Identifying clusters of leprosy patients in India: A comparison of methods.
Background Preventive interventions with post-exposure prophylaxis (PEP) are needed in leprosy high-endemic areas to interrupt the transmission of Mycobacterium leprae. Program managers intend to use Geographic Information Systems (GIS) to target preventive interventions considering efficient use of...
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ftdoajarticles:oai:doaj.org/article:f6b216a1af9d47eab91f6a79c65e5142 2023-05-15T15:15:41+02:00 Identifying clusters of leprosy patients in India: A comparison of methods. Anneke T Taal Akshat Garg Suchitra Lisam Ashok Agarwal Josafá G Barreto Wim H van Brakel Jan Hendrik Richardus David J Blok 2022-12-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0010972 https://doaj.org/article/f6b216a1af9d47eab91f6a79c65e5142 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0010972 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0010972 https://doaj.org/article/f6b216a1af9d47eab91f6a79c65e5142 PLoS Neglected Tropical Diseases, Vol 16, Iss 12, p e0010972 (2022) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2022 ftdoajarticles https://doi.org/10.1371/journal.pntd.0010972 2022-12-30T19:24:48Z Background Preventive interventions with post-exposure prophylaxis (PEP) are needed in leprosy high-endemic areas to interrupt the transmission of Mycobacterium leprae. Program managers intend to use Geographic Information Systems (GIS) to target preventive interventions considering efficient use of public health resources. Statistical GIS analyses are commonly used to identify clusters of disease without accounting for the local context. Therefore, we propose a contextualized spatial approach that includes expert consultation to identify clusters and compare it with a standard statistical approach. Methodology/principal findings We included all leprosy patients registered from 2014 to 2020 at the Health Centers in Fatehpur and Chandauli districts, Uttar Pradesh State, India (n = 3,855). Our contextualized spatial approach included expert consultation determining criteria and definition for the identification of clusters using Density Based Spatial Clustering Algorithm with Noise, followed by creating cluster maps considering natural boundaries and the local context. We compared this approach with the commonly used Anselin Local Moran's I statistic to identify high-risk villages. In the contextualized approach, 374 clusters were identified in Chandauli and 512 in Fatehpur. In total, 75% and 57% of all cases were captured by the identified clusters in Chandauli and Fatehpur, respectively. If 100 individuals per case were targeted for PEP, 33% and 11% of the total cluster population would receive PEP, respectively. In the statistical approach, more clusters in Chandauli and fewer clusters in Fatehpur (508 and 193) and lower proportions of cases in clusters (66% and 43%) were identified, and lower proportions of population targeted for PEP was calculated compared to the contextualized approach (11% and 11%). Conclusion A contextualized spatial approach could identify clusters in high-endemic districts more precisely than a standard statistical approach. Therefore, it can be a useful alternative to detect preventive ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 16 12 e0010972 |
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Directory of Open Access Journals: DOAJ Articles |
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English |
topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Anneke T Taal Akshat Garg Suchitra Lisam Ashok Agarwal Josafá G Barreto Wim H van Brakel Jan Hendrik Richardus David J Blok Identifying clusters of leprosy patients in India: A comparison of methods. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Background Preventive interventions with post-exposure prophylaxis (PEP) are needed in leprosy high-endemic areas to interrupt the transmission of Mycobacterium leprae. Program managers intend to use Geographic Information Systems (GIS) to target preventive interventions considering efficient use of public health resources. Statistical GIS analyses are commonly used to identify clusters of disease without accounting for the local context. Therefore, we propose a contextualized spatial approach that includes expert consultation to identify clusters and compare it with a standard statistical approach. Methodology/principal findings We included all leprosy patients registered from 2014 to 2020 at the Health Centers in Fatehpur and Chandauli districts, Uttar Pradesh State, India (n = 3,855). Our contextualized spatial approach included expert consultation determining criteria and definition for the identification of clusters using Density Based Spatial Clustering Algorithm with Noise, followed by creating cluster maps considering natural boundaries and the local context. We compared this approach with the commonly used Anselin Local Moran's I statistic to identify high-risk villages. In the contextualized approach, 374 clusters were identified in Chandauli and 512 in Fatehpur. In total, 75% and 57% of all cases were captured by the identified clusters in Chandauli and Fatehpur, respectively. If 100 individuals per case were targeted for PEP, 33% and 11% of the total cluster population would receive PEP, respectively. In the statistical approach, more clusters in Chandauli and fewer clusters in Fatehpur (508 and 193) and lower proportions of cases in clusters (66% and 43%) were identified, and lower proportions of population targeted for PEP was calculated compared to the contextualized approach (11% and 11%). Conclusion A contextualized spatial approach could identify clusters in high-endemic districts more precisely than a standard statistical approach. Therefore, it can be a useful alternative to detect preventive ... |
format |
Article in Journal/Newspaper |
author |
Anneke T Taal Akshat Garg Suchitra Lisam Ashok Agarwal Josafá G Barreto Wim H van Brakel Jan Hendrik Richardus David J Blok |
author_facet |
Anneke T Taal Akshat Garg Suchitra Lisam Ashok Agarwal Josafá G Barreto Wim H van Brakel Jan Hendrik Richardus David J Blok |
author_sort |
Anneke T Taal |
title |
Identifying clusters of leprosy patients in India: A comparison of methods. |
title_short |
Identifying clusters of leprosy patients in India: A comparison of methods. |
title_full |
Identifying clusters of leprosy patients in India: A comparison of methods. |
title_fullStr |
Identifying clusters of leprosy patients in India: A comparison of methods. |
title_full_unstemmed |
Identifying clusters of leprosy patients in India: A comparison of methods. |
title_sort |
identifying clusters of leprosy patients in india: a comparison of methods. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2022 |
url |
https://doi.org/10.1371/journal.pntd.0010972 https://doaj.org/article/f6b216a1af9d47eab91f6a79c65e5142 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 16, Iss 12, p e0010972 (2022) |
op_relation |
https://doi.org/10.1371/journal.pntd.0010972 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0010972 https://doaj.org/article/f6b216a1af9d47eab91f6a79c65e5142 |
op_doi |
https://doi.org/10.1371/journal.pntd.0010972 |
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PLOS Neglected Tropical Diseases |
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16 |
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12 |
container_start_page |
e0010972 |
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