Assessing the Association Between Combined Sewer Overflow Events and Acute Gastrointestinal Illness in the Merrimack Valley of Northern Massachusetts

Combined sewer systems (CSS) are common wastewater infrastructure features that exist in more than 700 US cities primarily in the Northeast and Midwest. In CSS, residential sewage, industrial waste, and stormwater are collected in a common pipe and flow together to a wastewater treatment facility. CSS discharge untreated combined wastewater directly into rivers and lakes during heavy rainfall events when the volume of water in the collection pipe exceeds treatment capacity. Discharge events—combined sewer overflow (CSO) events—are driven by intensity and frequency of precipitation and introduce microbial pathogens and chemical pollutants to surface waterbodies, posing a risk to downstream communities. Observed and predicted increases in total precipitation and frequency of heavy rainfall events in regions containing CSS underscore the importance of understanding associated health risks. The objective of this study is to evaluate the relationship between CSO events and acute gastrointestinal illness (AGI) in communities that source their drinking water downstream from CSS outfalls. The Merrimack River (New Hampshire and northern Massachusetts) receives CSS discharge from five communities, three of which are upstream of drinking water intakes that collectively supply water for over 500,000 people. We assess the association between upstream CSO events and emergency room (ER) visits for AGI depending on primary community drinking water source over the study period (2014-2019). We also evaluate the relationship between heavy precipitation and AGI to determine whether CSO activation or precipitation is a stronger predictor of AGI in communities with a CSO-impaired drinking water source. Community-level exposure status is assigned based on drinking water source, and individual-level data on ER visits for AGI are used to determine community-level counts of AGI. We use a Poisson regression model with a distributed lag nonlinear regression framework, which allows for temporally lagged and nonlinear associations between exposure and outcome of emergency room visits for AGI. We present our findings along with implications for CSO mitigation, source water protection, and health in the Merrimack region under increasing climate pressure.