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’, ‘socioeconomic 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 papers have not been included in the review of 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 Abstracts Online search and a Google Scholar search were also conducted to identify pertinent recent grey literature. 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 [23–25].
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 major driver of 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 to what they perceive as a life of powerlessness and despair’ (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 deprivation 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 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 0.72–0.84).
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 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 (riskier) sex.
Two prospective cohort studies examining the relationship 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 controlling 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 underlying factors. Wealthier individuals tend to live in 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 tend to be better educated, with better knowledge of HIV 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 infection, even if higher economic status protects individuals from acquiring HIV. Both the above-stated limitations can be overcome by using prospective cohorts to track HIV incidence. This volume presents three such studies with differing results: (i) Lopman et al. in Manicaland, Zimbabwe, reported a significantly lower male HIV incidence (between baseline in 1998/2000 and follow-up in 2001/2003) in the wealthiest asset tercile (15.4/1000 person-years) compared with the lowest tercile (27.4/1000 person-years), controlling for age and site of residence. This trend was even more marked in young men under 17–24 years of age. No such association between wealth and HIV seroconvesion was observed among women. Mortality rates were significantly lower in both men and women of higher wealth groups. They also found a decrease in HIV prevalence across all asset wealth groups during the study period, with the largest decrease in the wealthiest tercile for both men at 25% and women at 21%. (ii) Controlling for place of residence, migration status, partnership status, sex and age, a study in rural KwaZulu Natal by Bärnighausen et al. found that individuals from households in the middle asset wealth tercile had a significantly higher hazard of HIV seroconversion (1.7 times that of the poorest tercile), whereas there was no significant difference between the wealthiest and poorest terciles. Per capita household expenditures on the other hand did not significantly influence the hazard of HIV seroconversion. (iii) In a study of HIV incidence in Limpopo Province of South Africa between 2001 and 2004, Hargreaves et al. did not find a statistically significant association between HIV seroconversion and economic status (assessed through participatory wealth ranking methods) in either men or women.
A few other longitudinal studies have added to our understanding of socioeconomic differentials in HIV transmission. Those studies are nationally representative rural household panel surveys, unlike the studies reviewed above (in which the national level surveys are cross-sectional and longitudinal cohorts are limited to provinces). Although they do not directly measure HIV prevalence or incidence, they do employ innovative methodologies to infer the extent of HIV-related prime age adult mortality.
A nationally representative rural panel data survey (2001–2004) in Zambia , sought to determine the ex ante socioeconomic characteristics of individuals who died in their prime age (15–59 years). When ranked by asset levels, relatively wealthier men were 43% more likely to die of disease-related causes than men in poor households, with no clear association among women.
In contrast, a nationwide rural panel survey (1997–2004) in Kenya , performing similar analyses to the above, reported men and women from relatively asset-poor households to be more likely to die than those from wealthier households. The authors also found a shift in the relationship between landholding size and prime-age mortality in which no significant association was observed between 1997 and 2000, but in both the 2000–2002 and 2000–2004 periods, access to more land was associated with reduced male mortality.
Does education reduce exposure to HIV?
Education is one of the most studied socioeconomic factors in the context of AIDS epidemics. Although education and economic resources are often jointly determined, empirical evidence has shown that education predicts health independently of income .
A systematic review in 2002 of 27 studies , mostly cross-sectional, with data predominantly collected before 1996, found that increased schooling was either not associated with HIV infection or was associated with an increased risk of HIV infection among men and women from both rural and urban communities in Africa. As the epidemic within countries has advanced, the evidence suggests a shift towards a reduced relative risk of HIV infection among adults, especially younger women, who have a secondary education [9,10,36].
The hypothesis that the ability to process and access information is one channel through which education affects health outcomes has been examined in a study in Uganda , in which changes in association between schooling levels, HIV prevalence, and condom use were estimated among a population-based rural cohort in Masaka District between 1989/1990 and 1999/2000. During the early years of the epidemic in 1990, there was no robust relationship between HIV and years of education for either sex for all individuals older than 17 years of age. By 2000, however, each additional year of education was found to lower the risk of being HIV positive significantly among 18–29-year-old women (aOR 0.863, 95% CI 0.77–0.96). Condom use was found to be positively associated (using bivariate analysis only) with schooling levels between 1995 and 2000, with the gradient between higher educational achievement and greater condom use being steeper for women than men (chi-square for trend of odds in 1996/1997, 69.10 for men and 82.13 for women, and in 1999/2000, 103.01 for men and 164.18 for women).
One study in Cote d'Ivoire  found more highly educated people to be more likely to engage in multiple sexual partnerships, although they were also more likely to use condoms, thus offsetting some of the risk of exposure to HIV. Similar observations of a higher probability of condom use among the more educated have been reported elsewhere [38,39].
A cross-sectional study in Botswana and Swaziland found that higher educated women were less likely to report a lack of control in sexual relationships (aOR 0.36, 95% CI 0.36–0.37), were less likely to report inconsistent condom use (aOR 0.72, 95% CI 0.57–0.91) and intergenerational sex (aOR 0.68, 95% CI 0.53–0.86). No association between risk behaviours and education among men was observed . Studies employing longitudinal rural panel datasets from Zambia, Kenya and Ethiopia have shown a pattern of negative association between educational attainment and disease-related mortality  (A. Chapoto et al., unpublished).
As with economic status, few studies have prospectively investigated the relationship between education and HIV incidence. Two such studies presented in this volume found a significantly protective effect of education, especially among women [16,18]. Hargreaves et al. found that among women (but not men) HIV seroconversion was negatively associated with education (aOR comparing attended secondary school versus none/primary 0.49, 95% CI 0.28–0.85; comparing those completing secondary school versus none/primary 0.25, 95% CI 0.12–0.53). Bärnighausen et al. reported that one additional grade of educational attainment reduced the hazard of HIV seroconversion by approximately 7%.
In sum, a relatively clear picture emerges for education, the majority of studies suggest that education is increasingly associated with less risky behaviours. Sustained efforts to improve education levels as well as targeted and tailored messages on HIV prevention efforts can yield positive results.
Poverty and HIV: pathways and interactions
Links between socioeconomic conditions, such as wealth and education, and HIV risk and vulnerability are clearly complex, perhaps too complex for a single explanation. A major analytical challenge is to define the causal pathways operating from distal socioeconomic factors to proximal individual behaviours and ultimately physiological factors. Different socioeconomic factors may affect health at different times in the life course [40,41], operating at different levels (e.g. individual, household and neighbourhoods) [42,43] and through different causal pathways [44,45].
The sections below highlight some of the more important factors and processes that condition the relationship between poverty, wealth and HIV. Here we focus on the key issues of gender inequality, mobility and social ecology. Malnutrition is another potentially important conditioning factor affecting the risk of HIV infection. Given space limitations, the reader is referred to other reviews and ongoing work in this area .
Gender and economic asymmetries
The issue of gender is front and central to any discussion of HIV and poverty. Women's dependence on men's economic support throughout much of the developing world means that women's personal resources, including their sexuality, has economic potential. Economic asymmetries within a couple are reinforced by various contextual factors, such as family and peer pressures, social and economic institutions and pervasive and deeply entrenched sex-based inequalities. Social norms in many sub-Saharan African contexts, for example, permit (and even encourage) men to engage in sex with multiple partners, with much younger partners, and to dominate sexual decision-making. In a study of four communities in a southern province of Zambia  respondents blamed women. Women were perceived to move around and ‘give love for money’; women who some believe could otherwise work hard and do not need to have sex for money. The fact that men, often much older than girls/women, pay for sex was rarely mentioned as a cause of the problem.
Pre and extramarital sex may involve the male to female transfer of material resources, such as money and gifts. Such exchanges may take the form of commercial sex or more informal transactional sex, which is common in high HIV contexts [26,46,47]. In a study of young Luo men in Kenya , male to female transfers were given in three-quarters of recent non-marital partnerships, and transfers were substantial. Men on average provided approximately US$8.50 (Ksh600) to each non-marital partner in the past month, equivalent to 9% of a male mean monthly income. The author also reported a negative and significant relationship between the value of transfer and reported consistent condom use . For every transfer of (monetary or non-monetary) Ksh500, approximately the mean amount given in transfers per non-marital partnership, the probability of condom use decreased approximately 8%.
Evidence points to significant positive associations between larger age differences between partners, the value of economic transactions and unsafe sexual behaviours [46–48]. In South Africa, low socioeconomic status has been found not only to increase female odds of exchanging sex for money or goods, but also to raise female chances of experiencing coerced sex, and male and female odds of having multiple sexual partners. It also lowers female chances of abstinence, female and male age at sexual debut, condom use at last sex, and communication with most recent sexual partner about sensitive topics. Low socioeconomic status has more consistent negative effects on female than on male sexual behaviours; it also raises the female risk of early pregnancy .
A few interesting recent studies have suggested that increased economic inequality between men and women leads to partnerships that are riskier in terms of HIV exposure. In one (B. Penman, B. Ozler, K. Beegle, S. Baird, unpublished data), a basic model of HIV epidemiology was combined with population demographic processes, factoring in the marital and economic status of sexually active heterosexual individuals. Using a few simple assumptions regarding partnership patterns, the data generate a clear correlation between gender inequality (defined by economic inequality between young women and older men) and HIV prevalence in a completely susceptible population after 20–25 years. As expected, if rich men or poor women contribute a higher share of their respective populations to the high sexual activity group, then the relationship between gender inequality and HIV prevalence becomes even stronger. Finally, they show how the relationship between inequality and HIV is stronger when inequality is generated more by higher proportions of richer men than poorer women.
Using a combination of data sources on HIV status at the individual level and poverty and inequality measures at the community level, a study in Kenya (K. Beegle, B. Ozler, unpublished data) found, conditional on a set of individual and community characteristics, gender inequality between young women and adult men to be significantly correlated with the individual's HIV-positive status. This effect is stronger for young women, especially in western Kenya where HIV prevalence is highest, and is robust to various definitions of economic inequality between young women and older men.
In Botswana and Swaziland, food insufficiency among women was found to be significantly associated with inconsistent condom use with a non-primary partner (aOR 1.73, 95% CI 1.27–2.36), sex exchange (aOR 1.84, 95% CI 1.74–1.93), intergenerational sexual relationships (aOR 1.46, 95% CI 1.03–2.08), and lack of control in sexual relationships (aOR 1.68, 95% CI 1.24–2.28). For men, food insufficiency was associated with only a 14% increase in the odds of reporting unprotected sex, and was not associated with other risky sexual behaviours . Although food insufficiency is certainly influenced by income, it is a distinct entity with different causes and consequences; there are many steps between an aggregated household income variable and the ability of an individual woman to access, control and use income to buy food. A specific focus on protecting and promoting access to food may thus decrease exposure to HIV, especially among women.
The link between mobility and the spread of HIV is determined by the structure of the migration process, the conditions under which it occurs, including poverty, exploitation, separation from families and partners, and separation from the sociocultural norms that guide behaviours within communities . Mobility can increase vulnerability to high-risk sexual behaviour as migrants' multilocal social networks create opportunities for sexual networking. Mobility also makes individuals more difficult to reach for preventive, care or treatment services.
There is convincing empirical evidence of a link between human mobility and the risk of HIV transmission. In sub-Saharan Africa, the risk of HIV infection has been found to be higher near roads, and among individuals who either have personal migration experience or have sexual partners who are migrants [18,49–53].
In eastern and southern Africa, mining, plantations and related agricultural industries (typically producing tea, coffee, tobacco, sugar cane, and rice) are often associated with situations of significant risk. Risks may be enhanced by regularized single-sex migration as in the case of southern African mines ; high and seasonal demands for agriculture labour on estates; workers moving on their own, sometimes from considerable distances and lodged in single-sex dormitories; long and often irregular pay intervals; and a dependent population of occasional or commercial sex workers from nearby villages or further afield [26,54,55]. Ownership structures, the national policy environment, and the economics of the industries are all important drivers of HIV transmission risk.
The social ecology of HIV
The socio-ecological systems perspective of disease transmission fosters a deliberate analysis of the dynamics of population patterns of health and wellbeing at each level of biological, ecological and social organization . Although most attention has been paid to the socioeconomic conditions of individuals and their households, relatively little attention has been paid to the socioecological conditions that shape norms, behaviours and access to various resources.
For example, in a Tanzanian study , community characteristics, such as the type of economic activity, ratio of bar girls to men, share of migrants, and distance to big cities have all been found to correlate positively with HIV seroprevalence; traits that are usually associated with higher income.
Slum populations may be particularly vulnerable. In Kenya, for example, slum residence has been found to be unique in its adverse impact on sexual outcomes, presumably because monetary currency is central to existence in cities where difficult economic circumstances coerce women to use sex as a means of survival . Using two separate indicators of deprivation, a Kenyan study has shown that although poverty is significantly associated with the examined sexual outcomes in all settings, the urban poor were significantly more likely than their rural counterparts to have an early sexual debut and multiple sexual partnerships, even among married women . Complementing their quantitative evidence with qualitative research, the authors posited that, beyond purely economic factors, other social conditions contributed to higher levels of sexual activity in the very poor slum communities. Young children were socialized into sexual behaviour because of: a lack of alternate recreational opportunities; residential conditions that precluded privacy for adult sexual activity; and role modeling by adults who either transacted sex for money or were more generally involved in casual sexual activity.
Consistent with these findings, Bärnighausen et al. showed that living in urban and peri-urban areas increases the hazard of HIV seroconversion. Community poverty rates in the Cape Area Panel Study by Dinkelman et al. significantly predicted earlier sexual debut for girls and boys, and higher rates of unprotected recent sex for boys.
The structural context of labour arrangements also contributes to the demand for transactional sex. Although considered very arduous and physically demanding, cane cutting jobs, for example, command higher monthly wages than most permanent positions. In a Zambian study , in the two worker compounds that make up the study area in Mazabuka, there was widespread awareness that married women sleep with cane cutters to access resources they either need or want.
Social cohesion and social capital are other important conditioning factors. A higher HIV risk has been found to be significantly associated with structural factors related to the community in a study in Limpopo, South Africa . Such factors included easier access to a trading centre, higher proportions of short-term residents, and lower levels of social capital (particularly significant among men); the latter being an index based on social network membership and responses to questions on levels of trust, reciprocity, solidarity in a time of crisis, collective action (positive) and local serious and violent crime rate (negative). In other words, HIV prevalence was higher in settings in which the social order had broken down, or had never been established in the first place. Among men, higher HIV prevalence was also seen among communities with easier access to a local mine, a higher density and activity of local bars, a higher numbers of sex workers per village, and lower proportions of outmigrants. More research is needed on social cohesion and HIV risk.
In conclusion, this paper has drawn upon recent studies to examine what is known about the degree to which, and the ways in which, socioeconomic status is associated with HIV transmission. The notion that poverty is the main driver of HIV transmission is too simplistic. Relative wealth appears to have a mixed influence on HIV risk depending on an array of contextual factors. Gender inequality appears to be particularly important. In the most comprehensive multicountry, cross-sectional study to date , the residual effect of wealth was found to be statistically insignificant after controlling for variables such as urban residence, age, education and differences in sexual behaviour. There are very few cohort studies that are able to relate wealth or poverty to the incidence of HIV, and they tend to be rurally based. One such study, reported here, shows the highest risk of infection in the middle wealth group.
Education in general appears to be protective with regard to HIV risk, and the interaction effects between education and wealth could be very positive; when individuals have resources and the ability to use those resources, they can act on safeguarding their sexual health.
In investigating the relationship between poverty, wealth and HIV it is important to state the following qualifications: (i) Many of the studies presented in this volume and elsewhere suffer from important limitations such as: low statistical power (especially when measuring HIV incidence); high attrition rates (especially of educated, mobile men); difficulty in tracking certain individuals (e.g. mobile commercial sex workers, truck drivers); a paucity of longitudinal nationally representative panels tracking HIV incidence; and a lack of a comprehensive measure of economic status. (ii) On the latter, different aspects of economic status are likely to behave differently; e.g. assets, income, expenditures, cash flows, sources and control of income, and their intrahousehold and sex differentials. Most studies here use an asset-based index of household wealth. (iii) Most studies also examine relative not absolute wealth. Many of the wealthiest groups in affected communities may actually fall below an absolute poverty line. (iv) Patterns are not uniform across, or even within, countries, and a variety of socioeconomic factors and processes are likely to be at play in complex and interlinked ways. (v) The relationship between wealth and HIV is dynamic and may change over time. Most studies are cross-sectional in nature, not longitudinal, and thus ‘snapshots’ at a single point in time. It is important therefore to track the rates of new infections. If incidence is higher in groups moving out of poverty, this implies a greater need to ‘HIV-proof’ poverty reduction so as to make the options for increasing wealth less risky. (vi) The role and influence of social capital, social cohesion and community-level structural factors is under-researched, little understood but potentially very important; especially given the known association between different forms of inequality and risk and vulnerability. (vii) Likewise, the literature is somewhat biased towards explaining the relationship between socioeconomic conditions and HIV through high-risk behaviour adoption pathways, with less attention being paid to the ways in which pre-existing health and nutritional status may have compromised the immune status of individuals .
In summary, when examining the interplay between wealth or poverty and HIV transmission, there is no simple explanation, no magic bullet. AIDS cannot accurately be termed a ‘disease of poverty’. Although 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 socioeconomic status and HIV.
Sponsorship: Financial support was provided by the Joint United Nations Programme on HIV/AIDS (UNAIDS) and the RENEWAL programme of the International Food Policy Research Institute.
Conflicts of interest: None.
1. Wojciki JM. Socioeconomic status
as a risk factor for HIV
infection in women in East, Central and Southern Africa: a systematic review. J Biosoc Sci 2005; 37:1–36.
2. Nyindo M. Complementary factors contributing to the rapid spread of HIV
-1 in sub-Saharan Africa: a review. East Afr Med J 2005; 82:40–46.
3. Buve A, Lagarde E, Carael M, Rutenberg N, Ferry B, Glynn JR, et al
. Study Group on Heterogeneity of HIV
Epidemics in African Cities. Interpreting sexual behavior data: validity issues in the multicentre study on factors determining the differential spread of HIV
in four African cities. AIDS 2001; 15(Suppl. 4):S117–S126.
4. Ainsworth M, Semali I. Who is most likely to die of AIDS? Socioeconomic correlates of adult deaths in Kagera Region, Tanzania. In: Ainsworth M, Fransen L, Over M, editors. Confronting AIDS: evidence for developing world. Brussels: European Commission; 1998.
5. Gregson S, Waddell H, Chandiwana S. School education and HIV
control in sub-Saharan Africa: from discord to harmony? J Int Dev 2001; 13:467–485.
6. Gregson S, Garnett GP, Nyamukapa CA, Hallett TB, Lewis JJ, Mason PR, et al
decline associated with behaviour change in eastern Zimbabwe. Science 2006; 311:664–666.
7. Hargreaves JR, Glynn JR. Educational attainment and HIV
-1 infection in developing countries: a systematic review. Trop Med Int Health 2002; 7:489–498.
8. Anderson RM, May RM, Boily MC, Garnett GP, Rowley JT. The spread of HIV
-1 in Africa – sexual contact patterns and the predicted demographic-impact of AIDS. Nature 1991; 352:581–589.
9. Glynn JR, Carael M, Buvé A, Anagonou S, Zekeng L, Kahindo M, et al
. Does increased general schooling protect against HIV
infection? A study in four African cities. Trop Med Int Health 2004; 9:4–14.
10. De Walque D, Nakiyingi-Miiro JS, Busingye J, Whitworth JA. 2005. Changing association between schooling levels and HIV
-1 infection over 11 years in a rural population cohort in south-west Uganda. Trop Med Int Health 2005; 10:993–1001.
11. Dinkelman T, Lam D, Leibbrandt M. Household and community income, economic shocks and risky sexual behavior of young adults: evidence from the Cape Area Panel Study 2002 and 2005. AIDS 2007; 21(Suppl. 7):S49–S56.
12. Weiser SD, Leiter K, Bangsberg DR, Butler ML, Percy-de Korte F, Hlanze Z, et al
. Food insufficiency is associated with high risk sexual behavior among women in Botswana and Swaziland. PLoS Medicine 2007; 4:e260.
13. Johnson K, Way A. Risk factors for HIV
infection in a national adult population: evidence from 2003 Kenya Demographic and Health Survey. J Acquir Immune Defic Syndr 2006; 42:627–636.
14. Nii-Amoo Dodoo F, Zulu EM, Ezeh AC. Urban–rural differences in the socioeconomic deprivation – sexual behavior link in Kenya. Soc Sci Med 2007; 64:1019–1031.
15. Lopman B, Lewis J, Nyamukapa C, Mushati P, Chandiwana S, Gregson S. HIV
incidence and poverty
in Manicaland, Zimbabwe: is HIV
becoming a disease of the poor? AIDS 2007; 21(Suppl. 7):S57–S66.
16. Hargreaves JR, Bonell CP, Morison LA, Kim JC, Phetla G, Porter JDH, et al
. Explaining continued high HIV
prevalence in South Africa: socioeconomic factors, HIV
incidence and sexual behaviour change among a rural cohort, 2001–2004. AIDS 2007; 21(Suppl. 7):S39–S48.
17. Mishra et al
2007; 21 (Suppl. 7)
18. Bärnighausen T, Hosegood V, Timaeus IM, Newell ML. The socioeconomic determinants of HIV
incidence: evidence from a longitudinal, population-based study in rural South Africa. AIDS 2007; 21(Suppl. 7):S29–S38.
19. Chapoto A, Jayne T. Socio-economic characteristics of individuals afflicted by AIDS-related prime-age mortality in Zambia. In: Gillespie SR, editor. AIDS, poverty
and hunger: challenges and responses. Washington DC: International Food Policy Research Institute; 2006.
20. Kirimi L, Jayne T. Poverty, gender and working-age adult mortality: evidence from rural Kenya
. Working paper. Nairobi: Egerton University/Tegemeo Institute; 2007.
21. Luke N. Exchange and condom use in informal sexual relationships in urban Kenya. Econ Dev Cult Change 2006; 54:319–348.
22. Luke N. Economic status, informal exchange, and sexual risk in Kisumu, Kenya
. Econ Dev Cult Change
2007; In press.
23. Greener R. The impact of HIV/AIDS on poverty and inequality
. Chapter 5. In: Haacker M, editor. The macroeconomics of HIV/AIDS
. Washington DC: International Monetary Fund; 2004.
24. Gillespie SR, Kadiyala S. HIV/AIDS and food and nutrition security: from evidence to action.
Food policy review no. 7. Washington DC: International Food Policy Research Institute; 2005.
25. Gillespie SR, editor. AIDS, poverty and hunger: challenges and responses.
Washington DC: International Food Policy Research Institute; 2006.
26. Byron E, Gillespie SR, Hamazakaza P. Local perceptions of risk and HIV prevention
in southern Zambia. RENEWAL working paper. 2006. Available at: www.ifpri.org/renewal
. Accessed: October 2007.
27. Bryceson D, Fonseca J. An enduring or dying peasantry: interactive impact of famine and HIV
/AIDS in rural Malawi. In: Gillespie SR, editor. AIDS, poverty
and hunger: challenges and responses. Washington DC: IFPRI; 2006.
28. Lewis O. Five families: Mexican case studies in the culture of poverty
. New York: Basic Books; 1976.
29. Tladi LS. Poverty
/AIDS in South Africa: an empirical contribution. SAHARA J 2006; 3:369–381.
30. Brook DW, Morojele NK, Zhang C, Brook JS. South African adolescents: pathways to risky sexual behavior. AIDS Educ Prev 2006; 18:259–272.
31. Kaufman C, Clark S, Manzini N, May J. Communities, opportunities, and adolescents' sexual behavior in KwaZulu-Natal, South Africa. Stud Fam Plann 2004; 35:261–274.
32. Kimuna S, Djamba Y. Wealth and extramarital sex among men in Zambia. Int Fam Plan Perspect 2005; 31:83–89.
33. Tanzania Commission for AIDS (TACAIDS), National Bureau of Statistics (NBS), and ORC Macro. Tanzania HIV/AIDS Indicator Survey 2003–04
. Calverton, Maryland USA: TACAIDS, NBS, and ORC Macro; 2005.
34. Lachaud JP. HIV prevalence and poverty in Africa: micro- and macro-econometric evidences applied to Burkina Faso
. J Health Econ
:483–504. Epub ahead of print 17 November 2006.
35. Deaton A. Health, inequality
, and economic development. J Econ Lit 2003; 41:113–158.
36. Michelo C, Sandoy IF, Fylkesnes K. Marked HIV
prevalence declines in higher educated young people: evidence from population-based surveys (1995–2003) in Zambia. AIDS 2006; 20:1031–1038.
37. Cogneau D, Grimm M. Socioeconomic status
, sexual behavior, and differential AIDS mortality: evidence from Cote D'Ivoire. Popul Res Pol Rev 2006; 25:393–407.
38. Font A, Puigpinos R, Chichango IE, Cabrero N, Borrell C. AIDS-related knowledge and behaviors in Mozambique. Rev Epidemiol Sante Pub 2006; 54:341–354.
39. Sandoy IF, Michelo C, Siziya S, Fylkesnes K. Associations between sexual behaviour change in young people and decline in HIV prevalence in Zambia.BMC Pub Health
2007; 7:60. Published online 2007 April 23
40. Smith GD, Hart C, Blane D, Hole D. Adverse socioeconomic conditions in childhood and cause specific adult mortality: prospective observational study. BMJ 1998; 316:1631–1635.
41. Galobardes B, Lynch JW, Davey SG. Childhood socioeconomic circumstances and cause-specific mortality in adulthood: Systematic review and interpretation. Epidemiol Rev 2004; 26:7–21.
42. Yen IH, Syme SL. The social environment and health: a discussion of the epidemiologic literature. Annu Rev Publ Health 1999; 20:287–308.
43. Pickett KE, Pearl M. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Commun H 2001; 55:111–122.
44. Link BG, Phelan J. Social conditions as fundamental causes of disease. J Health Soc Behav 1995; 35:80–94.
45. Krieger N. Theories of social epidemiology in the 21st century: an ecosocial perspective. Int J Epidemiol 2001; 30:668–677.
46. Luke N. Confronting the ‘sugar daddy’ stereotype: age and economic asymmetries and risky sexual behavior in urban Kenya. Int Fam Plan Perspect 2005; 31:6–14.
47. Luke N. Age and economic asymmetries in the sexual relationships of adolescent girls in sub-Saharan Africa. Stud Family Plann 2003; 34:67–86.
48. Hallman K. Socioeconomic disadvantage and unsafe sexual behaviors among young women and men in South Africa
. Policy Research Division working paper no. 190. New York: Population Council; 2004.
49. Crush J, Frayne B, Grant M. Linking migration, HIV/AIDS and urban food security in southern and eastern Africa
. RENEWAL working paper. Washington DC: International Food Policy Research Institute; 2006. Available at: http://www.ifpri.org/renewal/renewalpub.asp
. Accessed: October 2007.
50. Bloom SS, Urassa M, Ng'weshemi J, Boerma JT. Community effects on the risk of HIV
infection in rural Tanzania. Sex Transm Infect 2002; 78:261–266.
51. Zuma K, Gouws E, Williams B, Lurie M. Risk factors for HIV
infection among women in Carletonville, South Africa: migration, demography and sexually transmitted diseases. Int J STD AIDS 2003; 14:814–817.
52. Boerma JT, Gregson S, Nyamukapa C, Urassa M. Understanding the uneven spread of HIV
within Africa: comparative study of biologic, behavioral, and contextual factors in rural populations in Tanzania and Zimbabwe. Sex Transm Dis 2003; 30:779–787.
53. Lagarde E, Schim M, Enel C, Holmgren B, Dray-Spira R, Pison G, et al
. Mobility and the spread of human immunodeficiency virus into rural areas of West Africa. Int J Epidemiol 2003; 32:744–752.
54. Ngwira N, Bota S, Loevinsohn M. HIV/AIDS, Agriculture and food security in Malawi: background to action
. RENEWAL working paper no. 1. The Hague: International Service for National Agricultural Research; and Lilongwe: Ministry of Agriculture and Irrigation; 2001.
55. Kisamba Mugerwa W, Nduhura D. Background paper on HIV/AIDS and agriculture in Uganda
. Washington DC: RENEWAL; 2002. Available at: www.ifpri.org/renewal
. Accessed: October 2007.
56. Zulu EM, Dodoo FN, Ezeh AC. Sexual risk taking in the slums of Nairobi, Kenya, 1993–98. Popul Stud – J Demogr 2002; 56:311–323.
57. Pronyk PM, Morison LA, Euripodou R, Phetla G, Hargreaves JR, Kim JC, et al.The role of structural factors in explaining variations in community HIV prevalence: a study in rural South Africa
. Sex Transm Infect
2007; In press.
58. Stillwaggon E. The ecology of poverty
, nutrition, parasites and vulnerability to HIV
/AIDS. In: Gillespie SR, editor. AIDS, poverty
and hunger: challenges and responses. Washington DC: International Food Policy Research Institute; 2006.
Keywords:© 2007 Lippincott Williams & Wilkins, Inc.
socioeconomic status; poverty; inequality; HIV; gender; prevention