Seasonal Associations Between Foodborne Campylobacter Infections and Ambient Temperature in US, 2010–2019

Abstract

Abstract Objectives In order to effectively mitigate the effects of climate change on human health, spatiotemporal relationships between Campylobacter infections and environmental drivers have to be systematically examined to determine whether common seasonal summer peaks observed in Campylobacter infections are well aligned with peaks in raising ambient temperatures. This study aims to evaluate the seasonal relationship between monthly ambient temperature and the monthly Campylobacter infections routinely collected by the CDC Foodborne Diseases Active Surveillance Network (FoodNet) in the United States in 2010–2019. Methods We created time series of monthly Campylobacter infection rates from the FoodNet Fast platform for ten participating states from January 2010 to December 2019 (120 months). We estimated average monthly temperatures for the ten states for the study period using the National Climate and Data Center's Global Summary of the Day database. To assess the seasonal synchronization and determine the lag effect, we used serial cross-correlation analysis. To examine the associations between Campylobacter rates with ambient temperature and adjust for seasonality and trend, we applied a harmonic negative binomial mixed-effects regression model. We also estimated peak timing and amplitude for infections and temperature using the δ-method. Results Serial synchronization between monthly Campylobacter infection rates and ambient temperature was observed in all FoodNet Surveillance states except for California. A one-month delay in peak infection after the peak in temperature was detected in Colorado, Connecticut, Maryland, Minnesota, New Mexico, New York, and Oregon. After adjusting for seasonality and trend across ten states, the relative risk of infection rates increased by 5% with the increase in monthly average temperature by 5.45°C equivalent to a shift from 75th to 95th percentile (95%CI: 1.02–1.09, p < 0.002). Conclusions An increase in monthly average temperature is associated with an increased risk of Campylobacter infection after adjusting for common summer seasonal patterns. Knowledge of the relationship will potentially improve the reliability and accuracy of integrated early warning outbreak forecasts and could guide climate mitigation strategies. Funding Sources None.