TY - JOUR
T1 - Exploring clustering of leprosy in the Comoros and Madagascar: a geospatial analysis
AU - Ortuno-Gutierrez, Nimer
AU - Mzembaba, Aboubacar
AU - Ramboarina, Stephanie
AU - Andriamira, Randrianantoandro
AU - Baco, Abdallah
AU - Braet, Sofie
AU - Younoussa, Assoumani
AU - Cauchoix, Bertrand
AU - Salim, Zahara
AU - Amidy, Mohamed
AU - Grillone, Saverio
AU - Rasamoelina, Tahinamandranto
AU - Cambau, Emmanuelle
AU - Geluk, Annemieke
AU - de Jong, Bouke C.
AU - Richardus, Jan Hendrik
AU - Hasker, Epco
N1 - FTX; DOAJ; OGOA; (CC BY-NC-ND 4.0)
PY - 2021
Y1 - 2021
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. 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 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/).
AB - 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 (4 4.0%) belonged to 2735 households included in a cluster. Thirty-eight percent of leprosy patients versus 10% of the total population live 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/).
KW - Clustering
KW - Leprosy
KW - Active case finding
KW - Spatial analysis
KW - POSTEXPOSURE PROPHYLAXIS
KW - RIFAMPICIN
KW - CONTACTS
KW - DESIGN
KW - TRIAL
U2 - 10.1016/j.ijid.2021.05.014
DO - 10.1016/j.ijid.2021.05.014
M3 - A1: Web of Science-article
SN - 1201-9712
VL - 108
SP - 96
EP - 101
JO - International Journal of Infectious Diseases
JF - International Journal of Infectious Diseases
ER -