A Thesis Submitted to the Faculty of Health Sciences, University of the Witwatersrand in partial fulfilment of the requirements for the Degree of Master of Science in Epidemiology Major Area-Subject: Biostatistics and Epidemiology November 2014 / Background: Malaria is a serious public health challenge in sub-Saharan Africa with children under five being the most vulnerable, and a child dies every 30 seconds from it. Therefore, it is important to investigate malaria’s direct and indirect determinants in specific sub-Saharan populations as well as identifying malaria hotspots in order to have informed and targeted preventative interventions. Rationale: Given the extent and seriousness of malaria in Southern Africa, understanding fully the factors associated with malaria is important in successfully fighting it. Therefore, understanding the determinants of malaria in children under five is important in working towards eliminating malaria in sub-Saharan populations. Objectives: This study’s objectives were:  To describe demographic, behavioral and environmental determinants (factors) associated with malaria episodes in under fives in households in Malawi in the year 2012  To investigate the determinants of malaria episodes in children under five years in Malawi in 2012  To compare spatial distribution of malaria episodes in households in Malawi in 2012. Methods: This study was a secondary data analysis based on data from the Malawi 2012 Malaria Indicator Survey (MIS) obtained from Demographic and Health Survey (DHS) program website. The outcome variable was positive blood smear result for malaria in children less than five years, after an initial positive rapid malaria diagnostic test done at the homestead. We controlled for confounders after propensity score matching in order to reduce selection bias. Cases and controls were matched based on their propensity scores. Statistical modelling was done using logistic regression as well as generalized structural equation modeling (G-SEM) to model direct and indirect effects on the outcome. Poisson regression was done to determine associations between the outcome (positive blood smear malaria result) and selected explanatory variables at household level and we then introduced a structured and unstructured random effect to measure spatial effects if any of malaria morbidity in children under the age of five. Results: The matched data had 1 325 children with 367 (24.3%) having blood smear positive malaria. Female children made up approximately 53% of the total study participants. Child related variables (age, haemoglobin and position in household) as well as wealth index were significant (directly and indirectly) with p values <0.001. Socio-economic status (SES) [Odds ratio (OR) = 0.96, 95% Confidence interval (CI) = 0.92, 0.99] and primary level of education [OR = 0.50, 95%CI = 0.32, 0.77] were important determinants. The 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=9.0x10-4, 0.38). The spatially adjusted significant variables on malaria morbidity were; type of place of residence (Urban or Rural) [posterior odds ratio (POR) =2.06; CI = 1.27, 3.34], not owning land [RR=1.77; CI= 1.19, 2.64], not staying in a slum [RR=0.52; CI= 0.33, 0.83] and enhanced vegetation index [RR=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: Socio-economic status (directly and indirectly) and education are important factors that influence malaria control. The study showed malaria as a disease of poverty with significant results in slum, type of place of residence as well as ownership of land. It is important that these factors be taken into consideration when planning malaria control programs in order to have effective programs. Direct and indirect effect modelling can also provide an alternative modelling technique that incorporates indirect effects that might not be of significance when modeled directly. This will help in improving malaria control. Enhanced vegetation index was also an important factor in malaria morbidity but precipitation and temperature suitability index were not significant factors.