potentially be more of a threat to the urban older than to

their rural counterparts. The majority of older Javanese in

our study were still engaged in agricultural production

and were typically more economically productive and

stable compared to their urban counterparts. Our data

reaffirmed the results from the IFLS conducted in 1993

that showed older Indonesian men and women often

remain economically active; males and younger age

groups were more active than women and older age

groups. The IFLS data indicated that older men who co-

resided still worked about 30 hours per week, while those

who did not co-reside worked about 38 hours per week.

The IFLS data also showed that the availability of

intergenerational financial transfer does not necessarily

influence parent’s labour supply (27).

Family and local community support for older people

is still reliable in rural Java. Only a very small proportion

of older Indonesians receive a pension as their source of

income (about 13% of males and 4% of females in 1985

with no significant change since then). Those who receive

a pension are mainly urban dwellers who had worked in

government sectors, the military or industries. Pensions

are not paid to urban poor or traditional agricultural

workers (14). The National Social Security Law for poor

people, proposed by the government in 2004, has yet to

be agreed by the legislative body and operationalised by

Fig. 1. The odds ratio for poor health among different education and socio-economic groups among 11,753 adults aged 50 years

and over in Purworejo District, 2007.

Cluster of households with at least one member being in the worst quintiles of quality of lifeCentre: 109.972 °E, 7.640 °S Radius: 13.2 kmRR: 1.51 (p<0.001)

Cluster of households with at least one member being in the worst quintiles of health scoreCentre: 109.890 °E, 7.742 °S Radius: 12.4 kmRR: 1.40 (p<0.001)

Cluster of households with at least one member being in the worst quintiles of functioningCentre: 109.955 °E, 7.741°S Radius: 7.9 kmRR: 1.45 (p<0.001)

Fig. 2. Spatial distribution of poor health indices among 11,753 adults aged 50 years and over in Purworejo District, 2007.

Nawi Ng et al.


the government. This delay has resulted in inefficient and

inappropriate distribution of funds to the needy. The

Indonesian Government has fostered community parti-

cipation in the provision of care and economic support

to the elderly, mainly to reduce community dependence

on the insufficient resources provided directly by the

government.

Living arrangements are an important influence on

care of older people. Our respondents who were not in

any marital relationship reported significantly worse QoL

(OR�1.32), worse functioning (OR�1.56) and worse

health status (OR�1.56) compared to those in a marital

relationship. Traditional Indonesian values mean that

children are supposed to stay together and to take care of

their parents, especially when they are no longer econom-

ically productive. However, changes in social values over

the past few decades have led to increasing migration to

larger cities for better career opportunities, changes that

may have affected elder care practices (10). The IFLS in

1993 showed that 60% of those over 60 years shared a

house with their children. The data shows that older

household heads are less likely to co-reside. The pre-

dictors for co-residence include households with large

numbers of children, households where the family head is

currently married, households in urban areas, or areas

with expensive housing. None of these factors were

identified as significant predictors for the transition to

co-residence with a child in the follow-up survey in 1997

(28). Data from this SAGE survey can be linked to the

longitudinal HDSS data to assess how changes in living

arrangements and migration over time could potentially

affect older people’s welfare and well-being in the study

population.

Our findings show clusters of households with poor

self-reported health, functioning and QoL among older

people. This points to the existence of health inequality in

the study area and signifies the need to identify factors

determining the distributions of poor health outcomes in

this rural population. Knowledge of the epidemiological

burden of poor health outcomes and their associated

factors is an important prerequisite for the government to

develop health promotion and intervention programmes

for the older population in Purworejo District and

throughout Indonesia (6).

Results from this study that show clusters of house-

holds with poor self-reported health outcome may poten-

tially indicate areas with higher risk of subsequent

mortality. Assessing the future morbidity and mortality

patterns longitudinally using this SAGE survey as base-

line data could prove this hypothesis. Results from the

multivariable analysis are supported by the results from

the spatial analysis that showed clusters of households

with poor QoL located in the northern part of Purworejo

District, which is a largely remote, mountainous area that

is not easily accessed by public transportation. This area

has lower socio-economic development and a higher

proportions of the population with low levels of educa-

tion, who might have poor perceptions of health, well-

being and illnesses, and thus have higher morbidity and

lower use of health care services.

The hardworking Javanese population views ‘life as a

continuous series of misfortunes, calamities and hard-

ships which a human being has to experience and to

endure readily’. The Javanese lead an active life through

constant endeavour (ichtiyar) in activities relating to

agricultural production, economic life and social and

family matters. Despite their view of life (ichtiyar), the

Javanese peasant population accepts what comes (nrimo)

and accepts fate willingly (ingkang nrimah), an attitude

which helps them to avoid disappointment or emotional

upset when things go wrong. When discussing the

burdens of life, the Javanese typically surrender and

accept fate (pasrah lan sumarah). Older Javanese people

appear to be content to await death, hopefully sur-

rounded by their children and grandchildren when their

time comes (29). The Javanese acceptance of fate in their

life might explain why the majority reported good health

and QoL when asked in the study.

QoL is one of a number of complex components of

successful ageing covering life expectancy, life satisfac-

tion, mental and psychological health, cognitive function,

physical health and functioning, income, living condi-

tions and arrangements, social support and social net-

works. Measuring QoL is also a complex exercise,

especially among older people (14). In addition to the

above-mentioned aspects, QoL is also very bound by

culture and may represent different constructs in different

settings. Results from health status assessments can

usually be used to predict QoL for older populations;

however, it is not uncommon to observe discrepancies

between these two measures (30).

Service provision for older people, particularly health

promotion and social services, is generally lacking in

Indonesia. Most older people care institutions are based

in urban areas and there is no alternative care for older

people in rural settings. Since the mid-1980s the Ministry

of Health in Indonesia has promoted services to older

people through ‘the Integrated Health Post Service for

Elderly People’ programme (Posyandu Lansia) (31). This

is a community-organised health promotion centre at

village level supervised by staff from the nearest primary

health care centre. The concept of the Integrated Health

Post Service was initially developed to address maternal

and child health issues and later expanded to cover the

ageing population. However, the programme lacks a

strong health promotion dimension, and puts a lot of

focus on the often-inadequate therapeutic aspects of

older people’s illnesses. Activities to promote healthy

ageing and healthy life-styles to enhance older people’s

well-being are mainly lacking in the programme. As



Indonesia is predicted to have the world’s fastest growing

older population during the 1990�2025 period (14), the

government needs to immediately address the issues

through policy and action to promote the well-being

and health of its older population.

Limitations of the studyWhile assessments of self-reported health and QoL

have been extensively researched in many countries,

corresponding methodological developments, particu-

larly in low-middle income country settings, are still

challenging. Efforts have been undertaken to derive

cross-culturally comparable instruments, yet researchers

are still attempting to validate instruments across

different settings through development of new validation

techniques such as vignettes (32, 33). This article has not

addressed the issue of comparability of our results to

other settings. Further analyses that take the rich data on

vignettes into account might provide better insights on

how our data on older people’s well-being can be

compared to data from other settings.

We observed that there were considerably more posi-

tive responses of data obtained from the Likert-scale

questions used in the study, and while this might reveal

the true levels of health and QoL in our study population,

it might also reflect how this rural population valued

their health and life, regardless of the true levels of their

health and QoL. Good protocols and periodic training of

interviewers hopefully reduced the possibility of social

desirability bias in our study.

ConclusionBeing female, old, unmarried and having a low education

and socio-economic level are significant predictors of

self-reported poor QoL and health status, and disability

among older people in Purworejo District. This study

shows the existence of geographic pockets of vulnerable

older people in Purworejo District, and emphasises the

need to take immediate action to address issues on older

people’s health and QoL. Lack of care and services for

older people has to be addressed, and the Indonesian

health system, through its Posyandu Lansia, should

increase the balance of ‘curing sick older people’ and

‘caring for healthy older people and promoting their

health and well-being’.

Acknowledgements

The authors would like to acknowledge Dr. Somnath Chatterji, Dr.

Paul Kowal, and Ms. Nirmala Naidoo of WHO and the INDEPTH

Adult Health working group for their support in data analysis and

interpretation of the data. SaTScanTM is a trademark of Martin

Kulldorff. The SaTScanTM software was developed under the joint

auspices of (a) Martin Kulldorff, (b) the National Cancer Institute

and (c) Farzad Mostashari of the New York City Department of

Health and Mental Hygiene.

Conflict of interest and fundingThis research has been supported by special grants from

the Swedish Council for Social and Work Life Research

(FAS), Grant No. 2003-0075. Coordination for preparing

this article has been supported by the Umea Centre for

Global Health Research, with support from FAS, the

Swedish Council for Working Life and Social Research

(Grant No. 2006-1512).

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*Nawi NgDepartment of Public Health and Clinical MedicineCentre for Global Health Research, Epidemiology and Global HealthUmea University, SE-901 85 Umea, SwedenTel: �46 90 7851391Fax: �46 90 138977Email: [email protected]



http://www.who.int/healthinfo/systems/sage/en/index.html

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Social gradients in self-reportedhealth and well-being among adultsaged 50 and over in Pune District,IndiaSiddhivinayak Hirve1,2*, Sanjay Juvekar1,2, Pallavi Lele1,2 andDhiraj Agarwal1,2

1Vadu Rural Health Program, KEM Hospital Research Center, Pune, Maharashtra, India; 2INDEPTHNetwork, Accra, Ghana

Background: India’s older population is projected to increase up to 96 million by 2011 with older people

accounting for 18% of its population by 2051. The Study on Global Ageing and Adult Health aims to

improve empirical understanding of health and well-being of older adults in developing countries.

Objectives: To examine age and socio-economic changes on a range of key domains in self-reported health

and well-being amongst older adults.

Design: A cross-sectional survey of 5,430 adults aged 50 and over using a shortened version of the SAGE

questionnaire to assess self-reported assessments (scales of 1�5) of performance, function, disability, quality of

life and well-being. Self-reported responses were calibrated using anchoring vignettes in eight key domains of

mobility, self-care, pain, cognition, interpersonal relationships, sleep/energy, affect, and vision. WHO

Disability Assessment Schedule Index and WHO health scores were calculated to examine for associations

with socio-demographic variables.

Results: Disability in all domains increased with increasing age and decreasing levels of education. Females

and the oldest old without a living spouse reported poorer health status and greater disability across all

domains. Performance and functionality self-reports were similar across all SES quintiles. Self-reports on

quality of life were not significantly influenced by socio-demographic variables.

Discussion: The study provides standardised and comparable self-rated health data using anchoring vignettes

in an older population. Though expectations of good health, function and performance decrease with age,

self-reports of disability severity significantly increased with age, more so if female, if uneducated and living

without a spouse. However, the presence or absence of spouse did not significantly alter quality of life self-

reports, suggesting a possible protective effect provided by traditional joint family structures in India, where

older people are social if not financial assets for their children.

Keywords: ageing; self-reported health; well-being; quality of life; INDEPTH WHO-SAGE





Received: 3 November 2009; Revised: 9 May 2010; Accepted: 8 July 2010; Published: 27 September 2010

India’s population is rapidly moving towards an older

age structure consequent on declining mortality and

high fertility in the twentieth century, followed by a

rapid decline in fertility and access to better health care in

recent times as successively larger cohorts step into old

age. The 2001 Census accounts for 7.5% of the popula-

tion being aged 60 years and over i.e. more than 76

million, a sharp increase from 25 million (5.63%) in 1961;

33 million (6%) in 1971; 43 million (6.49%) in 1981; and

57 million (6.76%) in 1991 (1). Life expectancy at birth


Global Health Action 2010. # 2010 Siddhivinayak Hirve et al. This is an Open Access article distributed under the terms of the Creative CommonsAttribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, andreproduction in any medium, provided the original work is properly cited.

88



has likewise increased from 50.5 in 1971 to 60.8 years for

males (49 to 62.5 years for females) in 2001. Kerala and

Maharashtra amongst others have taken the lead in

ushering in this demographic transition in India (2).

This demographic evolution seen in recent decades has

major consequences on the economy, disease burden

facing society and important implications for govern-

mental health, economic and social policies such as

health care for the elderly, retirement benefits, old age

homes, food and personal security, economic growth, etc.

Elderly women face a double burden � not only because

of their advancing age and the prevailing societal gender

differential, but also because they survive without their

life partners (approximately 50% widows amongst elderly

women compared to 15% widowers amongst elderly

men).

India’s older population is projected to increase to 96

million (8.2%) by the next census in 2011, with older

people accounting for approximately 18% of its popula-

tion by 2051. This calls for a shift from demographically

based programmes and policies to economically oriented

policies and programmes which would take care of the

economic, health and social security and quality of life

concerns of older people, so that they can lead a dignified

life in their closing years without adding to the millions

below the poverty line (3).

The Madrid International Plan of Action on Aging

2002 prioritises Advancing Health and Well-being into

old age as a central theme. There is not enough evidence

to say whether longer life expectancy is accompanied by

improved health or simply more years of poor health �especially in the context of changing familial norms

towards small families and altered social and personal

support systems (4).

Of the different patterns of living among older people

such as living with a spouse, or with children or in old age

homes, living alone or with a spouse tends to be most

stable for those aged 65 years and over, whereas living

with a child or grandchild is the most stable living

arrangement for the oldest old (5). Financial dependence

has increased, leisure time and social cohesion have

decreased, and life styles have changed for older people

with a gradual breakdown of the traditional joint family

system (6�8).

Ageing research in India has focused mostly on disease

states and risk factors. Evidence on elderly health,

physical performance and disability is limited to under-

standing the psycho-social or socio-behavioural risk

factors (9�13). There is a shift from the traditional

assessment of health based on risk factors, mortality

and utilisation of health care services to an assessment

that focuses on functioning and disability in multiple

health and related domains of daily life (14). Self-rated

health (SRH) has often been used in large survey settings

to rapidly assess health status, and has been shown to be

related to impending morbidity and mortality. However,

health valuation is multi-faceted and influenced not only

by disease experience and disease perception but also by

health expectations which in turn are influenced by the

socio-cultural context of the individual (15). Conse-

quently, there arises a need to standardise the ways in

which individuals report their health status, as people

from varying socio-cultural backgrounds may rate their

health differently. As self-assessments of health play an

increasing role in measurement of health outcomes, an

approach using ‘anchoring vignettes’ can improve the

utility of SRH by addressing issues of comparability

amongst individuals and populations.

The Study on Global Ageing and Adult Health

(SAGE) aims to improve the empirical understanding

of health and well-being of older adults, and ageing, in

developing countries. This paper explores the socio-

demographic gradients of older people’s health with a

focus on physical performance and function, using the

short SAGE version implemented at the Vadu, India,

Health and Demographic Surveillance System (HDSS).

Methods

Study area and study sampleThe SAGE is designed as a longitudinal data platform in

six countries including India, based on methodological

advances created by the WHO’s World Health Survey

programme (16). The shortened version of SAGE has

been implemented by the INDEPTH Network in eight of

its member DSS sites (Agincourt in South Africa, Ifakara

in Tanzania, Nairobi in Kenya, Navrongo in Ghana,

Filabavi in Viet Nam, Matlab in Bangladesh, Purworejo

in Indonesia and Vadu in India), each site having an

initial enrolment target of 5,000 adults (except the urban

slum-based site of Nairobi with a target of 2,000) aged 50

and over. Of these, Agincourt, Navrongo and Vadu

implemented both the shorter and longer version to

complement the national SAGE implementation in their

respective countries. The Vadu HDSS monitors demo-

graphic trends in its population of some 80,000 people

spread over 22 villages in Pune district in Maharashtra,

India. The SAGE short version was administered in 2007

by trained graduate field-based researchers, to a ran-

domly selected sample of 6,000 individuals aged 50 and

over.

SAGE toolThe SAGE tool has been adapted from the WHO’s World

Health Survey implemented in 70 countries, from 16

other cross-sectional and longitudinal studies on ageing

including the US Health and Retirement Study and

English Longitudinal Study on Ageing, and cognitive

testing of the draft tool in South Africa and Viet Nam in

Social gradients in self-reported health status


2004. The resulting SAGE tool was piloted in India,

Ghana and Tanzania in 2005.

The long SAGE tool comprises three main question-

naires: household, individual and proxy (http://

www.who.int/healthinfo/systems/sage/en/index1.html).

The household questionnaire includes the household

roster, and details of housing, family support networks

and transfers, household assets and income and household

expenditure. The individual questionnaire includes socio-

demographic characteristics, and information on work

history and benefits, health state descriptions, anthropo-

metry, physical and cognitive performance tests and

biomarkers, risk factors and preventive health behaviours,

chronic conditions and health service coverage, health care

utilisation, social cohesion, subjective well-being and

quality of life, and impact of care giving on older people.

The proxy questionnaire was for a proxy respondent if the

interviewer felt that the subject selected did not

possess the cognitive ability to complete the individual

questionnaire.

The shortened version of the SAGE instrument, used

for this study, includes only the salient two to four self-

assessment ratings per domain from the longer SAGE

tool, and covers eight different health domains of

mobility, self-care, pain and discomfort, cognition, parti-

cipation in interpersonal activities, sleep/energy, affect

and vision. The shortened version comprises three main

sections � the first section is a self-assessment of health

state descriptions, function and disability in these eight

domains supplemented by summary self-assessment rat-

ings of overall health and function. The second section is

a self-assessment of overall subjective well-being and

quality of life. The third section includes four sets of 20

vignettes each, applied in rotation to different respon-

dents. Each vignette set covers two of the eight health

domains; with five vignettes for each domain question.

For each self-assessment question, the respondent is

asked to rate his/her own health, function and disability

on a 5-point categorical scale (1 to 5) where the score 1

denotes the best health (categories range from very good

to very bad) or least difficulty in a function or the least

disability (categories range from none to extreme diffi-

culty or cannot do). The SRH measurement is supple-

mented by age, sex, education, socio-economic status

quintiles, and marital status information collected on all

individuals every 6 months as part of routine demo-

graphic surveillance in the Vadu HDSS site.

The anchoring vignette serves to describe a concrete

level in a given health domain that the respondent

evaluates using the same question and response categories

used for self-assessment on that domain. The vignettes

are ‘fixed’ across all respondents so that any variation in

self-assessment can be attributed to differences in re-

sponse category cut-points that reflect the respondent’s

expectations for health � in the same way that the self-

ratings do for the respondent’s own levels of health (17).

The average score for each health domain for each

respondent was calculated. As an example, if the respon-

dent had mild difficulty in washing/bathing or dressing

(score of 2) and no difficulty in taking care of or

maintaining general appearance (score of 1) and mild

difficulty in staying by himself for a few days (score of 2),

then the average score for the respondent for the self-care

domain was calculated as 1.67. Though the self-assessment

ratings were categorical, the summary score average

becomes a continuous variable with a narrow range from

1 to 5. As a result, most of these average scores did not have

normal distributions and hence the average summary score

was re-coded as categorical (1 to 5) with cut points 0�1,

1.1�2, 2.1�3, 3.1�4 and 4.1�5.

A mean WHO Quality of Life score was calculated

based on eight self-assessment ratings addressing satis-

faction with various health domains. The mean WHO-

QoL score ranges from 1 to 5 (where 5 indicates poor

satisfaction with quality of life) and this was transformed

into a 0 to 100 scale, in which a higher score indicates a

higher quality of life.

A WHO Disability Assessment Schedule (WHODAS)

index was calculated based on standard weights applied

to 12 self-assessment ratings of limitations of function in

various health domains. The index ranges from 0 to 100

(where 100 indicates extreme disability), and was then

inverted into a score designated WHODASi, with a range

from 0 to 100 in which a higher score indicated a higher

functional ability.

Health status scores were derived using Item Response

Theory (IRT) parameter estimates in Winsteps, a Rasch

measurement software. IRT uses Maximum Likelihood

Estimation (MLE) which combines the pattern of re-

sponses as well as the characteristics of each specific item

for the multiple health questions (each with multiple

response categories) to produce the final health scores

(18).The health status score was then transformed to a

scale of 0 to 100, with higher scores representing better

health status.

These three 0 to 100 scores thus represent different

aspects of self-reported health, but all follow a 0 to 100

scale in which higher scores represent better outcomes.

The distribution of self-reported responses to each of

the health domains was compared across age groups, sex,

marital status, socio-economic status quintiles and edu-

cational levels for significant differences between the

lowest and highest categories of the function and

performance-rating variables (Kolmogorov Smirnov

equality of distribution test).

ResultsWe analysed data on 5,430 individuals aged 50 and over,

with adequate cognitive ability to complete the survey,

Siddhivinayak Hirve et al.




and who could be linked to the DSS database. Table 1

gives the socio-demographic profile of subjects. As

expected, older women were significantly less educated

than younger ones. A significantly higher proportion of

older women were widows (35%) compared to older men

as widowers (9.5%). There was no significant sex

differential for any other socio-demographic variable.

Fig. 1 shows an example comparing self-ratings with

anchored vignettes (ordered in increasing levels of

difficulty) for two mobility questions. There was good

response consistency in ratings of the five vignettes used

for describing different levels of difficulty in mobility for

both the mobility questions, thus validating the use

of anchoring vignettes for comparison of self-ratings of

mobility between individuals. The average self-rating of

mobility by older adults aged 50 and over lay somewhere

between the level of mobility described by vignette 1 (‘xxx

has no problems with walking, running or using her

hands, arms and legs. S/he jogs 4 kms twice a week’) and

2 (‘XXX is able to walk distances of up to 200 metres

without any problems but feels tired after walking 1 km

or climbing up more than one flight of stairs. S/he has no

problem with day-to-day physical activities such as

carrying food from the market’).

Table 1. Socio-demographic profile of 5,475 adults aged 50 and over in Vadu, India

Males (n�2,850) Females (n�2,625) Test of significance

51.6% 48.4% NS

Mean age (SD) years 63.1 (8.9) 62.5 (8.9) NS

Age group (years)

50�59 (%) 39.5 39.7 NS

60�69 (%) 36.1 38.9

70�79 (%) 19.1 16.5

80 and over 5.1 4.8

Education x2�632.8

No formal education (%) 36.9 8.1 pB0.001

56 years (%) 55.6 79.6

�6 years (%) 7.5 12.2

Marital status

Now single (%) 9.5 35 pB0.001

Socio-economic status

Poorest quintile (%) 10.5 12.7 NS

Second quintile (%) 15.6 15.1

Third quintile (%) 21.2 22.7

Fourth quintile (%) 22.3 19.7

Least poor quintile (%) 30.2 29.6

Mean number of household members (SD) 6.9 (3.5) 6.8 (3.6) NS

Mean number of people aged 50 years and over in household (SD) 1.77 (0.78) 1.77 (0.78) NS

Fig. 1. Self-assessments and vignette ratings for two mobility questions among 5,475 adults aged 50 and over in Vadu, India.



As age increased, self-ratings of difficulty with mobility

increased significantly (Fig. 2). Increasing difficulty in

ratings for self-care, pain, cognition, sleep and vision were

also seen as age increased. However, there was no

statistically significant change in participation in inter-

personal activities or affect with increasing age.

Older males rated higher levels of difficulty in perform-

ing functions and tasks in all health domains compared to

older females. Similar statistically significant trends were

seen for older men or women who had lost their spouse

compared to their married contemporaries (Fig. 3), with

the single elderly female widow rating the most difficulty

in performing tasks in any of the health domains.

Education was directly related to function in all health

domains. At lower educational levels, the self-ratings for

difficulty in performing functions in all health domains

were higher (Fig. 4). Self-ratings of function and dis-

ability were similar across all quintiles of socio-economic

status.

Table 2 shows that males self-reported significantly less

disability, and significantly better overall health than

their female contemporaries. However, there was no

significant difference in self-reported quality of life across

age groups and sex.

Multivariate analysis showed that males self-reported

better health status compared to females; self-reports of

poorer health status increased as people became older;

older people without any formal education were signifi-

cantly more likely (70%) to self-rate their health status as

poor compared to their more educated contemporaries;

and older people without a spouse were marginally more

likely to rate poor health status compared to those living

with their spouse (Table 3). Socio-economic status did

not appear to influence self-reports of health.

Self-reported quality of life was not significantly

influenced by age, sex or education. The elderly popula-

tion belonging to the lowest SES quintiles, as well those

without a living spouse, rate poorer quality of life than

their better off counterparts and those with a living

spouse

DiscussionThe 20th century challenged us with population growth �the 21st century challenge is to cope with ageing. India is

home to one of the world’s largest populations which is

ageing rapidly. It is projected that by 2030 about 45% of the

health burden in India, largely due to non-communicable

diseases, will be borne by the older adults (19). To cope

with an ageing India, policy makers need to be informed

with evidence on interrelated domains including work and

retirement benefits, private wealth and income security, the

implications of family and societal level transfer systems,

health and well-being of the ageing population. As

populations age, the social and economic demands on

Fig. 2. Age differentials in self-ratings in different health domains among 5,475 adults aged 50 and over in Vadu, India.



Fig. 3. Differentials in self-ratings of health domains by marital status among 5,475 adults aged 50 and over in Vadu, India.

Fig. 4. Differentials in self-ratings of health domains by education level among 5,475 adults aged 50 and over in Vadu, India.



families, communities and nations will grow with implica-

tions for the formal and informal social and medical care

systems (20). Well-being, a person’s perceived level of

satisfaction with his work, his marriage, his health and life

as a whole, though hard to measure, continues to be a good

measure of success of governmental programmes and

policies.

The mobility domain vignette example introduced the

concept of vignettes to anchor self-ratings of health in the

mobility domain to a concrete level of function or

disability. Vignettes have been used in the social sciences

since the 1950s (21) and more recently in health and

medicine (22, 23). The difference is that we use vignettes

as scale anchors rather than as random variants of the

same vignette. This means that a vignette describes the

same level of function or health status to all respondents.

Also, the vignette is anchored to the self-rating through

the use of identical questions and response categories.

The underlying assumption for anchoring vignettes is

that of response consistency (i.e. a person evaluates a

hypothetical level of health in the same way s/he would

self-assess his/her own health) and vignette equivalence

(i.e. the level described by a vignette is understood

similarly by individuals independent of age, sex, educa-

tion or any other characteristic). Hence, the primary

purpose of anchoring vignettes linked to self-assessments

is to detect and adjust for differences in response category

cut-points so as to make categorical self-reports more

comparable. This approach allows for studying differ-

ences in categorical cut-points between and within

populations across different socio-demographic groups,

or within populations over time.

This paper underlines the importance of socio-demo-

graphic factors as predictors which influence SRH in

various health domains. Despite lowered expectations of

function and performance, the self-reports of disability

significantly increased with age (biological influence) as

well as environment (socio-cultural influence). The older

woman, though with a longer life expectancy compared

to her male contemporary, is disadvantaged on multiple

fronts � due to her advancing age; due to societal norms

of being a woman which limit her mobility and function;

due to her being less educated, less empowered. This

inability to perform and function and the consequent

deleterious effect on health, are compounded if the older

woman loses her spouse at an early age. The presence or

absence of the spouse of an older person significantly

altered self-reports on health and quality of life. The lack

of significant associations between age, sex, education

and quality of life, seen otherwise with health, needs

further study to understand the linkages between health

and quality of life in its various dimensions.

Table 2. Age and sex differentials in health, disability and

quality of life outcomes for 5,475 adults aged 50 and over in

Vadu, India

Males

(n�2,850)

Females

(n�2,625) p-value


50�59 years 80.0 (13.1) 77.4 (13.4) B0.001

60�69 years 78.3 (13.8) 75.4 (13.5) B0.001

70�79 years 75.4 (14.0) 72.9 (14.1) 0.006

80 years and over 74.9 (15.2) 70.0 (17.7) 0.01


50�59 years 69.8 (11.2) 67.3 (9.7) B0.001

60�69 years 67.8 (9.8) 66.0 (8.7) B0.001

70�79 years 65.9 (9.0) 64.6 (8.5) 0.025

80 years and over 65.9 (9.8) 62.6 (8.9) 0.003


50�59 years 75.3 (4.5) 74.8 (4.5) 0.02

60�69 years 74.8 (4.7) 74.5 (4.5) 0.09

70�79 years 74.1 (5.0) 74.1 (5.2) NS

80 years and over 74.7 (5.4) 73.3 (6.1) 0.049

Table 3. Factors associated with self-rated poor health and

quality of lifea for 5,475 adults aged 50 and over in Vadu,

India

Poor quality of life

OR (95% CI)

Poor health

OR (95% CI)

Sex

Males 1.07 (0.93�1.22) 0.73 (0.64�0.83)

Females 1 1

Age

50�59 years 1 1

60�69 years 1.01 (0.87�1.17) 1.18 (1.03�1.35)

70�79 years 1.13 (0.95�1.36) 1.53 (1.29�1.83)

80 years and over 1.05 (0.78�1.41) 1.78 (1.32�2.39)

Education

No formal education 1.04 (0.77�1.44) 1.7 (1.27�2.26)

56 years 1.22 (1.03�1.44) 1.39 (1.19�1.63)

�6 years 1 1

Marital status

Now single 1.19 (1.01�1.41) 1.05 (0.89�1.24)

Currently in partnership 1 1

Socio-economic status

First quintile 1.56 (1.25�1.95) 1.05 (0.85�1.31)

Second quintile 1.41 (1.16�1.71) 1.36 (1.12�1.64)

Third quintile 1.18 (0.99�1.41 1.10 (0.93�1.30)

Fourth quintile 1.07 (0.9�1.28) 0.85 (0.72�1.01)

Fifth quintile 1 1

aLogistic model controlling for family size.



Late life outcomes are wide ranging. Old age experi-

ence is very different for those who are financially secure

and educated than for those who are poor and unedu-

cated; those who are healthy than those who are ill; and

those who find themselves alone than those who are

embedded in strong social networks. Understanding

health, disability and well-being in later life has wide

implications for informing policy as India matures

demographically.

Acknowledgements

The study has been supported by the INDEPTH Network, through

a supplemental grant to the World Health Organization, Geneva, by

the National Institute on Aging, USA. The authors acknowledge the

role of Stephen Tollman, Somnath Chatterjee and Paul Kowal in

leading this INDEPTH WHO-SAGE initiative and to Nawi Ng and

Kathy Kahn for coordinating efforts for a concerted publication.

Thanks are due to Nirmala Naidoo for statistical support in

estimating IRT health scores. Finally, the authors thank the Vadu

DSS field-based staff for their quality work and the older subjects of

Vadu who willingly consented to the study.



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*Siddhivinayak HirveKEM Hospital Research CentreRasta Peth, Pune 411011Maharashtra, IndiaTel: �91 20 66037336Fax: �91 20 26125603Email: [email protected]



http://www.cehat.org/humanrights/rajan.pdf

http://www.cehat.org/humanrights/rajan.pdf

Health inequalities among older menand women in Africa and Asia: evidencefrom eight Health and DemographicSurveillance System sites in theINDEPTH WHO-SAGE studyNawi Ng1,2,3*#, Paul Kowal4,5, Kathleen Kahn1,2,6#, NirmalaNaidoo4, Salim Abdullah2,7, Ayaga Bawah2, Fred Binka2,Nguyen T.K. Chuc2,8, Cornelius Debpuur2,9, ThaddeusEgondi2,10, F. Xavier Gomez-Olive2,6, Mohammad Hakimi2,3,Siddhivinayak Hirve2,11, Abraham Hodgson2,9, SanjayJuvekar2,11, Catherine Kyobutungi2,10, Hoang Van Minh2,8,Mathew A. Mwanyangala2,7, Rose Nathan2,7, AbdurRazzaque2,12, Osman Sankoh2, P. Kim Streatfield2,12,Margaret Thorogood2,13, Stig Wall1#, Siswanto Wilopo2,3,Peter Byass1#, Stephen M. Tollman1,2,6# andSomnath Chatterji4

1Department of Public Health and Clinical Medicine, Centre for Global Health Research,Epidemiology and Global Health, Umea University, Umea, Sweden; 2INDEPTH Network, Accra,Ghana; 3Purworejo HDSS, Faculty of Medicine, Gadjah Mada University, Yogyakarta, Indonesia;4World Health Organization, Multi-Country Studies Unit, Geneva, Switzerland; 5University ofNewcastle Research Centre on Gender, Health and Ageing, Newcastle, NSW, Australia; 6MRC/WitsRural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health,University of the Witwatersrand, Johannesburg, South Africa; 7Ifakara Health Institute, Ifakara,Morogoro, Tanzania; 8FilaBavi HDSS, Faculty of Public Health, Hanoi Medical University, Hanoi, VietNam; 9Navrongo HDSS, Navrongo, Ghana; 10African Population & Health Research Center, Nairobi,Kenya; 11Vadu Rural Health Programme, KEM Hospital Research Centre, Pune, Maharashtra, India;12Matlab HDSS, ICDDR,B, Dhaka, Bangladesh; 13Warwick Medical School, University of Warwick,Coventry, UK

Background: Declining rates of fertility and mortality are driving demographic transition in all regions of the

world, leading to global population ageing and consequently changing patterns of global morbidity and

mortality. Understanding sex-related health differences, recognising groups at risk of poor health and

identifying determinants of poor health are therefore very important for both improving health trajectories

and planning for the health needs of ageing populations.

Objectives: To determine the extent to which demographic and socio-economic factors impact upon measures

of health in older populations in Africa and Asia; to examine sex differences in health and further explain

how these differences can be attributed to demographic and socio-economic determinants.

Methods: A total of 46,269 individuals aged 50 years and over in eight Health and Demographic

Surveillance System (HDSS) sites within the INDEPTH Network were studied during 2006�2007 using an

abbreviated version of the WHO Study on global AGEing and adult health (SAGE) Wave I instrument.

#Editor, Nawi Ng, Supplement Editor, Kathleen Kahn, Chief Editor, Stig Wall, Deputy Editor, Peter Byass, Supplement Editor, Stephen M.Tollman, have not participated in the review and decision process for this paper.


Global Health Action 2010. # 2010 Nawi Ng et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproductionin any medium, provided the original work is properly cited.

96


The survey data were then linked to longitudinal HDSS background information. A health score was

calculated based on self-reported health derived from eight health domains. Multivariable regression and

post-regression decomposition provide ways of measuring and explaining the health score gap between men

and women.

Results: Older men have better self-reported health than older women. Differences in household socio-

economic levels, age, education levels, marital status and living arrangements explained from about 82%

and 71% of the gaps in health score observed between men and women in South Africa and Kenya,

respectively, to almost nothing in Bangladesh. Different health domains contributed differently to the

overall health scores for men and women in each country.

Conclusion: This study confirmed the existence of sex differences in self-reported health in low- and middle-

income countries even after adjustments for differences in demographic and socio-economic factors. A

decomposition analysis suggested that sex differences in health differed across the HDSS sites, with the

greatest level of inequality found in Bangladesh. The analysis showed considerable variation in how

differences in socio-demographic and economic characteristics explained the gaps in self-reported health

observed between older men and women in African and Asian settings. The overall health score was a robust

indicator of health, with two domains, pain and sleep/energy, contributing consistently across the HDSS sites.

Further studies are warranted to understand other significant individual and contextual determinants to

which these sex differences in health can be attributed. This will lay a foundation for a more evidence-based

approach to resource allocation, and to developing health promotion programmes for older men and women

in these settings.

Keywords: ageing; survey methods; public health; burden of disease; demographic transition; disability; well-being; health

status; INDEPTH WHO-SAGE





Received: 28 June 2010; Revised: 8 July 2010; Accepted: 8 July 2010; Published: 27 September 2010

Declining rates of fertility and mortality are

driving demographic transitions in all regions

of the world, leading to global population

ageing. This includes substantial growth in the numbers

and proportions of older adults in low- and middle-

income countries, estimated at an annual growth rate of

2.6%. In 2010, about 9.9% of the total Asian and 5.4% of

the total African populations are aged 60 years and over.

By 2050, these population proportions of older people are

projected to increase to 23.6% and 10.7%, respectively.

Along with population ageing, the burden of morbidity

and mortality in the population will also undergo change

from burden profiles dominated by infectious diseases to

those affected by chronic non-communicable diseases

(NCD) (1). The chronic NCD burden is predicted to

increase over the next 20 years from 60% to 79% in Asia

and from 28% to 51% in Africa (2). The impact of HIV/

AIDS in eastern and southern Africa has been extreme,

leading to major reversals in mortality and different

patterns of demographic transition. The dominant sce-

nario in many sub-Saharan African countries will be

co-existing chronic infectious and non-communicable

disease (3). The consequences for population ageing are

considerable and impact the roles played by older people,

especially women. Widespread availability of antiretro-

virals is improving the quality and length of life lived with

HIV, but the overall effects on mortality patterns, life

expectancy, population structure and social roles will be

considerable for years to come. All this furthers the idea

that multiple transitions are underway in contrasting

settings.

Estimates of life expectancies at birth and at 60 years of

age provide an objective way of measuring and comparing

the health status of populations over time. In most

countries, the life expectancies of women exceed those of

men and these differences are expected to widen in low-

income countries over the next 30�40 years. Despite living

longer, there are indications that, compared with men,

women in low-income countries report poorer health

(4�6). Understanding sex-related health differences along

with gendered aspects of health, recognising groups at risk

of poor health and identifying determinants of poor health

are all critical for planning the health needs of ageing

populations and improving health trajectories.

This article discusses this pattern in eight Health and

Demographic Surveillance System (HDSS) sites within the

INDEPTH Network (International Network for the

Demographic Evaluation of Populations and Their

Health inequalities among older men and women in Africa and Asia



Health, http://www.indepth-network.org) across Africa

and Asia. The study used data collected using a modified,

summary version of the WHO Study on global AGEing

and adult health (SAGE) which was linked with long-

itudinal HDSS background variables. This collaboration

between WHO-SAGE and the INDEPTH�HDSS sites

links the SAGE survey tools with longitudinal HDSS data

collection platform in order to improve understanding of

the determinants of adult health and ageing in low- and

middle-income countries in Africa and Asia.

The work underscores the importance of using interna-

tional survey data on self-reported health and function-

ality of older adults to complement statistics on life

expectancy and burden of illness. Our goal is to determine

the extent to which various factors impact upon measures

of health, and how this occurs differentially for men and

women. We measure differences in self-reported health by

sex, and explain how these differences can be attributed to

demographic and socio-economic determinants measured

in this study. These analyses inform an understanding of

the distribution and the socio-demographic and economic

determinants of self-reported health, which can contribute

to the development of health-promotion programmes and

more general support and development initiatives for older

men and women.

Methods

Study populationThis multi-centre INDEPTH WHO-SAGE study was

conducted during 2006�2007 in eight HDSS sites in Africa

and Asia: Agincourt (South Africa), Ifakara (Tanzania),

Nairobi (Kenya), Navrongo (Ghana), Filabavi (Viet

Nam), Matlab (Bangladesh), Purworejo (Indonesia) and

Vadu (India) (7). The HDSS sites were selected to include

different geographic and socio-economic contexts. A total

of 93,347 individuals aged 50 years and over were

identified from the surveillance databases across all eight

field sites. In six sites, all adults 50 years and over were

targeted for face-to-face interview; in the other two

sites (Navrongo and Matlab) a random sample of house-

holds with at least one member aged 50 years and over was

selected. Respondents within these households were

selected using Kish tables (8). In both cases, older

individuals had a known non-zero probability of selection.

A total of 58,004 respondents aged 50 years and over were

invited to participate, and the response rate was 80%,

resulting in a final total sample of 46,269, ranging from

2,072 in Nairobi to 12,395 in Purworejo. A total of 2,334

respondents (5.0%) were later excluded from the analysis

because of incomplete socio-demographic information

[item non-response: age (n�11); education (n�450);

socio-economic status (n�1,627); marital status (n�121);

living arrangements (n�125)], giving a total sample of

43,935.

Study instruments and variablesThis study used a modified and shortened INDEPTH

WHO-SAGE instrument consisting of health status

description, subjective well-being and quality of life

modules (see information at the end of the abstract).

The study questionnaire was developed through a con-

sultative process between INDEPTH and WHO-SAGE

with the goal of integrating a feasible number of useful

SAGE modules into routine surveillance update activities

with minimum impact on existing HDSS procedures and

maximum return on measuring health and well-being. The

survey instrument consisted of questions in eight health

domains (affect, cognition, interpersonal relationships,

mobility, pain, self-care, sleep/energy and vision) with

related anchoring vignettes. In each domain, two ques-

tions were asked to assess how much difficulty the

respondent had in performing activities during the last

30 days. The summary instrument also assessed functional

status using Activities of Daily Living (ADL) or Instru-

mental Activities of Daily Living (IADL) type of ques-

tions, and covered subjective well-being and quality of life

issues. This instrument was translated and back-translated

in eight local languages. Standardised training, interview

protocols and quality assurance procedures were used

across all participating sites. Centralised training was

provided to principal investigators from each site, who in

turn trained their respective survey teams: site-based

training averaged 4.5 days in duration across the sites.

Mean interview time was 20 min. Three sites integrated

the INDEPTH WHO-SAGE module into their routine

HDSS surveillance, while the remaining five sites con-

ducted the INDEPTH WHO-SAGE study as a separate

data collection activity. Detailed descriptions of instru-

ments, survey protocols and quality control measures are

described in a companion article in this volume (9).

The INDEPTH WHO-SAGE questionnaire also col-

lected information on overall self-reported health using

the question ‘In general, how would you rate your health

today?’, using a 5-point response scale. However, the

main outcome of interest in this article is the health score.

In brief, the composite health score was calculated based

on self-reported health derived from the eight health

domain items. Each item response was based on a 5-point

ordered categorical scale. Due to its multidimensionality,

the health score provided a more robust assessment of

individual health levels than a single overall self-rated

general health question and was subsequently used as the

health outcome variable in the planned analyses (10, 11).

The composite health scores were calculated using item

response theory with a partial credit model (12). Each

item was calibrated using chi-squared fit statistics to

assess its contribution to the composite health score.

The raw scores were transformed through Rasch model-

ling into a continuous cardinal scale, with 0 representing

worst health and a maximum score of 100 representing

Nawi Ng et al.



best health (9). The psychometric properties of the health

score have been assessed and reported elsewhere (13).

Background information for each respondent was

obtained by linking the SAGE results to selected,

standardised variables from the HDSS site databases,

which contain extensive data on individual demographic

characteristics as well as household-level information.

The variables were harmonised across sites to ensure

comparability. The socio-economic index for households

in each site was based on a locally derived wealth index;

all households in a site were allocated to wealth quintiles

which were developed using principal component factor

analysis (14) on a range of asset variables including

dwelling characteristics and household possessions (such

as livestock and durable goods). The wealth index was

derived by each HDSS independently. Since these are

relative measures, it was not possible to make direct

comparisons of quintiles across sites, but it is possible to

compare health outcomes across wealth quintiles within

each site/country, and time-trends in outcomes by wealth

quintile across all sites.

Data analysesDescriptive results are presented for demographic and

socio-economic variables at each site. Means and 95%

confidence intervals (CI) for the health scores are

presented to describe variations in different population

subgroups across the eight HDSS sites.

The health score was used as the dependent variable in

regression analyses. A mean score for each domain was

obtained by taking the average of responses in the two

domain-specific questions. The contribution of each

health domain (affect, cognition, interpersonal relation-

ships, mobility, pain, self-care, sleep/energy and vision) to

the health score was determined using its regression

coefficient, and the analyses were adjusted by household

wealth quintiles and living arrangements, and respon-

dents’ age, education levels and marital status. Differ-

ences in health score by sex were then analysed to

ascertain how much demographic and socio-economic

factors contributed to the observed differences.

Multivariable linear regression was used to assess

statistical associations between socio-economic and de-

mographic characteristics as independent variables, and

the health score as the dependent variable, for all

respondents, separately by sex and HDSS sites. A post-

regression decomposition based on Blinder-Oaxaca

methods (15, 16) was performed in order to show the

extent to which sex-based differences in outcomes were

attributable to differences in sex distributions of socio-

economic and demographic characteristics, and how

much to other factors. Together, multivariable regression

and decomposition provided a way of measuring and

explaining an outcome gap, which in this case was the

mean difference in health score between men and women.

All the analyses were weighted by the 2007 population

age and sex distribution at each HDSS site. The

descriptive results were standardised to the WHO world

standard population distribution to account for the

different population distributions across HDSS sites

(17). All statistical analyses were conducted in STATA

Version 10.0 (18).

Ethical considerationsThe research was approved by the Ethical Committee or

Board in each HDSS site and/or their host institutions,

and the Ethics Review Committee at WHO, Geneva.

Informed consent was obtained from each individual

prior to the study.

ResultsA total of 43,935 respondents aged 50 years and over

(24,434 women and 19,501 men) were included in the

analyses. Table 1 provides demographic and socio-

economic characteristics of the respondents. The smaller

number of women in Nairobi and men in Agincourt

reflects the dynamics of labour and social migration

occurring in these two settings. Overall, more women

participated than men (55.6% and 44.4%, respectively)

with substantial variation across the sites. In Agincourt,

women constituted three-quarters of respondents, com-

pared to only 35% in Nairobi. The majority of respon-

dents were aged between 50 and 59 years (42%), along

with a substantial proportion of the oldest old (6.8% aged

80 years and over). Nairobi had 72% of respondents aged

50�59 years and only 2.3% aged 80 years and over. In

contrast, Filabavi had 7.4% men and 14.3% women

respondents aged 80 years and over. In general, women

respondents and those from African sites had lower

education levels than men and those from Asian sites.

Almost two-thirds of male respondents in Filabavi

reported more than six years of education, in contrast

to only 6% in Ifakara and 13% in Navrongo. The

corresponding figures for women ranged from 2.3% in

Ifakara to 33% in Filabavi. Over 88% of male respon-

dents in Asian sites were in current partnerships; while in

the African settings, the corresponding proportion ran-

ged from 76% in Agincourt to 87% in Nairobi. There

were more older women in African sites who were not

currently in a relationship compared to women in Asian

sites. Notably, 74% of women respondents in Nairobi

were either widowed, divorced or never married. Overall,

more than 90% of respondents lived with other family

members, except in Nairobi where up to 29% of men and

21% of women lived alone.

Tables 2 and 3 show the distributions of the health

score for men and women by different demographic and

socio-economic characteristics across the HDSS sites. In

all sites, both men and women aged 80 years and over

consistently had lower health scores compared to respon-



Table 1. Distribution of study populations in eight Health and Demographic Surveillance System (HDSS) sites in Africa and Asia, 2006�2007

Agincourt,

South Africa

Ifakara,

Tanzania

Nairobi,

Kenya

Navrongo,

Ghana

Filabavi,

Viet Nam

Matlab,

Bangladesh

Purworejo,

Indonesia

Vadu,

India

Characteristics M F M F M F M F M F M F M F M F

Total subjects 949 2,890 2,388 2,636 1,298 693 1,634 2,660 3,462 5,054 1,999 2,005 5,420 6,333 2,351 2,163

Age group (years)

50�59 40.6 39.2 45.5 45.7 72.2 58.0 41.9 44.7 41.7 35.4 44.0 43.1 38.0 35.9 46.0 45.3

60�69 33.0 27.8 33.9 29.6 19.9 25.2 33.5 37.8 29.5 24.4 32.1 35.7 31.5 34.8 35.5 37.2

70�79 19.4 24.4 16.5 18.1 5.7 10.1 18.8 14.3 21.5 25.8 18.9 17.7 22.8 22.5 14.7 13.3

80 and over 7.1 8.6 4.1 6.5 2.3 6.7 5.7 3.2 7.4 14.3 4.9 3.5 7.7 6.7 3.8 4.1

Education levels

No formal 49.9 63.8 20.9 56.1 25.5 52.7 NA NA 2.0 10.6 41.3 72.2 14.2 36.7 4.6 7.2

At most 6 years 23.7 20.4 72.9 41.6 59.5 42.6 87.3 95.0 34.2 55.7 33.2 23.5 62.5 51.4 56.8 84.5

More than 6 years 26.4 15.8 6.2 2.3 15.0 4.6 12.7 5.0 63.8 33.7 25.6 4.3 23.3 11.9 38.6 8.3

Marital status

In partnership 76.4 41.1 84.5 50.1 86.8 26.5 81.9 35.4 92.8 60.5 96.4 53.4 88.0 60.4 91.3 66.8

Single 23.6 58.9 15.5 49.9 13.2 73.5 18.1 64.6 7.2 39.5 3.6 46.6 12.0 39.6 8.7 33.2

Living arrangements

Living together in household 89.0 96.4 97.5 98.2 70.7 79.4 96.4 94.7 98.7 91.1 99.6 95.0 96.3 90.2 99.0 96.5

Living alone 11.0 3.6 2.5 1.8 29.3 20.6 3.6 5.3 1.3 8.9 0.4 5.0 3.7 9.8 1.0 3.5

Household socio-economic status

First quintile (lowest) 16.5 15.4 21.6 16.8 27.6 15.9 30.8 26.2 8.1 16.2 13.8 16.5 18.5 20.5 10.2 12.6

Second quintile 17.8 19.1 23.2 16.5 13.1 22.0 26.7 23.7 17.0 18.8 16.7 16.4 19.0 19.8 15.5 14.7

Third quintile 17.8 19.7 21.9 20.1 18.8 23.9 21.7 22.5 22.1 20.7 17.9 16.8 20.3 20.0 21.3 22.8

Fourth quintile 19.5 21.1 33.4 46.6 20.5 24.6 16.2 20.5 26.5 22.4 22.3 24.5 21.5 19.9 22.8 20.2

Fifth quintile (highest) 28.4 24.6 NA NA 20.0 13.7 4.6 7.1 26.3 21.8 29.2 25.9 20.8 19.8 30.2 29.6

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Table 2. Distribution of health score across subgroups of men in eight Health and Demographic Surveillance System (HDSS) sites in Africa and Asia, 2006�2007

Mean health score and 95% confidence interval

Characteristics

Agincourt,

South Africa

Ifakara,

Tanzania

Nairobi,

Kenya

Navrongo,

Ghana

Filabavi,

Viet Nam

Matlab,

Bangladesh

Purworejo,

Indonesia

Vadu,

India

Age group (years)

50�59 67.8 (66.5�69.1) 74.6 (73.8�75.4) 74.4 (73.5�75.3) 68.4 (67.7�69.1) 72.5 (71.9�73.1) 65.7 (65.1�66.3) 77.3 (76.8�77.7) 70.1 (69.3�70.9)

60�69 66.6 (65.3�67.9) 71.5 (70.7�72.4) 70.5 (68.9�72.0) 65.9 (65.2�66.6) 68.8 (68.2�69.4) 62.2 (61.6�62.8) 73.2 (72.7�73.7) 67.9 (67.2�68.6)

70�79 65.5 (64.3�66.7) 67.0 (65.9�68.1) 69.1 (66.1�72.1) 62.1 (61.2�63.1) 65.3 (64.6�65.9) 59.3 (58.4�60.2) 68.4 (67.9�69.0) 65.6 (64.8�66.5)

80 and over 62.6 (60.8�64.3) 61.4 (59.9�63.0) 60.1 (56.4�63.9) 61.0 (59.1�62.9) 59.7 (58.7�60.8) 54.9 (53.4�56.5) 64.0 (63.0�65.0) 65.8 (64.0�67.6)

Education levels

No formal 65.9 (64.8�66.9) 71.5 (70.1�72.9) 69.8 (68.0�71.6) NA 65.8 (63.7�68.0) 62.5 (61.9�63.0) 72.7 (71.7�73.8) 66.0 (64.3�67.6)

At most 6 years 66.4 (64.9�67.9) 71.5 (71.0�72.1) 71.6 (70.1�73.1) 65.8 (65.4�66.3) 68.2 (67.5�68.9) 62.7 (62.0�63.3) 73.5 (73.1�73.8) 67.8 (67.1�68.4)

More than 6 years 68.8 (67.3�70.2) 71.8 (69.9�73.7) 75.0 (72.5�77.5) 67.3 (64.3�70.4) 70.2 (69.7�70.6) 63.8 (63.1�64.5) 74.4 (73.8�75.0) 69.7 (68.8�70.5)

Marital status

In partnership 67.1 (66.2�68.0) 71.6 (71.1�72.2) 71.4 (70.5�72.3) 66.4 (65.9�66.9) 69.3 (68.9�69.6) 62.8 (62.4�63.2) 73.8 (73.5�74.1) 68.5 (68.0�69.0)

Single 65.5 (64.0�67.0) 70.3 (68.9�71.6) 69.1 (66.6�71.6) 64.4 (63.3�65.5) 66.9 (65.1�68.7) 62.5 (60.2�64.8) 72.2 (71.2�73.2) 66.3 (64.3�68.3)

Living arrangements

Living together in household 66.5 (65.7�67.3) 71.4 (70.9�71.9) 71.2 (70.1�72.2) 66.0 (65.6�66.5) 69.3 (68.9�69.6) 62.8 (62.4�63.2) 73.6 (73.3�73.9) 68.3 (67.9�68.8)

Living alone 68.0 (65.7�70.3) 74.2 (71.4�77.1) 72.0 (70.4�73.5) 66.3 (64.0�68.6) 67.2 (64.7�69.8) 64.5 (60.4�68.5) 72.4 (70.2�74.6) 69.2 (61.2�77.2)


First quintile (lowest) 65.6 (64.0�67.3) 70.7 (69.7�71.6) 71.1 (69.6�72.6) 66.1 (65.4�66.9) 66.7 (65.4�68.0) 62.6 (61.6�63.5) 73.0 (72.3�73.8) 67.1 (65.9�68.4)

Second quintile 66.3 (64.5�68.0) 72.5 (71.5�73.5) 71.8 (69.5�74.1) 66.1 (65.2�67.0) 68.2 (67.3�69.0) 61.8 (60.9�62.8) 72.7 (72.1�73.4) 67.2 (66.0�68.4)

Third quintile 66.6 (64.9�68.4) 71.5 (70.4�72.6) 73.9 (71.7�76.2) 65.4 (64.5�66.3) 69.2 (68.5�69.9) 62.6 (61.6�63.6) 74.1 (73.4�74.8) 67.4 (66.5�68.3)

Fourth quintile 65.6 (64.5�66.8) 71.2 (70.3�72.1) 69.6 (68.2�71.1) 66.2 (65.1�67.3) 69.5 (68.8�70.2) 62.6 (61.8�63.3) 73.9 (73.4�74.5) 69.3 (68.3�70.4)

Fifth quintile (highest) 68.0 (66.5�69.6) NA 71.8 (69.3�74.3) 67.8 (65.1�70.5) 70.6 (69.9�71.3) 63.7 (63.1�64.4) 74.2 (73.6�74.8) 69.3 (68.5�70.2)

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Table 3. Distribution of health score across subgroups of women in eight Health and Demographic Surveillance System (HDSS) sites in Africa and Asia, 2006�2007

Mean health score and 95% confidence interval

Characteristics

Agincourt,

South Africa

Ifakara,

Tanzania

Nairobi,

Kenya

Navrongo,

Ghana

Filabavi,

Viet Nam

Matlab,

Bangladesh

Purworejo,

Indonesia

Vadu,

India

Age group (years)

50�59 66.2 (65.6�66.8) 72.1 (71.5�72.8) 69.6 (68.4�70.8) 65.2 (64.7�65.6) 68.8 (68.4�69.2) 57.8 (57.3�58.2) 74.7 (74.3�75.1) 67.1 (66.4�67.7)

60�69 65.7 (65.0�66.3) 68.3 (67.6�69.0) 64.1 (62.5�65.7) 62.1 (61.6�62.5) 64.9 (64.4�65.3) 55.4 (54.9�56.0) 70.0 (69.6�70.4) 66.0 (65.4�66.6)

70�79 62.7 (62.1�63.4) 64.4 (63.4�65.4) 60.7 (57.9�63.5) 59.1 (58.4�59.7) 61.9 (61.4�62.3) 51.4 (50.5�52.3) 66.0 (65.5�66.5) 63.9 (63.1�64.7)

80 and over 60.3 (59.2�61.4) 58.6 (57.0�60.2) 56.4 (53.8�59.0) 55.7 (53.9�57.4) 57.7 (57.1�58.3) 51.1 (49.1�53.0) 62.7 (61.7�63.7) 62.5 (60.9�64.0)

Education levels

No formal 65.0 (64.5�65.4) 69.2 (68.6�69.9) 64.4 (63.1�65.6) NA 63.3 (61.9�64.7) 55.1 (54.7�55.5) 70.5 (69.9�71.1) 65.3 (63.8�66.8)

At most six years 65.0 (64.2�65.7) 67.9 (67.2�68.5) 67.0 (65.7�68.2) 62.5 (62.2�62.8) 65.2 (64.9�65.6) 56.3 (55.5�57.0) 71.1 (70.7�71.4) 65.7 (65.3�66.1)

More than six years 66.7 (65.5�67.9) 71.0 (68.1�74.0) 64.5 (61.7�67.4) 62.7 (61.2�64.2) 67.4 (66.7�68.1) 58.0 (56.6�59.3) 72.9 (72.1�73.7) 67.5 (66.0�69.0)

Marital status

In partnership 65.8 (65.2�66.3) 69.5 (68.9�70.2) 69.0 (66.4�71.6) 64.1 (63.6�64.6) 66.2 (65.9�66.5) 56.0 (55.5�56.5) 71.6 (71.2�71.9) 65.8 (65.3�66.3)

Single 64.6 (64.1�65.1) 68.1 (67.4�68.7) 65.0 (63.9�66.0) 61.9 (61.5�62.2) 64.7 (64.2�65.2) 55.3 (54.8�55.9) 70.2 (69.7�70.6) 65.8 (65.0�66.6)

Living arrangements

Living together in household 65.1 (64.7�65.5) 68.7 (68.3�69.1) 65.7 (64.6�66.8) 62.5 (62.2�62.8) 65.7 (65.4�66.0) 55.4 (55.1�55.8) 71.0 (70.7�71.3) 65.8 (65.4�66.2)

Living alone 63.7 (62.0�65.4) 67.8 (64.8�70.7) 65.1 (63.5�66.6) 62.8 (61.2�64.3) 64.6 (63.4�65.8) 57.7 (56.0�59.3) 70.0 (69.0�71.0) 66.9 (65.0�68.8)


First quintile (lowest) 65.6 (64.6�66.5) 67.3 (66.3�68.3) 66.8 (64.5�69.2) 62.9 (62.3�63.4) 64.0 (63.3�64.7) 54.9 (54.1�55.7) 70.2 (69.7�70.8) 65.5 (64.4�66.6)

Second quintile 64.2 (63.4�65.0) 69.4 (68.3�70.4) 64.8 (63.1�66.4) 62.5 (61.9�63.0) 65.1 (64.6�65.7) 54.8 (53.9�55.7) 71.1 (70.6�71.7) 65.0 (64.0�66.1)

Third quintile 65.3 (64.5�66.2) 69.4 (68.5�70.4) 64.7 (62.9�66.4) 62.3 (61.7�62.9) 65.8 (65.3�66.4) 55.0 (54.2�55.8) 71.2 (70.6�71.8) 65.4 (64.6�66.2)

Fourth quintile 64.3 (63.6�65.1) 68.6 (68.0�69.3) 66.0 (64.0�67.9) 62.5 (61.8�63.1) 65.8 (65.2�66.3) 56.0 (55.4�56.7) 70.8 (70.2�71.3) 66.5 (65.6�67.4)

Fifth quintile (highest) 65.8 (65.0�66.5) NA 66.7 (64.8�68.7) 62.1 (60.7�63.4) 66.8 (66.3�67.4) 56.2 (55.6�56.9) 71.4 (70.8�71.9) 66.3 (65.6�67.0)

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dents in younger age groups. The discrepancies in health

score between the lowest and the highest age groups were

less in Agincourt and Vadu than in other HDSS sites.

Both men and women with higher levels of education also

consistently had higher health scores compared to

respondents with lower levels of education, except for

women in Nairobi and Navrongo where the patterns were

not entirely clear. In all sites, both men and women who

were not in current partnerships also had marginally

lower health scores than those with partners. There were

no statistically significant within-site differences in health

scores observed between those who lived alone and those

who lived together with other family members, nor across

different household socio-economic quintiles. There was

a clear gradient in health score across different levels of

self-reported health categories. The average health scores

ranged from 52.0 (95% CI: 50.4�53.6) in men who

reported their health as ‘very bad’ to 76.7 (75.9�77.5) in

men who reported their health as ‘very good’. The

corresponding figures were 48.0 (46.5�49.5) and 74.5

(73.5�75.5) for women (data not shown).

Each of the eight health domains contributed differ-

ently to the overall health score in each site. Table 4 shows

the commonalities and differences in contributions from

each domain across the sites. Matlab had the least

dispersion across the domains, whereas Purworejo had

the most. Four health domains were identified as

contributing the most to the overall health score: pain/

discomfort (in Ifakara and Purworejo men and women,

and in Matlab and Filabavi women), vision (in Nairobi

and Vadu), mobility (in Matlab and Filabavi men) and

sleep/energy (in Navrongo and Agincourt). Interpersonal

relations contributed relatively less to the overall health

score than the other domains, except in Vadu. Self-care

contributed the least with the regression coefficients

ranging from �0.14 among women from Ifakara (com-

pared to a pain domain coefficient of �3.01 in the same

site) to 0.94 in men from Purworejo.

A decomposition of the health score by sex was

conducted using a separate regression model adjusted

for the effects of socio-economic and demographic

characteristics. Table 5 shows that in all sites, men had

higher health scores than women across all age-groups

(pB0.001). The gaps in the health score between men and

women were significantly larger in Matlab and Nairobi

compared to the other HDSS sites. There were large

discrepancies in the proportion of the health score

difference between men and women attributable to group

differences in socio-economic and demographic charac-

teristics; and similarly in the proportion of the gap that

was attributed to other influences not adjusted for in the

model, such as gender discrimination. Within the propor-

tion of the inequality attributed to individual character-

istics, sex differences in age contributed from �13.4% to

24.8% of the disparity observed in health score between

men and women in Navrongo and Filabavi, respectively.

Inclusion of additional determinants (level of education,

marital status, living arrangements and household wealth

quintiles) showed that up to 82% of the sex difference in

the mean health score in Agincourt was attributable to

the distribution of the determinants between the two

groups, with a remaining 18% attributable to other

factors not included in the model. In contrast, almost

none of the health score disparity between men and

women in Matlab was attributable to this set of determi-

nants. The results of the fully adjusted model, therefore,

provide a better understanding of the way in which

known factors contributed to sex differences in health

scores across the fieldsites.

DiscussionThis article presents novel findings on how the differences

in health between men and women can be partially

explained by socio-demographic and social factors, by

unexplained inequality, and by the differences in unex-

plained inequality between settings. The aim of the

decomposition analysis was to move beyond a basic

comparison of sex differences in self-reported health,

and instead begin to unravel the determinants of the

differences and variations across contrasting African and

Asian settings. By statistically regressing available (and

commonly used) independent variables, such as age,

education, marital status, socio-economic status and

living arrangements, the decomposition technique char-

acterised the association of other factors � potentially

gender-related issues � on health scores. Referring to Table

5, model 5, a possible interpretation is that gendered

aspects of society in the Matlab area of rural Bangladesh

contribute more to the differences in reported health

between men and women than in the Agincourt area of

rural South Africa. This suggests that the influence of

gendered aspects of health warrants closer examination

when investigating sex-based differences in health. How-

ever, caution should be taken with this hypothesis until the

limitations outlined below are taken into account.

Three key results emerge from this cross-site study on

health and ageing in eight low- and middle-income

countries. Firstly, despite women having higher life

expectancy than men, older men reported better health

than older women in these settings. These results are in

line with findings from Europe and North America

showing that women reported poorer health than men

(19, 20). The INDEPTH WHO-SAGE results also

indicated significantly larger sex differences in health in

Nairobi and Matlab than in the other HDSS sites. A

previous study from Matlab also reported poorer self-

reported health in women than in men, independent of

age. However, the contribution of sex to self-reported

health disappeared after controlling for objective physical



Table 4. Regression coefficients for each domain (ranked from highest to lowest) with health score as outcome in eight Health and Demographic Surveillance System (HDSS)

sites in Africa and Asia, 2006�2007

Agincourt, South Africa Ifakara, Tanzania Nairobi, Kenya Navrongo, Ghana Filabavi, Viet Nam Matlab, Bangladesh Purworejo, Indonesia Vadu, India

Men

�2.48 Sleep/energy �3.51 Pain �3.88 Vision �2.66 Sleep/energy �2.81 Mobility �2.09 Mobility �4.06 Pain �3.17 Vision

�2.25 Cognition �3.19 Mobility �3.57 Sleep/energy �2.20 Affect �2.70 Sleep/energy �1.85 Pain �3.41 Cognition �2.61 Mobility

�2.24 Affect �2.60 Vision �3.53 Pain �2.15 Mobility �2.38 Pain �1.82 Affect �3.18 Vision �2.59 Pain

�2.23 Pain �2.42 Sleep/energy �3.38 Affect �2.04 Pain �2.14 Cognition �1.66 Sleep/energy �2.81 Sleep/energy �2.52 Affect

�1.80 Vision �2.30 Cognition �2.56 Mobility �2.00 Cognition �1.88 Affect �1.55 Cognition �2.43 Affect �2.43 Interpersonal

�1.70 Mobility �1.89 Affect �2.38 Cognition �1.54 Interpersonal �1.80 Vision �1.48 Vision �2.19 Mobility �2.26 Cognition

�1.50 Interpersonal �0.56 Interpersonal �1.87 Interpersonal �1.51 Vision �1.10 Interpersonal �0.96 Self-care �0.94 Interpersonal �1.39 Self-care

�0.50 Self-care �0.46 Self-care 0.16 Self-care �0.19 Self-care 0.20 Self-care �0.94 Interpersonal 0.94 Self-care �1.39 Sleep/energy

Women

�2.29 Sleep/energy �3.01 Pain �2.53 Pain �1.90 Sleep/energy �2.10 Pain �1.51 Pain �3.40 Pain �2.38 Vision

�2.22 Pain �2.99 Mobility �2.41 Mobility �1.78 Mobility �2.04 Sleep/energy �1.51 Interpersonal �3.04 Cognition �2.35 Pain

�2.12 Cognition �2.16 Vision �2.30 Vision �1.74 Pain �2.03 Mobility �1.49 Affect �2.68 Vision �2.19 Interpersonal

�2.01 Affect �2.15 Cognition �2.14 Cognition �1.71 Affect �1.88 Cognition �1.46 Vision �2.30 Mobility �2.11 Mobility

�1.58 Mobility �2.07 Sleep/energy �2.02 Affect �1.69 Cognition �1.59 Affect �1.46 Mobility �2.30 Sleep/energy �2.02 Affect

�1.53 Vision �1.92 Affect �1.94 Sleep/energy �1.49 Interpersonal �1.48 Vision �1.39 Self-care �2.16 Affect �1.91 Cognition

�1.31 Interpersonal �0.71 Interpersonal �1.74 Interpersonal �1.34 Vision �1.21 Interpersonal �1.37 Sleep/energy �1.31 Interpersonal �1.51 Self-care

�0.80 Self-care �0.14 Self-care �0.54 Self-care �0.81 Self-care �0.59 Self-care �1.34 Cognition 0.68 Self-care �1.50 Sleep/energy

Note: Numbers represent regression coefficients for each health domain derived from separate regression analyses for each site. Health score was used as the outcome variable, and the

regression analyses were adjusted for age (as continuous variable), education level, marital status, living arrangements, and wealth quintiles.

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performance, limitations in activities of daily living, and

acute and chronic morbidity (21).

Secondly, individual and household socio-economic

determinants contributed differently across settings

in explaining the sex differences in reported health.

Differences in household socio-economic levels and

living arrangements, and respondent’s age, education and

marital status, provided virtually no explanation in

Bangladesh while accounting for 71% and 82% of the

sex difference in health score observed in Nairobi, Kenya

and Agincourt, South Africa, respectively. Importantly,

inequalities observed in the health score, and the sex

differences between sites, may also be explained by

individual and contextual factors not assessed in this

study, such as occupational status, history of chronic

morbidity, presence of physical disabilities and other

environmental and socio-demographic risk factors at

household and village levels.

Thirdly, different health domains contributed differ-

ently to the overall health score for men and women in

each setting. Questions on self-care, which assess respon-

dents’ difficulties in washing/dressing or bathing and

maintaining general appearance, have been used exten-

sively in different health measurement tools (22, 23) but

consistently contributed least to overall health scores, in

both men and women and in almost all the study sites.

This might be due to the help given by members of

extended families in many of these field settings. This

study provides deeper understanding on how various

functional domains affect people’s perception of their

health. Despite its usefulness in predicting future mor-

bidity and mortality in both developed and developing

countries (24�26), a single question on self-rated health

provides little indepth understanding of something as

complex and multifaceted as health. This study, however,

showed a consistent trend towards better health scores in

people who rated their health as ‘very good’ compared to

those who rated their health as ‘very bad’. This domain-

specific knowledge is vital in laying the foundation for

rational resource allocation and for developing appro-

priate evidence-based health promotion programmes for

older adults.

The study attempts to measure and compare the health

of older adults in low- and middle-income countries,

information largely lacking in resource-constrained set-

tings. Increasing longevity will have substantial health,

economic and social impacts in all countries, and will

particularly affect under-resourced and under-performing

health systems in low-income countries, which are gen-

erally poorly prepared to provide the chronic care needed

to manage non-communicable conditions in older people

(3, 27, 28). This study has highlighted prominent sex

differences in the health of older adults and raises the

need to further study the factors contributing to these

disparities. This will be important for developing targetedTab

le5.

Dec

om

po

siti

on

an

aly

sis

of

pre

dic

tors

of

ineq

uali

tyin

hea

lth

sco

res

bet

wee

nm

ena

nd

wo

men

inei

gh

tIN

DE

PT

HH

DS

Ssi

tes,

20

06�2

00

7

Chara

cte

ristics

Ag

inco

urt

,

So

uth

Afr

ica

Ifakara

,

Tanza

nia

Nairo

bi,

Kenya

Navro

ng

o,

Ghana

Fila

bavi,

Vie

tN

am

Matlab

,

Bang

lad

esh

Purw

ore

jo,

Ind

onesia

Vad

u,

Ind

ia

Mean

health

sco

re

Men

66.6

771.7

873.1

365.8

768.9

362.8

672.9

268.4

5

Wo

men

64.6

268.7

466.6

562.8

164.3

955.5

670.2

866.0

0

Diff

ere

nce

betw

een

men

and

wo

men

2.0

53.0

46.4

83.0

74.5

57.3

02.6

42.4

5

Mo

del1:

%exp

lain

ed

by

inclu

sio

no

fag

e5.1

6.2

21.1

�13.4

24.8

�1.9

�0.9

�1.0

Mo

del2:

%exp

lain

ed

by

inclu

sio

no

fag

eand

ed

ucatio

nle

vel

20.8

6.6

30.3

�9.6

46.1

1.9

8.0

24.8

Mo

del3:

%exp

lain

ed

by

inclu

sio

no

fag

e,

ed

ucatio

nle

vel,

and

marita

lsta

tus

48.2

21.3

67.5

21.8

44.6

1.6

24.6

36.2

Mo

del4:

%exp

lain

ed

by

inclu

sio

no

fag

e,

ed

ucatio

nle

vel,

marita

l

sta

tus,

and

livin

garr

ang

em

ents

79.7

28.7

70.5

23.5

45.7

0.8

24.8

35.1

Mo

del5:

%exp

lain

ed

by

inclu

sio

no

fag

e,

ed

ucatio

nle

vel,

marita

l

sta

tus,

livin

garr

ang

em

ents

,and

ho

useho

ldw

ealth

quin

tile

s

81.5

30.3

69.1

22.1

44.6

�0.4

22.5

35.6



interventions to address differences in health between

men and women.

The study was designed as an add-on to established

HDSS sites in Africa and Asia. Embedding this study

within HDSS sites allowed for data linkages between this

cross-sectional study and the rich demographic and

socio-economic information available in HDSS data-

bases. The infrastructure established for this research

provides the unique opportunity to follow these popula-

tions longitudinally in a scientifically reliable manner.

Linking the health and function indices with future

morbidity and mortality data, collected routinely as

part of regular HDSS update rounds, will allow deeper

understanding of the dynamics of health transition and

population ageing in low- and middle-income countries

(29�31). The results of this study may also serve as a

baseline for observing trends and changes in older

people’s health in the future, whether occurring naturally

or following policy shifts.

There are some limitations to this study. Firstly, the

study subjects may not have been representative of older

people in their respective countries � although, in all

cases, they reflect poorer, often rural, populations. In

some HDSS sites, a random sample of the older adults

under surveillance was recruited into the study, whereas

others surveyed the entire surveillance population aged

50 years and over. Due to the differing population

structure within each HDSS and differences in sampling

strategies, all prevalence data were standardised to the

WHO standard population (17). Secondly, the compar-

ability of this cross-national study on self-reported health

may be compromised by the dynamics of ageing and the

cultural influences on health in the different settings.

The instrument used to assess self-reported health in the

different domains might not be able to fully capture

people’s experiences and expectations for their health.

However, this method for measuring health has been used

as part of the World Health Survey in some 70 countries

with robust results (32). Future research should compare

how these self-reported health items are correlated with

more objective measures, such as blood pressure and

other findings from medical examination. Thirdly, since

the wealth quintiles, serving as a proxy for socio-

economic status, were constructed by each HDSS, they

are relative rather than absolute measures and were not

harmonised across sites. The expected patterns of health

by wealth were not clearly demonstrated within or across

HDSS sites and did not contribute significantly to the

decomposition results. This may need to be addressed

in future analyses of the dataset using longitudinal

approaches. Fourthly, the cross-sectional nature of the

data limits the possibility of drawing causal associations

on how health influences socio-economic status or vice-

versa. The potential to use these cross-sectional data as a

baseline for further longitudinal data analyses strength-

ens the benefit of embedding the INDEPTH WHO-

SAGE study in the HDSS operation.

This comparative study may therefore benefit from

analyses incorporating vignette-based adjustments (data

for which have been collected) that map self-reported

health to a common comparable scale in each domain

(32, 33). These adjustments might improve the cross-site

comparability of the results. Similarly, subsequent ana-

lyses correlating health outcomes by sex with observed

mortality � a robust potential with HDSS longitudinal

data collection � will probably be enlightening.

Despite these limitations, the study provides a robust

data set, baseline and data collection platform that can be

used to inform future interventions � and their evaluation

� for older people’s health across contrasting geographic

and socio-cultural settings.

ConclusionThis INDEPTH WHO-SAGE study examined sex differ-

ences in health among older adults within low- and

middle-income countries and found that men reported

significantly better health than women. It also unveiled

wide variation in how individual and household socio-

economic characteristics explain the gaps in self-reported

health observed between men and women in Africa and

Asia. Further studies are needed to examine individual

and contextual determinants to which the health gaps

between older men and women can be attributed,

including gender roles, thus addressing the health in-

equalities observed. We expect such analyses to inform

our understanding of the distribution and determinants

of health and well-being by sex and age, and to provide

stronger evidence on which to base national and global

policies on population health and ageing. While the

gender paradox between health and life expectancy exists

in all these settings, our results affirm that old age will

bring particular problems for women in low-resource

societies. There will be clear need for gender-sensitive

health interventions to address the higher level of poor

health reported in older women and the documented

health differences between the sexes.

Acknowledgements

The authors would like to acknowledge the help of Dr. Richard

Gibson and Dr. Jenny Stewart Williams from the Research Center

on Gender, Health, and Ageing, University of Newcastle, Australia

for their statistical advice.

Conflict of interest and fundingFinancial support was provided by the US National

Institute on Aging through an interagency agreement

with the World Health Organization, supplemented by

support from Umea University for the Filabavi and

Purworejo sites. Both WHO and INDEPTH contributed

Nawi Ng et al.


financial and human resources to the collaboration. The

Umea Centre for Global Health Research (supported by

FAS, the Swedish Council for Working Life and Social

Research, Grant No. 2006-1512) provided technical

support and advice to the sites and co-hosted with

INDEPTH an analytic and writing workshop in 2008.

The Health and Population Division, School of Public

Health, University of the Witwatersrand, South Africa

serves as the satellite secretariat providing scientific

leadership, technical and administrative support for the

INDEPTH Adult Health and Ageing initiative.

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*Nawi NgDepartment of Public Health and Clinical MedicineCentre for Global Health Research, Epidemiology and Global HealthUmea UniversitySE-901 85 Umea, SwedenTel: �46 90 7851391Fax: �46 90 138977Email: [email protected]



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