Zambia, along with a handful of countries within Southern and Eastern Africa, is on track to reduce malaria cases 40% by 2020. Relative to 2015, the country’s progress so far translates to as many as 700,000 cases prevented annually. Such tremendous strides speak to the success of preventative interventions, such as improved access to indoor residual spraying (IRS) and insecticide treated nets (ITNs), but the work is far from over.
The citizens and communities across Zambia rely on annual district planning to determine where, how much of, and what interventions are needed in a particular population. In answering these questions, district teams begin their microplanning processes, determining on a local level the nuances of the year’s malaria interventions. These teams must deeply understand the communities they serve. Who is at greatest risk of infection? Where do they live? What settlements ought to be targeted? And what resources are needed to bring a community out of harm’s way? Lacking this information, district teams cannot fully grasp the extent of preventable malaria cases and consequently limit their capacity to act.
Traditionally, teams of local community health workers aggregate this information on foot and process it in hard copy. This costly and time consuming work flow jeopardizes the data’s accuracy, totality, and speedy delivery to key decision makers. System bottlenecks, limited resources, or a lack of confidence in the data can then undermine the quantitative foundation of an intervention. Weary decision makers might turn instead to outdated data and an imprecise understanding of the population they aim to serve. What ought to be a concrete step in the year’s plan bends to inefficacious circumstances.