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Estimation of the relationship between meteorological factors and measles using spatiotemporal Bayesian model in Shandong Province, China

BACKGROUND: Measles-containing vaccine (MCV) has been effective in controlling the spread of measles. Some countries have declared measles elimination. But recently years, the number of cases worldwide has increased, posing a challenge to the global goal of measles eradication. This study estimated...

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Autores principales: Jia, Yan, Xu, Qing, Zhu, Yuchen, Li, Chunyu, Qi, Chang, She, Kaili, Liu, Tingxuan, Zhang, Ying, Li, Xiujun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369697/
https://www.ncbi.nlm.nih.gov/pubmed/37491220
http://dx.doi.org/10.1186/s12889-023-16350-y
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author Jia, Yan
Xu, Qing
Zhu, Yuchen
Li, Chunyu
Qi, Chang
She, Kaili
Liu, Tingxuan
Zhang, Ying
Li, Xiujun
author_facet Jia, Yan
Xu, Qing
Zhu, Yuchen
Li, Chunyu
Qi, Chang
She, Kaili
Liu, Tingxuan
Zhang, Ying
Li, Xiujun
author_sort Jia, Yan
collection PubMed
description BACKGROUND: Measles-containing vaccine (MCV) has been effective in controlling the spread of measles. Some countries have declared measles elimination. But recently years, the number of cases worldwide has increased, posing a challenge to the global goal of measles eradication. This study estimated the relationship between meteorological factors and measles using spatiotemporal Bayesian model, aiming to provide scientific evidence for public health policy to eliminate measles. METHODS: Descriptive statistical analysis was performed on monthly data of measles and meteorological variables in 136 counties of Shandong Province from 2009 to 2017. Spatiotemporal Bayesian model was used to estimate the effects of meteorological factors on measles, and to evaluate measles risk areas at county level. Case population was divided into multiple subgroups according to gender, age and occupation. The effects of meteorological factors on measles in subgroups were compared. RESULTS: Specific meteorological conditions increased the risk of measles, including lower relative humidity, temperature, and atmospheric pressure; higher wind velocity, sunshine duration, and diurnal temperature variation. Taking lowest value (Q1) as reference, RR (95%CI) for higher temperatures (Q2–Q4) were 0.79 (0.69–0.91), 0.54 (0.44–0.65), and 0.48 (0.38–0.61), respectively; RR (95%CI) for higher relative humidity (Q2–Q4) were 0.76 (0.66–0.88), 0.56 (0.47–0.67), and 0.49 (0.38–0.63), respectively; RR (95%CI) for higher wind velocity (Q2–Q4) were 1.43 (1.25–1.64), 1.85 (1.57–2.18), 2.00 (1.59–2.52), respectively. 22 medium-to-high risk counties were identified, mainly in northwestern, southwestern and central Shandong Province. The trend was basically same in the effects of meteorological factors on measles in subgroups, but the magnitude of the effects was different. CONCLUSIONS: Meteorological factors have an important impact on measles. It is crucial to integrate these factors into public health policies for measles prevention and control in China. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-023-16350-y.
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spelling pubmed-103696972023-07-27 Estimation of the relationship between meteorological factors and measles using spatiotemporal Bayesian model in Shandong Province, China Jia, Yan Xu, Qing Zhu, Yuchen Li, Chunyu Qi, Chang She, Kaili Liu, Tingxuan Zhang, Ying Li, Xiujun BMC Public Health Research BACKGROUND: Measles-containing vaccine (MCV) has been effective in controlling the spread of measles. Some countries have declared measles elimination. But recently years, the number of cases worldwide has increased, posing a challenge to the global goal of measles eradication. This study estimated the relationship between meteorological factors and measles using spatiotemporal Bayesian model, aiming to provide scientific evidence for public health policy to eliminate measles. METHODS: Descriptive statistical analysis was performed on monthly data of measles and meteorological variables in 136 counties of Shandong Province from 2009 to 2017. Spatiotemporal Bayesian model was used to estimate the effects of meteorological factors on measles, and to evaluate measles risk areas at county level. Case population was divided into multiple subgroups according to gender, age and occupation. The effects of meteorological factors on measles in subgroups were compared. RESULTS: Specific meteorological conditions increased the risk of measles, including lower relative humidity, temperature, and atmospheric pressure; higher wind velocity, sunshine duration, and diurnal temperature variation. Taking lowest value (Q1) as reference, RR (95%CI) for higher temperatures (Q2–Q4) were 0.79 (0.69–0.91), 0.54 (0.44–0.65), and 0.48 (0.38–0.61), respectively; RR (95%CI) for higher relative humidity (Q2–Q4) were 0.76 (0.66–0.88), 0.56 (0.47–0.67), and 0.49 (0.38–0.63), respectively; RR (95%CI) for higher wind velocity (Q2–Q4) were 1.43 (1.25–1.64), 1.85 (1.57–2.18), 2.00 (1.59–2.52), respectively. 22 medium-to-high risk counties were identified, mainly in northwestern, southwestern and central Shandong Province. The trend was basically same in the effects of meteorological factors on measles in subgroups, but the magnitude of the effects was different. CONCLUSIONS: Meteorological factors have an important impact on measles. It is crucial to integrate these factors into public health policies for measles prevention and control in China. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-023-16350-y. BioMed Central 2023-07-25 /pmc/articles/PMC10369697/ /pubmed/37491220 http://dx.doi.org/10.1186/s12889-023-16350-y Text en © The Author(s) 2023 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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
Jia, Yan
Xu, Qing
Zhu, Yuchen
Li, Chunyu
Qi, Chang
She, Kaili
Liu, Tingxuan
Zhang, Ying
Li, Xiujun
Estimation of the relationship between meteorological factors and measles using spatiotemporal Bayesian model in Shandong Province, China
title Estimation of the relationship between meteorological factors and measles using spatiotemporal Bayesian model in Shandong Province, China
title_full Estimation of the relationship between meteorological factors and measles using spatiotemporal Bayesian model in Shandong Province, China
title_fullStr Estimation of the relationship between meteorological factors and measles using spatiotemporal Bayesian model in Shandong Province, China
title_full_unstemmed Estimation of the relationship between meteorological factors and measles using spatiotemporal Bayesian model in Shandong Province, China
title_short Estimation of the relationship between meteorological factors and measles using spatiotemporal Bayesian model in Shandong Province, China
title_sort estimation of the relationship between meteorological factors and measles using spatiotemporal bayesian model in shandong province, china
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369697/
https://www.ncbi.nlm.nih.gov/pubmed/37491220
http://dx.doi.org/10.1186/s12889-023-16350-y
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