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HIV/AIDS, poverty and risky sexual behaviour in South Africa Frederik le R Booysen Published online: 11 Nov 2009.

To cite this article: Frederik le R Booysen (2004) HIV/AIDS, poverty and risky sexual behaviour in South Africa, African Journal of AIDS Research, 3:1, 57-67, DOI: 10.2989/16085900409490319 To link to this article: http://dx.doi.org/10.2989/16085900409490319

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African Journal of AIDS Research 2004, 3(1): 57–67 Printed in South Africa — All rights reserved

AJAR ISSN 1608–5906

HIV/AIDS, poverty and risky sexual behaviour in South Africa Frederik le R Booysen

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Department of Economics and Centre for Health Systems Research & Development, University of the Free State, PO Box 339, Bloemfontein 9300, South Africa e-mail: [email protected]

This paper employs data from the 1998 South African Demographic and Health Survey in exploring the nature of socio-economic inequalities in and determinants of risky sexual behaviour. Risky sexual behaviour was associated with poverty only in the case of multiple partnerships. Affluent women that have engaged in risky sexual behaviour were shown to be more likely to have cited negative perceptions about condom use as main reason for not using a condom at last sex. Poor women in turn were more likely to cite lack of knowledge about condoms and abstinence from condom use as main reason. Poverty plays little part in explaining differences in risky sexual behaviour, although higher education in some cases was associated positively with risky sexual behaviour. Risky sexual behaviour was associated with differences in age, urban residence and marital status. Coloured, Asian and White women were less likely than African women to have engaged in risky sexual behaviour. Violence and coercion were also associated with risky sexual behaviour. Women in affluent households that had engaged in risky sexual behaviour were more likely to have been mistreated by a husband or partner compared to poor women. There is no evidence of a definitive one-way causal relationship between poverty, gender and sexual behaviour and further research is required to elucidate this complex relationship. Keywords: coercion, condom use, gender, violence

Introduction South Africa is one of the countries worst affected by the HIV/AIDS pandemic. The estimated HIV prevalence among the adult population (20.1%) is amongst the highest in the world (UNAIDS, 2002). The transmission of HIV in South Africa occurs mainly through heterosexual intercourse, with the largest proportion of new infections occurring amongst younger women, as is the case in sub-Saharan Africa (EsuWilliams, 2000; Poku, 2001; Ackerman & De Klerk, 2002; Gilbert & Walker, 2002). Awareness and prevention efforts centred around information campaigns on safe sex remain central in the government’s current policy towards HIV/AIDS (Department of Health, 2000). In South Africa, the recent debate on the relationship between poverty and HIV/AIDS has attracted considerable attention, a controversy that stands in stark contrast to established evidence of a positive, dual relationship between health and wealth (Todaro, 1994; Burkey, 1996; DFID, 1999; Jack, 1999; Smith, 1999). According to Butler (2000), ‘ignorance of the mechanisms of transmission was not associated with poverty… in the early days of HIV transmission’. However, this is no longer the case. Ainsworth and Semali (1997) argue that the positive relationship between socio-economic status and HIV status found in many earlier studies of the epidemic may disappear as HIV/AIDS becomes more endemic to African countries, thus in future shifting HIV infection increasingly to persons of lower socioeconomic status. This argument is supported by the evidence of a negative relationship between risk of HIV infection and socio-economic status reported in some studies

(Ainsworth & Semali, 1997; Filmer, 1997). Poverty, therefore, evidently may be necessary, though not sufficient, for the spread of an epidemic of this scale. According to De Guzman (2001), the traditional infectious disease model ignores the importance of social vulnerability (and poverty therefore) insofar as it puts the individual and individual decisions at the centre stage. Thus, it ignores the fact that people may be at risk due to their position in society, which is dependent on a range of social, economic and political factors, and not only on their sexual behaviour (De Guzman, 2001; Marks, 2002). Earlier work by the author of the analysis of the relationship between poverty and risky sexual behaviour using the South African Demographic and Health Survey (SADHS) data has shown that women in poorer households are slightly less knowledgeable about HIV/AIDS. Although the socioeconomic inequalities in risky sexual behaviour were negligible, the evidence does suggest that knowledge is not being translated into behaviour change, with a high percentage of women who know the sexual transmission routes of HIV engaging in risky sexual behaviour (Booysen & Summerton, 2002). Eaton, Flisher and Aarø (2003), in a review of South African research on unsafe sexual behaviour amongst the youth, conclude that between 50% and 60% of sexually active persons reported never having used a condom. Venier, Ross and Akande (1998), Harrison, Smit and Myer (2000) and Mendez, Hulsey and Archer (2001) also emphasise this apparent failure of mass awareness and prevention campaigns to encourage safe sex amongst

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young adults. Although a number of more recent studies have explored the determinants of risk of HIV infection with the aid of data from women and/or sexual surveys, much of the earlier work in this regard has focused exclusively on constraints sex workers face in negotiating with their customers about condom use (Karim, Karim, Soldan & Zondi, 1995; Deneheffe, Caraël & Noumbissi, 1997; Wojcicki & Malala, 2001). This paper explores the relationship between poverty and risky sexual behaviour, focusing on a number of alternative definitions of risky sexual behaviour and exploring the likely determinants of risky sexual behaviour. The section that follows provides an overview of the proposed relationship(s) between HIV/AIDS, poverty and risky sexual behaviour, the nature of the likely determinants of risky sexual behaviour, and definitions of risky sexual behaviour. My section entitled Data and Method discusses the methods and data employed in the analysis reported on in this paper, while Results, Discussion and Conclusion respectively present and discuss the results and limitations of these analyses. HIV/AIDS, poverty and risky sexual behaviour Poverty, apart from being associated with poor nutrition and a breakdown of immune systems, is likely to increase the vulnerability of people to HIV/AIDS. Poverty contributes to unsafe sexual practices as a result of lack of knowledge and lack of access to means of protection. Women’s inability to negotiate about condom use with sexual partners as a result of entrenched gender roles and power relations also contribute to unsafe sexual practices (MacPhail & Campbell, 2001; Upton, 2001; Whiteside, 2001; Buseh & McElmurry, 2002; Myer, Matthews & Little, 2002; Whiteside, 2002). Both Desmond (2001) and Whiteside (2002) emphasise the complex, multi-faceted nature of the relationship between poverty and HIV/AIDS, e.g. how labour migration induced by rural poverty can contribute to the spread of the disease and how poor, single mothers may be forced to become occasional sex workers in order to survive (Desmond, 2001; Poku, 2001). Gillies, Tolley and Wolstenholm (1996) and Nyamathi, Flaskerud, Leake and Chen (1996) moreover, highlight the importance of homelessness, urban/rural migration patterns, migrant labour practices and the breakdown of social support networks in communities with limited access to social service delivery and in developing countries in increasing the vulnerability of poor people to HIV/AIDS. Poverty, moreover, plays an important role in the largescale transmission of tuberculosis, an opportunistic infection that in the HIV/AIDS era has become an even more important concern of health authorities (Dye, Scheele, Dolin, Pathania & Raviglione, 1999; Floyd, Reid, Wilkinson & Gilks, 1999). In fact, both HIV/AIDS and tuberculosis can be described as diseases of poverty (Killewo, 2002). Louria (2000), moreover, argues that the vicious cycle of poverty and health stands to translate into a continuous emergence and re-emergence of a variety of infectious diseases, including HIV/AIDS. A number of studies on sexual behaviour argue that poor women may be particularly vulnerable to HIV-infection

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based on evidence of sustained high-risk sexual practices by women who are aware of HIV/AIDS transmission and prevention (Geringer, Marks, Allen & Armstrong, 1993). Poverty appears to play a prominent role in influencing sexual decision-making by limiting individuals’ decision-making powers in sexual relationships. Gender power imbalances, which are rooted in gender role norms, play a pivotal role in sexual decision-making and in explaining the vulnerability of women to HIV infection. Gender is employed here in its original sense, i.e. ‘describing distinctions between, and the relative status of, women and men’, as well as the biological sense, i.e. ‘groups defined by the biology of sexual reproduction’ (Krieger, 2003, pp. 652–653). Women are generally perceived to be subservient, submissive and dependent, whilst men are bestowed with qualities of superiority, leadership and decision-making. These gender-specific attributes serve to condone men’s oppressive behaviour over women and fail to sanction behaviour that deems women powerless in their sexual relationships. In essence, women have a low self-efficacy in their interactions with men due to their low economic and social status and because of the power that men have over women’s sexuality (Karim et al., 1995; Warwick, Bharat, Castro, Garcia, Leshabari, SinghanetraRenard & Aggleton, 1998; Weiss, Whelan & Gupta, 2000; De Guzman, 2001; Gupta, 2002; Jewkes, 2002a; Pulerwitz, Amaro, De Jong, Gortmaker & Rudd, 2002; Renwick, 2002; Jewkes, Levin & Penn-Kekana 2003a; 2003b; Krieger, 2003). Women may also abandon safe sexual practices in exchange for economic and financial security, despite knowing the risks of doing so, which is the result of ‘women’s socially structured dependence on men for material and economic resources’ (Zierler & Krieger, 1997, p. 418). Young women, for example, often engage in sexual intercourse at an early age for material gain or favours from sexual partners (Weiss et al., 2000; Ackermann & De Klerk, 2002). Hence, the vulnerability of women to HIV/AIDS is often closely associated with so-called ‘transactional sex’ (Hunter, 2002) and need not only be thought about in terms of ‘formal’ prostitution. Buseh et al., 2002, for example, found that rural women in Swaziland may have multiple sexual partners as a means to economic security. De Guzman (2001) moreover refers to evidence of how an income-generating program in Zambia aimed at reducing women’s and adolescent’s dependence on risky partnerships for economic support has contributed to decreasing their social vulnerability to the disease. Poku (2001) argues that West African women, whom he claims are better integrated into economic life than elsewhere on the continent, are less dependent on men for survival and better able to negotiate condom use. HIV prevalence, he points out, is consistently lower in West African countries than in East and southern Africa. In this view, poverty and limited economic opportunity contribute to the increase in HIV-infection rates since sex becomes a sustainable livelihood opportunity for those most vulnerable to infection, i.e. women. Violence and coercion, which according to Heise, Pitanguy and Germaine (1995) represents a ‘profound health problem for women across the globe’ (p. ix) and is endemic to the South African society (Jewkes, 2002b;

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Jewkes & Abrahams, 2002), is another important determinant of risky sexual behaviour, given that it plays a significant role in increasing the vulnerability of women to HIV infection, especially in the case of poor women (Whiteside, 2001; 2002). Risk of HIV infection can be the result of childhood sexual abuse, which according to Zierler and Krieger (1997) often later in life results in ‘high-risk sex, prostitution, crack use, injection drug use, recurrent sexual assault, homelessness and incarceration’ (p. 420). Other ways in which violence can increase risk of infection include the detrimental effect of violence and coercion on these women’s ability to negotiate safe sex or other preventive behaviours and the exposure of women rendered homeless as a result of domestic abuse to rape, drugs and sex for economic survival (Zierler & Krieger, 1997; Maman, Campbell, Sweat & Gielen, 2000; Jewkes, 2002a; Jewkes et al., 2003a; 2003b). Alcohol abuse and drug use, moreover, may contribute to violence against women and to risk of HIV infection by increasing risk-taking behaviour and negatively influencing rational decision-making (LaBrie, Schiffman & Earlywine, 2002). Therefore, alcohol abuse and drug use are viewed as important factors contributing to poorer women’s limited ability to control their sexual practices, thus increasing their vulnerability to HIV infection, often as a result of alcohol abuse or drug use accompanying violent and coercive behaviour (Heise, Pitanguy & Germaine, 1995; Dingle & Oei, 1997; Zierler & Krieger, 1997; Jewkes, 2002a). In the light of the above evidence, therefore, social inequalities in general and poverty and gender in particular are key in explaining the risk of HIV infection amongst poor women and women in general (Zierler & Krieger, 1997; 1998; Kalipeni, 2000; Bancroft, 2001; Gilbert & Walker, 2002). The main aim of this paper is to employ the SADHS to explore the nature and determinants of risky sexual behaviour in South Africa in relation to poverty or socio-economic status. Three alternative definitions of risky sexual behaviour are employed for this purpose. Based on the information about sexual and marital relationships collected from female respondents, risky sexual behaviour is defined here as women having last had sex with a casual acquaintance, someone they have just met or a commercial sex worker and not having protected themselves against HIV by using a condom during last sex (‘AIDSRISK1’). The second definition is the same, but applies only to those non-married women that had a regular sexual partner at the time, thus excluding all married or co-habiting women, whom are assumed to have a regular sexual partner (‘AIDSRISK2’). The third proxy of risk sexual behaviour focuses on women with multiple sexual partners (‘AIDSRISK3’). These definitions, although similar to ones employed elsewhere in the literature in studies of the HIV risk via heterosexual transmission (Brunswick, Aidala, Dobkin, Howard, Titus & Banaszak, 1993; Nyamathi et al., 1996; Deneheffe et al., 1997; Filmer, 1997; Buseh & McElmurry, 2002; Mize, Robinson, Bockting & Scheltma, 2002), assumed these behaviours to be ‘risky’ only where the women also were knowledgeable about HIV/AIDS and its sexual transmission routes based on responses to questions on HIV/AIDS in the AIDS module of

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the survey. Such an approach allows one to explore socioeconomic inequalities in risky sexual behaviour in the context of long-standing prevention and awareness campaigns in South Africa aimed at curbing HIV infection. Data and method The relationship between HIV/AIDS, poverty and risky sexual behaviour is explored with the aid of data from the SADHS. Macro International has since 1984 conducted more than 70 nationally representative DHS surveys in more than 50 countries (Sahn & Stifel, 2000). The first such survey in South African was conducted in 1998. The survey represents a nationally representative probability sample of 11 735 women aged 15–49. Three sets of questionnaires were employed in the survey, i.e. a household, woman and adult questionnaire. Data from the household questionnaire was used to develop a measure of poverty, more details about which is provided in the next pages. The data employed in investigating the specific link between HIV/AIDS, poverty and risky sexual behaviour comes from the modules on ‘AIDS’, ‘marital and sexual relations’, ‘treatment of women in the household’ and ‘woman’s work and residence’ included in the women’s questionnaire (Department of Health, 1998; 2002). The DHS normally does not include questions on income and expenditure. As a result, it is not possible to employ income or expenditure as measures of socio-economic status. Consequently, Filmer and Pritchett’s (1998) asset index approach to the measurement of poverty, which the World Bank (2000) employed in their HNP country reports, is used here to quantify differences in socio-economic status. The variables included in the index are those items that measure household ownership of consumer durables, access to services and aspects of housing infrastructure. The asset index is estimated with the aid of iterated factor analysis. The value of the index is calculated by summing the score on each variable across all the variables included in the factor analysis. The asset index for Asset Variables 1–n for Household J can be represented as: Aj = f1 x (aj1–m1)/(s1) + … fn x (ajn–mn)/(sn) (1) where Aj represents the asset index, f the scoring factors or co-efficients for each asset, a the particular household’s score on the particular asset, and m and s the mean and standard deviation of each asset variable (Filmer & Pritchett, 1998, p. 6). An analysis of the resulting asset index estimates for South Africa has shown that the asset index represents an internally coherent, robust and comparable measure of poverty (Booysen, 2002). Established economic concepts and tools are employed to assess socio-economic inequalities in risky sexual behaviour. Women are assigned the score on the asset index for the particular household to which they belong. For the purpose of these comparisons, scores on the asset index are divided into population quintiles, with comparisons being made across the five quintiles. Quintile 1 include the poorest 20% of the population and quintile 5 the richest 20%. Results allow for the sampling design of the survey. Following this, a concentration index was calculated for each of these variables. The concentration index reflects the

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extent to which a particular health status is distributed across the population. The Concentration Index (CI) is derived from a Lorenz curve function (Ls) in which the cumulative proportion of the population, ranked by socio-economic status, is plotted against the cumulative proportion of illhealth. CI represents twice the area between Ls and the diagonal. An index of –1 and +1 respectively means that illhealth is concentrated in the most disadvantaged as opposed to the most advantaged person in the population, with zero denoting complete equality (Kakwani, Wagstaff & Van Doorslaer, 1997). In the case of a positive health indicator (e.g. access to health care services) the opposite will be true. Hence, the sign of the index indicates whether the particular health status is negatively or positively associated with poverty. So, for example, infant mortality and immunisation coverage will respectively have a negative and positive concentration curve (Filmer & Pritchett, 1998; World Bank, 2000). The concentration index estimates reported in these pages were calculated from grouped data using the following equation: CI = 2/µ Σ (ƒt µ t Rt) – 1 (2) where m represents the mean of the particular health indicator and µt and ƒt respectively represents the value of the health indicator and population share for the t th socio-economic group. Rt is the relative rank of the t t h socio-economic group, defined as Rt = Σƒg+0.5ƒt, which indicates the cumulative proportion of the population up to the midpoint of each interval group (Kakwani et al., 1997). Such analysis, however, has certain limitations. The analysis is only descriptive and cannot be employed in an analysis of the causal relationship between poverty, risky sexual behaviour and vulnerability to HIV infection. Such analysis also provides only an economic perspective on poverty and does not include parameters of social exclusion such as gender or ethnicity in quantifying poverty (World Bank, 2000; Mwabu, 2001). The analysis reported in the subsequent pages explores three issues. Firstly, the extent of socio-economic inequalities in risky sexual behaviour is explored, focusing on those women who are knowledgeable about HIV/AIDS (see section entitled Socio-economic inequalities in risky sexual behaviour). Secondly, socio-economic inequalities in possible determinants of risky sexual behaviour are explored (see section entitled Socio-economic inequalities in select deter-

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minants of risky sexual behaviour), given that women from different socio-economic classes may be exposed to HIV infection for different reasons. This part of the analysis focuses on women that are knowledgeable about HIV/AIDS and that have engaged in risky sexual behaviour, comparing those who have used a condom at last sex to those who have not. These risk factors include gender and power relations, violence and coercion and aspects of condom use. The svymean command in Stata7 was employed to calculate the proportion of women in each wealth quintile exhibiting those characteristics indicative of risky sexual behaviour and of determinants of risky sexual behaviour (Statacorp, 2001). Finally, logistical regression analysis is employed in exploring the relative importance of poverty, other related risk factors and select socio-demographic characteristics in explaining differences in risky sexual behaviour amongst women knowledgeable about HIV/AIDS and its sexual transmission routes (see section entitled Multivariate analysis of determinants of risky sexual behaviour), using the svylogit command in Stata7 (Statacorp, 2001). The discussion in these pages focuses only on those socio-economic inequalities and determinants of risky sexual behaviour found to be statistically significant in the respective analysis. Results Socio-economic inequalities in risky sexual behaviour Table 1 reports the estimated socio-economic inequalities in risky sexual behaviour. Only a very small proportion of women knowledgeable about HIV/AIDS actually engaged in risky sexual behaviour, i.e. less than 4%. The concentration index for having engaged in risky sexual behaviour was negative in all three cases, which means that poorer women are more likely to have engaged in risky sexual behaviour compared to women in more affluent households. However, the concentration index differed significantly from zero only in the case of the variable defining risk in terms of multiple partnerships. The concentration index is relatively high, taking on a value of –0.226, with six times as many women in the poorest quintile (0.6%) having engaged in risky sex compared to women in households in the top quintile (0.1%). The other two estimates did not statistically differ significantly from zero.

Table 1: HIV/AIDS, poverty and risky sexual behaviour in South Africa (1998) Indicator 1. Know about HIV/AIDS and its sexual transmission routes BUT have last had sex with a casual acquaintance, someone they have just met or a commercial sex worker AND have not used a condom at last sex (AIDSRISK1) 2. Know about HIV/AIDS and its sexual transmission routes and who have a regular sexual partner BUT have last had sex with a casual acquaintance, someone they have just met or a commercial sex worker AND have not used a condom at last sex (AIDSRISK2) 3. Know about HIV/AIDS and its sexual transmission routes AND have multiple sexual partners (AIDSRISK3) Sample (n)

1 3.0

Wealth quintile 2 3 3.4 5.6

4 3.8

5 2.0

Conc. average index 3.7 –0.034

2.3

2.3

4.3

3.3

1.5

2.8

–0.019

0.6

0.3

0.4

0.1

0.1

0.3

–0.266*

2 074 2 116 2 335 2 337 2 071 1 0933

* Averages were calculated by weighting the quintile rate for the particular indicator by the proportion of the total number of individuals at risk in that specific quintile. Index values with an asterisk differ significantly from zero at the 95% level based on a t-statistic (P < 0.05)

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Socio-economic inequalities in select determinants of risky sexual behaviour Table 2 reports socio-economic inequalities in a number of determinants of risky sexual behaviour. This analysis employed AIDSRISK1 as criteria for risky sexual behaviour, i.e. women having last had sex with a casual acquaintance, someone they have just met or a commercial sex worker and not having protected themselves against HIV by using a condom during last sex despite being knowledgeable about HIV/AIDS and its sexual transmission routes. The results for AIDSRISK2 were largely similar, but are not reported here due to constraints of space. The determinants or risky sexual behaviour are represented with the following proxies. Power relations here were defined with the aid of three proxies derived from questions determining the extent to which women are able to partake jointly with partners in making decisions that affect them. In all cases, outcomes were coded as 1 if women did not partake in such decisions and zero otherwise. The first variable reflects the extent to which women jointly participated in decisions about contraception use where the couple had discussed family planning. A second proxy of power rela-

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tions is whether women have a say in deciding how the money they themselves earn will be used. Women were also asked whether their husband or partner has ‘regularly not provided money [they] need for food, rent or bills but has money for other things’, which represent the third proxy of power relations employed in this analysis. Three proxies of violence are employed in this analysis. The first indicates whether the particular woman has ever been mistreated by a husband, by a partner or by a boyfriend, i.e. ‘kicked, bitten, slapped, hit or thrown with something’. The second and third respectively indicates whether the person who mistreated the woman was intoxicated at the time (i.e. ‘on drugs or alcohol’) and whether the particular woman has ever left a husband, partner or boyfriend because of being mistreated. In addition, the analysis employs two proxies of sexual coercion. The first is based on the question put to all women as to whether they have ever been coerced into having sexual intercourse, either physically or through persuasion, the emphasis being on the fact that it was against their will. The second is derived from responses to a question on whether any man has touched the respondent in a sexual way against their will

Table 2: Socio-economic inequalities in determinants of risky sexual behaviour in South Africa (1998) Indicator 1 A. Power relations, violence and mistreatment of women 1. Does not have say in choice of method of contraception 2. Does not have say in how own income is spent if currently working and earning an income 3. Husband/partner does not provide money needed for food, rent or bills when money available 4. Ever forced to or persuaded to have sexual intercourse against their will 5. Ever forced to be touched by a man or touch a man against her will before age 15 6. Ever mistreated by husband, partner or boyfriend 7. Ever mistreated by husband, partner or boyfriend while intoxicated 8. Ever left a husband, partner or boyfriend because of mistreatment B. Main reason for not using a condom 1. Problems with the availability of condoms 2. Lack of knowledge about condoms 3. Abstinence from condom use due to cultural or religious prohibition 4. Perceived low risk of STD/HIV 5. Negative perceptions about condom use a. Respondent dislikes condom b. Partner dislikes condom c. Inconvenient to use condom d. No/less sensation with condom e. Suggests lack of trust of partner f. Suggests lack of love of partner g. Respondent/partner has burning sensation or discomfort h. Prefers sex ‘flesh to flesh’ Sample (n)

2

Wealth quintile 3 4

Average 5

Conc. index

96.0 64.7

86.8 81.8

96.6 75.5

97.2 66.7

94.6 51.9

94.4 71.2

0.009 -0.027

96.0

89.7

93.3

99.7

100.0

94.9

0.013

11.3 0.0

6.9 1.5

11.5 3.4

18.5 4.8

0.4 0.0

11.3 2.4

0.071 0.239

3.9 1.7 2.5

12.0 9.8 8.5

11.7 10.4 7.5

15.3 5.8 6.2

23.0 19.4 4.8

12.1 8.6 6.3

0.200* 0.165 0.041

14.6 23.3 11.0 2.5 48.0 14.1 23.1 0.0 0.0 12.1 0.0 0.0 19.4 68

12.1 15.2 5.2 0.0 55.1 16.0 26.6 4.2 0.0 8.7 0.0 1.1 11.1 82

12.1 8.0 6.1 1.2 69.3 22.3 28.1 2.8 1.0 17.3 2.5 0.6 16.4 127

15.1 4.2 5.6 1.0 68.1 14.0 36.9 0.8 1.3 14.2 5.2 0.0 8.9 78

15.6 4.5 4.8 4.8 68.4 5.3 18.0 0.0 1.9 46.3 9.7 0.0 17.3 33

13.4 11.2 6.6 1.4 62.2 16.4 27.8 2.0 0.7 16.4 2.7 0.4 14.4 388

0.020 -0.323* -0.119 0.130 0.067* -0.037 0.042 -0.092 0.462* 0.206 0.555* -0.188 -0.056

* This analysis employed AIDSRISK1 as criteria for risky sexual behaviour. The results for AIDSRISK2 were largely similar, but are not reported here due to constraints of space. Statistically significant results for this variable are referred to in the discussion in the text where different from the results reported in Table 2. Due to the small number of women that engaged in risky sexual behaviour as defined by AIDSRISK3 (n = 42), socio-economic inequalities could not be assessed for this parameter of risky sexual behaviour. Averages were calculated by weighting the quintile rate for the particular indicator by the proportion of the total number of individuals at risk in that specific quintile. Index values with an asterisk differ significantly from zero at the 95% level based on a t-statistic (P < 0.05)

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and/or whether any man has forced them to touch his private parts against their will before they were 15 years old. All women who indicated that they did not use a condom at last sex were asked to indicate the reasons why they did not do so. The question allowed for multiple responses and listed 24 possible specific responses. These responses were combined into four main categories of reasons, i.e. negative perceptions about condom use (e.g. respondent or partner disliking condoms), problems with the availability of condoms (e.g. condoms being too expensive or not knowing where to get condoms), lack of knowledge about condoms (e.g. did not know about condoms or how to use them), and reasons for abstinence from condom use (e.g. cultural or religious prohibition). Perceived low risk of HIV and STDs, which was considered as a reason for abstaining from condom use, presents a reason for lack of condom use specifically related to HIV/AIDS. Consequently, socio-economic inequalities for this outcome are reported separately, as are the socio-economic inequalities related to specific negative perceptions about condom use. The most predominant factors associated with risky sexual behaviour for women in general are related to power relations and the main reasons for not using condoms at last sex. The majority of the women that had engaged in risky sexual behaviour reported having little control over decisions pertaining to contraception use (71.2%) and matters pertaining to financial issues (almost 95%). Just more than 60% of women attributed their failure to use a condom at last sex to negative perceptions about condom use. However, five correlates only of risky sexual behaviour exhibited any significant differences across socio-economic status. The lack of statistical significance is driven by the small number of women in each of the five quintiles, which is a function of the dataset used and the criteria used for selection. Firstly, women in more affluent households that had engaged in risky sexual behaviour were also more likely to have been mistreated by a husband, partner or boyfriend. The concentration index took on a relatively large positive value of 0.200. In the top quintile, 23% of women reported ever having been mistreated compared to 4% only in the bottom quintile. In the second instance, women in poorer households who engaged in risky sexual behaviour were more likely to cite lack of knowledge about condoms as the main reason for not using a condom at last sex. The corresponding concentration index took on a negative and was relatively large (–0.323), with 23.3% and 4.5% respectively of women in the bottom and top quintiles attributing their decision to lack of knowledge about condoms. Women in more affluent households who engaged in risky sexual behaviour in turn were more likely to cite negative perceptions about condom use as the main reason for not using a condom at last sex, i.e. 68% in the top quintile compared to 48% in the bottom quintile. The estimate of the concentration index, which took on a positive value, was relatively small (0.067), but the gradient was more pronounced in the case of two particular negative perceptions of condom use, albeit cited by a relatively small proportion of women (i.e. less than 3%). Affluent women who engaged in risky

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sexual behaviour were more likely to ascribe non-condom use to experiencing no or less sensation with a condom (index = 0.462) and to condom use being suggestive of a lack of love for their partner (index = 0.555). When the analysis was limited to non-married women (i.e. using variable AIDSRISK2 as proxy of risky sexual behaviour), two more variables exhibited statistically significant differences across socio-economic status. Interestingly, women from poorer households who had engaged in risky sexual behaviour in this instance were more likely to attribute non-condom use to abstinence for cultural or religious reasons. The concentration index for this outcome was negative and took on a relative large value of –0.313. In the poorest quintile, 13.8% of women attributed their failure to use a condom at last sex to abstinence compared to zero in the top quintile. Relatively more affluent women in turn now attributed lack of condom use to suggestions of lack of trust of their partner (index = 0.260), a negative perception cited by almost 17% of women who had engaged in risky sexual behaviour as a reason for not using a condom at last sex. Here, a staggering 45% of women in the top quintile cited this negative perception compared to just 7% of women in the bottom quintile. Multivariate analysis of determinants of risky sexual behaviour Logistical regression analyses were employed in exploring the relative importance of poverty, other related risk factors, and select socio-demographic characteristics in explaining differences in risky sexual behaviour amongst women knowledgeable about HIV/AIDS and its sexual transmission routes. These results are reported in Table 3. No regression results are reported for AIDSRISK3 (i.e. the risk variable focusing on multiple partnerships) due to the fact that the small number of women in this group (n = 54) did not exhibit sufficient variability across the socio-demographic and other characteristics reported here to allow a meaningful comparison with the other two analyses. The three proxies of power relations included in the preceding analysis (see Table 2) were not included in these regression models, given that these variables exhibited too little variation across the dependent and independent variables included in the models and resulted in reduced samples that precluded the meaningful analysis of the role of other determinants. Three regressions were run, one including both the asset index and education (Model 1), and two respectively excluding these two proxies of socio-economic status (Models 2 and 3). The results of the latter two regressions are included here so as to assess the robustness of these findings, i.e. to determine whether the use of a combination of or of single proxies of socio-economic status changes the results. The results in Table 3 confirm the absence of a socioeconomic gradient in risky sexual behaviour evident from Table 1. Poverty, it appears, plays little part in explaining differences in risky sexual behaviour as defined in this paper. The exception is the application of regression models 1 and 2 to risky sexual behaviour amongst non-married women (AIDSRISK2). In these cases, women with some primary (1–7 years) or secondary (8–12 years) education were more likely than women with no education to have engaged in

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Table 3: Determinants of risky sexual behaviour amongst South African women (1998), odds ratios (OR) Socio-demographic characteristics

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1 Asset index (quintile 1 = 1.000) Quintile 2 Quintile 3 Quintile 4 Quintile 5 Education (none = 1.000) 1–7 years 8–12 years >12 years Age (15–19 years = 1.000) 20–24 years 25–29 years 30–34 years 35–39 years 40–44 years 45–49 years Population group (African = 1.000) Coloured White Asian Rural place of residence (urban = 1.000) Currently married Ever forced to or persuaded to have sexual intercourse against their will Ever forced to be touched by a man or touch a man against her will before age 15 Ever mistreated by husband/partner/boyfriend Ever mistreated by husband/partner/boyfriend under influence of alcohol/drugs Ever left a partner husband/partner/boyfriend because of mistreatment Personally knows someone diagnosed with HIV/AIDS or who died of AIDS F statistic (P < 0.01) Sample (n)

AIDSRISK1 2

1.102 1.260 0.834 0.885 1.094 1.122 0.955

1.204 1.097 0.903

1.596* 1.732* 2.037* 2.167* 2.014* 0.930

1.662* 1.870* 1.954* 1.996* 1.916* 1.054

0.407* 0.122* 0.439 0.516* 0.073* 1.632** 0.725 1.667 0.724 0.665 1.155 7.4 10 197

0.426* 0.104* 0.382** 0.531* 1.570** 0.884 1.608 0.703 0.405 0.586 8.0 10 516

3

1

1.109 1.267 0.837 0.877

0.800 1.086 0.797 0.773

AIDSRISK2 2

3 0.829 1.145 0.859 0.829

1.603 1.979** 1.586

1.726** 1.945** 1.594

1.578* 1.703* 2.004* 2.126* 1.972* 0.905

1.278 0.640** 0.895 0.686 0.700 0.386*

1.365 0.731 0.883 0.673 0.711 0.427**

1.264 0.617** 0.833 0.622** 0.621 0.332*

0.410* 0.120* 0.440 0.515* 0.079* 1.635** 0.714 1.663 0.728 0.666 1.156 7.7 10 197

0.137* 0.084* 0.287** 0.440*

0.122* 0.073* 0.253* 0.444* 0.073* 2.181* 0.534 1.404 0.687 0.865 1.153 4.6 10 516

0.138* 0.085* 0.296** 0.432*

2.203* 0.554 1.507 0.628 0.857 1.256 3.8 10 197

2.215* 0.542 1.494 0.621 0.867 1.287 4.5 10 197

* Marital status was excluded from the regressions with AIDSRISK2, given that married or co-habiting women were assumed to have a regular sexual partner. Odds ratios (OR) with one asterisk are statistically significant at the 5% level, while ones with two asterisks are significant at the 10% level. Source: author’s own calculations from the SADHS

risky sexual behaviour, with odds ratios for these educational statuses exceeding 1. Age generally plays a significant part in explaining differences in risky sexual behaviour, with women in their sexually active years being more likely to engage in risky sexual behaviour compared to women aged 15–19 years and peaking in the age groups 30–39 years. Interestingly, this association between age and risky sexual behaviour is inverted when applying the analysis to non-married women only (i.e. using variable AIDSRISK2). Here the likelihood of risky sexual behaviour declines as age increases, thus indicating that younger non-married women are relatively more likely to engage in risky sexual behaviour, which can be expected as the likelihood of marriage increases with age. Marital status, as expected, protected women from risky sexual behaviour, with married women being much less likely to have engaged in risky sexual behaviour (i.e. odds ratios are close to zero), regardless of the measure of socio-economic status employed in the model. Three other variables featured as significant determinants of risky sexual behaviour in the case of all six models. Women belonging to households residing in rural areas were less likely to have engaged in risky sexual behaviour in

all cases, with odds ratios taking on values between 0.4 and 0.5. Coloured, Asian and White women were less likely than African women to have engaged in risky sexual behaviour, with odds ratios for these population groups failing below zero. Finally, and most importantly, women reporting ever having been coerced into sexual intercourse against their will were more likely to have engaged in risky sexual behaviour, odds ratios exceeding one in the case of this variable. None of the remaining determinants of risky sexual behaviour included in the analysis featured of significant determinants. Discussion and conclusion In terms of the likely factors associated with risky sexual behaviour, more affluent women were shown to be more likely to cite negative perceptions about condom use as main reason for not using a condom at last sex, particularly due to experiencing no or less sensation when using a condom during sex and due to condom use being suggestive of lack of trust or love for a partner, while poorer women were more likely to cite lack of knowledge about condoms and abstinence from condom use as the main reason. Therefore,

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some factors associated with risky sexual behaviour exhibited a negative relation with socio-economic status, while others were positively associated with poverty. Jewkes et al. (2003a), in an analysis of the relationship between select proxies of gender inequalities and the likelihood of South African women from three provinces discussing HIV with their partner and suggesting condom use to a partner, likewise report both positive and negative directions of association, highlighting the ‘need for a more nuanced understanding of gender inequalities and their relationship to HIV risk’ (p. 125). However, caution is also required insofar as the educational level and cultural background of women also influence the way in which they will respond to the questions on negative perceptions about condom use. Evidence of a significant socio-economic gradient in risky sexual behaviour existed only when ‘risk’ was defined in terms of multiple partnerships, with poorer women being more likely to engage in risky sexual behaviour. According to the results of the multivariate analysis, poverty plays little part in explaining differences in risky sexual behaviour as defined in this paper, with the exception of education being a significant determinant in the case of risky sexual behaviour amongst non-married women. Filmer’s (1997) multicountry study on risky sexual behaviour likewise reported no significant association between asset ownership and risky sexual behaviour, neither for sexual intercourse with a nonregular partner, nor for condom use with a non-regular partner. The likelihood of risky sexual behaviour amongst women in general increases with age and peaks at ages 30–39, while amongst non-married women the likelihood decreases with age. Marital status, as expected, protected women from risky sexual behaviour. Risky sexual behaviour was less likely amongst rural women. In terms of population groups, Coloured, Asian and White women were less likely than African women to have engaged in risky sexual behaviour. This result raises the question as to whether race still represents a better proxy of socio-economic status than does education or household wealth, particularly in the context of the results presented in this paper. However, the results presented here do share certain commonalities with the international evidence on correlates and determinants of risky sexual behaviour. In an earlier multi-country study of the socio-economic correlates of risky sexual behaviour amongst non-married men and women, Deneheffe et al. (1997) found similar evidence to us on the role of age, education and place of residence, with lower ages, higher educational levels and urban residence being associated with risky sexual behaviour. Filmer’s (1997) multi-country study on risky sexual behaviour likewise reported that the likelihood of risky sexual behaviour is higher amongst women residing in urban areas and, similar to our study, lower amongst married women. However, Filmer (1997) also found a strong, negative association between the likelihood of risky sexual behaviour and education, unlike our analysis which reported no significant association with education for women in general and a somewhat weak but positive association for non-married women. A large proportion of women who had engaged in risky

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sexual behaviour had little control over decisions pertaining to contraception use and household finances. Moreover, violence and coercion featured as a significant factor associated with risky sexual behaviour, not only specifically in the context of socio-economic status, but also amongst women in general. Women in more affluent households who had engaged in risky sexual behaviour were more likely to ever have been mistreated by a husband, partner or boyfriend. Jewkes (2002a) also reports evidence of poverty protecting South African women from intimate partner violence in contrast to the expected positive association between violence and poverty. Sexual coercion in turn featured as a significant determinant of risky sexual behaviour in the multivariate analysis focusing on women in general, with women reported ever having been coerced into sexual intercourse against their will being more likely to have engaged in risky sexual behaviour. The current literature provides further evidence of this link between violence and coercion and risky sexual behaviour. Studies in the United States and from sub-Saharan Africa have also found evidence of the role of violence and coercion in increasing the vulnerability of women to HIV infection. Zierler and Krieger (1997) cite evidence from numerous studies in the US which have found that women in relationships characterised by violence are less likely to be able to negotiate with their partners about sex, while studies of HIV-infected women have recorded levels of sexual or physical violence by current or past partners ranging from 67% to 83%. Heise et al. (1995), Maman et al. (2000), and Heise, Ellsberg and Gottmoeler (2002) in turn cite evidence from a range of studies conducted in developing and developed countries which — through the use of both direct and indirect evidence — emphasises the role of childhood abuse in increasing the risk of HIV infection in later life via its effect on the number of sexual partners, age at first intercourse, the likelihood of subsequent sexual abuse and on drug use. Jewkes and Abrahams (2002) report evidence from a three-province study in South Africa that shows a positive association between domestic violence and violence in childhood, alcohol abuse and low education. The danger is that an AIDS prevention strategy focusing exclusively on negotiation of condom use, as does the South African strategy to a large extent, does not specifically address the role of power relations in increasing the vulnerability of women to HIV infection, particularly so where violence and coercion is a common occurrence in many relationships (Venier et al., 1998; Harrison et al., 2000; Jewkes & Abrahams, 2002). According to Heise et al. (1995), what is required are ‘interventions that address both the causes and symptoms of violence and coercion, including interventions in primary prevention, justice system reform, health care response, programmes to assist victims and treatment of reeducation programs for perpetrators’ (p. ix). Jewkes (2002a), from a South African perspective, emphasises the importance of ‘primary preventive interventions aimed at improving the status of women and reducing norms of violence, poverty and alcohol consumption’ (p. 1423) in addressing this evil and the associated threat of HIV infection. Crucial as well is the role of interventions aimed at fostering male responsibility for HIV risk appraisal and safe sex

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(Dowsett, Aggleton, Abega, Jenkins, Marshall, Runganga, Schifter, Tan & Tarr, 1998; Warwick et al., 1998), the focus of awareness and prevention activities thus far having been almost exclusively on women. In conclusion then, poverty or low socio-economic status in itself does not drive the transmission of HIV through risky sexual behaviour (Filmer, 1997). Risky sexual behaviour is associated rather with age, education, place of residence, race and marital status (Deneheffe et al., 1997; Filmer, 1997). In addition, the vulnerability of women to HIV infection results from their lack of power in negotiating about condom use, their economic dependence on men and the violent and coercive nature of their relationships with their partners (Heise et al., 1995; Zierler & Krieger, 1997; Maman et al., 2000; Heise et al., 2002; Jewkes & Abrahams, 2002; Jewkes, 2002a). Much criticism, however, has been levelled at the validity of responses to survey questions on sensitive issues such as sexual behaviour and relationships in general and of surveys on violence and coercion in particular (Jewkes & Abrahams, 2002). These problems in large part result from the complex nature of sexual behaviour and relationships (Wojcicki & Malala, 2001), which is influenced by factors at the personal level as well as the proximal (e.g. interpersonal relationships and physical environment) and distal context (e.g. culture) (Eaton et al., 2003). In addition, the cross-sectional design of most of the existing surveys makes it impossible to investigate the causal relationships between HIV infection, HIV risk and the various risk factors described in these pages (Maman et al., 2000), as does the fact that most surveys investigating risk behaviours interview either a sample of men or women but not paired partners (Sly, Harrison, Moore & Soler, 2001). Delor and Hubert (2000) likewise emphasise this dynamic or inter-temporal quality of risk-taking processes, drawing from the wider literature on vulnerability in the social sciences. The results of this analysis need to be interpreted in the context of this problem, given that these shortcomings apply to the SADHS. The lack of significance of poverty and of various proxies of power relations in explaining differences in risky sexual behaviour, which stands in stark contrast to some evidence on the association between poverty, gender and HIV infection reported elsewhere in the literature, perhaps is the result of the limitations of the DHS survey design in studying these issues rather than these factors being unimportant in establishing the dynamics of risk of HIV infection and the association of HIV infection with poverty. The problem of insignificance of these factors may also be the result of the omission in the multivariate analysis of certain important factors associated with risky sexual behaviour, such as province, occupation, partner characteristics and community characteristics. Multivariate analyses of this nature also need to explore the simultaneous interaction between various risk factors in explaining differences in risky sexual behaviour, for example alcohol use, power and violence. However, the lack of heterogeneity in the relatively small group of respondents that exhibit risky sexual behaviour across all co-variates often preclude such analysis, as was the case here. As such, as Jewkes et al. (2003a) suggest, the evidence (and often lack thereof) on correlates and

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determinants of risky sexual behaviour presented and discussed in this paper highlights the need for further work in this area to elucidate the complex nature of the relationship between poverty, gender and risky sexual behaviour. Acknowledgements — I am indebted to Kiersten Johnston (Macro International), Tom Moultrie (University of Cape Town) and Debbie Bradshaw (MRC), who provided me with invaluable assistance, as well as three anonymous referees, who provided useful comments on an earlier draft of this paper. The South African data employed in the analysis has been obtained from the National Department of Health. The views presented in this paper are those of the author and should not be attributed to the Department. The author — Frikkie Booysen has a PhD in Economics. His main research interest is poverty analysis, with a particular focus on the relationship between health, poverty and HIV/AIDS, including the socio-economic impact of HIV/AIDS on households.

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