Temporal change in multimorbidity prevalence, clustering patterns, and the association with mortality: findings from the China Kadoorie Biobank study in Jiangsu Province
ObjectivesThe characteristics of multimorbidity in the Chinese population are currently unclear. We aimed to determine the temporal change in multimorbidity prevalence, clustering patterns, and the association of multimorbidity with mortality from all causes and four major chronic diseases.MethodsTh...
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Frontiers Media S.A.
2024-04-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1389635/full |
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| author | Hao Yu Hao Yu Ran Tao Jinyi Zhou Jian Su Yan Lu Yujie Hua Jianrong Jin Pei Pei Canqing Yu Canqing Yu Canqing Yu Dianjianyi Sun Dianjianyi Sun Dianjianyi Sun Zhengming Chen Liming Li Liming Li Liming Li Jun Lv Jun Lv Jun Lv |
| author_facet | Hao Yu Hao Yu Ran Tao Jinyi Zhou Jian Su Yan Lu Yujie Hua Jianrong Jin Pei Pei Canqing Yu Canqing Yu Canqing Yu Dianjianyi Sun Dianjianyi Sun Dianjianyi Sun Zhengming Chen Liming Li Liming Li Liming Li Jun Lv Jun Lv Jun Lv |
| author_sort | Hao Yu |
| collection | DOAJ |
| description | ObjectivesThe characteristics of multimorbidity in the Chinese population are currently unclear. We aimed to determine the temporal change in multimorbidity prevalence, clustering patterns, and the association of multimorbidity with mortality from all causes and four major chronic diseases.MethodsThis study analyzed data from the China Kadoorie Biobank study performed in Wuzhong District, Jiangsu Province. A total of 53,269 participants aged 30–79 years were recruited between 2004 and 2008. New diagnoses of 15 chronic diseases and death events were collected during the mean follow-up of 10.9 years. Yule's Q cluster analysis method was used to determine the clustering patterns of multimorbidity. A Cox proportional hazards model was used to estimate the associations of multimorbidity with mortalities.ResultsThe overall multimorbidity prevalence rate was 21.1% at baseline and 27.7% at the end of follow-up. Multimorbidity increased more rapidly during the follow-up in individuals who had a higher risk at baseline. Three main multimorbidity patterns were identified: (i) cardiometabolic multimorbidity (diabetes, coronary heart disease, stroke, and hypertension), (ii) respiratory multimorbidity (tuberculosis, asthma, and chronic obstructive pulmonary disease), and (iii) mental, kidney and arthritis multimorbidity (neurasthenia, psychiatric disorders, chronic kidney disease, and rheumatoid arthritis). There were 3,433 deaths during the follow-up. The mortality risk increased by 24% with each additional disease [hazard ratio (HR) = 1.24, 95% confidence interval (CI) = 1.20–1.29]. Compared with those without multimorbidity at baseline, both cardiometabolic multimorbidity and respiratory multimorbidity were associated with increased mortality from all causes and four major chronic diseases. Cardiometabolic multimorbidity was additionally associated with mortality from cardiovascular diseases and diabetes, with HRs of 2.64 (95% CI = 2.19–3.19) and 28.19 (95% CI = 14.85–53.51), respectively. Respiratory multimorbidity was associated with respiratory disease mortality, with an HR of 9.76 (95% CI = 6.22–15.31).ConclusionThe prevalence of multimorbidity has increased substantially over the past decade. This study has revealed that cardiometabolic multimorbidity and respiratory multimorbidity have significantly increased mortality rates. These findings indicate the need to consider high-risk populations and to provide local evidence for intervention strategies and health management in economically developed regions. |
| first_indexed | 2024-04-24T07:52:01Z |
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| id | doaj.art-457f9fd26e1c49e9982909d7c601f3b1 |
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| last_indexed | 2024-04-24T07:52:01Z |
| publishDate | 2024-04-01 |
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| spelling | doaj.art-457f9fd26e1c49e9982909d7c601f3b12024-04-18T10:26:38ZengFrontiers Media S.A.Frontiers in Public Health2296-25652024-04-011210.3389/fpubh.2024.13896351389635Temporal change in multimorbidity prevalence, clustering patterns, and the association with mortality: findings from the China Kadoorie Biobank study in Jiangsu ProvinceHao Yu0Hao Yu1Ran Tao2Jinyi Zhou3Jian Su4Yan Lu5Yujie Hua6Jianrong Jin7Pei Pei8Canqing Yu9Canqing Yu10Canqing Yu11Dianjianyi Sun12Dianjianyi Sun13Dianjianyi Sun14Zhengming Chen15Liming Li16Liming Li17Liming Li18Jun Lv19Jun Lv20Jun Lv21Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, ChinaDepartment of Noncommunicable Chronic Disease and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, ChinaDepartment of Noncommunicable Chronic Disease and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, ChinaDepartment of Noncommunicable Chronic Disease and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, ChinaDepartment of Noncommunicable Chronic Disease and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, ChinaDepartment of Noncommunicable Chronic Disease Control and Prevention, Suzhou City Center for Disease Control and Prevention, Suzhou, ChinaDepartment of Noncommunicable Chronic Disease Control and Prevention, Suzhou City Center for Disease Control and Prevention, Suzhou, ChinaDepartment of Noncommunicable Chronic Disease Control and Prevention, Wuzhong District Center for Disease Control and Prevention, Suzhou, ChinaPeking University Center for Public Health, Epidemic Preparedness and Response, Beijing, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, ChinaPeking University Center for Public Health, Epidemic Preparedness and Response, Beijing, ChinaKey Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, ChinaPeking University Center for Public Health, Epidemic Preparedness and Response, Beijing, ChinaKey Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, ChinaClinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United KingdomDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, ChinaPeking University Center for Public Health, Epidemic Preparedness and Response, Beijing, ChinaKey Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, ChinaPeking University Center for Public Health, Epidemic Preparedness and Response, Beijing, ChinaKey Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, ChinaObjectivesThe characteristics of multimorbidity in the Chinese population are currently unclear. We aimed to determine the temporal change in multimorbidity prevalence, clustering patterns, and the association of multimorbidity with mortality from all causes and four major chronic diseases.MethodsThis study analyzed data from the China Kadoorie Biobank study performed in Wuzhong District, Jiangsu Province. A total of 53,269 participants aged 30–79 years were recruited between 2004 and 2008. New diagnoses of 15 chronic diseases and death events were collected during the mean follow-up of 10.9 years. Yule's Q cluster analysis method was used to determine the clustering patterns of multimorbidity. A Cox proportional hazards model was used to estimate the associations of multimorbidity with mortalities.ResultsThe overall multimorbidity prevalence rate was 21.1% at baseline and 27.7% at the end of follow-up. Multimorbidity increased more rapidly during the follow-up in individuals who had a higher risk at baseline. Three main multimorbidity patterns were identified: (i) cardiometabolic multimorbidity (diabetes, coronary heart disease, stroke, and hypertension), (ii) respiratory multimorbidity (tuberculosis, asthma, and chronic obstructive pulmonary disease), and (iii) mental, kidney and arthritis multimorbidity (neurasthenia, psychiatric disorders, chronic kidney disease, and rheumatoid arthritis). There were 3,433 deaths during the follow-up. The mortality risk increased by 24% with each additional disease [hazard ratio (HR) = 1.24, 95% confidence interval (CI) = 1.20–1.29]. Compared with those without multimorbidity at baseline, both cardiometabolic multimorbidity and respiratory multimorbidity were associated with increased mortality from all causes and four major chronic diseases. Cardiometabolic multimorbidity was additionally associated with mortality from cardiovascular diseases and diabetes, with HRs of 2.64 (95% CI = 2.19–3.19) and 28.19 (95% CI = 14.85–53.51), respectively. Respiratory multimorbidity was associated with respiratory disease mortality, with an HR of 9.76 (95% CI = 6.22–15.31).ConclusionThe prevalence of multimorbidity has increased substantially over the past decade. This study has revealed that cardiometabolic multimorbidity and respiratory multimorbidity have significantly increased mortality rates. These findings indicate the need to consider high-risk populations and to provide local evidence for intervention strategies and health management in economically developed regions.https://www.frontiersin.org/articles/10.3389/fpubh.2024.1389635/fullmultimorbiditycluster analysiscohort studyprevalencemortality |
| spellingShingle | Hao Yu Hao Yu Ran Tao Jinyi Zhou Jian Su Yan Lu Yujie Hua Jianrong Jin Pei Pei Canqing Yu Canqing Yu Canqing Yu Dianjianyi Sun Dianjianyi Sun Dianjianyi Sun Zhengming Chen Liming Li Liming Li Liming Li Jun Lv Jun Lv Jun Lv Temporal change in multimorbidity prevalence, clustering patterns, and the association with mortality: findings from the China Kadoorie Biobank study in Jiangsu Province Frontiers in Public Health multimorbidity cluster analysis cohort study prevalence mortality |
| title | Temporal change in multimorbidity prevalence, clustering patterns, and the association with mortality: findings from the China Kadoorie Biobank study in Jiangsu Province |
| title_full | Temporal change in multimorbidity prevalence, clustering patterns, and the association with mortality: findings from the China Kadoorie Biobank study in Jiangsu Province |
| title_fullStr | Temporal change in multimorbidity prevalence, clustering patterns, and the association with mortality: findings from the China Kadoorie Biobank study in Jiangsu Province |
| title_full_unstemmed | Temporal change in multimorbidity prevalence, clustering patterns, and the association with mortality: findings from the China Kadoorie Biobank study in Jiangsu Province |
| title_short | Temporal change in multimorbidity prevalence, clustering patterns, and the association with mortality: findings from the China Kadoorie Biobank study in Jiangsu Province |
| title_sort | temporal change in multimorbidity prevalence clustering patterns and the association with mortality findings from the china kadoorie biobank study in jiangsu province |
| topic | multimorbidity cluster analysis cohort study prevalence mortality |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1389635/full |
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