Study design and setting
We used a mixed-methods cross-sectional study design. Quantitative data was used to determine compliance and identify associated factors. Qualitative data were collected simultaneously to supplement the factors associated with compliance. The data collected were from primary care facilities and administrative staff at district level in Mbale from June to August 2020.
Mbale district is one of the districts found in the mid-eastern region of Eastern Uganda. It is divided into three constituencies: two counties (Bungokho south and north) and Mbale municipality, with its largest city being Mbale city. Mbale district has 36 public health facilities, of which 35 are primary care facilities, and one is a regional referral hospital that serves the eastern region(3). The district had an estimated population projection of 586,300 people in 2020 (24). The district was chosen for the study because of its huge decline in health sector performance from being in the top 10 districts in 2016/2017 to 34th and 45th in 2017/2018 and 2019/2020, respectively(25–27).
Study population
Our study units comprised primary care health facilities in Mbale district from which respondents were purposively selected. We included in the study all primary care facilities funded by the government. The selected facilities were from health centre levels II, III, and IV. HCs levels I, II, III, and IV are the only facilities in the healthcare system that provide primary care in Uganda (2, 28). They deliver the first connection between the public and the formal health sector and focus mainly on infectious disease prevention and treatment services. Village Health Teams (VHTs) constitute health centre I and are attached to a nearby health facility(29).
HCs level IV are mandated to serve a target population of 100,000 people and have provisions for an operating theatre, inpatient, and laboratory services, and act as a referral facility for HCs level III in their jurisdiction. HCs level III have a target population of 20,000 people and have provisions for basic laboratory services, maternity care, and inpatient care. HCs level II, on the other hand, are lower-level facilities and are mandated to serve a target population of about 5,000 people providing outpatient services and outreach programs only (28).
The health facilities are staffed differently based on their level of care, determined by the MOH. HCs level IV have between 10 to 21 staff, which include; 1 doctor, clinical officers, qualified nurses, midwives, and nursing aides. HCs level III have 10 to 6 staff, including clinical officers, qualified nurses, midwives, and nurse aides. HCs level II, as lower-level facilities, have approximately four staff including a qualified nurse, midwife, and nurse aides (28). Specific cadres are employed depending on the services to be offered across the different facility levels, although currently, staffing levels stand at only 45%, 20%, and 13% of HCs levels II, III, and IV, respectively(11). In addition to the staffing constraints, nationally, HCs levels IV, III and II received 2.6%, 6.3%, and 2.3%, respectively, of the National Medical Store (NMS) expenditure in the 2020/2021 financial year (11).
Ordering of medicines follows a pull system at Hospitals and HCs level IV and a push or kit system at HCs level III and II(4). The pull system requires each health facility to determine what and how much to order. On the other hand, the push system requires each health facility to receive predefined kits(5). The health facility in charge approves bi-monthly orders for medicines at each facility before submission. A graduate pharmacist makes orders at the hospital level, dispensers with qualifications of diploma in pharmacy at HCs level IV, and stores in charge at HCs level III and II. Annual facility budgets are divided into six cycles to approximate the value of commodities ordered every cycle. The quantity to order each month is calculated using a formula incorporating the quantity on hand and maximum stock. When the total amount exceeds the cycle budget, vetting commodities is done using VEN classification to prioritize the vital items(30).
Study sample size
We required a sample size of at least 30 health facilities according to the WHO guidelines on survey of health facilities(31). The sample size for quantitative data was determined using Yamane’s proportionate method since the district had a finite population size. This sampling method was used to combine responses into categories and sample size based on proportions(32).
The proportions of each of these facilities were used to determine the exact number of participating facilities. Once the number had been determined, these were then randomly sampled. The sample size was calculated using the formula below based on a finite number of facilities.
n = (35) / (1+35(0.05)2 = 33
Proportions for each level of facility in the study area were (3 HCs level IV (8.6%), 23 HCs level III (65.7%), and 9 HCs level II (25.7%)). Therefore, 3 HCs level IV, 22 HCs level III, and 8 HCs level II were sampled. The sample size of respondents at health facility level was calculated based on the assumption that a facility had one in charge and one store in charge. The total number of respondents was, therefore, 66. The District Health Officer (DHO) and four Medicines Management Supervisors (MMS) were purposively selected for key informant interviews. MMSs are health workers that provide supportive supervision to improve medicines management in public health facilities(33, 34).
Variables
The outcome variable of the study was compliance with EMHS redistribution guidelines. This was a binary outcome, where a facility would be compliant or non-compliant. A facility was reported compliant if it scored equal to or above an arbitrary cut-off of 80% on the observational checklist. The independent variables were categorical and included constructs of the EMHS redistribution guidelines, including the steps and triggers of redistribution. Others were the factors that affect stock levels and redistribution at health facilities, the availability of funds, bureaucratic processes, knowledge of staff, up-to-date stock cards, availability of EMHS, and communication channels.
Data collection
Three data collection tools were used in the study, including a questionnaire, a key informant guide, and an observational checklist. The tools were distributed among the respondents that had been purposively selected.
The tools were administered by the principal investigator together with two research assistants. The assistants had a minimum qualification of a bachelor’s degree and were trained on data collection procedures before commencement of the exercise. A pilot study was conducted in 5% of the calculated sample size before data collection to pre-test the data collection tools.
Administrative clearance was obtained from the offices of the Chief Administrative Officer(CAO), the DHO, the town clerk, and the manager responsible for each health facility. On arrival at the facility or district office for the study, the researchers introduced themselves and stated their reason for the visit. The researchers found the selected respondents at their respective workstations between 8:30 am and 4:30 pm. They were given introductory letters, briefed about the study being conducted, given consent forms, and allowed to participate freely without coercion. Only respondents that gave informed consent were allowed to participate in the study. The interviews ran for about 30 to 45 minutes each, and these were supplemented with on-site observations using an observational checklist.
Quantitative data collection
Quantitative data was gathered using a questionnaire and checklist. The questionnaire was used to collect data from respondents at the health centres to determine their socio-demographic characteristics and possible factors associated with non-compliance. The questionnaire was administered by research assistants and had both open and closed-ended questions allowing the respondents to explain where necessary. The questions were formulated based on the activities involved in the process of redistribution as stated in the guidelines, and some questions were adapted from a similar questionnaire used to carry out a scoping study about compliance with redistribution guidelines(1, 23). Questions asked included knowledge of the steps of redistribution and its financing, if the facility had excess stock, if the guidelines were available and if they had ever been trained. Probing for interesting responses and observation of nonverbal responses were also done.
The observational checklist supplemented the responses and was guided by triggers, including; A facility has an excess of one EMHS while another has a deficit, the facility's stock is expected to expire before being used, the facility has EMHS distributed to it in error, especially when a lower-level facility received supplies meant for a higher-level facility and facility has more EMHS of a short shelf life than what was forecasted for use.
While using the checklist, we reviewed copies of the issue and requisition vouchers and stock records. We checked for occurrences of excess stock or deficits, availability of the guidelines, and the use of any communication system to alert departments. A percentage score was computed from the scores among the facilities that experienced triggers of redistribution to determine compliance.
Selection of tracer commodities
We used documents of six tracer items to investigate compliance, including Artemether/Lumefantrine tablets, isoniazid tablets, cotrimoxazole tablets, oxytocin injections, metformin tablets, and rapid diagnostic test kits for malaria. We selected two tracer commodities, Artemether/Lumefantrine and malaria rapid diagnostic test kits, because of the high burden of malaria and the large number of people at risk of getting the disease (35, 36). Isoniazid and cotrimoxazole were selected for their use among HIV patients for preventive treatment against tuberculosis and as prophylaxis against opportunistic infections. We also selected metformin because of the increasing burden of Diabetes Mellitus and oxytocin because of its importance in managing obstetric conditions (35, 37).
Qualitative data collection
We used a key informant interview guide to conduct interviews with the DHO and the MMSs, who had the most information about the overall redistribution process in the district. These interviews increased our understanding of the findings as they had extended probing. Open-ended questions were used during the interviews, and the sessions were recorded using a mini digital voice recorder to reduce the likelihood of omitting relevant information.
Data quality control and management
To ensure the quality of data collected was assured, the tools used were pre-tested on two health facilities in a similar setting before their use in the field. The tools were then revised based on the feedback to ensure that these would be comprehended and appropriate responses obtained from the respondents.
The research assistants were also trained before data collection to ensure that all relevant information was captured. They were trained on the study objectives, how to use the audio recorders and the ethics of working when interacting with the respondents. Completeness of data was ensured by checking filled tools in real time to identify any missing data.
Data Analysis
Quantitative data was transferred from a Microsoft Excel 2016 spreadsheet (Microsoft Corporation, Washington, USA) and entered into Epi-Info version 3.5.1(CDC, Atlanta, Georgia). to be checked for consistency. Cleaned data was then exported to IBM SPSS Version 24.0 for analysis. Descriptive statistics were determined for the population and respondents from the univariate analysis. Test for association was done using logistics regression through multivariate analysis. Associations with the diverse factors were determined using odds ratios, p-values, and a confidence interval of 95%.
Qualitative data were analysed using ATLAS.ti version 24.0(Scientific Software Development GmbH, Berlin, Germany)Thematic analysis was utilized. Qualitative data collected from recordings of interviews were transcribed, and a codebook was created for the different variables. The coded qualitative data were then categorized and grouped into themes for analysis. Continuous theme searching and reviewing were done until no new codes were observed from the scripts.