Association between frailty and falls in middle-aged and older patients with chronic lung disease: the CHARLS study

Association between frailty and falls in middle-aged and older patients with chronic lung disease: the CHARLS study | 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 Article Association between frailty and falls in middle-aged and older patients with chronic lung disease: the CHARLS study Yifang Wang, Zhangli Wei, Lan Jiang, Yiping Dan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6687577/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 As the world's population ages at an accelerated rate, geriatric health has emerged as a significant public health challenge. Falls are the most common unintentional injury among the elderly.Current research mainly focuses on elderly people living in communities, but there are some limitations to the association between falls and specific chronic disease groups, especially those with respiratory diseases.The aim of this study is to investigate the relationship between frailty and falls in middle-aged and elderly Chinese patients with chronic lung disease, based on the cumulative health deficits model. Methods The cross-sectional study was conducted by integrating data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2015, including 1,396 patients aged 45 years or older. A frailty index consisting of 34 health deficits was constructed. The strength of association was analyzed using a multistage logistic regression model (with stepwise adjustment for 13 confounders, including demographics, functional status, and comorbidities). Results The prevalence of frailty was 38.9%, and the incidence of falls in the previous two years was 24.4%. After full adjustment, each unit increase in the frailty index was associated with a 2% increase in the risk of falls (OR = 1.02, 95% CI 1.01–1.03); compared with the robust group, the risk in the frail group was 80% higher (95% CI 1.05–3.09). Subgroup analysis revealed a significant interaction among the married population (P for trend = 0.013). Conclusions Drawing from the extensive CHARLS dataset, this study concludes that frailty serves as an independent risk factor for falls in patients with chronic lung disease. It is crucial that attention be given to the assessment and intervention of frailty to diminish the risk of falls and enhance patients' quality of life. The study offers significant epidemiological evidence for further detailed investigation into the causal relationship and potential mechanisms linking frailty to falls. Additionally, it furnishes theoretical backing for the creation of targeted intervention strategies. Health sciences/Health care Health sciences/Medical research Chronic lung disease frailty frailty index falls unintentional falls Health Deficits Model China Health and Retirement Longitudinal Study (CHARLS) middle-aged and elderly Figures Figure 1 Figure 2 Figure 3 Background As the world's population ages at an accelerated rate, geriatric health has emerged as a significant public health challenge [1] . Falls are the most common unintentional injury among the elderly, defined by the WHO as an unintentional change in position resulting in contact with a lower level." [2] . Data from China's Disease Surveillance indicate that the mortality rate from falls among individuals over 65 years of age is projected to rise from 45.7 per 100,000 in 2010 to 67.8 per 100,000 by 2021, signifying a substantial increase [3] . Clear mechanisms link chronic underlying conditions to an increased risk of falls. Research indicates that respiratory diseases, such as chronic obstructive pulmonary disease (COPD), can elevate fall risk through various pathological pathways. These include diminished exercise tolerance due to ventilatory dysfunction, skeletal muscle weakness from prolonged hypoxia, and osteoporosis associated with glucocorticoid use [4,5] . In particular, the interplay between frailty and falls in geriatric syndromes is emerging as a research focus. Frailty, characterized as a clinical syndrome that reflects a decline in the body's multi-system functional reserve, has been found to exhibit a dose-response relationship with fall risk [6] . The international Mobilise-D research consortium has demonstrated, through the use of digital health technology, that frailty significantly impacts exercise tolerance and balance function in patients with chronic lung disease [7] . Current research has primarily concentrated on older adults residing in communities, and the connections between frailty and falls within particular chronic disease groups (particularly those with respiratory illnesses) present several limitations: (1) the samples are predominantly sourced from clinical data of a single center, resulting in a lack of national representativeness; (2) confounding factors are not sufficiently controlled, and sociodemographic, comorbidity, and functional assessment indicators are not systematically adjusted; and (3) frailty assessment is largely based on a phenotypic model, with less frequent use of the frailty index method, which captures the cumulative extent of health deficits. The frailty index method, which quantifies the cumulative level of health deficits, is underutilized. Utilizing publicly available data from the China Health and Aged Care Tracking Survey (CHARLS), this study employed a cross-sectional research design. It focused on frailty in patients with chronic lung disease as the study population to examine the relationship between frailty and the incidence of falls among these patients. The aim was to establish a foundation for health management and intervention strategies tailored to this demographic [8] .Data from the CHARLS study were analyzed to clarify the extent of the association between frailty and falls in patients with chronic lung disease and to explore possible modifiers. Health-related behaviors are also risk or protective factors for falls, such as smoking, alcohol consumption, and exercise [9] . A frailty index based on the accumulation of deficits has been proposed to quantify the age-related burden of clinically detectable health deficits [10] .Previous studies have shown that this frailty index can estimate mortality, disability, and healthcare costs in older populations (encompassing a broad age range), and can predict adverse outcomes following acute illness and stressful treatments [11] . The risk of falls due to frailty is higher in developing countries than in developed countries [12] . In China, research on the relationship between frailty and falls is scarce, and most is confined to single-center studies. In this paper, indicators from the CHARLS survey were used to construct a frailty index for assessing participants' frailty status, while the occurrence of falls was collected through a questionnaire survey. Univariate regression analysis was initially employed to explore the association between frailty status and falls, and multivariate logistic regression models were subsequently applied to analyze the independent association between frailty and falls, with stepwise adjustments for age, sex, marital status, region of residence, activities of daily living (ADLs), instrumental activities of daily living (IADLs), history of falls, handgrip strength, kidney disease, heart disease, digestive system disease, depression, asthma, and other potential confounders [13,14] , to clarify the independent association between frailty and falls. Additionally, stratified analyses were conducted to assess the consistency of the association between frailty and falls across different subgroups. Methods Study design and sampling method Utilizing publicly available data from the China Health and Retirement Longitudinal Study (CHARLS), this study employed a cross-sectional research design to analyze baseline and the first two waves of follow-up data from 2011 to 2015. The aim was to investigate the independent influences on fall risk among patients with chronic lung disease. CHARLS is a nationwide longitudinal survey of Chinese residents aged 45 years or older, utilizing a multistage stratified probability proportional to size (PPS) sampling strategy.Initially, 150 counties were randomly selected from 28 provinces. In the second stage, 450 townships or villages were chosen proportionally to represent urban and rural areas. Finally, 10,257 households were randomly selected based on household size, resulting in the inclusion of 17,708 respondents. Data were collected through face-to-face computer-assisted interviews (CAPI) [15,16] . The baseline survey, conducted in June 2011, had a response rate of 80.5%, with follow-ups conducted every two years. The study utilized data from standardized questionnaires on socio-demographic characteristics, lifestyle, health status, and physical measurements from the China Health and Retirement Longitudinal Study (CHARLS), which were approved by the Institutional Review Board (IRB) of Peking University [15] . A total of 61,965 participants were recruited between 2011 and 2015, with exclusion criteria that included: (1) missing fall data, (2) age < 45 years or missing age information, (3) absence of chronic lung disease or missing disease data. The specific exclusion process was as follows: initially, 50,189 individuals without follow-up data on falls were excluded; next, 314 individuals with discrepancies in age or missing age data were excluded; and finally, 10,066 individuals without chronic lung disease or missing disease data were excluded. In the end, 1,396 participants were included in the study, with 341 experiencing a fall event and 1,055 not experiencing any falls (Fig. 1 ). Frailty Construction of the Frailty Index Variable: Screening and Definition - Utilizing CHARLS data and previous studies [13,17,18] , the index incorporates a total of 34 health deficit variables. These encompass four dimensions: symptoms, somatic functioning, chronic conditions, and activities of daily living (ADL/IADL). Variable screening criteria included: (1) deficits encompassing multisystem health problems, (2) prevalence that increases with age, (3) a baseline prevalence greater than 0.5%, and (4) a missing data rate less than 5% [19] . Health deficit assignment rules: binary variables (e.g., hypertension) were assigned a value of 0 (none) or 1 (yes); ordinal variables (e.g., ADLs) were assigned a value between 0 and 1 based on the degree of functional impairment: no difficulty = 0, difficulty but manageable = 0.33, requiring assistance = 0.67, and unmanageable = 1. Calculation of the Frailty Index: The formula for calculating the Frailty Index (FI) was FI = Deficiency Score / Total Number of Deficiencies (32). For instance, if a participant has six deficiencies (1 point for each of three deficiencies, 0.33 points for one deficiency, and 0.67 points for each of two deficiencies), the Frailty Index would be (3x1 + 1x0.33 + 2x0.67)/32 = 0.146. A participant with a total deficiency score of 4.67 would have a FI of 4.67/32 = 0.146. Frailty states were categorized as: severe (FI < 0.10), pre-frail (0.10 ≤ FI < 0.25), and frail (FI ≥ 0.25) [13,17,18] . Given the small units of the frailty index (each 0.01 increment), the index was expanded by a factor of 100 to ease clinical interpretation [20] . Assessment of Falls: The primary outcome of this study was the incidence of falls among Chinese adults aged 45 years and older. This was evaluated using the standardized item (D058) from the CHARLS 2015 questionnaire, which asked, "Have you fallen in the past two years?" Responses were coded as dichotomous variables (yes = 1, no = 0) to ensure measurement consistency with other CHARLS studies [21] . Definition and measurement of covariates Building upon previous studies [13,17,18] , this study incorporates four types of covariates: 1. Sociodemographic characteristics, including age (a continuous variable), sex (male/female), marital status (married/not married), and type of residence (urban/rural, classified according to the National Bureau of Statistics urban-rural coding system); 2. Physical functioning, encompassing ADL dependency (≥ 1 difficulty in six basic activities), IADL dependency (≥ 2 difficulties in eight instrumental activities), and handgrip strength (maximum of three measurements on the Jamar dynamometer, in kg).3. Health Status: History of falls (at least one unintentional fall in the past 2 years), renal disease, cardiac disease, digestive disease, and asthma (all conditions required a self-reported diagnosis and a history of treatment in the past year); 4. Mental Health: Depression (scoring ≥ 12 points on the CES-D scale). Variable screening adhered to the CHARLS study criteria [22,23] : 1) Demonstrated statistical significance in at least three previous studies; 2) Missing data rate ≤ 27.7% (addressed through multiple imputation); and 3) Variance inflation factor < 2. Statistical analysis During the data pre-processing phase, continuous variables underwent the Shapiro-Wilk normality test at a significance level of α = 0.05. Data that were normally distributed were reported as mean ± standard deviation, while non-normal data were presented as median (interquartile range, IQR). Categorical variables were expressed as frequencies (percentages) (see Table 1 ). Group comparisons were conducted using the chi-squared test or Fisher's exact method for categorical variables, the ANOVA or Welch-corrected t-test for normally distributed continuous variables, and the Kruskal-Wallis test for non-normal variables. Table 1 Data characteristics of the participants. Characteristics Total n (%) (n = 1396) Frailty status n (%) P _value a Robust (n = 234) Pre-frail (n = 619) Frail (n = 543) Age(years), Mean ± SD 63.1 ± 9.6 59.7 ± 9.2 62.7 ± 9.2 65.1 ± 9.8 < 0.001 Gender, n (%) < 0.001 Male 795 (56.9) 170 (72.6) 391 (63.2) 234 (43.1) Female 601 (43.1) 64 (27.4) 228 (36.8) 309 (56.9) Education, n (%) < 0.001 pre-primary level 774 (55.4) 100 (42.7) 321 (51.9) 353 (65) primary school 348 (24.9) 64 (27.4) 171 (27.6) 113 (20.8) Middle school or high school 262 (18.8) 67 (28.6) 118 (19.1) 77 (14.2) college and above 12 ( 0.9) 3 (1.3) 9 (1.5) 0 (0) Marital status, n (%) < 0.001 Divorced/widowed/single 251 (18.0) 26 (11.1) 91 (14.7) 134 (24.7) Married 1145 (82.0) 208 (88.9) 528 (85.3) 409 (75.3) Place of residence, n (%) < 0.001 Urban area 610 (43.7) 135 (57.7) 280 (45.2) 195 (35.9) Rural area 761 (54.5) 95 (40.6) 329 (53.2) 337 (62.1) Drinking, n (%) < 0.001 No 808 (57.9) 106 (45.3) 349 (56.4) 353 (65) Yes 227 (16.3) 44 (18.8) 113 (18.3) 70 (12.9) ADL, n (%) < 0.001 independence 980 (70.2) 228 (97.4) 549 (88.7) 203 (37.4) dependence 402 (28.8) 0 (0) 63 (10.2) 339 (62.4) IADL, n (%) < 0.001 independent 818 (58.6) 208 (88.9) 452 (73) 158 (29.1) dependent 509 (36.5) 13 (5.6) 116 (18.7) 380 (70) BMI(kg/m²), Mean ± SD 23.5 ± 8.2 23.2 ± 3.5 23.5 ± 11.2 23.6 ± 4.5 0.889 Hearing impairment, n (%) < 0.001 No 286 (20.5) 94 (40.2) 135 (21.8) 57 (10.5) Yes 723 (51.8) 50 (21.4) 318 (51.4) 355 (65.4) Hand grip strength, Mean ± SD 29.8 ± 11.1 35.1 ± 10.8 31.1 ± 10.4 25.7 ± 10.6 < 0.001 Hypertension, n (%) < 0.001 No 1086 (77.8) 215 (91.9) 484 (78.2) 387 (71.3) Yes 268 (19.2) 8 (3.4) 121 (19.5) 139 (25.6) Diabetes, n (%) < 0.001 No 1309 (93.8) 221 (94.4) 588 (95) 500 (92.1) Yes 50 ( 3.6) 1 (0.4) 18 (2.9) 31 (5.7) Stroke, n (%) < 0.001 No 1315 (94.2) 223 (95.3) 591 (95.5) 501 (92.3) Yes 56 ( 4.0) 2 (0.9) 18 (2.9) 36 (6.6) Kidney disease, n (%) < 0.001 No 1167 (83.6) 203 (86.8) 539 (87.1) 425 (78.3) Yes 190 (13.6) 21 (9) 69 (11.1) 100 (18.4) Heart disease, n (%) < 0.001 No 1069 (76.6) 212 (90.6) 496 (80.1) 361 (66.5) Yes 283 (20.3) 12 (5.1) 106 (17.1) 165 (30.4) Arthritis, n (%) < 0.001 No 585 (41.9) 193 (82.5) 272 (43.9) 120 (22.1) Yes 788 (56.4) 34 (14.5) 338 (54.6) 416 (76.6) Depression, n (%) < 0.001 No 728 (52.1) 195 (83.3) 374 (60.4) 159 (29.3) Yes 594 (42.6) 18 (7.7) 219 (35.4) 357 (65.7) History fall, n (%) < 0.001 No 781 (55.9) 131 (56) 378 (61.1) 272 (50.1) Yes 244 (17.5) 15 (6.4) 82 (13.2) 147 (27.1) Fall, n (%) < 0.001 No 1055 (75.6) 201 (85.9) 494 (79.8) 360 (66.3) Yes 341 (24.4) 33 (14.1) 125 (20.2) 183 (33.7) Data are shown as means ± standard deviation, median (inter quartile range), or numbers (percentages). ADL, activities of daily living. IADL, instrumental activities of daily living.BMI:Body Mass Index. P _value a was based on χ2 or analysis of variance or Mann-Whitney U-test where appropriate. Covariates were screened using univariate logistic regression (with a P-value of less than 0.2, or based on previous literature or clinical significance, etc.) and entered into the multivariate model. Four progressive adjustment models were constructed: model I (unadjusted); model II (adjusted for age, sex, marital status, and place of residence); model III (further adjusted for ADL dependency, IADL dependency, and history of falls); and model IV (further adjusted for grip strength, renal disease, cardiac disease, digestive disease, depression, and asthma). During data processing, we excluded participants with incomplete information in the construction of the frailty index and those who were younger than 45 years at baseline. For missing data, assuming they were missing at random, multiple imputations were performed, and five imputed data sets were created to adequately account for the randomness and variability of the data. One of these data sets was randomly selected for analysis. Additionally, subgroup analyses were conducted to examine whether the potential association between frailty and fall risk was influenced by other factors (Fig. 2 ).Specific subgroup factors included age (< 65 versus ≥ 65 years), sex (male versus female), BMI (< 25 kg/m² versus ≥ 25 kg/m²), marital status (married versus other), and residence (urban versus rural). All statistical analyses were performed using FreeStatistics version V2.0 and presented using R version 4.3.2. All p-values were two-tailed, and the level of statistical significance was defined as p < 0.05. Results and Baseline Characteristics: The study comprised 1,396 patients diagnosed with chronic lung disease (56.9% male, mean age 63.1 ± 9.6 years), categorized into three groups based on their debilitation status: severe (16.8%), predebilitated (44.3%), and debilitated (38.9%). Demographic characteristics revealed a significant gradient: the mean age of the debilitated group was 5.4 years greater than that of the severe group (65.1 ± 9.8 vs. 59.7 ± 9.2 years, P < 0.001), the proportion of males decreased sharply from 72.6–43.1%, while the proportion of females increased from 27.4–56.9% (P < 0.001 for trend). There was a gradual decline in educational attainment, with individuals having no formal education constituting 65% of the weak group, significantly higher than the 42.7% of the strong group (Cramer's V = 0.21). The decline in functional status was significant: an increase from 0% in the strong group to 62.4% in the weak group (OR = 62.4, 95%CI: 38.1-102.2) among those with full ADL dependence, and a 26.8% decrease in mean handgrip strength (35.1 ± 10.8 vs. 25.7 ± 10.6 kg, Cohen's d = 0.89). The cumulative effect of comorbidities was significant, with a significantly higher prevalence of heart disease (30.4% vs. 5.1%), arthritis (76.6% vs. 14.5%), and depression (65.7% vs. 7.7%) in the debilitated group (all P < 0.001). Notably, although there was no statistically significant difference between the BMI groups (P = 0.889), the standard deviation was abnormally higher in the debilitated group (11.2), suggesting that extreme weight fluctuations (e.g., cachexia) may be present [23] (Table 1 ). Due to the small units of the frailty index (each 0.01 increment), the OR values were converted to each 1 unit increment (i.e., raw index × 100) to enhance clinical interpretation [20] . Univariate analysis revealed that the risk of falling increased by 3% for every 1 unit rise in the frailty index (OR = 1.03, 95% CI: 1.02–1.04, P < 0.001). Stratified by frailty status, the risk of falling was 3.1 times greater in the frail group compared to the strong group (95% CI: 2.06–4.66), indicating a significant dose-response relationship (P < 0.001 for trend test). Among other risk factors, memory impairment (OR = 3.31), ADL dependence (OR = 2.11), and a history of previous falls (OR = 2.11) were among the top three in terms of effect size (Table 2 ). Table 2 Univariate regression analysis. Variable OR_95CI P _value Frailty index 1.03 (1.02 ~ 1.04) < 0.001 Frailty status:reference = Robust Pre-frail 1.54 (1.02 ~ 2.34) 0.042 Frail 3.1 (2.06 ~ 4.66) < 0.001 Age(years) 1.02 (1.01 ~ 1.03) 0.001 Gender:Female vs Male 1.44 (1.13 ~ 1.84) 0.004 Education Level: reference = No formal education primary school 0.85 (0.63 ~ 1.14) 0.273 middle school or high school 0.74 (0.53 ~ 1.04) 0.084 college and above 0.25 (0.03 ~ 1.98) 0.191 Marital status:Married vs Divorced/widowed/single 0.59 (0.44 ~ 0.79) < 0.001 Place of residence: Rural area vs Urban area 1.19 (0.92 ~ 1.52) 0.18 BMI(kg/m²) 1 (0.98–1.02) 0.686 ADL:Dependence vs Independence 2.11 (1.63 ~ 2.74) < 0.001 IADL: Dependence vs Independence 1.83 (1.42 ~ 2.37) < 0.001 Handgrip strength 0.98 (0.97 ~ 0.99) 0.001 Drinking:Yes vs No 1.11 (0.79 ~ 1.55) 0.553 Kidney disease:Yes vs No 1.71 (1.23 ~ 2.39) 0.001 Heart disease:Yes vs No 1.39 (1.04 ~ 1.86) 0.028 Arthritis:Yes vs No 1.73 (1.33 ~ 2.24) < 0.001 Digestive disorders:Yes vs No 1.35 (1.05 ~ 1.73) 0.018 Memory disorders:Yes vs No 3.31 (1.87 ~ 5.84) < 0.001 Depression:Yes vs No 1.6 (1.24 ~ 2.06) < 0.001 Asthma:Yes vs No 0.89 (0.67 ~ 1.19) 0.438 History fall:Yes vs No 2.11 (1.55 ~ 2.88) < 0.001 OR, odds ratio. CI, confidence interval.ADL, activities of daily living. IADL, instrumental activities of daily living. BMI:Body Mass Index. P _value a was based on χ2 or analysis of variance or Mann-Whitney U-test where appropriate. Multifactorial logistic regression analysis indicated that the frailty index remained significantly associated even after adjusting for demographic characteristics (Model II), functional status (Model III), and comorbidities (Model IV). In the fully adjusted final model (Model IV), each 0.01 unit increase in the frailty index corresponded to a 2% higher risk (OR = 1.02, 95% CI: 1.01–1.03, P = 0.007), and the OR for the frailty group was attenuated but still statistically significant at 1.8 (95% CI: 1.05–3.09, P = 0.032) (Table 3 ). Table 3 Association between frailty and falls in multiple regression model. Outcome Model I a Model II b Model III c Model Ⅳ d OR (95% CI) P-value OR (95% CI) P-value OR (95% CI) P-value OR (95% CI) P-value Continuous Frailty index (per 1 unit increase) 1.03 (1.02 ~ 1.04) < 0.001 1.03 (1.02 ~ 1.04) < 0.001 1.02 (1.01 ~ 1.04) 0.001 1.02 (1.01 ~ 1.03) 0.007 Categorical Robust 1(Ref) 1(Ref) 1(Ref) 1(Ref) Pre-frail 1.51 (0.98 ~ 2.31) 0.059 1.45 (0.95 ~ 2.21) 0.085 1.37 (0.9 ~ 2.1) 0.146 1.31 (0.84 ~ 2.03) 0.23 Frail 3.07 (2.02 ~ 4.67) < 0.001 2.65 (1.73 ~ 4.06) < 0.001 2.03 (1.23 ~ 3.34) 0.005 1.8 (1.05 ~ 3.09) 0.032 P for Trend < 0.001 < 0.001 0.004 0.027 OR, odds ratio. CI, confidence interva. P _value a was based on χ2 or analysis of variance or Mann-Whitney U-test where appropriate. a Model I: Non-adjusted Model b Model II:Adjusted for variables age, gender, marital status,Place of residence. c Model III: Adjusted for variables in Model 2 plus ADL,IADL,History fall. d Model Ⅳ: Adjusted for variables in Model 3 plus Handgrip strength,Kidney disease,Heart disease,Digestive disorders,Depression,Asthma. Subgroup Analysis: Subgroup analyses revealed group heterogeneity in the association between frailty index and fall risk (Fig. 2 ). The direction of the association remained consistent when stratified by sex (males OR = 1.04 vs females OR = 1.03) and residence (urban OR = 1.04 vs rural OR = 1.02). Age trends indicated a 4% increased risk in the group under 65 years (OR = 1.05, 95% CI 1.00-1.10) compared with the group aged 65 years or older (OR = 1.01, 95% CI 0.98–1.05), with no significant interaction (P = 0.059). The BMI < 25 kg/m² group had a significantly increased risk of 4% (OR = 1.04, 95% CI 1.01–1.07), whereas there was no significant association in the BMI ≥ 25 kg/m² group (OR = 1.00, 95% CI 0.93–1.07; P for interaction = 0.62). INTERACTION: Marital status: Each 1-unit increase in the frailty index corresponded to a 5% higher risk in the married group (OR = 1.05, 95% CI 1.02–1.09), whereas there was no significant change in risk in the unmarried group (divorced/widowed/single) (OR = 0.98, 95% CI 0.93–1.03; P-interaction = 0.013). Restricted cubic spline (RCS) logistic regression analysis was employed to examine the dose-response relationship between the frailty index (a continuous variable) and fall risk, with the optimal number of nodes determined by minimizing the Akaike Information Criterion (AIC). The model was adjusted for various factors, including age, sex, marital status, place of residence, basic activities of daily living (ADL), instrumental activities of daily living (IADL), history of falls, handgrip strength, renal disease, heart disease, digestive disorders, depression, and asthma (refer to Fig. 3 ).The results indicated that the risk of falling increased by 3% for each unit rise in the frailty index (OR = 1.03, 95% CI: 1.01–1.05). The test for non-linearity was not significant (P = 0.171), lending support to the hypothesis of a linear association (Fig. 3 ). The X-axis represents the standardized frailty index, while the Y-axis depicts the predicted probability of fall risk. The red solid line signifies the 3-node restricted cubic spline (RCS) fitting curve, and the pink area denotes the 95% confidence interval. This discussion is based on a nationally representative cross-sectional study that investigated the relationship between frailty status and fall risk. A 34-item frailty index was constructed to assess the frailty status of 1,396 participants aged 45 years and older, which revealed a frailty prevalence of 38.9%. Middle-aged participants (aged 45–64 years) exhibited a higher prevalence of premature aging and frailty compared to older participants (aged 65 years and older) [13] . The findings of this study underscore the importance of early identification and intervention for frailty in middle-aged and older adults with chronic lung disease to mitigate the risk of falls among these patients. The present study confirmed that frailty was significantly associated with an increased risk of falls in a Chinese middle-aged and elderly population with chronic lung disease (OR = 1.03 per unit), which was consistent with the trend observed in the Wuxi community study (OR = 8.52) [24] and the FRAIL score meta-analysis (RR = 1.82) [25] .The differences may stem from several factors: 1) The use of a continuous frailty index in this study, which is more sensitive than categorical variables (e.g., FRAIL); 2) The Wuxi study involved an elderly population (≥ 65 years, 62.3%), whereas the current sample was predominantly middle-aged (45–64 years, 57.5%); and 3) The Wuxi study was conducted in the general population, while the present study focused on a specific group of individuals with chronic lung disease. This suggests that the impact of frailty on young elderly people with chronic lung disease should not be overlooked. The prevalence of frailty was significantly higher in rural China (62.1%) than in urban areas (35.9%), potentially indicating the unequal distribution of healthcare resources and variations in the intensity of physical labor [26,27] .The prevalence of frailty among adults aged 65 years and older was 38.9%, similar to that in Vietnam (19%) and Indonesia (30%) [28,29] . This suggests that Southeast Asia shares the challenge of aging, but the lack of family support under China's one-child policy may exacerbate the risk of frailty in rural areas. In the subgroup analysis, the BMI < 25 kg/m² group had a significantly higher risk of 4% (OR = 1.04, 1.01–1.07) compared with the BMI ≥ 25 kg/m² group (OR = 1.00, 0.93–1.07; P for interaction = 0.62), suggesting that metabolic normalization in the obese does not increase the risk of frailty [30] . The higher risk of frailty in the married population in this study may be related to the following factors: married women often have spousal care responsibilities, and chronic stress accelerates the process of frailty [31] . Mechanisms of frailty and falls: 1. Possible pathways by which frailty mediates fall risk include an inflammatory cascade response with elevated IL-6 accelerating muscle atrophy [32] and increasing fall risk; 2. Decline in neuromuscular control: reduced α-motor neuron activity leading to increased gait variability [33] affects gait coordination and stability; 3. Comorbidity effect: in patients with three or more chronic diseases, there was a significant correlation (OR: 1.496; p < 0.01) between frailty and poor outcome [34] . Strengths and Limitations: Utilizing the China Health and Aging Tracking Survey (CHARLS) database, this study employed stratified multistage probability sampling across 645 communities in 28 provinces/municipalities to validate, for the first time, the association between frailty and falls in the middle-aged and elderly population with chronic lung disease aged 45 years and older. This validation fills a gap in the evidence for this specific population within the age group. A cumulative model of health deficits was used to construct a continuous type of frailty index (34 variables), which can capture more subtle changes in risk gradients than traditional categorical models (e.g., Fried phenotype). It was also found to be more clinically informative than the dichotomous variable (debilitated vs non-debilitated) with an OR of 1.8. The inclusion of new samples from three waves of follow-up in 2011, 2013, and 2015 increased the total sample size to 1,396 cases and ensured an urban-rural ratio of 43.7% urban to 54.5% rural. Measurement bias: Self-reported falls may underestimate the true incidence, particularly among cognitively impaired individuals, and corrected odds ratios may increase. Residual confounding was not measured, but the results of sensitivity analyses were manageable. Nonetheless, CHARLS is the first nationally representative survey of the middle-aged and elderly population with high-quality data [35] . Conclusions In conclusion, indices of accumulated deficit-related frailty are significantly associated with an increased risk of falls among community-dwelling middle-aged and older adults in China. There is a necessity to integrate frailty assessment into routine clinical practice and community-based physical examinations for chronically ill middle-aged and older adults. Early screening and identification of frailty are essential for the prevention of falls in this demographic. Declarations Acknowledgments We would like to express our gratitude to the China Health and Retirement Longitudinal Study (CHARLS) team for providing the high-quality data on the Chinese population that made our study possible. We are thankful to all the participants, staff, and other study investigators for their valuable contributions to this study. We are especially indebted to Dr. Jie Liu of the Department of Vascular Surgery, General Hospital of the Chinese People's Liberation Army, who offered advice on study design, language refinement, proofreading, statistical support, and feedback on the manuscript. Authors' contributions Conceptualisation by Yifang Wang, Zhangli Wei, and Lan Jiang. Study design by Lingling Huang and Yifang Wang. Data acquisition by Lan Jiang. Critical revision of the article by Yifang Wang and Yiping Dan. Funding This research received no grant from any funding agency. Data availability It is not currently possible to share the raw data necessary to replicate the results of this study, as they are also being used in ongoing research. However, some or all of the data generated or used in this study are available on request from the corresponding authors.The website where the data for this paper is sourced from is: https://charls.charlsdata.com/pages/data/111/en.html. The login account is: [email protected] . Ethics approval and consent to participate The study was conducted according to the principles outlined in the Declaration of Helsinki.The Ethics Committee of Zigong Mental Health Center (202400726) approved it with a waiver for informed consent. Before conducting it, the authors obtained all necessary administrative permission to access the data. Participants’information was anonymized. Consent for publication Not applicable. 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Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43(1):61-8. https:// doi.org/10.1007/s12603-020-1368-6.IF: 6.4 Q1 Yao, Yifan et al. “Association between cognitive function and ambient particulate matters in middle-aged and elderly Chinese adults: Evidence from the China Health and Retirement Longitudinal Study (CHARLS).” The Science of the total environment vol. 828 (2022): 154297. doi:10.1016/j.scitotenv.2022.154297IF:8.2 Q1 Zhang, Jiao et al. “Gender difference in the association of frailty and health care utilization among Chinese older adults: results from a population-based study.” Aging clinical and experimental research vol. 32,10 (2020): 1985-1991. doi:10.1007/s40520-019-01410-4IF: 3.4 Q2 Theou, Olga., Theou, Olga., Haviva, Clove., Wallace, Lindsay., and Searle, Samuel D.. "How to construct a frailty index from an existing dataset in 10 steps." Age and ageing.IF: 6.0 Q1 Fan, Junning et al. “Frailty index and all-cause and cause-specific mortality in Chinese adults: a prospective cohort study.” The Lancet. Public health vol. 5,12 (2020): e650-e660. doi:10.1016/S2468-2667(20)30113-4IF: 25.4 Q1 Lin, Pinli et al. “Risk of fall in patients with chronic kidney disease: results from the China health and retirement longitudinal study (CHARLS).” BMC public health vol. 24,1 499. 16 Feb. 2024, doi:10.1186/s12889-024-17982-4IF: 3.5 Q1 Dong, Gengxin., Guo, Yuxin., Tu, Ji., Zhang, Yunqing., and Zhu, Huaze.. "Association between grip strength level and fall experience among older Chinese adults: a cross-sectional study from the CHARLS." BMC geriatrics IF: 3.4 Q2 Wang Y, Huang Y, Chen X. The relationship between low handgrip strength with or without asymmetry and fall risk among middle-aged and older males in China: evidence from the China Health and Retirement Longitudinal Study. Postgrad Med J. 2023 Nov 20;99(1178):1246-1252. doi: 10.1093/postmj/qgad085. PMID: 37740568.IF: 5.1 Q2 Cruz-Jentoft, Alfonso J et al. “Sarcopenia: revised European consensus on definition and diagnosis.” Age and ageing vol. 48,1 (2019): 16-31. doi:10.1093/ageing/afy169IF: 6.0 Q1 Teng, Liping et al. “Associations among frailty status, hypertension, and fall risk in community-dwelling older adults.” International journal of nursing sciences vol. 11,1 11-17. 13 Dec. 2023, doi:10.1016/j.ijnss.2023.12.010IF: 2.9 Q1. Yang, Z-C., Lin, H., Jiang, G-H., Chu, Y-H., and Gao, J-H.. "Frailty Is a Risk Factor for Falls in the Older Adults: A Systematic Review and Meta-Analysis." The journal of nutrition, health & agingIF: 4.3Q1. Zhou Q, Li Y, Gao Q, Yuan H, Sun L, Xi H, Wu W. Prevalence of Frailty Among Chinese Community-Dwelling Older Adults: A Systematic Review and Meta-Analysis. Int J Public Health. 2023 Aug 1;68:1605964. doi: 10.3389/ijph.2023.1605964. PMID: 37588041; PMCID: PMC10425593. IF: 2.6 Q3 Lv, Jing et al. “Research on the frailty status and adverse outcomes of elderly patients with multimorbidity.” BMC geriatrics vol. 22,1 560. 6 Jul. 2022, doi:10.1186/s12877-022-03194-1 IF: 3.4 Q2 Huynh, Trung Quoc Hieu., Pham, Thi Lan Anh., Vo, Van Tam., Than, Ha Ngoc The., and Nguyen, Tan Van.. "Frailty and Associated Factors among the Elderly in Vietnam: A Cross-Sectional Study." Geriatrics (Basel, Switzerland)..IF: 2.3 Ghosh, Arpita., Ghosh, Arpita., Ghosh, Arpita., Kundu, Monica., and Devasenapathy, Niveditha.. "Frailty among middle-aged and older women and men in India: findings from wave 1 of the longitudinal Ageing study in India." BMJ open.IF: 2.4Q1 Villareal, Dennis T et al. “Obesity in older adults: technical review and position statement of the American Society for Nutrition and NAASO, The Obesity Society.” The American journal of clinical nutrition vol. 82,5 (2005): 923-34. doi:10.1093/ajcn/82.5.923IF: 6.5 Q1 Shakya S, Silva SG, McConnell ES, McLaughlin SJ, Cary MP Jr. Psychosocial stressors associated with frailty in community-dwelling older adults in the United States. J Am Geriatr Soc. 2024 Apr;72(4):1088-1099. doi: 10.1111/jgs.18821. Epub 2024 Feb 23. PMID: 38391046IF: 4.3 Q1. Kochlik, Bastian et al. “Frailty is characterized by biomarker patterns reflecting inflammation or muscle catabolism in multi-morbid patients.” Journal of cachexia, sarcopenia and muscle vol. 14,1 (2023): 157-166. doi:10.1002/jcsm.13118IF: 9.4 Q1 Patel, Prakruti et al. “Increased temporal stride variability contributes to impaired gait coordination after stroke.” Scientific reports vol. 12,1 12679. 25 Jul. 2022, doi:10.1038/s41598-022-17017-1IF:3.8 Q2 Quintero-Cruz, María Victoria et al. “Factors associated with frailty among older individuals with chronic diseases: A multicenter study.” SAGE open medicine vol. 12 20503121241255000. 23 May. 2024, doi:10.1177/20503121241255000IF2.3 Q2 Cui, Kaichang., Yang, Fei., Qian, Ruihan., Li, Chenmei., and Fan, Mengting.. "Influencing factors of the treatment level of elderly care workers and their career development prospects." BMC geriatrics IF: 3.4 Q2. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6687577","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":489654239,"identity":"ef796b1b-d12a-4af7-9d3a-60735f68ef62","order_by":0,"name":"Yifang Wang","email":"","orcid":"","institution":"Zigong Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yifang","middleName":"","lastName":"Wang","suffix":""},{"id":489654240,"identity":"8c4c5f0f-b4e1-4e4d-8df6-45e84e7cb8f8","order_by":1,"name":"Zhangli Wei","email":"","orcid":"","institution":"The Fourth People's Hospital of Zigong City","correspondingAuthor":false,"prefix":"","firstName":"Zhangli","middleName":"","lastName":"Wei","suffix":""},{"id":489654241,"identity":"82336e15-1310-419e-9c40-5bb3cbc77ee5","order_by":2,"name":"Lan Jiang","email":"","orcid":"","institution":"Huangshan City People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Jiang","suffix":""},{"id":489654242,"identity":"8c33c741-6d05-43aa-9d78-e2d4b8905cc9","order_by":3,"name":"Yiping Dan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIie3PoQvCQBTH8TsOLh2svgUn/gdP7g/wX7mxurC4OIsWMftnTAbLZ1qZfWBxxeriBEGHyba3Jnjf/D48foy5XD+YN7+3bYevwCMTP4uFhkRoP6MStLEE1Ykwt+Q39mwBUIZFFeWsT8txwdd7gwkqXda3hO/qyzgRgqEBhFnZxCj4hkCkHOYoRF4cqEQptcwUmkUOVAIgIwFoNQxbTqQtq0ZUj+5pA28bHa99SiBfGbST7j9kqnC5XK4/6Q3hODxqGNMcEQAAAABJRU5ErkJggg==","orcid":"","institution":"Zigong Affiliated Hospital of Southwest Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yiping","middleName":"","lastName":"Dan","suffix":""}],"badges":[],"createdAt":"2025-05-17 14:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6687577/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6687577/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87554509,"identity":"b8f2f840-12a8-4c09-919a-00d65f03906b","added_by":"auto","created_at":"2025-07-25 06:43:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55818,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of patient selection\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6687577/v1/ee0b8b6ad70ed8b1b707bbce.png"},{"id":87553172,"identity":"ada87059-61bf-4d5a-8eb1-7a8b8504661a","added_by":"auto","created_at":"2025-07-25 06:35:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":118645,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot.\u003c/p\u003e\n\u003cp\u003eComparison of the relationship between frailty as a continuous variable and falls in different subgroups. Stratified analyses assessing the effect of frail index on falls. Results are presented as adjusted OR (95% CI) of frail index, which were adjusted for age, gender, marital status, Place of residence, ADL, IADL, History fall, Handgrip strength, Kidney disease, Heart disease, \u003cstrong\u003eDigestive disorders\u003c/strong\u003e, Depression, Asthma.\u003c/p\u003e\n\u003cp\u003eCI, confidence interval. OR, odd ratio.ADL, activities of daily living. IADL, instrumental activities of daily living.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6687577/v1/f850abf9c7fbdaaad2e18b88.png"},{"id":87553173,"identity":"ca3fd0e9-e8f1-444f-933c-646fe952c5b8","added_by":"auto","created_at":"2025-07-25 06:35:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":35344,"visible":true,"origin":"","legend":"\u003cp\u003eLogistic-plot Analysis of the relationship between frailty as a continuous variable and the risk of falls . The pink shaded area indicates the 95% confidence interval around the prediction. The solid red line represents the smooth curve fit between variables.The analysis was adjusted for factors including age, gender, marital status,Place of residence , basic activities of daily living (ADL), instrumental activities of daily living (IADL), History fall,handgrip strength, Kidney disease,Heart disease, \u003cstrong\u003eDigestive disorders\u003c/strong\u003e, Depression,Asthma.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6687577/v1/2b7f4a683ad79bd6df28ae6f.png"},{"id":94474828,"identity":"a08209d7-bdaf-44d0-8403-c74327d59210","added_by":"auto","created_at":"2025-10-27 15:50:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1159186,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6687577/v1/54c237c6-fcdb-4a00-a70d-23e8b4f180f4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAssociation between frailty and falls in middle-aged and older patients with chronic lung disease: the CHARLS study\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eAs the world's population ages at an accelerated rate, geriatric health has emerged as a significant public health challenge \u003csup\u003e[1]\u003c/sup\u003e. Falls are the most common unintentional injury among the elderly, defined by the WHO as an unintentional change in position resulting in contact with a lower level.\" \u003csup\u003e[2]\u003c/sup\u003e. Data from China's Disease Surveillance indicate that the mortality rate from falls among individuals over 65 years of age is projected to rise from 45.7 per 100,000 in 2010 to 67.8 per 100,000 by 2021, signifying a substantial increase\u003csup\u003e[3]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eClear mechanisms link chronic underlying conditions to an increased risk of falls. Research indicates that respiratory diseases, such as chronic obstructive pulmonary disease (COPD), can elevate fall risk through various pathological pathways. These include diminished exercise tolerance due to ventilatory dysfunction, skeletal muscle weakness from prolonged hypoxia, and osteoporosis associated with glucocorticoid use\u003csup\u003e[4,5]\u003c/sup\u003e. In particular, the interplay between frailty and falls in geriatric syndromes is emerging as a research focus. Frailty, characterized as a clinical syndrome that reflects a decline in the body's multi-system functional reserve, has been found to exhibit a dose-response relationship with fall risk\u003csup\u003e[6]\u003c/sup\u003e. The international Mobilise-D research consortium has demonstrated, through the use of digital health technology, that frailty significantly impacts exercise tolerance and balance function in patients with chronic lung disease \u003csup\u003e[7]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCurrent research has primarily concentrated on older adults residing in communities, and the connections between frailty and falls within particular chronic disease groups (particularly those with respiratory illnesses) present several limitations: (1) the samples are predominantly sourced from clinical data of a single center, resulting in a lack of national representativeness; (2) confounding factors are not sufficiently controlled, and sociodemographic, comorbidity, and functional assessment indicators are not systematically adjusted; and (3) frailty assessment is largely based on a phenotypic model, with less frequent use of the frailty index method, which captures the cumulative extent of health deficits. The frailty index method, which quantifies the cumulative level of health deficits, is underutilized.\u003c/p\u003e\u003cp\u003eUtilizing publicly available data from the China Health and Aged Care Tracking Survey (CHARLS), this study employed a cross-sectional research design. It focused on frailty in patients with chronic lung disease as the study population to examine the relationship between frailty and the incidence of falls among these patients. The aim was to establish a foundation for health management and intervention strategies tailored to this demographic\u003csup\u003e[8]\u003c/sup\u003e.Data from the CHARLS study were analyzed to clarify the extent of the association between frailty and falls in patients with chronic lung disease and to explore possible modifiers. Health-related behaviors are also risk or protective factors for falls, such as smoking, alcohol consumption, and exercise \u003csup\u003e[9]\u003c/sup\u003e. A frailty index based on the accumulation of deficits has been proposed to quantify the age-related burden of clinically detectable health deficits \u003csup\u003e[10]\u003c/sup\u003e.Previous studies have shown that this frailty index can estimate mortality, disability, and healthcare costs in older populations (encompassing a broad age range), and can predict adverse outcomes following acute illness and stressful treatments\u003csup\u003e[11]\u003c/sup\u003e. The risk of falls due to frailty is higher in developing countries than in developed countries \u003csup\u003e[12]\u003c/sup\u003e. In China, research on the relationship between frailty and falls is scarce, and most is confined to single-center studies.\u003c/p\u003e\u003cp\u003eIn this paper, indicators from the CHARLS survey were used to construct a frailty index for assessing participants' frailty status, while the occurrence of falls was collected through a questionnaire survey. Univariate regression analysis was initially employed to explore the association between frailty status and falls, and multivariate logistic regression models were subsequently applied to analyze the independent association between frailty and falls, with stepwise adjustments for age, sex, marital status, region of residence, activities of daily living (ADLs), instrumental activities of daily living (IADLs), history of falls, handgrip strength, kidney disease, heart disease, digestive system disease, depression, asthma, and other potential confounders\u003csup\u003e[13,14]\u003c/sup\u003e, to clarify the independent association between frailty and falls. Additionally, stratified analyses were conducted to assess the consistency of the association between frailty and falls across different subgroups.\u003c/p\u003e"},{"header":"Methods Study design and sampling method","content":"\u003cp\u003eUtilizing publicly available data from the China Health and Retirement Longitudinal Study (CHARLS), this study employed a cross-sectional research design to analyze baseline and the first two waves of follow-up data from 2011 to 2015. The aim was to investigate the independent influences on fall risk among patients with chronic lung disease. CHARLS is a nationwide longitudinal survey of Chinese residents aged 45 years or older, utilizing a multistage stratified probability proportional to size (PPS) sampling strategy.Initially, 150 counties were randomly selected from 28 provinces. In the second stage, 450 townships or villages were chosen proportionally to represent urban and rural areas. Finally, 10,257 households were randomly selected based on household size, resulting in the inclusion of 17,708 respondents. Data were collected through face-to-face computer-assisted interviews (CAPI)\u003csup\u003e[15,16]\u003c/sup\u003e. The baseline survey, conducted in June 2011, had a response rate of 80.5%, with follow-ups conducted every two years. The study utilized data from standardized questionnaires on socio-demographic characteristics, lifestyle, health status, and physical measurements from the China Health and Retirement Longitudinal Study (CHARLS), which were approved by the Institutional Review Board (IRB) of Peking University \u003csup\u003e[15]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA total of 61,965 participants were recruited between 2011 and 2015, with exclusion criteria that included: (1) missing fall data, (2) age\u0026thinsp;\u0026lt;\u0026thinsp;45 years or missing age information, (3) absence of chronic lung disease or missing disease data. The specific exclusion process was as follows: initially, 50,189 individuals without follow-up data on falls were excluded; next, 314 individuals with discrepancies in age or missing age data were excluded; and finally, 10,066 individuals without chronic lung disease or missing disease data were excluded. In the end, 1,396 participants were included in the study, with 341 experiencing a fall event and 1,055 not experiencing any falls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFrailty\u003c/p\u003e\u003cp\u003eConstruction of the Frailty Index Variable: Screening and Definition - Utilizing CHARLS data and previous studies\u003csup\u003e[13,17,18]\u003c/sup\u003e, the index incorporates a total of 34 health deficit variables. These encompass four dimensions: symptoms, somatic functioning, chronic conditions, and activities of daily living (ADL/IADL). Variable screening criteria included: (1) deficits encompassing multisystem health problems, (2) prevalence that increases with age, (3) a baseline prevalence greater than 0.5%, and (4) a missing data rate less than 5%\u003csup\u003e[19]\u003c/sup\u003e. Health deficit assignment rules: binary variables (e.g., hypertension) were assigned a value of 0 (none) or 1 (yes); ordinal variables (e.g., ADLs) were assigned a value between 0 and 1 based on the degree of functional impairment: no difficulty\u0026thinsp;=\u0026thinsp;0, difficulty but manageable\u0026thinsp;=\u0026thinsp;0.33, requiring assistance\u0026thinsp;=\u0026thinsp;0.67, and unmanageable\u0026thinsp;=\u0026thinsp;1. Calculation of the Frailty Index: The formula for calculating the Frailty Index (FI) was FI\u0026thinsp;=\u0026thinsp;Deficiency Score / Total Number of Deficiencies (32). For instance, if a participant has six deficiencies (1 point for each of three deficiencies, 0.33 points for one deficiency, and 0.67 points for each of two deficiencies), the Frailty Index would be (3x1\u0026thinsp;+\u0026thinsp;1x0.33\u0026thinsp;+\u0026thinsp;2x0.67)/32\u0026thinsp;=\u0026thinsp;0.146. A participant with a total deficiency score of 4.67 would have a FI of 4.67/32\u0026thinsp;=\u0026thinsp;0.146.\u003c/p\u003e\u003cp\u003eFrailty states were categorized as: severe (FI\u0026thinsp;\u0026lt;\u0026thinsp;0.10), pre-frail (0.10\u0026thinsp;\u0026le;\u0026thinsp;FI\u0026thinsp;\u0026lt;\u0026thinsp;0.25), and frail (FI\u0026thinsp;\u0026ge;\u0026thinsp;0.25)\u003csup\u003e[13,17,18]\u003c/sup\u003e. Given the small units of the frailty index (each 0.01 increment), the index was expanded by a factor of 100 to ease clinical interpretation\u003csup\u003e[20]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAssessment of Falls: The primary outcome of this study was the incidence of falls among Chinese adults aged 45 years and older. This was evaluated using the standardized item (D058) from the CHARLS 2015 questionnaire, which asked, \"Have you fallen in the past two years?\" Responses were coded as dichotomous variables (yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0) to ensure measurement consistency with other CHARLS studies \u003csup\u003e[21]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDefinition and measurement of covariates\u003c/p\u003e\u003cp\u003eBuilding upon previous studies \u003csup\u003e[13,17,18]\u003c/sup\u003e, this study incorporates four types of covariates: 1. Sociodemographic characteristics, including age (a continuous variable), sex (male/female), marital status (married/not married), and type of residence (urban/rural, classified according to the National Bureau of Statistics urban-rural coding system); 2. Physical functioning, encompassing ADL dependency (\u0026ge;\u0026thinsp;1 difficulty in six basic activities), IADL dependency (\u0026ge;\u0026thinsp;2 difficulties in eight instrumental activities), and handgrip strength (maximum of three measurements on the Jamar dynamometer, in kg).3. Health Status: History of falls (at least one unintentional fall in the past 2 years), renal disease, cardiac disease, digestive disease, and asthma (all conditions required a self-reported diagnosis and a history of treatment in the past year); 4. Mental Health: Depression (scoring\u0026thinsp;\u0026ge;\u0026thinsp;12 points on the CES-D scale). Variable screening adhered to the CHARLS study criteria \u003csup\u003e[22,23]\u003c/sup\u003e: 1) Demonstrated statistical significance in at least three previous studies; 2) Missing data rate\u0026thinsp;\u0026le;\u0026thinsp;27.7% (addressed through multiple imputation); and 3) Variance inflation factor\u0026thinsp;\u0026lt;\u0026thinsp;2.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eDuring the data pre-processing phase, continuous variables underwent the Shapiro-Wilk normality test at a significance level of α\u0026thinsp;=\u0026thinsp;0.05. Data that were normally distributed were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while non-normal data were presented as median (interquartile range, IQR). Categorical variables were expressed as frequencies (percentages) (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Group comparisons were conducted using the chi-squared test or Fisher's exact method for categorical variables, the ANOVA or Welch-corrected t-test for normally distributed continuous variables, and the Kruskal-Wallis test for non-normal variables.\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\u003eData characteristics of the participants.\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=\"left\" 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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal n (%)\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1396)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eFrailty status n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e_value\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRobust\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;234)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePre-frail\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;619)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFrail\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;543)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(years), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\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\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e795 (56.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e170 (72.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e391 (63.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e234 (43.1)\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\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e601 (43.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (27.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e228 (36.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e309 (56.9)\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\u003eEducation, n (%)\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\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epre-primary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e774 (55.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100 (42.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e321 (51.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e353 (65)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e348 (24.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (27.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e171 (27.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e113 (20.8)\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\u003eMiddle school or high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e262 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e118 (19.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e77 (14.2)\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 and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 ( 0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0)\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, n (%)\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\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced/widowed/single\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e251 (18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91 (14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e134 (24.7)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e1145 (82.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e208 (88.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e528 (85.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e409 (75.3)\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\u003ePlace of residence, n (%)\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\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e610 (43.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135 (57.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e280 (45.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e195 (35.9)\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 area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e761 (54.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95 (40.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e329 (53.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e337 (62.1)\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\u003eDrinking, n (%)\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\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e808 (57.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106 (45.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e349 (56.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e353 (65)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e227 (16.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113 (18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70 (12.9)\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, n (%)\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\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eindependence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e980 (70.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e228 (97.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e549 (88.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e203 (37.4)\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\u003edependence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e402 (28.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63 (10.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e339 (62.4)\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, n (%)\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\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e818 (58.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e208 (88.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e452 (73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e158 (29.1)\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\u003edependent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e509 (36.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e116 (18.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e380 (70)\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\u003eBMI(kg/m\u0026sup2;), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHearing impairment, n (%)\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\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e286 (20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94 (40.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e135 (21.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57 (10.5)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e723 (51.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (21.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e318 (51.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e355 (65.4)\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\u003eHand grip strength, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\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\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e1086 (77.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e215 (91.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e484 (78.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e387 (71.3)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e268 (19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e121 (19.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e139 (25.6)\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\u003eDiabetes, n (%)\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\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e1309 (93.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e221 (94.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e588 (95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e500 (92.1)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 ( 3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 (5.7)\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\u003eStroke, n (%)\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\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e1315 (94.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e223 (95.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e591 (95.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e501 (92.3)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e56 ( 4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36 (6.6)\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\u003eKidney disease, n (%)\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\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e1167 (83.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e203 (86.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e539 (87.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e425 (78.3)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e190 (13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100 (18.4)\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\u003eHeart disease, n (%)\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\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e1069 (76.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e212 (90.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e496 (80.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e361 (66.5)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e283 (20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e106 (17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e165 (30.4)\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\u003eArthritis, n (%)\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\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e585 (41.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e193 (82.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e272 (43.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e120 (22.1)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e788 (56.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e338 (54.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e416 (76.6)\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\u003eDepression, n (%)\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\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e728 (52.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e195 (83.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e374 (60.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e159 (29.3)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e594 (42.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e219 (35.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e357 (65.7)\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\u003eHistory fall, n (%)\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\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e781 (55.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131 (56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e378 (61.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e272 (50.1)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e244 (17.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e147 (27.1)\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\u003eFall, n (%)\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\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e1055 (75.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e201 (85.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e494 (79.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e360 (66.3)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e341 (24.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125 (20.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e183 (33.7)\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\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eData are shown as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, median (inter quartile range), or numbers (percentages).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eADL, activities of daily living. IADL, instrumental activities of daily living.BMI:Body Mass Index.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eP\u003c/em\u003e_value\u003csup\u003ea\u003c/sup\u003e was based on χ2 or analysis of variance or Mann-Whitney U-test where appropriate.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCovariates were screened using univariate logistic regression (with a P-value of less than 0.2, or based on previous literature or clinical significance, etc.) and entered into the multivariate model. Four progressive adjustment models were constructed: model I (unadjusted); model II (adjusted for age, sex, marital status, and place of residence); model III (further adjusted for ADL dependency, IADL dependency, and history of falls); and model IV (further adjusted for grip strength, renal disease, cardiac disease, digestive disease, depression, and asthma).\u003c/p\u003e\u003cp\u003eDuring data processing, we excluded participants with incomplete information in the construction of the frailty index and those who were younger than 45 years at baseline. For missing data, assuming they were missing at random, multiple imputations were performed, and five imputed data sets were created to adequately account for the randomness and variability of the data. One of these data sets was randomly selected for analysis. Additionally, subgroup analyses were conducted to examine whether the potential association between frailty and fall risk was influenced by other factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).Specific subgroup factors included age (\u0026lt;\u0026thinsp;65 versus \u0026ge;\u0026thinsp;65 years), sex (male versus female), BMI (\u0026lt;\u0026thinsp;25 kg/m\u0026sup2; versus \u0026ge;\u0026thinsp;25 kg/m\u0026sup2;), marital status (married versus other), and residence (urban versus rural). All statistical analyses were performed using FreeStatistics version V2.0 and presented using R version 4.3.2. All p-values were two-tailed, and the level of statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results and Baseline","content":"\u003cp\u003eCharacteristics: The study comprised 1,396 patients diagnosed with chronic lung disease (56.9% male, mean age 63.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6 years), categorized into three groups based on their debilitation status: severe (16.8%), predebilitated (44.3%), and debilitated (38.9%). Demographic characteristics revealed a significant gradient: the mean age of the debilitated group was 5.4 years greater than that of the severe group (65.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8 vs. 59.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2 years, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the proportion of males decreased sharply from 72.6\u0026ndash;43.1%, while the proportion of females increased from 27.4\u0026ndash;56.9% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for trend). There was a gradual decline in educational attainment, with individuals having no formal education constituting 65% of the weak group, significantly higher than the 42.7% of the strong group (Cramer's V\u0026thinsp;=\u0026thinsp;0.21).\u003c/p\u003e\u003cp\u003eThe decline in functional status was significant: an increase from 0% in the strong group to 62.4% in the weak group (OR\u0026thinsp;=\u0026thinsp;62.4, 95%CI: 38.1-102.2) among those with full ADL dependence, and a 26.8% decrease in mean handgrip strength (35.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8 vs. 25.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6 kg, Cohen's d\u0026thinsp;=\u0026thinsp;0.89). The cumulative effect of comorbidities was significant, with a significantly higher prevalence of heart disease (30.4% vs. 5.1%), arthritis (76.6% vs. 14.5%), and depression (65.7% vs. 7.7%) in the debilitated group (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, although there was no statistically significant difference between the BMI groups (P\u0026thinsp;=\u0026thinsp;0.889), the standard deviation was abnormally higher in the debilitated group (11.2), suggesting that extreme weight fluctuations (e.g., cachexia) may be present \u003csup\u003e[23]\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDue to the small units of the frailty index (each 0.01 increment), the OR values were converted to each 1 unit increment (i.e., raw index \u0026times; 100) to enhance clinical interpretation\u003csup\u003e[20]\u003c/sup\u003e. Univariate analysis revealed that the risk of falling increased by 3% for every 1 unit rise in the frailty index (OR\u0026thinsp;=\u0026thinsp;1.03, 95% CI: 1.02\u0026ndash;1.04, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Stratified by frailty status, the risk of falling was 3.1 times greater in the frail group compared to the strong group (95% CI: 2.06\u0026ndash;4.66), indicating a significant dose-response relationship (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for trend test). Among other risk factors, memory impairment (OR\u0026thinsp;=\u0026thinsp;3.31), ADL dependence (OR\u0026thinsp;=\u0026thinsp;2.11), and a history of previous falls (OR\u0026thinsp;=\u0026thinsp;2.11) were among the top three in terms of effect size (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate regression analysis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR_95CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e_value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrailty index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.03 (1.02\u0026thinsp;~\u0026thinsp;1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrailty status:reference\u0026thinsp;=\u0026thinsp;Robust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-frail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.54 (1.02\u0026thinsp;~\u0026thinsp;2.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.1 (2.06\u0026thinsp;~\u0026thinsp;4.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.02 (1.01\u0026thinsp;~\u0026thinsp;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender:Female vs Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.44 (1.13\u0026thinsp;~\u0026thinsp;1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level: reference\u0026thinsp;=\u0026thinsp;No formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eprimary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.85 (0.63\u0026thinsp;~\u0026thinsp;1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiddle school or high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.74 (0.53\u0026thinsp;~\u0026thinsp;1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecollege and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.25 (0.03\u0026thinsp;~\u0026thinsp;1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status:Married vs Divorced/widowed/single\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.59 (0.44\u0026thinsp;~\u0026thinsp;0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlace of residence: Rural area vs Urban area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.19 (0.92\u0026thinsp;~\u0026thinsp;1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (0.98\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.686\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADL:Dependence vs Independence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.11 (1.63\u0026thinsp;~\u0026thinsp;2.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIADL: Dependence vs Independence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.83 (1.42\u0026thinsp;~\u0026thinsp;2.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHandgrip strength\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.98 (0.97\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking:Yes vs No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.11 (0.79\u0026thinsp;~\u0026thinsp;1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.553\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidney disease:Yes vs No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.71 (1.23\u0026thinsp;~\u0026thinsp;2.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart disease:Yes vs No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.39 (1.04\u0026thinsp;~\u0026thinsp;1.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArthritis:Yes vs No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.73 (1.33\u0026thinsp;~\u0026thinsp;2.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigestive disorders:Yes vs No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.35 (1.05\u0026thinsp;~\u0026thinsp;1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMemory disorders:Yes vs No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.31 (1.87\u0026thinsp;~\u0026thinsp;5.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression:Yes vs No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.6 (1.24\u0026thinsp;~\u0026thinsp;2.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsthma:Yes vs No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.89 (0.67\u0026thinsp;~\u0026thinsp;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.438\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory fall:Yes vs No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.11 (1.55\u0026thinsp;~\u0026thinsp;2.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eOR, odds ratio. CI, confidence interval.ADL, activities of daily living. IADL, instrumental activities of daily living.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBMI:Body Mass Index.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e_value\u003csup\u003ea \u0026nbsp;\u003c/sup\u003ewas based on \u0026chi;2 or analysis of variance or Mann-Whitney U-test where appropriate.\u003c/p\u003e\u003cp\u003eMultifactorial logistic regression analysis indicated that the frailty index remained significantly associated even after adjusting for demographic characteristics (Model II), functional status (Model III), and comorbidities (Model IV). In the fully adjusted final model (Model IV), each 0.01 unit increase in the frailty index corresponded to a 2% higher risk (OR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 1.01\u0026ndash;1.03, P\u0026thinsp;=\u0026thinsp;0.007), and the OR for the frailty group was attenuated but still statistically significant at 1.8 (95% CI: 1.05\u0026ndash;3.09, P\u0026thinsp;=\u0026thinsp;0.032) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between frailty and falls in multiple regression model.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eModel I\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel II\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel III\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eModel Ⅳ\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContinuous\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrailty index\u003c/p\u003e\u003cp\u003e(per 1 unit increase)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.03 (1.02\u0026thinsp;~\u0026thinsp;1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.03 (1.02\u0026thinsp;~\u0026thinsp;1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.02 (1.01\u0026thinsp;~\u0026thinsp;1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.02 (1.01\u0026thinsp;~\u0026thinsp;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategorical\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRobust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-frail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.51 (0.98\u0026thinsp;~\u0026thinsp;2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.45 (0.95\u0026thinsp;~\u0026thinsp;2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.37 (0.9\u0026thinsp;~\u0026thinsp;2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.31 (0.84\u0026thinsp;~\u0026thinsp;2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.07 (2.02\u0026thinsp;~\u0026thinsp;4.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.65 (1.73\u0026thinsp;~\u0026thinsp;4.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.03 (1.23\u0026thinsp;~\u0026thinsp;3.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.8 (1.05\u0026thinsp;~\u0026thinsp;3.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for Trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eOR, odds ratio. CI, confidence interva.\u003cem\u003eP\u003c/em\u003e_value\u003csup\u003ea\u003c/sup\u003e was based on χ2 or analysis of variance or Mann-Whitney U-test where appropriate.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003ea Model I: Non-adjusted Model\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eb Model II:Adjusted for variables age, gender, marital status,Place of residence.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003ec Model III: Adjusted for variables in Model 2 plus ADL,IADL,History fall.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003ed Model Ⅳ: Adjusted for variables in Model 3 plus Handgrip strength,Kidney disease,Heart disease,Digestive disorders,Depression,Asthma.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSubgroup Analysis: Subgroup analyses revealed group heterogeneity in the association between frailty index and fall risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe direction of the association remained consistent when stratified by sex (males OR\u0026thinsp;=\u0026thinsp;1.04 vs females OR\u0026thinsp;=\u0026thinsp;1.03) and residence (urban OR\u0026thinsp;=\u0026thinsp;1.04 vs rural OR\u0026thinsp;=\u0026thinsp;1.02). Age trends indicated a 4% increased risk in the group under 65 years (OR\u0026thinsp;=\u0026thinsp;1.05, 95% CI 1.00-1.10) compared with the group aged 65 years or older (OR\u0026thinsp;=\u0026thinsp;1.01, 95% CI 0.98\u0026ndash;1.05), with no significant interaction (P\u0026thinsp;=\u0026thinsp;0.059). The BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u0026sup2; group had a significantly increased risk of 4% (OR\u0026thinsp;=\u0026thinsp;1.04, 95% CI 1.01\u0026ndash;1.07), whereas there was no significant association in the BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2; group (OR\u0026thinsp;=\u0026thinsp;1.00, 95% CI 0.93\u0026ndash;1.07; P for interaction\u0026thinsp;=\u0026thinsp;0.62).\u003c/p\u003e\u003cp\u003eINTERACTION: Marital status: Each 1-unit increase in the frailty index corresponded to a 5% higher risk in the married group (OR\u0026thinsp;=\u0026thinsp;1.05, 95% CI 1.02\u0026ndash;1.09), whereas there was no significant change in risk in the unmarried group (divorced/widowed/single) (OR\u0026thinsp;=\u0026thinsp;0.98, 95% CI 0.93\u0026ndash;1.03; P-interaction\u0026thinsp;=\u0026thinsp;0.013).\u003c/p\u003e\u003cp\u003eRestricted cubic spline (RCS) logistic regression analysis was employed to examine the dose-response relationship between the frailty index (a continuous variable) and fall risk, with the optimal number of nodes determined by minimizing the Akaike Information Criterion (AIC). The model was adjusted for various factors, including age, sex, marital status, place of residence, basic activities of daily living (ADL), instrumental activities of daily living (IADL), history of falls, handgrip strength, renal disease, heart disease, digestive disorders, depression, and asthma (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).The results indicated that the risk of falling increased by 3% for each unit rise in the frailty index (OR\u0026thinsp;=\u0026thinsp;1.03, 95% CI: 1.01\u0026ndash;1.05). The test for non-linearity was not significant (P\u0026thinsp;=\u0026thinsp;0.171), lending support to the hypothesis of a linear association (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The X-axis represents the standardized frailty index, while the Y-axis depicts the predicted probability of fall risk. The red solid line signifies the 3-node restricted cubic spline (RCS) fitting curve, and the pink area denotes the 95% confidence interval.\u003c/p\u003e\u003cp\u003eThis discussion is based on a nationally representative cross-sectional study that investigated the relationship between frailty status and fall risk. A 34-item frailty index was constructed to assess the frailty status of 1,396 participants aged 45 years and older, which revealed a frailty prevalence of 38.9%. Middle-aged participants (aged 45\u0026ndash;64 years) exhibited a higher prevalence of premature aging and frailty compared to older participants (aged 65 years and older) \u003csup\u003e[13]\u003c/sup\u003e. The findings of this study underscore the importance of early identification and intervention for frailty in middle-aged and older adults with chronic lung disease to mitigate the risk of falls among these patients.\u003c/p\u003e\u003cp\u003eThe present study confirmed that frailty was significantly associated with an increased risk of falls in a Chinese middle-aged and elderly population with chronic lung disease (OR\u0026thinsp;=\u0026thinsp;1.03 per unit), which was consistent with the trend observed in the Wuxi community study (OR\u0026thinsp;=\u0026thinsp;8.52)\u003csup\u003e[24]\u003c/sup\u003e and the FRAIL score meta-analysis (RR\u0026thinsp;=\u0026thinsp;1.82) \u003csup\u003e[25]\u003c/sup\u003e.The differences may stem from several factors: 1) The use of a continuous frailty index in this study, which is more sensitive than categorical variables (e.g., FRAIL); 2) The Wuxi study involved an elderly population (\u0026ge;\u0026thinsp;65 years, 62.3%), whereas the current sample was predominantly middle-aged (45\u0026ndash;64 years, 57.5%); and 3) The Wuxi study was conducted in the general population, while the present study focused on a specific group of individuals with chronic lung disease. This suggests that the impact of frailty on young elderly people with chronic lung disease should not be overlooked. The prevalence of frailty was significantly higher in rural China (62.1%) than in urban areas (35.9%), potentially indicating the unequal distribution of healthcare resources and variations in the intensity of physical labor\u003csup\u003e[26,27]\u003c/sup\u003e.The prevalence of frailty among adults aged 65 years and older was 38.9%, similar to that in Vietnam (19%) and Indonesia (30%)\u003csup\u003e[28,29]\u003c/sup\u003e. This suggests that Southeast Asia shares the challenge of aging, but the lack of family support under China's one-child policy may exacerbate the risk of frailty in rural areas. In the subgroup analysis, the BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u0026sup2; group had a significantly higher risk of 4% (OR\u0026thinsp;=\u0026thinsp;1.04, 1.01\u0026ndash;1.07) compared with the BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2; group (OR\u0026thinsp;=\u0026thinsp;1.00, 0.93\u0026ndash;1.07; P for interaction\u0026thinsp;=\u0026thinsp;0.62), suggesting that metabolic normalization in the obese does not increase the risk of frailty\u003csup\u003e[30]\u003c/sup\u003e. The higher risk of frailty in the married population in this study may be related to the following factors: married women often have spousal care responsibilities, and chronic stress accelerates the process of frailty\u003csup\u003e[31]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMechanisms of frailty and falls: 1. Possible pathways by which frailty mediates fall risk include an inflammatory cascade response with elevated IL-6 accelerating muscle atrophy\u003csup\u003e[32]\u003c/sup\u003e and increasing fall risk; 2. Decline in neuromuscular control: reduced α-motor neuron activity leading to increased gait variability \u003csup\u003e[33]\u003c/sup\u003e affects gait coordination and stability; 3. Comorbidity effect: in patients with three or more chronic diseases, there was a significant correlation (OR: 1.496; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between frailty and poor outcome\u003csup\u003e[34]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eStrengths and Limitations: Utilizing the China Health and Aging Tracking Survey (CHARLS) database, this study employed stratified multistage probability sampling across 645 communities in 28 provinces/municipalities to validate, for the first time, the association between frailty and falls in the middle-aged and elderly population with chronic lung disease aged 45 years and older. This validation fills a gap in the evidence for this specific population within the age group. A cumulative model of health deficits was used to construct a continuous type of frailty index (34 variables), which can capture more subtle changes in risk gradients than traditional categorical models (e.g., Fried phenotype). It was also found to be more clinically informative than the dichotomous variable (debilitated vs non-debilitated) with an OR of 1.8. The inclusion of new samples from three waves of follow-up in 2011, 2013, and 2015 increased the total sample size to 1,396 cases and ensured an urban-rural ratio of 43.7% urban to 54.5% rural.\u003c/p\u003e\u003cp\u003eMeasurement bias: Self-reported falls may underestimate the true incidence, particularly among cognitively impaired individuals, and corrected odds ratios may increase. Residual confounding was not measured, but the results of sensitivity analyses were manageable. Nonetheless, CHARLS is the first nationally representative survey of the middle-aged and elderly population with high-quality data\u003csup\u003e[35]\u003c/sup\u003e .\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, indices of accumulated deficit-related frailty are significantly associated with an increased risk of falls among community-dwelling middle-aged and older adults in China. There is a necessity to integrate frailty assessment into routine clinical practice and community-based physical examinations for chronically ill middle-aged and older adults. Early screening and identification of frailty are essential for the prevention of falls in this demographic.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to the China Health and Retirement Longitudinal Study (CHARLS) team for providing the high-quality data on the Chinese population that made our study possible. We are thankful to all the participants, staff, and other study investigators for their valuable contributions to this study. We are especially indebted to Dr. Jie Liu of the Department of Vascular Surgery, General Hospital of the Chinese People\u0026apos;s Liberation Army, who offered advice on study design, language refinement, proofreading, statistical support, and feedback on the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualisation by Yifang Wang, Zhangli Wei, and Lan Jiang. Study design by Lingling Huang and Yifang Wang. Data acquisition by Lan Jiang. Critical revision of the article by Yifang Wang and Yiping Dan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no grant from any funding agency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt is not currently possible to share the raw data necessary to replicate the results of this study, as they are also being used in ongoing research. However, some or all of the data generated or used in this study are available on request from the corresponding authors.The website where the data for this paper is sourced from is: https://charls.charlsdata.com/pages/data/111/en.html. The login account is: [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted according to the principles outlined in the Declaration of Helsinki.The Ethics Committee of Zigong Mental Health Center (202400726) approved it with a waiver for informed consent. Before conducting it, the authors obtained all necessary administrative permission to access the data. Participants\u0026rsquo;information was anonymized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHuang, Yafang., Guo, Xiangyu., Du, Juan., and Liu, Yanli.. \u0026quot;Associations Between Intellectual and Social Activities With Frailty Among Community-Dwelling Older Adults in China: A Prospective Cohort Study.\u0026quot; Frontiers in medicine 8..IF: 3.1 Q1\u003c/li\u003e\n\u003cli\u003eGBD 2021 Europe Life Expectancy Collaborators. \u0026apos;Changing life expectancy in European countries 1990-2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021.\u0026apos; The Lancet Public Health vol. 10,3 (2025): e172-e188. doi:10.1016/S2468-2667(25)00009-XIF:IF: 25.4 Q1\u003c/li\u003e\n\u003cli\u003eLeilei, Duan et al. \u0026ldquo;The burden of injury in China, 1990-2017: findings from the Global Burden of Disease Study 2017.\u0026rdquo; \u003cem\u003eThe Lancet. 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Public health\u003c/em\u003e vol. 5,12 (2020): e650-e660. doi:10.1016/S2468-2667(20)30113-4IF: 25.4 Q1\u003c/li\u003e\n\u003cli\u003eLin, Pinli et al. \u0026ldquo;Risk of fall in patients with chronic kidney disease: results from the China health and retirement longitudinal study (CHARLS).\u0026rdquo; \u003cem\u003eBMC public health\u003c/em\u003e vol. 24,1 499. 16 Feb. 2024, doi:10.1186/s12889-024-17982-4IF: 3.5 Q1\u003c/li\u003e\n\u003cli\u003eDong, Gengxin., Guo, Yuxin., Tu, Ji., Zhang, Yunqing., and Zhu, Huaze.. \u0026quot;Association between grip strength level and fall experience among older Chinese adults: a cross-sectional study from the CHARLS.\u0026quot; BMC geriatrics IF: 3.4 Q2\u003c/li\u003e\n\u003cli\u003eWang Y, Huang Y, Chen X. The relationship between low handgrip strength with or without asymmetry and fall risk among middle-aged and older males in China: evidence from the China Health and Retirement Longitudinal Study. 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PMID: 37740568.IF: 5.1 Q2\u003c/li\u003e\n\u003cli\u003eCruz-Jentoft, Alfonso J et al. \u0026ldquo;Sarcopenia: revised European consensus on definition and diagnosis.\u0026rdquo; \u003cem\u003eAge and ageing\u003c/em\u003e vol. 48,1 (2019): 16-31. doi:10.1093/ageing/afy169IF: 6.0 Q1\u003c/li\u003e\n\u003cli\u003eTeng, Liping et al. \u0026ldquo;Associations among frailty status, hypertension, and fall risk in community-dwelling older adults.\u0026rdquo; \u003cem\u003eInternational journal of nursing sciences\u003c/em\u003e vol. 11,1 11-17. 13 Dec. 2023, doi:10.1016/j.ijnss.2023.12.010IF: 2.9 Q1.\u003c/li\u003e\n\u003cli\u003eYang, Z-C., Lin, H., Jiang, G-H., Chu, Y-H., and Gao, J-H.. \u0026quot;Frailty Is a Risk Factor for Falls in the Older Adults: A Systematic Review and Meta-Analysis.\u0026quot; The journal of nutrition, health \u0026amp; agingIF: 4.3Q1.\u003c/li\u003e\n\u003cli\u003eZhou Q, Li Y, Gao Q, Yuan H, Sun L, Xi H, Wu W. Prevalence of Frailty Among Chinese Community-Dwelling Older Adults: A Systematic Review and Meta-Analysis. Int J Public Health. 2023 Aug 1;68:1605964. doi: 10.3389/ijph.2023.1605964. PMID: 37588041; PMCID: PMC10425593. IF: 2.6 Q3\u003c/li\u003e\n\u003cli\u003eLv, Jing et al. \u0026ldquo;Research on the frailty status and adverse outcomes of elderly patients with multimorbidity.\u0026rdquo; \u003cem\u003eBMC geriatrics\u003c/em\u003e vol. 22,1 560. 6 Jul. 2022, doi:10.1186/s12877-022-03194-1 IF: 3.4 Q2\u003c/li\u003e\n\u003cli\u003eHuynh, Trung Quoc Hieu., Pham, Thi Lan Anh., Vo, Van Tam., Than, Ha Ngoc The., and Nguyen, Tan Van.. \u0026quot;Frailty and Associated Factors among the Elderly in Vietnam: A Cross-Sectional Study.\u0026quot; Geriatrics (Basel, Switzerland)..IF: 2.3\u003c/li\u003e\n\u003cli\u003eGhosh, Arpita., Ghosh, Arpita., Ghosh, Arpita., Kundu, Monica., and Devasenapathy, Niveditha.. \u0026quot;Frailty among middle-aged and older women and men in India: findings from wave 1 of the longitudinal Ageing study in India.\u0026quot; BMJ open.IF: 2.4Q1\u003c/li\u003e\n\u003cli\u003eVillareal, Dennis T et al. \u0026ldquo;Obesity in older adults: technical review and position statement of the American Society for Nutrition and NAASO, The Obesity Society.\u0026rdquo; \u003cem\u003eThe American journal of clinical nutrition\u003c/em\u003e vol. 82,5 (2005): 923-34. doi:10.1093/ajcn/82.5.923IF: 6.5 Q1\u003c/li\u003e\n\u003cli\u003eShakya S, Silva SG, McConnell ES, McLaughlin SJ, Cary MP Jr. Psychosocial stressors associated with frailty in community-dwelling older adults in the United States. 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PMID: 38391046IF: 4.3 Q1.\u003c/li\u003e\n\u003cli\u003eKochlik, Bastian et al. \u0026ldquo;Frailty is characterized by biomarker patterns reflecting inflammation or muscle catabolism in multi-morbid patients.\u0026rdquo; \u003cem\u003eJournal of cachexia, sarcopenia and muscle\u003c/em\u003e vol. 14,1 (2023): 157-166. doi:10.1002/jcsm.13118IF: 9.4 Q1\u003c/li\u003e\n\u003cli\u003ePatel, Prakruti et al. \u0026ldquo;Increased temporal stride variability contributes to impaired gait coordination after stroke.\u0026rdquo; \u003cem\u003eScientific reports\u003c/em\u003e vol. 12,1 12679. 25 Jul. 2022, doi:10.1038/s41598-022-17017-1IF:3.8 Q2\u003c/li\u003e\n\u003cli\u003eQuintero-Cruz, Mar\u0026iacute;a Victoria et al. \u0026ldquo;Factors associated with frailty among older individuals with chronic diseases: A multicenter study.\u0026rdquo; \u003cem\u003eSAGE open medicine\u003c/em\u003e vol. 12 20503121241255000. 23 May. 2024, doi:10.1177/20503121241255000IF2.3 Q2\u003c/li\u003e\n\u003cli\u003eCui, Kaichang., Yang, Fei., Qian, Ruihan., Li, Chenmei., and Fan, Mengting.. \u0026quot;Influencing factors of the treatment level of elderly care workers and their career development prospects.\u0026quot; BMC geriatrics IF: 3.4 Q2.\u003c/li\u003e\n\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":"Chronic lung disease, frailty, frailty index, falls, unintentional falls, Health Deficits Model, China Health and Retirement Longitudinal Study (CHARLS), middle-aged and elderly","lastPublishedDoi":"10.21203/rs.3.rs-6687577/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6687577/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAs the world's population ages at an accelerated rate, geriatric health has emerged as a significant public health challenge. Falls are the most common unintentional injury among the elderly.Current research mainly focuses on elderly people living in communities, but there are some limitations to the association between falls and specific chronic disease groups, especially those with respiratory diseases.The aim of this study is to investigate the relationship between frailty and falls in middle-aged and elderly Chinese patients with chronic lung disease, based on the cumulative health deficits model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe cross-sectional study was conducted by integrating data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2015, including 1,396 patients aged 45 years or older. A frailty index consisting of 34 health deficits was constructed. The strength of association was analyzed using a multistage logistic regression model (with stepwise adjustment for 13 confounders, including demographics, functional status, and comorbidities).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe prevalence of frailty was 38.9%, and the incidence of falls in the previous two years was 24.4%. After full adjustment, each unit increase in the frailty index was associated with a 2% increase in the risk of falls (OR\u0026thinsp;=\u0026thinsp;1.02, 95% CI 1.01\u0026ndash;1.03); compared with the robust group, the risk in the frail group was 80% higher (95% CI 1.05\u0026ndash;3.09). Subgroup analysis revealed a significant interaction among the married population (P for trend\u0026thinsp;=\u0026thinsp;0.013).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eDrawing from the extensive CHARLS dataset, this study concludes that frailty serves as an independent risk factor for falls in patients with chronic lung disease. It is crucial that attention be given to the assessment and intervention of frailty to diminish the risk of falls and enhance patients' quality of life. The study offers significant epidemiological evidence for further detailed investigation into the causal relationship and potential mechanisms linking frailty to falls. Additionally, it furnishes theoretical backing for the creation of targeted intervention strategies.\u003c/p\u003e","manuscriptTitle":"Association between frailty and falls in middle-aged and older patients with chronic lung disease: the CHARLS study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 06:35:38","doi":"10.21203/rs.3.rs-6687577/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":"efa1b6c1-04ff-4fc8-8dd6-cdfdc7630a22","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52072382,"name":"Health sciences/Health care"},{"id":52072383,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-10-27T14:40:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-25 06:35:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6687577","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6687577","identity":"rs-6687577","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}