Weighted Multilevel Models for Malaria Indicator Survey

Abstract

Background: The data generated from malaria indicator surveys have a multilevel structure where children or households are nested within cluster and this may result in dependent data. In a survey, clusters or units within clusters may be selected with unequal probabilities and often surveys are subjected to non-response. Modeling of these data must take into consideration these aspects as these could lead to incorrect inferences. The main purpose of this study is to assess the factors that affect child malaria diagnostic outcomes and to estimate the proportion of overall child-level variation in malaria outcome that is attributable to child and household-level predictors and the cluster-level predictors using various weighted multilevel models. In this study, cluster represents a "Kebele", which is the smallest administrative unit in regional states in Ethiopia. Methods: A sample of 4,384 under five years age children were in this study from three regional states of Ethiopia. Various multilevel models with random cluster effects were used taking into account the survey design weights. These weights are scaled to address unequal probabilities of selection within clusters. Results: The asymptotic chi-square mixture distribution test results suggested the need for the cluster-level random effects in all the models. The findings related to child / household- and cluster-level predictors are consistent with those available in existing literature. The overall variability has been partitioned into child/household-level and cluster-level variability and the results revealed that some of the differences between clusters in under five years age children malaria RDT outcomes explained by child/household factors. Conclusions: The use of weighted multilevel model to examine within- and between cluster variability in malaria RDT outcomes for under five years age children and of the extent to which between clusters variability is explained by child / household and cluster-level factors. The approach used in this paper allows investigation of how individual- and cluster-level factors including the public health authorities’ interventions plans related to health outcomes simultaneously. Such collective assessment approach may lead to more effective public health strategies and could have important policy implications for health promotion and for the reduction of health disparities.

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