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Multilevel logistic regression modelling to quantify variation in malaria prevalence in Ethiopia
Bereket Tessema Zewude1, Legesse Kassa Debusho1, Tadele Akeba Diriba1
1Department of Statistics, University of South Africa, Johannesburg, South Africa.
Plos One
|September 29, 2022
Summary
Multilevel logistic regression revealed significant variations in malaria prevalence between Ethiopian regions. Key factors influencing malaria include household mosquito net access, water source, and regional altitude, guiding targeted public health interventions.
Area of Science:
- Epidemiology
- Public Health
- Biostatistics
Background:
- Malaria remains a significant public health issue in Ethiopia, despite lower prevalence compared to other African nations.
- Traditional logistic regression models may yield biased results for hierarchical malaria indicator survey (MIS) data due to ignored intra-cluster correlation.
- Multilevel modeling is crucial for accurately analyzing complex, clustered survey data.
Purpose of the Study:
- To quantify malaria prevalence variations across Sample Enumeration Areas (SEAs) in Ethiopia.
- To assess the impact of cluster characteristics on malaria prevalence using intra-class correlation.
- To identify significant individual, household, and SEA-level factors affecting malaria prevalence via multilevel logistic regression.
Main Methods:
- Utilized data from the 2011 Ethiopian National Malaria Indicator Surveys (EMIS) for three major regions (Amhara, Oromia, SNNP), encompassing 9272 individuals.
- Applied multilevel logistic regression models with random SEA effects, incorporating survey design weights to account for unequal selection probabilities.
- Assessed spatial clustering of malaria prevalence using the Getis-Ord statistic on model random effects predictions.
Main Results:
- Significant malaria risk factors identified include age, gender, household mosquito net ownership, dwelling window presence, drinking water source, region, and median altitude.
- Approximately 45% of residual malaria prevalence variation was attributed to SEAs, with an Odds Ratio (MOR) of 4.784 indicating substantial unexplained heterogeneity between SEAs.
- High SEA variability was confirmed, with 80% interval odds ratios for SEA-level variables containing one.
Conclusions:
- Multilevel logistic regression identified five individual/household and two SEA-level risk factors for malaria infection in Ethiopia.
- Public health policies should prioritize factors like improved access to clean drinking water and targeted interventions based on spatial clustering findings.
- Addressing SEA-level variations and identified risk factors is essential for effective malaria control planning and geographically targeted interventions.
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