title: Spatial and socio-economic effects on malaria morbidity in children under 5 years in Malawi in 2012.
abstract: BACKGROUND
  Malaria is a major health challenge in sub-Saharan Africa with children under 5 being most vulnerable. Identifying regions of greater malarial burden is vital in targeting interventions.
  
  
  METHODS
  This study analysed malaria morbidity using data from the Malawi 2012 Malaria Indicator Survey that were obtained from Demographic and Health Survey (DHS) program website. These data captured malaria related information on children under 5. Poisson regression was done to determine associations between outcome (number of children under 5 with malaria in household) and explanatory variables. A Bayesian smoothing approach was employed to adjust for spatial random effects on child related variables.
  
  
  RESULTS
  There were 1878 households in 140 clusters. The number of children under five was 1900. Spatially structured effects accounted for more than 90% of random effects as these had a mean of 1.32 (95% Credible Interval (CI)=0.37, 2.50) whilst spatially unstructured had a mean of 0.10 (CI=9.0 × 10(-4), 0.38). Spatially adjusted significant variables were; type of place of residence (urban or rural) [posterior odds ratio (POR)=2.06; CI= 1.27, 3.34], not owning land [POR=1.77; CI=1.19, 2.64], not staying in a slum [POR=0.52; CI=0.33, 0.83] and enhanced vegetation index [POR=0.02; CI=0.00, 1.08]. A trend was observed on usage of insecticide treated mosquito nets [POR=0.80; CI=0.63, 1.03].
  
  
  CONCLUSIONS
  This study showed that malaria is a disease of poverty. Enhanced vegetation index was an important factor in malaria morbidity. The central region was identified as the area with greatest disease burden.
authors.affiliation: Division 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. Electronic address: simangachitunhu@gmail.com.
authors.forename: Simangaliso
authors.initials: S
authors.lastname: Chitunhu
authors.affiliation: Division 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. Electronic address: Eustasius.Musenge@wits.ac.za.
authors.forename: Eustasius
authors.initials: E
authors.lastname: Musenge
mesh_terms: D001499:Bayes Theorem; D002675:Child, Preschool; D016000:Cluster Analysis; D003710:Demography / Q000706:statistics & numerical data; D005260:Female; D006306:Health Surveys / Q000706:statistics & numerical data*; D006801:Humans; D007223:Infant; D008288:Malaria / Q000453:epidemiology*; D008295:Malawi / Q000453:epidemiology; D008297:Male; D012959:Socioeconomic Factors; D062206:Spatial Analysis
keywords: Bayesian spatial smoothing; Childhood malaria; Conditional autoregressive model; Geographical information system; Structured random effects; Unstructured random effects