In this study, we found that health facilities located in malaria hypoendemic regions scored lower than those in mesoendemic regions in terms of the overall quality of services provided for malaria, overall site readiness to provide care, availability of malaria reference materials, availability of HMIS tools, and patient satisfaction. However, there was no difference in the clinical management factors between facilities in hypoendemic compared to mesoendemic regions. Facilities located in urban areas were also found to score lower than those in rural areas, and those without implementing partner support scored lower during the MSDQI assessments.
Site Readiness
Facilities located in malaria mesoendemic regions were found to have higher readiness scores compared to those in hypoendemic regions; this is consistent with findings from a study among 826 health facilities in Kenya, Namibia, and Senegal (28). This might be explained by having a higher proportion of facilities located in rural settings included in the analytical samples. Malaria has traditionally been a rural disease; the higher malaria burden may lead to improved facility readiness in mesoendemic settings (28, 29).
The type of health facility did not influence the readiness to provide care for malaria patients. This contradicts previous findings from studies conducted in low and middle-income countries, including Tanzania (30), which showed there were differences in readiness by the type of the facility. This might be due to having a different composition in the types of facilities; more than three quarters (81.5%) of the facilities involved in this study were dispensaries (i.e., lowest facility level). Another possible reason might be that malaria is among the most common diagnoses attended in health facilities in the country and the standards for operating facilities require malaria services to be delivered in facilities at all levels; this might explain the high level of facility readiness in our analysis regardless of facility type.
Facilities in mesoendemic regions were found to have a higher availability of reference materials. A possible explanation for the observed difference could be due to the presence of implementing partners providing case management support, which includes printing and distribution of reference materials to health facilities. A higher proportion of facilities in the mesoendemic regions had implementing partner support compared to those in hypoendemic regions. Another reason could be due to the number of reference materials used in the calculation of the indicator. This indicator was comprised of five different reference materials, some of which might not be frequently used in facilities in hypoendemic regions, resulting in lower scores during the evaluation.
In both endemicity settings, health facilities scored best in the availability of HMIS tools compared to other factors. This could be explained by the recent health systems strengthening efforts for improving the HMIS in Tanzania (31). The tools evaluated by this indicator were not specific for malaria, they included the HMIS registers, tally sheets and monthly summary forms, which are standard throughout the country.
Staff training on appropriate malaria case management scored lowest among the site readiness factors in both endemicity settings. We expected the scores to be high as the institutions (universities and colleges) training clinicians in Tanzania have standardised curriculums that include the management of malaria. The low scores might be caused by how the assessment is conducted for this indicator. The MSDQI OPD checklist documents the number of available clinical staff, the number of staff who had received formal, or on-the-job training on malaria case management, including artesunate injectable prescription, and the mean of these scores is calculated. Injectable artesunate was introduced in Tanzania in 2013 and incorporated in the National Guidelines for Diagnosis and Treatment of Malaria in 2014 (32). Facilities with a higher proportion of clinical staff who had graduated more than 8 years ago, before the introduction of artesunate, might score low in the first part of the checklist. Although on-the-job trainings on injection artesunate preparation and administration were provided when it was being introduced, a recent study shows that health care worker knowledge is still low (33).
Clinical Management
In both endemicity settings, health facilities scored the lowest in clinical management factors compared to the others assessed.
Facilities scored poorly in history taking and clinical examinations conducted for febrile patients in both endemicity settings. This might be explained by the human resource for health crisis in the country leading to a high workload (34). The evaluation tool required the observer to assess fine details in history taking and physical examination that might have been skipped in a clinical setting with a high burden or turnover of patients. Patients might be referred quickly for laboratory confirmation without a thorough history and physical examination.
Health facilities scored higher in the testing rate for patients suspected to have malaria. However, there was no statistical difference in scores between the endemicity settings. This was not the case in Angola during a study which found there was a lower testing rate in low transmission settings (35).
In both endemicity settings, approximately 25% of the malaria test results (mRDT and microscopy) were not properly interpreted or the patient was diagnosed clinically to have malaria (i.e., without a test result). This proportion is lower compared to previous findings from the Service Provision Assessment for Malaria conducted in 2016 (36), but still reflects an unacceptably high number of facilities prescribing anti-malarial drugs for patients either not tested or having negative test results. A review of the HMIS data for the same period that this study was conducted showed that across the country approximately 3% of all malaria cases were diagnosed clinically without testing. This practice is contrary to the national guidelines on malaria diagnosis and treatment, and the recommendations of WHO, which require treatment with anti-malarial drugs be limited to patients with parasitological confirmation of malaria infection (13, 16, 32).
Clinicians should improve on history taking skills and the performance of thorough physical examinations, refer for or conduct diagnostic tests for suspected malaria cases, and properly interpret and adhere to the test results before prescribing an appropriate antimalarial.
Outcome Factor
The level of patient satisfaction was high, and higher than that found in the Service Provision Assessment for Malaria, 2016 (36). Patients were more satisfied with the services received in the mesoendemic regions compared to the hypoendemic regions. However, the MSDQI OPD checklist used an exit interview to assess this level of satisfaction. This could be subject to courtesy biases. The checklist also gave scores when the patient could correctly explain the use of dispensed drugs at home, a situation that might favour the facilities in mesoendemic regions given they were more likely to prescribe medication as they attend more malaria cases.
Strengths and Limitations
Results from this study illustrated the quality of malaria case management services provided in outpatient departments across Tanzania mainland by utilising routine health facility assessment data obtained through the MSDQI package. This data was collected using a standardized approach that is sustainable. The approach aids in the collection, monitoring, and evaluation of malaria performance indicators at the facility level that provides timely, accurate information and data for decision making.
The analysis methods enabled comparison between different malaria endemicity settings. While the differences in scores observed in our analysis were statistically significant, the magnitude of these differences are likely not large enough for Tanzania’s national malaria program to prioritize corrective interventions based on facility location, i.e., by endemicity or rural versus urban settings. Nonetheless, this comparison may help decision makers to identify areas that need to be targeted for improvement in future analyses. Possible areas to focus improvements based on our analysis are site readiness, especially staff training, and clinical management that includes all transactions made between patients and health service providers from the time of history taking up to when patients exit the facility.
Findings from this study may serve as a baseline that could facilitate ongoing monitoring of case management performance at health facilities as MSDQI assessment data collection continues.
Countrywide analysis using the MSDQI OPD data before this study could not be performed. We merged MSDQI data from several sources including those that were collected using both paper-based and electronic tools that were initially stored in separate servers.
We assigned facilities to urban or rural locations; however, misclassification might have occurred in differentiating between facilities located in peri-urban settings.
Misclassification might have also occurred in differentiating endemicity settings by malaria prevalence estimates at the regional level. There is heterogeneity within regions. A better approach might be to perform the analysis at the district level and further differentiate the endemicity settings into more strata as previously defined using Tanzania HMIS and population survey data (37).
Cross-sectional health facility performance data that was collected between September 2017 and December 2018 was linked to estimates of malaria survey data conducted between October and December 2017. A better approach could have been to use data from real-time surveillance that would have enabled the detection of hot spots, especially in the hypoendemic regions.
The study analysed data from less than a quarter of all operating facilities in Tanzania mainland. The distribution of facilities across the country that were included was not uniform as some regions started the implementation of MSDQI supervisions earlier or had a higher rate of conducting supervisions than others. This might be influenced by the presence of implementing partners in the regions and the accessibility of the facilities by road for supervision during the dry and wet seasons. These might affect the generalisability of the results. As the coverage of the MSDQI assessments increases, more frequent analyses using data from all modules are warranted.