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The Texas flood registry: a flexible tool for environmental and public health practitioners and researchers.
Miranda, Marie Lynn; Callender, Rashida; Canales, Joally M; Craft, Elena; Ensor, Katherine B; Grossman, Max; Hopkins, Loren; Johnston, Jocelyn; Shah, Umair; Tootoo, Joshua.
Afiliación
  • Miranda ML; Children's Environmental Health Initiative, University of Notre Dame, South Bend, IN, USA. mlm@nd.edu.
  • Callender R; Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, South Bend, IN, USA. mlm@nd.edu.
  • Canales JM; Children's Environmental Health Initiative, Rice University, Houston, TX, USA.
  • Craft E; Children's Environmental Health Initiative, Rice University, Houston, TX, USA.
  • Ensor KB; Environmental Defense Fund, Austin, TX, USA.
  • Grossman M; Department of Statistics, Rice University, Houston, TX, USA.
  • Hopkins L; Children's Environmental Health Initiative, University of Notre Dame, South Bend, IN, USA.
  • Johnston J; Department of Statistics, Rice University, Houston, TX, USA.
  • Shah U; Houston Health Department, Houston, TX, USA.
  • Tootoo J; Children's Environmental Health Initiative, University of Notre Dame, South Bend, IN, USA.
J Expo Sci Environ Epidemiol ; 31(5): 823-831, 2021 09.
Article en En | MEDLINE | ID: mdl-34175888
ABSTRACT

BACKGROUND:

Making landfall in Rockport, Texas in August 2017, Hurricane Harvey resulted in unprecedented flooding, displacing tens of thousands of people, and creating environmental hazards and exposures for many more.

OBJECTIVE:

We describe a collaborative project to establish the Texas Flood Registry to track the health and housing impacts of major flooding events.

METHODS:

Those who enroll in the registry answer retrospective questions regarding the impact of storms on their health and housing status. We recruit both those who did and did not flood during storm events to enable key comparisons. We leverage partnerships with multiple local health departments, community groups, and media outlets to recruit broadly. We performed a preliminary analysis using multivariable logistic regression and a binomial Bayesian conditional autoregressive (CAR) spatial model.

RESULTS:

We find that those whose homes flooded, or who came into direct skin contact with flood water, are more likely to experience a series of self-reported health effects. Median household income is inversely related to adverse health effects, and spatial analysis provides important insights within the modeling approach.

SIGNIFICANCE:

Global climate change is likely to increase the number and intensity of rainfall events, resulting in additional health burdens. Population-level data on the health and housing impacts of major flooding events is imperative in preparing for our planet's future.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Salud Pública / Inundaciones Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: America do norte Idioma: En Revista: J Expo Sci Environ Epidemiol Asunto de la revista: EPIDEMIOLOGIA / SAUDE AMBIENTAL Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Salud Pública / Inundaciones Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: America do norte Idioma: En Revista: J Expo Sci Environ Epidemiol Asunto de la revista: EPIDEMIOLOGIA / SAUDE AMBIENTAL Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos