How useful are malaria risk maps at the country level? Perceptions of decision-makers in Kenya, Malawi and the Democratic Republic of Congo

Abstract

Background Declining malaria prevalence and pressure on external funding have increased the need for efficiency in malaria control in sub-Saharan Africa (SSA). Modelled Plasmodium falciparum parasite rate (PfPR) maps are increasingly becoming available and provide information on the epidemiological situation of countries. However, how these maps are understood or used for national malaria planning is rarely explored. In this study, the practices and perceptions of national decision-makers on the utility of malaria risk maps, showing prevalence of parasitaemia or incidence of illness, was investigated. Methods A document review of recent National Malaria Strategic Plans was combined with 64 in-depth interviews with stakeholders in Kenya, Malawi and the Democratic Republic of Congo (DRC). The document review focused on the type of epidemiological maps included and their use in prioritising and targeting interventions. Interviews (14 Kenya, 17 Malawi, 27 DRC, 6 global level) explored drivers of stakeholder perceptions of the utility, value and limitations of malaria risk maps. Results Three different types of maps were used to show malaria epidemiological strata: malaria prevalence using a PfPR modelled map (Kenya); malaria incidence using routine health system data (Malawi); and malaria prevalence using data from the most recent Demographic and Health Survey (DRC). In Kenya the map was used to target preventative interventions, including long-lasting insecticide-treated nets (LLINs) and intermittent preventive treatment in pregnancy (IPTp), whilst in Malawi and DRC the maps were used to target in-door residual spraying (IRS) and LLINs distributions in schools. Maps were also used for operational planning, supply quantification, financial justification and advocacy. Findings from the interviews suggested that decision-makers lacked trust in the modelled PfPR maps when based on only a few empirical data points (Malawi and DRC). Conclusions Maps were generally used to identify areas with high prevalence in order to implement specific interventions. Despite the availability of national level modelled PfPR maps in all three countries, they were only used in one country. Perceived utility of malaria risk maps was associated with the epidemiological structure of the country and use was driven by perceived need, understanding (quality and relevance), ownership and trust in the data used to develop the maps.

Secondary malaria vectors in western Kenya include novel species with unexpectedly high densities and parasite infection rates

“…Malaria transmission occurs throughout the year in western Kenya, with peaks corresponding to rainfall in mid-April to July and November to December. It is classified as a lake endemic region with a Plasmodium falciparum prevalence of 20%-50% [ 3 ]. Anopheles gambiae (s.s.), An.…”

“…Approximately 75% of the population in Kenya is at risk of the disease and 16% of outpatient consultations are malaria related [ 2 ]. Disease transmission in the country is variable with regions being endemic, epidemic-prone, seasonal transmission, or low risk zones, with prevalence rates of Plasmodium falciparum as high as 36.5% in parts of western Kenya [ 2 , 3 ].…”

Predicting malaria epidemics in Burkina Faso with machine learning

“…3. Implementation of the machine-learning algorithms is a highly debated one, with even the simplest of data representation (risk maps) being not completely effective [23]. Translating error bars and risk analysis to interventions is something that must be carried out beyond the machine-learning domain, and exists as a continuing discussion between data-scientists, epidemiologists and key decision makers.…”

Maplaria: a user friendly web-application for spatio-temporal malaria prevalence mapping

“…Over the last decade, model-based geostatistical (MBG) methods have become an established set of modern statistical tools for interpolating malaria risk within a geographical area of interest using cross-sectional survey data [ 7 , 8 ]. As a result, risk maps generated from MBG models have been increasingly adopted by NMCPs in SSA, where the burden remains high relative to other regions, to inform policy decisions for monitoring, evaluation and to inform health policies [ 9 , 10 ].…”