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Leveraging data science to enhance suicide prevention research: a literature review
  1. Avital Rachelle Wulz1,
  2. Royal Law2,
  3. Jing Wang2,
  4. Amy Funk Wolkin2
  1. 1 Oak Ridge Associated Universities (ORAU), Division of Injury Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
  2. 2 Division of Injury Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
  1. Correspondence to Avital Rachelle Wulz, Division of Injury Prevention, Centers for Disease Control and Prevention, Atlanta, GA 30341, USA; AWulz{at}


Objective The purpose of this research is to identify how data science is applied in suicide prevention literature, describe the current landscape of this literature and highlight areas where data science may be useful for future injury prevention research.

Design We conducted a literature review of injury prevention and data science in April 2020 and January 2021 in three databases.

Methods For the included 99 articles, we extracted the following: (1) author(s) and year; (2) title; (3) study approach (4) reason for applying data science method; (5) data science method type; (6) study description; (7) data source and (8) focus on a disproportionately affected population.

Results Results showed the literature on data science and suicide more than doubled from 2019 to 2020, with articles with individual-level approaches more prevalent than population-level approaches. Most population-level articles applied data science methods to describe (n=10) outcomes, while most individual-level articles identified risk factors (n=27). Machine learning was the most common data science method applied in the studies (n=48). A wide array of data sources was used for suicide research, with most articles (n=45) using social media and web-based behaviour data. Eleven studies demonstrated the value of applying data science to suicide prevention literature for disproportionately affected groups.

Conclusion Data science techniques proved to be effective tools in describing suicidal thoughts or behaviour, identifying individual risk factors and predicting outcomes. Future research should focus on identifying how data science can be applied in other injury-related topics.

  • suicide/self-harm
  • public health
  • media

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  • Contributors ARW was involved in all aspects of the study, including planning, analysing and reporting the results in the article. RL was involved in analysing, writing and reviewing the article. JW was involved in reviewing the article and providing subject matter expertise to the overall project. AFW was involved in the planning, writing and reviewing of the article.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Disclaimer The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

  • Competing interests None declared.

  • 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.