Marie C D Stoner1,2, Daniel Westreich3, Jennifer Ahern4, Jessie Edwards3, F Xavier Gómez-Olivé5, Stephen M Tollman5,6, Sheri Lippman5,7, Kathleen Kahn5,6,8, Audrey Pettifor1,3,5. 1. Carolina Population Center, University of North Carolina, Chapel Hill, North Carolina, USA. 2. Women's Global Health Imperative, RTI International, San Francisco, California, USA. 3. Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. 4. School of Public Health, Division of Epidemiology and Biostatistics, University of California, Berkeley, Berkeley, California, USA. 5. Medical Research Council/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. 6. INDEPTH Network, Accra, Ghana. 7. Department of Medicine, University of California, San Francisco, San Francisco, California, USA. 8. Epidemiology and Global Health Unit, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden.
Abstract
BACKGROUND: Combination interventions may be an effective way to prevent human immunodeficiency virus (HIV) in adolescent girls and young women. However, current studies are not designed to understand which specific interventions and combinations will be most effective. We estimate the possible impacts of interventions on a combination of factors associated with HIV. METHODS: We used the g-formula to model interventions on combinations of HIV risk factors to identify those that would prevent the most incident HIV infections, including low school attendance, intimate partner violence, depression, transactional sex, and age-disparate partnerships. We used data from the HIV Prevention Trials Network (HPTN) 068 study in rural South Africa from 2011 to 2017. We estimated HIV incidence under a potential intervention that reduced each risk factor and compared this to HIV incidence under the current distribution of these risk factors. RESULTS: Although many factors had strong associations with HIV, potential intervention estimates did not always suggest large reductions in HIV incidence because the prevalence of risk factors was low. When modeling combination effects, an intervention to increase schooling, decrease depression, and decease transactional sex showed the largest reduction in incident infection (risk difference, -1.4%; 95% confidence interval [CI], -2.7% to -.2%), but an intervention on only transactional sex and depression still reduced HIV incidence by -1.3% (95% CI, -2.6% to -.2%). CONCLUSIONS: To achieve the largest reductions in HIV, both prevalence of the risk factor and strength of association with HIV must be considered. Additionally, intervening on more risk factors may not necessarily result in larger reductions in HIV incidence.
BACKGROUND: Combination interventions may be an effective way to prevent human immunodeficiency virus (HIV) in adolescent girls and young women. However, current studies are not designed to understand which specific interventions and combinations will be most effective. We estimate the possible impacts of interventions on a combination of factors associated with HIV. METHODS: We used the g-formula to model interventions on combinations of HIV risk factors to identify those that would prevent the most incident HIV infections, including low school attendance, intimate partner violence, depression, transactional sex, and age-disparate partnerships. We used data from the HIV Prevention Trials Network (HPTN) 068 study in rural South Africa from 2011 to 2017. We estimated HIV incidence under a potential intervention that reduced each risk factor and compared this to HIV incidence under the current distribution of these risk factors. RESULTS: Although many factors had strong associations with HIV, potential intervention estimates did not always suggest large reductions in HIV incidence because the prevalence of risk factors was low. When modeling combination effects, an intervention to increase schooling, decrease depression, and decease transactional sex showed the largest reduction in incident infection (risk difference, -1.4%; 95% confidence interval [CI], -2.7% to -.2%), but an intervention on only transactional sex and depression still reduced HIV incidence by -1.3% (95% CI, -2.6% to -.2%). CONCLUSIONS: To achieve the largest reductions in HIV, both prevalence of the risk factor and strength of association with HIV must be considered. Additionally, intervening on more risk factors may not necessarily result in larger reductions in HIV incidence.
Authors: Marie C D Stoner; Jessie K Edwards; William C Miller; Allison E Aiello; Carolyn T Halpern; Aimée Julien; Katherine B Rucinski; Amanda Selin; Rhian Twine; James P Hughes; Jing Wang; Yaw Agyei; Francesc Xavier Gómez-Olivé; Ryan G Wagner; Oliver Laeyendecker; Catherine Macphail; Kathleen Kahn; Audrey Pettifor Journal: J Acquir Immune Defic Syndr Date: 2018-09-01 Impact factor: 3.731
Authors: Daniel Westreich; Jessie K Edwards; Elizabeth T Rogawski; Michael G Hudgens; Elizabeth A Stuart; Stephen R Cole Journal: Am J Public Health Date: 2016-06 Impact factor: 9.308
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Authors: Marie C D Stoner; Jessie K Edwards; Daniel Westreich; Kelly Kilburn; Jennifer Ahern; Sheri A Lippman; F Xavier Gómez-Olivé; Kathleen Kahn; Audrey Pettifor Journal: AIDS Behav Date: 2021-01-21