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Spatiotemporal trends and ecological determinants of cardiovascular mortality among 2844 counties in mainland China, 2006–2020: a Bayesian modeling study of national mortality registries

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death in China. No previous study has reported CVD mortality at county-level, and little was known about the nonmedical ecological factors of CVD mortality at such small scale in mainland China. Understanding the spatiotemporal variati...

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Autores principales: Wang, Wei, Li, Junming, Liu, Yunning, Ye, Pengpeng, Xu, Chengdong, Yin, Peng, Liu, Jiangmei, Qi, Jinlei, You, Jinling, Lin, Lin, Song, Ziwei, Wang, Limin, Wang, Lijun, Huo, Yong, Zhou, Maigeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714200/
https://www.ncbi.nlm.nih.gov/pubmed/36451190
http://dx.doi.org/10.1186/s12916-022-02613-9
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author Wang, Wei
Li, Junming
Liu, Yunning
Ye, Pengpeng
Xu, Chengdong
Yin, Peng
Liu, Jiangmei
Qi, Jinlei
You, Jinling
Lin, Lin
Song, Ziwei
Wang, Limin
Wang, Lijun
Huo, Yong
Zhou, Maigeng
author_facet Wang, Wei
Li, Junming
Liu, Yunning
Ye, Pengpeng
Xu, Chengdong
Yin, Peng
Liu, Jiangmei
Qi, Jinlei
You, Jinling
Lin, Lin
Song, Ziwei
Wang, Limin
Wang, Lijun
Huo, Yong
Zhou, Maigeng
author_sort Wang, Wei
collection PubMed
description BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death in China. No previous study has reported CVD mortality at county-level, and little was known about the nonmedical ecological factors of CVD mortality at such small scale in mainland China. Understanding the spatiotemporal variations of CVD mortality and examining its nonmedical ecological factors would be of great importance to tailor local public health policies. METHODS: By using national mortality registration data in China, this study used hierarchical spatiotemporal Bayesian model to demonstrate spatiotemporal distribution of CVD mortality in 2844 counties during 2006 to 2020 and investigate how nonmedical ecological determinants have affected CVD mortality inequities from the spatial perspectives. RESULTS: During 2006–2020, the age-standardized mortality rate (ASMR) of CVD decreased from 284.77 per 100,000 in 2006 to 241.34 per 100,000 in 2020. Among 2844 counties, 1144 (40.22%) were hot spots counties with a higher CVD mortality risk compared to the national average and located mostly in northeast, north central, and westernmost regions; on the contrary, 1551 (54.53%) were cold spots counties and located mostly in south and southeast coastal counties. CVD mortality risk decreased from 2006 to 2020 was larger in counties where CVD mortality rate had been higher in 2006 in most of the counties, vice versa. Nationwide, nighttime light intensity (NTL) was the major influencing factor of CVD mortality, a higher NTL appeared to be negatively associated with a lower CVD mortality, with one unit increase in NTL, and the CVD mortality risk will decrease 11% (relative risk of NTL was estimated as 0.89 with 95% confidence interval of 0.83–0.94). CONCLUSIONS: Substantial between-county discrepancies of CVD mortality distribution were observed during past 15 years in mainland China. Nonmedical ecological determinants were estimated to significantly explain the overall and local spatiotemporal patterns of this CVD mortality risk. Targeted considerations are needed to integrate primary care with clinical care through intensifying further strategies to narrow unequally distribution of CVD mortality at local scale. The approach to county-level analysis with small area models has the potential to provide novel insights into Chinese disease-specific mortality burden. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02613-9.
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spelling pubmed-97142002022-12-02 Spatiotemporal trends and ecological determinants of cardiovascular mortality among 2844 counties in mainland China, 2006–2020: a Bayesian modeling study of national mortality registries Wang, Wei Li, Junming Liu, Yunning Ye, Pengpeng Xu, Chengdong Yin, Peng Liu, Jiangmei Qi, Jinlei You, Jinling Lin, Lin Song, Ziwei Wang, Limin Wang, Lijun Huo, Yong Zhou, Maigeng BMC Med Research Article BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death in China. No previous study has reported CVD mortality at county-level, and little was known about the nonmedical ecological factors of CVD mortality at such small scale in mainland China. Understanding the spatiotemporal variations of CVD mortality and examining its nonmedical ecological factors would be of great importance to tailor local public health policies. METHODS: By using national mortality registration data in China, this study used hierarchical spatiotemporal Bayesian model to demonstrate spatiotemporal distribution of CVD mortality in 2844 counties during 2006 to 2020 and investigate how nonmedical ecological determinants have affected CVD mortality inequities from the spatial perspectives. RESULTS: During 2006–2020, the age-standardized mortality rate (ASMR) of CVD decreased from 284.77 per 100,000 in 2006 to 241.34 per 100,000 in 2020. Among 2844 counties, 1144 (40.22%) were hot spots counties with a higher CVD mortality risk compared to the national average and located mostly in northeast, north central, and westernmost regions; on the contrary, 1551 (54.53%) were cold spots counties and located mostly in south and southeast coastal counties. CVD mortality risk decreased from 2006 to 2020 was larger in counties where CVD mortality rate had been higher in 2006 in most of the counties, vice versa. Nationwide, nighttime light intensity (NTL) was the major influencing factor of CVD mortality, a higher NTL appeared to be negatively associated with a lower CVD mortality, with one unit increase in NTL, and the CVD mortality risk will decrease 11% (relative risk of NTL was estimated as 0.89 with 95% confidence interval of 0.83–0.94). CONCLUSIONS: Substantial between-county discrepancies of CVD mortality distribution were observed during past 15 years in mainland China. Nonmedical ecological determinants were estimated to significantly explain the overall and local spatiotemporal patterns of this CVD mortality risk. Targeted considerations are needed to integrate primary care with clinical care through intensifying further strategies to narrow unequally distribution of CVD mortality at local scale. The approach to county-level analysis with small area models has the potential to provide novel insights into Chinese disease-specific mortality burden. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02613-9. BioMed Central 2022-11-30 /pmc/articles/PMC9714200/ /pubmed/36451190 http://dx.doi.org/10.1186/s12916-022-02613-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (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 Article
Wang, Wei
Li, Junming
Liu, Yunning
Ye, Pengpeng
Xu, Chengdong
Yin, Peng
Liu, Jiangmei
Qi, Jinlei
You, Jinling
Lin, Lin
Song, Ziwei
Wang, Limin
Wang, Lijun
Huo, Yong
Zhou, Maigeng
Spatiotemporal trends and ecological determinants of cardiovascular mortality among 2844 counties in mainland China, 2006–2020: a Bayesian modeling study of national mortality registries
title Spatiotemporal trends and ecological determinants of cardiovascular mortality among 2844 counties in mainland China, 2006–2020: a Bayesian modeling study of national mortality registries
title_full Spatiotemporal trends and ecological determinants of cardiovascular mortality among 2844 counties in mainland China, 2006–2020: a Bayesian modeling study of national mortality registries
title_fullStr Spatiotemporal trends and ecological determinants of cardiovascular mortality among 2844 counties in mainland China, 2006–2020: a Bayesian modeling study of national mortality registries
title_full_unstemmed Spatiotemporal trends and ecological determinants of cardiovascular mortality among 2844 counties in mainland China, 2006–2020: a Bayesian modeling study of national mortality registries
title_short Spatiotemporal trends and ecological determinants of cardiovascular mortality among 2844 counties in mainland China, 2006–2020: a Bayesian modeling study of national mortality registries
title_sort spatiotemporal trends and ecological determinants of cardiovascular mortality among 2844 counties in mainland china, 2006–2020: a bayesian modeling study of national mortality registries
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714200/
https://www.ncbi.nlm.nih.gov/pubmed/36451190
http://dx.doi.org/10.1186/s12916-022-02613-9
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