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Trend analysis and prediction of injury incidence in China from 1990 to 2019 based on Bayesian age–period–cohort model
  1. Yuanjie Meng1,
  2. Chaocai Wang2,
  3. Yan Liu1
  1. 1Department of Public Health, Qinghai University Medical College, Xining, Qinghai, China
  2. 2Qinghai Provincial Center for Disease Control and Prevention, Xining, China
  1. Correspondence to Dr Yan Liu, Department of Public Health, Qinghai University Medical College, Xining, Qinghai, China; yanliu{at}qhu.edu.cn

Abstract

Background Injury is a major challenge to global public health. Analysing the trend of injury incidence in China from 1990 to 2019 and predicting future trends in incidence can provide a theoretical basis for injury prevention and control in China.

Methods We collected age-standardised incidence rates of injuries in China from 1990 to 2019 from the Global Burden of Disease 2019 study. We analysed trends using joinpoint regression and age–period–cohort models. A prediction study was conducted using the Bayesian age-period-cohort model.

Results From 1990 to 2019, there was an increasing trend in transport injuries, a decreasing trend in unintentional injuries and a decreasing trend in self-harm and interpersonal violence. The high-risk age for transport injuries, unintentional injuries and self-harm and interpersonal violence were 20–69 years (relative risk (RR)>1), ≤14 and ≥80 years (RR>1) and 20–24 years (RR=2.311, 95% CI 2.296 to 2.326), respectively. Projections indicate that by 2030, the incidence of transport and unintentional injuries will increase, whereas the incidence of self-harm and interpersonal violence will decrease.

Conclusion The age group with the highest risk of transport injuries, unintentional injuries and self-harm and interpersonal violence were the 20–69 years, ≤ 14 and ≥80 years and 20–24 years age groups, respectively. Transport injuries and unintentional injuries will increase in 2020–2030, while self-harm and interpersonal violence will decrease. These can serve as a basis for developing measures to prevent and manage the impact of injuries.

  • Burden Of Disease
  • Time series
  • Public Health

Data availability statement

Data are available in a public, open access repository.

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Data availability statement

Data are available in a public, open access repository.

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Footnotes

  • YM and CW are joint first authors.

  • Contributors YM completed data analysis and article writing; CW is responsible for improving the study design, proposing amendments and providing financial support; YL is responsible for proposing research ideas and guiding the paper.YL is designated as the guarantor.

  • Funding Qinghai Province 'Kunlun Talent' action plan ([Qing Talent Word (2020) No.18]).

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer-reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.