To analyze the risk factors for cognitive impairment in patients with multimorbidity.

Studies on the occurrence of cognitive impairment in patients with multimorbidity were identified by searching Chinese and foreign databases: PubMed, Web of Science, Embase, Cochrane library, CNKI, Wan Fang Data, VIP, SinoMed. The search period was from database inception to July 1, 2024, meta-analysis was performed using RevMan5.4 and Stata18.0 software.

A total of 12 studies were included, comprising 1 137 737 patients. The meta-analysis indicated that smoking (OR=1.11，95% CI=1.05-1.18), low social activity (OR=1.52，95% CI=1.06-2.17), having two chronic diseases (OR=1.15，95% CI=1.05-1.26), having three chronic diseases (OR=1.37，95% CI=0.96-1.94), having four or more chronic diseases (OR=1.67，95% CI=1.49-1.87), having two cardiovascular metabolic diseases (OR=1.72，95% CI=1.42-2.07), having three or more cardiovascular metabolic diseases (OR=2.53，95% CI=1.41-4.54), and eight multimorbidity models were the neuropsychiatric (OR=2.05，95% CI=1.83-2.29), cancer/sensory impairment (OR=1.32，95% CI=1.18-1.47) diabetes/heart disease (OR=2.17，95% CI=1.68-2.08) diabetes/stroke (OR=2.92，95% CI=1.49-5.71), diabetes/hypertension (OR=1.93，95% CI=1.72-2.17), hypertension/heart disease (OR=1.56，95% CI=1.40-1.74), stroke/heart disease (OR=2.65，95% CI=1.89-3.71), and stroke/diabetes-heart disease (OR=3.95，95% CI=2.81-5.56) showed statistically significant differences (all P<0.05).

Current evidence suggests that smoking, low social activity, number of chronic diseases, and different patterns of multimorbidity are all risk factors for cognitive impairment in patients with multimorbidity, and that early clinical screening and intervention is warranted to reduce the progression of cognitive impairment in patients with multimorbidity.

To explore the relationship between body roundness index (BRI) and new onset arthritis, and provide a new perspective for the prevention and treatment of arthritis.

A cohort study design was adopted, using the data of 2011 and 2020 in the China longitudinal study on the China Health and Retirement Longitudinal Study (CHARLS), taking BRI as the exposure factor, high-density lipoprotein cholesterol (HDL-C), total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C) as the intermediary variables, including demographic characteristics such as age and gender, smoking, alcohol consumption and other health behaviors as confounding factors, and new onset arthritis as the outcome. Statistical analysis was carried out using multiple model logistic regression strategy, restricted cubic spline, subgroup analysis and intermediary analysis. Compared with relevant studies, the main purpose of the dose-response relationship we explored is to study whether there is a nonlinear relationship between exposure and outcome, that is, whether the risk of outcome will change accordingly with the change of exposure level.

A total of 5 166 participants were included, and BRI was positively correlated with arthritis, with an OR value of 1.196 (95% CI: 1.047-1.367). The prevalence of new onset arthritis increased by 19.6% when BRI increased by a quartile range; The dose-response relationship showed that BRI > 3.943 was positively correlated with the incidence of arthritis; Subgroup analysis showed that the positive correlation between BRI and new onset arthritis was not affected by many factors; Mediation analysis showed that HDL-C had a protective effect on arthritis, mediating about 6.84% of the impact of BRI on the incidence of arthritis. The mediation effect of total cholesterol, triglycerides and LDL-C was not significant; The AUC of BRI for predicting new onset arthritis (0.546) was higher than that of BMI (0.527).

The increased level of BRI may lead to an increased risk of arthritis, and HDL-C may alleviate this risk. In the future, its clinical application potential and regulatory strategies should be explored to reduce the incidence of arthritis and improve the prognosis.

To analyze the relationship between different combinations of chronic diseases and health-related quality of life (HRQoL) among rural patients with chronic diseases in Shandong Province, and to provide references for improving the HRQoL of patients with chronic diseases.

A multi-stage random sampling method was used to select 531 patients with chronic diseases in Shandong Province for face-to-face questionnaires. The EuroQol five-dimensional five-level questionnaire (EQ-5D-5L) was used to evaluate the patients’ HRQoL. The Tobit regression model was used to analyze the influencing factors of patients’HRQoL.

Among rural patients with chronic diseases, the combinations of chronic diseases with relatively high prevalence rates were arthritis + other diseases (excluding digestive system diseases), pure metabolic diseases, and stroke + metabolic diseases or stroke + metabolic diseases + heart disease, accounting for 27.68%, 19.59%, and 12.81% respectively. The results of the Tobit model showed that the influencing factors of the health utility value of rural patients with chronic diseases were age ≥ 75 years, the course of multiple chronic diseases exceeding 10 years, and disease combinations: patients with any one of digestive system diseases, arthritis, or stroke as part of multiple chronic diseases had significantly lower HRQoL than those with other combinations of chronic diseases.

There are significant differences in the HRQoL of rural patients with chronic diseases with different combinations of chronic diseases. Attention should be paid to health promotion among rural residents, and early screening and prevention should be carried out for high-risk patients with digestive system diseases, arthritis, and stroke to improve the HRQoL of rural residents.

To understand the prevalence and influencing factors of Chronic Obstructive Pulmonary Disease (COPD) among middle-aged and elderly residents in the community, and to construct and validate a Nomogram prediction model to provide a theoretical basis for the prevention of COPD onset and deterioration.

This cross-sectional study involved 3 250 middle-aged and elderly community residents in Chengdu Eastern New District. Basic information and risk factors for COPD were collected through pulmonary function tests, physical examinations, and questionnaires. The study population was divided into training and validation sets. Elastic net analysis was performed in the training set to select coefficients, and LASSO regression and random forest models were utilized to analyze the influencing factors of COPD and severe COPD, leading to the establishment of a Nomogram prediction model.

The detection rates of COPD and severe COPD among community residents in Chengdu were 26% and 5.38%, respectively. LASSO regression indicated that age, male, being unmarried, low income, childhood pneumonia hospitalization, childhood cough, and parental respiratory disease history were common risk factors for COPD and severe COPD. Unique risk factors for COPD included low BMI, occupation, dust exposure, allergy history, workplace smoking, family smoking, and personal smoking.Unique risk factors for severe COPD included low educational level, waist-to-hip ratio, and maternal smoking during pregnancy. A Nomogram risk prediction model for COPD and severe COPD was established based on these indicators, with AUC values of 69.34%(95%CI: 66.90%-71.79%) and 76.81% (95%CI: 72.71%-80.90%), respectively, demonstrating good predictive performance. Internal validation through 1 000 bootstrap resampling showed that the calibration curves of the Nomogram model closely matched the actual occurrence of COPD and severe COPD, indicating a good fit.

Gender, age, educational level, marital status, family income, waist-to-hip ratio, childhood pneumonia hospitalization history, childhood cough, maternal smoking, paternal respiratory disease history, and maternal respiratory disease history are independent risk factors for COPD and severe COPD among community residents in Chengdu. The constructed Nomogram prediction model can provide a concise and intuitive personalized risk assessment for COPD and severe COPD for community residents and high-risk populations.

To use the multi-state model to study the risks of developing Parkinson’s disease and death after stroke and explore their influencing factors, so as to provide a scientific basis for the prognosis of stroke patients and the prevention of Parkinson’s disease.

This study was based on 345 585 participants registered in the UK Biobank database from 2006 to 2010, with follow-up until November 30, 2021. The multi-state model was used to analyze the risks of developing Parkinson’s disease and death in stroke patients.

Among the six outcome paths, the cumulative risk from stroke to death was the highest, followed by that of Parkinson’s disease and from the baseline to death. The risk probability of developing Parkinson’s disease after stroke was 2.25 times that of the baseline state, and the death probability of stroke patients was 11.36 times that of the baseline population. The results of the influencing factors in the multi-state model showed that advanced age (over 60 years old), male, depression, smoking, alcohol consumption, and childhood obesity were all risk factors for the transition of the baseline population to the three states (stroke, Parkinson’s disease, and death). In the transition path from stroke to Parkinson’s disease, advanced age (over 60 years old) was a risk factor, while alcohol consumption and female gender were protective factors for stroke patients to develop Parkinson’s disease (HR=0.569, 95%CI: 0.357-0.909), (HR=0.521, 95%CI: 0.344-0.788). In the path from stroke to death, advanced age (over 50 years old), depression (HR=1.980, 95%CI: 1.656-2.369), smoking (HR=1.504,95%CI: 1.358-1.647), and a family history of stroke could increase the risk of stroke to death, while alcohol consumption was a protective factor (HR=0.872, 95%CI: 0.774-0.984). Advanced age (over 60 years old), depression (HR=1.783, 95%CI: 1.295-2.451), and smoking (HR=1.781, 95%CI: 1.397-2.295) were risk factors for the death of Parkinson’s patients, and the mortality rate of female patients was lower than that of male patients (HR=0.797, 95%CI: 0.686-0.926).

Advanced age (over 60 years old), male gender, smoking, depression, and childhood obesity are risk factors for stroke, Parkinson’s disease, and death in the baseline population; advanced age, male, smoking, depression, and a family history of stroke are risk factors for stroke patients to develop Parkinson’s disease and die. The multi-state model can be used to demonstrate the influencing factors and extent of disease transitions, revealing the patterns of disease progression.

To understand the current situation of adult tobacco prevalence in each sub-district and township of Dingzhou city, Hebei Province, provide data support for formulating grass-roots tobacco control policies, and provide a reference for estimating tobacco prevalence indicators at the county-district level.

A multi-stage stratified cluster sampling method and Probability Proportional to Size (PPS) sampling method were used to conduct a questionnaire survey among permanent residents aged 15 and above in Dingzhou city. The survey data were cleaned, weighted, and analyzed using SAS 9.4. The current smoking rate, quitting rate, and second-hand smoke exposure rate in Dingzhou city were reported. The Small Area Estimation (SAE) method was used to estimate the above-mentioned indicators for each sub-district and township.

A total of 7 660 valid questionnaires were collected in this survey, with an overall response rate of 68.70%. In 2023, the current smoking rate among people aged 15 and above in Dingzhou city was 22.27%, the quitting rate was 9.60%, and the second-hand smoke exposure rate among non-smokers was 36.84%. The estimation results of the SAE model showed that there were differences in tobacco prevalence monitoring indicators among different sub-districts and townships. Townships such as Xingyi Town, Liqinggu Town, and Dongliuchun Township in the southern part of Dingzhou city had relatively high current smoking rates (25%-35%). Some townships in the northwest and southeast regions had relatively low quitting rates (less than 10%), while the sub-districts and townships with relatively high second-hand smoke exposure rates were more scattered, with the rates in Dongting Town and Xizhong Town exceeding 50%.

The SAE model can be used to estimate tobacco prevalence monitoring indicators at the county-district and even sub-district and township levels. Currently, the tobacco prevalence situation in some sub-districts and townships of Dingzhou city is relatively severe. Targeted grass-roots tobacco control work should be carried out, the construction of smoke-free environments should be strengthened, and grass-roots smoking cessation services should be promoted.

To explore whether the newly proposed cardiovascular health indicator LE8 provides greater predictive value for adverse cardiovascular events (ACE) than LS7 in the Chinese population.

Based on the China Health and Nutrition Survey (CHNS), 5 772 participants who had not experienced ACE prior to the 2009 survey were included. The cardiovascular health indicators LE8 and LS7 were calculated, and the Kaplan-Meier method and Cox proportional hazards model were used to estimate the 5-year risk of ACE occurrence. The differences in predictive value for ACE occurrence between the two indicators were compared using C statistics.

For LE8, the participants in the highest percentile had a 5-year ACE risk of 2.3%,whereas those in the lowest percentile had a risk approximately six times higher. The results of the Cox proportional hazards model indicated that the C statistic for LS7 was 0.728 (95%CI: 0.706-0.75), while that for LE8 was 0.726 (95%CI: 0.703-0.748), with no statistically significant difference (P=0.51).

In the Chinese population, both LE8 and LS7 are associated with new ACE. However, LE8 incurs higher collection costs and has not demonstrated superior predictive value; therefore, LS7, which is easier to implement in clinical practice, is recommended as the risk prediction indicator for new cardiovascular events.

To analyze the epidemiological characteristics of Hepatitis B Virus (HBV) in the Xinjiang Uygur Autonomous Region from 2005 to 2023 and to predict the incidence trend from 2024 to 2026, thereby providing scientific basis for the prevention and control of Hepatitis B.

Data on HBV incidence were extracted from the Chinese Disease Prevention and Control Information System for descriptive analysis, and the gray prediction model was employed to forecast the incidence trends.Visualization of the data was performed using Origin 2022 software.

From 2005 to 2023, a total of 724 286 cases of HBV were reported in the Xinjiang Uygur Autonomous Region, with an average annual incidence rate of 172.97 per 100 000 population. Cases were reported across various prefectures and cities, with the highest cumulative incidence observed in Kashgari Prefecture (171 395 cases), Urumqi city (132 256 cases), and Aksu Prefecture (90 145 cases). Among the reported cases, males (401 266 cases) outnumbered females (323 020 cases), with the primary affected demographic being individuals aged 20-39 years. The GM (1,1) model predicted a declining incidence trend for Hepatitis B in the Xinjiang Uygur Autonomous Region from 2024 to 2026, with projected rates of 104.809, 97.406, and 90.065 per 100 000 population, respectively.

The overall incidence level of Hepatitis B in the Xinjiang Uygur Autonomous Region is relatively high; however, a downward trend in incidence is anticipated from 2024 to 2026.It is recommended to enhance the promotion of Hepatitis B vaccination, strengthen prevention and control efforts in key populations and areas, and improve public awareness of Hepatitis B to achieve the goal of eliminating its prevalence.