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BMC Public Health
Published in: BMC Public Health 1/2024

Open Access 01-12-2024 | Arthritis | Research

Trajectories network analysis of chronic diseases among middle-aged and older adults: evidence from the China Health and Retirement Longitudinal Study (CHARLS)

Authors: Jiade Chen, Fan Zhang, Yuan Zhang, Ziqiang Lin, Kaisheng Deng, Qingqin Hou, Lixia Li, Yanhui Gao

Published in: BMC Public Health | Issue 1/2024

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Abstract

Background

Given the increased risk of chronic diseases and comorbidity among middle-aged and older adults in China, it is pivotal to identify the disease trajectory of developing chronic multimorbidity and address the temporal correlation among chronic diseases.

Method

The data of 15895 participants from the China Health and Retirement Longitudinal Study (CHARLS 2011 – 2018) were analyzed in the current study. Binomial tests and the conditional logistic regression model were conducted to estimate the associations among 14 chronic diseases, and the disease trajectory network analysis was adopted to visualize the relationships.

Results

The analysis showed that hypertension is the most prevalent disease among the 14 chronic conditions, with the highest cumulative incidence among all chronic diseases. In the disease trajectory network, arthritis was found to be the starting point, and digestive diseases, hypertension, heart diseases, and dyslipidemia were at the center, while memory-related disease (MRD), stroke, and diabetes were at the periphery of the network.

Conclusions

With the chronic disease trajectory network analysis, we found that arthritis was prone to the occurrence and development of various other diseases. In addition, patients of heart diseases/hypertension/digestive disease/dyslipidemia were under higher risk of developing other chronic conditions. For patients with multimorbidity, early prevention can preclude them from developing into poorer conditions, such as stroke, MRD, and diabetes. By identifying the trajectory network of chronic disease, the results provided critical insights for developing early prevention and individualized support services to reduce disease burden and improve patients’ quality of life.
Appendix
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Literature
1.
2.
3.
Nguyen H, Manolova G, Daskalopoulou C, Vitoratou S, Prince M, Prina AM. Prevalence of multimorbidity in community settings: A systematic review and meta-analysis of observational studies. J Comorb. 2019;9:2235042X19870934.CrossRefPubMedPubMedCentral
4.
Zhou X, Zhang D, Health P. Multimorbidity in the elderly: a systematic bibliometric analysis of research output. Int J Environ Res Public Health. 2021;19(1):353.CrossRefPubMedPubMedCentral
5.
Soley-Bori M, Ashworth M, Bisquera A, Dodhia H, Lynch R, Wang Y, et al. Impact of multimorbidity on healthcare costs and utilisation: a systematic review of the UK literature. Bri J Gen Pract. 2021;71(702):e39-46.CrossRef
6.
Noël PH, Chris Frueh B, Larme AC, Pugh JA. Collaborative care needs and preferences of primary care patients with multimorbidity. Health Expect. 2005;8(1):54–63.CrossRefPubMedPubMedCentral
7.
Schneider J, Algharably EAE, Budnick A, Wenzel A, Dräger D, Kreutz RJ. High prevalence of multimorbidity and polypharmacy in elderly patients with chronic pain receiving home care are associated with multiple medication-related problems. 2021:1180.
8.
Molist-Brunet N, Sevilla-Sánchez D, Puigoriol-Juvanteny E, Bajo-Peñas L, Cantizano-Baldo I, Cabanas-Collell L, et al. Individualized medication review in older people with multimorbidity: a comparative analysis between patients living at home and in a nursing home. Int J Environ Res Public Health. 2022;19(6):3423.CrossRefPubMedPubMedCentral
9.
Skou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ, et al. Multimorbidity. Nat Rev Dis Primers. 2022;8(1):1–22.CrossRef
10.
11.
Hone T, Stokes J, Trajman A, Saraceni V, Coeli CM, Rasella D, et al. Racial and socioeconomic disparities in multimorbidity and associated healthcare utilisation and outcomes in Brazil: a cross-sectional analysis of three million individuals. BMC Public Health. 2021;21:1–11.CrossRef
12.
Boro B, Saikia N. Association of multimorbidity and physical activity among older adults in India: an analysis from the longitudinal ageing survey of India (2017–2018). BMJ Open. 2022;12(5):e053989.CrossRefPubMedPubMedCentral
13.
Jensen AB, Moseley PL, Oprea TI, Ellesøe SG, Eriksson R, Schmock H, et al. Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nat Commun. 2014;5(1):1–10.CrossRef
14.
Yang H, Pawitan Y, He W, Eriksson L, Holowko N, Hall P, et al. Disease trajectories and mortality among women diagnosed with breast cancer. Breast Cancer Res. 2019;21(1):1–8.CrossRef
15.
Andersen RK, Jørgensen IF, Reguant R, Jemec GBE, Brunak S. Disease trajectories for hidradenitis suppurativa in the Danish population. JAMA Dermatol. 2020;156(7):780–6.CrossRef
16.
Han X, Hou C, Yang H, Chen W, Ying Z, Hu Y, et al. Disease trajectories and mortality among individuals diagnosed with depression: a community-based cohort study in UK Biobank. Mol Psychiatry. 2021;26(11):6736–46.CrossRefPubMedPubMedCentral
17.
Meng S-D, Wang W, Ying P, Wang L-J, Liu Y-N, Liu J-M, et al. Analysis on the burden of four major chronic diseases among the elderly (≥60 years old) in China in 2005 and 2020. Chin J Prev Contr Chronic Dis. 2022;30(5):321–6.
18.
Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol. 2014;43(1):61–8.CrossRefPubMed
19.
Liu Y. China health and retirement longitudinal study wave 4 user’s guide.
20.
21.
Moisset X, Perié M, Pereira B, Dumont E, Lebrun-Frenay C, Lesage F-X, et al. Decreased prevalence of cancer in patients with multiple sclerosis: a case-control study. Plos One. 2017;12(11):e0188120.CrossRefPubMedPubMedCentral
22.
Adjei IA, Karim R. An application of bootstrapping in logistic regression model. Open Access Lib J. 2016;3(9):1–9.
23.
Siggaard T, Reguant R, Jørgensen IF, Haue AD, Lademann M, Aguayo-Orozco A, et al. Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients. Nat Commun. 2020;11(1):4952.CrossRefPubMedPubMedCentralADS
24.
Castillo-Cañón JC, Trujillo-Cáceres SJ, Bautista-Molano W, Valbuena-García AM, Fernández-Ávila DG, Acuña-Merchán L. Rheumatoid arthritis in Colombia: a clinical profile and prevalence from a national registry. Clin Rheumatol. 2021;40(9):3565–73.CrossRefPubMed
25.
26.
Krist AH, Davidson KW, Mangione CM, Barry MJ, Cabana M, Caughey AB, et al. Behavioral counseling interventions to promote a healthy diet and physical activity for cardiovascular disease prevention in adults with cardiovascular risk factors: US Preventive Services Task Force recommendation statement. JAMA. 2020;324(20):2069–75.CrossRefPubMed
27.
Abbasi A, Corpeleijn E, Gansevoort RT, Gans RO, Hillege HL, Stolk RP, et al. Role of HDL cholesterol and estimates of HDL particle composition in future development of type 2 diabetes in the general population: the PREVEND study. J Clin Endocrinol Metab. 2013;98(8):E1352–9.CrossRefPubMed
28.
Reitz C. Dyslipidemia and the risk of Alzheimer’s disease. Curr Atheroscler Rep. 2013;15:1–9.CrossRef
29.
Herzig K-H, Purhonen A-K, Räsänen KM, Idziak J, Juvonen P, Phillps R, et al. Fecal pancreatic elastase-1 levels in older individuals without known gastrointestinal diseases or diabetes mellitus. BMC Geriatr. 2011;11(1):1–5.CrossRef
30.
Du Y, Su T, Song X, Gao J, Zou D, Zuo C, et al. Efficacy and safety of cinitapride in the treatment of mild to moderate postprandial distress syndrome–predominant functional dyspepsia. J Clin Gastroenterol. 2014;48(4):328–35.CrossRefPubMed
31.
Semb AG, Ikdahl E, Wibetoe G, Crowson C, Rollefstad S. Atherosclerotic cardiovascular disease prevention in rheumatoid arthritis. Nat Rev Rheumatol. 2020;16(7):361–79.CrossRefPubMed
32.
Han M, Chen Q, Liu L, Li Q, Ren Y, Zhao Y, et al. Stage 1 hypertension by the 2017 American College of Cardiology/American Heart Association hypertension guidelines and risk of cardiovascular disease events: systematic review, meta-analysis, and estimation of population etiologic fraction of prospective cohort studies. J Hypertens. 2020;38(4):573–8.CrossRefPubMed
33.
Chen H, Zhou Y, Huang L, Xu X, Yuan C. Multimorbidity burden and developmental trajectory in relation to later-life dementia: a prospective study. Alzheimers Dement. 2024;2022:19.
34.
Demant MN, Gislason GH, Køber L, Vaag A, Torp-Pedersen C, Andersson CJD. Association of heart failure severity with risk of diabetes: a Danish nationwide cohort study. Diabetologia. 2014;57(8):1595–600.CrossRefPubMed
35.
Islam MR, Lbik D, Sakib MS, Maximilian Hofmann R, Berulava T, Jiménez Mausbach M, et al. Epigenetic gene expression links heart failure to memory impairment. EMBO Mol Med. 2021;13(3):e11900.CrossRefPubMedPubMedCentral
36.
Aradine E, Hou Y, Cronin CA, Chaturvedi S. Chaturvedi S, Current status of dyslipidemia treatment for stroke prevention. Current neurology and neuroscience reports. 2020;20(8):1–8.CrossRef
37.
Vergès B. Metabolism. Dyslipidemia in type 1 diabetes: a masked danger. Trends Endocrinol Metab. 2020;31(6):422–34.CrossRefPubMed
38.
Silva MVF. Loures CdMG, Alves LCV, de Souza LC, Borges KBG, Carvalho MdGJJobs. Alzheimer’s disease: risk factors and potentially protective measures. 2019;26(1):1–11.
39.
Metadata
Title
Trajectories network analysis of chronic diseases among middle-aged and older adults: evidence from the China Health and Retirement Longitudinal Study (CHARLS)
Authors
Jiade Chen
Fan Zhang
Yuan Zhang
Ziqiang Lin
Kaisheng Deng
Qingqin Hou
Lixia Li
Yanhui Gao
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2024
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-024-17890-7

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