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County-level hurricane exposure and birth rates: application of difference-in-differences analysis for confounding control.
Grabich, Shannon C; Robinson, Whitney R; Engel, Stephanie M; Konrad, Charles E; Richardson, David B; Horney, Jennifer A.
Afiliación
  • Grabich SC; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 135 Dauer Drive 2101 McGavran-Greenberg Hall, CB #7435, Chapel Hill, NC 27599 USA.
  • Robinson WR; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 135 Dauer Drive 2101 McGavran-Greenberg Hall, CB #7435, Chapel Hill, NC 27599 USA.
  • Engel SM; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 135 Dauer Drive 2101 McGavran-Greenberg Hall, CB #7435, Chapel Hill, NC 27599 USA.
  • Konrad CE; Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC USA.
  • Richardson DB; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 135 Dauer Drive 2101 McGavran-Greenberg Hall, CB #7435, Chapel Hill, NC 27599 USA.
  • Horney JA; Department of Epidemiology and Biostatistics, School of Rural Public Health, Texas A&M Health Science Center, College Station, TX USA.
Emerg Themes Epidemiol ; 12: 19, 2015.
Article en En | MEDLINE | ID: mdl-26702293
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Emerg Themes Epidemiol Año: 2015 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Emerg Themes Epidemiol Año: 2015 Tipo del documento: Article