[Analysis on Neisseria gonorrhoeae infection status and related factors in outpatients of sexually transmitted diseases in Shenzhen].

Objective: To understand the prevalence of Neisseria gonorrhoeae (NG) infection and related factors in outpatients of sexually transmitted disease (STD) in Shenzhen and provide scientific evidence for targeted intervention. Methods: A cross-sectional study was conducted in STD outpatients who were aged 18-49 years, had had sexual intercourse, had not received antibiotics in the last 2 weeks and sought medical cares in 22 medical institutions in 6 districts of Shenzhen from 15 April, 2018 to 16 May, 2018, their basic information and urine samples were collected, and NG in urine sample was detected by nucleic acid amplification test. Logistic regression analysis was conducted to identify the related factors for NG infection. Results: The information collection and sample collection were completed for 8 324 STD outpatients, among these STD outpatients, 196 were NG positive, with a positive rate of 2.4% (196/8 324). The positive rate of NG in men (5.8%, 148/2 567) was higher than that in women (0.8%, 47/5 742) (χ(2)=189.43, P<0.05). Aged 24 years or below (OR=2.11, 95%CI: 1.44-3.09), single/divorced/widowed (OR=1.98, 95%CI:1.38-2.84), having casual sex in the last 3 months (OR=1.77, 95%CI:1.29-2.43) were the related factors for NG infection. Conclusions: We found that NG infection rate was high in STD outpatients in Shenzhen. The infection rates in men and in women differed obviously. It is necessary to conduct gonorrhea screening and intervention in STD outpatients with high-risk behaviors, such as extramarital and casual sex behaviors, and standardize the treatment to reduce the incidence and spread of gonorrhea and other STDs.

Time series analysis and forecasting of chlamydia trachomatis incidence using surveillance data from 2008 to 2019 in Shenzhen, China.

Chlamydia trachomatis (CT) infection has been a major public health threat globally. Monitoring and prediction of CT epidemic status and trends are important for programme planning, allocating resources and assessing impact; however, such activities are limited in China. In this study, we aimed to apply a seasonal autoregressive integrated moving average (SARIMA) model to predict the incidence of CT infection in Shenzhen city, China. The monthly incidence of CT between January 2008 and June 2019 in Shenzhen was used to fit and validate the SARIMA model. A seasonal fluctuation and a slightly increasing pattern of a long-term trend were revealed in the time series of CT incidence. The monthly CT incidence ranged from 4.80/100 000 to 21.56/100 000. The mean absolute percentage error value of the optimal model was 8.08%. The SARIMA model could be applied to effectively predict the short-term CT incidence in Shenzhen and provide support for the development of interventions for disease control and prevention.