1.Characteristics of HIV-infected persons without long term disease progress and related factors in Guangxi Zhuang Autonomous Region.
X J ZHOU ; Q Y ZHU ; J J LI ; G H LAN ; S S LIANG ; S F LIU ; X H LIU ; Q MENG ; C X ZHOU ; Z Y SHEN
Chinese Journal of Epidemiology 2019;40(1):70-73
Objective: To understand the characteristics of HIV infected persons without long term disease progress [also known as long term non-progressors (LTNPs)], and related factors in Guangxi Zhuang Autonomous Region (Guangxi). Methods: Data of persons living with HIV and receiving no antiretroviral therapy in Guangxi by the end of 2016 were collected from the national HIV/AIDS comprehensive control and prevention information system of China. Results: By the end of 2016, there were 313 LTNPs in Guangxi, accounting for 2.3% of those being reported for more than 10 years, 5.4% of those being reported for more than 10 years and surviving, and 26.6% of those being reported for more than 10 years, surviving and receiving no antiretroviral therapy. Among the LTNPs, 87.2%(273) were men, 94.9% (297) were aged ≤ 40 years, 32.3% (101) were farmers, 55.6% (174) were single, divorced or widowed, 69.3% (217) were of Han ethnic group, 68.1% (213) were injecting drug users, and 52.1% (163) were from custody facilities. Multiple logistic regression analysis indicated that factors associated with delayed disease progression included age ≤40 years (compared with age >40 years, aOR=1.55, 95%CI: 1.31-3.12) and injection drug use (compared with sexual transmission, aOR=1.23, 95%CI: 1.10-1.74). Conclusions: A number of LTNPs existed in HIV-infected individuals in Guangxi. Further research are needed to identify the related factors, and it is necessary to conduct large sample size studies on host immunology, genetics and the virology of HIV to explore the related mechanism.
Adolescent
;
Adult
;
Age Distribution
;
China/epidemiology*
;
Drug Users/statistics & numerical data*
;
Ethnicity/statistics & numerical data*
;
HIV Infections/ethnology*
;
Humans
;
Male
;
Socioeconomic Factors
2.Effect of baseline CD(4)(+) T cell count on drop-out of antiretroviral therapy in HIV infected persons in Guangxi Zhuang Autonomous Region, 2008-2015.
X H LIU ; Q Y ZHU ; J M SU ; Q MENG ; X J ZHOU ; Z Y SHEN ; Z Z TANG ; W M YANG ; Y H RUAN ; Y M SHAO
Chinese Journal of Epidemiology 2018;39(9):1216-1221
Objective: To investigate the effect of baseline CD(4)(+) T cell count (CD(4)) on drop-out of antiretroviral therapy (ART) in HIV infected persons. Methods: Retrospective cohort was conducted in this study. HIV infected persons aged≥18 years and receiving free ART for the first time in Guangxi Zhuang Autonomous Region (Guangxi) from 2008 to 2015 were selected from the antiretroviral treatment database of National Comprehensive HIV/AIDS Information System, with follow-up conducted till May 30, 2016. Cause-specific Cox proportional hazard models were used to evaluate effect of different CD(4) on the drop-out of ART in the HIV infected persons. Results: A total of 58 502 eligible study participants were included in this retrospective cohort study. The average drop-out ratio was 4.8/100 person-years. After controlling the following baseline covariates: age, sex, marital status, route of HIV infection, WHO clinical stage before ART, initial/current ART regiment, ART regiment adjustment, and year of initiating ART for potential confounding, the adjusted HR of drop-out for HIV infected persons with 200- cells/μl, 351-cells/μl and ≥500 cells/μl were 1.110 (95%CI: 1.053-1.171, P<0.001), 1.391 (95%CI: 1.278-1.514, P<0.001) and 1.695 (95%CI: 1.497-1.918, P<0.001), respectively, in risk for drop-out compared with those with baseline CD(4)<200 cells/μl. Among the HIV infected persons, 56.0% (1 601/2 861) of drug withdrawal was due to poor compliance with medication. Conclusions: With the increase of baseline CD(4) when initiating ART, the risk for the drop-out in HIV infected persons increased significantly. To further reduce the drop-out of ART, it is important to take CD(4) into account in initiating ART and to strengthen the health education on treatment compliancy and training for healthcare providers.
Adolescent
;
Anti-Retroviral Agents/administration & dosage*
;
CD4 Lymphocyte Count
;
China/epidemiology*
;
HIV
;
HIV Infections/virology*
;
Humans
;
Incidence
;
Medication Adherence
;
Retrospective Studies
;
T-Lymphocytes
5.Application of generalized estimation equations to establish prediction equation for tuberculosis drug resistance in Zhejiang province.
Q WANG ; X M WANG ; W M CHEN ; L ZHOU ; Q MENG ; S H CHEN ; Z W LIU ; W B WANG
Chinese Journal of Epidemiology 2018;39(3):368-373
Objective: Drug-resistant tuberculosis (TB) may be resistant to one or multiple anti-TB drugs. We used generalized estimation equations to analysis the risk factors of drug-resistant TB and provide information for the establishment of a warning model for these non-independent data. Methods: The drug susceptibility test and questionnaire survey were performed in sputum positive TB patients from 30 anti TB drug-resistance surveillance sites in Zhejiang province. The generalized estimation model was established by the GENMOD module of SAS, with resistance to 13 kinds of anti-TB drugs as dependent variables and possible influencing factors, such as age, having insurance, HBV infection status, and history of anti-TB drug intake, as independent variables. Results: In this study, the probability of drug resistance at baseline level was 20.26%. Age, insurance, whether being co-infected with HBV, and treatment history or treatment withdrawal were statistically significantly correlated with anti-TB drug resistance. The prediction equation was established according to the influence degree of the factors mentioned above on drug resistance. Conclusion: The generalized estimation equations can effectively and robustly analyze the correlated binary outcomes, and thus provide more comprehensive information for drug resistance risk factor evaluation and warning model establishment.
Antitubercular Agents/therapeutic use*
;
Drug Resistance, Multiple, Bacterial
;
Humans
;
Models, Statistical
;
Mycobacterium tuberculosis/drug effects*
;
Risk Factors
;
Sputum/microbiology*
;
Surveys and Questionnaires
;
Tuberculosis/epidemiology*
;
Tuberculosis, Multidrug-Resistant
6.Family history and risk of coronary heart disease.
J H SI ; R R MENG ; C Q YU ; Y GUO ; Z BIAN ; Y L TAN ; P PEI ; J S CHEN ; Z M CHEN ; J LYU ; L M LI
Chinese Journal of Epidemiology 2018;39(2):173-178
Objective: To evaluate the association of family history with risk of major coronary events (MCE) and ischemic heart disease (IHD). Methods: After excluding participants with heart disease, stroke or cancer at baseline survey, a total of 485 784 participants from the China Kadoorie Biobank, who had no missing data on critical variables, were included in the analysis. Cox regression analysis was used to estimate the hazard ratios (HR) and 95% CI. Subgroup analyses were performed according to the baseline characteristics. Results: During a median of 7.2 years of follow-up, we documented 3 934 incident cases of MCE and 24 537 cases of IHD. In multivariable-adjusted models, family history was significantly associated with risk of MCE and IHD. The adjusted HRs (95%CI) were 1.41 (1.19-1.65) and 1.25 (1.18-1.33), respectively. History of disease among siblings was more strongly associated with early-onset MCE than parental history (HR=2.97, 95%CI: 1.80-4.88). Moreover, the association of family history with MCE and IHD was stronger in persons who were overweight or obesive, and the association between family history and MEC was stronger in smokers. Conclusion: This large-scale, prospective study indicated that family history was an independent risk factor for MCE and IHD in China. The intervention targeting major known lifestyle risk factors and the management of chronic diseases should be strengthened for Chinese population, especially for the individuals with family history were at high risk.
Asian People/statistics & numerical data*
;
China/epidemiology*
;
Coronary Disease/genetics*
;
Humans
;
Incidence
;
Myocardial Ischemia/genetics*
;
Overweight/ethnology*
;
Proportional Hazards Models
;
Prospective Studies
;
Risk Assessment
;
Risk Factors
;
Smoking/ethnology*