Sex-specific transitions of frailty states and modifiable determinants in community-dwelling older adults: a 16-year Chinese nationwide cohort study

Sex-specific transitions of frailty states and modifiable determinants in community-dwelling older adults: a 16-year Chinese nationwide cohort 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 Research Article Sex-specific transitions of frailty states and modifiable determinants in community-dwelling older adults: a 16-year Chinese nationwide cohort study Xinyao Liu, Tianlu Yin, Hongpu Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8353056/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Evidence on sex differences in transitions of frailty states remains inconclusive. This study therefore aimed to analyze sex-specific transitions in frailty states and their modifiable determinants in older Chinese adults. Methods We analyzed data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) collected between 2002 and 2018. Frailty states were characterized using a frailty index. A multi-state Markov model was used to estimate transition intensities, probabilities, and mean sojourn times between states, and to assess the impact of demographic, social, and lifestyle factors on these transitions. Results The study included 8,445 participants (mean age 82.62 ± 10.64 years; 50.18% male). The median follow-up time was 6.02 years (IQR: 2.59–9.12). Men had a lower likelihood of transitioning from robust to pre-frail (transition intensity: 0.229, 95% CI: 0.215–0.243) than women (0.297, 95% CI: 0.279–0.316). However, men were more likely to revert from pre-frail to robust (0.170, 95% CI: 0.155–0.188) compared to women (0.137, 95% CI: 0.125–0.151). In contrast, the transition intensities between pre-frail and frail states were comparable between sexes. Furthermore, the determinants of frailty states transitions differed by sex: lifestyle factors (exercise and dietary patterns) were primary influencers in men, whereas social determinants (education level, social participation, and marital status) played a more prominent role in women. Conclusions Frailty states transition in late life follows distinct, sex-specific pathways shaped by differential exposure to modifiable risks. To promote healthy aging, sex-specific public health strategies are recommended, focusing on behavioral interventions for men and enhanced social support for women. China older adults frailty states transition multi-state Markov model Figures Figure 1 Figure 2 Introduction With the accelerated aging process of the global population, frailty has emerged as a critical public health challenge worldwide. Frailty is a medical syndrome with multiple causes and contributing factors, characterized by a decline in the functioning of multiple physiological systems along with increased sensitivity to stressors [ 1 , 2 ]. Globally, the pooled prevalence rate of frailty in older people is estimated to be 11%, and 10% in China [ 3 , 4 ]. Frailty leads to personal disability, falls, dementia, reduced quality of life, and death, but also long-term socioeconomic burdens [ 2 ]. Frailty is not a static trait but a dynamic clinical syndrome characterized by reversible transitions between robust, pre-frail, and frail states [ 2 , 5 ]. This dynamic nature provides a critical window for early intervention. However, current evidence on the determinants of these transitions remains inconsistent, particularly regarding the role of sex. Some studies indicate that women are more likely to experience improvements or deteriorations in frailty states [ 5 , 6 ], while others suggest that men are more prone to such changes [ 7 , 8 ], and some reports find no significant sex differences [ 9 ]. Notably, the majority of this evidence originates from Western high‑income societies. This evidence gap is significant because sex, as a socially constructed variable, profoundly influences exposure pathways to various diseases and the distribution mechanisms of risk factors [ 10 ]. China, with the world’s largest aging population and distinct traditional gender norms, offers a crucial social context for further exploration of this issue. Within the sociocultural environment of China, older men and women often occupy different social spheres, which may channel risk factors through divergent pathways to health. A recent study on sex differences in mortality risk among the Chinese population supports this pattern, indicating that women’s health is more influenced by social factors, whereas men’s health is more driven by unhealthy lifestyle behaviors [ 11 ]. Therefore, this study aims not only to document sex differences but to elucidate the sex‑divergent pathways linking modifiable factors to frailty states transitions. We hypothesize that among older Chinese adults, frailty states transitions are primarily driven by lifestyle factors in men and by social determinants in women. To test this hypothesis, we employ a multi‑state Markov model, which offers distinct advantages in characterizing the dynamic progression of chronic diseases—enabling estimation of transition intensities and probabilities between states, mean sojourn times in each state, and identification of key drivers of specific transition pathways [ 12 – 15 ]. Using nationally representative longitudinal data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), this study applies a multi‑state Markov model to provide empirical evidence for developing precise, sex‑sensitive intervention strategies to promote healthy aging. Methods Study population The population for this study was derived from the CLHLS conducted by Peking University and Duke University. A comprehensive overview of the CLHLS study has been provided in previous studies [ 16 ]. In brief, since 1998, the CLHLS has randomly selected community residents aged ≥ 65 years in 23 of 31 provinces, covering approximately 85% of China’s population. Its purpose is to collect comprehensive individual-level data on a range of factors to fill the current gaps in scientific research and policy analysis on healthy ageing in China. The CLHLS study was approved by the Research Ethics Committee of Peking University and Duke University (IRB00001052-13074). Written informed consent was obtained from all participants or their legal representatives. The present study utilized data from the CLHLS, collected between 2002 and 2018. The inclusion criteria were as follows: (1) participants aged 65–105 years; (2) FI missing data in each survey at baseline and follow-up of < 20%; and (3) surveyed at least twice between 2002–2018; (4) no frailty at baseline (FI < 0.25). The exclusion criteria were as follows: (1) participants aged 105 years; (2) FI missing data in each survey at baseline and follow-up ≥ 20%; (3) only one survey data; and (4) frailty at baseline (FI ≥ 0.25). Finally, 8,445 participants were included in this study. A flowchart illustrating the participant selection process is shown in Appendix p9. FI The FI was constructed in accordance with standard procedures [ 17 , 18 ]. Referring to previous studies [ 19 , 20 ], 40 health variables were selected for this study, including mental health indicators (5 items), activities of daily living (6 items), instrumental activities of daily living (8 items), physical function limitations (6 items), sensory functions (2 items), cognitive functions (1 item), chronic diseases (10 items), and subjective and objectively evaluated functioning (2 items). Each health variable was assigned a value between 0.00 and 1.00, with 0.00 indicating no health defects and 1.00 indicating the presence of health defects (Appendix p4). The scores of the 40 health variables were summed and divided by the theoretical maximum score of 40 points. The resulting score was used as the FI for each participant, ranging from 0 to 1. As the proportion of missing data for all items was < 10%, and the effectiveness and stability of median interpolation are not inferior to the complex missing data filling methods [ 21 , 22 ], this study employed the median of the corresponding items to impute the missing data, thereby maximizing the sample size. Based on previous studies [ 2 , 23 , 24 ], three frailty states were defined: (1) robust: FI < 0.1; (2) pre-frail: FI between 0.1 and 0.25; and (3) frail: FI ≥ 0.25. Covariate In accordance with the findings of previous study [ 20 ], the potential influence of various factors, including demographic characteristics, social determinants, and lifestyle factors on the transition of frailty was investigated. Demographic characteristics included age. Socioeconomic determinants included the level of education (0 years of education/≥1 years), residence (urban/rural), marital status (married/other, other includes divorced, widowed or never married) and social participation (yes/no). Lifestyles included smoking status (current/never), current drinking (yes/no), dietary pattern (unfavorable/intermediate or favorable), exercise (yes/no), and body mass index (BMI, < 18.5 or ≥ 28 kg/m 2 / ≥18.5 to < 28 kg/m 2 ). Detailed information on covariates is available in Appendix p11. Statistical analysis Multiple imputations were used to fill in missing covariates. Categorical variables were expressed as frequency (%), and continuous variables were expressed as mean and standard deviation (SD) or median and interquartile range. The c 2 test was used for categorical variables, and analysis of variance or the Kruskal–Wallis test was used for continuous variables to compare the differences in risk factors between the sexes. In this study, participants' states were categorized as robust, pre-frail, frail, and deceased, with death being an absorbing state and the other states being transient. The following possible state transition paths were considered: remaining unchanged: robust→robust, pre-frail→pre-frail, frail→frail; deteriorating transitions: robust→pre-frail, pre-frail→frail; recovering transitions: pre-frail→robust, frail→pre-frail; transitions to death: robust→deceased, pre-frail→deceased, frail→deceased. A schematic diagram is provided in Appendix p10. In this study, transitions from robust to frail and from frail to robust were not allowed. The reasons for this are based on previous research findings: (1) pre-frail individuals are more likely to transition to robust than frail individuals; (2) transitions from robust to frail or from frail to robust are rare, with probabilities of approximately 3–4% [ 5 ]. A multi-state Markov model was used to calculate transition intensities between different frailty states, estimate transition probabilities between different frailty states over specific time periods, estimate the mean sojourn time in each frailty states for individuals, and assess the effects of covariates on transitions between different frailty states. The covariates included in the model were age (continuous variable), education level, residence, marital status, social participation, smoking status, current drinking, dietary pattern, exercise, and BMI. Covariates were measured at baseline and treated as time-fixed variables. Detailed information on the multi-state Markov model is provided in Appendix p12. The R4.4.1 msm package was used to construct the multi-state Markov model [ 25 ]. Additional analyses were conducted using SAS version 9.4. A two-tailed P -value of < 0.05 was considered statistically significant. Results Baseline characteristics of study population This study included 8,445 participants, with a mean age of 82.62 ± 10.64 years, of whom 50.18% were man. The median follow-up time was 6.02 years (IQR: 2.59–9.12). Statistically significant differences were observed between man and woman for most risk factors, including age, education level, marital status, social participation, smoking, drinking, dietary patterns, exercise, and BMI (Table 1 ). Table 1 Baseline characteristics of the study population All (n = 8445) Men (n = 4238) Women (n = 4207) P value Age (years), mean ± SD 82.62 ± 10.64 81.72 ± 10.04 83.52 ± 11.13 < 0.0001 Social determinants Education level (years of schooling) < 0.0001 Ever (≥ 1) 3737 (44.25) 2877 (67.89) 860 (20.44) None (0) 4708 (55.75) 1361 (32.11) 3347 (79.56) Residence 0.40 Urban 3716 (44.00) 1884 (44.45) 1832 (43.55) Rural 4729 (56.00) 2354 (55.55) 2375 (56.45) Marital status < 0.0001 Married 3208 (37.99) 2182 (51.49) 1026 (24.39) Other 5237 (62.01) 2056 (48.51) 3181 (75.61) Social participation < 0.0001 Yes 7117 (84.27) 3721 (87.80) 3396 (80.72) No 1328 (15.73) 517 (12.20) 811 (19.28) Lifestyle Smoking status < 0.0001 Current 5414 (64.11) 1756 (41.43) 3658 (86.95) Never 3031 (35.89) 2482 (58.57) 549 (13.05) Current drinking < 0.0001 Yes 2079 (24.62) 1539 (36.31) 540 (12.84) No 6366 (75.38) 2699 (63.69) 3667 (87.16) Dietary pattern < 0.0001 Favorable 2540 (30.08) 1479 (34.90) 1061 (25.22) Intermediate/ unfavorable 5905 (69.92) 2759 (34.90) 3146 (74.78) Exercise < 0.0001 Yes 3475 (41.15) 2038 (48.09) 1437 (34.16) No 4970 (58.85) 2200 (51.91) 2770 (65.84) BMI (kg/m 2 ) < 0.0001 18.5 ~ 28 4705 (55.71) 2538 (59.89) 2167 (51.51) < 18.5 or ≥ 28 3740 (44.29) 1700 (40.11) 2040 (48.49) Values are numbers (percentages) unless stated otherwise * At baseline, a higher proportion of men were robust (59.20% vs. 40.80%), while more women were pre-frail (58.68% vs. 41.32%). By the end of the follow-up period, men still had higher proportion in the robust (60.76% vs. 39.24%) and deceased states (54.18% vs. 45.82%), but lower proportion in the pre-frail (45.62% vs. 54.38%) and frail states (36.36% vs. 63.64%) compared to women (Table 2 ). Throughout the follow-up period, men had higher rates of maintaining robustness, recovering from pre-frailty, and transitioning to death from any state, but lower rates of deterioration (Appendix p6). Table 2 Frailty states transition statistics States Men Women Initial checkup Final checkup Initial checkup Final checkup Robust 2478 (59.20%) 96 (60.76%) 1708 (40.80%) 62 (39.24%) Pre-frail 1760 (41.32%) 99 (45.62%) 2499 (58.68%) 118 (54.38%) Frail 0 (0.00%) 60 (36.36%) 0 (0.00%) 105 (63.64%) Death 0 (0.00%) 188 (54.18%) 0 (0.00%) 159 (45.82%) Total 4238 (50.18%) 443 (49.94%) 4207 (49.82%) 444 (50.06%) Estimated mean sojourn time and transitions intensity Model estimates showed men had a longer mean sojourn time in the robust state than women (2.77 vs. 2.02 years), but shorter times in pre-frail (1.99 vs. 2.13 years) and frail states (2.09 vs. 2.66 years) (Fig. 1 A-B). Analysis of transition intensities indicated that men were less likely to transition from robust to pre-frail (0.229, 95% CI 0.215–0.243) compared to women (0.297, 95% CI 0.279–0.316), but more likely to revert from pre-frail to robust (0.170, 95% CI 0.155–0.188) than women (0.137, 95% CI 0.125–0.151). Furthermore, men were consistently more likely to transition from all states to death. However, the likelihood of transitions between pre-frail and frail states was comparable between sexes (Fig. 1 A-B). Estimated mean sojourn times (95% CIs) for each state and transitions intensities (95% CIs) between different frailty states are presented for men (A) and women (B). Arrow colors indicate transitions between states: black indicates maintenance of the current state, red indicates worsening of the condition or death, and blue indicates recovery to a healthier state. The bold values indicate a higher transition intensity for this specific pathway in the current gender compared to the other gender. Transitions probabilities of frailty states Figure 2 illustrates the dynamic transition probabilities over 16-years. The analysis reveals that the probability of remaining in the same state consistently decreased over time in both sexes (Figs. 2 A–C), whereas the probability of death showed a continuous increasing trend, with men consistently exhibiting higher mortality probabilities than women across all states (Figs. 2 H–J). Transition probabilities for both deterioration and recovery pathways peaked around the first three years of follow-up and then declined. Sex-based differences followed a similar pattern, widening initially and then narrowing. In the first three years, men had lower probabilities of deteriorating (robust→pre-frail: 15.2%→21.8% vs. 20.0%→29.1%; pre-frail→frail: 12.0%→13.9% vs. 13.0%→17.1%) and a higher probability of recovering from pre-frail to robust (11.3%→16.2% vs. 8.3%→10.9%), but a lower probability of recovering from frail to pre-frail (8.2%→9.5% vs. 8.2%→11.0%) (Fig. 2 D-G). Original data are in Appendix p7. The figure displays the transition probabilities of frailty states for older men and women at follow-up years 1, 2, 3, 4, 5, 6, 9, 12, and 16. The blue and yellow lines represent men and women, respectively. Covariate effects on frailty states transitions Increasing age elevated the risk of deterioration and reduced the likelihood of recovery for both sexes (Table 3 ). For modifiable factors, distinct sex-specific patterns emerged: In men, lack of exercise (HR = 1.393, 95% CI: 1.199–1.619) and an intermediate or unfavorable dietary pattern (HR = 1.299, 95% CI: 1.124–1.501) significantly increased the risk of progression from robust to pre-frail. Notably, current drinking (HR = 1.257, 95% CI: 1.003–1.575) and lack of exercise (HR = 1.595, 95% CI: 1.265–2.010) were associated with an increased likelihood of reverting from pre-frail to robust (Table 3 ). In women, lack of formal education (HR = 1.275, 95% CI: 1.065–1.526) and social participation (HR = 1.425, 95% CI: 1.110–1.828) were significantly associated with an increased risk of transitioning from robust to pre-frail. Conversely, a change in marital status (e.g., widowed) was associated with a higher likelihood of recovery from frail to pre-frail (HR = 1.607, 95% CI: 1.018–2.536) (Table 3 ). In the transition from pre-frail state to death, men were primarily influenced by social participation and BMI, whereas women were more associated with marital status and social participation. In the progression from frail state to death, education level was the main influencing factor for men, while place of residence played a more significant role for women (Table 3 ). Furthermore, the analysis of covariates related to the transition from robust state to death is provided in Appendix P8. Table 3 Effects of covariates on transitions among frailty states Covariate Level Hazard Ratio (95% CI) Deteriorate transitions Recovery transitions Death transitions Robust → Pre-frail Pre-frail → Frail Pre-frail → Robust Frail → Pre-frail Pre-frail → Deceased Frail → Deceased Age Men — 1.047 (1.038, 1.056) 1.041 (1.027, 1.055) 0.966 (0.951, 0.981) 0.981 (0.934, 1.030) 1.067 (1.057, 1.078) 1.015 (1.003, 1.026) Women — 1.059 (1.048, 1.070) 1.050 (1.041, 1.059) 0.991 (0.975, 1.007) 0.963 (0.938, 0.988) 1.064 (1.055, 1.072) 1.031 (1.022, 1.041) Social determinants Education level Men None (0) 1.294 (0.961, 1.327) 0.866 (0.713, 1.050) 0.879 (0.699, 1.105) 1.150 (0.650, 2.034) 0.943 (0.795, 1.118) 1.226 (1.013, 1.484) Women None (0) 1.275 (1.065, 1.526) 1.111 (0.907, 1.360) 1.000 (0.771, 1.297) 1.051 (0.598, 1.846) 1.0423(0.827, 1.313) 1.005 (0.798, 1.265) Residence Men Rural 1.064 (0.917, 1.235) 0.812 (0.616, 1.070) 0.984 (0.728, 1.329) 0.863 (0.485, 1.534) 0.980(0.823, 1.166) 1.083 (0.890, 1.319) Women Rural 1.075 (0.917, 1.261) 0.990 (0.846, 1.159) 0.976 (0.777, 1.227) 1.446 (0.931, 2.246) 1.053 (0.894, 1.240) 1.224 (1.037, 1.444) Marital status Men Other 1.077 (0.920, 1.261) 1.109 (0.915, 1.344) 1.201 (0.939, 1.537) 0.961 (0.552, 1.673) 1.349 (1.132, 1.607) 1.044 (0.861, 1.267) Women Other 0.867 (0.737, 1.021) 1.104 (0.918, 1.327) 0.863 (0.685, 1.088) 1.607 (1.018, 2.536) 1.398 (1.099, 1.777) 1.088 (0.869, 1.363) Social participation Men No 1.347 (0.954, 1.902) 1.029 (0.715, 1.482) 1.351 (0.863, 2.116) 1.565 (0.632, 3.876) 1.473 (1.188, 1.828) 0.916 (0.662, 1.267) Women No 1.425 (1.110, 1.828) 1.101 (0.884, 1.373) 1.227 (0.875, 1.720) 1.582 (0.968, 2.586) 1.328 (1.102, 1.599) 0.830 (0.674, 1.022) Lifestyle Smoking status Men Current 1.021(0.884, 1.179) 0.957 (0.791, 1.159) 0.899 (0.718, 1.126) 0.985 (0.566, 1.714) 1.050 (0.887, 1.243) 1.046 (0.866, 1.264) Women Current 0.875 (0.705, 1.086) 0.891 (0.716, 1.108) 0.756 (0.546, 1.047) 0.614 (0.295, 1.277) 1.330 (1.076, 1.644) 1.076 (0.850, 1.363) Table 3 Effects of covariates on transitions among frailty states (continued) Covariate Level Hazard Ratio (95% CI) Deteriorate transitions Recovery transitions Death transitions Robust → Pre-frail Pre-frail → Frail Pre-frail → Robust Frail → Pre-frail Pre-frail → Deceased Frail → Deceased Lifestyle Current drinking Men Yes 1.125 (0.973, 1.300) 1.170 (0.961, 1.423) 1.257 (1.003, 1.575) 1.412 (0.834, 2.391) 0.863 (0.723, 1.031) 1.145 (0.948, 1.382) Women Yes 0.977 (0.766, 1.244) 0.809 (0.637, 1.027) 1.198 (0.849, 1.689) 1.161 (0.618, 2.181) 0.955 (0.770, 1.184) 0.944 (0.745, 1.196) Exercise Men No 1.393 (1.199, 1.619) 0.861 (0.707, 1.049) 1.595 (1.265, 2.010) 1.229 (0.686, 2.201) 1.263 (1.056, 1.511) 0.926 (0.762, 1.125) Women No 1.126 (0.958, 1.325) 1.016 (0.864, 1.194) 0.944 (0.745, 1.197) 1.302 (0.829, 2.046) 1.050 (0.872, 1.264) 0.997 (0.836, 1.188) Dietary pattern Men Intermediate/ Unfavorable 1.299 (1.124, 1.501) 1.091 (0.897, 1.328) 1.096 (0.875, 1.374) 1.421 (0.800, 2.524) 0.999 (0.828, 1.204) 1.146 (0.936, 1.402) Women Intermediate/ Unfavorable 1.044 (0.884, 1.233) 0.980 (0.822, 1.170) 0.961 (0.748, 1.236) 0.959 (0.599, 1.532) 1.007 (0.830, 1.221) 1.019 (0.842, 1.234) BMI Men < 18.5 or ≥ 28 kg/m 2 0.911 (0.788, 1.053) 0.914 (0.759, 1.101) 0.822 (0.653, 1.036) 1.130 (0.693, 1.843) 1.435 (1.219, 1.689) 0.956 (0.792, 1.153) Women < 18.5 or ≥ 28 kg/m 2 1.027 (0.883, 1.195) 1.061 (0.912, 1.234) 1.088 (0.876, 1.351) 1.214 (0.834, 1.766) 1.304 (1.108, 1.534) 0.963 (0.820, 1.130) Reference of covariates: education level, ref = ever (≥ 1); residence, ref = city and town; marital status, ref = married; smoking status, ref = never; current drinking, ref = no; dietary pattern, ref = favorable; exercise, ref = yes; social participation, ref = yes; BMI, ref = 18.5 ~ 28 kg/m 2 . Boldface indicates statistical significance ( P < 0.05). Discussion Our 16-year nationwide cohort study reveals two principal findings. First, there are significant sex differences in the dynamic patterns of frailty state transitions: women have a higher risk of progressing from robust to pre-frail, whereas men show a greater propensity for recovery from pre-frail to robust, yet face a significantly higher mortality risk once in a pre-frail or frail state. Second, and more importantly, the pathways influencing these transitions are sexually divergent. Transitions in men are predominantly driven by modifiable lifestyle factors (physical activity and diet), while transitions in women are more strongly associated with social determinants (education, social participation, and marital status). This supports our primary hypothesis and underscores that frailty progression is channeled through distinct, gender-shaped pathways. Sex-specific transition patterns in frailty states The systematic review by Kojima et al. [ 5 ] and an Italian study [ 6 ] both reported that women were more likely to experience transitions in frailty states, whether toward improvement or deterioration. In contrast, a German study presented contradictory findings, indicating that men were more prone to transitions toward deterioration [ 7 ], A multinational European study yielded results partially consistent with ours, observing that men were less likely to enter a frailty trajectory at a healthy baseline but faced a higher mortality risk once frailty had developed [ 8 ]. However, a key difference lies in the finding of that European study, which reported that men had a higher risk of progressing from pre-frail to frail state [ 8 ], whereas our study demonstrated that women exhibited higher probabilities of bidirectional transitions between pre-frail and frail states. It is noteworthy that the intrinsic transition intensities between these states were similar between sexes in our study, suggesting that the observed probability differences may be attributed to a competing risk effect. Specifically, the higher mortality among men in pre-frail or frail states may have shortened their observation time, thereby reducing opportunities for further state transitions. These variations in transition patterns of frailty states across populations may stem from complex physiological mechanisms and context-specific sociocultural factors. In the Chinese context, women generally have a longer life expectancy than men [ 26 ], which may extend their exposure window to frailty-related risks. Meanwhile, men tend to engage more frequently in health-risk behaviors [ 11 ], which may contribute to their elevated mortality risk once a frailty trajectory has been initiated. Sex differences in risk factor of frailty state transitions Social determinants [ 22 , 27 – 31 ]and lifestyle factors [ 28 , 32 – 34 ] have been established as significant risk factors for frailty. Building on this foundation, our study further investigates the differential impacts of these factors on transitions of frailty states between men and women. The analysis reveals that transitions of frailty states in men are primarily driven by exercise and dietary patterns, mainly affecting their progression from robust to pre-frail. In contrast, women's frailty states transitions were more significantly associated with social determinants. Specifically, lower education levels and lack of social participation increased women's risk of progressing from robust to pre-frail states, while changes in marital status such as widowhood or divorce promoted recovery from frail to pre-frail states. Recent research has also found that socioeconomic status is exclusively associated with increased frailty risk in women [ 35 ]. The observed association between marital loss and frailty recovery in women aligns with previous findings that older women who lose partners may experience lower frailty probability [ 36 , 37 ]., possibly due to reduced caregiving burdens or shifts in social roles. These sex-based differences likely mirror traditional gender norms in Chinese society, where women have historically been oriented toward domestic spheres and men toward public life, thereby shaping distinct exposure pathways to health risks and resources. Implications for practice and public health These findings underscore the necessity of incorporating a gender perspective into public health strategies and clinical interventions by implementing targeted approaches: prioritizing the management of risk behaviors in men, while strengthening socioeconomic support and enhancing quality of life for women. Furthermore, this study reveals that the mean sojourn time in each frailty state among older adults is only about two years, and the transition probabilities between frailty states—as well as sex-based differences—are most pronounced during the initial follow-up period, particularly within the first three years. Therefore, it is crucial to implement routine frailty screening in the overall older population and maintain heightened vigilance during this early stage. The primary strength of this study lies in its use of 16-year nationwide longitudinal data and the application of a multi-state model, which uniquely captures the complete dynamic process of frailty transitions. However, this study also has several limitations. First, reliance on self-reported data may introduce bias, and treating covariates as time-fixed ignores potential changes over the long follow-up period. Second, certain findings, such as the association between alcohol consumption or lack of exercise and recovery from frailty among men, should be interpreted with caution. These results may reflect reverse causality (e.g., recent recovery leading to reduced physical activity), self-report bias, or complex behavioral patterns—although previous studies have reported an association between the highest level of alcohol consumption and the lowest risk of frailty [ 38 ]. Finally, most covariates exhibited primarily unidirectional effects (influencing either the progression or recovery of frailty states), which may be because deterioration is inherently more common than recovery in older populations [ 5 ]. The relatively low incidence of recovery events may also have resulted in limited statistical power due to insufficient sample size, making it difficult to detect statistically significant associations. Conclusion This study demonstrates that frailty transitions in late life are not governed by a universal set of risk factors but are channeled through pathways shaped by lifelong gender socialization. By identifying lifestyle factors as key levers for men and social determinants as critical points of entry for women, our research provides a scientific blueprint for developing precise and effective interventions to mitigate the burden of frailty in China’s aging population. Declarations Clinical trial number: not applicable. Contributors: XL contributed to the acquisition and analysis of data, and writing of the original draft. TY contributed to the manuscript reviewing and editing efforts. HH gave final approval of the version to be published, and agreed to be accountable for all aspects of the work. All authors read and approved the final manuscript. Funding: This work was supported by Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (CIFMS) (Grant No. 2022-I2M-1-019). Data sharing statement: CLHLS data are available via the website: https://opendata.pku.edu.cn/ (accessed on 1 December 2021). Declaration of interests: We have no conflict of interest to declare. Acknowledgements: We would like to thank all the individuals participating and investigators of the CLHLS, as well as the CLHLS team for their selfless sharing of survey data. 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Luo YX, Zhou XH, Heng T, Yang LL, Zhu YH, Hu P, Yao XQ: Bidirectional transitions of sarcopenia states in older adults: The longitudinal evidence from CHARLS . Journal of cachexia, sarcopenia and muscle 2024, 15 (5):1915-1929. Sanz-Blasco R, Ruiz-Sánchez de León JM, Ávila-Villanueva M, Valentí-Soler M, Gómez-Ramírez J, Fernández-Blázquez MA: Transition from mild cognitive impairment to normal cognition: Determining the predictors of reversion with multi-state Markov models . Alzheimer's & dementia : the journal of the Alzheimer's Association 2022, 18 (6):1177-1185. Han Y, Hu Y, Yu C, Guo Y, Pei P, Yang L, Chen Y, Du H, Sun D, Pang Y et al : Lifestyle, cardiometabolic disease, and multimorbidity in a prospective Chinese study . European heart journal 2021, 42 (34):3374-3384. Zeng Y: Towards Deeper Research and Better Policy for Healthy Aging --Using the Unique Data of Chinese Longitudinal Healthy Longevity Survey . China economic journal 2012, 5 (2-3):131-149. 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Journal of clinical epidemiology 2022, 145 :70-80. Cao X, Ma C, Zheng Z, He L, Hao M, Chen X, Crimmins EM, Gill TM, Levine ME, Liu Z: Contribution of life course circumstances to the acceleration of phenotypic and functional aging: A retrospective study . EClinicalMedicine 2022, 51 :101548. Song X, Mitnitski A, Rockwood K: Prevalence and 10-year outcomes of frailty in older adults in relation to deficit accumulation . Journal of the American Geriatrics Society 2010, 58 (4):681-687. Fan J, Yu C, Guo Y, Bian Z, Sun Z, Yang L, Chen Y, Du H, Li Z, Lei Y et al : Frailty index and all-cause and cause-specific mortality in Chinese adults: a prospective cohort study . The Lancet Public health 2020, 5 (12):e650-e660. Jackson C: Multi-state models for panel data: the msm package for R . Journal of statistical software 2011, 38 :1-28. Bai R, Liu Y, Zhang L, Dong W, Bai Z, Zhou M: Projections of future life expectancy in China up to 2035: a modelling study . The Lancet Public health 2023, 8 (12):e915-e922. Dugravot A, Fayosse A, Dumurgier J, Bouillon K, Rayana TB, Schnitzler A, Kivimaki M, Sabia S, Singh-Manoux A: Social inequalities in multimorbidity, frailty, disability, and transitions to mortality: a 24-year follow-up of the Whitehall II cohort study . The Lancet Public health 2020, 5 (1):e42-e50. Brunner EJ, Shipley MJ, Ahmadi-Abhari S, Valencia Hernandez C, Abell JG, Singh-Manoux A, Kawachi I, Kivimaki M: Midlife contributors to socioeconomic differences in frailty during later life: a prospective cohort study . The Lancet Public health 2018, 3 (7):e313-e322. Makizako H, Shimada H, Doi T, Tsutsumimoto K, Hotta R, Nakakubo S, Makino K, Lee S: Social Frailty Leads to the Development of Physical Frailty among Physically Non-Frail Adults: A Four-Year Follow-Up Longitudinal Cohort Study . International journal of environmental research and public health 2018, 15 (3). Davies K, Maharani A, Chandola T, Todd C, Pendleton N: The longitudinal relationship between loneliness, social isolation, and frailty in older adults in England: a prospective analysis . The Lancet Healthy longevity 2021, 2 (2):e70-e77. Jarach CM, Tettamanti M, Nobili A, D'Avanzo B: Social isolation and loneliness as related to progression and reversion of frailty in the Survey of Health Aging Retirement in Europe (SHARE) . Age and ageing 2021, 50 (1):258-262. Gil-Salcedo A, Dugravot A, Fayosse A, Dumurgier J, Bouillon K, Schnitzler A, Kivimäki M, Singh-Manoux A, Sabia S: Healthy behaviors at age 50 years and frailty at older ages in a 20-year follow-up of the UK Whitehall II cohort: A longitudinal study . PLoS medicine 2020, 17 (7):e1003147. Fan J, Yu C, Pang Y, Guo Y, Pei P, Sun Z, Yang L, Chen Y, Du H, Sun D et al : Adherence to Healthy Lifestyle and Attenuation of Biological Aging in Middle-Aged and Older Chinese Adults . The journals of gerontology Series A, Biological sciences and medical sciences 2021, 76 (12):2232-2241. Moreno-Tamayo K, Manrique-Espinoza B, Morales-Carmona E, Salinas-Rodríguez A: Sleep duration and incident frailty: The Rural Frailty Study . BMC geriatrics 2021, 21 (1):368. Dong P, Zhang XQ, Yin WQ, Li ZY, Li XN, Gao M, Shi YL, Guo HW, Chen ZM: The relationship among socioeconomic status, social support and frailty: is there a gender difference? Aging clinical and experimental research 2025, 37 (1):111. Trevisan C, Grande G, Vetrano DL, Maggi S, Sergi G, Welmer AK, Rizzuto D: Gender Differences in the Relationship Between Marital Status and the Development of Frailty: A Swedish Longitudinal Population-Based Study . Journal of women's health (2002) 2020, 29 (7):927-936. Trevisan C, Veronese N, Maggi S, Baggio G, De Rui M, Bolzetta F, Zambon S, Sartori L, Perissinotto E, Crepaldi G et al : Marital Status and Frailty in Older People: Gender Differences in the Progetto Veneto Anziani Longitudinal Study . Journal of women's health (2002) 2016, 25 (6):630-637. Kojima G, Liljas A, Iliffe S, Jivraj S, Walters K: A systematic review and meta-analysis of prospective associations between alcohol consumption and incident frailty . Age and ageing 2018, 47 (1):26-34. Additional Declarations No competing interests reported. Supplementary Files Appendix.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 08 Jan, 2026 Editor invited by journal 16 Dec, 2025 Editor assigned by journal 14 Dec, 2025 Submission checks completed at journal 14 Dec, 2025 First submitted to journal 13 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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09:22:55","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":143291,"visible":true,"origin":"","legend":"","description":"","filename":"179a5be63f7f4019af8afb4607bfc7881structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8353056/v1/a50bbd4c399ae83d05de889b.xml"},{"id":100367291,"identity":"629cf2fa-0bd2-46b6-a135-6d795a523662","added_by":"auto","created_at":"2026-01-16 07:56:56","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":154926,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8353056/v1/97180f9b58675c98ea96d4e9.html"},{"id":100125515,"identity":"6f12093d-285b-44e0-97e2-c8cbd8373927","added_by":"auto","created_at":"2026-01-13 09:22:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47057,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTransitions intensity between different frailty states by gender\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEstimated mean sojourn times (95% CIs) for each state and transitions intensities (95% CIs) between different frailty states are presented for men (A) and women (B). Arrow colors indicate transitions between states: black indicates maintenance of the current state, red indicates worsening of the condition or death, and blue indicates recovery to a healthier state. The bold values indicate a higher transition intensity for this specific pathway in the current gender compared to the other gender.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8353056/v1/3bafbbb9d8ef7d4ca25f6761.png"},{"id":100125518,"identity":"a06b47c0-0f80-4bce-b0a8-aca0a00eeccf","added_by":"auto","created_at":"2026-01-13 09:22:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63255,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePossible transition states of frailty and corresponding transition probabilities at 1-6, 9, 12, and 16 years, stratified by sex\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe figure displays the transition probabilities of frailty states for older men and women at follow-up years 1, 2, 3, 4, 5, 6, 9, 12, and 16. The blue and yellow lines represent men and women, respectively.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8353056/v1/80a82a1619c5dd933d5e35f2.png"},{"id":100382317,"identity":"c2f85a20-8a92-40b7-b160-a65075122c20","added_by":"auto","created_at":"2026-01-16 10:42:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3252541,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8353056/v1/c86e1703-6c73-4533-a14a-cbfc37ebdc59.pdf"},{"id":100366117,"identity":"68f52c86-4082-45be-b74d-00b02152754d","added_by":"auto","created_at":"2026-01-16 07:55:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1342445,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8353056/v1/1dcdbc61ddea089885ffb0af.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sex-specific transitions of frailty states and modifiable determinants in community-dwelling older adults: a 16-year Chinese nationwide cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the accelerated aging process of the global population, frailty has emerged as a critical public health challenge worldwide. Frailty is a medical syndrome with multiple causes and contributing factors, characterized by a decline in the functioning of multiple physiological systems along with increased sensitivity to stressors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Globally, the pooled prevalence rate of frailty in older people is estimated to be 11%, and 10% in China [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Frailty leads to personal disability, falls, dementia, reduced quality of life, and death, but also long-term socioeconomic burdens [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrailty is not a static trait but a dynamic clinical syndrome characterized by reversible transitions between robust, pre-frail, and frail states [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This dynamic nature provides a critical window for early intervention. However, current evidence on the determinants of these transitions remains inconsistent, particularly regarding the role of sex. Some studies indicate that women are more likely to experience improvements or deteriorations in frailty states [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], while others suggest that men are more prone to such changes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and some reports find no significant sex differences [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Notably, the majority of this evidence originates from Western high‑income societies.\u003c/p\u003e \u003cp\u003eThis evidence gap is significant because sex, as a socially constructed variable, profoundly influences exposure pathways to various diseases and the distribution mechanisms of risk factors [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. China, with the world\u0026rsquo;s largest aging population and distinct traditional gender norms, offers a crucial social context for further exploration of this issue. Within the sociocultural environment of China, older men and women often occupy different social spheres, which may channel risk factors through divergent pathways to health. A recent study on sex differences in mortality risk among the Chinese population supports this pattern, indicating that women\u0026rsquo;s health is more influenced by social factors, whereas men\u0026rsquo;s health is more driven by unhealthy lifestyle behaviors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, this study aims not only to document sex differences but to elucidate the sex‑divergent pathways linking modifiable factors to frailty states transitions. We hypothesize that among older Chinese adults, frailty states transitions are primarily driven by lifestyle factors in men and by social determinants in women. To test this hypothesis, we employ a multi‑state Markov model, which offers distinct advantages in characterizing the dynamic progression of chronic diseases\u0026mdash;enabling estimation of transition intensities and probabilities between states, mean sojourn times in each state, and identification of key drivers of specific transition pathways [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Using nationally representative longitudinal data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), this study applies a multi‑state Markov model to provide empirical evidence for developing precise, sex‑sensitive intervention strategies to promote healthy aging.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe population for this study was derived from the CLHLS conducted by Peking University and Duke University. A comprehensive overview of the CLHLS study has been provided in previous studies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In brief, since 1998, the CLHLS has randomly selected community residents aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years in 23 of 31 provinces, covering approximately 85% of China\u0026rsquo;s population. Its purpose is to collect comprehensive individual-level data on a range of factors to fill the current gaps in scientific research and policy analysis on healthy ageing in China. The CLHLS study was approved by the Research Ethics Committee of Peking University and Duke University (IRB00001052-13074). Written informed consent was obtained from all participants or their legal representatives.\u003c/p\u003e \u003cp\u003eThe present study utilized data from the CLHLS, collected between 2002 and 2018. The inclusion criteria were as follows: (1) participants aged 65\u0026ndash;105 years; (2) FI missing data in each survey at baseline and follow-up of \u0026lt;\u0026thinsp;20%; and (3) surveyed at least twice between 2002\u0026ndash;2018; (4) no frailty at baseline (FI\u0026thinsp;\u0026lt;\u0026thinsp;0.25). The exclusion criteria were as follows: (1) participants aged\u0026thinsp;\u0026lt;\u0026thinsp;65 or \u0026gt;\u0026thinsp;105 years; (2) FI missing data in each survey at baseline and follow-up \u0026ge;\u0026thinsp;20%; (3) only one survey data; and (4) frailty at baseline (FI\u0026thinsp;\u0026ge;\u0026thinsp;0.25). Finally, 8,445 participants were included in this study. A flowchart illustrating the participant selection process is shown in Appendix p9.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFI\u003c/h3\u003e\n\u003cp\u003eThe FI was constructed in accordance with standard procedures [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Referring to previous studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], 40 health variables were selected for this study, including mental health indicators (5 items), activities of daily living (6 items), instrumental activities of daily living (8 items), physical function limitations (6 items), sensory functions (2 items), cognitive functions (1 item), chronic diseases (10 items), and subjective and objectively evaluated functioning (2 items). Each health variable was assigned a value between 0.00 and 1.00, with 0.00 indicating no health defects and 1.00 indicating the presence of health defects (Appendix p4). The scores of the 40 health variables were summed and divided by the theoretical maximum score of 40 points. The resulting score was used as the FI for each participant, ranging from 0 to 1. As the proportion of missing data for all items was \u0026lt;\u0026thinsp;10%, and the effectiveness and stability of median interpolation are not inferior to the complex missing data filling methods [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], this study employed the median of the corresponding items to impute the missing data, thereby maximizing the sample size. Based on previous studies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], three frailty states were defined: (1) robust: FI\u0026thinsp;\u0026lt;\u0026thinsp;0.1; (2) pre-frail: FI between 0.1 and 0.25; and (3) frail: FI\u0026thinsp;\u0026ge;\u0026thinsp;0.25.\u003c/p\u003e\n\u003ch3\u003eCovariate\u003c/h3\u003e\n\u003cp\u003eIn accordance with the findings of previous study [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], the potential influence of various factors, including demographic characteristics, social determinants, and lifestyle factors on the transition of frailty was investigated. Demographic characteristics included age. Socioeconomic determinants included the level of education (0 years of education/\u0026ge;1 years), residence (urban/rural), marital status (married/other, other includes divorced, widowed or never married) and social participation (yes/no). Lifestyles included smoking status (current/never), current drinking (yes/no), dietary pattern (unfavorable/intermediate or favorable), exercise (yes/no), and body mass index (BMI, \u0026lt;\u0026thinsp;18.5 or \u0026ge;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e/ \u0026ge;18.5 to \u0026lt;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e). Detailed information on covariates is available in Appendix p11.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eMultiple imputations were used to fill in missing covariates. Categorical variables were expressed as frequency (%), and continuous variables were expressed as mean and standard deviation (SD) or median and interquartile range. The c\u003csup\u003e2\u003c/sup\u003e test was used for categorical variables, and analysis of variance or the Kruskal\u0026ndash;Wallis test was used for continuous variables to compare the differences in risk factors between the sexes.\u003c/p\u003e \u003cp\u003eIn this study, participants' states were categorized as robust, pre-frail, frail, and deceased, with death being an absorbing state and the other states being transient. The following possible state transition paths were considered: remaining unchanged: robust\u0026rarr;robust, pre-frail\u0026rarr;pre-frail, frail\u0026rarr;frail; deteriorating transitions: robust\u0026rarr;pre-frail, pre-frail\u0026rarr;frail; recovering transitions: pre-frail\u0026rarr;robust, frail\u0026rarr;pre-frail; transitions to death: robust\u0026rarr;deceased, pre-frail\u0026rarr;deceased, frail\u0026rarr;deceased. A schematic diagram is provided in Appendix p10. In this study, transitions from robust to frail and from frail to robust were not allowed. The reasons for this are based on previous research findings: (1) pre-frail individuals are more likely to transition to robust than frail individuals; (2) transitions from robust to frail or from frail to robust are rare, with probabilities of approximately 3\u0026ndash;4% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA multi-state Markov model was used to calculate transition intensities between different frailty states, estimate transition probabilities between different frailty states over specific time periods, estimate the mean sojourn time in each frailty states for individuals, and assess the effects of covariates on transitions between different frailty states. The covariates included in the model were age (continuous variable), education level, residence, marital status, social participation, smoking status, current drinking, dietary pattern, exercise, and BMI. Covariates were measured at baseline and treated as time-fixed variables. Detailed information on the multi-state Markov model is provided in Appendix p12.\u003c/p\u003e \u003cp\u003eThe R4.4.1 msm package was used to construct the multi-state Markov model [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additional analyses were conducted using SAS version 9.4. A two-tailed \u003cem\u003eP\u003c/em\u003e-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of study population\u003c/h2\u003e \u003cp\u003eThis study included 8,445 participants, with a mean age of 82.62\u0026thinsp;\u0026plusmn;\u0026thinsp;10.64 years, of whom 50.18% were man. The median follow-up time was 6.02 years (IQR: 2.59\u0026ndash;9.12). Statistically significant differences were observed between man and woman for most risk factors, including age, education level, marital status, social participation, smoking, drinking, dietary patterns, exercise, and BMI (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll (n\u0026thinsp;=\u0026thinsp;8445)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen (n\u0026thinsp;=\u0026thinsp;4238)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWomen (n\u0026thinsp;=\u0026thinsp;4207)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.62\u0026thinsp;\u0026plusmn;\u0026thinsp;10.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.72\u0026thinsp;\u0026plusmn;\u0026thinsp;10.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.52\u0026thinsp;\u0026plusmn;\u0026thinsp;11.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial determinants\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level (years of schooling)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver (\u0026ge;\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3737 (44.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2877 (67.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e860 (20.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4708 (55.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1361 (32.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3347 (79.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3716 (44.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1884 (44.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1832 (43.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4729 (56.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2354 (55.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2375 (56.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \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\u003e3208 (37.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2182 (51.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1026 (24.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5237 (62.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2056 (48.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3181 (75.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial participation\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7117 (84.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3721 (87.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3396 (80.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1328 (15.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e517 (12.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e811 (19.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLifestyle\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5414 (64.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1756 (41.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3658 (86.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3031 (35.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2482 (58.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e549 (13.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinking\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2079 (24.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1539 (36.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e540 (12.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6366 (75.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2699 (63.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3667 (87.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary pattern\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2540 (30.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1479 (34.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1061 (25.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate/ unfavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5905 (69.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2759 (34.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3146 (74.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3475 (41.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2038 (48.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1437 (34.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4970 (58.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2200 (51.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2770 (65.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026thinsp;~\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4705 (55.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2538 (59.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2167 (51.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5 or \u0026ge;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3740 (44.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1700 (40.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2040 (48.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eValues are numbers (percentages) unless stated otherwise\u003csup\u003e*\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt baseline, a higher proportion of men were robust (59.20% vs. 40.80%), while more women were pre-frail (58.68% vs. 41.32%). By the end of the follow-up period, men still had higher proportion in the robust (60.76% vs. 39.24%) and deceased states (54.18% vs. 45.82%), but lower proportion in the pre-frail (45.62% vs. 54.38%) and frail states (36.36% vs. 63.64%) compared to women (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Throughout the follow-up period, men had higher rates of maintaining robustness, recovering from pre-frailty, and transitioning to death from any state, but lower rates of deterioration (Appendix p6).\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\u003eFrailty states transition statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInitial checkup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinal checkup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInitial checkup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFinal checkup\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRobust\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2478 (59.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96 (60.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1708 (40.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62 (39.24%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-frail\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1760 (41.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99 (45.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2499 (58.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e118 (54.38%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrail\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60 (36.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e105 (63.64%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeath\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e188 (54.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e159 (45.82%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4238 (50.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e443 (49.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4207 (49.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e444 (50.06%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEstimated mean sojourn time and transitions intensity\u003c/h3\u003e\n\u003cp\u003eModel estimates showed men had a longer mean sojourn time in the robust state than women (2.77 vs. 2.02 years), but shorter times in pre-frail (1.99 vs. 2.13 years) and frail states (2.09 vs. 2.66 years) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B).\u003c/p\u003e \u003cp\u003eAnalysis of transition intensities indicated that men were less likely to transition from robust to pre-frail (0.229, 95% CI 0.215\u0026ndash;0.243) compared to women (0.297, 95% CI 0.279\u0026ndash;0.316), but more likely to revert from pre-frail to robust (0.170, 95% CI 0.155\u0026ndash;0.188) than women (0.137, 95% CI 0.125\u0026ndash;0.151). Furthermore, men were consistently more likely to transition from all states to death. However, the likelihood of transitions between pre-frail and frail states was comparable between sexes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEstimated mean sojourn times (95% CIs) for each state and transitions intensities (95% CIs) between different frailty states are presented for men (A) and women (B). Arrow colors indicate transitions between states: black indicates maintenance of the current state, red indicates worsening of the condition or death, and blue indicates recovery to a healthier state. The bold values indicate a higher transition intensity for this specific pathway in the current gender compared to the other gender.\u003c/p\u003e\n\u003ch3\u003eTransitions probabilities of frailty states\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the dynamic transition probabilities over 16-years. The analysis reveals that the probability of remaining in the same state consistently decreased over time in both sexes (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;C), whereas the probability of death showed a continuous increasing trend, with men consistently exhibiting higher mortality probabilities than women across all states (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH\u0026ndash;J).\u003c/p\u003e \u003cp\u003eTransition probabilities for both deterioration and recovery pathways peaked around the first three years of follow-up and then declined. Sex-based differences followed a similar pattern, widening initially and then narrowing. In the first three years, men had lower probabilities of deteriorating (robust\u0026rarr;pre-frail: 15.2%\u0026rarr;21.8% vs. 20.0%\u0026rarr;29.1%; pre-frail\u0026rarr;frail: 12.0%\u0026rarr;13.9% vs. 13.0%\u0026rarr;17.1%) and a higher probability of recovering from pre-frail to robust (11.3%\u0026rarr;16.2% vs. 8.3%\u0026rarr;10.9%), but a lower probability of recovering from frail to pre-frail (8.2%\u0026rarr;9.5% vs. 8.2%\u0026rarr;11.0%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-G). Original data are in Appendix p7.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe figure displays the transition probabilities of frailty states for older men and women at follow-up years 1, 2, 3, 4, 5, 6, 9, 12, and 16. The blue and yellow lines represent men and women, respectively.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCovariate effects on frailty states transitions\u003c/h2\u003e \u003cp\u003eIncreasing age elevated the risk of deterioration and reduced the likelihood of recovery for both sexes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For modifiable factors, distinct sex-specific patterns emerged:\u003c/p\u003e \u003cp\u003eIn men, lack of exercise (HR\u0026thinsp;=\u0026thinsp;1.393, 95% CI: 1.199\u0026ndash;1.619) and an intermediate or unfavorable dietary pattern (HR\u0026thinsp;=\u0026thinsp;1.299, 95% CI: 1.124\u0026ndash;1.501) significantly increased the risk of progression from robust to pre-frail. Notably, current drinking (HR\u0026thinsp;=\u0026thinsp;1.257, 95% CI: 1.003\u0026ndash;1.575) and lack of exercise (HR\u0026thinsp;=\u0026thinsp;1.595, 95% CI: 1.265\u0026ndash;2.010) were associated with an increased likelihood of reverting from pre-frail to robust (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn women, lack of formal education (HR\u0026thinsp;=\u0026thinsp;1.275, 95% CI: 1.065\u0026ndash;1.526) and social participation (HR\u0026thinsp;=\u0026thinsp;1.425, 95% CI: 1.110\u0026ndash;1.828) were significantly associated with an increased risk of transitioning from robust to pre-frail. Conversely, a change in marital status (e.g., widowed) was associated with a higher likelihood of recovery from frail to pre-frail (HR\u0026thinsp;=\u0026thinsp;1.607, 95% CI: 1.018\u0026ndash;2.536) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the transition from pre-frail state to death, men were primarily influenced by social participation and BMI, whereas women were more associated with marital status and social participation. In the progression from frail state to death, education level was the main influencing factor for men, while place of residence played a more significant role for women (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, the analysis of covariates related to the transition from robust state to death is provided in Appendix P8.\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\u003eEffects of covariates on transitions among frailty states\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"char\" char=\".\" 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 \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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c10\" namest=\"c3\"\u003e \u003cp\u003eHazard Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eDeteriorate transitions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eRecovery transitions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eDeath transitions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobust \u0026rarr; Pre-frail\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePre-frail \u0026rarr; Frail\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePre-frail \u0026rarr; Robust\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFrail \u0026rarr; Pre-frail\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePre-frail \u0026rarr; Deceased\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFrail \u0026rarr; Deceased\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.047 (1.038, 1.056)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.041 (1.027, 1.055)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.966 (0.951, 0.981)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.981 (0.934, 1.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.067 (1.057, 1.078)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.015 (1.003, 1.026)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.059 (1.048, 1.070)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.050 (1.041, 1.059)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.991 (0.975, 1.007)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.963 (0.938, 0.988)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.064 (1.055, 1.072)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.031 (1.022, 1.041)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial determinants\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.294 (0.961, 1.327)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.866 (0.713, 1.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.879 (0.699, 1.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.150 (0.650, 2.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.943 (0.795, 1.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.226 (1.013, 1.484)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.275 (1.065, 1.526)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.111 (0.907, 1.360)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000 (0.771, 1.297)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.051 (0.598, 1.846)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.0423(0.827, 1.313)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.005 (0.798, 1.265)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\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 \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.064 (0.917, 1.235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.812 (0.616, 1.070)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.984 (0.728, 1.329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.863 (0.485, 1.534)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.980(0.823, 1.166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.083 (0.890, 1.319)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.075 (0.917, 1.261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.990 (0.846, 1.159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.976 (0.777, 1.227)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.446 (0.931, 2.246)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.053 (0.894, 1.240)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.224 (1.037, 1.444)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.077 (0.920, 1.261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.109 (0.915, 1.344)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.201 (0.939, 1.537)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.961 (0.552, 1.673)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.349 (1.132, 1.607)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.044 (0.861, 1.267)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.867 (0.737, 1.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.104 (0.918, 1.327)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.863 (0.685, 1.088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.607 (1.018, 2.536)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.398 (1.099, 1.777)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.088 (0.869, 1.363)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial participation\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 \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.347 (0.954, 1.902)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.029 (0.715, 1.482)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.351 (0.863, 2.116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.565 (0.632, 3.876)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.473 (1.188, 1.828)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.916 (0.662, 1.267)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.425 (1.110, 1.828)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.101 (0.884, 1.373)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.227 (0.875, 1.720)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.582 (0.968, 2.586)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.328 (1.102, 1.599)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.830 (0.674, 1.022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLifestyle\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"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 \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.021(0.884, 1.179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.957 (0.791, 1.159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.899 (0.718, 1.126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.985 (0.566, 1.714)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.050 (0.887, 1.243)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.046 (0.866, 1.264)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.875 (0.705, 1.086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.891 (0.716, 1.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.756 (0.546, 1.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.614 (0.295, 1.277)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.330 (1.076, 1.644)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.076 (0.850, 1.363)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffects of covariates on transitions among frailty states (continued)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e \u003cp\u003eHazard Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eDeteriorate transitions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eRecovery transitions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eDeath transitions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobust \u0026rarr; Pre-frail\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePre-frail \u0026rarr; Frail\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePre-frail \u0026rarr; Robust\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFrail \u0026rarr; Pre-frail\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePre-frail \u0026rarr; Deceased\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eFrail \u0026rarr; Deceased\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLifestyle\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinking\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\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.125 (0.973, 1.300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.170 (0.961, 1.423)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.257 (1.003, 1.575)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.412 (0.834, 2.391)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.863 (0.723, 1.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.145 (0.948, 1.382)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.977 (0.766, 1.244)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.809 (0.637, 1.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.198 (0.849, 1.689)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.161 (0.618, 2.181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.955 (0.770, 1.184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.944 (0.745, 1.196)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise\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\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.393 (1.199, 1.619)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.861 (0.707, 1.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.595 (1.265, 2.010)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.229 (0.686, 2.201)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.263 (1.056, 1.511)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.926 (0.762, 1.125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.126 (0.958, 1.325)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.016 (0.864, 1.194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.944 (0.745, 1.197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.302 (0.829, 2.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1.050 (0.872, 1.264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.997 (0.836, 1.188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary pattern\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\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate/ Unfavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.299 (1.124, 1.501)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.091 (0.897, 1.328)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.096 (0.875, 1.374)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.421 (0.800, 2.524)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.999 (0.828, 1.204)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.146 (0.936, 1.402)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate/ Unfavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.044 (0.884, 1.233)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.980 (0.822, 1.170)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.961 (0.748, 1.236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.959 (0.599, 1.532)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1.007 (0.830, 1.221)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.019 (0.842, 1.234)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\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\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5 or \u0026ge;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.911 (0.788, 1.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.914 (0.759, 1.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.822 (0.653, 1.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.130 (0.693, 1.843)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.435 (1.219, 1.689)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.956 (0.792, 1.153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5 or \u0026ge;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.027 (0.883, 1.195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.061 (0.912, 1.234)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.088 (0.876, 1.351)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.214 (0.834, 1.766)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.304 (1.108, 1.534)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.963 (0.820, 1.130)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eReference of covariates: education level, ref\u0026thinsp;=\u0026thinsp;ever (\u0026ge;\u0026thinsp;1); residence, ref\u0026thinsp;=\u0026thinsp;city and town; marital status, ref\u0026thinsp;=\u0026thinsp;married; smoking status, ref\u0026thinsp;=\u0026thinsp;never; current drinking, ref\u0026thinsp;=\u0026thinsp;no; dietary pattern, ref\u0026thinsp;=\u0026thinsp;favorable; exercise, ref\u0026thinsp;=\u0026thinsp;yes; social participation, ref\u0026thinsp;=\u0026thinsp;yes; BMI, ref\u0026thinsp;=\u0026thinsp;18.5\u0026thinsp;~\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBoldface indicates statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur 16-year nationwide cohort study reveals two principal findings. First, there are significant sex differences in the dynamic patterns of frailty state transitions: women have a higher risk of progressing from robust to pre-frail, whereas men show a greater propensity for recovery from pre-frail to robust, yet face a significantly higher mortality risk once in a pre-frail or frail state. Second, and more importantly, the pathways influencing these transitions are sexually divergent. Transitions in men are predominantly driven by modifiable lifestyle factors (physical activity and diet), while transitions in women are more strongly associated with social determinants (education, social participation, and marital status). This supports our primary hypothesis and underscores that frailty progression is channeled through distinct, gender-shaped pathways.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSex-specific transition patterns in frailty states\u003c/h2\u003e \u003cp\u003eThe systematic review by Kojima et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and an Italian study [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] both reported that women were more likely to experience transitions in frailty states, whether toward improvement or deterioration. In contrast, a German study presented contradictory findings, indicating that men were more prone to transitions toward deterioration [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], A multinational European study yielded results partially consistent with ours, observing that men were less likely to enter a frailty trajectory at a healthy baseline but faced a higher mortality risk once frailty had developed [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, a key difference lies in the finding of that European study, which reported that men had a higher risk of progressing from pre-frail to frail state [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], whereas our study demonstrated that women exhibited higher probabilities of bidirectional transitions between pre-frail and frail states. It is noteworthy that the intrinsic transition intensities between these states were similar between sexes in our study, suggesting that the observed probability differences may be attributed to a competing risk effect. Specifically, the higher mortality among men in pre-frail or frail states may have shortened their observation time, thereby reducing opportunities for further state transitions.\u003c/p\u003e \u003cp\u003eThese variations in transition patterns of frailty states across populations may stem from complex physiological mechanisms and context-specific sociocultural factors. In the Chinese context, women generally have a longer life expectancy than men [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which may extend their exposure window to frailty-related risks. Meanwhile, men tend to engage more frequently in health-risk behaviors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], which may contribute to their elevated mortality risk once a frailty trajectory has been initiated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSex differences in risk factor of frailty state transitions\u003c/h2\u003e \u003cp\u003eSocial determinants [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]and lifestyle factors [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] have been established as significant risk factors for frailty. Building on this foundation, our study further investigates the differential impacts of these factors on transitions of frailty states between men and women. The analysis reveals that transitions of frailty states in men are primarily driven by exercise and dietary patterns, mainly affecting their progression from robust to pre-frail. In contrast, women's frailty states transitions were more significantly associated with social determinants. Specifically, lower education levels and lack of social participation increased women's risk of progressing from robust to pre-frail states, while changes in marital status such as widowhood or divorce promoted recovery from frail to pre-frail states. Recent research has also found that socioeconomic status is exclusively associated with increased frailty risk in women [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The observed association between marital loss and frailty recovery in women aligns with previous findings that older women who lose partners may experience lower frailty probability [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]., possibly due to reduced caregiving burdens or shifts in social roles.\u003c/p\u003e \u003cp\u003eThese sex-based differences likely mirror traditional gender norms in Chinese society, where women have historically been oriented toward domestic spheres and men toward public life, thereby shaping distinct exposure pathways to health risks and resources.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImplications for practice and public health\u003c/h2\u003e \u003cp\u003eThese findings underscore the necessity of incorporating a gender perspective into public health strategies and clinical interventions by implementing targeted approaches: prioritizing the management of risk behaviors in men, while strengthening socioeconomic support and enhancing quality of life for women. Furthermore, this study reveals that the mean sojourn time in each frailty state among older adults is only about two years, and the transition probabilities between frailty states\u0026mdash;as well as sex-based differences\u0026mdash;are most pronounced during the initial follow-up period, particularly within the first three years. Therefore, it is crucial to implement routine frailty screening in the overall older population and maintain heightened vigilance during this early stage.\u003c/p\u003e \u003cp\u003eThe primary strength of this study lies in its use of 16-year nationwide longitudinal data and the application of a multi-state model, which uniquely captures the complete dynamic process of frailty transitions. However, this study also has several limitations. First, reliance on self-reported data may introduce bias, and treating covariates as time-fixed ignores potential changes over the long follow-up period. Second, certain findings, such as the association between alcohol consumption or lack of exercise and recovery from frailty among men, should be interpreted with caution. These results may reflect reverse causality (e.g., recent recovery leading to reduced physical activity), self-report bias, or complex behavioral patterns\u0026mdash;although previous studies have reported an association between the highest level of alcohol consumption and the lowest risk of frailty [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Finally, most covariates exhibited primarily unidirectional effects (influencing either the progression or recovery of frailty states), which may be because deterioration is inherently more common than recovery in older populations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The relatively low incidence of recovery events may also have resulted in limited statistical power due to insufficient sample size, making it difficult to detect statistically significant associations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that frailty transitions in late life are not governed by a universal set of risk factors but are channeled through pathways shaped by lifelong gender socialization. By identifying lifestyle factors as key levers for men and social determinants as critical points of entry for women, our research provides a scientific blueprint for developing precise and effective interventions to mitigate the burden of frailty in China’s aging population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors:\u0026nbsp;\u003c/strong\u003eXL contributed to the acquisition and analysis of data, and writing of the original draft. TY contributed to the manuscript reviewing and editing efforts. HH gave final approval of the version to be published, and agreed to be accountable for all aspects of the work. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (CIFMS) (Grant No. 2022-I2M-1-019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing statement:\u0026nbsp;\u003c/strong\u003eCLHLS data are available via the website: https://opendata.pku.edu.cn/ (accessed on 1 December 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests:\u0026nbsp;\u003c/strong\u003eWe have no conflict of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe would like to thank all the individuals participating and investigators of the CLHLS, as well as the CLHLS team for their selfless sharing of survey data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eFrailty in older adults: evidence for a phenotype\u003c/strong\u003e. \u003cem\u003eThe journals of gerontology Series A, Biological sciences and medical sciences \u003c/em\u003e2001, \u003cstrong\u003e56\u003c/strong\u003e(3):M146-156.\u003c/li\u003e\n\u003cli\u003eHoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP: \u003cstrong\u003eFrailty: implications for clinical practice and public health\u003c/strong\u003e. \u003cem\u003eLancet (London, England) \u003c/em\u003e2019, \u003cstrong\u003e394\u003c/strong\u003e(10206):1365-1375.\u003c/li\u003e\n\u003cli\u003eCollard RM, Boter H, Schoevers RA, Oude Voshaar RC: \u003cstrong\u003ePrevalence of frailty in community-dwelling older persons: a systematic review\u003c/strong\u003e. \u003cem\u003eJournal of the American Geriatrics Society \u003c/em\u003e2012, \u003cstrong\u003e60\u003c/strong\u003e(8):1487-1492.\u003c/li\u003e\n\u003cli\u003eHe B, Ma Y, Wang C, Jiang M, Geng C, Chang X, Ma B, Han L: \u003cstrong\u003ePrevalence and Risk Factors for Frailty among Community-Dwelling Older People in China: A Systematic Review and Meta-Analysis\u003c/strong\u003e. \u003cem\u003eThe journal of nutrition, health \u0026amp; aging \u003c/em\u003e2019, \u003cstrong\u003e23\u003c/strong\u003e(5):442-450.\u003c/li\u003e\n\u003cli\u003eKojima G, Taniguchi Y, Iliffe S, Jivraj S, Walters K: \u003cstrong\u003eTransitions between frailty states among community-dwelling older people: A systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eAgeing research reviews \u003c/em\u003e2019, \u003cstrong\u003e50\u003c/strong\u003e:81-88.\u003c/li\u003e\n\u003cli\u003eTrevisan C, Veronese N, Maggi S, Baggio G, Toffanello ED, Zambon S, Sartori L, Musacchio E, Perissinotto E, Crepaldi G\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eFactors Influencing Transitions Between Frailty States in Elderly Adults: The Progetto Veneto Anziani Longitudinal Study\u003c/strong\u003e. \u003cem\u003eJournal of the American Geriatrics Society \u003c/em\u003e2017, \u003cstrong\u003e65\u003c/strong\u003e(1):179-184.\u003c/li\u003e\n\u003cli\u003eMielke N, Schneider A, Huscher D, Ebert N, Schaeffner E: \u003cstrong\u003eGender differences in frailty transition and its prediction in community-dwelling old adults\u003c/strong\u003e. \u003cem\u003eScientific reports \u003c/em\u003e2022, \u003cstrong\u003e12\u003c/strong\u003e(1):7341.\u003c/li\u003e\n\u003cli\u003eBorrat-Besson C, Ryser V-A, Wernli B: \u003cstrong\u003e15 Transitions between frailty states \u0026ndash; a European comparison\u003c/strong\u003e. 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(2002) \u003c/em\u003e2016, \u003cstrong\u003e25\u003c/strong\u003e(6):630-637.\u003c/li\u003e\n\u003cli\u003eKojima G, Liljas A, Iliffe S, Jivraj S, Walters K: \u003cstrong\u003eA systematic review and meta-analysis of prospective associations between alcohol consumption and incident frailty\u003c/strong\u003e. \u003cem\u003eAge and ageing \u003c/em\u003e2018, \u003cstrong\u003e47\u003c/strong\u003e(1):26-34.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"China, older adults, frailty, states transition, multi-state Markov model","lastPublishedDoi":"10.21203/rs.3.rs-8353056/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8353056/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEvidence on sex differences in transitions of frailty states remains inconclusive. This study therefore aimed to analyze sex-specific transitions in frailty states and their modifiable determinants in older Chinese adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) collected between 2002 and 2018. Frailty states were characterized using a frailty index. A multi-state Markov model was used to estimate transition intensities, probabilities, and mean sojourn times between states, and to assess the impact of demographic, social, and lifestyle factors on these transitions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study included 8,445 participants (mean age 82.62\u0026thinsp;\u0026plusmn;\u0026thinsp;10.64 years; 50.18% male). The median follow-up time was 6.02 years (IQR: 2.59\u0026ndash;9.12). Men had a lower likelihood of transitioning from robust to pre-frail (transition intensity: 0.229, 95% CI: 0.215\u0026ndash;0.243) than women (0.297, 95% CI: 0.279\u0026ndash;0.316). However, men were more likely to revert from pre-frail to robust (0.170, 95% CI: 0.155\u0026ndash;0.188) compared to women (0.137, 95% CI: 0.125\u0026ndash;0.151). In contrast, the transition intensities between pre-frail and frail states were comparable between sexes. Furthermore, the determinants of frailty states transitions differed by sex: lifestyle factors (exercise and dietary patterns) were primary influencers in men, whereas social determinants (education level, social participation, and marital status) played a more prominent role in women.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFrailty states transition in late life follows distinct, sex-specific pathways shaped by differential exposure to modifiable risks. To promote healthy aging, sex-specific public health strategies are recommended, focusing on behavioral interventions for men and enhanced social support for women.\u003c/p\u003e","manuscriptTitle":"Sex-specific transitions of frailty states and modifiable determinants in community-dwelling older adults: a 16-year Chinese nationwide cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 09:22:50","doi":"10.21203/rs.3.rs-8353056/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-01-08T10:48:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-16T06:46:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-15T02:40:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-15T02:38:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-12-13T13:23:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f93717b-9750-4991-81b6-1ac08318f8ce","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-13T09:22:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 09:22:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8353056","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8353056","identity":"rs-8353056","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}