Informing targeted public health measures with data-driven decision modeling
SPECIAL SEMINAR
W. Alton Russell, PhD
Assistant Professor
Dept of Epidemiology, Biostatistics & Occupational Health
SPGH | McGill University
WHERE: In-Person | 2001 McGill College, Rm 1203 | Zoom
Abstract
Decision-analytic models can inform measures to address important problems in population health and health systems. Traditionally, decision analysts have focused on the aggregate or average impact of measures on a population. Increasingly, policy makers seek to understand the distribution of impacts across diverse populations. This is for two main reasons: to understand the equity implications of policies and to enable targeted public health measures.
I will describe how integrating data-driven methods like machine learning into decision analysis can improve estimation of the impact of public health measures on diverse populations, informing targeted interventions. I will describe two applications: dispensing the overdose reversal drug naloxone to patients receiving prescription opioids and tailoring the frequency of blood donations to each donors’ estimated trajectory for recovering iron stores.
Bio
W. Alton Russell, PhD, is an Assistant Professor in the McGill School of Population and Global Health and director of the Data-Driven Decision Modeling Lab, or D3Mod lab. The D3Mod lab aims to enable the efficient, effective, and equitable use of finite healthcare resources by developing, assessing, and applying traditional decision modeling methods (mathematical modeling, simulation, optimization) together with data-driven methods (machine learning, Bayesian statistics). Dr. Russell received undergraduate training in Industrial Engineering and Public Health at North Carolina State University, Masters and Doctoral training in Management Science and Engineering at Stanford University, and postdoctoral training at the Massachusetts General Hospital Institute for Technology Assessment and Harvard Medical School.