Leveraging data science to enhance suicide prevention research: a literature review

Abstract

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.

Keywords: media; public health; suicide/self-harm.

Figure 1

Literature review screening process.

Figure 2

Counts of individual-level, population-level and overall articles published in each year.

Figure 3

Counts of individual-level and population-level articles categorised by reason for applying data science methods. ‘Multiple’ refers to any article with more than one reason for applying data science methods. These counts are not included in the other groups.