global health action supplement 2
TRANSCRIPT
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.
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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.
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Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5442
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.
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Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5480
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
6 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5480
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5493
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5493 9
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
10 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5493
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302
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.
12 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302 13
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.
14 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302
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).
Ageing and adult health status in eight lower-income countries
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302 15
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.
16 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302
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?
‘Was it none, mild, moderate,
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?
‘Was it none, mild, moderate,
severe or extreme or cannot
do this?’
Q4 How much difficulty did
[Someshni] have in vigorousactivities?
‘Was it none, mild, moderate,
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
Ageing and adult health status in eight lower-income countries
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302 17
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.
18 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302
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.
Ageing and adult health status in eight lower-income countries
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302 19
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.
20 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302
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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*Paul KowalMulti-Country Studies UnitWorld Health Organization20 Avenue AppiaCH-1211 Geneva, SwitzerlandEmail: [email protected]
Paul Kowal et al.
22 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302
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
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: 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.
�INDEPTH WHO-SAGE Supplement
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126
(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.
24 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126 25
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)
F. Xavier Gomez-Olive et al.
26 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126
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
Nationality of origin
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
Occupational status in 2004
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
Cross-sectional survey of older people in rural South Africa
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126 27
(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
F. Xavier Gomez-Olive et al.
28 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126
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)
Household asset score (quintiles)
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
Nationality of origin
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
Occupational status in 2004
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
Cross-sectional survey of older people in rural South Africa
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126 29
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
Household with and without people aged less than 50
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
Nationality of origin
South African 1 1
Mozambican 0.95 (0.82, 1.09) 0.76 (0.64, 0.91)
Occupational status in 2004
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.
F. Xavier Gomez-Olive et al.
30 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126
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
In a partnership 545 (29.3) 369 (19.8) 323 (17.4) 343 (18.4) 282 (15.2) df�4
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
Less than 6 years 214 (28.3) 149 (19.7) 127 (16.8) 147 (19.4) 120 (15.9) pB0.001
Six years or more 206 (34.7) 130 (21.9) 99 (16.7) 89 (15) 70 (11.8)
Household asset score (quintiles)
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)
Household with and without people aged less than 50
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
Nationality of origin
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
Occupational status in 2004
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.
Cross-sectional survey of older people in rural South Africa
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126 31
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
Agincourt sub-district, 2006
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)
In current partnership 1 1
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)
Household asset score (quintiles)
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)
Occupational status in 2004
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.
F. Xavier Gomez-Olive et al.
32 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126
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
In a partnership 432 (23.2) 394 (21.2) 371 (19.9) 371 (19.9) 292 (15.7) df�4
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
Less than 6 years 169 (22.3) 163 (21.5) 131 (17.3) 164 (21.7) 129 (17.0) pB0.001
Six years or more 157 (26.4) 151 (25.4) 102 (17.2) 91 (15.3) 93 (15.7)
Household asset score (quintiles)
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)
Household with and without people aged less than 50
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
Nationality of origin
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
Occupational status in 2004
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.
Cross-sectional survey of older people in rural South Africa
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126 33
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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Variables
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Multivariate model OR
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Female 1.17 (1.01, 1.35)
Age group (years)
50�59 1 1
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final model
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Occupational status in 2004Working 1 1
Not working 1.50 (1.22, 1.83) 1.32 (1.07, 1.64)
aWHOQoL: The World Health Organization Quality of Life score
was calculated and then divided into quintiles. A better quality of
life was defined as those included in the three lowest quintiles;
while a worse quality of life was defined as those included in the
two highest quintiles.
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*F. Xavier Gomez-OliveMRC/Wits Rural Public Health and Health Transitions Research Unit(Agincourt)School of Public HealthFaculty of Health SciencesUniversity of the Witwatersrand7 York Road, Parktown 2193Johannesburg, South AfricaEmail: [email protected]
Cross-sectional survey of older people in rural South Africa
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126 35
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
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: 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
�INDEPTH WHO-SAGE Supplement
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2142
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2142 37
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.
38 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2142
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.
Health status and quality of life among adults in Tanzania
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2142 39
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
80 years and over 82.1 86.5
Table 3. Distribution of self-reported health status outcomes
by age and sex
Variables Men (n�2,463) Women (n�2,668)
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
80 years and over 82.7 84.7
Mathew A. Mwanyangala et al.
40 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2142
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
In current partnership 1 10
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
Health status and quality of life among adults in Tanzania
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2142 41
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]
Mathew A. Mwanyangala et al.
44 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2142
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
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: 17 November 2009; Revised: 27 June 2010; Accepted: 8 July 2010; Published: 27 September 2010
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
�INDEPTH WHO-SAGE Supplement
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138
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.
46 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138
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
The health and well-being of older people in Nairobi’s slums
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138 47
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)
Mean WHOQoL score (SD)
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%
Mean health status score (SD)
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%
Catherine Kyobutungi et al.
48 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138
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
The health and well-being of older people in Nairobi’s slums
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138 49
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)
Less than or equal to
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
Catherine Kyobutungi et al.
50 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138
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
The health and well-being of older people in Nairobi’s slums
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138 51
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.
Catherine Kyobutungi et al.
52 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138
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]
The health and well-being of older people in Nairobi’s slums
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138 53
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
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: 27 November 2009; Revised: 4 June 2010; Accepted: 8 July 2010; Published: 27 September 2010
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
�INDEPTH WHO-SAGE Supplement
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151 55
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.
56 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151
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.
Self-reported health and functional limitations among older people in Ghana
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151 57
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
80 years and over 4.5 4.2
Education level (%)
No formal education 90.7 85.2
Less than or equal to
6 years
3.9 3.5
More than 6 years 5.4 11.5
Marital status (%)
Now single 46.3 50.5
In current partnership 53.7 49.5
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
Fifth quintile 7.4 15.7
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
Cornelius Debpuur et al.
58 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151
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.
Self-reported health and functional limitations among older people in Ghana
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151 59
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)
Mean WHODASi score (SD)
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
Mean health status score (SD)
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.
Cornelius Debpuur et al.
60 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151
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
No formal education 1.00 1.00
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]
In current partnership 1 1
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.
Self-reported health and functional limitations among older people in Ghana
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151 61
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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*Cornelius DebpuurNavrongo Health Research CentreP.O. Box 114Navrongo, UER, GhanaTel: �233 74222310Fax: �233 74222320Email: [email protected]
Self-reported health and functional limitations among older people in Ghana
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151 63
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
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: 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.
�INDEPTH WHO-SAGE Supplement
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2124
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2124 65
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.
66 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2124
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)
Mean health status score (SD)
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
Variables Men (n�3,469) Women (n�5,066)
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.
WHO-SAGE study on older adults in rural community of Viet Nam
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2124 67
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.
68 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2124
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.
Conflict of interest and fundingThe authors have not received any funding or benefits
from industry to conduct this study.
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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]
WHO-SAGE study on older adults in rural community of Viet Nam
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2124 69
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
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: 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
�INDEPTH WHO-SAGE Supplement
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.4618
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.4618 71
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.
72 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.4618
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
No formal education 56.3 57.4
Less than or equal to 6 years 28.7 28.7
More than 6 years 14.9 13.9
Marital status
Now single 23.8 29.7
In current partnership 76.2 70.3
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
Fifth quintile 27.4 25.9
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
80 years and over 48.9 24.2
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
80 years and over 55.6 50.7
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
80 years and over 76.4 71.4
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
80 years and over 54.6 42.0
Socio-demographic differentials of adult health indicators in Matlab
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.4618 73
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.
Abdur Razzaque et al.
74 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.4618
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.
Socio-demographic differentials of adult health indicators in Matlab
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.4618 75
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.
Conflict of interest and fundingThe authors have not received any funding or benefits
from industry to conduct this study.
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Abdur Razzaque et al.
76 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.4618
21. Chen LC, Huq E, D’Souza S. Sex bias in the family allocation of
food and health care in rural Bangladesh. Popul Dev Rev 1981;
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22. Antonovsky A, Bernstein J. Social class and infant mortality.
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*Abdur RazzaqueHDSUICDDR,BMohakhali, Dhaka-1212BangladeshEmail: [email protected]
Socio-demographic differentials of adult health indicators in Matlab
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.4618 77
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.
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]
Keywords: adult health; health status; clustering; quality of life; disability; ageing; Purworejo; Indonesia; INDEPTH
WHO-SAGE
Received: 3 November 2009; Revised: 28 June 2010; Accepted: 8 July 2010; Published: 27 September 2010
#Editor, Nawi Ng, Deputy Editor, Peter Byass, Chief Editor, Stig Wall, have not participated in the review and decision process for this paper.
�INDEPTH WHO-SAGE Supplement
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2125
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2125 79
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.
80 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2125
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)
Less than or equal to
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.
Health of older people in Indonesia
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2125 81
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)
Percentage in the worst quintile (95% CI)
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)
Percentage in the worst quintile (95% CI)
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.
82 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2125
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.
Health
of
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83
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.
84 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2125
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
Health of older people in Indonesia
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2125 85
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]
Health of older people in Indonesia
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2125 87
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
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: 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
�INDEPTH WHO-SAGE Supplement
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2128
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2128 89
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.
90 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2128
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.
Social gradients in self-reported health status
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2128 91
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.
Siddhivinayak Hirve et al.
92 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2128
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.
Social gradients in self-reported health status
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2128 93
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
Mean WHODASi score (SD)
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
Mean health status score (SD)
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
Mean WHOQoL score (SD)
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.
Siddhivinayak Hirve et al.
94 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2128
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.
Conflict of interest and fundingThe authors have not received any funding or benefits
from industry to conduct this study.
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*Siddhivinayak HirveKEM Hospital Research CentreRasta Peth, Pune 411011Maharashtra, IndiaTel: �91 20 66037336Fax: �91 20 26125603Email: [email protected]
Social gradients in self-reported health status
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2128 95
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.
�INDEPTH WHO-SAGE Supplement
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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5420
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
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: 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
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5420 97
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.
98 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5420
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-
Health inequalities among older men and women in Africa and Asia
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5420 99
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)
Household socio-economic status
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)
Household socio-economic status
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
Health inequalities among older men and women in Africa and Asia
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5420 103
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
Health inequalities among older men and women in Africa and Asia
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5420 105
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.
106 Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5420
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]
Health inequalities among older men and women in Africa and Asia
Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5420 107