Keywords
Comorbidity model
Daily living ability
Latent category analysis
Healthy aging
CHARLS
Cross-sectional studies
Categories
How to Cite
Abstract
Objective: To analyze the current prevalence and clustering patterns of chronic comorbidities among elderly people in China, explore the association between different comorbidity patterns and impairment of basic daily living ability (BADL) and instrumental ability of daily living (IADL), and test the moderating effects of physical exercise and social support, providing empirical evidence for formulating precise elderly health management strategies. Methods: Using cross-sectional data from the 2018 China Health and Elderly Care Tracking Survey (CHARLS), 11,246 elderly people aged 60 and above were selected as study subjects. Latent category analysis (LCA) was used to identify chronic disease comorbidity patterns, and logistic regression models were used to analyze the number of comorbidities and their association with BADEL and IADL impairment, to test interaction effects, and to conduct multiple robustness tests. Results: The chronic disease comorbidity rate among the elderly in China was 59.9%, with severe comorbidities (≥ 5 types) accounting for 10.5%. LCA identified five comorbidity patterns: cardiometabolic (26.8%), musculoskeletal-digestive (20.5%, with Chinese population specificity), respiratory-psychiatric (15.3%), multisystem impairment (14.0%), and relative health (23.4%, with 4.2% still having BADAL impairment and 14.3% having IADL impairment in this group). The number of comorbidities was significantly graded and cumulatively associated with impaired daily living ability. The risk of BADAL and IADL impairment in severely comorbid elderly patients was 9.12 times higher (OR=9.12, 95% CI: 7.28-11.43) and 7.89 times higher (OR=7.89, 95% CI: 6.65-9.36), respectively. There was significant heterogeneity in the impact of different comorbidity modes on function, with multisystem impairment at the highest risk, and respiratory-psychiatric types causing significantly more IADL damage than cardiometabolic types. Interaction analysis showed that physical exercise and social support had a significant buffering effect on the association between comorbidity and IADL impairment (interaction term p<0.05). The burden of comorbidities is heavier among women, rural areas, and elderly people with low socioeconomic status. Conclusion: The phenomenon of chronic disease comorbidities among the elderly in China is common and shows a clear pattern of clustering. The association between comorbidity and impaired daily living ability has hierarchical cumulative effects and pattern heterogeneity; physical exercise and social support can buffer the negative effects of comorbidities. It is recommended to include comorbidity pattern identification in comprehensive elderly assessments, implement integrated interventions targeting high-risk patterns such as respiratory-psychotic types, and focus on health inequalities in rural and vulnerable groups.
References
[1] World Health Organization. (2019). World health statistics 2019. Geneva: World Health Organization.
[2] Marengoni, A., Angleman, S., Melis, R., Mangialasche, F., Karp, A., Garmen, A., Meinow, B., & Fratiglioni, L. (2011). Aging with multimorbidity: A systematic review of the literature. Ageing Research Reviews, 10(4), 430–439.
[3] Du Peng, Li Long. (2016). Current Situation and Countermeasures of Chronic Diseases Among the Elderly in China. China Health Policy Research, 9(3), 1–9
[4] Wang Jing, Wang Dewen. (2016). Status and Trends of Chronic Diseases Among the Elderly in China. Journal of Population Studies, 38(4), 80–88
[5] Prados‑Torres, A., Calderón‑Larrañaga, A., Hancco‑Saavedra, J., Poblador‑Plou, B., & Gimeno‑Feliu, L. A. (2014). Multimorbidity patterns: A systematic review. Journal of Clinical Epidemiology, 67(3), 254–266.
[6] Fried, L. P., Ferrucci, L., Darer, J., Williamson, J. D., & Anderson, G. (2004). Untangling the concepts of disability, frailty, and comorbidity: Implications for improved targeting and care. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 59(3), M255–M263.
[7] Fortin, M., Bravo, G., Hudon, C., Dubois, M. F., & Almirall, J. (2006). Relationship between multimorbidity and health‑related quality of life of patients in primary care. Quality of Life Research, 15(1), 83–91.
[8] Verbrugge, L. M., & Jette, A. M. (1994). The disablement process. Social Science & Medicine, 38(1), 1–14.
[9] Yang, S., Zhong, P., Shen, X., & Zhang, Y. (2024). A multi‑state model study of the disability‑free life expectancy among older adults with chronic multimorbidity based on CHARLS — China, 2011–2020. China CDC Weekly, 6(28), 695–698.
[10] Zeng Yi, Liu Yuzhi, Xiao Zhenyu. (2004). Analysis of Factors Influencing Elderly in China. Population Research, 28(4), 9–16
[11] Huntley, A. L., Johnson, R., Purdy, S., Valderas, J. M., & Salisbury, C. (2012). Measures of multimorbidity and morbidity burden for use in primary care and community settings: A systematic review and guide. Annals of Family Medicine, 10(2), 134–141.
[12] Violan, C., Foguet‑Boreu, Q., Flores‑Mateo, G., et al. (2014). Prevalence, determinants and patterns of multimorbidity in primary care: A systematic review of observational studies. PLOS ONE, 9(7), e102149.
[13] Iijima, K., Iizuka, T., Suzuki, K., et al. (2019). Multimorbidity patterns in older adults: A prospective cohort study using patient‑centered priority assessment. Journal of the American Medical Directors Association, 20(11), 1408–1413.
[14] House, J. S., Lantz, P. M., & Herd, P. (2005). Continuity and change in the social stratification of aging and health over the life course: Evidence from a nationally representative longitudinal study from 1986 to 2001/2002 (Americans' Changing Lives Study). The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 60(Supplement 2), S15–S26.
[15] St Sauver, J. L., Boyd, C. M., Grossardt, B. R., et al. (2015). Risk of developing multimorbidity across all ages in an historical cohort study: Differences by sex and ethnicity. BMJ Open, 5(2), e006413.
[16] Huang Jianquan, Tang Zhe, Xiang Hua. (2018). Analysis of the Prevalence and Disease Spectrum of Chronic Diseases among the Elderly in Beijing. Chinese Journal of Epidemiology, 39(3), 274–279
[17] Xu, X., Geldsetzer, P., Ebert, C., et al. (2018). Multimorbidity among middle‑aged and older adults in China and India: Results from the China health and retirement longitudinal study (CHARLS) and the longitudinal aging study in India (LASI). The Lancet, 392, S8.
[18] Wang, H., Du, S., He, Y., et al. (2021). Multimorbidity and health‑related quality of life among the community‑dwelling elderly: A longitudinal study in China. BMJ Open, 11(9), e048193.
[19] Quiñones, A. R., Liang, J., Bennett, J. M., & Xu, X. (2011). How does the trajectory of multimorbidity vary across black, white, and Mexican Americans in middle and old age? The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 66(6), 739–749.
[20] Liu, X., Guo, Q., Feng, X., et al. (2021). Multimorbidity and its patterns, and all‑cause mortality among middle‑aged and older adults in China: Competing‑risks analysis of national longitudinal study. Scientific Reports, 11(1), 21811.
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