Multilevel Analysis Applied to Binary Data: Malaria Prevalence in Sauri Millenium Village, Kenya

Abstract

Millennium Villages Project is an initiative that is meant to demonstrate that the millennium development goals could be achieved in an integrated approach in putting a combination of interventions in place. Among these interventions are the health interventions of reducing the prevalence of common diseases. Malaria is one of these common diseases. In 2005 Sauri Millennium Village was started in western Kenya which was then followed by 13 other villages across Africa. A baseline study was done in 2005 to measure the bench marks of the millennium development goals indicators in the village. As part of these surveys, blood data was collected to estimate the baseline prevalence of malaria in the Sauri Millennium village. This data was linked to socioeconomic data to study factors affecting malaria prevalence. Malaria affects individuals who are clustered in households and villages. In addition to individual effects, households and villages have characteristics that influence malaria prevalence. The individual characteristics under study were age and gender. The household characteristics were income and the education status of the household the individual belongs. The village level factors were the counts of water . bodies and the area covered by woods of the villages. Logistic regression models were applied to understand the determinants of malaria. Considering the multilevel structure of data, the analysis goes beyond the single-level modelling and explores the value of multilevel modelling in understanding the malaria risk factors. The analysis showed that malaria prevalence among the population at baseline was about 50% and was similar for males and females. The results also showed that malaria prevalence decreases with age. Income and education status of the households were also found to have an effect on malaria prevalence. The utility of the multilevel techniques in answering the research questions clearly demonstrated the value of statistical techniques in understanding factors affecting health outcomes. The recognition of complex structures of data in statistical modelling processes, yield reliable results that help health strategists make informed decisions in taming malaria.