# Evidence based targeting of districts for active surveillance of skin-related neglected tropical diseases in Ghana

## TL;DR

This study developed a tool to identify Ghanaian districts where active surveillance for skin-related neglected tropical diseases would be most effective.

## Contribution

The paper introduces an evidence-based framework and interactive dashboard to prioritize districts for skin NTD surveillance using open-source data.

## Key findings

- 17 districts in Ghana were identified as top priorities for active case detection of skin NTDs.
- Six districts scored high for both Buruli ulcer and lymphatic filariasis, making them key areas for intervention.
- The framework uses open-source data and can be adapted for other countries or diseases.

## 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.

Skin-related neglected tropical diseases (NTDs), such as Buruli ulcer and lymphatic filariasis, are often under-reported because they affect remote communities with limited access to healthcare. Finding these cases early is important, but “active case detection” can be costly. To make the best use of limited resources, we developed a practical approach to identify the districts in Ghana where case-finding is most likely to have an impact. We began by building a conceptual framework—a structured flowchart to think about where undiagnosed cases might be found—made up of five key domains: 1. Evidence for endemicity, 2. Population vulnerability to disease, 3. Accessibility of health services, 4. Availability of health services, 5. Risk factors for poor clinical outcomes. For each domain, we identified contributing factors (e.g., predicted disease risk, poverty levels, travel time to clinics, number of health centres, and under-five mortality) and found open-source datasets that could represent them. We summarised these data for each district, converted them into comparable scores, and gave each domain equal weight to ensure a balanced final ranking. We then presented the results in an interactive online dashboard (website) so that decision-makers can explore district-level maps and scores without needing specialist mapping software. Using this method, we identified 17 districts in Ghana as top priorities for active case detection of skin NTDs. Because the approach uses only open-source data, it can be adapted for other countries, different geographic scales, or even other diseases. This makes it a flexible tool for public health teams to target interventions where they are most needed.