Associations of activities of daily living disability and instrumental activities of daily living disability with all-cause mortality: Evidence from Five Major Longitudinal Studies

Associations of activities of daily living disability and instrumental activities of daily living disability with all-cause mortality: Evidence from Five Major Longitudinal Studies | 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 Associations of activities of daily living disability and instrumental activities of daily living disability with all-cause mortality: Evidence from Five Major Longitudinal Studies Heng Wang, Cong Li, Kaibo Yang, Chingyu Cheng, Jinghua Jiao, Han Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7084168/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background With the global aging of the population, it is increasingly crucial for older people to maintain their independence through activities of daily living (ADL) and instrumental activities of daily living (IADL). There is evidence that ADL/IADL disability is associated with increased mortality, but there is still limited evidence of this in middle-aged and old people globally. Methods We conducted a multi-cohort pooled study using data from five major longitudinal studies in the Global Aging Dataset: the Health and Retirement Study (HRS), China Health and Retirement Longitudinal Study (CHARLS), Survey of Health, Aging and Retirement in Europe (SHARE), English Longitudinal Study of Aging (ELSA), and Mexican Health and Aging Study (MHAS). We used Cox proportional hazard models to examine the associations of ADL and IADL disabilities with mortality. Furthermore, we conducted analysis to explore the interaction between ADL and IADL on mortality, and further mediation analysis to explore the roles of chronic diseases, depression, and socioeconomic status in the association. Findings: The final sample included 10,089 participants from CHARLS, 7,218 from ELSA, 20,702 from HRS, 12,411 from MHAS, and 33,650 from SHARE. The Cox proportional hazards model revealed that ADL/IADL disabilities were significantly associated with increased mortality across all cohorts. The pooled results showed that the hazard ratio (HR) for mortality with one disability in terms of ADL was 1.31 (95% CI: 1.13, 1.52) compared with those without disabilities, while it was 1.84 (1.41, 2.42) for two or more disabilities. Meanwhile, the HR was 1.44 (1.28, 1.62) for one disability in terms of IADL, but 2.11 (1.66, 2.69) for two or more disabilities. Significant mediating effects of chronic diseases and depression were found across all cohorts. The most pronounced additive interaction was observed in CHARLS, with relative excess risk due to interaction (RERI) of 2.29 (95% CI: 0.70, 3.89). Interpretation: This study provides evidence that ADL/IADL disabilities significantly elevate the long-term mortality risk among middle-aged and old people. Chronic diseases and depression substantially mediate this association. Our findings underscore the disproportionate health inequities faced by individuals with disabilities and highlight the urgent need for global health systems to adapt, ensuring that people with disabilities are better understood and included in health policies and services. Figures Figure 1 Introduction With the rapid aging of populations globally, understanding the factors that influence the health and lifespan of the old people has become increasingly crucial. One key factor is disability, which encompasses individuals with long-term physical, mental, intellectual, or sensory impairments( 1 ). These disabilities can interact with various health conditions, impair an individual’s ability to perform daily activities, and limit their full participation in social life( 2 ). The definition of disability highlights the varying degrees and dimensions of barriers and potential health issues that individuals may face, all of which contribute to an elevated risk of premature mortality( 3 ). Existing studies have established a clear association between disability and mortality. In low- and middle-income countries, individuals with disabilities experience a mortality rate double that of non-disabled individuals, and their risk of mortality increases with the number of disabilities reported. Similar trends have also been observed in developed countries( 4 – 6 ). Against this background, there is a crucial need to obtain a deeper understanding of the relationship between disability and mortality, as it can provide vital insights for the development of targeted policies and the optimal allocation of limited healthcare resources. Activities of daily living (ADL) and instrumental activities of daily living (IADL) are central to maintaining independence in later life. ADL refer to basic self-care tasks (sometimes referred to as “basic activities of daily living” or BADLs), such as bathing, dressing, and eating, while IADL involve more complex tasks necessary for independent living, such as managing finances, preparing meals, and taking medication( 7 , 8 ). IADL often require higher cognitive function than ADL. Aging, disabling diseases (such as stroke) and injuries are the primary factors leading to ADL/IADL disability. Epidemiological surveys show that elderly-friendly living environments, accessible medical services and improved education levels can reduce the incidence rate of ADL/IADL disability. Previous studies consistently showed that individuals with ADL disabilities face a significantly increased risk of mortality compared with those without such disabilities( 9 ). And greater disability is often associated with higher mortality( 10 ). For example, in a previous study in Chinese Community, old people with disabilities in at least one ADL item were reported to have a 63% higher risk of mortality over a 7-year period( 11 ). Disability data collected within health information systems are essential for understanding mortality risk; however, they often lack a universal classification framework( 12 – 14 ). ADL and IADL offer quantifiable measures of limitations in daily activities and provide a straightforward reflection of disability at the individual level( 15 ). Given their broad applicability across different economic and cultural contexts, ADL/IADL assessments have been included in many large cohort studies, presenting an opportunity to examine the relationship between disability and mortality across diverse populations. In this study, we leverage data from five major longitudinal studies, each incorporating extensive aging-related data collected globally and utilizing consistent stratified sampling, follow-up procedures, and questionnaire designs. This enables a comprehensive analysis across various cultural, economic, and healthcare settings. Our objective is to address critical gaps in the literature by investigating the nuanced relationships and interaction effects between disability—specifically ADL/IADL disability—and all-cause mortality among middle-aged and older adults, while also exploring potential mediating factors. Methods Study design, participants and data extraction In this study, we used data from five nationally representative longitudinal surveys of community-dwelling older adults. The specific follow-up time of each cohort is shown in the Supplementary Figure S1 . The cohorts include the Health and Retirement Study (HRS) (waves 10 to 15, 2010–2020), the Survey of Health, Ageing and Retirement in Europe (SHARE) (waves 2 to 7, 2007–2017), the English Longitudinal Study of Ageing (ELSA) (waves 2 to 7, 2004–2014), the Mexican Health and Aging Study (MHAS) (waves 1 to 3, 2001–2013), and the China Health and Retirement Longitudinal Study (CHARLS) (waves 1 to 4, 2011–2019). Since CHARLS only assesses ADL/IADL in individuals aged 50 and above, we set the minimum age for inclusion in the study as 50. We also extracted covariates, including the following: demographic variables (age at the start of follow-up, sex, education level, marital status, region), BMI (measured physically or self-reported by respondents during follow-up), smoking and drinking status, physician-diagnosed chronic disease (using the acceptable minimum subset of data available between cohorts; MHAS did not provide information on heart-related diseases at the beginning of follow-up), depression (assessed using different versions of the CES-D scale)( 16 ), socioeconomic status (SES) (owing to variation in the concept of “savings” in different cultural and economic contexts, we used household annual total income as a continuous variable). Only individuals with complete ADL/IADL questionnaire results and complete information on demographic variables at the start of follow-up were included. The data processing methods of covariates can be found in the Supplementary Table S1 - 2 Exposure Limitations/disabilities in ADL (for brevity, we use the term “disabilities” elsewhere) are defined as difficulties in performing corresponding activities in the ADL scale, as is similarly the case for IADL. However, the answer options for the ADL/IADL scales applied in each cohort are not uniform (e.g., CHARLS uses a five-level scale for answering, while HRS uses a three-level one). We used data from the harmonized and cleaned coordinated version, where an item is coded as “1” if the individual has difficulty (of any severity) in performing one of the ADL, and “0” otherwise; we did the same for IADL( 9 , 15 ). The sum of each item in the ADL/IADL scale is the ADL/IADL Disability Score. We also assessed the ADL/IADL Status, divided into no disability in ADL/IADL, disability in one item, or disability in two or more items. The details of the ADL/IADL scales for each cohort are shown in the Supplementary Table S3 - 4 . Outcomes The outcome event in this study was the death of the individual during follow-up. The survival status and survival time of individuals at the end of follow-up were extracted from harmonized longitudinal cohorts, with all cohorts identifying death cases through national or municipal mortality registers (ELSA, HRS) or by interviewing informants or knowledgeable individuals (CHARLS, MHAS, SHARE). For participants who died during follow-up, survival time was calculated by subtracting the baseline year of the cohort from the year in which the participant was confirmed to have died. Statistical Analysis We performed descriptive statistics for all participants and generated Kaplan-Meier (K-M) survival curves, using the log-rank test to evaluate the differences in survival probability between these categories ( Supplementary Figure S2 ). We did not observe any crossing of the survival curves. We determined the covariates constituting the minimum sufficient adjustment set using a directed acyclic graph (DAG) ( Supplementary Figure S3 ). We conducted main analyses and produced result summaries for all cohorts, using the Cox proportional hazards model to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Model 1 was adjusted according to the minimum sufficient adjustment set provided by the DAG, including demographic variables (age, sex, education, marital status, region), BMI, and smoking/drinking status. Model 2 further adjusted for chronic diseases, CES-D scale score, and SES. We also conducted a meta-analysis on the results of model 2 to summarize the results of each cohort, using the inverse variance method for weighting. We also used each item in the ADL/IADL scale as exposure, adjusted using model 2, and calculated its association with the mortality rate. To evaluate the additive interaction of ADL and IADL disabilities, we further calculated the relative excess risk due to interaction (RERI), the attributable proportion due to interaction (AP), and the synergy index (SI). Since the use of multiplicative interaction in the Cox model is equivalent to additive interaction, we did not evaluate the product term. We used the “mediation” in R for causal mediation analysis, estimating the natural direct and indirect effects, calculating the proportion of mediators using the nonparametric bootstrap method, with Monte Carlo sampling set to 2500 times, and the 95% CI calculated by quasi-Bayesian approximation. In the mediation model, we used ADL/IADL disability as exposure, calculating the proportions of depression, chronic diseases, and SES as mediators in the association between disability and mortality rate. To ensure the robustness of our study results, we conducted several sensitivity analyses ( Supplementary Table S5 ). In the main analysis, we used multiple imputation (R package “mice” version 3.16.0) to fill in the missing values ( Supplementary Table S6 ). As multiple imputation is not ideal for imputing binary variables, for robustness of conclusions, we used a value of 0.5 to fill in missing values in the ADL/IADL questionnaire. Data analysis was performed using R software (version 4.4.1) and Stata (version 18.0). A two-sided P value < 0.05 was considered statistically significant. All statistical tests were adjusted using individual-level weights provided by the cohort. Patient and Public Involvement Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research Ethical Statement As this study primarily involves secondary analysis of publicly available datasets, the need for ethical approval of this study was waived ( Supplementary Table S7 ). Role of the funding source The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Human Ethics and Consent to Participate Not applicable, because the data of this study are derived from large cohort study data. All cohort studies have been approved by the relevant ethics committees. For details, please see the Supplementary Table S7 . Results The process of participant selection is shown in Supplementary Figure S4 . After completion of the selection process, this study included 10,089 CHARLS participants, 7,218 ELSA participants, 20,702 HRS participants, 12,411 MHAS participants, and 33,650 SHARE participants. Table 1 shows the features of the participants in these five cohorts. The proportion of outcome events during the follow-up period in the cohorts varied from 4.39% in CHARLS to 28.99% in HRS. Among the included samples, 65.1% were female and 18.4% died during the follow-up period. Meanwhile, among the excluded samples, 51.1% were female and 3.7% died during the follow-up period. The relationship between ADL/IADL disability and mortality rate, as described in detail in Table 2 , involved significant associations with a higher risk of mortality in all cohorts in model 1, which adjusted for the minimum sufficient adjustment set, and the risk of mortality was significantly higher for those with two or more ADL/IADL disabilities than for those with one. In model 2, which further adjusted for SES, depression, and chronic diseases, these associations remained significant. In both model 1 and model 2, the highest HRs were found in CHARLS [ADL disability: model 1 HR: 3.44 (2.77, 4.27), model 2 HR: 2.71 (2.13, 3.44); IADL disability: model 1 HR: 3.45 (2.78, 4.29), model 2 HR: 2.78 (2.20, 3.52)]. The meta-analysis of these findings produced summarized HRs ( Supplementary Figure S5-8 ), emphasizing the robustness of these associations in different populations and environments. Subgroup analysis did not reveal consistent between-group differences across cohorts. Detailed results are shown in Supplementary Tables S8-12. The analysis of the association between each disability items in the ADL/IADL scale and death found similar patterns within each cohort, as described in detail in Figure 1 . Having disabilities in eating, shopping for daily necessities, and preparing hot meals had relatively large negative impacts on the likelihood of survival. The results of the interaction analysis are shown in Table 3 . The interaction between ADL disabilities and IADL disabilities was relatively strong in the CHARLS, HRS, and ELSA cohorts [CHARLS, RERI: 2.29 (0.70, 3.89); HRS, RERI: 1.91 (1.52, 2.29); ELSA, RERI: 1.24 (0.07, 2.40)], and relatively weak in MHAS and SHARE [MHAS, RERI: 0.87 (0.54, 1.19); SHARE, RERI: 0.58 (0.37, 0.79)]. The results of the mediation analysis are shown in Table 4. The proportion of mediation by chronic diseases varied among the cohorts, from 10.3% (6.8%, 16.0%) for IADL in CHARLS to 32.3% (19.4%, 61.0%) for IADL in ELSA. The mediating effect of depression was observed in all cohorts except ELSA, ranging from 1.1% (1.0%, 21.0%) for IADL in CHARLS to 31.7% (22.8%, 46.0%) for ADL in ELSA. We did not observe a mediating effect of SES in any cohort. Overall, the main results of this study were maintained after comprehensive sensitivity analyses ( Supplementary Table S13-17 ). Similar patterns were found in complete case analysis and after multiple imputations. In the analysis using the sample excluding outcome events occurring within 1 year of the start of follow-up, the association between having one ADL/IADL disability and death was no longer significant in CHARLS; the other results were basically consistent with those in the main analysis. Excluding HRS data related to the COVID-19 pandemic did not significantly change the HRS results. The results of model 2 without adjusting for chronic disease covariates did not change significantly. In the analysis using the maximum ADL/IADL items of each cohort as the dataset, the association between having one ADL/IADL disability item and death was no longer significant in CHARLS and MHAS, while the other results were consistent with those in the main analysis. Description of samples included and excluded in the study is shown in the Supplementary Table S18 Discussion Our study used data from five large longitudinal cohorts, which included over 80,000 participants from different ethnic, cultural, and socio-economic backgrounds, to study the association between disability and long-term mortality rate from the perspective of ADL/IADL disabilities. We found that, in middle-aged and old people, individuals with disabilities had a significantly increased risk of mortality, with this risk for those with two or more ADL/IADL disabilities being approximately double that for those without any disabilities. Analysis of individual ADL/IADL items found that disabilities in eating, preparing hot meals, and shopping for daily necessities had larger negative impacts on the likelihood of survival across all cohorts. Across all cohorts, there was an additive interaction between ADL and IADL disabilities, further emphasizing the synergistic impact of multiple disabilities on the risk of mortality. The increased risk of mortality associated with ADL/IADL disabilities was partially mediated by chronic diseases and depression, suggesting that chronic diseases and depression are key factors for improving health outcomes among the old disabled population. Our use of the ADL/IADL scale to study the association between disability and mortality allowed us to systematically compare and summarize results across different cohorts, but it also hindered our ability to compare our findings with other disability-related research. Our main results regarding the effects of ADL/IADL and disability levels are consistent with existing research, namely, that middle-aged and old people with ADL/IADL disabilities have a significantly increased risk of mortality. Smythe et al. found that the mortality rate for people with disabilities in low- and middle-income countries was double that of non-disabled people, which is consistent with our results, emphasizing the higher risk of mortality faced by the disabled population(5). Kuper et al., using a mixed research method, found that the mortality rate for people with disabilities was 2.24 times that of non-disabled people, and life expectancy models estimated that, on average, people with disabilities die 13.8 years earlier than the general population worldwide(4). Meanwhile, Landes reached similar conclusions in a study of the adult population in the United States, where the difference in mortality rates compared with that of non-disabled people was greatest for people with ADL disabilities and combinations of one or more other disabilities(17). Our analysis of the ADL/IADL scale sub-items found that the associations between disability across all cohorts. Disabilities in activities such as eating, preparing hot meals, and shopping for daily necessities have larger negative impacts on the likelihood of survival, which may be because these activities are closely related to nutritional intake, and malnutrition has been proven to be significantly associated with an increased risk of mortality(18). Age-related changes in metabolism are known to make the old people particularly vulnerable to malnutrition. To our knowledge, this is the first finding that the interaction of ADL and IADL disability significantly increases the risk of mortality, especially when both are present. We speculate that the reason for this interaction may be that the individual experiences losses of physiological function, social support, and mental health, among others, making them more susceptible to disease progression and death. Further research may be needed to determine the specific mechanism involved. We also observed the mediating roles of chronic diseases and depression in the association between disability and mortality. In the United States, chronic diseases are the main causes of disability and mortality(19). Similarly, the occurrence of disabilities lowers the quality of life and ability to access medical resources among the old people, further increasing the risk of chronic diseases. Existing community-based systems for managing chronic disease should be more inclusive of people with disabilities, thereby alleviating deaths caused by disability. This would help to reduce the burden of chronic diseases and disabilities at the same time, but more research is needed to investigate the extent to which deaths caused by disabilities can be attributed to chronic diseases. Similar to reported findings in chronic diseases, depression is also a modifiable variable that mediates the association between disability and mortality(16). On the one hand, disabilities increase the mental burden on the old people, while on the other hand, they hinder their degree of social participation. Common ways in which the old people obtain social support from other members of the community may not be available to disabled people. More targeted measures should be taken to alleviate the social isolation and loneliness suffered by people with disabilities, such as peer support groups and professional psychological counseling(20). Our study emphasizes the importance of using internationally comparable, standardized definitions and frameworks for disability. A sustainable future that is inclusive and accepting of disabled groups should be built on accurate recognition and classification of the disabled population(21, 22). The Disability Data Report published by the Disability Data Initiative in 2021 found that internationally comparable questions on disability were only included in surveys undertaken in 84 out of 180 countries reviewed and in 16% of household surveys and censuses(14). In the future, there is a need for more unified recording and classification of disabled groups in healthcare databases in order to discover the specific challenges faced by those with different types of disability in different environments. The COVID-19 pandemic has intensified the scarcity of disability data(23), and the lack of such data has excluded disabled people from discussions on health equity and data-driven policy. Collecting data on disabled populations in a reliable manner and classifying it in a standardized way is crucial to realizing the rights of the estimated 1.3 billion disabled people worldwide. Our study, consistent with the conclusions drawn in other disability-related meta-analyses, found that disabled people have higher mortality rates and suffer from health inequalities, and this worse mortality rate is universally found among those with different economic and cultural backgrounds(4, 5, 17, 24). There is thus a need to change existing healthcare systems to make them more inclusive of disabled people, improving their access to medical care and its affordability for them (25). Training for healthcare workers can be strengthened to provide a professional level of care and an inclusive attitude towards those with disabilities. By incorporating disabled people into mainstream medical plans through accessible medical infrastructure and disability databases, and providing more targeted healthcare for disabled people(26, 27). Our study has several strengths. First, the cross-national longitudinal nature of this study makes our conclusions widely applicable. Moreover, the disability data came from rigorous data collection and similar research designs, making the results of different cohorts comparable. Second, we conducted in-depth analysis of the data, discovering the mediating roles of chronic diseases and depression, the significant increase in mortality risk from the interaction between ADL and IADL, and the difference in the impacts of disabilities in different ADL/IADL items on mortality risk. However, our study also has significant limitations. Most importantly, the entire disabled population cannot be identified through the use of ADL/IADL scales alone(28). The WHO’s ICF conceptual framework divides functional disorders into three aspects: physical function disorders or physical structure changes; activity limitations; and restrictions on participation. As such, when using ADL disabilities alone, it is not possible to accurately describe the characteristics and intensity levels of an individual’s disability. As another limitation, the scarcity and lack of standardization of disability data in aging cohorts hindered us from including more low- and middle-income countries in this study, which may have weakened our ability to identify the specific challenges faced by these disabled groups. A further limitation associated with our data is that death in the cohorts was identified through questionnaire surveys and linked registry data (ELSA and HRS). While the variability in data sources across different cohorts may introduce some inconsistencies in mortality reporting, our findings remain robust, as the overall patterns and associations observed are consistent across datasets. In addition, we used HRs to report the main results, but since HR may change over specific periods during follow-up, the average HR may not be valuable as a reference. Although the Cox proportional hazards model can adjust for potential confounders, our model may still be subject to residual confounding due to unmeasured confounders and measurement error. Although our study included 89% of the respondents, the model may still be affected by selection bias Conclusion Overall, our study results show that, at the multi-cohort level, ADL/IADL disabilities significantly increase the long-term mortality risk of the middle-aged and old people. Chronic diseases and depression largely mediate this association, and we also found that the interaction of ADL and IADL disabilities significantly increases the risk of mortality. These conclusions emphasize that disability significantly shortens life expectancy, and that changes are needed in healthcare systems to better accommodate disabled people, including reducing the barriers (physical and psychological) to accessing healthcare services, providing more targeted chronic disease management and mental health services for disabled people, and collecting disability data within a more standardized and inclusive framework. Establishing healthcare systems that accept and include disabled people requires the joint efforts of all healthcare workers and policy planners, which is crucial to realizing the health rights of the estimated 1.3 billion disabled people worldwide. Declarations Data sharing statement Data for this study were obtained from several major cohort studies, through the CHARLS (https://charls.pku.edu.cn/), ELSA (http://www.elsa-project.ac.uk/), HRS (https://hrs.isr.umich.edu/), MHAS (https://www.mhasweb.org/), SHARE (https://share-eric.eu/). Contributors HW: Conceptualization, Data curation, Formal analysis, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing; CL: Investigation, Methodology, Validation, Writing – review & editing; KBY: Investigation, Methodology, Validation, Writing – review & editing; CYC: Formal analysis, Methodology, Software, Validation; JHJ: Resources, Supervision, Writing – review & editing, Validation; HZ: Conceptualization, Resources, Supervision, Writing – review & editing; LL: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing. All authors contributed to the critical revision of the manuscript and read and approved the final version of the manuscript. All authors had full access to all the data in the study and accepted responsibility for the decision to submit for publication. Declaration of interests The authors declare no conflict of interest regarding this manuscript. Fundings : This study was funded by GDPH Supporting Fund for Talent Program (KY0120220263), LiaoNing Revitalization Talents Program (XLYC2203192), Guangzhou School (hospital) Enterprise Joint Funding Project (2025A03J3901), and 2024 High-end Foreign Experts Recruitment Plan of China (S20240245) References WHO Guidelines Approved by the Guidelines Review Committee. World Report on Disability 2011. Geneva: World Health Organization Copyright © World Health Organization 2011.; 2011. The Lancet Public H. Disability-a neglected issue in public health. Lancet Public Health. 2021;6(6):e346. Heslop P, Blair PS, Fleming P, Hoghton M, Marriott A, Russ L. The Confidential Inquiry into premature deaths of people with intellectual disabilities in the UK: a population-based study. Lancet. 2014;383(9920):889–95. Kuper H, Rotenberg S, Azizatunnisa L, Banks LM, Smythe T. 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Kuper H, Azizatunnisa L, Gatta DR, Rotenberg S, Banks LM, Smythe T, et al. Building disability-inclusive health systems. Lancet Public Health. 2024;9(5):e316–25. Nguyen TV, Kane S. Towards an agenda of action and research for making health systems responsive to the needs of people with disabilities. Lancet Reg Health West Pac. 2024;52:101225. Palmer M, Harley D. Models and measurement in disability: an international review. Health Policy Plan. 2012;27(5):357–64. Tables Table-1. Baseline characteristics of 84,070 participants from five cohort studies. Cohort CHARLS (n = 10,089) ELSA (n = 7,218) HRS (n = 20,702) MHAS (n = 12,411) SHARE (n = 33,650) Variable Survived Deceased Survived Deceased Survived Deceased Survived Deceased Survived Deceased (n = 9,646) (n = 443) (n = 6,802) (n = 416) (n = 14,701) (n = 6,001) (n = 9,208) (n = 3,203) (n = 28,255) (n = 5,395) Follow-up time (Year), Median (Q₁, Q₃) 9 (9, 9) 1 (1, 2) 10 (10, 10) 4 (2, 6) 10 (10, 10) 5 (3, 8) 12 (12, 12) 7 (4, 10) 10 (10, 10) 5 (3, 8) Age (Year), Median (Q₁, Q₃) 59 (55, 65) 72 (63, 78) 64 (57, 71) 75 (68, 82) 61 (55, 70) 77 (69, 84) 58 (53, 65) 69 (61, 76) 62 (56, 70) 76 (68, 81) Sex, n (%) Female 5026 (52.1) 190 (42.9) 3706 (54.5) 172 (41.3) 8636 (58.7) 3245 (54.1) 5167 (56.1) 1560 (48.7) 15811 (56.0) 2546 (47.2) Male 4620 (47.9) 253 (57.1) 3096 (45.5) 244 (58.7) 6065 (41.3) 2756 (45.9) 4041 (43.9) 1643 (51.3) 12444 (44.0) 2849 (52.8) Education, n (%) Less than high school 8655 (89.7) 423 (95.5) 2642 (38.8) 215 (51.7) 2435 (16.6) 1655 (27.6) 8216 (89.2) 3009 (93.9) 13051 (46.2) 3458 (64.1) High school or equivalent 866 (9.0) 18 (4.1) 3175 (46.7) 154 (37.0) 4826 (32.8) 2206 (36.8) 220 (2.4) 45 (1.4) 9406 (33.3) 1304 (24.2) College or above 125 (1.3) 2 (0.5) 985 (14.5) 47 (11.3) 7440 (50.6) 2140 (35.7) 772 (8.4) 149 (4.7) 5798 (20.5) 633 (11.7) Marita Status, n (%) Married 8549 (88.6) 281 (63.4) 4788 (70.4) 273 (65.6) 9608 (65.4) 2975 (49.6) 7307 (79.4) 2163 (67.5) 21530 (76.2) 3353 (62.2) Unmarried 1097 (11.4) 162 (36.6) 2014 (29.6) 143 (34.4) 5093 (34.6) 3026 (50.4) 1901 (20.6) 1040 (32.5) 6725 (23.8) 2042 (37.8) Region, n (%) Rural 6167 (63.9) 275 (62.1) NA NA 3928 (26.7) 1944 (32.4) 2300 (25.0) 889 (27.8) 7780 (27.5) 1548 (28.7) Urban 3479 (36.1) 168 (37.9) NA NA 10773 (73.3) 4057 (67.6) 6908 (75.0) 2314 (72.2) 20475 (72.5) 3847 (71.3) Alcohol Consumption, n (%) Never 5849 (60.6) 249 (56.2) 698 (10.3) 73 (17.5) 5741 (39.1) 3529 (58.8) 6134 (66.6) 2392 (74.7) 4347 (15.4) 1092 (20.2) Ever 3797 (39.4) 194 (43.8) 6104 (89.7) 343 (82.5) 8960 (60.9) 2472 (41.2) 3074 (33.4) 811 (25.3) 23908 (84.6) 4303 (79.8) Smoking, n (%) Never 5803 (60.2) 205 (46.3) 2526 (37.1) 118 (28.4) 6707 (45.6) 2237 (37.3) 5303 (57.6) 1613 (50.4) 14865 (52.6) 2784 (51.6) Ever 3843 (39.8) 238 (53.7) 4276 (62.9) 298 (71.6) 7994 (54.4) 3764 (62.7) 3905 (42.4) 1590 (49.6) 13390 (47.4) 2611 (48.4) SES, n (%) Q1 2350 (24.4) 173 (39.1) 1670 (24.6) 135 (32.5) 3060 (20.8) 2117 (35.3) 2064 (22.4) 1057 (33.0) 6788 (24.0) 1623 (30.1) Q2 2408 (25.0) 114 (25.7) 1673 (24.6) 131 (31.5) 3260 (22.2) 1916 (31.9) 2112 (22.9) 974 (30.4) 6642 (23.5) 1778 (33.0) Q3 2442 (25.3) 80 (18.1) 1707 (25.1) 97 (23.3) 3883 (26.4) 1290 (21.5) 2440 (26.5) 661 (20.6) 7155 (25.3) 1261 (23.4) Q4 2446 (25.4) 76 (17.2) 1752 (25.8) 53 (12.7) 4498 (30.6) 678 (11.3) 2592 (28.1) 511 (16.0) 7670 (27.1) 733 (13.6) CES-D, Median (Q₁, Q₃) 7 (4, 12) 11 (6, 16) 1 (0, 2) 1 (0, 3) 1 (0, 2) 1 (0, 3) 3 (1, 5) 4 (2, 6) 1 (0, 2) 2 (1, 4) BMI (Kg/m2), Mean (Q₁, Q₃) 23.1 (21.1, 25.3) 21.7 (19.8, 23.7) 27.5 (25.3, 30.3) 26.8 (24.8, 29.6) 29.6 (27.6, 32.1) 28.5 (26.5, 31.0) 26.9 (24.8, 29.3) 26.1 (24.0, 28.4) 26.2 (23.8, 29.0) 26.0 (23.4, 29.0) ADL Status, n (%) Disability items= 0 8110 (84.1) 245 (55.3) 1859 (27.3) 41 (9.9) 12912 (87.8) 3954 (65.9) 8408 (91.3) 2579 (80.5) 26065 (92.2) 4063 (75.3) Disability items= 1 783 (8.1) 55 (12.4) 2227 (32.7) 113 (27.2) 939 (6.4) 763 (12.7) 441 (4.8) 254 (7.9) 1265 (4.5) 545 (10.1) Disability items>= 2 753 (7.8) 143 (32.3) 2716 (39.9) 262 (63.0) 850 (5.8) 1284 (21.4) 359 (3.9) 370 (11.6) 925 (3.3) 787 (14.6) IADL Status, n (%) Disability items= 0 7765 (80.5) 223 (50.3) 5603 (82.4) 271 (65.1) 13190 (89.7) 3863 (64.4) 8568 (93.0) 2534 (79.1) 26625 (94.2) 4034 (74.8) Disability items= 1 1042 (10.8) 58 (13.1) 643 (9.5) 71 (17.1) 958 (6.5) 836 (13.9) 457 (5.0) 323 (10.1) 988 (3.5) 567 (10.5) Disability items>= 2 839 (8.7) 162 (36.6) 556 (8.2) 74 (17.8) 553 (3.8) 1302 (21.7) 183 (2.0) 346 (10.8) 642 (2.3) 794 (14.7) ADL/IADL Disability Score, Median (Q₁, Q₃) 0.0 (0.0, 1.0) 1.0 (0.0, 5.0) 0.0 (0.0, 0.0) 0.0 (0.0, 1.0) 0.0 (0.0, 0.0) 0.5 (0.0, 2.0) 0.0 (0.0, 0.0) 0.0 (0.0, 1.0) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) ADL Disability Score, Median (Q₁, Q₃) 0.0 (0.0, 0.0) 0.0 (0.0, 2.5) 0.0 (0.0, 0.0) 0.0 (0.0, 1.0) 0.0 (0.0, 0.0) 0.0 (0.0, 1.0) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) 0.0 (0.0, 1.0) IADL Disability Score, Median (Q₁, Q₃) 0.0 (0.0, 0.0) 0.5 (0.0, 3.0) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) 0.0 (0.0, 1.0) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) Chronic Diseases, Median (Q₁, Q₃) 1.0 (0.0, 1.0) 1.0 (1.0, 2.0) 1.0 (0.0, 2.0) 2.0 (1.0, 3.0) 2.0 (1.0, 3.0) 3.0 (2.0, 4.0) 1.0 (0.0, 1.0) 1.0 (0.0, 2.0) 1.0 (0.0, 2.0) 2.0 (1.0, 3.0) ADL: activities of daily living; IADL: instrumental activities of daily living; CI: confidence interval; HRS: Health and Retirement Study; ELSA: English Longitudinal Study of Ageing; SHARE: Survey of Health, Ageing and Retirement in Europe; CHARLS: China Health and Retirement Longitudinal Study; MHAS: Mexican Health and Aging Study; CES-D: Center for Epidemiologic Studies Depression; SES: Socioeconomic status; BMI: Body mass index; NA: not applicable. Table-2. Association between ADL/IADL status and mortality. Cohort CHARLS (n=10,089) ELSA (n=7,218) HRS (n=20,702) MHAS (n=12,411) SHARE (n=33,650) Pooled (n=84,070) Exposure Model 1 HR (95%CI) Model 2 HR (95%CI) Model 1 HR (95%CI) Model 2 HR (95%CI) Model 1 HR (95%CI) Model 2 HR (95%CI) Model 1 HR (95%CI) Model 2 HR (95%CI) Model 1 HR (95%CI) Model 2 HR (95%CI) Model 1 HR (95%CI) Model 2 HR (95%CI) ADL Disability items = 0 ref ref ref ref ref ref ref ref ref ref ref ref Disability items = 1 1.70(1.26, 2.28) 1.46(1.08, 1.97) 1.58(1.22, 2.06) 1.42(1.08, 1.87) 1.65(1.52, 1.78) 1.51(1.39, 1.63) 1.25(1.09, 1.42) 1.10(0.96, 1.25) 1.42(1.30, 1.56) 1.19(1.09, 1.31) 1.48(1.32, 1.67) 1.31(1.13, 1.52) Disability items≥ 2 3.44(2.77, 4.27) 2.71(2.13, 3.44) 1.84(1.42, 2.39) 1.52(1.14, 2.03) 2.62(2.45, 2.80) 2.36(2.20, 2.54) 1.84(1.65, 2.06) 1.57(1.40, 1.76) 1.99(1.84, 2.16) 1.41(1.29, 1.54) 2.27(1.87, 2.75) 1.84(1.41, 2.42) IADL Disability items = 0 ref ref ref ref ref ref ref ref ref ref ref ref Disability items = 1 1.52(1.13, 2.03) 1.31(0.98, 1.76) 1.87(1.39, 2.51) 1.63(1.20, 2.21) 1.92(1.77, 2.07) 1.70(1.57, 1.83) 1.45(1.29, 1.63) 1.33(1.18, 1.50) 1.63(1.49, 1.79) 1.34(1.22, 1.47) 1.67(1.47, 1.89) 1.44(1.28, 1.62) Disability items≥ 2 3.45(2.78, 4.29) 2.78(2.20, 3.52) 2.70(1.95, 3.75) 2.15(1.52, 3.05) 2.92(2.73, 3.13) 2.50(2.33, 2.69) 2.19(1.94, 2.47) 1.89(1.67, 2.13) 2.11(1.94, 2.29) 1.55(1.41, 1.70) 2.60(2.15, 3.14) 2.11(1.66, 2.69) ADL: activities of daily living; IADL: instrumental activities of daily living; HR: hazard ratio; CI: confidence interval; HRS: Health and Retirement Study; ELSA: English Longitudinal Study of Ageing; SHARE: Survey of Health, Ageing and Retirement in Europe; CHARLS: China Health and Retirement Longitudinal Study; MHAS: Mexican Health and Aging Study Model 1 was adjusted for demographic variables (age, gender, region, education, marital status), BMI, smoking, drinking status. Model 2 was adjusted for demographic variables (age, gender, region, education, marital status), BMI, smoking, drinking status, chronic diseases, CES-D scale score, and SES. Table-3. Synergistic effect between ADL/IADL disability on increased risk of mortality. Cohort CHARLS ELSA HRS MHAS SHARE ADL disability absent * IADL disability absent 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) ADL disability absent * IADL disability present 1.66 (1.33, 2.08) 1.56 (1.19, 2.06) 1.67 (1.56, 1.78) 1.48 (1.33, 1.63) 1.35 (1.24, 1.47) ADL disability present * IADL disability absent 1.68 (1.34, 2.11) 1.24 (0.98, 1.59) 1.44 (1.35, 1.54) 1.14 (1.03, 1.26) 1.13 (1.04, 1.23) ADL disability present * IADL disability present 4.64 (3.03, 7.08) 3.05 (1.81, 5.11) 4.02 (3.55, 4.54) 2.48 (2.06, 2.98) 2.06 (1.77, 2.4) RERI (95%CI) 2.29 (0.70, 3.89) 1.24 (0.07, 2.40) 1.91 (1.52, 2.29) 0.87 (0.54, 1.19) 0.58 (0.37, 0.79) AP (95%CI) 0.49 (0.36, 0.63) 0.41 (0.23, 0.58) 0.47 (0.44, 0.51) 0.35 (0.28, 0.41) 0.28 (0.22, 0.34) SI (95%CI) 2.71 (2.02, 3.63) 2.53 (1.89, 3.39) 2.71 (2.51, 2.93) 2.41 (2.17, 2.67) 2.20 (1.98, 2.45) ADL: activities of daily living; IADL: instrumental activities of daily living; CI: confidence interval; HRS: Health and Retirement Study; ELSA: English Longitudinal Study of Ageing; SHARE: Survey of Health, Ageing and Retirement in Europe; CHARLS: China Health and Retirement Longitudinal Study; MHAS: Mexican Health and Aging Study; RERI: relative excess risk due to interaction; AP: attributable proportion due to interaction, SI: synergy index. Null hypothesis for addictive interaction: AP = 0, RERI = 0, SI = 1. Adjusted for demographic variables (age, gender, region, education, marital status), BMI, smoking, drinking status, chronic diseases, CES-D scale score, and SES. Table-4. Mediation analysis using ADL/IADL disability as an exposure. Cohort CHARLS ELSA HRS MHAS SHARE Exposure ADL disability IADL disability ADL disability IADL disability ADL disability IADL disability ADL disability IADL disability ADL disability IADL disability CES-D Mediation proportion (%) 11.4 (2.0, 22.0) 1.06 (1.0, 21.0) 2.9 (-15.6, 25.0) 0.27 (-17.2, 17.0) 4.8 (2.0, 8.0) 3.2 (0.9, 6.0) 15.2 (8.2, 26.0) 7.6 (4.0, 12.0) 31.7 (22.8, 46.0) 25.0 (17.3, 34.0) P value 0.022 0.038 0.69 0.96 <0.0001 0.014 <0.0001 <0.0001 <0.0001 <0.0001 Chronic Diseases Mediation proportion (%) 13.5 (8.0, 21.0) 10.3 (6.8, 16.0) 32.3 (19.4, 61.0) 26.1 (16.9, 44.0) 21.3 (18.7, 24.0) 19.3 (17.3, 22.0) 21.9 (15.9, 32.0) 13.4 (9.6, 18.0) 20.6 (14.8, 30.0) 14.1 (10.8, 19.0) P value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 SES Mediation proportion (%) 0.0 (-0.01, 0.01) 0.0 (-0.01, 0.01) 1.2 (-1.7, 7.0) 0.50 (-1.1, 4.0) 0.50 (0.0, 2.0) 0.48 (0.0, 1.0) 0.15 (-1.1, 2.0) 0.0 (-1.1, 2.0) 0.31 (-3.7, 4.0) 0.68 (-1.8, 3.0) P value 0.70 0.70 0.31 0.45 0.32 0.31 0.69 0.85 0.85 0.56 ADL: activities of daily living; IADL: instrumental activities of daily living; CI: confidence interval; HRS: Health and Retirement Study; ELSA: English Longitudinal Study of Ageing; SHARE: Survey of Health, Ageing and Retirement in Europe; CHARLS: China Health and Retirement Longitudinal Study; MHAS: Mexican Health and Aging Study; CES-D: Center for Epidemiologic Studies Depression; SES: Socioeconomic status. Adjusted for demographic variables (age, gender, region, education, marital status), BMI, smoking, drinking status. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx STROBEchecklistcohort.docx Graphicabstract.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Feb, 2026 Reviews received at journal 06 Feb, 2026 Reviews received at journal 04 Feb, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviewers agreed at journal 04 Oct, 2025 Reviewers invited by journal 16 Jul, 2025 Editor invited by journal 15 Jul, 2025 Editor assigned by journal 14 Jul, 2025 Submission checks completed at journal 14 Jul, 2025 First submitted to journal 09 Jul, 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7084168","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486437713,"identity":"ba4c28da-6e63-45c4-b92d-2c67fb83b2c1","order_by":0,"name":"Heng Wang","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Heng","middleName":"","lastName":"Wang","suffix":""},{"id":486437714,"identity":"cd11b118-65ab-46c5-9aad-d73707358e69","order_by":1,"name":"Cong Li","email":"","orcid":"","institution":"Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Li","suffix":""},{"id":486437717,"identity":"81028c9b-a2ec-4ca3-ac8a-8a6820803bba","order_by":2,"name":"Kaibo Yang","email":"","orcid":"","institution":"First Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kaibo","middleName":"","lastName":"Yang","suffix":""},{"id":486437720,"identity":"b0c0f4b3-7911-4893-9ee5-f5dd5c3df165","order_by":3,"name":"Chingyu Cheng","email":"","orcid":"","institution":"Singapore Eye Research Institute, Singapore National Eye Centre","correspondingAuthor":false,"prefix":"","firstName":"Chingyu","middleName":"","lastName":"Cheng","suffix":""},{"id":486437724,"identity":"45ec9127-8b91-43c4-9fc8-0f2d709d967b","order_by":4,"name":"Jinghua Jiao","email":"","orcid":"","institution":"Guangzhou Eighth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jinghua","middleName":"","lastName":"Jiao","suffix":""},{"id":486437727,"identity":"aa146dbd-1f3f-4ddc-95e6-b663961211ae","order_by":5,"name":"Han Zhang","email":"","orcid":"","institution":"First Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Zhang","suffix":""},{"id":486437728,"identity":"4f7c9a5e-6ab9-4871-87d8-2773adaf20ce","order_by":6,"name":"Lei Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIie3RPQrCQBCG4U8CsVncdm08w9hLcpVIwMoiB1DJAaJ1xFPYW0wIaCOxDSRFKmvtLCyMf3U2neC+zTbzwA4DmEw/WM9Ch0EjgIUmsa16GDRpQ/AkSNuQriC+BCdnVSwZ190cchM2fUxQElPhr8vM68TnA1TJzSQVNaF8SpbgPUh5GuROWVsCYudDZjrEDpKIfK9fHuulmIXKG4iU6ba63R23V0TD6sKLgYwbyLdxqN4H0r0O4EK93oW2MJlMpv/pAQVjRCg4P02OAAAAAElFTkSuQmCC","orcid":"","institution":"Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-07-09 13:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7084168/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7084168/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87104811,"identity":"a4a75306-3609-46f8-841b-b4b0599923ed","added_by":"auto","created_at":"2025-07-19 14:24:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":340105,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between ADL/IADL scale items disability and mortality.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHR: hazard ratio; CI: confidence interval; HRS: Health and Retirement Study; ELSA: English Longitudinal Study of Ageing; SHARE: Survey of Health, Ageing and Retirement in Europe; CHARLS: China Health and Retirement Longitudinal Study; MHAS: Mexican Health and Aging Study\u003c/p\u003e\n\u003cp\u003eAdjusted for demographic variables (age, gender, region, education, marital status), BMI, smoking, drinking status, chronic diseases, CES-D scale score, and SES.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7084168/v1/6ba26182a83452e3253d2d82.png"},{"id":87105283,"identity":"77c25b99-7523-4af2-ba5c-d58bddea3c0b","added_by":"auto","created_at":"2025-07-19 14:40:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1617137,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7084168/v1/20ed3ebd-4b00-4d67-925e-433924fb88f6.pdf"},{"id":87104817,"identity":"816deaf0-7fa2-4054-8c45-0e232541bdb4","added_by":"auto","created_at":"2025-07-19 14:24:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3860393,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7084168/v1/7a4f25ad4abbe616369e13b6.docx"},{"id":87104814,"identity":"e5353a48-00f4-44a8-a40b-4703974e28f2","added_by":"auto","created_at":"2025-07-19 14:24:06","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":33950,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklistcohort.docx","url":"https://assets-eu.researchsquare.com/files/rs-7084168/v1/e8553b2550dec4025828b60f.docx"},{"id":87104892,"identity":"c3781551-8b0c-43e0-9df5-fb4908914cfe","added_by":"auto","created_at":"2025-07-19 14:32:06","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":776821,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicabstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7084168/v1/5324bf531b62d17e76e66841.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations of activities of daily living disability and instrumental activities of daily living disability with all-cause mortality: Evidence from Five Major Longitudinal Studies","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the rapid aging of populations globally, understanding the factors that influence the health and lifespan of the old people has become increasingly crucial. One key factor is disability, which encompasses individuals with long-term physical, mental, intellectual, or sensory impairments(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). These disabilities can interact with various health conditions, impair an individual\u0026rsquo;s ability to perform daily activities, and limit their full participation in social life(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The definition of disability highlights the varying degrees and dimensions of barriers and potential health issues that individuals may face, all of which contribute to an elevated risk of premature mortality(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Existing studies have established a clear association between disability and mortality. In low- and middle-income countries, individuals with disabilities experience a mortality rate double that of non-disabled individuals, and their risk of mortality increases with the number of disabilities reported. Similar trends have also been observed in developed countries(\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Against this background, there is a crucial need to obtain a deeper understanding of the relationship between disability and mortality, as it can provide vital insights for the development of targeted policies and the optimal allocation of limited healthcare resources.\u003c/p\u003e\u003cp\u003eActivities of daily living (ADL) and instrumental activities of daily living (IADL) are central to maintaining independence in later life. ADL refer to basic self-care tasks (sometimes referred to as \u0026ldquo;basic activities of daily living\u0026rdquo; or BADLs), such as bathing, dressing, and eating, while IADL involve more complex tasks necessary for independent living, such as managing finances, preparing meals, and taking medication(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). IADL often require higher cognitive function than ADL. Aging, disabling diseases (such as stroke) and injuries are the primary factors leading to ADL/IADL disability. Epidemiological surveys show that elderly-friendly living environments, accessible medical services and improved education levels can reduce the incidence rate of ADL/IADL disability. Previous studies consistently showed that individuals with ADL disabilities face a significantly increased risk of mortality compared with those without such disabilities(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). And greater disability is often associated with higher mortality(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). For example, in a previous study in Chinese Community, old people with disabilities in at least one ADL item were reported to have a 63% higher risk of mortality over a 7-year period(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDisability data collected within health information systems are essential for understanding mortality risk; however, they often lack a universal classification framework(\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). ADL and IADL offer quantifiable measures of limitations in daily activities and provide a straightforward reflection of disability at the individual level(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Given their broad applicability across different economic and cultural contexts, ADL/IADL assessments have been included in many large cohort studies, presenting an opportunity to examine the relationship between disability and mortality across diverse populations. In this study, we leverage data from five major longitudinal studies, each incorporating extensive aging-related data collected globally and utilizing consistent stratified sampling, follow-up procedures, and questionnaire designs. This enables a comprehensive analysis across various cultural, economic, and healthcare settings. Our objective is to address critical gaps in the literature by investigating the nuanced relationships and interaction effects between disability\u0026mdash;specifically ADL/IADL disability\u0026mdash;and all-cause mortality among middle-aged and older adults, while also exploring potential mediating factors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy design, participants and data extraction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, we used data from five nationally representative longitudinal surveys of community-dwelling older adults. The specific follow-up time of each cohort is shown in the \u003cb\u003eSupplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. The cohorts include the Health and Retirement Study (HRS) (waves 10 to 15, 2010\u0026ndash;2020), the Survey of Health, Ageing and Retirement in Europe (SHARE) (waves 2 to 7, 2007\u0026ndash;2017), the English Longitudinal Study of Ageing (ELSA) (waves 2 to 7, 2004\u0026ndash;2014), the Mexican Health and Aging Study (MHAS) (waves 1 to 3, 2001\u0026ndash;2013), and the China Health and Retirement Longitudinal Study (CHARLS) (waves 1 to 4, 2011\u0026ndash;2019). Since CHARLS only assesses ADL/IADL in individuals aged 50 and above, we set the minimum age for inclusion in the study as 50. We also extracted covariates, including the following: demographic variables (age at the start of follow-up, sex, education level, marital status, region), BMI (measured physically or self-reported by respondents during follow-up), smoking and drinking status, physician-diagnosed chronic disease (using the acceptable minimum subset of data available between cohorts; MHAS did not provide information on heart-related diseases at the beginning of follow-up), depression (assessed using different versions of the CES-D scale)(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), socioeconomic status (SES) (owing to variation in the concept of \u0026ldquo;savings\u0026rdquo; in different cultural and economic contexts, we used household annual total income as a continuous variable). Only individuals with complete ADL/IADL questionnaire results and complete information on demographic variables at the start of follow-up were included. The data processing methods of covariates can be found in the \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-\u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eExposure\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLimitations/disabilities in ADL (for brevity, we use the term \u0026ldquo;disabilities\u0026rdquo; elsewhere) are defined as difficulties in performing corresponding activities in the ADL scale, as is similarly the case for IADL. However, the answer options for the ADL/IADL scales applied in each cohort are not uniform (e.g., CHARLS uses a five-level scale for answering, while HRS uses a three-level one). We used data from the harmonized and cleaned coordinated version, where an item is coded as \u0026ldquo;1\u0026rdquo; if the individual has difficulty (of any severity) in performing one of the ADL, and \u0026ldquo;0\u0026rdquo; otherwise; we did the same for IADL(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The sum of each item in the ADL/IADL scale is the ADL/IADL Disability Score. We also assessed the ADL/IADL Status, divided into no disability in ADL/IADL, disability in one item, or disability in two or more items. The details of the ADL/IADL scales for each cohort are shown in the \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e-\u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe outcome event in this study was the death of the individual during follow-up. The survival status and survival time of individuals at the end of follow-up were extracted from harmonized longitudinal cohorts, with all cohorts identifying death cases through national or municipal mortality registers (ELSA, HRS) or by interviewing informants or knowledgeable individuals (CHARLS, MHAS, SHARE). For participants who died during follow-up, survival time was calculated by subtracting the baseline year of the cohort from the year in which the participant was confirmed to have died.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eWe performed descriptive statistics for all participants and generated Kaplan-Meier (K-M) survival curves, using the log-rank test to evaluate the differences in survival probability between these categories (\u003cb\u003eSupplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). We did not observe any crossing of the survival curves. We determined the covariates constituting the minimum sufficient adjustment set using a directed acyclic graph (DAG) (\u003cb\u003eSupplementary Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). We conducted main analyses and produced result summaries for all cohorts, using the Cox proportional hazards model to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Model 1 was adjusted according to the minimum sufficient adjustment set provided by the DAG, including demographic variables (age, sex, education, marital status, region), BMI, and smoking/drinking status. Model 2 further adjusted for chronic diseases, CES-D scale score, and SES. We also conducted a meta-analysis on the results of model 2 to summarize the results of each cohort, using the inverse variance method for weighting. We also used each item in the ADL/IADL scale as exposure, adjusted using model 2, and calculated its association with the mortality rate. To evaluate the additive interaction of ADL and IADL disabilities, we further calculated the relative excess risk due to interaction (RERI), the attributable proportion due to interaction (AP), and the synergy index (SI). Since the use of multiplicative interaction in the Cox model is equivalent to additive interaction, we did not evaluate the product term. We used the \u0026ldquo;mediation\u0026rdquo; in R for causal mediation analysis, estimating the natural direct and indirect effects, calculating the proportion of mediators using the nonparametric bootstrap method, with Monte Carlo sampling set to 2500 times, and the 95% CI calculated by quasi-Bayesian approximation. In the mediation model, we used ADL/IADL disability as exposure, calculating the proportions of depression, chronic diseases, and SES as mediators in the association between disability and mortality rate. To ensure the robustness of our study results, we conducted several sensitivity analyses (\u003cb\u003eSupplementary Table S5\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eIn the main analysis, we used multiple imputation (R package \u0026ldquo;mice\u0026rdquo; version 3.16.0) to fill in the missing values (\u003cb\u003eSupplementary Table S6\u003c/b\u003e). As multiple imputation is not ideal for imputing binary variables, for robustness of conclusions, we used a value of 0.5 to fill in missing values in the ADL/IADL questionnaire. Data analysis was performed using R software (version 4.4.1) and Stata (version 18.0). A two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical tests were adjusted using individual-level weights provided by the cohort.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePatient and Public Involvement\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research\u003c/p\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical Statement\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs this study primarily involves secondary analysis of publicly available datasets, the need for ethical approval of this study was waived (\u003cstrong\u003eSupplementary Table S7\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRole of the funding source\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHuman Ethics and Consent to Participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable, because the data of this study are derived from large cohort study data. All cohort studies have been approved by the relevant ethics committees. For details, please see the \u003cstrong\u003eSupplementary Table S7\u003c/strong\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe process of participant selection is shown in \u003cstrong\u003eSupplementary Figure S4\u003c/strong\u003e. After completion of the selection process, this study included 10,089 CHARLS participants, 7,218 ELSA participants, 20,702 HRS participants, 12,411 MHAS participants, and 33,650 SHARE participants. \u003cstrong\u003eTable 1\u003c/strong\u003e shows the features of the participants in these five cohorts. The proportion of outcome events during the follow-up period in the cohorts varied from 4.39% in CHARLS to 28.99% in HRS. Among the included samples, 65.1% were female and 18.4% died during the follow-up period. Meanwhile, among the excluded samples, 51.1% were female and 3.7% died during the follow-up period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe relationship between ADL/IADL disability and mortality rate, as described in detail in \u003cstrong\u003eTable 2\u003c/strong\u003e, involved significant associations with a higher risk of mortality in all cohorts in model 1, which adjusted for the minimum sufficient adjustment set, and the risk of mortality was significantly higher for those with two or more ADL/IADL disabilities than for those with one. In model 2, which further adjusted for SES, depression, and chronic diseases, these associations remained significant. In both model 1 and model 2, the highest HRs were found in CHARLS [ADL disability: model 1 HR: 3.44 (2.77, 4.27), model 2 HR: 2.71 (2.13, 3.44); IADL disability: model 1 HR: 3.45 (2.78, 4.29), model 2 HR: 2.78 (2.20, 3.52)]. The meta-analysis of these findings produced summarized HRs (\u003cstrong\u003eSupplementary Figure S5-8\u003c/strong\u003e), emphasizing the robustness of these associations in different populations and environments. Subgroup analysis did not reveal consistent between-group differences across cohorts. Detailed results are shown in \u003cstrong\u003eSupplementary Tables S8-12.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of the association between each disability items in the ADL/IADL scale and death found similar patterns within each cohort, as described in detail in \u003cstrong\u003eFigure 1\u003c/strong\u003e. Having disabilities in eating, shopping for daily necessities, and preparing hot meals had relatively large negative impacts on the likelihood of survival.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of the interaction analysis are shown in \u003cstrong\u003eTable 3\u003c/strong\u003e. The interaction between ADL disabilities and IADL disabilities was relatively strong in the CHARLS, HRS, and ELSA cohorts [CHARLS, RERI: 2.29 (0.70, 3.89); HRS, RERI: 1.91 (1.52, 2.29); ELSA, RERI: 1.24 (0.07, 2.40)], and relatively weak in MHAS and SHARE [MHAS, RERI: 0.87 (0.54, 1.19); SHARE, RERI: 0.58 (0.37, 0.79)].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of the mediation analysis are shown in \u003cstrong\u003eTable 4.\u003c/strong\u003e The proportion of mediation by chronic diseases varied among the cohorts, from 10.3% (6.8%, 16.0%) for IADL in CHARLS to 32.3% (19.4%, 61.0%) for IADL in ELSA. The mediating effect of depression was observed in all cohorts except ELSA, ranging from 1.1% (1.0%, 21.0%) for IADL in CHARLS to 31.7% (22.8%, 46.0%) for ADL in ELSA. We did not observe a mediating effect of SES in any cohort.\u003c/p\u003e\n\u003cp\u003eOverall, the main results of this study were maintained after comprehensive sensitivity analyses (\u003cstrong\u003eSupplementary Table S13-17\u003c/strong\u003e). Similar patterns were found in complete case analysis and after multiple imputations. In the analysis using the sample excluding outcome events occurring within 1 year of the start of follow-up, the association between having one ADL/IADL disability and death was no longer significant in CHARLS; the other results were basically consistent with those in the main analysis. Excluding HRS data related to the COVID-19 pandemic did not significantly change the HRS results. The results of model 2 without adjusting for chronic disease covariates did not change significantly. In the analysis using the maximum ADL/IADL items of each cohort as the dataset, the association between having one ADL/IADL disability item and death was no longer significant in CHARLS and MHAS, while the other results were consistent with those in the main analysis. Description of samples included and excluded in the study is shown in the \u003cstrong\u003eSupplementary Table S18\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study used data from five large longitudinal cohorts, which included over 80,000 participants from different ethnic, cultural, and socio-economic backgrounds, to study the association between disability and long-term mortality rate from the perspective of ADL/IADL disabilities. We found that, in middle-aged and old people, individuals with disabilities had a significantly increased risk of mortality, with this risk for those with two or more ADL/IADL disabilities being approximately double that for those without any disabilities. Analysis of individual ADL/IADL items found that disabilities in eating, preparing hot meals, and shopping for daily necessities had larger negative impacts on the likelihood of survival across all cohorts. Across all cohorts, there was an additive interaction between ADL and IADL disabilities, further emphasizing the synergistic impact of multiple disabilities on the risk of mortality. The increased risk of mortality associated with ADL/IADL disabilities was partially mediated by chronic diseases and depression, suggesting that chronic diseases and depression are key factors for improving health outcomes among the old disabled population.\u003c/p\u003e\n\u003cp\u003eOur use of the ADL/IADL scale to study the association between disability and mortality allowed us to systematically compare and summarize results across different cohorts, but it also hindered our ability to compare our findings with other disability-related research. Our main results regarding the effects of ADL/IADL and disability levels are consistent with existing research, namely, that middle-aged and old people with ADL/IADL disabilities have a significantly increased risk of mortality. Smythe et al. found that the mortality rate for people with disabilities in low- and middle-income countries was double that of non-disabled people, which is consistent with our results, emphasizing the higher risk of mortality faced by the disabled population(5). Kuper et al., using a mixed research method, found that the mortality rate for people with disabilities was 2.24 times that of non-disabled people, and life expectancy models estimated that, on average, people with disabilities die 13.8 years earlier than the general population worldwide(4). Meanwhile, Landes reached similar conclusions in a study of the adult population in the United States, where the difference in mortality rates compared with that of non-disabled people was greatest for people with ADL disabilities and combinations of one or more other disabilities(17).\u003c/p\u003e\n\u003cp\u003eOur analysis of the ADL/IADL scale sub-items found that the associations between disability across all cohorts. Disabilities in activities such as eating, preparing hot meals, and shopping for daily necessities have larger negative impacts on the likelihood of survival, which may be because these activities are closely related to nutritional intake, and malnutrition has been proven to be significantly associated with an increased risk of mortality(18). Age-related changes in metabolism are known to make the old people particularly vulnerable to malnutrition. To our knowledge, this is the first finding that the interaction of ADL and IADL disability significantly increases the risk of mortality, especially when both are present. We speculate that the reason for this interaction may be that the individual experiences losses of physiological function, social support, and mental health, among others, making them more susceptible to disease progression and death. Further research may be needed to determine the specific mechanism involved. We also observed the mediating roles of chronic diseases and depression in the association between disability and mortality. In the United States, chronic diseases are the main causes of disability and mortality(19). Similarly, the occurrence of disabilities lowers the quality of life and ability to access medical resources among the old people, further increasing the risk of chronic diseases. Existing community-based systems for managing chronic disease should be more inclusive of people with disabilities, thereby alleviating deaths caused by disability. This would help to reduce the burden of chronic diseases and disabilities at the same time, but more research is needed to investigate the extent to which deaths caused by disabilities can be attributed to chronic diseases. Similar to reported findings in chronic diseases, depression is also a modifiable variable that mediates the association between disability and mortality(16). On the one hand, disabilities increase the mental burden on the old people, while on the other hand, they hinder their degree of social participation. Common ways in which the old people obtain social support from other members of the community may not be available to disabled people. More targeted measures should be taken to alleviate the social isolation and loneliness suffered by people with disabilities, such as peer support groups and professional psychological counseling(20).\u003c/p\u003e\n\u003cp\u003eOur study emphasizes the importance of using internationally comparable, standardized definitions and frameworks for disability. A sustainable future that is inclusive and accepting of disabled groups should be built on accurate recognition and classification of the disabled population(21, 22). The Disability Data Report published by the Disability Data Initiative in 2021 found that internationally comparable questions on disability were only included in surveys undertaken in 84 out of 180 countries reviewed and in 16% of household surveys and censuses(14). In the future, there is a need for more unified recording and classification of disabled groups in healthcare databases in order to discover the specific challenges faced by those with different types of disability in different environments. The COVID-19 pandemic has intensified the scarcity of disability data(23), and the lack of such data has excluded disabled people from discussions on health equity and data-driven policy. Collecting data on disabled populations in a reliable manner and classifying it in a standardized way is crucial to realizing the rights of the estimated 1.3 billion disabled people worldwide.\u003c/p\u003e\n\u003cp\u003eOur study, consistent with the conclusions drawn in other disability-related meta-analyses, found that disabled people have higher mortality rates and suffer from health inequalities, and this worse mortality rate is universally found among those with different economic and cultural backgrounds(4, 5, 17, 24). There is thus a need to change existing healthcare systems to make them more inclusive of disabled people, improving their access to medical care and its affordability for them (25). Training for healthcare workers can be strengthened to provide a professional level of care and an inclusive attitude towards those with disabilities. By incorporating disabled people into mainstream medical plans through accessible medical infrastructure and disability databases, and providing more targeted healthcare for disabled people(26, 27).\u003c/p\u003e\n\u003cp\u003eOur study has several strengths. First, the cross-national longitudinal nature of this study makes our conclusions widely applicable. Moreover, the disability data came from rigorous data collection and similar research designs, making the results of different cohorts comparable. Second, we conducted in-depth analysis of the data, discovering the mediating roles of chronic diseases and depression, the significant increase in mortality risk from the interaction between ADL and IADL, and the difference in the impacts of disabilities in different ADL/IADL items on mortality risk. However, our study also has significant limitations. Most importantly, the entire disabled population cannot be identified through the use of ADL/IADL scales alone(28). The WHO\u0026rsquo;s ICF conceptual framework divides functional disorders into three aspects: physical function disorders or physical structure changes; activity limitations; and restrictions on participation. As such, when using ADL disabilities alone, it is not possible to accurately describe the characteristics and intensity levels of an individual\u0026rsquo;s disability. As another limitation, the scarcity and lack of standardization of disability data in aging cohorts hindered us from including more low- and middle-income countries in this study, which may have weakened our ability to identify the specific challenges faced by these disabled groups. A further limitation associated with our data is that death in the cohorts was identified through questionnaire surveys and linked registry data (ELSA and HRS). While the variability in data sources across different cohorts may introduce some inconsistencies in mortality reporting, our findings remain robust, as the overall patterns and associations observed are consistent across datasets. In addition, we used HRs to report the main results, but since HR may change over specific periods during follow-up, the average HR may not be valuable as a reference. Although the Cox proportional hazards model can adjust for potential confounders, our model may still be subject to residual confounding due to unmeasured confounders and measurement error. Although our study included 89% of the respondents, the model may still be affected by selection bias\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, our study results show that, at the multi-cohort level, ADL/IADL disabilities significantly increase the long-term mortality risk of the middle-aged and old people. Chronic diseases and depression largely mediate this association, and we also found that the interaction of ADL and IADL disabilities significantly increases the risk of mortality. These conclusions emphasize that disability significantly shortens life expectancy, and that changes are needed in healthcare systems to better accommodate disabled people, including reducing the barriers (physical and psychological) to accessing healthcare services, providing more targeted chronic disease management and mental health services for disabled people, and collecting disability data within a more standardized and inclusive framework. Establishing healthcare systems that accept and include disabled people requires the joint efforts of all healthcare workers and policy planners, which is crucial to realizing the health rights of the estimated 1.3 billion disabled people worldwide.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData sharing statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData for this study were obtained from several major cohort studies, through the CHARLS (https://charls.pku.edu.cn/), ELSA (http://www.elsa-project.ac.uk/), HRS (https://hrs.isr.umich.edu/), MHAS (https://www.mhasweb.org/), SHARE (https://share-eric.eu/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHW: Conceptualization, Data curation, Formal analysis, Methodology, Software, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing; CL: Investigation, Methodology, Validation, Writing \u0026ndash; review \u0026amp; editing; KBY: Investigation, Methodology, Validation, Writing \u0026ndash; review \u0026amp; editing; CYC: Formal analysis, Methodology, Software, Validation; JHJ: Resources, Supervision, Writing \u0026ndash; review \u0026amp; editing, Validation; HZ: Conceptualization, Resources, Supervision, Writing \u0026ndash; review \u0026amp; editing; LL: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. All authors contributed to the critical revision of the manuscript and read and approved the final version of the manuscript. All authors had full access to all the data in the study and accepted responsibility for the decision to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest regarding this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was funded by GDPH Supporting Fund for Talent Program (KY0120220263), LiaoNing Revitalization Talents Program (XLYC2203192), Guangzhou School (hospital) Enterprise Joint Funding Project (2025A03J3901), and 2024 High-end Foreign Experts Recruitment Plan of China (S20240245)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO Guidelines Approved by the Guidelines Review Committee. World Report on Disability 2011. Geneva: World Health Organization Copyright \u0026copy; World Health Organization 2011.; 2011.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThe Lancet Public H. Disability-a neglected issue in public health. Lancet Public Health. 2021;6(6):e346.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeslop P, Blair PS, Fleming P, Hoghton M, Marriott A, Russ L. The Confidential Inquiry into premature deaths of people with intellectual disabilities in the UK: a population-based study. Lancet. 2014;383(9920):889\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuper H, Rotenberg S, Azizatunnisa L, Banks LM, Smythe T. The association between disability and mortality: a mixed-methods study. Lancet Public Health. 2024;9(5):e306\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmythe T, Kuper H. The association between disability and all-cause mortality in low-income and middle-income countries: a systematic review and meta-analysis. Lancet Glob Health. 2024;12(5):e756\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDugravot A, Fayosse A, Dumurgier J, Bouillon K, Rayana TB, Schnitzler A, et al. Social inequalities in multimorbidity, frailty, disability, and transitions to mortality: a 24-year follow-up of the Whitehall II cohort study. Lancet Public Health. 2020;5(1):e42\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKatz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW, STUDIES OF ILLNESS IN THE AGED. THE INDEX OF ADL: A STANDARDIZED MEASURE OF BIOLOGICAL AND PSYCHOSOCIAL FUNCTION. JAMA. 1963;185:914\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist. 1969;9(3):179\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGill TM, Allore HG, Holford TR, Guo Z. Hospitalization, restricted activity, and the development of disability among older persons. JAMA. 2004;292(17):2115\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarberger-Gateau P, Rainville C, Letenneur L, Dartigues JF. A hierarchical model of domains of disablement in the elderly: a longitudinal approach. Disabil Rehabil. 2000;22(7):308\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu Z, Zheng B, Kaminga AC, Zhou F, Xu H. Association Between Functional Limitations and Incident Cardiovascular Diseases and All-Cause Mortality Among the Middle-Aged and Older Adults in China: A Population-Based Prospective Cohort Study. Front Public Health. 2022;10:751985.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJajtner KM, Brucker DL, Mitra S. Midlife Work Limitations are Associated with Lower Odds of Survival and Healthy Aging. J Gerontol B Psychol Sci Soc Sci. 2022;77(4):790\u0026ndash;802.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReed NS, Meeks LM, Swenor BK. Disability and COVID-19: who counts depends on who is counted. Lancet Public Health. 2020;5(8):e423.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThe Lancet Global H. Disability: measurement matters. Lancet Glob Health. 2021;9(8):e1028.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGill TM, Allore HG, Gahbauer EA, Murphy TE. Change in disability after hospitalization or restricted activity in older persons. JAMA. 2010;304(17):1919\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Liu M, Yang F, Chen H, Wang Y, Liu J. The associations of socioeconomic status, social activities, and loneliness with depressive symptoms in adults aged 50 years and older across 24 countries: findings from five prospective cohort studies. Lancet Healthy Longev. 2024;5(9):100618.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLandes SD. Disability Mortality Disparity: Risk Of Mortality For Disabled Adults Nearly Twice That For Nondisabled Adults, 2008-19. Health Aff (Millwood). 2024;43(8):1128\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNorman K, Ha\u0026szlig; U, Pirlich M. Malnutrition in Older Adults-Recent Advances and Remaining Challenges. Nutrients. 2021;13(8).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBauer UE, Briss PA, Goodman RA, Bowman BA. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet. 2014;384(9937):45\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFalvey JR, Cohen AB, O'Leary JR, Leo-Summers L, Murphy TE, Ferrante LE. Association of Social Isolation With Disability Burden and 1-Year Mortality Among Older Adults With Critical Illness. JAMA Intern Med. 2021;181(11):1433\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoallemi EA, Malekpour S, Hadjikakou M, Raven R, Szetey K, Moghadam MM, et al. Local Agenda 2030 for sustainable development. Lancet Planet Health. 2019;3(6):e240\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThe world's. goals to save humanity are hugely ambitious - but they are still the best option. Nature. 2023;621(7978):227\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuper H, Smythe T. Are people with disabilities at higher risk of COVID-19-related mortality? a systematic review and meta-analysis. Public Health. 2023;222:115\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEhrlich JR, Ramke J, Macleod D, Burn H, Lee CN, Zhang JH, et al. Association between vision impairment and mortality: a systematic review and meta-analysis. Lancet Glob Health. 2021;9(4):e418\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArmitage R, Nellums LB. The COVID-19 response must be disability inclusive. Lancet Public Health. 2020;5(5):e257.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuper H, Azizatunnisa L, Gatta DR, Rotenberg S, Banks LM, Smythe T, et al. Building disability-inclusive health systems. Lancet Public Health. 2024;9(5):e316\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen TV, Kane S. Towards an agenda of action and research for making health systems responsive to the needs of people with disabilities. Lancet Reg Health West Pac. 2024;52:101225.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalmer M, Harley D. Models and measurement in disability: an international review. Health Policy Plan. 2012;27(5):357\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable-1. Baseline characteristics of 84,070 participants from five cohort studies.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 17.0406%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHARLS (n = 10,089)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 17.0406%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eELSA (n = 7,218)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 17.0406%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHRS (n = 20,702)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 17.0406%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMHAS (n = 12,411)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 17.0406%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSHARE (n = 33,650)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003eSurvived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003eDeceased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003eSurvived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003eDeceased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003eSurvived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003eDeceased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003eSurvived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003eDeceased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003eSurvived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003eDeceased\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e(n = 9,646)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e(n = 443)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e(n = 6,802)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e(n = 416)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e(n = 14,701)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e(n = 6,001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e(n = 9,208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e(n = 3,203)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e(n = 28,255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e(n = 5,395)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eFollow-up time (Year), Median (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e9 (9, 9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1 (1, 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e10 (10, 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e4 (2, 6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e10 (10, 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e5 (3, 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e12 (12, 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e7 (4, 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e10 (10, 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e5 (3, 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eAge (Year), Median (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e59 (55, 65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e72 (63, 78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e64 (57, 71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e75 (68, 82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e61 (55, 70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e77 (69, 84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e58 (53, 65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e69 (61, 76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e62 (56, 70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e76 (68, 81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e5026 (52.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e190 (42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e3706 (54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e172 (41.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e8636 (58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e3245 (54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e5167 (56.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1560 (48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e15811 (56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2546 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e4620 (47.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e253 (57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e3096 (45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e244 (58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e6065 (41.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2756 (45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e4041 (43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1643 (51.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e12444 (44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2849 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eEducation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Less than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e8655 (89.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e423 (95.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e2642 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e215 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e2435 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1655 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e8216 (89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e3009 (93.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e13051 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e3458 (64.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; High school or equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e866 (9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e18 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e3175 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e154 (37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e4826 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2206 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e220 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e45 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e9406 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1304 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; College or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e125 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e985 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e47 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e7440 (50.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2140 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e772 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e149 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e5798 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e633 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eMarita Status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e8549 (88.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e281 (63.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e4788 (70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e273 (65.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e9608 (65.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2975 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e7307 (79.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2163 (67.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e21530 (76.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e3353 (62.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Unmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e1097 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e162 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e2014 (29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e143 (34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e5093 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e3026 (50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e1901 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1040 (32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e6725 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2042 (37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eRegion, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e6167 (63.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e275 (62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e3928 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1944 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e2300 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e889 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e7780 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1548 (28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e3479 (36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e168 (37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e10773 (73.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e4057 (67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e6908 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2314 (72.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e20475 (72.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e3847 (71.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eAlcohol Consumption, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Never\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e5849 (60.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e249 (56.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e698 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e73 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e5741 (39.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e3529 (58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e6134 (66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2392 (74.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e4347 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1092 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e3797 (39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e194 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e6104 (89.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e343 (82.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e8960 (60.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2472 (41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e3074 (33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e811 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e23908 (84.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e4303 (79.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eSmoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Never\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e5803 (60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e205 (46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e2526 (37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e118 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e6707 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2237 (37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e5303 (57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1613 (50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e14865 (52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2784 (51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Ever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e3843 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e238 (53.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e4276 (62.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e298 (71.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e7994 (54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e3764 (62.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e3905 (42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1590 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e13390 (47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2611 (48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eSES, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e2350 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e173 (39.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e1670 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e135 (32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e3060 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2117 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e2064 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1057 (33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e6788 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1623 (30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e2408 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e114 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e1673 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e131 (31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e3260 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1916 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e2112 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e974 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e6642 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1778 (33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e2442 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e80 (18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e1707 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e97 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e3883 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1290 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e2440 (26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e661 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e7155 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1261 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e2446 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e76 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e1752 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e53 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e4498 (30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e678 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e2592 (28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e511 (16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e7670 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e733 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eCES-D, Median (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e7 (4, 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e11 (6, 16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e1 (0, 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1 (0, 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e1 (0, 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1 (0, 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e3 (1, 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e4 (2, 6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e1 (0, 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2 (1, 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eBMI (Kg/m2), Mean (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e23.1 (21.1, 25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e21.7 (19.8, 23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e27.5 (25.3, 30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e26.8 (24.8, 29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e29.6 (27.6, 32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e28.5 (26.5, 31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e26.9 (24.8, 29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e26.1 (24.0, 28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e26.2 (23.8, 29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e26.0 (23.4, 29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eADL Status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eDisability items= 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e8110 (84.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e245 (55.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e1859 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e41 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e12912 (87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e3954 (65.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e8408 (91.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2579 (80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e26065 (92.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e4063 (75.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eDisability items= 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e783 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e55 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e2227 (32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e113 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e939 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e763 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e441 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e254 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e1265 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e545 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eDisability items\u0026gt;= 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e753 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e143 (32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e2716 (39.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e262 (63.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e850 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1284 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e359 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e370 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e925 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e787 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eIADL Status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eDisability items= 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e7765 (80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e223 (50.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e5603 (82.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e271 (65.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e13190 (89.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e3863 (64.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e8568 (93.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2534 (79.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e26625 (94.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e4034 (74.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eDisability items= 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e1042 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e58 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e643 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e71 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e958 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e836 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e457 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e323 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e988 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e567 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eDisability items\u0026gt;= 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e839 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e162 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e556 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e74 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e553 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1302 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e183 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e346 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e642 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e794 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eADL/IADL Disability Score, Median (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e0.0 (0.0, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1.0 (0.0, 5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e0.0 (0.0, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e0.5 (0.0, 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e0.0 (0.0, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eADL Disability Score, Median (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e0.0 (0.0, 2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e0.0 (0.0, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e0.0 (0.0, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e0.0 (0.0, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eIADL Disability Score, Median (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e0.5 (0.0, 3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e0.0 (0.0, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8156%;\"\u003e\n \u003cp\u003eChronic Diseases, Median (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e1.0 (0.0, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1.0 (1.0, 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e1.0 (0.0, 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2.0 (1.0, 3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e2.0 (1.0, 3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e3.0 (2.0, 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e1.0 (0.0, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e1.0 (0.0, 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.309%;\"\u003e\n \u003cp\u003e1.0 (0.0, 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.7315%;\"\u003e\n \u003cp\u003e2.0 (1.0, 3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eADL: activities of daily living; IADL: instrumental activities of daily living; CI: confidence interval; HRS: Health and Retirement Study; ELSA: English Longitudinal Study of Ageing; SHARE: Survey of Health, Ageing and Retirement in Europe; CHARLS: China Health and Retirement Longitudinal Study; MHAS: Mexican Health and Aging Study; CES-D: Center for Epidemiologic Studies Depression; SES: Socioeconomic status; BMI: Body mass index; NA: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable-2. Association between ADL/IADL status and mortality.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10.1192%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 12.9409%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHARLS (n=10,089)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 12.9409%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eELSA (n=7,218)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 12.9409%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHRS (n=20,702)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 12.9409%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMHAS (n=12,411)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 12.9409%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSHARE (n=33,650)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0.5838%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 12.9409%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePooled (n=84,070)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10.1192%;\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eModel 1 HR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eModel 2 HR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eModel 1 HR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eModel 2 HR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eModel 1 HR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eModel 2 HR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eModel 1 HR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eModel 2 HR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eModel 1 HR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eModel 2 HR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0.5838%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eModel 1 HR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eModel 2 HR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 3.7947%;\"\u003e\n \u003cp\u003eADL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.2272%;\"\u003e\n \u003cp\u003eDisability items = 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0.5838%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.2272%;\"\u003e\n \u003cp\u003eDisability items = 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.70(1.26, 2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.46(1.08, 1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.58(1.22, 2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.42(1.08, 1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.65(1.52, 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.51(1.39, 1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.25(1.09, 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.10(0.96, 1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.42(1.30, 1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.19(1.09, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0.5838%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.48(1.32, 1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.31(1.13, 1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.2272%;\"\u003e\n \u003cp\u003eDisability items\u0026ge;\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e3.44(2.77, 4.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e2.71(2.13, 3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.84(1.42, 2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.52(1.14, 2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e2.62(2.45, 2.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e2.36(2.20, 2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.84(1.65, 2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.57(1.40, 1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.99(1.84, 2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.41(1.29, 1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0.5838%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e2.27(1.87, 2.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.84(1.41, 2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 3.7947%;\"\u003e\n \u003cp\u003eIADL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.2272%;\"\u003e\n \u003cp\u003eDisability items = 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0.5838%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.2272%;\"\u003e\n \u003cp\u003eDisability items = 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.52(1.13, 2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.31(0.98, 1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.87(1.39, 2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.63(1.20, 2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.92(1.77, 2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.70(1.57, 1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.45(1.29, 1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.33(1.18, 1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.63(1.49, 1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.34(1.22, 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0.5838%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.67(1.47, 1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.44(1.28, 1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.2272%;\"\u003e\n \u003cp\u003eDisability items\u0026ge;\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e3.45(2.78, 4.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e2.78(2.20, 3.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e2.70(1.95, 3.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e2.15(1.52, 3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e2.92(2.73, 3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e2.50(2.33, 2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e2.19(1.94, 2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.89(1.67, 2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e2.11(1.94, 2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e1.55(1.41, 1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0.5838%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e2.60(2.15, 3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.4218%;\"\u003e\n \u003cp\u003e2.11(1.66, 2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eADL: activities of daily living; IADL: instrumental activities of daily living; HR: hazard ratio; CI: confidence interval; HRS: Health and Retirement Study; ELSA: English Longitudinal Study of Ageing; SHARE: Survey of Health, Ageing and Retirement in Europe; CHARLS: China Health and Retirement Longitudinal Study; MHAS: Mexican Health and Aging Study\u003c/p\u003e\n\u003cp\u003eModel 1 was adjusted for demographic variables (age, gender, region, education, marital status), BMI, smoking, drinking status.\u003c/p\u003e\n\u003cp\u003eModel 2 was adjusted for demographic variables (age, gender, region, education, marital status), BMI, smoking, drinking status, chronic diseases, CES-D scale score, and SES.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable-3. Synergistic effect between ADL/IADL disability on increased risk of mortality.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHARLS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eELSA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMHAS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSHARE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eADL disability absent * IADL disability absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eADL disability absent * IADL disability present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.66 (1.33, 2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.56 (1.19, 2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.67 (1.56, 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.48 (1.33, 1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.35 (1.24, 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eADL disability present * IADL disability absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.68 (1.34, 2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.24 (0.98, 1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.44 (1.35, 1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.14 (1.03, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.13 (1.04, 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eADL disability present * IADL disability present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e4.64 (3.03, 7.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e3.05 (1.81, 5.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e4.02 (3.55, 4.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.48 (2.06, 2.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.06 (1.77, 2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eRERI (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.29 (0.70, 3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.24 (0.07, 2.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.91 (1.52, 2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.87 (0.54, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.58 (0.37, 0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eAP (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.49 (0.36, 0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.41 (0.23, 0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.47 (0.44, 0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.35 (0.28, 0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.28 (0.22, 0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eSI (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.71 (2.02, 3.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.53 (1.89, 3.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.71 (2.51, 2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.41 (2.17, 2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.20 (1.98, 2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eADL: activities of daily living; IADL: instrumental activities of daily living; CI: confidence interval; HRS: Health and Retirement Study; ELSA: English Longitudinal Study of Ageing; SHARE: Survey of Health, Ageing and Retirement in Europe; CHARLS: China Health and Retirement Longitudinal Study; MHAS: Mexican Health and Aging Study; RERI: relative excess risk due to interaction; AP: attributable proportion due to interaction, SI: synergy index.\u003c/p\u003e\n\u003cp\u003eNull hypothesis for addictive interaction: AP\u0026thinsp;=\u0026thinsp;0, RERI\u0026thinsp;=\u0026thinsp;0, SI\u0026thinsp;=\u0026thinsp;1. Adjusted for demographic variables (age, gender, region, education, marital status), BMI, smoking, drinking status, chronic diseases, CES-D scale score, and SES.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable-4. Mediation analysis using ADL/IADL disability as an exposure.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.359%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.5869%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHARLS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.5869%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eELSA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.5869%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.5869%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMHAS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.5869%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSHARE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.359%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADL disability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIADL disability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADL disability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIADL disability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADL disability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIADL disability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADL disability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIADL disability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADL disability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIADL disability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 6.4957%;\"\u003e\n \u003cp\u003eCES-D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8632%;\"\u003e\n \u003cp\u003eMediation proportion (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e11.4 (2.0, 22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e1.06 (1.0, 21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e2.9 (-15.6, 25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.27 (-17.2, 17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e4.8 (2.0, 8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e3.2 (0.9, 6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e15.2 (8.2, 26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e7.6 (4.0, 12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e31.7 (22.8, 46.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e25.0 (17.3, 34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8632%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 6.4957%;\"\u003e\n \u003cp\u003eChronic Diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8632%;\"\u003e\n \u003cp\u003eMediation proportion (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e13.5 (8.0, 21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e10.3 (6.8, 16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e32.3 (19.4, 61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e26.1 (16.9, 44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e21.3 (18.7, 24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e19.3 (17.3, 22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e21.9 (15.9, 32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e13.4 (9.6, 18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e20.6 (14.8, 30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e14.1 (10.8, 19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8632%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 6.4957%;\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8632%;\"\u003e\n \u003cp\u003eMediation proportion (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.0 (-0.01, 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.0 (-0.01, 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e1.2 (-1.7, 7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.50 (-1.1, 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.50 (0.0, 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.48 (0.0, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.15 (-1.1, 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.0 (-1.1, 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.31 (-3.7, 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.68 (-1.8, 3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8632%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.70\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.70\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.2934%;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eADL: activities of daily living; IADL: instrumental activities of daily living; CI: confidence interval; HRS: Health and Retirement Study; ELSA: English Longitudinal Study of Ageing; SHARE: Survey of Health, Ageing and Retirement in Europe; CHARLS: China Health and Retirement Longitudinal Study; MHAS: Mexican Health and Aging Study; CES-D: Center for Epidemiologic Studies Depression; SES: Socioeconomic status.\u003c/p\u003e\n\u003cp\u003eAdjusted for demographic variables (age, gender, region, education, marital status), BMI, smoking, drinking status.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7084168/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7084168/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eWith the global aging of the population, it is increasingly crucial for older people to maintain their independence through activities of daily living (ADL) and instrumental activities of daily living (IADL). There is evidence that ADL/IADL disability is associated with increased mortality, but there is still limited evidence of this in middle-aged and old people globally.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a multi-cohort pooled study using data from five major longitudinal studies in the Global Aging Dataset: the Health and Retirement Study (HRS), China Health and Retirement Longitudinal Study (CHARLS), Survey of Health, Aging and Retirement in Europe (SHARE), English Longitudinal Study of Aging (ELSA), and Mexican Health and Aging Study (MHAS). We used Cox proportional hazard models to examine the associations of ADL and IADL disabilities with mortality. Furthermore, we conducted analysis to explore the interaction between ADL and IADL on mortality, and further mediation analysis to explore the roles of chronic diseases, depression, and socioeconomic status in the association.\u003c/p\u003e\u003ch2\u003eFindings:\u003c/h2\u003e\u003cp\u003eThe final sample included 10,089 participants from CHARLS, 7,218 from ELSA, 20,702 from HRS, 12,411 from MHAS, and 33,650 from SHARE. The Cox proportional hazards model revealed that ADL/IADL disabilities were significantly associated with increased mortality across all cohorts. The pooled results showed that the hazard ratio (HR) for mortality with one disability in terms of ADL was 1.31 (95% CI: 1.13, 1.52) compared with those without disabilities, while it was 1.84 (1.41, 2.42) for two or more disabilities. Meanwhile, the HR was 1.44 (1.28, 1.62) for one disability in terms of IADL, but 2.11 (1.66, 2.69) for two or more disabilities. Significant mediating effects of chronic diseases and depression were found across all cohorts. The most pronounced additive interaction was observed in CHARLS, with relative excess risk due to interaction (RERI) of 2.29 (95% CI: 0.70, 3.89).\u003c/p\u003e\u003ch2\u003eInterpretation:\u003c/h2\u003e\u003cp\u003eThis study provides evidence that ADL/IADL disabilities significantly elevate the long-term mortality risk among middle-aged and old people. Chronic diseases and depression substantially mediate this association. Our findings underscore the disproportionate health inequities faced by individuals with disabilities and highlight the urgent need for global health systems to adapt, ensuring that people with disabilities are better understood and included in health policies and services.\u003c/p\u003e","manuscriptTitle":"Associations of activities of daily living disability and instrumental activities of daily living disability with all-cause mortality: Evidence from Five Major Longitudinal Studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-19 14:24:02","doi":"10.21203/rs.3.rs-7084168/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-20T15:57:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T13:24:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-04T19:36:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105787271968769797355761924947569410","date":"2026-01-09T06:58:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133009860518127439155608054750716151053","date":"2025-10-04T21:09:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-16T07:40:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-15T07:01:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-14T13:30:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-14T13:28:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-07-09T13:11:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0e4ad00d-a710-449e-a41d-9194ae757802","owner":[],"postedDate":"July 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T15:54:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-19 14:24:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7084168","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7084168","identity":"rs-7084168","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}