Using CLA to Improve Malaria Data Quality and Decision Making in Guinea

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Author(s):
Aissata Fofana
Organization(s):
Institution(s):
Date Published:
September 12, 2018
Contribution:
Community Contribution
StopPalu, a USAID-funded activity (May 2013–December 2017), assisted the Government of Guinea to reduce malaria morbidity and mortality. One of StopPalu’s specific objectives was to improve the Ministry of Health’s (MOH’s) capacity to collect, manage, and use malaria health information for monitoring, evaluation, and surveillance. At the start of StopPalu, only 30% of health facilities were submitting their monthly malaria reports to the Prefectural Health Directorate (DPS), and the data were not being regularly reviewed or used. The collaborating, learning, and adapting (CLA) approach presented a sustainable way to solve the reporting quality issues. We felt that using the CLA approach in collaboration with the National Malaria Control Program (NMCP) would build its capacity to identify underlying issues and make changes, and to assess and solve similar challenges in the future.
Through structured and resourced collaboration forums with key stakeholders, we identified issues contributing to poor data quality. Actionable solutions based on the findings of this first meeting included access to and training on standardized national data collection tools. However, at the end of the first year, StopPalu noted that many health centers were still not submitting their monthly reports, despite implementing these solutions. To learn more about this problem, the activity organized another pause and reflect session that included a wider range of stakeholders, including those from lower tiers of the health system. By consulting actors from the field, the session participants adapted by proposing monthly data review meetings at each level to analyze, validate, and compile data before submission to the next level.
Through this iterative CLA approach, the percentage of health centers that submit monthly reports improved from less than 30% in 2013 to more than 95% in 2017. Due to the success in increasing data management capacity, the team is now using the CLA approach to improve the use of data for decision making.

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