Nimer Ortuño-Gutiérrez1, Aboubacar Mzembaba2, Stéphanie Ramboarina3, Randrianantoandro Andriamira4, Abdallah Baco2, Sofie Braet5, Assoumani Younoussa2, Bertrand Cauchoix3, Zahara Salim2, Mohamed Amidy2, Saverio Grillone2, Tahinamandranto Rasamoelina6, Emmanuelle Cambau7, Annemieke Geluk8, Bouke C de Jong5, Jan Hendrik Richardus9, Epco Hasker5. 1. Damien Foundation, Brussels, Belgium. Electronic address: Nimer.OrtunoGutierrez@damiaanactie.be. 2. National Tuberculosis and Leprosy Control Program, Moroni, Comoros. 3. Raoul Follereau Foundation, Antananarivo, Madagascar. 4. National Leprosy Control Program, Antananarivo, Madagascar. 5. Institute of Tropical Medicine, Antwerp, Belgium. 6. Centre d'Infectiologie Charles Mérieux, Université d'Antananarivo, Antananarivo, Madagascar. 7. INSERM, IAME UMR1137, Université de Paris, 75018 Paris, France; APHP GHU Nord, Service de Mycocactériologie spécialisée et de référence, Centre National de Référence des Mycobactéries et de la Résistance des Mycobactéries aux Antituberculeux - Laboratoire Associé, Paris, France. 8. Leiden University Medical Center (LUMC), Department of Infectious Diseases, Leiden, The Netherlands. 9. Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
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.
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 leprosypatient. RESULTS: We identified 455 leprosypatients; 200 (44.0%) belonged to 2735 households included in a cluster. Thirty-eight percent of leprosypatients versus 10% of the total population live ≤25 m from another leprosypatient. 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 leprosypatient, 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.