Chronic conditions and intrinsic capacity trajectories among Chinese middle-aged and older adults: a nationally representative longitudinal study

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Methods Cohort data was from three waves of the China Health and Retirement Longitudinal Study with a final sample of 8,836 participants. IC score was calculated by six different sub-domains. Parallel process latent growth curve model was applied to examine the association between chronic condition and IC trajectories. Results Over the study period, IC decreased significantly (β = −0.182, p < 0.001), with the largest reductions observed in vision and hearing. Increase in number of chronic conditions (β = -0.200, p < 0.001) and residing in rural areas (β = -0.052, p = 0.045) was related to a faster decline in IC. Conclusions Integrated care for older people, complex interventions on multimorbidity, and policies to narrow down rural-urban disparity were warranted to slow IC decline. Intrinsic capacity chronic condition trajectory older population Figures Figure 1 Introduction The globe is experiencing rapid growth in both the size and the proportion of older adults aged 60 years and older, with the absolute number increasing from 0.9 billion in 2015 to 2.1 billion in 2050 and the proportion doubling from about 11% in 2000 to 22% in 2050 1 . As people live longer, individuals are more likely to experience multimorbidity (coexistence of two and more chronic conditions) and geriatric syndromes such as falls, frailty, and sarcopenia, manifesting a complexity of health states related to heterogeneous care needs and survivals or other outcomes 2 – 5 . The World Health Organization (WHO) has proposed a paradigm shift from disease-centred to function- and person-centred approach for older people, based on the core concept of intrinsic capacity (IC) 6 . IC is defined as the composite of all the physical and mental capacities of an individual, determined not only by diseases, injuries, and broader geriatric syndromes but also the environments older adults inhabit and their interaction with them 3 . The IC construct differs from other approaches by being framed as a dynamic continuum and its trajectories can be monitored across the second half of a person's life course to provide insight into the effectiveness of clinical actions, as well as its effectiveness in public health, and on the needs of older population 7 . Although it is assumed that 80% of older population will be living in low- and middle-income countries (LMICs) in 2050, limited evidence on IC trajectories has been collected from these areas 8 , 9 . Home to 1/5 of the world’s older people, China bears heavy burden of aging and will continue to age rapidly for decades, where there will be 395 million people aged 65 years and older by 2050, equivalent to 1.2 times the current population of the United States (US) 10 . However, few studies have investigated IC trajectories in China and other LMICs. Most of these studies were based on regional data and provided inconsistent results, and few have reported based on nationally representative data 11 , 12 . Furthermore, existing studies were limited in analyzing IC trajectories, which focused on state transition, thus suppressing valuable information on IC trajectories preceding and occurring after the decline 11 , 13 . Another research gap is the limited understanding of the determinants of IC trajectories, particularly on the relationship between chronic condition and IC trajectories. Partly due to the regional data or inconsistent measurement of IC 14 , 15 , previous studies found the association of chronic condition with IC trajectories was inconsistent. Additionally, previous studies also suffered from methodological limitations in considering chronic condition as a time-independent variable, constraining the capacity to analyze the dynamic interplay between the progression of chronic condition and the trajectories of IC 16 , 17 . Since individuals with chronic condition were more likely to develop multimorbidity, time-varying statistical models including both trajectories of chronic condition and IC were warranted. To address these gaps, the current study examined how changes of number of chronic conditions shaped trajectories of IC by applying a WHO-proposed framework for IC and parallel process latent growth curve model, drawing data from a nationally representative longitudinal cohort of middle-aged and older Chinese adults. This will be accomplished by: (1) describing trajectories of IC among middle-aged and older adults in China; (2) characterizing the longitudinal relationship between trajectories of chronic condition and IC. Methods Data source and study sample In this longitudinal analysis, we used data from three waves of the China Health and Retirement Longitudinal Study (CHARLS) run in 2011, 2013 and 2015. CHARLS collects high-quality data through one-on-one interviews using structured questionnaires from a nationally representative sample of Chinese residents aged 45 years and older. The survey employs a multistage stratified probability-proportionate-to-size sampling method, covering 28 provinces, 150 counties/districts and 450 villages/communities 18 . The CHARLS survey included a total of 17,708 respondents in 2011, who were followed up every two years for repeated interviews. Among the respondents in baseline, 858 were excluded due to age below 45, 6,174 were excluded due to absence of intrinsic capacity measurement in baseline, 23 were excluded due to missing data for covariates and weight in baseline, and 567 were excluded due to loss of follow-up in 2013 or 2015. The final analytical sample size was 8,836. To address potential non-response bias and to ensure that the estimation was nationally representative, we weighed the samples using a survey weight variable provided by CHARLS, which gave sampled units (households and individuals) weights inversely proportional to their probability of having been selected and having responded 18 . Measurement Following guidelines proposed by WHO as well as previous studies, IC score was calculated by six different sub-domains (depressive symptoms, cognition, vision, hearing, locomotor, vitality) with a scale ranging from 0 to 6 19–21 . Depressive symptoms were measured using the Center for Epidemiological Studies-Depression Scale (CES-D) with a Cronbach's alpha of 0.85, including 10 items that assessed how respondents felt and behaved during the previous week, covering aspects such as bother, attention, depression, difficulties, the future, fear, sleep, happiness, loneliness, and life 22 , 23 . A four-level scale was used, ranging from 0 to 3 (0 = rarely or none of the time [less than 1 day]; 1 = some or a little of the time [1–2 days]; 2 = occasionally or a moderate amount of time [3–4 days]; 3 = most or all of the time [5–7 days]). The scoring methods for questions 5 and 8 about positive performance are reversed. The total score is between 0 and 30, with scores below 12 considered as having psychological capacity 24 . Cognition was measured by episodic memory, including immediate and delayed word recall. The maximum score for memory tests was 20 points, with one point for each correctly recalled word 25 . Consistent to previous studies, scoring at least 1 standard deviation (SD) below the average standard in baseline (2011) was defined as not having cognitive capacity, while scores above this threshold were considered as having cognitive capacity 26 . Both vision and hearing were defined based on the self-reported assessments. For vision, participants were asked “How good is your vision for seeing things at a distance (with glasses or corrective lenses), like recognizing a friend from across the street?” Hearing status was assessed by asking participants to rate their hearing (using a hearing aid if they used one). Both questions were rated on a five-point scale (excellent, very good, good, fair, or poor). According to previous studies, participants who reported “excellent/very good/good” were classified having vision or hearing capacity 27 . Locomotor was measured through three separate static balance tests, which was progressively more difficult and included side-by-side, semi-tandem and full tandem tests. Participants holding the position in all three tests for at least 10 seconds were considered as having locomotor capacity 19 . Vitality was measured through the handgrip strength test. Handgrip strength (kg) of the dominant hand was assessed using a hand-held dynamometer. In line with previous study, vitality was defined by the maximum hand strength measurement of the dominant hand 28 , and values below the threshold (28.0 kg for men and 18.0 kg for women) by the Asian Working Group for Sarcopenia were classified as not having vitality capacity 29 . Number of chronic conditions was categorized into 6 groups (0/1/2/3/4/≥5). Chronic condition included 12 self-reported non-communicable diseases in CHARLS, including hypertension, diabetes, dyslipidemia, heart disease, stroke, cancer, chronic lung disease, digestive disease, liver disease, kidney disease, arthritis, and asthma. Following previous studies, covariates included six baseline characteristics: age (45–49, 50–54, 55–59, 60–64, 65–69, 70–74, ≥ 75 years old), gender (female, male), residential status (urban, rural), marital status (divorced or others, married and partnered), education level (illiterate, elementary school, middle school, high school and above), and health insurance (no, yes) 14 , 30 . All covariates were assessed at baseline in 2011. Data analysis Latent growth curve model (LGCM) is a method for modeling repeated measures as latent variables composed of a random intercept and random slope(s) that permit individual cases to have unique trajectories of change over time 31 . Intercept refers to the initial status in chronic condition or IC of the sample, while slope represents the change rate of chronic condition or IC. Parallel process LGCM estimates the parameters of the growth factors (intercepts and slopes) of chronic condition and IC and the relationship between them. We conducted two sets of analysis: First, unconditional LGCM was performed to estimate the baseline (latent intercept) and change (latent slope) of IC over time. Second, parallel process LGCM was performed to examine the association between change of chronic conditions and IC trajectories, adjusted for baseline covariates (descried in previous subsection). Missing values were dealt with by maximum likelihood estimation under the missing at random assumption. Missing-data analysis for subdomains of IC and chronic condition was conducted using Little’s test with the command mcartest in Stata 17.0 software, with a result (p = 0.19) consistent with the assumption 32 . The available data on each subdomain of IC are shown in Supplementary Table S1 . The model fits the data well if it satisfies most of following criteria: χ2 statistic > 0.05, comparative fit index (CFI) and Tucker-Lewis Index (TLI) > 0.90, the root-mean-square error of approximation (RMSEA) < 0.06, and a standardized root-mean-square residual (SRMR) < 0.08 33 . Estimates with a p-value of < 0.05 were interpreted as statistically significant. Descriptive analysis was conducted using Stata 17.0 (StataCorp LP, College Station, Texas) software and modeling analysis using Mplus 7.4 (Muthén & Muthén) software. Results Among the baseline cohort of 8,836 participants (Table 1 ), the mean age was 58.26 (SD = 0.16) years, slightly more than half were female and lived in rural areas. 84.7% were married, 41.8% had an education level of primary school, and 93.0% had health insurance. Approximately 29% and 37% had one chronic condition and multimorbidity at baseline, respectively. Table 1 Baseline characteristics of participants n Proportion (%) Age 45–49 1,818 21.1 50–54 1,403 16.0 55–59 1,958 21.6 60–64 1,649 17.7 65–69 1,027 11.8 70–74 621 7.3 ≥ 75 360 4.5 Gender Female 4,558 51.6 Male 4,278 48.4 Residence status Urban 3,131 15.3 Rural 5,705 84.7 Marital status Divorced or others 1,293 41.8 Married and partnered 7,543 58.2 Education level Illiterate 2,171 23.1 Primary school 3,744 42.0 Middle school and above 2,921 34.9 Health insurance No 588 7.0 Yes 8,248 93.0 Number of chronic conditions 0 2,944 33.7 1 2,635 29.1 2 1,787 20.6 3 856 9.3 4 369 4.3 ≥ 5 245 2.9 Overall, intrinsic capacity in our sample decreased over four years (Table 2 ). Four out of the six subdomains, such as cognition, vision, hearing, and vitality, have decreased. The largest reductions were observed in vision and hearing, decreasing from 39.6% and 47.4% in 2011 to 27.9% and 32.5% in 2015, respectively. No significant difference over time was found for locomotor. Table 2 Change of retained subdomains of intrinsic capacity among middle-aged and older adults from 2011 to 2015 Dimension 2011 2013 2015 Proportion % (95%CI) Depressive symptoms 76.1 (74.4, 77.7) 79.3 (77.9, 80.7) 76.0 (74.4, 77.5) Cognition 87.0 (85.7, 88.2) 86.7 (85.6, 87.7) 80.0 (78.4, 81.6) Vision 39.6 (37.5, 41.7) 35.9 (33.9, 38.0) 27.9 (26.5, 29.3) Hearing 47.4 (45.4, 49.4) 40.3 (38.4, 42.2) 32.5 (31.0, 34.0) Locomotor 80.4 (78.6, 82.0) 81.6 (80.1, 83.0) 82.4 (81.1, 83.7) Vitality 90.9 (89.7, 91.9) 89.0 (87.9, 90.1) 84.5 (83.1, 85.9) Full capacity 17.9 (16.6, 19.3) 14.8 (13.6, 16.2) 10.4 (9.2, 11.8) Notes: CI = confidence interval. Unconditional LGCM was applied to capture the trajectories of IC over time (Table 3 ). On average, initial IC score was 4.235 and decreased significantly (β = -0.182, p < 0.001) among the study sample over the 4-year study period, which had different initial status (β = 0.715, p < 0.001) and change rates (β = 0.070, p < 0.001) in IC. However, the association between the intercept and slope was not significant. Table 3 Unconditional latent growth curve model for intrinsic capacity Estimate S.E. p Means Intercept 4.235 0.026 < 0.001 Slope -0.182 0.013 < 0.001 Variances Intercept 0.715 0.040 < 0.001 Slope 0.070 0.021 0.001 Covariance Intercept-Slope -0.027 0.024 0.244 Model fit X 2 19.702 < 0.001 RMSEA 0.046 CFI 0.994 TLI 0.981 SRMR 0.020 Notes: Estimate = unstandardized coefficient; S.E. = standard error; RMSEA = root-mean-square error of approximation; CFI = comparative fit index; TLI = Tucker-Lewis Index; SRMR = standardized root-mean-square residual. Parallel process LGCM was conducted to estimate the longitudinal associations between number of chronic conditions and IC trajectories over time, adjusted for baseline characteristics. The model fitted the data well (Table 4 ). Figure 1 shows that more chronic conditions at baseline was associated with lower IC scores (β = -0.175, p < 0.001) and slower decline (β = 0.024, p = 0.008) in IC over time. Increase in number of chronic conditions was related to the faster decline in IC over time (β = -0.200, p < 0.001). Older age, being female, having lower education level, living in rural areas were negatively associated with IC lower scores at baseline. Middle-aged and older adults living in rural areas had faster decline in IC scores (β = -0.052, p = 0.045). Discussion In 2020, the United Nations General Assembly declared 2021–2030 the Decade of Healthy Ageing, highlighting the importance for policymakers across the world to focus on improving the lives of older people, both today and in the future 8 . With the world’s largest older population, China bears heavy burden from rapidly ageing populations such as increased prevalence of multimorbidity and IC decline, stressing the importance of retaining IC and achieving “healthy ageing” 10 , 34 , 35 . The current study found that increase in number of chronic conditions was related to the faster decrease in IC among middle-aged and older Chinese adults over the 4-year follow up period. To our knowledge, this is the first longitudinal study to characterize the trajectories of IC and to examine how change in number of chronic conditions shaped IC trajectories using nationally representative data. We conducted a systematic literature search for published papers on the relationship of chronic condition and IC trajectories until May 31, 2024, using the keywords “chronic condition”, “multimorbidity”, and “intrinsic capacity” on Pubmed, Web of Science, and Scopus, and eight longitudinal studies were identified with five studies from China 11 – 13 , 16 , 30 , one from Mexico 36 , two from Europe 37 , 38 . Despite the longitudinal study design of the studies in China, they were limited by regional data such as sample from western regions of China, suggesting no significant relationship between chronic conditions and IC trajectories 11 . Furthermore, IC was a novel concept proposed by WHO, no consistent assessment frame for which has been constructed 19 . Additionally, chronic condition was treated as a time-independent variable in previous studies, which was measured at baseline to evaluate its association with IC trajectories 37 . Since single chronic condition easily developed into multimorbidity, it was necessary to consider its time-varying effect. Our study extends the knowledge of IC trajectories by employing a nationally representative longitudinal data to discern the relationship between changes in number of chronic conditions and IC trajectories by utilizing a WHO-proposed framework and parallel process LGCM. This longitudinal study identified a decline in IC scores among a nationally representative population over a 4-year follow up period. Evidence from LMICs indicated a decrease in the proportion of retaining full IC with advancing age 20 . The largest reductions were observed in sensory subdomain, namely vision and hearing. This aligned with research in west China by Jia et al., which suggested that sensory impairment as the most prevalent impaired IC domains was a strong predictor of worsened or persistent poor IC 11 . Possible reasons for this might include that sensory impairment can lead to subsequent cognitive decline, depression, and decreased mobility 25 , 39 , 40 . Those residing in rural areas had lower IC scores at baseline and a more rapid decline over the follow-up period, which was in line with the strand of literature on the rural-urban health disparity in China. Despite great efforts taken to narrow down the disparity by Chinese government, gaps persisted across various dimensions, such as access to healthcare services, healthcare quality, health literacy, and socioeconomic factors 41 – 43 , contributing to poorer health outcomes and IC in rural populations. Additionally, our study has added to the small body of research examining the relationship of chronic condition and IC trajectories, specifically focusing on the association between increase in the number of chronic conditions and faster decrease in IC scores. Potential factors underlying this association might involve escalated depressive symptoms, reduced mobility and physical activity, as well as the complexities and challenges related to polypharmacy and the management from multimorbidity 44 – 46 . Prior research has established connections between chronic condition and depression, revealing that the odds of experiencing depressive disorders increased by 45% with each additional chronic condition compared with no chronic condition 44 . Older adults with multimorbidity were significantly more likely to engage in low levels of physical activity, which was subdomains of IC as well 45 . Additionally, polypharmacy, prevalent among individuals with multimorbidity, often resulted in adverse events, drug–drug and disease–drug interactions, and a lower level of adherence to more complex treatment regimens 46 . Intriguingly, higher number of chronic conditions at baseline were associated with a more gradual decline in IC scores. This link could be attributed to a baseline effect, where those already exhibiting low IC levels due to chronic conditions had a limited scope for further decline, or it might reflect increased healthcare resource utilization by older adults with chronic conditions endeavoring to manage and preserve their health status 16 . There are several policy implications for this study. Our findings demonstrated a significant correlation of increase in the number of chronic conditions with a more rapid decline in IC scores. Given these insights, early screening was of great importance for individuals with chronic condition yet showing no IC decline, which might enable more timely intervention to maintain IC levels. Evidence from a randomized controlled trial in China with a follow-up of 6 months has proved the feasibility of the approach called integrated care for older people (ICOPE), including screening for IC and tailored intervention for community-dwelling older adults, which was supported by its acceptance among key stakeholders and impact on health outcomes 47 . Future studies should consider longer follow-up period to examine its robust and sustainable impact on health status. Furthermore, since the association was mediated by adverse effect on subdomains of IC from multimorbidity and management of multimorbidity 44 – 46 , multidomain or complex interventions on multimorbidity were warranted to offer a patient-centered approach, potentially slowing down the decline of IC among older adults with multimorbidity 48 , 49 . This approach underscores the importance of addressing functional limitation, depressive symptoms, and problems of polypharmacy to improve their health-related quality of life and reduce their burden of illness 49 , 50 . Additionally, efforts should be taken to minimize the rural-urban disparity in China, a crucial factor contributing to lower IC levels and rapid decrease among middle-aged and older adults. These gaps primarily manifested in terms of the access of quality primary healthcare (PHC). To ensure quality care and access to effective services in rural areas, measures to strengthen PHC should be taken including build a strong PHC workforce centered on village doctors with gradual expansion to nurses 51 . Previous studies have shown that the potential role of non-physician PHC providers such as rehabilitation specialists and nutritionists based in PHC setting in improving health outcomes and patient satisfaction 47 . Moreover, both financial and non-financial incentives should be employed to attract and retain the workforce, including salaries supplemented with performance-based compensation and social recognition within the community 52 , 53 . Study limitations must also be acknowledged. First, key variables such as chronic condition and partial subdomains of IC relied on self-report, rendering them subject to recall bias and social expectation bias. Future research would benefit from the inclusion of objective performance tests related to chronic conditions and IC. Second, due to lack of data in other waves, the long-term trajectories of IC cannot be identified. Since CHARLS was not designed with the purpose of this study, thus the variables available for inclusion in our analysis had their limitations 18 . However, a WHO-proposed framework for evaluating IC was employed in this study. Future studies should expand the observation time-frame and apply the life course perspective to examine the longitudinal relationship between chronic condition and IC trajectories. Finally, this study considered the number of chronic conditions, yet the impact of different chronic condition might vary. Future research could explore the relationship between specific disease types or multimorbidity patterns and IC trajectories. Conclusion The present study characterized the longitudinal relationship between the change in number of chronic conditions and the trajectories of IC among middle-aged and older adults in China, revealing that increase in number of chronic conditions was significantly associated with a more rapid decline in IC over a four-year period. Therefore, it is highlighted for integrated care and complex interventions on multimorbidity to mitigate IC decline among older adults. Additionally, addressing the rural-urban disparity in access to quality PHC and health literacy is critical for sustaining the wellbeing and IC among older adults. Declarations Ethics approval and consent to participate The data used in this study were approved by the Peking University Institutional Review Boards (IRB) (IRB00001052-11015). Written informed consent was obtained from all participants. Consent for publication Not applicable. Availability of data and materials This study is based on publicly-available datasets. The health data can be accessed from the website: http://charls.pku.edu.cn/. Competing interests The authors declare no competing interests. Funding This work was supported by National Natural Science Foundation of China (Grant Number 72174009). Authors' contributions XM has full access to all data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: ZZ and XM. Data analysis: ZZ. 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J Am Med Dir Assoc. 2024;25(5):757–e7634. Salisbury C, Man MS, Bower P, et al. Management of multimorbidity using a patient-centred care model: a pragmatic cluster-randomised trial of the 3D approach. Lancet. 2018;392(10141):41–50. Man MS, Chaplin K, Mann C, et al. Improving the management of multimorbidity in general practice: protocol of a cluster randomised controlled trial (The 3D Study). BMJ Open. 2016;6(4):e011261. Yip W, Fu H, Jian W, et al. Universal health coverage in China part 2: addressing challenges and recommendations. Lancet Public Health. 2023;8(12):e1035–42. Li X, Lu J, Hu S, et al. The primary health-care system in China. Lancet. 2017;390(10112):2584–94. Ma X, Wang H, Yang L, et al. Realigning the incentive system for China's primary healthcare providers. BMJ. 2019;365:l2406. Additional Declarations No competing interests reported. Supplementary Files BMCsup.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 22 May, 2026 Reviews received at journal 08 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviews received at journal 07 Jan, 2026 Reviewers agreed at journal 20 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers invited by journal 11 Jun, 2025 Editor invited by journal 14 May, 2025 Editor assigned by journal 11 Apr, 2025 Submission checks completed at journal 11 Apr, 2025 First submitted to journal 10 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6420100","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469832774,"identity":"a62b5ef0-2740-404a-919c-c8e0b13e11fb","order_by":0,"name":"Ziyin Zhao","email":"","orcid":"","institution":"School of Public Health, Peking University","correspondingAuthor":false,"prefix":"","firstName":"Ziyin","middleName":"","lastName":"Zhao","suffix":""},{"id":469832775,"identity":"a31afcc0-bd04-495c-a2da-b652cbc6f57a","order_by":1,"name":"Xiaochen Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYHACxsdgigdEsBGnhdmYZC1s0qRpMbh2+Fl1wR87BnOeMwYMH8oOM/DPbsCvRXJ2mtntmW3JDJa9PQaMM84dZpC4cwC/Fn7pBLPbvA0HGAzO8xgw87YdZjCQSCDkkfRvxTx/oFr+EqOFXzrHjJmHDajlbI8BMyMxWiRn5xRL87Yl81j2HCs42HMunUfiBgEtBrfTN37m+WMnZ86TvPHBjzJrOf4ZBLTAAI8BkDjAAI0e4oAB8UpHwSgYBaNgpAEAfHM6ffmsZlEAAAAASUVORK5CYII=","orcid":"","institution":"China Center for Health Development Studies, Peking University","correspondingAuthor":true,"prefix":"","firstName":"Xiaochen","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-04-10 12:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6420100/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6420100/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84817184,"identity":"218b14f5-e7aa-470d-bd08-34e2ae24f76e","added_by":"auto","created_at":"2025-06-17 15:47:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43263,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of the parallel process latent growth curve model to estimate baseline and change of chronic condition and IC over time.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6420100/v1/f5bb5f2558deb96a8d39cbd2.png"},{"id":84818940,"identity":"0a1fb168-4328-4981-b4e7-9a6e3dd8b378","added_by":"auto","created_at":"2025-06-17 15:56:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":708625,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6420100/v1/eea348be-e669-43d7-9eca-fabf7c24fc54.pdf"},{"id":84817181,"identity":"877554c8-46f8-42a1-a70b-baa187bb3f59","added_by":"auto","created_at":"2025-06-17 15:47:56","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21255,"visible":true,"origin":"","legend":"","description":"","filename":"BMCsup.docx","url":"https://assets-eu.researchsquare.com/files/rs-6420100/v1/78723b7cbf6da825157d496f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Chronic conditions and intrinsic capacity trajectories among Chinese middle-aged and older adults: a nationally representative longitudinal study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe globe is experiencing rapid growth in both the size and the proportion of older adults aged 60 years and older, with the absolute number increasing from 0.9\u0026nbsp;billion in 2015 to 2.1\u0026nbsp;billion in 2050 and the proportion doubling from about 11% in 2000 to 22% in 2050\u003csup\u003e1\u003c/sup\u003e. As people live longer, individuals are more likely to experience multimorbidity (coexistence of two and more chronic conditions) and geriatric syndromes such as falls, frailty, and sarcopenia, manifesting a complexity of health states related to heterogeneous care needs and survivals or other outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The World Health Organization (WHO) has proposed a paradigm shift from disease-centred to function- and person-centred approach for older people, based on the core concept of intrinsic capacity (IC)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. IC is defined as the composite of all the physical and mental capacities of an individual, determined not only by diseases, injuries, and broader geriatric syndromes but also the environments older adults inhabit and their interaction with them\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The IC construct differs from other approaches by being framed as a dynamic continuum and its trajectories can be monitored across the second half of a person's life course to provide insight into the effectiveness of clinical actions, as well as its effectiveness in public health, and on the needs of older population\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough it is assumed that 80% of older population will be living in low- and middle-income countries (LMICs) in 2050, limited evidence on IC trajectories has been collected from these areas\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Home to 1/5 of the world\u0026rsquo;s older people, China bears heavy burden of aging and will continue to age rapidly for decades, where there will be 395\u0026nbsp;million people aged 65 years and older by 2050, equivalent to 1.2 times the current population of the United States (US)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, few studies have investigated IC trajectories in China and other LMICs. Most of these studies were based on regional data and provided inconsistent results, and few have reported based on nationally representative data\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Furthermore, existing studies were limited in analyzing IC trajectories, which focused on state transition, thus suppressing valuable information on IC trajectories preceding and occurring after the decline\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAnother research gap is the limited understanding of the determinants of IC trajectories, particularly on the relationship between chronic condition and IC trajectories. Partly due to the regional data or inconsistent measurement of IC\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, previous studies found the association of chronic condition with IC trajectories was inconsistent. Additionally, previous studies also suffered from methodological limitations in considering chronic condition as a time-independent variable, constraining the capacity to analyze the dynamic interplay between the progression of chronic condition and the trajectories of IC\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Since individuals with chronic condition were more likely to develop multimorbidity, time-varying statistical models including both trajectories of chronic condition and IC were warranted.\u003c/p\u003e \u003cp\u003eTo address these gaps, the current study examined how changes of number of chronic conditions shaped trajectories of IC by applying a WHO-proposed framework for IC and parallel process latent growth curve model, drawing data from a nationally representative longitudinal cohort of middle-aged and older Chinese adults. This will be accomplished by: (1) describing trajectories of IC among middle-aged and older adults in China; (2) characterizing the longitudinal relationship between trajectories of chronic condition and IC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and study sample\u003c/h2\u003e \u003cp\u003eIn this longitudinal analysis, we used data from three waves of the China Health and Retirement Longitudinal Study (CHARLS) run in 2011, 2013 and 2015. CHARLS collects high-quality data through one-on-one interviews using structured questionnaires from a nationally representative sample of Chinese residents aged 45 years and older. The survey employs a multistage stratified probability-proportionate-to-size sampling method, covering 28 provinces, 150 counties/districts and 450 villages/communities\u003csup\u003e18\u003c/sup\u003e. The CHARLS survey included a total of 17,708 respondents in 2011, who were followed up every two years for repeated interviews. Among the respondents in baseline, 858 were excluded due to age below 45, 6,174 were excluded due to absence of intrinsic capacity measurement in baseline, 23 were excluded due to missing data for covariates and weight in baseline, and 567 were excluded due to loss of follow-up in 2013 or 2015. The final analytical sample size was 8,836. To address potential non-response bias and to ensure that the estimation was nationally representative, we weighed the samples using a survey weight variable provided by CHARLS, which gave sampled units (households and individuals) weights inversely proportional to their probability of having been selected and having responded\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurement\u003c/h3\u003e\n\u003cp\u003eFollowing guidelines proposed by WHO as well as previous studies, IC score was calculated by six different sub-domains (depressive symptoms, cognition, vision, hearing, locomotor, vitality) with a scale ranging from 0 to 6\u003csup\u003e19\u0026ndash;21\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDepressive symptoms were measured using the Center for Epidemiological Studies-Depression Scale (CES-D) with a Cronbach's alpha of 0.85, including 10 items that assessed how respondents felt and behaved during the previous week, covering aspects such as bother, attention, depression, difficulties, the future, fear, sleep, happiness, loneliness, and life\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. A four-level scale was used, ranging from 0 to 3 (0\u0026thinsp;=\u0026thinsp;rarely or none of the time [less than 1 day]; 1\u0026thinsp;=\u0026thinsp;some or a little of the time [1\u0026ndash;2 days]; 2\u0026thinsp;=\u0026thinsp;occasionally or a moderate amount of time [3\u0026ndash;4 days]; 3\u0026thinsp;=\u0026thinsp;most or all of the time [5\u0026ndash;7 days]). The scoring methods for questions 5 and 8 about positive performance are reversed. The total score is between 0 and 30, with scores below 12 considered as having psychological capacity\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCognition was measured by episodic memory, including immediate and delayed word recall. The maximum score for memory tests was 20 points, with one point for each correctly recalled word\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Consistent to previous studies, scoring at least 1 standard deviation (SD) below the average standard in baseline (2011) was defined as not having cognitive capacity, while scores above this threshold were considered as having cognitive capacity\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBoth vision and hearing were defined based on the self-reported assessments. For vision, participants were asked \u0026ldquo;How good is your vision for seeing things at a distance (with glasses or corrective lenses), like recognizing a friend from across the street?\u0026rdquo; Hearing status was assessed by asking participants to rate their hearing (using a hearing aid if they used one). Both questions were rated on a five-point scale (excellent, very good, good, fair, or poor). According to previous studies, participants who reported \u0026ldquo;excellent/very good/good\u0026rdquo; were classified having vision or hearing capacity\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLocomotor was measured through three separate static balance tests, which was progressively more difficult and included side-by-side, semi-tandem and full tandem tests. Participants holding the position in all three tests for at least 10 seconds were considered as having locomotor capacity\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eVitality was measured through the handgrip strength test. Handgrip strength (kg) of the dominant hand was assessed using a hand-held dynamometer. In line with previous study, vitality was defined by the maximum hand strength measurement of the dominant hand\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, and values below the threshold (28.0 kg for men and 18.0 kg for women) by the Asian Working Group for Sarcopenia were classified as not having vitality capacity\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNumber of chronic conditions was categorized into 6 groups (0/1/2/3/4/\u0026ge;5). Chronic condition included 12 self-reported non-communicable diseases in CHARLS, including hypertension, diabetes, dyslipidemia, heart disease, stroke, cancer, chronic lung disease, digestive disease, liver disease, kidney disease, arthritis, and asthma.\u003c/p\u003e \u003cp\u003eFollowing previous studies, covariates included six baseline characteristics: age (45\u0026ndash;49, 50\u0026ndash;54, 55\u0026ndash;59, 60\u0026ndash;64, 65\u0026ndash;69, 70\u0026ndash;74, \u0026ge;\u0026thinsp;75 years old), gender (female, male), residential status (urban, rural), marital status (divorced or others, married and partnered), education level (illiterate, elementary school, middle school, high school and above), and health insurance (no, yes)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. All covariates were assessed at baseline in 2011.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eLatent growth curve model (LGCM) is a method for modeling repeated measures as latent variables composed of a random intercept and random slope(s) that permit individual cases to have unique trajectories of change over time\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Intercept refers to the initial status in chronic condition or IC of the sample, while slope represents the change rate of chronic condition or IC. Parallel process LGCM estimates the parameters of the growth factors (intercepts and slopes) of chronic condition and IC and the relationship between them. We conducted two sets of analysis: First, unconditional LGCM was performed to estimate the baseline (latent intercept) and change (latent slope) of IC over time. Second, parallel process LGCM was performed to examine the association between change of chronic conditions and IC trajectories, adjusted for baseline covariates (descried in previous subsection). Missing values were dealt with by maximum likelihood estimation under the missing at random assumption. Missing-data analysis for subdomains of IC and chronic condition was conducted using Little\u0026rsquo;s test with the command \u003cem\u003emcartest\u003c/em\u003e in Stata 17.0 software, with a result (p\u0026thinsp;=\u0026thinsp;0.19) consistent with the assumption\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The available data on each subdomain of IC are shown in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The model fits the data well if it satisfies most of following criteria: χ2 statistic\u0026thinsp;\u0026gt;\u0026thinsp;0.05, comparative fit index (CFI) and Tucker-Lewis Index (TLI)\u0026thinsp;\u0026gt;\u0026thinsp;0.90, the root-mean-square error of approximation (RMSEA)\u0026thinsp;\u0026lt;\u0026thinsp;0.06, and a standardized root-mean-square residual (SRMR)\u0026thinsp;\u0026lt;\u0026thinsp;0.08\u003csup\u003e33\u003c/sup\u003e. Estimates with a p-value of \u0026lt;\u0026thinsp;0.05 were interpreted as statistically significant. Descriptive analysis was conducted using Stata 17.0 (StataCorp LP, College Station, Texas) software and modeling analysis using Mplus 7.4 (Muth\u0026eacute;n \u0026amp; Muth\u0026eacute;n) software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the baseline cohort of 8,836 participants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the mean age was 58.26 (SD\u0026thinsp;=\u0026thinsp;0.16) years, slightly more than half were female and lived in rural areas. 84.7% were married, 41.8% had an education level of primary school, and 93.0% had health insurance. Approximately 29% and 37% had one chronic condition and multimorbidity at baseline, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced or others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried and partnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle school and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of chronic conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, intrinsic capacity in our sample decreased over four years (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Four out of the six subdomains, such as cognition, vision, hearing, and vitality, have decreased. The largest reductions were observed in vision and hearing, decreasing from 39.6% and 47.4% in 2011 to 27.9% and 32.5% in 2015, respectively. No significant difference over time was found for locomotor.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChange of retained subdomains of intrinsic capacity among middle-aged and older adults from 2011 to 2015\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eProportion % (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepressive symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.1 (74.4, 77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.3 (77.9, 80.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.0 (74.4, 77.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87.0 (85.7, 88.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.7 (85.6, 87.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.0 (78.4, 81.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.6 (37.5, 41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.9 (33.9, 38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.9 (26.5, 29.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHearing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.4 (45.4, 49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.3 (38.4, 42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.5 (31.0, 34.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocomotor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.4 (78.6, 82.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.6 (80.1, 83.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.4 (81.1, 83.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.9 (89.7, 91.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89.0 (87.9, 90.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.5 (83.1, 85.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.9 (16.6, 19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.8 (13.6, 16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.4 (9.2, 11.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: CI\u0026thinsp;=\u0026thinsp;confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUnconditional LGCM was applied to capture the trajectories of IC over time (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). On average, initial IC score was 4.235 and decreased significantly (β = -0.182, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among the study sample over the 4-year study period, which had different initial status (β\u0026thinsp;=\u0026thinsp;0.715, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and change rates (β\u0026thinsp;=\u0026thinsp;0.070, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in IC. However, the association between the intercept and slope was not significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnconditional latent growth curve model for intrinsic capacity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept-Slope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: Estimate\u0026thinsp;=\u0026thinsp;unstandardized coefficient; S.E. = standard error; RMSEA\u0026thinsp;=\u0026thinsp;root-mean-square error of approximation; CFI\u0026thinsp;=\u0026thinsp;comparative fit index; TLI\u0026thinsp;=\u0026thinsp;Tucker-Lewis Index; SRMR\u0026thinsp;=\u0026thinsp;standardized root-mean-square residual.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eParallel process LGCM was conducted to estimate the longitudinal associations between number of chronic conditions and IC trajectories over time, adjusted for baseline characteristics. The model fitted the data well (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that more chronic conditions at baseline was associated with lower IC scores (β = -0.175, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and slower decline (β\u0026thinsp;=\u0026thinsp;0.024, p\u0026thinsp;=\u0026thinsp;0.008) in IC over time. Increase in number of chronic conditions was related to the faster decline in IC over time (β = -0.200, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Older age, being female, having lower education level, living in rural areas were negatively associated with IC lower scores at baseline. Middle-aged and older adults living in rural areas had faster decline in IC scores (β = -0.052, p\u0026thinsp;=\u0026thinsp;0.045).\u003c/p\u003e \u003cp\u003e\u003cimg 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\" height=\"508\" width=\"408\"\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn 2020, the United Nations General Assembly declared 2021\u0026ndash;2030 the Decade of Healthy Ageing, highlighting the importance for policymakers across the world to focus on improving the lives of older people, both today and in the future\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. With the world\u0026rsquo;s largest older population, China bears heavy burden from rapidly ageing populations such as increased prevalence of multimorbidity and IC decline, stressing the importance of retaining IC and achieving \u0026ldquo;healthy ageing\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The current study found that increase in number of chronic conditions was related to the faster decrease in IC among middle-aged and older Chinese adults over the 4-year follow up period. To our knowledge, this is the first longitudinal study to characterize the trajectories of IC and to examine how change in number of chronic conditions shaped IC trajectories using nationally representative data.\u003c/p\u003e \u003cp\u003eWe conducted a systematic literature search for published papers on the relationship of chronic condition and IC trajectories until May 31, 2024, using the keywords \u0026ldquo;chronic condition\u0026rdquo;, \u0026ldquo;multimorbidity\u0026rdquo;, and \u0026ldquo;intrinsic capacity\u0026rdquo; on Pubmed, Web of Science, and Scopus, and eight longitudinal studies were identified with five studies from China\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, one from Mexico\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, two from Europe\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Despite the longitudinal study design of the studies in China, they were limited by regional data such as sample from western regions of China, suggesting no significant relationship between chronic conditions and IC trajectories\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Furthermore, IC was a novel concept proposed by WHO, no consistent assessment frame for which has been constructed\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Additionally, chronic condition was treated as a time-independent variable in previous studies, which was measured at baseline to evaluate its association with IC trajectories\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Since single chronic condition easily developed into multimorbidity, it was necessary to consider its time-varying effect. Our study extends the knowledge of IC trajectories by employing a nationally representative longitudinal data to discern the relationship between changes in number of chronic conditions and IC trajectories by utilizing a WHO-proposed framework and parallel process LGCM.\u003c/p\u003e \u003cp\u003eThis longitudinal study identified a decline in IC scores among a nationally representative population over a 4-year follow up period. Evidence from LMICs indicated a decrease in the proportion of retaining full IC with advancing age\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The largest reductions were observed in sensory subdomain, namely vision and hearing. This aligned with research in west China by Jia et al., which suggested that sensory impairment as the most prevalent impaired IC domains was a strong predictor of worsened or persistent poor IC\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Possible reasons for this might include that sensory impairment can lead to subsequent cognitive decline, depression, and decreased mobility\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Those residing in rural areas had lower IC scores at baseline and a more rapid decline over the follow-up period, which was in line with the strand of literature on the rural-urban health disparity in China. Despite great efforts taken to narrow down the disparity by Chinese government, gaps persisted across various dimensions, such as access to healthcare services, healthcare quality, health literacy, and socioeconomic factors\u003csup\u003e\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, contributing to poorer health outcomes and IC in rural populations.\u003c/p\u003e \u003cp\u003eAdditionally, our study has added to the small body of research examining the relationship of chronic condition and IC trajectories, specifically focusing on the association between increase in the number of chronic conditions and faster decrease in IC scores. Potential factors underlying this association might involve escalated depressive symptoms, reduced mobility and physical activity, as well as the complexities and challenges related to polypharmacy and the management from multimorbidity\u003csup\u003e\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Prior research has established connections between chronic condition and depression, revealing that the odds of experiencing depressive disorders increased by 45% with each additional chronic condition compared with no chronic condition\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Older adults with multimorbidity were significantly more likely to engage in low levels of physical activity, which was subdomains of IC as well\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Additionally, polypharmacy, prevalent among individuals with multimorbidity, often resulted in adverse events, drug\u0026ndash;drug and disease\u0026ndash;drug interactions, and a lower level of adherence to more complex treatment regimens\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Intriguingly, higher number of chronic conditions at baseline were associated with a more gradual decline in IC scores. This link could be attributed to a baseline effect, where those already exhibiting low IC levels due to chronic conditions had a limited scope for further decline, or it might reflect increased healthcare resource utilization by older adults with chronic conditions endeavoring to manage and preserve their health status\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThere are several policy implications for this study. Our findings demonstrated a significant correlation of increase in the number of chronic conditions with a more rapid decline in IC scores. Given these insights, early screening was of great importance for individuals with chronic condition yet showing no IC decline, which might enable more timely intervention to maintain IC levels. Evidence from a randomized controlled trial in China with a follow-up of 6 months has proved the feasibility of the approach called integrated care for older people (ICOPE), including screening for IC and tailored intervention for community-dwelling older adults, which was supported by its acceptance among key stakeholders and impact on health outcomes\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Future studies should consider longer follow-up period to examine its robust and sustainable impact on health status. Furthermore, since the association was mediated by adverse effect on subdomains of IC from multimorbidity and management of multimorbidity\u003csup\u003e\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, multidomain or complex interventions on multimorbidity were warranted to offer a patient-centered approach, potentially slowing down the decline of IC among older adults with multimorbidity\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. This approach underscores the importance of addressing functional limitation, depressive symptoms, and problems of polypharmacy to improve their health-related quality of life and reduce their burden of illness\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Additionally, efforts should be taken to minimize the rural-urban disparity in China, a crucial factor contributing to lower IC levels and rapid decrease among middle-aged and older adults. These gaps primarily manifested in terms of the access of quality primary healthcare (PHC). To ensure quality care and access to effective services in rural areas, measures to strengthen PHC should be taken including build a strong PHC workforce centered on village doctors with gradual expansion to nurses\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Previous studies have shown that the potential role of non-physician PHC providers such as rehabilitation specialists and nutritionists based in PHC setting in improving health outcomes and patient satisfaction\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Moreover, both financial and non-financial incentives should be employed to attract and retain the workforce, including salaries supplemented with performance-based compensation and social recognition within the community\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStudy limitations must also be acknowledged. First, key variables such as chronic condition and partial subdomains of IC relied on self-report, rendering them subject to recall bias and social expectation bias. Future research would benefit from the inclusion of objective performance tests related to chronic conditions and IC. Second, due to lack of data in other waves, the long-term trajectories of IC cannot be identified. Since CHARLS was not designed with the purpose of this study, thus the variables available for inclusion in our analysis had their limitations\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, a WHO-proposed framework for evaluating IC was employed in this study. Future studies should expand the observation time-frame and apply the life course perspective to examine the longitudinal relationship between chronic condition and IC trajectories. Finally, this study considered the number of chronic conditions, yet the impact of different chronic condition might vary. Future research could explore the relationship between specific disease types or multimorbidity patterns and IC trajectories.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study characterized the longitudinal relationship between the change in number of chronic conditions and the trajectories of IC among middle-aged and older adults in China, revealing that increase in number of chronic conditions was significantly associated with a more rapid decline in IC over a four-year period. Therefore, it is highlighted for integrated care and complex interventions on multimorbidity to mitigate IC decline among older adults. Additionally, addressing the rural-urban disparity in access to quality PHC and health literacy is critical for sustaining the wellbeing and IC among older adults.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe data used in this study were approved by the Peking University Institutional Review Boards (IRB) (IRB00001052-11015). Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThis study is based on publicly-available datasets. The health data can be accessed from the website: http://charls.pku.edu.cn/.\u003c/p\u003e\n\u003ch2\u003eCompeting interests \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation of China (Grant Number 72174009).\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eXM has full access to all data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: ZZ and XM. Data analysis: ZZ. Interpretation of data: XM. Drafting of the manuscript: ZZ. Critical revision of the manuscript for important intellectual content and supervision: XM. XM is guarantor.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eWe would like to extend our thanks to the invaluable contributions by the study participants and data collection staff.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Global strategy and action plan on ageing and health. World Health Organization; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkou ST, Mair FS, Fortin M, et al. Multimorbidity. Nat Rev Dis Primers. 2022;8(1):48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. World report on ageing and health. Geneva: World Health Organization; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInouye SK, Studenski S, Tinetti ME, et al. Geriatric syndromes: clinical, research, and policy implications of a core geriatric concept. 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BMJ. 2019;365:l2406.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Intrinsic capacity, chronic condition, trajectory, older population","lastPublishedDoi":"10.21203/rs.3.rs-6420100/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6420100/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to identify trajectories of intrinsic capacity (IC) and examine the longitudinal relationship between trajectories of chronic condition and IC among middle-aged and older adults in China.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eCohort data was from three waves of the China Health and Retirement Longitudinal Study with a final sample of 8,836 participants. IC score was calculated by six different sub-domains. Parallel process latent growth curve model was applied to examine the association between chronic condition and IC trajectories.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOver the study period, IC decreased significantly (β = \u0026minus;0.182, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the largest reductions observed in vision and hearing. Increase in number of chronic conditions (β = -0.200, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and residing in rural areas (β = -0.052, p\u0026thinsp;=\u0026thinsp;0.045) was related to a faster decline in IC.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIntegrated care for older people, complex interventions on multimorbidity, and policies to narrow down rural-urban disparity were warranted to slow IC decline.\u003c/p\u003e","manuscriptTitle":"Chronic conditions and intrinsic capacity trajectories among Chinese middle-aged and older adults: a nationally representative longitudinal study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 15:47:52","doi":"10.21203/rs.3.rs-6420100/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-22T15:35:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T14:41:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T02:34:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255567346638691162304304241496969093712","date":"2026-04-30T00:31:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79708400856353604873298139396472474637","date":"2026-04-24T16:25:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-07T09:51:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161462061720489392707421234917787515518","date":"2025-06-20T10:35:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50865338447500446035460614991424187635","date":"2025-06-13T14:12:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-11T11:28:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-14T10:33:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-11T05:48:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-11T05:44:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-04-10T12:11:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d9ea13a9-7cee-4b0b-b8f8-298b1a2b3f11","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-22T15:35:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T14:41:55+00:00","index":98,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T02:34:22+00:00","index":97,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-22T15:41:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 15:47:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6420100","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6420100","identity":"rs-6420100","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}