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Spatial and socio-economic effects on malaria morbidity in children under 5 years in Malawi in 2012

Simangaliso Chitunhu1, Eustasius Musenge1

  • 1Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, 27 St Andrews' Road, Parktown, Johannesburg 2193, South Africa.

Spatial and Spatio-Temporal Epidemiology
|February 28, 2016
PubMed

Insights

Malaria disproportionately affects young children in sub-Saharan Africa. This study identified poverty, urban/rural residence, and vegetation as key factors, with the central region showing the highest malaria burden.

Area of Science:

  • Epidemiology
  • Public Health
  • Spatial Analysis

Background:

  • Malaria remains a significant public health concern in sub-Saharan Africa, particularly impacting children under five.
  • Targeted interventions require precise identification of high-burden geographical areas.

Purpose of the Study:

  • To analyze malaria morbidity patterns in children under five in Malawi.
  • To identify key socio-economic and environmental determinants of malaria risk.
  • To map spatial variations in malaria burden.

Main Methods:

  • Utilized data from the 2012 Malawi Malaria Indicator Survey (Demographic and Health Survey program).
  • Employed Poisson regression to assess associations between household malaria cases and explanatory variables.
  • Applied a Bayesian smoothing approach to account for spatial random effects.

Main Results:

  • Spatially structured effects significantly influenced malaria distribution, accounting for over 90% of random effects.
  • Significant risk factors included urban/rural residence, land ownership, slum dwelling, and enhanced vegetation index.
  • A trend towards reduced risk was observed with the use of insecticide-treated mosquito nets.

Conclusions:

  • Malaria prevalence is strongly linked to poverty indicators.
  • Enhanced Vegetation Index emerged as a critical environmental factor influencing malaria morbidity.
  • The central region of Malawi was identified as having the highest malaria disease burden.
Abstract

Keywords:
Bayesian spatial smoothingChildhood malariaConditional autoregressive modelGeographical information systemStructured random effectsUnstructured random effects

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