Material and methods : Cross sectional study

Introductions

Although sub-Saharan Africa continues to bear the largest burden of malaria with 88% of all cases and 9 in every 10 deaths globally, recent studies show that malaria is on the decline in the African region; there was a decline of 48% in the number of cases and 66% in deaths between 2000 and 2015 [1]. These declines may be attributed to improved access to malaria prevention and control tools, such as insecticide-treated bed nets (ITNs), indoor residual spraying (IRS), and artemisinin-based combination therapy (ACT). To consolidate the achievements in malaria prevention and control, the World Health Organization (WHO) updated its framework concerning malaria by requiring all countries where malaria is endemic to ensure that all suspected cases of the disease are tested via microscopy or rapid diagnostic test (RDT) and that every confirmed case is treated with a quality-assured anti-malarial drug [2].

Following the update of the WHO Framework for malaria prevention and control in 2012, Uganda, similarly to other countries where malaria is endemic, adopted these updated policies [3]. Uganda updated its national malaria treatment guidelines that same year to indicate first-line treatment and their alternatives for all cases of malaria, which includes any artemisinin-based combinations that has been recommended by the WHO and Ministry of Health (MOH) and is registered with the National Drug Authority. Adherence to the treatment guidelines has numerous benefits including improved use of anti-malarial drugs and reductions in the current high costs of ACT. It is, therefore, important that healthcare providers in both public and private sectors adhere to these guidelines for effective control of the disease.

Despite these efforts, Uganda has the third highest number of deaths from malaria and some of the highest recorded rates of malaria transmission in Africa, especially in areas around Lake Kyoga [4]. Both Buyende and Kaliro, two districts bordering Lake Kyoga, have hyperendemic transmission intensity rates (≥ 75%) for malaria at 85 and 100% of their populations, respectively. Meanwhile, these two districts are among those districts specifically targeted with heavy malaria interventions for being hyperendemic areas [5]. Despite many interventions, such as distributing free ITNs and increasing access to malaria diagnostic services and ACT by the Government of Uganda in the two districts, the area remains highly malaria burdened [5].

Several factors, including adherence to Integrated Management of Malaria (IMM) guidelines, may be majorly contributing to this consistent burden. Poor adherence, for example, can lead to increased malaria burden, parasite resistance, resource wastage, and treatment failure. This research sought to determine the level of adherence and associated factors to IMM guidelines among healthcare providers in Buyende and Kaliro. Findings may assist development partners and policy makers in identifying gaps related to IMM adherence and avert possible consequences, such as drug resistance, loss of lives and increased medical costs.

MATERIAL AND METHODS

Study area

This study was conducted in the lakeshores of the Buyende and Kaliro districts in Uganda; lakeshores were defined as parishes that share borders with Lake Kyoga in Buyende and Kaliro districts [6]. Both Buyende and Kaliro are in Busoga, a sub-region in Eastern Uganda and surrounded by Lake Kyoga. Buyende, difficult to reach due to poor transport infrastructure, has a total population of 323,100 [7]. Kaliro has a total population of 236,200 [7]. Global positioning system (GPS) coordinates of the study facilities were taken, and Global Information System (GIS) software was used to generate a map of these areas.

Study design

This study followed a cross-sectional design where data were collected from selected health facilities in September and October 2016 using a structured questionnaire. Primary data including socio-demographics, illness diagnoses, and medicine prescriptions were collected from patients and/or their attendants exiting the selected health facilities. Where available, the researcher or research assistant looked at patients’ medical forms to triangulate the information collected through the interview. Additionally, data on attitudes was collected from healthcare providers who were directly involved in malaria management in the selected health facilities.

Study exposure and outcome

Exposure variables in this study were healthcare provider factors, such as cadre of healthcare providers, age, gender, level of education, training on national malaria guidelines, and health facility-related factors, including the type of facility and ownership. These factors were examined to see if they influence adherence to IMM guidelines. The individual health worker was linked to all the patients he/she had treated in order to establish whether the health worker had adhered to the guidelines. The study outcome was level of adherence to IMM guidelines. Adherence was defined as the proportion of diagnosis and prescriptions made in line with the national malaria management guidelines.

Sample size and participant selection

Using the Leslie Kish 1965 formula [8],
=(Z0.95)2(P(1P)/D2)
with 95% confidence interval (i.e. Z0.95 = 1.96), 31% proportion (P) for fever in Uganda [9], and absolute precision (D) of 5%, a total sample of 329 participants was considered sufficient for this study. Buyende district has a total of 23 health facilities (5 at the lake shores) and while Kaliro has 21 (6 at the lakeshores) [6]. This study included all health facilities (11) along the shores of Lake Kyoga, 5 of which were in Buyende and 6 in Kaliro. These were the only ones that had information regarding the number of patients with fevers treated at each facility prior. Exit interviews were used, and every patient participant who met the inclusion criteria was included. Health workers involved in the diagnosis and treatment of suspected malaria were also included in the study. Private health facilities were anticipated to have a client flow of 10–20 clients a day while public health facilities had at least 50 clients. A total of 286 patients were screened; 223 were eligible, i.e., seeking treatment for illness including fever. Of the 223 eligible caregivers, 11% (26) declined to participate in the study. Extending the enrollment for this study was not possible due to insufficient funding.

Data collection

Data were collected electronically using the Open Data Kit (ODK) software installed on Samsung Galaxy smart phones. Each phone was enabled to capture GPS coordinates to provide facility locations. The client/patient caregiver questionnaires were programmed and uploaded using ODK. The programmed tool had built-in validation steps to ensure no errors were made. Adult patients and caregivers of children below the age of 15 years were interviewed on exit from selected facilities after seeking diagnosis and treatment of fever. The data collectors were positioned at the exit of the outpatient side of the health facility to interview the patient participants while following interview protocol procedures to obtain the required information. Information regarding laboratory diagnosis and prescription was verified from a patient’s medical form or book while clinical diagnosis information was as reported by the participant. Similarly, questionnaires were administered to health workers who directly managed patients with suspected malaria.

Data management and analysis

The data collected were downloaded as a Microsoft Excel workbook and then exported to SPSS version 17 for analysis. For univariate analysis, each individual variable was summarized using frequency tables, charts and graphs. For bivariate analysis, each independent variable was cross-tabulated with the dependent variable (adherence to national malaria management guidelines) to establish possible associations. The Chi Square test, or Fisher Exact Test (FET) for cases when the expected number was less than 5, was used to determine association between these categorical variables at a statistical significance level of 0.05. For multivariate level analysis, binary regression was conducted on those highly significant independent variables to determine independent factors’ influence on adherence to the national malaria guidelines, with 95% confidence intervals for the odds ratios and significance levels of 0.05. Data quality was ensured through comprehensive training of research assistants, pre-testing of tools, and constant supervision.

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