Assessment of Data Use for Malaria Program Decision Making in the Democratic Republic of the Congo
- 1. Assessment of Data Use for Malaria Program Decision Making in the Democratic Republic of the Congo Background Olivier Kakesa1, Jadhoul Nkongolo1,, Johanna Karemere1, Yazoume Ye1, Ramine Bahrambengi1, Ismael Nyanzi 3, Joris Likwela2, Isabel Brodsky3 1MEASURE Evaluation, ICF, 2National Malaria Control Program in the DRC, 3MEASURE Evaluation, Palladium The assessment gave insight into the main barriers preventing data use for decision making among the national and provincial teams. For example, most of the 38 participants acknowledged that they are not using data for decision making, because of data quality concerns. These findings paved the process for strategic action planning to overcome these barriers and, ultimately, to improve the culture of data use. Participants prioritized three key programmatic questions to answer with data in the two months following the assessment. They were also encouraged to use action plans to delegate tasks and responsibilities and to produce timelines for these activities to ensure completion. The assessment process increased participants’ commitment to the data use cycle—in which data use improves quality, which in turn leads to further use. These activities will launch the data use cycle in the DRC and are anticipated to lead to better quality data. This research has been supported by the President’s Malaria Initiative (PMI) through the United States Agency for International Development (USAID) under the terms of MEASURE Evaluation cooperative agreement AIDOAA-L-14-00004. The opinions expressed are those of the authors and do not necessarily reflect the views of USAID, or the United States Government. Availability of data is not assured because data are stored in DHIS 2, which is a web-based platform. Sometimes access is compromised (due to lack of internet) when data are needed for a decision. Access to Data for Decision Making Comparison of national level respondents’ current skills with perceived skills Frequency of data review meetings Use of data in decision making about resource allocation Data Sources and Data Management Primary sources of routine data: • DHIS 2 • NMCP database • Weekly epidemiological surveillance reports 63 63 75 38 88 50 13 50 50 25 Calculate averages Explain M&E results Use data to set targets Communicate variation from… Calculate rates Actual Skill Perceived Skill Comparison of Provincial level respondents’ current skills with perceived skills 63 58 74 42 95 79 42 37 47 47 Calculate averages Explain M&E results Use data to set targets Communicate varation… Calculate rates Actual Skill Perceived Skill Primary sources of non-routine data: • Qualitative and quantitative evaluations • Operations research Objective Materials and Methods The use of data and information to guide policy and program implementation is critical to reaching intervention targets. However, most national malaria control programs and departments of health information systems in malaria endemic countries face challenges to data use, particularly at the subnational level. The USAID-funded MEASURE Evaluation project conducted an assessment in the Democratic Republic of the Congo (DRC) to identify how data are being used for decision making and how future interventions can be designed to promote data demand and use for decision making. • The assessment was conducted at the national level and at the provincial level in Haut Katanga, Kinshasa, and Lualaba. • The methodology comprised individual interviews, a group assessment, and a site visit checklist to look at technical, organizational, and behavioral determinants of data use.. • Tool included 15 open-ended questions that cover 8 overarching data use topics. • Interviewed 12 key informants from DSNIS, NMCP, and a national-level NGO • Respondents came from three levels of the health system: o National, provincial, health zone Areas Related to Decision Making • Performance management (i.e., where and how often to conduct supportive supervision and monitoring visits) • Resource allocation (drugs and commodities, human resources, etc.) • Investigation and response to epidemics and other disease outbreaks • Where to implement programs and outreach services Conclusion Results (continued)ResultsBackground