Evidence map›Paper›PMID 40761358›Full record

ArticleBMJ public health2025

Multimorbidity progression and the heterogeneous impact of healthy ageing risk factors: a multicohort study.

Shasha Han, Wangyue Chen, Muzi Shen, Ruitai Shao, Weizhong Yang, Chen Wang

Abstract read
In one paragraph

Article in BMJ public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Shasha Han *School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0000-0001-7388-8125
Wangyue Chen *School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Muzi ShenSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Ruitai ShaoSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Weizhong YangSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Chen WangSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Community HealthEpidemiologyHealth Transition

Identifiers

PMID40761358
PMCPMC12320038

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.