ArticleAmerican journal of epidemiology2019
Spatiotemporal Error in Rainfall Data: Consequences for Epidemiologic Analysis of Waterborne Diseases.
Article in American journal of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed.
- Urban flooding as a biohazard risk: A call to address environmental and health inequities in tropical cities.PLoS neglected tropical diseases · 2026Article
- Variability of enteric pathogen infections by season and meteorological conditions in a low-income, urban setting in Mozambique.PLOS global public health · 2026Article
- Article
- Variability of enteric pathogen infections by season and meteorological conditions in a low-income, urban setting in Mozambique.medRxiv : the preprint server for health sciences · 2025Article
- Rainfall events and daily mortality across 645 global locations: two stage time series analysis.BMJ (Clinical research ed.) · 2024Article
- The Association Between Rainfall, Temperature, and Reported Drinking Water Source: A Multi-Country Analysis.GeoHealth · 2022Article
- Combined Sewer Overflows and Gastrointestinal Illness in Atlanta, 2002-2013: Evaluating the Impact of Infrastructure Improvements.Environmental health perspectives · 2022Article
- Article
- Heavy weather events, water quality and gastroenteritis in Norway.One health (Amsterdam, Netherlands) · 2021Article
- Meteorological factors and childhood diarrhea in Peru, 2005-2015: a time series analysis of historic associations, with implications for climate change.Environmental health : a global access science source · 2021Article
- Measuring Environmental Exposure to Enteric Pathogens in Low-Income Settings: Review and Recommendations of an Interdisciplinary Working Group.Environmental science & technology · 2020Review
- Heavy Rainfall Events and Diarrheal Diseases: The Role of Urban-Rural Geography.The American journal of tropical medicine and hygiene · 2020Article
- Assessing the magnitude and uncertainties of the burden of selected diseases attributable to extreme heat and extreme precipitation under a climate change scenario in Michigan for the period 2041-2070.Environmental health : a global access science source · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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.
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