Spatiotemporal Error in Rainfall Data: Consequences for Epidemiologic Analysis of Waterborne Diseases.
Am J Epidemiol
; 188(5): 950-959, 2019 05 01.
Article
em En
| MEDLINE
| ID: mdl-30689681
ABSTRACT
The relationship between rainfall, especially extreme rainfall, and increases in waterborne infectious diseases is widely reported in the literature. Most of this research, however, has not formally considered the impact of exposure measurement error contributed by the limited spatiotemporal fidelity of precipitation data. Here, we evaluate bias in effect estimates associated with exposure misclassification due to precipitation data fidelity, using extreme rainfall as an example. We accomplished this via a simulation study, followed by analysis of extreme rainfall and incident diarrheal disease in an epidemiologic study in Ecuador. We found that the limited fidelity typical of spatiotemporal rainfall data sets biases effect estimates towards the null. Use of spatial interpolations of rain-gauge data or satellite data biased estimated health effects due to extreme rainfall (occurrence) and wet conditions (accumulated totals) downwards by 35%-45%. Similar biases were evident in the Ecuadorian case study analysis, where spatial incompatibility between exposed populations and rain gauges resulted in the association between extreme rainfall and diarrheal disease incidence being approximately halved. These findings suggest that investigators should pay greater attention to limitations in using spatially heterogeneous environmental data sets to assign exposures in epidemiologic research.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Chuva
/
Doenças Transmitidas pela Água
/
Análise Espaço-Temporal
Limite:
Humans
País/Região como assunto:
America do sul
/
Ecuador
Idioma:
En
Revista:
Am J Epidemiol
Ano de publicação:
2019
Tipo de documento:
Article