Abstract
BackgroundThere is limited empirical research on the underlying gender inequity norms shaping gender-based violence, power, and HIV risks in sub-Saharan Africa, or how risk pathways may differ for men and women. This study is among the first to directly evaluate the adherence to gender inequity norms and epidemiological relationships with violence and sexual risks for HIV infection.MethodsData were derived from population-based cross-sectional samples recruited through two-stage probability sampling from the 5 highest HIV prevalence districts in Botswana and all districts in Swaziland (2004–5). Based on evidence of established risk factors for HIV infection, we aimed 1) to estimate the mean adherence to gender inequity norms for both men and women; and 2) to model the independent effects of higher adherence to gender inequity norms on a) male sexual dominance (male-controlled sexual decision making and rape (forced sex)); b) sexual risk practices (multiple/concurrent sex partners, transactional sex, unprotected sex with non-primary partner, intergenerational sex).FindingsA total of 2049 individuals were included, n = 1255 from Botswana and n = 796 from Swaziland. In separate multivariate logistic regression analyses, higher gender inequity norms scores remained independently associated with increased male-controlled sexual decision making power (AORmen = 1.90, 95%CI:1.09–2.35; AORwomen = 2.05, 95%CI:1.32–2.49), perpetration of rape (AORmen = 2.19 95%CI:1.22–3.51), unprotected sex with a non-primary partner (AORmen = 1.90, 95%CI:1.14–2.31), intergenerational sex (AORwomen = 1.36, 95%CI:1.08–1.79), and multiple/concurrent sex partners (AORmen = 1.42, 95%CI:1.10–1.93).InterpretationThese findings support the critical evidence-based need for gender-transformative HIV prevention efforts including legislation of women's rights in two of the most HIV affected countries in the world.
Highlights
There is limited empirical research on the underlying gender inequity norms shaping gender-based violence, power, and HIV risks in sub-Saharan Africa, or how risk pathways may differ for men and women
Sexual Power and HIV Risk Measures Based on research of established risk factors for HIV infection in sub-Saharan Africa and theoretical concepts of sexual power and HIV [7,8,9,10,26], we examined two outcomes to capture male sexual dominance and four measures of sexual risk practices: 1) Male-controlled sexual decision making –defined based on a power differential in response to the following two questions: ‘‘Who generally decides when you have sex?’’ and ‘‘In your sexual encounters, who usually decides whether you use a condom.?’’
‘‘It is ok for men to have more than one partner’’ ‘‘It is a woman’s duty to have sex with her spouse/partner even if she does not want to’’ ‘‘It is more important for a woman to respect her spouse/partner than it is for a man to respect his spouse/partner’’ ‘‘A man may beat this spouse/partner if she disobeys him’’ ‘‘A man may beat this spouse/partner if he believes she is having sex with another man’’ ‘‘It is more important for a boy to get an education than a girl’’
Summary
Population Setting Data were derived from a population-based cross-sectional study conducted in Botswana and Swaziland between November 2004 and May 2005. Covariates Based on previous research [7,8,9,10], socio-demographic variables considered apriori as potential confounders of the relationship between gender inequity norms and our outcomes of interest included: age (continuous, per year), relationship status (defined as single, married, or cohabitating), education ($high school vs ,high school education), annual household income (dichotomized at the ordinal variable closest to the sample median in each country), rural residence (vs urban), and risky alcohol use (defined as heavy drinking, problem drinking vs moderate/no drinking using the National Institute of Alcohol Abuse and Alcoholism definitions). Separate multivariate logistic regression models were constructed to obtain adjusted affects of the relationship between mean scores for gender inequity norms and each of the outcome measures, controlling for potential confounders and variables significant in bivariate analyses. We conducted country-specific models to evaluate the trends in associations between our mean gender inequity norms and HIV risk outcomes in each setting. Given that the same trends in associations were observed for all our outcome measures in country-specific models, we report the results for the global model, controlling for differences by country
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