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Geospatial epidemiology of leprosy in northwest Bangladesh: a 20-year retrospective observational study

BACKGROUND: Leprosy is known to be unevenly distributed between and within countries. High risk areas or ‘hotspots’ are potential targets for preventive interventions, but the underlying epidemiologic mechanisms that enable hotspots to emerge, are not yet fully understood. In this study, we identifi...

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Autores principales: Bulstra, Caroline A., Blok, David J., Alam, Khorshed, Butlin, C. Ruth, Roy, Johan Chandra, Bowers, Bob, Nicholls, Peter, de Vlas, Sake J., Richardus, Jan Hendrik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7986508/
https://www.ncbi.nlm.nih.gov/pubmed/33752751
http://dx.doi.org/10.1186/s40249-021-00817-4
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author Bulstra, Caroline A.
Blok, David J.
Alam, Khorshed
Butlin, C. Ruth
Roy, Johan Chandra
Bowers, Bob
Nicholls, Peter
de Vlas, Sake J.
Richardus, Jan Hendrik
author_facet Bulstra, Caroline A.
Blok, David J.
Alam, Khorshed
Butlin, C. Ruth
Roy, Johan Chandra
Bowers, Bob
Nicholls, Peter
de Vlas, Sake J.
Richardus, Jan Hendrik
author_sort Bulstra, Caroline A.
collection PubMed
description BACKGROUND: Leprosy is known to be unevenly distributed between and within countries. High risk areas or ‘hotspots’ are potential targets for preventive interventions, but the underlying epidemiologic mechanisms that enable hotspots to emerge, are not yet fully understood. In this study, we identified and characterized leprosy hotspots in Bangladesh, a country with one of the highest leprosy endemicity levels globally. METHODS: We used data from four high-endemic districts in northwest Bangladesh including 20 623 registered cases between January 2000 and April 2019 (among ~ 7 million population). Incidences per union (smallest administrative unit) were calculated using geospatial population density estimates. A geospatial Poisson model was used to detect incidence hotspots over three (overlapping) 10-year timeframes: 2000–2009, 2005–2014 and 2010–2019. Ordinal regression models were used to assess whether patient characteristics were significantly different for cases outside hotspots, as compared to cases within weak (i.e., relative risk (RR) of one to two), medium (i.e., RR of two to three), and strong (i.e., RR higher than three) hotspots. RESULTS: New case detection rates dropped from 44/100 000 in 2000 to 10/100 000 in 2019. Statistically significant hotspots were identified during all timeframes and were often located at areas with high population densities. The RR for leprosy was up to 12 times higher for inhabitants of hotspots than for people living outside hotspots. Within strong hotspots (1930 cases among less than 1% of the population), significantly more child cases (i.e., below 15 years of age) were detected, indicating recent transmission. Cases in hotspots were not significantly more likely to be detected actively. CONCLUSIONS: Leprosy showed a heterogeneous distribution with clear hotspots in northwest Bangladesh throughout a 20-year period of decreasing incidence. Findings confirm that leprosy hotspots represent areas of higher transmission activity and are not solely the result of active case finding strategies. [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40249-021-00817-4.
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spelling pubmed-79865082021-03-24 Geospatial epidemiology of leprosy in northwest Bangladesh: a 20-year retrospective observational study Bulstra, Caroline A. Blok, David J. Alam, Khorshed Butlin, C. Ruth Roy, Johan Chandra Bowers, Bob Nicholls, Peter de Vlas, Sake J. Richardus, Jan Hendrik Infect Dis Poverty Research Article BACKGROUND: Leprosy is known to be unevenly distributed between and within countries. High risk areas or ‘hotspots’ are potential targets for preventive interventions, but the underlying epidemiologic mechanisms that enable hotspots to emerge, are not yet fully understood. In this study, we identified and characterized leprosy hotspots in Bangladesh, a country with one of the highest leprosy endemicity levels globally. METHODS: We used data from four high-endemic districts in northwest Bangladesh including 20 623 registered cases between January 2000 and April 2019 (among ~ 7 million population). Incidences per union (smallest administrative unit) were calculated using geospatial population density estimates. A geospatial Poisson model was used to detect incidence hotspots over three (overlapping) 10-year timeframes: 2000–2009, 2005–2014 and 2010–2019. Ordinal regression models were used to assess whether patient characteristics were significantly different for cases outside hotspots, as compared to cases within weak (i.e., relative risk (RR) of one to two), medium (i.e., RR of two to three), and strong (i.e., RR higher than three) hotspots. RESULTS: New case detection rates dropped from 44/100 000 in 2000 to 10/100 000 in 2019. Statistically significant hotspots were identified during all timeframes and were often located at areas with high population densities. The RR for leprosy was up to 12 times higher for inhabitants of hotspots than for people living outside hotspots. Within strong hotspots (1930 cases among less than 1% of the population), significantly more child cases (i.e., below 15 years of age) were detected, indicating recent transmission. Cases in hotspots were not significantly more likely to be detected actively. CONCLUSIONS: Leprosy showed a heterogeneous distribution with clear hotspots in northwest Bangladesh throughout a 20-year period of decreasing incidence. Findings confirm that leprosy hotspots represent areas of higher transmission activity and are not solely the result of active case finding strategies. [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40249-021-00817-4. BioMed Central 2021-03-22 /pmc/articles/PMC7986508/ /pubmed/33752751 http://dx.doi.org/10.1186/s40249-021-00817-4 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Bulstra, Caroline A.
Blok, David J.
Alam, Khorshed
Butlin, C. Ruth
Roy, Johan Chandra
Bowers, Bob
Nicholls, Peter
de Vlas, Sake J.
Richardus, Jan Hendrik
Geospatial epidemiology of leprosy in northwest Bangladesh: a 20-year retrospective observational study
title Geospatial epidemiology of leprosy in northwest Bangladesh: a 20-year retrospective observational study
title_full Geospatial epidemiology of leprosy in northwest Bangladesh: a 20-year retrospective observational study
title_fullStr Geospatial epidemiology of leprosy in northwest Bangladesh: a 20-year retrospective observational study
title_full_unstemmed Geospatial epidemiology of leprosy in northwest Bangladesh: a 20-year retrospective observational study
title_short Geospatial epidemiology of leprosy in northwest Bangladesh: a 20-year retrospective observational study
title_sort geospatial epidemiology of leprosy in northwest bangladesh: a 20-year retrospective observational study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7986508/
https://www.ncbi.nlm.nih.gov/pubmed/33752751
http://dx.doi.org/10.1186/s40249-021-00817-4
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