Evaluation of Remotely Sensed Inundation Data Sets to Estimate Flood‐Associated Emergency Department Visits After Hurricane Harvey

Floods can increase the risk of adverse health outcomes through multiple pathways, including contamination of food and water. Remotely sensed (RS) inundation extents can help identify regions with expected heightened flood-related health risks, but variations across inundation data sets and their integration into health risk assessments may affect intervention targeting. We examined if the association between census tract (CT) flooding and intestinal infectious disease related emergency department (IID-ED) visits differed by RS-based exposure estimation methods. Two Hurricane Harvey Inundation data sets with different spatiotemporal resolutions were used to estimate CT-level exposure as percent land flooded and percent population flooded, yielding four exposure variables. These were linked to ED visits by residential CT, and the effect estimates for association between IID-ED visits and flooding were derived. A 10% increase in land flooded was associated with a 6% (1%-10%) higher risk of IID-ED visits, while percent population flooded was not significantly associated with IID-ED visits. No statistically significant differences were found in the effect estimates between the inundation data sets or the exposure representation methods. Combining data sets to identify flooded CTs improved model fitness compared to using either alone, indicating a 1.30 (1.16-1.45) times greater risk of IID-ED visits in flooded CTs compared to non-flooded CTs. CTs where the data sets disagreed also showed a 25% (8%-10%) higher risk of IID-ED visits compared to the mutually agreed non-flooded CTs. Combining remotely sensed inundation data sets of different specifications can address limitations of individual products and improve identifying intervention areas to mitigate flood-related health risks.