With the increasing life expectancy and lifestyle changes of patients with chronic hepatitis B (CHB), the significance of comorbidities of chronic non-communicable diseases (NCDs) in disease progression and health prognosis of CHB patients is gaining prominence. This study aims to explore the association between CHB and NCDs comorbidities, focusing on the impact of common metabolism-related diseases, such as metabolic syndrome and diabetes, on the health outcomes of CHB patients. We also summarize studies on integrating the management of comorbidities in CHB patients and provide relevant recommendations for effective management. The findings of this study serve as a foundation for understanding the clinical characteristics and prevalence trends, reducing the disease burden of comorbidities among CHB patients, and establishing a comprehensive and coordinated management system for comorbidities.

The disease risk prediction model is the basis of precision prevention and an essential reference for clinical treatment decisions. The development of risk prediction models requires the support of a large amount of high-quality data. A large population cohort study is an important basis for this study. The United Kingdom Biobank (UKB), as a mega-population cohort and biobank, has played an essential role in the exploration of disease etiology and research related to disease prevention and control, with its rich baseline and follow-up data and concepts and mechanisms shared globally. This study followed PRISMA guidelines and included 210 articles with corresponding authors from 18 countries, of which 58 (27.62%) were from the UKB. A total of 491 disease risk prediction models were extracted for cancer, cardiovascular and cerebrovascular diseases, endocrine and metabolic diseases, respiratory diseases, and other diseases and their subgroups, of which 132 were developed by UKB without validation, 183 were developed by UKB with internal validation, 17 were developed by UKB with external validation, and 159 were developed by external development with UKB validation. A total of 188 models used only macro variables (38.29%), and 303 models combined macro and micro variables (61.71%). Model construction methods included survival outcome models, logistic regression, and machine learning. Survival outcome models were dominated by Cox proportional risk regression models and a few models considering competitive risk, accelerated failure models, or different baseline risk functions. Machine learning models included random forest, XGBoost, CatBoost, support vector machine, convolutional neural network, and other methods. The UKB is an essential resource for multiple disease risk prediction modeling studies.

Objective:To analyze the disease burden in middle-aged and elderly population aged ≥55 in Shenzhen from 2016 to 2030 and provide evidence for the development of healthy aging strategies.Methods:The years of life lost (YLL), years lost due to disability (YLD), and the disability-adjusted life year (DALY) in this population from 2016 to 2022 were calculated. Joinpoint log-linear regression model was used to analyze the time trend. Bayesian age-period-cohort model and grey system model were used to predict YLL, YLD, and DALY in this population in 2030.Results:From 2016 to 2022, the crude DALY rate showed a transient fluctuation in age group 55-74 years, but a pronounced increase in age group ≥85 years. The proportions of YLL and YLD due to non-communicable diseases in all age groups was considerably higher than those due to communicable and nutritional diseases and injuries. In 2022, in all age groups, the YLL due to neoplasms (55-74 years old) and cardiovascular disease (≥75 years old) ranked first, and the YLD due to musculoskeletal disorder ranked first. By 2030, the causes of YLL and YLD ranking first in each age group would be remained, while the ranks of some causes would increase.Conclusions:The age specific characteristics of current and predicted disease burden differed in individuals aged ≥55 years. Therefore, it is necessary to allocate social and medical resources according to the disease burden pattern.

Objective:To understand the infection status and characteristics of 2019-nCoV infection in different areas in China after the adjustment of the national prevention and control strategy of 2019-nCoV infection.Methods:The online questionnaire survey was conducted among staff and students of 39 public health schools in 23 provinces (municipalities) in China from 12: 00 on December 20 to 9: 00 on December 23, 2022. The infection rates in staff and students in all the provinces were estimated. The risk factors, demographic and clinical characteristics of 2019-nCoV infections were explored.Results:A total of 28 901 valid questionnaires were obtained (26 355 from students and 2 546 from staff) with a qualified rate of 94.3%. The infection rates varied greatly among provinces and cities; the infection rates in students and staff in Beijing reached 78.55% and 76.40%, respectively. Infection rates in students and staff in Tianjin and Hebei also exceeded 65.00%, and 96.76% of infections occurred on and after December 1, 2022. Students had lower risk for the infection compared with staff ( OR=0.72, 95% CI: 0.60-0.86). Compared with age group ≤20 years, the OR of age groups 21-30, 31-40, 41-50, 51-60 and > 60 years were 1.22 (95% CI: 1.14-1.30), 1.54 (95% CI: 1.30-1.84), 1.25 (95% CI: 0.99-1.58), 1.29 (95% CI: 0.94-1.78) and 1.19 (95% CI: 0.51-2.80), respectively. The longer the period after the last vaccination, the higher the risk for the infection. Compared with those who received the last vaccination in the past 3 months, the OR of those who received the last vaccination in the past 4-6 months, 7-9 months, 10-12 months, 13-15 months and ≥16 months were 1.56 (95% CI: 1.34-1.82), 1.59 (95% CI: 1.36-1.86), 1.67 (95% CI: 1.45-1.93), 1.86 (95% CI:1.58-2.19) and 2.46 (95% CI: 2.09-2.90), respectively. Compared with those living alone, the OR of those living with 1-2, 3-4 and ≥5 roommates were 17.55 (95% CI: 15.91-19.39), 20.22 (95% CI: 18.25-22.43) and 11.78 (95% CI: 10.40-13.36), respectively. Only 5.94% of the staff and 7.19% of the students reported asymptomatic infections. Among those with symptoms, 88.18% of students and 85.65% of staff reported symptom of fever. Conclusions:The transmission dynamics of 2019-nCoV infection varied significantly across the country. The speed of transmission of 2019-nCoV and clinical severity of the infection were far beyond our knowledge. Organized epidemiological survey should be regularly carried out to provide reliable data support for more accurate prediction of the epidemic and medical resource allocation.

Objective:To understand the characteristics of sexualized drug use (SDU) among men who have sex with men (MSM) in Chengdu and classify their behavioral patterns by applying the two-step cluster (TSC) algorithm and by exploring the association between SDU patterns and high risk sexual behaviors, in order to provide a reference basis for the development of HIV prevention interventions.Methods:Supported by an MSM-friendly organization in Chengdu from December 2021 to February 2022, MSM were recruited by on-site survey and peer referral to collect information on sociodemographics, SDU characteristics, sexual behaviors, STD diagnosis, and HIV status. TSC was performed to classify the characteristics of SDU, and the differences between groups were compared. Chi-square test and multiple binary logistic regressions were used to identify the relationship between SDU clusters and HIV-related risk sexual behaviors.Results:A total of 727 MSM were surveyed, and 39.8% (289/727) of the respondents reported SDU experience with a same-sex partner in the last six months. TSC clustered SDU-MSM into three behavioral pattern groups. There were significant differences in monthly income, types of drugs used for SDU, mode, frequency, polydrug use, multiple sexual partners, non-adherence to condom use, and group sex among the three groups of SDU-MSM ( P<0.05). Multivariate binary logistic regression analysis showed that the third category of SDU-MSM was 2.22 (95% CI: 1.06-4.66) times more likely than the first category not to use condoms consistently; the third and second categories were 2.82 (95% CI: 1.18-6.77) times and 8.78 (95% CI: 3.42-22.42) times. Conclusions:The prevalence of SDU among MSM in Chengdu was more than 1/3, and different SDU clustering pattern was associated with high-risk sexual behaviors. MSM with higher SDU frequency, drug use, and polydrug abuse are more difficult to adhere to condom use and more likely to have group sex and high risk sexual behaviors, increasing the risk of STD and HIV infection, and need to strengthen SDU surveillance and intervention.

Objective: To analyze the spatiotemporal distribution of life expectancy (LE) and health-adjusted life expectancy (HALE) in Guangzhou from 2010 to 2019, and quantize the comprehensive impact of different causes and sequelae on health. Methods: The LE, HALE, and cause-excluded health adjusted life expectancy (CEHALE) were estimated using cause-of-death surveillance datasets from Guangzhou Municipal Center for Disease Control and Prevention from 2010 to 2019 and open data from the Global Burden of Disease Study. Joinpoint log-linear regression model was used to analyze the temporal trend and described spatial distribution. Results: In 2019, the LE in residents in Guangzhou was 82.9 years (80.1 years in men and 85.9 years in women), and the HALE was 75.6 years (74.0 years in men and 77.3 years in women). Compared with the urban fringe, the central urban area had higher LE and HALE, and the differences between LE and HALE were small. The LE and HALE in Guangzhou showed an increasing trend from 2010 to 2019. The LE increased by 2.8 years (AAPC=0.4, 95%CI: 0.3-0.4), with the increase of 2.8 years in men and 2.9 years in women. The HALE increased by 2.4 years (AAPC=0.3, 95%CI: 0.3-0.4), with the increase of 2.5 years in men and 2.2 years in women. The median healthy life lost due to communicable, maternal, neonatal, and nutritional diseases was 6.2 years (AAPC=-4.2, 95%CI: -5.3--3.1), while the median healthy life lost due to non-communicable diseases was 14.7 years (AAPC=1.6, 95%CI: 0.9-2.3), the median healthy life expectancy reduced by injury was 6.3 years (AAPC=-3.5, 95%CI: -4.5--2.6). Musculoskeletal disorders, skin and subcutaneous diseases, cardiovascular diseases, nutritional deficiencies, diabetes and kidney diseases were the top five diseases causing healthy life expectancy loss. Conclusion: The LE and HALE in residents in Guangzhou increased steadily from 2010 to 2019, but the quality of life in the urban fringe was lower than that of the central urban area. Non-communicable diseases were the leading causes of healthy life expectancy loss. Health policies and prevention measures should be developed according to area specific characteristics, and social medical resources should be rationally allocated to key diseases to reduce their disease burden.
Cost of Illness ; Female ; Health Status ; Humans ; Infant, Newborn ; Life Expectancy ; Male ; Noncommunicable Diseases ; Quality of Life

Adolescent ; Adult ; Age Distribution ; Aged ; Basic Reproduction Number ; COVID-19/virology* ; China/epidemiology* ; Disease Outbreaks ; Female ; Humans ; Male ; Middle Aged ; SARS-CoV-2 ; Sex Ratio ; Young Adult

This study aims to provide a brief overview of the history and development of global health education (GHE) as academic degree programs worldwide,and to identify GHE's development opportunities and obstacles in China.This is a state-of-the-art review of published and unpublished information that described and evaluated disci-plinary development of global health degree programs worldwide,written in English,and published or shared be-tween 1990 and 2020.Data were derived from official websites of leading global health institutions,like "Google Scholar","PubMed",and unpublished information such as presentation files and unpublished manuscripts col-lected from knowledgeable leaders in the field.We retrieved and reviewed a total of 35 articles and a large amount of unpublished information or sources on the internet.Global Health emerged as a new discipline around the end of the last millennium and proliferated in the last two decades in developed nations,especially the United States and the United Kingdom.The development of China's GHE programs was built on China's increasing engagement in global health affairs and research.In 2012,Wuhan University established the first official global health de-partment in China.Several universities such as Peking University and Duke Kunshan University subsequently set up departments or programs to offer undergraduate and postgraduate majors and degrees.The first school-level global health unit was established in Shanghai in 2019.The Consortium of Chinese Universities for Global Health(CCUGH) grew from 10 founding members in 2013 to 25 in 2020.Major desirable attributes "unique" to stu-dents majoring in global health include global-mindedness,health interests,compassion,intercultural sensitivity,and adventurous spirit.Graduates from GHE programs have a diverse set of career choices spanning research,government,not-for-profit,and private sector occupations.We identified a number of strengths,weaknesses,opportunities,and threats to the future development of GHE in China.To ensure sustainable future growth,we advocate addressing the following key aspects:(1) clearer disciplinary distinctions;(2) multidisciplinary col-laborations;(3) public-sector investments;and (4) non-public sectors participation.Amidst China's increasing engagement in health affairs globally and the proliferation of GHE programs in developed nations,China has experienced fast growth in GHE degree programs since 2012 while a number of challenges remain for its future development.

Objective:To analyze the status and its influencing factors of job burnout among female workers of labor-intensive enterprises.Methods:A total of 750 female workers from 5 labor-intensive enterprises in Guangdong Province were selected as the study subjects by random cluster sampling method in August, 2019. 665 valid questionnaires were collected, and the effective recovery rate was 88.67%. The Maslach Burnout Inventory-General Survey was used to assess job burnout and its influencing factors were analyzed.Results:Among 665 female workers, 429 (64.51%) found to have different levels of burnout, among which 380 (57.14%) were mild to moderate burnout and 49 (7.37%) were severe burnout. The comprehensive scores of job burnout in different age, marital status, current post working age, working time per week, personal monthly income, working system and occupational stress groups were statistically significant ( P<0.01) . There were significant differences in the score of emotional exhaustion in different age, marital status, current working age, working time per week, personal monthly income and occupational stress groups ( P<0.05) . There were significant differences in the dimensions of depersonalization in different age, weekly work time, personal monthly income, working system and occupational stress groups ( P<0.05) . There were significant differences in the dimensions of low individual achievement in different education levels, weekly work time, working system and occupational stress groups ( P<0.05) . Conclusion:The female workers of labor-intensive enterprises are generally have mild to moderate job burnout. The main influencing factors of job burnout are weekly work time and occupational stress.

Objective:To analyze the status and its influencing factors of job burnout among female workers of labor-intensive enterprises.Methods:A total of 750 female workers from 5 labor-intensive enterprises in Guangdong Province were selected as the study subjects by random cluster sampling method in August, 2019. 665 valid questionnaires were collected, and the effective recovery rate was 88.67%. The Maslach Burnout Inventory-General Survey was used to assess job burnout and its influencing factors were analyzed.Results:Among 665 female workers, 429 (64.51%) found to have different levels of burnout, among which 380 (57.14%) were mild to moderate burnout and 49 (7.37%) were severe burnout. The comprehensive scores of job burnout in different age, marital status, current post working age, working time per week, personal monthly income, working system and occupational stress groups were statistically significant ( P<0.01) . There were significant differences in the score of emotional exhaustion in different age, marital status, current working age, working time per week, personal monthly income and occupational stress groups ( P<0.05) . There were significant differences in the dimensions of depersonalization in different age, weekly work time, personal monthly income, working system and occupational stress groups ( P<0.05) . There were significant differences in the dimensions of low individual achievement in different education levels, weekly work time, working system and occupational stress groups ( P<0.05) . Conclusion:The female workers of labor-intensive enterprises are generally have mild to moderate job burnout. The main influencing factors of job burnout are weekly work time and occupational stress.