Latent Profile Analysis and Determinants of Social Isolation among Older Adults in China

Latent Profile Analysis and Determinants of Social Isolation among Older Adults in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Latent Profile Analysis and Determinants of Social Isolation among Older Adults in China Xiaoxing Lai, Zhiyuan Zhang, Yonghua Cai, Yang Li, Rui Sun, Shuai Zhang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7756154/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study aimed to identify social isolation profiles among older adults in China using latent profile analysis and to explore their associations with demographic and health factors. Methods A cross-sectional study was conducted between July and December 2023 in the Chaoyang and Dongcheng districts of Beijing, China. Participants aged 60 years or older were recruited through convenience sampling from six communities. Latent profile analysis was applied to classify patterns of social isolation. Multivariate logistic regression models were used to assess factors associated with different profiles of social isolation. Results A total of 529 participants were included. Three social isolation profiles were identified: the mild social isolation group (n = 239), the moderate social isolation group (n = 213), and the severe social isolation group (n = 77). In the context of comparing mild to severe social isolation, multivariate analysis revealed that older adults living alone without care had significantly higher odds of severe social isolation compared to those living with children (OR = 0.099, 95% CI: 0.034–0.377, P < 0.001). Frailty (OR = 0.141, 95% CI: 0.039–0.588, P = 0.033), severe ADL dependence (OR = 0.249, 95% CI: 0.145–0.454, P = 0.048), and moderate to severe IADL dependence (OR = 0.448 to 0.307, P < 0.05) were also significantly associated with severe social isolation. Similarly, in the comparison between moderate and severe group, compared to those living with children, older adults living alone without care were more likely to experience severe social isolation (OR = 0.199, 95% CI: 0.078–0.328, P = 0.001). In terms of functional status, severe ADL dependence (OR = 0.152, 95% CI: 0.061–0.505, P = 0.015) and moderate to severe IADL dependence (OR = 0.231 to 0.128, P < 0.05) were significantly associated with severe social isolation. Conclusions Older adults living alone without care, with frailty, and with functional dependence are at significantly higher risk of severe social isolation. Understanding the specific factors contributing to different profiles can inform tailored support strategies aimed at enhancing social engagement and improving the quality of life within this population. Social Isolation Older Adults Latent Profile Analysis Determinants Figures Figure 1 Background Social isolation among older adults has emerged as a critical public health concern due to its substantial impact on both mental and physical well-being 1 . In China, the prevalence of social loneliness among middle-aged and elderly individuals is estimated at 16.48%, with particularly high rates observed among women and those living in rural areas 2 . Comparable rates have been reported in European countries, underscoring the widespread nature of this issue 3 . Social isolation is commonly defined as a state of limited social engagement, absence of meaningful interpersonal relationships, and persistent feelings of loneliness, all of which significantly impair quality of life in later years 3 . Extensive evidence indicates that social networks built through sustained interpersonal interactions provide crucial emotional, instrumental, and informational support, thereby mitigating many of the adverse effects associated with aging 4 , 5 . The importance of social connectedness cannot be overstated, as it is closely linked to improved psychological resilience, enhanced cognitive functioning, and better physical health outcomes 6 . However, the prevalence of social isolation among the elderly in China remains a cause for concern. Several contributing factors have been identified, including age-related declines in physical and cognitive function, as well as shifting familial and societal roles resulting from changes in traditional family structures 7 , 8 . Moreover, the rapid advancement of digital technology, while offering new avenues for communication, has also inadvertently widened the digital divide. Many older adults face difficulties in adopting new technologies due to barriers such as limited digital literacy and access, which may exacerbate their sense of disconnection 9 , 10 . The cumulative impact of these challenges has been associated with markedly increased health risks, including a 29% higher risk of incident coronary heart disease, a 50% higher risk of developing dementia, and a 32% higher risk of all-cause mortality among socially isolated older adults 11 – 13 . Despite growing awareness, most existing research remains focused on population-level estimates, often overlooking the heterogeneity of social isolation experiences among older individuals 14 – 16 . Furthermore, traditional assessment tools typically rely on aggregated scale scores, which may obscure nuanced patterns and individual differences in social isolation 14 , 17 . To address these gaps, this study applies Latent Profile Analysis (LPA), a person-centered statistical technique that identifies distinct subgroups based on multidimensional response patterns related to social isolation. This approach enables a more refined understanding of the typologies of social isolation in older adults. The aim of this study is to identify latent profiles of social isolation among older adults using LPA and to examine the sociodemographic, health-related, and psychosocial factors associated with each profile. Methods Study design and Participants This cross-sectional study used convenience sampling to recruit older adults from six communities in Chaoyang and Dongcheng districts of Beijing, in China between July and December 2023. Inclusion criteria included: age ≥ 60 years; Participants who provided informed consent, were voluntary, and were capable of normal communication and cooperation to complete the study. Exclusion criteria included: psychiatric disorders; severe organic diseases involving the heart, brain, liver, or kidneys; or being in the terminal stage of illness. The study protocol was approved by the Ethics Committee of Peking Union Medical College Hospital (Approval No. ZS-2943), and all participants signed informed consent forms. Data Collection General Information Questionnaire (Supplementary material 1): Developed by the research team based on literature review, this questionnaire collected demographic data including age, sex, occupation, education, marital status, living arrangement, number of children, household economic status, and method of medical expense payment. It also included clinical history variables such as multimorbidity, polypharmacy, family history of chronic illness, and smoking and alcohol consumption. Lubben Social Network Scale (LSNS) 18 : This 12-item scale was used to assess social isolation, consisting of two subscales: family network (items 1–6) and friend network (items 7–12). It evaluates the number of family members or friends with whom the individual can maintain contact, converse, or seek help. Response options include “none,” “1,” “2,” “3–4,” “5–8,” and “9 or more,” scored from 0 to 5. Total scores range from 0 to 60, with lower scores indicating more limited social networks and a higher likelihood of social isolation. A total score below 19 suggests the presence of social isolation. The Chinese version of the LSNS exhibited good internal consistency, with a Cronbach’s α of 0.83 19 . Fried Frailty Phenotype (FFP) 20 : Based on the frailty cycle model, this scale assesses physical frailty using five criteria: unintentional weight loss, slowed walking speed, reduced grip strength, decreased physical activity, and fatigue. Each criterion is assigned 1 point if present. A total score of 3 or more indicates frailty; 1–2 points indicates pre-frailty; and 0 points indicates non-frailty. Montreal Cognitive Assessment (MoCA) 21 : Developed by Nasreddine et al. in 2005, the MoCA is used to assess cognitive function. It was translated into Chinese by Wang Wei et al. in 2007 for use among populations with varying degrees of cognitive impairment. The Cronbach’s α coefficient of the scale is 0.836 22 . The MoCA includes 12 items across 8 domains. Each item is scored 1 point for a correct answer and 0 points for an incorrect answer, yielding a total score ranging from 0 to 30. A score below 26 indicates cognitive impairment, with higher scores reflecting better cognitive performance. Barthel Index (BI) 23 : The BI is used to evaluate Basic Activities of Daily Living (BADL), including feeding, transfers between bed and wheelchair, personal hygiene, toileting, bathing, ambulation (45 m), stair climbing, dressing, bowel control, and bladder control. Total scores range from 0 to 100. Scores of 100 indicate independence; 75–95, mild dependence; 50–70, moderate dependence; 25–45, severe dependence; and 0–20, total dependence. Lawton-Brody Instrumental Activities of Daily Living (IADL) Scale 24 : This scale assesses eight areas: shopping, transportation, cooking, housekeeping, laundry, telephone use, medication management, and financial handling. The maximum score is 8 points, with higher scores indicating greater functional independence. A score of 8 reflects full independence; scores below 8 indicate varying degrees of dependence. Specifically, scores of 6–7 represent mild dependence; 3–5, moderate dependence; and ≤ 2, severe dependence. The Cronbach’s α coefficient of the scale is 0.880. Prior to data collection, all research staff underwent standardized training. The study team established communication with community health personnel to explain the study objectives and procedures and to obtain cooperation. Before administering the questionnaire, researchers provided standardized instructions explaining the study’s purpose, significance, and survey procedures. All data were collected directly by trained investigators. Upon completion of each questionnaire, responses were immediately reviewed. For unclear responses, investigators followed up to clarify and verify the information. Completed questionnaires were collected on site. Any questionnaire with more than 20% missing data was excluded from analysis. Double data entry and cross-checking were performed to ensure accuracy. A total of 540 questionnaires were distributed. Five individuals withdrew after signing informed consent, and six questionnaires were incomplete. Thus, 529 valid questionnaires were collected, yielding a response rate of 98.0%. Double data entry and verification were performed to ensure data accuracy. Sample Size Calculation According to the sample size estimation method for regression analysis 25 , which recommends a sample size 10 to 20 times the number of independent variables, and considering a 20% attrition rate, a minimum of 225 participants was required for 18 predictors. Statistical Analysis For normally distributed continuous variables, data were described as mean ± standard deviation; for non-normally distributed variables, data were reported as median and interquartile range. Categorical and ordinal variables were described using frequencies and percentages. Latent profile analysis was conducted using Mplus 8.3 to classify individuals based on distinct scoring patterns. Model fit was assessed using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and adjusted BIC (aBIC), with smaller values indicating better fit. The Bootstrap Likelihood Ratio Test (BLRT) and the Lo-Mendell-Rubin (LMR) test were used to compare models with k versus k-1 classes; a significant P value (< 0.05) indicated that the k-class model was superior. Entropy values, ranging from 0 to 1, were used to evaluate classification precision, with higher values indicating better accuracy. Descriptive statistics, chi-square tests, analysis of variance, and Kruskal-Wallis H tests were performed using SPSS version 26.0. A multivariate logistic regression analysis was performed to ascertain factors associated with the different latent profiles of social isolation. P value < 0.05 was considered statistically significant. Results Baseline Characteristics A total of 529 older adults aged 64 to 100 years (mean age 81.35 ± 6.25) were included in the study. The majority of participants were female (60.7%), previously employed as workers (50.9%), and married (66.7%), while 31.9% were widowed. Regarding living arrangements, 47.8% resided with a spouse, 29.5% lived with children, and 8.7% lived alone. Most participants were covered by urban employee health insurance (59.5%), and 26.8% received government-funded care. Monthly household income varied significantly, with 60.1% earning between 6,500 and 25,000 yuan, and 27.2% earning less than 2,500 yuan. Multimorbidity was present in 68.8% of participants; 41.9% reported a family history of chronic disease, and 53.9% have polypharmacy (Table 1 ). Table 1 Univariate Analysis of Latent Profiles of Social Isolation among Older Adults. Variables Total Mild Social Isolation (n = 239) Moderate Social Isolation (n = 213) Severe Social Isolation (n = 77) P Age 529 81.06 ± 6.24 81.91 ± 6.48 80.71 ± 5.55 0.223 Sex 0.034 Female 321 139 (43.30%) 125 (38.94%) 57 (17.76%) Male 208 100 (48.08%) 88 (42.31%) 20 (9.62%) Occupation 0.060 Cadre 178 82 (46.07%) 74 (41.57%) 22 (12.36%) Worker 269 126 (46.84%) 104 (38.66%) 39 (14.50%) Farmer 69 29 (42.03%) 26 (37.68%) 14 (20.29%) Cultural Worker 7 0 (0.00%) 7 (100.00%) 0 (0.00%) Other 6 2 (33.33%) 2 (33.33%) 2 (33.33%) Highest Educational Attainment 0.171 No Formal Education 43 13 (30.23%) 20 (46.51%) 10 (23.26%) Primary School 88 44 (50.00%) 30 (34.09%) 14 (15.91%) Junior High School 128 66 (51.56%) 44 (34.38%) 18 (14.06%) Senior High School 88 34 (38.64%) 40 (45.45%) 14 (15.91%) College Degree or Above 182 82 (45.05%) 79 (43.41%) 21 (11.54%) Marital Status 0.000 Never Married 2 1 (50.00%) 0 (0.00%) 1 (50.00%) Married 353 173 (49.01%) 161 (45.61%) 19 (5.38%) Divorced 5 2 (40.00%) 0 (0.00%) 3 (60.00%) Widowed 169 63 (37.28%) 52 (30.77%) 54 (31.95%) Living Arrangement 0.000 Living Alone Without Care 74 0 (0.00%) 3 (4.05%) 71 (95.95%) Living Alone With Care 46 20 (43.48%) 26 (56.52%) 0 (0.00%) Living with Spouse 253 131 (51.78%) 118 (46.64%) 4 (1.58%) Living with Children 156 88 (56.41%) 66 (42.31%) 2 (1.28%) Number of Children 529 3.00 (2.00, 3.00) 2.00 (1.00, 3.00) 2.00 (1.00, 3.50) 0.023 Medical Payment Method 0.148 Government-Funded Care 142 65 (45.77%) 62 (43.66%) 15 (10.56%) Urban Health Insurance 315 144 (45.71%) 124 (39.37%) 47 (14.92%) Rural Health Insurance 71 30 (42.25%) 27 (38.03%) 14 (19.72%) Out-of-Pocket Payment 1 0 (0.00%) 0 (0.00%) 1 (100.00%) Monthly Household Income 0.393 < 2,500 yuan 144 61 (42.36%) 66 (45.83%) 17 (11.81%) 2,500–6,500 yuan 67 27 (40.30%) 28 (41.79%) 12 (17.91%) 6,500–25,000 yuan 318 151 (47.48%) 119 (37.42%) 48 (15.09%) Multimorbidity 0.830 No 165 75 (45.45%) 64 (38.79%) 26 (15.76%) Yes 364 164 (45.05%) 149 (40.93%) 51 (14.01%) Family History of Chronic Disease 0.520 No 307 139 (45.28%) 119 (38.76%) 49 (15.96%) Yes 221 100 (45.25%) 93 (42.08%) 28 (12.67%) Smoking and Alcohol Use History 0.977 No 504 228 (45.24%) 203 (40.28%) 73 (14.48%) Yes 25 11 (44.00%) 10 (40.00%) 4 (16.00%) Polypharmacy 0.520 No 244 116 (47.54%) 92 (37.70%) 36 (14.75%) Yes 285 123 (43.16%) 121 (42.46%) 41 (14.39%) Fried Frailty Phenotype (FP) 0.003 Non-Frail 220 110 (50.00%) 85 (38.64%) 25 (11.36%) Pre-Frailty 216 102 (47.22%) 86 (39.81%) 28 (12.96%) Frailty 93 27 (29.03%) 42 (45.16%) 24 (25.81%) Montreal Cognitive Assessment (MoCA) 0.766 Normal Cognition 381 170 (44.62%) 157 (41.21%) 54 (14.17%) Cognitive Impairment 148 69 (46.62%) 56 (37.84%) 23 (15.54%) ADL 0.000 Independent 249 126 (50.60%) 103 (41.37%) 20 (8.03%) Mild Dependence 203 89 (43.84%) 85 (41.87%) 29 (14.29%) Moderate Dependence 59 21 (35.59%) 21 (35.59%) 17 (28.81%) Severe Dependence 18 3 (16.67%) 4 (22.22%) 11 (61.11%) IADL 0.000 Independent 297 157 (52.86%) 105 (35.35%) 35 (11.78%) Mild Dependence 92 35 (38.04%) 47 (51.09%) 10 (10.87%) Moderate Dependence 122 43 (35.25%) 56 (45.90%) 23 (18.85%) Severe Dependence 18 4 (22.22%) 5 (27.78%) 9 (50.00%) Significant differences in social isolation profile membership were identified across subgroups defined by sex, marital status, living arrangement, number of children, frailty status, and both basic and instrumental activities of daily living (P < 0.05). Detailed results are presented in Table 1 . Scores for Social Isolation, Frailty, Cognitive Function, BADL, and IADL The Lubben Social Network Scale scores ranged from 5 to 40, with a mean of 28.17 (± 8.67). A total of 239 participants (45.0%) met the criteria for social isolation. Fried frailty scores varied from 0 to 5, with a mean of 1.26 (± 1.292), and 309 participants were classified as frail. The MoCA scores ranged from 9 to 30, yielding a mean score of 25.35 (± 4.434), with 148 participants exhibiting cognitive impairment. Barthel Index scores ranged from 5 to 100, with a mean of 90.02 (± 14.794); 166 participants demonstrated limitations in BADL. Finally, IADL scores ranged from 0 to 8, with a mean of 6.65 (± 1.919), and 232 participants displayed dependence in IADL. Discussion This study investigated social isolation among 529 community-dwelling older adults of China, revealing a significant prevalence of social isolation, with 45% of participants meeting the established criteria. Through LPA, three distinct social isolation profiles were identified: Mild Social Isolation, Low Friend Network-Moderate Social Isolation, and Severe Social Isolation. This classification enhances the understanding of social dynamics within aged populations and underscores the necessity for tailored interventions to address varying degrees of isolation. Social Isolation Profiles and Implications The Mild Social Isolation group (45.18%) had relatively strong family and friend networks, whereas the Severe Social Isolation group (14.55%) exhibited low scores across both domains, reflecting comprehensive disconnection. Notably, the Moderate Social Isolation group (40.27%) maintained family ties but had limited engagement with friends and the broader community. This distinction is particularly important in the Chinese context, where family traditionally provides primary support; a robust family network alone may not fully prevent social isolation. Identifying these subgroups allows public health practitioners and policymakers to develop interventions that target specific deficits, such as promoting community engagement for those with intact family support but limited social networks outside the household. Living Arrangements and Family Support Living alone without care was a strong predictor of severe social isolation. Among 74 participants living alone without care, nearly all (95.95%) were classified as severely isolated. This aligns with previous research showing that the absence of family support significantly reduces emotional and instrumental resources, increasing the risk of social disconnection 26 27 . In China, the family remains the cornerstone of older adults’ social support system, and rapid urbanization and smaller family sizes may exacerbate isolation for those without co-residing children 28 , 29 , 30 . These findings suggest that interventions should not only encourage family involvement but also establish formal community support systems, such as home visit programs, social clubs, or volunteer networks, to support older adults living alone. Functional Status and Community Engagement Dependence in basic and instrumental activities of daily living (ADL/IADL) was significantly associated with more severe social isolation profiles. Individuals with severe functional limitations were more likely to fall into the Moderate or Severe Social Isolation groups. Physical impairments can restrict mobility, limit participation in social activities, and reduce opportunities to maintain friendships, even when family support is present 31 , 32 . Longitudinal studies have also demonstrated that declines in ADL and IADL predict subsequent increases in social isolation and loneliness 31 32 . In our study, dependence in ADL and IADL was significantly associated with a higher likelihood of being in the severe isolation group, particularly among those with reduced social networks beyond their immediate family. This underscores the importance of addressing functional limitations in interventions aimed at reducing social isolation among older adults. Frailty and Its Bidirectional Relationship with Social Isolation Frailty was another significant factor associated with social isolation. Frailty, a state of increased vulnerability to stressors, has a complex and bidirectional relationship with social isolation. Non-frail participants were more likely to belong to the Mild Social Isolation group, whereas frail individuals were overrepresented in the Moderate and Severe Social Isolation groups. Frail individuals are less able to participate in social activities due to reduced physiological reserves, fatigue, and decreased mobility, while social isolation itself may accelerate the development or progression of frailty 33 . Frailty limits physiological reserves and mobility, reducing participation in social activities, while social isolation itself may accelerate frailty through decreased physical activity and psychosocial stimulation 34 , 35 36 . These findings highlight the importance of integrating frailty screening into routine geriatric care. Early identification and multicomponent interventions—including physical exercise, nutritional support, and psychosocial engagement—may mitigate both frailty and social isolation, ultimately improving quality of life 33 , 37 . Limitations This study has several limitations. First, the participants were limited to community-dwelling older adults, and no data were collected from rural or hospitalized populations. Second, the survey was confined to urban districts in Beijing, which may limit the generalizability of the findings. Third, the study was cross-sectional in design, precluding any longitudinal assessment of changes in social isolation over time. Future research should expand the sample size, incorporate multi-center designs, and collaborate with community health authorities to conduct comprehensive assessments of social isolation among older adults. Longitudinal follow-up is also needed to better understand temporal trends and transitions in social isolation status. Conclusions This study identified distinct patterns of social isolation among community-dwelling older adults in China, including mild social isolation, moderate social isolation, and severe social isolation. Older adults living alone without caregivers were more likely to experience severe social isolation. Those with moderate or severe dependence in ADL and IADL were more likely to fall into the moderate isolation group with limited engagement with friends and the broader community. In contrast, the absence of frailty was associated with a higher probability of belonging to the mild social isolation group. These findings underscore the need for individualized interventions based on specific social isolation profiles. Continuous monitoring, tailored support, and personalized management strategies should be implemented to mitigate social isolation in older adults. Abbreviations LPA Latent Profile Analysis LSNS Lubben Social Network Scale FFP Fried Frailty Phenotype MoCA Montreal Cognitive Assessment BI Barthel Index ADL Activities of Daily Living BADL Basic Activities of Daily Living IADL Instrumental Activities of Daily Living AIC Akaike Information Criterion BIC Bayesian Information Criterion aBIC adjusted Bayesian Information Criterion BLRT Bootstrap Likelihood Ratio Test LMR Lo-Mendell-Rubin C1 Class 1 C2 Class 2 C3 Class 3 Declarations Ethics approval and consent to participate All research procedures were conducted in accordance with the relevant guidelines and regulations established by the Declaration of Helsinki. Ethical approval was granted by the Ethics Committee of Peking Union Medical College Hospital (Ethics approval number: ZS-2943). Informed consent was obtained from all participants. To protect privacy and confidentiality, all data were deidentified, and access was restricted to authorized personnel. Participants received no compensation, except for access to quality medical care, and no identifying information was included. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Clinical trial number Not applicable. Funding This study was supported by the Chinese Nursing Association Research Funding (ZHKYQ202420), Peking Union Medical College Hospital Talent Cultivation Program Category C (UBJ10309), and Chinese Aging Well Association Research Funding (24 − 02). Author Contribution XL：Conceptualization，Methodology，Writing – original draft，Writing – review and editing，Funding acquisition. XH：Data curation，Supervision，Project administration，Funding acquisition，Writing–original draft. ZZ：Investigation，Resources，Writing – original draft. YC：Formal analysis，Visualization，Writing – review and editing. YL：Software，Data curation，Writing – review and editing. RS：Formal analysis，Investigation，Writing – review and editing. SZ：Writing – review and editing，Methodology，Investigation. KZ：Data curation，Validation，Writing – review and editing. HD：Validation，Writing – review and editing. SL：Validation，Writing – review and editing. JH: Validation，Writing – review and editing. All authors reviewed and approved the manuscript. Acknowledgements Not applicable. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Sherman DW, Alfano AR, Alfonso F, et al. A Systematic Review of the Relationship between Social Isolation and Physical Health in Adults. Healthcare (Basel, Switzerland) . Jun. 2024;1(11). 10.3390/healthcare12111135 . Zhang L, Jing S, Yang XL, Wang YP, Su XY. Epidemiology of Social Isolation Among Middle-aged and Older Adults in China. Zhongguo yi xue ke xue yuan xue bao Acta Academiae Medicinae Sinicae . May 21 2025; 10.3881/j.issn.1000-503X.16657 Nakahara K, Yokoi K. 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Understanding intergenerational dynamics and social support's impact on health and well-being of older adults in South Asia: a scoping review. Syst Rev Apr. 2025;11(1):86. 10.1186/s13643-025-02833-z . Pereira C, Bravo J, Raimundo A, Tomas-Carus P, Mendes F, Baptista F. Risk for physical dependence in community-dwelling older adults: The role of fear of falling, falls and fall-related injuries. Int J Older People Nurs Sep. 2020;15(3):e12310. 10.1111/opn.12310 . Guo L, An L, Luo F, Yu B. Social isolation, loneliness and functional disability in Chinese older women and men: a longitudinal study. Age Ageing Jun. 2021;28(4):1222–8. 10.1093/ageing/afaa271 . Gheorghe AC, Bălășescu E, Hulea I, et al. Frailty and Loneliness in Older Adults: A Narrative Review. Geriatrics (Basel) Sep. 2024;13(5). 10.3390/geriatrics9050119 . Hanlon P, Wightman H, Politis M, et al. The relationship between frailty and social vulnerability: a systematic review. The lancet Healthy longevity Mar. 2024;5(3):e214–26. 10.1016/s2666-7568(23)00263-5 . Pearson E, Siskind D, Hubbard R, et al. Frailty and Treatment-Resistant Schizophrenia: A Retrospective Cohort Study. Community mental health journal Jan. 2023;59(1):105–9. 10.1007/s10597-022-00998-8 . Saeki N, Mizutani M, Tanimura S, Nishide R. Types and frequency of social participation and comprehensive frailty among community-dwelling older people in Japan. Preventive Med reports Dec. 2023;36:102443. 10.1016/j.pmedr.2023.102443 . Lenti MV, Brera AS, Di Sabatino A, Corazza GR. Empowering the vulnerable in internal medicine: a narrative review on physical exercise as a tool to tackle frailty. Intern Emerg Med Jun. 2025;1. 10.1007/s11739-025-03988-2 . Additional Declarations No competing interests reported. Supplementary Files GeneralQuestionnaire.docx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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12:06:11","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":143761,"visible":true,"origin":"","legend":"","description":"","filename":"3b8b9cc1ae584359b622441706c4029a1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7756154/v1/a0f4fa920966004423d1705d.xml"},{"id":96286807,"identity":"06f47988-2184-46d4-a670-d87e7d91acaf","added_by":"auto","created_at":"2025-11-19 12:06:11","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152612,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7756154/v1/1f3ac425053c1b31d35328eb.html"},{"id":96286799,"identity":"13c3feec-2615-422d-a359-09f13ce913f4","added_by":"auto","created_at":"2025-11-19 12:06:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThree Latent Profiles of Social Isolation among Older Adults.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7756154/v1/3e29dd6d738c569aa80ee5c5.png"},{"id":105365325,"identity":"62f6742c-e56c-483d-bbf5-828359c761cc","added_by":"auto","created_at":"2026-03-25 08:29:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1081957,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7756154/v1/6c9cfa3d-295c-4b82-87a2-ac4a8b4de7c4.pdf"},{"id":96286800,"identity":"10de3653-c39d-4b42-9ac2-50206f3ad8df","added_by":"auto","created_at":"2025-11-19 12:06:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16366,"visible":true,"origin":"","legend":"","description":"","filename":"GeneralQuestionnaire.docx","url":"https://assets-eu.researchsquare.com/files/rs-7756154/v1/bc1af90a065955be32f9db8b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Latent Profile Analysis and Determinants of Social Isolation among Older Adults in China","fulltext":[{"header":"Background","content":"\u003cp\u003eSocial isolation among older adults has emerged as a critical public health concern due to its substantial impact on both mental and physical well-being\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In China, the prevalence of social loneliness among middle-aged and elderly individuals is estimated at 16.48%, with particularly high rates observed among women and those living in rural areas\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Comparable rates have been reported in European countries, underscoring the widespread nature of this issue\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Social isolation is commonly defined as a state of limited social engagement, absence of meaningful interpersonal relationships, and persistent feelings of loneliness, all of which significantly impair quality of life in later years\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eExtensive evidence indicates that social networks built through sustained interpersonal interactions provide crucial emotional, instrumental, and informational support, thereby mitigating many of the adverse effects associated with aging\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The importance of social connectedness cannot be overstated, as it is closely linked to improved psychological resilience, enhanced cognitive functioning, and better physical health outcomes\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHowever, the prevalence of social isolation among the elderly in China remains a cause for concern. Several contributing factors have been identified, including age-related declines in physical and cognitive function, as well as shifting familial and societal roles resulting from changes in traditional family structures\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Moreover, the rapid advancement of digital technology, while offering new avenues for communication, has also inadvertently widened the digital divide. Many older adults face difficulties in adopting new technologies due to barriers such as limited digital literacy and access, which may exacerbate their sense of disconnection\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe cumulative impact of these challenges has been associated with markedly increased health risks, including a 29% higher risk of incident coronary heart disease, a 50% higher risk of developing dementia, and a 32% higher risk of all-cause mortality among socially isolated older adults\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Despite growing awareness, most existing research remains focused on population-level estimates, often overlooking the heterogeneity of social isolation experiences among older individuals\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Furthermore, traditional assessment tools typically rely on aggregated scale scores, which may obscure nuanced patterns and individual differences in social isolation\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. To address these gaps, this study applies Latent Profile Analysis (LPA), a person-centered statistical technique that identifies distinct subgroups based on multidimensional response patterns related to social isolation. This approach enables a more refined understanding of the typologies of social isolation in older adults.\u003c/p\u003e\u003cp\u003eThe aim of this study is to identify latent profiles of social isolation among older adults using LPA and to examine the sociodemographic, health-related, and psychosocial factors associated with each profile.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and Participants\u003c/h2\u003e\u003cp\u003eThis cross-sectional study used convenience sampling to recruit older adults from six communities in Chaoyang and Dongcheng districts of Beijing, in China between July and December 2023.\u003c/p\u003e\u003cp\u003eInclusion criteria included: age\u0026thinsp;\u0026ge;\u0026thinsp;60 years; Participants who provided informed consent, were voluntary, and were capable of normal communication and cooperation to complete the study.\u003c/p\u003e\u003cp\u003eExclusion criteria included: psychiatric disorders; severe organic diseases involving the heart, brain, liver, or kidneys; or being in the terminal stage of illness.\u003c/p\u003e\u003cp\u003e The study protocol was approved by the Ethics Committee of Peking Union Medical College Hospital (Approval No. ZS-2943), and all participants signed informed consent forms.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eGeneral Information Questionnaire (Supplementary material 1): Developed by the research team based on literature review, this questionnaire collected demographic data including age, sex, occupation, education, marital status, living arrangement, number of children, household economic status, and method of medical expense payment. It also included clinical history variables such as multimorbidity, polypharmacy, family history of chronic illness, and smoking and alcohol consumption.\u003c/p\u003e\u003cp\u003eLubben Social Network Scale (LSNS) \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e: This 12-item scale was used to assess social isolation, consisting of two subscales: family network (items 1\u0026ndash;6) and friend network (items 7\u0026ndash;12). It evaluates the number of family members or friends with whom the individual can maintain contact, converse, or seek help. Response options include \u0026ldquo;none,\u0026rdquo; \u0026ldquo;1,\u0026rdquo; \u0026ldquo;2,\u0026rdquo; \u0026ldquo;3\u0026ndash;4,\u0026rdquo; \u0026ldquo;5\u0026ndash;8,\u0026rdquo; and \u0026ldquo;9 or more,\u0026rdquo; scored from 0 to 5. Total scores range from 0 to 60, with lower scores indicating more limited social networks and a higher likelihood of social isolation. A total score below 19 suggests the presence of social isolation. The Chinese version of the LSNS exhibited good internal consistency, with a Cronbach\u0026rsquo;s α of 0.83\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFried Frailty Phenotype (FFP) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e: Based on the frailty cycle model, this scale assesses physical frailty using five criteria: unintentional weight loss, slowed walking speed, reduced grip strength, decreased physical activity, and fatigue. Each criterion is assigned 1 point if present. A total score of 3 or more indicates frailty; 1\u0026ndash;2 points indicates pre-frailty; and 0 points indicates non-frailty.\u003c/p\u003e\u003cp\u003eMontreal Cognitive Assessment (MoCA) \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e: Developed by Nasreddine et al. in 2005, the MoCA is used to assess cognitive function. It was translated into Chinese by Wang Wei et al. in 2007 for use among populations with varying degrees of cognitive impairment. The Cronbach\u0026rsquo;s α coefficient of the scale is 0.836 \u003csup\u003e22\u003c/sup\u003e. The MoCA includes 12 items across 8 domains. Each item is scored 1 point for a correct answer and 0 points for an incorrect answer, yielding a total score ranging from 0 to 30. A score below 26 indicates cognitive impairment, with higher scores reflecting better cognitive performance.\u003c/p\u003e\u003cp\u003eBarthel Index (BI) \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e: The BI is used to evaluate Basic Activities of Daily Living (BADL), including feeding, transfers between bed and wheelchair, personal hygiene, toileting, bathing, ambulation (45 m), stair climbing, dressing, bowel control, and bladder control. Total scores range from 0 to 100. Scores of 100 indicate independence; 75\u0026ndash;95, mild dependence; 50\u0026ndash;70, moderate dependence; 25\u0026ndash;45, severe dependence; and 0\u0026ndash;20, total dependence.\u003c/p\u003e\u003cp\u003eLawton-Brody Instrumental Activities of Daily Living (IADL) Scale \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e: This scale assesses eight areas: shopping, transportation, cooking, housekeeping, laundry, telephone use, medication management, and financial handling. The maximum score is 8 points, with higher scores indicating greater functional independence. A score of 8 reflects full independence; scores below 8 indicate varying degrees of dependence. Specifically, scores of 6\u0026ndash;7 represent mild dependence; 3\u0026ndash;5, moderate dependence; and \u0026le;\u0026thinsp;2, severe dependence. The Cronbach\u0026rsquo;s α coefficient of the scale is 0.880.\u003c/p\u003e\u003cp\u003ePrior to data collection, all research staff underwent standardized training. The study team established communication with community health personnel to explain the study objectives and procedures and to obtain cooperation. Before administering the questionnaire, researchers provided standardized instructions explaining the study\u0026rsquo;s purpose, significance, and survey procedures. All data were collected directly by trained investigators. Upon completion of each questionnaire, responses were immediately reviewed. For unclear responses, investigators followed up to clarify and verify the information. Completed questionnaires were collected on site. Any questionnaire with more than 20% missing data was excluded from analysis. Double data entry and cross-checking were performed to ensure accuracy. A total of 540 questionnaires were distributed. Five individuals withdrew after signing informed consent, and six questionnaires were incomplete. Thus, 529 valid questionnaires were collected, yielding a response rate of 98.0%. Double data entry and verification were performed to ensure data accuracy.\u003c/p\u003e\n\u003ch3\u003eSample Size Calculation\u003c/h3\u003e\n\u003cp\u003eAccording to the sample size estimation method for regression analysis\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, which recommends a sample size 10 to 20 times the number of independent variables, and considering a 20% attrition rate, a minimum of 225 participants was required for 18 predictors.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eFor normally distributed continuous variables, data were described as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation; for non-normally distributed variables, data were reported as median and interquartile range. Categorical and ordinal variables were described using frequencies and percentages. Latent profile analysis was conducted using Mplus 8.3 to classify individuals based on distinct scoring patterns. Model fit was assessed using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and adjusted BIC (aBIC), with smaller values indicating better fit. The Bootstrap Likelihood Ratio Test (BLRT) and the Lo-Mendell-Rubin (LMR) test were used to compare models with k versus k-1 classes; a significant \u003cem\u003eP\u003c/em\u003e value (\u0026lt;\u0026thinsp;0.05) indicated that the k-class model was superior. Entropy values, ranging from 0 to 1, were used to evaluate classification precision, with higher values indicating better accuracy. Descriptive statistics, chi-square tests, analysis of variance, and Kruskal-Wallis H tests were performed using SPSS version 26.0. A multivariate logistic regression analysis was performed to ascertain factors associated with the different latent profiles of social isolation. \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\u003cp\u003eA total of 529 older adults aged 64 to 100 years (mean age 81.35\u0026thinsp;\u0026plusmn;\u0026thinsp;6.25) were included in the study. The majority of participants were female (60.7%), previously employed as workers (50.9%), and married (66.7%), while 31.9% were widowed. Regarding living arrangements, 47.8% resided with a spouse, 29.5% lived with children, and 8.7% lived alone. Most participants were covered by urban employee health insurance (59.5%), and 26.8% received government-funded care. Monthly household income varied significantly, with 60.1% earning between 6,500 and 25,000 yuan, and 27.2% earning less than 2,500 yuan. Multimorbidity was present in 68.8% of participants; 41.9% reported a family history of chronic disease, and 53.9% have polypharmacy (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eUnivariate Analysis of Latent Profiles of Social Isolation among Older Adults.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMild Social Isolation\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;239)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate Social Isolation\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;213)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSevere Social Isolation\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81.06\u0026thinsp;\u0026plusmn;\u0026thinsp;6.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.91\u0026thinsp;\u0026plusmn;\u0026thinsp;6.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80.71\u0026thinsp;\u0026plusmn;\u0026thinsp;5.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.034\u003c/p\u003e\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\u003e321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139 (43.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125 (38.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57 (17.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003e208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100 (48.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88 (42.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (9.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupation\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCadre\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (46.07%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74 (41.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (12.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126 (46.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e104 (38.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39 (14.50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (42.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (37.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (20.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCultural Worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (100.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (33.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (33.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (33.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHighest Educational Attainment\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Formal Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (30.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (46.51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (23.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (50.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (34.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (15.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJunior High School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66 (51.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44 (34.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18 (14.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSenior High School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (38.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 (45.45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (15.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege Degree or Above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (45.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79 (43.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21 (11.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever Married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (50.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (50.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e173 (49.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e161 (45.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19 (5.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (40.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (60.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63 (37.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52 (30.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54 (31.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving Arrangement\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving Alone Without Care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (4.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71 (95.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving Alone With Care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (43.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (56.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving with Spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131 (51.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e118 (46.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (1.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving with Children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88 (56.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66 (42.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (1.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.00 (2.00, 3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00 (1.00, 3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.00 (1.00, 3.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical Payment Method\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment-Funded Care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (45.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62 (43.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (10.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban Health Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e144 (45.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e124 (39.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47 (14.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural Health Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (42.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (38.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (19.72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOut-of-Pocket Payment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (100.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonthly Household Income\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2,500 yuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (42.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66 (45.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17 (11.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2,500\u0026ndash;6,500 yuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (40.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28 (41.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12 (17.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6,500\u0026ndash;25,000 yuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e151 (47.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e119 (37.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48 (15.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultimorbidity\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.830\u003c/p\u003e\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\u003e165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75 (45.45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64 (38.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26 (15.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003e364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e164 (45.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e149 (40.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51 (14.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily History of Chronic Disease\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.520\u003c/p\u003e\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\u003e307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139 (45.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e119 (38.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49 (15.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003e221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100 (45.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93 (42.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28 (12.67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking and Alcohol Use History\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.977\u003c/p\u003e\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\u003e504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e228 (45.24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e203 (40.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73 (14.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (44.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (40.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (16.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolypharmacy\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.520\u003c/p\u003e\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\u003e244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116 (47.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92 (37.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36 (14.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003e285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123 (43.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e121 (42.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41 (14.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFried Frailty Phenotype (FP)\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Frail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110 (50.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85 (38.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25 (11.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-Frailty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102 (47.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86 (39.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28 (12.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrailty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (29.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42 (45.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24 (25.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMontreal Cognitive Assessment (MoCA)\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.766\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal Cognition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e170 (44.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e157 (41.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54 (14.17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCognitive Impairment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (46.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (37.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23 (15.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADL\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndependent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126 (50.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103 (41.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (8.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild Dependence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89 (43.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85 (41.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29 (14.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate Dependence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (35.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (35.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17 (28.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere Dependence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (16.67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (22.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (61.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIADL\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndependent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e157 (52.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e105 (35.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35 (11.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild Dependence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (38.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47 (51.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (10.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate Dependence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (35.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (45.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23 (18.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere Dependence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (22.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (27.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9 (50.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSignificant differences in social isolation profile membership were identified across subgroups defined by sex, marital status, living arrangement, number of children, frailty status, and both basic and instrumental activities of daily living (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eScores for Social Isolation, Frailty, Cognitive Function, BADL, and IADL\u003c/h3\u003e\n\u003cp\u003eThe Lubben Social Network Scale scores ranged from 5 to 40, with a mean of 28.17 (\u0026plusmn;\u0026thinsp;8.67). A total of 239 participants (45.0%) met the criteria for social isolation. Fried frailty scores varied from 0 to 5, with a mean of 1.26 (\u0026plusmn;\u0026thinsp;1.292), and 309 participants were classified as frail. The MoCA scores ranged from 9 to 30, yielding a mean score of 25.35 (\u0026plusmn;\u0026thinsp;4.434), with 148 participants exhibiting cognitive impairment. Barthel Index scores ranged from 5 to 100, with a mean of 90.02 (\u0026plusmn;\u0026thinsp;14.794); 166 participants demonstrated limitations in BADL. Finally, IADL scores ranged from 0 to 8, with a mean of 6.65 (\u0026plusmn;\u0026thinsp;1.919), and 232 participants displayed dependence in IADL.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated social isolation among 529 community-dwelling older adults of China, revealing a significant prevalence of social isolation, with 45% of participants meeting the established criteria. Through LPA, three distinct social isolation profiles were identified: Mild Social Isolation, Low Friend Network-Moderate Social Isolation, and Severe Social Isolation. This classification enhances the understanding of social dynamics within aged populations and underscores the necessity for tailored interventions to address varying degrees of isolation.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSocial Isolation Profiles and Implications\u003c/h2\u003e\u003cp\u003eThe Mild Social Isolation group (45.18%) had relatively strong family and friend networks, whereas the Severe Social Isolation group (14.55%) exhibited low scores across both domains, reflecting comprehensive disconnection. Notably, the Moderate Social Isolation group (40.27%) maintained family ties but had limited engagement with friends and the broader community. This distinction is particularly important in the Chinese context, where family traditionally provides primary support; a robust family network alone may not fully prevent social isolation. Identifying these subgroups allows public health practitioners and policymakers to develop interventions that target specific deficits, such as promoting community engagement for those with intact family support but limited social networks outside the household.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eLiving Arrangements and Family Support\u003c/h2\u003e\u003cp\u003eLiving alone without care was a strong predictor of severe social isolation. Among 74 participants living alone without care, nearly all (95.95%) were classified as severely isolated. This aligns with previous research showing that the absence of family support significantly reduces emotional and instrumental resources, increasing the risk of social disconnection\u003csup\u003e26 27\u003c/sup\u003e. In China, the family remains the cornerstone of older adults\u0026rsquo; social support system, and rapid urbanization and smaller family sizes may exacerbate isolation for those without co-residing children\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. These findings suggest that interventions should not only encourage family involvement but also establish formal community support systems, such as home visit programs, social clubs, or volunteer networks, to support older adults living alone.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eFunctional Status and Community Engagement\u003c/h2\u003e\u003cp\u003eDependence in basic and instrumental activities of daily living (ADL/IADL) was significantly associated with more severe social isolation profiles. Individuals with severe functional limitations were more likely to fall into the Moderate or Severe Social Isolation groups. Physical impairments can restrict mobility, limit participation in social activities, and reduce opportunities to maintain friendships, even when family support is present\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Longitudinal studies have also demonstrated that declines in ADL and IADL predict subsequent increases in social isolation and loneliness\u003csup\u003e31 32\u003c/sup\u003e. In our study, dependence in ADL and IADL was significantly associated with a higher likelihood of being in the severe isolation group, particularly among those with reduced social networks beyond their immediate family. This underscores the importance of addressing functional limitations in interventions aimed at reducing social isolation among older adults.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eFrailty and Its Bidirectional Relationship with Social Isolation\u003c/h2\u003e\u003cp\u003eFrailty was another significant factor associated with social isolation. Frailty, a state of increased vulnerability to stressors, has a complex and bidirectional relationship with social isolation. Non-frail participants were more likely to belong to the Mild Social Isolation group, whereas frail individuals were overrepresented in the Moderate and Severe Social Isolation groups. Frail individuals are less able to participate in social activities due to reduced physiological reserves, fatigue, and decreased mobility, while social isolation itself may accelerate the development or progression of frailty\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Frailty limits physiological reserves and mobility, reducing participation in social activities, while social isolation itself may accelerate frailty through decreased physical activity and psychosocial stimulation\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, 35 36\u003c/sup\u003e. These findings highlight the importance of integrating frailty screening into routine geriatric care. Early identification and multicomponent interventions\u0026mdash;including physical exercise, nutritional support, and psychosocial engagement\u0026mdash;may mitigate both frailty and social isolation, ultimately improving quality of life\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations. First, the participants were limited to community-dwelling older adults, and no data were collected from rural or hospitalized populations. Second, the survey was confined to urban districts in Beijing, which may limit the generalizability of the findings. Third, the study was cross-sectional in design, precluding any longitudinal assessment of changes in social isolation over time. Future research should expand the sample size, incorporate multi-center designs, and collaborate with community health authorities to conduct comprehensive assessments of social isolation among older adults. Longitudinal follow-up is also needed to better understand temporal trends and transitions in social isolation status.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study identified distinct patterns of social isolation among community-dwelling older adults in China, including mild social isolation, moderate social isolation, and severe social isolation. Older adults living alone without caregivers were more likely to experience severe social isolation. Those with moderate or severe dependence in ADL and IADL were more likely to fall into the moderate isolation group with limited engagement with friends and the broader community. In contrast, the absence of frailty was associated with a higher probability of belonging to the mild social isolation group. These findings underscore the need for individualized interventions based on specific social isolation profiles. Continuous monitoring, tailored support, and personalized management strategies should be implemented to mitigate social isolation in older adults.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLPA \u0026nbsp; \u0026nbsp;Latent Profile Analysis\u003c/p\u003e\n\u003cp\u003eLSNS \u0026nbsp; \u0026nbsp;Lubben Social Network Scale\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFFP \u0026nbsp; \u0026nbsp;Fried Frailty Phenotype\u003c/p\u003e\n\u003cp\u003eMoCA \u0026nbsp; \u0026nbsp;Montreal Cognitive Assessment\u003c/p\u003e\n\u003cp\u003eBI \u0026nbsp; \u0026nbsp;Barthel Index\u003c/p\u003e\n\u003cp\u003eADL \u0026nbsp; \u0026nbsp;Activities of Daily Living\u003c/p\u003e\n\u003cp\u003eBADL \u0026nbsp; \u0026nbsp;Basic Activities of Daily Living \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIADL \u0026nbsp; \u0026nbsp;Instrumental Activities of Daily Living\u003c/p\u003e\n\u003cp\u003eAIC \u0026nbsp; \u0026nbsp;Akaike Information Criterion\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBIC \u0026nbsp; \u0026nbsp;Bayesian Information Criterion\u003c/p\u003e\n\u003cp\u003eaBIC \u0026nbsp; \u0026nbsp;adjusted Bayesian Information Criterion\u003c/p\u003e\n\u003cp\u003eBLRT \u0026nbsp; \u0026nbsp;Bootstrap Likelihood Ratio Test\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLMR \u0026nbsp; \u0026nbsp;Lo-Mendell-Rubin\u003c/p\u003e\n\u003cp\u003eC1 \u0026nbsp; \u0026nbsp;Class 1\u003c/p\u003e\n\u003cp\u003eC2 \u0026nbsp; \u0026nbsp;Class 2\u003c/p\u003e\n\u003cp\u003eC3 \u0026nbsp; \u0026nbsp;Class 3\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e All research procedures were conducted in accordance with the relevant guidelines and regulations established by the Declaration of Helsinki. Ethical approval was granted by the Ethics Committee of Peking Union Medical College Hospital (Ethics approval number: ZS-2943). Informed consent was obtained from all participants. To protect privacy and confidentiality, all data were deidentified, and access was restricted to authorized personnel. Participants received no compensation, except for access to quality medical care, and no identifying information was included.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eClinical trial number\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by the Chinese Nursing Association Research Funding (ZHKYQ202420), Peking Union Medical College Hospital Talent Cultivation Program Category C (UBJ10309), and Chinese Aging Well Association Research Funding (24\u0026thinsp;\u0026minus;\u0026thinsp;02).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXL：Conceptualization，Methodology，Writing \u0026ndash; original draft，Writing \u0026ndash; review and editing，Funding acquisition. XH：Data curation，Supervision，Project administration，Funding acquisition，Writing\u0026ndash;original draft. ZZ：Investigation，Resources，Writing \u0026ndash; original draft. YC：Formal analysis，Visualization，Writing \u0026ndash; review and editing. YL：Software，Data curation，Writing \u0026ndash; review and editing. RS：Formal analysis，Investigation，Writing \u0026ndash; review and editing. SZ：Writing \u0026ndash; review and editing，Methodology，Investigation. KZ：Data curation，Validation，Writing \u0026ndash; review and editing. HD：Validation，Writing \u0026ndash; review and editing. SL：Validation，Writing \u0026ndash; review and editing. JH: Validation，Writing \u0026ndash; review and editing. All authors reviewed and approved the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSherman DW, Alfano AR, Alfonso F, et al. A Systematic Review of the Relationship between Social Isolation and Physical Health in Adults. \u003cem\u003eHealthcare (Basel, Switzerland)\u003c/em\u003e. Jun. 2024;1(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/healthcare12111135\u003c/span\u003e\u003cspan address=\"10.3390/healthcare12111135\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang L, Jing S, Yang XL, Wang YP, Su XY. 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Intern Emerg Med Jun. 2025;1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11739-025-03988-2\u003c/span\u003e\u003cspan address=\"10.1007/s11739-025-03988-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Social Isolation, Older Adults, Latent Profile Analysis, Determinants","lastPublishedDoi":"10.21203/rs.3.rs-7756154/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7756154/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThis study aimed to identify social isolation profiles among older adults in China using latent profile analysis and to explore their associations with demographic and health factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional study was conducted between July and December 2023 in the Chaoyang and Dongcheng districts of Beijing, China. Participants aged 60 years or older were recruited through convenience sampling from six communities. Latent profile analysis was applied to classify patterns of social isolation. Multivariate logistic regression models were used to assess factors associated with different profiles of social isolation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 529 participants were included. Three social isolation profiles were identified: the mild social isolation group (n\u0026thinsp;=\u0026thinsp;239), the moderate social isolation group (n\u0026thinsp;=\u0026thinsp;213), and the severe social isolation group (n\u0026thinsp;=\u0026thinsp;77). In the context of comparing mild to severe social isolation, multivariate analysis revealed that older adults living alone without care had significantly higher odds of severe social isolation compared to those living with children (OR\u0026thinsp;=\u0026thinsp;0.099, 95% CI: 0.034\u0026ndash;0.377, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Frailty (OR\u0026thinsp;=\u0026thinsp;0.141, 95% CI: 0.039\u0026ndash;0.588, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033), severe ADL dependence (OR\u0026thinsp;=\u0026thinsp;0.249, 95% CI: 0.145\u0026ndash;0.454, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048), and moderate to severe IADL dependence (OR\u0026thinsp;=\u0026thinsp;0.448 to 0.307, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were also significantly associated with severe social isolation. Similarly, in the comparison between moderate and severe group, compared to those living with children, older adults living alone without care were more likely to experience severe social isolation (OR\u0026thinsp;=\u0026thinsp;0.199, 95% CI: 0.078\u0026ndash;0.328, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). In terms of functional status, severe ADL dependence (OR\u0026thinsp;=\u0026thinsp;0.152, 95% CI: 0.061\u0026ndash;0.505, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) and moderate to severe IADL dependence (OR\u0026thinsp;=\u0026thinsp;0.231 to 0.128, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were significantly associated with severe social isolation.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOlder adults living alone without care, with frailty, and with functional dependence are at significantly higher risk of severe social isolation. Understanding the specific factors contributing to different profiles can inform tailored support strategies aimed at enhancing social engagement and improving the quality of life within this population.\u003c/p\u003e","manuscriptTitle":"Latent Profile Analysis and Determinants of Social Isolation among Older Adults in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 12:06:06","doi":"10.21203/rs.3.rs-7756154/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"919fe8fd-fc56-4c55-8c95-402ff7664c0a","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T08:28:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 12:06:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7756154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7756154","identity":"rs-7756154","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}