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Poverty, sexual behaviour, gender and HIV infection among young black men and women in Cape Town, South Africa a

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Nicoli Nattrass , Brendan Maughan-Brown , Jeremy Seekings & Alan Whiteside

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Centre for Social Science Research (CSSR) , University of Cape Town , Private Bag, Rondebosch 7701 , Cape Town , South Africa b

Southern Africa Labour and Development Research Unit , University of Cape Town , Rondebosch 7701 , Cape Town , South Africa c

Health Economics and HIV/AIDS Research Division (HEARD) , University of KwaZuluNatal , Westville Campus, Private Bag X54001, Durban , 4000 , South Africa Published online: 12 Dec 2012.

To cite this article: Nicoli Nattrass , Brendan Maughan-Brown , Jeremy Seekings & Alan Whiteside (2012) Poverty, sexual behaviour, gender and HIV infection among young black men and women in Cape Town, South Africa, African Journal of AIDS Research, 11:4, 307-317, DOI: 10.2989/16085906.2012.754830 To link to this article: http://dx.doi.org/10.2989/16085906.2012.754830

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ISSN 1608-5906 EISSN 1727-9445 http://dx.doi.org/10.2989/16085906.2012.754830

Poverty, sexual behaviour, gender and HIV infection among young black men and women in Cape Town, South Africa Nicoli Nattrass1*, Brendan Maughan-Brown2, Jeremy Seekings1 and Alan Whiteside3 Centre for Social Science Research (CSSR), University of Cape Town, Private Bag, Rondebosch 7701, Cape Town, South Africa 2University of Cape Town, Southern Africa Labour and Development Research Unit, Rondebosch 7701, Cape Town, South Africa 3Health Economics and HIV/AIDS Research Division (HEARD), University of KwaZulu-Natal, Westville Campus, Private Bag X54001, Durban 4000, South Africa *Corresponding author, e-mail: [email protected]

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This article contributes methodologically and substantively to the debate over the importance of poverty, sexual behaviour and circumcision in relation to HIV infection, using panel data on young black men and women in Cape Town, South Africa. Methodological challenges included problems of endogeneity and blunt indicator variables, especially for the measurement of sexual behaviour. Noting these difficulties, we found that the importance of socioeconomic and sexual-behavioural factors differed between men and women. While we found a clear association between the number of years of sexual activity and HIV status among both men and women, we found that past participation in a concurrent sexual partnership increased the odds of HIV infection for men but not women. Women, but not men, who made the transition from school to tertiary education (our key indicator of socioeconomic status) were less likely to be HIV-positive than those who made the transition from school to unemployment. Both poverty and sexual behaviour matter to individuals’ HIV risk, but in gendered ways. Keywords: Cape Area Panel Study, circumcision, endogeneity, HIV/AIDS, panel data, prevalence, sex differentials, socioeconomic factors, surveys

Introduction There are many reasons why HIV infection in Africa is affected by socioeconomic as well as sexual-behavioural and biological factors. Different studies have reached contradictory conclusions on the importance of each set of factors, and the relative importance of these factors remains unclear. This article considers some of the methodological difficulties — including problems of endogeneity and blunt measurement — in assessing the importance of these factors, and uses data from a panel study in Cape Town, South Africa, to explore some ways out of the analytic impasse. Using panel data to reduce problems of endogeneity, and selecting variables to improve measurement, we show that, in the case of people living in Cape Town, the importance of socioeconomic and sexual-behavioural factors is gendered, differing between young men and young women. While we found a clear association between the number of years of sexual activity and HIV status among both men and women, we found that past participation in a concurrent sexual partnership increased the odds of HIV infection for men only. Women, but not men, who made the transition from school to tertiary education (our key indicator of socioeconomic status) were less likely to be HIV-positive

than those who made the transition from school to unemployment. Poverty, sex and HIV The fact that two-thirds of the global HIV-positive population live in the world’s poorest region, sub-Saharan Africa (UNAIDS, 2011), fuels arguments that a lack of development, poverty or low socioeconomic status must, in some way, be a key driver of the HIV epidemic. The situation is complicated by the uneven nature of the African HIV epidemic. Table 1 provides data on the nine countries with the largest numbers of HIV-positive people; all are African. Some countries with very high adult HIV prevalence have small populations (Lesotho, Botswana, Swaziland and Namibia) and thus are not on this list. The country-level data in Table 1 do not suggest any obvious relationship between socioeconomic indicators and HIV prevalence. HIV prevalence is highest in the richest of these nine countries (South Africa), but prevalence varies greatly among both the poorest countries (contrast Zimbabwe and Uganda) and among countries with intermediate incomes (contrast Nigeria and Zambia). This mirrors the cross-country econometric literature that finds no clear

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or consistent relationship between per capita income and HIV prevalence (e.g. Gillespie, Kadiyala & Greener, 2007; Nattrass, 2009, Wamai, Morris, Failis, Sokal, Klausner, Appleton et al., 2011). Although there are suggestions that some measures of poverty (such as calories per capita consumed) matter in relation to HIV prevalence (see Stillwaggon, 2002; Sawers & Stillwaggon, 2010a), such results are contingent on the specification of the model (Nattrass, 2009). The complexity of the relationship between HIV and socioeconomic status is revealed by individual-level data showing that HIV prevalence is often higher among higherincome people (Fylkesnes, Musonda, Kasumba, Ndhlovu, Mluanda, Kaetano & Chipaila, 1997; Senkoro, Boerma, Klokke, Ng’weshemi, Muro, Gabone & Borgdoff, 2000; Mishra, Vaessen, Boerma, Arnold, Barrere, Cross et al., 2006; Gillespie et al., 2007; Fox, 2010) and that the relationship can change over time (Parkhurst, 2010). This suggests socioeconomic status probably matters, but that there are various, even diametrically opposed pathways between socioeconomic status and HIV infection, with both poverty and affluence driving HIV infection in different cases. Poverty can increase individual risk of HIV infection if it affects sexual behaviour — for example, by encouraging transactional sex or through lack of access to or information about condoms (Booysen & Summerton, 2002; Buve, Bishikwabo-Nsarhaza & Mutangadura, 2002; Fenton, 2004; Nattrass, 2004). But relative poverty also seems to matter, in so far as sexual liaisons may be motivated by the desire to obtain the trappings of modern lifestyles associated with the rich (Hunter, 2002; Leclerc-Madlala, 2003). Furthermore, the lifestyles associated with higher-income earners (including travel and extended sexual networks) also pose HIV risks (Shelton, Cassell & Adetunji, 2005; Fox, 2010; Parkhurst, 2010). The relationship between socioeconomic status and HIV risk is therefore likely to vary across socioeconomic gradient, time and space — both between and within countries. This is the case in sub-Saharan Africa, though the dominant pattern appears to be that HIV risk rises with wealth (Mishra et al., 2006; Fox, 2010). With regard to South Africa, several studies have found a negative relationship between education levels and HIV (Shisana & Simbayi, 2002; Bärnighausen, Hosegood, Timaeus & Newell, 2007), and a positive association

between living in an urban informal area/shack and HIV (Auvert, Ballard, Campbell, Caraël, Carton, Fehler et al., 2001; Connolly, Shisana, Colvin & Stoker, 2004). This suggests that lower socioeconomic status is a risk factor for HIV. The evidence is complicated however. In Kenya, Bärnighausen et al. (2007) found that rural households in the middle of the income distribution had significantly higher risk of HIV infection than the poorest; a panel study in KwaZulu-Natal Province, South Africa, found no significant differences in prior labour-market participation between young adults who died of HIV-related illnesses and those who died of other causes (Sienaert, 2007). In South Africa almost all HIV infections occur through sex. Empirical studies in several parts of the world have found that the correlates of HIV status include: age at first penetrative sexual intercourse (Kaestle, Halpern, Miller & Ford, 2005); number of lifetime sexual partners (Auvert et al., 2001; Munro, Pradeep, Jayachandran, Lowndes, Mahapatra, Ramesh et al., 2008); general condom use (Pettifor et al., 2005); and unwilling sexual intercourse (Jewkes, Dunkle, Nduna & Shai, 2010). There is some debate about the role of concurrent sexual partnerships in driving the HIV epidemic. Modelling, taking into account the period of acute infection, indicates strongly that HIV is likely to spread faster through concurrent partnerships (Halperin & Epstein, 2007). The relevance of this modelling is contested however (see Lurie & Rosenthal, 2010; Sawers & Stillwaggon, 2010b). Biological factors also mediate the risk of HIV acquisition. The risk of infection is affected by other sexually transmitted infections (Gray, Wawer, Sewankambo, Serwadda, Li, Moulton et al., 1999; Galvin & Cohen, 2004; Guwatudde, Wabwire-Mangen, Eller & Eller, 2009), hormonal contraceptives (Heffron, Donnell, Rees, Celum, Mugo, Were et al., 2012), and medical male circumcision (Auvert, Taljaard, Lagarde, Sobngwi-Tambekou, Sita & Puren, 2005; Bailey, Moses, Parker, Agot, MacLean, Krieger et al., 2007; Gray et al., 2007; Siegfried, Muller, Volmink, Deeks, Egger, Low et al., 2009). Circumcision seems to be especially important. In clinical trials, medical male circumcision reduced the risk of HIV acquisition by more than 60%. Cross-national and national data on circumcision present contrary findings, however. On the one hand, no relationship was found between male circumcision and HIV status in a South

Table 1: Development Indicators for the countries with the nine largest HIV-positive populations

Country South Africa Nigeria Kenya Mozambique Tanzania Uganda Zimbabwe Zambia Malawi

Number of Gross national HIV-positive income per capita people in 2010 in 2010 (US$) 5.6 million 6 100 3.3 million 1 180 1.5 million 780 1.4 million 440 1.4 million 530 1.2 million 490 1.2 million 460 978 000 1 070 925 000 330

Literacy rate (%) 89 61 87 55 73 73 92 71 74

Poverty HIV prevalence Life expectancy Births with skilled headcount ratio (age group 15–49 at birth (years) health staff (%) (%) years) (%) 23 52 91 18 55 48 39 4 46 55 44 6 55 48 55 12 33 56 43 6 25 53 42 7 72 45 60 14 59 46 47 14 53 54 54 11

Sources: World Bank () and UNAIDS ().

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African HIV survey (Connolly et al., 2004), and Garenne (2008) argues that demographic and health survey (DHS) data from African countries do not indicate that circumcision has any protective effect. On the other hand, multivariate analysis using pooled African DHS data shows that the odds of acquiring HIV were over four-times higher for uncircumcised men, and this effect was even more pronounced once sexual behaviour and other co-variates were included in the regression (Gebremedhin, 2010). Warren (2010) argues, using multiple regressions to control for a set of socioeconomic and behavioural factors, that there are strong regional differences in southern Africa. Circumcision is associated with safer sexual behaviour and lower rates of HIV infection in Botswana and Swaziland, for example, but not in Lesotho. In sum, there remains considerable uncertainty as to the extent to which poverty, sexual behaviour and biological factors have driven HIV in Africa. One reasonable conclusion is that one should take regional variation seriously, explore local dynamics carefully, and tailor HIV prevention and treatment interventions accordingly (UNAIDS, 2010). As Parkhurst (2010, p. 524) states in his recent analysis of the available statistical sources for 12 African countries: “Neither poverty nor wealth per se drives the HIV epidemic. Being poor or being wealthy may be associated with sets of behaviours that are either protective or risky for HIV infection…. A bottom-up focus is necessary to identify factors that drive the risk of HIV infection in both wealthy and poorer groups in a given setting.” This article analyzes the factors associated with HIV infection in one setting, the South African city of Cape Town. It also considers the methodological challenges of studying the drivers of HIV infection outside of a laboratory setting. Indeed, we argue that these methodological challenges help to explain some of the variation in findings in the existing literature. Methods This article explores the correlates of HIV status in a sample of young black African adults in Cape Town, focusing on socioeconomic status and sexual behaviour. The data come from the Cape Area Panel Study (CAPS, version v1203). The first wave of CAPS (in 2002) surveyed a representative sample of 4 752 adolescents and young adults living in Cape Town, using a two-stage sample stratified by race. The respondents were subsequently re-interviewed up to four more times, most recently in 2009 (Wave 5), when the cohort was aged 20–30. In all waves of the survey, the respondents were asked detailed questions on a variety of demographic, socioeconomic, and behavioural topics. In Wave 5, HIV tests were conducted on the black respondents (information about the HIV-testing protocol, ethical approval and questionnaire can be found at ). Ninety-four percent of these respondents consented to an HIV test, with 86% providing a dry blood-spot specimen and a further 8% opting for the saliva test. HIV prevalence was 8.8% among the black males and 30.1% among the black females. When the data were weighted to take into account sampling design and selection into the HIV test, these figures adjusted to 9.5%

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HIV prevalence for black men and 30.0% for black women. In the analysis presented here, we use data adjusted by the sampling weights. The CAPS data are broadly consistent with data on women from antenatal clinics (ANCs) as well as predictions generated by the ASSA2008 demographic model (see Figure 1, which shows test-based observations and the predictions from ASSA models). HIV prevalence among females in our sample was close to the ANC data for the Western Cape Province and even closer to the ANC data for Khayelitsha (31%), which is Cape Town’s largest township (Médecin Sans Frontières, 2010) and home to a high proportion of the CAPS respondents. Thus, female HIV prevalence in the CAPS data was a little higher than the ASSA2008 estimate of 28%, but male HIV prevalence in the CAPS data was very close to the ASSA2008 predicted figure of 9.2% for 2009. While it might seem more sensible to assess the quality of a model through comparing its predictions with actual data, the fact that the ASSA2008 model has been calibrated and validated using a wide range of other, actual data should give us some confidence that the CAPS sample is reasonably representative. The HIV-test data, in conjunction with a rich panel data set, provides us with a unique opportunity to explore a wide range of factors during adolescence and early adulthood that potentially correlate with subsequent HIV status. But the analysis of the determinants of HIV — and the relative importance of socioeconomic and behavioural factors — faces several severe methodological challenges. First, the CAPS data do not tell us precisely when HIV-positive respondents sero-converted or when (if ever) they first knew their HIV status. This poses problems of possible endogeneity. For example, people who reported having used a condom the last time they had sex might have done so either to protect themselves from HIV infection or, if they knew that they were already HIV-positive, to protect their partner. Similarly, poverty might increase HIV risk, but acquiring HIV or AIDS is likely to erode income and wealth. These sorts of dynamics, in which reverse causation may be present, inevitably introduce noise into the data analysis and limit the variables we can meaningfully use to ‘explain’ HIV prevalence. Second, ‘sexual behaviour’ is not easily measured in ways that are appropriate to the conceptual reasoning. The problem, essentially, is that a person becomes infected with HIV as the result of a single sexual act; it is not possible to identify this particular sexual act, or even the partnership, or to separate data on this from data on other acts or partnerships. Survey data provide, at best, a general picture of the respondents’ sexual histories, including condom use in particular relationships. These are blunt indicators, which are likely to have an oblique, if any, relationship with HIV status. Not only are they indirect indicators of the sexualbehaviour information that ideally we would like, but they are fraught with recall error, social desirability bias and missing values (respondents may prefer to say they do not know or refuse to answer). Studies that have found an association between sexual behaviour and HIV status have had to use measures of sexual behaviour that may serve as crude proxies for higher-risk behaviour by individuals and their partners. These blunt indicators include age

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ASSA South African women aged 20–29

30

Khayelitsha ANC pregnant women (MSF data)

HIV PREVALENCE (%)

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25

Western Cape ANC pregnant women 20

ASSA Western Cape women aged 20–29

15

ASSA South African men aged 20–29 ASSA Western Cape men aged 20–29

10

CAPS women aged 20–29 5 CAPS men aged 20–29 2002 2003 2004 2005 2006 2007 2008 2009 YEAR Figure 1: Panel data showing HIV prevalence among young men and women in South Africa (ANC = antenatal clinic; ASSA = Actuarial Society of South Africa; CAPS = Cape Area Panel Study; MSF = Médecin Sans Frontières) Sources: Constructed using data from CAPS version v1203, MSF and the ASSA2008 model ().

at first penetrative sexual intercourse (Kaestle et al., 2005), number of lifetime partners (Auvert et al., 2001; Munro et al., 2008), general condom use (Pettifor et al., 2005), and unwilling sexual intercourse (Jewkes et al., 2010). We explore these indicators (or variants of them) in this study. Concurrency is especially difficult to measure sensibly. The biological mechanisms are well understood: people are most infectious during the acute early stage of HIV infection, when viral loads are high, and so concurrency increases the risk that HIV is passed on through sexual networks. But the risk of any individual becoming HIV-infected depends not on whether he or she has concurrent sexual relationships, but on the viral load of the individual’s sexual partner(s). Ideally we would have a measure of the partners’ viral load; failing that, we would like to measure concurrency among the individual’s partners (Morris, 2010). It is extremely difficult, if not impossible, to obtain reliable empirical data on the behaviour of respondents’ partners in order to test the hypothesis. Some scholars argue that the absence of evidence that concurrency drives the HIV epidemic means that we should not be treating it as if it is (Lurie & Rosenthal, 2010; Sawers & Stillwaggon, 2010b). Given the difficulties in collecting the required data, however, an absence of evidence should not be conflated with evidence of absence. The CAPS survey asked respondents whether they thought that their various sexual partners had been in concurrent relationships. Because this is a measure of perceptions we did not include these data in our analysis. Yet it is worth noting that previous analysis of the CAPS

data found a strong correlation between individual concurrency and perceived partner concurrency (Kenyon, Boulle, Badri & Asselman, 2010; Mah, & Halperin 2010). It is thus likely that individual concurrency is both a proxy for partner concurrency and an indicator that the respondent is participating in higher-risk sexual networks. Our measure of individual concurrency comes from Wave 5 of CAPS, where the respondents themselves filled in the answer to the following question: ‘Have you ever been in a sexual relationship with someone and had sex with somebody else? This includes main partners, side-partners, roll-ons and one night stands.’ (‘Roll-on’ is a common term used in Cape Town for a concurrent partner: Mah & MaughanBrown [2009].) In addition to asking about a respondent’s own concurrency (and partners’ perceived concurrency), CAPS asked many other questions about sexual behaviour. From information provided across all the waves of the survey, we constructed variables for ‘years sexually active,’ ‘condom use at first sex,’ average age difference for reported sexual partnerships, having had more than two sexual partners in total (and whether serial or concurrent), and whether respondents had ever been forced or tricked into having sex. Most quantitative studies of the relationship between HIV and socioeconomic status use data on income or wealth (usually an index of household assets) collected at the same time as the HIV test is conducted. Such studies are prone to endogeneity because HIV status could be both a reflection of socioeconomic status and a cause of lower socioeconomic status, to the extent that HIV-related

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illnesses pose additional burdens on households. In this article, we circumvent the problem by using household wealth data from the first wave of the panel study (in 2002) when most of the respondents were teenagers. Although some respondents may well have already become HIV-positive, they were unlikely to be suffering from HIV-related illnesses already, and thus their HIV status is unlikely to have had any material impact on the household. We use measures of young adults’ transitions after school — to tertiary education, employment or unemployment — as indicators of their socioeconomic trajectory. Those who made one or more of these transitions were classified according to the highest-ranked transition: making the transition to tertiary education ranked higher than making a transition to employment, which ranked higher than making a transition to unemployment. In other words, those classified as having made the transition from school to unemployment had never had a job or any tertiary education. Individuals who had ever made the transition from school to enrolment in any form of post-school education are likely to be on a relatively high socioeconomic trajectory. Such individuals probably lived in households that supported them to complete secondary schooling successfully, and they had the initiative and the opportunity to enrol in further education. Individuals who were able to find some employment are probably on a middle-level socioeconomic trajectory as they were able to find a job; although without the benefits of tertiary education, occupational mobility is likely to be limited. Those who only transitioned to unemployment are likely to be on a relatively low socioeconomic trajectory, as unemployment and lack of education are key markers of low socioeconomic status in South Africa (Seekings & Nattrass, 2005). Table 2 displays means of the socioeconomic measures, using various educational and income/wealth indicators, for those young black men and women respondents who made a transition from school to tertiary education, as compared with those who did not. The results refer to the 94% who completed an HIV test. Note that there were no significant differences between the characteristics of this group and those of the 6% of black respondents interviewed at the same time who did not give an HIV test (data not shown). Table 2 shows that individuals who had transitioned to tertiary education had (as to be expected) significantly more years of schooling and a higher grade-12 completion rate than those who did not. It also shows that those who made the transition to tertiary education lived in households with significantly greater income/wealth at the time of both the first and the most recent wave of the CAPS survey (in 2002 and 2009, respectively). This is true also for men and women separately (data not shown). The results support our hypothesis that having made the transition to tertiary education is a marker of socioeconomic status as well as an indication that the individual is on a higher socioeconomic trajectory than are those who do not make this transition. Circumcision status is also not as easily measured as it might seem. Traditional male circumcision, undertaken by the majority of Xhosa men in our sample, does not necessarily remove as much of the foreskin as a medical circumcision (Lagarde, Taljaard, Puren, Rain-Taljaard & Auvert, 2003; Bailey et al., 2007). This means that males

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Table 2: Means of the socioeconomic measures (by transition from school to tertiary education, or to employment/unemployment); data for black respondents tested for HIV (SD = standard deviation) School School to to tertiary employment or education unemployment Mean ± SD Mean ± SD or percentage or percentage Highest school grade 11.7 ± 1.2 10.1 ± 1.7 attained Passed grade 12 85% 23% Household assets in 6.3 ± 2.7 4.2 ± 2.5 2002 Household monthly 517 ± 482 335 ± 332 per capita income in 2002 (Rand) Household monthly 1 203 ± 1 284 685 ± 808 per capita income in 2009 (Rand)

Difference

1.6*** 62%*** 2.1*** 182*** 518***

***p ≤ 0.01 (statistical significance of differences between means calculated using t-tests). Note: Number of young people who made the transition to tertiary education varies between 259 and 296; data were missing most often on household incomes.

who are circumcised according to cultural practices may be less protected if any remaining foreskin continues to provide targets for HIV infection (Wamai et al., 2011). Previous research using the CAPS data on the risk of HIV infection among young Xhosa men in Cape Town shows that those with partial circumcisions had a higher risk of HIV infection than those who were fully circumcised (Maughan-Brown, Venkataramani, Nattrass, Seekings & Whiteside, 2011). DHS data on circumcision status are limited to whether the respondent reported being circumcised, without any evidence of the extent of the circumcision. The fact that traditional male circumcision varies in the extent to which the foreskin is removed may well be an important explanation for why the link between males’ circumcision status and HIV-infection status is not always clear from national and even cross-national survey data. In the CAPS survey, men were asked whether some or all of their foreskin had been removed, and, in a confidential part of the questionnaire that they filled in themselves, they were presented with illustrations of male penises showing different extents of circumcision and asked which picture most closely resembled their own penis. This enabled us to construct a set of variables on the extent of circumcision among the respondents. In this article we use a dummy variable ‘fully circumcised or not’ in our exploration of the correlates of HIV infection. About 10% of those reporting that they had been circumcised seem to have been only partially circumcised. We categorised these as not fully circumcised. Most (83%) of our male respondents had been fully circumcised. In addition, we included a binary measure of whether the respondent had ever had a sexually transmitted infection (STI) (either a known disease itself or a history of dysuria, genital discharge, ulcers or sores). The CAPS survey asked women if they had ever used an injected contraceptive, and 44% said they had. There was no difference between the sample of women who were

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HIV-positive and those who were HIV-negative, which is unsurprising because whether a women had ‘ever’ used such a contraceptive is unlikely to be correlated with HIV status — as the measure is too blunt. Thus we did not include this in the multivariate models. Finally, we created a dummy variable taking the value of ‘1’ if the respondent reported ever having had an STI. This might have played a biological role in acquiring HIV (lesions from an STI render people more vulnerable to HIV infection), but more likely is a further indication of higherrisk sexual behaviour. In our analysis we first consider the descriptive statistics from the CAPS data for men and women, and then according to their HIV status. Next, we model the relationship between socioeconomic factors and HIV status using a series of logistic regression models and reporting the odds ratios. We model the results for men and women together, then men only and women only. The first models include only the socioeconomic measures. For men and women, we then ran a second model that includes only sexualbehaviour variables. For men and women, men only and women only, we then ran a model that includes both the socioeconomic and sexual-behavioural variables. All analyses were conducted with Stata 12.0. The results are weighted and standard errors are adjusted for clustering using the ‘svy’ commands in Stata. Results and discussion Table 3 presents some descriptive statistics for the sample of black men and women respondents who also tested for HIV. There were no significant differences between men and women with regard to mean age, mean number of household assets (in Wave 1), reported history of STIs, or the rate at which the respondents had transitioned from

Nattrass, Maughan-Brown, Seekings and Whiteside

school to tertiary education. Women had a marginally higher mean number of years of schooling but were more likely than men to have made the transition from school to unemployment than from school to employment. With regard to sexual behaviour, men consistently reported significantly higher levels of risk behaviour (earlier age of sexual debut, a greater proportion having had more than two sexual partners, lower rates of condom use at first sex, and higher rates of individual concurrency, i.e. admitting to having had concurrent partnerships). Women reported higher rates of being ‘tricked or forced’ into sex than men. There were significant differences between the men and women with regard to average age difference within sexual partnerships: sexual partners were on average 3.6 years older for women, and one year younger for men. Table 4 presents the respondents’ socioeconomic and sexual-behavioural characteristics by gender and HIV status. Among both the men and women, HIV-positive individuals were older, had been sexually active for longer, and reported having had more than two sexual partners. Household assets were negatively correlated with HIV status for women but not for men. HIV-positive women were much less likely to have made the transition from school to tertiary education than were HIV-negative women. Having had an STI and ever having been forced or tricked into having sex was significantly associated with HIV risk for women, but not for men. Men who were HIV-positive were more likely not to have used condoms at first sex and to have had other partners besides their main sexual partners. Average age difference between sexual partners was not significantly linked to HIV status for either men or women. Table 5 presents the results of the multivariate logistic regression analysis for men and women together, and then separately. It uses a parsimonious set of socioeconomic, sexual-behavioural and biological variables. Table 5 shows

Table 3: Descriptive statistics of the black respondents who also tested for HIV, by gender (SD = standard deviation) Mean ± SD or percentage HIV status (HIV-positive = 1)† Age (years) Socioeconomic factors Highest school grade attained Transitioned from school to tertiary education† Transitioned from school to employment† Transitioned from school to unemployment† Sum of household assets in 2002 Behavioural factors Any history of an STI† Years sexually active Age at first sex Condom used at first sex† More than two sexual partners† Individual concurrency (respondents report having had at least one sexual partnership concurrently with another) † Ever had forced sex† Mean age difference with sexual partners (years) n

Men 9.5% 25 (2.7)

21% (0.02)* 0.2 (0.12)

10.3 (1.9) 24% 64% 12% 4.8 (2.8)

10.6 (1.6) 24% 55% 20% 4.7 (2.6)

0.3 (0.13)* 1% (0.03) 9% (0.03)* 8% (0.02)* 0.1 (0.2)

26% 9.4 (3.1) 15.5 (1.9) 50% 84% 55%

24% 8.2 (2.9) 16.6 (1.6) 58% 62% 26%

1% (0.03) 1.2 (0.2)* 1.1 (0.1)* 8% (0.03)* 23% (0.03)* 29% (0.04)*

11% –1.0 (1.9) 518–545

25% 3.6 (2.5) 685–700

14% (0.02)* 4.6 (0.1)*

*p ≤ 0.01 (statistical significance of differences between means calculated using t-tests). Dummy variable (percentages are reported and standard deviations are not reported).

†

Women 30% 24.8 (2.5)

Difference (standard error)

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that gender is the strongest single determinant of HIV status: controlling for a set of socioeconomic and sexual-behaviour variables, young women are five-times more likely to be HIV-positive than young men. Using the full sample, Model 1 includes only the socioeconomic variables, Model 2 included only the sexual-behaviour indicators, and Model 3 includes all variables. A comparison of these three models reveals that sexual-behaviour indicators (years sexually active, having had an STI, and individual concurrency) were statistically significant predictors of HIV status in all models. The only socioeconomic status variable that showed any statistical significance was having made the transition to tertiary education: the odds of being HIV-positive for those had made the transition to tertiary education were just over half (53%) that for those had made the transition to unemployment — but only once the behavioural characteristics were controlled for (Model 3). The odds of being HIV-positive for those who made the transition to employment (compared with those who made the transition to unemployment) were also lower, but measured imprecisely, and hence of no statistical significance. Models 4 and 5 run the specifications of Models 1 and 3 but for women only. They show that for women, having made the transition to tertiary education (relative to having made the transition to unemployment) was significant both in the socioeconomic model (Model 4) and in the model controlling for sexual behaviour (Model 5). The size of the coefficient

increases from Model 4 to Model 5, suggesting something related to being on this lower socioeconomic trajectory beyond the sexual-behavioural factors picked up by the control variables in Model 5. Controlling for sexual behaviour and the other variables in Model 5, women who made the transition to tertiary education had 45% the odds of being HIV-positive than did women who made the transition to unemployment. Model 6 presents the results from the fully specified model for men, which can be compared to Model 5 for women. Number of years of sexual activity remains a significant determinant of HIV status for both men and women, though the coefficient is larger for men. However, comparing the regressions reveals some important differences between the men and women. First, the overall negative relationship between having made the transition to tertiary education (as opposed to unemployment) which was evident in the full sample (Model 3) was actually driven by a strong relationship in the data for women. For men, the relationship was positive, but statistically insignificant. Second, we can see that the relationship in the general sample (Model 3) between individual concurrency and HIV status (respondents who reported ever having had concurrent sexual partners had 1.6-times the odds of being HIV-positive than those who did not) was driven primarily by the data for men. Model 6 shows that men who reported individual concurrency had twice the odds of being HIV-positive. Model 5 (women only) indicated a

Table 4: Black respondents’ socioeconomic and behavioural characteristics by gender and HIV status (SD = standard deviation; SE = standard error)

Age (years) Socioeconomic factors Highest school grade attained Transitioned from school to tertiary education† Transitioned from school to employment† Transitioned from school to unemployment† Sum of household assets in 2002 Sexual-behavioural factors Any history of an STI† Fully circumcised† Years sexually active Age at first sex Condom used at first sex† More than two sexual partners† Individual concurrency ever† Ever had forced sex† Mean age difference with sexual partners (years) n

HIV-positive

Men HIV-negative

HIV-positive

Women HIV-negative

Mean ± SD or percentage 26.1 ± 2.3

Mean ± SD or percentage 24.9 ± 2.7

Difference (SE)

Mean ± SD or percentage 24.7 ± 2.6

Difference (SE)

1.2 (0.34)***

Mean ± SD or percentage 25.1 ± 2.5

9.9 ± 2.6 20%

10.3 ± 1.8 24%

0.4 (0.36) 4% (0.07)

10.4 ± 1.5 17%

10.7 ± 1.7 28%

0.3 (0.2) 10% (0.04)***

71%

63%

8% (0.08)

58%

54%

4% (0.04)

8%

13%

4% (0.04)

25%

18%

6% (0.04)

4.4 ± 3.2

4.8 ± 2.8

4 (0.5)

4.2 ± 2.5

4.9 ± 2.7

0.7 (0.2)***

35% 74% 10.6 ± 2.7 15.5 ± 1.7 38% 95% 71% 16% –1.4 ± 2.1

25% 85% 9.2 ± 3.1 15.5 ± 1.9 51% 83% 53% 11% –1.0 ± 1.9

10% (0.07) 11% (0.07) 1.4 (0.4)*** 0.01 (0.3) 14% (0.08)* 12% (0.04)*** 18% (0.07)*** 5% (0.06) 0.4 (0.4)

30% – 8.7 ± 2.6 16.4 ± 1.6 57% 67% 31% 29% 3.8 ± 2.2

22% – 8 ± 2.9 16.6 ± 1.6 59% 59% 24% 23% 3.6 ± 2.7

8% (0.04)** – 0.7 (0.2)*** 0.2 (0.01) 2% (0.04) 8% (0.04)* 7% (0.04) 6% (0.04)* 0.2 (0.2)

47–48

470–497

207–211

478–489

Note: Statistical significance of differences between means calculated using t-tests. *p ≤ 0.1 **p ≤ 0.05 ***p ≤ 0.01 †Dummy variable (percentages are reported and standard deviations are not reported).

0.4 (0.2)*

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Table 5: Modelling HIV status using logistic regression models (odds ratios with 95% confidence intervals in parentheses)

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Model 1 4.16*** Female† (2.99–5.76) Years of school 0.96 (0.86–1.07) Transition from school to tertiary 0.68 education† (0.38–1.19) Transition from school to 0.91 employment† (0.60–1.38) Sum of household assets in 0.94 2002 (0.88–1.01) Any history of an STI† Number of years sexually active Condom used at first sex† More than two sexual partners† Individual concurrency ever† Ever had forced sex† Mean age difference with sexual partners (years) Circumcised fully† Observations

1 239

Men and women Model 2 Model 3 5.35*** 5.37*** (3.27–8.76) (3.31–8.71) 0.97 (0.87–1.09) 0.53** (0.29–0.97) 0.72 (0.46–1.13) 0.95 (0.90–1.02) 1.41** 1.46** (1.01–1.97) (1.04–2.07) 1.08** 1.08*** (1.02–1.15) (1.02–1.15) 0.96 1.01 (0.69–1.34) (0.72–1.41) 1.30 1.30 (0.89–1.91) (0.88–1.91) 1.54** 1.57** (1.06–2.25) (1.07–2.31) 1.29 1.22 (0.86–1.34) (0.82–1.82) 1.00 1.00 (0.93–1.07) (0.93–1.07)

1 144

1 141

Women only Model 4 Model 5

0.99 (0.87–1.12) 0.54* (0.29–1.02) 0.81 (0.51–1.27) 0.93 (0.87–1.00)

695

1.00 (0.89–1.13) 0.45** (0.23–0.86) 0.67 (0.41–1.09) 0.96 (0.89–1.03) 1.53** (1.04–2.26) 1.07* (1.00–1.15) 1.20 (0.79–1.83) 1.26 (0.84–1.90) 1.42 (0.89–2.24) 1.19 (0.80–1.77) 1.03 (0.96–1.10)

651

Men only Model 6 Model 7

0.89 (0.72–1.10) 1.14 (0.28–4.63) 1.15 (0.36–3.66) 0.91 (0.79–1.05) 1.21 (0.58–2.54) 1.13** (1.02–1.26) 0.61 (0.30–1.22) 2.35 (0.49–11.32) 2.13** (1.04–4.35) 1.64 (0.65–4.15) 0.92 (0.77–1.09)

490

0.90 (0.72–1.12) 1.30 (0.32–5.25) 1.20 (0.38–3.83) 0.92 (0.79–1.06) 1.19 (0.57–2.49) 1.15** (1.04–1.29) 0.61 (0.31–1.21) 2.30 (0.51–10.35) 2.18** (1.08–4.43) 1.61 (0.64–4.04) 0.91 (0.77–1.09) 0.41** (0.17–0.97) 487

Note: Statistical significance of differences between means calculated using t-tests. *p ≤ 0.1 **p ≤ 0.05 ***p ≤ 0.01 † Dummy variable (percentages are reported and standard deviations are not reported). Table 6: Sexual behaviour characteristics of the respondents, by transition to tertiary education, men and women (sample of the black respondents tested for HIV)

School to tertiary education n

%

School to employment or unemployment n %

Difference (standard error)

Men and women ^Percent condom used at last sex 296 73 928 64 8% (0.02)*** Φ Higher-risk unprotected sex 284 16 888 30 14% (0.03)*** Men only Percent condom used at last sex 125 80 408 72 8% (0.03)*** Higher-risk unprotected sex 120 10 390 23 13% (0.04)*** Women only Percent condom used at last sex 171 66 520 58 8% (0.03)*** Higher-risk unprotected sex 164 21 498 35 14% (0.04)*** Notes: ^Percent condom used at last sex throughout study Waves 1, 3, 4 and 5. ΦHigher-risk unprotected sex = 1 if the individual reported not using condoms always with a partner of unknown or positive HIV serostatus; data from study Wave 5 (in 2009). ***p ≤ 0.01 (statistical significance of differences between means calculated using t-tests).

similarly positive relationship, but it was statistically insignificant. Third, having a history of an STI increased the odds of being HIV-infected by a factor of 1.5 for women, but was statistically insignificant for men.

If concurrency is more of a HIV-risk factor for men than women, it implies that men who have had concurrent relationships are either disproportionately likely to be having sex with HIV-positive women (or, more precisely,

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African Journal of AIDS Research 2012, 11(4): 307–317

with women with high viral loads) or are having more sex with their partners. It is possible that, in Cape Town, black men in concurrent relationships are having sex with women at higher risk of exposure to HIV, whereas black women in concurrent relationships are having sex with men at lower risk of exposure to HIV. Model 7 provides a further model for men only, this time including a variable indicating whether the respondent was fully circumcised or not. It shows that controlling for the other variables in the model, being fully circumcised reduced the odds of being HIV-positive by almost 60%. Adding this variable did not change the significance or the strength of the variables that were significant in Model 6 — thereby indicating that there are distinct biological and behavioural factors involved in driving the risk of HIV infection for men. What is it about transition to tertiary education (relative to the transition to unemployment) that is protective for women? We have so far regarded this variable as an indicator of socioeconomic status. However, it may also be a further indicator of sexual behaviour if people who had made the transition to tertiary education practice safer sex in ways not captured by our sexual-behaviour control variables. As noted earlier, we were limited in our choice of control variables by the challenge of avoiding endogeneity. We chose not to use variables such as average percentage use of condoms over the life of the panel study, or current higher-risk sexual behaviour, because it is possible that the causality runs from HIV status to condom use for those who discover or suspect that they are HIV-positive. Instead we restricted our measure to condom use at first sex. Precisely because our measures of sexual behaviour are limited, it is thus very likely that the socioeconomic status variables were picking up unmeasured variation in sexual behaviour in the regressions. As shown in Table 6, men and women who had made the transition from school to tertiary education more frequently engaged in safer sex. They were substantially less likely to report inconsistent condom use with a partner whose HIV status was either unknown or positive, and average condom use (with sexual partners reported over the life of the panel study) was higher. This was true for men and women together, as well as separately. Marshall (2012), using early waves of the CAPS data, found that being in higher education (and controlling for other factors such as school results and home background) was strongly associated with condom use among black women especially. Conclusions Despite the difficulties involved in developing explanatory variables for HIV status (avoiding measures for which reverse causation is possible, missing data, social desirability bias, and recall error) we found some strong and significant results. Most importantly, our study reiterates the importance of (full) male circumcision as an HIV-prevention intervention for men. We also found that years of sexual activity was significantly linked to HIV status for both men and women, but that there were important differences between men and women in other respects. Notably, individual concurrency increased the odds of being HIV-positive for men, but not for

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women, whereas reporting ever having had an STI increased the odds for women, but not for men. There is, of course, a great deal of ‘noise’ in this data set regarding reported sexual behaviour; and, as discussed earlier, our choice of variables was limited by what was available and the challenge of avoiding endogeneity. That we were able to find strong and statistically significant results for some of the sexual-behaviour variables at all is noteworthy. By contrast, our indicators for socioeconomic status — with the exception of having made the transition to tertiary education for women — did not deliver statistically significant results. This does not mean that socioeconomic status does not matter for the HIV epidemic among black Africans in Cape Town, as our indicator variables were also limited. Furthermore, it is important to re-emphasise that our analysis was limited to black Africans in Cape Town (i.e. to relatively poor people). A larger study, across all population groups, would no doubt pick up greater variation in both socioeconomic status and HIV prevalence, and thus would be better placed than this study to make definitive statements about the role of socioeconomic status. We argue that our transition variables are indicators of socioeconomic status, with those who had made the transition from school to tertiary education likely to be from betteroff households than those who had made the transition from school to employment or to unemployment. But if those who make the transition to tertiary education are also more risk averse, it may well be that this variable is also proxying for sexual behaviour, albeit in a way linked to social class. As shown in Table 6, both men and women who had made the transition to tertiary education were more likely to practice safer sex. But whereas this was reflected also as a statistically significant protective effect for women in the multivariate regression, it was not the same for men. Rather, not having ever engaged in a concurrent partnership and being fully circumcised were key factors for the men. Finally, it is important to note that none of our regression models explained a high proportion of the variance in HIV infections. This is likely to be due in part to the methodological difficulties of measuring socioeconomic and behavioural factors, as well as remaining problems of endogeneity. It is also likely to be due in part to the randomness of HIV infections. For young black African men and women in Cape Town, living in neighbourhoods where a significant proportion of people are HIV-positive, and where there are social pressures to have unprotected sex, it is often a matter of bad luck whether an individual’s sexual partner is HIV-infected, or even whether an individual’s sexual partner has unprotected sex with someone else who is HIV-infected. Our analysis points to the importance of better data — including, especially, panel data, which helps us circumvent problems of endogeneity. Because the CAPS survey included data on HIV status from one point in time only (in 2009), we cannot address problems of endogeneity entirely, yet the use of panel data on sexual behaviour and socioeconomic factors is preferable to relying on data from cross-sectional surveys. Ideally, we would have data on the sexual histories of the respondents’ sexual partners (and their viral loads). Realistically though, we can try to identify more fully which variables on the individuals

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