Relationship Between Multimorbidity, Combination and All-Cause Mortality Among Older Adults: A Retrospective Cohort Analysis

Relationship Between Multimorbidity, Combination and All-Cause Mortality Among Older Adults: A Retrospective Cohort Analysis | 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 Relationship Between Multimorbidity, Combination and All-Cause Mortality Among Older Adults: A Retrospective Cohort Analysis Kun He, Wenli Zhang, Xueqi Hu, Hao Zhao, Bingxin Guo, Zhan Shi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-122536/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background: Previous studies have evaluated the association of multimorbidity with higher mortality, but epidemiologic data on the association between the combination of multimorbidity and all-cause mortality risk are rare. We aimed to examine the relationship between multimorbidity (number/combination) and all-cause mortality in Chinese older adults. Methods: We conducted a population-based study of 50,100 Chinese participants. Cox regression models were used to estimate the impact of long-term conditions (LTCs) on all-cause mortality. Results: The prevalence of multimorbidity was 31.35% and all-cause mortality was 8.01% (50,100 participants). In adjusted Cox models, the hazard rations (HRs) and 95% confidence intervals (CIs) of all-cause mortality risk for those with 1, 2, and ≥ 3 LTCs compared with those with no LTCs was 1.10 (1.01-1.20), 1.21 (1.10-1.33), and 1.46 (1.27-1.67), respectively ( P trend <0.001). In the LTCs ≥ 2 category, the combination of chronic diseases that included hypertension, diabetes, CHD, COPD, and stroke had the greatest impact on mortality. In the stratified model by age and sex, absolute all-cause mortality was higher among the ≥ 75 age group with an increasing number of LTCs. However, the relative effect size of the increasing number of LTCs on higher mortality risk was larger among those < 75 years. Conclusions: The risk of all-cause mortality is increased with the number of multimorbidity among Chinese older adults, particularly combinations. Health Economics & Outcomes Research Infectious Diseases Health Policy Older adults Chronic conditions Multimorbidity All-cause mortality Combination Figures Figure 1 Figure 1 Background Aging around the world poses a global challenge, previous studies have reported that thousands of persons turn 65 years old every day[ 1 ]. To our knowledge, China has the largest population base in the world, and the size of the elderly population is enormous. In 2018, the number of Chinese people aged 65 or above had reached 166.58 million, accounting for 11.9% of the total population, which means that China is aging at an unprecedented rate[ 2 ]. The phenomenon of ageing has led to a substantial increase in chronic conditions, which consequently results in a rising prevalence of multimorbidity, most commonly described as the presence of two or more long-term conditions (LTCs)[ 3 ]. Studies have found that multimorbidity leads to poor quality of life[ 4 ], increased use of inpatient and ambulatory greater health care[ 5 , 6 ], greater complexity regarding of clinical treatment and patient management[ 7 , 8 ], and more importantly, an increase in mortality[ 9 , 10 ]. For example, previous studies, mainly based on the UK population, had suggested that the all-cause mortality rate for people with multimorbidity was 4.74% [ 11 ]. Thus, the management of multimorbidity has become a public health priority for public health care professionals and health care systems[ 7 , 12 ]. The study found that hypertension, diabetes, stroke, heart disease, chronic respiratory disease and cancer are among the most common types of multimorbidity and are the most common causes of long-term disability and premature mortality worldwide[ 13 – 16 ]. As a result of the change of healthy lifestyle and the progress of medical level, the mortality rate from multimorbidity such as heart disease and cancer has been significantly reduced in high-income countries[ 17 , 18 ], whereas rates of mortality caused by multimorbidity are rising rapidly in low- and middle-income countries, posing a serious social burden[ 19 ]. Thus, a better understanding of the epidemiology of multimorbidity is necessary to develop interventions to reduce the burden of death. However, the relationship between multimorbidity (number/combination) and all-cause mortality has not been well described in the Chinese population. Therefore, we designed a retrospective cohort study to explore the relationship in Chinese older adults. Methods Study design and participants We performed a retrospective cohort analysis of 103,729 participants who entered the health check-up program in Xinzheng City, Henan Province, Central China, from January 1, 2014 to November 15, 2019. A total of 84,353 (81.3%) participants were successfully followed up. We excluded participants with age < 65 at baseline (n = 31,900), or missing data for date of birth and gender at baseline (n = 2,353). A total of 50,100 individuals were included in the main analysis (Supplemental Fig. 1) . The study was approved by the Ethics Committee of Zhengzhou University, and written informed consent was obtained from all participants. Data Collection A standardized questionnaire was used by trained research staff under stringent quality control to collect information. Marital status was categorized as living with partner and without partner. Smokers were defined as ever smoking at least 100 cigarettes in their lifetime and classified as nonsmokers and previous/current smokers[ 20 ]. Alcohol consumption was divided into three categories: never, occasionally or daily. Physical activity was a categorical variable based on the self-reporting and classified as never, occasionally or daily. The measurements for height and weight were performed with the subjects wearing light clothing without shoes[ 21 ]. Blood pressure was measured by an automatic sphygmomanometer (Omron HEM-7125, Kyoto, Japan) after subjects had rested in a seated position for at least 5 minutes, and the mean value was recorded when the subjects were measured three times[ 22 ]. Blood samples were obtained after an overnight fast of at least 8 hours to assess levels of fasting plasma glucose (FPG) using an automatic biochemical analyzer (DIRUI CS380, Changchun, China)[ 22 ]. In this study, diabetes was defined as fasting glucose levels ≥ 7.0 mmol/L and/or current treatment with anti-diabetes medication according to the China guideline for T2DM[ 23 ]. Hypertension was defined as positive for participants who were considered hypertensive with systolic blood pressure (SBP) ≥ 140 mm Hg or diastolic blood pressure (DBP) ≥ 90 mm Hg or current use of antihypertensive medication[ 24 ]. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m 2 ), which was classified based on the WHO classification criteria into underweight (< 18.5 kg/m 2 ), desirable (18.5–24.9 kg/m 2 ), overweight (25.0–29.9 kg/m 2 ), obesity class I (30.0–34.9 kg/m 2 ), obesity class II (35.0–39.9 kg/m 2 ), and obesity class III (≥ 40.0 kg/m 2 )[ 25 ]. Information on the 7 chronic conditions diagnosed by a physician or reported by the study participants was recorded: (1) hypertension, (2) diabetes, (3) coronary heart disease (CHD), (4) stroke, (5) chronic obstructive pulmonary disease (COPD), (6) cancer, and (7) mental system disease. Multimorbidity was classified based on number of chronic conditions into no LTCs, 1 LTC, 2 LTCs, and ≥ 3 LTCs. The outcome variable for the present study was all-cause mortality. Death certificates were obtained as well. Statistical analysis All quantitative variables are described using the median (interquartile range [IQR]) for skewed distribution, and qualitative variables are expressed as frequency (%). Cox regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of all-cause mortality by groups of LTC categories. With no LTCs as the reference group, we constructed three multivariate-adjusted Cox models: model 1 was adjusted for age, sex and marital status; model 2 was adjusted for model 1 plus smoking status, alcohol status and physical activity; and model 3 was further adjusted for BMI. In order to understand the pattern and effect of multimorbidity, we further analyzed the common combination of multimorbidity in older adults. A test for multiplicative interaction showed that age (< 75/≥75 years) and sex at baseline modified the association of LTCs and risk of all-cause mortality (both p -interaction < 0.001). Therefore, for subgroup analyses, we stratified participants by age and sex at baseline. Finally, sensitivity analysis was performed using LTCs defined by the medical examination diagnostic record at study baseline and not including self-reported history. Statistical analyses were performed using SAS 9.1 (SAS Institute, Cary, NC, USA), the forest plot was performed by GraphPad Prism 8, and the level of significance was considered at P < 0.05 (two-tailed). Results The baseline characteristics of study participants with the four LTC groups are presented in Table 1 . A total of 4,012 (8.01%) of the 50,100 participants died. The median age at baseline was 69.18 years (range 65–106 years), and 53.87% of the population was female. The proportion of participants with 0, 1, 2, and 3 or more LTCs was 12,334 (24.62%), 22,060 (44.03%), 12,866 (25.68%), and 2,840 (5.67%), respectively. In addition, the prevalence of multimorbidity in female was higher than in male (67.47% vs 63.58%). The most frequent diseases in participants with 3 or more LTCs were hypertension (98.87%), CHD (89.65%), and diabetes (88.59%). Table 1 Relationship of multimorbidity with demographics and health-related behaviour at baseline. No LTCs N = 12,334(24.62%) 1 LTC N = 22,060(44.03%) 2 LTCs N = 12,866(25.68%) ≥ 3 LTCs N = 2,840(5.67%) Over all N = 50,100 Age; missing values n = 0 Age in years-median (IQR) 68.85 (65.95–75.04) 69.35 (65.98–75.69) 69.20 (65.95–75.42) 69.15 (65.88–75.19) 69.18 (65.96–75.46) Sex; missing values n = 0 Male Female 6,394 (51.84) 5,940 (48.16) 10,437 (47.31) 11,623 (52.69) 5,244 (40.76) 7,622 (59.24) 1,034 (36.41) 1,806 (63.59) 23,109 (46.13) 26,991 (53.87) Marital status; missing values n = 140(0.28%) Living with partner Living without partner 9,852 (80.11) 2,446 (19.89) 17,180 (78.09) 4,821 (21.91) 10,117 (78.88) 2,709 (21.12) 2,215 (78.13) 620 (21.87) 39,364 (78.79) 10,596 (21.21) Smoking status; missing values n = 614(1.24%) Never Current or previous 10,171 (84.50) 1,865 (15.50) 18,402 (84.31) 3,425 (15.69) 11,122 (86.94) 1,671 (13.06) 2,475 (87.46) 355 (12.54) 42,170 (85.21) 7,316 (14.78) Alcohol status; missing values n = 767(1.55%) Never Occasionally Daily 11,224 (93.53) 499 (4.16) 278 (2.32) 20,243 (93.02) 931 (4.28) 589 (2.71) 11,903 (93.3) 490 (3.84) 356 (2.79) 2,634 (93.40) 107 (3.79) 79 (2.80) 46,004 (93.25) 2,027 (4.11) 1,302 (2.64) Physical activity; missing values n = 723(1.46%) Never Occasionally Daily 8,404 (69.78) 1,085 (9.01) 2,554 (21.21) 13,860 (63.71) 2,351 (10.81) 5,543 (25.48) 7,678 (60.18) 1,434 (11.24) 3,647 (28.58) 1,676 (59.41) 306 (10.85) 839 (29.74) 31,618 (64.03) 5,176 (10.48) 12,583 (25.48) BMI; missing values n = 1,323(2.71%) < 18.5 18.5–24.9 25-29.9 30-34.9 35-39.9 ≥ 40 336 (2.82) 8,091 (68.00) 3,129 (26.30) 324 (2.72) 15 (0.13) 3 (0.03) 407 (1.90) 11,943 (55.62) 7,612 (35.45) 1,378 (6.42) 122 (0.57) 11 (0.05) 173 (1.37) 5,833 (46.24) 5,306 (42.06) 1,175 (9.31) 114 (0.90) 14 (0.11) 34 (1.22) 1,215 (43.53) 1,187 (42.53) 316 (11.32) 36 (1.29) 3 (0.11) 950 (1.95) 27,082 (55.52) 17,234 (35.33) 3,193 (6.55) 287 (0.59) 31 (0.06) Chronic conditions Hypertension Diabetes CHD Stroke COPD Tumour Mental disorders 0 (0.00) 0 (0.00) 0 (0.00) 0 (0.00) 0 (0.00) 0 (0.00) 0 (0.00) 17,057 (77.32) 2,242 (10.16) 2,420 (10.97) 77 (0.35) 157 (0.71) 18 (0.08) 79 (0.36) 12,162 (94.53) 6,962 (54.11) 5,714 (44.41) 424 (3.30) 352 (2.74) 22 (0.17) 93 (0.72) 2,808 (98.87) 2,516 (88.59) 2,546 (89.65) 425 (14.96) 266 (9.37) 17 (0.60) 81 (2.85) 32,027 (63.93) 11,720 (23.39) 10,680 (21.32) 926 (1.85) 775 (1.55) 57 (0.11) 253 (0.50) LTCs, long-term conditions; IQR, interquartile range; BMI, body mass index; CHD, coronary heart disease; COPD, chronic obstructive pulmonary disease. During 205,022.95 person-years of follow-up, the mortality rates (per 1,000 person-years) were 15.92, 19.83, 21.18, and 24.80 in the no LTCs,1 LTC, 2 LTCs, and ≥ 3 LTCs groups, respectively. The number of LTCs at baseline had a significant association with all-cause mortality observed in the three models (Table 2 ). In adjusted Cox model 3, the HRs (95% CIs) for all-cause mortality in participants with 1, 2, and ≥ 3 LTCs compared with those with no LTCs were 1.10 (1.01–1.20), 1.21 (1.10–1.33), and 1.46 (1.27–1.67), respectively ( P trend <0.001). Table 2 Multimorbidity and all-cause mortality No LTCs 1 LTC 2 LTCs ≥ 3 LTCs P trend No. of deaths 760 1,809 1,146 297 No. of person-years 47,741.92 91,206.57 54,098.89 11,975.57 a Cumulative mortality rate 15.92 19.83 21.18 24.80 HR (95% CI) HR (95% CI) HR (95% CI) b Model1 Reference 1.04 (0.95,1.13) 1.11 (1.02,1.22) 1.33 (1.17,1.53) < 0.001 c Model2 Reference 1.06 (0.98,1.16) 1.15 (1.05,1.26) 1.38 (1.20,1.58) < 0.001 d Model3 Reference 1.10 (1.01,1.20) 1.21 (1.10,1.33) 1.46 (1.27,1.67) < 0.001 LTCs, long-term conditions, HR denotes hazard ratio and 95% CI denotes 95% confidence interval. a Per 1000 person-years. b Model 1 is adjusted for age (< 75 or ≥ 75), sex (female or male) and marital status (living with partner or living without partner). c Model 2 is adjusted for covariates in model 1 plus smoking status (never, current or previous) and alcohol status (never, occasions or daily). d Model 3 is adjusted for covariates in model 2 plus body mass index (< 18.5, 18.5–24.9, 25-29.9, 30-34.9, 35-39.9 or ≥ 40). In the study, we assessed the impact of 16 different combinations of 7 chronic diseases on the risk of all-cause mortality, as shown in Table 3 . In the LTCs = 2 category, the combination of hypertension + diabetes (HR 1.32, 95% CI 1.18–1.48), hypertension + stroke (HR 1.69, 95% CI 1.27–2.26), hypertension + COPD (HR 1.91, 95% CI 1.40–2.62), and diabetes + COPD (HR 3.49, 95% CI 1.31–9.34) had significant effects on mortality. For those with 3 LTCs, the combination of hypertension + diabetes + CHD (HR 1.29, 95% CI 1.10–1.52), hypertension + diabetes + stroke (HR 2.52, 95% CI 1.71–3.70), and hypertension + CHD + COPD (HR 1.88, 95% CI 1.16–3.05) had significant effects on mortality. For the population with LTCs = 4, the combination of hypertension + diabetes + CHD + stroke (HR 2.14, 95% CI 1.11–4.14) had significant effects on mortality. Hypertension appears most frequently in the combination of multimorbidity. Table 3 The most impactful LTCs combinations in stratified Cox regression analysis for mortality, for different multimorbidity categories (based on LTC count) Categories No. of deaths No. of person-years Cumulative mortality rate* Adjusted** HR (95% CI) 2 LTCs (Total number of deaths N = 1146) Hypertension + diabetes 570 26,753.84 21.31 1.32 (1.18,1.48) Hypertension + CHD 416 21,575.80 19.28 1.02 (0.90,1.16) Hypertension + stroke 58 1,581.35 36.68 1.69 (1.27,2.26) Hypertension + COPD 44 992.24 44.34 1.91 (1.40,2.62) Diabetes + CHD 28 2,162.11 12.95 0.86 (0.58,1.26) CHD + COPD 11 312.92 35.15 1.37 (0.74,2.57) Hypertension + Mental disorders 8 284.27 28.14 1.32 (0.63,2.79) Diabetes + COPD 4 89.62 44.63 3.49 (1.31,9.34) 3 LTCs (Total number of deaths N = 279) Hypertension + diabetes + CHD 188 8,849.08 21.25 1.29 (1.10,1.52) Hypertension + diabetes + stroke 29 660.92 43.88 2.52 (1.71,3.70) Hypertension + stroke + CHD 17 652.11 26.07 1.50 (0.93,2.42) Hypertension + CHD + COPD 17 477.75 35.58 1.88 (1.16,3.05) Hypertension + diabetes + COPD 9 294.51 30.56 1.54 (0.77,3.08) 4 LTCs (Total number of deaths N = 18) Hypertension + diabetes + stroke + CHD 9 274.78 32.75 2.14(1.11,4.14) LTCs, long-term conditions; One asterisk Per 1000 person-years; Two asterisk adjusted for age, sex, marital status, smoking status, alcohol status, body mass index, and physical activity levels at baseline; CHD, coronary heart disease; COPD, chronic obstructive pulmonary disease. The analysis of all-cause mortality for multimorbidity stratified by age and sex is shown in Fig. 1 . In the multivariable-adjusted models, the absolute event rate for all-cause mortality was higher for the age group ≥ 75 years, but the relative effect sizes for mortality risk with increasing number of LTCs were higher for the age group < 75 years. Absolute mortality was highest in the age group ≥ 75 years with ≥ 3 LTCs (6.48% for males and 3.71% for females). However, participants in the age group < 75 years with ≥ 3 LTCs had the highest relative risk of all-cause mortality (HR 1.85, 95% CI 1.40–2.44 for males and HR 1.84, 95% CI 1.34–2.54 for females) compared to participants with no LTCs in the same age group. The observed effect size was similar for both female and male. In addition, we found that being older, living without partner, and being underweight had a higher risk of mortality. In contrast, participants who were female, overweight, class I obesity, and physically active had a significantly lower adjusted risk of all-cause mortality ( Supplemental Table 1 ). The sensitivity analysis yielded similar findings as our main results, and the risk of death increased with the increase in LTC count when the LTC count was defined only by the physical examination records and did not include the self-reported conditions ( Supplemental Table 2 ). Discussion This cohort study demonstrates that multimorbidity has a positive association with all-cause mortality. The results from sensitivity analyses were robust. In the LTCs ≥ 2 category, the chronic disease combination that included hypertension, diabetes, CHD, COPD, and stroke had the greatest impact on mortality. In the stratified model by age and sex, absolute all-cause mortality was higher in the age group ≥ 75 years, but the relative magnitude of the effect on mortality risk was greater among those < 75 years. The higher all-cause mortality risk among participants with 1, 2, and ≥ 3 LTCs found in this study is consistent with previous studies that have focused on older adults[ 26 , 27 ]. The study of Martinez-Gomez et al. observed significant upward trends with 1 LTC (HR 1.26), 2 LTC (HR 1.78), and ≥ 3 LTC (HR 2.27) than those without LTC[ 26 ]. Similarly, Nunes et al. conducted a meta-analysis of 26 studies and observed similar effect sizes[ 10 ]. And we also found that LTCs combinations were most strongly associated with all-cause mortality relative to the number of multimorbidity alone, whereas previous studies mainly focused on the role of a single disease in death, such as diabetes[ 28 ]. Moreover, the present study found that hypertension was the most frequent occurrence, with presence in 11 of the 16 different combinations, suggesting that there may be a potential link between hypertension and a variety of chronic diseases. However, a large sample of participants aged 40 to 69 years study based on 36 chronic conditions showed that conditions such as hypertension, diabetes, and asthma were at the center of common multimorbidity[ 16 ]. These findings suggest there is a need for further research to explore the links between these diseases and their possible interactions, which is of great significance for the guidance and treatment of multimorbidity, and also provides a theoretical basis for the formulation of health management measures and allocation of medical resources for the elderly population to some extent. A prospective population-based cohort study of England people aged 37 to 73 years shown that participants in the younger age group 37–49 years with ≥ 4 LTCs had the highest relative risk of all-cause mortality[ 11 ]. And we found that the association on risk of all-cause mortality with an increasing number of LTCs was particularly evident among younger age groups (< 75 vs ≥ 75 years old), which not only fills in the gap of the age of participants, but also is basically consistent with the trend of previous studies. This study found that female had a higher prevalence of multimorbidity than male, which was consistent with previous findings[ 29 , 30 ]. However, the observed magnitude of mortality effect size was similar for both female and male. Potential explanations for the phenomenon are that females are generally more sensitized to their health[ 30 ], more likely to report more conditions[ 31 ], and more likely to engage in preventive health behavior[ 32 ]. These findings suggest the need for early intervention to manage and prevent chronic diseases and to reduce the prevalence of multiple diseases as much as possible. Studies have shown that the causes of death from multimorbidity can be attributed to 4 major underlying risk factors: smoking, alcohol consumption, underweight, and physical inactivity[ 11 , 33 ]. While previous studies found that smoking was a risk factor for all-cause mortality[ 11 ], our results found no statistically significant difference, which is possibly explained by the quitter bias (people may stop smoking because they are in poor health and may be advised not to continue). The results of the risk study on alcohol consumption were basically consistent with the results of Ortolá et al. , which showed that there was no statistically significant difference in mortality between light-to-moderate alcohol consumption and no alcohol consumption among people over 60 years old[ 34 ]. As well, we found the greater all-cause mortality risk among persons with underweight (BMI < 18.5 kg/m 2 ) in this study, which agreed with previous studies[ 21 , 35 , 36 ]. In addition, this study suggests that physical activity is associated with a lower mortality risk, and the underlying mechanism behind this finding may be that physical activity delays disease progression by preventing many chronic diseases, including diabetes, cardiovascular and respiratory diseases, and some types of cancer[ 37 ]. Therefore, once any of these chronic conditions is diagnosed, physical activity is often incorporated into treatment plans to improve quality of life and survival[ 26 , 37 , 38 ]. Our study has several strengths. First, the determination of chronic diseases was relatively accurate and comprehensive, including the self-reported condition of participants and the diagnosis made by the physician based on the professional comprehensive examination. Second, our study was also novel in that we explored combinations of multimorbidity that were associated with the highest risk of death. In addition, it is more convincing to assess the relationship between multimorbidity and all-cause mortality based on a large sample size. Finally, the main results remained robust after conducting sensitivity analyses. However, the study has some limitations. Since we included only seven chronic diseases registered at baseline, some other diseases associated with older people such as hyperlipidemia and arthritis could not be taken into account, which may underestimate the prevalence of many diseases in this study, and there was no way to estimate the severity of conditions as well. Another restriction is that our participants were people over 65 years, which should caution generalizing our findings to younger age groups. In addition, although we adjusted for various covariates, there is still a possibility of residual confounding, such as diet factors. Last, recall bias is unavoidable in self-reported information. Conclusion Our findings suggest an increased risk of all-cause mortality with an elevated number of multimorbidity, particularly LTC combinations, which provides scientific basis for the treatment, prevention and control of the multimorbidity, and has important public health significance in guiding the rational allocation of medical and health resources. Abbreviations LTCs: Long-term conditions; FPG: Fasting plasma glucose; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; BMI: Body mass index; CHD: Coronary heart disease; COPD: Chronic obstructive pulmonary disease; IQR: Interquartile range; HRs: Hazard ratios; CIs: Confidence intervals. Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of Zhengzhou University, and written informed consent was obtained from all participants. Consent for Publication Not applicable. Availability of data and material The datasets used and/or analyzed in this study were obtained from third parties and cannot be publicly available to confidentiality requirements. Competing interests The authors declare that they have no competing interests. Funding This study was supported by National Key Research and Development Program “Research on prevention and control of major chronic non-communicable diseases” of China (Grant NO: 2017YFC1307705). The funders were not involved in the study design, data collection, analysis and interpretation, and manuscript writing. Author contributions K.H., W.Z., and X.H. substantially contributed to the design and drafting of the study and the analysis and interpretation of the data. K.H. wrote the manuscript. K.H., W.Z., X.H., H.Z., B.G., Z.S., X.Z., C.Y. revised it critically for important intellectual content. All authors were involved in the collection of data and approve of the final version of the manuscript. Acknowledgements The investigators are grateful to the dedicated participants and all research staff of the study. Author details 1 Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China. 2 Department of pharmacy, Zhengzhou people's hospital, Zhengzhou, Henan, People’s Republic of China. 3 Department of Neurology, Chinese People's Liberation Army General Hospital, Beijing, People’s Republic of China. References Cohen JE. Human population: the next half century. Science. 2003;302(5648):1172–5. The 2018 Statistical Communique on National Economic and Social Development . http :// www . stats . gov . cn /tjsj/zxfb/201902/ t20190228_1651265 . html . Forman DE, Maurer MS, Boyd C, Brindis R, Salive ME, Horne FM, Bell SP, Fulmer T, Reuben DB, Zieman S, et al. Multimorbidity in Older Adults With Cardiovascular Disease. J Am Coll Cardiol. 2018;71(19):2149–61. 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Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research . cosponsored by the American Society of Preventive Oncology. 2016;25(1):16–27. Bertram MY, Sweeny K, Lauer JA, Chisholm D, Sheehan P, Rasmussen B, Upreti SR, Dixit LP, George K, Deane S. Investing in non-communicable diseases: an estimation of the return on investment for prevention and treatment services. Lancet. 2018;391(10134):2071–8. Han C, Liu Y, Sun X, Luo X, Zhang L, Wang B, Ren Y, Zhou J, Zhao Y, Zhang D, et al. Prediction of a new body shape index and body adiposity estimator for development of type 2 diabetes mellitus: The Rural Chinese Cohort Study. Br J Nutr. 2017;118(10):771–6. Cheng FW, Gao X, Mitchell DC, Wood C, Still CD, Rolston D, Jensen GL. Body mass index and all-cause mortality among older adults. Obesity (Silver Spring Md). 2016;24(10):2232–9. Li S, Guo B, Chen H, Shi Z, Li Y, Tian Q, Shi S. The role of the triglyceride (triacylglycerol) glucose index in the development of cardiovascular events: a retrospective cohort analysis. Scientific reports. 2019;9(1):7320. Jia W, Weng J, Zhu D, Ji L, Lu J, Zhou Z, Zou D, Guo L, Ji Q, Chen L, et al. Standards of medical care for type 2 diabetes in China 2019. Diab/Metab Res Rev. 2019;35(6):e3158. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, Jones DW, Materson BJ, Oparil S, Wright JT Jr, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. Jama. 2003;289(19):2560–72. Clinical guidelines on the identification , evaluation , and treatment of overweight and obesity in adults : executive summary . Expert Panel on the Identification , Evaluation , and Treatment of Overweight in Adults . The American journal of clinical nutrition 1998, 68 (4):899–917. Martinez-Gomez D, Guallar-Castillon P, Garcia-Esquinas E, Bandinelli S, Rodríguez-Artalejo F: Physical Activity and the Effect of Multimorbidity on All - Cause Mortality in Older Adults . Mayo Clinic proceedings 2017, 92 (3):376–382. Halonen P, Raitanen J, Jämsen E, Enroth L, Jylhä M. Chronic conditions and multimorbidity in population aged 90 years and over: associations with mortality and long-term care admission. Age Ageing. 2019;48(4):564–70. Sadarangani KP, Hamer M, Mindell JS, Coombs NA, Stamatakis E. Physical activity and risk of all-cause and cardiovascular disease mortality in diabetic adults from Great Britain: pooled analysis of 10 population-based cohorts. Diabetes Care. 2014;37(4):1016–23. Diaz E, Poblador-Pou B, Gimeno-Feliu LA, Calderón-Larrañaga A, Kumar BN, Prados-Torres A. Multimorbidity and Its Patterns according to Immigrant Origin. A Nationwide Register-Based Study in Norway. PloS one. 2015;10(12):e0145233. Pache B, Vollenweider P, Waeber G, Marques-Vidal P. Prevalence of measured and reported multimorbidity in a representative sample of the Swiss population. BMC Public Health. 2015;15:164. Verbrugge LM. The twain meet: empirical explanations of sex differences in health and mortality. Journal of health social behavior. 1989;30(3):282–304. Rogers RG, Everett BG, Onge JM, Krueger PM. Social, behavioral, and biological factors, and sex differences in mortality. Demography. 2010;47(3):555–78. Salive ME. Multimorbidity in older adults. Epidemiol Rev. 2013;35:75–83. Ortolá R, García-Esquinas E, López-García E, León-Muñoz LM, Banegas JR, Rodríguez-Artalejo F. Alcohol consumption and all-cause mortality in older adults in Spain: an analysis accounting for the main methodological issues. Addiction. 2019;114(1):59–68. Winter JE, MacInnis RJ, Wattanapenpaiboon N, Nowson CA. BMI and all-cause mortality in older adults: a meta-analysis. Am J Clin Nutr. 2014;99(4):875–90. Flegal KM, Graubard BI, Williamson DF, Gail MH. Excess deaths associated with underweight, overweight, and obesity. Jama. 2005;293(15):1861–7. Pareja-Galeano H, Garatachea N, Lucia A. Exercise as a Polypill for Chronic Diseases. Prog Mol Biol Transl Sci. 2015;135:497–526. Naci H, Ioannidis JP. Comparative effectiveness of exercise and drug interventions on mortality outcomes: metaepidemiological study. Br J Sports Med. 2015;49(21):1414–22. Supplementary Files supplementalmaterials.docx Supplementary Figure 1. Flow diagram of participant selection. Supplemental Table 1. Multimorbidity and all-cause mortality: Cox regression analysis. This table shows that being older, living without partner, and being underweight had a higher risk of mortality. In contrast, participants who were female, overweight, class I obesity, and physically active had a significantly lower adjusted risk of all-cause mortality. Supplemental Table 2. Comparison of LTCs (self-report vs physical examination) in prediction of all-cause mortality. This table shows that The sensitivity analysis yielded similar findings as our main results, and the risk of death increased with the increase in LTC count. supplementalmaterials.docx Supplementary Figure 1. Flow diagram of participant selection. Supplemental Table 1. Multimorbidity and all-cause mortality: Cox regression analysis. This table shows that being older, living without partner, and being underweight had a higher risk of mortality. In contrast, participants who were female, overweight, class I obesity, and physically active had a significantly lower adjusted risk of all-cause mortality. Supplemental Table 2. Comparison of LTCs (self-report vs physical examination) in prediction of all-cause mortality. This table shows that The sensitivity analysis yielded similar findings as our main results, and the risk of death increased with the increase in LTC count. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 21 Feb, 2021 Review # 2 received at journal 20 Feb, 2021 Reviewer # 2 agreed at journal 03 Feb, 2021 Review # 1 received at journal 04 Jan, 2021 Reviewer # 1 agreed at journal 14 Dec, 2020 Reviewers invited by journal 03 Dec, 2020 Editor assigned by journal 02 Dec, 2020 Submission checks completed at journal 02 Dec, 2020 Editor invited by journal 02 Dec, 2020 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-122536","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":5911724,"identity":"74e6b30b-c36f-4ebe-a9ea-6ed7cb2d863f","order_by":0,"name":"Kun He","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"He","suffix":""},{"id":5911725,"identity":"28ae8207-08c1-43f1-9cd0-d000c040da5b","order_by":1,"name":"Wenli Zhang","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Wenli","middleName":"","lastName":"Zhang","suffix":""},{"id":5911726,"identity":"55f7ab5e-7bba-4e81-a33c-9cec7ff71422","order_by":2,"name":"Xueqi Hu","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xueqi","middleName":"","lastName":"Hu","suffix":""},{"id":5911727,"identity":"37e1f41d-22bc-446b-98e7-22c06bd7c88f","order_by":3,"name":"Hao Zhao","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Zhao","suffix":""},{"id":5911728,"identity":"0492550f-1e34-40c3-9ded-1fb8dfa90791","order_by":4,"name":"Bingxin Guo","email":"","orcid":"","institution":"zhengzhou university","correspondingAuthor":false,"prefix":"","firstName":"Bingxin","middleName":"","lastName":"Guo","suffix":""},{"id":5911729,"identity":"02a3aced-fcbb-4955-a53f-b98580255a8c","order_by":5,"name":"Zhan Shi","email":"","orcid":"","institution":"Zhengzhou First People Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhan","middleName":"","lastName":"Shi","suffix":""},{"id":5911730,"identity":"f63c5dd0-5160-446f-93cf-8d7bd5c9ab66","order_by":6,"name":"Xiaoyan Zhao","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Zhao","suffix":""},{"id":5911731,"identity":"1300c726-290d-4aa0-8d93-1c24a07927b9","order_by":7,"name":"Chunyu Yin","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chunyu","middleName":"","lastName":"Yin","suffix":""},{"id":5911732,"identity":"05f4a27a-48b5-4769-8f22-a5c8cca62754","order_by":8,"name":"Songhe Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqUlEQVRIiWNgGAWjYLACxgYbHn72BtK0pMlI9hwgTcthG4MbDkSqlu8/Y/zx547zPAw3GBg/fMwhyoIzZtK8Z27zMM5uYJacuY0ILcyMPWbMjG23eZhlDrAx8xKjhY2ZB+iwtnM8bBIJRGrhYeMxkOBtO8DDQ7QWCR62MmnetmQeCZ6DzcT5Rb7/8Gagw+zs7Y83H/zwkRgtSICxgTT1o2AUjIJRMApwAwCvEi+YoQ4aHgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-2214-1440","institution":"Zhengzhou University","correspondingAuthor":true,"prefix":"","firstName":"Songhe","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2020-12-05 13:55:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-122536/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-122536/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":4112189,"identity":"54d5db56-20f0-4b6c-a3d0-bce583fdf72b","added_by":"auto","created_at":"2020-12-08 23:45:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":110986,"visible":true,"origin":"","legend":"All-cause mortality for multimorbidity stratified by age and sex, and adjusted for marital status, smoking status, alcohol status, physical activity, and body mass index. One asterisk Per 1000 person-years; Two asterisk adjusted for marital status, smoking status, alcohol status, body mass index, and physical activity levels at baseline.","description":"","filename":"Onlinefloatimage1.Png","url":"https://assets-eu.researchsquare.com/files/rs-122536/v1/4b0343099cd00a227b643655.Png"},{"id":4112186,"identity":"4cb116ad-92bb-4157-b1f2-4c6758c3abf2","added_by":"auto","created_at":"2020-12-08 23:45:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":110986,"visible":true,"origin":"","legend":"All-cause mortality for multimorbidity stratified by age and sex, and adjusted for marital status, smoking status, alcohol status, physical activity, and body mass index. One asterisk Per 1000 person-years; Two asterisk adjusted for marital status, smoking status, alcohol status, body mass index, and physical activity levels at baseline.","description":"","filename":"Onlinefloatimage1.Png","url":"https://assets-eu.researchsquare.com/files/rs-122536/v1/bec6e3fb8702eba3cffa438f.Png"},{"id":13629989,"identity":"f4955363-88b3-4c90-886f-96f08996da67","added_by":"auto","created_at":"2021-09-17 08:10:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":896003,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-122536/v1/97952a9b-ca83-40a3-aad5-584d278a8d41.pdf"},{"id":4112188,"identity":"74855b9e-9783-4482-ac3e-18848517e9b7","added_by":"auto","created_at":"2020-12-08 23:45:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":196295,"visible":true,"origin":"","legend":"Supplementary Figure 1. Flow diagram of participant selection. Supplemental Table 1. Multimorbidity and all-cause mortality: Cox regression analysis. This table shows that being older, living without partner, and being underweight had a higher risk of mortality. In contrast, participants who were female, overweight, class I obesity, and physically active had a significantly lower adjusted risk of all-cause mortality. Supplemental Table 2. Comparison of LTCs (self-report vs physical examination) in prediction of all-cause mortality. This table shows that The sensitivity analysis yielded similar findings as our main results, and the risk of death increased with the increase in LTC count.","description":"","filename":"supplementalmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-122536/v1/05afcdf05696d044d99deaaf.docx"},{"id":4112185,"identity":"c9ec98d3-5a7f-435a-b7cf-153a531b390e","added_by":"auto","created_at":"2020-12-08 23:45:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":196295,"visible":true,"origin":"","legend":"Supplementary Figure 1. Flow diagram of participant selection. Supplemental Table 1. Multimorbidity and all-cause mortality: Cox regression analysis. This table shows that being older, living without partner, and being underweight had a higher risk of mortality. In contrast, participants who were female, overweight, class I obesity, and physically active had a significantly lower adjusted risk of all-cause mortality. Supplemental Table 2. Comparison of LTCs (self-report vs physical examination) in prediction of all-cause mortality. This table shows that The sensitivity analysis yielded similar findings as our main results, and the risk of death increased with the increase in LTC count.","description":"","filename":"supplementalmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-122536/v1/bc66db5e7f8ea3cbb6c15c0d.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eRelationship Between Multimorbidity, Combination and All-Cause Mortality Among Older Adults: A Retrospective Cohort Analysis\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eAging around the world poses a global challenge, previous studies have reported that thousands of persons turn 65\u0026nbsp;years old every day[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. To our knowledge, China has the largest population base in the world, and the size of the elderly population is enormous. In 2018, the number of Chinese people aged 65 or above had reached 166.58\u0026nbsp;million, accounting for 11.9% of the total population, which means that China is aging at an unprecedented rate[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The phenomenon of ageing has led to a substantial increase in chronic conditions, which consequently results in a rising prevalence of multimorbidity, most commonly described as the presence of two or more long-term conditions (LTCs)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Studies have found that multimorbidity leads to poor quality of life[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], increased use of inpatient and ambulatory greater health care[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], greater complexity regarding of clinical treatment and patient management[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and more importantly, an increase in mortality[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For example, previous studies, mainly based on the UK population, had suggested that the all-cause mortality rate for people with multimorbidity was 4.74% [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Thus, the management of multimorbidity has become a public health priority for public health care professionals and health care systems[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study found that hypertension, diabetes, stroke, heart disease, chronic respiratory disease and cancer are among the most common types of multimorbidity and are the most common causes of long-term disability and premature mortality worldwide[\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. As a result of the change of healthy lifestyle and the progress of medical level, the mortality rate from multimorbidity such as heart disease and cancer has been significantly reduced in high-income countries[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], whereas rates of mortality caused by multimorbidity are rising rapidly in low- and middle-income countries, posing a serious social burden[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThus, a better understanding of the epidemiology of multimorbidity is necessary to develop interventions to reduce the burden of death. However, the relationship between multimorbidity (number/combination) and all-cause mortality has not been well described in the Chinese population. Therefore, we designed a retrospective cohort study to explore the relationship in Chinese older adults.\u003c/p\u003e "},{"header":"Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eWe performed a retrospective cohort analysis of 103,729 participants who entered the health check-up program in Xinzheng City, Henan Province, Central China, from January 1, 2014 to November 15, 2019. A total of 84,353 (81.3%) participants were successfully followed up. We excluded participants with age\u0026thinsp;\u0026lt;\u0026thinsp;65\u0026nbsp;at baseline (n\u0026thinsp;=\u0026thinsp;31,900), or missing data for date of birth and gender at baseline (n\u0026thinsp;=\u0026thinsp;2,353). A total of 50,100 individuals were included in the main analysis \u003cb\u003e(Supplemental Fig.\u0026nbsp;1)\u003c/b\u003e. The study was approved by the Ethics Committee of Zhengzhou University, and written informed consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e \n\u003ch2\u003eData Collection\u003c/h2\u003e\n \u003cp\u003eA standardized questionnaire was used by trained research staff under stringent quality control to collect information. Marital status was categorized as living with partner and without partner. Smokers were defined as ever smoking at least 100 cigarettes in their lifetime and classified as nonsmokers and previous/current smokers[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Alcohol consumption was divided into three categories: never, occasionally or daily. Physical activity was a categorical variable based on the self-reporting and classified as never, occasionally or daily. The measurements for height and weight were performed with the subjects wearing light clothing without shoes[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Blood pressure was measured by an automatic sphygmomanometer (Omron HEM-7125, Kyoto, Japan) after subjects had rested in a seated position for at least 5 minutes, and the mean value was recorded when the subjects were measured three times[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Blood samples were obtained after an overnight fast of at least 8 hours to assess levels of fasting plasma glucose (FPG) using an automatic biochemical analyzer (DIRUI CS380, Changchun, China)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In this study, diabetes was defined as fasting glucose levels\u0026thinsp;\u0026ge;\u0026thinsp;7.0\u0026nbsp;mmol/L and/or current treatment with anti-diabetes medication according to the China guideline for T2DM[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Hypertension was defined as positive for participants who were considered hypertensive with systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;140\u0026nbsp;mm Hg or diastolic blood pressure (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;90\u0026nbsp;mm Hg or current use of antihypertensive medication[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m\u003csup\u003e2\u003c/sup\u003e), which was classified based on the WHO classification criteria into underweight (\u0026lt;\u0026thinsp;18.5\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e), desirable (18.5\u0026ndash;24.9\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25.0\u0026ndash;29.9\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e), obesity class I (30.0\u0026ndash;34.9\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e), obesity class II (35.0\u0026ndash;39.9\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e), and obesity class III (\u0026ge;\u0026thinsp;40.0\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Information on the 7 chronic conditions diagnosed by a physician or reported by the study participants was recorded: (1) hypertension, (2) diabetes, (3) coronary heart disease (CHD), (4) stroke, (5) chronic obstructive pulmonary disease (COPD), (6) cancer, and (7) mental system disease. Multimorbidity was classified based on number of chronic conditions into no LTCs, 1 LTC, 2 LTCs, and \u0026ge;\u0026thinsp;3 LTCs. The outcome variable for the present study was all-cause mortality. Death certificates were obtained as well.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll quantitative variables are described using the median (interquartile range [IQR]) for skewed distribution, and qualitative variables are expressed as frequency (%). Cox regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of all-cause mortality by groups of LTC categories. With no LTCs as the reference group, we constructed three multivariate-adjusted Cox models: model 1 was adjusted for age, sex and marital status; model 2 was adjusted for model 1 plus smoking status, alcohol status and physical activity; and model 3 was further adjusted for BMI. In order to understand the pattern and effect of multimorbidity, we further analyzed the common combination of multimorbidity in older adults. A test for multiplicative interaction showed that age (\u0026lt;\u0026thinsp;75/\u0026ge;75\u0026nbsp;years) and sex at baseline modified the association of LTCs and risk of all-cause mortality (both \u003cem\u003ep\u003c/em\u003e-interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Therefore, for subgroup analyses, we stratified participants by age and sex at baseline. Finally, sensitivity analysis was performed using LTCs defined by the medical examination diagnostic record at study baseline and not including self-reported history. Statistical analyses were performed using SAS 9.1 (SAS Institute, Cary, NC, USA), the forest plot was performed by GraphPad Prism 8, and the level of significance was considered at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed).\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003cp\u003eThe baseline characteristics of study participants with the four LTC groups are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 4,012 (8.01%) of the 50,100 participants died. The median age at baseline was 69.18\u0026nbsp;years (range 65\u0026ndash;106\u0026nbsp;years), and 53.87% of the population was female. The proportion of participants with 0, 1, 2, and 3 or more LTCs was 12,334 (24.62%), 22,060 (44.03%), 12,866 (25.68%), and 2,840 (5.67%), respectively. In addition, the prevalence of multimorbidity in female was higher than in male (67.47% vs 63.58%). The most frequent diseases in participants with 3 or more LTCs were hypertension (98.87%), CHD (89.65%), and diabetes (88.59%).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eRelationship of multimorbidity with demographics and health-related behaviour at baseline.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNo LTCs\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;12,334(24.62%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e1 LTC\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;22,060(44.03%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e2 LTCs\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;12,866(25.68%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;3 LTCs\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,840(5.67%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOver all\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;50,100\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge; missing values n\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\n\u003cp\u003eAge in years-median (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68.85 (65.95\u0026ndash;75.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.35 (65.98\u0026ndash;75.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.20 (65.95\u0026ndash;75.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.15 (65.88\u0026ndash;75.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.18 (65.96\u0026ndash;75.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSex; missing values n\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,394 (51.84)\u003c/p\u003e\n\u003cp\u003e5,940 (48.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10,437 (47.31)\u003c/p\u003e\n\u003cp\u003e11,623 (52.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5,244 (40.76)\u003c/p\u003e\n\u003cp\u003e7,622 (59.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,034 (36.41)\u003c/p\u003e\n\u003cp\u003e1,806 (63.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e23,109 (46.13)\u003cbr /\u003e26,991 (53.87)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarital status; missing values n\u0026thinsp;=\u0026thinsp;140(0.28%)\u003c/p\u003e\n\u003cp\u003eLiving with partner\u003c/p\u003e\n\u003cp\u003eLiving without partner\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9,852 (80.11)\u003c/p\u003e\n\u003cp\u003e2,446 (19.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17,180 (78.09)\u003c/p\u003e\n\u003cp\u003e4,821 (21.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10,117 (78.88)\u003c/p\u003e\n\u003cp\u003e2,709 (21.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,215 (78.13)\u003c/p\u003e\n\u003cp\u003e620 (21.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39,364 (78.79)\u003c/p\u003e\n\u003cp\u003e10,596 (21.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking status; missing values n\u0026thinsp;=\u0026thinsp;614(1.24%)\u003c/p\u003e\n\u003cp\u003eNever\u003c/p\u003e\n\u003cp\u003eCurrent or previous\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10,171 (84.50)\u003c/p\u003e\n\u003cp\u003e1,865 (15.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18,402 (84.31)\u003c/p\u003e\n\u003cp\u003e3,425 (15.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11,122 (86.94)\u003c/p\u003e\n\u003cp\u003e1,671 (13.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,475 (87.46)\u003c/p\u003e\n\u003cp\u003e355 (12.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42,170 (85.21)\u003c/p\u003e\n\u003cp\u003e7,316 (14.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlcohol status; missing values n\u0026thinsp;=\u0026thinsp;767(1.55%)\u003c/p\u003e\n\u003cp\u003eNever\u003c/p\u003e\n\u003cp\u003eOccasionally\u003c/p\u003e\n\u003cp\u003eDaily\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11,224 (93.53)\u003c/p\u003e\n\u003cp\u003e499 (4.16)\u003c/p\u003e\n\u003cp\u003e278 (2.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20,243 (93.02)\u003c/p\u003e\n\u003cp\u003e931 (4.28)\u003c/p\u003e\n\u003cp\u003e589 (2.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11,903 (93.3)\u003c/p\u003e\n\u003cp\u003e490 (3.84)\u003c/p\u003e\n\u003cp\u003e356 (2.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,634 (93.40)\u003c/p\u003e\n\u003cp\u003e107 (3.79)\u003c/p\u003e\n\u003cp\u003e79 (2.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46,004 (93.25)\u003c/p\u003e\n\u003cp\u003e2,027 (4.11)\u003c/p\u003e\n\u003cp\u003e1,302 (2.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhysical activity; missing values n\u0026thinsp;=\u0026thinsp;723(1.46%)\u003c/p\u003e\n\u003cp\u003eNever\u003c/p\u003e\n\u003cp\u003eOccasionally\u003c/p\u003e\n\u003cp\u003eDaily\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,404 (69.78)\u003c/p\u003e\n\u003cp\u003e1,085 (9.01)\u003c/p\u003e\n\u003cp\u003e2,554 (21.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13,860 (63.71)\u003c/p\u003e\n\u003cp\u003e2,351 (10.81)\u003c/p\u003e\n\u003cp\u003e5,543 (25.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7,678 (60.18)\u003c/p\u003e\n\u003cp\u003e1,434 (11.24)\u003c/p\u003e\n\u003cp\u003e3,647 (28.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,676 (59.41)\u003c/p\u003e\n\u003cp\u003e306 (10.85)\u003c/p\u003e\n\u003cp\u003e839 (29.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31,618 (64.03)\u003c/p\u003e\n\u003cp\u003e5,176 (10.48)\u003c/p\u003e\n\u003cp\u003e12,583 (25.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI; missing values n\u0026thinsp;=\u0026thinsp;1,323(2.71%)\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e\n\u003cp\u003e18.5\u0026ndash;24.9\u003c/p\u003e\n\u003cp\u003e25-29.9\u003c/p\u003e\n\u003cp\u003e30-34.9\u003c/p\u003e\n\u003cp\u003e35-39.9\u003c/p\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e336 (2.82)\u003c/p\u003e\n\u003cp\u003e8,091 (68.00)\u003c/p\u003e\n\u003cp\u003e3,129 (26.30)\u003c/p\u003e\n\u003cp\u003e324 (2.72)\u003c/p\u003e\n\u003cp\u003e15 (0.13)\u003c/p\u003e\n\u003cp\u003e3 (0.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e407 (1.90)\u003c/p\u003e\n\u003cp\u003e11,943 (55.62)\u003c/p\u003e\n\u003cp\u003e7,612 (35.45)\u003c/p\u003e\n\u003cp\u003e1,378 (6.42)\u003c/p\u003e\n\u003cp\u003e122 (0.57)\u003c/p\u003e\n\u003cp\u003e11 (0.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e173 (1.37)\u003c/p\u003e\n\u003cp\u003e5,833 (46.24)\u003c/p\u003e\n\u003cp\u003e5,306 (42.06)\u003c/p\u003e\n\u003cp\u003e1,175 (9.31)\u003c/p\u003e\n\u003cp\u003e114 (0.90)\u003c/p\u003e\n\u003cp\u003e14 (0.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (1.22)\u003c/p\u003e\n\u003cp\u003e1,215 (43.53)\u003c/p\u003e\n\u003cp\u003e1,187 (42.53)\u003c/p\u003e\n\u003cp\u003e316 (11.32)\u003c/p\u003e\n\u003cp\u003e36 (1.29)\u003c/p\u003e\n\u003cp\u003e3 (0.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e950 (1.95)\u003c/p\u003e\n\u003cp\u003e27,082 (55.52)\u003c/p\u003e\n\u003cp\u003e17,234 (35.33)\u003c/p\u003e\n\u003cp\u003e3,193 (6.55)\u003c/p\u003e\n\u003cp\u003e287 (0.59)\u003c/p\u003e\n\u003cp\u003e31 (0.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic conditions\u003c/p\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003cp\u003eDiabetes\u003c/p\u003e\n\u003cp\u003eCHD\u003c/p\u003e\n\u003cp\u003eStroke\u003c/p\u003e\n\u003cp\u003eCOPD\u003c/p\u003e\n\u003cp\u003eTumour\u003c/p\u003e\n\u003cp\u003eMental disorders\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17,057 (77.32)\u003c/p\u003e\n\u003cp\u003e2,242 (10.16)\u003c/p\u003e\n\u003cp\u003e2,420 (10.97)\u003c/p\u003e\n\u003cp\u003e77 (0.35)\u003c/p\u003e\n\u003cp\u003e157 (0.71)\u003c/p\u003e\n\u003cp\u003e18 (0.08)\u003c/p\u003e\n\u003cp\u003e79 (0.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12,162 (94.53)\u003c/p\u003e\n\u003cp\u003e6,962 (54.11)\u003c/p\u003e\n\u003cp\u003e5,714 (44.41)\u003c/p\u003e\n\u003cp\u003e424 (3.30)\u003c/p\u003e\n\u003cp\u003e352 (2.74)\u003c/p\u003e\n\u003cp\u003e22 (0.17)\u003c/p\u003e\n\u003cp\u003e93 (0.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,808 (98.87)\u003c/p\u003e\n\u003cp\u003e2,516 (88.59)\u003c/p\u003e\n\u003cp\u003e2,546 (89.65)\u003c/p\u003e\n\u003cp\u003e425 (14.96)\u003c/p\u003e\n\u003cp\u003e266 (9.37)\u003c/p\u003e\n\u003cp\u003e17 (0.60)\u003c/p\u003e\n\u003cp\u003e81 (2.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32,027 (63.93)\u003c/p\u003e\n\u003cp\u003e11,720 (23.39)\u003c/p\u003e\n\u003cp\u003e10,680 (21.32)\u003c/p\u003e\n\u003cp\u003e926 (1.85)\u003c/p\u003e\n\u003cp\u003e775 (1.55)\u003c/p\u003e\n\u003cp\u003e57 (0.11)\u003c/p\u003e\n\u003cp\u003e253 (0.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eLTCs, long-term conditions; IQR, interquartile range; BMI, body mass index; CHD, coronary heart disease; COPD, chronic obstructive pulmonary disease.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eDuring 205,022.95 person-years of follow-up, the mortality rates (per 1,000 person-years) were 15.92, 19.83, 21.18, and 24.80 in the no LTCs,1 LTC, 2 LTCs, and \u0026ge;\u0026thinsp;3 LTCs groups, respectively. The number of LTCs at baseline had a significant association with all-cause mortality observed in the three models (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In adjusted Cox model 3, the HRs (95% CIs) for all-cause mortality in participants with 1, 2, and \u0026ge;\u0026thinsp;3 LTCs compared with those with no LTCs were 1.10 (1.01\u0026ndash;1.20), 1.21 (1.10\u0026ndash;1.33), and 1.46 (1.27\u0026ndash;1.67), respectively (\u003cem\u003eP\u003c/em\u003e \u003csub\u003etrend\u003c/sub\u003e \u0026lt;0.001).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultimorbidity and all-cause mortality\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNo LTCs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e1 LTC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e2 LTCs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;3 LTCs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo. of deaths\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e760\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,809\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,146\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e297\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo. of person-years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47,741.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91,206.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54,098.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11,975.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eCumulative mortality rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eModel1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04 (0.95,1.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.11 (1.02,1.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.33 (1.17,1.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eModel2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.06 (0.98,1.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.15 (1.05,1.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.38 (1.20,1.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003eModel3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.10 (1.01,1.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.21 (1.10,1.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.46 (1.27,1.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eLTCs, long-term conditions, HR denotes hazard ratio and 95% CI denotes 95% confidence interval.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e Per 1000 person-years.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003e Model 1 is adjusted for age (\u0026lt;\u0026thinsp;75 or \u0026ge;\u0026thinsp;75), sex (female or male) and marital status (living with partner or living without partner).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e\u003csup\u003ec\u003c/sup\u003e Model 2 is adjusted for covariates in model 1 plus smoking status (never, current or previous) and alcohol status (never, occasions or daily).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e\u003csup\u003ed\u003c/sup\u003e Model 3 is adjusted for covariates in model 2 plus body mass index (\u0026lt;\u0026thinsp;18.5, 18.5\u0026ndash;24.9, 25-29.9, 30-34.9, 35-39.9 or \u0026ge;\u0026thinsp;40).\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn the study, we assessed the impact of 16 different combinations of 7 chronic diseases on the risk of all-cause mortality, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. In the LTCs\u0026thinsp;=\u0026thinsp;2 category, the combination of hypertension\u0026thinsp;+\u0026thinsp;diabetes (HR 1.32, 95% CI 1.18\u0026ndash;1.48), hypertension\u0026thinsp;+\u0026thinsp;stroke (HR 1.69, 95% CI 1.27\u0026ndash;2.26), hypertension\u0026thinsp;+\u0026thinsp;COPD (HR 1.91, 95% CI 1.40\u0026ndash;2.62), and diabetes\u0026thinsp;+\u0026thinsp;COPD (HR 3.49, 95% CI 1.31\u0026ndash;9.34) had significant effects on mortality. For those with 3 LTCs, the combination of hypertension\u0026thinsp;+\u0026thinsp;diabetes\u0026thinsp;+\u0026thinsp;CHD (HR 1.29, 95% CI 1.10\u0026ndash;1.52), hypertension\u0026thinsp;+\u0026thinsp;diabetes\u0026thinsp;+\u0026thinsp;stroke (HR 2.52, 95% CI 1.71\u0026ndash;3.70), and hypertension\u0026thinsp;+\u0026thinsp;CHD\u0026thinsp;+\u0026thinsp;COPD (HR 1.88, 95% CI 1.16\u0026ndash;3.05) had significant effects on mortality. For the population with LTCs\u0026thinsp;=\u0026thinsp;4, the combination of hypertension\u0026thinsp;+\u0026thinsp;diabetes\u0026thinsp;+\u0026thinsp;CHD\u0026thinsp;+\u0026thinsp;stroke (HR 2.14, 95% CI 1.11\u0026ndash;4.14) had significant effects on mortality. Hypertension appears most frequently in the combination of multimorbidity.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe most impactful LTCs combinations in stratified Cox regression analysis for mortality, for different multimorbidity categories (based on LTC count)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCategories\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNo. of deaths\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNo. of person-years\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCumulative mortality rate*\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAdjusted** HR (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 LTCs (Total number of deaths N\u0026thinsp;=\u0026thinsp;1146)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u0026thinsp;+\u0026thinsp;diabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e570\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26,753.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.32 (1.18,1.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u0026thinsp;+\u0026thinsp;CHD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e416\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21,575.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.02 (0.90,1.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u0026thinsp;+\u0026thinsp;stroke\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,581.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.69 (1.27,2.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u0026thinsp;+\u0026thinsp;COPD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e992.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e44.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.91 (1.40,2.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes\u0026thinsp;+\u0026thinsp;CHD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2,162.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.86 (0.58,1.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCHD\u0026thinsp;+\u0026thinsp;COPD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e312.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.37 (0.74,2.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u0026thinsp;+\u0026thinsp;Mental disorders\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e284.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.32 (0.63,2.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes\u0026thinsp;+\u0026thinsp;COPD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e89.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e44.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.49 (1.31,9.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 LTCs (Total number of deaths N\u0026thinsp;=\u0026thinsp;279)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u0026thinsp;+\u0026thinsp;diabetes\u0026thinsp;+\u0026thinsp;CHD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e188\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8,849.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.29 (1.10,1.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u0026thinsp;+\u0026thinsp;diabetes\u0026thinsp;+\u0026thinsp;stroke\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e660.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.52 (1.71,3.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u0026thinsp;+\u0026thinsp;stroke\u0026thinsp;+\u0026thinsp;CHD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e652.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.50 (0.93,2.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u0026thinsp;+\u0026thinsp;CHD\u0026thinsp;+\u0026thinsp;COPD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e477.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.88 (1.16,3.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u0026thinsp;+\u0026thinsp;diabetes\u0026thinsp;+\u0026thinsp;COPD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e294.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.54 (0.77,3.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 LTCs (Total number of deaths N\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u0026thinsp;+\u0026thinsp;diabetes\u0026thinsp;+\u0026thinsp;stroke\u0026thinsp;+\u0026thinsp;CHD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e274.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e32.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.14(1.11,4.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eLTCs, long-term conditions;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eOne asterisk Per 1000 person-years;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eTwo asterisk adjusted for age, sex, marital status, smoking status, alcohol status, body mass index, and physical activity levels at baseline;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eCHD, coronary heart disease;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eCOPD, chronic obstructive pulmonary disease.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe analysis of all-cause mortality for multimorbidity stratified by age and sex is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. In the multivariable-adjusted models, the absolute event rate for all-cause mortality was higher for the age group\u0026thinsp;\u0026ge;\u0026thinsp;75 years, but the relative effect sizes for mortality risk with increasing number of LTCs were higher for the age group\u0026thinsp;\u0026lt;\u0026thinsp;75\u0026nbsp;years. Absolute mortality was highest in the age group\u0026thinsp;\u0026ge;\u0026thinsp;75\u0026nbsp;years with \u0026ge;\u0026thinsp;3 LTCs (6.48% for males and 3.71% for females). However, participants in the age group\u0026thinsp;\u0026lt;\u0026thinsp;75\u0026nbsp;years with \u0026ge;\u0026thinsp;3 LTCs had the highest relative risk of all-cause mortality (HR 1.85, 95% CI 1.40\u0026ndash;2.44 for males and HR 1.84, 95% CI 1.34\u0026ndash;2.54 for females) compared to participants with no LTCs in the same age group. The observed effect size was similar for both female and male. In addition, we found that being older, living without partner, and being underweight had a higher risk of mortality. In contrast, participants who were female, overweight, class I obesity, and physically active had a significantly lower adjusted risk of all-cause mortality (\u003cstrong\u003eSupplemental Table\u0026nbsp;1\u003c/strong\u003e). The sensitivity analysis yielded similar findings as our main results, and the risk of death increased with the increase in LTC count when the LTC count was defined only by the physical examination records and did not include the self-reported conditions (\u003cstrong\u003eSupplemental Table\u0026nbsp;2\u003c/strong\u003e).\u003c/p\u003e"},{"header":"Discussion","content":" \u003cp\u003eThis cohort study demonstrates that multimorbidity has a positive association with all-cause mortality. The results from sensitivity analyses were robust. In the LTCs\u0026thinsp;\u0026ge;\u0026thinsp;2 category, the chronic disease combination that included hypertension, diabetes, CHD, COPD, and stroke had the greatest impact on mortality. In the stratified model by age and sex, absolute all-cause mortality was higher in the age group\u0026thinsp;\u0026ge;\u0026thinsp;75 years, but the relative magnitude of the effect on mortality risk was greater among those\u0026thinsp;\u0026lt;\u0026thinsp;75\u0026nbsp;years.\u003c/p\u003e \u003cp\u003eThe higher all-cause mortality risk among participants with 1, 2, and \u0026ge;\u0026thinsp;3 LTCs found in this study is consistent with previous studies that have focused on older adults[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The study of \u003cem\u003eMartinez-Gomez et al.\u003c/em\u003e observed significant upward trends with 1 LTC (HR 1.26), 2 LTC (HR 1.78), and \u0026ge;\u0026thinsp;3 LTC (HR 2.27) than those without LTC[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Similarly, \u003cem\u003eNunes et al.\u003c/em\u003e conducted a meta-analysis of 26 studies and observed similar effect sizes[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. And we also found that LTCs combinations were most strongly associated with all-cause mortality relative to the number of multimorbidity alone, whereas previous studies mainly focused on the role of a single disease in death, such as diabetes[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Moreover, the present study found that hypertension was the most frequent occurrence, with presence in 11 of the 16 different combinations, suggesting that there may be a potential link between hypertension and a variety of chronic diseases. However, a large sample of participants aged 40 to 69\u0026nbsp;years study based on 36 chronic conditions showed that conditions such as hypertension, diabetes, and asthma were at the center of common multimorbidity[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These findings suggest there is a need for further research to explore the links between these diseases and their possible interactions, which is of great significance for the guidance and treatment of multimorbidity, and also provides a theoretical basis for the formulation of health management measures and allocation of medical resources for the elderly population to some extent.\u003c/p\u003e \u003cp\u003eA prospective population-based cohort study of England people aged 37 to 73\u0026nbsp;years shown that participants in the younger age group 37\u0026ndash;49\u0026nbsp;years with \u0026ge;\u0026thinsp;4 LTCs had the highest relative risk of all-cause mortality[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. And we found that the association on risk of all-cause mortality with an increasing number of LTCs was particularly evident among younger age groups (\u0026lt;\u0026thinsp;75 vs\u0026thinsp;\u0026ge;\u0026thinsp;75\u0026nbsp;years old), which not only fills in the gap of the age of participants, but also is basically consistent with the trend of previous studies. This study found that female had a higher prevalence of multimorbidity than male, which was consistent with previous findings[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, the observed magnitude of mortality effect size was similar for both female and male. Potential explanations for the phenomenon are that females are generally more sensitized to their health[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], more likely to report more conditions[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and more likely to engage in preventive health behavior[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These findings suggest the need for early intervention to manage and prevent chronic diseases and to reduce the prevalence of multiple diseases as much as possible.\u003c/p\u003e \u003cp\u003eStudies have shown that the causes of death from multimorbidity can be attributed to 4 major underlying risk factors: smoking, alcohol consumption, underweight, and physical inactivity[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. While previous studies found that smoking was a risk factor for all-cause mortality[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], our results found no statistically significant difference, which is possibly explained by the quitter bias (people may stop smoking because they are in poor health and may be advised not to continue). The results of the risk study on alcohol consumption were basically consistent with the results of \u003cem\u003eOrtol\u0026aacute; et al.\u003c/em\u003e, which showed that there was no statistically significant difference in mortality between light-to-moderate alcohol consumption and no alcohol consumption among people over 60\u0026nbsp;years old[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. As well, we found the greater all-cause mortality risk among persons with underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e) in this study, which agreed with previous studies[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In addition, this study suggests that physical activity is associated with a lower mortality risk, and the underlying mechanism behind this finding may be that physical activity delays disease progression by preventing many chronic diseases, including diabetes, cardiovascular and respiratory diseases, and some types of cancer[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, once any of these chronic conditions is diagnosed, physical activity is often incorporated into treatment plans to improve quality of life and survival[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study has several strengths. First, the determination of chronic diseases was relatively accurate and comprehensive, including the self-reported condition of participants and the diagnosis made by the physician based on the professional comprehensive examination. Second, our study was also novel in that we explored combinations of multimorbidity that were associated with the highest risk of death. In addition, it is more convincing to assess the relationship between multimorbidity and all-cause mortality based on a large sample size. Finally, the main results remained robust after conducting sensitivity analyses. However, the study has some limitations. Since we included only seven chronic diseases registered at baseline, some other diseases associated with older people such as hyperlipidemia and arthritis could not be taken into account, which may underestimate the prevalence of many diseases in this study, and there was no way to estimate the severity of conditions as well. Another restriction is that our participants were people over 65 years, which should caution generalizing our findings to younger age groups. In addition, although we adjusted for various covariates, there is still a possibility of residual confounding, such as diet factors. Last, recall bias is unavoidable in self-reported information.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eOur findings suggest an increased risk of all-cause mortality with an elevated number of multimorbidity, particularly LTC combinations, which provides scientific basis for the treatment, prevention and control of the multimorbidity, and has important public health significance in guiding the rational allocation of medical and health resources.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eLTCs: Long-term conditions; FPG: Fasting plasma glucose; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; BMI: Body mass index; CHD: Coronary heart disease; COPD: Chronic obstructive pulmonary disease; IQR: Interquartile range; HRs: Hazard ratios; CIs: Confidence intervals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of Zhengzhou University, and written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed in this study were obtained from third parties and cannot be publicly available to confidentiality requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by National Key Research and Development Program \u0026ldquo;Research on prevention and control of major chronic non-communicable diseases\u0026rdquo; of China (Grant NO: 2017YFC1307705). The funders were not involved in the study design, data collection, analysis and interpretation, and manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.H., W.Z., and X.H. substantially contributed to the design and drafting of the study and the analysis and interpretation of the data. K.H. wrote the manuscript. K.H., W.Z., X.H., H.Z., B.G., Z.S., X.Z., C.Y. revised it critically for important intellectual content. All authors were involved in the collection of data and approve of the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe investigators are grateful to the dedicated participants and all research staff of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People\u0026rsquo;s Republic of China. \u003csup\u003e2\u003c/sup\u003eDepartment of pharmacy, Zhengzhou people's hospital, Zhengzhou, Henan, People\u0026rsquo;s Republic of China. \u003csup\u003e3\u003c/sup\u003eDepartment of Neurology, Chinese People's Liberation Army General Hospital, Beijing, People\u0026rsquo;s Republic of China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCohen JE. 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J Am Coll Cardiol. 2018;71(19):2149\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTyack Z, Frakes KA, Barnett A, Cornwell P, Kuys S, McPhail S. Predictors of health-related quality of life in people with a complex chronic disease including multimorbidity: a longitudinal cohort study. Quality of life research: an international journal of quality of life aspects of treatment care rehabilitation. 2016;25(10):2579\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalisbury C, Johnson L, Purdy S, Valderas JM, Montgomery AA. Epidemiology and impact of multimorbidity in primary care: a retrospective cohort study. The British journal of general practice: the journal of the Royal College of General Practitioners. 2011;61(582):e12\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolff JL, Starfield B, Anderson G. Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch Intern Med. 2002;162(20):2269\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380(9836):37\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParekh AK, Barton MB. The challenge of multiple comorbidity for the US health care system. Jama. 2010;303(13):1303\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGijsen R, Hoeymans N, Schellevis FG, Ruwaard D, Satariano WA, van den Bos GA. Causes and consequences of comorbidity: a review. J Clin Epidemiol. 2001;54(7):661\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNunes BP, Flores TR, Mielke GI, Thum\u0026eacute; E, Facchini LA. Multimorbidity and mortality in older adults: A systematic review and meta-analysis. 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Health Aff. 2017;36(11):1866\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal, \u003cb\u003eregional\u003c/b\u003e, \u003cb\u003eand national age\u003c/b\u003e-\u003cb\u003esex specific mortality for 264 causes of death\u003c/b\u003e, \u003cb\u003e1980\u003c/b\u003e\u0026ndash;\u003cb\u003e2016\u003c/b\u003e: \u003cb\u003ea systematic analysis for the Global Burden of Disease Study\u003c/b\u003e 2016. \u003cem\u003eLancet (London, England)\u003c/em\u003e 2017, \u003cb\u003e390\u003c/b\u003e(10100):1151\u0026ndash;1210.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZemedikun DT, Gray LJ, Khunti K, Davies MJ, Dhalwani NN: \u003cb\u003ePatterns of Multimorbidity in Middle\u003c/b\u003e-\u003cb\u003eAged and Older Adults\u003c/b\u003e: \u003cb\u003eAn Analysis of the UK Biobank Data\u003c/b\u003e. \u003cem\u003eMayo Clinic proceedings\u003c/em\u003e 2018, \u003cb\u003e93\u003c/b\u003e(7):857\u0026ndash;866.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Cesare M, Bennett JE, Best N, Stevens GA, Danaei G, Ezzati M. The contributions of risk factor trends to cardiometabolic mortality decline in 26 industrialized countries. Int J Epidemiol. 2013;42(3):838\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorre LA, Siegel RL, Ward EM, Jemal A. \u003cb\u003eGlobal Cancer Incidence and Mortality Rates and Trends\u0026ndash;An Update\u003c/b\u003e. \u003cem\u003eCancer epidemiology, biomarkers \u0026amp; prevention: a publication of the American Association for Cancer Research\u003c/em\u003e. cosponsored by the American Society of Preventive Oncology. 2016;25(1):16\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBertram MY, Sweeny K, Lauer JA, Chisholm D, Sheehan P, Rasmussen B, Upreti SR, Dixit LP, George K, Deane S. Investing in non-communicable diseases: an estimation of the return on investment for prevention and treatment services. 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Scientific reports. 2019;9(1):7320.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia W, Weng J, Zhu D, Ji L, Lu J, Zhou Z, Zou D, Guo L, Ji Q, Chen L, et al. Standards of medical care for type 2 diabetes in China 2019. Diab/Metab Res Rev. 2019;35(6):e3158.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, Jones DW, Materson BJ, Oparil S, Wright JT Jr, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. Jama. 2003;289(19):2560\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eClinical guidelines on the identification\u003c/b\u003e, \u003cb\u003eevaluation\u003c/b\u003e, \u003cb\u003eand treatment of overweight and obesity in adults\u003c/b\u003e: \u003cb\u003eexecutive summary\u003c/b\u003e. \u003cb\u003eExpert Panel on the Identification\u003c/b\u003e, \u003cb\u003eEvaluation\u003c/b\u003e, \u003cb\u003eand Treatment of Overweight in Adults\u003c/b\u003e. \u003cem\u003eThe American journal of clinical nutrition\u003c/em\u003e 1998, \u003cb\u003e68\u003c/b\u003e(4):899\u0026ndash;917.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinez-Gomez D, Guallar-Castillon P, Garcia-Esquinas E, Bandinelli S, Rodr\u0026iacute;guez-Artalejo F: \u003cb\u003ePhysical Activity and the Effect of Multimorbidity on All\u003c/b\u003e-\u003cb\u003eCause Mortality in Older Adults\u003c/b\u003e. \u003cem\u003eMayo Clinic proceedings\u003c/em\u003e 2017, \u003cb\u003e92\u003c/b\u003e(3):376\u0026ndash;382.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalonen P, Raitanen J, J\u0026auml;msen E, Enroth L, Jylh\u0026auml; M. Chronic conditions and multimorbidity in population aged 90 years and over: associations with mortality and long-term care admission. Age Ageing. 2019;48(4):564\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadarangani KP, Hamer M, Mindell JS, Coombs NA, Stamatakis E. Physical activity and risk of all-cause and cardiovascular disease mortality in diabetic adults from Great Britain: pooled analysis of 10 population-based cohorts. Diabetes Care. 2014;37(4):1016\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiaz E, Poblador-Pou B, Gimeno-Feliu LA, Calder\u0026oacute;n-Larra\u0026ntilde;aga A, Kumar BN, Prados-Torres A. Multimorbidity and Its Patterns according to Immigrant Origin. A Nationwide Register-Based Study in Norway. PloS one. 2015;10(12):e0145233.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePache B, Vollenweider P, Waeber G, Marques-Vidal P. Prevalence of measured and reported multimorbidity in a representative sample of the Swiss population. BMC Public Health. 2015;15:164.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerbrugge LM. The twain meet: empirical explanations of sex differences in health and mortality. Journal of health social behavior. 1989;30(3):282\u0026ndash;304.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRogers RG, Everett BG, Onge JM, Krueger PM. Social, behavioral, and biological factors, and sex differences in mortality. Demography. 2010;47(3):555\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalive ME. Multimorbidity in older adults. Epidemiol Rev. 2013;35:75\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtol\u0026aacute; R, Garc\u0026iacute;a-Esquinas E, L\u0026oacute;pez-Garc\u0026iacute;a E, Le\u0026oacute;n-Mu\u0026ntilde;oz LM, Banegas JR, Rodr\u0026iacute;guez-Artalejo F. Alcohol consumption and all-cause mortality in older adults in Spain: an analysis accounting for the main methodological issues. Addiction. 2019;114(1):59\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinter JE, MacInnis RJ, Wattanapenpaiboon N, Nowson CA. BMI and all-cause mortality in older adults: a meta-analysis. Am J Clin Nutr. 2014;99(4):875\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlegal KM, Graubard BI, Williamson DF, Gail MH. Excess deaths associated with underweight, overweight, and obesity. Jama. 2005;293(15):1861\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePareja-Galeano H, Garatachea N, Lucia A. Exercise as a Polypill for Chronic Diseases. Prog Mol Biol Transl Sci. 2015;135:497\u0026ndash;526.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaci H, Ioannidis JP. 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Br J Sports Med. 2015;49(21):1414\u0026ndash;22.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Older adults, Chronic conditions, Multimorbidity, All-cause mortality, Combination","lastPublishedDoi":"10.21203/rs.3.rs-122536/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-122536/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003ePrevious studies have evaluated the association of multimorbidity with higher mortality, but\u003cstrong\u003e \u003c/strong\u003eepidemiologic data on the association between the combination of multimorbidity and all-cause mortality risk are rare. We aimed to examine the relationship between multimorbidity (number/combination) and all-cause mortality in Chinese older adults. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe conducted a population-based study of 50,100 Chinese participants. Cox regression models were used to estimate the impact of long-term conditions (LTCs) on all-cause mortality. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe prevalence of multimorbidity was 31.35% and all-cause mortality was 8.01% (50,100 participants). In adjusted Cox models, the hazard rations (HRs) and 95% confidence intervals (CIs) of all-cause mortality risk for those with 1, 2, and ≥ 3 LTCs compared with those with no LTCs was 1.10 (1.01-1.20), 1.21 (1.10-1.33), and 1.46 (1.27-1.67), respectively (\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e \u0026lt;0.001). In the LTCs ≥ 2 category, the combination of chronic diseases that included hypertension, diabetes, CHD, COPD, and stroke had the greatest impact on mortality. In the stratified model by age and sex, absolute all-cause mortality was higher among the ≥ 75 age group with an increasing number of LTCs. However, the relative effect size of the increasing number of LTCs on higher mortality risk was larger among those \u0026lt; 75 years.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe risk of all-cause mortality is increased with the number of multimorbidity among Chinese older adults, particularly combinations.\u003c/p\u003e","manuscriptTitle":"Relationship Between Multimorbidity, Combination and All-Cause Mortality Among Older Adults: A Retrospective Cohort Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-12-08 23:45:06","doi":"10.21203/rs.3.rs-122536/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-02-22T00:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-02-21T00:00:00+00:00","index":2,"fulltext":"Recommendation: Accept after minor essential revisions\nForm responses:\n---\n\nComments to Author:\n---\nThank you for this interesting paper which addresses a major contemporary health issue in multiple chronic conditions. I have a few comments which could help strengthen the paper.\nMinor comments\n1. On line 54 the paper states that China is ageing at a high rate but the proportion aged over 65 is only presented at one point. This does not give any indication of a rate of change over time, but rather a high proportion at one point.\n2. Line 62-63 gives an all-cause mortality rate of 4.74%, I assume this is annually\n3. Line 110-112 Did you consider using Asian cut offs for BMI? There are several studies available which show risk of metabolic and cardiovascular outcomes occurring at lower ages in Asian populations\n4. Is there any indication of the quality, or completeness of the cause of death data? From whom were the mortality reports collected ie from hospitals? Or from civil registration system?\n5. It is stated in the Methods section, but it would to make clear at various points through the manuscript what the reference group is for the hazard ratios you present. We have regression models presented in Table 3 which compare lots of different combinations of conditions. This table would benefit from a footnote explaining the reference groups for the hazard ratios presented.\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons after the final decision on the manuscript has been made. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **Yes**\n* Declaration of competing interests: **I declare that i have no competing interests.**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. (default)**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **Yes**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **Yes**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"reviewerAgreed","content":"","date":"2021-02-04T00:00:00+00:00","index":2,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-01-05T00:00:00+00:00","index":1,"fulltext":"Recommendation: Major revisions required\nForm responses:\n---\n\nComments to Author:\n---\nThe authors examined the relationship between multimorbidity (number/combination) and all-cause mortality in Chinese older adults.\n\nMinor comment:\n1. In the title and abstract, I would suggest using the term 'disease combination or cluster' instead of 'multimorbidity combination'.\n\nMajor comments:\n1. The authors mentioned that they included in this study participants aged 65 and older who entered the health check-up program in Xinzheng City, Henan Province, Central China, from January 1, 2014 to November 15, 2019. The information was collected using a standardized questionnaire. It is not clear, if this was a fixed or dynamic cohort? What was the follow up period for each participant?\n\n2. The COX regression analysis, like any statistical test, is based on multiple assumptions, including: random censoring, proportional hazard assumption, and testing nonlinearity. I did not find any information on checking assumptions. However, a violation of these assumptions limits the applicability of COX regression analysis of the data.\n\n3. It would be great to describe the study outcome - all-cause mortality, in the Methods section, and present the details on the follow-up period. If the cohort is dynamic, the authors should clearly mention the min and max, or average follow-up period for the study participants.\n\n4. What methodology was used for examining the modification effect?\n5. What methodology was used for examining the multiplicative interaction effects presented in the Results section?\n\n6. The authors are speaking about prevention of mortality in multimorbid patients. I would suggest calculating the multimorbidity-attributable mortality prevention number for the specific period of time to indicate how many cases of multimorbidity would need to be avoided in order to reduce the death for one person in China.\n\n\n\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons after the final decision on the manuscript has been made. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **No**\n* Declaration of competing interests: **I declare that I have no competing interests**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. (default)**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **No**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **No**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"reviewerAgreed","content":"","date":"2020-12-15T00:00:00+00:00","index":1,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-12-04T00:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-12-03T00:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-12-02T23:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-12-02T23:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"74837326-388a-4242-b15d-51f70793667e","owner":[],"postedDate":"December 8th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":1382305,"name":"Health Economics \u0026 Outcomes Research"},{"id":1382306,"name":"Infectious Diseases"},{"id":1382307,"name":"Health Policy"}],"tags":[],"updatedAt":"2020-12-08T23:45:07+00:00","versionOfRecord":[],"versionCreatedAt":"2020-12-08 23:45:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-122536","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-122536","identity":"rs-122536","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}