Costs attributable to criminal justice involvement in injuries: a systematic review.

CONTEXT: Costs related to criminal justice are an important component of the economic burden of injuries; such costs could include police involvement, judicial and corrections costs, among others. If the literature has sufficient information on the criminal justice costs related to injury, it could be added to existing estimates of the economic burden of injury. OBJECTIVE: To examine research on injury-related criminal justice costs, and what extent cost information is available by type of injury. DATA SOURCES: Medline, PsycINFO, Sociological Abstracts ProQuest, EconLit and National Criminal Justice Reference Service were searched from 1998 to 2021. DATA EXTRACTION: Preferred Reporting Items for Systematic reviews and Meta-Analyses was followed for data reporting. RESULTS: Overall, 29 studies reported criminal justice costs and the costs of crime vary considerably. CONCLUSIONS: This study illustrates possible touchpoints for cost inputs and outputs in the criminal justice pathway, providing a useful conceptualisation for better estimating criminal justice costs of injury in the future. However, better understanding of all criminal justice costs for injury-related crimes may provide justification for prevention efforts and potentially for groups who are disproportionately affected. Future research may focus on criminal justice cost estimates from injuries by demographics to better understand the impact these costs have on particular populations.

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

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.