Abstract
Objectives: To identify patterns of spatial clustering of leprosy. Design: We performed a baseline survey for a trial on post-exposure prophylaxis for leprosy in Comoros and Madagascar. We screened 64 villages, door-to-door, and recorded results of screening, demographic data and geographic coordinates. To identify clusters, we fitted a purely spatial Poisson model using Kulldorff's spatial scan statistic. We used a regular Poisson model to assess the risk of contracting leprosy at the individual level as a function of distance to the nearest known leprosy patient. Results: We identified 455 leprosy patients; 200 (44.0%) belonged to 2735 households included in a cluster. Thirty-eight percent of leprosy patients versus 10% of the total population live ≤25 m from another leprosy patient. Risk ratios for being diagnosed with leprosy were 7.3, 2.4, 1.8, 1.4 and 1.7, for those at the same household, at 1–<25 m, 25–<50 m, 50–<75 m and 75–<100 m as/from a leprosy patient, respectively, compared to those living at ≥100 m. Conclusions: We documented significant clustering of leprosy beyond household level, although 56% of cases were not part of a cluster. Control measures need to be extended beyond the household, and social networks should be further explored.
| Original language | English |
|---|---|
| Pages (from-to) | 96-101 |
| Number of pages | 6 |
| Journal | International Journal of Infectious Diseases |
| Volume | 108 |
| DOIs | |
| Publication status | Published - 1 Jul 2021 |
Bibliographical note
Funding Information:This study is part of the PEOPLE project, which is part of the EDCTP2 programme supported by the European Union (grant number RIA2017NIM-1847-PEOPLE ). The views and opinions of authors expressed herein do not necessarily state or reflect those of EDCTP.
Publisher Copyright:
© 2021 The Author(s)
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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