The unexpected nature of disasters leaves little time or resources for organized health surveillance of the affected population, and even less for those who are unaffected. An ideal epidemiologic study would monitor both groups equally well, but would typically be decided against as infeasible or costly. Exposure and health outcome data at the level of the individual can be difficult to obtain. Despite these challenges, the health effects of a disaster can be approximated. Approaches include 1) the use of publicly available exposure data in geographic detail, 2) health outcomes data - collected before, during, and after the event, and 3) statistical modeling designed to compare the observed frequency of health outcomes with the counterfactual frequency hidden by the disaster itself. We applied these strategies to Hurricane Sandy, which struck the northeastern United States in October 2012. Hospital admissions data from the state of New York with information on primary payer as well as patient demographic characteristics were analyzed. To illustrate the method, we present multivariate logistic regression results for the first 2 months after the hurricane. Inferential implications of admissions data on nearly the entire target population in the wake of a disaster are discussed.
Bibliographical noteFunding Information:
Author affiliations: Division of Environmental Health Sciences, School of Public Health, University of Minnesota– Twin Cities, Minneapolis, Minnesota (Steven J. Mongin, Hyun Kim); Barry Commoner Center for Health and the Environment, Queens College, City University of New York, New York, New York (Sherry L. Baron); Department of Occupational Medicine, Epidemiology and Prevention, Hofstra Northwell Health School of Medicine, Manhasset, New York (Rebecca M. Schwartz); and Department of Population Health Science and Policy and Institute for Translational Epidemiology, Icahn School of Medicine at Mount Sinai, New York, New York (Bian Liu, Emanuela Taioli). S.J.M. is currently with the Division of Biostatistics, School of Public Health, University of Minnesota–Twin Cites, Minneapolis, Minnesota. This work was supported by the Office of the Assistant Secretary for Preparedness and Response (US Department of Health and Human Services) under grant HITEP150029-01-00. Conflict of interest: none declared.
- Hurricane Sandy
- counterfactual inference
- finite population
- public data