Spatial and socio-economic effects on malaria morbidity in children under 5 years in Malawi in 2012. (February 2016)

• Record Type: Journal Article
• Authors: Chitunhu, Simangaliso; Musenge, Eustasius
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 16(2016)
• Page Start: 21
• Page End: 33
• Publication Date: 2016-02
• Subjects: CAR conditional autoregressive model -- CI Credible Interval -- DHS Demographic and Health Survey -- DIC deviance information criterion -- DST Department of Science and Technology -- EA enumeration area -- EVI enhanced vegetation index -- GMRF Gaussian Markov Random Fields -- GIS geographical information system -- GLM generalised linear model -- INLA integrated nested laplace approximation -- ITN insecticide insecticide-treated bed nets -- MCMC Markov chain Monte Carlo -- MH Metropolis–Hastings -- MIS Malaria Indicator Survey -- NRF National Research Foundation -- POR posterior odds ratio -- RR relative risk -- SACEMA South African Centre of Excellence in Epidemiological Modelling and Analysis -- SES socio-economic status -- TSI temperature suitability index -- UN United Nations -- WHO World Health Organization; Childhood malaria -- Structured random effects -- Unstructured random effects -- Bayesian spatial smoothing -- Geographical information system -- Conditional autoregressive model; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2015.11.001
• Languages: English
• ISSNs: 1877-5845

Abstract

Highlights: Malaria morbidity is linked to socio-economic status. The central region of Malawi was most affected by malaria morbidity in 2012. Adjusting for cluster sample weights is important in spatial Bayesian modelling. Bayesian smoothing approach is advantageous in handling spatial random effects. 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]. Conclusion: 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.