Objective: To analyze the spatial distribution of tuberculosis (TB) and identify the clustering areas in Qinghai province from 2014 to 2016, and provide evidence for the prevention and control of TB. Methods: The data of pulmonary TB cases confirmed by clinical and laboratory diagnosis in Qinghai during this period were collected from National Disease Reporting Information System. The visualization of annual reported incidence, three-dimensional trend analysis and local Getis-Ord G(i)(*) spatial autocorrelation analysis of TB were performed by using software ArcGIS 10.2.2, and global Moran's I spatial autocorrelation analysis were analyzed by using software OpenGeoDa 1.2.0 to describe and analyze the spatial distribution characteristics and high incidence areas of TB in Qinghai from 2014 to 2016. Results: A total of 20 609 pulmonary TB cases were reported in Qinghai during this period. The reported incidences were 101.16/100 000, 123.26/100 000 and 128.70/100 000 respectively, an increasing trend with year was observed (trend χ(2)=187.21, P<0.001). The three-dimensional trend analysis showed that the TB incidence increased from northern area to southern area, and up-arch trend from the east to the west. Global Moran's I spatial autocorrelation analysis showed that annual reported TB incidence in different areas had moderate spatial clustering (Moran's I values were 0.631 3, 0.605 4, and 0.587 3, P<0.001). And local G(i)(*) analysis showed that there were some areas with high TB incidences, such as 10 counties of Yushu and Guoluo prefectures (Gande, Banma and Dari counties, etc., located in the southwest of Qinghai), and some areas with low TB incidences, such as Huangzhong county, Chengdong district and Chengbei district of Xining city and Dachaidan county of Haixi prefecture, and the reported TB incidences in the remaining areas were moderate. Conclusion: The annual reported TB incidence increased year by year in Qinghai from 2014 to 2016. The distribution of TB cases showed obvious spatial clustering, and Yushu and Guoluo prefectures were the key areas in TB prevention and control. In addition, the spatial clustering analysis could provide the important evidence for the development of TB prevention and control measures in Qinghai.
China/epidemiology* ; Cluster Analysis ; Disease Notification/statistics & numerical data* ; Female ; Geographic Information Systems ; Humans ; Incidence ; Male ; Spatial Analysis ; Spatio-Temporal Analysis ; Tuberculosis/microbiology* ; Tuberculosis, Pulmonary/ethnology*

Objective: To explore the sampling method in China National Human Biomonitoring Program (HBP) and the related errors, so as to calculate and evaluate the study design in sampling. Methods: The sampling method of HBP is of multistage nature. Taking the results of sampling method from Guizhou province as an example, results related to sampling error and variation coefficient were calculated, using the multistage unequal probability sampling error method. Results: The HBP covered 152 monitoring sites in 31 provinces (autonomous regions and municipalities) and with 21 888 residents selected. The replacement rates at various stages were 5.26%, 6.35% and 40.6% respectively. The sampling error in Guizhou province was 3 207 594, and the coefficient of variation was 0.097. Conclusions: According to the multi-stage unequal probability sampling method, the sampling coefficient variability appeared small with high precision, in Guizhou province. However, this method did not consider the weight adjustment of non-sampling errors such as population missing rate and response rate. Methods related to the calculation on multi-stage sampling error among large-scale public health monitoring projects need to be further studied.
China ; Cities ; Environmental Monitoring ; Humans ; Research Design

Both the increasing prevalence and growing burden of diabetes mellitus have caused global public health concerns. With the development of bio-psycho-social medical model, the impact of psychosocial factors on diabetes has attracted more attentions among the researchers. This paper summarizes findings from epidemiological studies that focusing on the association between diabetes and related psychosocial risk factors. Foreign studies have shown that psychological factors are closely related to diabetes, but the conclusions on social factors are inconsistent. Domestic studies have only targeted on small-sample-sized and cross-sectional studies. More longitudinal research is needed to confirm the impact of psychosocial factors on the risk of diabetes.
China/epidemiology* ; Cross-Sectional Studies ; Diabetes Mellitus/psychology* ; Humans ; Incidence ; Prevalence ; Risk Factors ; Social Environment ; Social Support

Objective: To explore the survival factors and construct a prognostic index (PI) for oral squamous cell carcinoma (OSCC). Methods: From January 2004 to June 2016, a total of 634 patients with pathologically confirmed OSCC were recruited in a hospital of Fujian. The clinical and follow-up data of all the patients with pathologically confirmed OSCC were collected to identify the factors influencing the prognosis of OSCC. All the patients were randomly divided into two groups: modeling group (modeling dataset, n=318) and validation group (validation dataset, n=316). Randomization was carried out by using computer-generated random numbers. In the modeling dataset, survival rates were calculated using Kaplan-Meier method and compared using the log-rank test. Cox regression model was used to estimate the hazard ratio (HRs) and 95% confidence intervals (CIs) of prognosis factors. An PI for OSCC patients prognostic prediction model was developed based on β value of each significant variable obtained from the multivariate Cox regression model. Using the tertile analysis, patients were divided into high-risk group, moderate-risk group, and low-risk group according to the PI, the Akaike information criterion (AIC) and Harrell's c-statistic (C index) were used to evaluated the model's predictability. Results: Results from the multivariate Cox regression model indicated that aged ≥55 years (HR=2.22, 95%CI: 1.45-3.39), poor oral hygiene (HR=2.12, 95%CI: 1.27-3.54), first diagnosis of lymph node metastasis (HR=5.78, 95%CI: 3.60-9.27), TNM stage Ⅲ-Ⅳ (stage Ⅰ as reference) (HR=2.43, 95%CI: 1.10-5.37) and poor differentiation (well differentiation as reference) (HR=2.53, 95%CI: 1.60-4.01) were the risk factors influencing the prognosis of OSCC. The PI model had a high predictability in modeling group and validation group (AIC and C index were 1 205.80, 0.700 2 and 1 150.47, 0.737 3). Conclusion: Age, poor oral hygiene, first diagnosis of lymph node metastasis, TNM stage and histological grade were factors associated with the prognosis of OSCC, and the PI model has a certain significance in the clinical treatment of OSCC.
Carcinoma, Squamous Cell/therapy* ; China/epidemiology* ; Humans ; Lymphatic Metastasis ; Middle Aged ; Mouth Neoplasms/therapy* ; Prognosis ; Proportional Hazards Models ; Risk Factors ; Survival Rate ; Treatment Outcome

Objective: To understand the prevalence of drug resistance in treatment-naive injecting drug users (IDUs) infected with HIV-1 in Guangzhou. Methods: HIV-1 RNA were extracted from the serum specimens of the newly confirmed HIV-1 positive IDUs living in Guangzhou, being infected through injecting drug use and receiving no antiretroviral therapy at the time of confirmation during 2008-2015. Full sequence of pol protease (PR) gene and partial sequence of reverse transcriptase (RT) gene were amplified by nested reverse transcription polymerase chain reaction (nested-PCR) and sequenced. After that, data were submitted to the HIV resistance database of Stanford University for drug resistance analysis. Results: Among the 518 HIV-1 infected IDUs, HIV-1pol gene segments were successfully obtained from the serum samples of 407 HIV-1 infected IDUs (78.57%) aged 18-64 (37.44±8.14) years. Among them, males accounted for 89.68% (365/407), those of Han ethnic group accounted for 89.93% (366/407), the unmarried accounted for 55.28% (225/407), and those with education level of junior high school or below accounted for 83.78% (341/407). The distribution of subtypes was predominated by CRF07_BC (47.18%, 192/407), followed by CRF01_AE (23.83%, 97/407), CRF08_BC (22.85%, 93/407), and other subtypes (6.14%, 25/407). The overall prevalence of drug resistance was 3.44% (14/407). The prevalence of drug resistance to protease inhibitors, nucleoside reverse transcriptase inhibitors and non-nucleoside reverse transcriptase inhibitors were 1.47%(6/407), 0.25% (1/407) and 1.72% (7/407) respectively. The mutation rate was 12.29% (50/407). No major drug resistance mutation was detected in protease and nucleoside reverse transcriptase regions. Higher rate of V179E mutation in the non-nucleoside reverse transcriptase region was detected in other subtypes and subtype CRF07_BC. Mutation seemed to have occurred in all 8 cases of subtype CRF55_01B in other subtypes. The highest mutation rate of E138A was detected in subtype CRF08_BC (3.23%). Two cases were resistant to all four drugs of NNRTIs. Conclusions: The prevalence of drug resistance in treatment-naive HIV-1 positive IDUs remained at a relatively low level during 2008-2015, in Guangzhou. Most infections were sensitive to existing antiviral drugs. However, drug resistance surveillance in IDUs infected with HIV should be strengthened to prevent the prevalence of multi-drug resistance and cross drug resistance.
Adolescent ; Adult ; Child ; Drug Resistance, Viral/genetics* ; Drug Users ; Genes, pol/genetics* ; Genotype ; HIV Infections/psychology* ; HIV-1/isolation & purification* ; Humans ; Male ; Mutation ; Prevalence ; RNA, Viral/genetics* ; Young Adult