Abstract
Objective:Better identification of at-risk groups could benefit HIV-1 care programmes. We systematically identified HIV-1 risk factors in two nationally representative cohorts of women in the Demographic and Health Surveys.Methods:We identified and replicated the association of 1415 social, economic, environmental, and behavioral factors with HIV-1 status. We used the 2007 and 2013–2014 surveys conducted among 5715 and 15 433 Zambian women, respectively (688 shared factors). We used false discovery rate criteria to identify factors that are strongly associated with HIV-1 in univariate and multivariate models of the entire population, as well as in subgroups stratified by wealth, residence, age, and past HIV-1 testing.Results:In the univariate analysis, we identified 102 and 182 variables that are associated with HIV-1 in the two surveys, respectively (79 factors were associated in both). Factors that were associated with HIV-1 status in full-sample analyses and in subgroups include being formerly married (adjusted OR 2007, 2.8, P < 10−16; 2013–2014 2.8, P < 10−29), widowhood (aOR 3.7, P < 10−12; and 4.2, P < 10−30), genital ulcers within 12 months (aOR 2.4, P < 10−5; and 2.2, P < 10−6), and having a woman head of the household (aOR 1.7, P < 10−7; and 2.1, P < 10−26), while owning a bicycle (aOR 0.6, P < 10−6; and 0.6, P < 10−8) and currently breastfeeding (aOR 0.5, P < 10−9; and 0.4, P < 10−26) were associated with decreased risk. Area under the curve for HIV-1 positivity was 0.76–0.82.Conclusion:Our X-wide association study identifies under-recognized factors related to HIV-1 infection, including widowhood, breastfeeding, and gender of head of the household. These features could be used to improve HIV-1 identification programs.
Highlights
Approaching the public health goals for global HIV-1 such as ‘90–90–90’ (90% of those with HIV-1 aware of their status, 90% in regular treatment, and 90% of those on treatment virally suppressed) requires effective identification of at-risk and infected individuals [1,2]
We used false discovery rate criteria to identify factors that are strongly associated with HIV-1 in univariate and multivariate models of the entire population, as well as in subgroups stratified by wealth, residence, age, and past HIV-1 testing
In the univariate analysis, we identified 102 and 182 variables that are associated with HIV-1 in the two surveys, respectively (79 factors were associated in both)
Summary
Approaching the public health goals for global HIV-1 such as ‘90–90–90’ (90% of those with HIV-1 aware of their status, 90% in regular treatment, and 90% of those on treatment virally suppressed) requires effective identification of at-risk and infected individuals [1,2]. Despite large efforts to expand testing, treatment, and retention services, only 45% of HIV-infected individuals in Sub-Saharan Africa knew their HIV-1 status in 2013, and estimated antiretroviral therapy (ART) coverage in 2016 exceeded 60% of all those infected in only five aDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, bCenter for Health Policy and the Center for Primary Care and Outcomes Research, Stanford University, Stanford, California, cNational Bureau of Economic Research, Cambridge, Massachusetts, dStanford Prevention Research Center, and eDivision of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, California, USA. The work cannot be changed in any way or used commercially without permission from the journal
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