The overall goal of this proposal is to accelerate the translation of scientific results from observational and experimental studies into public health policy. Epidemiology is at a crossroads. The vast majority of studies of exposure-outcome relationships in observational and randomized settings alike ask questions about exposure effects: questions which relate to individuals, and to lifestyle choices. For example, a study might ask about how smoking cessation affects risk of heart attack in people with HIV - a question relating largely to exposure-disease etiology. Such estimates are useful, but they are only the first step in making effective public policy, which requires the articulation of a specifi health intervention, and issues of the uptake of the intervention and its real world effectiveness. At present, few methods exist for assessing interventional effects in complex longitudinal data with time-varying exposures: the mainstay of modern epidemiology. This proposal aims to develop and apply innovative epidemiologic methods to create policy-ready, actionable epidemiologic results. Specifically, we will develop and extend two of the g-methods: the parametric g-formula, and inverse probability weighted marginal structural models. We will apply these methodological innovations to critical and timely problems within HIV in both domestic and international contexts. First, we will examine smoking-associated morbidity and mortality among HIV-positive individuals in the United States from the national CNICS database, and estimate the impact of specific smoking cessation interventions on all-cause mortality in this population. Second, we will examine the joint effects of hormonal contraception and pregnancy on response to highly active antiretroviral therapy among HIV-positive women in South Africa, and estimate the impact of specific interventions to reduce unintended pregnancy in this population on population health outcomes including response to treatment. The proposed methods work will help to bridge the gaps between epidemiology and implementation science, and will thus have extremely broad application: not only to the critical issues in HIV, but to epidemiology and public health as a whole. The proposed substantive work will fill needed gaps in the HIV literature, and our approach - rooted in the methods we will develop - will be well-positioned to drive public health policy in these areas. Thus, we propose a high-risk, high-reward effort to create methods to move from exposures to interventions and from patients to policy: through clearly communicated and usable methodological innovation, and high-impact substantive applications.

Public Health Relevance

Typical population-based studies address questions of exposure-disease relationships, which are most relevant to individual lifestyle decisions and clinical care;such studies implicitly compare hypothetical populations in which everyone is exposed, and no one is exposed. Findings from such studies are difficult to apply to questions of health policy for numerous reasons. Here, we propose to innovate and extend epidemiologic methods to create usable tools for translating scientific results of typical studies into public health policy. We will apply these tools to two specific substantive areas related to HIV: mortalit and morbidity attributable to smoking among people in care for HIV;and how pregnancy and use of hormonal contraception affect women's response to antiretroviral therapy.

National Institute of Health (NIH)
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
NIH Director’s New Innovator Awards (DP2)
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Special Emphasis Panel (ZRG1-MOSS-C (56))
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Russo, Denise
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University of North Carolina Chapel Hill
Public Health & Prev Medicine
Schools of Public Health
Chapel Hill
United States
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Keil, Alexander P; Edwards, Jessie K (2018) You are smarter than you think: (super) machine learning in context. Eur J Epidemiol :
Breskin, Alexander; Cole, Stephen R; Westreich, Daniel (2018) Exploring the Subtleties of Inverse Probability Weighting and Marginal Structural Models. Epidemiology 29:352-355
Keil, Alexander P; Richardson, David B (2018) Quantifying Cancer Risk from Radiation. Risk Anal 38:1474-1489
Hong, Jin-Liern; Jonsson Funk, Michele; LoCasale, Robert et al. (2018) Generalizing Randomized Clinical Trial Results: Implementation and Challenges Related to Missing Data in the Target Population. Am J Epidemiol 187:817-827
Keil, Alexander P; Mooney, Stephen J; Jonsson Funk, Michele et al. (2018) RESOLVING AN APPARENT PARADOX IN DOUBLY ROBUST ESTIMATORS. Am J Epidemiol 187:891-892
Keil, Alexander P; Richardson, David B; Westreich, Daniel et al. (2018) Estimating the Impact of Changes to Occupational Standards for Silica Exposure on Lung Cancer Mortality. Epidemiology 29:658-665
Breskin, Alexander; Cole, Stephen R; Hudgens, Michael G (2018) The Authors Respond. Epidemiology 29:e51
Lesko, Catherine R; Buchanan, Ashley L; Westreich, Daniel et al. (2018) The Authors Respond. Epidemiology 29:e14-e15
Stuart, Elizabeth A; Ackerman, Benjamin; Westreich, Daniel (2018) Generalizability of randomized trial results to target populations: Design and analysis possibilities. Res Soc Work Pract 28:532-537
Richardson, David B; Kinlaw, Alan C; Keil, Alexander P et al. (2018) Inverse Probability Weights for the Analysis of Polytomous Outcomes. Am J Epidemiol 187:1125-1127

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