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

The aging water delivery infrastructure is becoming a growing issue in the United States, with deferred maintenance creating a system that is ‘deeply stressed,’ ‘over-worked and under-budgeted’, according to the US Environmental Protection Agency (USEPA 2011a). There are an estimated 240,000 water main breaks per year in the USA, and the number of breaks increases substantially near the end of the system's service life (USEPA 2011b). Pipe breaks and/or low and negative pressure events in the drinking water distribution system can result in intrusion of pathogenic microorganisms if an external source of contamination is present (Besner et al. 2011).

Several outbreaks of waterborne disease have been attributed to public drinking water systems, including the largest reported waterborne disease outbreak ever documented, in Milwaukee, Wisconsin, in 1993, with over 400,000 people affected (MacKenzie et al. 1995). Between 1971 and 2006, 282 outbreaks of acute gastrointestinal (GI) illness in community water systems were reported to the US Waterborne Disease and Outbreak Surveillance System (WBDOSS). Contamination within the distribution system and in-premise plumbing accounted for 79 (9.9%) and 65 (8.1%), respectively, of the non-legionellosis waterborne disease outbreaks where a deficiency in the water system could be identified. While there has been a decrease in the number of reported outbreaks over time, in this period there was no change in the annual proportion of outbreaks associated with distribution system deficiencies (Craun et al. 2010). Between 2009 and 2010, distribution system deficiencies accounted for 15.2% (5/33) of identified outbreak deficiencies in drinking water-associated disease outbreaks (CDC 2013).

However, outbreaks of waterborne disease represent only the disease events that impact a large enough number of people within a relatively short period of time to be recognized by public health agencies. Linking illness to drinking water is inherently difficult through outbreak investigation methods, because most persons have daily exposure to tap water (Tostmann et al. 2012; CDC 2013). 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 with control periods without disturbances in the same geographical area, in a study using Swedish national Health Call Center data. A recent review of the impact of distribution system deficiencies on endemic GI illness found that tap water consumption in malfunctioning distribution networks and system deficiencies, such as water outages, were both associated with GI disease (Ercumen et al. 2014). Risk assessment models also suggest that distribution systems can be a source of GI illness (Teunis et al. 2010; USA; Lambertini et al. 2012; USA).

Intervention studies comparing homes with water treatment devices versus those without have had mixed results, with some reporting increased risk of GI illness attributable to drinking tap water (Payment et al. 1991, 1997; Canada), some reporting increased risk only in sensitive sub-populations (Colford et al. 2009; USA), and others reporting no difference (Hellard et al. 2001; Australia; Colford et al. 2005; USA). In their meta-analysis, Ercumen et al. (2014) found elevated risk of GI illness for consumers of tap water versus point-of-use treated water in unblinded studies, but no association for studies that blinded participants to their point-of-use water treatment status.

Epidemiological studies of distribution system contributions to GI illness are challenging to design and implement because of the difficulty in estimating exposure to waterborne pathogens, detecting the relevant health outcomes, and controlling for the effect of confounding factors. Our group has taken advantage of an extensive dataset of emergency department (ED) visits and detailed information on water distribution systems in Metro Atlanta, GA to address this challenge. We previously conducted an analysis of the associations between ED visits for acute GI illness and water residence time (WRT) in the distribution system, as estimated using hydraulic models developed by two water utilities. 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 5–7%, among people living in ZIP codes served by water with a long average WRT (top 10%) compared with intermediate residence times (11th to 89th percentile), after controlling for potential confounding factors such as patient age and markers of socioeconomic status (Tinker et al. 2009).

In this previous analysis, we based the WRT exposure assignment on a ZIP code average level. However, important variation in WRT likely exists within ZIP codes, as the lengths of water pipes between the fixed-location water treatment plants and homes vary, depending on the size of the ZIP code and the location of homes. Because we are ultimately interested in the water that reaches the end user, we hypothesized that a more spatially refined characterization of WRT reduces exposure misclassification and thus may reduce any bias to the null of our previous epidemiologic findings. Therefore, in the analysis presented here, we refined our exposure metric by estimating patient-level WRT based on patients' residential address and proximity to the nearest distribution system node in a calibrated hydraulic model of the distribution system.

The greater Atlanta metropolitan area is served by six major water utilities. The water treatment technology, age of the infrastructure, size of the service area, demographics of the population served, and various other factors differ among these utilities. In this way, Atlanta provides a good example of the range of conditions that exist in drinking water distribution systems in major US cities. This study focuses on two utilities in the greater Atlanta area. Utility 1 and Utility 2 each operate two treatment plants that supply water to their respective distribution pipeline networks. Utility 1 serves 680,000 customers over a 650 square mile (1,680 km²) area, and Utility 2 serves 1.2 million customers over a 348 square mile (900 km²) area. The demographics of the populations served differ substantially: Utility 1 serves a 65% Caucasian population with an average Census 2000 block-level median household income of $60,569 (s.d. $20,017), whereas Utility 2 serves an 85% African American population with an average Census 2000 block-level median household income of $28,894 (s.d. $16,674).

Our ED database comprised over 10 million ED visit records from hospitals in the 20-county Atlanta metropolitan area during 1993–2004, in which 41 of 42 acute care hospitals provided data for all or part of the study period. Relevant data elements for the current analysis included: patient medical record number, unique visit number, date of admission, primary and secondary International Classification of Diseases, Ninth Revision (ICD-9) diagnostic codes, patient age, date of birth, gender, race, ZIP code of residence, residential street address, and method of payment for the visit (e.g. Medicaid).

Our GI illness outcome encompassed ED visits for which the primary, or any available secondary ICD-9, code had one of the following diagnoses: infectious GI illness (001–004, 005.0, 005.4, 005.89, 005.89, 005.9, 006–007, 008.0, 008.42–008.44, 008.47, 008.49, 008.5, 008.6, 008.8, 009), non-infectious GI illness (558.9), and nausea and vomiting plausibly related to GI illness (787.01–787.03, 787.91). Non-infectious GI illness was included in the case definition because previous research has shown that many infectious cases of GI illness are misclassified into this diagnostic category (Lew et al. 1991; Gangarosa et al. 1992; Schwartz et al. 1997). The control group included non-injury ED visits without GI illness. The spatial distribution of ED usage for injuries might be different from the spatial distribution of the source population, so we excluded ED visits for injuries in the comparison group in an effort to better track the spatial distribution of the source population (i.e. those that would go to the ED for a GI illness if they had a GI illness). Repeat visits by a patient within a single day were counted as a single visit.

This study protocol was approved by the Social, Humanist, and Behavioral Committee for the Protection of Human Subjects, by the Institutional Review Board at Emory University, Atlanta, GA, USA (Protocol #IRB00045761).

WRT was estimated using extended period hydraulic models provided by the two water utilities. These models estimate the water travel times to all pipe intersections (‘nodes’), which represents a flow-weighted average time for all travel paths between the treatment plant and the node within the distribution system pipe network. Further details of water age calculations are provided in Tinker et al. (2009). Tinker et al. (2009) utilized a usage-weighted average of the node WRT by ZIP code. Here we used individual node-level WRT estimates. WRT was estimated for Utility 1 for 1999–2003 and Utility 2 for 1993–2004.

All ED visit records were geocoded using patient residential address information to enable assignment of WRT at the closest node in the water distribution system. Geocoding was conducted using a standard geocoding algorithm in Streetmap for ArcGIS version 9.2 (ESRI, Redlands, CA). Records with invalid address information and observations with a geocoding confidence score of <50% were removed (Figure 1).

We joined node-level WRT data, geocoded ED visit data, and census block-level median household income and percentage minority data from the 2000 US Census (United States Census Bureau 2000) in ArcMap version 9.3 (ESRI, Redlands, CA), separately for each of the two service areas. The joining function assigned the nearest water distribution system node to each ED patient residence and calculated the Euclidean distance for each pairing. At this step, we excluded ED visits with residences that were located outside of the service area, as defined by geographic boundary data obtained from each utility (Figure 1). This addressed the issue that in addition to serving individual household customers directly, both utilities sell their water to several wholesale city customers that are located within the service areas, but whose water nodes are not included in the hydraulic models.

Separate models were run for Utility 1 and Utility 2. First, WRT was modeled in three categories, with apriori cut points: ≤10th percentile, 10th–90th percentile, ≥90th percentile. The middle category (10th–90th percentile) was the reference category, following Tinker et al. (2009). We also modeled WRT for Utility 2 using the cut points for Utility 1 as a comparison, in order to compare the same absolute levels for WRT decile categorization. Second, for ease of interpretation for water systems engineers, WRT was modeled using absolute number of hours, in nine categories of 12-hour increments, with <12 h as the reference category.

Control variables (covariates) included: Medicaid status (patient level indicator – yes, no, or missing), median household income (census block level – continuous), percentage minority (census block level – continuous), age (patient level indicator with four levels: 0–5, 6–18, 19–64, ≥65), Euclidean distance between the hospital where the ED visit occurred and the geocoded patient residence (patient level indicator with three levels: <20th, 20th–80th, >80th percentile), hospital indicator variables denoting the hospital in which the ED visit occurred, year indicator variables, day-of-week indicator variables, season indicator variable, and product terms between patient age and hospital, patient age and distance to hospital, and patient age and Medicaid status. Control variables were chosen on the basis of being potential risk factors for GI illness that also vary spatially, and thus could also be associated with WRT.

For both utilities, we also used a logistic regression model to compare the odds of ED patients having GI illness as a function of straight-line Euclidian distance between x-y coordinates from households to nodes, controlling for the same set of variables as described above. We compared distances of 30–150 m and > 150 m with a reference category of <30 m.

Frequencies of visits for GI illness among non-injury ED visits during the study periods are shown in Table 1. The geocoding procedure resulted in 698,705 visits with valid geocodes for Utility 1 and 708,330 visits with valid geocodes for Utility 2. This represents an overall success rate of 80.6%. Total numbers of visits and nodes included in the analysis are presented in Table 1. Geocoded data were available for only approximately half of the hospitals and total visits included in the analysis by Tinker et al. (2009). Utility 1 had a significantly higher average WRT than Utility 2 (p < 0.0001), with a mean of 55.9 h (range: 0.51–336 h) versus 16.1 h (range: 0.24–336 h), as expected given the difference in the size of their service areas.

Because the cut point for long WRT was substantially greater for Utility 1 than for Utility 2, and because we observed stronger associations with GI ED visits for Utility 1, we also considered the decile cut points for Utility 1 in the analysis for Utility 2. This allowed for a direct comparison of the two utilities relative to the same WRT categorization. This analysis showed elevated risk for longer WRT, but with a very large confidence interval for the long WRT category risk ratio.

We also evaluated the odds of increased risk of GI illness ED visits with Euclidian distance between geocoded patient residences and the location of the nearest distribution system node (Table 2). For Utility 1, we observed an increased odds of GI illness for patients living >150 m from the nearest node, compared with a reference category of <30 m (OR = 1.09, 95% CI: 1.03–1.15).

Ultimately, exposure to microbial contamination within the distribution system is dependent upon the occurrence of low-pressure conditions, the presence of a source of contamination, and the existence of a pathway for entry of external contaminants, which together can create the necessary conditions for intrusion events. The concentrations of microbes and duration of intrusion events are key factors influencing exposure (Besner et al. 2011). We use WRT here as a proxy for a large number of processes within the drinking water distribution system that determine whether a person opens their tap at the time when a contaminant is passing through and drinks that water. Our assumption is that longer WRT increases the probability of a number of intrusion events occurring.

We conducted this analysis to explore how a more geographically resolved exposure assignment might affect the associations between WRT and ED visits for GI illness observed by Tinker et al. (2009), who assigned aggregated WRT by ZIP code. We performed this analysis for two drinking water utilities in the greater Atlanta metropolitan area, with different demographic and distribution system characteristics. In particular, Utility 1 had a substantially higher average WRT compared with Utility 2.

Comparing long WRT (≥90th percentile) with intermediate residence times (11th to 89th percentile), we observed a modestly increased risk for GI illness for Utility 1 (OR = 1.07, 95% CI: 1.02–1.13) but not for Utility 2 (OR = 0.98, 95% CI: 0.94–1.02), whereas Tinker et al. (2009) observed a modestly increased risk for GI illness for both utilities (Utility 1: OR = 1.07, 95% CI: 1.03–1.10; Utility 2: OR = 1.05, 95% CI: 1.02–1.08). When we compared the same categories of WRT for both utilities (i.e. based on quintiles of WRT at Utility 1), we observed an increased odds of illness but with a large amount of uncertainty in the estimates (OR = 1.25, 95% CI: 0.96–1.64), because of the few WRT observations that occurred in the long WRT category at Utility 2 in this analysis. Fewer than 1% of the Utility 2 observations occurred in the highest 20th percentile of Utility 1 WRTs.

It is important to note that Tinker et al. (2009) made an a priori decision to use the intermediate WRT category as the reference group and to explore the possibility of increased risk related to short WRTs due to reduced contact time with disinfectant in the distribution system, in addition to exploring the possibility of increased risk related to longer WRTs due to increased probability of contamination within the distribution system. In order to maintain consistency between the analyses, we also used the intermediate WRT category as the reference group.

The analysis of 12-hour increments of WRT in relation to ED visits for GI illness provided additional insights that are more interpretable to water treatment plant operators. Examining these finer increments of WRT, we found that only exposures >48 h were associated with increased risk of GI illness, and exposures of >96 h had the strongest associations (Figure 3), although none of these relationships was statistically significant. While there was very little data for Utility 2 in the longest WRT categories, in both utilities we observed an upward trend in risk at the longest absolute WRTs. Again, these relationships were not statistically significant.

By comparison, Payment et al. (1991, 1997) reported WRT ranging from 0.3 h to 34 h in Montreal, in studies that found no correlation between WRT and highly credible GI illness. Hellard et al. (2001) reported average WRTs of 24–36 h in Melbourne, where no difference was found between groups with functional versus sham household water treatment devices. Thus, the distribution systems observed in these studies may not have reached values of WRT to cause elevated risk of GI illness. However, because WRT is a proxy for likelihood of pathogen intrusion, and each distribution system is unique, we would not necessarily expect the same absolute values across systems (e.g. an older system may have more intrusions within the same WRT relative to a newer system).

This analysis provided the opportunity to compare different approaches to WRT exposure assignment. The node-level WRT used here provided a more refined exposure assignment than WRT aggregated by ZIP code, and provided the opportunity for an individual-level, rather than ecological analysis. Aggregation of WRT estimates across nodes within a ZIP code smooths out heterogeneous exposures across an area. Thus, a small number of ZIP codes can have a large influence on the results, and might misrepresent the overall exposure for a particular ZIP code.

However, it is important to note that there were differences in the two datasets analyzed (Table 1), and our refined exposure assignment approach presented several limitations. Assigning node-level WRT to ED patients reduced the sample size available for the analysis as it required limiting the dataset to ED visits with patient residential address information; this reduced the number of hospitals and our total sample size by about half compared with the Tinker et al. (2009) analysis. More specific exposure assignment also opens up the possibility of errors related to address and node assignment. Assigning WRT to a specific node in the distribution system might assume too much specificity; i.e. the nearest node by Euclidian distance might not in fact be the node from which the household receives its water, due to complexities in the distribution system. In addition, individuals may consume water outside of the home, but still in the same general vicinity, for example at a local school or childcare center. More aggregated exposure assessment averages out these types of errors.

Our exposure assessment was also limited to describing WRT at distribution system nodes, rather than the tap, at the point of use. The ideal measure of WRT would describe time for water to reach the user at their residence. The availability of geocoded data for visitors to the ED, as well as the locations of nodes, allowed us to examine Euclidian distance between residence and node in the distribution system. We found that for Utility 1, ED visitors living >150 m from the nearest node in the distribution system had 9% increased odds (95% CI: 3–15%) of having GI illness compared with those living <30 m from the nearest node (Table 2). This analysis must be interpreted with caution, because Euclidian distance to a distribution system node does not necessarily represent whether a household receives water from that distribution system node. However, distance between households and drinking water delivery nodes might be an area to explore in future investigations.

It is also important to note that we had no data on water consumption patterns, so our analyses relied on the assumption that patients were consuming water directly from the distribution system, and we also had no information on quality of premise plumbing. Consumption of purchased water or point-of-use treatment of distribution system water would therefore affect the results we observed. Additionally, the assignment of WRT at each node is subject to error due to factors such as misspecifications of the hydraulic model, mixing of water of different ages, and assignment of only one WRT measurement per node per year.

Our results suggest that drinking water utilities might consider reducing absolute WRTs to <2–3 days or consider adding booster disinfection in areas of the distribution system with longer WRT in order to minimize the risk of GI illness from consumption of municipal water. Continued investment is needed to maintain the current quality of drinking water infrastructure.

Financial support was provided by United States Environmental Protection Agency (USEPA) Grants #R834250, #R831629, #R82921301; by National Institute of Environmental Health Sciences Grant #R01ES011294; by National Institute for Allergy and Infectious Diseases Grant #1K01AI103544; and by Electric Power Research Institute Grants #W03253-07, EP-P27723/C13172, EP-P4353/C2124. We would also like to thank the water utility collaborators who provided hydraulic models for the analysis. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the USEPA. Further, USEPA does not endorse the purchase of any commercial products or services mentioned in the publication.