Factors influencing malaria infection in Rwanda 2010: a cross-sectional survey study using generalized structural equation modeling

Abstract Background Malaria is one of the primary public health concerns in the world and an important cause of morbidity and mortality in sub-Saharan Africa. Malaria morbidity is associated with poverty and vulnerability as it is not easy for the poor people to access preventive treatment and protective measures. In Rwanda, malaria prevention has become a major problem against the double-barreled burden of an overstretched health system and strained financial resources.Methods This work was a cross-sectional survey study design based on data from Rwanda collected in 2010 through the Malaria Indicator Survey as part of the Demographic and Health Survey. The primary outcome variable was an ordinal variable with three categories; no malaria, probable malaria, and confirmed malaria cases. The outcome variable was formulated by combining rapid malaria test and confirmatory blood smear laboratory test. Statistical analysis was done using survey ordinal logistic regression modelling adjusting for random effects for direct effects and generalised structural equation modelling (G-SEM) to obtain total (direct and indirect) effects of malaria morbidity. Results The 11,865 participants had a mean age of 22 years, and two-thirds of the participants were females (67%). Household related variables (socio-economic status, health insurance, age in years) showed a significant total effect on malaria infection. Socioeconomic status had the greatest total effect which was a sum of the direct and indirect effects influenced indirectly by education, health insurance and the number of rooms for sleeping in isolation. Conclusion Poverty is still the core issue to the morbidity patterns driving the malaria epidemic in Rwanda. Access to health facility and health insurance has a high positive impact on decreasing disease as such a special focus on some regions can be an effective intervention strategy. A better understanding of the drivers of morbidity directly and/or indirectly can better target interventions to be more efficient in those affected areas.