Factors associated with youth gang membership in low- and middle-income countries: a systematic review.

Lorraine Mazerolle,Kathryn Benier,Angela Higginson,Joseph Murray,Yulia Shenderovich,Laura Bedford

doi:10.4073/csr.2018.11

Abstract

This Campbell systematic review examines the predictors of youth gang membership in low‐ and middle‐income countries. The review summarises findings from eight reports from five countries and the Caribbean region. The lack of available evidence limits the extent to which clear conclusions can be drawn about the factors associated with youth gang membership. The review is based on a very small number of studies, and has significant limitations in coverage. The limited evidence of the correlates of youth gang membership suggests factors that may drive gang membership and suggests areas where interventions may prove promising in the family, school, and community domains, as well as provide a starting point for future studies.

Highlights

The studies were conducted in Turkey, Trinidad and Tobago, the Caribbean, El Salvador, China and Brazil
Delinquency, alcohol and soft drug use, male gender, risky sexual behaviours, employment, psychological risk factors, and victimisation were each associated with significantly higher odds of youth gang membership
The lack of available evidence limits the extent to which we can draw any clear conclusions about the factors associated with youth gang membership

Summary

Objectives

This review addresses two key objectives: (1) to synthesize the published and unpublished empirical evidence on the factors associated with membership of youth gangs in low- and middleincome countries; (2) to assess the relative strength of the different factors across the domains of individual, family, school, peer group and community. We conducted a broad abstract screening of over 54,000 titles and abstracts, followed by a close abstract screening of 1509 abstracts. Nine studies met the eligibility criteria and were included in the review. One of these studies did not report sufficient data to allow the calculation of a standardized effect size, and so was not included in the analyses. A total of 85 independent effect sizes were extracted from the eight studies with sufficient data to create a standardized effect size. We calculated Cohen’s d from continuous data and the Log Odds Ratio from dichotomous data. All effects were categorized into the five predictor domains, and further classified into conceptually similar group and risk or protective factors. We synthesized the data using multiple random effects meta-analyses with inverse variance weighting

Methods

Results

Discussion

Conclusion