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. To assess at the individual level the risk of suffering from leprosy as a function of distance to the nearest known leprosy patient, we used a regular Poisson model.
RESULTS: We identified 455 leprosy patients, 200 (44.0%) belonging to 2,735 households included in a cluster. Thirty-eight percent of leprosy patients versus ten percent of the population live within 25 meters of another leprosy patient. The risk ratios for being diagnosed with leprosy were 7.3 (5.1-10.4), 2.4 (1.7-3.4), 1.8 (1.3-2.5), 1.4 (1.0-2.1) and 1.7 (1.1-2.4) respectively for those living in the same household, at 1-<25 m, at 25-<50 m, at 50-<75 m and at 75-<100 m, compared to those living at ≥100 m.
CONCLUSIONS: We documented significant clustering of leprosy beyond the household-level, though 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.
BT - International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases C1 - https://www.ncbi.nlm.nih.gov/pubmed/33991682 DA - 05/2021 DO - 10.1016/j.ijid.2021.05.014 J2 - Int J Infect Dis LA - eng N2 -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. To assess at the individual level the risk of suffering from leprosy as a function of distance to the nearest known leprosy patient, we used a regular Poisson model.
RESULTS: We identified 455 leprosy patients, 200 (44.0%) belonging to 2,735 households included in a cluster. Thirty-eight percent of leprosy patients versus ten percent of the population live within 25 meters of another leprosy patient. The risk ratios for being diagnosed with leprosy were 7.3 (5.1-10.4), 2.4 (1.7-3.4), 1.8 (1.3-2.5), 1.4 (1.0-2.1) and 1.7 (1.1-2.4) respectively for those living in the same household, at 1-<25 m, at 25-<50 m, at 50-<75 m and at 75-<100 m, compared to those living at ≥100 m.
CONCLUSIONS: We documented significant clustering of leprosy beyond the household-level, though 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.
PY - 2021 T2 - International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases TI - Exploring clustering of leprosy in the Comoros and Madagascar: a geospatial analysis. UR - https://www.ijidonline.com/action/showPdf?pii=S1201-9712%2821%2900415-X SN - 1878-3511 ER -