Volume 21 Supplement 7 November 2007
Poverty, HIV and AIDS: Vulnerability and
Impact in Southern Africa
Editors: Stuart Gillespie
Sponsored by UNAIDS, RENEWAL and HEARD
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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.
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AIMS AND SCOPE
AIDS publishes papers reporting original scientifi c, clinical, epidemiological, and social research which are of a high
standard and contribute to the overall knowledge of the fi eld of the acquired immune defi ciency syndrome. The
Journal publishes Original Papers, Concise Communications, Research Letters and Correspondence, as well as
invited Editorial Reviews and Editorial Comments.
© Wolters Kluwer Health | Lippincott Williams & Wilkins
Investigating the empirical evidence for understanding vulnerability and the associations between poverty, HIV
infection and AIDS impact
Stuart Gillespie, Robert Greener, Alan Whiteside and James Whitworth
Is poverty or wealth driving HIV transmission?
Stuart Gillespie, Suneetha Kadiyala and Robert Greener
HIV infection does not disproportionately affect the poorer in sub-Saharan Africa
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
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
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
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?
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
Michael R. Carter, Julian May, Jorge Agüero and Sonya Ravindranath
The fi nancial impact of HIV/AIDS on poor households in South Africa
Daryl L. Collins and Murray Leibbrandt
Father fi gures: the progress at school of orphans in South Africa
Ian M. Timaeus and Tania Boler
Exploring the Cinderella myth: intrahousehold differences in child wellbeing between orphans and non-orphans in
Amajuba District, South Africa
Anokhi Parikh, Mary Bachman DeSilva, Mandisa Cakwe, Tim Quinlan, Jonathon L. Simon, Anne Skalicky and
List of contributors
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Investigating the empirical evidence for
understanding vulnerability and the associations
between poverty, HIV infection and AIDS impact
Stuart Gillespiea, Robert Greenerb, Alan Whitesidecand
AIDS 2007, 21 (suppl 7):S1–S4
It is just over 25 years since the first cases of AIDS were
reported. Over this quarter-century, AIDS has become
one of most highly studied diseases in history. There
have been significant medical advances in understanding
the consequences of HIV infection and treating AIDS, as
is well documented in many journals, including AIDS.
The complex and place-specific social, economic,
behavioural and psychological drivers of the spread of
HIV remain less well delineated. The consequences of
increased illness and death in poorcountries and commu-
nities are still unfolding.
agenda by UN Security Council Resolution 1308, which
stated: ‘the spread of HIV can have a uniquely devastating
impact on all sectors and levels of society’. A year later, in
July 2001, there was a UN General Assembly Special
Session on HIV/AIDS. Since then our understanding of
the epidemic and its potential impacts has deepened. This
supplement, written by social scientists, looks at how
socioeconomic determinants drive HIV spread and how
AIDS illness and mortality is impacting on communities.
It is helpful to locate the contents of this supplement in
overarching points to be made in introduction. First, the
epidemic is complex both in terms of what is driving it
and the effects it has. It has been described as a ‘long wave
event’. It takes years for the epidemic to spread through
society and generations for the full impact to be felt. A
recent book highlights the nature of such long wave
events . ‘Singled out: how two million women
survived without men after theFirstWorld War’describes
unable to marry, as the men they would have partnered
were dead, killed in the First World War. It is only in the
past decade that the last of these spinsters has died. The
impacts of AIDS will take even longer to work through
Second, HIV is diverse in its spread. Early fears that the
virus would spread rapidly outside Africa have not
materialized. For example, the UNAIDS 2006 ‘Report
on the global AIDS epidemic’ estimated that there were
5.7 million people living with HIV in India. In July 2007,
less spread of the infection than had been feared .
Similar downward revisions of estimates have been made
in China. In a recent book, James Chin  argued that
there are many populations in which heterosexual
epidemics will not occur in the general population and
the epidemic will remain confined to specific risk groups.
has been overstated are primarily from Asia, and in
particular China and the Philippines. This is not to
understate the individual tragedy of each infection, but
rather to recognize that there are countries where AIDS
will have a considerable impact and others where its
importance can be downgraded.
It is not just globally that there is wide variation. In
mainland sub-Saharan Africa HIV prevalence in adults
ranges from 0.7% in Mauritania to 33.4 % in Swaziland.
The hardest-hit countries are all in southern Africa; these
are shown in Fig. 1, the so-called ‘red’ countries. Adult
HIV prevalence exceeds 20% in four of these countries:
Swaziland, Lesotho, Botswana and Zimbabwe. South
Africa, Namibia, Zambia, Mozambique, and Malawi all
have adult prevalence rates in the range of 10–20% .
These countries are the focus of this supplement.
From theaInternational Food Policy Research Institute, Geneva, Switzerland, thebJoint United Nations Programme on HIV/AIDS,
Geneva, Switzerland, thecHealth Economics and HIV/AIDS Research Division, University of KwaZulu-Natal, South Africa, and
thedWellcome 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: email@example.com
ISSN 0269-9370 Q 2007 Wolters Kluwer Health | Lippincott Williams & Wilkins
Third, social science faces problems in addressing the
phenomenon of HIVand itsconsequences. The epidemic
still unfolding. Social science relies on assessing what has
happened. This is done through surveys and panel data,
For example in the 1980s it was suggested, on the basis of
models, that AIDS would cause economies to grow more
slowly than otherwise would be the case. In 2007, at the
individual country level, this does not seen to have
occurred. Uganda had the worst epidemic in the world
during the early 1990s yet managed consistent economic
growth estimated at 6.5% per annum from 1991 to 2002.
Botswana’s growth rate over the same period was 5.6%.
South Africa has seen steady growth since 1999. Yet it is
only through longitudinal and cross-sectional studies that
we can hope to understand the impact of the disease.
Longitudinal panel data give a picture of what has
happened in a population over the period for which the
data are collected. An alternative is to gather cross-
sectional data: if we can understand what has happened in
Uganda will it help predict what might happen in
Lesotho? The one thing we have not been good at is
predicting the future, although UNAIDS made a brave
to 2025’ report launched in March 2005 .
A brief history of 25 years of response
The AIDS epidemic was recognized in 1981, initally
officially named ‘acquired immune deficiency syndrome’
(AIDS) in July 1982, and in 1983 the human immuno-
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
heterosexual adults, not just gay men, individuals with
drug users, who formed the main groups at risk in
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
medical interventions. Early responses were therefore
predominantly scientific and technical in nature.
It soon became apparent, however, that this was not
enough, and attention shifted to understanding why
people were being exposed. This led to early knowledge
attitude and practice surveys, which sought to understand
high-risk behaviours  p.73. This emphasis on
prevention gained momentum because medical scientists
an effective vaccine soon faded and is now seen to be
many years, if not decades, away.
Internationally, the World Health Organization (WHO)
took the lead in response to HIV in 1986; teams visited
most developing countries to establish short and
medium-term AIDS programmes, which then evolved
into national AIDS programmes . International
responses to HIV were, however, limited and character-
ized by denial, underestimation, and oversimplification.
HIV was not placed high on the agenda of any other
United Nations agency. Although life expectancy was
plummeting in certain African countries, for example,
the United Nations Development Programme waited
until 1997 to take this into account in calculating its
human development index .
By the 1990s there was a new perspective developing, as
interest in the individual, social, and economic milieux
that lead tovulnerability to HIV infection began to grow.
Academics and programme officers increasingly recog-
nized that social justice, poverty and equity issues were
driving the uneven spread of the virus within and
between communities and societies [12–15].
In 1996, there were major changes in response to HIV,
reflecting and reflected in the scholarship of the time. In
S2AIDS2007, Vol 21 (suppl 7)
Fig. 1. Map of adult HIV prevalence in Africa.
the 1994 book ‘AIDS in Africa’ of 33 chapters only three
were on preventive strategies and four on socioeconomic
impact, the rest were scientific or epidemiological .
By 1996, when the second edition of ‘AIDS in theworld’
was published, of 41 chapters onlyapproximately 18 were
pure science .
In 1996, the new UN agency charged with coordinating
in Geneva. This was significant as it acknowledged that
the international health body the WHO was not able to
respond to the epidemic in all its facets, and there needed
to be international coordination for an exceptional
disease. At the XIth International AIDS Conference in
Vancouver, the arrival of new drugs in developed
countries to treat AIDS was announced, and mortality
among those being treated plummeted.
At the XIIIth International AIDS Conference in
Durban, South Africa, in July 2000, Nelson Mandela,
closed the conference with a call for drugs to be made
accessible to all. Since then, the response to AIDS has
been dominated by new initiatives for making treatment
accessible, especially in developing countries. The price
of drugs has fallen dramatically with the manufacture of
generic drugs.1In 2001, United Nation’s Secretary
General, Kofi Annan, called for spending on AIDS to be
increased 10-fold in developing countries, and the
Global Fund for AIDS, TB and Malaria was established.
The same year, President George W. Bush announced
the Presidential Emergency Plan for AIDS Relief
(PEPFAR) targeting 15 developing countries. In 2003,
the WHO and UNAIDS proclaimed the ‘3 by 5’ plan, to
treat 3 million people in poor countries by the end
Over the decade from 1996 to 2006, more financial
resources than ever before were made available for the
response to AIDS, with emphasis increasingly on making
treatment available in developing countries. In 1996,
there was approximately US$300 million for HIV/AIDS
in low and middle-income countries; by 2006, this
increased to US$8.3 billion. It is noteworthy that this
response, largely a result of treatment becoming
available and affordable, led to a ‘remedicalization’ of
It is not clear why southern Africa has been so hard hit by
HIV. Socioeconomic variables, cultural factors and sexual
behaviour all play a role. Poverty, income inequality, sex
inequity, long-term concurrent partnerships, the lack of
male circumcision, and the prevalence of co-infections
are factors that have been identified and need further
examination. There are no easy solutions to curbing the
spread of the epidemic. There are countries, outside
southern Africa, where the epidemic appears to be under
control: Uganda brought early hope to Africa by showing
how high levels of political commitment and com-
munity-led responses can work to stabilize HIV
prevalence. In other locations, such as Tanzania, infection
rates peaked at a lower level than those currently seen in
most of southern Africa.
The focus of this supplement is on bringing together and
understanding the data on the socioeconomic dimensions
of the epidemic. It came out of a meeting sponsored by
UNAIDS and hosted by the Health Economics and
HIV/AIDS Research Division of the University of
KwaZulu-Natal held in Durban from 16 to 18 October
2006. The aim of the symposium was to bring together
people, especiallythoseinvolvedinfieldresearch,to share
knowledge and experience and to address gaps in our
understanding of the spread of HIVand impact of AIDS.
In particular, we were looking for community-
based longitudinal studies currently being carried out
The outputs of this meeting were to be a review of the
main longitudinal socioeconomic data collections in
Africa with a bearing on HIV, the publication of the
participants’ best papers, and an opportunity to network
and share ideas.
The meeting was a qualified success in that papers were
presented and we have this interesting and thought-
provoking supplement. There are, however, a number of
caveats, and these cut to the heart of the issues we are
dealing with. South African research and papers
dominate. Of the 11 papers we publish, eight are from
South Africa, two compare datafrom acrossthe continent
and one is from Zimbabwe. This is also true of the
authors, the vast majority are either South African or
based in the developed world. Clearly, there are real issues
emphasis is on delivery not research, but, as this
supplement shows, quality data and good science are
Of the ten paperswe publish, seven are from South Africa
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
the epidemic are complex and policy needs to take this
Although poor individualsandhouseholdsarelikely tobe
hit harder by the downstream impacts of AIDS than their
less poor counterparts, their chances of being exposed to
Introduction Whiteside et al. S3
1Presentation by Peter Graaf of the HIV/AIDS Department of the
WHO to an ‘Informal technical consultation on the relevance and
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
Organization, and Swedish/Norwegian HIV/AIDS Team on 25 June
HIV in the first place are not necessarily greater than
wealthier individuals or households. It is too simplistic to
refer to AIDS as a ‘disease of poverty’. As an infectious
disease, it is appropriate that the primary core response to
HIVfocuseson publichealthprevention strategiesand on
medical treatment and care. But if we are to make further
strides in combating the epidemic we need broad-based
prevention, that is, prevention that deals with the
contextual environment and the underlying socio-
economic, behavioural and psychological drivers of the
epidemic. Like the virus, these strategies need to cut
across all socioeconomic strata of society.
On the downstream side, although AIDS impoverishes
responses need to take account of the context-specificity
and dynamic nature of the stresses, shocks and local
responses brought by AIDS, so that mitigation measures
are appropriately designed.
Finally, as is always the case with a publication, there are
people who need to be thanked. In Durban, Marisa
Casale took charge of organizing the meeting. UNAIDS
sponsored both the meeting and publication. Alan
Whiteside’s time was largely supported through a DFID
Research Partners Consortium grant. Stuart Gillespie’s
time was supported by the RENEWAL programme
through support from Irish Aid and the Swedish
International Development Cooperation Agency, and
by UNAIDS. We also acknowledge the extensive inputs
of Suneetha Kadiyala of the International Food Policy
Research Unit throughout the preparation of this
Conflicts of interest: None.
1. Nicholson V. Singled out: how two million women survived
without men after the First World War. London: Viking; 2007.
UNAIDS. 2006 Report on the Global AIDS epidemic. 2006.
Available at: http://www.unaids.org/en/HIV_data/2006Global-
Report/default.asp. Accessed: September 2007.
Chin J. The AIDS pandemic: the collision of epidemiology with
political correctness. Oxford: Radcliffe Publishing; 2006.
UNAIDS. AIDS in Africa: three scenarios to 2025. Geneva:
Centers for Disease Control and Prevention. MMWR Morb
Mortal Wkly Rep.
BayleyA. AggressiveKaposi’ssarcoma in Zambia. Lancet 1984;
Hooper E. The river: a journey back to the source of HIV and
AIDS. London: Allen Lane/The Penguin Press; 1999. Copyright
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Iliffe J. The African AIDS epidemic: a history. Oxford: James
Shilts R. And the band played on: people politics and the AIDS
epidemic. London: Viking; 1988.
Mann J, Tarantola D, editors. Government national AIDS pro-
grams, Chap. 30. In: AIDS in the world II. Oxford: Oxford
University Press; 1996.
Whiteside A, Barnett T, George G, Van Niekerk A. Through a
glass, darkly: data and uncertainty in the AIDS debate. In:
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Whiteside A. AIDS – socio-economic causes and conse-
quences. Occasional paper no 28. Economic Research Unit,
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Gruskin S, Hendriks A, Tomasevski K. Human rights and the
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Oxford University; 1996.
S4 AIDS2007, Vol 21 (suppl 7)
Is poverty or wealth driving HIV transmission?
Stuart Gillespiea, Suneetha Kadiyalaband 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
first place are not necessarily greater than wealthier individuals or households. What is
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.
? 2007 Wolters Kluwer Health | Lippincott Williams & Wilkins
AIDS 2007, 21 (suppl 7):S5–S16
Keywords: socioeconomic status, poverty, inequality, HIV, gender, prevention
Evidence of the association between HIV transmission
and socioeconomic status is mixed [1–3]. Although early
studies tended to find positive correlations between
economic resources, education and HIV infection [4,5],
as the epidemic has progressed, it has increasingly been
degree, type and dynamics of the influence of socio-
settings and at various stages of the AIDS epidemic is,
however, still rudimentary. This paper seeks to bring
together what is known on this, drawing especiallyon the
findings of some recent studies, including several in
and women have higher rates of partner change because
they have greater personal autonomy and spatial mobility
[4,6,7].Although the richer and bettereducated are likely
to have better access to reproductive healthcare, condom
use is generally low in Africa and other parts of the
developing world. Pre-existing sexual behaviour patterns
(from ‘pre-HIV’times) therefore make the richer and the
better educated more vulnerable to HIV infection,
especially in the early stages of the epidemic, when
information about the virus and how to protect oneself is
usually low [6,8]. At a later stage, however, it has been
argued that individuals with higher socioeconomic status
tend to adopt safer sexual practices, once the effects of
AIDS-related morbidity and mortality become more
apparent, adding greater credibility to HIV prevention
Another currently postulated dynamic is that poverty
(possibly itself fuelled by AIDS) is increasingly placing
individuals from poor households at greater risk of
exposure to HIV via the economically driven adoption of
risky behaviours. Poverty and food insecurityare thought
to increase sexual risk taking, particularly among women
From theaInternational Food Policy Research Institute, Geneva, Switzerland, thebInternational Food Policy Research Institute,
Washington, DC, USA, and thecJoint United Nations Programme on HIV/AIDS, Geneva, Switzerland.
CorrespondenceandrequestsforreprintstoStuartGillespie,International FoodPolicyResearch Institute, c/oUNAIDS, 20Avenue
Appia, CH-1211 Geneva 27, Switzerland.
ISSN 0269-9370 Q 2007 Wolters Kluwer Health | Lippincott Williams & Wilkins
who may engage in transactional sex to procure food
for themselves and their children. Women’s economic
dependenceontheir partners mayalsomake itdifficult for
them to insist on safer sex (e.g. condom use). In addition,
poor people are more likely to be food insecure and
malnourished. Malnutrition is known to weaken the
HIV transmission in any unprotected sexual encounter
(although this remains under-researched). This strand of
in the distribution of the epidemic across population
subsequent rate of HIV transmission.
We aim to present an overview of the findings of key
recent African studies (primarily 2004–2007) examining
the relationship between economic resources/status and
the risk of HIV infection (see Table 1). The starting point
was the evidence presented in this supplement on this
relationship, but our search then expanded to draw upon
other recent literature from sub-Saharan Africawhere the
epidemic is most severe.
First, PUBMED and ECONLIT searches (2004–2007)
were used to identify all studies addressing the link
between socioeconomic status (poverty and education in
particular) and the risk of HIV. Searches were limited to
English language and Africa. Keywords pertaining to the
explanatory variables were ‘poverty’, ‘wealth’, ‘socio-
economic status’, ‘socioeconomic’, ‘education’ and
‘education level’. Keywords pertaining to the outcome
variable of interest were ‘HIV risk’, ‘HIV transmission’,
‘sexual behaviour’ and ‘HIV prevalence’. Studies on
special groups of populations such as truck drivers and
uniformed services have been excluded. Conceptual/
theoretical papershave not been included in the reviewof
the association between socioeconomic status, poverty,
education and the risk of heterosexual HIV transmission,
although such studies have been used from a reference
perspective. Quantitative studies with only descriptive
statistics have been excluded. Sixteen of the 49 retrieved
articles were thus excluded. In addition, a Dissertation
AbstractsOnlinesearchand a Google Scholar search were
Whenever possible, the authors of such papers that met
the above criteria were contacted for the latest drafts and
updates on the status of their articles.
As such, this overview is intended to complement earlier
reviews examining this relationship [23,24]. It then seeks
to delve deeper into the pathways and interactions that
contextualize the link between wealth/poverty and
heterosexual HIV transmission risk. We stress at the
outset that we are not reviewing evidence of the
downstream impacts of AIDS on poverty, a subject that
has been comprehensively covered recently elsewhere
Does poverty increase exposure to HIV?
At the country level there is a weak positive relationship
between national wealth and HIV prevalence across
countries in sub-Saharan Africa, where higher prevalence
is seen in the wealthier countries of southern Africa
(Fig. 1). Strong urban–rural economic linkages, good
transport links and high professional mobility may
translate into both higher incomes and higher HIV
incidence. National poverty rates, on the other hand, do
not show a strong association with HIV prevalence
(Fig. 2). There is, however, a clear and significant pattern
of association between income inequality and HIV
prevalence across countries; countries with greater
inequality have higher HIV prevalence, especially in
sub-Saharan Africa but also to a lesser extent in Asia and
Latin America (Fig. 3).
Household level evidence that poverty is a majordriverof
the epidemic is rather mixed. It is important, however, to
note that most studies focus on relative poverty in the
context of generalized chronic poverty. In most cases, it is
only the highest one or two quintiles (or possibly three in
middle-income southern African countries) that can be
thought of as representing the non-poor, using the
standard poverty line definitions, or the US$1 or US$2
per day measures adopted for the purpose of global
comparison. Comparisons are thus between ‘wealthier’
and ‘poorer’ groups.
Studies adopting ethnographic methodologies suggest
that material poverty increases the risks of contracting
HIV mainly through the channel of high-risk behaviour
adoption. The respondents of an ethnographic study in
the southern province of Zambia  identified frequent
droughts and limited wage labour opportunities, after the
post-economic liberalization closure of companies, as the
‘push’ factors behind the increasing resort of women to
transactional sex. In a qualitative study in Malawi 
certain social groups were found to continue to engage in
high-risk behaviours despite knowing the risks. They did
so, the authors contend, to affirm their social identity and
to deny that ‘anything they do makes a difference towhat
theyperceiveasa lifeofpowerlessnessanddespair’(p. 17).
The ‘culture of poverty’, as documented by Lewis  in
Latin America, may thus be as significant as material
poverty in motivating risky behaviours.
The findings from several recent quantitative surveys that
investigated the relationship between economic depri-
vation and the adoption of high-risk behaviours are
generally consistent with much of the qualitative research
[29–31], although there are important differences
between behaviours and regarding the influence of
gender in different contexts [12,14,32].
Employing the Cape Area Panel Study, which surveys
individual youths aged 14–22 years in Cape Town, South
S6AIDS2007, Vol 21 (suppl 7)
Table 1. Recent quantitative studies examining the relationship between HIV and socioeconomic status.
StudyObjectiveStudy design and statistical analyses Key findings
Dinkelman et al. Estimate if sexual debut between 2002
and 2005, number of recent partners
and lack of condom use at last sex
in 2005 is affected by household
income constraints and income shocks.
Cape Area Panel Study data that surveyed
4752 boys and girls, 14–22 years of age
in Cape Town, South Africa (2002–2005).
Multivariate probit models
Household income negatively associated with sexual
debut, and economic shocks positively associated
with multiple partnerships among girls. Community
poverty rates predict earlier sexual debut and
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.
Food insufficiency associated with inconsistent
condom use with a non-primary partner, sex
exchange, intergenerational sexual relationships, and
lack of control in sexual relationships. For men,
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.
Wealth was positively related to HIV-positive
serostatus for both men and women. Women
with primary education were nearly twice as likely
to be HIV positive as those with no education.
Sexual behaviour factors were not significantly
associated with HIV serostatus.
Although poverty was significantly associated with
the examined sexual outcomes in all settings, the
urban poor are significantly more likely than their
rural counterparts to have an early sexual debut
and a greater incidence of multiple sexual partnerships.
The disadvantage of the urban poor is accentuated
for married women; those in Nairobi’s slums are at
least three times as likely to have multiple sexual
partners as their rural counterparts.
The greatest decrease in HIV prevalence occurred in
the highest wealth index tercile in both men and
women. In men (but not women), HIV incidence
was lowest in the top wealth index tercile. Mortality
rates were significantly lower in both men and women
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.
Among men, there was little evidence that HIV
seroconversion was associated with any
socioeconomic factor. Among women, HIV
seroconversion was negatively associated with
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.
In all eight countries, adults in the wealthiest quintiles
have higher prevalence of HIV than those in the
poorer quintiles, but the positive association
between wealth and HIV status was statistically
insignificant in multivariate models.
Weiser et al. Studies the association between food
insufficiency (not having enough food
to eat over the previous 12 months)
and inconsistent condom use, sex
exchange, and other measures of risky sex.
Cross-sectional population-based survey of
1255 adults in Botswana and 796 adults
Multivariable logistic regression analyses,
clustered by country, and stratified by sex.
Johnson and Way Investigates the association between
demographic, social, behavioural,
and biological variables and HIV
serostatus in Kenya.
Cross-sectional, 2003 Kenya Demographic
and Health Survey.
Multivariate logistic regression model
stratified by sex.
Nii-Amoo Dodoo et al.  Examines the relationship between
HIV-related sexual activity outcomes,
specifically age at first sex and multiple
sexual partnerships, and socioeconomic
deprivation amenities index, (based on
asset index and amenities index) in rural
and urban Kenya.
Quantitative data are drawn from the
Demographic & Health Surveys (DHS)
and qualitative data from the Sexual
Networking and Associated Reproductive
and Social Health Concerns study.
Multivariate Cox regressions.
Lopman et al. Studies the association between wealth
index (based on household asset ownership)
and HIV incidence, HIV mortality, sexual
risk behaviour, and sexual mixing patterns.
Manicaland, Zimbabwe HIV/STD Prevention
Project’s population-based open cohort
(baseline between 1998 and 2001 and
follow-up between 2001 and 2003).
Multivariate logistics and Poisson regression
Hargreaves et al. To assess the evidence that HIV incidence
rates and sexual behaviour patterns differed
by wealth, education and migration.
Prospective cohort of 1967 individuals
(14–35 years of age) in Limpopo province,
South Africa (2001 and 2004).
Multivariate logistic regression models,
stratified by sex.
Mishra et al. Examines the association between wealth
(index based on household ownership
of consumer durables) and HIV serostatus
of 15–49-year-old individuals.
Cross-sectional nationally representative
surveys from eight sub-Saharan African
countries conducted during 2003–2005.
Multivariate logistic regression models,
stratified by sex.
Poverty, wealth, HIV transmission Gillespie et al.
2007, Vol 21 (suppl 7)
Table 1. (continued)
Study Objective Study design and statistical analysesKey findings
Ba ¨rnighausen et al. Investigates the effect of educational
attainment, household wealth categories
(based on a ranking of households on an
assets index scale) and total household
expenditure, on HIV incidence.
Longitudinal data (2003–2005) on 3325 adults
from Africa Centre Demographic Information
System in KwaZulu-Natal, South Africa.
Semiparametric and parametric survival models.
Belonging to a household in the middle
wealth category increased the risk of
HIV seroconversion. One additional grade
of educational attainment reduced the
hazard of HIV seroconversion by
approximately 7%. Urban residence was
associated with a 65% increase in the
hazard of HIV seroconversion.
Relatively non-poor men (ranked by
assets levels) were 43% more likely
to die than poor men. Poor and non-poor
women were equally likely to die. No clear
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.
Over time, the probability of disease-related
death declined for both men and women.
A reversal in the effect of education on death
was observed, with more educated women
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.
No association between schooling and HIV
infection and a significant negative association
with herpes simplex 2 in women observed in
Kisumu or Ndola,. In Yaounde, women with
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.
In 1989/90, there was no significant relationship
between education and HIV prevalence.
In 1999–2000 women aged 18–29 years
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.
Men’s income was not significantly associated
with condom use. Having an adolescent
female partner does not have a significant
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
Chapoto and Jayne To determine the ex-ante socioeconomic
characteristics of individuals who died
in their prime age (15–59 years)
Nationally representative panel data set of
18821 individuals from 5420 households
surveyed between 2001 and 2004.
Multivariate probit models, stratified by sex
Kirimi and Jayne  Estimates the potentially changing
relationship over time between
household and individual-level
indicators of poverty and subsequent
death of prime-age adults in Kenya.
Nationwide data set of 5755 individuals
from 1500 Kenyan rural households
collected in 1997, 2000, 2002 and 2004.
Multivariate probit models, stratified by sex.
Glynn et al.  Investigates the associations between
schooling and both HIV and herpes
simplex 2 infection and risky behaviours
in Cotonou (Benin), Yaounde (Cameroon),
Kisumu (Kenya) and Ndola (Zambia).
Cross-sectional population-based survey
conducted in 1997–1998 in four African
cities including approximately
2000 adults in each city.
Multivariate models, stratified by sex.
De Walque et al. Investigates the association between
changing HIV prevalence, condom
use and education in rural south-west
Population-based cohort followed between
1989/1990 and 1999/2000.
Multivariate and bivariate (condom versus
Luke To study the trade-off between transfers
and condom use at last sexual intercourse
in non-commercial, non-marital sexual
relationships in Kenya.
Cross-sectional survey of Luo men aged
21–45 years in Kisumu, Kenya.
Multivariate models including male fixed
Africa (2002–2005), Dinkelman et al.  show that for
girls, sexual debut appears to be earlier in poor
households, especially those who have experienced an
economic shock (a death, illness or job loss). A recent
cross-sectional study in Kenya found asset poverty to be
significantly related to risky sexual outcomes, such as
early sexual debut, multiple sexual partnerships, in all
three residential settings studied . In a study in
Botswana and Swaziland , although protective in
unadjusted analyses, controlling for other variables,
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–
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
be positively associated with transactional sex and the
value of transfers . For every Ksh1000 in male
income, the probability of giving a transfer in the past
month increases approximately 1%, and the total amount
of transfers increases Ksh29 (US$0.40). Wealth (income
and inherited land) was not, however, correlated with
condom use, suggesting that larger transfers are not being
given by wealthier men as an incentive for condom-free
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
follow-up study (baseline between 1998 and 2001 and
follow-up between 2001 and 2003) in Manicaland,
Zimbabwe, Lopman et al. , found wealthier men
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
the result of differences in education across wealth levels.
Better-off women reported fewer partners and were less
likely to engage in transactional sex, adjusting for age,
education level and site type. Hargreaves et al.  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).
Using Demographic and Health Survey (DHS) data from
eight countries, Mishra et al.  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
the poorest individuals in these countries. This is partly
accounted for by an association of wealth with other
Empirical investigation of the connection
between economic status (income and
inherited land), transfers, and non-marital
non-commercial, sexual relationships
Cross-sectional survey of Luo men aged
21–45 years in Kisumu, Kenya.
Economic status was positively and significantly
associated with both the giving of transfers and the amount. For every additional acre
in inherited land, the total amount of transfers increases by Ksh10 on average. Wealth was
not correlated with condom use. Each
additional year of education increased the
probability of condom use by
Beegle and Ozler (unpublished)
Examines the relationship between HIV status
and gender inequality between young
women and adult men within an individual’s
community and to examine young
women’s poverty status on individual HIV
status in Kenya.
Three sources of cross-sectional data:
2003 Kenya Demographic and
Health Survey, 1999 Population
and Housing Census, Kenya Poverty
Multivariate probit models
A one standard deviation increase in gender inequality
was associated with a 1% increase in the probability
of being HIV positive for young women. For a given
level of gender inequality, age is protective. Similarly, the
effect of gender inequality for women decreased with
increasing household assets, although this effect was
not always significant. Conditional on gender inequality,
the share of young women who live in poverty in the
community did not increase the probability of
individual HIV infection.
Poverty, wealth, HIV transmission Gillespie et al. S9
underlying factors. Wealthier individuals tend to live in Download full-text
urban areas where HIV is more prevalent, they tend to be
more mobile, more likely to have multiple partners, more
likely to engage in sex with non-regular partners, and
they live longer; all factors that may present greater
lifetime HIV risks. On the other hand, however, they
prevention methods, and are more likely to use condoms;
factors that reduce their risk compared with poorer
individuals. Controlling for these associations, however,
does not reverse the conclusion: there is no apparent
association between low wealth status and HIV.
Using data from the cross-sectional, population-based
2003 Kenya Demographic and Health Survey, a recent
study found increased wealth to be positively related to
HIV infection, with the effect being stronger for women
than men; the wealthiest women being 2.6 times more
likely than the poorest women to be HIV positive .
Similar findings were reported in Tanzania  and in
Burkina Faso .
Studies of cross-sectional associations between HIV
serostatus and socioeconomic status (such as those above
and the cross-sectional studies featured in another
comprehensive review ) suffer from important
limitations: They are unable to distinguish between the
effect of economic status on HIV infection and the effect
of HIV infection on economic status, and they are unable
to control for the fact that individuals from richer
households may survive longer with HIV, and are thus
more likely to be present in the population to be tested,
thereby increasing HIV prevalence rates.
In a cross-sectional study, it is thus conceivable to find a
positive association between economic status and HIV
S10 AIDS 2007, Vol 21 (suppl 7)
Percentage below US$1 per day
Central African Republic
R squared = 0.0996
R squared = 0.0307
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.
GDP per capita (PPP, logarithmic scale)
Central African Republic
R squared = 0.2952
R squared = 0.0000
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.