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Two frequently encountered but underrecognized challenges for causal inference in studying the long-term health effects of disasters among survivors include: (a) time-varying effects of disasters on a time-to-event outcome and (b) selection bias due to selective attrition. We review approaches to overcome these challenges and show application of the approaches to a real-world longitudinal data of older adults who were directly impacted by the 2011 earthquake and tsunami (n=4,857). To illustrate the problem of time-varying effects of disasters, we examined the association between degree of damage due to the tsunami and all-cause mortality. We compared results from Cox regression assuming proportional hazards versus adjusted parametric survival curves allowing for time-varying hazard ratios. To illustrate the problem of selection bias, we examined the association between proximity to the coast (a proxy for housing damage from the tsunami) and depressive symptoms. We corrected for selection bias due to attrition in the two post-disaster follow-up surveys (conducted in 2013 and 2016) using multivariable adjustment, inverse probability censoring weighting, and survivor average causal effect estimation. Our results demonstrate that the analytic approaches ignoring time-varying effects on mortality and selection bias due to selective attrition may underestimate the long-term health effects of disasters. © The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail:


Koichiro Shiba, Takuya Kawahara, Jun Aida, Katsunori Kondo, Naoki Kondo, Peter James, Mariana Arcaya, Ichiro Kawachi. Causal Inference in Studying the Long-term Health Effects of Disasters: Challenges and Potential Solutions. American journal of epidemiology. 2021 Mar 17

PMID: 33728430

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