mycobacteria risks from water and ice machines 793
regularly flushed the new hospital water system during pre- occupancy to bring fresh chloramine-treated water through the plumbing. Water was sampled. Plumbing fixtures were func- tional 1 year prior to patient occupancy. Drinking water and ice machines (DWIMs) were installed in the new hospital 2 weeks prior to patient occupancy. Despite these efforts, pediatric HSCT patients became colonized with RGM at an increased incidence soon after the new hospital opened.3 These clinical cases of these 15 patients have been described in a separate report.3 We sought to determine the cause of the outbreak and to
eradicate the source of infection. Nontuberculous myco- bacteria (NTM) are relatively resistant to chloramine and are present in many municipal water supplies; a US Environ- mental Protection Agency (EPA) survey found mycobacteria in 61% of hospital water samples.4–6 NTM cause nosocomial outbreaks and pseudo-outbreaks.7 In contrast to the RGM bacteremia outbreak in our old hospital, this recent outbreak resulted primarily in positive RGM cultures from sputum, throat, or gastrointestinal sites.2,3 Therefore, we investigated potential ingested water sources. Water and ice from DWIMs quickly became the focus of our investigation in the new hospital, and we compared these results with results from the DWIMs in the old building.
methods
samples from the 4 DWIMs: (1) the first water from the machine (ie, first-drop water), (2) water collected after the line was allowed to flushed for 30 seconds (ie, 30-second water), and (3) water collected from melted ice that was collected from the ice machine dispenser (ie, ice). Standard methods were used for the evaluation of water.8 Samples were collected in sterile cups containing sodium thiosulfate to neutralize chlorine at time of collection. Serial dilutions were run through a 0.45-μmmem- brane filter placed on Reasoner’s 2 agar and incubated at 35°C for 7 days before colony-forming units (CFU/mL) were counted. The 7-day incubation was chosen because RGM colonies are not visible after 48 hours of incubation. To identify a strategy for ameliorating bacterial burden and
From April 2012 to December 2014,>5,000 water samples were collected from the city water supply entering the new hospital. We tested drinking water from the hospital domestic water system (ie, shower, hand-wash sink, and patient sink) and surge tanks, water hammer arrestors, and points leading to and inside the 4 DWIMs that served the pediatric HSCT patients: 2 from the blood and marrow transplant (BMT) ward and 2 from the pediatric intensive care unit. On an approximately weekly basis, we tested 3 types of water
escalation in the 4 DWIMs, a number of strategies were tested: (1) flushing ice and water frequently, (2) using 0.005-, 0.15-, and 0.20-µm water filters, (2) cleaning and disinfecting DWIMs, (3) changing piping to copper, (4) installing silver- impregnated machine components and silver filters, and (5) ultraviolet germicidal irradiation and ozone disinfection
(UV/ozone). Water and ice samples were collected before and after these interventions, samples were cultured, and HPCs were determined and compared (Table 1). Thresholds for these interventions were chosen based on the
water and ice HPCs from DWIMs in the old building because those levels had been safe for ingestion by the HSCT patients, and neither RGM bacteremia nor increased amounts of RGM in sputum or gastrointestinal tract cultures had been observed. At first, the target thresholds for total HPC were 500CFU/mL at 48 hours of incubation (ie, the EPA drinking water standard).9 We changed the thresholds (after 7 days incuba- tion) to <500CFU/mL as the goal, with 1,000CFU/mL as the target and 4,000CFU/mL as the action point.
Statistical Methods
For first-drop, 30-second, and ice water samples from each DWIM, the log-CFU was used due to the large positive skew for the distribution of CFU. For each DWIM, we recorded sample type, intervention, HPC, and percentage by which the sample exceeded the threshold (Table 1). To estimate changes in the mean log-CFU by intervention
type, a first-order, autoregressive, segmented regression model was used to estimate the mean level of change for each inter- vention type separately for eachDWIMand location (Table 1). The segmented interventions were included as binary indica- tors. Interventions were believed to be immediate and rela- tively constant due to the recurring filter replacement schedule; therefore, slope changes were not parameterized and estimated. Seasonality was not believed to affect drinking water; this hypothesis was corroborated by comparing the Akaike Information Criterion (AIC) when fitting a model with and without a seasonal component. The mean change for each main effect and a 95% confidence interval (CI) were calculated. The models assumed an autoregressive-1 (AR1) correlation over time. Descriptive scatter plots were used to assess the impact of each intervention on log-CFU from each sample; Friedman’s smooth lines were created to examine trends in the log-CFU over time. We analyzed the main effects of the following interventions:
silver-impregnated components and silver filter, and UV and ozone disinfection for machine 3, and (4) 3 filter sizes and silver- impregnated components and silver filter for machine 4. For machine 1, the single observation labeled “nonflushing” was removed because it was the sole observation of this type. Simi- larly, a single observation labeled “nonsilver” was removed from machine 2 analysis.
results
Between July 2011 and April 2012, 15 patients developedRGM colonization (n = 10) and/or infection (n = 6) (Figure 1).3 A 2-year retrospective revealed (1) no positive RGM cultures in this patient population and (2) a significant rate difference
(1) UV and ozone disinfection on machine 1, (2) a 0.005-µm filter combined with flushing for machine 2, (3) 3 filter sizes,
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