中华流行病学杂志最新文献

Objective: To compare the prevalence and progression of subclinical atherosclerosis (SA) in populations with different cardiovascular disease (CVD) risks in China, and clarify the relationship between CVD risk stratification and SA. Methods: All participants were from Beijing Community-Based Cohort of Atherosclerosis. A total of 1 462 participants underwent carotid ultrasound and coronary computed tomography scan during 2008-2009 and 2013-2014. After excluding 191 participants with history of CVD and incomplete baseline data, 1 271 participants were included in final analysis. The 10-year CVD risk for participants were calculated based on the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) equation, and risk stratification was performed. The prevalence and progression of SA was determined by carotid intima-media thickness (cIMT), carotid plaque score and coronary artery calcification (CAC) score. Results: In the participants included in this study, 536 (42.2%), 418 (32.9%) and 317 (24.9%) were classified to have low, intermediate and high 10-year risk, respectively. With the rising level of 10-year risk, the proportion of patients with SA and SA progression increased. In low, intermediate and high CVD risk groups, the proportions of participants with CAC were 16.4%, 36.4% and 52.0% (trend P<0.001); and 15.4%, 36.4% and 53.6% had progression of CAC during follow-up, respectively (trend P<0.001); compared with low-risk group, RRs for CAC progression of intermediate and high-risk groups were 2.316 (95%CI: 1.714-3.129) and 3.322 (95%CI: 2.472-4.463), respectively (trend P<0.001). The trend of relationship between CVD risk stratification and cIMT and carotid plaque progression were consistent with CAC. Conclusions: This current study shows CVD risk stratification is closely related to the prevalence and progression of atherosclerosis in Chinese population. However, many people with low CVD risk have atherosclerotic change in their carotid and coronary artery.

Objective: To explore the association between frailty index (FI) and the risk for cardiovascular disease (CVD) in adults in Inner Mongolia Autonomous Region, and provide new evidence for the prevention of CVD in adults in Inner Mongolia Autonomous Region. Methods: The FI was constructed by using the data from a prospective cohort with a sample size of 25 055 individuals in 6 years of follow-up, and the prevalence of frailty in adults in Inner Mongolia Autonomous Region was described by the FI, and Cox proportional hazard regression model was used to evaluate the association between the FI and the incidence of CVD in adults in Inner Mongolia Autonomous Region. Results: The FI of the study population was 0.24±0.09. The population in the pre-frail (FI: 0.21-0.27) and frail (FI≥0.28) phases had increased risk for CVD compared to non-frail (FI≤0.20) population [pre-frail: hazard ratio (HR)=1.232, 95%CI: 1.127-1.347; frail phase: HR=1.418, 95%CI:1.299-1.548]. For every 0.10 increase in FI, the risk for cardiovascular disease increased by 20.3% (HR=1.203,95%CI:1.156-1.252). Conclusions: In this study, we constructed a FI, which can suggest the risk for CVD. As the increase of frailty degree, the risk for CVD increases.

Extremely unbalanced data here refers to datasets where the values of independent or dependent variables exhibit severe unbalance in proportions, such as extremely unbalanced case-control ratio, very low incidence rate of disease, heavily censored time-to-event data, and low-frequency or rare variants. In such scenarios, the statistic derived from hypothesis test using the classical statistical method, e.g., logistic regression model and Cox proportional hazard regression model, might deviate from theoretical asymptotic distribution, resulting in inflation or deflation of type I error. With the increased availability and exploration of resources from large-scale population cohorts in genome-wide association study (GWAS), there is a growing demand for effective and accurate statistical approaches to handle extremely unbalanced data in independent and non-independent samples. Our study introduces classical statistical methods in genetic statistics firstly, then, summarizes the failure of classical statistical methods in dealing with extremely unbalanced data through simulation experiments to draw researchers' attention to the extremely unbalanced data in GWAS.

Multimorbidity is significantly associated with life quality decline, disability, and increased mortality risk. Additionally, it leads to greater consumption of healthcare resources, presenting substantial challenges to healthcare systems globally. To better assess the burden of multimorbidity, its impact on patient health outcomes and healthcare services, and to explore the underlying mechanisms in its development, this paper summarizes the existing methods used for measuring and analyzing multimorbidity in research and practice, including disease count, disease-weighted indices, multimorbidity pattern recognition (such as disease association analysis, clustering analysis, and network analysis) and longitudinal methods to provide references for the accurate assessment of the prevalence of multimorbidity and its changes and improve the validity and universality of research findings.

Objective: To describe the distribution of fat-free mass (FFM) and fat-free mass index (FFMI) in children aged 3-17 years in China. Methods: Data were collected from National Nutrition and Health Systematic Survey in 0-18 years old children in China. By using multi-stage stratified randomized cluster sampling method, the project was conducted in 28 survey points in urban and rural areas in 14 provinces (autonomous regions and municipalities) in 7 regions in China from 2019 to 2021. FFM was measured using bioelectrical impedance meter. Finally, the body composition data of 70 853 children were included in the analysis. M (Q₁, Q₃) was used to describe the gender and age specific FFM and FFMI of the children in different regions. Kruskal-Wallis H rank sum test was used to compare FFM and FFMI of boys and girls in same age group, boys in different age groups, girls in different age groups, as well as boys in same age group and girls in same age group in different regions. DSCF method was used for pairwise comparisons. Results: After the age of 11 years, the difference of FFMI between boys and girls increased year by year. The FFMI was 14.2 kg/m² in boys and 13.8 kg/m² in girls at 11 years old, the difference was significant (χ²=135.86, P<0.001). The difference of FFMI between boys and girls exceed 1.0 kg/m² from 12 years old, and FFMI was 15.3 kg/m² in boys and 14.2 kg/m² in girls at 12 year old, the difference was significant (χ²=597.27, P<0.001). The FFMI was 17.5 kg/m² in boys and 14.7 kg/m² in girls at 16 years old, the difference was significant (χ²=2 543.60, P<0.001). The FFMI was higher in boys in northeast China, while the FFMI was lower in both boys and girls in northwest China. Conclusions: Gender specific difference was observed in the increase of FFMI with age. The FFMI was significantly higher in boys than in girls after 11 years old. It is necessary to pay attention to the problem of FFM in children in northeastern and northwestern China.

Objective: To analyze the temporal distribution characteristics of hand, foot and mouth disease (HFMD) in Beijing and provide reference evidence in HFMD prevention and control. Methods: The monthly incidence data of HFMD in Beijing from 2008 to 2023 were collected from Notifiable Disease Management Information System of the Chinese Information System of Disease Control and Prevention, and the epidemiological characteristics of HFMD were analyzed by the methods of time series seasonal decomposition graph, concentration degree, and circular distribution.The WPS office software 2019 was used to clean the data, Python software 3.12 was used to analyze and make statistical charts. Results: The monthly incidence fluctuation of HFMD in Beijing from 2008 to 2015 was higher than that from 2016 to 2022. From 2016 to 2022, the fluctuation range of monthly incidence showed a gradually decreasing trend.From 2008 to 2015, the concentration (M) was 0.58, indicating a relatively strong seasonality; the mean angle (α) calculated by the circular distribution method was 174.95°, and the mean angle standard deviation (s) was 60.43°. The annual incidence peak occurred on June 27, and the incidence peak period was from April 27 to August 27. From 2016 to 2019 and 2023, the M was 0.57, indicating a relatively strong seasonality. The α was 228.05°, and s was 61.44°. The annual incidence peak occurred on August 20, and the incidence peak period was from June 18 to October 21. From 2020 to 2022, the M was 0.42, indicating a seasonality, the α was 238.27° and s was 76.35°. The annual incidence peak occurred on July 15, and the incidence peak period was from June 14 to November 14. The α of 2008-2015, 2016-2019 and 2023, and 2020-2022 were tested by the Watson-Williams method and the difference was statistically significant (F=33 443.09, P<0.001). In 2023, the M was 0.77, indicating a strong seasonality. The incidence peak occurred on September 16, and the incidence peak period was from August 5 to October 28. Conclusions: The seasonality of HFMD in Beijing was obvious from 2008 to 2023, and the incidence peak day and peak period overall had rearward shifts. It is necessary to strengthen the comprehensive analysis of the distribution characteristics at different dimensions and the comprehensive prevention and control in key areas, places, and populations during the peak incidence period.

Objective: To analyze the survival status and death factors of confirmed HIV-infected patients in Hangzhou to provide a basis for the formulation of AIDS prevention and treatment strategies. Methods: A retrospective cohort study was conducted. The data were from the HIV/AIDS Comprehensive Response Information Management System of the Chinese Disease Control and Prevention Information System.Epidemiological characteristics of HIV-infected patients were comparied in Hangzhou City from 2004 to 2023 by using chi-square Test. The survival rate of HIV-infected patients in Hangzhou was calculated by the life table method, the survival curves of different subgroups were described by the Kaplan-Meier method, and the Cox proportional hazard regression model was used to analyze the influencing factors of death risk. The SPSS 26.0 software was used for statistical analysis. Results: Among the 9 457 subjects, the total follow-up time was 58 004.18 person-years, 494 patients died, fatality rate of all-cause cases was 0.85 per 100 person-years.The average survival time was 18.59 (95%CI:18.40-18.78) years. Malignant neoplasms and pneumocystis pneumonia were the first (14.37%,71/494) and second (10.73%, 53/494) causes of death, respectively. Death within 6 months after diagnosis accounted for 42.51% (210/494), and suicide accounted for 4.25% (21/494). Multivariate Cox regression analysis showed that compared with those who received antiviral treatment (ART) within 3 months of diagnosis, those who received ART outside 3 months and those who did not receive ART had a 1.65 (95%CI:1.25-2.19) and 20.68 (95%CI:15.80-27.06) times risk of death, respectively. The HIV-infected patients with high CD4⁺T lymphocytes (CD4) counts for the first time had a lower risk of death. The risk of death of patients with baseline CD4 counts of 200-349 cells/µl, 350-499 cells/µl, and ≥500 cells/µl was 0.38 (95%CI:0.29-0.49), 0.26 (95%CI:0.19-0.36), 0.21 (95%CI:0.14-0.31) times higher than that of baseline CD4 counts <200 cells/µl, respectively. Conclusions: The overall survival of the HIV-infected patients was good in Hangzhou from 2004 to 2023. Early detection of HIV infection and timely mobilization to participate in ART was the key to improving the survival rate of patients. At the same time, given the suicide problem of HIV-infected patients, suicide surveillance and depression and anxiety screening of HIV-infected patients should be further strengthened, and targeted psychological intervention policies should be implemented.

Objective: This study aims to obtain the prevalence and features associated with Violence Against Children (VAC) in China and, thus, formulate a prevention strategy. Methods: The mortality-related data of VAC was sourced from the National Disease Surveillance Points System (DSP) during 2013-2021. We analyzed the DSP data regarding children aged 0-17 years old who died from violence. The hospital cases of VAC was sourced from the National Injury Surveillance System (NISS), 2013-2021. We analyzed the data from NISS with the parameter of "intentional injury" caused by VAC in children aged between 0-17 years. Using robust linear regression, we analyze the time trend in the proportion of violence incidence. To understand the variations in the incidence of different types of violence across genders, we apply the chi-square test and adjusted Pearson residuals. Results: The overall trend of death caused by VAC has declined; it was reduced to 0.14/100 000 in 2021 from 0.33/100 000 in 2013. In 2021, male VAC mortality (0.15/100 000) was higher than females (0.13/100 000). The proportion of VAC cases to all injury cases has declined from 3.34% in 2013 to 2.29% in 2021. Among 9 344 VAC cases supervised by hospitals in 2021, the number of males (7 503 cases) was around 4 times that of females (1 841 cases), and the top three modes of violence were blunt tools (64.77%), falls (7.46%) and sharp instruments (6.18%), and 45 cases of sexual violence included 38 girls and 7 boys. Conclusions: The declining death rate due to VAC may be related to the benign development of Chinese society. Prevention strategies targeting training in parenting skills and problem-solving should be prioritized.

Objective: To construct a risk prediction model for diabetes kidney disease (DKD). Methods: Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation. Results: The study included 49 706 subjects, with an median (Q₁, Q₃) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions: This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.