ArticlePLOS global public health2026
Evidence based targeting of districts for active surveillance of skin-related neglected tropical diseases in Ghana.
Article in PLOS global public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
To improve control and management of skin-neglected tropical diseases (NTDs), district-level integration of case finding and management is recommended. However, these strategies are costly and should be targeted to co-endemic areas. Identifying districts with high burdens of undiagnosed cases, particularly where access to healthcare is limited, can better direct efforts. We developed an evidence consensus framework-a structured decision-making approach that combines information from multiple sources to support decision-makers. Using this approach, we built an interactive dashboard that brings together data on factors such as indicators of disease endemicity from model predictions, population vulnerability to disease, access to and availability of health services, and risk factors for poor clinical outcomes. Each factor is given a score, which is then adjusted so that no single type of information outweighs the others. Districts were scored and ranked based on levels of each indicator, and districts scoring highest across combined criteria were identified. We visualised results on an interactive dashboard, or webpage, intended for use by decision-makers in NTD programs in Ghana. We identified 108 districts potentially endemic for both Buruli ulcer (BU) and lymphatic filariasis (LF). Of these, 17 districts ranked in the highest quintile for overall score and were deemed suitable for active case detection of skin NTDs. Notably, Pru East, Shama, and Nzema East scored highest, despite mixed BU endemicity. Six districts, including Shama, Awutu Senya East, and Ekumfi, scored high for both BU and LF, making them priority areas for active BU detection. This evidence-based framework offers a practical method for integrating datasets to guide surveillance and decision-making in skin NTDs. It emphasizes prioritizing districts with high overall scores and predicted LF or BU prevalence, while addressing gaps in knowledge about BU risk factors. By simplifying data integration, this framework enhances surveillance efforts, improving coverage and resource allocation.
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