Development and Validation of a Risk Prediction Model for Mild Cognitive Impairment in Older Chinese Adults with Chronic Diseases

Development and Validation of a Risk Prediction Model for Mild Cognitive Impairment in Older Chinese Adults with Chronic Diseases | 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 Development and Validation of a Risk Prediction Model for Mild Cognitive Impairment in Older Chinese Adults with Chronic Diseases Lulu Yan, Yuanyuan Peng, Chenjiao Guo, Entong Ren, Hao Chen, Yanan Ou, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7042025/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jan, 2026 Read the published version in BMC Geriatrics → Version 1 posted 10 You are reading this latest preprint version Abstract Background: As the population continues to age, the prevalence of mild cognitive impairment (MCI) has increased steadily. Studies have shown that older adults with chronic diseases are more likely to develop MCI than are those without chronic conditions, suggesting that chronic diseases may play a significant role in the onset of MCI.Therefore, this study is designed to develop a predictive model for MCI among older individuals with chronic diseases in China and to identify the major factors influencing the occurrence of MCI. Method: This study used data from the 2018 China Health and Retirement Longitudinal Study (CHARLS). A total of 4,712 participants who met the inclusion and exclusion criteria were included, with the dataset randomly divided into training and validation sets at a 7:3 ratio. Thirty indicators, including sociodemographic factors, lifestyle, health status, and psychological status, were analyzed. By combining the results from the Least Absolute Shrinkage and Selection Operator (LASSO) regression and random forest, nine optimal predictors were selected, and a nomogram was constructed on the basis of these factors. The model's discrimination, calibration, clinical applicability, and generalizability were assessed via receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and internal validation. Results: Age, education level, child satisfaction, marital status, depressive symptoms, ADL score, income, memory, and the number of chronic diseases were identified as significant predictors of MCI in older adults with chronic diseases. In the training set, the area under the curve (AUC) was 0.865, and in the validation set, it was 0.860. The calibration curves for both groups were close to the diagonal, and the P values of the Hosmer-Lemeshow test were all greater than 0.05, indicating that the predicted results of the model were highly consistent with the actual outcomes. Decision curve analysis (DCA) confirmed the strong clinical applicability of the model. Conclusion: The nomogram prediction model developed in this study demonstrated good predictive performance and may serve as a useful tool to help identify older adults with chronic diseases who are at increased risk of MCI. These findings may inform future strategies for individualized risk assessment and early management. Predictive model MCI Older adults CHARLS Chronic disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Background By 2035, China's older population is projected to exceed 400 million, comprising one-third of the total population, highlighting the country’s growing aging challenge [1,2]. The aging population, coupled with extended life expectancy, has contributed to a steady increase in the prevalence of chronic diseases, significantly impacting the health and daily lives of residents. Data show that approximately 75.8% of older adults suffer from at least one chronic condition, with hypertension, and coronary heart disease being the most common. Chronic diseases are now the leading contributors to the global disease burden [3], accounting for more than 70% of China’s total disease burden [4], leading to an increase in the demand for public health resources and medical expenditures. The treatment of chronic diseases is generally costly and often requires lifelong management [5]. These diseases not only impose a significant economic burden on patients but also severely affect the quality of life of older adults. As the population ages, the prevalence of Alzheimer’s disease (AD) is increasing. AD, characterized by cognitive decline and behavioral changes, is the most common form of dementia. In 2018, the global number of AD patients worldwide reached 50 million and is projected to exceed 152 million by 2050 [6]. AD severely affects daily functioning in older adults and imposes substantial economic and caregiving burdens on families and society [7].Evidence suggests that AD is preceded by a transitional stage during which cognitive decline begins gradually. Once this stage is surpassed, cognitive deterioration accelerates, significantly impairing daily activities [8]. Mild cognitive impairment (MCI) represents this transitional phase. As of 2020, there were over 38.77 million MCI patients in the older adult population [9]. These patients exhibit mild memory impairment but have not yet met the clinical diagnostic criteria for dementia and are in the early stages of dementia development[10,11]. Studies have shown that 15% to 28% of older adults with MCI progress to dementia within three years, with the likelihood rising to 70% within five to ten years [11,12]. Current pharmacological treatments for AD provide only symptomatic relief, with no effect on halting or reversing disease progression [13]. The clinical practice guidelines for MCI released by the American Academy of Neurology indicate that 14.4% to 55.6% of MCI patients can restore nervous system integrity and reduce the risk of AD through intervention [14]. Consequently, research has increasingly focused on early identification and intervention during the MCI stage, widely considered the "golden window" for dementia prevention.Studies have shown that patients with chronic diseases are more likely to experience cognitive decline due to factors such as chronic inflammation and immune dysfunction [15]. Older adults with chronic conditions are at greater risk of developing MCI than their healthy peers [16]. Chronic conditions may increase the risk of MCI by affecting brain structure and function, including through vascular damage [17], metabolic disorders [18], and inflammatory responses [19]. These conditions not only lead to a decline in physiological function but also increase the risk of MCI [20]. Existing prediction models fail to adequately reflect the cognitive risk in older adults with chronic diseases. Therefore, early identification of cognitive decline in older patients with chronic diseases, along with timely intervention and treatment, holds significant clinical importance in delaying or even reversing cognitive decline. In the medical field, risk prediction models are commonly used to assess the impact of various risk factors on the occurrence of outcome events. These models help identify high-risk populations and enable the development of personalized interventions in advance. Significant increases in MCI incidence were observed during the epidemic period, likely linked to the viral pandemic [21]. The 2020 data, focused mainly on the epidemic. This dataset could not provide sufficient predictors for the gradual return to normal life following the end of the epidemic phase. Therefore, this study uses 2018 data, which are considered the most accurate representation of cognitive characteristics in older adults during a normal phase. Using the China Health and Retirement Longitudinal Study (CHARLS) database, this study aims to develop a risk prediction model for MCI in older adults with chronic diseases, identify high-risk groups through early screening, and provide effective management and personalized interventions for high-risk individuals. 2. Materials and methods 2.1 Data sources The data for this study were obtained from the follow-up data of older adults with chronic diseases aged 60 years and above, from the 2018 CHARLS. The CHARLS is organized by the Research Center for Healthy Aging and Development and the National School of Development, both at Peking University. The survey covered 23 provinces and cities (including county-level cities) in China, representing 85% of the country's population, which ensures a high degree of representativeness [22]. To ensure data accuracy and reliability, the inclusion criteria for this study were as follows: ① age ≥60 years and ② patients with chronic diseases. The exclusion criteria were as follows: ① individuals with more than 20% missing data on individual variables; ② those with incomplete or invalid cognitive function scale data; and ③ individuals with missing sex information. 2.2 Data collection 2.2.1 Cognitive Functioning Scale The CHARLS survey assesses cognitive function via methods similar to those used in the U.S. Health and Retirement Study (HRS) [23], and cognitive function is assessed across three components: the Telephone Interview for Cognitive Status (TICS-10), word recall, and drawing. Scores range from 0 to 31.The orientation and attention section includes questions about the current season, day, year, month, and date, with 1 point awarded for each correct response. The word recall section involves recalling 10 words immediately and again after 4-10 minutes, for a total of 20 points—1 point per correctly recalled word. In the drawing section, participants are shown a picture and asked to replicate the figure on a piece of white paper. They receive 1 point if they can accurately draw the figure and complete all angles within a pentagon with two intersecting quadrilateral sides; otherwise, they receive 0 points. This section assesses the participants' visuospatial ability [24]. There is currently no universally accepted diagnostic standard for MCI. In this study, MCI was defined via age-associated cognitive decline (AACD), with the criterion set as one standard deviation below the mean for the same age group [25].This study classifies participants into age groups, with each group representing a 5-year interval, and those who meet the AACD criteria are classified as having MCI. 2.2.2 Methodology for determining chronic diseases In CHARLS, chronic disease is defined across a range of conditions, including hypertension, dyslipidemia (e.g., hyper- or hypolipidemia), diabetes or elevated blood glucose, malignancies (excluding mild skin cancer), chronic lung diseases (e.g., chronic bronchitis, emphysema, and pulmonary heart disease, excluding tumors or cancer), liver diseases (excluding fatty liver, tumors, or cancer), heart diseases (e.g., myocardial infarction, coronary heart disease, angina, heart failure), stroke (including infarction and hemorrhage), kidney disease (excluding cancer), digestive system disorders (excluding cancer), emotional and mental health issues, memory-related diseases (e.g., Alzheimer’s disease, brain atrophy), arthritis or rheumatism, and asthma, among others.The diagnosis was based on self-reports. The participants were asked whether a doctor had diagnosed them with each condition. A response of ‘‘yes’’ was given a score of 1, and ‘‘no’’ was given a score of 0. The scores were summed to determine the total number of chronic conditions. 2.2.3 Independent vari ables Through a literature review and consultation with relevant experts, this study identified 30 factors affecting MCI, which are categorized into four main areas: (1) sociodemographic factors; (2) lifestyle; (3) health status; and (4) psychological status. The values assigned to each variable are presented (Suplementary Table 1.). The following sections provide specific details for each theme: Sociodemographic factors included age (60-69, 70-79, ≥80), sex (female or male), education (less than junior high, high school/vocational, and college degree or above), marital status (with or without spouse), residence (rural or urban), income level (low, middle, high), and participation in social health insurance (yes or no).Income levels were categorized based on the total income quartiles: individuals below the lower quartile were classified as low-income, those above the upper quartile as high-income, and those in between as middle-income. Marital status was categorized such that married individuals were considered to have a spouse, whereas those who were divorced, widowed, or never married were considered to have no spouse. Lifestyle factors included sleep duration (short 8h), lunch break duration (none, ≤90 min, >90 min), socialization (yes or no), smoking status (yes or no), alcohol consumption (yes or no), and daily exercise (yes or no). Health status factors included activities of daily living (ADL) scores, near and distance vision (good, fair, poor), hearing (good, fair, poor), history of falls, disability (yes or no), memory (good, fair, poor), pain (none, a little, a lot), health satisfaction (satisfactory or unsatisfactory), hospitalization in the past year, history of hip fracture, and number of chronic diseases. The ADL scale assesses participants' basic daily activities [26], including toileting, bowel control, bathing, dressing, eating, and bed mobility. Scoring was based on a scale of 1 for no difficulty or difficulty but able to complete, and 0 for difficulty requiring assistance or inability to complete. Higher scores indicate greater self-care ability [27]. Psychological status factors included depression (yes or no), life satisfaction (satisfied or dissatisfied), child satisfaction (satisfied or dissatisfied), marital satisfaction (satisfied or dissatisfied), and air quality satisfaction (satisfied or dissatisfied). Depression was assessed via the Center for Epidemiologic Studies Depression Scale (CES-D10) scale, which consists of 10 items. Two items are scored negatively, whereas the remaining eight are scored positively, with a maximum score of 30. A score above 10 indicates depression [28], and the result is categorized as "yes" or "no." 2.3. Data analysis This study analyzed data from the 2018 CHARLS database.Categorical variables are presented as frequencies and percentages, with group comparisons made via the c 2 test or Fisher's exact test. Since continuous variables are non-normally distributed, they are reported as medians and interquartile ranges, with comparisons between groups performed via the rank sum test. Missing data were addressed via the KNN proximity method, implemented through the VIM and DMwR packages in R. The maximum proportion of missing values for any independent variable was less than 20%.To ensure randomness and reproducibility,a random seed was set in RStudio [29], and the sample was randomly divided into training and validation sets at a 7:3 ratio. The training set was used for feature selection and model building, whereas the validation set was used to assess the performance of the trained model. Univariate logistic regression analysis was performed on the training set to explore the associations between various variables and MCI in older adults with chronic diseases. Significant predictors of MCI were identified via both random forest and the least absolute shrinkage and selection operator (LASSO) regression models. Random forests evaluate the contribution of each variable to the MCI by calculating the mean decrease in the gini index(MDG). Compared with traditional regression methods, random forests are more resistant to data interference and effectively reduce the impact of outliers on model predictions. They also address the sensitivity issues seen in traditional regression, which arise from changes in the number of independent variables, improving model stability [30].LASSO regression was used to reduce multicollinearity among variables. A 10-fold cross-validation was applied to determine the optimal tuning parameter (λ), ensuring that the selected predictors were not overfit and that the resulting nomogram was accurate [31].Combining the significant variables identified by both methods, nine key variables were selected to construct the nomogram. In this study, the model's discriminative power of the model was assessed via the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The agreement between the predicted probabilities and actual outcomes was evaluated via the Hosmer-Lemeshow (H-L) goodness-of-fit test and calibration curves. Additionally, decision curve analysis (DCA) was used to assess the model's clinical validity. All data analyses were conducted via R software (version 4.1.0) and SPSS 25.0. Statistical tests were two-tailed, and a P value of <0.05 was considered to indicate statistical significance. 3. Results 3.1 Incidence and Baseline Characteristics of MCI in Participants The final sample included 4,712 older adults with chronic diseases, of whom 969 (20.6%) were identified as having MCI. The specific screening process is shown in Supplementary Fig. 1. Among these participants, 2,313 were male (49.1%) and 2,399 were female (50.9%). In terms of age distribution, the majority were aged 60-69 years(60.2%), followed by those aged 70-79 years(30.1%), and those aged 80 years and older (9.7%). The mean age of participants in this study was 69.07±7.09 years. The mean age of individuals in the MCI group was 72.41±8.27 years, which was significantly higher than that of the non-MCI group (68.21±6.48 years). The detailed distribution of MCI prevalence across different age groups is presented in Supplementary Fig. 2. Among the chronic conditions, hypertension was the most common (1,183 cases), followed by hyperlipidemia (976 cases), whereas emotional and mental disorders were the least common (131 cases). The specific distributions of the 14 diseases are shown in Supplementary Fig. 3. Further analysis revealed significant differences in sex, age, and social activity between the MCI and non-MCI groups(p<0.05). Detailed demographic and clinical characteristics are provided in Table 1. In this study, multicollinearity among the 30 independent variables was evaluated via the variance inflation factor (VIF). On the basis of the standard that VIF values below 10 indicate no multicollinearity, the results confirmed that there is no multicollinearity among the independent variables in this study [32]. Detailed results are provided in Supplementary Table 2. The full sample (n=4,712) was randomly split into a training set (n=3,276) and a validation set (n=1,436) at a 7:3 ratio. 3.2 Identification of Predictors The results of the univariate analysis and random forest analysis are presented in Table 2, and the results of the LASSO regression are shown in Fig. 1. The univariate analysis revealed that 22 variables were statistically significant. These variables were selected for LASSO regression analysis to reduce noise. When Lambda1se was set to 0.011940, this regression analysis identified 13 significant predictors out of 22 potential predictors of MCI, including gender, child satisfaction, place of residence, health satisfaction, depression, income, memory, sleep duration, education level, marital status, ADL score, number of chronic conditions, and age.In the random forest model, the lowest estimation error rate for out-of-bag samples (OBB = 19.08%) was observed when ntree=500 and mtry=5. According to the random forest analysis, the top 10 most significant predictors were ADL score, child satisfaction, depression, age, memory, education level, number of chronic diseases, income, marital status, and whether the individual was bothered by pain Fig. 2. By combining LASSO regression with the random forest algorithm, this study ultimately identified the nine most significant predictors: ADL score, child satisfaction, depression, age, memory, education level, number of chronic diseases, income, and marital status. 3.3 Construction of a nomogram model By selecting the top 9 predictors, this study constructed a nomogram prediction model was constructed (Fig.3). The model aggregates the scores of each independent variable to produce a total score. The predicted probabilities of the MCI for different total scores are displayed at the bottom of Fig.3. The higher the total score is, the greater the likelihood of MCI in older adults with chronic diseases. 3.4 Performance and Internal Validation of the Nomogram 3.4.1 Differentiation The AUC was used to assess the discriminatory power of the predictive model in both the training and validation sets (Figs.4A and 4B). In the training set, the model achieved an AUC of 0.865 (95% CI: 0.850-0.879), with a sensitivity of 78.8% and specificity of 75.9% (Fig.5A). In the validation set, the AUC was 0.860 (95% CI: 0.835-0.885), with a sensitivity of 77.9% and specificity of 81.6% (Fig.5B). As both the upper and lower bounds of the AUC exceeded 0.7, the model demonstrated strong discriminatory ability. Clinically, it offers valuable support for distinguishing MCI patients from non-MCI patients cases among older adults with chronic diseases, enhancing early screening, intervention, and overall management of cognitive health. 3.4.2 Cali bration The accuracy of the nomogram was evaluated via the H-L goodness-of-fit test and calibration curves, with a p-value > 0.05 indicating good model fit. The H-L test results showed good calibration in both the training set (c²=10.40, df=8, p=0.238) and the validation set (c²=13.13, df=8, p=0.108). The calibration curves for both sets (Figs.6A and 6B) closely aligned with the ideal reference line, indicating strong agreement between the predicted and observed probabilities. 3.4.3 Clinica l effectiveness In this study, DCA was used to evaluate the clinical utility of the nomogram in predicting MCI among older adults with chronic diseases. DCA curves were used to evaluate the clinical value of a model by comparing the net benefit of different clinical decision-making strategies. As shown in Figs.7A and 7B, the nomogram demonstrated a significantly greater net benefit in both the training and validation sets. This finding indicates strong clinical applicability, supporting its use in guiding effective decision-making and early intervention strategies. 4. Discussion This study revealed that the prevalence of MCI among older adults with chronic diseases was 20.6%, which was significantly higher than the rates reported for the general older population in the CHARLS and Chinese Longitudinal Healthy Longevity Survey (CLHLS) datasets (11.8% and 8.3%, respectively). This suggests a heightened risk of MCI in individuals with chronic conditions, likely due to the physiological impacts of these diseases. Most participants in this study had only one chronic condition, with hypertension being the most common condition, which is consistent with findings by Zheng et al [33]. Various predictive models for MCI have been developed, including logistic regression, random forests, decision trees, and extreme gradient boosting. However, the "black-box" nature of machine learning techniques complicates their comprehension and explanation, limiting their application in healthcare.Therefore, some researchers have used nomograms to present predicted results. By converting complex regression outputs into clear visual tools, the nomograms provide clinicians with a convenient way to assess patients’ conditions. Unlike other studies of the general older adult population, first, this study draws on a nationally representative sample, with particular emphasis on individuals with a high prevalence of chronic diseases. It also includes a targeted analysis of the number of chronic conditions, offering a larger scale and stronger representativeness. Second, to reduce the risk of overfitting, a combination of LASSO regression and random forest methods was employed for variable selection. Finally, the study revealed that factors such as nighttime sleep duration, place of residence, and participation in social activities had limited influence on cognitive impairment. These discrepancies may stem from differences in the cognitive assessment tools used across studies. Additionally, the high proportion of rural participants (71%) may have contributed to the variation in the results. This study identified age and education level as significant factors influencing MCI, which is consistent with prior research [34]. These studies have shown that age significantly impacts cognitive function in older adults, with an interactive relationship between age and education in shaping cognitive outcomes [35]. Cognitive decline is often linked to age-related physiological changes, which can impair attention, comprehension, and responsiveness, increasing the risk of MCI. Previous studies have confirmed a positive correlation between education and cognitive function, suggesting that higher educational attainment can slow cognitive deterioration in older adults [36]. This may be due to the effect of education on cognitive style; individuals with higher education tend to engage in more mentally stimulating activities and exhibit more active cognitive processes, which enhances cognitive reserve and helps mitigate the aging effects on cognition.Therefore, clinicians should be attentive to the influence of age on MCI risk and recognize the critical role of education in cognitive decline. Older adults with chronic diseases are encouraged to engage in mental, social, and spiritual activities and participate in cognitive training, which helps increase cognitive reserve and delays cognitive decline. ADL is a significant indicator of an individual's capacity for self-care. The prediction model in this study demonstrated that low ADL scores were significantly associated with MCI in older adults with chronic diseases,and random forests identified ADL scores as the most important predictor of MCI. This finding aligns with previous studies that have also identified a link between MCI and ADL deficits, thereby substantiating the reliability of ADL deficits as a predictor of MCI [37]. The implications of low ADL scores extend beyond diminished self-care abilities, potentially affecting patients' nutritional habits and heightening the risk of malnutrition. Decrease in physical function are frequently accompanied by reduced activity levels, which may lead to weakened muscle strength and decreased bone density, increasing the susceptibility of patients to sarcopenia and osteoporosis. These conditions, in turn, may decrease immunity and contribute to psychiatric issues [38]. Therefore, clinicians should comprehensively assess patients' daily living abilities and identify the risk of MCI early. Additionally, self-care training should be strengthened for older adults with chronic diseases, encouraging them to practice daily living activities, such as dressing, and eating, to help them regain independence. This study also identified marital status as a significant predictor of MCI risk. Older adults without a spouse were more likely to develop MCI than were those with a spouse, which is consistent with previous research [39]. Individuals with spouses often lead healthier lifestyles and are more socially engaged, which can help slow cognitive decline and delay the onset of MCI [40]. In contrast, those without a spouse (e.g.divorced or widowed) may be at greater risk due to factors such as pessimism, reduced quality of life, and limited emotional communication. These individuals often experience loneliness, lack confidence, and struggle with social integration, all of which increase the risk of MCI [41-43].The study also revealed an association between child satisfaction and MCI risk in older adults with chronic diseases. Research has suggested that a family environment with greater child satisfaction can reduce the incidence of MCI by providing psychological support, improving quality of life, and strengthening emotional bonds.Clinicians should therefore focus on the mental health of older adults with chronic diseases who are without a spouse. Encouraging participation in social activities and promoting family involvement, particularly from children, in the health management of older adults can offer crucial psychological support. Enhancing communication and emotional ties within the family may improve a patient's quality of life, reduce psychological stress, and help lower the risk of MCI. Depression is one of the most common neuropsychiatric symptoms and often serves as an early manifestation of MCI [44,45]. In this study, we found that depression and poor memory were significant risk factors for MCI in older adults with chronic diseases.Psychological disorders such as depression may increase the risk of MCI through immune system dysregulation and structural brain changes [46]. Depression has been linked to hippocampal atrophy [47], and studies have confirmed that reduced hippocampal volume is strongly associated with MCI [48]. Additionally, individuals with both depression and MCI often experience significant reductions in cortical and subcortical gray matter volume [49], suggesting shared neuroanatomical damage between the two conditions.These common pathophysiological changes imply that depression and MCI may share similar underlying mechanisms. Abnormalities in the hippocampus and prefrontal cortex, key regions for memory and cognitive processing, are central to memory loss [50]. Pathological changes, including neuroinflammation, can disrupt synaptic plasticity and neural network connectivity, impairing memory formation and information processing [46,47].Thus, clinicians should monitor mood and memory changes in older adults with chronic diseases, as these changes may be early indicators of MCI. Improved psychological and cognitive assessments could help with early detection and intervention, potentially slowing cognitive decline. Previous studies on predictive modeling of MCI in older adults have often overlooked income level as a variable. In this study, income was included on the basis of suggestions from epidemiologists, and it was found that higher income significantly protects against MCI. Income serves as an important indicator of a family's economic situation, income influences lifestyle, nutrition, physical health, social engagement, and access to information—factors that indirectly affect cognitive function. Adequate financial resources allow older adults to participate more in aerobic and social activities, both of which enhance cognitive function and reduce the risk of MCI. Furthermore, higher income contributes to greater life satisfaction and positive emotional experiences, which are beneficial for cognitive health [47,48]. Consequently, older adults with higher incomes are at a lower risk of developing MCI. Clinicians should assess patients' economic status, focus on high-risk individuals, and help them overcome financial barriers while promoting healthier lifestyles. The 2015 WHO Global Report on Aging and Health highlights chronic diseases as an increasing burden on public health systems. This study supports the idea that the increasing prevalence of chronic conditions negatively impacts MCI, which aligns with findings from other studies [51,52]. Chronic diseases such as coronary heart disease, hypertension, and diabetes may contribute to the development of MCI and related conditions in older adults [53] These conditions often lead to inflammatory responses, vascular damage, and metabolic disturbances, all of which can increase the risk of MCI in affected individuals [54]. This underscores the importance of increased vigilance in the older population with chronic conditions, highlighting the need for more frequent screening to identify those at risk of MCI early. MCI is a complex, multifactorial process, making it challenging to achieve optimal outcomes with a single intervention. Therefore, early screening for MCI in older adults with chronic diseases, particularly in high-risk groups, should be prioritized. Early assessment of factors such as depression, advanced age, reduced daily functioning, and the absence of a spouse is crucial for timely intervention.Personalized health management plans should then be developed on the basis of individual MCI risk factors. For example, patients with low child satisfaction and significant depressive symptoms should be prioritized for psychological interventions, whereas those with impaired daily functioning should be referred for rehabilitation.Clinical staff should implement multidimensional, interdisciplinary interventions. For older adults with chronic diseases, integrated approaches combining mental health support, social activity promotion, and cognitive training can help reduce the risk of MCI. This study has several limitations. First, no other dataset with measurement methods and variable definitions fully consistent with the CHARLS dataset was available, making it impossible to conduct clinical impact assessments or external validation of the model. This limits the external validity of the model, and its applicability and generalizability across different cultural contexts and socioeconomic conditions require further evaluated using data from other countries or regions. Second, information on chronic diseases in the database was based on self-reports rather than objective biochemical assessments, which may introduce recall bias. Additionally, due to the impact of the COVID-19 pandemic, data from 2020 were not available; thus, this study utilized data only from the year 2018 and employed a cross-sectional design, which inherently limits the ability to infer causal relationships. For example, depression, income level, and memory decline may be risk factors for MCI, but cognitive deficits associated with MCI may also contribute to these problems [55]. Finally, the development of MCI may be influenced by multiple factors such as dietary habits. However, due to limitations in the variable scope of the CHARLS dataset, not all potential influencing factors could be included in the current analysis. Future research should consider integrating data from additional sources and incorporating more comprehensive variables, as well as conducting longitudinal studies to enhance the overall robustness and predictive accuracy of the model. 5. Conclusion This study developed a nomogram model to predict MCI in older adults with chronic diseases using data from the CHARLS database. The model incorporated age, education level, child satisfaction, marital status, depressive symptoms, ADL score, income, memory, and the number of chronic conditions. Internal evaluation of the model's discriminative ability, calibration, and decision curve analysis demonstrated good performance across key indicators, suggesting potential clinical utility. Despite the limitations of the study, the model may serve as a useful tool for initial risk assessment in older adults with chronic diseases and could inform future clinical strategies. Abbreviations AACD Age-associated Cognitive Decline ADL Activities of Daily Living AUC Area under the curve CES-D10 Center for Epidemiologic Studies Depression Scale CHARLS China Health and Retirement Longitudinal Study CLHLS Chinese Longitudinal Healthy Longevity Survey DCA Decision Curve Analysis H-L Hosmer-Lemeshow HRS Health and Retirement Study LASSO Least absolute shrinkage and selection operator MCI Mild Cognitive Impairment MDG Mean Decrease In Gini Index TICS-10 Telephone Interview for Cognitive Status VIF Variance inflation factor Declarations Ethics approval and consent to participate This was a retrospective study based on the CHARLS database. Patient information was hidden before the study. Informed consent from patients was not needed and there were no ethical conflicts. The original CHARLS was approved by the Ethics Review Board of Peking University (IRB00001052-11015), and all participants signed an informed consent form at the time of participation. The study followed the guidelines of the Declaration of Helsinki. Clinical trial number: not applicable. Consent for publication Not applicable. Availability of data and materials These data are publicly accessible via the official website,http://CHARLS.pku.edu.cn. Competing interests All the other authors have no conflicts of interest to report. Funding This study was funded by the Science and Technology Projects in Guangzhou (2024B03J1245)，the Science and Technology Projects in Guangzhou (2023A03J0170）and Key Laboratory of Geriatric Long-term Care(Naval Medical University), Ministry of Education (LNZDPY-2023-01). Authors ’ contributions YLL and PYY created the study protocol, performed the data collection, statistical analysis,and wrote the first draft, GCJ and RET assisted with the study design and data collection, CH and OYN conflated the data and assisted with the statistical analyses. HJ and ZYT were involved in the interpretation of the data and revision of the manuscript. LWH and XL, as the senior authors, reviewed and edited all versions of the manuscript. All the authors read and approved the final manuscript. Acknowledgments We are grateful to CHARLS for providing us with these data. We appreciate all the coresearchers, reviewers, and editors. Authors' information 1 Health Science Center, Yangtze University, Jingzhou City 434023, Hubei Province, China 2 Department of Nursing, General Hospital of Southern Theatre Command of PLA, Guangzhou 510000, Guangdong province, China 3 Department of Geratic Cardiology, General Hospital of Southern Theater Command of PLA, Guangzhou 510000,Guangdong province, China 4 Guangdong Pharmaceutical University, Guangzhou 510310, Guangdong province, China References Li X, Fan L, Leng SX. The aging tsunami and senior healthcare development in China. J Am Geriatr Soc. 2018;66(8):1462-1468. doi:10.1111/jgs.15424. Chen YY, Kang L, Liu XH, Liu YS. Update on aging statistics and geriatrics development in China. 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Basic characteristics of relevant covariates in older patients with chronic disease MCI Variable Total(4712) No-MCI(3743) MCI(969) χ 2 /Z P Sex(%) 78.208 p ＜0.01 Female 2399(50.9％) 1783(47.6％) 616(63.6％) Male 2313(49.1％) 1960(52.4％) 353(36.4％) Physical exercise(%) 2.047 0.152 Yes 3044(64.6％) 2437(49.1％) 607(62.6％) No 1668(35.4％) 1306(34.9％) 362(37.4％) Social activity(%) 18.236 p ＜0.01 Yes 2413(51.2％) 1976(52.8％) 437(45.1％) No 2299(48.8％) 1767(47.2％) 532(54.9％) Marital satisfaction(%) 22.359 p ＜0.01 Yes 3911(83.0％) 3156(84.3％) 755(77.9％) No 801(17.0％) 587(15.7％) 214(22.1％) Child satisfaction(%) 275.584 p ＜0.01 Yes 4211(89.4％) 3487(93.2％) 724(74.7％) No 501(10.6％) 256(6.8％) 245(25.3％) Residence(%) 47.708 p ＜0.01 Urban 1334(28.3％) 1146(30.6％) 188(19.4％) Rural 3378(71.7％) 2597(69.4％) 781(80.6％) Air quality satisfaction(%) Yes 3733(79.2％) 2980(79.6％) 753(77.7％) 1.699 0.192 No 979(20.8％) 763(20.4％) 216(22.3％) Whether he has been hospitalized in the past year(%) 0.181 0.671 Yes 1285(27.3％) 1026(27.4％) 259(26.7％) No 3427(72.7％) 2717(72.6％) 710(73.3％) Fall down(%) 8.586 0.003 Yes 1162(24.7％) 888(23.7％) 274(28.3％) No 3550(75.3％) 2855(76.3％) 695(71.7％) Hip fracture(%) 0.117 0.733 Yes 179(3.8％) 144(3.8％) 35(3.6％) No 4533(96.2％) 3599(96.2％) 934(96.4％) Life satisfaction(%) 20.647 p ＜0.01 Yes 4101(87.0％) 3300(88.2％) 801(82.7％) No 611(13.0％) 443(11.8％) 168(17.3％) Health satisfaction(%) 12.173 p ＜0.01 Yes 2813(59.7％) 2282(11.8％) 531(54.8％) No 1899(40.3％) 1461(39.0％) 438(45.2％) Medical insurance(%) 11.841 p ＜0.01 Yes 4572(97.0％) 3648(97.5％) 924(95.4％) No 140(3.0％) 95(2.5％) 45(4.6％) Smoke(%) 0.599 0.439 Yes 2065(43.8％) 1651(44.1％) 414(42.7％) No 2647(56.2％) 2092(55.9％) 555(57.3％) Drink(%) 2.266 0.132 Yes 2280(48.4％) 1832(48.9％) 448(46.2％) No 2432(51.6％) 1911(51.1％) 521(53.8％) Physical disability(%) 1.342 0.247 Yes 1859(39.5％) 1461(39.0％) 398(41.1％) No 2853(60.5％) 2282(61.0％) 571(58.9％) Depression(%) 149.6122 p ＜0.01 Yes 1089(23.1％) 722(19.3％) 367(37.9％) No 3623(76.9％) 3021(80.7％) 602(62.1％) Distant object vision(%) -4.741 p ＜0.01 Good 1032(21.9％) 837(22.4％) 195(20.1％) Common 2322(49.3％) 1901(50.8％) 421(43.4％) Bad 1358(28.8％) 1005(26.9％) 353(36.4％) Near object vision(%) -3.316 p ＜0.01 Good 1190(25.3％) 961(25.7％) 229(23.6％) Common 2399(50.9％) 1939(51.8％) 460(47.5％) Bad 1123(23.8％) 843(22.5％) 280(28.9％) Hearing(%) -3.652 p ＜0.01 Good 1246(26.4％) 1007(26.9％) 239(24.7％) Common 2579(54.7％) 2084(55.7％) 495(51.1％) Bad 887(18.8％) 652(17.4％) 235(24.3％) Be vexed by pain(%) -2.127 0.033 No 1453(30.8％) 1165(31.1％) 288(29.7％) A little 2050(43.5％) 1652(44.1％) 398(41.1％) relatively much more 1209(25.7％) 926(24.7％) 283(29.2％) Education(%) -21.311 p ＜0.01 Below junior high school 2841(60.3％) 1963(52.4％) 878(90.6％) High school or vocational school 1415(30.0％) 1340(35.8％) 75(7.7％) College or above 456(9.7％) 440(11.8％) 16(1.7％) Memory(%) -11.926 p ＜0.01 Good 3350(71.1％) 2805(74.9％) 545(56.2％) Common 1190(25.3％) 844(22.5％) 346(35.7％) Bad 172(3.7％) 94(2.5％) 78(8.0％) Naptime(%) -2.997 0.003 ＞90min 1434(30.4％) 1155(30.9％) 279(28.8％) ≤90min 1214(25.8％) 1000(26.7％) 214(22.1％) 0min 2064(43.8％) 1588(42.4％) 476(49.1％) Nighttime sleep(%) -1.627 0.104 ＜6h 1847(39.2％) 1400(37.4％) 447(46.1％) ≥6h and ≤8h 2460(52.2％) 2084(55.7％) 376(38.8％) ＞8h 405(8.6％) 259(6.9％) 146(15.1％) Income(%) -11.828 p ＜0.01 Low income 1694(36.0％) 1205(32.2％) 489(50.5％) Middle income 2014(42.7％) 1638(43.8％) 376(38.8％) High income 1004(21.3％) 900(24.0％) 104(10.7％) Marriage(%) 273.683 p ＜0.01 have a spouse 3849(81.7％) 3235(86.4％) 614(63.4％) have no spouse 863(18.3％) 508(13.6％) 355(36.6％) Age -16.687 p ＜0.01 60~69 2836(60.2％) 2476(66.2％) 360(37.2％) 70~79 1418(30.1％) 980(26.2％) 438(45.2％) ≥80 458(9.7％) 287(7.7％) 171(17.6％) ADL score 6 (6,6) 6 (6,6) 6 (3,6) -25.275 p ＜0.01 Number of chronic diseases 1 (1,2) 1 (1,2) 1 (1,2) -4.222 p ＜0.01 Table 2. Results of univariate analysis and random forest in training set (n = 4712). Variables Groups Univariate logistic regression odds ratio (95％CI) P value Random forest GMP Sex 24.186 Female Ref Male 0.425 (0.355, 0.507) p ＜0.01* Physical exercise 20.823 No Ref Yes 0.941 (0.790， 1.121） 0.497 Social activity 25.053 No Ref Yes 0.860 (0.727, 1.017 0.078 Marital satisfaction 17.785 No Ref Yes 0.644 (0.522, 0.795) p ＜0.01* Child satisfaction 65.250 No Ref Yes 0.125 (0.098, 0.160) p ＜0.01* Residence 21.138 Rural Ref Urban 0.266 (0.205, 0.345) p ＜0.01* Air quality satisfaction 21.847 No Ref Yes 0.845 (0.695, 1.028) 0.092 Whether he has been hospitalized in the past year 20.877 No Ref Yes 0.899 (0.742, 1.088) 0.274 Fall down 18.992 No Ref Yes 1.171 (0.967, 1.417) 0.106 Hip fracture 5.653 No Ref Yes 1.893 (1.099, 3.259) 0.021 Life satisfaction 13.550 No Ref Yes 0.719 (0.569, 0.908) 0.006 Health satisfaction 30.228 No Ref Yes 0.822 (0.695, 0.972) 0.022 Medical insurance 7.123 No Ref Yes 0.524 (0.338, 0.814) 0.004 Smoke 23.589 No Ref Yes 1.180 (0.998, 1.396) 0.053 Drink 24.346 No Ref Yes 1.088 (0.919, 1.287) 0.327 Physical disability 26.553 No Ref Yes 1.111 (0.940, 1.315) 0.217 Depression 64.243 No Ref Yes 5.596 (4.608, 6.794) p ＜0.01* Distant object vision 36.583 Good Ref Common 0.941 (0.755, 1.172) 0.586 Bad 1.371 (1.086, 1.731) 0.008 Near object vision 38.413 Good Ref Common 0.967 (0.785, 1.191) 0.967 Bad 1.383 (1.097, 1.743) 1.383 Hearing 36.140 Good Ref Common 0.988 (0.806, 1.212) 0.910 Bad 1.465 (1.148, 1.869) 0.002 Be vexed by pain 42.637 No Ref A little 0.918 (0.753, 1.119) 0.396 relatively much more 1.085 (0.870, 1.353) 0.469 Education 54.486 Below junior high school Ref High school or vocational school 0.056 (0.035, 0.091) p ＜0.01* College or above 0.012 (0.002, 0.083) p ＜0.01* Memory 59.626 Good Ref Common 3.099 (2.570, 3.737) p ＜0.01* Bad 15.512 (9.501, 25.325) p ＜0.01* Naptime 38.107 ＞90min Ref ≤90min 1.536 (1.202, 1.964) p ＜0.01* 0min 1.099 (0.912, 1.324） 0.323 Night time sleep 39.721 ＜6h Ref ≥6h and ≤8h 0.588 (0.491, 0.706) p ＜0.01* ＞8h 1.728 (1.320, 2.263) p ＜0.01* Income 48.909 Low income Ref Middle income 0.627 (0.521, 0.753) p ＜0.01* High income 0.248 (0.188, 0.327) p ＜0.01* Marriage 41.603 have no spouse Ref have a spouse 0.219 (0.181, 0.266) p ＜0.01* Age 62.010 60~69 Ref 70~79 2.941 (2.437, 3.550) p ＜0.01* ≥80 3.774 (2.919, 4.878) p ＜0.01* ADL score 0.630 (0.587, 0.676) p ＜0.01* 65.966 Number of chronic diseases 1.270 (1.178, 1.369) p ＜0.01* 51.323 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7042025","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486451877,"identity":"74ac2f2e-31e3-4508-8033-f043194731af","order_by":0,"name":"Lulu Yan","email":"","orcid":"","institution":"Yangtze University","correspondingAuthor":false,"prefix":"","firstName":"Lulu","middleName":"","lastName":"Yan","suffix":""},{"id":486451878,"identity":"d0fd111f-88ce-4a02-91a0-ec51571be94f","order_by":1,"name":"Yuanyuan Peng","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Peng","suffix":""},{"id":486451879,"identity":"29e53b50-29b3-498e-920d-23aef5576b1b","order_by":2,"name":"Chenjiao Guo","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Chenjiao","middleName":"","lastName":"Guo","suffix":""},{"id":486451880,"identity":"81ef8345-6f2a-4ead-b9f6-2a9c1325446c","order_by":3,"name":"Entong Ren","email":"","orcid":"","institution":"Yangtze University","correspondingAuthor":false,"prefix":"","firstName":"Entong","middleName":"","lastName":"Ren","suffix":""},{"id":486451881,"identity":"2c2ecfa1-4f38-4db6-aa59-70e13ceb6206","order_by":4,"name":"Hao Chen","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Chen","suffix":""},{"id":486451882,"identity":"84ebae9e-8137-4c78-bf60-78f6f1675bd3","order_by":5,"name":"Yanan Ou","email":"","orcid":"","institution":"Yangtze University","correspondingAuthor":false,"prefix":"","firstName":"Yanan","middleName":"","lastName":"Ou","suffix":""},{"id":486451883,"identity":"9290e31a-f580-4f65-b69f-f28252296a89","order_by":6,"name":"Jiang Han","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Jiang","middleName":"","lastName":"Han","suffix":""},{"id":486451884,"identity":"9d4d120d-f291-4c9e-a910-f539c0df25b1","order_by":7,"name":"Yuntian Zhu","email":"","orcid":"","institution":"Jiangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuntian","middleName":"","lastName":"Zhu","suffix":""},{"id":486451885,"identity":"a1c2c821-95d7-4e4f-b068-0f6d2ec02e44","order_by":8,"name":"Weihua Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIie3SsWrDMBCA4TMCTVeUUaYleYUzgbgloXmVM4V0ETRjR4GHLH0Ah75Ep84XAunWrIEshq4Z7K1dSpPVg+2xg/7pBn0cBwIIhf5hBKaCcglgIfKU/c7QGN9FEIDpQpQsS724iQvpTTTXpd7OyHMHsU5bpu0oLlzyxrhHAomq2rWRp92FJK/WjYntEVPlVbx+797CQ+smxHTEOy9aXfUk6TfzJ5JwT3JtF0ws0oPgSd0yPSbrly+hzD9gXGzy1lvmKxcdqufpyH5kPvnx93Nj8k1VtxAYcHM4f4OW9+eMNIdQKBQKNfoD3HNQey706nsAAAAASUVORK5CYII=","orcid":"","institution":"General Hospital of Southern Theatre Command of PLA","correspondingAuthor":true,"prefix":"","firstName":"Weihua","middleName":"","lastName":"Li","suffix":""},{"id":486451887,"identity":"835329f7-7fd7-4dfc-a203-a5c28e46f665","order_by":9,"name":"Lin Xu","email":"","orcid":"","institution":"General Hospital of Southern Theater Command of PLA","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-07-04 01:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7042025/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7042025/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12877-025-06924-3","type":"published","date":"2026-01-05T15:57:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87365790,"identity":"bcd1aa13-e0ce-4abb-8245-cc427fd3c79c","added_by":"auto","created_at":"2025-07-23 06:27:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":67379,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO regression was performed on 30 independent variables. (A) Coefficient profile was generated from log (lambda). (B) The LASSO model uses tenfold cross-validation to select the optimal lambda. The partial likelihood deviation (binomial deviation) curves were plotted relative to the log (lambda). The left vertical dashed line indicates the minimum of the cross-validation error, and the right vertical dashed line indicates the minimum within one standard error of the minima.\u003c/p\u003e","description":"","filename":"OnlineFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7042025/v1/29060fbaeca35d6d52d66556.png"},{"id":87366011,"identity":"a87fde60-46d4-4949-9b61-b0cc0a6365cc","added_by":"auto","created_at":"2025-07-23 06:35:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":121455,"visible":true,"origin":"","legend":"\u003cp\u003eRanking of the importance of each characteristic variable in the prediction model.\u003c/p\u003e","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7042025/v1/bebb8a1d2bffcdbf40873230.png"},{"id":87365792,"identity":"fdfb6be3-856f-4159-b183-c49838e48b8f","added_by":"auto","created_at":"2025-07-23 06:27:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":134431,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram.\u003c/p\u003e","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7042025/v1/437ff2cf52ecd9b083b538e8.png"},{"id":87364706,"identity":"47632a7d-c8f5-4940-9fbe-7d50b2c7b15c","added_by":"auto","created_at":"2025-07-23 06:19:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":552816,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram ROC curves for the quantitative prediction of fall risk in the older individuals. (A) Nomogram-based ROC curve generated from the training dataset;(B) Nomogram-based ROC curve generated from the validation dataset.\u003c/p\u003e","description":"","filename":"OnlineFig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7042025/v1/73d05bdad981a16284c2d7a9.png"},{"id":87364708,"identity":"b2f40ae7-f446-42cc-aea6-1bc0212bcaf9","added_by":"auto","created_at":"2025-07-23 06:19:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":392983,"visible":true,"origin":"","legend":"\u003cp\u003eConfusion matrix. (A) Training set confusion matrix; (B) Validation set confusion matrix.\u003c/p\u003e","description":"","filename":"OnlineFig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7042025/v1/4089a502594c4380136ead93.png"},{"id":87365795,"identity":"74a6104c-978c-4831-88cd-a8ccc95c35aa","added_by":"auto","created_at":"2025-07-23 06:27:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":532911,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plots for the quantitative prediction of fall risk in the older individuals. (A) Calibration plot for the training dataset; (B) Calibration plot for the validation dataset.\u003c/p\u003e","description":"","filename":"OnlineFig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-7042025/v1/9502aa68258681aafe990308.png"},{"id":87365797,"identity":"be997e01-e92d-4c5f-8d2e-4037523add0d","added_by":"auto","created_at":"2025-07-23 06:27:45","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":470583,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve plots for the quantitative prediction of fall risk in the older individuals. (A) DCA curves for the training dataset; (B) DCA curves for the validation dataset.\u003c/p\u003e","description":"","filename":"OnlineFig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-7042025/v1/42b65863f97aacd20b38ce83.png"},{"id":100069261,"identity":"7cb44fcb-d414-4e3c-843a-12627cd2f878","added_by":"auto","created_at":"2026-01-12 16:12:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2155774,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7042025/v1/21dbe8fa-86f7-4b2b-8f27-3f8a8b63d77e.pdf"},{"id":87365794,"identity":"593fb870-a62c-47cd-bdaa-05919a16afc8","added_by":"auto","created_at":"2025-07-23 06:27:45","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":523809,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7042025/v1/004e688fbce0ffa827fae96a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Risk Prediction Model for Mild Cognitive Impairment in Older Chinese Adults with Chronic Diseases","fulltext":[{"header":"1. Background","content":"\u003cp\u003eBy 2035, China\u0026apos;s older population is projected to exceed 400 million, comprising one-third of the total population, highlighting the country\u0026rsquo;s growing aging challenge [1,2]. The aging population, coupled with extended life expectancy, has contributed to a steady increase in the prevalence of chronic diseases, significantly impacting the health and daily lives of residents. Data show that approximately 75.8% of older adults suffer from at least one chronic condition, with hypertension, and coronary heart disease being the most common. Chronic diseases are now the leading contributors to the global disease burden [3], accounting for more than 70% of China\u0026rsquo;s total disease burden [4], leading to an increase in the demand for public health resources and medical expenditures. The treatment of chronic diseases is generally costly and often requires lifelong management [5]. These diseases not only impose a significant economic burden on patients but also severely affect the quality of life of older adults.\u003c/p\u003e\n\u003cp\u003eAs the population ages, the prevalence of Alzheimer\u0026rsquo;s disease (AD) is increasing. AD, characterized by cognitive decline and behavioral changes, is the most common form of dementia. In 2018, the global number of AD patients worldwide reached 50 million and is projected to exceed 152 million by 2050 [6]. AD severely affects daily functioning in older adults and imposes substantial economic and caregiving burdens on families and society [7].Evidence suggests that AD is preceded by a transitional stage during which cognitive decline begins gradually. Once this stage is surpassed, cognitive deterioration accelerates, significantly impairing daily activities [8]. Mild cognitive impairment (MCI) represents this transitional phase. As of 2020, there were over 38.77 million MCI patients in the older adult population [9]. These patients exhibit mild memory impairment but have not yet met the clinical diagnostic criteria for dementia and are in the early stages of dementia development[10,11]. Studies have shown that 15% to 28% of older adults with MCI progress to dementia within three years, with the likelihood rising to 70% within five to ten years [11,12]. Current pharmacological treatments for AD provide only symptomatic relief, with no effect on halting or reversing disease progression [13]. The clinical practice guidelines for MCI released by the American Academy of Neurology indicate that 14.4% to 55.6% of MCI patients can restore nervous system integrity and reduce the risk of AD through intervention [14]. Consequently, research has increasingly focused on early identification and intervention during the MCI stage, widely considered the \u0026quot;golden window\u0026quot; for dementia prevention.Studies have shown that patients with chronic diseases are more likely to experience cognitive decline due to factors such as chronic inflammation and immune dysfunction [15]. Older adults with chronic conditions are at greater risk of developing MCI than their healthy peers [16]. Chronic conditions may increase the risk of MCI by affecting brain structure and function, including through vascular damage [17], metabolic disorders [18], and inflammatory responses [19]. These conditions not only lead to a decline in physiological function but also increase the risk of MCI [20]. Existing prediction models fail to adequately reflect the cognitive risk in older adults with chronic diseases. Therefore, early identification of cognitive decline in older patients with chronic diseases, along with timely intervention and treatment, holds significant clinical importance in delaying or even reversing cognitive decline.\u003c/p\u003e\n\u003cp\u003eIn the medical field, risk prediction models are commonly used to assess the impact of various risk factors on the occurrence of outcome events. These models help identify high-risk populations and enable the development of personalized interventions in advance. Significant increases in MCI incidence were observed during the epidemic period, likely linked to the viral pandemic [21]. The 2020 data, focused mainly on the epidemic. This dataset could not provide sufficient predictors for the gradual return to normal life following the end of the epidemic phase. Therefore, this study uses 2018 data, which are considered the most accurate representation of cognitive characteristics in older adults during a normal phase. Using the China Health and Retirement Longitudinal Study (CHARLS) database, this study aims to develop a risk prediction model for MCI in older adults with chronic diseases, identify high-risk groups through early screening, and provide effective management and personalized interventions for high-risk individuals.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Data sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study were obtained from the follow-up data of older adults with chronic diseases aged 60 years and above, from the 2018 CHARLS. The CHARLS is organized by the Research Center for Healthy Aging and Development and the National School of Development, both at Peking University. The survey covered 23 provinces and cities (including county-level cities) in China, representing 85% of the country\u0026apos;s population, which ensures a high degree of representativeness [22].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo ensure data accuracy and reliability, the inclusion criteria for this study were as follows: ① age \u0026ge;60 years and ② patients with chronic diseases. The exclusion criteria were as follows: ① individuals with more than 20% missing data on individual variables; ② those with incomplete or invalid cognitive function scale data; and ③ individuals with missing sex information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1 Cognitive Functioning Scale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHARLS survey assesses cognitive function via methods similar to those used in the U.S. Health and Retirement Study (HRS) [23], and cognitive function is assessed across three components: the Telephone Interview for Cognitive Status (TICS-10), word recall, and drawing. Scores range from 0 to 31.The orientation and attention section includes questions about the current season, day, year, month, and date, with 1 point awarded for each correct response. The word recall section involves recalling 10 words immediately and again after 4-10 minutes, for a total of 20 points\u0026mdash;1 point per correctly recalled word. In the drawing section, participants are shown a picture and asked to replicate the figure on a piece of white paper. They receive 1 point if they can accurately draw the figure and complete all angles within a pentagon with two intersecting quadrilateral sides; otherwise, they receive 0 points. This section assesses the participants\u0026apos; visuospatial ability [24].\u003c/p\u003e\n\u003cp\u003eThere is currently no universally accepted diagnostic standard for MCI. In this study, MCI was defined via age-associated cognitive decline (AACD), with the criterion set as one standard deviation below the mean for the same age group [25].This study classifies participants into age groups, with each group representing a 5-year interval, and those who meet the AACD criteria are classified as having MCI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2 Methodology for determining chronic diseases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn CHARLS, chronic disease is defined across a range of conditions, including hypertension, dyslipidemia (e.g., hyper- or hypolipidemia), diabetes or elevated blood glucose, malignancies (excluding mild skin cancer), chronic lung diseases (e.g., chronic bronchitis, emphysema, and pulmonary heart disease, excluding tumors or cancer), liver diseases (excluding fatty liver, tumors, or cancer), heart diseases (e.g., myocardial infarction, coronary heart disease, angina, heart failure), stroke (including infarction and hemorrhage), kidney disease (excluding cancer), digestive system disorders (excluding cancer), emotional and mental health issues, memory-related diseases (e.g., Alzheimer\u0026rsquo;s disease, brain atrophy), arthritis or rheumatism, and asthma, among others.The diagnosis was based on self-reports. The participants were asked whether a doctor had diagnosed them with each condition. A response of \u0026lsquo;\u0026lsquo;yes\u0026rsquo;\u0026rsquo; was given a score of 1, and \u0026lsquo;\u0026lsquo;no\u0026rsquo;\u0026rsquo; was given a score of 0. The scores were summed to determine the total number of chronic conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.3 Independent vari\u003c/strong\u003e\u003cstrong\u003eables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough a literature review and consultation with relevant experts, this study identified 30 factors affecting MCI, which are categorized into four main areas: (1) sociodemographic factors; (2) lifestyle; (3) health status; and (4) psychological status. The values assigned to each variable are presented (Suplementary Table 1.). The following sections provide specific details for each theme:\u003c/p\u003e\n\u003col class=\"decimal_type\"\u003e\n \u003cli\u003eSociodemographic factors included age (60-69, 70-79,\u0026nbsp;\u0026ge;80), sex (female or male), education (less than junior high, high school/vocational, and college degree or above), marital status (with or without spouse), residence (rural or urban), income level (low, middle, high), and participation in social health insurance (yes or no).Income levels were categorized based on the total income quartiles: individuals below the lower quartile were classified as low-income, those above the upper quartile as high-income, and those in between as middle-income. Marital status was categorized such that married individuals were considered to have a spouse, whereas those who were divorced, widowed, or never married were considered to have no spouse.\u003c/li\u003e\n \u003cli\u003eLifestyle factors included sleep duration (short \u0026lt;6h, medium 6-8h, long \u0026gt;8h), lunch break duration (none,\u0026nbsp;\u0026le;90 min, \u0026gt;90 min), socialization (yes or no), smoking status (yes or no), alcohol consumption (yes or no), and daily exercise (yes or no).\u003c/li\u003e\n \u003cli\u003eHealth status factors included activities of daily living (ADL) scores, near and distance vision (good, fair, poor), hearing (good, fair, poor), history of falls, disability (yes or no), memory (good, fair, poor), pain (none, a little, a lot), health satisfaction (satisfactory or unsatisfactory), hospitalization in the past year, history of hip fracture, and number of chronic diseases. The ADL scale assesses participants\u0026apos; basic daily activities [26], including toileting, bowel control, bathing, dressing, eating, and bed mobility. Scoring was based on a scale of 1 for no difficulty or difficulty but able to complete, and 0 for difficulty requiring assistance or inability to complete. Higher scores indicate greater self-care ability [27].\u003c/li\u003e\n \u003cli\u003ePsychological status factors included depression (yes or no), life satisfaction (satisfied or dissatisfied), child satisfaction (satisfied or dissatisfied), marital satisfaction (satisfied or dissatisfied), and air quality satisfaction (satisfied or dissatisfied). Depression was assessed via the Center for Epidemiologic Studies Depression Scale (CES-D10) scale, which consists of 10 items. Two items are scored negatively, whereas the remaining eight are scored positively, with a maximum score of 30. A score above 10 indicates depression [28], and the result is categorized as \u0026quot;yes\u0026quot; or \u0026quot;no.\u0026quot;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study analyzed data from the 2018 CHARLS database.Categorical variables are presented as frequencies and percentages, with group comparisons made via the\u0026nbsp;c\u003csup\u003e2\u003c/sup\u003e test or Fisher\u0026apos;s exact test. Since continuous variables are non-normally distributed, they are reported as medians and interquartile ranges, with comparisons between groups performed via the rank sum test. Missing data were addressed via the KNN proximity method, implemented through the VIM and DMwR packages in R. The maximum proportion of missing values for any independent variable was less than 20%.To ensure randomness and reproducibility,a random seed was set in RStudio [29], and the sample was randomly divided into training and validation sets at a 7:3 ratio.\u003c/p\u003e\n\u003cp\u003eThe training set was used for feature selection and model building, whereas the validation set was used to assess the performance of the trained model. Univariate logistic regression analysis was performed on the training set to explore the associations between various variables and MCI in older adults with chronic diseases. Significant predictors of MCI were identified via both random forest and the least absolute shrinkage and selection operator (LASSO) regression models. Random forests evaluate the contribution of each variable to the MCI by calculating the mean decrease in the gini index(MDG). Compared with traditional regression methods, random forests are more resistant to data interference and effectively reduce the impact of outliers on model predictions. They also address the sensitivity issues seen in traditional regression, which arise from changes in the number of independent variables, improving model stability [30].LASSO regression was used to reduce multicollinearity among variables. A 10-fold cross-validation was applied to determine the optimal tuning parameter (\u0026lambda;), ensuring that the selected predictors were not overfit and that the resulting nomogram was accurate\u0026nbsp;[31].Combining the significant variables identified by both methods, nine key variables were selected to construct the nomogram.\u003c/p\u003e\n\u003cp\u003eIn this study, the model\u0026apos;s discriminative power of the model was assessed via the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The agreement between the predicted probabilities and actual outcomes was evaluated via the Hosmer-Lemeshow (H-L) goodness-of-fit test and calibration curves. Additionally, decision curve analysis (DCA) was used to assess the model\u0026apos;s clinical validity. All data analyses were conducted via R software (version 4.1.0) and SPSS 25.0. Statistical tests were two-tailed, and a P value of \u0026lt;0.05 was considered to indicate statistical significance.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Incidence and Baseline Characteristics of MCI in Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final sample included 4,712 older adults with chronic diseases, of whom 969 (20.6%) were identified as having MCI. The specific screening process is shown in Supplementary Fig. 1. Among these participants, 2,313 were male (49.1%) and 2,399 were female (50.9%). In terms of age distribution, the majority were aged 60-69 years(60.2%), followed by those aged 70-79 years(30.1%), and those aged 80 years and older (9.7%). The mean age of participants in this study was 69.07\u0026plusmn;7.09 years. The mean age of individuals in the MCI group was 72.41\u0026plusmn;8.27 years, which was significantly higher than that of the non-MCI group (68.21\u0026plusmn;6.48 years). The detailed distribution of MCI prevalence across different age groups is presented in Supplementary Fig. 2.\u0026nbsp;Among the chronic conditions, hypertension was the most common (1,183 cases), followed by hyperlipidemia (976 cases), whereas emotional and mental disorders were the least common (131 cases). The specific distributions of the 14 diseases are shown in Supplementary Fig. 3. Further analysis revealed significant differences in sex, age, and social activity between the MCI and non-MCI groups(p\u0026lt;0.05). Detailed demographic and clinical characteristics are provided in Table 1.\u003c/p\u003e\n\u003cp\u003eIn this study, multicollinearity among the 30 independent variables was evaluated via the variance inflation factor (VIF). On the basis of the standard that VIF values below 10 indicate no multicollinearity, the results confirmed that there is no multicollinearity among the independent variables in this study [32]. Detailed results are provided in Supplementary Table 2. The full sample (n=4,712) was randomly split into a training set (n=3,276) and a validation set (n=1,436) at a 7:3 ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Identification of Predictors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the univariate analysis and random forest analysis are presented in Table 2, and the results of the LASSO regression are shown in Fig. 1. The univariate analysis revealed that 22 variables were statistically significant. These variables were selected for LASSO regression analysis to reduce noise. When Lambda1se was set to 0.011940, this regression analysis identified 13 significant predictors out of 22 potential predictors of MCI, including gender, child satisfaction, place of residence, health satisfaction, depression, income, memory, sleep duration, education level, marital status, ADL score, number of chronic conditions, and age.In the random forest model, the lowest estimation error rate for out-of-bag samples (OBB = 19.08%) was observed when ntree=500 and mtry=5. According to the random forest analysis, the top 10 most significant predictors were ADL score, child satisfaction, depression, age, memory, education level, number of chronic diseases, income, marital status, and whether the individual was bothered by pain Fig. 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy combining LASSO regression with the random forest algorithm, this study ultimately identified the nine most significant predictors: ADL score, child satisfaction, depression, age, memory, education level, number of chronic diseases, income, and marital status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Construction of a nomogram model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy selecting the top 9 predictors, this study constructed a nomogram prediction model was constructed (Fig.3). The model aggregates the scores of each independent variable to produce a total score. The predicted probabilities of the MCI for different total scores are displayed at the bottom of Fig.3. The higher the total score is, the greater the likelihood of MCI in older adults with chronic diseases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Performance and Internal Validation of the Nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.1 Differentiation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe AUC was used to assess the discriminatory power of the predictive model in both the training and validation sets (Figs.4A and 4B). In the training set, the model achieved an AUC of 0.865 (95% CI: 0.850-0.879), with a sensitivity of 78.8% and specificity of 75.9% (Fig.5A). In the validation set, the AUC was 0.860 (95% CI: 0.835-0.885), with a sensitivity of 77.9% and specificity of 81.6% (Fig.5B). As both the upper and lower bounds of the AUC exceeded 0.7, the model demonstrated strong discriminatory ability. Clinically, it offers valuable support for distinguishing MCI patients from non-MCI patients cases among older adults with chronic diseases, enhancing early screening, intervention, and overall management of cognitive health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.2 Cali\u003c/strong\u003e\u003cstrong\u003ebration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe accuracy of the nomogram was evaluated via the H-L goodness-of-fit test and calibration curves, with a p-value \u0026gt; 0.05 indicating good model fit. The H-L test results showed good calibration in both the training set (c\u0026sup2;=10.40, df=8, p=0.238) and the validation set (c\u0026sup2;=13.13, df=8, p=0.108). The calibration curves for both sets (Figs.6A and 6B) closely aligned with the ideal reference line, indicating strong agreement between the predicted and observed probabilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.3 Clinica\u003c/strong\u003e\u003cstrong\u003el effectiveness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, DCA was used to evaluate the clinical utility of the nomogram in predicting MCI among older adults with chronic diseases. DCA curves were used to evaluate the clinical value of a model by comparing the net benefit of different clinical decision-making strategies. As shown in Figs.7A and 7B, the nomogram demonstrated a significantly greater net benefit in both the training and validation sets. This finding indicates strong clinical applicability, supporting its use in guiding effective decision-making and early intervention strategies.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study revealed that the prevalence of MCI among older adults with chronic diseases was 20.6%, which was significantly higher than the rates reported for the general older population in the CHARLS and Chinese Longitudinal Healthy Longevity Survey (CLHLS) datasets (11.8% and 8.3%, respectively). This suggests a heightened risk of MCI in individuals with chronic conditions, likely due to the physiological impacts of these diseases. Most participants in this study had only one chronic condition, with hypertension being the most common condition, which is consistent with findings by Zheng et al [33]. Various predictive models for MCI have been developed, including logistic regression, random forests, decision trees, and extreme gradient boosting. However, the \u0026quot;black-box\u0026quot; nature of machine learning techniques complicates their comprehension and explanation, limiting their application in healthcare.Therefore, some researchers have used nomograms to present predicted results. By converting complex regression outputs into clear visual tools, the nomograms provide clinicians with a convenient way to assess patients\u0026rsquo; conditions.\u003c/p\u003e\n\u003cp\u003eUnlike other studies of the general older adult population, first, this study draws on a nationally representative sample, with particular emphasis on individuals with a high prevalence of chronic diseases. It also includes a targeted analysis of the number of chronic conditions, offering a larger scale and stronger representativeness. Second, to reduce the risk of overfitting, a combination of LASSO regression and random forest methods was employed for variable selection. Finally, the study revealed that factors such as nighttime sleep duration, place of residence, and participation in social activities had limited influence on cognitive impairment. These discrepancies may stem from differences in the cognitive assessment tools used across studies. Additionally, the high proportion of rural participants (71%) may have contributed to the variation in the results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study identified age and education level as significant factors influencing MCI, which is consistent with prior research [34]. These studies have shown that age significantly impacts cognitive function in older adults, with an interactive relationship between age and education in shaping cognitive outcomes [35]. Cognitive decline is often linked to age-related physiological changes, which can impair attention, comprehension, and responsiveness, increasing the risk of MCI. Previous studies have confirmed a positive correlation between education and cognitive function, suggesting that higher educational attainment can slow cognitive deterioration in older adults [36]. This may be due to the effect of education on cognitive style; individuals with higher education tend to engage in more mentally stimulating activities and exhibit more active cognitive processes, which enhances cognitive reserve and helps mitigate the aging effects on cognition.Therefore, clinicians should be attentive to the influence of age on MCI risk and recognize the critical role of education in cognitive decline. Older adults with chronic diseases are encouraged to engage in mental, social, and spiritual activities and participate in cognitive training, which helps increase cognitive reserve and delays cognitive decline.\u003c/p\u003e\n\u003cp\u003eADL is a significant indicator of an individual\u0026apos;s capacity for self-care. The prediction model in this study demonstrated that low ADL scores were significantly associated with MCI in older adults with chronic diseases,and random forests identified ADL scores as the most important predictor of MCI. This finding aligns with previous studies that have also identified a link between MCI and ADL deficits, thereby substantiating the reliability of ADL deficits as a predictor of MCI [37]. The implications of low ADL scores extend beyond diminished self-care abilities, potentially affecting patients\u0026apos; nutritional habits and heightening the risk of malnutrition. Decrease in physical function are frequently accompanied by reduced activity levels, which may lead to weakened muscle strength and decreased bone density, increasing the susceptibility of patients to sarcopenia and osteoporosis. These conditions, in turn, may decrease immunity and contribute to psychiatric issues [38]. Therefore, clinicians should comprehensively assess patients\u0026apos; daily living abilities and identify the risk of MCI early. Additionally, self-care training should be strengthened for older adults with chronic diseases, encouraging them to practice daily living activities, such as dressing, and eating, to help them regain independence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study also identified marital status as a significant predictor of MCI risk. Older adults without a spouse were more likely to develop MCI than were those with a spouse, which is consistent with previous research [39]. Individuals with spouses often lead healthier lifestyles and are more socially engaged, which can help slow cognitive decline and delay the onset of MCI [40]. In contrast, those without a spouse (e.g.divorced or widowed) may be at greater risk due to factors such as pessimism, reduced quality of life, and limited emotional communication. These individuals often experience loneliness, lack confidence, and struggle with social integration, all of which increase the risk of MCI [41-43].The study also revealed an association between child satisfaction and MCI risk in older adults with chronic diseases. Research has suggested that a family environment with greater child satisfaction can reduce the incidence of MCI by providing psychological support, improving quality of life, and strengthening emotional bonds.Clinicians should therefore focus on the mental health of older adults with chronic diseases who are without a spouse. Encouraging participation in social activities and promoting family involvement, particularly from children, in the health management of older adults can offer crucial psychological support. Enhancing communication and emotional ties within the family may improve a patient\u0026apos;s quality of life, reduce psychological stress, and help lower the risk of MCI.\u003c/p\u003e\n\u003cp\u003eDepression is one of the most common neuropsychiatric symptoms and often serves as an early manifestation of MCI [44,45]. In this study, we found that depression and poor memory were significant risk factors for MCI in older adults with chronic diseases.Psychological disorders such as depression may increase the risk of MCI through immune system dysregulation and structural brain changes [46]. Depression has been linked to hippocampal atrophy [47], and studies have confirmed that reduced hippocampal volume is strongly associated with MCI [48]. Additionally, individuals with both depression and MCI often experience significant reductions in cortical and subcortical gray matter volume [49], suggesting shared neuroanatomical damage between the two conditions.These common pathophysiological changes imply that depression and MCI may share similar underlying mechanisms. Abnormalities in the hippocampus and prefrontal cortex, key regions for memory and cognitive processing, are central to memory loss [50]. Pathological changes, including neuroinflammation, can disrupt synaptic plasticity and neural network connectivity, impairing memory formation and information processing [46,47].Thus, clinicians should monitor mood and memory changes in older adults with chronic diseases, as these changes may be early indicators of MCI. Improved psychological and cognitive assessments could help with early detection and intervention, potentially slowing cognitive decline.\u003c/p\u003e\n\u003cp\u003ePrevious studies on predictive modeling of MCI in older adults have often overlooked income level as a variable. In this study, income was included on the basis of suggestions from epidemiologists, and it was found that higher income significantly protects against MCI. Income serves as an important indicator of a family\u0026apos;s economic situation, income influences lifestyle, nutrition, physical health, social engagement, and access to information\u0026mdash;factors that indirectly affect cognitive function. Adequate financial resources allow older adults to participate more in aerobic and social activities, both of which enhance cognitive function and reduce the risk of MCI. Furthermore, higher income contributes to greater life satisfaction and positive emotional experiences, which are beneficial for cognitive health [47,48]. Consequently, older adults with higher incomes are at a lower risk of developing MCI. Clinicians should assess patients\u0026apos; economic status, focus on high-risk individuals, and help them overcome financial barriers while promoting healthier lifestyles.\u003c/p\u003e\n\u003cp\u003eThe 2015 WHO Global Report on Aging and Health highlights chronic diseases as an increasing burden on public health systems. This study supports the idea that the increasing prevalence of chronic conditions negatively impacts MCI, which aligns with findings from other studies [51,52]. Chronic diseases such as coronary heart disease, hypertension, and diabetes may contribute to the development of MCI and related conditions in older adults [53] These conditions often lead to inflammatory responses, vascular damage, and metabolic disturbances, all of which can increase the risk of MCI in affected individuals [54]. This underscores the importance of increased vigilance in the older population with chronic conditions, highlighting the need for more frequent screening to identify those at risk of MCI early.\u003c/p\u003e\n\u003cp\u003eMCI is a complex, multifactorial process, making it challenging to achieve optimal outcomes with a single intervention. Therefore, early screening for MCI in older adults with chronic diseases, particularly in high-risk groups, should be prioritized. Early assessment of factors such as depression, advanced age, reduced daily functioning, and the absence of a spouse is crucial for timely intervention.Personalized health management plans should then be developed on the basis of individual MCI risk factors. For example, patients with low child satisfaction and significant depressive symptoms should be prioritized for psychological interventions, whereas those with impaired daily functioning should be referred for rehabilitation.Clinical staff should implement multidimensional, interdisciplinary interventions. For older adults with chronic diseases, integrated approaches combining mental health support, social activity promotion, and cognitive training can help reduce the risk of MCI.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, no other dataset with measurement methods and variable definitions fully consistent with the CHARLS dataset was available, making it impossible to conduct clinical impact assessments or external validation of the model. This limits the external validity of the model, and its applicability and generalizability across different cultural contexts and socioeconomic conditions require further evaluated using data from other countries or regions. Second, information on chronic diseases in the database was based on self-reports rather than objective biochemical assessments, which may introduce recall bias. Additionally, due to the impact of the COVID-19 pandemic, data from 2020 were not available; thus, this study utilized data only from the year 2018 and employed a cross-sectional design, which inherently limits the ability to infer causal relationships. For example, depression, income level, and memory decline may be risk factors for MCI, but cognitive deficits associated with MCI may also contribute to these problems [55]. Finally, the development of MCI may be influenced by multiple factors such as dietary habits. However, due to limitations in the variable scope of the CHARLS dataset, not all potential influencing factors could be included in the current analysis. Future research should consider integrating data from additional sources and incorporating more comprehensive variables, as well as conducting longitudinal studies to enhance the overall robustness and predictive accuracy of the model.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study developed a nomogram model to predict MCI in older adults with chronic diseases using data from the CHARLS database. The model incorporated age, education level, child satisfaction, marital status, depressive symptoms, ADL score, income, memory, and the number of chronic conditions. Internal evaluation of the model\u0026apos;s discriminative ability, calibration, and decision curve analysis demonstrated good performance across key indicators, suggesting potential clinical utility. Despite the limitations of the study, the model may serve as a useful tool for initial risk assessment in older adults with chronic diseases and could inform future clinical strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAACD \u0026nbsp; \u0026nbsp; Age-associated Cognitive Decline\u003c/p\u003e\n\u003cp\u003eADL \u0026nbsp; \u0026nbsp; \u0026nbsp; Activities of Daily Living\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; Area under the curve\u003c/p\u003e\n\u003cp\u003eCES-D10 \u0026nbsp; Center for Epidemiologic Studies Depression Scale\u003c/p\u003e\n\u003cp\u003eCHARLS \u0026nbsp; China Health and Retirement Longitudinal Study\u003c/p\u003e\n\u003cp\u003eCLHLS \u0026nbsp; \u0026nbsp;Chinese Longitudinal Healthy Longevity Survey\u003c/p\u003e\n\u003cp\u003eDCA \u0026nbsp; \u0026nbsp; \u0026nbsp; Decision Curve Analysis\u003c/p\u003e\n\u003cp\u003eH-L \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hosmer-Lemeshow\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHRS \u0026nbsp; \u0026nbsp; \u0026nbsp; Health and Retirement Study\u003c/p\u003e\n\u003cp\u003eLASSO \u0026nbsp; \u0026nbsp;Least absolute shrinkage and selection operator\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMCI \u0026nbsp; \u0026nbsp; \u0026nbsp; Mild Cognitive Impairment\u003c/p\u003e\n\u003cp\u003eMDG \u0026nbsp; \u0026nbsp; \u0026nbsp;Mean Decrease In Gini Index\u003c/p\u003e\n\u003cp\u003eTICS-10 \u0026nbsp; \u0026nbsp;Telephone Interview for Cognitive Status\u003c/p\u003e\n\u003cp\u003eVIF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Variance inflation factor\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a retrospective study based on the CHARLS database. Patient information was hidden before the study. Informed consent from patients was not needed and there were no ethical conflicts. The original CHARLS was approved by the Ethics Review Board of Peking University (IRB00001052-11015), and all participants signed an informed consent form at the time of participation. The study followed the guidelines of the Declaration of Helsinki. Clinical trial number: not applicable.\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 materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese data are publicly accessible via the official website,http://CHARLS.pku.edu.cn.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the other authors have no conflicts of interest to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Science and Technology Projects in Guangzhou (2024B03J1245)，the Science and Technology Projects in Guangzhou (2023A03J0170）and Key Laboratory of Geriatric Long-term Care(Naval Medical University), Ministry of Education (LNZDPY-2023-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYLL and PYY created the study protocol, performed the data collection, statistical analysis,and wrote the first draft, GCJ and RET assisted with the study design and data collection, CH and OYN conflated the data and assisted with the statistical analyses. HJ and ZYT were involved in the interpretation of the data and revision of the manuscript. LWH and XL, as the senior authors, reviewed and edited all versions of the manuscript. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to CHARLS for providing us with these data. We appreciate all the coresearchers, reviewers, and editors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eHealth Science Center, Yangtze University, Jingzhou City 434023, Hubei Province, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDepartment of Nursing, General Hospital of Southern Theatre Command of PLA, Guangzhou 510000, Guangdong province, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eDepartment of Geratic Cardiology, General Hospital of Southern Theater Command of PLA, Guangzhou 510000,Guangdong province, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eGuangdong Pharmaceutical University, Guangzhou 510310, Guangdong province, China \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi X, Fan L, Leng SX. The aging tsunami and senior healthcare development in China. 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J Gerontol B Psychol Sci Soc Sci. 2011;66 Suppl 1:i162\u0026ndash;171. doi:10.1093/geronb/gbr048.\u003c/li\u003e\n\u003cli\u003eCao L, Zhao Z, Ji C, Xia Y. Association between solid fuel use and cognitive impairment: A cross-sectional and follow-up study in a middle-aged and older Chinese population. Environ Int. 2021;146:106251. doi:10.1016/j.envint.2020.106251.\u003c/li\u003e\n\u003cli\u003eRichards M, Touchon J, Ledesert B, Richie K. Cognitive decline in ageing: are AAMI and AACD distinct entities? Int J Geriatr Psychiatry. 1999;14(7):534-540. doi:10.1002/(sici)1099-1166(199907)14:7\u0026lt;534::aid-gps963\u0026gt;3.0.co;2-b.\u003c/li\u003e\n\u003cli\u003eKatz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of illness in the aged. The index of ADL: A standardized measure of biological and psychosocial function. JAMA. 1963;185:914-919. doi:10.1001/jama.1963.03060120024016.\u003c/li\u003e\n\u003cli\u003eBu F, Deng XH, Zhan NN, Cheng H, Wang ZL, Tang L, Zhao Y, Lyu QY. Development and validation of a risk prediction model for frailty in patients with diabetes. BMC Geriatr. 2023;23(1):172. doi:10.1186/s12877-023-03823-3.\u003c/li\u003e\n\u003cli\u003eJiang CH, Zhu F, Qin TT. Relationships between chronic diseases and depression among middle-aged and elderly people in China: A prospective study from CHARLS. Curr Med Sci. 2020;40(5):858-870. doi:10.1007/s11596-020-2270-5.\u003c/li\u003e\n\u003cli\u003eWu WT, Li YJ, Feng AZ, Li L, Huang T, Xu AD, Lyu J. Data mining in clinical big data: the frequently used databases, steps, and methodological models. Mil Med Res. 2021;8(1):44. doi:10.1186/s40779-021-00338-z.\u003c/li\u003e\n\u003cli\u003eRigatti SJ. Random Forest. J Insur Med. 2017;47(1):31-39. doi:10.17849/insm-47-01-31-39.1.\u003c/li\u003e\n\u003cli\u003eFriedman JD, Reece GR, Eldor L. The utility of the posterior thigh flap for complex pelvic and perineal reconstruction. Plast Reconstr Surg. 2010;126(1):146-155. doi:10.1097/PRS.0b013e3181da8769.\u003c/li\u003e\n\u003cli\u003eShen M, Zhang Y, Zhan R, Du T, Shen P, Lu X, Liu S, Guo R, Shen X. Predicting the risk of cardiovascular disease in adults exposed to heavy metals: Interpretable machine learning. Ecotoxicol Environ Saf. 2025 Jan 15;290:117570. doi:10.1016/j.ecoenv.2024.117570.\u003c/li\u003e\n\u003cli\u003eZheng Y, Zhang C, Liu Y. Risk prediction models of depression in older adults with chronic diseases. J Affect Disord. 2024;359:182-188. doi:10.1016/j.jad.2024.05.078.\u003c/li\u003e\n\u003cli\u003eSong Y, Yuan Q, Liu H, Gu K, Liu Y. Machine learning algorithms to predict mild cognitive impairment in older adults in China: A cross-sectional study. J Affect Disord. 2025;368:117-126. doi:10.1016/j.jad.2024.09.059.\u003c/li\u003e\n\u003cli\u003eRabbitt P, Lowe C. Patterns of cognitive ageing. Psychol Res. 2000;63(3):308-316. doi:10.1007/s004269900009.\u003c/li\u003e\n\u003cli\u003eSeblova D, Berggren R, L\u0026ouml;vd\u0026eacute;n M. Education and age-related decline in cognitive performance: Systematic review and meta-analysis of longitudinal cohort studies. Ageing Res Rev. 2020;58:101005. doi:10.1016/j.arr.2019.101005.\u003c/li\u003e\n\u003cli\u003eDi Carlo A, Baldereschi M, Lamassa M, Bovis F, Inzitari M. Daily function as predictor of dementia in cognitive impairment, no dementia (CIND) and mild cognitive impairment (MCI): an 8-year follow-up in the ILSA study. J Alzheimers Dis. 2016;53(2):505-515. doi:10.3233/JAD-160087.\u003c/li\u003e\n\u003cli\u003ePerna S, D\u0026apos;Arcy Francis M, Bologna C, Moncaglieri F, Riva A, Morazzoni P, Allegrini P, Isu A, Vigo B, Guerriero F, Rondanelli M. Performance of Edmonton Frail Scale on frailty assessment: its association with multi-dimensional geriatric conditions assessed with specific screening tools. BMC Geriatr. 2017;17(1):2. doi:10.1186/s12877-016-0382-3.\u003c/li\u003e\n\u003cli\u003eSommerlad A, Ruegger J, Singh-Manoux A, Lewis G, Livingston G. Marriage and risk of dementia: systematic review and meta-analysis of observational studies. J Neurol Neurosurg Psychiatry. 2018;89(3):231-238. doi:10.1136/jnnp-2017-316274.\u003c/li\u003e\n\u003cli\u003eLee Y, Chi I, Palinkas LA. Widowhood, leisure activity engagement, and cognitive function among older adults. Aging Ment Health. 2019;23(6):771-780. doi:10.1080/13607863.2018.1450837.\u003c/li\u003e\n\u003cli\u003eHajek A, Riedel-Heller SG, K\u0026ouml;nig HH. Perceived social isolation and cognitive functioning. Longitudinal findings based on the German Ageing Survey. Int J Geriatr Psychiatry. 2020;35(3):276-281. doi:10.1002/gps.5243.\u003c/li\u003e\n\u003cli\u003eZhang Z, Li LW, Xu H, Liu J. Does widowhood affect cognitive function among Chinese older adults? SSM Popul Health. 2018;7:100329. doi:10.1016/j.ssmph.2018.100329.\u003c/li\u003e\n\u003cli\u003eBrown SL, Lin IF, Vielee A, Mellencamp KA. Midlife marital dissolution and the onset of cognitive impairment. Gerontologist. 2021;61(7):1085-1094. doi:10.1093/geront/gnaa193.\u003c/li\u003e\n\u003cli\u003eGallagher D, Fischer CE, Iaboni A. Neuropsychiatric symptoms in mild cognitive impairment: an update on prevalence, mechanisms, and clinical significance. Can J Psychiatry. 2017;62(3):161-169. doi:10.1177/0706743716648296.\u003c/li\u003e\n\u003cli\u003eThomas PA, Umberson D. Do older parents\u0026apos; relationships with their adult children affect cognitive limitations, and does this differ for mothers and fathers? J Gerontol B Psychol Sci Soc Sci. 2018;73(6):1133-1142. doi:10.1093/geronb/gbx009.\u003c/li\u003e\n\u003cli\u003eChen H, Zhou Y, Huang L, Xu X, Yuan C. Multimorbidity burden and developmental trajectory in relation to later-life dementia: a prospective study. Alzheimers Dement. 2023;19(5):2024-2033. doi:10.1002/alz.12840.\u003c/li\u003e\n\u003cli\u003eLee GJ, Lu PH, Hua X, Lee S, Wu S, Nguyen K, Teng E, Leow AD, Jack CR Jr, Toga AW, Weiner MW, Bartzokis G, Thompson PM; Alzheimer\u0026apos;s Disease Neuroimaging Initiative. Depressive symptoms in mild cognitive impairment predict greater atrophy in Alzheimer\u0026apos;s disease-related regions. Biol Psychiatry. 2012;71(9):814-821. doi:10.1016/j.biopsych.2011.12.024.\u003c/li\u003e\n\u003cli\u003eWolf OT, Convit A, Thorn E, de Leon MJ. Salivary cortisol day profiles in elderly with mild cognitive impairment. Psychoneuroendocrinology. 2002;27(7):777-789. doi:10.1016/s0306-4530(01)00079-8.\u003c/li\u003e\n\u003cli\u003eXie C, Li W, Chen G, Ward BD, Franczak MB, Jones JL, Antuono PG, Li SJ, Goveas JS. The co-existence of geriatric depression and amnestic mild cognitive impairment detrimentally affect gray matter volumes: voxel-based morphometry study. Behav Brain Res. 2012;235(2):244-250. doi:10.1016/j.bbr.2012.08.007.\u003c/li\u003e\n\u003cli\u003eSquire LR, O\u0026apos;Dede AJ. Conscious and unconscious memory systems. Cold Spring Harb Perspect Biol. 2015;7(3):a021667. doi:10.1101/cshperspect.a021667.\u003c/li\u003e\n\u003cli\u003eShang X, Zhu Z, Zhang X, Huang Y, Zhang X, Liu J, Wang W, Tang S, Yu H, Ge Z, Yang X, He M. Association of a wide range of chronic diseases and apolipoprotein E4 genotype with subsequent risk of dementia in community-dwelling adults: A retrospective cohort study. EClinicalMedicine. 2022;45:101335. doi:10.1016/j.eclinm.2022.101335.\u003c/li\u003e\n\u003cli\u003eSong X, Mitnitski A, Rockwood K. Nontraditional risk factors combine to predict Alzheimer disease and dementia. Neurology. 2011;77(3):227-234. doi:10.1212/WNL.0b013e318225c6bc.\u003c/li\u003e\n\u003cli\u003eHu WH, Wang JW. Current situation and influencing factors of cognitive impairment in the elderly people aged 65 and above in Futian District, Shenzhen. Pop Sci Technol. 2022;24(1):73-76. 1008-1151(2022)01-0073-04.\u003c/li\u003e\n\u003cli\u003eVetrano DL, Rizzuto D, Calder\u0026oacute;n-Larra\u0026ntilde;aga A, Onder G, Welmer A-K, Bernabei R, Marengoni A, Fratiglioni L. Trajectories of functional decline in older adults with neuropsychiatric and cardiovascular multimorbidity: A Swedish cohort study. PLoS Med. 2018;15(3):e1002503. doi:10.1371/journal.pmed.1002503.\u003c/li\u003e\n\u003cli\u003eLi JQ, Tan L, Wang HF, Tan MS, Tan L, Xu W, Zhao QF, Wang J, Jiang T, Yu JT. Risk factors for predicting progression from mild cognitive impairment to Alzheimer\u0026apos;s disease: a systematic review and meta-analysis of cohort studies. J Neurol Neurosurg Psychiatry. 2016;87(5):476\u0026ndash;484. doi:10.1136/jnnp-2014-310095.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable1.\u0026nbsp;\u003c/strong\u003eBasic characteristics of relevant covariates in older patients with chronic disease MCI\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"686\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eTotal(4712)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eNo-MCI(3743)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eMCI(969)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e/Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eSex(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e78.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2399(50.9％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1783(47.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e616(63.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2313(49.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1960(52.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e353(36.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003ePhysical exercise(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3044(64.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2437(49.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e607(62.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1668(35.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1306(34.9％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e362(37.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eSocial activity(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e18.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2413(51.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1976(52.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e437(45.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2299(48.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1767(47.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e532(54.9％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eMarital satisfaction(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e22.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3911(83.0％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e3156(84.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e755(77.9％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e801(17.0％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e587(15.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e214(22.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eChild satisfaction(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e275.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e4211(89.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e3487(93.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e724(74.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e501(10.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e256(6.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e245(25.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eResidence(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e47.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1334(28.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1146(30.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e188(19.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3378(71.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2597(69.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e781(80.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eAir quality satisfaction(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3733(79.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2980(79.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e753(77.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e979(20.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e763(20.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e216(22.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eWhether he has been hospitalized in the past year(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1285(27.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1026(27.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e259(26.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3427(72.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2717(72.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e710(73.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eFall down(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e8.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1162(24.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e888(23.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e274(28.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3550(75.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2855(76.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e695(71.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eHip fracture(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e179(3.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e144(3.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e35(3.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e4533(96.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e3599(96.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e934(96.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eLife satisfaction(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e20.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e4101(87.0％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e3300(88.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e801(82.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e611(13.0％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e443(11.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e168(17.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eHealth satisfaction(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e12.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2813(59.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2282(11.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e531(54.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1899(40.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1461(39.0％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e438(45.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eMedical insurance(%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e11.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e4572(97.0％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e3648(97.5％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e924(95.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e140(3.0％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e95(2.5％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e45(4.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eSmoke(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2065(43.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1651(44.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e414(42.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2647(56.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2092(55.9％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e555(57.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eDrink(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2280(48.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1832(48.9％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e448(46.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2432(51.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1911(51.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e521(53.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003ePhysical disability(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1859(39.5％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1461(39.0％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e398(41.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2853(60.5％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2282(61.0％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e571(58.9％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eDepression(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e149.6122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1089(23.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e722(19.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e367(37.9％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3623(76.9％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e3021(80.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e602(62.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eDistant object vision(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-4.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1032(21.9％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e837(22.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e195(20.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eCommon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2322(49.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1901(50.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e421(43.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1358(28.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1005(26.9％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e353(36.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNear object vision(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-3.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1190(25.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e961(25.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e229(23.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eCommon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2399(50.9％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1939(51.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e460(47.5％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1123(23.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e843(22.5％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e280(28.9％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eHearing(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-3.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1246(26.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1007(26.9％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e239(24.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eCommon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2579(54.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2084(55.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e495(51.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e887(18.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e652(17.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e235(24.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eBe vexed by pain(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-2.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1453(30.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1165(31.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e288(29.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eA little\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2050(43.5％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1652(44.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e398(41.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003erelatively much more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1209(25.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e926(24.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e283(29.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eEducation(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-21.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eBelow junior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2841(60.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1963(52.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e878(90.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eHigh school or vocational school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1415(30.0％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1340(35.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e75(7.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e456(9.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e440(11.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e16(1.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eMemory(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-11.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3350(71.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2805(74.9％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e545(56.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eCommon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1190(25.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e844(22.5％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e346(35.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e172(3.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e94(2.5％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e78(8.0％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNaptime(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-2.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e＞90min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1434(30.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1155(30.9％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e279(28.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026le;90min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1214(25.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1000(26.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e214(22.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e0min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2064(43.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1588(42.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e476(49.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNighttime sleep(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-1.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e＜6h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1847(39.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1400(37.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e447(46.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026ge;6h and \u0026le;8h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2460(52.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2084(55.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e376(38.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e＞8h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e405(8.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e259(6.9％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e146(15.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eIncome(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-11.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eLow income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1694(36.0％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1205(32.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e489(50.5％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eMiddle income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2014(42.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1638(43.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e376(38.8％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eHigh income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1004(21.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e900(24.0％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e104(10.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eMarriage(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e273.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003ehave a spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3849(81.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e3235(86.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e614(63.4％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003ehave no spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e863(18.3％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e508(13.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e355(36.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-16.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e60~69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2836(60.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2476(66.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e360(37.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e70~79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1418(30.1％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e980(26.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e438(45.2％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026ge;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e458(9.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e287(7.7％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e171(17.6％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eADL score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e6 (6,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e6 (6,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e6 (3,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-25.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNumber of chronic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1 (1,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1 (1,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1 (1,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-4.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eResults of univariate analysis and random forest in training set (n = 4712).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"693\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eUnivariate logistic regression odds ratio (95％CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eRandom forest GMP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e24.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.425 (0.355, 0.507)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003ePhysical exercise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e20.823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.941 (0.790，\u0026nbsp;1.121）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eSocial activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e25.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.860 (0.727, 1.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eMarital satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e17.785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.644 (0.522, 0.795)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eChild satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e65.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.125 (0.098, 0.160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e21.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.266 (0.205, 0.345)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eAir quality satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e21.847\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.845 (0.695, 1.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eWhether he has been hospitalized in the past year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e20.877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.899 (0.742, 1.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eFall down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e18.992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e1.171 (0.967, 1.417)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eHip fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e5.653\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e1.893 (1.099, 3.259)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eLife satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e13.550\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.719 (0.569, 0.908)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eHealth satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e30.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.822 (0.695, 0.972)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eMedical insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e7.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.524 (0.338, 0.814)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eSmoke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e23.589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e1.180 (0.998, 1.396)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eDrink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e24.346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e1.088 (0.919, 1.287)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003ePhysical disability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e26.553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e1.111 (0.940, 1.315)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e64.243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e5.596 (4.608, 6.794)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eDistant object vision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e36.583\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eCommon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.941 (0.755, 1.172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e1.371 (1.086, 1.731)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eNear object vision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e38.413\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eCommon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.967 (0.785, 1.191)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e1.383 (1.097, 1.743)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eHearing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e36.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eCommon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.988 (0.806, 1.212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e1.465 (1.148, 1.869)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eBe vexed by pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e42.637\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eA little\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.918 (0.753, 1.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003erelatively much more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e1.085 (0.870, 1.353)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e54.486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eBelow junior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eHigh school or vocational school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.056 (0.035, 0.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.012 (0.002, 0.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eMemory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e59.626\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eCommon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e3.099 (2.570, 3.737)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e15.512 (9.501, 25.325)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eNaptime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e38.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e＞90min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026le;90min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e1.536 (1.202, 1.964)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e1.099 (0.912, 1.324）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eNight time sleep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e39.721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e＜6h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026ge;6h and \u0026le;8h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.588 (0.491, 0.706)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e＞8h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e1.728 (1.320, 2.263)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e48.909\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eLow income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eMiddle income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.627 (0.521, 0.753)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eHigh income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.248 (0.188, 0.327)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eMarriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e41.603\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003ehave no spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003ehave a spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.219 (0.181, 0.266)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e62.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e60~69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e70~79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e2.941 (2.437, 3.550)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026ge;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e3.774 (2.919, 4.878)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eADL score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e0.630 (0.587, 0.676)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e65.966\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eNumber of chronic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e1.270 (1.178, 1.369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e＜0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e51.323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Predictive model, MCI, Older adults, CHARLS, Chronic disease","lastPublishedDoi":"10.21203/rs.3.rs-7042025/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7042025/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e As the population continues to age, the prevalence of mild cognitive impairment (MCI) has increased steadily. Studies have shown that older adults with chronic diseases are more likely to develop MCI than are those without chronic conditions, suggesting that chronic diseases may play a significant role in the onset of MCI.Therefore, this study is designed to develop a predictive model for MCI among older individuals with chronic diseases in China and to identify the major factors influencing the occurrence of MCI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e This study used data from the 2018 China Health and Retirement Longitudinal Study (CHARLS). A total of 4,712 participants who met the inclusion and exclusion criteria were included, with the dataset randomly divided into training and validation sets at a 7:3 ratio. Thirty indicators, including sociodemographic factors, lifestyle, health status, and psychological status, were analyzed. By combining the results from the Least Absolute Shrinkage and Selection Operator (LASSO) regression and random forest, nine optimal predictors were selected, and a nomogram was constructed on the basis of these factors. The model's discrimination, calibration, clinical applicability, and generalizability were assessed via receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and internal validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAge, education level, child satisfaction, marital status, depressive symptoms, ADL score, income, memory, and the number of chronic diseases were identified as significant predictors of MCI in older adults with chronic diseases. In the training set, the area under the curve (AUC) was 0.865, and in the validation set, it was 0.860. The calibration curves for both groups were close to the diagonal, and the P values of the Hosmer-Lemeshow test were all greater than 0.05, indicating that the predicted results of the model were highly consistent with the actual outcomes. Decision curve analysis (DCA) confirmed the strong clinical applicability of the model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The nomogram prediction model developed in this study demonstrated good predictive performance and may serve as a useful tool to help identify older adults with chronic diseases who are at increased risk of MCI. These findings may inform future strategies for individualized risk assessment and early management.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Risk Prediction Model for Mild Cognitive Impairment in Older Chinese Adults with Chronic Diseases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 06:19:40","doi":"10.21203/rs.3.rs-7042025/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-22T09:08:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T00:51:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-16T12:08:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302878894747127786466218007019971544167","date":"2025-07-19T18:24:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82598812169592721665904710816649557903","date":"2025-07-18T22:49:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-16T14:04:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-16T13:43:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-14T08:23:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-13T07:06:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-07-13T07:02:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8ea9abf9-abd4-4d1b-ad19-68f0c847fe1d","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:02:38+00:00","versionOfRecord":{"articleIdentity":"rs-7042025","link":"https://doi.org/10.1186/s12877-025-06924-3","journal":{"identity":"bmc-geriatrics","isVorOnly":false,"title":"BMC Geriatrics"},"publishedOn":"2026-01-05 15:57:56","publishedOnDateReadable":"January 5th, 2026"},"versionCreatedAt":"2025-07-23 06:19:40","video":"","vorDoi":"10.1186/s12877-025-06924-3","vorDoiUrl":"https://doi.org/10.1186/s12877-025-06924-3","workflowStages":[]},"version":"v1","identity":"rs-7042025","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7042025","identity":"rs-7042025","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}