Evidence map›Paper›PMID 31841540›Full record

ArticlePloS one2019

Extending the use of the World Health Organisations'  water sanitation and hygiene assessment tool for surveys in hospitals - from WASH-FIT to WASH-FAST.

Michuki Maina, Olga Tosas-Auguet, Jacob McKnight, Mathias Zosi, Grace Kimemia, Paul Mwaniki, Arabella Hayter, Margaret Montgomery, Constance Schultsz, Mike English

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  4. Review
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  6. Article
  7. Potential secondary transmission of SARS-CoV-2 via wastewater.The Science of the total environment · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Michuki MainaHealth Services Research Group, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.ORCID 0000-0001-6874-8929
Olga Tosas-AuguetNuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
Jacob McKnightNuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
Mathias ZosiHealth Services Research Group, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.
Grace KimemiaHealth Services Research Group, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.
Paul MwanikiHealth Services Research Group, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.
Arabella HayterWater Sanitation and Hygiene Department, World Health Organization, Geneva, Switzerland.
Margaret MontgomeryWater Sanitation and Hygiene Department, World Health Organization, Geneva, Switzerland.
Constance SchultszAmsterdam University Medical Centres, University of Amsterdam, Amsterdam, The Netherlands.
Mike EnglishHealth Services Research Group, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.ORCID 0000-0002-7427-0826

Funding

EPA EP-C-15-003Wellcome Trust 107769/Z/10/ZWellcome Trust 207522Wellcome Trust 207522/Z/17/ZWorld Health Organization 001
6 · The paper itself

Abstract

backgroundPoor water sanitation and hygiene (WASH) in health care facilities increases hospital-associated infections, and the resulting greater use of second-line antibiotics drives antimicrobial resistance. Recognising the existing gaps, the World Health Organisations' Water and Sanitation for Health Facility Improvement Tool (WASH-FIT) was designed for self-assessment. The tool was designed for small primary care facilities mainly providing outpatient and limited inpatient care and was not designed to compare hospital performance. Together with technical experts, we worked to adapt the tool for use in larger facilities with multiple inpatient units (wards), allowing for comparison between facilities and prompt action at different levels of the health system.

methodsWe adapted the existing facility improvement tool (WASH-FIT) to create a simple numeric scoring approach. This is to illustrate the variation across hospitals and to facilitate monitoring of progress over time and to group indicators that can be used to identify this variation. Working with stakeholders, we identified those responsible for action to improve WASH at different levels of the health system and used piloting, analysis of interview data to establish the feasibility and potential value of the WASH Facility Survey Tool (WASH-FAST) to demonstrate such variability.

resultsWe present an aggregate percentage score based on 65 indicators at the facility level to summarise hospitals' overall WASH status and how this varies. Thirty-four of the 65 indicators spanning four WASH domains can be assessed at ward level enabling within hospital variations to be highlighted. Three levels of responsibility for WASH service monitoring and improvement were identified with stakeholders: the county/regional level, senior hospital management and hospital infection prevention and control committees.

conclusionWe propose WASH-FAST can be used as a survey tool to assess, measure and monitor the progress of WASH in hospitals in resource-limited settings, providing useful data for decision making and tracking improvements over time.

Indexed as

World Health OrganizationCross InfectionFeasibility StudiesGlobal HealthHand DisinfectionHand HygieneHealth Plan ImplementationHospitalsHumansPractice Guidelines as TopicQuality ImprovementSanitationSurveys and QuestionnairesTime FactorsWater PurificationWater Supply

Identifiers

PMID31841540
PMCPMC6913973

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.