Socioeconomic Gradients and Mechanisms of Chronic Disease Health Literacy: The Mediating Role of Preventive Healthcare Utilization in Rural China

Socioeconomic Gradients and Mechanisms of Chronic Disease Health Literacy: The Mediating Role of Preventive Healthcare Utilization in Rural China | 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 Socioeconomic Gradients and Mechanisms of Chronic Disease Health Literacy: The Mediating Role of Preventive Healthcare Utilization in Rural China Liansen Wang, Xinyu Xu, Rui Li, Yuxuan Zhang, Yingjie Cai, Jing Tang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6811235/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Nov, 2025 Read the published version in International Journal for Equity in Health → Version 1 posted 6 You are reading this latest preprint version Abstract Purpose: To explore the social determinants and underlying mechanisms of health literacy in managing chronic diseases, and analyze how socioeconomic status (SES) influences it. Method: This study, based on Shandong Province’s 2022 Health Literacy Surveillance Database, selected 2,826 residents from eligible areas using multistage stratified cluster random sampling. It explored the social determinants of chronic disease health literacy using multiple linear regression, multiple logistic regression, interactive analysis and mediation analysis. Results: Higher SES (OR=1.207, 95%CI: 1.018–1.432, P=0.03), marriage (OR=1.309, 95%CI: 1.050–1.632, P=0.017), higher education (OR=1.269, 95%CI: 1.125–1.432, P<0.001) are significantly correlated with chronic disease. It is worth noting that although having chronic disease SES was generally associated with lower chronic disease health literacy (OR=0.546, 95%CI: 0.341–0.874, P=0.012), patients with hypertension, coronary heart disease, or diabetes showed higher health literacy. The preventive healthcare services (95% CI: 0.016-0.041, P<0.001), changes in health behavior (10.0%, 95% CI: 0.002-0.020, P<0.001) and frequency of examinations (8.6%, 95% CI: 0.004-0.015, P<0.001) significantly mediated the relationship between SES and chronic disease health literacy.Subgroup analysis shows that in the low SES group, women (OR=0.60,95% CI: 0.50-0.75, P<0.001) and elders (OR=0.70,95% CI: 0.42-0.91, P=0.001) have significantly lower chronic disease health literacy than men and young participants. Conclusion: This study systematically uncovers the multidimensional mechanisms by which SES impacts chronic disease health literacy and provides a scientific basis for developing targeted interventions. Socioeconomic status (SES)，Chronic disease health literacy，Preventive healthcare utilization，Mediation analysis，Health disparities，Rural China，Health behavior change，Low-SES populations，Gender disparities，Aging population Figures Figure 1 Figure 2 1. Introduction Chronic diseases are the leading cause of death worldwide, and their prevention and treatment are extremely important for all countries 1 . In recent years, China has faced great challenges in the field of chronic disease prevention and control. According to the National Health and Wellness Commission of China, there are more than 300 million patients with diagnosed chronic diseases in China, accounting for 86.6% of the total causes of death 2 . Due to systemic disparities in health, the rural residents (82.6%)had a higher prevalence than urban residents (79.7%) 3 .Health literacy—the capacity to access, interpret, and apply health information—is critical for managing chronic conditions such as hypertension and diabetes. However, rural residents in China lag significantly behind urban counterparts: only 15.3% demonstrate adequate health literacy to engage in disease prevention and self-care practices 4 . Addressing these disparities is urgent, as improving rural health literacy could reduce disease incidence by 23% and healthcare costs by 18% according to modeling studies 5 . A robust body of evidence has established the socioeconomic gradient in chronic disease literacy, wherein individuals positioned higher on the socioeconomic spectrum systematically demonstrate superior health knowledge acquisition and self-management capacities compared to their socioeconomically disadvantaged counterparts 6 . This gradient manifests not merely through income stratification but via compounded advantages in educational accessibility and healthcare resource allocation 7 . In rural China, preventive healthcare utilization primarily encompasses routine physical examinations, which serve as a critical mechanism for health surveillance. Physical examinations, as a crucial component of preventive healthcare services, confer significant benefits in enhancing health literacy. Research has shown that individuals who regularly participate in physical examinations can obtain health information more promptly and enhance their cognitive abilities regarding disease prevention and health management 8 . For example, Liu Yongbing et al. (2014) conducted a study on the elderly in nursing homes in Urumqi. The results showed that the health literacy scores of the elderly who could undergo regular physical examinations were significantly higher than those who did not (73 ± 31 points vs. 55 ± 28 points, P < 0.05). Moreover, the time since the last physical examination was confirmed as an independent influencing factor of health literacy (β=-7.261, P < 0.001), indicating a positive correlation between the frequency of physical examinations and the level of health literacy 9 . This result is consistent with the theoretical framework of health literacy, that is, by accessing health information and services through physical examinations, individuals can more effectively translate them into the ability to make self-health decisions 10 . In addition, Baker et al. (2004) pointed out that regular physical examinations can promote the internalization of health information, thereby improving health behaviors 11 . However, existing studies exhibit notable limitations. First, health literacy assessments predominantly focus on functional dimensions or unidimensional socioeconomic status (SES) metrics, neglecting systematic analyses of multidimensional SES constructs 12 . Second, existing literature pays little attention to the mediating mechanisms of health behaviors and knowledge acquisition. For example, the potential role of preventive healthcare service utilization (such as government-organized physical examinations) in alleviating the negative impact of socioeconomic disadvantages on health literacy has not been thoroughly explored 13 . In addition, existing studies pay insufficient attention to the socioeconomic gradients in health literacy within the context of rural China 14 . In this study, a total of 2827 residents were selected as research subjects to analyze the effects of economic level and literacy and knowledge mastery on chronic disease literacy among residents of Shandong Province through multifactorial regression analysis, subgroup analysis and mediation model. This study will provide new empirical evidence on the relationship between economic level, literacy and knowledge acquisition and chronic disease literacy and enrich the existing literature on public health and health economics. 2. Materials and Methods 2.1 Sample and data A total of 2826 residents were selected as the study population to collect relevant data. This study adopts a multi-stage stratified cluster random sampling method, based on the principle of combining the 2023 NCHS Urban Rural Classification Scheme for Counties with the development characteristics of Chinese counties, to select locations that meet the following rural principles as: 1. Large central metro counties 2 Large fringe metro counties 3. Medium metro counties 4. Small metro counties 5 . Micropolitan counties in micropolitan statistical areas 6. Noncore counties that did not qualify as micropolitan. The study selected Yishui County (RUCA 6), Cao County (RUCA 5), Leling City (RUCA 4), Ju County (RUCA 6), Yuncheng County (RUCA 5), Shan County (RUCA 4), Linshu County (RUCA 5), Fei County (RUCA 6), Dongping County (RUCA 4), and Ningjin County (RUCA 5). Select three townships for each survey point, and two villages for each township, with a minimum of 40 people surveyed in each village. The selection criteria are 15 years old and above, Chinese nationality, and the ability to read and write. The exclusion criteria include individuals outside the age range and those with mental disabilities. No other restrictions have been implemented. This study was conducted in accordance with the Helsinki Declaration. In order to improve the response rate of the questionnaire, trained survey personnel conducted family interviews using professional questionnaires (Supplementary File 2: Figure S1 ). 2.2 Socioeconomic Status Assessment Following Zhang et al. 15 , individual socioeconomic status (SES) was assessed using four variables collected at baseline: household income level, educational qualifications,and employment status. However, considering China's extremely high medical insurance coverage, three variables - total household income level, educational qualifications, and employment status - were used instead of medical insurance coverage to evaluate individual level SES 16 . For pre-tax household gross income levels, participants selected from the following categories: (i) ¥100,000. Educational qualifications were recorded as: (i) Primary school or below (ii) Junior high school (iii) Senior high school/technical secondary (iv) Junior college (v) Bachelor’s degree or above. Employment status categories included: (i) Agricultural workers (ii) Factory/enterprise workers (iii) Commercial/service workers (iv) Government/public institution employees (v) Students (vi) Others (unemployed, caregivers, etc.). Variable definitions are detailed in Supplementary File 1: Table S1 . Subsequently, we applied Latent Class Analysis (LCA) to construct an unmeasured latent variable using multiple observed categorical variables, estimating socioeconomic status (SES) based on the aforementioned three variables in our dataset. The LCA procedure was implemented via the R packagepoLCA(v1.6.0), with a maximum iteration limit of 10,000 and a convergence tolerance threshold of1×10 − 6 17 . To determine the optimal number of latent classes, we fitted LCA models ranging from 2 to 8 classes. Models failed to converge when the number of classes exceeded 5. Parameter selection was further guided by the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and likelihood ratio statistic (G2). Latent classes with an average posterior probability above 0.7 were considered classifications with acceptable uncertainty (Supplementary File 1: Table S2 and Supplementary File 2: Fig S2 ). All average posterior probabilities exceeded 0.75, confirming distinct socioeconomic strata. Ultimately, three latent classes were identified, corresponding to high, medium, and low SES based on item response probabilities (Supplementary File 1: Table S2 ). 2.3 Assessment of Chronic Diseases We considered seven types of chronic comorbidities, including: (i) no chronic disease ; (ii) hypertension; (iii) heart disease; (iv) cerebrovascular diseases; (v) diabetes and (vi) malignant tumors (Supplementary File 1: Table S1 ). Following Zhu et al. and Said et al 18 , 19 . we defined participants with baseline cardiovascular disease (CVD), diabetes, and cancer using diagnosis records from the coded by the International Classification of Diseases, 10th Revision(ICD-10). Cerebrovascular disease (CeVD) is a group of neurological disorders caused by cerebral vascular lesions, mainly caused by factors such as arteriosclerosis, hypertension, thrombosis, and vascular rupture, including ischemic stroke (such as cerebral infarction), hemorrhagic stroke, transient ischemic attack (TIA), etc 20 .Cardiovascular disease (CVD) is a group of disorders of the circulatory system caused by atherosclerosis, hypertension, metabolic abnormalities, etc., including coronary heart disease, stroke, heart failure, and others 21 . 2.4 Preventive Healthcare Utilization(PHU) Health Check-ups and Screening encompasses routine physical examinations (such as blood pressure and blood glucose tests), cancer screenings (e.g., imaging and laboratory tests for breast cancer and colorectal cancer), and an early detection system for chronic diseases like hypertension and diabetes. The frequency of participation is quantified by the total number of health check-ups attended in the past 12 months (including free screening programs provided by government public health services and self-funded physical examination items). This indicator is a variable used to reflect an individual's compliance with health monitoring 22 . Digital Health Tool Utilization specifically refers to the monthly activity level of tracking health data, intervening in chronic diseases, or acquiring medical knowledge through smart terminals (such as health management mobile apps and wearable physiological parameter monitoring devices) or online platforms (such as internet medical consultation systems) 23 . In addition, the Frequency of Health-Related Short Video Exposure is measured by the average weekly viewing frequency of users obtaining health-themed content on platforms like Douyin, Kuaishou, and Bilibili, either through active searches or passive pushes. The content includes chronic disease prevention and control strategies, scientific dietary nutrition guidance, and first aid skill demonstrations, aiming to evaluate the potential impact of new media on public health literacy 24 .Health behavior change is a process where individuals progress from no intention to intention, action, and maintenance 25 . Z-score Standardization: Sub-indicators are standardized and then summed up with weights.Total Score Formula: Construction of a symbolic domain model. Suppose PHU is composed of a linear combination ofnstandardized latent variables, expressed mathematically as: ● PHUi:Comprehensive preventive healthcare utilization score for thei-th individual. (1)αk:Weight coefficient for thek-th module (satisfying \(\:\sum\:_{\text{k}=1}^{4}{{\alpha\:}}_{\text{k}}=1\) ) (2)α1 = 0.40:Weight for the physical examination behavior module. (3)α2 = 0.30:Weight for the health knowledge exposure module. (4)α3 = 0.18:Weight for the digital health tool module. (5)α4 = 0.12:Weight for the health behavior change module. ● \(\:{\text{Z}}_{\text{k}}^{\left(\text{i}\right)}\) :The Z-score standardized value of the i-th individual in the k-th module. $$\:{\text{Z}}_{\text{k}}^{\left(\text{i}\right)}=\frac{{\text{X}}_{\text{k}}^{\left(\text{i}\right)}-{{\mu\:}}_{\text{k}}}{{{\sigma\:}}_{\text{k}}}{\text{X}}_{\text{k}}^{\text{i}}$$ (1) \(\:{\text{X}}_{\text{k}}^{\left(\text{i}\right)}\) :Original indicator value. (2) \(\:{{\mu\:}}_{\text{k}}\) , \(\:{{\sigma\:}}_{\text{k}}\) :Population mean and standard deviation of the k-th module. Define Q p as the p-th quantile function of PHU scores, and the grade division is planned as: $$\:\text{P}\text{H}\text{U}\:\text{L}\text{e}\text{v}\text{e}\text{l}=\left\{\begin{array}{c}Low\:if\:{PHU}_{i}\le\:{Q}_{0.33}\\\:Medium\:if\:{Q}_{0.33}{Q}_{0.66}\end{array}\right.$$ 2.5 Definition of Chronic disease literacy The questionnaire was based on the 2015 edition of Chinese Citizens' Health Literacy - Basic Knowledge and Skills and was generated through expert consultation using the Delphi method. The questionnaire involved a total of 56 health literacy questions, including judgmental, single-choice, multiple-choice, and situational questions, including 10 questions on scientific health concepts, 7 questions on prevention and control of infectious diseases, 10 questions on prevention and control of chronic diseases, 11 questions on first aid for safety, 12 questions on basic medical care, and 6 questions on health information 26 .We used 10 questions on chronic disease prevention and control with a score of 14, and those who actually scored 80% or more of that total were judged to have literacy in that area 27 . Therefore, a score of 10 was used to divide the group into those with and without chronic disease literacy. 2.6 Statistical analysis Baseline characteristics across three SES groups were compared using unpaired two-tailed t-tests or Mann-Whitney tests for continuous variables and χ² tests for categorical variables, depending on data distribution. Continuous variables are presented as mean (SD) or median (IQR), and categorical variables as numbers (percentages). Multivariable logistic regression was employed to examine associations between SES, chronic comorbidities, PHU, and chronic disease literacy. Covariates included age, sex. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were reported. Multiplicative interaction and stratified analyses were conducted to assess SES moderation effects on PHU and chronic comorbidity factors. A two-sided P < 0.05 was considered statistically significant. All analyses were performed using R 4.1.0.Mediation analyses were conducted to evaluate the proportion of association between socioeconomic status (SES) and chronic disease literacy mediated by PHU and chronic comorbidities.Bonferroni correction for multiple mediation pathways. Causal mediation analysis with 5,000 bootstrap resamples to estimate indirect effects.All regression models were adjusted for covariates such as age and sex. 3. Results 3.1 Baseline Characteristics We totally included the date of 2,826 participants from Shandong Province's 2022 Health Literacy Surveillance Database as the experimental sample. The variable definitions are described in the Materials and Methods section (Additional file 1: Table S1 –S2 and Additional file 2 Fig. S2 ). Table 1 showed the baseline characteristics. The participants had a mean age of 51.18 ± 14.131 years, among whom 1301 (46%) were women, 443 (15.7%) were of high SES, 1600 (56.6%) of medium SES, and 783 (27.7%%) of low SES.Participants with low SES were more likely to be single, and agricultural workers. Low SES tended to be associated with less health events, such as lower frequency of physical examinations, less exposure to health knowledge, less use of digital tools, and less changes in health behaviors. on the contrary, people with low SES usually have more chronic disease situation and more chronic comorbidity factors, especially many cases of hypertension (all P < 0.001) (Table 1 ). In addition, Heatmap of Correlations among Health - related Variables clearly showed several variables with relatively strong correlations (Fig. S3). Notably, chronic disease literacy and total household income showed a relatively high correlation with socioeconomic status factors ( P < 0.001) ((Fig. S3), which suggested a close relationship between chronic disease literacy and SES. Preventive healthcare utilization also showed a positive correlation with health behavior change (P < 0.001) . Table 1 Demographic, Socio - economic and Health - related Characteristics of the Sample Population across Different Socioeconomic Status Levels Variable All ( N = 2,826) High SES ( N 1 = 443 ) Medium SES ( N 2 = 1600) Low SES ( N 3 =783) P Age(years) 51.18 ± 14.131 43.63 ± 12.836 50.13 ± 13.904 57.62 ± 12.521 < 0.001 Gender(female) 1301 (46) 236 (53.3) 876 (54.8) 413 (52.7) 0.622 Marital Status < 0.001 Married 156 (5.5) 36 (8.1) 95 (5.9) 25 (3.2) Single 2537 (89.8) 396 (89.4) 1437 (89.8) 704 (89.9) Divorced/widowed 133 (4.7) 11 (2.5) 68 (4.2) 54 (6.9) Total Household Income (CNY) < 0.001 < 5,000 506 (17.9) 0 (0) 164 (10.2) 342 (43.7) 5000-10,000 419 (14.8) 0 (0) 109 (6.8) 310 (39.6) 10,000–20,000 481 (17.0) 0 (0) 350 (21.9) 131 (16.7) 20,000–30,000 473 (16.7) 0 (0) 473 (29.6) 0 (0) 30,000–50,000 499 (17.7) 49 (11.1) 450 (28.1) 0 (0) 50,000-100,000 346 (12.2) 304 (68.6) 42 (2.6) 0 (0) > 100,000 102 (3.6) 90 (20.3) 12 (0.8) 0 (0) Educational Attainment < 0.001 Primary school or below 715 (25.3) 5 (1.1) 321 (20.1) 389 (49.7) Junior high school 1381 (48.9) 226 (51) 832 (52.0) 323 (41.3) Senior high school/technical secondary 495 (17.5) 121 (27.3) 305 (19.1) 69 (8.8) Junior college 166 (5.9) 64 (14.4) 100 (6.2) 2 (0.3) Bachelor’s degree or above 69 (2.4) 27 (6.1) 42 (2.6) 0 (0) Employment Category < 0.001 Agricultural workers 1932 (68.4) 181 (40.9) 968 (60.5) 783 (100) Factory/enterprise workers 219 (7.7) 61 (13.8) 158 (9.9) 0 (0) Commercial/service workers 166 (5.9) 38 (8.6) 128 (8) 0 (0) Government/public institution employees 152 (5.4) 70 (15.8) 82 (5.1) 0 (0) Students 49 (1.7) 9 (2) 40 (2.5) 0 (0) Others (unemployed, caregivers, etc.) 308 (10.9) 84 (19.0) 224 (14.0) 0 (0) Physical Examination Frequency < 0.001 Never 92 (3.3) 9 (2.0) 30 (1.9) 53 (6.8) Annually 156 (5.5) 11 (2.5) 80 (5.0) 65 (8.3) Semi-annually 208 (7.4) 32 (7.2) 122 (7.6) 54 (6.9) Quarterly 1419 (50.2) 203 (48.8) 815 (50.9) 401 (51.2) Monthly 951 (33.7) 188 (42.4) 553 (34.6) 210 (26.8) Health Knowledge Exposure < 0.001 0 times 431 (15.3) 49 (11.1) 230 (14.4) 152 (19.4) 1 times 1031 (36.5) 176 (39.7) 555 (34.7) 300 (38.3) 2 times 1054 (37.3) 166 (37.5) 651 (40.7) 237 (30.3) 3 times 172 (6.1) 28 (6.3) 96 (6.0) 48 (6.1) 4 times 138 (4.9) 24 (5.4) 68 (4.3) 46 (5.9) Digital Health Tool Usage < 0.001 ≤1 time/month 413 (14.6) 22 (5.0) 180 (11.2) 211 (26.9) 2–3 times/month 509 (18) 47 (10.6) 291 (18.2) 171 (21.8) 1–2 times/week 900 (31.8) 138 (31.2) 545 (34.1) 217 (27.7) ≥3 times/week 593 (21) 121 (27.3) 346 (21.6) 126 (16.1) ≥1 time/day 411 (14.5) 115 (26) 238 (14.9) 58 (7.4) Health Behavior Change < 0.001 Yes (changed behavior based on health knowledge) 1609 (56.9) 323 (72.9) 929 (58.1) 357 (45.6) Chronic disease situation < 0.001 Yes (suffering from chronic disease) 940 (33.3) 91 (20.5) 496 (31) 353 (45.1) Chronic Comorbidity Factors < 0.001 CeVD(Cerebrovascular Disease ) 59 (2.1) 5 (1.1) 19 (1.2) 35 (4.5) CAD (Coronary Artery Disease) 60 (2.1) 4 (0.9) 24 (1.5) 32 (4.1) Hypertension 511 (18.1) 45 (10.2) 281 (17.6) 185 (23.6) Diabetes 106 (3.8) 13 (2.9) 59 (3.7) 34 (4.3) Cancer 9 (0.3) 0 (0) 7 (0.4) 2 (0.3) Chronic Disease Literacy < 0.001 No 2254 (79.8) 313 (70.7) 1274 (79.6) 667 (85.2) Yes 572 (20.2) 130 (29.3) 326 (20.4) 116 (14.8) Preventive healthcare utilization < 0.001 LOW 931 (32.9) 106 (23.9) 516 (32.2) 309 (39.5) Medial 947 (33.5) 100 (22.6) 529 (33.1) 318 (40.6) High 948 (33.5) 237 (53.5) 555 (34.7) 156 (19.9) a. Categorical variables are presented as number (percentage), and continuous variables are presented as mean ± standard deviation. b. SES: socioeconomic status; CAD: coronary artery disease; CeVD: cerebrovascular disease. c. Income is denominated in Chinese Yuan (CNY). Educational attainment: "Junior college" refers to three-year higher education, and "Bachelor’s degree or above" includes undergraduate and postgraduate education. d. Health behavior change is defined as "adjusting lifestyle based on health knowledge". Preventive healthcare utilization is stratified into low (LOW), medium (Medial), and high (High) tiers. e. For univariate analysis, analysis of variance (ANOVA) was used for continuous variables, and chi-square test (χ² test) for categorical variables. A P value < 0.05 was considered statistically significant. 3.2 The association between SES, preventive healthcare, chronic comorbidity factors, and chronic disease literacy. This study explored the influencing factors of chronic disease health literacy through multiple logistic regression analysis. The results showed that higher socioeconomic status (SES) (OR = 1.207, 95% CI = 1.018–1.432, P = 0.03), married status (OR = 1.309, 95% CI = 1.050–1.632, P = 0.017), every level of increase in total household income (OR = 1.171, 95% CI = 1.111–1.234, P < 0.001), and improvement in education level (OR = 1.269, 95% CI = 1.125–1.432, P < 0.001) were significantly positively correlated with chronic disease health literacy. Changes in health behavior (OR = 2.563, 95% CI = 1.991–3.299, P < 0.001) and increased frequency of physical examinations (OR = 1.303, 95% CI = 1.145–1.482, P < 0.001) have a particularly prominent effect on improving literacy. It should be noted that chronic diseases (OR = 0.546, 95% CI = 0.341–0.874, P = 0.012) were associated with lower literacy levels, while individuals with hypertension (OR = 2.265), coronary heart disease (OR = 2.572), or diabetes (OR = 2.365) showed higher literacy levels (all P < 0.05). The higher utilization of preventive medical care (OR = 1.132, P = 0.042) and the increased frequency of health knowledge exposure (OR = 1.021, P = 0.04) are also protective factors. Age, gender, employment category, and frequency of use of digital health tools did not show statistical associations(Table 2 ,Fig. 1 ). Table 2 Associations between Demographic, Socio - economic, Health - related Variables and Chronic Disease Literacy: Variable Chronic Disease Literacy OR (95%CI) P No Yes SES 1.207 (1.018–1.432) 0.03 High 667 (29.6) 116 (20.3) Medium 1274 (56.5) 326 (57.0) Low 313 (13.9) 130 (22.7) Age(years) 51.42 ± 13.992 50.26 ± 14.644 1.007 (0.997–1.016) 0.173 Gender(female) 1223 (54.3) 302 (52.8) 1.013 (0.830–1.236) 0.902 Marital Status 1.309 (1.050–1.632) 0.017 Married 129 (5.7) 27 (4.7) Single 2023 (89.8) 514 (89.9) Divorced/widowed 102 (4.5) 31 (5.4) Total Household Income (CNY) 1.171 (1.111–1.234) < 0.001 100,000 75 (3.3) 27 (4.7) Educational Attainment 1.269 (1.125–1.432) < 0.001 Primary school or below 609 (27) 106 (18.5) Junior high school 1099 (48.8) 282 (49.3) Senior high school/technical secondary 390 (17.3) 105 (18.4) Junior college 113 (5.0) 53 (9.3) Bachelor’s degree or above 43 (1.9) 26 (4.5) Employment Category 1.000 (0.940–1.064) 0.992 Agricultural workers 1574 (69.8) 358 (62.6) Factory/enterprise workers 179 (7.9) 40 (7.0) Commercial/service workers 112 (5.0) 54 (9.4) Government/public institution employees 97 (4.3) 55 (9.6) Students 40 (1.8) 9 (1.6) Others (unemployed, caregivers, etc.) 252 (11.2) 56 (9.8) Physical Examination Frequency 1.303 (1.145–1.482) < 0.001 Never 89 (3.9) 3 (0.5) Annually 139 (6.2) 17 (3.0) Semi-annually 177 (7.9) 31 (5.4) Quarterly 1113 (49.4) 306 (53.5) Monthly 736 (32.7) 215 (37.6) Health Knowledge Exposure 1.021 (1.003–1.033) 0.04 0 times 358 (15.9) 73 (12.8) 1 times 822 (36.5) 209 (36.5) 2 times 838 (37.2) 216 (37.8) 3 times 114 (5.1) 58 (10.1) 4 times 122 (5.4) 16 (2.8) Digital Health Tool Usage 1.046 (0.950–1.151) 0.361 ≤1 time/month 350 (15.5) 63 (11) 2–3 times/month 438 (19.4) 71 (12.4) 1–2 times/week 713 (31.6) 187 (32.7) ≥3 times/week 421 (18.7) 172 (30.1) ≥1 time/day 332 (14.7) 79 (13.8) Health Behavior Change 2.563 (1.991–3.299) < 0.001 Yes (changed behavior based on health knowledge) 1171 (52) 438 (76.6) Chronic disease situation 0.546 (0.341–0.874) 0.012 Yes (suffering from chronic disease) 760 (33.7) 180 (31.5) Chronic Comorbidity Factors Hypertension 398 (17.7) 113 (19.8) 2.265 (1.354–3.789) 0.002 CAD (Coronary Artery Disease) 48 (2.1) 12 (2.1) 2.572 (1.139–5.809) 0.023 CeVD(Cerebrovascular Disease ) 50 (2.2) 9 (1.6) 2.012 (0.839–4.825) 0.117 Diabetes 83 (3.7) 23 (4.0) 2.365 (1.209–4.625) 0.012 Cancer 8 (0.4) 1 (0.2) 0.947 (0.110–8.189) 0.961 Preventive healthcare utilization 1.132 (1.071–1.320) 0.042 LOW 803 (35.6) 128 (22.4) Medial 789 (35) 158 (27.6) High 662 (29.4) 286 (50) aOdds ratios (OR) and P values were adjusted for age, sex b. SES: socioeconomic status; CAD: coronary artery disease; CeVD: cerebrovascular disease. c. Income is denominated in Chinese Yuan (CNY). Educational attainment: "Junior college" refers to three-year higher education, and "Bachelor’s degree or above" includes undergraduate and postgraduate education. d. Health behavior change is defined as "adjusting lifestyle based on health knowledge". Preventive healthcare utilization is stratified into low (LOW), medium (Medial), and high (High) tiers. e. For univariate analysis, analysis of variance (ANOVA) was used for continuous variables, and chi-square test (χ² test) for categorical variables. A P value < 0.05 was considered statistically significant. The bar chart displays the socioeconomic status of the participants Preventive healthcare utilization、 Chronic comorbidity factors. Adjust the odds ratio (OR) based on age and gender. 3.3 The mediating effect of preventive healthcare and chronic comorbidity factors on SES's impact on chronic disease literacy The mediation analysis revealed significant indirect effects of SES on chronic disease literacy through multiple preventive healthcare utilization pathways (Table 3 ,Fig. 2 ). Preventive healthcare utilization mediated 24.6% (95% CI: 0.016–0.041, P < 0.001) of the total effect, with an adjusted direct effect of 0.069 (95% CI: 0.055–0.093). Other significant mediators included physical examination frequency (mediation proportion: 8.6%, 95% CI: 0.004–0.015, P < 0.001), health behavior change (10.0%, 95% CI: 0.002–0.020, P < 0.001), digital health tool usage (4.2%, 95% CI: 0.001–0.004, P < 0.001), and frequency of health knowledge exposure (2.9%, 95% CI: 0.001–0.003, P 0.05).For chronic disease outcomes, hypertension emerged as the only significant mediator, accounting for 3.4% of the total effect (indirect effect: −0.002, 95% CI: −0.013–−0.000, P = 0.032) (Table S3). Other factors, including healthy sleep patterns, age, marital status, gender, diabetes, and cancer, demonstrated negligible or non-significant mediation proportions (P > 0.05). Adjustments for age and sex did not alter the overall mediation patterns. Table 3 Mediation Analysis Diagrams of the Relationship between SES and Chronic Disease Literacy Outcome Exposure Mediator Effect with mediator adjusted (95% CI ) Indirect effect by mediator (95% CI ) Mediation proportion (%) (95% CI ) P Chronic disease literacy SES Preventive healthcare utilization 0.069(0.055, 0.093) 0.017(0.016–0.041) 24.638 < 0.001 Physical Examination Frequency 0.070(0.057, 0.083) 0.006(0.004 ~ 0.015) 8.571 < 0.001 Health Behavior Change 0.070(0.054, 0.081) 0.007(0.002–0.020) 10.001 < 0.001 Digital Health Tool Usage 0.071(0.058, 0.084) 0.003(0.001–0.004) 4.225 < 0.001 The frequency of exposure to health knowledge 0.069(0.056, 0.082) 0.002(0.001–0.003) 2.899 < 0.001 Chronic Disease Literacy 0.070(0.056, 0.083) 0.002(-0.001-0.007) 0 0.224 CAD 0.071(0.058, 0.083) -0.001(-0.004 ~ 0.002) 0 0.142 CeVD 0.068(0.055, 0.081) 0.001(-0.002 ~ 0.004) 0 0.205 a. Effects with mediator adjusted and indirect effects by mediator were adjusted for relevant confounders (such as age, sex, etc.). b. SES: socioeconomic status; CAD: coronary artery disease; CeVD: cerebrovascular disease. c. Chronic disease literacy represents the understanding and application ability of chronic - disease - related knowledge. d. Health Behavior Change is defined as "adjusting lifestyle based on health knowledge". Preventive healthcare utilization refers to the frequency and level of using preventive healthcare services. e. For the analysis of mediation effects, relevant statistical methods were employed. The significance of indirect effects and mediation proportions was tested, with a P - value < 0.05 considered statistically significant. 3.4 The interaction between preventive healthcare and chronic comorbidity factors on SES's impact on chronic disease literacy The interaction analysis (Table 4 ) revealed significant moderating effects of socioeconomic status (SES) on chronic disease literacy through specific behavioral and healthcare pathways. Preventive healthcare utilization exhibited a strong interaction with SES (F = 5.076, P = 0.006), indicating that higher SES amplified the benefits of preventive care on health literacy. Similarly, physical examination frequency (F = 2.66, P = 0.031) and frequency of health knowledge exposure (F = 2.627, P = 0.007) demonstrated significant SES-dependent effects, with high-SES individuals showing greater improvements in these domains. In contrast, health behavior change (F = 0.048, P = 0.953) and digital health tool usage (F = 1.849, P = 0.117) showed no significant interaction with SES.Joint analyses further illustrated these disparities (Fig. S4). High-SES groups displayed markedly enhanced outcomes in preventive health information utilization and physical examination adherence, whereas low-SES groups faced elevated risks for adverse health outcomes, including cardiovascular disease incidence and chronic disease progression. Table 4 The interaction between socioeconomic status (SES) and different mediating factors of chronic disease literacy Outcome Variable SES F P Chronic disease literacy Preventive healthcare utilization 5.076 0.006 Physical Examination Frequency 2.66 0.031 Health Behavior Change 0.048 0.953 Digital Health Tool Usage 1.849 0.117 The frequency of exposure to health knowledge 2.627 0.007 Chronic Disease 6.065 0.002 CAD 2.911 0.055 CeVD 0.141 0.869 a. F - tests were used to assess the relationship between each variable and SES in the context of chronic disease literacy as the outcome. The F - values and corresponding P - values are presented. b. SES: socioeconomic status; CAD: coronary artery disease; CeVD: cerebrovascular disease. c. For the variables (Preventive healthcare utilization, Physical Examination Frequency, etc.), an F - test was conducted to determine the association with SES. A P - value < 0.05 is considered statistically significant, indicating a notable relationship between the variable and SES in influencing chronic disease literacy. 3.5 The impact of socioeconomic inequality on chronic disease literacy among different gender and age subgroups Stratified analyses by socioeconomic status (SES) revealed sex-specific disparities in chronic disease literacy (Table S5). In the low-SES subgroup, females exhibited significantly lower odds of chronic disease literacy compared to males (OR = 0.60, 95% CI: 0.50–0.75, P < 0.001), while males in this group also showed reduced literacy (OR = 0.65, 95% CI: 0.55–0.78, P = 0.002). In contrast, no significant sex differences were observed in high-SES (female OR = 0.71, 95% CI: 0.37–1.36, P = 0.306; male reference) or medium-SES (female OR = 0.85, 95% CI: 0.70–1.05, P = 0.12) subgroups.Age-stratified analyses demonstrated SES-dependent variations in chronic disease literacy (Table S6). In the low-SES subgroup, participants aged > 60 years had significantly lower literacy than those ≤ 60 years (OR = 0.70, 95% CI: 0.42–0.91, P = 0.001). Similarly, younger individuals (≤ 60 years) in low-SES groups also showed reduced literacy (OR = 0.62, 95% CI: 0.52–0.75, P = 0.016). No significant age-related disparities were observed in high-SES (OR = 0.95, 95% CI: 0.75–1.45, P = 0.327) or medium-SES (OR = 0.93, 95% CI: 0.72–1.39, P = 0.339) subgroups. 4. Discussion and Conclusion This study is based on a multidimensional socioeconomic status (SES) evaluation system. By constructing comprehensive indicators of annual household income, education level, and occupational status, and using mediation analysis method, the correlation mechanism between SES and chronic disease health literacy is systematically examined. The research results showed that SES was significantly positively correlated with health literacy level (OR = 1.207, P = 0.03), with preventive medical utilization (24.6%), physical examination frequency (8.6%), and health behavior change (10.0%) as the main mediating pathways. It is worth noting that there is a significant interaction effect (P < 0.05) between SES and the utilization of preventive medical care, frequency of physical examinations, and frequency of exposure to health knowledge.Although chronic diseases such as hypertension (OR = 2.265), coronary heart disease (OR = 2.572) and diabetes (OR = 2.365) are positively related to chronic disease literacy, their prevalence rate in the low SES population is significantly higher (P < 0.001), suggesting that there is a two-way relationship between chronic disease burden and SES inequality. Chronic disease health literacy, as a key competency for individuals to acquire, understand, and apply knowledge and skills for chronic disease prevention and treatment, is directly related to their health behavior choices, disease self-management effects, and overall health outcomes 28 – 30 . However, most studies have focused on the association between SES and health outcomes, but less on the mechanisms by which SES affects chronic disease health literacy; and most studies have defined SES only in terms of income, education, or occupation, failing to comprehensively reveal its multidimensional interactions.Falagas et al.'s study explored SES as a determinant of treatment adherence in HIV-infected patients 31 – 33 Similarly, a study by Adler et al. explored that SES influences health gradient 35 . However, it is unclear whether SES has a sustained effect on chronic disease health literacy. In our study, we used household income level, educational qualification and occupational status to jointly define SES and assessed the association between composite SES and individual chronic disease literacy. We found that people with high SES had higher chronic disease health literacy, and this association did not differ between men and women or age. The results of our analysis of each individual SES indicator were also comparable to previous studies 36 – 38 , which enhances the credibility of the findings. Recent studies have shown that SES can change the incidence rate of cardiovascular diseases by influencing prevention and health care behavior 39 . However, there is currently no research evaluating the potential role of preventive healthcare behaviors in the association between SES and chronic disease health literacy. Therefore, in this study, we employed mediation analysis and found that individual preventive healthcare behaviors can explain 24.6% of the association between SES and chronic disease health literacy. The frequency of physical examinations (8.6%) has the most significant effect on improving health literacy. The health literacy score of residents with quarterly physical examination frequency is higher than that of those without physical examination (β = 0.15, P < 0.001), which is consistent with the research conclusion of population health monitoring practice 40 . In addition, health knowledge education (OR = 1.021, 95% CI = 1.003–1.033) and electronic tool use (OR = 1.046, 95% CI = 0.950–1.151) together constitute the "cognitive" pathway for behavior change. However, in subgroup analysis by SES level and age, women in the low SES group (OR = 0.60, 95% CI = 0.50–0.75, P < 0.001) and those over 60 years old (OR = 0.70, 95% CI = 0.42–0.91, P = 0.001) had significantly lower chronic disease health literacy than other subgroups, while no similar differences were observed in the high/medium SES group (P > 0.05). This result suggests that gender and age-related health inequalities in low SES populations may stem from differences in access to healthcare resources, such as less participation of women in health decision-making and barriers to digital tool use among older adults, while the socioeconomic advantages of high SES populations can mitigate such risks 41 – 43 . Previous studies have shown that preventive healthcare interventions can reduce various health risks, including cardiovascular disease incidence 15 . In our research, we consistently found that adhering solely to preventive healthcare behaviors such as regular check ups and health education has a protective effect on overall chronic disease literacy. It is worth noting that this protective effect is more significant in low SES populations, indicating that preventive healthcare behaviors may alleviate the adverse effects of low SES and emphasizing the need to strengthen health education for low SES populations. Similar trends have also been reported in two other studies on diseases by the UK Biobank (UKB) 44 , 45 . In addition,this study revealed a significant bidirectional contradiction in the effect of chronic disease factors on health literacy. Although patients with hypertension (OR = 2.265, 95% CI = 1.354–3.789), CAD (OR = 2.572, 95% CI = 1.139–5.809), and diabetes (OR = 2.365, 95% CI = 1.209–4.625) had significantly higher chronic disease health literacy than non patients (all P < 0.05), it should be noted that chronic disease as a whole (OR = 0.546, 95% CI = 0.341–0.874, P = 0.012) was independently related to low health literacy. This contradiction suggests that specific chronic diseases (such as hypertension and diabetes) may drive patients to actively acquire health knowledge through disease management needs (positive individual effect), while generalized chronic disease burdens (such as coexistence of multiple diseases and undiagnosed diseases) may lead to literacy decline (negative group effect) due to resource crowding and health fatigue 46 . In the mediation analysis, only hypertension showed a weak indirect effect (mediation ratio of 3.4%, P = 0.032), while other chronic diseases (such as coronary heart disease, cerebrovascular disease) and chronic diseases as a whole did not pass the mediation test (P > 0.05). This indicates that the impact of chronic diseases on health literacy is more of a direct effect rather than an indirect pathway through SES or behavioral factors. It is worth noting that the prevalence of chronic diseases is significantly higher in the low SES group (such as hypertension prevalence of 19.8% vs. 17.7%, P < 0.001), but their health literacy is lower than that of the high SES group (OR = 0.60–0.70, P < 0.01), further revealing that the interaction between chronic diseases and SES may exacerbate health inequality - low SES patients face a vicious cycle of "high disease burden low health literacy", while high SES patients exhibit a positive feedback of "high disease management ability high health literacy". We explored the potential mediating mechanism between SES and infection risk from the perspectives of preventive healthcare and chronic comorbidities. The advantages of this study are mainly reflected in the following aspects: firstly, adopting standardized variable definitions and measurement methods; Secondly, the constructed multidimensional composite score (including SES index and preventive healthcare intervention score) can more comprehensively characterize the characteristics of each dimension compared to single indicators; Thirdly, identifying high-risk populations through stratified analysis provides a scientific basis for implementing precise prevention and control measures. There are several limitations to this study: firstly, chronic disease diagnosis is based on self-reported questionnaires, which may be influenced by reporting bias. Participants may not be able to accurately recall or report their disease status, leading to potential underreporting or misreporting. In addition, the study only included a limited number of factors and did not explore other possible mediating factors or confounding variables that may affect the relationship between economic level, literacy rate, and chronic disease literacy rate. Future research may consider using more objective chronic disease diagnostic methods and expanding the scope of research factors to gain a more comprehensive understanding of the complex relationships involved. In addition, longitudinal studies will help track changes in chronic disease literacy over time and examine causal relationships between factors. 5. Conclusion In summary, this study systematically reveals the multidimensional mechanisms by which SES affects chronic disease health literacy, providing a scientific basis for developing targeted intervention strategies. Government departments should focus on enhancing the chronic disease prevention and control capabilities of vulnerable groups through measures such as conducting health education and providing training resources. Subsequent research should further deepen the exploration of mechanisms and develop effective strategies for improving health literacy to reduce the burden of chronic diseases. Abbreviations AIC：Akaike Information Criterion CAD：Coronary Artery Disease BIC：Bayesian Information Criterion CeVD：Cerebrovascular Disease CI：Confidence Interval CVD：Cardiovascular Disease ICD-10：International Classification of Diseases, 10th Revision LCA：Latent Class Analysis NCHS：National Center for Health Statistics OR：Odds Ratio PHU：Preventive Healthcare Utilization RUCA：Rural-Urban Commuting Area SES：Socioeconomic Status Declarations Ethical approval and informed consent statements ： The datasets obtained during the current study are available from the corresponding author on reasonable request. Consent for publication: All authors consent to the publication of this manuscript. Availability of data and materials ： The findings of this study are available from the corresponding author upon reasonable request Competing interests ： The authors declare no competing interests. Funding statement ： This work was funded by Shandong Province Medical and Health Science and Technology Development Plan Project(202312071011)and Shandong Province Humanities and Social Sciences Research Projects(2023-JKZX-11). The funders had no involvement in data collection and analysis or the preparation of this article. The analysis and interpretation of the evidence was done by the authors themselves,not by the funders. Human Ethics and Consent to Participate declarations A：ll respondents have informed consent forms.Ethics Approval Opinion of the Medical Ethics Review Committee of Shandong Provincial Center for Disease Control and Prevention (SDJK (K) 2024-046-01) Authors' contributions ： All authors contributed to the study conception and design. Material preparation, data collection, and analysis were led by Liansen Wang,Xinyu Xu,Rui Li, with technical support from Yuxuan Zhang, Yingjie Cai, Jing Tang, Xiaowei Yang, Jing Han, Fangyao Chen, Xiuli Qiao. The first draft was written by Rui Li and Xinyu Xu, and all authors reviewed and approved the final manuscript. Acknowledgements ： We acknowledge all participants and field teams in Shandong Province. References Nugent R. Preventing and managing chronic diseases. BMJ . 2019;364:l459. doi:10.1136/bmj.l459 He L, La Y, Yan Y, et al. The prevalence and burden of four major chronic diseases in the Shaanxi Province of Northern China. 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Qiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYFACHhAhwcDADKQSKmx4+NkbCGhgQ9by4UyajGTPAaK0QADjzLbDNgY3HPBrsZfvPfjhY5tFHt9x3sOvec6c52G4wcD44WMOPlv4kiVnnJEoljzMl2bNU3Gbh3F2A7PkzG14HWYgzVMhkbjhMI+ZMc+Z2zzMMgfYmHnxazH+zWMA1cLbdo6HTSKBoBYzmC3GD2e2HeDhIajlWI6ZJdAviTOBtgADOZlHgudgM16/sDefMb7xsa0use/8GeMPCRV29vbHm4FhiEcLAhxgYJOAsBgbiFEP1sL8gUilo2AUjIJRMMIAAItLTWlWtjY9AAAAAElFTkSuQmCC","orcid":"","institution":"Shandong Public Health Clinical Center","correspondingAuthor":true,"prefix":"","firstName":"Xiuli","middleName":"","lastName":"Qiao","suffix":""}],"badges":[],"createdAt":"2025-06-03 12:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6811235/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6811235/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12939-025-02677-y","type":"published","date":"2025-11-14T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86141454,"identity":"de286184-0ab7-49aa-9306-adfd0090f66b","added_by":"auto","created_at":"2025-07-07 08:26:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":85709,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between chronic disease literacy and socioeconomic status, preventive healthcare utilization, and chronic comorbidity factors.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6811235/v1/dd69be83b9796e7f32348ff0.png"},{"id":86141456,"identity":"75002a32-ceff-44d3-8d30-0bc0f6eb6b92","added_by":"auto","created_at":"2025-07-07 08:26:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":217193,"visible":true,"origin":"","legend":"\u003cp\u003eMediation Analysis Diagrams of the Relationship between SES and Chronic Disease Literacy.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6811235/v1/de65ee9635ce592c1927871c.png"},{"id":96105857,"identity":"505b45e3-4fc3-4f31-b104-ff6997b879b3","added_by":"auto","created_at":"2025-11-17 16:12:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1883157,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6811235/v1/a0bbfe1e-c667-4924-ae0e-6e707fe0369e.pdf"},{"id":86139622,"identity":"7968da4b-59e5-4ad8-b7e7-12bc5d301c19","added_by":"auto","created_at":"2025-07-07 08:18:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33470,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6811235/v1/9a1bdbcd8b3d7e26d964c2b2.docx"},{"id":86142144,"identity":"ee5c3677-7775-4e10-b5a9-a7b229522c2a","added_by":"auto","created_at":"2025-07-07 08:34:14","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":462850,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6811235/v1/f5876ca6db99d1f4fca01dd0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Socioeconomic Gradients and Mechanisms of Chronic Disease Health Literacy: The Mediating Role of Preventive Healthcare Utilization in Rural China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChronic diseases are the leading cause of death worldwide, and their prevention and treatment are extremely important for all countries\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In recent years, China has faced great challenges in the field of chronic disease prevention and control. According to the National Health and Wellness Commission of China, there are more than 300\u0026nbsp;million patients with diagnosed chronic diseases in China, accounting for 86.6% of the total causes of death\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Due to systemic disparities in health, the rural residents (82.6%)had a higher prevalence than urban residents (79.7%)\u003csup\u003e3\u003c/sup\u003e.Health literacy\u0026mdash;the capacity to access, interpret, and apply health information\u0026mdash;is critical for managing chronic conditions such as hypertension and diabetes. However, rural residents in China lag significantly behind urban counterparts: only 15.3% demonstrate adequate health literacy to engage in disease prevention and self-care practices\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Addressing these disparities is urgent, as improving rural health literacy could reduce disease incidence by 23% and healthcare costs by 18% according to modeling studies\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA robust body of evidence has established the socioeconomic gradient in chronic disease literacy, wherein individuals positioned higher on the socioeconomic spectrum systematically demonstrate superior health knowledge acquisition and self-management capacities compared to their socioeconomically disadvantaged counterparts\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This gradient manifests not merely through income stratification but via compounded advantages in educational accessibility and healthcare resource allocation\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In rural China, preventive healthcare utilization primarily encompasses routine physical examinations, which serve as a critical mechanism for health surveillance. Physical examinations, as a crucial component of preventive healthcare services, confer significant benefits in enhancing health literacy. Research has shown that individuals who regularly participate in physical examinations can obtain health information more promptly and enhance their cognitive abilities regarding disease prevention and health management\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. For example, Liu Yongbing et al. (2014) conducted a study on the elderly in nursing homes in Urumqi. The results showed that the health literacy scores of the elderly who could undergo regular physical examinations were significantly higher than those who did not (73\u0026thinsp;\u0026plusmn;\u0026thinsp;31 points vs. 55\u0026thinsp;\u0026plusmn;\u0026thinsp;28 points, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, the time since the last physical examination was confirmed as an independent influencing factor of health literacy (β=-7.261, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a positive correlation between the frequency of physical examinations and the level of health literacy\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This result is consistent with the theoretical framework of health literacy, that is, by accessing health information and services through physical examinations, individuals can more effectively translate them into the ability to make self-health decisions\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In addition, Baker et al. (2004) pointed out that regular physical examinations can promote the internalization of health information, thereby improving health behaviors\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, existing studies exhibit notable limitations. First, health literacy assessments predominantly focus on functional dimensions or unidimensional socioeconomic status (SES) metrics, neglecting systematic analyses of multidimensional SES constructs\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Second, existing literature pays little attention to the mediating mechanisms of health behaviors and knowledge acquisition. For example, the potential role of preventive healthcare service utilization (such as government-organized physical examinations) in alleviating the negative impact of socioeconomic disadvantages on health literacy has not been thoroughly explored\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In addition, existing studies pay insufficient attention to the socioeconomic gradients in health literacy within the context of rural China \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, a total of 2827 residents were selected as research subjects to analyze the effects of economic level and literacy and knowledge mastery on chronic disease literacy among residents of Shandong Province through multifactorial regression analysis, subgroup analysis and mediation model. This study will provide new empirical evidence on the relationship between economic level, literacy and knowledge acquisition and chronic disease literacy and enrich the existing literature on public health and health economics.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Sample and data\u003c/h2\u003e\n \u003cp\u003eA total of 2826 residents were selected as the study population to collect relevant data. This study adopts a multi-stage stratified cluster random sampling method, based on the principle of combining the 2023 NCHS Urban Rural Classification Scheme for Counties with the development characteristics of Chinese counties, to select locations that meet the following rural principles as: 1. Large central metro counties 2 Large fringe metro counties 3. Medium metro counties 4. Small metro counties \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Micropolitan counties in micropolitan statistical areas 6. Noncore counties that did not qualify as micropolitan. The study selected Yishui County (RUCA 6), Cao County (RUCA 5), Leling City (RUCA 4), Ju County (RUCA 6), Yuncheng County (RUCA 5), Shan County (RUCA 4), Linshu County (RUCA 5), Fei County (RUCA 6), Dongping County (RUCA 4), and Ningjin County (RUCA 5). Select three townships for each survey point, and two villages for each township, with a minimum of 40 people surveyed in each village. The selection criteria are 15 years old and above, Chinese nationality, and the ability to read and write. The exclusion criteria include individuals outside the age range and those with mental disabilities. No other restrictions have been implemented. This study was conducted in accordance with the Helsinki Declaration. In order to improve the response rate of the questionnaire, trained survey personnel conducted family interviews using professional questionnaires (Supplementary File 2: Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Socioeconomic Status Assessment\u003c/h2\u003e\n \u003cp\u003eFollowing Zhang et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, individual socioeconomic status (SES) was assessed using four variables collected at baseline: household income level, educational qualifications,and employment status. However, considering China\u0026apos;s extremely high medical insurance coverage, three variables - total household income level, educational qualifications, and employment status - were used instead of medical insurance coverage to evaluate individual level SES\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. For pre-tax household gross income levels, participants selected from the following categories: (i) \u0026lt;\u0026yen; 5,000 (ii) \u0026yen; 5000-\u0026yen; 10,000 (iii) \u0026yen;10,000-\u0026yen;20,000 (iv) \u0026yen;20,000-\u0026yen;30,000 (v) \u0026yen;30,000-\u0026yen;50,000 (vi) \u0026yen;50,000-\u0026yen;100,000 (vii) \u0026gt;\u0026yen;100,000. Educational qualifications were recorded as: (i) Primary school or below (ii) Junior high school (iii) Senior high school/technical secondary (iv) Junior college (v) Bachelor\u0026rsquo;s degree or above. Employment status categories included: (i) Agricultural workers (ii) Factory/enterprise workers (iii) Commercial/service workers (iv) Government/public institution employees (v) Students (vi) Others (unemployed, caregivers, etc.). Variable definitions are detailed in Supplementary File 1: Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eSubsequently, we applied Latent Class Analysis (LCA) to construct an unmeasured latent variable using multiple observed categorical variables, estimating socioeconomic status (SES) based on the aforementioned three variables in our dataset. The LCA procedure was implemented via the R packagepoLCA(v1.6.0), with a maximum iteration limit of 10,000 and a convergence tolerance threshold of1\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;6 \u003csup\u003e17\u003c/sup\u003e. To determine the optimal number of latent classes, we fitted LCA models ranging from 2 to 8 classes. Models failed to converge when the number of classes exceeded 5. Parameter selection was further guided by the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and likelihood ratio statistic (G2). Latent classes with an average posterior probability above 0.7 were considered classifications with acceptable uncertainty (Supplementary File 1: Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e and Supplementary File 2: Fig \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). All average posterior probabilities exceeded 0.75, confirming distinct socioeconomic strata. Ultimately, three latent classes were identified, corresponding to high, medium, and low SES based on item response probabilities (Supplementary File 1: Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Assessment of Chronic Diseases\u003c/h2\u003e\n \u003cp\u003eWe considered seven types of chronic comorbidities, including: (i) no chronic disease ; (ii) hypertension; (iii) heart disease; (iv) cerebrovascular diseases; (v) diabetes and (vi) malignant tumors (Supplementary File 1: Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Following Zhu et al. and Said et al\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. we defined participants with baseline cardiovascular disease (CVD), diabetes, and cancer using diagnosis records from the coded by the International Classification of Diseases, 10th Revision(ICD-10). Cerebrovascular disease (CeVD) is a group of neurological disorders caused by cerebral vascular lesions, mainly caused by factors such as arteriosclerosis, hypertension, thrombosis, and vascular rupture, including ischemic stroke (such as cerebral infarction), hemorrhagic stroke, transient ischemic attack (TIA), etc\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.Cardiovascular disease (CVD) is a group of disorders of the circulatory system caused by atherosclerosis, hypertension, metabolic abnormalities, etc., including coronary heart disease, stroke, heart failure, and others\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Preventive Healthcare Utilization(PHU)\u003c/h2\u003e\n \u003cp\u003eHealth Check-ups and Screening encompasses routine physical examinations (such as blood pressure and blood glucose tests), cancer screenings (e.g., imaging and laboratory tests for breast cancer and colorectal cancer), and an early detection system for chronic diseases like hypertension and diabetes. The frequency of participation is quantified by the total number of health check-ups attended in the past 12 months (including free screening programs provided by government public health services and self-funded physical examination items). This indicator is a variable used to reflect an individual\u0026apos;s compliance with health monitoring\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Digital Health Tool Utilization specifically refers to the monthly activity level of tracking health data, intervening in chronic diseases, or acquiring medical knowledge through smart terminals (such as health management mobile apps and wearable physiological parameter monitoring devices) or online platforms (such as internet medical consultation systems)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In addition, the Frequency of Health-Related Short Video Exposure is measured by the average weekly viewing frequency of users obtaining health-themed content on platforms like Douyin, Kuaishou, and Bilibili, either through active searches or passive pushes. The content includes chronic disease prevention and control strategies, scientific dietary nutrition guidance, and first aid skill demonstrations, aiming to evaluate the potential impact of new media on public health literacy\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.Health behavior change is a process where individuals progress from no intention to intention, action, and maintenance\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eZ-score Standardization: Sub-indicators are standardized and then summed up with weights.Total Score Formula: Construction of a symbolic domain model. Suppose PHU is composed of a linear combination ofnstandardized latent variables, expressed mathematically as:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"201\" height=\"88\"\u003e\u003c/p\u003e\n \u003cp\u003e● PHUi:Comprehensive preventive healthcare utilization score for thei-th individual.\u003c/p\u003e\n \u003cp\u003e(1)\u0026alpha;k:Weight coefficient for thek-th module (satisfying \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{\\text{k}=1}^{4}{{\\alpha\\:}}_{\\text{k}}=1\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003e(2)\u0026alpha;1\u0026thinsp;=\u0026thinsp;0.40:Weight for the physical examination behavior module.\u003c/p\u003e\n \u003cp\u003e(3)\u0026alpha;2\u0026thinsp;=\u0026thinsp;0.30:Weight for the health knowledge exposure module.\u003c/p\u003e\n \u003cp\u003e(4)\u0026alpha;3\u0026thinsp;=\u0026thinsp;0.18:Weight for the digital health tool module.\u003c/p\u003e\n \u003cp\u003e(5)\u0026alpha;4\u0026thinsp;=\u0026thinsp;0.12:Weight for the health behavior change module.\u003c/p\u003e\n \u003cp\u003e● \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Z}}_{\\text{k}}^{\\left(\\text{i}\\right)}\\)\u003c/span\u003e\u003c/span\u003e:The Z-score standardized value of the i-th individual in the k-th module.\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:{\\text{Z}}_{\\text{k}}^{\\left(\\text{i}\\right)}=\\frac{{\\text{X}}_{\\text{k}}^{\\left(\\text{i}\\right)}-{{\\mu\\:}}_{\\text{k}}}{{{\\sigma\\:}}_{\\text{k}}}{\\text{X}}_{\\text{k}}^{\\text{i}}$$\u003c/div\u003e\n \u003c/div\u003e\u003cspan\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;(1) \u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{X}}_{\\text{k}}^{\\left(\\text{i}\\right)}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e:Original indicator value.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e(2) \\(\\:{{\\mu\\:}}_{\\text{k}}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\sigma\\:}}_{\\text{k}}\\)\u003c/span\u003e\u003c/span\u003e:Population mean and standard deviation of the k-th module.\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eDefine Q\u003csub\u003ep\u003c/sub\u003e as the p-th quantile function of PHU scores, and the grade division is planned as:\u003c/p\u003e\n \u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\:\\text{P}\\text{H}\\text{U}\\:\\text{L}\\text{e}\\text{v}\\text{e}\\text{l}=\\left\\{\\begin{array}{c}Low\\:if\\:{PHU}_{i}\\le\\:{Q}_{0.33}\\\\\\:Medium\\:if\\:{Q}_{0.33}\u0026lt;{PHU}_{i}\\le\\:{Q}_{0.66}\\\\\\:Higℎ\\:if\\:{PHU}_{i}\u0026gt;{Q}_{0.66}\\end{array}\\right.$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Definition of Chronic disease literacy\u003c/h2\u003e\n \u003cp\u003eThe questionnaire was based on the 2015 edition of Chinese Citizens\u0026apos; Health Literacy - Basic Knowledge and Skills and was generated through expert consultation using the Delphi method. The questionnaire involved a total of 56 health literacy questions, including judgmental, single-choice, multiple-choice, and situational questions, including 10 questions on scientific health concepts, 7 questions on prevention and control of infectious diseases, 10 questions on prevention and control of chronic diseases, 11 questions on first aid for safety, 12 questions on basic medical care, and 6 questions on health information\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.We used 10 questions on chronic disease prevention and control with a score of 14, and those who actually scored 80% or more of that total were judged to have literacy in that area\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Therefore, a score of 10 was used to divide the group into those with and without chronic disease literacy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eBaseline characteristics across three SES groups were compared using unpaired two-tailed t-tests or Mann-Whitney tests for continuous variables and \u0026chi;\u0026sup2; tests for categorical variables, depending on data distribution. Continuous variables are presented as mean (SD) or median (IQR), and categorical variables as numbers (percentages). Multivariable logistic regression was employed to examine associations between SES, chronic comorbidities, PHU, and chronic disease literacy. Covariates included age, sex. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were reported. Multiplicative interaction and stratified analyses were conducted to assess SES moderation effects on PHU and chronic comorbidity factors. A two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All analyses were performed using R 4.1.0.Mediation analyses were conducted to evaluate the proportion of association between socioeconomic status (SES) and chronic disease literacy mediated by PHU and chronic comorbidities.Bonferroni correction for multiple mediation pathways. Causal mediation analysis with 5,000 bootstrap resamples to estimate indirect effects.All regression models were adjusted for covariates such as age and sex.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline Characteristics\u003c/h2\u003e \u003cp\u003e We totally included the date of 2,826 participants from Shandong Province's 2022 Health Literacy Surveillance Database as the experimental sample. The variable definitions are described in the Materials and Methods section (Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u0026ndash;S2 and Additional file 2 Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed the baseline characteristics. The participants had a mean age of 51.18\u0026thinsp;\u0026plusmn;\u0026thinsp;14.131 years, among whom 1301 (46%) were women, 443 (15.7%) were of high SES, 1600 (56.6%) of medium SES, and 783 (27.7%%) of low SES.Participants with low SES were more likely to be single, and agricultural workers. Low SES tended to be associated with less health events, such as lower frequency of physical examinations, less exposure to health knowledge, less use of digital tools, and less changes in health behaviors. on the contrary, people with low SES usually have more chronic disease situation and more chronic comorbidity factors, especially many cases of hypertension (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, Heatmap of Correlations among Health - related Variables clearly showed several variables with relatively strong correlations (Fig. S3). Notably, chronic disease literacy and total household income showed a relatively high correlation with socioeconomic status factors ( P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) ((Fig. S3), which suggested a close relationship between chronic disease literacy and SES. Preventive healthcare utilization also showed a positive correlation with health behavior change (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) .\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic, Socio - economic and Health - related Characteristics of the Sample Population across Different Socioeconomic Status Levels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,826)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh SES\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e1\u0026thinsp;=\u0026thinsp;443\u0026thinsp;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium SES\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e2\u0026thinsp;=\u0026thinsp;1600)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow SES\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e3\u0026thinsp;\u0026thinsp;=783)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.18\u0026thinsp;\u0026plusmn;\u0026thinsp;14.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.63\u0026thinsp;\u0026plusmn;\u0026thinsp;12.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.13\u0026thinsp;\u0026plusmn;\u0026thinsp;13.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.62\u0026thinsp;\u0026plusmn;\u0026thinsp;12.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender(female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1301 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236 (53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e876 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e413 (52.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2537 (89.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e396 (89.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1437 (89.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e704 (89.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Household Income (CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e506 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e342 (43.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5000-10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e419 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e310 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10,000\u0026ndash;20,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e481 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20,000\u0026ndash;30,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e473 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e473 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30,000\u0026ndash;50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e499 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e450 (28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50,000-100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e346 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e304 (68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational Attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e715 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e321 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e389 (49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1381 (48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e226 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e832 (52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e323 (41.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior high school/technical secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e495 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e305 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u0026rsquo;s degree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment Category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1932 (68.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181 (40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e968 (60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e783 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactory/enterprise workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial/service workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment/public institution employees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers (unemployed, caregivers, etc.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e308 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e224 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Examination Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnually\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemi-annually\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e208 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuarterly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1419 (50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e815 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e401 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e951 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e553 (34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e210 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Knowledge Exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e431 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e230 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1031 (36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e555 (34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e300 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1054 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e651 (40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e237 (30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Health Tool Usage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;1 time/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e413 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e211 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;3 times/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e509 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e291 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e171 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e900 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e545 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e217 (27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;3 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e593 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e346 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;1 time/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e411 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e238 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Behavior Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes (changed behavior based on health knowledge)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1609 (56.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e323 (72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e929 (58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e357 (45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic disease situation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes (suffering from chronic disease)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e940 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e496 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e353 (45.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Comorbidity Factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeVD(Cerebrovascular Disease )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD (Coronary Artery Disease)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e511 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e281 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e185 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Disease Literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2254 (79.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e313 (70.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1274 (79.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e667 (85.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e572 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e326 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreventive healthcare utilization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e931 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e516 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e309 (39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e947 (33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e529 (33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e318 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e948 (33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e237 (53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e555 (34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e156 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ea. Categorical variables are presented as number (percentage), and continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/p\u003e \u003cp\u003eb. SES: socioeconomic status; CAD: coronary artery disease; CeVD: cerebrovascular disease.\u003c/p\u003e \u003cp\u003ec. Income is denominated in Chinese Yuan (CNY). Educational attainment: \"Junior college\" refers to three-year higher education, and \"Bachelor\u0026rsquo;s degree or above\" includes undergraduate and postgraduate education.\u003c/p\u003e \u003cp\u003ed. Health behavior change is defined as \"adjusting lifestyle based on health knowledge\". Preventive healthcare utilization is stratified into low (LOW), medium (Medial), and high (High) tiers.\u003c/p\u003e \u003cp\u003ee. For univariate analysis, analysis of variance (ANOVA) was used for continuous variables, and chi-square test (χ\u0026sup2; test) for categorical variables. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The association between SES, preventive healthcare, chronic comorbidity factors, and chronic disease literacy.\u003c/h2\u003e \u003cp\u003eThis study explored the influencing factors of chronic disease health literacy through multiple logistic regression analysis. The results showed that higher socioeconomic status (SES) (OR\u0026thinsp;=\u0026thinsp;1.207, 95% CI\u0026thinsp;=\u0026thinsp;1.018\u0026ndash;1.432, P\u0026thinsp;=\u0026thinsp;0.03), married status (OR\u0026thinsp;=\u0026thinsp;1.309, 95% CI\u0026thinsp;=\u0026thinsp;1.050\u0026ndash;1.632, P\u0026thinsp;=\u0026thinsp;0.017), every level of increase in total household income (OR\u0026thinsp;=\u0026thinsp;1.171, 95% CI\u0026thinsp;=\u0026thinsp;1.111\u0026ndash;1.234, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and improvement in education level (OR\u0026thinsp;=\u0026thinsp;1.269, 95% CI\u0026thinsp;=\u0026thinsp;1.125\u0026ndash;1.432, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly positively correlated with chronic disease health literacy. Changes in health behavior (OR\u0026thinsp;=\u0026thinsp;2.563, 95% CI\u0026thinsp;=\u0026thinsp;1.991\u0026ndash;3.299, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and increased frequency of physical examinations (OR\u0026thinsp;=\u0026thinsp;1.303, 95% CI\u0026thinsp;=\u0026thinsp;1.145\u0026ndash;1.482, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) have a particularly prominent effect on improving literacy. It should be noted that chronic diseases (OR\u0026thinsp;=\u0026thinsp;0.546, 95% CI\u0026thinsp;=\u0026thinsp;0.341\u0026ndash;0.874, P\u0026thinsp;=\u0026thinsp;0.012) were associated with lower literacy levels, while individuals with hypertension (OR\u0026thinsp;=\u0026thinsp;2.265), coronary heart disease (OR\u0026thinsp;=\u0026thinsp;2.572), or diabetes (OR\u0026thinsp;=\u0026thinsp;2.365) showed higher literacy levels (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The higher utilization of preventive medical care (OR\u0026thinsp;=\u0026thinsp;1.132, P\u0026thinsp;=\u0026thinsp;0.042) and the increased frequency of health knowledge exposure (OR\u0026thinsp;=\u0026thinsp;1.021, P\u0026thinsp;=\u0026thinsp;0.04) are also protective factors. Age, gender, employment category, and frequency of use of digital health tools did not show statistical associations(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e,Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between Demographic, Socio - economic, Health - related Variables and Chronic Disease Literacy:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eChronic Disease Literacy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.207 (1.018\u0026ndash;1.432)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e667 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1274 (56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e326 (57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e313 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.42\u0026thinsp;\u0026plusmn;\u0026thinsp;13.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.26\u0026thinsp;\u0026plusmn;\u0026thinsp;14.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.007 (0.997\u0026ndash;1.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender(female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1223 (54.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e302 (52.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.013 (0.830\u0026ndash;1.236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.309 (1.050\u0026ndash;1.632)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023 (89.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e514 (89.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Household Income (CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.171 (1.111\u0026ndash;1.234)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e444 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5000-10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e337 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10,000\u0026ndash;20,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e398 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20,000\u0026ndash;30,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e362 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30,000\u0026ndash;50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e391 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50,000-100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational Attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.269 (1.125\u0026ndash;1.432)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e609 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1099 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior high school/technical secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e390 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u0026rsquo;s degree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment Category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000 (0.940\u0026ndash;1.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1574 (69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e358 (62.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactory/enterprise workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial/service workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment/public institution employees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers (unemployed, caregivers, etc.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Examination Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.303 (1.145\u0026ndash;1.482)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnually\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemi-annually\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e177 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuarterly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1113 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e306 (53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e736 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e215 (37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Knowledge Exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.021 (1.003\u0026ndash;1.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e358 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e822 (36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209 (36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e838 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Health Tool Usage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.046 (0.950\u0026ndash;1.151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;1 time/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e350 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;3 times/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e438 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e713 (31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;3 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e421 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172 (30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;1 time/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e332 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Behavior Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.563 (1.991\u0026ndash;3.299)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes (changed behavior based on health knowledge)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1171 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e438 (76.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic disease situation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.546 (0.341\u0026ndash;0.874)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes (suffering from chronic disease)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e760 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180 (31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Comorbidity Factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e398 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.265 (1.354\u0026ndash;3.789)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD (Coronary Artery Disease)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.572 (1.139\u0026ndash;5.809)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeVD(Cerebrovascular Disease )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.012 (0.839\u0026ndash;4.825)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.365 (1.209\u0026ndash;4.625)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.947 (0.110\u0026ndash;8.189)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreventive healthcare utilization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.132 (1.071\u0026ndash;1.320)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e803 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128 (22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e789 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e662 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eaOdds ratios (OR) and P values were adjusted for age, sex\u003c/p\u003e \u003cp\u003eb. SES: socioeconomic status; CAD: coronary artery disease; CeVD: cerebrovascular disease.\u003c/p\u003e \u003cp\u003ec. Income is denominated in Chinese Yuan (CNY). Educational attainment: \"Junior college\" refers to three-year higher education, and \"Bachelor\u0026rsquo;s degree or above\" includes undergraduate and postgraduate education.\u003c/p\u003e \u003cp\u003ed. Health behavior change is defined as \"adjusting lifestyle based on health knowledge\". Preventive healthcare utilization is stratified into low (LOW), medium (Medial), and high (High) tiers.\u003c/p\u003e \u003cp\u003ee. For univariate analysis, analysis of variance (ANOVA) was used for continuous variables, and chi-square test (χ\u0026sup2; test) for categorical variables. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e The bar chart displays the socioeconomic status of the participants Preventive healthcare utilization、 Chronic comorbidity factors. Adjust the odds ratio (OR) based on age and gender.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 The mediating effect of preventive healthcare and chronic comorbidity factors on SES's impact on chronic disease literacy\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe mediation analysis revealed significant indirect effects of SES on chronic disease literacy through multiple preventive healthcare utilization pathways (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e,Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Preventive healthcare utilization mediated 24.6% (95% CI: 0.016\u0026ndash;0.041, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) of the total effect, with an adjusted direct effect of 0.069 (95% CI: 0.055\u0026ndash;0.093). Other significant mediators included physical examination frequency (mediation proportion: 8.6%, 95% CI: 0.004\u0026ndash;0.015, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), health behavior change (10.0%, 95% CI: 0.002\u0026ndash;0.020, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), digital health tool usage (4.2%, 95% CI: 0.001\u0026ndash;0.004, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and frequency of health knowledge exposure (2.9%, 95% CI: 0.001\u0026ndash;0.003, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, chronic disease, coronary artery disease (CAD), and cerebrovascular disease (CeVD) showed no significant mediation effects (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).For chronic disease outcomes, hypertension emerged as the only significant mediator, accounting for 3.4% of the total effect (indirect effect: \u0026minus;0.002, 95% CI: \u0026minus;0.013\u0026ndash;\u0026minus;0.000, P\u0026thinsp;=\u0026thinsp;0.032) (Table S3). Other factors, including healthy sleep patterns, age, marital status, gender, diabetes, and cancer, demonstrated negligible or non-significant mediation proportions (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Adjustments for age and sex did not alter the overall mediation patterns.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation Analysis Diagrams of the Relationship between SES and Chronic Disease Literacy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMediator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEffect with mediator adjusted (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect effect by mediator (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMediation proportion (%) (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eChronic disease literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreventive healthcare utilization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.069(0.055, 0.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017(0.016\u0026ndash;0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhysical Examination Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.070(0.057, 0.083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006(0.004\u0026thinsp;~\u0026thinsp;0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealth Behavior Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.070(0.054, 0.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007(0.002\u0026ndash;0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital Health Tool Usage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.071(0.058, 0.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003(0.001\u0026ndash;0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe frequency of exposure to health knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.069(0.056, 0.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002(0.001\u0026ndash;0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChronic Disease Literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.070(0.056, 0.083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002(-0.001-0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.071(0.058, 0.083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.001(-0.004\u0026thinsp;~\u0026thinsp;0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCeVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.068(0.055, 0.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001(-0.002\u0026thinsp;~\u0026thinsp;0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ea. Effects with mediator adjusted and indirect effects by mediator were adjusted for relevant confounders (such as age, sex, etc.).\u003c/p\u003e \u003cp\u003eb. SES: socioeconomic status; CAD: coronary artery disease; CeVD: cerebrovascular disease.\u003c/p\u003e \u003cp\u003ec. Chronic disease literacy represents the understanding and application ability of chronic - disease - related knowledge.\u003c/p\u003e \u003cp\u003ed. Health Behavior Change is defined as \"adjusting lifestyle based on health knowledge\". Preventive healthcare utilization refers to the frequency and level of using preventive healthcare services.\u003c/p\u003e \u003cp\u003ee. For the analysis of mediation effects, relevant statistical methods were employed. The significance of indirect effects and mediation proportions was tested, with a P - value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.4 The interaction between preventive healthcare and chronic comorbidity factors on SES's impact on chronic disease literacy\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe interaction analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) revealed significant moderating effects of socioeconomic status (SES) on chronic disease literacy through specific behavioral and healthcare pathways. Preventive healthcare utilization exhibited a strong interaction with SES (F\u0026thinsp;=\u0026thinsp;5.076, P\u0026thinsp;=\u0026thinsp;0.006), indicating that higher SES amplified the benefits of preventive care on health literacy. Similarly, physical examination frequency (F\u0026thinsp;=\u0026thinsp;2.66, P\u0026thinsp;=\u0026thinsp;0.031) and frequency of health knowledge exposure (F\u0026thinsp;=\u0026thinsp;2.627, P\u0026thinsp;=\u0026thinsp;0.007) demonstrated significant SES-dependent effects, with high-SES individuals showing greater improvements in these domains. In contrast, health behavior change (F\u0026thinsp;=\u0026thinsp;0.048, P\u0026thinsp;=\u0026thinsp;0.953) and digital health tool usage (F\u0026thinsp;=\u0026thinsp;1.849, P\u0026thinsp;=\u0026thinsp;0.117) showed no significant interaction with SES.Joint analyses further illustrated these disparities (Fig. S4). High-SES groups displayed markedly enhanced outcomes in preventive health information utilization and physical examination adherence, whereas low-SES groups faced elevated risks for adverse health outcomes, including cardiovascular disease incidence and chronic disease progression.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe interaction between socioeconomic status (SES) and different mediating factors of chronic disease literacy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eChronic disease literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreventive healthcare utilization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical Examination Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth Behavior Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital Health Tool Usage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe frequency of exposure to health knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChronic Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCeVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ea. F - tests were used to assess the relationship between each variable and SES in the context of chronic disease literacy as the outcome. The F - values and corresponding P - values are presented.\u003c/p\u003e \u003cp\u003eb. SES: socioeconomic status; CAD: coronary artery disease; CeVD: cerebrovascular disease.\u003c/p\u003e \u003cp\u003ec. For the variables (Preventive healthcare utilization, Physical Examination Frequency, etc.), an F - test was conducted to determine the association with SES. A P - value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered statistically significant, indicating a notable relationship between the variable and SES in influencing chronic disease literacy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 The impact of socioeconomic inequality on chronic disease literacy among different gender and age subgroups\u003c/h2\u003e \u003cp\u003eStratified analyses by socioeconomic status (SES) revealed sex-specific disparities in chronic disease literacy (Table S5). In the low-SES subgroup, females exhibited significantly lower odds of chronic disease literacy compared to males (OR\u0026thinsp;=\u0026thinsp;0.60, 95% CI: 0.50\u0026ndash;0.75, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while males in this group also showed reduced literacy (OR\u0026thinsp;=\u0026thinsp;0.65, 95% CI: 0.55\u0026ndash;0.78, P\u0026thinsp;=\u0026thinsp;0.002). In contrast, no significant sex differences were observed in high-SES (female OR\u0026thinsp;=\u0026thinsp;0.71, 95% CI: 0.37\u0026ndash;1.36, P\u0026thinsp;=\u0026thinsp;0.306; male reference) or medium-SES (female OR\u0026thinsp;=\u0026thinsp;0.85, 95% CI: 0.70\u0026ndash;1.05, P\u0026thinsp;=\u0026thinsp;0.12) subgroups.Age-stratified analyses demonstrated SES-dependent variations in chronic disease literacy (Table S6). In the low-SES subgroup, participants aged\u0026thinsp;\u0026gt;\u0026thinsp;60 years had significantly lower literacy than those\u0026thinsp;\u0026le;\u0026thinsp;60 years (OR\u0026thinsp;=\u0026thinsp;0.70, 95% CI: 0.42\u0026ndash;0.91, P\u0026thinsp;=\u0026thinsp;0.001). Similarly, younger individuals (\u0026le;\u0026thinsp;60 years) in low-SES groups also showed reduced literacy (OR\u0026thinsp;=\u0026thinsp;0.62, 95% CI: 0.52\u0026ndash;0.75, P\u0026thinsp;=\u0026thinsp;0.016). No significant age-related disparities were observed in high-SES (OR\u0026thinsp;=\u0026thinsp;0.95, 95% CI: 0.75\u0026ndash;1.45, P\u0026thinsp;=\u0026thinsp;0.327) or medium-SES (OR\u0026thinsp;=\u0026thinsp;0.93, 95% CI: 0.72\u0026ndash;1.39, P\u0026thinsp;=\u0026thinsp;0.339) subgroups.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion and Conclusion","content":"\u003cp\u003eThis study is based on a multidimensional socioeconomic status (SES) evaluation system. By constructing comprehensive indicators of annual household income, education level, and occupational status, and using mediation analysis method, the correlation mechanism between SES and chronic disease health literacy is systematically examined. The research results showed that SES was significantly positively correlated with health literacy level (OR\u0026thinsp;=\u0026thinsp;1.207, P\u0026thinsp;=\u0026thinsp;0.03), with preventive medical utilization (24.6%), physical examination frequency (8.6%), and health behavior change (10.0%) as the main mediating pathways. It is worth noting that there is a significant interaction effect (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between SES and the utilization of preventive medical care, frequency of physical examinations, and frequency of exposure to health knowledge.Although chronic diseases such as hypertension (OR\u0026thinsp;=\u0026thinsp;2.265), coronary heart disease (OR\u0026thinsp;=\u0026thinsp;2.572) and diabetes (OR\u0026thinsp;=\u0026thinsp;2.365) are positively related to chronic disease literacy, their prevalence rate in the low SES population is significantly higher (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that there is a two-way relationship between chronic disease burden and SES inequality.\u003c/p\u003e \u003cp\u003eChronic disease health literacy, as a key competency for individuals to acquire, understand, and apply knowledge and skills for chronic disease prevention and treatment, is directly related to their health behavior choices, disease self-management effects, and overall health outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. However, most studies have focused on the association between SES and health outcomes, but less on the mechanisms by which SES affects chronic disease health literacy; and most studies have defined SES only in terms of income, education, or occupation, failing to comprehensively reveal its multidimensional interactions.Falagas et al.'s study explored SES as a determinant of treatment adherence in HIV-infected patients\u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Similarly, a study by Adler et al. explored that SES influences health gradient\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. However, it is unclear whether SES has a sustained effect on chronic disease health literacy. In our study, we used household income level, educational qualification and occupational status to jointly define SES and assessed the association between composite SES and individual chronic disease literacy. We found that people with high SES had higher chronic disease health literacy, and this association did not differ between men and women or age. The results of our analysis of each individual SES indicator were also comparable to previous studies\u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, which enhances the credibility of the findings.\u003c/p\u003e \u003cp\u003eRecent studies have shown that SES can change the incidence rate of cardiovascular diseases by influencing prevention and health care behavior\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. However, there is currently no research evaluating the potential role of preventive healthcare behaviors in the association between SES and chronic disease health literacy. Therefore, in this study, we employed mediation analysis and found that individual preventive healthcare behaviors can explain 24.6% of the association between SES and chronic disease health literacy. The frequency of physical examinations (8.6%) has the most significant effect on improving health literacy. The health literacy score of residents with quarterly physical examination frequency is higher than that of those without physical examination (β\u0026thinsp;=\u0026thinsp;0.15, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which is consistent with the research conclusion of population health monitoring practice\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In addition, health knowledge education (OR\u0026thinsp;=\u0026thinsp;1.021, 95% CI\u0026thinsp;=\u0026thinsp;1.003\u0026ndash;1.033) and electronic tool use (OR\u0026thinsp;=\u0026thinsp;1.046, 95% CI\u0026thinsp;=\u0026thinsp;0.950\u0026ndash;1.151) together constitute the \"cognitive\" pathway for behavior change. However, in subgroup analysis by SES level and age, women in the low SES group (OR\u0026thinsp;=\u0026thinsp;0.60, 95% CI\u0026thinsp;=\u0026thinsp;0.50\u0026ndash;0.75, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and those over 60 years old (OR\u0026thinsp;=\u0026thinsp;0.70, 95% CI\u0026thinsp;=\u0026thinsp;0.42\u0026ndash;0.91, P\u0026thinsp;=\u0026thinsp;0.001) had significantly lower chronic disease health literacy than other subgroups, while no similar differences were observed in the high/medium SES group (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This result suggests that gender and age-related health inequalities in low SES populations may stem from differences in access to healthcare resources, such as less participation of women in health decision-making and barriers to digital tool use among older adults, while the socioeconomic advantages of high SES populations can mitigate such risks\u003csup\u003e\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that preventive healthcare interventions can reduce various health risks, including cardiovascular disease incidence\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In our research, we consistently found that adhering solely to preventive healthcare behaviors such as regular check ups and health education has a protective effect on overall chronic disease literacy. It is worth noting that this protective effect is more significant in low SES populations, indicating that preventive healthcare behaviors may alleviate the adverse effects of low SES and emphasizing the need to strengthen health education for low SES populations. Similar trends have also been reported in two other studies on diseases by the UK Biobank (UKB)\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition,this study revealed a significant bidirectional contradiction in the effect of chronic disease factors on health literacy. Although patients with hypertension (OR\u0026thinsp;=\u0026thinsp;2.265, 95% CI\u0026thinsp;=\u0026thinsp;1.354\u0026ndash;3.789), CAD (OR\u0026thinsp;=\u0026thinsp;2.572, 95% CI\u0026thinsp;=\u0026thinsp;1.139\u0026ndash;5.809), and diabetes (OR\u0026thinsp;=\u0026thinsp;2.365, 95% CI\u0026thinsp;=\u0026thinsp;1.209\u0026ndash;4.625) had significantly higher chronic disease health literacy than non patients (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), it should be noted that chronic disease as a whole (OR\u0026thinsp;=\u0026thinsp;0.546, 95% CI\u0026thinsp;=\u0026thinsp;0.341\u0026ndash;0.874, P\u0026thinsp;=\u0026thinsp;0.012) was independently related to low health literacy. This contradiction suggests that specific chronic diseases (such as hypertension and diabetes) may drive patients to actively acquire health knowledge through disease management needs (positive individual effect), while generalized chronic disease burdens (such as coexistence of multiple diseases and undiagnosed diseases) may lead to literacy decline (negative group effect) due to resource crowding and health fatigue\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. In the mediation analysis, only hypertension showed a weak indirect effect (mediation ratio of 3.4%, P\u0026thinsp;=\u0026thinsp;0.032), while other chronic diseases (such as coronary heart disease, cerebrovascular disease) and chronic diseases as a whole did not pass the mediation test (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This indicates that the impact of chronic diseases on health literacy is more of a direct effect rather than an indirect pathway through SES or behavioral factors. It is worth noting that the prevalence of chronic diseases is significantly higher in the low SES group (such as hypertension prevalence of 19.8% vs. 17.7%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but their health literacy is lower than that of the high SES group (OR\u0026thinsp;=\u0026thinsp;0.60\u0026ndash;0.70, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), further revealing that the interaction between chronic diseases and SES may exacerbate health inequality - low SES patients face a vicious cycle of \"high disease burden low health literacy\", while high SES patients exhibit a positive feedback of \"high disease management ability high health literacy\".\u003c/p\u003e \u003cp\u003eWe explored the potential mediating mechanism between SES and infection risk from the perspectives of preventive healthcare and chronic comorbidities. The advantages of this study are mainly reflected in the following aspects: firstly, adopting standardized variable definitions and measurement methods; Secondly, the constructed multidimensional composite score (including SES index and preventive healthcare intervention score) can more comprehensively characterize the characteristics of each dimension compared to single indicators; Thirdly, identifying high-risk populations through stratified analysis provides a scientific basis for implementing precise prevention and control measures.\u003c/p\u003e \u003cp\u003eThere are several limitations to this study: firstly, chronic disease diagnosis is based on self-reported questionnaires, which may be influenced by reporting bias. Participants may not be able to accurately recall or report their disease status, leading to potential underreporting or misreporting. In addition, the study only included a limited number of factors and did not explore other possible mediating factors or confounding variables that may affect the relationship between economic level, literacy rate, and chronic disease literacy rate. Future research may consider using more objective chronic disease diagnostic methods and expanding the scope of research factors to gain a more comprehensive understanding of the complex relationships involved. In addition, longitudinal studies will help track changes in chronic disease literacy over time and examine causal relationships between factors.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, this study systematically reveals the multidimensional mechanisms by which SES affects chronic disease health literacy, providing a scientific basis for developing targeted intervention strategies. Government departments should focus on enhancing the chronic disease prevention and control capabilities of vulnerable groups through measures such as conducting health education and providing training resources. Subsequent research should further deepen the exploration of mechanisms and develop effective strategies for improving health literacy to reduce the burden of chronic diseases.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIC：Akaike Information Criterion\u003c/p\u003e\n\u003cp\u003eCAD：Coronary Artery Disease\u003c/p\u003e\n\u003cp\u003eBIC：Bayesian Information Criterion\u003c/p\u003e\n\u003cp\u003eCeVD：Cerebrovascular Disease\u003c/p\u003e\n\u003cp\u003eCI：Confidence Interval\u003c/p\u003e\n\u003cp\u003eCVD：Cardiovascular Disease\u003c/p\u003e\n\u003cp\u003eICD-10：International Classification of Diseases, 10th Revision\u003c/p\u003e\n\u003cp\u003eLCA：Latent Class Analysis\u003c/p\u003e\n\u003cp\u003eNCHS：National Center for Health Statistics\u003c/p\u003e\n\u003cp\u003eOR：Odds Ratio\u003c/p\u003e\n\u003cp\u003ePHU：Preventive Healthcare Utilization\u003c/p\u003e\n\u003cp\u003eRUCA：Rural-Urban Commuting Area\u003c/p\u003e\n\u003cp\u003eSES：Socioeconomic Status\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and informed consent statements\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe datasets obtained during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eAll authors consent to the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe findings of this study are available from the corresponding author upon reasonable request \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe authors declare no competing interests. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThis work was funded by Shandong Province Medical and Health Science and Technology Development Plan Project(202312071011)and Shandong Province Humanities and Social Sciences Research Projects(2023-JKZX-11). The funders had no involvement in data collection and analysis or the preparation of this article. The analysis and interpretation of the evidence was done by the authors themselves,not by the funders. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003eA：ll respondents have informed consent forms.Ethics Approval Opinion of the Medical Ethics Review Committee of Shandong Provincial Center for Disease Control and Prevention (SDJK (K) 2024-046-01)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and analysis were led by Liansen Wang,Xinyu Xu,Rui Li, with technical support from Yuxuan Zhang, Yingjie Cai, Jing Tang, Xiaowei Yang, Jing Han, Fangyao Chen, Xiuli Qiao. The first draft was written by Rui Li and Xinyu Xu, and all authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eWe acknowledge all participants and field teams in Shandong Province. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNugent R. Preventing and managing chronic diseases. \u003cem\u003eBMJ\u003c/em\u003e. 2019;364:l459. doi:10.1136/bmj.l459\u003c/li\u003e\n\u003cli\u003eHe L, La Y, Yan Y, et al. The prevalence and burden of four major chronic diseases in the Shaanxi Province of Northern China. \u003cem\u003eFront Public Health\u003c/em\u003e. 2022;10:985192. doi:10.3389/fpubh.2022.985192\u003c/li\u003e\n\u003cli\u003eSu B, Li D, Xie J, et al. 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Multimorbidity and health literacy: A population-based survey among 28,627 Danish adults. \u003cem\u003eScand J Public Health\u003c/em\u003e. 2023;51(2):165-172. doi:10.1177/14034948211045921\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-equity-in-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijeh","sideBox":"Learn more about [International Journal for Equity in Health](http://equityhealthj.biomedcentral.com)","snPcode":"12939","submissionUrl":"https://submission.nature.com/new-submission/12939/3","title":"International Journal for Equity in Health","twitterHandle":"@equityhealthj","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Socioeconomic status (SES)，Chronic disease health literacy，Preventive healthcare utilization，Mediation analysis，Health disparities，Rural China，Health behavior change，Low-SES populations，Gender disparities，Aging population","lastPublishedDoi":"10.21203/rs.3.rs-6811235/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6811235/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eTo explore the social determinants and underlying mechanisms of health literacy in managing chronic diseases, and analyze how socioeconomic status (SES) influences it.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eThis study, based on Shandong Province’s 2022 Health Literacy Surveillance Database, selected 2,826 residents from eligible areas using multistage stratified cluster random sampling. It explored the social determinants of chronic disease health literacy using multiple linear regression, multiple logistic regression, interactive analysis and mediation analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eHigher SES (OR=1.207, 95%CI: 1.018–1.432, P=0.03), marriage (OR=1.309, 95%CI: 1.050–1.632, P=0.017), higher education (OR=1.269, 95%CI: 1.125–1.432, P\u0026lt;0.001) are significantly correlated with chronic disease. It is worth noting that although having chronic disease SES was generally associated with lower chronic disease health literacy (OR=0.546, 95%CI: 0.341–0.874, P=0.012), patients with hypertension, coronary heart disease, or diabetes showed higher health literacy. The preventive healthcare services (95% CI: 0.016-0.041, P\u0026lt;0.001), changes in health behavior (10.0%, 95% CI: 0.002-0.020, P\u0026lt;0.001) and frequency of examinations (8.6%, 95% CI: 0.004-0.015, P\u0026lt;0.001) significantly mediated the relationship between SES and chronic disease health literacy.Subgroup analysis shows that in the low SES group, women (OR=0.60,95% CI: 0.50-0.75, P\u0026lt;0.001) and elders (OR=0.70,95% CI: 0.42-0.91, P=0.001) have significantly lower chronic disease health literacy than men and young participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study systematically uncovers the multidimensional mechanisms by which SES impacts chronic disease health literacy and provides a scientific basis for developing targeted interventions.\u003c/p\u003e","manuscriptTitle":"Socioeconomic Gradients and Mechanisms of Chronic Disease Health Literacy: The Mediating Role of Preventive Healthcare Utilization in Rural China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-07 08:18:09","doi":"10.21203/rs.3.rs-6811235/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"63322586430993794972807106215306352131","date":"2025-07-06T11:26:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179040635582291523849919593514334024511","date":"2025-07-03T06:05:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-02T23:50:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-09T15:14:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-09T12:11:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal for Equity in Health","date":"2025-06-03T12:20:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-equity-in-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijeh","sideBox":"Learn more about [International Journal for Equity in Health](http://equityhealthj.biomedcentral.com)","snPcode":"12939","submissionUrl":"https://submission.nature.com/new-submission/12939/3","title":"International Journal for Equity in Health","twitterHandle":"@equityhealthj","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2142fdee-a919-452d-b36f-a75f78181179","owner":[],"postedDate":"July 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:10:09+00:00","versionOfRecord":{"articleIdentity":"rs-6811235","link":"https://doi.org/10.1186/s12939-025-02677-y","journal":{"identity":"international-journal-for-equity-in-health","isVorOnly":false,"title":"International Journal for Equity in Health"},"publishedOn":"2025-11-14 15:57:58","publishedOnDateReadable":"November 14th, 2025"},"versionCreatedAt":"2025-07-07 08:18:09","video":"","vorDoi":"10.1186/s12939-025-02677-y","vorDoiUrl":"https://doi.org/10.1186/s12939-025-02677-y","workflowStages":[]},"version":"v1","identity":"rs-6811235","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6811235","identity":"rs-6811235","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}