Multilevel logistic regression modelling to quantify variation in malaria prevalence in Ethiopia.

ABSTRACT:

Background

Ethiopia has low malaria prevalence compared to most other malaria-endemic countries in Africa. However, malaria is still a major public health problem in the country. The binary logistic regression model has been widely used to analyse malaria indicator survey (MIS) data. However, most MIS have a hierarchical structure which may result in dependent data. Since this model assumes that conditional on the covariates the malaria statuses of individuals are independent, it ignores potential intra-cluster correlation among observations within a cluster and may generate biased analysis results and conclusions. Therefore, the aim of this study was to quantify the variation in the prevalence of malaria between sample enumeration areas (SEAs) or clusters, the effects of cluster c

SUBMITTER: Zewude BT 

PROVIDER: S-EPMC9521912 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature