Prevalence and risk factors of malaria in children under the age of five years old in Uganda.

Abstract

Malaria is considered to be one of the main global health problems, with it causing close to a million deaths each year. Ninety percent of these deaths occur in Sub- Saharan Africa and 70% are of children under the age of 5 years. Uganda, ranked 6th worldwide in the number of malaria cases and 3rd in the number of malaria deaths in 2008, experiences weather conditions that often allow malaria transmission to occur all year round with only a few areas that experience low or unstable transmission. Malaria is the leading cause of morbidity in Uganda with 95% of the population at risk and it killing between 70,000 and 100,000 children every year. Children under the age of five years are among the most vulnerable to malaria infection as they have not yet developed any immunity to the disease. In order to apply successful implementations to eradicate malaria, there is a continuous need to understand the epidemiology and risk factors associated with the disease. Although a large number of studies done worldwide have identified a wide variety of risk factors; socioeconomic, environmental, demographic, and others, associated with malaria infection, there is still a great need to identify the influence of these factors in a local context to allow a successful formulation of a national malaria-control strategy. There have, however, been very few studies done in Uganda on malaria indicators and risk factors. These studies have also been specific to one community at a time. Most recent studies on malaria in Uganda have been hospital-based, investigating clinical malaria among young children and pregnant women. One of the aims of this thesis was to identify significant socio-economic, demographic and environmental risk factors associated with malaria infection, based on the result of a microscopy test conducted on 3,972 children under the age of five during a nationally represented Malaria Indicator Survey (MIS) done in Uganda in 2009. The MIS sample was stratified according to 10 regions of Uganda and was not spread geographically in proportion to the population, but rather equally across the regions. The survey consisted of a two-stage sample design where the first stage involved selecting clusters, with probability proportional to size, from a list of enumeration areas. The second stage involved systematic sampling of households from a list of households in each cluster. Surveys carried out using these sampling techniques are referred to as having complex survey designs. The response variable of interest is binary, indicating whether a child tested positive or negative for malaria. Logistic regression is commonly used to explore the relationship between a binary response variable and a set of explanatory variables. However, this method of analysis is not valid if the data come from complex survey designs. Failure to account for the complex design of a study may result in an overestimation of standard errors, therefore leading to incorrect results. There are many methods of dealing with this design of the study. Two such commonly used approaches are design-based and model-based statistical methods. A designed-based method, which involves the extension of logistic regression to complex survey designs, is survey logistic regression. For design-based methods, parameter estimates and inferences are based on the sampling weights, and only inferences concerning the effects of certain covariates on the response variable are of interest. However, model-based methods are used when interest is also on estimating the proportion of variation in the response variable that is attributable to each of the multiple levels of sampling. In this case, inference on the variance components of the model may also be of interest. Such methods include generalized linear mixed models and generalized estimating equations. This thesis discusses these three methods of analyzing complex survey designs and compares the results of each applied to the MIS data.