Producing Malaria Indicators Through District Health Information Software (DHIS2): Practices, Processes And Challenges In Kenya

Globally there is increasing interest in malaria indicators produced through routine information systems. Deficiencies in routine health information systems in many malaria endemic countries are well recognized and interventions such as the computerization of District Health 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 where these data are collected and reported. Using an ethnographic approach, this thesis critically examined how data for constructing malaria indicators are collected and reported through the District Health Information Software (DHIS2) in Kenya. The study was conducted over 18-months in four frontline health facilities and two sub-county health records offices. Data collection involved observations, review of tools and data quality audits, interviews and document reviews. Data were analysed using a thematic analysis approach. This study found that malaria indicator data generation at the health facility level was undermined by a range of factors including: understaffing; human resource management challenges; stock-out of essential commodities; poorly designed tools; and unclear/missing instructions for data collection and collation. In response to these challenges, health workers adopted various coping mechanisms such as informal task shifting and role sharing. They also used improvised tools which sustained the data collection process but had varied implications for the outcome of the process. Data quality problems were concealed in aggregated monthly reports. The DHIS2 autocorrected errors and masked data quality problems. Problems were compounded by inadequate data collection support systems such as supervision. Many challenges for malaria data generation were not HMIS or disease specific but reflected wider health system weaknesses. Any interventions seeking to improve routine malaria data generation must therefore look beyond malaria or HMIS initiatives to also include those that address the broader contextual factors that shape malaria data generation.