The relationship between socioeconomic status and health is well documented. There is ample evidence that wealthier populations do better on most measures of health status, including nutrition, morbidity and mortality, and of healthcare utilization [1–3]. Consistent with these findings, there is evidence of an inverse relationship between socioeconomic status and the risk of sexually transmitted infections (STI), such as herpes, chlamydia, gonorrhoea, syphilis, and bacterial vaginosis [4–13]. Although much of this evidence is from developed countries, it is reasonable to expect that poverty increases vulnerability to HIV in the same manner in low and middle-income countries. It is indeed often argued that poverty is the root cause of the spread of HIV . A recent article in the Lancet argued that ‘[s]ince poverty plays a role in creating an environment in which individuals are particularly susceptible and vulnerable to HIV/AIDS, poverty reduction will undoubtedly be at the core of a sustainable solution to HIV/AIDS’ . Analogous views have been expressed in numerous public statements and publications, and guide HIV prevention efforts in several countries.
At the global level, there is evidence of a positive correlation between countries' HIV prevalence and poverty, as measured by per capita income, income inequality, or absolute poverty . The HIV epidemic in sub-Saharan Africa represents a notable exception to this general pattern. On the one hand, at the macro level African nations with high HIV prevalence, such as South Africa and Botswana, tend to be the wealthier countries in the region [17,18]. On the other hand, at the individual level, wealth has been found to be positively associated with HIV serostatus [19–21]. Reviews of the existing literature about the association between socioeconomic status and HIV infection indicate that only a few studies have found a negative association, whereas most have found a positive or no association [22,23]. To account for this finding, it has been argued that a greater prevalence of risky sexual behaviours among the wealthier may increase their vulnerability to HIV infection, whereas better nutritional status, greater access to healthcare, and greater use of antiretroviral drugs may improve their survival if infected .
In this study, using data from eight recent population-based, nationally representative surveys with HIV testing in sub-Saharan Africa, we conducted an in-depth analysis of the association between household wealth status and HIV prevalence in sub-Saharan Africa. Our aim is to identify what specific characteristics and behaviours of the wealthier are associated with HIV infection, and to what extent confounding factors such as place of residence and other risk factors mediate this association.
We conceptualized the association between wealth and HIV as being influenced by a host of underlying background factors and mediated by several proximate factors (Fig. 1). We viewed this relationship as transitional in nature, operating within and depending on the social and epidemiological context. For wealth to have an effect on HIV incidence and prevalence, the underlying factors must affect one or more of the proximate factors, which in turn affect either the rate of infection or the duration of infectivity with HIV  (a detailed discussion of this conceptual framework and possible pathways between wealth and HIV status is provided elsewhere ).
We analysed data from six Demographic and Health Surveys (DHS; Kenya, Malawi, Lesotho, Cameroon, Ghana, and Burkina Faso) and two AIDS Indicator Surveys (AIS; Tanzania and Uganda) with linked HIV test results. These surveys, conducted during the period 2003–2005, collected sociodemographic and behavioural data as well as blood samples for HIV testing from nationally representative samples of adult women and men. Table 1 gives basic information about the eight surveys. The sampling design and survey implementation procedures for each country are described in detail in the individual country survey reports [26–33]. Although the age ranges of adults included in the surveys varied across countries (see Table 1), for consistency the present analysis is limited to men and women aged 15–49 years in each country.
In all surveys, HIV testing was carried out using dried blood spot samples collected on a special filter paper using capillary blood from a finger prick, except in Uganda where venous blood was collected. Participation in HIV testing was voluntary and, before collecting blood samples for HIV testing, each selected participant was asked to provide informed consent to the testing . Informed consent was obtained separately for the questionnaire interview. In each country, HIV testing was conducted in a central laboratory by following a standard testing algorithm designed to maximize the sensitivity and specificity of HIV test results, and an approved quality assurance and quality control plan . The testing algorithm used two HIV enzyme immunosorbent assays, based on different antigens. All discordant samples that were positive on the first test and negative on the second test were retested with the same enzyme immunosorbent assays, and if still discordant, were resolved by Western blot testing. These steps were also repeated for 5–10% of randomly selected samples that tested negative on the first test. For external quality assessment, a subset of dried blood spot samples (usually approximately 5%) was retested at an outside reference laboratory using the same algorithm.
In order to ensure confidentiality, the HIV test results were anonymously linked to individual and household questionnaire information through bar codes, after scrambling the household and cluster identifiers . All HIV testing procedures were reviewed by the ethical review boards of Macro International Inc. (a US-based company that provides technical assistance to DHS/AIS surveys around the world), and the host country.
As DHS/AIS surveys do not include direct questions on income or expenditure, we measured household wealth by means of an index based on household ownership of consumer durables (such as a television and a bicycle; materials used for housing construction; and the availability of amenities such as electricity, source of drinking water, and type of toilet facility) that tend to be correlated with household economic status. The index, constructed using principal components analysis, is a composite measure of the cumulative living standard of a household, which places individual households on a continuous scale of relative wealth [36,37]. The wealth index is divided into population quintiles, with the lowest quintile representing the poorest 20% and the highest quintile representing the wealthiest 20% of households within each country. The wealth index defined in this manner captures well the relative economic status within each country, and it correlates strongly with the health and wellbeing of people .
Using the conceptual framework illustrated in Fig. 1, we systematically examined the association between wealth and HIV infection. For each country, we first descriptively evaluated whether household wealth was associated with key risk behaviours and protective factors, including those that may increase the risk of HIV exposure [age at first sexual intercourse, age at first cohabitation, number of times married, duration in current union, polygamy, partner living elsewhere (for women only), number of lifetime and recent sexual partners, sexual intercourse with a non-regular (non-marital, non-cohabiting) partner], and those that may be associated with an increased risk of transmission per exposure [condom use with the last non-regular partner and consistent condom use, alcohol use at last sexual intercourse, reported STI or STI symptoms, circumcision (for men only)]. We also examined the association with knowledge of the respondent's HIV status, and knowledge of HIV prevention methods.
We used multivariate logistic regression to measure the independent relationship between household wealth and HIV status after controlling for underlying and mediating proximate factors. In particular, for women and men aged 15–49 years who reported ever having sex, we estimated five alternative logistic regression models. The first model estimated unadjusted effects of household wealth on HIV prevalence. The second model added controls for several underlying background factors, including age, ethnicity, religion, urban/rural residence, and geographical region of residence. The third model additionally controlled for education, occupation, media exposure, marital status, duration in union, number of years at current place of residence, alcohol use at last sex in the previous 12 months, knowledge of HIV prevention methods, and knowledge of own HIV status. The fourth model added a set of proximate factors that were likely to mediate the relationship between the underlying background factors and HIV prevalence (as indicated in Fig. 1). These included age at first sexual intercourse, the number of lifetime sexual partners (replaced with whether the respondent had two or more partners in the previous 12 months in Kenya, Ghana, Burkina Faso, and Malawi, where information on lifetime partners was not available), reported STI or STI symptoms in the previous 12 months, circumcision (for men only), and consistent condom use in the previous 12 months. Finally, the fifth model added a control for community-level wealth, computed by averaging the household wealth scores in each cluster. We also fitted a similar set of models to the data for cohabiting couples to examine the association between household wealth and the likelihood that one or both partners was HIV positive. Results from only the first model (unadjusted) and the fifth model (adjusted for all potential confounders and mediating factors) are presented. The analysis accounts for the complex survey design of the DHS/AIS to estimate efficient regression coefficients and robust standard errors adjusting for intracluster correlation and by using sampling weights.
The overall HIV prevalence in the eight countries considered ranges from 1.8% in Burkina Faso to 23.5% in Lesotho, with women having a higher HIV prevalence than men in all countries except Burkina Faso (Table 2). In all countries, HIV prevalence tends to be much higher among adults belonging to the wealthiest 20% of households than among those from the poorest 20%. This pattern holds for men and women separately, with the exception of men in Ghana and Lesotho where HIV prevalence in the highest wealth quintile is slightly lower than in the lowest quintile. In most cases, HIV prevalence increases monotonically with household wealth status, with the notable exception of Ghana where there is an inverted U-shaped relationship between the two. Similarly, for cohabiting couples the likelihood that one or both partners is HIV infected increases with household wealth, with the wealthiest 20% of couples being two to seven times more likely to have HIV than the poorest 20%. The only exception is Lesotho, where this ratio is smaller (1.2) but in the same direction.
Wealthier men and women tend to be more educated, more mobile, and more likely to live in urban areas, where HIV is more prevalent. Wealthier men and women are also more likely to be older, regularly exposed to mass media, to be working in professional or service jobs than poorer individuals (not shown). In addition, consistently across countries, wealthier individuals tend to start cohabiting at an older age than poorer individuals, with an average age difference between the highest and lowest wealth quintile of 2–4 years in most cases (Tables 3 and 4). Wealthier men and women are less likely to be in a polygamous union than poorer men and women. Knowledge of HIV prevention (being faithful to one's regular partner and using condoms) increases with household wealth for both men and women in all countries, except for men in Tanzania, Malawi, and Cameroon and for women in Lesotho, where there is little difference in such knowledge by wealth status. Wealthier men are more likely to report having had two or more sexual partners in the past 12 months than poorer men, with the notable exception of Tanzania where the pattern is reversed. Wealthier men also tend to have more lifetime sexual partners and are more likely to have sex with non-regular partners than poorer men. In all countries, wealthier men and women are more likely to use condoms than poorer individuals. Also, wealthier men are more likely than poorer men to be circumcised, except in Lesotho.
In Table 5 we present unadjusted and adjusted odds of HIV infection by wealth quintile. Unadjusted odds indicate that, in all countries except Lesotho and Ghana, men belonging to the highest wealth quintile are more likely to be HIV infected than those belonging to the lowest wealth quintile. In Lesotho and Ghana, there is an inverted U-shaped relationship between household wealth and HIV prevalence among men; in other terms, the odds of HIV infection peak in the middle wealth quintile. Higher HIV prevalence with increasing household wealth is also observed for women; the odds of HIV infection are two to five times greater in the highest wealth quintile than in the lowest wealth quintile (statistically significant in all countries). This suggests a stronger positive effect of wealth on HIV infection among women than among men.
The strong, positive association between wealth and HIV infection in the unadjusted models is diminished considerably when a number of underlying factors, selected proximate factors, and community-level wealth are controlled for in alternative models (not shown). Even with all underlying and proximate factors controlled, however, the odds of HIV infection remain greater than one in the highest wealth quintile in four of the eight countries considered for men, and in seven out of the eight countries considered for women, but lose statistical significance in most cases.
Results are similar for cohabiting couples. In all but one country the unadjusted odds of one or both partners being HIV infected are two to seven times greater among couples in the highest wealth quintile than among those in the lowest wealth quintile. Adding controls for selected underlying factors, proximate factors, and community-level wealth progressively diminishes the strength of this association. With all factors controlled for, the odds of one or both partners being HIV infected remain greater than one in six out of the eight countries considered, but statistically significant at the 5% level in only one country (Tanzania).
This study found that, contrary to evidence for other infectious diseases and theoretical expectations, HIV prevalence is not disproportionately higher among adults living in poorer households in sub-Saharan Africa. In all eight countries included in the present analysis, wealthier men and women tend to have a higher prevalence of HIV than poorer individuals. In most cases, the positive association between wealth status and HIV is considerably diminished when a number of underlying factors (such as education, urban/rural residence, and community wealth) and some of the behavioural and biological pathways (proximate factors, such as sexual risk taking, condom use, and male circumcision) are taken into account. The results indicate that much of the positive association between wealth and HIV is caused by these underlying or mediating factors. Even after accounting for these various factors, however, in most countries wealthier adults remain at least as likely as poorer individuals to be infected with HIV, if not more. The results are similar for cohabiting couples.
Our analysis indicates that several factors may be responsible for the observed higher HIV prevalence among wealthier individuals in these countries. First, the wealthier are more likely to live in urban areas and to live in wealthier communities, where HIV is more prevalent. Wealthier adults, especially men, tend to be more mobile, more likely to have multiple partners, and more likely to engage in sex with non-regular partners, behaviours that tend to be associated with a higher HIV prevalence. On the other hand, wealthier men and women tend to be more educated and have a greater knowledge of HIV prevention methods. As such, they may be more likely to receive healthcare, to use condoms (both with non-regular partners and consistently with all partners), and less likely to use alcohol when having sex. Wealthier men are more likely to be circumcised, which may reduce their risk of HIV infection. Also, wealthier adults may live longer with HIV than poorer individuals as a result of their better health and nutritional status.
Women are less likely than men to report having multiple partners and non-regular partners. We found that the positive association between wealth status and HIV prevalence tends to be stronger for women than for men in most countries, suggesting disproportionately greater vulnerability of women in the wealthier groups.
There are several limitations of this study that should be kept in mind when interpreting our findings. One important limitation is that DHS/AIS surveys do not collect data on household income or expenditure, which would traditionally be included in an assessment of wealth status. The assets-based wealth index used here is only a proxy indicator for household economic status . In addition, wealth index scores cannot be compared across countries both because the level and distribution of wealth differs from one country to another and because the choice of assets included in the construction of the index varies somewhat from country to country. In spite of these issues, in developing-country settings, the wealth index has been shown to produce superior results and equal or greater distinctions in health outcomes than household expenditure-based measures . Moreover, as income and expenditure measures can be volatile and temporary, wealth status (which results from the accumulation of income) is a preferred measure to relate to HIV prevalence (which results from an accumulation of incidence).
A second limitation is differential non-response in the surveys considered. Non-response rates for HIV testing tend to be higher among the wealthier, urban, and more educated adults, who also tend to have higher HIV prevalence. Previous research has, however, indicated that in these surveys differential non-response has small and insignificant effects on the observed HIV prevalence, so any bias caused by differential non-response by wealth status should be small . In any case, if there were no differential non-response by wealth status, the positive association between wealth status and HIV prevalence would be even stronger. In addition, the surveys considered exclude population groups that are difficult to locate or interview, most notably the homeless. The observed positive association between wealth status and HIV prevalence may be overestimated to the extent that the homeless are poorer and have higher HIV prevalence than those included in the survey. Given that the proportion of the homeless in the total population tends to be small, any effect of excluding this group on the observed associations is likely to be small.
Another limitation is that our analysis is based on self-reported behaviours. There is evidence that women tend to underreport and men tend to exaggerate their premarital and extramarital sexual activity . Epidemiological studies in Africa have also observed weak associations between self-reported risky sexual behaviour and HIV status . The findings of our study may be biased to the extent that men and women misreport their number of sexual partners, sex with non-regular partners, condom use, and other related behaviours, or to the extent that the degree of misreporting is different across the wealth quintiles.
A fourth limitation is that the surveys included in the analysis did not collect data on concurrent partnerships and sexual networks. We were thus unable to examine the extent to which wealthier individuals are more likely to engage in such complex patterns of sexual relations, which may increase the risk of HIV infection in Africa by allowing the virus to spread rapidly to others [41–45].
Moreover, because of the cross-sectional nature of the data used in this study, endogeneity might bias our results at several levels. First, when considering the effect of wealth status on HIV prevalence we do not allow for the opposite, detrimental effect of HIV infection on wealth status, which is well established [46,47]. Excluding HIV-positive individuals who reported being seriously ill for three or more months in the previous 12 months (in Tanzania, Uganda, Cameroon, and Malawi, where such information was collected) had virtually no effect on the observed associations between wealth and HIV status (data not shown). Second, if HIV-positive adults were aware of their serostatus, they might have adjusted their sexual and reproductive behaviour. We tested this by excluding HIV-positive individuals who were previously tested and received the result, which made little difference to the observed associations between wealth and HIV status (data not shown). Third, when infected with HIV, wealthier individuals are likely to survive longer than poorer individuals because of better nutrition and access to healthcare. Cross-sectional data used in this study did not allow taking into account such selective survival of wealthier respondents. A lack of information on the availability and access to treatment and care (antiretroviral drugs in particular) further limited the possibility of disentangling this effect. Antiretroviral therapy coverage was, however, still very low at the time of the survey data collection in most countries.
Finally, for many HIV-positive adults, the infection may have preceded their sexual and other behaviours recorded in the survey, which may have biased some of the associations. Moreover, the strength and direction of the relationship between wealth status and HIV prevalence and the roles of risk behaviours and protective factors are likely to change over time, depending on the stage and spread of the epidemic . Cross-sectional data used in our study do not allow the examination of causal effects and these transitional phenomena.
In conclusion, this study found a positive association between household economic status and HIV prevalence among adult men and women in sub-Saharan Africa. Accounting for various underlying factors and proximate determinants explains much of this positive association, but in most cases wealthier adults remain at least as likely as poorer individuals to be HIV infected. We found that HIV prevalence does not follow the same pattern of association with poverty within countries in sub-Saharan Africa as most other diseases. Although poverty reduction is an essential strategy to improve health and combat the HIV epidemic, our analysis suggests that HIV prevention, care, and treatment programmes should also be focused on the better-off segments of the population. Focusing on the most important modes of exposure will probably be more effective than focusing broadly on poverty reduction [49,50]. It will also be important to extend programmes to the rural areas where a majority of the population in sub-Saharan Africa resides.
It is important to stress that our findings do not imply that the poor are not disproportionately affected by HIV when they do get infected. Poverty reduction is an extremely important goal in itself for many reasons, and it will certainly help combat the HIV epidemic in the long run and deal with its many adverse consequences.
The authors wish to thank the numerous people who provided comments on the two presentations and on earlier drafts of this paper.
Sponsorship: Funding for this research was provided by the Joint United Nations Programme on HIV/AIDS (UNAIDS) (no. HQ/05/423963). Additional support was provided by the United States Agency for International Development (USAID) through the MEASURE DHS project (no. GPO-C-00-03-00002-00).
The preliminary findings of this research were presented at the US President's Emergency Plan for AIDS Relief (PEPFAR) Annual Meeting in Durban, South Africa, on 12–15 June 2006, and at the International AIDS Economic Network Meeting in Toronto, Canada, on 11–12 August 2006.
The views expressed are those of the authors and do not necessarily reflect the views of UNAIDS, USAID, the United States Government, or the organizations with which the authors are affiliated.
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