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Nonparametric Regression and Spatial Variation of Malaria Incidences : Linking Disease Risk to Climatic Variability in Malawi

S. D. Kampondeni, C. Chilingulo,K. B. Seydel,M. J Potchen,W. G. Bradley,M. T. Latourette,G. L. Birbeck,J. E. Siebert,T. E. Taylor, P. Kapito-Tembo, D. P. Mathanga, J. Fiore, K. Seydel, M. Liomba, A. Bauleni, P. Pensulo, R. Mukadam, O., Nyirenda, M. Laufer

semanticscholar(2013)

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Abstract
Introduction Malaria is a major public health problem in terms of both morbidity and mortality. Due to severe health economic costs of malaria, there is a need for methods that will help to understand the geographical variation of the disease risk and its association with climate. This paper analyses the variation of hospital diagnosed malaria incidences in relation to Rainfall, Temperatures and Humidity that are measured at district level from 2002 to 2010 in Malawi. Methods Using district hospital and health facilities malaria case records and climatic factors, a non-parametric regression model based on generalized additive mixed models (GAMM) was developed. Modeling and inference is within full Bayesian framework through Markov Chain Monte Carlo (MCMC) simulation techniques. Results There is a decreasing trend of malaria incidences in the study period and an evidence of spatial variation in the risk of having malaria which is higher in Warm Wet Season (November to March) with RR = 1.07, 95% CI = [1.02-1.07] mainly in districts along lakes and rivers such as Rumphi, NkhataBay, Nkhotakota, Salima, Ntcheu, Balaka, Mwanza, Chikhwawa and Nsanje. Marginal changes in environmental factors greatly affect the risk of increased malaria incidences. Malaria incidences in a given month are strongly positively associated with minimum temperatures of around 20 degree Celsius the previous month. Conclusions and Recommendations Modeling the impact of known factors alone is not sufficient to produce a satisfactory fit to the observations, geographical variation needs to be considered to improve the fit and account for heterogeneity. Ignoring a nonlinear relationship may result in misleading estimates of residual spatial surface which would have been overlooked by a parametric linear model. Association between weather and malaria should be considered in the development and implementation of malaria interventions. History of Malaria Testing Among Out-Patient Adults Receiving Antimalarial Drugs at Queen Elizabeth Central Hospital (QECH), Southern Malawi, 2012 A.Kamoto1, A S. Muula2 1.Fourth Year Student, Department of Pharmacy, University Of Malawi, College Of Medicine, Blantyre Malawi. Email: akamoto@medcol.mw 2.Department of Community Health, University of Malawi, College Of Medicine, Blantyre Malawi
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