County-level hurricane exposure and birth rates: application of difference-in-differences analysis for confounding control Public Deposited

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Creator
  • Robinson, Whitney
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Grabich, Shannon C
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Horney, Jennifer A
    • Other Affiliation: Department of Epidemiology and Biostatistics, School of Rural Public Health, Texas A&M Health Science Center, College Station, USA
  • Engel, Stephanie
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Konrad, Charles
    • Affiliation: College of Arts and Sciences, Department of Geography
  • Richardson, David
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
Abstract
  • Abstract Background Epidemiological analyses of aggregated data are often used to evaluate theoretical health effects of natural disasters. Such analyses are susceptible to confounding by unmeasured differences between the exposed and unexposed populations. To demonstrate the difference-in-difference method our population included all recorded Florida live births that reached 20 weeks gestation and conceived after the first hurricane of 2004 or in 2003 (when no hurricanes made landfall). Hurricane exposure was categorized using ≥74 mile per hour hurricane wind speed as well as a 60 km spatial buffer based on weather data from the National Oceanic and Atmospheric Administration. The effect of exposure was quantified as live birth rate differences and 95 % confidence intervals [RD (95 % CI)]. To illustrate sensitivity of the results, the difference-in-differences estimates were compared to general linear models adjusted for census-level covariates. This analysis demonstrates difference-in-differences as a method to control for time-invariant confounders investigating hurricane exposure on live birth rates. Results Difference-in-differences analysis yielded consistently null associations across exposure metrics and hurricanes for the post hurricane rate difference between exposed and unexposed areas (e.g., Hurricane Ivan for 60 km spatial buffer [−0.02 births/1000 individuals (−0.51, 0.47)]. In contrast, general linear models suggested a positive association between hurricane exposure and birth rate [Hurricane Ivan for 60 km spatial buffer (2.80 births/1000 individuals (1.94, 3.67)] but not all models. Conclusions Ecological studies of associations between environmental exposures and health are susceptible to confounding due to unmeasured population attributes. Here we demonstrate an accessible method of control for time-invariant confounders for future research.
Date of publication
Identifier
  • doi:10.1186/s12982-015-0042-7
Resource type
  • Article
Rights statement
  • In Copyright
Rights holder
  • Grabich et al.
Language
  • English
Bibliographic citation
  • Emerging Themes in Epidemiology. 2015 Dec 22;12(1):19
Publisher
  • BioMed Central
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