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Factors Influencing Malaria Morbidity and Mortality Among Under 15 Years Children in Parts of Ghana: The Role of Guardians’ Socio-Demographic Characteristics Demonstrated Through Modelling


 Background To understand the underlying factors that led to failure to achieve the millennium development goal 6c by Ghana, we modelled guardians’ socio-demographic characteristics and the factors that could influence child health to provide directions for improving anti-malaria interventions in Ghana and sub-Sahara Africa.Methods Clinical data of 274 malaria patients, age up to 15 years, were collected from a cross-sectional study conducted in two districts in the central region of Ghana. Guardians of the children were interviewed to obtain socio-demographic characteristics and factors that could influence child health. The data obtained were analysed using multivariate GLM, type III ANOVA and multilayer perceptron network analysis.Results Of the 274 children enrolled into the study, 69.34% were between the age of 0-5 years. The median parasite density, haemoglobin and platelet were 89,923 (64,056-149,541) parasites/mm3, 8.40 (4.70-11.60) g/dL and 126 (87-146) x10^9/L respectively. 12.41% of the enrolled children had severe clinical conditions. The outcome using multivariate GLM analysis indicate a significant association between the guardians’ socio-demographic characteristics and factors that influence child health, p<0.0001. CSOC, FPOCCH, DOOSCS, and CS were all influenced by the guardians’ occupational status, p<0.0001; CSOC and MCID were influenced by the guardians’ educational levels, p<0.0001 and FPOCCH and MCID were also influenced by guardians age category, p<0.0001. The multilayer perceptron network analysis showed that the association between guardians’ socio-demographic characteristics and factors that influence child health are affected by several hidden confounding factors.Conclusion It was demonstrated an association between factors that influence child health care and guardians’ socio-demographic characteristics and this is confounded by hidden socio-economic factors and biases through modelling. I, therefore, propose a model called a network of socio-economic progenitors to provide a simple explanation for the underlying factors that influence child health in malaria-endemic areas.