Risk ratios for contagious outcomes
Year: 2018
Type: article
Abstract: Epidemiologists commonly use the risk ratio to summarize the relationship between a binary covariate and outcome, even when outcomes may be dependent. Investigations of transmissible diseases in clusters-households, villages or small groups-often report risk ratios. Epidemiologists have warned that risk ratios may be misleading when outcomes are contagious, but the nature of this error is poorly understood. In this study, we assess the meaning of the risk ratio when outcomes are contagious. We provide a mathematical definition of infectious disease transmission within clusters, based on the canonical stochastic susceptible-infective model. From this characterization, we define the individual-level ratio of instantaneous infection risks as the inferential target, and evaluate the properties of the risk ratio as an approximation of this quantity. We exhibit analytically and by simulation the circumstances under which the risk ratio implies an effect whose direction is opposite that of the true effect of the covariate. In particular, the risk ratio can be greater than one even when the covariate reduces both individual-level susceptibility to infection, and transmissibility once infected. We explain these findings in the epidemiologic language of confounding and Simpson's paradox, underscoring the pitfalls of failing to account for transmission when outcomes are contagious.
Authors: Olga Morozova, Ted Cohen, Forrest W. Crawford
Institution: Yale University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funders: National Institutes of Health, National Institute on Drug Abuse, National Institute of Allergy and Infectious Diseases, National Institute of Child Health and Human Development, National Center for Research Resources, National Center for Advancing Translational Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: Yale Biomedical Supercomputer, G2854942511, G292646880, Making Better Decisions: Policy Modeling for AIDS and Drug Abuse, Optimizing Medication-Assisted Therapy for Opioid Dependence in Ukraine: HIV Impact Modeling and Cost-Effectiveness Evaluation, Evaluating health and economic effects of targeted strategies in TB/HIV, Network-based epidemiology for hidden and hard-to-reach populations, High Performance Computing Instrumentation for Yale University Biomedical HPC Cen, Yale Clinical and Translational Science Award
OpenAlex ID: W2738209754