Browsing by Author "Stanton, Michelle"

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    Developing a digital platform for sub-district level malaria burden stratification mapping using routine health facility case data – guiding the national transition to targeted malaria control, version 1.0
    (Kamuzu University of Health Sciences, 2022-07-26) Stanton, Michelle; Chirombo, James
    Study type: Secondary data analysis Research problem: With the continued reduction of malaria prevalence at the national level after many years of intensive malaria interventions, community-level transmission becomes more important. Focusing on the trends in malaria transmission at the national level may mask the heterogeneities that exist at the sub-district level. Therefore, it is important to understand the disease dynamics at the community level to inform strategies that can eventually lead to the elimination of the disease. Main objective: The main objective is to use routinely reported health facility-level malaria case data to produce sub-district level estimates of clinical malaria incidence in Malawi and stratify the country by different levels of incidence for more targeted malaria intervention. Specific objectives: • Develop standardised procedures for data collation and data management of routinely reported data • Undertake a spatial analysis to stratify the country into the predefined risk categories at regular intervals, using available data on current intervention coverage, climate/environment, and evidence of insecticide resistance. • Develop a digital dashboard which can be accessed by the National Malaria Control Programme (NMCP) and district programme coordinators to display interactive maps, summaries of the resulting analysis and generate automated reports • Train staff at the NMCP and the Central Monitoring and Evaluation Division (CMED) to update the data and maintenance analyses on a regular basis with the support of the Ministry of Health’s Digital Heath Division (DHD). • Support relevant technical working groups (TWGs) to use the maps when planning upcoming interventions. Methodology: We will obtain monthly aggregated malaria case data from the Health Management Information System (HMIS) known as DHIS21 at the health facility level. After data processing, we will apply geospatial statistical methods to estimate malaria incidence over time, incorporating the effects of climate, environment, and malaria intervention activities. Our results will then be visualised within a digital dashboard which will be integrated within NMCP surveillance systems at the national and district levels. Expected results and their dissemination We anticipate that our analysis will reveal the spatial and temporal heterogeneities in malaria transmission across the country. Results will be shared with COMREC and the Ministry of Health at relevant technical working group meetings and the joint KUHeS and MLW research dissemination conference. We will also publish the results in peer-reviewed journals.