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.

Automated measurement of malaria parasitaemia among asymptomatic blood donors in Malawi using the Sysmex XN-31 analyser: could such data be used to complement national malaria surveillance in real time?

“…The reference group for location was non-urban and December for the month of collection OR odds ratio, CI confidence interval prevalence rate in Malawi, which has been consistently reported [22,23]. In contrast, the low prevalence Southern Highlands (6.1%) zone, showed the opposite, with a consistent downward trend with the lowest donor malaria prevalence in April, whereas the Northern (7%) and Shire Valley (12%) zones had variable patterns.…”

“…The median age of blood donors was 21 years (IQR 19-28), with 95% of donors between 16 and 47 years, and the oldest 64 years. Female donors were significantly younger (p < 0.001, Mann-Whitney test) with median age of 19 years (IQR 18-25) compared with males (median 21 years, IQR [19][20][21][22][23][24][25][26][27][28]. There were also differences in age distribution based on collection centre.…”

Stratification of malaria incidence in Papua New Guinea (2011–2019): Contribution towards a sub-national control policy

“…A similar approach to the one applied in this work has previously been used elsewhere, for example, in Tanzania [24]. Modelling tools have been used to stratify malaria and improve national malaria programs' decision-making [24,26,[68][69][70][71]. Both geostatistical and dynamic modelling methods could assist decision-making on malaria control by examining different allocation scenarios of interventions at operational units.…”