Open AccessCausal Inference in Studying the Long-Term Health Effects of Disasters: Challenges and Potential Solutions
Author(s) -
Koichiro Shiba,
Takuya Kawahara,
Jun Aida,
Katsunori Kondo,
Naoki Kondo,
Peter James,
Mariana Arcaya,
Ichiro Kawachi
Publication year - 2021
Publication title -
american journal of epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.33
H-Index - 256
eISSN - 1476-6256
pISSN - 0002-9262
DOI - 10.1093/aje/kwab064
Subject(s) - causal inference , inverse probability weighting , censoring (clinical trials) , confounding , attrition , marginal structural model , selection bias , proportional hazards model , hazard ratio , proxy (statistics) , hazard , econometrics , statistics , medicine , propensity score matching , confidence interval , mathematics , chemistry , dentistry , organic chemistry
Two frequently encountered but underrecognized challenges for causal inference in studying the long-term health effects of disasters among survivors include 1) time-varying effects of disasters on a time-to-event outcome and 2) selection bias due to selective attrition. In this paper, we review approaches for overcoming these challenges and demonstrate application of the approaches to a real-world longitudinal data set of older adults who were directly affected by the 2011 Great East Japan 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 analysis assuming proportional hazards with those derived using 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 2 postdisaster follow-up surveys (conducted in 2013 and 2016) using multivariable adjustment, inverse probability of censoring weighting, and survivor average causal effect estimation. Our results demonstrate that analytical approaches which ignore time-varying effects on mortality and selection bias due to selective attrition may underestimate the long-term health effects of disasters.