Relationship between health literacy and health status among rural residents in western China: a cross-sectional study

Relationship between health literacy and health status among rural residents in western China: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Relationship between health literacy and health status among rural residents in western China: a cross-sectional study Kaihui Jiang, Haijiao Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9397188/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Health literacy is a crucial determinant of health outcomes. This study aimed to investigate the levels of health literacy and health status, analyse their influencing factors, and explore their relationship among rural residents in western China, providing evidence for public health policy improvement. Methods A cross-sectional study was conducted from August to September 2023 in Lingui District, Guilin City. A total of 603 rural residents aged 15–69 were selected via multi-stage random sampling. Data were collected using the standardized "2023 National Residents Health Literacy Monitoring Questionnaire". Health literacy was assessed across three dimensions: knowledge, behavior, and skills, with a score ≥ 58/73 indicating adequate literacy. Health status was evaluated via self-rated health, two-week morbidity, and chronic disease presence. Data were analyzed using SPSS 26.0 for descriptive statistics, t-tests, ANOVA, and multivariate regression (linear, binary, and ordinal logistic). Results The proportion of residents with adequate health literacy was 24.54% (148/603). The levels for basic health knowledge, healthy lifestyles/behaviors, and basic skills were 31.51%, 24.71%, and 25.70%, respectively. Linear regression identified education level (β = 6.993, p < 0.001) and annual household income (β = 4.547, p < 0.001) as significant positive predictors of health literacy score, while age was a negative predictor (β=-4.410, p < 0.001). Ordinal logistic regression showed that health literacy (OR = 1.016, 95%CI: 1.006–1.026), education level (OR = 1.643, 95%CI: 1.192–2.266), and annual household income (OR = 1.426, 95%CI: 1.106–1.839) were significantly associated with better self-rated health. Chronic disease status positively predicted two-week morbidity (OR = 1.802, 95%CI: 1.099–2.954). Health literacy (OR = 1.014, 95%CI: 1.002–1.027) and occupation (OR = 1.348, 95%CI: 1.035–1.755) were positively associated, while age (OR = 0.482, 95%CI: 0.354–0.656) was negatively associated, with the absence of chronic diseases. Conclusions Health literacy among rural residents in this western region is relatively low and is influenced by age, education, and income. Health literacy, along with education and income, also positively affects health status. Targeted health education interventions focusing on improving health literacy, especially in older, less-educated, and lower-income groups, are needed to narrow urban-rural health disparities. Health Literacy Health Status Rural Population Western China Cross-Sectional Studies Health Disparities Background Health is a fundamental human pursuit and a critical marker of national prosperity. The concept of health has evolved from a purely biomedical model to a biopsychosocial one, emphasizing the interplay of physical, mental, and social factors [1]. Health literacy, broadly defined as the cognitive and social skills that determine the motivation and ability of individuals to access, understand, and use information to promote and maintain good health, has emerged as a key modifiable determinant of health outcomes [2]. A robust body of evidence, primarily from Western countries, has established that limited health literacy is associated with poorer health knowledge, less effective self-management of chronic diseases, lower utilization of preventive services, higher rates of hospitalization, and increased mortality [3–5]. This relationship underscores the critical role of health literacy in achieving health equity and improving population health. The measurement of health literacy is essential for monitoring population levels and evaluating interventions. Internationally, several validated tools exist, such as the Short Test of Functional Health Literacy in Adults (S-TOFHLA) and the Health Literacy Survey questionnaire (HLS) series [6, 7]. In China, efforts to develop and validate culturally appropriate instruments have been ongoing. Studies have validated the Chinese version of the Health Literacy Scale Short-Form (HLS-SF12) [8], the HLS19-Q12 instrument for the general adult population [9], and the Chinese Resident Health Literacy Scale [10]. Disease-specific scales, such as the Chinese Health Literacy Scale for Chronic Care (CHLCC) [11] and the Falls Health Literacy Scale (FHLS) [12], have also been developed. These tools have facilitated national monitoring, revealing that while China's average health literacy level has been steadily increasing, significant urban-rural and regional disparities persist, with western and rural areas consistently lagging behind [13]. China's rapid socioeconomic development has been accompanied by a widening gap in health resources and outcomes between urban and rural areas. Rural residents, particularly in the western regions, face compounded challenges including lower educational attainment, economic disadvantage, limited access to quality healthcare, and poorer environmental sanitation [14, 15]. These social determinants of health create a context where low health literacy is both a cause and a consequence of health inequities. Studies have shown that income and education are primary contributors to health inequality in rural western China [16]. Furthermore, vulnerabilities such as limited access to health services and higher burdens of chronic diseases exacerbate the health risks for rural populations [17, 18]. The COVID-19 pandemic has further highlighted and potentially widened these rural-urban health disparities [19]. Within this context, understanding the specific levels of health literacy, its sociodemographic correlates, and its relationship with health status in underserved rural western China is crucial for designing effective, targeted interventions. While national reports provide macro-level data, detailed community-based studies from specific western rural localities are limited. Such studies can uncover local nuances and provide actionable evidence for grassroots health promotion. Therefore, this study aimed to: (1) assess the level of health literacy and its three core dimensions (knowledge, behavior, skills) among rural residents in Lingui District, a representative area in western China; (2) identify sociodemographic factors associated with health literacy; (3) analyze factors influencing health status; and (4) explore the relationship between health literacy and health status. The findings are intended to inform targeted health promotion strategies within the framework of China's Rural Revitalization and Healthy China initiatives, ultimately aiming to improve health equity in underdeveloped rural regions. Methods Study Design and Participants A community-based cross-sectional study was conducted in August-September 2023 in Lingui District, Guilin City, Guangxi Zhuang Autonomous Region, located in western China. The study population comprised non-institutionalized permanent residents aged 15–69 years from six townships (Liangjiang, Sitang, Huixian, Liutang, Lingu, and Wantian Yao Ethnic Township). Permanent residence was defined as living locally for over six months in the past year. Sampling Method and Sample Size A multi-stage random sampling method was employed, following the national health literacy monitoring protocol [20]. Stage 1: All six townships in Lingui District (a municipal-level monitoring point) were included. Stage 2: Two villages/neighborhood committees were randomly selected from each township using Probability Proportional to Size (PPS) sampling. Stage 3: From each selected village, a minimum of 65 households were randomly selected from the household registration list. Stage 4: One eligible resident per selected household was randomly chosen for interview using the KISH grid method. The minimum required sample size for a municipal monitoring point is 600. Accounting for potential non-response, 603 participants were successfully surveyed, yielding a response rate satisfactory for analysis. Data Collection and Measures Data were collected through face-to-face interviews using the standardized electronic "2023 National Residents Health Literacy Monitoring Questionnaire". The questionnaire included four sections: Basic Demographic Information: Age, gender, education, occupation, marital status, household size, and annual household income. Health Literacy Assessment: 56 items evaluating three dimensions: basic health knowledge and concepts (33 points), healthy lifestyles and behaviors (22 points), and basic health skills (18 points). Item types included true/false, single-choice, multiple-choice, and scenario-based questions. Scoring followed the national guideline: 1 point for correct true/false or single-choice, 2 points for correct multiple-choice, 0 points otherwise. The total possible score was 73. Adequate health literacy was defined as a total score ≥ 58 (≥ 80% of total). Adequate dimension-specific literacy was defined as scoring ≥ 80% of the points available in that dimension (Knowledge: ≥26; Behavior: ≥18; Skills: ≥14) [21, 22]. Health Status Indicators: * Self-rated health (SRH): "In the past year, how would you rate your health?" Responses: Good (4), Fairly good (3), Average (2), Fairly poor (1), Poor (0). * Two-week morbidity: "Have you experienced any of the following symptoms in the last two weeks?" (e.g., fever, cough). Categorized as "No" (1) or "Yes" (0). * Chronic disease status: "Have you been diagnosed with the following chronic diseases?" (e.g., hypertension, diabetes). Categorized as "No" (1) or "Yes" (0). 4. Health-related Behaviors: Smoking status, physical activity, BMI. Quality Control Strict quality control procedures were implemented. Investigators underwent standardized training. Supervisors rechecked 10% of questionnaires on-site for consistency. Data were double-entered independently using EpiData software, and inconsistencies were resolved by referring to the original questionnaires. Statistical Analysis Data analysis was performed using SPSS 26.0. Descriptive statistics (frequencies, percentages, mean ± SD) described sample characteristics and health literacy/status levels. Group comparisons used t-tests or ANOVA. Multivariate linear regression identified independent factors associated with the health literacy score. Binary logistic regression analyzed factors associated with two-week morbidity and chronic disease status. Ordinal logistic regression analyzed factors associated with self-rated health. Variance Inflation Factor (VIF) < 5 indicated no multicollinearity. A two-tailed p-value < 0.05 was considered statistically significant. Ethical Considerations The study was approved by the Ethics Review Committee of Guilin Medical University. Verbal informed consent was obtained from all participants before the interview, as approved by the ethics committee and in accordance with the national monitoring protocol which uses verbal consent. All data were anonymized for analysis. Results 1. Sociodemographic Characteristics of Participants A total of 603 participants were included. The mean age was 51.19 ± 10.56 years, with 51.7% aged 46–60. Males constituted 56.6%. Most participants were married (82.4%), had an education level of primary school or below (47.1%) or middle school (50.7%), and were farmers (83.1%). Regarding household income, 41.3% earned ≤ 10,000 CNY/year, and 50.2% earned 10,001–50,000 CNY/year. (Table 1 ). Table 1 Sociodemographic characteristics of the participants (n = 603) Variable Category n % Gender Male 341 56.6 Female 262 43.4 Age (years) 15–30 20 3.3 31–45 154 25.5 46–60 312 51.7 ≥ 61 117 19.4 Education Level Primary school or below 284 47.1 Middle school 306 50.7 College or above 13 2.2 Occupation Farmer 501 83.1 Worker/Enterprise employee 50 8.3 Others* 52 8.6 Annual Household Income (CNY) ≤ 10,000 249 41.3 10,001–50,000 303 50.2 50,001-100,000 46 7.6 > 100,000 5 0.8 *Includes civil servant, teacher, healthcare worker, student, etc. 2. Levels of Health Literacy Only 24.54% (148/603) of participants had adequate health literacy (total score ≥ 58). The adequate level was highest for basic health knowledge and concepts (31.51%), followed by basic skills (25.70%) and healthy lifestyles and behaviors (24.71%). The mean total health literacy score was 43.49 ± 16.72. (Table 2 ). Table 2 Health literacy levels among participants (n = 603) Health Literacy Dimension Adequate, n (%) Score, Mean ± SD Overall Health Literacy 148 (24.54) 43.49 ± 16.72 Basic Knowledge & Concepts 190 (31.51) 20.36 ± 7.69 Healthy Lifestyles & Behaviors 149 (24.71) 13.20 ± 5.42 Basic Skills 155 (25.70) 9.93 ± 4.64 3. Factors Associated with Health Literacy Score Univariate analysis showed significant differences in health literacy scores across age groups, education levels, occupations, and annual household income (p < 0.001 for all), but not by gender, marital status, or family size. (Table 3 ). Table 3 Univariate analysis of factors associated with health literacy scores (Mean ± SD) Factor Category Health Literacy Score F/t p-value Age Group 15–30 yrs 56.80 ± 12.80 24.177 < 0.001 31–45 yrs 51.09 ± 15.75 46–60 yrs 41.04 ± 15.79 ≥ 61 yrs 37.75 ± 16.45 Education Primary or below 37.71 ± 15.41 42.955 < 0.001 Middle school 47.99 ± 16.13 College or above 63.85 ± 7.08 Annual Household Income (CNY) ≤ 10,000 38.61 ± 16.57 15.85 100,000 55.20 ± 14.67 Multivariate linear regression (Table 4 ) confirmed that higher education level (β = 6.993, p < 0.001) and higher annual household income (β = 4.547, p < 0.001) were independent positive predictors of a higher health literacy score, while older age (β=-4.410, p < 0.001) was a negative predictor. The model explained 19.2% of the variance in health literacy (R²=0.192). Table 4 Multivariate linear regression analysis of factors associated with health literacy score (n = 603) Predictor Unstandardized Coefficient B (SE) Standardized Coefficient Beta t p-value 95% CI for B Constant 38.080 (6.064) 6.28 < 0.001 26.163 to 49.997 Gender (Male vs. Female) 1.746 (1.291) 0.052 1.353 0.177 -0.789 to 4.281 Age (increasing) -4.410 (0.938) -0.199 -4.703 < 0.001 -6.252 to -2.568 Education Level (increasing) 6.993 (1.299) 0.226 5.385 < 0.001 4.441 to 9.545 Annual Household Income (increasing) 4.547 (1.033) 0.176 4.401 < 0.001 2.517 to 6.577 Marital Status 0.907 (0.811) 0.043 1.119 0.264 -0.686 to 2.500 Occupation -0.761 (0.671) -0.043 -1.133 0.258 -2.078 to 0.557 Family Size 0.760 (0.870) 0.033 0.873 0.383 -0.951 to 2.471 Model Summary: R² = 0.192, Adjusted R² = 0.182, F(7,595) = 20.167, p < 0.001. 4. Factors Associated with Health Status 4.1 Self-rated health (SRH): 34.16% reported "Good" SRH. In ordinal logistic regression (Table 5 ), higher health literacy score (OR = 1.016, 95%CI: 1.006–1.026), higher education level (OR = 1.643, 95%CI: 1.192–2.266), and higher annual household income (OR = 1.426, 95%CI: 1.106–1.839) were significantly associated with better SRH. Table 5 Ordinal logistic regression analysis of factors associated with self-rated health (n = 603) Predictor Coefficient (SE) OR (95% CI) p-value Health Literacy Score 0.016 (0.005) 1.016 (1.006 to 1.026) 0.002 Education Level 0.497 (0.164) 1.643 (1.192 to 2.266) 0.002 Annual Household Income 0.355 (0.130) 1.426 (1.106 to 1.839) 0.006 Gender 0.075 (0.159) 1.078 (0.790 to 1.471) 0.635 Age 0.122 (0.117) 1.130 (0.898 to 1.422) 0.299 Marital Status -0.061 (0.099) 0.941 (0.775 to 1.143) 0.541 Occupation -0.085 (0.085) 0.919 (0.778 to 1.084) 0.315 Family Size -0.007 (0.107) 0.993 (0.806 to 1.224) 0.951 McFadden's Pseudo R² = 0.032. 4.2 Two-week Morbidity: 15.59% reported illness in the past two weeks. Binary logistic regression indicated that having a chronic disease was the only significant predictor, increasing the odds of two-week morbidity (OR = 1.802, 95%CI: 1.099–2.954, p = 0.020). 4.3 Chronic Disease Status: 79.27% reported having at least one chronic disease. Binary logistic regression (Table 6 ) showed that a higher health literacy score (OR = 1.014, 95%CI: 1.002–1.027) and having a non-farmer occupation (OR = 1.348, 95%CI: 1.035–1.755) were associated with higher odds of not having a chronic disease (coded as 1), while older age (OR = 0.482, 95%CI: 0.354–0.656) was associated with lower odds. Table 6 Binary logistic regression analysis of factors associated with the absence of chronic diseases (n = 603) Predictor Coefficient (SE) OR (95% CI) p-value Health Literacy Score 0.014 (0.006) 1.014 (1.002 to 1.027) 0.028 Age -0.730 (0.157) 0.482 (0.354 to 0.656) < 0.001 Occupation (Non-farmer vs. Farmer) 0.299 (0.134) 1.348 (1.035 to 1.755) 0.026 Discussion This study revealed a low level of health literacy (24.54%) among rural residents in Lingui District, western China, which is lower than the 2023 national average for rural residents (26.23%) and much lower than for urban residents (33.25%) [13]. The knowledge dimension scored highest, aligning with national trends, suggesting that translating knowledge into sustained healthy behaviors and practical skills remains a challenge [23]. Consistent with previous research [24, 25], we found that higher education and income were strong positive enablers of health literacy, likely because they facilitate access to, comprehension of, and resources to act upon health information. The negative association with age highlights older adults as a vulnerable group requiring tailored, accessible health education interventions. Importantly, health literacy demonstrated a significant, independent positive association with better self-rated health, even after adjusting for socioeconomic factors. This aligns with the conceptual model where health literacy empowers individuals to make informed decisions, engage in preventive behaviors, and navigate the healthcare system more effectively, leading to better perceived and objective health outcomes [3, 5]. The finding that chronic disease status was the primary driver of recent morbidity underscores the disease burden in this aging rural population. The strong positive effects of education and income on both health literacy and health status illustrate a syndemic of social disadvantage. In western rural China, lower educational opportunities and economic constraints create a barrier to health literacy acquisition, which in turn perpetuates health inequities. This calls for integrated policies that combine poverty alleviation and educational investment with health promotion, as envisioned in China's Rural Revitalization Strategy. Strengths and Limitations Strengths include a rigorous, nationally standardized sampling and assessment methodology, and a focus on an understudied western rural population. Limitations should be noted. The cross-sectional design precludes causal inference. Self-reported data, especially for health status, are subject to bias. The single geographic location limits generalizability to all western regions. The modest explanatory power of some regression models (e.g., for SRH) indicates other unmeasured factors (e.g., social support, environmental factors) are at play. Conclusions Health literacy among rural residents in this western Chinese region is suboptimal and is shaped by age, education, and income. Health literacy itself, along with education and income, contributes to better health status. Public health strategies must prioritize health literacy promotion, employing context-specific, simple, and repetitive communication channels suitable for rural areas. Interventions should particularly target older adults and those with lower socioeconomic status. Future longitudinal or intervention studies are needed to establish causality and test the effectiveness of tailored health literacy programs in similar settings. Abbreviations * SRH Self-Rated Health * OR Odds Ratio * CI Confidence Interval Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of Nanxishan Hospital of Guangxi Zhuang Autonomous Region. The need for informed consent was waived due to the retrospective analysis of anonymized data. This research was conducted as part of the following funded projects: Guangxi T-wave Change Population's Physical Status and Physical Function Assessment (Grant No.Z-C20250194). Consent to participate: Not applicable for this secondary analysis. The original informed consent was obtained by the Health and Retirement Study (HRS). Please refer to the supplementary file (Additional File 1: 2023 National Residents Health Literacy Monitoring Questionnaire.pdf ) for details regarding consent documentation. Consent for publication Not applicable. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding Guangxi T-wave Change Population's Physical Status and Physical Function Assessment(Grant No.Z-C20250194). Authors' contributions Kaihui Jiang: Conceptualization, Investigation, Formal Analysis, Writing – Original Draft. Haijiao Zhang: Supervision, Methodology, Writing – Review & Editing, Project Administration. All authors read and approved the final manuscript. 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"https://doi.org/10.16168/j.cnki.issn.1002-9982.2021.06.001" (https://doi.org/10.16168/j.cnki.issn.1002-9982.2021.06.001) Fleary SA, Ettienne R. Social disparities in health literacy in the United States. Health Lit Res Pract. 2019;3(1):e47-e52. "https://doi.org/10.3928/24748307-20190131-01" (https://doi.org/10.3928/24748307-20190131-01) Xiao L, Ma Y, Li Y, et al. Study on the status and influencing factors of health literacy among Chinese urban and rural residents. Chinese Journal of Health Education. 2009;25(5):323-326. "https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7iLik5jEcCI09uHa3oBxtwoGwBq5wUvn5pS5t9JNwVk6kPx7_1Fc9c8hW5_7vZ4qj&uniplatform=NZKPT" (https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7iLik5jEcCI09uHa3oBxtwoGwBq5wUvn5pS5t9JNwVk6kPx7_1Fc9c8hW5_7vZ4qj&uniplatform=NZKPT). Accessed 10 Apr 2026. Additional Declarations No competing interests reported. Supplementary Files 2023NationalResidentsHealthLiteracyMonitoringQuestionnaireEnglishTranslation.pdf Additional File 1: Questionnaire_2023 National Residents Health Literacy Monitoring Questionnaire.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 18 Apr, 2026 Editor invited by journal 15 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Submission checks completed at journal 13 Apr, 2026 First submitted to journal 12 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9397188","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627369775,"identity":"9e429262-720e-4d22-a3eb-80caa9506ccd","order_by":0,"name":"Kaihui Jiang","email":"","orcid":"","institution":"Guangxi Nanshan Hospital (The Second People's Hospital of Guangxi Zhuang Autonomous Region)","correspondingAuthor":false,"prefix":"","firstName":"Kaihui","middleName":"","lastName":"Jiang","suffix":""},{"id":627369776,"identity":"3687572c-b347-410e-b963-b73bb1b75925","order_by":1,"name":"Haijiao Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACfvnDBx//qbCRY2NvIFKL5Ay2ZAOeM2nGfDwHiNRicIPHTIC35XDiPIkEYm2Z3WDGINnAzNgm+XjjDYYam2iCWvhlDqQ9MNzBxswmnVZswXAsLbeBoC0NCccNEs/wsLFJ55hJMDYcJqzF4EBim8TBNgkeNskzxGq5kcwm2dhmIMEmwUOkFsmeY8zGDGcSDNh4gH5JIMYv/Oz9Hx8zVPyvn99+eOONDzU2hLWgOJLoqEHSQqqOUTAKRsEoGBkAAE/5Pb5BjhiGAAAAAElFTkSuQmCC","orcid":"","institution":"Guangxi Nanshan Hospital (The Second People's Hospital of Guangxi Zhuang Autonomous Region)","correspondingAuthor":true,"prefix":"","firstName":"Haijiao","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-04-12 23:53:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9397188/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9397188/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107910007,"identity":"eb19558c-ca41-40c5-829b-c989aa758b76","added_by":"auto","created_at":"2026-04-27 13:12:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":311617,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9397188/v1/c46db437-1830-4020-b46e-5d02f4642cd9.pdf"},{"id":107909949,"identity":"491fab46-e115-488f-a31b-141c06982f8c","added_by":"auto","created_at":"2026-04-27 13:12:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":228769,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File 1: Questionnaire_2023 National Residents Health Literacy Monitoring Questionnaire.pdf\u003c/p\u003e","description":"","filename":"2023NationalResidentsHealthLiteracyMonitoringQuestionnaireEnglishTranslation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9397188/v1/cb1bdba734fe13daa3133bb1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eRelationship between health literacy and health status among rural residents in western China: a cross-sectional study\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eHealth is a fundamental human pursuit and a critical marker of national prosperity. The concept of health has evolved from a purely biomedical model to a biopsychosocial one, emphasizing the interplay of physical, mental, and social factors [1]. Health literacy, broadly defined as the cognitive and social skills that determine the motivation and ability of individuals to access, understand, and use information to promote and maintain good health, has emerged as a key modifiable determinant of health outcomes [2]. A robust body of evidence, primarily from Western countries, has established that limited health literacy is associated with poorer health knowledge, less effective self-management of chronic diseases, lower utilization of preventive services, higher rates of hospitalization, and increased mortality [3\u0026ndash;5]. This relationship underscores the critical role of health literacy in achieving health equity and improving population health.\u003c/p\u003e \u003cp\u003eThe measurement of health literacy is essential for monitoring population levels and evaluating interventions. Internationally, several validated tools exist, such as the Short Test of Functional Health Literacy in Adults (S-TOFHLA) and the Health Literacy Survey questionnaire (HLS) series [6, 7]. In China, efforts to develop and validate culturally appropriate instruments have been ongoing. Studies have validated the Chinese version of the Health Literacy Scale Short-Form (HLS-SF12) [8], the HLS19-Q12 instrument for the general adult population [9], and the Chinese Resident Health Literacy Scale [10]. Disease-specific scales, such as the Chinese Health Literacy Scale for Chronic Care (CHLCC) [11] and the Falls Health Literacy Scale (FHLS) [12], have also been developed. These tools have facilitated national monitoring, revealing that while China's average health literacy level has been steadily increasing, significant urban-rural and regional disparities persist, with western and rural areas consistently lagging behind [13].\u003c/p\u003e \u003cp\u003eChina's rapid socioeconomic development has been accompanied by a widening gap in health resources and outcomes between urban and rural areas. Rural residents, particularly in the western regions, face compounded challenges including lower educational attainment, economic disadvantage, limited access to quality healthcare, and poorer environmental sanitation [14, 15]. These social determinants of health create a context where low health literacy is both a cause and a consequence of health inequities. Studies have shown that income and education are primary contributors to health inequality in rural western China [16]. Furthermore, vulnerabilities such as limited access to health services and higher burdens of chronic diseases exacerbate the health risks for rural populations [17, 18]. The COVID-19 pandemic has further highlighted and potentially widened these rural-urban health disparities [19].\u003c/p\u003e \u003cp\u003eWithin this context, understanding the specific levels of health literacy, its sociodemographic correlates, and its relationship with health status in underserved rural western China is crucial for designing effective, targeted interventions. While national reports provide macro-level data, detailed community-based studies from specific western rural localities are limited. Such studies can uncover local nuances and provide actionable evidence for grassroots health promotion.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to: (1) assess the level of health literacy and its three core dimensions (knowledge, behavior, skills) among rural residents in Lingui District, a representative area in western China; (2) identify sociodemographic factors associated with health literacy; (3) analyze factors influencing health status; and (4) explore the relationship between health literacy and health status. The findings are intended to inform targeted health promotion strategies within the framework of China's Rural Revitalization and Healthy China initiatives, ultimately aiming to improve health equity in underdeveloped rural regions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Participants\u003c/h2\u003e \u003cp\u003eA community-based cross-sectional study was conducted in August-September 2023 in Lingui District, Guilin City, Guangxi Zhuang Autonomous Region, located in western China. The study population comprised non-institutionalized permanent residents aged 15\u0026ndash;69 years from six townships (Liangjiang, Sitang, Huixian, Liutang, Lingu, and Wantian Yao Ethnic Township). Permanent residence was defined as living locally for over six months in the past year.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling Method and Sample Size\u003c/h3\u003e\n\u003cp\u003eA multi-stage random sampling method was employed, following the national health literacy monitoring protocol [20].\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStage 1: All six townships in Lingui District (a municipal-level monitoring point) were included.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStage 2: Two villages/neighborhood committees were randomly selected from each township using Probability Proportional to Size (PPS) sampling.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStage 3: From each selected village, a minimum of 65 households were randomly selected from the household registration list.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStage 4: One eligible resident per selected household was randomly chosen for interview using the KISH grid method.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe minimum required sample size for a municipal monitoring point is 600. Accounting for potential non-response, 603 participants were successfully surveyed, yielding a response rate satisfactory for analysis.\u003c/p\u003e\n\u003ch3\u003eData Collection and Measures\u003c/h3\u003e\n\u003cp\u003eData were collected through face-to-face interviews using the standardized electronic \"2023 National Residents Health Literacy Monitoring Questionnaire\". The questionnaire included four sections:\u003c/p\u003e \u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eBasic Demographic Information: Age, gender, education, occupation, marital status, household size, and annual household income.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHealth Literacy Assessment: 56 items evaluating three dimensions: basic health knowledge and concepts (33 points), healthy lifestyles and behaviors (22 points), and basic health skills (18 points). Item types included true/false, single-choice, multiple-choice, and scenario-based questions. Scoring followed the national guideline: 1 point for correct true/false or single-choice, 2 points for correct multiple-choice, 0 points otherwise. The total possible score was 73. Adequate health literacy was defined as a total score\u0026thinsp;\u0026ge;\u0026thinsp;58 (\u0026ge;\u0026thinsp;80% of total). Adequate dimension-specific literacy was defined as scoring\u0026thinsp;\u0026ge;\u0026thinsp;80% of the points available in that dimension (Knowledge: \u0026ge;26; Behavior: \u0026ge;18; Skills: \u0026ge;14) [21, 22].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHealth Status Indicators:\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e \u003cp\u003e* Self-rated health (SRH): \"In the past year, how would you rate your health?\" Responses: Good (4), Fairly good (3), Average (2), Fairly poor (1), Poor (0).\u003c/p\u003e \u003cp\u003e* Two-week morbidity: \"Have you experienced any of the following symptoms in the last two weeks?\" (e.g., fever, cough). Categorized as \"No\" (1) or \"Yes\" (0).\u003c/p\u003e \u003cp\u003e* Chronic disease status: \"Have you been diagnosed with the following chronic diseases?\" (e.g., hypertension, diabetes). Categorized as \"No\" (1) or \"Yes\" (0).\u003c/p\u003e \u003cp\u003e4. Health-related Behaviors: Smoking status, physical activity, BMI.\u003c/p\u003e\n\u003ch3\u003eQuality Control\u003c/h3\u003e\n\u003cp\u003eStrict quality control procedures were implemented. Investigators underwent standardized training. Supervisors rechecked 10% of questionnaires on-site for consistency. Data were double-entered independently using EpiData software, and inconsistencies were resolved by referring to the original questionnaires.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData analysis was performed using SPSS 26.0. Descriptive statistics (frequencies, percentages, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) described sample characteristics and health literacy/status levels. Group comparisons used t-tests or ANOVA. Multivariate linear regression identified independent factors associated with the health literacy score. Binary logistic regression analyzed factors associated with two-week morbidity and chronic disease status. Ordinal logistic regression analyzed factors associated with self-rated health. Variance Inflation Factor (VIF)\u0026thinsp;\u0026lt;\u0026thinsp;5 indicated no multicollinearity. A two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003e The study was approved by the Ethics Review Committee of Guilin Medical University. Verbal informed consent was obtained from all participants before the interview, as approved by the ethics committee and in accordance with the national monitoring protocol which uses verbal consent. All data were anonymized for analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003e1. Sociodemographic Characteristics of Participants\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA total of 603 participants were included. The mean age was 51.19\u0026thinsp;\u0026plusmn;\u0026thinsp;10.56 years, with 51.7% aged 46\u0026ndash;60. Males constituted 56.6%. Most participants were married (82.4%), had an education level of primary school or below (47.1%) or middle school (50.7%), and were farmers (83.1%). Regarding household income, 41.3% earned\u0026thinsp;\u0026le;\u0026thinsp;10,000 CNY/year, and 50.2% earned 10,001\u0026ndash;50,000 CNY/year. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics of the participants (n\u0026thinsp;=\u0026thinsp;603)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \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\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.4\u003c/p\u003e \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\u003e15\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorker/Enterprise employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual Household Income (CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,001\u0026ndash;50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50,001-100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\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\u003e*Includes civil servant, teacher, healthcare worker, student, etc.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2. Levels of Health Literacy\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOnly 24.54% (148/603) of participants had adequate health literacy (total score\u0026thinsp;\u0026ge;\u0026thinsp;58). The adequate level was highest for basic health knowledge and concepts (31.51%), followed by basic skills (25.70%) and healthy lifestyles and behaviors (24.71%). The mean total health literacy score was 43.49\u0026thinsp;\u0026plusmn;\u0026thinsp;16.72. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\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\u003eHealth literacy levels among participants (n\u0026thinsp;=\u0026thinsp;603)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Literacy Dimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdequate, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScore, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Health Literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e148 (24.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e43.49\u0026thinsp;\u0026plusmn;\u0026thinsp;16.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic Knowledge \u0026amp; Concepts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e190 (31.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e20.36\u0026thinsp;\u0026plusmn;\u0026thinsp;7.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthy Lifestyles \u0026amp; Behaviors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e149 (24.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e13.20\u0026thinsp;\u0026plusmn;\u0026thinsp;5.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic Skills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155 (25.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.93\u0026thinsp;\u0026plusmn;\u0026thinsp;4.64\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\u003e3. Factors Associated with Health Literacy Score\u003c/p\u003e \u003cp\u003eUnivariate analysis showed significant differences in health literacy scores across age groups, education levels, occupations, and annual household income (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all), but not by gender, marital status, or family size. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eUnivariate analysis of factors associated with health literacy scores (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealth Literacy Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF/t\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;30 yrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.80\u0026thinsp;\u0026plusmn;\u0026thinsp;12.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;45 yrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e51.09\u0026thinsp;\u0026plusmn;\u0026thinsp;15.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u0026ndash;60 yrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e41.04\u0026thinsp;\u0026plusmn;\u0026thinsp;15.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;61 yrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e37.75\u0026thinsp;\u0026plusmn;\u0026thinsp;16.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.71\u0026thinsp;\u0026plusmn;\u0026thinsp;15.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e47.99\u0026thinsp;\u0026plusmn;\u0026thinsp;16.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e63.85\u0026thinsp;\u0026plusmn;\u0026thinsp;7.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual Household Income (CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.61\u0026thinsp;\u0026plusmn;\u0026thinsp;16.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,001\u0026ndash;50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e47.89\u0026thinsp;\u0026plusmn;\u0026thinsp;15.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50,001-100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e52.83\u0026thinsp;\u0026plusmn;\u0026thinsp;14.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e55.20\u0026thinsp;\u0026plusmn;\u0026thinsp;14.67\u003c/p\u003e \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\u003eMultivariate linear regression (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) confirmed that higher education level (β\u0026thinsp;=\u0026thinsp;6.993, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and higher annual household income (β\u0026thinsp;=\u0026thinsp;4.547, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independent positive predictors of a higher health literacy score, while older age (β=-4.410, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was a negative predictor. The model explained 19.2% of the variance in health literacy (R\u0026sup2;=0.192).\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\u003eMultivariate linear regression analysis of factors associated with health literacy score (n\u0026thinsp;=\u0026thinsp;603)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstandardized Coefficient B (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardized Coefficient Beta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI for B\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e38.080 (6.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.163 to 49.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male vs. Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.746 (1.291)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.789 to 4.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (increasing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.410 (0.938)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.252 to -2.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation Level (increasing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.993 (1.299)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.441 to 9.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual Household Income (increasing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.547 (1.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.517 to 6.577\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 \u003cp\u003e0.907 (0.811)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.686 to 2.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.761 (0.671)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.078 to 0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.760 (0.870)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.951 to 2.471\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\u003eModel Summary: R\u0026sup2; = 0.192, Adjusted R\u0026sup2; = 0.182, F(7,595)\u0026thinsp;=\u0026thinsp;20.167, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003e4. Factors Associated with Health Status\u003c/p\u003e\u003cp\u003e4.1 Self-rated health (SRH): 34.16% reported \"Good\" SRH. In ordinal logistic regression (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), higher health literacy score (OR\u0026thinsp;=\u0026thinsp;1.016, 95%CI: 1.006\u0026ndash;1.026), higher education level (OR\u0026thinsp;=\u0026thinsp;1.643, 95%CI: 1.192\u0026ndash;2.266), and higher annual household income (OR\u0026thinsp;=\u0026thinsp;1.426, 95%CI: 1.106\u0026ndash;1.839) were significantly associated with better SRH.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOrdinal logistic regression analysis of factors associated with self-rated health (n\u0026thinsp;=\u0026thinsp;603)\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Literacy Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.016 (0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.016 (1.006 to 1.026)\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=\"c1\"\u003e \u003cp\u003eEducation Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.497 (0.164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.643 (1.192 to 2.266)\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=\"c1\"\u003e \u003cp\u003eAnnual Household Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.355 (0.130)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.426 (1.106 to 1.839)\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=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.075 (0.159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.078 (0.790 to 1.471)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.122 (0.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.130 (0.898 to 1.422)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.299\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 \u003cp\u003e-0.061 (0.099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.941 (0.775 to 1.143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.085 (0.085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.919 (0.778 to 1.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.007 (0.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.993 (0.806 to 1.224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.951\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\u003eMcFadden's Pseudo R\u0026sup2; = 0.032.\u003c/p\u003e \u003cp\u003e4.2 Two-week Morbidity: 15.59% reported illness in the past two weeks. Binary logistic regression indicated that having a chronic disease was the only significant predictor, increasing the odds of two-week morbidity (OR\u0026thinsp;=\u0026thinsp;1.802, 95%CI: 1.099\u0026ndash;2.954, p\u0026thinsp;=\u0026thinsp;0.020).\u003c/p\u003e \u003cp\u003e4.3 Chronic Disease Status: 79.27% reported having at least one chronic disease. Binary logistic regression (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) showed that a higher health literacy score (OR\u0026thinsp;=\u0026thinsp;1.014, 95%CI: 1.002\u0026ndash;1.027) and having a non-farmer occupation (OR\u0026thinsp;=\u0026thinsp;1.348, 95%CI: 1.035\u0026ndash;1.755) were associated with higher odds of not having a chronic disease (coded as 1), while older age (OR\u0026thinsp;=\u0026thinsp;0.482, 95%CI: 0.354\u0026ndash;0.656) was associated with lower odds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinary logistic regression analysis of factors associated with the absence of chronic diseases (n\u0026thinsp;=\u0026thinsp;603)\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Literacy Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.014 (0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.014 (1.002 to 1.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.730 (0.157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.482 (0.354 to 0.656)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eOccupation (Non-farmer vs. Farmer)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.299 (0.134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.348 (1.035 to 1.755)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study revealed a low level of health literacy (24.54%) among rural residents in Lingui District, western China, which is lower than the 2023 national average for rural residents (26.23%) and much lower than for urban residents (33.25%) [13]. The knowledge dimension scored highest, aligning with national trends, suggesting that translating knowledge into sustained healthy behaviors and practical skills remains a challenge [23].\u003c/p\u003e \u003cp\u003eConsistent with previous research [24, 25], we found that higher education and income were strong positive enablers of health literacy, likely because they facilitate access to, comprehension of, and resources to act upon health information. The negative association with age highlights older adults as a vulnerable group requiring tailored, accessible health education interventions.\u003c/p\u003e \u003cp\u003eImportantly, health literacy demonstrated a significant, independent positive association with better self-rated health, even after adjusting for socioeconomic factors. This aligns with the conceptual model where health literacy empowers individuals to make informed decisions, engage in preventive behaviors, and navigate the healthcare system more effectively, leading to better perceived and objective health outcomes [3, 5]. The finding that chronic disease status was the primary driver of recent morbidity underscores the disease burden in this aging rural population.\u003c/p\u003e \u003cp\u003eThe strong positive effects of education and income on both health literacy and health status illustrate a syndemic of social disadvantage. In western rural China, lower educational opportunities and economic constraints create a barrier to health literacy acquisition, which in turn perpetuates health inequities. This calls for integrated policies that combine poverty alleviation and educational investment with health promotion, as envisioned in China's Rural Revitalization Strategy.\u003c/p\u003e \u003cp\u003eStrengths and Limitations\u003c/p\u003e \u003cp\u003eStrengths include a rigorous, nationally standardized sampling and assessment methodology, and a focus on an understudied western rural population. Limitations should be noted. The cross-sectional design precludes causal inference. Self-reported data, especially for health status, are subject to bias. The single geographic location limits generalizability to all western regions. The modest explanatory power of some regression models (e.g., for SRH) indicates other unmeasured factors (e.g., social support, environmental factors) are at play.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eHealth literacy among rural residents in this western Chinese region is suboptimal and is shaped by age, education, and income. Health literacy itself, along with education and income, contributes to better health status. Public health strategies must prioritize health literacy promotion, employing context-specific, simple, and repetitive communication channels suitable for rural areas. Interventions should particularly target older adults and those with lower socioeconomic status. Future longitudinal or intervention studies are needed to establish causality and test the effectiveness of tailored health literacy programs in similar settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e* SRH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSelf-Rated Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e* OR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e* CI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of Nanxishan Hospital of Guangxi Zhuang Autonomous Region. The need for informed consent was waived due to the retrospective analysis of anonymized data. This research was conducted as part of the following funded projects: Guangxi T-wave Change Population\u0026apos;s Physical Status and Physical Function Assessment (Grant No.Z-C20250194).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable for this secondary analysis. The original informed consent was obtained by the Health and Retirement Study (HRS). Please refer to the supplementary file (Additional File 1: \u003cem\u003e2023 National Residents Health Literacy Monitoring Questionnaire.pdf\u003c/em\u003e) for details regarding consent documentation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuangxi T-wave Change Population\u0026apos;s Physical Status and Physical Function Assessment(Grant No.Z-C20250194).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaihui Jiang: Conceptualization, Investigation, Formal Analysis, Writing \u0026ndash; Original Draft. Haijiao Zhang: Supervision, Methodology, Writing \u0026ndash; Review \u0026amp; Editing, Project Administration. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the staff from the Lingui District Center for Disease Control and Prevention and all township hospitals for their assistance in data collection. We are also grateful to all the participants for their time and cooperation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. 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Chinese Journal of Health Education. 2009;25(5):323-326. \u0026quot;https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7iLik5jEcCI09uHa3oBxtwoGwBq5wUvn5pS5t9JNwVk6kPx7_1Fc9c8hW5_7vZ4qj\u0026amp;uniplatform=NZKPT\u0026quot; (https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7iLik5jEcCI09uHa3oBxtwoGwBq5wUvn5pS5t9JNwVk6kPx7_1Fc9c8hW5_7vZ4qj\u0026amp;uniplatform=NZKPT). Accessed 10 Apr 2026.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Health Literacy, Health Status, Rural Population, Western China, Cross-Sectional Studies, Health Disparities","lastPublishedDoi":"10.21203/rs.3.rs-9397188/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9397188/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHealth literacy is a crucial determinant of health outcomes. This study aimed to investigate the levels of health literacy and health status, analyse their influencing factors, and explore their relationship among rural residents in western China, providing evidence for public health policy improvement.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted from August to September 2023 in Lingui District, Guilin City. A total of 603 rural residents aged 15\u0026ndash;69 were selected via multi-stage random sampling. Data were collected using the standardized \"2023 National Residents Health Literacy Monitoring Questionnaire\". Health literacy was assessed across three dimensions: knowledge, behavior, and skills, with a score\u0026thinsp;\u0026ge;\u0026thinsp;58/73 indicating adequate literacy. Health status was evaluated via self-rated health, two-week morbidity, and chronic disease presence. Data were analyzed using SPSS 26.0 for descriptive statistics, t-tests, ANOVA, and multivariate regression (linear, binary, and ordinal logistic).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe proportion of residents with adequate health literacy was 24.54% (148/603). The levels for basic health knowledge, healthy lifestyles/behaviors, and basic skills were 31.51%, 24.71%, and 25.70%, respectively. Linear regression identified education level (β\u0026thinsp;=\u0026thinsp;6.993, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and annual household income (β\u0026thinsp;=\u0026thinsp;4.547, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as significant positive predictors of health literacy score, while age was a negative predictor (β=-4.410, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Ordinal logistic regression showed that health literacy (OR\u0026thinsp;=\u0026thinsp;1.016, 95%CI: 1.006\u0026ndash;1.026), education level (OR\u0026thinsp;=\u0026thinsp;1.643, 95%CI: 1.192\u0026ndash;2.266), and annual household income (OR\u0026thinsp;=\u0026thinsp;1.426, 95%CI: 1.106\u0026ndash;1.839) were significantly associated with better self-rated health. Chronic disease status positively predicted two-week morbidity (OR\u0026thinsp;=\u0026thinsp;1.802, 95%CI: 1.099\u0026ndash;2.954). Health literacy (OR\u0026thinsp;=\u0026thinsp;1.014, 95%CI: 1.002\u0026ndash;1.027) and occupation (OR\u0026thinsp;=\u0026thinsp;1.348, 95%CI: 1.035\u0026ndash;1.755) were positively associated, while age (OR\u0026thinsp;=\u0026thinsp;0.482, 95%CI: 0.354\u0026ndash;0.656) was negatively associated, with the absence of chronic diseases.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHealth literacy among rural residents in this western region is relatively low and is influenced by age, education, and income. Health literacy, along with education and income, also positively affects health status. Targeted health education interventions focusing on improving health literacy, especially in older, less-educated, and lower-income groups, are needed to narrow urban-rural health disparities.\u003c/p\u003e","manuscriptTitle":"Relationship between health literacy and health status among rural residents in western China: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-27 13:11:18","doi":"10.21203/rs.3.rs-9397188/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-19T02:15:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-15T07:21:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T23:36:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-13T23:35:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-04-12T23:39:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a76af649-1a60-45de-84f6-904fcb1c8471","owner":[],"postedDate":"April 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T13:11:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-27 13:11:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9397188","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9397188","identity":"rs-9397188","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}