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HIV incidence and poverty in Manicaland, Zimbabwe: is HIV becoming a disease of the poor?

Lopman, Bena; Lewis, Jamesb; Nyamukapa, Constancea,c; Mushati, Phyllisc; Chandiwana, Stevenc,d,✠; Gregson, Simona,c

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doi: 10.1097/01.aids.0000300536.82354.52
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Similar to other countries in southern Africa, the HIV epidemic in Zimbabwe has a precarious relationship with socioeconomic development [1]. Zimbabwe has one of the more developed infrastructures in sub-Saharan Africa, with widespread access to education and the highest adult literacy in the region [2]. Zimbabwe is also experiencing one of the largest national epidemics. HIV prevalence in the adult population was 20.1% in 2005, down from 22.1% in 2003. There are two contributory factors to the decline in HIV prevalence in Zimbabwe [3]. Mortality rates are high [4], as a result of high HIV incidence in the past, but this decrease cannot be explained by mortality alone [5]. Sexual risk behaviour is also changing; condom distribution has increased, young people are delaying their sexual debut, and there has been a reduction in the numbers of casual partnerships [3,6].

Some investigators have suggested that as the HIV epidemic progresses, risk would shift from the wealthier (who, as a result of their relative wealth, are part of a larger sexual network) [7] to the poorer (who, because of their lower educational attainment and social position, are less empowered to change their sexual behaviour) [8,9]. It is suspected that the HIV epidemic in Zimbabwe initially affected more mobile and more educated men as a result of their ability to attract sexual partners, but as early as 1998/2000 risk was similar, or perhaps slightly lower for those with secondary education [10]. If this trend is realized, the HIV epidemic threatens to become an endemic disease of poverty in Zimbabwe.

The changing relationship between socioeconomics and HIV must be seen in the context of sweeping macroeconomic changes in Zimbabwe. The Zimbabwean economy has been in severe decline, with negative growth since 1997 [11]. Over the period 1997–2005, gross domestic product declined by more than 30%. In 2003, annual inflation was approximately 250%, and this has since accelerated to over 1000% per annum [11]. The economic factors that partly underlie partnership formation [12], including behaviours ranging from sex work to marriage, are likely to be highly unstable, making understanding the link between poverty and HIV extremely timely yet difficult to study.


Study population

The Manicaland HIV/STD Prevention Project is an ongoing population-based open cohort study. Full details of the study can be found elsewhere [6,13]. In short, the study population were resident in small towns (two) forestry, tea and coffee estates (four) and rural areas (six, including four subsistence farming and two roadside trading centres) in the province of Manicaland in eastern Zimbabwe. All local residents were enumerated in an initial household census (conducted between July 1998 and February 2000; referred to here as baseline), which was repeated 3 years later in each site (referred to here as follow-up). Men aged 17–54 years and women aged 15–44 years were recruited into a cohort study of HIV transmission. A maximum of one member of each marital group was selected for recruitment to the cohort, members of multiple married couples and all unmarried individuals from a single household were eligible.

Totals of 8376 and 7102 of the households identified in the survey areas at baseline and at follow-up, respectively, were enumerated. Male and female participation rates in the individual cohort study survey were 78% (4320/5561) and 80% (5134/6419) at baseline and 77% (3047/3958) and 80% (3972/4936) at follow-up, respectively. Approximately 3 years after baseline 54% (2242/4142) of the men and 66% (3265/4922) of the women who were not known to have died were re-interviewed at follow-up. This loss to follow-up rate compared favourably with other community cohorts in rural African settings [6]. Enumerators were notified of deaths by surviving household members or community informants if the household dissolved completely.

At each round, after written informed consent was given, information on demographic, socioeconomic and sexual behaviour data were collected through an interviewer-led questionnaire [14]. Dried blood spots were collected for HIV serological testing for the purposes of research only. Testing was performed using a highly sensitive and specific antibody dipstick assay (> 99% for both) [15].

Socioeconomic status

Individual wealth was measured on the basis of the asset ownership of the household of residence. Data were collected on household ownership of ‘fixed’ and ‘sellable’ assets. Fixed assets include water supply, toilet facilities, electricity supply, housing structure and floor type. Sellable assets included ownership of radio, television, bicycle, motorbike and automobile. Chi-squared tests demonstrated significant differences of all assets (except automobile, which was owned by only 1.2% of households) between towns, estates and rural areas.

A simple summed score asset ownership was created (see Justification of ‘summed score’ as measure of wealth below). The binary and ordinal measures were each transformed to lie between 0 and 1. For example, bike ownership conferred a score of 0 or 1, and type of floor conferred a value of 0 for natural floor (earth/sand/dung), 0.5 for rudimentary floor (e.g. planks/bamboo), or 1 for finished floor (wood/cement). The 10 variables were added and expressed as a percentage.

In order to augment the study power, a wealth index was created by splitting the summed score into three equal groups (terciles) from the whole population. Preliminary analyses demonstrated that the distribution of the wealth index differed between towns and other areas; therefore, analyses were conducted separately for towns, estate and rural areas or were controlled for site type in multivariable regression.

Analysis of HIV incidence and mortality

Seroconversions, defined as individuals who tested negative at baseline and positive at follow-up, were assumed to have been infected halfway through the period of observation. Poisson regression models were fitted with incident infection as the outcome and wealth index tercile the explanatory variable. Models were controlled for age and site type and are presented separately for men and women.

Mortality rates were modelled using the same approach. Only deaths of participants who were HIV-positive at baseline were included, with deaths of HIV-negative participants omitted. This was done for two reasons. First, deaths from non-AIDS causes are likely to differ by wealth index and would therefore obscure the relationship between wealth index and HIV-associated mortality. Second, with the aim of examining how new infections and deaths contribute to the changing prevalence of HIV, the rate of becoming infected must be compared with the rate of dying from infection.

Differentials in sexual behaviour

Reported sexual behaviours, collected at follow-up survey, were analysed for differences associated with the wealth index. The summed score of the wealth index was modelled as a continuous variable. The influence of the wealth index on sexual debut, the number and type of partnerships, and condom usage was modelled, controlling for age and site type.

Mixing patterns and wealth index

Mixing patterns are not directly analysable from the baseline and follow-up of the Manicaland cohort because participants cannot be directly linked to their marital or non-marital partners. Participants were asked whether or not their last partner had secondary education. Having secondary education was significantly correlated with the respondents' wealth index status (R2 = 0.11 and P < 0.0001 for men; R2 = 0.14 and P < 0.0001 for women). Therefore, we roughly approximate mixing patterns by participants' education level with their partner's education level. We represent the degree of assortative (like-with-like) mixing by Q. Q is 1 when mixing is completely assortative and 0 when completely random [16]. Results are presented separately for individuals aged under and over 30 years.


Wealth score

In the HIV serosurvey, 9842 eligible men (aged 17–54 years) and women (aged 15–44 years) were tested at baseline and 7728 were tested at follow-up. Complete wealth index data were missing from 209 of individuals at baseline (2.7%) and 215 at follow-up (2.6%), who were excluded from analyses. At follow-up, the summed score of the wealth index in men and women followed a roughly normal distribution by visual assessment. The mean summed score of the wealth index for men and women was higher in towns (0.44 and 0.43, respectively) than in either estates (0.34 and 0.32) or rural areas (0.34 and 0.32). Therefore, men and women in towns were more frequently categorized in the higher wealth index tercile. Rather than constructing separate wealth index categories for each site type, analyses are either controlled for area of residence, or presented separately when appropriate. There was also greater variance in wealth index in towns compared with estates and rural areas (Table 1), highlighting greater socioeconomic heterogeneity in urban areas. The mean summed wealth index score did not substantially or significantly change between baseline and follow-up in any of the site types.

Table 1:
Mean and distribution of wealth index in towns, estates and rural areas in Manicaland, Zimbabwe at follow-up study (2001–2003).

Follow-up rates decreased with an increasing wealth index (66, 61, and 58%, chi-squared P < 0.0001), increasing education (primary/none 70%, secondary/higher 55%, chi-squared P < 0.001) and being more mobile at baseline (64 and 53%, chi-squared P < 0.0001). Follow-up appeared not to be directly dependent on wealth, however, the wealth index was not an independent predictor of follow-up after controlling for education and mobility (Wald test P = 0.23).


As previously reported [6], HIV prevalence fell in the open cohort between baseline and follow-up. HIV prevalence fell in each wealth index tercile in both men and women (Table 2). The largest decrease in prevalence was in the highest wealth index tercile in both men at 25% (compared with 11% in the poorest tercile) and women at 21% (compared with 18% in the poorest tercile).

Table 2:
HIV prevalence at baseline (1998–2001) and follow-up (2001–2003) in wealth groups and change in prevalence in the open cohort.


In men, HIV incidence was lower in the top wealth index tercile (15.4 per 1000 person-years) compared with the lowest tercile (27.4 per 1000 person-years; Fig. 1a–c). There was a significant trend of decreasing incidence by wealth index tercile after controlling for site type and age [incidence rate ratio 0.73; 95% confidence interval (CI) 0.56–0.93; P = 0.03, Poisson regression]. This trend was even more marked in young men 17–24 years of age, in whom rates in the highest wealth index group were 8.3 per 1000 person-years and 23.3 in the lowest wealth index group (incidence rate ratio 0.69; 95% CI 0.50–0.95; P = 0.02, Poisson regression).

Fig. 1:
HIV incidence by wealth tercile in Manicaland Zimbabwe, 1998–2003. Incidence is the number of new HIV infections per 1000 person-years. Person-years at risk are contributed by participants uninfected (HIV-negative) at baseline and followed up at round 2 of the survey. Points and whiskers show the observed cumulative incidence and 95% confidence intervals. Lines and shaded area illustrate fitted Poisson model and 95% interval controlling for age and site type. aSignificant linear trend (likelihood ratio test P < 0.05) Poisson regression model, controlling for age and site type.

No clear significant or monotonic trends in incidence by wealth index were observed in women of all ages or young women (Fig. 1d–f). Controlling for education or mobility (living outside the village in the past year) did not significantly improve the models for men or women (Wald test P > 0.35 for all tests). Mobility was not associated with the wealth index tercile for sex, in which education and wealth index tercile were positively associated for men (chi-squared P < 0.0001) and women (chi-squared P < 0.0001).


Overall, 300 HIV-positive deaths were observed in the cohort from 1998 to 2003 [4]. Mortality rates decreased from 25 to 20 to 15 deaths per 1000 person-years in increasing wealth index terciles for men, a trend statistically significant after controlling for site type and age (Poisson regression P = 0.024; Fig. 2a–c). Although not significant when split into young and older adulthood (35 years of age), the same trend of decreasing mortality was observed. Mortality was also lower in higher wealth index women between the ages of 15 and 34 years (Poisson regression Wald P = 0.024). In women over 35 years of age there was no apparent mortality trend by wealth index. Controlling for education level or mobility did not significantly improve the models for men or women (Wald test P > 0.45 for all tests).

Fig. 2:
Mortality rates of HIV-positive individuals by wealth tercile in Manicaland Zimbabwe, 1998–2003. Mortality rate is the number of deaths among HIV-positive individuals per 1000 person-years in the cohort excluding HIV-negative participants who died. Deaths among HIV-infected represents individuals leaving the population of infected and therefore is directly comparable to the incidence of new infections. Points and whiskers show observed cumulative incidence and 95% confidence intervals. Lines and shaded areas illustrate the fitted Poisson model and 95% interval controlling for age and site type. aSignificant linear trend (likelihood ratio test P < 0.10) Poisson regression model, controlling for age and site type. bSignificant linear trend (likelihood ratio test P < 0.05) Poisson regression model, controlling for age and site type.

Sexual behaviour

Considering the whole of the male study population, men of higher wealth index were more likely to have casual sexual partners and to have multiple partners in the 3-year follow-up period, but were also more likely to report consistent condom use in their casual relationships (all controlling for site type and age in logistic regression models; Table 3, model 1). In towns, however, men of higher wealth index did not report greater numbers of partnerships but did report higher condom usage in casual partnerships. In estates, relatively wealthier men were more likely to have casual partners but were more likely to use condoms and not engage in transactional sex.

Table 3:
Association of wealth and sexual behaviour: logistic regression models, men.

Women of higher wealth index were less likely to begin sex (under 25 year olds), have casual partners, have more than one partner in 3 years of follow-up, or engage in transactional sex (all controlling for site type and age in logistic regression models; Table 4, model 1). These differentials were most pronounced in towns, with all remaining significant when restricting analyses to urban women. Higher wealth index women in estates were less likely to engage in transactional sex. In rural areas in particular there were no significant (P < 0.05) associations between sexual behaviour and the wealth index. Condom use was not associated with the wealth index in women in any setting.

Table 4:
Association of wealth and sexual behaviour: logistic regression models, women.

Controlling for completed secondary education had no substantive effects on the estimates of the association of wealth index and the five sexual behaviours in men (Table 3 and Table 4, model 2). For women, secondary education was a stronger determinant of starting sex (for under 25 years olds) than the wealth index, but controlling for education levels had little effect on wealth index coefficients for other indicators of sexual behaviour.

Sexual mixing patterns

Sexual behaviour data suggest that higher wealth index men may be engaging in riskier sexual behaviours, at least for certain indicators such as having casual partners. Patterns of mixing will, however, predict the probability of engaging with an infected partner.

Both men and women in higher wealth index groups were more likely to have attended secondary or higher education (men 61, 74, and 76%; women 44, 52, and 63%; χ2 < 0.001 for both sexes). The proportions who had achieved secondary/higher education were much higher in those under the age of 30 years compared with those aged 30 years and over (Fig. 3a,b). In higher wealth index groups young individuals (< 30 years) were more likely to have secondary education, and mixing was increasingly assortative. This is important because in both sexes, HIV prevalence was lower among individuals with secondary education, although the difference was much greater in women (secondary/higher 24.8%; none/primary 12.5%; chi-squared P < 0.001) than men (secondary/higher 11.8%; none/primary 7.6%; chi-squared P = 0.017). In other words, young men and women in higher wealth index groups were more likely to be educated and to have an educated partner, and that partner was less likely to be infected, with this pattern more pronounced in men.

Fig. 3:
Uneven risk resulting from sexual mixing patters: Mixing patterns and education by wealth index and HIV prevalence by education level. (a) and (b) Education and mixing by wealth index.
Table 1
Secondary/higher educated; ––♦–– Q [degree of assortative (like-with-like) mixing]. (c) and (d) HIV prevalence by education. ▪ Primary/none; □ secondary/higher.

Some 61% of women without any secondary education reported that their last partner was of the same education level and 80% of women with secondary education reported that their last partner had the same level. Assortativeness of mixing and proportion with secondary education also increased with wealth index in older participants (30+ years, Fig. 3b and d), but in this older age group men and women with secondary education had a higher prevalence of HIV. Therefore, men and women in higher wealth index groups would be more likely to contact an infected individual. In summary, patterns of mixing appear to confer an increased risk for the higher wealth index groups in the older ages but a lower risk for the young.


HIV incidence was associated with poverty in men, especially young men, from 1998 to 2003 in Manicaland, Zimbabwe. No such trend was observed in women. Lower HIV incidence in men of higher wealth index is partly explained and supported by other observations from this cohort. The study was undertaken during a period of general decline of HIV prevalence, but, overall, the biggest decreases in prevalence occurred in higher wealth index groups. By our ‘summed score’ measure of the wealth index, towns were the ‘wealthiest’ of the site types, but they also had the greatest variance in their wealth index. This finding, alongside the generally higher prevalence in towns, supports the suggestion that HIV transmission may be enhanced by heterogeneity when different social or economic groups mix [17].

The relationship between reported sexual behaviour and HIV incidence was not always straightforward. Men of higher wealth index reported having more partners and were more likely to have a casual partner. This is the same pattern observed early in the African HIV epidemic, which was used to explain the higher prevalence in the more mobile and relatively well off [1]. The evidence from the present study suggests that although higher wealth index men may be having more partners, they may be lower-risk relationships than those entered into by poorer men. This is for two reasons. First, men of higher wealth index in all sites (including towns where they do not have more partners) were more likely to use condoms in their casual partnerships. In effect, men reduce the transmission probability if encountering an infected woman. In addition, for each partnership that is formed, there may be a lower probability of serodiscordance in higher wealth index groups; if partnerships are assortative (made between members of the same wealth index) and HIV prevalence is lower in higher wealth index groups, these partnerships will tend to be less risky. Given the limitations of the present Manicaland data, we cannot measure directly the degree to which mixing is assortative by wealth index. Participant reports on the level of education of their most recent partners, however, suggest that higher wealth index men and women are markedly more likely to form partnerships with individuals with secondary education, and in turn, young people with secondary/higher education have substantially lower HIV prevalence. Therefore, men and women of higher wealth index are less likely to form partnerships with infected individuals. This crude measure of the sexual network requires substantial refinement in two ways. First, the level of education is only one dimension of HIV prevalence. Education as a function of age, as discussed briefly here, is another. As noted in a number of other studies in sub-Saharan Africa, the relationship between education and HIV vulnerability seems to be reversing, with education becoming protective [10,18,19]. Second, and preferably, the serostatus of each individual in a partnership would be known to understand the degree to which HIV has penetrated certain wealth index groups and to what degree serosorting is occurring in new partnerships. The association between HIV and education reversed completely, with education being protective in young people and a risk in older groups.

Analysis of mortality is one way to understand historical trends in incidence because there is approximately a 10-year period between infection and death [20,21]. As with incidence, mortality was lower in higher wealth index groups in both young men and young women, suggesting that the patterns of incidence have not changed markedly since the estimated 10-year period when the groups currently suffering mortality became infected. Modelling studies of the HIV epidemic in Zimbabwe suggest that behaviour change began in about 1992. (Hallett et al. unpublished information) and data from the Demographic and Health Survey from as early as 1994 show women of higher wealth index delaying sexual debut as well as the more frequent use of condoms by both men and women (Lopman et al., unpublished information). This suggests that behaviour change have been underway approximately 10 years before this study, with the resulting impact on infection only now becoming apparent. An alternative explanation is that survival rates are lower in poorer groups. If malnutrition leads to faster disease progression, as some research has suggested [22,23], and poorer groups are more malnourished, higher mortality rates could be caused by reduced survival, rather than different levels of infection.

The present analyses have focused on incidence in order to understand the direction of causation between the wealth index and HIV as well as to reflect contemporary patterns of infection. Previous analyses have examined poverty and prevalent infection, and provide an interesting comparison to the incidence findings. Seroprevalence was not associated with wealth index among men, whereas poorer women were more likely to be infected in the baseline survey of this cohort [24]. This is in contrast to lower incidence in higher wealth index men and no association with incidence in women. This suggests a general shift away from risk in higher wealth index groups, perhaps with the shift lagging behind in women. At baseline, poor women from rural areas were more likely to have started sex, whereas poor women from towns were more likely to engage in transactional sex. By the follow-up survey poorer women in towns were still more likely to engage in transactional sex, but were also more likely to have multiple and casual partners and to start sex younger. It may be expected that the first group of women to be motivated and able to change behaviour are relatively wealthy women in towns, and this is precisely what was observed.

HIV risk has reduced substantially among teenagers; however, the girls who are still becoming infected have an identifiable vulnerability such as being orphaned or having experienced the death of another household member [25]. Orphaned girls or girls with an HIV-infected parent are more likely to drop out of school and begin sex, leading to pregnancy, poor reproductive health and HIV. So, despite not observing a general trend of wealth index and incidence in women, there is a clear causal pathway from vulnerability to leaving school, ultimately leading to HIV infection in young women in this population. It has previously been observed that households experiencing a death, and particularly an AIDS death, disproportionably suffered the loss of the household head, increased healthcare expenditure, and were more likely to dissolve [26].

This highlights the fact that our measure of the wealth index may be limited in a number of ways. When grouped into terciles, it becomes a relative measure, with individuals categorized on the basis of the asset ownership of their household, compared with the asset ownership of other households. Therefore, the secular decrease in wealth index likely to be occurring because of AIDS mortality and the collapse of the Zimbabwean economy [11] has not been expressed in this measure. Furthermore, simplified as a relative measure, asset ownership may be a crude indicator of how the wealth index is a determinant of sexual behaviour. For example, falling below a certain poverty threshold may drive a woman to sex work; a dynamic that may not be adequately represented by radio ownership, floor type, etc., or any combination of these variables. Finally, the summed score measure is to some extent a marker of urban residence, as evidenced by the higher mean wealth index score in towns. Levels of follow-up were comparable to other major cohort studies in Africa [6], but the wealth index was not independently associated with a probability of follow-up, so it is unlikely that these results are biased with respect to the wealth analysis. Having secondary or higher education and being more mobile at baseline were, however, associated with lower follow-up rates. If survival or incidence rates differed in the lost-to-follow-up groups the analysis may be biased with respect to mobility and education. For example, if more educated groups left the Manicaland study sites to find employment in large cities, they may have been at increased risk because of the higher prevalence in cities and the possibility of meeting new sexual partners after relocation. The group of migrants that were followed up, however, did not have different levels of incidence or sexual behaviour, but this was a small group of the total migrant population [27].

Despite these limitations of the current data, we have observed a decreased risk of HIV incidence in higher wealth index men. Although such a trend was not observed in women, the finding that lower wealth index women engage in riskier behaviour combined with their tendency for having less-educated male partners suggests that future trends may follow the emerging pattern in men. At this advanced stage of the epidemic, a number of factors may contribute to infection and risk behaviour. HIV prevention activities in Zimbabwe have included the treatment of sexually transmitted infections, social marketing of condoms, voluntary counselling and testing, education through mass media and the activities of the National AIDS Trust Fund (which is supported by income tax). These initiatives, as well as fear of AIDS mortality, may have disproportionately affected those of higher wealth index. Risk reduction behaviour, ushered in by the relatively well off is a hopeful trend, but, in the frail Zimbabwean economy, where the poor are an increasing demographic, the clustering of HIV in lower wealth strata is cause for concern.

Appendix: Justification of ‘summed score’

There is a high level of correlation between all binary and ordinal wealth variables. Therefore, exploratory analyses were undertaken to reduce the 10 assets to a simplified measure of the wealth index [24]. A simplified measure was created using multidimensional scaling analysis (MDS), a statistical technique for exploring similarities and differences in data [28]. Starting from a correlation matrix between variables, MDS is used to assign a score to each individual using fewer dimensions coded in a reduced number of variables. The first dimension of MDS was compared with a summed score of all assets. For the summed score the binary and ordinal measures were each transformed to lie between 0 and 1. The 10 variables were added and expressed as a percentage. A high degree of correlation was found between the first dimension of the MDS and the summed score in subsistence farming areas (R2 = 0.96), roadside business centres (R2 = 0.97), commercial estates (R2 = 0.94) and towns (R2 = 0.95). The summed score was thus considered equivalent to the first dimension of the MDS and a general indicator of poverty. Given the reproducibility and more intuitive interpretation of the summed score, it was used for all analysis.

Conflicts of interest: None.


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Africa; Zimbabwe; AIDS; HIV; poverty; socioeconomic development

© 2007 Lippincott Williams & Wilkins, Inc.