Interventions to improve water supply and quality, sanitation and handwashing facilities in healthcare facilities, and their effect on healthcare-associated infections in low-income and middle-income countries: a systematic review and supplementary scoping review
Bibliographic record
Abstract
INTRODUCTION: Healthcare-associated infections (HCAIs) are the most frequent adverse event compromising patient safety globally. Patients in healthcare facilities (HCFs) in low-income and middle-income countries (LMICs) are most at risk. Although water, sanitation and hygiene (WASH) interventions are likely important for the prevention of HCAIs, there have been no systematic reviews to date. METHODS: As per our prepublished protocol, we systematically searched academic databases, trial registers, WHO databases, grey literature resources and conference abstracts to identify studies assessing the impact of HCF WASH services and practices on HCAIs in LMICs. In parallel, we undertook a supplementary scoping review including less rigorous study designs to develop a conceptual framework for how WASH can impact HCAIs and to identify key literature gaps. RESULTS: Only three studies were included in the systematic review. All assessed hygiene interventions and included: a cluster-randomised controlled trial, a cohort study, and a matched case-control study. All reported a reduction in HCAIs, but all were considered at medium-high risk of bias. The additional 27 before-after studies included in our scoping review all focused on hygiene interventions, none assessed improvements to water quantity, quality or sanitation facilities. 26 of the studies reported a reduction in at least one HCAI. Our scoping review identified multiple mechanisms by which WASH can influence HCAI and highlighted a number of important research gaps. CONCLUSIONS: Although there is a dearth of evidence for the effect of WASH in HCFs, the studies of hygiene interventions were consistently protective against HCAIs in LMICs. Additional and higher quality research is urgently needed to fill this gap to understand how WASH services in HCFs can support broader efforts to reduce HCAIs in LMICs. PROSPERO REGISTRATION NUMBER: CRD42017080943.
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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 | 0.020 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".