Multimorbidity progression and the heterogeneous impact of healthy ageing risk factors: a multicohort study
Introduction We aimed to quantify how diseases accumulate and diminish among ageing populations, and examine how modifiable risk factors influence these progressions.Methods In this multicohort study with four cohorts, China Health and Retirement Longitudinal Study, the English Longitudinal Study of...
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BMJ Publishing Group
2025-08-01
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| Series: | BMJ Public Health |
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| author | Chen Wang Ruitai Shao Shasha Han Wangyue Chen Weizhong Yang Muzi Shen |
| author_facet | Chen Wang Ruitai Shao Shasha Han Wangyue Chen Weizhong Yang Muzi Shen |
| author_sort | Chen Wang |
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| description | Introduction We aimed to quantify how diseases accumulate and diminish among ageing populations, and examine how modifiable risk factors influence these progressions.Methods In this multicohort study with four cohorts, China Health and Retirement Longitudinal Study, the English Longitudinal Study of Ageing (ELSA), the Health and Retirement Study, and the Survey of Health, Ageing and Retirement in Europe (SHARE), and 75 874 participants, we employed a multistage model that accommodated bidirectional transitions between four health stages (0, 1, 2, ≥3 conditions) from baseline to 8 years, and conducted matching analyses to examine the influence of age, sex, socioeconomic status (SES) and lifestyle factors on these transitions.Results Disease accumulated faster than diminished (0.08–0.44 vs 0.00–0.06). Transitions accelerated towards severe multimorbidity (0→1: 0.29 (95% CI 0.28 to 0.29), 1→2: 0.27 (95% CI 0.27 to 0.28) and 2→≥3: 0.44 (95% CI 0.43 to 0.45)). Mortality risk escalated with condition count: 0.08 (95% CI 0.08 to 0.09) for 0 conditions, 0.13 (95% CI 0.12 to 0.13) for 1 condition 0.17 (95% CI 0.16 to 0.18) for 2 conditions, and 0.27 (95% CI 0.26 to 0.27) for ≥3 conditions. Cohorts exhibited broadly similar progression patterns, though ELSA demonstrated slower transitions to ≥3 conditions and SHARE showed elevated mortality from 0 and 1 conditions. Key risk factor effects emerged: disease accumulation peaked at 55–65 years; females had higher disease accumulation but lower transitions to death than males; Low-SES populations had higher probabilities of developing ≥3 conditions than the middle-SES group, while middle-SES populations had higher accumulation probabilities for 0→≥2 and 2→≥3. Lifestyle factors exerted differential impacts: smoking increased 1→3 transitions and drinking increased 0→2 transitions, while physician inactivity increased 0→3 transitions. Sensitivity analyses confirmed robustness across 11 condition-specific models.Conclusions Multimorbidity progression accelerates nonlinearly, with risk factors exerting varying effects, depending on the magnitude of risk factors and initial health states. Precision interventions should target age, sex, SES and lifestyle-specific strategies. |
| format | Article |
| id | doaj-art-7ef6fd67d51a42a19d6dc5b562294b80 |
| institution | Kabale University |
| issn | 2753-4294 |
| language | English |
| publishDate | 2025-08-01 |
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| series | BMJ Public Health |
| spelling | doaj-art-7ef6fd67d51a42a19d6dc5b562294b802025-08-20T03:37:23ZengBMJ Publishing GroupBMJ Public Health2753-42942025-08-013210.1136/bmjph-2024-002474Multimorbidity progression and the heterogeneous impact of healthy ageing risk factors: a multicohort studyChen Wang0Ruitai Shao1Shasha Han2Wangyue Chen3Weizhong Yang4Muzi Shen5Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China–Japan Friendship Hospital, Beijing, Beijing, ChinaState Key Laboratory of Respiratory Health and Multimorbidity, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, Beijing, ChinaState Key Laboratory of Respiratory Health and Multimorbidity, Beijing, China1 Department of Epidemiology and Biostatistics, Institute of Basic Medical Science, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing, ChinaSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, ChinaIntroduction We aimed to quantify how diseases accumulate and diminish among ageing populations, and examine how modifiable risk factors influence these progressions.Methods In this multicohort study with four cohorts, China Health and Retirement Longitudinal Study, the English Longitudinal Study of Ageing (ELSA), the Health and Retirement Study, and the Survey of Health, Ageing and Retirement in Europe (SHARE), and 75 874 participants, we employed a multistage model that accommodated bidirectional transitions between four health stages (0, 1, 2, ≥3 conditions) from baseline to 8 years, and conducted matching analyses to examine the influence of age, sex, socioeconomic status (SES) and lifestyle factors on these transitions.Results Disease accumulated faster than diminished (0.08–0.44 vs 0.00–0.06). Transitions accelerated towards severe multimorbidity (0→1: 0.29 (95% CI 0.28 to 0.29), 1→2: 0.27 (95% CI 0.27 to 0.28) and 2→≥3: 0.44 (95% CI 0.43 to 0.45)). Mortality risk escalated with condition count: 0.08 (95% CI 0.08 to 0.09) for 0 conditions, 0.13 (95% CI 0.12 to 0.13) for 1 condition 0.17 (95% CI 0.16 to 0.18) for 2 conditions, and 0.27 (95% CI 0.26 to 0.27) for ≥3 conditions. Cohorts exhibited broadly similar progression patterns, though ELSA demonstrated slower transitions to ≥3 conditions and SHARE showed elevated mortality from 0 and 1 conditions. Key risk factor effects emerged: disease accumulation peaked at 55–65 years; females had higher disease accumulation but lower transitions to death than males; Low-SES populations had higher probabilities of developing ≥3 conditions than the middle-SES group, while middle-SES populations had higher accumulation probabilities for 0→≥2 and 2→≥3. Lifestyle factors exerted differential impacts: smoking increased 1→3 transitions and drinking increased 0→2 transitions, while physician inactivity increased 0→3 transitions. Sensitivity analyses confirmed robustness across 11 condition-specific models.Conclusions Multimorbidity progression accelerates nonlinearly, with risk factors exerting varying effects, depending on the magnitude of risk factors and initial health states. Precision interventions should target age, sex, SES and lifestyle-specific strategies.https://bmjpublichealth.bmj.com/content/3/2/e002474.full |
| spellingShingle | Chen Wang Ruitai Shao Shasha Han Wangyue Chen Weizhong Yang Muzi Shen Multimorbidity progression and the heterogeneous impact of healthy ageing risk factors: a multicohort study BMJ Public Health |
| title | Multimorbidity progression and the heterogeneous impact of healthy ageing risk factors: a multicohort study |
| title_full | Multimorbidity progression and the heterogeneous impact of healthy ageing risk factors: a multicohort study |
| title_fullStr | Multimorbidity progression and the heterogeneous impact of healthy ageing risk factors: a multicohort study |
| title_full_unstemmed | Multimorbidity progression and the heterogeneous impact of healthy ageing risk factors: a multicohort study |
| title_short | Multimorbidity progression and the heterogeneous impact of healthy ageing risk factors: a multicohort study |
| title_sort | multimorbidity progression and the heterogeneous impact of healthy ageing risk factors a multicohort study |
| url | https://bmjpublichealth.bmj.com/content/3/2/e002474.full |
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