Tangena, J.-A.A. and Mategula, D. and Sedda, L. and Atkinson, P.M. (2023) Unravelling the impact of insecticide-treated bed nets on childhood malaria in Malawi. Malaria Journal, 22 (1): 16. ISSN 1475-2875
Full text not available from this repository.Abstract
Background: To achieve malaria elimination it is essential to understand the impact of insecticide-treated net (ITNs) programmes. Here, the impact of ITN access and use on malaria prevalence in children in Malawi was investigated using Malaria Indicator Survey (MIS) data. Methods: MIS data from 2012, 2014 and 2017 were used to investigate the relationship between malaria prevalence in children (6–59 months) and ITN use. Generalized linear modelling (GLM), geostatistical mixed regression modelling and non-stationary GLM were undertaken to evaluate trends, spatial patterns and local dynamics, respectively. Results: Malaria prevalence in Malawi was 27.1% (95% CI 23.1–31.2%) in 2012 and similar in both 2014 (32.1%, 95% CI 25.5–38.7) and 2017 (23.9%, 95% CI 20.3–27.4%). ITN coverage and use increased during the same time period, with household ITN access growing from 19.0% (95% CI 15.6–22.3%) of households with at least 1 ITN for every 2 people sleeping in the house the night before to 41.7% (95% CI 39.1–44.4%) and ITN use from 41.1% (95% CI 37.3–44.9%) of the population sleeping under an ITN the previous night to 57.4% (95% CI 55.0–59.9%). Both the geostatistical and non-stationary GLM regression models showed child malaria prevalence had a negative association with ITN population access and a positive association with ITN use although affected by large uncertainties. The non-stationary GLM highlighted the spatital heterogeneity in the relationship between childhood malaria and ITN dynamics across the country. Conclusion: Malaria prevalence in children under five had a negative association with ITN population access and a positive association with ITN use, with spatial heterogeneity in these relationships across Malawi. This study presents an important modelling approach that allows malaria control programmes to spatially disentangle the impact of interventions on malaria cases.