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Background: HIV prevalence varies between 0% and 1.6% in India's states. The factors underpinning this variation are poorly defined. Methods: We evaluated the relationship between HIV prevalence by state and a range of risk factors in the Indian 2015 National Family Health Survey. Pearson’s correlation was used to assess the relationship between HIV prevalence and each variable. The prevalence of each risk factor was compared between five high-HIV-prevalence states (>1% prevalence) and a large low-HIV-prevalence state (Uttar Pradesh; HIV prevalence, 0.06%). Results: There was an association between HIV prevalence and men's mean lifetime number of partners (r = 0.55; P = 0.001) and men reporting sex with a non-married, non-cohabiting partner (r = 0.40; P = 0.014). In general, men in high-prevalence states were less likely to be circumcised and (with the exception of Chandigarh) use condoms at last sex. In two high prevalence states (Mizoram and Nagaland), men reported a higher number of lifetime partners and a higher prevalence of multiple partners and high-risk sex in the past year. Conclusions: Variation in circumcision prevalence and sexual behavior may contribute to the large variations in HIV prevalence by state in India.

Background: HIV prevalence varies between 0% and 1.6% in India's states. The factors underpinning this variation are poorly defined. Methods: We evaluated the relationship between HIV prevalence by state and a range of risk factors in the Indian 2015 National Family Health Survey. Pearson’s correlation was used to assess the relationship between HIV prevalence and each variable. The prevalence of each risk factor was compared between five high-HIV-prevalence states (>1% prevalence) and a large low-HIV-prevalence state (Uttar Pradesh; HIV prevalence, 0.06%). Results: There was an association between HIV prevalence and men's mean lifetime number of partners (r = 0.55; P = 0.001) and men reporting sex with a non-married, non-cohabiting partner (r = 0.40; P = 0.014). In general, men in high-prevalence states were less likely to be circumcised and (with the exception of Chandigarh) use condoms at last sex. In two high prevalence states (Mizoram and Nagaland), men reported a higher number of lifetime partners and a higher prevalence of multiple partners and high-risk sex in the past year. Conclusions: Variation in circumcision prevalence and sexual behavior may contribute to the large variations in HIV prevalence by state in India.

Background: It is unclear why HIV prevalence varies by nearly two orders of magnitude between regions within countries in sub-Saharan Africa. In this ecological study, we assess if HIV prevalence by region is associated with any of four markers of higher risk sexual behavior: lifetime number of partners, multiple partners in past year, higher risk sex (defined as sex with non-cohabiting, non-marital partners) and age at debut. Methods: We performed Pearson’s correlation between the 4 behavioral risk factors and HIV prevalence by region in 47 nationally representative surveys from 27 sub-Saharan African countries, separately by gender. In addition, principal components analysis was used to reduce the eight risk factors (four for each gender) to two principal components (PCs). Mixed effects linear regression was used to assess the relationship between the resulting two PCs and HIV prevalence after controlling for the prevalence of male circumcision. Results: HIV prevalence varied by a median 3.7 fold (IQR 2.9-7.9) between regions within countries. HIV prevalence was strongly associated with higher risk sex and, to a lesser extent, the other risk factors evaluated. Both PCs were strongly associated with HIV prevalence when assessed via linear regression. Conclusions: Differences in sexual behavior may underpin the large differences in HIV-prevalence between subpopulation within sub-Saharan African countries.