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Citation Information

Type Journal Article - Hagenlocher and Castro Population Health Metrics
Title Mapping malaria risk and vulnerability in the United Republic of Tanzania: a spatial explicit model
Author(s)
Volume 13
Issue 2
Publication (Day/Month/Year) 2015
URL https://pophealthmetrics.biomedcentral.com/track/pdf/10.1186/s12963-015-0036-2?site=http://pophealth​metrics.biomedcentral.com
Abstract
Background: Outbreaks 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.
Methods: Building 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.
Results: The 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.
Conclusions: The 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.

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