Background: Globally, there is increasing interest in malaria indicators produced through routine information systems due to their potential to provide near real time data for driving malaria control decisions. Deficiencies in routine health information systems are well recognized and interventions such as the computerization of District Information Systems have been implemented to improve data quality, demand and use. However, little is known about the micro-practices and processes that shape routine malaria data generation at the frontline. This study critically examined how data for constructing routine malaria indicators are constructed and reported through the DHIS2 in Kenya.
Methods: The study was conducted over 18-months in four frontline health facilities and two sub-county health records offices located in two malaria endemic counties. The study employed an ethnographic approach to data collection which involved observations, document review, records review, and interviews (n=27). Data were analysed using a thematic content analysis approach.
Results: Routine malaria data generation at the frontline was undermined by a range of factors such as understaffing, human resource management challenges, stock-out of essential commodities, poorly designed tools, and unclear instructions for data collection and collation. In response to these challenges, health workers adopted various coping mechanisms such as informal task shifting and use of improvised tools which sustained the data collection process but undermine data quality. Data quality problems were concealed in aggregated reports entered in the DHIS2. Problems were compounded by inadequate data collection support systems such as supervision.
Conclusion: Challenges to routine malaria data generation and reporting through the DHIS2 are embedded within the broader challenges faced by the health system. Any intervention seeking to improve routine malaria data generation must therefore look beyond malaria or the health information system-specific initiatives to also include those that address the broader contextual factors that shape malaria data generation.