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... Show moreObjectives: 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 (4 4.0%) 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. (c) 2021 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-ncnd/4.0/). Show less
Corstjens, P.L.A.M.; Hooij, A. van; Fat, E.M.T.K.; Alam, K.; Vrolijk, L.B.; Dlamini, S.; ... ; Geluk, A. 2019
