University of North Carolina Modelling combination interventions to prevent HIV among girls of school age in South Africa HPTN 068 Marie Stoner, PhD University of North Carolina Chapel Hill, NC, USA 23 July, 2019

Introduction Combination prevention strategies may be an effective way to prevent HIV in adolescent girls and young women (AGYW) Example: combining a cash transfer with adolescent friendly health services Resource intensive Modelling strategies are a cost-effective way to determine what might work before implementation

Factors associated with incident HIV infection in the HPTN 068 cohort Depression Intimate Partner Violence (IPV) Age-disparate relationships Transactional Sex Low school attendance 2,533 AGYW 13-20 years in rural South Africa

Goal Identify combination interventions by modeling the effects of known population interventions among AGYW on these five HIV-associated risk factors Depression IPV Age-disparate relationships Transactional sex Low school attendance

The G-Formula Create a simulated cohort (N=50,000) based on observed distribution of key characteristics Ask more nuanced questions that are relevant for policy and planning Conventionally studies compare: Risk of HIV in girls who are depressed versus in those who are not depressed Using G-formula: If we intervened to reduce depression by 25%, how much would this reduce HIV incidence?

Estimating the effects of interventions Percent of risk factor observed in data Entirely exposed vs. entirely unexposed (standard comparison) C. Observed vs. no one exposed D. Observed vs reduced by X% Westreich 2017, Epidemiology

Objectives Estimate population intervention effects on HIV acquisition in AGYW Quantify the potential reduction in HIV incidence after intervening on a combination of the 5 HIV-associated risk factors

Effective interventions to reduce HIV-associated risk factors 36.2% reduction in depression (Chibanda et al. 2016) 3.9% reduction low school attendance (Baird et al 2010) 10% reduction in IPV (Pettifor et al. 2016) 3% reduction in transactional sex (3%) (Cluver et.al 2013) 1.5% reduction in age-disparate partnership (Dupas et. al. 2011)

Age-disparate Partnership Entirely exposed vs. entirely unexposed (standard comparison)   Low School Attendance  Depression Ever IPV Age-disparate Partnership Transactional Sex Risk Difference (%) -8.5 (-8.8, -8,1) -3.2 (-3.5, -2.9) -0.3 (-0.7,0.0) -2.6 (-2.9, -2.2) -6.6 (-6.9, -6.2) Risk Ratio 0.41 (0.39,0.43) 0.64 (0.61,0.67) 0.95 (0.9,1) 0.70 (0.67,0.74) 0.46 (0.44,0.48) Bold p<0.005

Age-disparate Partnership Observed vs. no one exposed   Low School Attendance  Depression Ever IPV Age-disparate Partnership Transactional Sex Prevalence 6.8% 18.1% 16.9% 7.8% 9.3% Risk Difference (%) -0.5 (-0.7, -0.2) -0.6 (-0.9,-0.3) -0.2 (-0.5,0.1) -0.7 (-1.0, -0.4) Risk Ratio 0.93 (0.88,0.97) 0.64 (0.61,0.67) 0.97 (0.93,1.02) (0.92,1.02) 0.89 (0.84,0.94) Bold p<0.005

Observed vs reduced by X% Intervention 1: school attendance alone (3.9% reduction) Intervention 2: depression alone (36.2% reduction) Intervention 3: transactional sex alone (3% reduction)

Combination intervention effects Intervention 4: school attendance, depression and transactional sex Intervention 5: school attendance and depression Intervention 6: school attendance and transactional sex Intervention 7: transactional sex and depression

Conclusions and next steps Intervening on depression alone will reduce risk of HIV infection in AGYW Transactional sex, depression and low school attendance had the largest population effects We need stronger interventions to substantially reduce these risk factors G-formula is a useful tool to determine how combination interventions will reduce HIV incidence across populations

Acknowledgements UNC Audrey Pettifor Amanda Selin Linda Kimaru Aimee Julien Mary Jane Hill Stephanie DeLong Jess Edwards Carolyn Halpern Allison Aiello Bill Miller Pettifor Advisee Group National Institutes of Health (NIH) [R01MH087118 and R01 MH110186] ) Carolina Population Center [P2C HD050924]. South Africa Kathleen Kahn Xavier Gomez-Olive Catherine MacPhail (WrHI/UOW) Ryan Wagner Stephen Tollman Rhian Twine Audrey Khosa HTPN 068 study team and participants HPTN Laboratory Center Susan Eshleman Oliver Laeyendecker Yaw Agyei HIV Prevention Trials Network

The HIV Prevention Trials Network is funded by the National Institute of Allergy and Infectious Diseases (UM1AI068619, UM1AI068613, UM1AI1068617), with co-funding from the National Institute of Mental Health, and the National Institute on Drug Abuse, all components of the U.S. National Institutes of Health. The work presented here was funded by NIH grants UM1AI068619, UM1AI068613 and UM1AI1068617. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Leadership and Operations Center (LOC): Grant #: UM1AI068619 Laboratory (LC): Grant #: UM1AI 068613 Statistical and Data Management Center (SDMC): Grant #: UM1AI 068617 Please remove any award number not applicable to presented research

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