Mapping malaria risk and vulnerability in the United Republic of Tanzania: a spatial explicit model

Abstract

BackgroundOutbreaks of vector-borne diseases (VBDs) impose a heavy burden on vulnerable populations. Despite recent progress in eradication and control, malaria remains the most prevalent VBD. Integrative approaches that take into account environmental, socioeconomic, demographic, biological, cultural, and political factors contributing to malaria risk and vulnerability are needed to effectively reduce malaria burden. Although the focus on malaria risk has increasingly gained ground, little emphasis has been given to develop quantitative methods for assessing malaria risk including malaria vulnerability in a spatial explicit manner.MethodsBuilding on a conceptual risk and vulnerability framework, we propose a spatial explicit approach for modeling relative levels of malaria risk - as a function of hazard, exposure, and vulnerability - in the United Republic of Tanzania. A logistic regression model was employed to identify a final set of risk factors and their contribution to malaria endemicity based on multidisciplinary geospatial information. We utilized a Geographic Information System for the construction and visualization of a malaria vulnerability index and its integration into a spatially explicit malaria risk map.ResultsThe spatial pattern of malaria risk was very heterogeneous across the country. Malaria risk was higher in Mainland areas than in Zanzibar, which is a result of differences in both malaria entomological inoculation rate and prevailing vulnerabilities. Areas of high malaria risk were identified in the southeastern part of the country, as well as in two distinct “hotspots” in the northwestern part of the country bordering Lake Victoria, while concentrations of high malaria vulnerability seem to occur in the northwestern, western, and southeastern parts of the mainland. Results were visualized using both 10×10 km2 grids and subnational administrative units.ConclusionsThe presented approach makes an important contribution toward a decision support tool. By decomposing malaria risk into its components, the approach offers evidence on which factors could be targeted for reducing malaria risk and vulnerability to the disease. Ultimately, results offer relevant information for place-based intervention planning and more effective spatial allocation of resources.Electronic supplementary materialThe online version of this article (doi:10.1186/s12963-015-0036-2) contains supplementary material, which is available to authorized users.

Modelling homogeneous regions of social vulnerability to malaria in Rwanda

“…Because no single approach to malaria elimination can be successful across the country, strategies that account for both socioeconomic and environmental settings in each region must be implemented in an integrated approach Mboera et al, 2014;Hagenlocher and Castro, 2015). An integrated vector management (IVM) programme was recently adopted in Rwanda as a comprehensive framework for malaria interventions, based on local ecology, disease epidemiology and socioeconomic factors (USAID, 2014).…”

“…Successful malaria elimination therefore needs to expand on classic approaches, which mostly focus on environmental factors, malaria parasites, and vectors, and should also consider the social, economic, demographic, and accessrelated (e.g. access to health care) factors that shape the vulnerability of the population (De Plaen et al, 2004;Kienberger and Hagenlocher, 2014;Hagenlocher and Castro, 2015).…”

“…In this regard a holistic risk and vulnerability framework, including a methodology for mapping social vulnerability to malaria, was developed by Kienberger and Hagenlocher (2014), and was recently applied to the entire East African Community (EAC) region. Hagenlocher and Castro (2015) also proposed a spatially explicit approach for mapping relative levels of malaria risk as a function of hazard (represented by malaria EIR), exposure, and vulnerability in Tanzania, where the final set of risk factors and their contribution to malaria endemicity was identified based on regression analysis. The results from the studies outlined above provide place-specific options for targeting malaria interventions among the most vulnerable populations.…”

“…Thus, there is a need to apply a high-resolution approach for modelling social vulnerability to malaria in Rwanda to bridge this gap. To model the regions of social vulnerability to malaria in Rwanda, this study is based on recently published works focusing on East Africa, such as Kienberger and Hagenlocher (2014) and Hagenlocher and Castro (2015). We applied a similar conceptual and methodological framework as the published works outlined above, but the advance of this paper relies on the spatial scale and better quality of data, which helped to reveal the hidden social vulnerability in Rwanda.…”

Predicting Malaria Transmission Risk in Endemic Areas of Iran: A Multilevel Modeling Using Climate and Socioeconomic Indicators

“…The effects of climatic variables on malaria transmission are well known and these variables have been widely used in many studies regarding malaria transmission (8,9). The socioeconomic factors are, also, among the significant variables associated with malaria transmission (10,11) and have been used by some studies (12,13). A fewer number of studies have used a combination of climatic variables along with socioeconomic variables for the estimation of malaria transmission (14,15).…”