ArticleBMC infectious diseases2022
Determining target populations for leprosy prophylactic interventions: a hotspot analysis in Indonesia.
Article in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 18 citations in OpenAlex.
- Persistent and Emerging High-Risk Clusters of Leprosy Detection in Brazil: A Nationwide Spatiotemporal Analysis, 2001-2023.Tropical medicine & international health : TM & IH · 2026Article
- Strategic automated external defibrillator deployment based on geospatial analysis of historical cardiac arrest locations.International journal of cardiology. Heart & vasculature · 2026Article
- Participatory development of Indonesia's national action plan for zero leprosy: strategies and interventions.Frontiers in public health · 2025Article
- Socioeconomic determinants of the leprosy risk in Yunnan Province, China: a county-level spatiotemporal study.Frontiers in public health · 2025Article
- Spatiotemporal pattern of leprosy in southwest China from 2010 to 2020: an ecological study.BMC public health · 2024Observational
- Identifying clusters of leprosy patients in India: A comparison of methods.PLoS neglected tropical diseases · 2022Article
- Community intervention programmes with people affected by leprosy: Listening to the voice of professionals.PLoS neglected tropical diseases · 2022Article
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
Abstract
backgroundLeprosy incidence remained at around 200,000 new cases globally for the last decade. Current strategies to reduce the number of new patients include early detection and providing post-exposure prophylaxis (PEP) to at-risk populations. Because leprosy is distributed unevenly, it is crucial to identify high-risk clusters of leprosy cases for targeting interventions. Geographic Information Systems (GIS) methodology can be used to optimize leprosy control activities by identifying clustering of leprosy cases and determining optimal target populations for PEP.
methodsThe geolocations of leprosy cases registered from 2014 to 2018 in Pasuruan and Pamekasan (Indonesia) were collected and tested for spatial autocorrelation with the Moran's I statistic. We did a hotspot analysis using the Heatmap tool of QGIS to identify clusters of leprosy cases in both areas. Fifteen cluster settings were compared, varying the heatmap radius (i.e., 500 m, 1000 m, 1500 m, 2000 m, or 2500 m) and the density of clustering (low, moderate, and high). For each cluster setting, we calculated the number of cases in clusters, the size of the cluster (km
resultsThe distribution of cases was more focused in Pasuruan (Moran's I = 0.44) than in Pamekasan (0.27). The proportion of total cases within identified clusters increased with heatmap radius and ranged from 3% to almost 100% in both areas. The proportion of the population in clusters targeted for PEP decreased with heatmap radius from > 100% to 5% in high and from 88 to 3% in moderate and low density clusters. We have developed an example of a practical guideline to determine optimal cluster settings based on a given PEP strategy, distribution of cases, resources available, and proportion of population targeted for PEP.
conclusionPolicy and operational decisions related to leprosy control programs can be guided by a hotspot analysis which aid in identifying high-risk clusters and estimating the number of people targeted for prophylactic interventions.
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