Background: Improved Water, Sanitation and Hygiene (WASH) in Healthcare facilities (HCFs) is of significant public health importance. It is associated with a reduction in the transmission of healthcare acquired infections (HAIs), increased trust and uptake of healthcare services, cost saving from infections averted, increased efficiency and improved staff morale. Despite these benefits, there is limited evidence on availability of WASH services in HCFs in the Greater Kampala Metropolitan Area (GKMA). This study assessed the availability and status of WASH services within HCFs in the GKMA in order to inform policy and WASH programming. Methods: A cross-sectional study was conducted in 60 HCFs. Availability of WASH services in the study HCFs was assessed using a validated WASH Conditions (WASHCon) tool comprising of structured interviews, HCF observations and microbial water quality analysis. Data were analysed using Stata 14 software and R software. Results: Overall, 84.5% (49/58) and 12.1% (7/58) of HCFs had limited and basic WASH service respectively. About 48.3% (28/58) had limited water service, 84.5% (49/58) had limited sanitation service, 50.0% (29/58) had limited environmental cleanliness service, 56.9% (33/58) had limited hand hygiene service, and 51.7% (30/58) had limited waste management service. About 94.4% of public HCFs had limited WASH service compared to only 68.2% of private not for profit facilities. More health centre IIIs, 92.5% and health centre IVs (85.7%) had limited WASH service compared to hospitals (54.5%). Conclusions: Our findings indicate that provision of water, sanitation, hand hygiene, environmental cleanliness, and health care waste management services within HCFs is largely hindered by structural and performance limitations. In spite of these limitations, it is evident that environmental cleanliness and treatment of infectious waste can be attained with better oversight and dedicated personnel. Attaining universal WASH coverage in HCFs will require deliberate and strategic investments across the different domains.
This cross sectional study utilised quantitative methods to collect data from selected public and private not for profit (PNFP) HCFs in the GKMA from January to March 2019. The GKMA includes the districts of Kampala, Wakiso and Mukono whose HCFs serve over 14% of Uganda’s population [6]. In this study, we considered HCFs at level III and above since these have a core mandate to deliver Maternal, New-born and Child Health (MNCH) services. In Uganda, the health care system is organised into a four-tier system (i.e., hospitals, health centres of levels IV, III and II) [7]. Level II health centres (HCs) have a catchment population of about 5000 people and only provide outpatient care and community outreach services. Level III HCs with a catchment population of about 20,000 people provide basic preventive, promotive, laboratory and curative services. They have limited inpatient capacity mainly maternity and general patient wards. Level IV HCs (catchment population 100,000) provide outpatient and inpatient services, maternity, children and adults’ wards, laboratory and blood transfusion services as well as an operating theatre. General hospitals (catchment population 500,000) provide preventive, promotive, curative, maternity, and inpatient health services and surgery, blood transfusion, laboratory, and medical imaging services. We sampled 60 out of 105 HCFs in the GKMA. In the sampling, we included all public and PNFP hospitals and HC IVs since these provide MNCH services to majority of the population in the GKMA. High volume PNFP hospitals and HC IVs were also purposively selected. We selected all the 8 PNFP hospitals, and 2 out of the 4 PNFP HC IVs. We purposively selected 28 out of 42 public, and 13 out of the 29 PNFP HC IIIs. HC IIIs with the largest catchment population were sampled. Data collection was conducted using the validated WASH Conditions (WASHCon) tool on the Commcare mobile data collection platform. The tool, developed by the Centre for Global Safe Water, Sanitation, and Hygiene (CGSW) at Emory University has been used to evaluate WASH conditions within HCFs in low- and middle-income countries including Uganda [8–10]. The WASHCon tool relies on data collected through surveys, observational checklists and water quality testing. Data collection was done using mobile devices. The data was then uploaded into pre-programmed dashboards via a cellular or wireless internet network (not required during data collection). For this study, the outcome of the WASHCon tool was WASH service which was categorized as basic, limited or unimproved/no service similar to the JMP WASH service ladders [8]. Based on WASHCon indicators, WASH service is a composite variable generated from five variables (water, sanitation, environmental cleanliness, hand hygiene and waste management services). In order to establish the water service, data was collected on source and accessibility, quantity and quality of water. Sanitation service was assessed by collecting data on accessibility to toilet facilities, number of toilets and existence of the infrastructure, while for hand hygiene services data was collected on availability of hand hygiene facilities and availability of associated supplies. Assessment of environmental cleanliness service was based on availability of cleaning supplies, cleaning practices and frequency, and facility hygiene. In order to establish the availability of waste management service, data were collected on segregation, treatment and disposal of healthcare waste. Using the WASHCon dashboard, evaluation scores were calculated on a scale of 1–3 for each of the WASH domains, as well as an overall score that is an average of all the domains. The scores were determined based on the responses to the survey questions, observation checklists, and water quality testing results (Additional file 1). These scores were further categorized into basic, limited or unimproved/ no service. HCFs that scored between 2.8 to 3.0 were classified as basic, and were considered to meet the minimum WASH in HCF requirements or were on track to meet them; HCFs that scored between 1.9 to 2.7 were classified as limited, and were considered to have made some progress towards meeting minimum requirements for WASH in HCFs but were not on track to meet them; while HCFs that scored between 1.0 to 1.8 were classified as having no service or unimproved (Additional file 1). Such facilities were considered to have made little or no progress towards achieving the minimum requirements for WASH in HCFs [8]. The independent variables included ownership (public vs. PNFP) and level of facility (HC III, HC IV and Hospital). Prior to data collection, study enumerators received training on the use of the WASHCon tool, quality control and research ethics. The observations and interviews were conducted by trained enumerators who had a minimum of a Bachelor’s degree in Environmental Health Science; Nursing; or Social Sciences. All the study enumerators were supervised to ensure quality control. In order to determine the availability of water services in HCFs, observations were done to establish the type of water source and availability of water, and this was followed by collection of duplicate water samples for microbial analysis. Water samples were collected from maternity wards, which were prioritised due to an elevated risk of transmission of HAIs compared to other patient care areas [11]. Water samples were collected using Whirl-Pak bags of 100 mls (with sodium thiosulfate to halt chlorine action in chlorinated supplies) and stored on ice until laboratory analysis. All samples were analysed within 4 hours from the time of collection. Water was tested for faecal coliform, i.e. E. coli using the membrane filtration method [12]. Chromocult agar was used for culturing E-Coli at 37 °C for 24 h. Colonies of E-coli (i.e. dark blue to violet in colour) were counted and results recorded per 100 ml of sample. The data obtained using the WASHCon Commcare app, preinstalled on a mobile device were uploaded onto a server managed by Makerere University School of Public Health and Emory University CGSW. Forms were synchronized daily by each enumerator. The investigators had access to preliminary results through a pre-programmed dashboard. Analysis was performed using Stata version 14 (StataCorp, Texas) and R 3.5.2. Descriptive statistics such as frequencies and proportions were used to summarize quantitative categorical data. Continuous data were expressed as means and standard deviations. Classification of WASH service and its five domains into basic, limited and unimproved/no service was guided by the scoring tool shown in additional file 1.