Drinking water residence time in distribution networks and emergency department visits for gastrointestinal illness in Metro Atlanta, Georgia

Abstract

We examined whether the average water residence time, the time it takes water to travel from the treatment plant to the user, for a zip code was related to the proportion of emergency department (ED) visits for gastrointestinal (GI) illness among residents of that zip code. Individual-level ED data were collected from all hospitals located in the five-county metro Atlanta area from 1993 to 2004. Two of the largest water utilities in the area, together serving 1.7 million people, were considered. People served by these utilities had almost three million total ED visits, 164,937 of them for GI illness. The relationship between water residence time and risk for GI illness was assessed using logistic regression, controlling for potential confounding factors, including patient age and markers of socioeconomic status (SES). We observed a modestly increased risk for GI illness for residents of zip codes with the longest water residence times compared to intermediate residence times (odds ratio (OR) for Utility 1 = 1.07, 95% confidence interval (CI) = 1.03, 1.10; OR for Utility 2 = 1.05, 95% CI = 1.02, 1.08). The results suggest that drinking water contamination in the distribution system may contribute to the burden of endemic GI illness.

Refined assessment of associations between drinking water residence time and emergency department visits for gastrointestinal illness in Metro Atlanta, Georgia

“…We consider WRT as a proxy for microorganism intrusion into the distribution system, because when it takes longer for water to reach the consumer there is a higher probability of an intrusion event occurring. As hypothesized, we observed a modest increased risk for GI illness, approximately five to seven percent, among people living in ZIP codes served by water with a long average WRT (top 10 percent) compared to intermediate residence times (11 th to 89 th percentile), after controlling for potential confounding factors such as patient age and markers of socioeconomic status (Tinker et al 2009). …”

“…The current analysis was based on a subset of our overall database, and a subset of those records analyzed by Tinker et al (2009). In this analysis, records were selected for inclusion for patients who visited any of the participating hospital EDs during the study period, had full residential address data available, and resided in the service area of the selected water companies at the time of the ED visit.…”

“…In this analysis, records were selected for inclusion for patients who visited any of the participating hospital EDs during the study period, had full residential address data available, and resided in the service area of the selected water companies at the time of the ED visit. Our initial selection resulted in 1,772,787 records from 15 hospitals for which full residential address information was available and which were made by patients with residential ZIP codes in either the Utility 1 (884,643 visits) or Utility 2 (888,144 visits) service areas, as defined by Tinker et al (2009). This dataset differs from that used in the Tinker et al (2009) analysis, which included 2,092,735 records from 27 hospitals, because that analysis was not restricted by a requirement for full residential address information.…”

“…Our initial selection resulted in 1,772,787 records from 15 hospitals for which full residential address information was available and which were made by patients with residential ZIP codes in either the Utility 1 (884,643 visits) or Utility 2 (888,144 visits) service areas, as defined by Tinker et al (2009). This dataset differs from that used in the Tinker et al (2009) analysis, which included 2,092,735 records from 27 hospitals, because that analysis was not restricted by a requirement for full residential address information. A flow chart of the data processing steps we followed is shown in Figure 1, and a comparison of the data used in the present analysis to the Tinker et al (2009) analysis is presented in Table 1.…”

“…Epidemiological studies can be used to evaluate the endemic burden of waterborne disease in the population, and to examine risks associated with distribution system deficiencies and intermittent contamination. A number of observational studies have found associations between GI illness and drinking tap water compromised by low pressure events (Hunter et al 2005; UK), main breaks or maintenance work (Nygard et al 2007; Norway), declines of residual chlorine concentrations (Egorov et al 2002; Russia), increased water age (Tinker et al 2009; USA), increased water turbidity (Egorov et al 2003; Russia; Tinker et al 2010; USA), water system outages (CDC 2011; USA), and virus detection in non-disinfected groundwater (Borchardt et al 2012; USA). On the other hand, Malm et al (2013; Sweden) found no evidence of increased complaints of GI illness associated with disturbances at the water works or in the distribution network compared to control periods without disturbances in the same geographical area, in a study using Swedish national Health Call Center data.…”