1. Volume 21 Supplement 7 November 2007
Poverty, HIV and AIDS: Vulnerability and
Impact in Southern Africa
Editors: Stuart Gillespie
Robert Greener
Jimmy Whitworth
Alan Whiteside
Sponsored by UNAIDS, RENEWAL and HEARD
This publication was made possible through support provided by the Joint United Nations Programme on HIV/AIDS (UNAIDS), and
through additional grants to the Regional Network on AIDS, Livelihoods and Food Security (RENEWAL), facilitated by the Interna-
tional Food Policy Research Institute (IFPRI), from Irish Aid, SIDA and USAID. Support to HEARD (the Health Economics and HIV/
AIDS Research Division of the University of KwaZulu-Natal, South Africa) was provided by a DFID Research Partner’s Consortium and
a Joint Financing Agreement involving SIDA, Royal Netherlands Embassy, Irish Aid, UNAIDS and DFID.

2. www.aidsonline.com
EDITORS
Jay A Levy (Editor-in-Chief, San Francisco)
Brigitte Autran (Paris)
Roel A Coutinho (Amsterdam)
John P Phair (Chicago)
EDITORIAL BOARD
P Aggleton, London (2008) J Goedert, Rockville (2007) M-L Newell, London (2009)
AA Ansari, Atlanta (2009) F Gotch, London (2009) G Pantaleo, Lausanne (2008)
T Boerma, Geneva (2009) M-L Gougeon, Paris (2007) M Peeters, Montpellier (2009)
M Bulterys, Atlanta (2008) R Gray, Baltimore (2009) D Pieniazek, Atlanta (2009)
S Butera, Atlanta (2009) A Greenberg, Washington (2007) G Poli, Milan (2008)
A Buvé, Antwerp (2008) S Gregson, London (2008) B Polsky, New York (2009)
A Carr, Sydney (2007) S Grinspoon, Boston (2009) M Prins, Amsterdam (2008)
M Carrington, Bethesda (2008) A Grulich, Sydney (2009) B Richardson, Seattle (2009)
B Clotet, Badalona (2007) D Havlir, San Francisco (2008) CA Rietmeijer, Denver (2007)
B Conway, Vancouver (2007) NA Hessol, San Francisco (2009) Y Rivière, Paris (2009)
H Coovadia, Natal (2008) A Hill, London (2007) S Rowland-Jones, Oxford (2008)
A Cossarizza, Modena (2007) JP Ioannidis, Ioannina (2007) C Sabin, London (2007)
D Costagliola, Paris (2008) C Katlama, Paris (2009) H Schuitemaker, Amsterdam (2008)
B Cullen, Durham (2007) D Katz, London (2008) Y Shao, Beijing (2008)
E Daar, Los Angeles (2008) D Katzenstein, Stanford (2009) V Soriano, Madrid (2009)
F Dabis, Bordeaux (2009) HA Kessler, Chicago (2007) S Spector, La Jolla (2008)
J del Amo, Alicante (2007) S Kippax, Sydney (2008) S Strathdee, La Jolla (2008)
E Delwart, San Francisco (2009) D Kuritzkes, Boston (2007) M Tardieu, Paris (2008)
T Folks, Atlanta (2009) J Lundgren, Hvidovre (2009) P van de Perre, Montpellier (2009)
A Fontanet, Paris (2008) D Margolis, Chapel Hill (2009) C van der Horst, Chapel Hill (2009)
M French, Perth (2007) J-P Moatti, Marseille (2008) C Wanke, Boston (2007)
A Ghani, London (2009) R Montelaro, Pittsburgh (2007) D Wolday, Addis Ababa (2008)
J Glynn, London (2007) RL Murphy, Chicago (2007)
Statistical advisers:
VT Farewell (University College London, London), F Lampe, A Cozzi Lepri, A Mocroft, AN Phillips
C Sabin, C Smith, Z Fox, W Bannister (Royal Free and University College Medical School, London).
AIMS AND SCOPE
AIDS publishes papers reporting original scientific, clinical, epidemiological, and social research which are of a high
standard and contribute to the overall knowledge of the field of the acquired immune deficiency syndrome. The
Journal publishes Original Papers, Concise Communications, Research Letters and Correspondence, as well as
invited Editorial Reviews and Editorial Comments.

3. Contents
Introduction
Investigating the empirical evidence for understanding vulnerability and the associations between poverty, HIV S1
infection and AIDS impact
Stuart Gillespie, Robert Greener, Alan Whiteside and James Whitworth
Is poverty or wealth driving HIV transmission? S5
Stuart Gillespie, Suneetha Kadiyala and Robert Greener
HIV infection does not disproportionately affect the poorer in sub-Saharan Africa S17
Vinod Mishra, Simona Bignami-Van Assche, Robert Greener, Martin Vaessen, Rathavuth Hong, Peter D. Ghys,
J. Ties Boerma, Ari Van Assche, Shane Khan and Shea Rutstein
The socioeconomic determinants of HIV incidence: evidence from a longitudinal, population-based study in rural S29
South Africa
Till Bärnighausen, Victoria Hosegood, Ian M. Timaeus and Marie-Louise Newell
Explaining continued high HIV prevalence in South Africa: socioeconomic factors, HIV incidence and sexual S39
behaviour change among a rural cohort, 2001–2004
James R. Hargreaves, Christopher P. Bonell, Linda A. Morison, Julia C. Kim, Godfrey Phetla, John D.H. Porter,
Charlotte Watts and Paul M. Pronyk
Household and community income, economic shocks and risky sexual behavior of young adults: evidence from the S49
Cape Area Panel Study 2002 and 2005
Taryn Dinkelman, David Lam and Murray Leibbrandt
HIV incidence and poverty in Manicaland, Zimbabwe: is HIV becoming a disease of the poor? S57
Ben Lopman, James Lewis, Constance Nyamukapa, Phyllis Mushati, Steven Chandiwana and Simon Gregson
The economic impacts of premature adult mortality: panel data evidence from KwaZulu-Natal, South Africa S67
Michael R. Carter, Julian May, Jorge Agüero and Sonya Ravindranath
The financial impact of HIV/AIDS on poor households in South Africa S75
Daryl L. Collins and Murray Leibbrandt
Father figures: the progress at school of orphans in South Africa S83
Ian M. Timaeus and Tania Boler
Exploring the Cinderella myth: intrahousehold differences in child wellbeing between orphans and non-orphans in S95
Amajuba District, South Africa
Anokhi Parikh, Mary Bachman DeSilva, Mandisa Cakwe, Tim Quinlan, Jonathon L. Simon, Anne Skalicky and
Tom Zhuwau
S104
List of contributors
© Wolters Kluwer Health | Lippincott Williams & Wilkins

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6. Investigating the empirical evidence for
understanding vulnerability and the associations
between poverty, HIV infection and AIDS impact
Stuart Gillespiea, Robert Greenerb, Alan Whitesidec and
James Whitworthd
AIDS 2007, 21 (suppl 7):S1–S4
It is just over 25 years since the first cases of AIDS were were dead, killed in the First World War. It is only in the
reported. Over this quarter-century, AIDS has become past decade that the last of these spinsters has died. The
one of most highly studied diseases in history. There impacts of AIDS will take even longer to work through
have been significant medical advances in understanding the population.
the consequences of HIV infection and treating AIDS, as
is well documented in many journals, including AIDS. Second, HIV is diverse in its spread. Early fears that the
The complex and place-specific social, economic, virus would spread rapidly outside Africa have not
behavioural and psychological drivers of the spread of materialized. For example, the UNAIDS 2006 ‘Report
HIV remain less well delineated. The consequences of on the global AIDS epidemic’ estimated that there were
increased illness and death in poor countries and commu- 5.7 million people living with HIV in India. In July 2007,
nities are still unfolding. this was revised downward to 2.5 million, reflecting much
less spread of the infection than had been feared [2].
In 2000, HIV was placed firmly on the global development Similar downward revisions of estimates have been made
agenda by UN Security Council Resolution 1308, which in China. In a recent book, James Chin [3] argued that
stated: ‘the spread of HIV can have a uniquely devastating there are many populations in which heterosexual
impact on all sectors and levels of society’. A year later, in epidemics will not occur in the general population and
July 2001, there was a UN General Assembly Special the epidemic will remain confined to specific risk groups.
Session on HIV/AIDS. Since then our understanding of Chin’s examples of where the potential for HIVepidemics
the epidemic and its potential impacts has deepened. This has been overstated are primarily from Asia, and in
supplement, written by social scientists, looks at how particular China and the Philippines. This is not to
socioeconomic determinants drive HIV spread and how understate the individual tragedy of each infection, but
AIDS illness and mortality is impacting on communities. rather to recognize that there are countries where AIDS
will have a considerable impact and others where its
It is helpful to locate the contents of this supplement in importance can be downgraded.
the context of the history of the epidemic. There are three
overarching points to be made in introduction. First, the It is not just globally that there is wide variation. In
epidemic is complex both in terms of what is driving it mainland sub-Saharan Africa HIV prevalence in adults
and the effects it has. It has been described as a ‘long wave ranges from 0.7% in Mauritania to 33.4 % in Swaziland.
event’. It takes years for the epidemic to spread through The hardest-hit countries are all in southern Africa; these
society and generations for the full impact to be felt. A are shown in Fig. 1, the so-called ‘red’ countries. Adult
recent book highlights the nature of such long wave HIV prevalence exceeds 20% in four of these countries:
events [1]. ‘Singled out: how two million women Swaziland, Lesotho, Botswana and Zimbabwe. South
survived without men after the First World War’ describes Africa, Namibia, Zambia, Mozambique, and Malawi all
how in the United Kingdom a generation of women were have adult prevalence rates in the range of 10–20% [2].
unable to marry, as the men they would have partnered These countries are the focus of this supplement.
From the aInternational Food Policy Research Institute, Geneva, Switzerland, the bJoint United Nations Programme on HIV/AIDS,
Geneva, Switzerland, the cHealth Economics and HIV/AIDS Research Division, University of KwaZulu-Natal, South Africa, and
the dWellcome Trust, London, United Kingdom
Correspondence to Alan Whiteside, Health Economics and HIV/AIDS Research Division, University of KwaZulu-Natal, Block
J418 Westville, University Road Westville, Private Bag XS4001, Durban, 4000, South Africa.
Fax: +27 (31) 260 25 87; e-mail: whitesid@ukzn.ac.za
ISSN 0269-9370 Q 2007 Wolters Kluwer Health | Lippincott Williams & Wilkins S1

7. S2 AIDS 2007, Vol 21 (suppl 7)
deficiency virus (HIV) was identified as the cause. The
number of cases rose rapidly across the United States and
was quickly identified in Europe, Australia, New Zealand
and Latin America. In central Africa, health workers were
observing new illnesses such as Kaposi’s sarcoma (a cancer)
in Zambia, cryptococcosis (an unusual fungal infection) in
Kinshasa, and there were reports of ‘slim disease’ and
unexpectedly high rates of death in Lake Victoria fishing
villages in Uganda [6–8]. These illnesses were occurring in
heterosexual adults, not just gay men, individuals with
haemophilia, blood transfusion recipients, and intravenous
drug users, who formed the main groups at risk in
developed countries. By 1982, cases were being seen in the
partners and infants of those infected [8,9].
The initial response of public health specialists, epide-
miologists and scientists was to try to identify what was
causing the disease and to understand how it was
spreading. This would inform prevention strategies and
Fig. 1. Map of adult HIV prevalence in Africa. 20–34%; medical interventions. Early responses were therefore
10–< 20%; 5–< 10%; 1–< 5%; < 1%. predominantly scientific and technical in nature.
Third, social science faces problems in addressing the It soon became apparent, however, that this was not
phenomenon of HIVand its consequences. The epidemic enough, and attention shifted to understanding why
is only 25 years old, which means that it, and its effects, are people were being exposed. This led to early knowledge
still unfolding. Social science relies on assessing what has attitude and practice surveys, which sought to understand
happened. This is done through surveys and panel data, high-risk behaviours [3] p.73. This emphasis on
and sometimes the picture is at odds with what we expect. prevention gained momentum because medical scientists
For example in the 1980s it was suggested, on the basis of had not yet discovered drugs that could cure, or even slow,
models, that AIDS would cause economies to grow more the progress of the disease. Initial optimism for developing
slowly than otherwise would be the case. In 2007, at the an effective vaccine soon faded and is now seen to be
individual country level, this does not seen to have many years, if not decades, away.
occurred. Uganda had the worst epidemic in the world
during the early 1990s yet managed consistent economic Internationally, the World Health Organization (WHO)
growth estimated at 6.5% per annum from 1991 to 2002. took the lead in response to HIV in 1986; teams visited
Botswana’s growth rate over the same period was 5.6%. most developing countries to establish short and
South Africa has seen steady growth since 1999. Yet it is medium-term AIDS programmes, which then evolved
only through longitudinal and cross-sectional studies that into national AIDS programmes [10]. International
we can hope to understand the impact of the disease. responses to HIV were, however, limited and character-
Longitudinal panel data give a picture of what has ized by denial, underestimation, and oversimplification.
happened in a population over the period for which the HIV was not placed high on the agenda of any other
data are collected. An alternative is to gather cross- United Nations agency. Although life expectancy was
sectional data: if we can understand what has happened in plummeting in certain African countries, for example,
Uganda will it help predict what might happen in the United Nations Development Programme waited
Lesotho? The one thing we have not been good at is until 1997 to take this into account in calculating its
predicting the future, although UNAIDS made a brave human development index [11].
attempt at this through its ‘AIDS in Africa: three scenarios
to 2025’ report launched in March 2005 [4]. By the 1990s there was a new perspective developing, as
interest in the individual, social, and economic milieux
that lead to vulnerability to HIV infection began to grow.
Academics and programme officers increasingly recog-
A brief history of 25 years of response nized that social justice, poverty and equity issues were
driving the uneven spread of the virus within and
1981–1996 between communities and societies [12–15].
The AIDS epidemic was recognized in 1981, initally
among gay men in New York and San Francisco [5]. It was 1996–2007
officially named ‘acquired immune deficiency syndrome’ In 1996, there were major changes in response to HIV,
(AIDS) in July 1982, and in 1983 the human immuno- reflecting and reflected in the scholarship of the time. In

8. Introduction Whiteside et al. S3
the 1994 book ‘AIDS in Africa’ of 33 chapters only three inequity, long-term concurrent partnerships, the lack of
were on preventive strategies and four on socioeconomic male circumcision, and the prevalence of co-infections
impact, the rest were scientific or epidemiological [16]. are factors that have been identified and need further
By 1996, when the second edition of ‘AIDS in the world’ examination. There are no easy solutions to curbing the
was published, of 41 chapters only approximately 18 were spread of the epidemic. There are countries, outside
pure science [17]. southern Africa, where the epidemic appears to be under
control: Uganda brought early hope to Africa by showing
In 1996, the new UN agency charged with coordinating how high levels of political commitment and com-
the response to the epidemic, UNAIDS, began operations munity-led responses can work to stabilize HIV
in Geneva. This was significant as it acknowledged that prevalence. In other locations, such as Tanzania, infection
the international health body the WHO was not able to rates peaked at a lower level than those currently seen in
respond to the epidemic in all its facets, and there needed most of southern Africa.
to be international coordination for an exceptional
disease. At the XIth International AIDS Conference in The focus of this supplement is on bringing together and
Vancouver, the arrival of new drugs in developed understanding the data on the socioeconomic dimensions
countries to treat AIDS was announced, and mortality of the epidemic. It came out of a meeting sponsored by
among those being treated plummeted. UNAIDS and hosted by the Health Economics and
HIV/AIDS Research Division of the University of
At the XIIIth International AIDS Conference in KwaZulu-Natal held in Durban from 16 to 18 October
Durban, South Africa, in July 2000, Nelson Mandela, 2006. The aim of the symposium was to bring together
closed the conference with a call for drugs to be made people, especially those involved in field research, to share
accessible to all. Since then, the response to AIDS has knowledge and experience and to address gaps in our
been dominated by new initiatives for making treatment understanding of the spread of HIV and impact of AIDS.
accessible, especially in developing countries. The price In particular, we were looking for community-
of drugs has fallen dramatically with the manufacture of based longitudinal studies currently being carried out
generic drugs.1 In 2001, United Nation’s Secretary in Africa.
General, Kofi Annan, called for spending on AIDS to be
increased 10-fold in developing countries, and the The outputs of this meeting were to be a review of the
Global Fund for AIDS, TB and Malaria was established. main longitudinal socioeconomic data collections in
The same year, President George W. Bush announced Africa with a bearing on HIV, the publication of the
the Presidential Emergency Plan for AIDS Relief participants’ best papers, and an opportunity to network
(PEPFAR) targeting 15 developing countries. In 2003, and share ideas.
the WHO and UNAIDS proclaimed the ‘3 by 5’ plan, to
treat 3 million people in poor countries by the end The meeting was a qualified success in that papers were
of 2005. presented and we have this interesting and thought-
provoking supplement. There are, however, a number of
Over the decade from 1996 to 2006, more financial caveats, and these cut to the heart of the issues we are
resources than ever before were made available for the dealing with. South African research and papers
response to AIDS, with emphasis increasingly on making dominate. Of the 11 papers we publish, eight are from
treatment available in developing countries. In 1996, South Africa, two compare data from across the continent
there was approximately US$300 million for HIV/AIDS and one is from Zimbabwe. This is also true of the
in low and middle-income countries; by 2006, this authors, the vast majority are either South African or
increased to US$8.3 billion. It is noteworthy that this based in the developed world. Clearly, there are real issues
response, largely a result of treatment becoming with developing capacity in African countries. The global
available and affordable, led to a ‘remedicalization’ of emphasis is on delivery not research, but, as this
HIV/AIDS. supplement shows, quality data and good science are
essential.
It is not clear why southern Africa has been so hard hit by
HIV. Socioeconomic variables, cultural factors and sexual Of the ten papers we publish, seven are from South Africa
behaviour all play a role. Poverty, income inequality, sex two compare data from across the continent and one is
from Zimbabwe. This is a good spread. What do the
papers tell us? Put simply, the causes and consequences of
1
Presentation by Peter Graaf of the HIV/AIDS Department of the the epidemic are complex and policy needs to take this
WHO to an ‘Informal technical consultation on the relevance and into account.
modalities of implementation of an observatory for HIV commodities
in Africa’ organized by Health Economics and HIV/AIDS Research
Division (HEARD), University of KwaZulu Natal, the World Health Although poor individuals and households are likely to be
Organization, and Swedish/Norwegian HIV/AIDS Team on 25 June hit harder by the downstream impacts of AIDS than their
2007. less poor counterparts, their chances of being exposed to

9. S4 AIDS 2007, Vol 21 (suppl 7)
HIV in the first place are not necessarily greater than References
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refer to AIDS as a ‘disease of poverty’. As an infectious 1. Nicholson V. Singled out: how two million women survived
disease, it is appropriate that the primary core response to without men after the First World War. London: Viking; 2007.
HIV focuses on public health prevention strategies and on 2. UNAIDS. 2006 Report on the Global AIDS epidemic. 2006.
Available at: http://www.unaids.org/en/HIV_data/2006Global-
medical treatment and care. But if we are to make further Report/default.asp. Accessed: September 2007.
strides in combating the epidemic we need broad-based 3. Chin J. The AIDS pandemic: the collision of epidemiology with
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4. UNAIDS. AIDS in Africa: three scenarios to 2025. Geneva:
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economic, behavioural and psychological drivers of the 5. Centers for Disease Control and Prevention. MMWR Morb
epidemic. Like the virus, these strategies need to cut Mortal Wkly Rep.
across all socioeconomic strata of society. 6. Bayley A. Aggressive Kaposi’s sarcoma in Zambia. Lancet 1984;
ii:1318–1320.
7. Hooper E. The river: a journey back to the source of HIV and
On the downstream side, although AIDS impoverishes AIDS. London: Allen Lane/The Penguin Press; 1999. Copyright
households, its effects are not uniform. Again, appropriate Edward Hooper 2000.
8. Iliffe J. The African AIDS epidemic: a history. Oxford: James
responses need to take account of the context-specificity Currey; 2006.
and dynamic nature of the stresses, shocks and local 9. Shilts R. And the band played on: people politics and the AIDS
responses brought by AIDS, so that mitigation measures epidemic. London: Viking; 1988.
are appropriately designed. 10. Mann J, Tarantola D, editors. Government national AIDS pro-
grams, Chap. 30. In: AIDS in the world II. Oxford: Oxford
University Press; 1996.
Finally, as is always the case with a publication, there are 11. Whiteside A, Barnett T, George G, Van Niekerk A. Through a
people who need to be thanked. In Durban, Marisa glass, darkly: data and uncertainty in the AIDS debate. In:
Developing world bioethics, issue 3. Oxford: Blackwell Publish-
Casale took charge of organizing the meeting. UNAIDS ers Ltd.; 2003.
sponsored both the meeting and publication. Alan 12. Whiteside A. AIDS – socio-economic causes and conse-
Whiteside’s time was largely supported through a DFID quences. Occasional paper no 28. Economic Research Unit,
University of Natal, Durban; 1993.
Research Partners Consortium grant. Stuart Gillespie’s 13. Gruskin S, Hendriks A, Tomasevski K. Human rights and the
time was supported by the RENEWAL programme response to HIV/AIDS. In: AIDS in the world II. Edited by Mann
through support from Irish Aid and the Swedish J, Tarantola D. Oxford: Oxford University Press; 1996.
International Development Cooperation Agency, and 14. Loewenson R, Whiteside A. Social and economic issues of HIV/
AIDS in southern Africa: a review of current research. SAfAIDS
by UNAIDS. We also acknowledge the extensive inputs 1997;.
of Suneetha Kadiyala of the International Food Policy 15. Barnett T, Whiteside A. HIV/AIDS and development: case studies
Research Unit throughout the preparation of this and a conceptual framework. Eur J Dev Res 1999; 11:200–234.
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17. Mann J, Tarantola D, editors. AIDS in the world II. Oxford:
Conflicts of interest: None. Oxford University; 1996.

10. Is poverty or wealth driving HIV transmission?
Stuart Gillespiea, Suneetha Kadiyalab and Robert Greenerc
Evidence of associations between socioeconomic status and the spread of HIV in
different settings and at various stages of the epidemic is still rudimentary. Few existing
studies are able to track incidence and to control effectively for potentially confounding
factors. This paper reviews the findings of recent studies, including several included in
this volume, in an attempt to uncover the degree to which, and the pathways through
which, wealth or poverty is driving transmission in sub-Saharan Africa. We investigate
the question of whether the epidemic is transitioning from an early phase in which
wealth was a primary driver, to one in which poverty is increasingly implicated. The
paper concludes by demonstrating the complexity and context-specificity of associ-
ations and the critical influence of certain contextual factors such as location, sex and
age asymmetries, the mobility of individuals, and the social ecology of HIV trans-
mission. Whereas it is true that poor individuals and households are likely to be hit
harder by the downstream impacts of AIDS, their chances of being exposed to HIV in the
first place are not necessarily greater than wealthier individuals or households. What is
clear is that approaches to HIV prevention need to cut across all socioeconomic strata of
society and they need to be tailored to the specific drivers of transmission within
different groups, with particular attention to the vulnerabilities faced by youth and
women, and to the dynamic and contextual nature of the relationship between socio-
economic status and HIV. ß 2007 Wolters Kluwer Health | Lippincott Williams & Wilkins
AIDS 2007, 21 (suppl 7):S5–S16
Keywords: socioeconomic status, poverty, inequality, HIV, gender, prevention
Introduction to have better access to reproductive healthcare, condom
use is generally low in Africa and other parts of the
Evidence of the association between HIV transmission developing world. Pre-existing sexual behaviour patterns
and socioeconomic status is mixed [1–3]. Although early (from ‘pre-HIV’ times) therefore make the richer and the
studies tended to find positive correlations between better educated more vulnerable to HIV infection,
economic resources, education and HIV infection [4,5], especially in the early stages of the epidemic, when
as the epidemic has progressed, it has increasingly been information about the virus and how to protect oneself is
assumed that this relationship is changing. Evidence of the usually low [6,8]. At a later stage, however, it has been
degree, type and dynamics of the influence of socio- argued that individuals with higher socioeconomic status
economic factors on rates of HIV transmission in different tend to adopt safer sexual practices, once the effects of
settings and at various stages of the AIDS epidemic is, AIDS-related morbidity and mortality become more
however, still rudimentary. This paper seeks to bring apparent, adding greater credibility to HIV prevention
together what is known on this, drawing especially on the messages [9,10].
findings of some recent studies, including several in
this supplement. Another currently postulated dynamic is that poverty
(possibly itself fuelled by AIDS) is increasingly placing
In most countries, relatively rich and better educated men individuals from poor households at greater risk of
and women have higher rates of partner change because exposure to HIV via the economically driven adoption of
they have greater personal autonomy and spatial mobility risky behaviours. Poverty and food insecurity are thought
[4,6,7]. Although the richer and better educated are likely to increase sexual risk taking, particularly among women
From the aInternational Food Policy Research Institute, Geneva, Switzerland, the bInternational Food Policy Research Institute,
Washington, DC, USA, and the cJoint United Nations Programme on HIV/AIDS, Geneva, Switzerland.
Correspondence and requests for reprints to Stuart Gillespie, International Food Policy Research Institute, c/o UNAIDS, 20 Avenue
Appia, CH-1211 Geneva 27, Switzerland.
E-mail: s.gillespie@cgiar.org
ISSN 0269-9370 Q 2007 Wolters Kluwer Health | Lippincott Williams & Wilkins S5

11. S6 AIDS 2007, Vol 21 (suppl 7)
who may engage in transactional sex to procure food Does poverty increase exposure to HIV?
for themselves and their children. Women’s economic
dependence on their partners may also make it difficult for At the country level there is a weak positive relationship
them to insist on safer sex (e.g. condom use). In addition, between national wealth and HIV prevalence across
poor people are more likely to be food insecure and countries in sub-Saharan Africa, where higher prevalence
malnourished. Malnutrition is known to weaken the is seen in the wealthier countries of southern Africa
immune system, which in turn may lead to a greater risk of (Fig. 1). Strong urban–rural economic linkages, good
HIV transmission in any unprotected sexual encounter transport links and high professional mobility may
(although this remains under-researched). This strand of translate into both higher incomes and higher HIV
literature on HIV transmission in Africa stresses the reversal incidence. National poverty rates, on the other hand, do
in the distribution of the epidemic across population not show a strong association with HIV prevalence
subgroups as the epidemic advances within countries, with (Fig. 2). There is, however, a clear and significant pattern
those of lower socioeconomic status experiencing a higher of association between income inequality and HIV
subsequent rate of HIV transmission. prevalence across countries; countries with greater
inequality have higher HIV prevalence, especially in
We aim to present an overview of the findings of key sub-Saharan Africa but also to a lesser extent in Asia and
recent African studies (primarily 2004–2007) examining Latin America (Fig. 3).
the relationship between economic resources/status and
the risk of HIV infection (see Table 1). The starting point Household level evidence that poverty is a major driver of
was the evidence presented in this supplement on this the epidemic is rather mixed. It is important, however, to
relationship, but our search then expanded to draw upon note that most studies focus on relative poverty in the
other recent literature from sub-Saharan Africa where the context of generalized chronic poverty. In most cases, it is
epidemic is most severe. only the highest one or two quintiles (or possibly three in
middle-income southern African countries) that can be
First, PUBMED and ECONLIT searches (2004–2007) thought of as representing the non-poor, using the
were used to identify all studies addressing the link standard poverty line definitions, or the US$1 or US$2
between socioeconomic status (poverty and education in per day measures adopted for the purpose of global
particular) and the risk of HIV. Searches were limited to comparison. Comparisons are thus between ‘wealthier’
English language and Africa. Keywords pertaining to the and ‘poorer’ groups.
explanatory variables were ‘poverty’, ‘wealth’, ‘socio-
economic status’, ‘socioeconomic’, ‘education’ and Studies adopting ethnographic methodologies suggest
‘education level’. Keywords pertaining to the outcome that material poverty increases the risks of contracting
variable of interest were ‘HIV risk’, ‘HIV transmission’, HIV mainly through the channel of high-risk behaviour
‘sexual behaviour’ and ‘HIV prevalence’. Studies on adoption. The respondents of an ethnographic study in
special groups of populations such as truck drivers and the southern province of Zambia [26] identified frequent
uniformed services have been excluded. Conceptual/ droughts and limited wage labour opportunities, after the
theoretical papers have not been included in the review of post-economic liberalization closure of companies, as the
the association between socioeconomic status, poverty, ‘push’ factors behind the increasing resort of women to
education and the risk of heterosexual HIV transmission, transactional sex. In a qualitative study in Malawi [27]
although such studies have been used from a reference certain social groups were found to continue to engage in
perspective. Quantitative studies with only descriptive high-risk behaviours despite knowing the risks. They did
statistics have been excluded. Sixteen of the 49 retrieved so, the authors contend, to affirm their social identity and
articles were thus excluded. In addition, a Dissertation to deny that ‘anything they do makes a difference to what
Abstracts Online search and a Google Scholar search were they perceive as a life of powerlessness and despair’ (p. 17).
also conducted to identify pertinent recent grey literature. The ‘culture of poverty’, as documented by Lewis [28] in
Whenever possible, the authors of such papers that met Latin America, may thus be as significant as material
the above criteria were contacted for the latest drafts and poverty in motivating risky behaviours.
updates on the status of their articles.
The findings from several recent quantitative surveys that
As such, this overview is intended to complement earlier investigated the relationship between economic depri-
reviews examining this relationship [23,24]. It then seeks vation and the adoption of high-risk behaviours are
to delve deeper into the pathways and interactions that generally consistent with much of the qualitative research
contextualize the link between wealth/poverty and [29–31], although there are important differences
heterosexual HIV transmission risk. We stress at the between behaviours and regarding the influence of
outset that we are not reviewing evidence of the gender in different contexts [12,14,32].
downstream impacts of AIDS on poverty, a subject that
has been comprehensively covered recently elsewhere Employing the Cape Area Panel Study, which surveys
[23–25]. individual youths aged 14–22 years in Cape Town, South

12. Table 1. Recent quantitative studies examining the relationship between HIV and socioeconomic status.
Study Objective Study design and statistical analyses Key findings
Dinkelman et al. [11] Estimate if sexual debut between 2002 Cape Area Panel Study data that surveyed Household income negatively associated with sexual
and 2005, number of recent partners 4752 boys and girls, 14–22 years of age debut, and economic shocks positively associated
and lack of condom use at last sex in Cape Town, South Africa (2002–2005). with multiple partnerships among girls. Community
in 2005 is affected by household Multivariate probit models poverty rates predict earlier sexual debut and
income constraints and income shocks. higher rates of unprotected recent sex for boys.
Schooling positively associated with a significant
condom use, but negatively associated with
multiple partners for both boys and girls.
Weiser et al. [12] Studies the association between food Cross-sectional population-based survey of Food insufficiency associated with inconsistent
insufficiency (not having enough food 1255 adults in Botswana and 796 adults condom use with a non-primary partner, sex
to eat over the previous 12 months) in Swaziland. exchange, intergenerational sexual relationships, and
and inconsistent condom use, sex Multivariable logistic regression analyses, lack of control in sexual relationships. For men,
exchange, and other measures of risky sex. clustered by country, and stratified by sex. food insufficiency was associated with increase in
the odds of unprotected sex only. Higher
educated women, but not men, were less likely to
report high-risk behaviours.
Johnson and Way [13] Investigates the association between Cross-sectional, 2003 Kenya Demographic Wealth was positively related to HIV-positive
demographic, social, behavioural, and Health Survey. serostatus for both men and women. Women
and biological variables and HIV Multivariate logistic regression model with primary education were nearly twice as likely
serostatus in Kenya. stratified by sex. to be HIV positive as those with no education.
Sexual behaviour factors were not significantly
associated with HIV serostatus.
Nii-Amoo Dodoo et al. [14] Examines the relationship between Quantitative data are drawn from the Although poverty was significantly associated with
HIV-related sexual activity outcomes, Demographic & Health Surveys (DHS) the examined sexual outcomes in all settings, the
specifically age at first sex and multiple and qualitative data from the Sexual urban poor are significantly more likely than their
sexual partnerships, and socioeconomic Networking and Associated Reproductive rural counterparts to have an early sexual debut
deprivation amenities index, (based on and Social Health Concerns study. and a greater incidence of multiple sexual partnerships.
asset index and amenities index) in rural Multivariate Cox regressions. The disadvantage of the urban poor is accentuated
and urban Kenya. for married women; those in Nairobi’s slums are at
least three times as likely to have multiple sexual
Poverty, wealth, HIV transmission Gillespie et al.
partners as their rural counterparts.
Lopman et al. [15] Studies the association between wealth Manicaland, Zimbabwe HIV/STD Prevention The greatest decrease in HIV prevalence occurred in
index (based on household asset ownership) Project’s population-based open cohort the highest wealth index tercile in both men and
and HIV incidence, HIV mortality, sexual (baseline between 1998 and 2001 and women. In men (but not women), HIV incidence
risk behaviour, and sexual mixing patterns. follow-up between 2001 and 2003). was lowest in the top wealth index tercile. Mortality
Multivariate logistics and Poisson regression rates were significantly lower in both men and women
models. of higher wealth index. Men of higher wealth
index reported more sexual partners, but were also
more likely to use condoms, controlling for age and
site type. Better-off women reported fewer partners
and were less likely to engage in transactional sex.
Hargreaves et al. [16] To assess the evidence that HIV incidence Prospective cohort of 1967 individuals Among men, there was little evidence that HIV
rates and sexual behaviour patterns differed (14–35 years of age) in Limpopo province, seroconversion was associated with any
by wealth, education and migration. South Africa (2001 and 2004). socioeconomic factor. Among women, HIV
Multivariate logistic regression models, seroconversion was negatively associated with
stratified by sex. education, but not wealth or migration. Migrant
men more often reported multiple partners. Migrant
and more educated individuals of both sexes, and
women from wealthier households, reported
higher levels of condom use.
Mishra et al. [17] Examines the association between wealth Cross-sectional nationally representative In all eight countries, adults in the wealthiest quintiles
(index based on household ownership surveys from eight sub-Saharan African have higher prevalence of HIV than those in the
of consumer durables) and HIV serostatus countries conducted during 2003–2005. poorer quintiles, but the positive association
of 15–49-year-old individuals. Multivariate logistic regression models, between wealth and HIV status was statistically
stratified by sex. insignificant in multivariate models.
(continued overleaf )
S7

13. S8
AIDS
Table 1. (continued )
Study Objective Study design and statistical analyses Key findings
2007, Vol 21 (suppl 7)
Barnighausen et al. [18]
¨ Investigates the effect of educational Longitudinal data (2003–2005) on 3325 adults Belonging to a household in the middle
attainment, household wealth categories from Africa Centre Demographic Information wealth category increased the risk of
(based on a ranking of households on an System in KwaZulu-Natal, South Africa. HIV seroconversion. One additional grade
assets index scale) and total household Semiparametric and parametric survival models. of educational attainment reduced the
expenditure, on HIV incidence. hazard of HIV seroconversion by
approximately 7%. Urban residence was
associated with a 65% increase in the
hazard of HIV seroconversion.
Chapoto and Jayne [19] To determine the ex-ante socioeconomic Nationally representative panel data set of Relatively non-poor men (ranked by
characteristics of individuals who died 18 821 individuals from 5420 households assets levels) were 43% more likely
in their prime age (15–59 years) surveyed between 2001 and 2004. to die than poor men. Poor and non-poor
in Zambia. Multivariate probit models, stratified by sex women were equally likely to die. No clear
and assets. relationship observed between education
attainment and probability of prime-age
mortality. Poor women with business
income were 15% less likely, and non-poor
women with business income 7% more
likely, to die than those without business income.
Kirimi and Jayne [20] Estimates the potentially changing Nationwide data set of 5755 individuals Over time, the probability of disease-related
relationship over time between from 1500 Kenyan rural households death declined for both men and women.
household and individual-level collected in 1997, 2000, 2002 and 2004. A reversal in the effect of education on death
indicators of poverty and subsequent Multivariate probit models, stratified by sex. was observed, with more educated women
death of prime-age adults in Kenya. and men, and particularly younger ones,
being at greater risk of death. Although weak,
there is also a delayed but significant
negative effect of landholding size and asset
value on male mortality.
Glynn et al. [9] Investigates the associations between Cross-sectional population-based survey No association between schooling and HIV
schooling and both HIV and herpes conducted in 1997–1998 in four African infection and a significant negative association
simplex 2 infection and risky behaviours cities including approximately with herpes simplex 2 in women observed in
in Cotonou (Benin), Yaounde (Cameroon), 2000 adults in each city. Kisumu or Ndola,. In Yaounde, women with
Kisumu (Kenya) and Ndola (Zambia). Multivariate models, stratified by sex. more schooling were less likely to be HIV
positive. Similar association observed among
men in Cotonou for herpes simplex 2. In all
cities, those with more education tended to
report less risky sexual behaviours.
De Walque et al. [10] Investigates the association between Population-based cohort followed between In 1989/90, there was no significant relationship
changing HIV prevalence, condom 1989/1990 and 1999/2000. between education and HIV prevalence.
use and education in rural south-west Multivariate and bivariate (condom versus In 1999–2000 women aged 18–29 years
Uganda. education) analyses. with post-primary education were at
significantly lower risk of HIV-1 infection
than women with no education. Condom
use increased during the study period and
this increase has been concentrated among
more educated individuals.
Luke [21] To study the trade-off between transfers Cross-sectional survey of Luo men aged Men’s income was not significantly associated
and condom use at last sexual intercourse 21–45 years in Kisumu, Kenya. with condom use. Having an adolescent
in non-commercial, non-marital sexual Multivariate models including male fixed female partner does not have a significant
relationships in Kenya. effects models. effect on condom use. For every Ksh500,
approximately the mean amount given in
transfers per partnership, the probability
of condom use decreased by approximately 8%.
Trade-off between transfers and condom use
does not vary between adolescents and
adult women.

14. Poverty, wealth, HIV transmission Gillespie et al. S9
level of gender inequality, age is protective. Similarly, the
not always significant. Conditional on gender inequality,
Africa (2002–2005), Dinkelman et al. [11] show that for
effect of gender inequality for women decreased with
the share of young women who live in poverty in the
was associated with a 1% increase in the probability
girls, sexual debut appears to be earlier in poor
increasing household assets, although this effect was
A one standard deviation increase in gender inequality
of being HIV positive for young women. For a given
households, especially those who have experienced an
in inherited land, the total amount of transfers
community did not increase the probability of
Economic status was positively and significantly
economic shock (a death, illness or job loss). A recent
increases by Ksh10 on average. Wealth was
associated with both the giving of transfers
and the amount. For every additional acre
additional year of education increased the
cross-sectional study in Kenya found asset poverty to be
not correlated with condom use. Each
significantly related to risky sexual outcomes, such as
early sexual debut, multiple sexual partnerships, in all
three residential settings studied [14]. In a study in
probability of condom use by
Botswana and Swaziland [12], although protective in
individual HIV infection.
unadjusted analyses, controlling for other variables,
approximately 3.4%.
income was not associated with intergenerational sex
and a lack of control in sexual relationships among
women. Wealthier men reported having more sex
exchange [adjusted odds ratios (aOR) 1.94, 95%
confidence interval (CI) 1.59–2.37] but were also more
likely to report condom use (aOR 0.78, 95% CI 0.72–
0.84).
Another recent cross-sectional study of Luo men aged
21–45 years of age in urban Kisumu, Kenya, found male
economic status, controlling for age and education, to
Cross-sectional survey of Luo men aged
and Housing Census, Kenya Poverty
be positively associated with transactional sex and the
Three sources of cross-sectional data:
value of transfers [22]. For every Ksh1000 in male
Health Survey, 1999 Population
21–45 years in Kisumu, Kenya.
2003 Kenya Demographic and
income, the probability of giving a transfer in the past
month increases approximately 1%, and the total amount
Multivariate probit models
of transfers increases Ksh29 (US$0.40). Wealth (income
Multivariate models.
and inherited land) was not, however, correlated with
condom use, suggesting that larger transfers are not being
Map (2003).
given by wealthier men as an incentive for condom-free
(riskier) sex.
Two prospective cohort studies examining the relation-
ship between economic resources and high-risk sexual
behaviours are presented in this volume. In a 3-year
women and adult men within an individual’s
follow-up study (baseline between 1998 and 2001 and
Examines the relationship between HIV status
women’s poverty status on individual HIV
inherited land), transfers, and non-marital
follow-up between 2001 and 2003) in Manicaland,
Empirical investigation of the connection
between economic status (income and
and gender inequality between young
non-commercial, sexual relationships
Zimbabwe, Lopman et al. [15], found wealthier men
community and to examine young
reporting more sexual partners, but also more frequent
use of condoms, controlling for age and site type. This
relationship became insignificant, however, after con-
trolling for education level, in addition to age and site
type, suggesting that the effect of wealth is at least partly
status in Kenya.
the result of differences in education across wealth levels.
Better-off women reported fewer partners and were less
in Kenya.
likely to engage in transactional sex, adjusting for age,
education level and site type. Hargreaves et al. [16] in
Limpopo, South Africa (2001–2004) found women, but
not men, from wealthier households reporting higher
levels of condom use (aOR comparing household ‘doing
OK’ with ‘very poor’ 2.03, 95% CI 1.29–3.20).
Beegle and Ozler (unpublished)
Using Demographic and Health Survey (DHS) data from
eight countries, Mishra et al. [17] found a positive
association between an asset-based wealth index and HIV
status. This relationship was stronger for women, and it
was clear that HIV prevalence was generally lower among
Luke [22]
the poorest individuals in these countries. This is partly
accounted for by an association of wealth with other

15. S10 AIDS 2007, Vol 21 (suppl 7)
35%
Swaziland
30%
Botswana
25%
Lesotho
HIV prevalence 20% Zimbabwe Namibia
Southern Africa Zambia
R squared = 0.2952 Mozambique South Africa
15% not significant
Malawi
Central African Republic
10%
Gabon
Côte d'Ivoire
Tanzania Kenya
E&W Africa Uganda
5%
R squared = 0.0000 Angola
not significant Sierra leone
Ethiopia
0%
US$100 US$1 000 US$10000
GDP per capita (PPP, logarithmic scale)
Fig. 1. HIV and per-capita gross domestic product in Africa. Sources: Economic data from UNDP Human Development Report
2006; HIV prevalence data from UNAIDS Epi Update, May 2006.
underlying factors. Wealthier individuals tend to live in likely than the poorest women to be HIV positive [13].
urban areas where HIV is more prevalent, they tend to be Similar findings were reported in Tanzania [33] and in
more mobile, more likely to have multiple partners, more Burkina Faso [34].
likely to engage in sex with non-regular partners, and
they live longer; all factors that may present greater Studies of cross-sectional associations between HIV
lifetime HIV risks. On the other hand, however, they serostatus and socioeconomic status (such as those above
tend to be better educated, with better knowledge of HIV and the cross-sectional studies featured in another
prevention methods, and are more likely to use condoms; comprehensive review [1]) suffer from important
factors that reduce their risk compared with poorer limitations: They are unable to distinguish between the
individuals. Controlling for these associations, however, effect of economic status on HIV infection and the effect
does not reverse the conclusion: there is no apparent of HIV infection on economic status, and they are unable
association between low wealth status and HIV. to control for the fact that individuals from richer
households may survive longer with HIV, and are thus
Using data from the cross-sectional, population-based more likely to be present in the population to be tested,
2003 Kenya Demographic and Health Survey, a recent thereby increasing HIV prevalence rates.
study found increased wealth to be positively related to
HIV infection, with the effect being stronger for women In a cross-sectional study, it is thus conceivable to find a
than men; the wealthiest women being 2.6 times more positive association between economic status and HIV
25% Botswana
Lesotho
Zimbabwe
20% Namibia
South Africa
Southern Africa
R squared = 0.0996 Zambia
Mozambique
not significant
HIV prevalence
15% Malawi
Central African Republic
10%
E&W Africa
Côte d'Ivoire Uganda
Tanzania R squared = 0.0307
Kenya not significant
5% Cameroon
Nigeria
Rwanda Burundi
Ghana
Ethiopia Gambia Mali
Burkina Faso Niger
Senegal
Mauritania Sierra Leone Madagascar
0%
0 10 20 30 40 50 60 70 80
Percentage below US$1 per day
Fig. 2. HIV and poverty in Africa. Sources: Economic data from UNDP Human Development Report 2006; HIV prevalence data
from UNAIDS Epi Update, May 2006.