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Epidemiological features and sociodemographic factors associated with mumps in mainland China from 2004 to 2018.

Xiaofang FuMinjie GeWucheng XuMin YuJiangang JuYonghong ZhongHuaqiong Huang
Published in: Journal of medical virology (2022)
Mumps is an acute infectious disease that spreads widely around the world. The aim of this study was to investigate the epidemiological features and sociodemographic factors associated with mumps in mainland China from 2004 to 2018. Incidence data for mumps during the period 2004-2018 were collected from the Public Health Sciences Data Center of China. Joinpoint regression analysis was performed to explore the trends of mumps. Space-time clustering analysis was conducted to spatial and temporal aggregation areas of mumps. A generalized linear model was used to explore sociodemographic factors associated with the incidence of mumps. The average annual incidence of mumps was 21.44/100 000 in mainland China. It was increased dramatically during 2004-2012 (annual percentage change​ [​​​​​​APC] = 7.51, 95% confidence interval [CI]: 2.28-13.00). After 2012, it remained stable, however, significantly increased in intermediately developed regions from 2015 to 2018 (APC = 25.84, 95% CI: 3.59-52.86). The first-level spatial and temporal aggregation areas were distributed in Xinjiang, Gansu, Qinghai, Ningxia and Shaanxi, Tibet, Sichuan, Yunnan, Chongqing, Guizhou, and Guangxi, with gathering times from January 1, 2006 to December 31, 2012 (relative risk [RR] = 1.87, p < 0.001). The percentage of the population aged 0-14 years, number of health workers per capital, and number of passengers were found to be positively associated with the incidence of mumps. Overall, after 2012, the incidence of mumps in mainland China remained stable. High-risk periods, clusters of regions, and sociodemographic factors for mumps were identified, which will help the government develop the disease- and location-specific interventive measures.
Keyphrases
  • public health
  • risk factors
  • mental health
  • infectious diseases
  • intensive care unit
  • hepatitis b virus
  • big data
  • climate change
  • social media
  • neural network
  • mechanical ventilation
  • health information