Producing routine malaria data: an exploration of the micro-practices and processes shaping routine malaria data quality in frontline health facilities in Kenya

Abstract

BackgroundRoutine health information systems can provide near real-time data for malaria programme management, monitoring and evaluation, and surveillance. There are widespread concerns about the quality of the malaria data generated through routine information systems in many low-income countries. However, there has been little careful examination of micro-level practices of data collection which are central to the production of routine malaria data.MethodsDrawing on fieldwork conducted in two malaria endemic sub-counties in Kenya, this study examined the processes and practices that shape routine malaria data generation at frontline health facilities. The study employed ethnographic methods—including observations, records review, and interviews—over 18-months in four frontline health facilities and two sub-county health records offices. Data were analysed using a thematic analysis approach.ResultsMalaria data generation was influenced by a range of factors including human resource shortages, tool design, and stock-out of data collection tools. Most of the challenges encountered by health workers in routine malaria data generation had their roots in wider system issues and at the national level where the framing of indicators and development of data collection tools takes place. In response to these challenges, health workers adopted various coping mechanisms such as informal task shifting and use of improvised tools. While these initiatives sustained the data collection process, they also had considerable implications for the data recorded and led to discrepancies in data that were recorded in primary registers. These discrepancies were concealed in aggregated monthly reports that were subsequently entered into the District Health Information Software 2.ConclusionChallenges to routine malaria data generation at frontline health facilities are not malaria or health information systems specific; they reflect wider health system weaknesses. Any interventions seeking to improve routine malaria data generation must look beyond just malaria or health information system initiatives and include consideration of the broader contextual factors that shape malaria data generation.

Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania

“…The ndings from this study and others [2,9,10,21], indicate that HMIS in low-and-middle income countries are characterized by poor data quality, thus compromising its use in decision making and planning. There is evidence that improved use of routine health data improves the quality of the data as more attention is paid to its demand and usability [10,[32][33]. This means, inadequate data use creates a vicious cycle of inadequate data demands and production of good quality information.…”

The relationship between facility-based malaria test positivity rate and community-based parasite prevalence

“…Variations in the testing rates have been associated with levels of endemicity, staffing or workload, inadequate training and lack of supervision of health care workers, shortages and stock-outs of mRDTs, and patient-level factors [23]. In Kenya, there is increasing evidence that coverage, completeness, quality of routine reliable malaria information remains woefully inadequate [60][61][62]. These inadequacies are not insurmountable.…”

Relationships between test positivity rate, total laboratory confirmed cases of malaria, and malaria incidence in high burden settings of Uganda: An ecological analysis

“…However, the utility of routine data from health facilities may be limited by incomplete or inaccurate reporting, lack of diagnostic testing in patients with suspected malaria, and poor quality laboratory diagnostics. Despite these challenges, an increased emphasis on laboratory-based con rmation of malaria and widespread availability of RDTs has improved the quality and utility of routine health facility-based data [9,[13][14][15].…”