To understand the prevalence status and influencing factors of sleep rhythm problems among community residents aged ≥15 in 7 provinces (municipality) of China, thus providing a scientific basis for formulating intervention measures for sleep rhythm problems.

From March to October 2024, a multi-stage stratified cluster random sampling method was used to survey 50 400 community residents aged ≥15 in seven provinces (municipality), including Hebei, Liaoning, Jiangsu, Henan, Guangdong, Chongqing, and Shaanxi. The prevalence status and influencing factors of delayed sleep phase problem (DSPP) and advanced sleep phase problem (ASPP) were analyzed.

Among the 46 193 community residents included in the analysis, 172 and 8 354 were detected with DSPP and ASPP, respectively, with weighted detection rates of 1.07% and 11.90%. The results of the multivariate unconditional logistic regression analysis showed that students or had current smoking, drinking, frequent use of electronic products before bedtime, and frequently eating late-night snacks were associated with a higher risk of DSPP, while those aged ≥40 had a lower risk of DSPP. Age ≥ 40, living in rural areas, drinking, and having chronic diseases were associated with a higher risk of ASPP, while the educational background of junior high school or above, office workers, students, other occupations, engaging in physical activity 3 to 4 times a week, and frequently eating late-night snacks were associated with a lower risk of ASPP.

The detection rate of sleep rhythm problems among community residents aged ≥15 in 7 provinces (municipality) of China is relatively high, with age, educational background, occupation, drinking habits, and whether frequently eating late-night snacks being common influencing factors for the detection of DSPP and ASPP.

To understand the associations between different smoking behavior characteristics and sleep quality among Chinese male residents aged 15 years and above, thus providing scientific evidence for developing relevant intervention measures.

From March to October 2024, a multi-stage stratified cluster random sampling method was used to select survey subjects from 70 counties/districts in 7 provinces/autonomous regions/municipalities across China. Face-to-face interviews were conducted with electronic questionnaires, and 22 333 individuals were ultimately included in the analysis. The Rao-Scott χ² test based on complex sampling design was conducted to compare demographic characteristics of male residents of different smoking status, and univariate analysis was performed to examine the associations between different smoking behavior characteristics and sleep quality. Multivariate logistic regression was adopted to analyze the associations of different smoking status and smoking behavior characteristics (smoking index, daily cigarette consumption, and smoking duration) with sleep quality among males.

Among the 22 333 male residents aged 15 and above in China, 44.92% (8 561), 42.84% (9 905), and 12.24% (3 867) were non-smokers, current smokers, and former smokers, respectively. The prevalence of poor sleep quality was 20.42%. Univariate analysis showed that the prevalence of poor sleep quality differed among male residents of different smoking status (χ² = 83.893, P < 0.000 1). In both current smokers and former smokers, there were statistically significant differences in the associations of daily cigarette consumption (χ²_{current smoker} = 26.821; χ²_{former smoker} = 10.958), smoking duration (χ²_{current smoker} = 39.380; χ²_{former smoker} = 9.078), and smoking index (χ²_{current smoker} = 26.177; χ²_{former smoker} = 9.389) with sleep quality (P < 0.05). Cochran-Armitage trend tests showed that as daily cigarette consumption (χ²_{current smoker} = –6.741, χ²_{former smoker} = –3.955), smoking duration (χ²_{current smoker} = –13.775, χ²_{former smoker} = –5.536), and smoking index (χ²_{current smoker} = –9.294, χ²_{former smoker} = –5.081) increased, the prevalence of poor sleep quality showed a linear increasing trend (P < 0.000 1). Multivariate logistic regression analysis, adjusted for confounders including age, gender household registration, geographic region, educational background, occupation, annual household income, alcohol consumption, exercise frequency, BMI, and chronic diseases, revealed that compared to non-smoking, both current smoking (OR = 1.312, 95%CI: 1.107–1.556) and former smoking (OR = 1.475, 95%CI: 1.173–1.854) were associated with a higher risk of poor sleep quality. Compared to non-smoking, the smoking index of <10 pack-years (OR = 1.293, 95%CI: 1.047–1.598), 10 to <20 pack-years (OR = 1.472, 95%CI: 1.177–1.841), 20 to <30 pack-years (OR = 1.470, 95%CI: 1.085–1.992), and ≥30 pack-years (OR = 1.283, 95%CI: 1.086–1.514) were associated with a higher risk of poor sleep quality. Compared to non-smoking, daily cigarette consumption of ≤10 cigarettes (OR = 1.197, 95%CI: 1.001–1.431), 11–20 cigarettes (OR = 1.421, 95%CI: 1.169-1.727), 21–30 cigarettes (OR = 1.608, 95%CI: 1.166–2.216), and >30 cigarettes (OR = 1.681, 95%CI: 1.208-2.338) were associated with a higher risk of poor sleep quality. Compared to non-smoking, smoking duration of 10 to <31 years (OR = 1.339, 95%CI: 1.109–1.618), 31 to <40 years (OR = 1.296, 95%CI: 1.093–1.536), and ≥40 years (OR = 1.327, 95%CI: 1.116–1.578) were associated with a higher risk of poor sleep quality.

Smoking associates with high risk of poor sleep quality, and the detrimental effect of smoking on sleep quality exhibits a dose-response relationship, suggesting that the cumulative effect of long-term nicotine exposure may be a key pathway for disrupting sleep quality.

To examine the association between secondhand smoke (SHS) exposure and sleep quality among female non-current smokers aged ≥15 years in China, thereby providing evidence for formulating tobacco control strategies to improve sleep health.

A cross-sectional survey was conducted between March and October 2024. Through a multistage stratified cluster random sampling design, a total of 21 870 female non-current smokers aged ≥15 years were recruited from seven provinces (autonomous regions, municipalities) in China. Data on SHS exposure and sleep-related variables were collected through face-to-face electronic questionnaires administered by trained investigators. Complex sampling weights were adopted to describe the demographic characteristics and SHS exposure of participants. Rao-Scott chi-square tests were conducted to assess subgroup differences, and multivariate logistic regression models were employed to evaluate the association between SHS exposure and sleep quality after adjusting for potential confounders.

Among the 21 870 participants, 12 082 (45.32%) resided in urban areas and 9 788 (54.68%) in rural areas. The overall prevalence of SHS exposure was 44.01%, with a higher exposure rate at home (22.79%) than in the workplace (15.90%). Univariate analysis revealed that females exposed to SHS had higher prevalence of poor sleep quality than those unexposed (32.54% vs. 23.59%, χ² = 26.97, P < 0.01). Specifically, SHS exposure at home was associated with higher prevalence of poor sleep quality than non-exposure (36.10% vs. 25.00%, P < 0.01). A similar trend was observed in the workplace (29.50% vs. 18.00%, P < 0.01). After adjusting for confounders, multivariate logistic regression showed that SHS exposure was a significant risk factor for poor sleep quality at the overall level (OR = 1.87, 95%CI: 1.56–2.23), at home (OR = 1.76, 95%CI: 1.47–2.12), and in the workplace (OR = 1.62, 95%CI: 1.19–2.20).

SHS exposure was significantly associated with poor sleep quality among female non-current smokers aged ≥15 years in China, indicating that SHS exposure may be a modifiable risk factor for poor sleep quality in females.

To analyze typical cases of AI-empowered proactive health management for chronic disease patients, providing mature references for advancing the application of AI in healthcare institutions and chronic disease patient management.

From October 20 to November 1, 2024, we searched platforms including the China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP, Chinese Medical Journal Full-text Database, Health News, and the official websites of the National Health Commission and the Chinese Center for Disease Control and Prevention for the typical cases of AI-empowered proactive health management for chronic disease patients. Using the text analysis method based on key dimensions of proactive health concepts, we established a thematic framework through hierarchical coding to distill the practical outcomes of these cases.

A total of 43 AI-empowered proactive health management cases for chronic disease patients were included as primary textual sources for framework construction. Three thematic frameworks—individual health responsibility, interpersonal relationship support, and disease management—were constructed. Individual health responsibility involved health monitoring, health assessment, and health education. Interpersonal relationship support included social support and psychological support. Disease management encompassed medication management, diet management, and personalized intervention. Through further screening, 10 representative cases were finalized, and an integrated pathway for AI-empowered proactive health management was distilled. That is, by integrating big data with human-machine interaction, a bidirectional proactive health management cycle was established between healthcare institutions and chronic disease patients, encompassing health monitoring, assessment/diagnosis, intervention reminders, and medication management.

AI enables integrated health management by bidirectionally empowering both healthcare institutions and chronic disease patients. This approach not only enhances patient engagement but also alleviates the workload of primary care providers.

To compare the accuracy of multiple large language models (LLM) and manual judgment in predicting infectious disease trends.

Using monthly report data of 17 infectious diseases in Guangzhou from January to December 2024, we used five LLM and expert judgments from public health physicians for prediction and compared the predicted results with actual trends.

A total of 99 predictions were conducted for 17 infectious diseases. The accuracy rate of prediction by public health physicians was 84.85%, and that of the AI-integrated prediction was 73.74%. The prediction accuracy of AI products followed the trend of Kimi (70.71%), Doubao (68.69%), Lingxi (68.69%, parallel), ERNIE Bot (65.66%), and Deep Seek (63.64%). On the basis of 99 referred data items related to dengue fever, norovirus infection, hand-foot-mouth disease, pertussis, and influenza, in the identification of epidemic inflection points, public health physicians showed the accuracy of 50.0%, followed by Kimi (30.0%), AI integration (20.0%), and Doubao (20.0%, parallel); public health physicians showed the F1 score of 0.56, followed by Kimi (0.40), Doubao (0.38), and AI integration (0.27).

Currently, public health physicians have higher accuracy in predicting infectious disease trends than AI, especially in identifying the impacts of complex social factors.

To develop an intelligent question-answering model based on retrieval-augmented generation (RAG) to assist public health professionals in efficiently and accurately extracting key information from technical documents such as regulations, guidelines, manuals, and literature, thereby improving knowledge acquisition efficiency and standardized application capabilities.

On the basis of RAG, this study integrates an automated text preprocessing mechanism and employs the LangChain framework to construct a context-aware unified retrieval-augmented question-answering model powered by large language models (CURA-LLM). This model combines a threshold-managed information retrieval module with a response generation module supported by historical dialogue records.

CURA-LLM demonstrated outstanding performance on three representative public health datasets related to operational guidelines, testing procedures, and legal regulations, with the F1 scores of 0.941, 0.891, and 0.947, and the cosine similarities of 0.968, 0.929, and 0.963, respectively. Moreover, CURA-LLM outperformed three existing domain-specific question-answering models in terms of retrieval relevance and answer accuracy.

CURA-LLM provides effective support for the intelligent interpretation and rapid extraction of knowledge from public health technical documents. It can assist personnel in quickly responding and standardizing execution in scenarios such as emergency responses, routine management, and professional training, offering strong potential for practical application.

To investigate the associations of serum triglyceride (TG) levels with various metabolic abnormalities among adult residents in 5 cities of western China, thereby providing evidence for the prevention and management of metabolic syndrome in this region.

A multistage stratified cluster sampling method was used to recruit 990 adult permanent residents ≥18 years old from Chifeng, Hohhot, and Ordos in Inner Mongolia Autonomous Region, Lhasa in Xizang Autonomous Region, and Dali in Yunnan province between March and October 2023. Data were collected through questionnaires, physical examinations, and laboratory tests. Multivariate unconditional logistic regression models were used to analyze the associations between serum TG levels and various metabolic abnormalities, including overweight and obesity, central obesity, hypertension, elevated low-density lipoprotein cholesterol (LDL-C), decreased high-density lipoprotein cholesterol (HDL-C), elevated total cholesterol (TC), insulin resistance, diabetes, and hyperuricemia.

A total of 939 adults were included in the final analysis. Among the surveyed population, 611 (65.0%), 163 (17.4%), and 165 (17.6%) had normal TG levels, borderline high TG, and elevated TG, respectively. The number (detection rates) of respondents with overweight and obesity, central obesity, hypertension, elevated LDL-C, decreased HDL-C, elevated TC, insulin resistance, diabetes, and hyperuricemia was 536 (57.1%), 432 (46.0%), 389 (41.4%), 94 (10.0%), 82 (8.7%), 237 (25.2%), 310 (33.0%), 227(24.2%), and 310 (33.0%), respectively. After adjusting for sex, age group, region, educational background, per capita monthly household income, smoking status, drinking status, physical activity level, and daily intake of cereals, tubers, vegetables, fruits, animal-based foods, dairy products, soy and nuts, salt, and oil, multivariate unconditional logistic regression analyses showed that adults with borderline high TG had higher detection rates of overweight and obesity (OR = 2.70, 95%CI: 1.80–4.04), central obesity (OR = 1.67, 95%CI: 1.13–2.48), hypertension (OR = 1.71, 95%CI: 1.15–2.54), elevated LDL-C (OR = 3.65, 95%CI: 2.03–6.59), elevated TC (OR = 3.42, 95%CI: 2.22–5.26), insulin resistance (OR = 2.65, 95%CI: 1.79–3.90), and hyperuricemia (OR = 2.31, 95%CI: 1.55–3.44) than the adults with normal serum TG levels. Adults with elevated TG had higher detection rates of overweight and obesity (OR = 4.68, 95%CI: 2.96–7.40), central obesity (OR = 4.25, 95%CI: 2.75–6.58), hypertension (OR = 1.83, 95%CI: 1.22–2.74), elevated LDL-C (OR = 4.66, 95%CI: 2.55–8.50), reduced HDL-C (OR = 5.85, 95%CI: 2.59–13.23), elevated TC (OR = 6.24, 95%CI: 3.98–9.79), insulin resistance (OR = 5.22, 95%CI: 3.47–7.83), diabetes (OR = 2.30, 95%CI: 1.50–3.52), and hyperuricemia (OR = 6.41, 95%CI: 4.22–9.73).

Elevated serum TG among adults in 5 cities of western China are positively associated with multiple metabolic abnormalities, including overweight and obesity, central obesity, hypertension, elevated LDL-C, decreased HDL-C, elevated TC, insulin resistance, diabetes, and hyperuricemia.

To construct and evaluate an index system for assessing the medical care-prevention integration capabilities of clinical medical professionals, providing a reference for advancing the national strategy of medical care-prevention integration.

A literature review and a systematic review were conducted to preliminarily establish the framework for the index system. Twenty experts in relevant fields were selected for Delphi expert consultation, and the analytic hierarchy process (AHP) was employed to calculate the weights for each level of indexes. The established evaluation index system was then adopted to assess the medical care-prevention integration capabilities of 360 randomly selected undergraduates majoring in clinical medicine and 220 licensed clinical physicians in Fujian province in May 2025 via an online questionnaire.

The expert positive coefficients in the two rounds of expert consultation were 90% (18/20) and 100.0% (18/18), indicating a high positive degree of experts. The expert authority coefficients in the two rounds were 0.564 and 0.777, with the second round demonstrating higher expert authority. The Kendall’s coefficients of concordance in the two rounds of expert consultation were 0.187 and 0.542 (both P < 0.001), being higher in the second round (χ² = 325.89, P < 0.001), which indicated better concordance among experts. Following expert consultation, the final evaluation index system for medical care-prevention integration capabilities of clinical medical professional was established, comprising 4 first-level indexes, 12 second-level indexes, and 36 third-level indexes. The mean importance scores of the first-, second-, and third-level indexes ranged from 4.67 to 4.89, 4.39 to 4.78, and 4.31 to 4.94, respectively. The full-score rates of the first-, second-, and third-level indexes ranged from 44.44% to 72.22%, 38.89% to 72.22%, and 33.33% to 61.11%, with coefficients of variation ranging from 0.06 to 0.10, 0.08 to 0.12, and 0.04 to 0.13, respectively. The weights of the first-, second-, and third-level indexes ranged from 0.075 to 0.331, 0.139 to 0.528, and 0.075 to 0.625, respectively. The final sample included 342 undergraduates majoring in clinical medicine and 211 licensed clinical physicians from Fujian province, who showed the overall mean scores of (2.096 ± 0.696) and (3.076 ± 0.632), respectively, in medical care-prevention integration capabilities. The overall average score of licensed clinical physicians was higher than that of undergraduates majoring in clinical medicine (t = 9.609, P < 0.001). Licensed clinical physicians scored higher than undergraduates on all the three levels of indexes except the second-level index “preventive medicine knowledge” (all P < 0.001).

The established evaluation index system for medical care-prevention integration capabilities among clinical medical professionals demonstrates sound scientific validity and reliability. It provides a reference for accurately assessing the medical care-prevention integration service capabilities of clinical medical professionals.

To investigate the awareness levels of human papillomavirus (HPV) and cervical cancer screening, as well as the acceptance of point-of-care testing (POCT) technology among women in Jinan, Shandong province, analyze the influencing factors, and assess their satisfaction with and attitudes toward the promotion of POCT technology.

A cross-sectional survey was conducted from May 2024 to June 2025 among 629 women aged 35–64 years who participated in the two-cancer screening program at two community health service centers in Jinan. Information on sociodemographic characteristics, awareness of HPV and cervical cancer screening, and acceptance of POCT technology was collected. Statistical analyses were performed with the chi-square test and binary logistic regression.

A total of 629 valid questionnaires were obtained. Regarding awareness of HPV and cervical cancer screening, 46.26% of participants had heard of HPV, among whom 70.10% knew it as the primary cause of cervical cancer. Overall, 80.76% had heard of cervical cancer screening, whereas only 44.09% were aware of specific screening methods. The awareness rates of HPV testing, cytological examination, and visual inspection with acetic acid/Lugol′s iodine (VIA/VILI) were 33.86%, 24.61%, and 6.99%, respectively. Multivariable analysis showed that compared with women aged 35–44 years, those aged 55–64 years had the lowest awareness level of HPV and cervical cancer screening (OR = 0.17, 95%CI: 0.10–0.31). Compared with women with primary school education or below, those with college education or above had the highest awareness level (OR = 3.71, 95%CI: 1.44–9.56). Regarding acceptance of POCT, 55.96% of women expressed willingness to use POCT in future screening. Multivariable analysis indicated that compared with women with primary school education or below, those with senior high school education (OR = 0.43, 95%CI: 0.22–0.81) and those with college or undergraduate education and above (OR = 0.37, 95%CI: 0.17–0.81) showed increased acceptance of POCT. Freelancers (OR = 1.93, 95%CI: 1.08–3.42) and workers (OR = 2.33, 95%CI: 1.10–4.92) showed lower acceptance of POCT technology than unemployed women. In terms of satisfaction and attitudes toward promotion, 84.27% of participants believed that POCT could significantly save time and improve the efficiency of conventional screening; 83.94% believed that it could significantly improve the accuracy of conventional screening; 85.21% reported being very satisfied overall; 83.78% held a positive attitude toward its long-term application; and 83.62% were willing to recommend the technology to others.

Women in Jinan have established basic awareness of HPV and cervical cancer screening, while their understanding of specific screening technologies remains insufficient. Acceptance of POCT technology is at a moderate level, whereas satisfaction with and willingness to promote this technology are relatively high.