Abstract
BACKGROUND AND AIM: Climate change and extreme weather are predicted to have a significant impact on diarrheal diseases. However, rainfall depends on local conditions and studies use varied measurements (e.g., absolute rainfall, heavy rainfall, or antecedent conditions) to define exposures. We systematically explored the influence of different rainfall measures on the association between acute gastrointestinal illness (AGI) and rainfall in North Carolina. METHODS: Common measures of rainfall were derived from recent studies (e.g., absolute rainfall (mm); heavy rainfall (90th, 95th, 99th percentile) and constructed from PRISM gridded daily weather data spatially aggregated to the ZIP code level. Rate ratios between rainfall (lagged 0-7 days) and AGI were estimated using quasi-Poisson time series models. All-cause AGI was defined by the daily emergency department (ED) visits per ZIP code using ICD-9 diagnosis codes from North Carolina’s syndromic surveillance system (2008-2015). Unadjusted and adjusted (for mean temperature and relative humidity) model estimates and goodness-of-fit measures were compared across rainfall measures. RESULTS: Between 2008-2015, there were 1.07M ZIP code-days with at least one ED visits for all-cause AGI per ZIP code per day in North Carolina. Adjusting for lagged mean temperature and relative humidity, we observed a 4.2% increase (RR=1.035; 95% CI: 1.029-1.040) and 5.1% (1.051; 1.035-1.067) in AGI ED visits respectively following lagged 90th and 99th percentile rainfall (1 day lag), with similar patterns for lags 2-7. By contrast, a 0.1% change in AGI ED visits was associated with a 1 mm increase in rainfall depending on the lag (e.g., lag 1: 1.001-1.001, 1.002 vs. lag 7: 0.999; 0.999-1.000). CONCLUSIONS: Heavy rainfall was associated with an increase in AGI rates, but absolute rainfall had a much smaller and ambiguous effect. These results suggest the importance of rainfall measurement and adjustment for other weather variables. KEYWORDS: diarrhea, rainfall, weather, climate, time series, exposure measurement
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