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Activity Number:
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278
- Innovative Methods for Predicting Public Health Outcomes and Informing Policy
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Type:
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Topic-Contributed
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Date/Time:
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Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract #317550
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Title:
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Spillover Effects of Pre-Exposure Prophylaxis Delivery for HIV Prevention: Evaluating the Importance of Effect Modification using an Agent-Based Model
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Author(s):
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Ashley Buchanan* and Carolyn Park and Sam Bessey and William Goedel and Eleanor Murray and Samuel Friedman and M. Elizabeth Halloran and Natallia Katenka and Brandon DL Marshall
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Companies:
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College of Pharmacy, University of Rhode Island and Brown School of Public Health and Brown School of Public Health and Brown School of Public Health and Boston University and Department of Population Health, NYU Grossman School of Medicine and Public Health Sciences Division, Fred Hutch and Department of Computer Science and Statistics, University of Rhode Island and Brown School of Public Health
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Keywords:
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Agent based models;
Causal Inference;
Spillover;
HIV/AIDS;
Interference;
Sexual networks
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Abstract:
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We developed an agent-based model using a trial emulation approach to quantify the impact of effect measure modification by key variables on spillover effects of pre-exposure prophylaxis (PrEP) among men who have sex with men (MSM) in Atlanta, GA. Eligible components (with ? 3 agents and ? 1 HIV+ agent) were first randomized to intervention or control. Within intervention components, agents were randomized to PrEP according to intervention coverage of 70%, which provides insight into a high PrEP coverage strategy. The spillover effect was defined as HIV incidence among agents not on PrEP but possibly sharing sexual risk with those on PrEP, as compared to those in control components. As spillover effects may depend on component features, we evaluated effect modification by component characteristics and estimated spillover effects using randomization-based estimators. The spillover effect was stronger among components either with higher HIV prevalence and greater density. Persons not on PrEP may benefit from being in a network with higher PrEP coverage levels, and this benefit may be larger when component-level risk factors are more prevalent.
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Authors who are presenting talks have a * after their name.
