Mitigation of opportunistic drinking water pathogens by onsite monochloramine disinfection in a hospital water system
Bibliographic record
Abstract
In acute care hospitals, susceptible patients and large, legacy water systems contribute to increased risk of nosocomial infections associated with drinking water pathogens. This study aimed to evaluate the long-term (>1-year) impact of onsite monochloramine treatment on Legionella pneumophila (Lp), nontuberculous mycobacteria (NTMs), Vermamoeba vermiformis (Vv), and physico-chemical water quality in a hospital hot water system. Using an innovative sampling approach, the efficacy of treatment was assessed at 22 distal sites (faucets, showerheads, handwashing stations) and compared to 10 control points representing the main flowing distribution system (return loops, heaters, remote sites). Monochloramine nearly eliminated Lp, achieving up to 3-log reductions in culturability (<24 h) and gene copies (4-week). Mean Vv concentrations decreased by 2-log within 24 h, with no evidence of a shift towards increased NTMs. Optimal reductions in all organisms were observed at monochloramine concentrations of 2-3 mg/L combined with temperatures exceeding 55 °C. However, these conditions were only consistently maintained at control points, where post-treatment mean concentrations were systematically 1-log lower than those at distal sites. The interruption of dosage (5-day and 4-week) also revealed significant and rapid rebounds of Legionella, NTMs, and Vv (>1-2-log), highlighting their persistence in biofilms. Short-term increases in metal release were observed, with mean copper and lead concentrations rising 1.8- and 4.6-fold, respectively. Overall, results confirmed the high and rapid efficacy of onsite monochloramine to control Lp and other organisms. Analysis of water quality, temperature distribution, and usage patterns emphasize the importance of maintaining optimized hydraulic and thermal regimes to ensure effective pathogen control at points of exposure. This study provides actionable insights and practical evidence to support healthcare facilities in implementing robust long-term monitoring and control strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".