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Spatial–temporal patterns and risk factors for human leptospirosis in Thailand, 2012–2018
Leptospirosis is a globally important zoonotic disease. The disease is particularly important in tropical and subtropical countries. Infections in humans can be caused by exposure to infected animals or contaminated soil or water, which are suitable for Leptospira. To explore the cluster area, the G...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8948194/ https://www.ncbi.nlm.nih.gov/pubmed/35332199 http://dx.doi.org/10.1038/s41598-022-09079-y |
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author | Chadsuthi, Sudarat Chalvet-Monfray, Karine Geawduanglek, Suchada Wongnak, Phrutsamon Cappelle, Julien |
author_facet | Chadsuthi, Sudarat Chalvet-Monfray, Karine Geawduanglek, Suchada Wongnak, Phrutsamon Cappelle, Julien |
author_sort | Chadsuthi, Sudarat |
collection | PubMed |
description | Leptospirosis is a globally important zoonotic disease. The disease is particularly important in tropical and subtropical countries. Infections in humans can be caused by exposure to infected animals or contaminated soil or water, which are suitable for Leptospira. To explore the cluster area, the Global Moran’s I index was calculated for incidences per 100,000 population at the province level during 2012–2018, using the monthly and annual data. The high-risk and low-risk provinces were identified using the local indicators of spatial association (LISA). The risk factors for leptospirosis were evaluated using a generalized linear mixed model (GLMM) with zero-inflation. We also added spatial and temporal correlation terms to take into account the spatial and temporal structures. The Global Moran’s I index showed significant positive values. It did not demonstrate a random distribution throughout the period of study. The high-risk provinces were almost all in the lower north-east and south parts of Thailand. For yearly reported cases, the significant risk factors from the final best-fitted model were population density, elevation, and primary rice crop arable areas. Interestingly, our study showed that leptospirosis cases were associated with large areas of rice production but were less prevalent in areas of high rice productivity. For monthly reported cases, the model using temperature range was found to be a better fit than using percentage of flooded area. The significant risk factors from the model using temperature range were temporal correlation, average soil moisture, normalized difference vegetation index, and temperature range. Temperature range, which has strongly negative correlation to percentage of flooded area was a significant risk factor for monthly data. Flood exposure controls should be used to reduce the risk of leptospirosis infection. These results could be used to develop a leptospirosis warning system to support public health organizations in Thailand. |
format | Online Article Text |
id | pubmed-8948194 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-89481942022-03-28 Spatial–temporal patterns and risk factors for human leptospirosis in Thailand, 2012–2018 Chadsuthi, Sudarat Chalvet-Monfray, Karine Geawduanglek, Suchada Wongnak, Phrutsamon Cappelle, Julien Sci Rep Article Leptospirosis is a globally important zoonotic disease. The disease is particularly important in tropical and subtropical countries. Infections in humans can be caused by exposure to infected animals or contaminated soil or water, which are suitable for Leptospira. To explore the cluster area, the Global Moran’s I index was calculated for incidences per 100,000 population at the province level during 2012–2018, using the monthly and annual data. The high-risk and low-risk provinces were identified using the local indicators of spatial association (LISA). The risk factors for leptospirosis were evaluated using a generalized linear mixed model (GLMM) with zero-inflation. We also added spatial and temporal correlation terms to take into account the spatial and temporal structures. The Global Moran’s I index showed significant positive values. It did not demonstrate a random distribution throughout the period of study. The high-risk provinces were almost all in the lower north-east and south parts of Thailand. For yearly reported cases, the significant risk factors from the final best-fitted model were population density, elevation, and primary rice crop arable areas. Interestingly, our study showed that leptospirosis cases were associated with large areas of rice production but were less prevalent in areas of high rice productivity. For monthly reported cases, the model using temperature range was found to be a better fit than using percentage of flooded area. The significant risk factors from the model using temperature range were temporal correlation, average soil moisture, normalized difference vegetation index, and temperature range. Temperature range, which has strongly negative correlation to percentage of flooded area was a significant risk factor for monthly data. Flood exposure controls should be used to reduce the risk of leptospirosis infection. These results could be used to develop a leptospirosis warning system to support public health organizations in Thailand. Nature Publishing Group UK 2022-03-24 /pmc/articles/PMC8948194/ /pubmed/35332199 http://dx.doi.org/10.1038/s41598-022-09079-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Chadsuthi, Sudarat Chalvet-Monfray, Karine Geawduanglek, Suchada Wongnak, Phrutsamon Cappelle, Julien Spatial–temporal patterns and risk factors for human leptospirosis in Thailand, 2012–2018 |
title | Spatial–temporal patterns and risk factors for human leptospirosis in Thailand, 2012–2018 |
title_full | Spatial–temporal patterns and risk factors for human leptospirosis in Thailand, 2012–2018 |
title_fullStr | Spatial–temporal patterns and risk factors for human leptospirosis in Thailand, 2012–2018 |
title_full_unstemmed | Spatial–temporal patterns and risk factors for human leptospirosis in Thailand, 2012–2018 |
title_short | Spatial–temporal patterns and risk factors for human leptospirosis in Thailand, 2012–2018 |
title_sort | spatial–temporal patterns and risk factors for human leptospirosis in thailand, 2012–2018 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8948194/ https://www.ncbi.nlm.nih.gov/pubmed/35332199 http://dx.doi.org/10.1038/s41598-022-09079-y |
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