Long-Lasting Insecticide-Treated Net Use Gaps and Severity Predictors in a Pre-Elimination Landscape: A Retrospective Observational Study in Mberengwa, Zimbabwe

Although significant progress has been made in reducing malaria transmission in Zimbabwe, the path to elimination remains challenging. The disease remains a persistent threat, particularly in resource-constrained areas such as Mberengwa, necessitating an urgent need to understand the demographic, behavioural, socioeconomic, and structural factors influencing long-lasting insecticide-treated net use and case severity. This study investigated these factors using individual malaria case data to inform the development of locally tailored strategies for malaria elimination. Individual malaria case data from 2019 to 2024 were collected from the District Health Information System Tracker-2 database for this study. Data were triangulated with line list and health facility register data to verify records and complete the missing data. The resulting 662 cases were analysed using stratified descriptive analysis, multivariate logistic regression, and Random Forest classification models. There is an overall gradual decline in the annual Test Positivity Rate, despite seasonal peaks. A critical finding was the disparity between long-lasting insecticide-treated net ownership (95%) and use (7.7%), suggesting that ownership does not translate to protective use. In the multivariate logistic regression, none of the tested variables were significant determinants of Long-Lasting Insecticide Net use. However, random forest modelling identified age, time to seek care, religious group, distance to health facilities, and education level as the top 5 influential factors. For malaria case severity, greater distance to a health facility (<i>P</i> < .001) and increasing age (<i>P</i> = .002) were consistently identified as significant factors associated with severity. The Random Forest model demonstrated enhanced performance in discriminating case severity compared to Logistic Regression. The findings of this study highlight that effective malaria elimination requires a combined focus on behavioural change, structural improvements in healthcare access, and data-driven programming supported by advanced analytics. Tailored malaria elimination strategies must address the long-lasting insecticide-treated net use gap and structural barriers.