Associations between Flood Risk and United States Census Tract-Level Health Outcomes.

Bayesian spatial analysis flood risk health outcomes social vulnerability

Journal

American journal of epidemiology
ISSN: 1476-6256
Titre abrégé: Am J Epidemiol
Pays: United States
ID NLM: 7910653

Informations de publication

Date de publication:
06 Jun 2024
Historique:
received: 18 05 2023
revised: 06 04 2024
medline: 7 6 2024
pubmed: 7 6 2024
entrez: 6 6 2024
Statut: aheadofprint

Résumé

Human-induced climate change has led to more frequent and severe flooding throughout the globe. We examined the association between flood risk and the prevalence of coronary heart disease, high blood pressure, asthma, and poor mental health in the UnitedStates, while taking into account different levels of social vulnerability. We aggregated flood risk variables from First Street Foundation by census tract and used principal component analysis to derive a set of five interpretable flood risk factors. The dependent variables were census-tract level disease prevalences generated by the Centers for Disease Control and Prevention. Bayesian spatial conditional autoregressive models were fit on this data to quantify the relationship between flood risk and health outcomes under different stratifications of social vulnerability. We showed that three flood risk principal components had small but significant associations with each of the health outcomes, across the different stratifications of social vulnerability. Our analysis gives the first United States-wide estimates of the associated effects of flood risk on specific health outcomes. We also show that social vulnerability is an important moderator of the relationship between flood risk and health outcomes. Our approach can be extended to other ecological studies that examine the health impacts of climate hazards.

Identifiants

pubmed: 38844537
pii: 7689063
doi: 10.1093/aje/kwae093
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2024.

Auteurs

Alvin Sheng (A)

North Carolina State University, Department of Statistics.

Brian J Reich (BJ)

North Carolina State University, Department of Statistics.

Kyle P Messier (KP)

National Institute of Environmental Health Sciences, Division of Translational Toxicology, Predictive Toxicology Branch and National Institute of Environmental Health Sciences, Division of Intramural Research, Biostatistics and Computational Biology Branch.

Classifications MeSH