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County-level hurricane exposure and birth rates: application of difference-in-differences analysis for confounding control
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4688997/ https://www.ncbi.nlm.nih.gov/pubmed/26702293 http://dx.doi.org/10.1186/s12982-015-0042-7 |
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author | Grabich, Shannon C. Robinson, Whitney R. Engel, Stephanie M. Konrad, Charles E. Richardson, David B. Horney, Jennifer A. |
author_facet | Grabich, Shannon C. Robinson, Whitney R. Engel, Stephanie M. Konrad, Charles E. Richardson, David B. Horney, Jennifer A. |
author_sort | Grabich, Shannon C. |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12982-015-0042-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4688997 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-46889972015-12-24 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. Emerg Themes Epidemiol Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12982-015-0042-7) contains supplementary material, which is available to authorized users. BioMed Central 2015-12-22 /pmc/articles/PMC4688997/ /pubmed/26702293 http://dx.doi.org/10.1186/s12982-015-0042-7 Text en © Grabich et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Grabich, Shannon C. Robinson, Whitney R. Engel, Stephanie M. Konrad, Charles E. Richardson, David B. Horney, Jennifer A. County-level hurricane exposure and birth rates: application of difference-in-differences analysis for confounding control |
title | County-level hurricane exposure and birth rates: application of difference-in-differences analysis for confounding control |
title_full | County-level hurricane exposure and birth rates: application of difference-in-differences analysis for confounding control |
title_fullStr | County-level hurricane exposure and birth rates: application of difference-in-differences analysis for confounding control |
title_full_unstemmed | County-level hurricane exposure and birth rates: application of difference-in-differences analysis for confounding control |
title_short | County-level hurricane exposure and birth rates: application of difference-in-differences analysis for confounding control |
title_sort | county-level hurricane exposure and birth rates: application of difference-in-differences analysis for confounding control |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4688997/ https://www.ncbi.nlm.nih.gov/pubmed/26702293 http://dx.doi.org/10.1186/s12982-015-0042-7 |
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