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The three different risk assessment methods yield significantly different estimates of ED AGI visit rates attributable to microbial contamination in CWSs (Table 3.2), although they tended to show similar geospatial distributions when mapped according to within-method risk percentiles. The DWAR method estimated that 32,200 (95% CI: 31,200-33,100) AGI ED visits annually may be attributed to microbial contamination in NC CWSs. This estimate is one order of magnitude higher than the rate estimated with the QMRA method, which attributed 4,000 (95% CI: 2,900-5,400)

annual ED visits to microbial contamination of drinking water. The QMRA method risk estimate is an order of magnitude higher than the estimate from the PIM method, which attributed 380 (95% CI: 150-630) annual ED AGI visits to microbial CWS contamination. Notably, none of the estimated 95% confidence intervals overlapped.

The PIM and QMRA models appeared to capture much more spatial variability than the DWAR method, with the PIM method capturing the most variability. Reflecting this increased capability to reflect variability, the coefficient of variation (standard deviation divided by the mean) of the PIM risk estimates across all the counties was 1.6, in comparison to 1.1 and 0.5 for the QMRA and DWAR methods, respectively (for estimates by county, see Table 3.S3). Using the PIM

approach, the mean estimated per-person annual risks ranged across counties from 0 to 0.00018 visits per person-year; the QMRA yielded county-level risks ranging from 0 to 0.0025 visits per person- year; and the DWAR model estimated risks ranging across counties from 0.0011 to 0.013 per person- year. Notably, the DWAR method, unlike the other two methods, suggests that in all counties, the per-person risk exceeds zero.

Despite the differences in the magnitude of the risk estimates among methods, all three showed similar geospatial distributions across the state (Figures 3.1, 3.S8, 3.S9). Specifically, all three methods identified 13 counties in the upper quartile of estimated risks; these counties are Anson, Beaufort, Bertie, Carteret, Chowan, Columbus, Craven, Edgecombe, Onslow, Pamlico, Perquimans, Wake, and Wayne (Table 3.S3). Eight of these 13 counties are located in the eastern-northeastern region of the state.

3.3.4 Sensitivity Analysis

A sensitivity analysis conducted by changing PIM model parameters to their lower and then upper 95% confidence interval (CI) values while leaving other parameters unchanged showed that the PIM estimates are most sensitive to uncertainty in the regression parameter describing the relationship between ED visits for AGI and monthly MCL violations (Figure 3.S10). Changing the parameter to

its lower 95% CI value decreased the estimated ED visits attributable to CWSs from 380 to 150, while increasing the parameter to its upper 95% CI value increased the estimated visits to more than 600. All other variables had a much smaller effect on the estimated risks. This result suggests that credible data on CWS compliance with microbiological standards are key to using the PIM approach to estimate AGI risks attributable to CWSs.

A similar sensitivity analysis indicated that the DWAR model is most sensitive to uncertainty in the function relating MCL violation rates to the fraction of AGI cases attributable to CWSs (Figure 3.S11). As previously discussed, Messner et al. developed this function from previous randomized, controlled trials in which some homes were provided with drinking water treatment systems and others with sham systems. Using the lower or upper 95% CIs of this function changed the estimated number of attributable AGI visits from 32,200 to about 11,000 and 55,000, respectively.

Finally, QMRA risk estimates are most sensitive to the uncertainty in the estimated total coliform concentrations in each CWS in each month, which are used to estimate pathogen

concentrations (Figure 3.S12). Changing these parameters for each CWS to their lower or upper 95% CI values changes the attributable AGI visits from 4,000 to 260 and 27,000, respectively. The results are generally insensitive to uncertainty in all other model parameters. Hence, the QMRA estimates could be improved if CWSs reported coliform concentration data, rather than just presence/absence data.

3.4 Discussion

The PIM approach to characterizing AGI risks attributable to drinking water required developing a multivariate linear regression model to predict the number of AGI ED visits in each county each month from demographic variables and data on the microbiological quality of CWSs and private wells. In conducting the regression analysis, we found significant associations between microbiological water quality and rates of ED visits for AGI. Counties with higher risks of exposure

to total coliform bacteria and/or E. coli in drinking water showed higher rates of AGI visits than counties with lower exposure risks. These effects were two orders of magnitude stronger for private wells than for CWSs, suggesting that CWSs provide important protective benefits against AGI, in comparison to private wells.

We found that the estimated AGI risk attributable to CWSs varies highly depending on the risk estimation method used. The mean values of estimated annual AGI ED visits were 380, 4,000, and 32,200 for the PIM, QMRA, and DWAR methods, respectively. All methods showed similar spatial patterns of risk, however, with counties in eastern and northeastern NC generally at higher risk than other counties. This spatial variability is mostly likely due to the comparatively high MCL violation rates (Figures 3.S4 and 3.S6) in these counties, in comparison to other parts of the state.