ArticleJournal of global health2025
Multimorbidity patterns and influencing factors in older Chinese adults: a national population-based cross-sectional survey.
Article in Journal of global health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neurobiological insights into the effects of ultra-processed food on lipid metabolism and associated mental health conditions: a scoping review.Frontiers in nutrition · 2025Pooled it
- Association between physical activity behaviors and hypertension with comorbid multiple chronic diseases in South Korean middle-aged and elderly: an analysis based on association rule.BMC public health · 2025Article
- Profiles of self-efficacy among cirrhotic patients in northern Guizhou Province, China: a latent profile analysis.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aims to develop specific multimorbidity relationships among the elderly and to explore the association of multidimensional factors with these relationships, thereby facilitating the formulation of personalised strategies for multimorbidity management. Methods: Cluster analysis identified chronic conditions that tend to cluster together, and then association rule mining was used to investigate relationships within these identified clusters more closely. Stepwise logistic regression analysis was conducted to explore the relationship between influencing factors and different health statuses in older adults. The results of this study were presented by network graph visualisation. Results: A total of 15 045 individuals were included in this study. The average age was 73.0 ± 6.8 years. The number of patients with multimorbidity was 7426 (49.4%). The most common binary disease combination was hypertension and depression. The four major multimorbidity clusters identified were the tumour-digestive disease cluster, the metabolic-circulatory disease cluster, the metal-psychological disease cluster, and the age-related degenerative disease cluster. Cluster analysis by sex and region revealed similar numbers and types of conditions in each cluster, with some variations. Gender and number of medications had a consistent effect across all disease clusters, while aging, body mass index (BMI), waist-to-hip ratio (WHR), cognitive impairment, plant-based foods, animal-based foods, highly processed foods and marital status had varying effects across different disease clusters. Conclusions: Multimorbidity is highly prevalent in the older population. The impact of lifestyle varies between different clusters of multimorbidity, and there is a need to implement different strategies according to different clusters of multimorbidity rather than an integrated approach to multimorbidity management.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.