Association of multimorbidity and mortality risk in senior adults: a population-based cohort study

Association of multimorbidity and mortality risk in senior adults: a population-based cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of multimorbidity and mortality risk in senior adults: a population-based cohort study Ming Gui, Chuan-jiang Chen, Yufeng Chen, Fan Wen, Yongqi Wang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7890602/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Feb, 2026 Read the published version in BMC Geriatrics → Version 1 posted 10 You are reading this latest preprint version Abstract Background This study aimed to evaluate the association of three multimorbidity indicators with mortality risk among senior adults, and compare their predictive performance on mortality risk. Methods This prospective cohort study selected 162,958 participants aged ≥ 65 years from Yuexiu Ageing and Health Cohort. Information on eleven system diseases was extracted; three multimorbidity indicators (condition count, multimorbidity patterns, and multimorbidity index) were created. Hazard ratio (HR) with 95% confidence intervals (CI) was calculated using Cox proportional hazard model after adjustment for confounders. The C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were used to compare the performance of multimorbidity indicators. Results 15,525 deaths were identified during a median of 4.79 years of follow-up. Compared with participants with no multimorbidity, those with multimorbidity had a 1.56-fold risk of all-cause mortality. Every one condition count increment was associated with a 17% increased risk of all-cause mortality. Three multimorbidity patterns labeled as Patterns I, II, and III were extracted and were significantly associated with the increased mortality risk, with HR being 1.97, 1.41, and 1.44 for Patterns I, II, and III respectively. Every 1-unit increment of multimorbidity index was associated with an 18% increased mortality risk. The multimorbidity index outperformed both multimorbidity pattern (IDI = 0.007, NRI = 0.0055), and condition count in predicting mortality (IDI = 0.003, NRI = 0.0046). Conclusions Three multimorbidity indicators were all associated with the increased mortality risk in community-dwelling older Chinese. The multimorbidity index had slightly better predictive performance for mortality than other two indicators. Multimorbidity Condition count Multimorbidity pattern Multimorbidity index Mortality Figures Figure 1 Introduction Multimorbidity is common among older adults. Multimorbidity is not only significantly associated with decreased quality of life, increased disability, but also leads to a large consumption of medical resources and poses a major challenge to the global health care system. In older adults, the prevalence of multimorbidity increased with age [ 1 , 2 ], and those with multimorbidity have a higher risk of death [ 3 , 4 ]. However, the measurement of multimorbidity varies across different studies. Condition count is one of commonly used approaches due to its simplicity and ease of access [ 5 ]; however, it often overlooks differences in disease severity and the complex interactions among conditions[ 6 , 7 ]. Multimorbidity patterns considered interactions between diseases [ 8 ] and can be extracted through various methods[ 9 , 10 ]. Unlike K-means (a hard clustering algorithm that forces each disease to belong to a single cluster), fuzzy c-means is a soft clustering technique that allows each disease to be simultaneously assigned to multiple clusters through membership probability[ 11 ], thus allowing establishment of multimorbidity patterns that consider all possible disease combinations[ 12 , 13 ]. However, it has not yet been applied to extract multimorbidity patterns. The multimorbidity index, a method taking into account the severity and type of diseases, is receiving increasing attention[ 14 , 15 ]. To date, there is no clear and effective "gold standard" for measuring multimorbidity index[ 16 ], and most indices developed in Western populations may have limited applicability in community-dwelling older adults in non-Western countries[ 17 ]. Some studies have developed new multimorbidity indices among Chinese community populations to predict outcomes, but these have not shown significant advantages over traditional indices like the Charlson Multimorbidity Index[ 18 , 19 ]. Thus, developing a new multimorbidity index specific to the disease conditions of old Chinese adults is necessary. Revealing the impact of multimorbidity on the mortality prediction in community-dwelling older populations has become an important issue in both clinical practice and research. Previous studies have mostly used a single type of indicator to predict mortality risk, and few studies have evaluated the predictive performance of different types of multimorbidity indicators. and the differences in predictive performance of various multimorbidity indicators for mortality risk remain unclear. Therefore, this study aims to comprehensively clarify the association and predictive performance between three multimorbidity indicators (condition count, multimorbidity patterns, and multimorbidity index) and the risk of all-cause and cause-specific mortality based on a large-scale cohort. Methods Participants Participants in cohort study were derived from the Yuexiu Ageing and Health Cohort (YAHC), which a dynamic cohort conducted since 2016 in Yuexiu District, Guangzhou, Southern China. The YAHC is coordinated by the Guangzhou Yuexiu District Center for Disease Control and Prevention, and is implemented by 18 community health service centers in its jurisdiction. YAHC is established based on the Older Health Management Project, one of the National Basic Public Health Service Programs in China. YAHC targets individuals aged 65 years and older, conducting annual health examinations and health management for community-dwelling older adults. Relevant data, including sociodemographic information, physical examination, lifestyle, laboratory data, history of chronic diseases, medical history, et al, are collected from each individual in each year. From January 2016 to October 2023, the YAHC successfully recruited 164,524 senior residents. After excluding 1,566 participants with a follow-up duration of less than 6 months, missing information on birthdate and gender, and incomplete medical history at baseline, a total of 162,958 participants were included in this study. This study was approved by the Medical Ethics Committee of Southern University of Science and Technology. The study was conducted in accordance with the Declaration of Helsinki. Written informed consent of survey was obtained from all participants before data collection. Assessment of multimorbidity All participants received detailed medical and physical examination each year, the diseases status and relevant information were recorded. The participants were also required to answer whether they were diagnosed to have the diseases by any doctors. Information on chronic diseases then was determined by checking medical examination results, case record information, questionnaire information, health file information. The International Classification of Diseases, 10th Revision (ICD-10) was used for disease coding. Information on 11 system-specific diseases were systematically retrieved from the cohort: including cardiovascular diseases, cerebrovascular diseases, endocrine system diseases, respiratory diseases, liver-related diseases, cancer, neurological and psychiatric disorders, kidney-related diseases, musculoskeletal-related diseases, gastrointestinal diseases, and eye-related diseases (Supplementary Table S1). Multimorbidity was defined as having two or more of the 11 chronic diseases forementioned. Three indicators for multimorbidity, including condition count, multimorbidity index, and multimorbidity pattern, were created; the detailed calculation methods for the three indicators can be seen in Supplementary Table S2. Ascertainment of mortality Mortality data up to 31 December 2023 were obtained from the National Death Registry of China. Mortality information includes the time of death and the cause of death. Causes of death were coded by professional medical workers using the ICD-10. The primary outcome of this study was all-cause mortality, and secondary outcomes were cause-specific mortality cardiovascular diseases (I00-I25, I70-I89), respiratory diseases (J00-J99), cancers (C00-C99), and cerebrovascular diseases (I60-I69) due to they were the dominant causes of the death. Assessment of covariates To collect information, trained medical staff from community health service centers created health records for residents who underwent annual physical examinations. A semi-structured questionnaire with in-depth interview was adopted to collected information. The social demographic information included age (years), gender (male, female), marital status (single, married, divorced, widowed), education (junior high school or below, senior high school, college or above), and healthcare service (self-funded, partially self-funded, public-funded). The lifestyle factors included smoking status, alcohol consumption, and physical activity. Partially self-funded healthcare service included fees from Basic Medical Insurance, Poverty Relief Money, Commercial Health Insurance, New Rural Cooperative Medical Scheme, or other health insurance. Smoking status was categorized as never smoked, former smoker, or current smoker, based on a lifetime smoking history of at least 100 cigarettes and current smoking status. Alcohol consumption was classified into two categories of nondrinker and drinker. Physical activity was categorized as "yes" or "no" based on whether exercise frequency exceeded once per week. Height and weight of the participants were measured, and body mass index (BMI, kg/m 2 ) was calculated as weight in kilograms divided by the square of height in meters. Statistical analysis All analyses were conducted using R version 4.4.2, and a P -value with two-side less than 0.05 was considered to be significant. The continuous variable with normal distribution was displayed by mean and standard deviation (SD), while non-normal distribution was represented by median and interquartile range (IQR); the distribution difference was tested by t-test, analysis of variance (ANOVA), or Mann-Whitney U Test. The categorical variables were expressed by frequency and percentage (%) and compared by the Chi-square test. Follow-up time was defined as the interval between baseline and death, loss to follow-up, or 31 December 2023, whichever occurred first, and the person-years were calculated. The Cox proportional hazards model was used to estimate the associations of all-cause and cause-specific mortality with each type of chronic diseases, condition count, multimorbidity index, and multimorbidity pattern, and hazard ratio (HR) and 95% confidence intervals (CI) was calculated after adjustment for confounders. Schoenfeld's global test did not find a violation of the proportional hazard assumption. The linear exposure-response relationship was tested by treating the median of each quartile of the multimorbidity index as a continuous variable in the model. The potential nonlinear relationship was examined using restricted cubic spline regression. The confounders in the adjusted model included age, gender, marital status, healthcare services, education, smoking status, alcohol consumption, physical activity, and BMI. To test the robustness and consistency of our results, a multiple-adjusted Fine and Gray competing risk regression model was employed to estimate the association of multimorbidity with the risk of specific-cause mortality; then the repeated analysis with the Cox proportional hazards model was done after excluding participants who died within the first year of follow-up. Furthermore, we calculated the adjusted the population attributable fraction (PAF, %) to determine the percentage of mortality that could be prevented if each specific chronic condition or multimorbidity index were eliminated [ 20 ]. The PAF provides an estimate of the proportion of disease cases that could be avoided by addressing the risk factors, Specifically, the PAF was calculated using the formula as follows: \(\:PAF=\frac{P\text{e}(HR-1)}{P\text{e}\left(HR-1\right)+1}X100\%\) , where P e represents the proportion of the exposure in the entire population. To compare the effectiveness of different multimorbidity indicators, the Cox proportional hazards model was used to construct a prediction model for the occurrence of all-cause mortality based on the above covariate and various indicators of multimorbidity [ 21 ]. Receiver operating characteristic (ROC) curves were created using the predicted survival probabilities and survival status of participants at the end of follow-up. The C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were used to compare the performance of different multimorbidity indices in predicting 5-year mortality rates. Results The median follow-up was 4.79 years. During 745,774 person-years of follow-up, 15,525 deaths occurred, including 4,195 (27.02%) due to cardiovascular diseases, 2,340 (15.07%) cerebrovascular diseases, 1,068 (6.88%) cancers, 4,426 (28.51%) respiratory diseases, and 3,496 (22.52%) other causes. The mean age of all participants was 72.9 (7.13) years and a total of 83,255 (48.91%) participants had multimorbidity (Table 1 ). Table 1 Socio-demographic, lifestyle, and health characteristics of the study population according to condition count of participants Variables Total Dead Survivor P Number of participants, N (%) 162,958 (100) 147,433 (90.47) 15,525 (9.53) < 0.0001 Age, years, mean (SD) 72.90 (7.13) 72.25 (6.71) 79.38 (7.75) < 0.0001 Body mass index, kg/m 2 , mean (SD) 23.30 (3.08) 23.34 (3.06) 22.65 (3.20) < 0.0001 Gender, N (%) < 0.0001 Male 70,859 (43.48) 62,963 (42.71) 7,896 (50.86) Female 92,099 (56.52) 84,470 (57.29) 7,629 (49.14) Education, N (%) < 0.0001 Junior high school or below 89,439 (54.88) 79,142 (53.68) 10,297 (66.33) Senior high school 49,577 (30.42) 46,186 (31.33) 3,391 (21.84) College or above 23,942 (14.69) 22,105 (14.99) 1,837 (11.83) Marital status, N (%) < 0.0001 Never married 2797 (1.72) 2449 (1.66) 348 (2.24) Married 137,906 (84.63) 126,769 (85.98) 11,137 (71.74) Divorced 2,318 (1.42) 2,113 (1.43) 205 (1.32) Widowed 19,937 (12.23) 16,102 (10.92) 3,835 (24.70) Healthcare service, N (%) < 0.0001 Self-funded 3,746 (2.30) 3,575 (2.42) 171 (1.10) Partially self-funded 149,199 (91.6) 134,971 (91.55) 14,228 (91.65) Public-funded 10,013 (6.14) 8,887 (6.03) 1,126 (7.25) Smoking status, N (%) < 0.0001 Nonsmoker 145,997 (89.59) 131,845 (89.43) 14,152 (91.16) Former smoker 5,318 (3.26) 4848 (3.29) 470 (3.03) Current smoker 11643 (7.14) 10740 (7.28) 903 (5.82) Alcohol consumption, N (%) < 0.0001 Nondrinker 152,687 (93.70) 137,836 (93.49) 14,851 (95.66) Drinker 10,271 (6.30) 9,597 (6.51) 674 (4.34) Physical Exercise, N (%) < 0.0001 Never 45,995 (28.23) 5,870 (37.81) 5,870 (37.81) Exercise 116,963 (71.77) 107,308 (72.78) 9,655 (62.19) Chronic diseases, N (%) Chronic diseases 89,998 (55.23) 79,352 (53.82) 10,646 (68.57) < 0.0001 Cardiovascular diseases 9,042 (5.55) 7,052 (4.78) 1,990 (12.82) < 0.0001 Cerebrovascular diseases 50,820 (31.19) 45,603 (30.93) 5,217 (33.60) < 0.0001 Endocrine system diseases 5888 (3.61) 4854 (3.29) 1034 (6.66) < 0.0001 Respiratory diseases 53,736 (32.98) 50,791 (34.45) 2,945 (18.97) < 0.0001 Liver- related diseases 11,646 (7.15) 9,870 (6.69) 1,776 (11.44) < 0.0001 Cancer 6,670 (4.09) 5,479 (3.72) 1,191 (7.67) < 0.0001 Neurological and psychiatric disorders 4,698 (2.88) 4,091 (2.77) 607 (3.91) < 0.0001 Kidney- related diseases 7,982 (4.90) 7,088 (4.81) 894 (5.76) < 0.0001 Musculoskeletal-related diseases 7,864 (4.83) 7,390 (5.01) 474 (3.05) < 0.0001 Gastrointestinal diseases 15,096 (9.26) 13,871 (9.41) 1,225 (7.89) < 0.0001 Multimorbidity, N (%) 79,703 (48.91) 71,156 (48.26) 8,547 (55.05) < 0.0001 Multimorbidity index, mean (SD) 2.00 (1.69) 1.94 (1.67) 2.53 (1.82) < 0.0001 ∗ P value from the t-test for normal distributed continuous variable, and from Chi-square test for the categorical variables. Compared with participants with no multimorbidity, participants with multimorbidity had a higher risk of all-cause mortality (HR:1.56, 95% CI:1.51–1.62). Comparing with participants with condition count of 0, those with 1, 2, 3, and ≥ 4 chronic diseases had 1.86-fold (95% CI: 1.77–1.95), 2.25-fold (95% CI: 2.14–2.36), 2.24-fold (95% CI: 2.11–2.37), and 2.03-fold (95% CI: 2.16–2.45) risk of all-cause mortality, respectively, after adjustment for potential confounders (Table 2 ); Every one condition count increment was associated with 17% (HR:1.17, 95% CI: 1.16–1.18) increased risk of all-cause mortality. Similar harmful effects were also found on cardiovascular mortality, cancer mortality, respiratory mortality, and cerebrovascular mortality. Table 2 Association of multimorbidity patterns, number of chronic conditions, and multimorbidity index with all-cause mortality and cause-specific mortality N0 All-Cause Mortality Cardiovascular Mortality Cancer Mortality Respiratory Mortality Cerebrovascular Mortality N1 HR (95%CI) N2 HR (95%CI) N3 HR (95%CI) N4 HR (95%CI) N5 HR (95%CI) Total 15,525 4,195 1,068 4,426 2,340 Condition count 0 34,865 2,638 1.00 630 1.00 140 1.00 875 1.00 360 1.00 1 41,412 4,340 1.86 (1.77, 1.95) 1,212 2.14 (1.94, 2.36) 288 2.31 (1.88, 2.83) 1231 1.69 (1.55, 1.85) 638 1.99 (1.74, 2.27) 2 37,115 4,374 2.25 (2.14, 2.36) 1,252 2.65 (2.40, 2.93) 289 2.67 (2.17, 3.28) 1205 2.05 (1.87, 2.25) 662 2.49 (2.18, 2.84) 3 21,820 2,527 2.24 (2.11, 2.37) 656 2.37 (2.12, 2.66) 216 3.42 (2.74, 4.26) 659 1.98 (1.78, 2.20) 404 2.62 (2.26, 3.04) ≥ 4 12,221 1,646 2.30 (2.16, 2.45) 445 2.51 (2.22, 2.85) 135 3.49 (2.74, 4.46) 456 2.17 (1.93, 2.44) 276 2.82 (2.40, 3.32) P for trend < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 Every one condition count increment 1.17 (1.16–1.18) 1.17 (1.14–1.19) 1.25 (1.20–1.30) 1.16 (1.14–1.18) 1.21 (1.18–1.25) Multimorbidity pattern Without Multimorbidity 76,277 6,978 1.00 1,842 1.00 428 1.00 2,106 1.00 998 1.00 Pattern I 14,097 2,791 1.97 (1.89, 2.06) 763 1.98 (1.82, 2.16) 119 1.44 (1.17, 1.77) 921 2.27 (2.09, 2.45) 541 2.59 (2.33, 2.88) Pattern II 41,393 4,101 1.41 (1.35, 1.47) 1282 1.65 (1.53, 1.77) 253 1.31 (1.11, 1.54) 966 1.21 (1.11, 1.30) 656 1.59 (1.43, 1.76) Pattern III 15,666 1,655 1.44 (1.36, 1.52) 308 1.00 (0.89, 1.14) 268 3.59 (3.07, 4.21) 433 1.35 (1.21, 1.50) 145 0.89 (0.75, 1.06) Multimorbidity index Quartile 1 (0–0.49) 43,729 2,984 1.00 683 1.00 158 1.00 952 1.00 393 1.00 Quartile 2 (0.49–1.73) 37,294 3,873 1.76 (1.68–1.85) 1,152 2.17 (1.97–2.39) 201 1.71 (1.38–2.1) 1,107 1.61 (1.47–1.75) 566 1.88 (1.65–2.14) Quartile 3 (1.73–2.91) 31,176 3,393 2.08 (1.97–2.18) 967 2.45 (2.22–2.71) 257 2.81 (2.30–3.45) 885 1.81 (1.64–1.98) 476 2.15 (1.88–2.47) Quartile 4 (2.91–13.29) 35,234 5,365 2.57 (2.45–2.69) 1,393 2.73 (2.48-3.00) 452 4.01 (3.32–4.83) 1,482 2.36 (2.17–2.57) 905 3.16 (2.79–3.57) P for trend < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 Every 1-unit increment 1.18 (1.17–1.19) 1.17 (1.16–1.19) 1.26 (1.22–1.3) 1.18 (1.16–1.19) 1.25 (1.22–1.27) Abbreviation: N0, N1, N2, N3, N4 and N5 represents the number of participants with survivors, all-cases mortality, cardiovascular mortality, cancer mortality, respiratory mortality, and cerebrovascular mortality, respectively. *Adjusted for age, gender, education, marital status, healthcare services, physical exercise, smoking status, alcohol consumption. The mean (SD) of the multimorbidity index was 1.94 (1.67) and 2.53 (1.82) for dead and survival participants, with a significant difference ( P < 0.001). Every 1-unit increment of the multimorbidity index was associated 1.18-fold (95% CI: 1.17–1.19) risk of all-cause mortality (Table 2 ). Compared to those within the quartile 1, participants within the quartile 2, the quartile 3 and quartile 4 had -adjusted HRs of 1.76 (95% CI: 1.68–1.85), 2.08 (95% CI: 1.97–2.18), and 2.57 (95% CI: 2.45–2.69), respectively, with significant linear trends ( P for trend < 0.001). A nonlinear relationship was observed between the multimorbidity index and all-cause mortality ( P for nonlinear < 0.001) in Fig. 1 . Similar harmful effects were also found on cardiovascular, cancer, respiratory, and cerebrovascular mortality. Three mutually exclusive multimorbidity patterns were extracted (Supplementary Table S3). Pattern I was featured with cerebrovascular diseases, neurological and psychiatric disorders, musculoskeletal-related disease, respiratory diseases, and gastrointestinal diseases. Pattern II was characterized with cardiovascular diseases, endocrine system diseases, and liver-related diseases. Pattern III was characterized with cancer, kidney-related diseases, and eye-related diseases. When comparing with participants without multimorbidity, those with the pattern I (HR: 1.97, 95% CI: 1.89–2.06), pattern II (HR: 1.41, 95% CI: 1.35–1.47) and pattern III (HR: 1.44, 95%CI:1.36–1.52) were associated with the increased risk of all-cause mortality (Table 2 ). Both the pattern I and the pattern II were associated with an increased risk of four cause-specific mortality, but the multimorbidity pattern III were only associated with an increased risk of cancer mortality and respiratory mortality. In sensitivity analysis, the consistent associations were remained when using the competitive risk model (Supplementary Table S4), and after excluding participants who died within the first year of follow-up (Supplementary Table S5). In condition count, the PAFs for all-cause mortality among those with 1, 2, 3, or ≥ 4 chronic diseases were 19.45%, 24.14%, 15.63%, and 9.96%, respectively (Table 3 ). For the multimorbidity pattern, patterns I, II, and III had PAFs for all-cause mortality of 9.13%, 10.27%, and 4.47%, respectively. For the multimorbidity index, Quartile 2, Quartile 3, and Quartile 4 had PAFs for 16.11%, 18.64%, and 28.12%, respectively. The fourth quartile of the multimorbidity index had the highest PAFs for cause-specific mortality, with 30.12%, 42.85%, 25.31%, and 34.99% for cardiovascular mortality, cancer mortality, respiratory mortality, and cerebrovascular mortality, respectively. Table 3 PAFs for all-cases mortality and cases-specific mortality associated with multimorbidity indicator Parameters All-Cause Mortality Cardiovascular Mortality Cancer Mortality Respiratory Mortality Cerebrovascular Mortality Multimorbidity patterns With no multimorbidity -- -- -- -- -- Pattern1 9.13 9.22 4.36 11.63 14.15 Pattern2 10.27 15.36 7.97 5.54 14.14 Pattern3 4.47 / 21.59 3.59 / Condition count 0 -- -- -- -- -- 1 19.45 24.25 26.89 16.23 21.75 2 24.14 29.58 29.83 21.09 27.50 3 15.63 16.99 26.56 12.77 19.49 > 4 9.96 11.39 17.48 9.05 13.41 Multimorbidity index Quartile 1 -- -- -- -- -- Quartile 2 16.11 22.81 15.03 13.35 18.19 Quartile 3 18.64 23.52 27.74 14.66 19.61 Quartile 4 28.12 30.12 42.85 25.31 34.99 *PAF was estimated by considering the effects of age, gender, education, marital status, healthcare services, physical exercise, smoking status, alcohol consumption. The comparison of the predictive performance of multimorbidity indicators for all-Cause mortality was shown in Table 4 and Supplementary Figure S1. The AUC values of these three indicators range from 0.755 to 0.775; these indicated that these three indicators perform similarly in terms of predictive performance. Based on IDI and NRI, all three multimorbidity indicators outperformed the base model in predicting 5-year all-cause mortality (IDI: >0, all p 0, all p < 0.05). The multimorbidity index demonstrated a slightly better discriminative ability compared to the condition count (C-statistic: p < 0.001; IDI: 0.003, p < 0.001; NRI: 0.0046, p < 0.001) and multimorbidity pattern (C-statistic: p < 0.001; IDI: 0.007, p < 0.001; NRI: 0.0055, p < 0.001). Table 4 C-statistics, IDIs, and NRIs for five-year all-cause mortality Measures of multimorbidity AUC C-statistics P Value IDIs P Value NRIs P Value Base model (As reference) 0.755 0.758 -- -- -- -- -- Condition count 0.773 0.767 < 0.001 0.015 < 0.001 0.0133 < 0.001 Multimorbidity pattern 0.766 0.764 < 0.001 0.011 < 0.001 0.0127 < 0.001 Multimorbidity index 0.775 0.770 < 0.001 0.018 < 0.001 0.0181 < 0.001 Multimorbidity pattern (As reference) 0.766 0.764 -- -- -- -- -- Condition count 0.773 0.767 < 0.001 0.004 < 0.001 0.0008 < 0.001 Multimorbidity index 0.775 0.770 < 0.001 0.007 < 0.001 0.0055 < 0.001 Condition count (As reference) 0.773 0.767 -- -- -- -- -- Multimorbidity index 0.775 0.770 < 0.001 0.003 < 0.001 0.0046 < 0.001 Abbrevaiiton: NRI, net reclassification improvement; IDI, integrated discrimination improvement *Base model considered the effect of age, gender, education, marital status, healthcare services, physical exercise, smoking status, and alcohol consumption., and other model considered the variable in base model, as well as the the specific moltimorbidity indicator itself . Discussion To our best knowledge, this is the first comprehensive study in China to examine the association of mortality risk with multimorbidity defined from three different dimensions. This large cohort study found that the multimorbidity, assessed by condition count, multimorbidity index, and multimorbidity pattern, was associated with an increased risk of all-cause mortality and four kinds of cause-specific mortality, regardless of the indicators. Compared to the condition count and multimorbidity pattern, the multimorbidity index performed better in predicting mortality. Condition count is a commonly used indicator in epidemiological studies and health services because it is simple and easy to obtain[ 22 ]. We found that in this study condition count was positively associated with an increased mortality risk in senior adults. A large epidemiological study from China Kadoorie Biobank also reported similar results among older adults[ 3 ]. The possible mechanism explanation may be due to that having more chronic diseases was associated with poorer health status, higher risks of organ dysfunction, sepsis, and death [ 23 ]. Multimorbidity index takes into account both disease severity and physical condition, thus enabling a more comprehensive quantification of health status and disease burden [ 6 ]. However, due to the heterogeneity between different multimorbidity indices[ 24 ], their comparability across studies is limited, therefore new, validated, and widely applicable tools need to be developed for multimorbidity evaluation. Our study developed a new multimorbidity index using a weighted approach with 10-fold cross-validation, and found a positive linear relationship of the multimorbidity index with the risk of all-causal mortality and four specific-causal mortalities. Similar results were also reported in two cohort studies among Americans [ 25 , 26 ]. The non-linear association between cardiovascular deaths and the multimorbidity index differs from that of deaths caused by other diseases. Firstly, cardiovascular diseases are more susceptible to the influence of medical services and social welfare measures, which mitigates the increase in cardiovascular death risk. Meanwhile, high-risk diseases emerge as the index increases, thereby reducing the likelihood of cardiovascular death occurrences. This suggests that early management of multimorbidity may help reduce mortality risk. Similar to the findings reported in previous studies [ 27 – 30 ], our research identified three multimorbidity patterns. Compared with participants without multimorbidity, those with three specific multimorbidity patterns had a higher risk of mortality when compared to those without multimorbidity, and the mortality risk varied across different patterns. Participants with the Pattern I which was characterized by the high prevalence of cerebrovascular diseases, neurological and mental disorders, musculoskeletal-related diseases, respiratory diseases, and digestive diseases, was similar to a pattern reported in another study[ 31 ]. We found that Pattern I was associated with a higher risk of mortality. This might be related to the composition of the main disease characteristics. For instance, stroke and COPD were among the leading causes of death in China, second only to ischemic heart disease [ 32 ]. Pattern II, characterized by cardiovascular, liver-related diseases, and endocrine system diseases, is commonly reported in different research [ 33 , 34 ]. Similar to other studies[ 10 , 35 ], Pattern II showed significant associations with both all-cause mortality and cause-specific mortality. Pattern III, characterized by tumors, eye-related diseases, and kidney-related diseases, has been rarely reported in previous studies. The SNAC-K study identified a similar "tumors and sensory impairments" pattern[ 30 ]. Pattern III may be related to immune responses and metabolic disorders [ 10 , 36 , 37 ]. The mortality risk for pattern III in our study was 44% higher compared to those without multimorbidity. The differences in mortality risk across different multimorbidity patterns suggest that multimorbidity research should focus on specific disease spectra based on the outcome of interest [ 22 ]. Early intervention and appropriate treatment for individuals with specific disease patterns could counteract the progression of frailty [ 38 ] and improve their quality of life [ 39 ]. In the multimorbidity indicators, the highest PAFs for all-cause mortality and cause-specific mortality were observed in the fourth quartile of the multimorbidity index. This indicates that the multimorbidity index can better measure the association between multimorbidity and mortality risk. Compared to a base model adjusted only for confounders, models that included multimorbidity indicators had higher C-statistics. The IDI and NRI indicated that, compared to condition count and multimorbidity patterns, the multimorbidity index provided improvements in overall discrimination and net reclassification, suggesting that assessing disease severity and type is important. Consistent with previous research[ 22 ], Despite the limited differences in predictive performance across different indicators, condition count methods remain a simple and practical tool for predicting mortality risk in clinical practice. The condition count, as a concise, intuitive, highly generalizable, and easy-to-interpret measurement metric, holds significant value in the preliminary assessment of mortality risk among old population. In comparison to other studies which only controlled for age and sex, our study additionally controlled for various confounders besides age and sex, resulting in more reliable study outcomes. The strengths of this study include its prospective cohort design, large sample size, and the broad range of multisystem chronic diseases considered. It is also the first study in China to use the fuzzy c-means clustering method to explore multimorbidity patterns and to comprehensively assess multimorbidity status using three different multimorbidity indicators from different dimensions. The consistent results from three different indicators manifested the robustness of the research results. Compared to previous studies[ 19 , 40 ], our study fully considered the impact of confounders on the predictive model, enhancing the accuracy and reliability of the results. Moreover, our study defines multimorbidity with systematic diseases, which can directly reflect the interactions between different systems. We calculated the PAFs of multimorbidity indices, which indicate that controlling and managing multimorbidity can reduce or lower the proportion of deaths. However, the study also has some limitations. First, there is no information on biomarkers or clinical care history, which could lead to the underestimation of disease burden and information bias. Second, the severity and extent of diseases within the same system are not consistent, and combining them into system-level diseases may affect the results, our future research will continue to refine the classification of diseases and provide more detailed results from different perspectives. In conclusion, condition count, multimorbidity index, and multimorbidity pattern were all significantly associated with the increased risk of mortality in the community-dwelling older Chinese and these three indicators had similar predictive performance for the mortality. The index and patterns provide more insights into understanding multimorbidity, but the conditional count remains a simple, cost-effective and easily implementable method for predicting mortality in both community and clinical settings. In real practice, multimorbidity indicators can be selected based on research objectives, study populations, and outcomes of interest. This study provides a set of effective and suitable methods for evaluating multimorbidity among Chinese people, and the results emphasize the importance of early identification and comprehensive management of multimorbidity in older populations. Abbreviations HR hazard ratio CI confidence intervals IDI integrated discrimination improvement NRI net reclassification improvement AUC area under curve YAHC Yuexiu Ageing and Health Cohort ICD-10 the International Classification of diseases,10th Revision BMI body mass index SD standard deviation IQR interquartile range (IQR) PAF population attributable fraction ROC receiver operating characteristic COPD chronic obstructive pulmonary disease SNAC-K Swedish National Study on Aging and Care in Kungsholmen. Declarations Acknowledgment : The authors are grateful to epidemiologists, nurses, and doctors in the Guangzhou Yuexiu District Center for Disease Control and Prevention, and eighteen community health service centers for data collection, and appreciate all study subjects for their participation. Conflict of Interest disclosure : The authors declare that there are no conflicts of interest Ethics approval statement : This study was approved by the Medical Ethics Committee of Southern University of Science and Technology. The study was performed in line with the Declaration of Helsinki and all participants provided informed consent. Funding statement : This work was supported by the Shenzhen Medical Research Fund (B2303004) and Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515010686). The founders had no role in the design, analysis, or writing of this manuscript. Data availability statement : The data used to support the findings of this study are available from the corresponding author upon request. A proposal with a detailed description of study objectives and a statistical analysis plan will be needed for the evaluation of the reasonability of requests if someone requests data sharing. Patient consent statement : The participants were required to sign a written informed consent form before joining the study. Patients and/or the public were not involved in the design, conduct, reporting, or dissemination plans of this research. Authors' contributions : XDL and WJZ conceived and designed the study; MG analyzed the data and drafted the manuscript; GM, YFC, CJC, YQW, JY, ZYF, YTT, ZLY collected and cleaned the data, FW and YFC coordinated the field investigation and data curation. WJZ, XDZ, and XDL reviewed and edited the manuscript. All co-authors provided comments and approved the final version. References Wei MY, Kabeto MU, Galecki AT, Langa KM. Physical Functioning Decline and Mortality in Older Adults With Multimorbidity: Joint Modeling of Longitudinal and Survival Data. journals Gerontol Ser Biol Sci Med Sci. 2019;74(2):226–32. Jani BD, Hanlon P, Nicholl BI, McQueenie R, Gallacher KI, Lee D, Mair FS. 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06:29:32","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":223507,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7890602/v1/74cc11344328a923a26c8702.html"},{"id":96246371,"identity":"edb2cb3e-c0e5-4474-ba3f-fb65d0488a8f","added_by":"auto","created_at":"2025-11-19 07:25:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75322,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response association between multimorbidity index with all-cases mortality and cases-specific mortality by using restricted cubic spline regression\u003c/p\u003e\n\u003cp\u003eThe potential non-linear relationship was examined using restricted cubic spline regression (knots on the 25th, 50th, and 75th percentiles), with HR and 95% CI being calculated based on the cox proportional risk model. HR (95% CI) was estimated by considering the effect of age, gender, education, marital status, healthcare services, physical exercise, smoking status, alcohol consumption.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7890602/v1/4e5521217617742f58d25b74.png"},{"id":102234199,"identity":"1e7b6469-5b77-40a5-a236-ff2664f109aa","added_by":"auto","created_at":"2026-02-09 16:07:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1281614,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7890602/v1/4751ba54-bcca-486b-b305-effe416df8a8.pdf"},{"id":96247083,"identity":"4a6bc1b4-fcf3-4324-aced-cad8ee9a08f6","added_by":"auto","created_at":"2025-11-19 07:27:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":76507,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7890602/v1/1451d68db417c14b2397c59a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAssociation of multimorbidity and mortality risk in senior adults: a population-based cohort study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultimorbidity is common among older adults. Multimorbidity is not only significantly associated with decreased quality of life, increased disability, but also leads to a large consumption of medical resources and poses a major challenge to the global health care system. In older adults, the prevalence of multimorbidity increased with age [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and those with multimorbidity have a higher risk of death [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the measurement of multimorbidity varies across different studies.\u003c/p\u003e\u003cp\u003eCondition count is one of commonly used approaches due to its simplicity and ease of access [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; however, it often overlooks differences in disease severity and the complex interactions among conditions[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Multimorbidity patterns considered interactions between diseases [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and can be extracted through various methods[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Unlike K-means (a hard clustering algorithm that forces each disease to belong to a single cluster), fuzzy c-means is a soft clustering technique that allows each disease to be simultaneously assigned to multiple clusters through membership probability[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], thus allowing establishment of multimorbidity patterns that consider all possible disease combinations[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, it has not yet been applied to extract multimorbidity patterns. The multimorbidity index, a method taking into account the severity and type of diseases, is receiving increasing attention[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To date, there is no clear and effective \"gold standard\" for measuring multimorbidity index[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and most indices developed in Western populations may have limited applicability in community-dwelling older adults in non-Western countries[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Some studies have developed new multimorbidity indices among Chinese community populations to predict outcomes, but these have not shown significant advantages over traditional indices like the Charlson Multimorbidity Index[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Thus, developing a new multimorbidity index specific to the disease conditions of old Chinese adults is necessary.\u003c/p\u003e\u003cp\u003eRevealing the impact of multimorbidity on the mortality prediction in community-dwelling older populations has become an important issue in both clinical practice and research. Previous studies have mostly used a single type of indicator to predict mortality risk, and few studies have evaluated the predictive performance of different types of multimorbidity indicators. and the differences in predictive performance of various multimorbidity indicators for mortality risk remain unclear. Therefore, this study aims to comprehensively clarify the association and predictive performance between three multimorbidity indicators (condition count, multimorbidity patterns, and multimorbidity index) and the risk of all-cause and cause-specific mortality based on a large-scale cohort.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eParticipants in cohort study were derived from the Yuexiu Ageing and Health Cohort (YAHC), which a dynamic cohort conducted since 2016 in Yuexiu District, Guangzhou, Southern China. The YAHC is coordinated by the Guangzhou Yuexiu District Center for Disease Control and Prevention, and is implemented by 18 community health service centers in its jurisdiction. YAHC is established based on the Older Health Management Project, one of the National Basic Public Health Service Programs in China. YAHC targets individuals aged 65 years and older, conducting annual health examinations and health management for community-dwelling older adults. Relevant data, including sociodemographic information, physical examination, lifestyle, laboratory data, history of chronic diseases, medical history, et al, are collected from each individual in each year. From January 2016 to October 2023, the YAHC successfully recruited 164,524 senior residents. After excluding 1,566 participants with a follow-up duration of less than 6 months, missing information on birthdate and gender, and incomplete medical history at baseline, a total of 162,958 participants were included in this study. This study was approved by the Medical Ethics Committee of Southern University of Science and Technology. The study was conducted in accordance with the Declaration of Helsinki. Written informed consent of survey was obtained from all participants before data collection.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssessment of multimorbidity\u003c/h3\u003e\n\u003cp\u003eAll participants received detailed medical and physical examination each year, the diseases status and relevant information were recorded. The participants were also required to answer whether they were diagnosed to have the diseases by any doctors. Information on chronic diseases then was determined by checking medical examination results, case record information, questionnaire information, health file information. The International Classification of Diseases, 10th Revision (ICD-10) was used for disease coding. Information on 11 system-specific diseases were systematically retrieved from the cohort: including cardiovascular diseases, cerebrovascular diseases, endocrine system diseases, respiratory diseases, liver-related diseases, cancer, neurological and psychiatric disorders, kidney-related diseases, musculoskeletal-related diseases, gastrointestinal diseases, and eye-related diseases (Supplementary Table S1). Multimorbidity was defined as having two or more of the 11 chronic diseases forementioned. Three indicators for multimorbidity, including condition count, multimorbidity index, and multimorbidity pattern, were created; the detailed calculation methods for the three indicators can be seen in Supplementary Table S2.\u003c/p\u003e\n\u003ch3\u003eAscertainment of mortality\u003c/h3\u003e\n\u003cp\u003eMortality data up to 31 December 2023 were obtained from the National Death Registry of China. Mortality information includes the time of death and the cause of death. Causes of death were coded by professional medical workers using the ICD-10. The primary outcome of this study was all-cause mortality, and secondary outcomes were cause-specific mortality cardiovascular diseases (I00-I25, I70-I89), respiratory diseases (J00-J99), cancers (C00-C99), and cerebrovascular diseases (I60-I69) due to they were the dominant causes of the death.\u003c/p\u003e\n\u003ch3\u003eAssessment of covariates\u003c/h3\u003e\n\u003cp\u003eTo collect information, trained medical staff from community health service centers created health records for residents who underwent annual physical examinations. A semi-structured questionnaire with in-depth interview was adopted to collected information. The social demographic information included age (years), gender (male, female), marital status (single, married, divorced, widowed), education (junior high school or below, senior high school, college or above), and healthcare service (self-funded, partially self-funded, public-funded). The lifestyle factors included smoking status, alcohol consumption, and physical activity. Partially self-funded healthcare service included fees from Basic Medical Insurance, Poverty Relief Money, Commercial Health Insurance, New Rural Cooperative Medical Scheme, or other health insurance. Smoking status was categorized as never smoked, former smoker, or current smoker, based on a lifetime smoking history of at least 100 cigarettes and current smoking status. Alcohol consumption was classified into two categories of nondrinker and drinker. Physical activity was categorized as \"yes\" or \"no\" based on whether exercise frequency exceeded once per week. Height and weight of the participants were measured, and body mass index (BMI, kg/m\u003csup\u003e2\u003c/sup\u003e) was calculated as weight in kilograms divided by the square of height in meters.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll analyses were conducted using R version 4.4.2, and a \u003cem\u003eP\u003c/em\u003e-value with two-side less than 0.05 was considered to be significant. The continuous variable with normal distribution was displayed by mean and standard deviation (SD), while non-normal distribution was represented by median and interquartile range (IQR); the distribution difference was tested by t-test, analysis of variance (ANOVA), or Mann-Whitney U Test. The categorical variables were expressed by frequency and percentage (%) and compared by the Chi-square test.\u003c/p\u003e\u003cp\u003eFollow-up time was defined as the interval between baseline and death, loss to follow-up, or 31 December 2023, whichever occurred first, and the person-years were calculated. The Cox proportional hazards model was used to estimate the associations of all-cause and cause-specific mortality with each type of chronic diseases, condition count, multimorbidity index, and multimorbidity pattern, and hazard ratio (HR) and 95% confidence intervals (CI) was calculated after adjustment for confounders. Schoenfeld's global test did not find a violation of the proportional hazard assumption. The linear exposure-response relationship was tested by treating the median of each quartile of the multimorbidity index as a continuous variable in the model. The potential nonlinear relationship was examined using restricted cubic spline regression. The confounders in the adjusted model included age, gender, marital status, healthcare services, education, smoking status, alcohol consumption, physical activity, and BMI. To test the robustness and consistency of our results, a multiple-adjusted Fine and Gray competing risk regression model was employed to estimate the association of multimorbidity with the risk of specific-cause mortality; then the repeated analysis with the Cox proportional hazards model was done after excluding participants who died within the first year of follow-up. Furthermore, we calculated the adjusted the population attributable fraction (PAF, %) to determine the percentage of mortality that could be prevented if each specific chronic condition or multimorbidity index were eliminated [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The PAF provides an estimate of the proportion of disease cases that could be avoided by addressing the risk factors, Specifically, the PAF was calculated using the formula as follows:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:PAF=\\frac{P\\text{e}(HR-1)}{P\\text{e}\\left(HR-1\\right)+1}X100\\%\\)\u003c/span\u003e\u003c/span\u003e, where \u003cem\u003eP\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e represents the proportion of the exposure in the entire population.\u003c/p\u003e\u003cp\u003eTo compare the effectiveness of different multimorbidity indicators, the Cox proportional hazards model was used to construct a prediction model for the occurrence of all-cause mortality based on the above covariate and various indicators of multimorbidity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Receiver operating characteristic (ROC) curves were created using the predicted survival probabilities and survival status of participants at the end of follow-up. The C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were used to compare the performance of different multimorbidity indices in predicting 5-year mortality rates.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe median follow-up was 4.79 years. During 745,774 person-years of follow-up, 15,525 deaths occurred, including 4,195 (27.02%) due to cardiovascular diseases, 2,340 (15.07%) cerebrovascular diseases, 1,068 (6.88%) cancers, 4,426 (28.51%) respiratory diseases, and 3,496 (22.52%) other causes. The mean age of all participants was 72.9 (7.13) years and a total of 83,255 (48.91%) participants had multimorbidity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSocio-demographic, lifestyle, and health characteristics of the study population according to condition count of participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDead\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSurvivor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of participants, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e162,958 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e147,433 (90.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15,525 (9.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72.90 (7.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72.25 (6.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e79.38 (7.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.30 (3.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.34 (3.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.65 (3.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70,859 (43.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62,963 (42.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7,896 (50.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92,099 (56.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84,470 (57.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7,629 (49.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJunior high school or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89,439 (54.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79,142 (53.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10,297 (66.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSenior high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49,577 (30.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46,186 (31.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,391 (21.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23,942 (14.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22,105 (14.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,837 (11.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2797 (1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2449 (1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e348 (2.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137,906 (84.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e126,769 (85.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11,137 (71.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,318 (1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,113 (1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e205 (1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19,937 (12.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16,102 (10.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,835 (24.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealthcare service, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-funded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,746 (2.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,575 (2.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e171 (1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePartially self-funded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e149,199 (91.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e134,971 (91.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14,228 (91.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic-funded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,013 (6.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8,887 (6.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,126 (7.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNonsmoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e145,997 (89.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e131,845 (89.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14,152 (91.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,318 (3.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4848 (3.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e470 (3.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11643 (7.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10740 (7.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e903 (5.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol consumption, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNondrinker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e152,687 (93.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e137,836 (93.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14,851 (95.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,271 (6.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9,597 (6.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e674 (4.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical Exercise, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45,995 (28.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,870 (37.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5,870 (37.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExercise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116,963 (71.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107,308 (72.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9,655 (62.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic diseases, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89,998 (55.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79,352 (53.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10,646 (68.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,042 (5.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7,052 (4.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,990 (12.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebrovascular diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50,820 (31.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45,603 (30.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5,217 (33.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEndocrine system diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5888 (3.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4854 (3.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1034 (6.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53,736 (32.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50,791 (34.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,945 (18.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver- related diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11,646 (7.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9,870 (6.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,776 (11.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,670 (4.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,479 (3.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,191 (7.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeurological and psychiatric disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,698 (2.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4,091 (2.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e607 (3.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidney- related diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,982 (4.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7,088 (4.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e894 (5.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMusculoskeletal-related diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,864 (4.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7,390 (5.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e474 (3.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15,096 (9.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13,871 (9.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,225 (7.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultimorbidity, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79,703 (48.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71,156 (48.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8,547 (55.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultimorbidity index, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00 (1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.94 (1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.53 (1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026lowast; P value from the t-test for normal distributed continuous variable, and from Chi-square test for the categorical variables.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCompared with participants with no multimorbidity, participants with multimorbidity had a higher risk of all-cause mortality (HR:1.56, 95% CI:1.51\u0026ndash;1.62). Comparing with participants with condition count of 0, those with 1, 2, 3, and \u0026ge;\u0026thinsp;4 chronic diseases had 1.86-fold (95% CI: 1.77\u0026ndash;1.95), 2.25-fold (95% CI: 2.14\u0026ndash;2.36), 2.24-fold (95% CI: 2.11\u0026ndash;2.37), and 2.03-fold (95% CI: 2.16\u0026ndash;2.45) risk of all-cause mortality, respectively, after adjustment for potential confounders (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e); Every one condition count increment was associated with 17% (HR:1.17, 95% CI: 1.16\u0026ndash;1.18) increased risk of all-cause mortality. Similar harmful effects were also found on cardiovascular mortality, cancer mortality, respiratory mortality, and cerebrovascular mortality.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of multimorbidity patterns, number of chronic conditions, and multimorbidity index with all-cause mortality and cause-specific mortality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eN0\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eAll-Cause Mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eCardiovascular Mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eCancer Mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eRespiratory Mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003eCerebrovascular Mortality\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eN4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eN5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15,525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4,195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1,068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4,426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e2,340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCondition count\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34,865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41,412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4,340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.86 (1.77, 1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.14 (1.94, 2.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.31 (1.88, 2.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.69 (1.55, 1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.99 (1.74, 2.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37,115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4,374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.25 (2.14, 2.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.65 (2.40, 2.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.67 (2.17, 3.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.05 (1.87, 2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e2.49 (2.18, 2.84)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21,820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.24 (2.11, 2.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.37 (2.12, 2.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.42 (2.74, 4.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.98 (1.78, 2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e2.62 (2.26, 3.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.30 (2.16, 2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.51 (2.22, 2.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.49 (2.74, 4.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.17 (1.93, 2.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e2.82 (2.40, 3.32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEvery one condition count increment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.17 (1.16\u0026ndash;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.17 (1.14\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.25 (1.20\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.16 (1.14\u0026ndash;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.21 (1.18\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMultimorbidity pattern\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWithout Multimorbidity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76,277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6,978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2,106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePattern I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14,097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.97 (1.89, 2.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.98 (1.82, 2.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.44 (1.17, 1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.27 (2.09, 2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e2.59 (2.33, 2.88)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePattern II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41,393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.41 (1.35, 1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.65 (1.53, 1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.31 (1.11, 1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.21 (1.11, 1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.59 (1.43, 1.76)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePattern III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15,666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.44 (1.36, 1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00 (0.89, 1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.59 (3.07, 4.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.35 (1.21, 1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.89 (0.75, 1.06)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMultimorbidity index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile 1 (0\u0026ndash;0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43,729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile 2 (0.49\u0026ndash;1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37,294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,873\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.76 (1.68\u0026ndash;1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.17 (1.97\u0026ndash;2.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.71 (1.38\u0026ndash;2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1,107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.61 (1.47\u0026ndash;1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.88 (1.65\u0026ndash;2.14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile 3 (1.73\u0026ndash;2.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31,176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.08 (1.97\u0026ndash;2.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.45 (2.22\u0026ndash;2.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.81 (2.30\u0026ndash;3.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.81 (1.64\u0026ndash;1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e2.15 (1.88\u0026ndash;2.47)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile 4 (2.91\u0026ndash;13.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35,234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.57 (2.45\u0026ndash;2.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.73 (2.48-3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.01 (3.32\u0026ndash;4.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1,482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.36 (2.17\u0026ndash;2.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e3.16 (2.79\u0026ndash;3.57)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEvery 1-unit increment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.18 (1.17\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.17 (1.16\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.26 (1.22\u0026ndash;1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.18 (1.16\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.25 (1.22\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eAbbreviation: N0, N1, N2, N3, N4 and N5 represents the number of participants with survivors, all-cases mortality, cardiovascular mortality, cancer mortality, respiratory mortality, and cerebrovascular mortality, respectively.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003e*Adjusted for age, gender, education, marital status, healthcare services, physical exercise, smoking status, alcohol consumption.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe mean (SD) of the multimorbidity index was 1.94 (1.67) and 2.53 (1.82) for dead and survival participants, with a significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Every 1-unit increment of the multimorbidity index was associated 1.18-fold (95% CI: 1.17\u0026ndash;1.19) risk of all-cause mortality (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared to those within the quartile 1, participants within the quartile 2, the quartile 3 and quartile 4 had -adjusted HRs of 1.76 (95% CI: 1.68\u0026ndash;1.85), 2.08 (95% CI: 1.97\u0026ndash;2.18), and 2.57 (95% CI: 2.45\u0026ndash;2.69), respectively, with significant linear trends (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A nonlinear relationship was observed between the multimorbidity index and all-cause mortality (\u003cem\u003eP\u003c/em\u003e for nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Similar harmful effects were also found on cardiovascular, cancer, respiratory, and cerebrovascular mortality.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThree mutually exclusive multimorbidity patterns were extracted (Supplementary Table S3). Pattern I was featured with cerebrovascular diseases, neurological and psychiatric disorders, musculoskeletal-related disease, respiratory diseases, and gastrointestinal diseases. Pattern II was characterized with cardiovascular diseases, endocrine system diseases, and liver-related diseases. Pattern III was characterized with cancer, kidney-related diseases, and eye-related diseases. When comparing with participants without multimorbidity, those with the pattern I (HR: 1.97, 95% CI: 1.89\u0026ndash;2.06), pattern II (HR: 1.41, 95% CI: 1.35\u0026ndash;1.47) and pattern III (HR: 1.44, 95%CI:1.36\u0026ndash;1.52) were associated with the increased risk of all-cause mortality (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both the pattern I and the pattern II were associated with an increased risk of four cause-specific mortality, but the multimorbidity pattern III were only associated with an increased risk of cancer mortality and respiratory mortality.\u003c/p\u003e\u003cp\u003eIn sensitivity analysis, the consistent associations were remained when using the competitive risk model (Supplementary Table S4), and after excluding participants who died within the first year of follow-up (Supplementary Table S5).\u003c/p\u003e\u003cp\u003eIn condition count, the PAFs for all-cause mortality among those with 1, 2, 3, or \u0026ge;\u0026thinsp;4 chronic diseases were 19.45%, 24.14%, 15.63%, and 9.96%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For the multimorbidity pattern, patterns I, II, and III had PAFs for all-cause mortality of 9.13%, 10.27%, and 4.47%, respectively. For the multimorbidity index, Quartile 2, Quartile 3, and Quartile 4 had PAFs for 16.11%, 18.64%, and 28.12%, respectively. The fourth quartile of the multimorbidity index had the highest PAFs for cause-specific mortality, with 30.12%, 42.85%, 25.31%, and 34.99% for cardiovascular mortality, cancer mortality, respiratory mortality, and cerebrovascular mortality, respectively.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePAFs for all-cases mortality and cases-specific mortality associated with multimorbidity indicator\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll-Cause Mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCardiovascular Mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCancer Mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRespiratory Mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCerebrovascular Mortality\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultimorbidity patterns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWith no multimorbidity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePattern1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePattern2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePattern3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCondition count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultimorbidity index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e*PAF was estimated by considering the effects of age, gender, education, marital status, healthcare services, physical exercise, smoking status, alcohol consumption.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe comparison of the predictive performance of multimorbidity indicators for all-Cause mortality was shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Supplementary Figure S1. The AUC values of these three indicators range from 0.755 to 0.775; these indicated that these three indicators perform similarly in terms of predictive performance. Based on IDI and NRI, all three multimorbidity indicators outperformed the base model in predicting 5-year all-cause mortality (IDI: \u0026gt;0, all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; NRI: \u0026gt;0, all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The multimorbidity index demonstrated a slightly better discriminative ability compared to the condition count (C-statistic: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; IDI: 0.003, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; NRI: 0.0046, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and multimorbidity pattern (C-statistic: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; IDI: 0.007, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; NRI: 0.0055, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eC-statistics, IDIs, and NRIs for five-year all-cause mortality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasures of multimorbidity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC-statistics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026nbsp;Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIDIs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026nbsp;Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNRIs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026nbsp;Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBase model\u003c/p\u003e\u003cp\u003e(As reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCondition count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultimorbidity pattern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultimorbidity index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultimorbidity pattern\u003c/p\u003e\u003cp\u003e(As reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCondition count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultimorbidity index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCondition count\u003c/p\u003e\u003cp\u003e(As reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultimorbidity index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbrevaiiton: NRI, net reclassification improvement; IDI, integrated discrimination improvement\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Base model considered the effect of age, gender, education, marital status, healthcare services, physical exercise, smoking status, and alcohol consumption., and other model considered the variable in base model, as well as the the specific moltimorbidity indicator itself .\u003c/td\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our best knowledge, this is the first comprehensive study in China to examine the association of mortality risk with multimorbidity defined from three different dimensions. This large cohort study found that the multimorbidity, assessed by condition count, multimorbidity index, and multimorbidity pattern, was associated with an increased risk of all-cause mortality and four kinds of cause-specific mortality, regardless of the indicators. Compared to the condition count and multimorbidity pattern, the multimorbidity index performed better in predicting mortality.\u003c/p\u003e\u003cp\u003eCondition count is a commonly used indicator in epidemiological studies and health services because it is simple and easy to obtain[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. We found that in this study condition count was positively associated with an increased mortality risk in senior adults. A large epidemiological study from China Kadoorie Biobank also reported similar results among older adults[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The possible mechanism explanation may be due to that having more chronic diseases was associated with poorer health status, higher risks of organ dysfunction, sepsis, and death [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMultimorbidity index takes into account both disease severity and physical condition, thus enabling a more comprehensive quantification of health status and disease burden [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, due to the heterogeneity between different multimorbidity indices[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], their comparability across studies is limited, therefore new, validated, and widely applicable tools need to be developed for multimorbidity evaluation. Our study developed a new multimorbidity index using a weighted approach with 10-fold cross-validation, and found a positive linear relationship of the multimorbidity index with the risk of all-causal mortality and four specific-causal mortalities. Similar results were also reported in two cohort studies among Americans [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The non-linear association between cardiovascular deaths and the multimorbidity index differs from that of deaths caused by other diseases. Firstly, cardiovascular diseases are more susceptible to the influence of medical services and social welfare measures, which mitigates the increase in cardiovascular death risk. Meanwhile, high-risk diseases emerge as the index increases, thereby reducing the likelihood of cardiovascular death occurrences. This suggests that early management of multimorbidity may help reduce mortality risk.\u003c/p\u003e\u003cp\u003eSimilar to the findings reported in previous studies [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], our research identified three multimorbidity patterns. Compared with participants without multimorbidity, those with three specific multimorbidity patterns had a higher risk of mortality when compared to those without multimorbidity, and the mortality risk varied across different patterns. Participants with the Pattern I which was characterized by the high prevalence of cerebrovascular diseases, neurological and mental disorders, musculoskeletal-related diseases, respiratory diseases, and digestive diseases, was similar to a pattern reported in another study[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. We found that Pattern I was associated with a higher risk of mortality. This might be related to the composition of the main disease characteristics. For instance, stroke and COPD were among the leading causes of death in China, second only to ischemic heart disease [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Pattern II, characterized by cardiovascular, liver-related diseases, and endocrine system diseases, is commonly reported in different research [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Similar to other studies[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], Pattern II showed significant associations with both all-cause mortality and cause-specific mortality. Pattern III, characterized by tumors, eye-related diseases, and kidney-related diseases, has been rarely reported in previous studies. The SNAC-K study identified a similar \"tumors and sensory impairments\" pattern[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Pattern III may be related to immune responses and metabolic disorders [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The mortality risk for pattern III in our study was 44% higher compared to those without multimorbidity. The differences in mortality risk across different multimorbidity patterns suggest that multimorbidity research should focus on specific disease spectra based on the outcome of interest [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Early intervention and appropriate treatment for individuals with specific disease patterns could counteract the progression of frailty [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and improve their quality of life [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the multimorbidity indicators, the highest PAFs for all-cause mortality and cause-specific mortality were observed in the fourth quartile of the multimorbidity index. This indicates that the multimorbidity index can better measure the association between multimorbidity and mortality risk.\u003c/p\u003e\u003cp\u003eCompared to a base model adjusted only for confounders, models that included multimorbidity indicators had higher C-statistics. The IDI and NRI indicated that, compared to condition count and multimorbidity patterns, the multimorbidity index provided improvements in overall discrimination and net reclassification, suggesting that assessing disease severity and type is important. Consistent with previous research[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], Despite the limited differences in predictive performance across different indicators, condition count methods remain a simple and practical tool for predicting mortality risk in clinical practice. The condition count, as a concise, intuitive, highly generalizable, and easy-to-interpret measurement metric, holds significant value in the preliminary assessment of mortality risk among old population. In comparison to other studies which only controlled for age and sex, our study additionally controlled for various confounders besides age and sex, resulting in more reliable study outcomes.\u003c/p\u003e\u003cp\u003eThe strengths of this study include its prospective cohort design, large sample size, and the broad range of multisystem chronic diseases considered. It is also the first study in China to use the fuzzy c-means clustering method to explore multimorbidity patterns and to comprehensively assess multimorbidity status using three different multimorbidity indicators from different dimensions. The consistent results from three different indicators manifested the robustness of the research results. Compared to previous studies[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], our study fully considered the impact of confounders on the predictive model, enhancing the accuracy and reliability of the results. Moreover, our study defines multimorbidity with systematic diseases, which can directly reflect the interactions between different systems. We calculated the PAFs of multimorbidity indices, which indicate that controlling and managing multimorbidity can reduce or lower the proportion of deaths. However, the study also has some limitations. First, there is no information on biomarkers or clinical care history, which could lead to the underestimation of disease burden and information bias. Second, the severity and extent of diseases within the same system are not consistent, and combining them into system-level diseases may affect the results, our future research will continue to refine the classification of diseases and provide more detailed results from different perspectives.\u003c/p\u003e\u003cp\u003eIn conclusion, condition count, multimorbidity index, and multimorbidity pattern were all significantly associated with the increased risk of mortality in the community-dwelling older Chinese and these three indicators had similar predictive performance for the mortality. The index and patterns provide more insights into understanding multimorbidity, but the conditional count remains a simple, cost-effective and easily implementable method for predicting mortality in both community and clinical settings. In real practice, multimorbidity indicators can be selected based on research objectives, study populations, and outcomes of interest. This study provides a set of effective and suitable methods for evaluating multimorbidity among Chinese people, and the results emphasize the importance of early identification and comprehensive management of multimorbidity in older populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehazard ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econfidence intervals\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIDI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eintegrated discrimination improvement\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNRI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enet reclassification improvement\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003earea under curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eYAHC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eYuexiu Ageing and Health Cohort\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICD-10\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethe International Classification of diseases,10th Revision\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003estandard deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einterquartile range (IQR)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePAF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epopulation attributable fraction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ereceiver operating characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003echronic obstructive pulmonary disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSNAC-K\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSwedish National Study on Aging and Care in Kungsholmen.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e: The authors are grateful to epidemiologists, nurses, and doctors in the Guangzhou Yuexiu District Center for Disease Control and Prevention, and eighteen community health service centers for data collection, and appreciate all study subjects for their participation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest disclosure\u003c/strong\u003e: The authors declare that there are no conflicts of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThis study was\u0026nbsp;approved by the Medical Ethics Committee of Southern University of Science and Technology. The study was performed in line with the Declaration of Helsinki and all participants provided informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e: This work was supported by the Shenzhen Medical Research Fund (B2303004) and Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515010686). The founders had no role in the design, analysis, or writing of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e: The data used to support the findings of this study are available from the corresponding author upon request. A proposal with a detailed description of study objectives and a statistical analysis plan will be needed for the evaluation of the reasonability of requests if someone requests data sharing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent statement\u003c/strong\u003e: The participants were required to sign a written informed consent form before joining the study. Patients and/or the public were not involved in the design, conduct, reporting, or dissemination plans of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e: XDL and WJZ conceived and designed the study; MG analyzed the data and drafted the manuscript; GM, YFC, CJC, YQW, JY, ZYF, YTT, ZLY collected and cleaned the data, FW and YFC coordinated the field investigation and data curation. WJZ, XDZ, and XDL reviewed and edited the manuscript. All co-authors provided comments and approved the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWei MY, Kabeto MU, Galecki AT, Langa KM. Physical Functioning Decline and Mortality in Older Adults With Multimorbidity: Joint Modeling of Longitudinal and Survival Data. journals Gerontol Ser Biol Sci Med Sci. 2019;74(2):226\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJani BD, Hanlon P, Nicholl BI, McQueenie R, Gallacher KI, Lee D, Mair FS. Relationship between multimorbidity, demographic factors and mortality: findings from the UK Biobank cohort. BMC Med. 2019;17(1):74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan J, Sun Z, Yu C, Guo Y, Pei P, Yang L, Chen Y, Du H, Sun D, Pang Y, et al. Multimorbidity patterns and association with mortality in 0.5 million Chinese adults. 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Bmj-Brit Med J 2020, 368.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnston MC, Crilly M, Black C, Prescott GJ, Mercer SW. Defining and measuring multimorbidity: a systematic review of systematic reviews. Eur J Pub Health. 2019;29(1):182\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlvarez-G\u0026aacute;lvez J, Ortega-Mart\u0026iacute;n E, Carretero-Bravo J, P\u0026eacute;rez-Mu\u0026ntilde;oz C, Su\u0026aacute;rez-Lled\u0026oacute; V, Ramos-Fiol B. Social determinants of multimorbidity patterns: A systematic review. Front public health 2023, 11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSturmer J, Franken DL, Ternus DL, Henn RL, Dias-da-Costa JS, Olinto MTA, Paniz VMV. Dietary pattern as a predictor of multimorbidity patterns: A population-based cross-sectional study with women. 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Autoimmun Rev. 2021;20(9):102892.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTong X, Xu J, Gong E, Zhang X, Li Y, Shao R, Xi H. Frailty as a breakthrough point for multimorbidity management among older adults: challenges and opportunities in China. BMJ (Clinical Res ed). 2024;387:e076767.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHo HE, Yeh CJ, Wei JCC, Chu WM, Lee MC. Trends of Multimorbidity Patterns over 16 Years in Older Taiwanese People and Their Relationship to Mortality. Int J Environ Res Public Health 2022, 19(6).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang S, Chen Y, Xiong L, Jin N, Zhao P, Liang Z, Cheng L, Kang L. Multimorbidity measures associated with cognitive function among community-dwelling older Chinese adults. Alzheimer's Dement J Alzheimer's Assoc. 2024;20(9):6221\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLleal M, Bar\u0026eacute; M, Herranz S, Or\u0026uacute;s J, Comet R, Jordana R, Bar\u0026eacute; M. Trajectories of chronic multimorbidity patterns in older patients: MTOP study. BMC Geriatr 2024, 24(1).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multimorbidity, Condition count, Multimorbidity pattern, Multimorbidity index, Mortality","lastPublishedDoi":"10.21203/rs.3.rs-7890602/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7890602/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThis study aimed to evaluate the association of three multimorbidity indicators with mortality risk among senior adults, and compare their predictive performance on mortality risk.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis prospective cohort study selected 162,958 participants aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years from Yuexiu Ageing and Health Cohort. Information on eleven system diseases was extracted; three multimorbidity indicators (condition count, multimorbidity patterns, and multimorbidity index) were created. Hazard ratio (HR) with 95% confidence intervals (CI) was calculated using Cox proportional hazard model after adjustment for confounders. The C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were used to compare the performance of multimorbidity indicators.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003e15,525 deaths were identified during a median of 4.79 years of follow-up. Compared with participants with no multimorbidity, those with multimorbidity had a 1.56-fold risk of all-cause mortality. Every one condition count increment was associated with a 17% increased risk of all-cause mortality. Three multimorbidity patterns labeled as Patterns I, II, and III were extracted and were significantly associated with the increased mortality risk, with HR being 1.97, 1.41, and 1.44 for Patterns I, II, and III respectively. Every 1-unit increment of multimorbidity index was associated with an 18% increased mortality risk. The multimorbidity index outperformed both multimorbidity pattern (IDI\u0026thinsp;=\u0026thinsp;0.007, NRI\u0026thinsp;=\u0026thinsp;0.0055), and condition count in predicting mortality (IDI\u0026thinsp;=\u0026thinsp;0.003, NRI\u0026thinsp;=\u0026thinsp;0.0046).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThree multimorbidity indicators were all associated with the increased mortality risk in community-dwelling older Chinese. The multimorbidity index had slightly better predictive performance for mortality than other two indicators.\u003c/p\u003e","manuscriptTitle":"Association of multimorbidity and mortality risk in senior adults: a population-based cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 06:29:26","doi":"10.21203/rs.3.rs-7890602/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-05T04:06:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-29T08:01:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-27T16:10:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21575729500957023542175572769167234662","date":"2025-12-24T15:22:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199108559749117536486770757282602827548","date":"2025-12-19T07:47:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-04T14:04:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-28T09:09:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-24T08:39:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-24T08:37:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-10-18T03:36:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"977c757a-a690-4588-a8b2-6d8ab5928963","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:04:55+00:00","versionOfRecord":{"articleIdentity":"rs-7890602","link":"https://doi.org/10.1186/s12877-026-07036-2","journal":{"identity":"bmc-geriatrics","isVorOnly":false,"title":"BMC Geriatrics"},"publishedOn":"2026-02-07 15:59:37","publishedOnDateReadable":"February 7th, 2026"},"versionCreatedAt":"2025-11-17 06:29:26","video":"","vorDoi":"10.1186/s12877-026-07036-2","vorDoiUrl":"https://doi.org/10.1186/s12877-026-07036-2","workflowStages":[]},"version":"v1","identity":"rs-7890602","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7890602","identity":"rs-7890602","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}