Exploring the distribution of risk factors for drop-out from Ponseti treatment for clubfoot across Bangladesh using geospatial cluster analysis

Submitted: 28 November 2022
Accepted: 25 February 2023
Published: 25 May 2023
Abstract Views: 612
PDF: 281
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Clubfoot is a congenital anomaly affecting 1/1,000 live births. Ponseti casting is an effective and affordable treatment. About 75% of affected children have access to Ponseti treatment in Bangladesh, but 20% are at risk of drop-out. We aimed to identify the areas in Bangladesh where patients are at high or low risk for drop-out. This study used a cross-sectional design based on publicly available data. The nationwide clubfoot program: ‘Walk for Life’ identified five risk factors for drop-out from the Ponseti treatment, specific to the Bangladeshi setting: household poverty, household size, population working in agriculture, educational attainment and travel time to the clinic. We explored the spatial distribution and clustering of these five risk factors. The spatial distribution of children <5 years with clubfoot and the population density differ widely across the different sub-districts of Bangladesh. Analysis of risk factor distribution and cluster analysis showed areas at high risk for dropout in the Northeast and the Southwest, with poverty, educational attainment and working in agriculture as the most prevalent driving risk factor. Across the entire country, twenty-one multivariate high-risk clusters were identified. As the risk factors for drop-out from clubfoot care are not equally distributed across Bangladesh, there is a need in regional prioritization and diversification of treatment and enrolment policies. Local stakeholders and policy makers can identify high-risk areas and allocate resources effectively.



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How to Cite

Pigeolet, M., Kucchal, T., Hey, M. T., Castro, M. C., Evans, A. M., Uribe-Leitz, T., Chowhury, M. M. H., & Juran, S. (2023). Exploring the distribution of risk factors for drop-out from Ponseti treatment for clubfoot across Bangladesh using geospatial cluster analysis. Geospatial Health, 18(1). https://doi.org/10.4081/gh.2023.1174