Chun Chen

Background: Health literacy (HL) is a major factor associated with population health. However, no visualized predictive model has been developed to identify populations with low HL in a cost-effective manner. Objective: This study aims to identify key determinants of HL and develop a predictive nomogram model for population-level HL screening. Methods: This study analyzed cross-sectional data from 56,340 residents (aged 15‐69 y) in Zhejiang Province (2023) And used the Chinese Health Literacy Scale to measure HL. We used 3 complementary analytical approaches: logistic regression (LR) analysis to identify HL-associated factors, machine learning models (gradient boosting decision tree [GBDT], extreme gradient boosting [XGBoost], LR, and random forest [RF]) with Shapley additive explanations (SHAP) to quantify predictor importance, and nomogram development using 2023 data with external validation on 2024 data. In the nomogram, model 1 used all the predictive variables, while model 2 used the 5 core predictive variables determined through SHAP analysis. Model performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis. Results: The nomogram models designed for predicting HL demonstrated robust stability, with good results in external validation. The area under the curve (AUC) values for both the test and validation sets of the machine learning models exceeded 0.75. At the 0.5 threshold, the sensitivities of the GBDT, XGBoost, LR, and RF test sets and validation sets were all >60%. The specificity of the test sets and validation sets of all 4 machine learning models was >75%. The positive predictive value (PPV) and negative predictive value (NPV) were both >60%. The AUC value of nomogram model 1 developed using the data from 2023 was 0.821, and the AUC value of the externally validated nomogram model 1 was 0.814. Similarly, nomogram model 2 had AUC values of 0.819 in the internal validation and 0.812 in the external validation. The PPV and NPV of both the internal validation model 1 and model 2 were all >60%. The integration of GBDT, XGBoost, LR, and RF models, along with the SHAP method, highlighted education level, age, annual household income, smoking, and occupation as the 5 most critical determinants. Conclusions: By using the GBDT, XGBoost, LR, RF, and nomogram models, a predictive model with substantial efficacy was effectively developed and validated for forecasting the HL levels of the population. This comprehensive model not only exhibits a commendable predictive capacity but also identifies the pivotal factors influencing HL. Such insights are crucial for shaping health policies and fostering healthier behaviors across the population.

Aim: To identify latent profiles of unmet home‐ and community‐based service needs among Chinese older adults and to develop an interpretable machine learning model to support person‐centered care. Design: Cross‐sectional, multisite study. Methods: We recruited 487 community‐dwelling adults aged 60 years and older from multiple provinces. Nineteen home‐ and community‐based service items across health services, social services and family caregiver support domains were assessed. A descriptive Jaccard network summarized co‐occurrence across services, latent class analysis identified subgroups with distinct patterns of unmet need, and TabNet classified participants into the combined higher‐unmet‐need group using eight selected demographic and health variables. Results: Pairwise co‐occurrence among unmet service needs was widespread. Three latent class profiles were identified: Generally Met (69.6%), Healthcare‐focused Unmet Needs (24.9%) and Extensive Unmet Needs (5.5%). TabNet showed moderate discrimination in the internal cross‐validation analysis (AUC 0.767, 95% CI 0.720–0.808). Conclusions: Latent profiles of unmet service needs were distinct. The interpretable eight‐variable TabNet model may support first‐stage community nursing screening by prioritizing older adults for subsequent professional assessment. External validation is required before routine implementation. Implications for the Profession and/or Patient Care: The model may help nursing teams prioritize older adults for follow‐up assessment; referral and care coordination should be determined through professional assessment and shared decision‐making. Impact: The study addressed unmet service needs among older adults and may inform resource allocation and person‐centered care planning in community‐based care. Reporting Method: TRIPOD+AI statement. Patient or Public Contribution: No patient or public contribution.

Health literacy has been increasingly emphasized as a critical factor shaping healthcare behaviors and costs. However, empirical evidence on its association with out-of-pocket (OOP) health expenditure remains complex, and little is known about how this relationship varies by chronic disease status. This study aimed to identify distinct health literacy profiles within a large Chinese population, examine their associations with OOP expenditure, and explore how these associations vary by chronic disease status. A cross-sectional study was conducted using data from a large-scale health literacy survey of 56,863 residents aged 15-69 in Zhejiang, China. Latent profile analysis (LPA) identified health literacy subgroups using the validated Chinese Health Literacy Scale. A Two-Part Model was employed to decompose expenditure into the probability of incurring costs and expenditure intensity. Stratified analyses were conducted to examine variation across chronic disease status. LPA identified three health literacy profiles with weighted proportions of: Relatively High (61.45%), Moderate (26.54%), and Low (12.01%). The analysis revealed an inverted U-shaped trajectory. The Moderate group exhibited significantly higher total OOP expenditure than the Low group (AME = 554.71 CNY, P < 0.05), driven primarily by elevated expenditure intensity. Conversely, the Relatively High group showed no significant difference (P > 0.05), indicating expenditure levels comparable to the baseline. Interaction analysis suggested potential heterogeneity by chronic disease status (P for interaction = 0.053, in Part 2), which prompted exploratory stratified analyses. Among those with one chronic disease, the Relatively High group showed higher probability of incurring expenditure (Part 1, P < 0.01) but significantly lower expenditure intensity (Part 2, P < 0.05). However, this association was not statistically significant among those with multimorbidity. The findings indicate that health literacy is associated with expenditure in a non-linear pattern. While higher literacy maintains expenditure at baseline levels, exploratory analyses suggest this protective effect may be attenuated under high disease burden. To enhance both equity and efficiency, policy strategies should focus on cultivating decision-making capabilities for appropriate healthcare utilization among general populations and strengthening financial protection for those with complex needs.

Background: Health literacy plays an important role in disease prevention and control. The aim of this study is to explore the health literacy patterns and associated factors among residents in Zhejiang Province. Methods: This study included 56,863 residents aged 15–69 years from the 2024 Zhejiang Province Health Literacy Survey. Latent Profile Analysis (LPA) was used to investigate health literacy patterns, and multinomial logistic regression analysis was employed to identify associated factors. Dominance analysis was performed to compare the relative contribution of the main variables associated with health literacy. Results: The analysis identified three distinct health literacy profiles: low literacy (15.13%), moderate literacy (32.24%), and relatively high literacy (52.63%). The low literacy group was characterized by an older demographic (with an average age of 58.71 years), lower educational attainment (20.72% had no formal education), a higher proportion of farmers (52.93%), and a significant share of low-income individuals (40.98%). Multinomial logistic regression and dominance analysis revealed that education level, age, and occupation were the most important associated factors of health literacy. Conclusion: The study findings highlighted the heterogeneity in health literacy among various population groups and emphasized the need for targeted interventions. This study provides empirical evidence to inform precision health promotion strategies in developed regions of China.

Background: Older adults’ psychological well‐being is recognized as a public health issue, and many factors may influence it. However, the longitudinal association between sleep quality and psychological well‐being among older adults remains insufficiently understood. Therefore, this study aimed to examine the longitudinal association between sleep quality and psychological well‐being among Chinese older adults. Methods: This study involved 2371 older adults aged 65 years and above who participated in all four waves (2008, 2011, 2014, and 2018) of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). A latent growth model (LGM) was used to examine the longitudinal association between sleep quality and psychological well‐being among older adults. Results: An unconditional quadratic growth curve model showed the best model fit. After controlling for time‐invariant and time‐varying covariates, better sleep quality was consistently associated with better psychological well‐being among older adults (2008: β = 0.159, p < 0.001; 2011: β = 0.193, p < 0.001; 2014: β = 0.207, p < 0.001; 2018: β = 0.266, p < 0.001). Conclusion: This study found that better sleep quality was longitudinally associated with better psychological well‐being among Chinese older adults over time. The results suggest that enhanced sleep quality among older adults in China is strongly associated with improved psychological well‐being.

With escalating population aging, the significance of ensuring equitable access to elderly care services is becoming increasingly prominent. This study exp

Background: In large tertiary hospitals across China, outpatient patients often encounter the “three longs and one short” (long registration time, long waiting time, long time for medicine collection, and short time for medical treatment) phenomenon. This scenario contributes to suboptimal patient experiences and declining satisfaction with health care services. To address the issue of long waiting times, many hospitals in China have implemented a range of measures. However, these measures have only improved individual aspects of the patient experience, with limited overall impact. Currently, there is a lack of comprehensive, intelligent reform for the entire patient service process in the medical system. Therefore, there is an urgent need to integrate and optimize the entire patient service process, providing real-time intelligent guidance within hospitals. This would help reduce waiting times for patients and enhance their satisfaction. Objective: This study aims to introduce a patient-centered intelligent guidance system and report on the impact of its implementation on outpatient waiting times and patient satisfaction in hospitals. Methods: The intelligent guidance system was designed with a patient-centered approach, leveraging internet and big data technologies. The system seamlessly connects various steps of the outpatient medical process, facilitating functions including automated check-in and comprehensive intelligent guidance for patients’ medical visits, thus enhancing the efficiency and quality of health care delivery. This system has been implemented in a tertiary hospital in China. To assess the system’s effectiveness, we compared outpatient visit data, waiting time data, and patient satisfaction levels between the preimplementation and postimplementation periods from 2019 to 2022. We analyzed the changes in patients’ average waiting times and satisfaction levels after the system was implemented. Results: One year after the introduction of the intelligent guidance system, the number of outpatient visits increased from 5,067,958 to 5,456,151. The waiting time for outpatient patients was significantly reduced. The waiting time for consultation decreased by 2.84 minutes (mean 41.14, SD 2.31 min vs mean 38.30, SD 1.89 min; P<.001). The waiting time for examination decreased by 3.35 minutes (mean 47.83, SD 1.10 min vs mean 44.48, SD 1.67 min; P<.001). Consultation time increased to 3.43 minutes (mean 2.85, SD 0.03 min vs mean 3.43, SD 0.26 min; P<.001). After the system was launched, patient satisfaction increased from 89.99% (SD 2.78%) in 2021 to 92.72% (SD 0.18%) in 2022 (P=.005). Conclusions: The patient-centered intelligent guidance system reported in this study proved beneficial for tertiary medical institutions striving to alleviate the outpatient burden caused by prolonged waiting times. Through continuous transformation and upgrading of the outpatient service process centered on patients, the efficiency of outpatient services and patient satisfaction improved. Therefore, the patient-centered principle method and process integration concepts for the system can be further promoted and implemented.

Home and community-based physical activity (HCBPA) has been extensively utilized among older adults. Nevertheless, the varying types of HCBPA, including different duration, intensity, and frequency, have sparked controversy regarding their impact on the quality of life in older adults. This study aims to explore the effects of HCBPA on QoL in older adults. We conducted a systematic review and retrieved studies published from January 2000 to April 2023 from multiple databases (PubMed, Embase, Cochrane, and the Web of Science Library). Seventeen articles met the inclusion criteria for this study. Long-term HCBPA interventions may have a more pronounced positive impact on older adults’ quality of life than short-term ones, with the intervention’s intensity and frequency playing a key role in its effectiveness. The results of the meta-analyses showed significant differences in PCS but not in MCS, both with low certainty of evidence. Policymakers should prioritize the importance of promoting HCBPA interventions with appropriate duration, intensity, and frequency to create a more age-inclusive society.

Home- and community-based services (HCBS) are optimal ways to deal with disability problems among older adults. This study aims to analyze urban–rural disparities in the relationship between HCBS utilization and levels of disability among Chinese older adults with disabilities, so as to meet the long-term care needs of them. In applying the Andersen Behavioral Model, bivariate analysis and multivariate regression models were employed using data from 843 older adults with disabilities from the 2018 China Longitudinal Aging Social Survey (CLASS). After adjusting covariates, disability levels among Chinese older adults with disabilities were significantly correlated with HCBS utilization in urban areas but not in rural areas. The urban–rural disparities may be due to the low utilization of HCBS in rural areas (only 11.2%) among older adults with disabilities compared with their urban counterparts (22.7%).