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30 July 2019 : Clinical Research  

Trends in the Epidemiology of Sexually Transmitted Disease, Acquired Immune Deficiency Syndrome (AIDS), Gonorrhea, and Syphilis, in the 31 Provinces of Mainland China

Xuechen Ye1CDEF, Jie Liu2BCDF, Zhe Yi3AE*

DOI: 10.12659/MSM.915732

Med Sci Monit 2019; 25:5657-5665

Abstract

BACKGROUND: This study aimed to investigate trends in the epidemiology of the leading sexually transmitted diseases (STDs), acquired immune deficiency syndrome (AIDS), gonorrhea, and syphilis, in the 31 provinces of mainland China.

MATERIAL AND METHODS: This retrospective study analyzed the incidence data of STDs from official reports in China between 2004 and 2016. The grey model first order one variable, or GM (1,1), time series forecasting model for epidemiological studies predicted the incidence of STDs based on the annual incidence reports from 31 Chinese mainland provinces. Hierarchical cluster analysis was used to group the prevalence of STDs within each province.

RESULTS: The prediction accuracy of the GM (1,1) model was high, based on data during the 13 years between 2004 and 2016. The model predicted that the incidence rates of AIDS and syphilis would continue to increase over the next two years. Cluster analysis showed that 31 provinces could be classified into four clusters according to similarities in the incidence of STDs. Group A (Sinkiang Province) had the highest reported prevalence of syphilis. Group B included provinces with a higher incidence of gonorrhea, mainly in the southeast coast of China. Group C consisted of southwest provinces with a higher incidence of AIDS.

CONCLUSIONS: The GM (1,1) model was predictive for the incidence of STDs in 31 provinces in China. The predicted incidence rates of AIDS and syphilis showed an upward trend. Regional distribution of the major STDs highlights the need for targeted prevention and control programs.

Keywords: Cluster Analysis, Forecasting, Incidence, Sexually Transmitted Diseases, Acquired Immunodeficiency Syndrome, Gonorrhea, HIV Infections, Models, Statistical, Prevalence, syphilis

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750