Reducing gunshot victimization in high-risk social networks through direct and spillover effects

Article metrics

Abstract

More than 60,000 people are victimized by gun violence each year in the United States. A large share of victims cluster in bounded and identifiable social networks. Despite a growing number of violence reduction programmes that leverage networks to broaden programmatic effects, there is little evidence that reductions in victimization are achieved through spillover effects on the peers of participants. This study estimates the direct and spillover effects of a gun violence field intervention in Chicago. Using a quasi-experimental design, we test whether a desistance-based programme reduced gunshot victimization among 2,349 participants. The study uses co-arrest network data to further test spillover effects on 6,132 non-participants. Direct effects were associated with a 3.2-percentage point reduction in victimization among seeds over two years, while potential spillover was associated with a 1.5-percentage point reduction among peers. Findings suggest that peer influence and the structure of networks might be leveraged to amplify gun violence reduction efforts.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Design of the intervention evaluation.
Fig. 2: The estimated effect of compliance on programme seeds (n = 2,349) and the estimated effect of compliance spillover on programme peers (n = 6,132).
Fig. 3

Data availability

The original data used in this study were provided to the corresponding author as part of a data-sharing agreement with the City of Chicago and the Chicago Police Department, and are prohibited from being shared directly. De-identified replication data generated and analysed in this study are available from the corresponding author upon request.

Code availability

All analyses were carried out in R. Code for reproducing the results of this study is available from the corresponding author upon request.

References

  1. 1.

    Wintemute, G. J. The epidemiology of firearm violence in the twenty-first century United States. Annu. Rev. Public Health 36, 5–19 (2015).

  2. 2.

    Web-based injury statistics query and reporting system. National Center for Injury Prevention and Control http://www.cdc.gov/injury/wisqars/index.html (2019).

  3. 3.

    Peterson, R. D. & Krivo, L. J. Divergent Social Worlds: Neighborhood Crime and the Racial-Spatial Divide (Russell Sage, 2010).

  4. 4.

    Harper, S., Lynch, J., Burris, S. & Davey Smith, G. Trends in the black-white life expectancy gap in the United States, 1983–2003. JAMA 297, 1224–1232 (2007).

  5. 5.

    Sharkey, P. The acute effect of local homicides on children’s cognitive performance. Proc. Natl Acad. Sci. USA 107, 11733–11738 (2010).

  6. 6.

    Tracy, M., Braga, A. A. & Papachristos, A. V. The transmission of gun and other weapon-involved violence within social networks. Epidemiol. Rev. 38, 70–86 (2016).

  7. 7.

    Papachristos, A. V., Wildeman, C. & Roberto, E. Tragic, but not random: the social contagion of nonfatal gunshot injuries. Soc. Sci. Med. 125, 139–150 (2015).

  8. 8.

    Papachristos, A. V., Braga, A. A. & Hureau, D. Social networks and the risk of gunshot injury. J. Urban Health 89, 992–1003 (2012).

  9. 9.

    Green, B., Thibaut, H. & Papachristos, A. V. Modeling contagion through social networks to explain and predict gunshot violence in Chicago, 2006 to 2014. JAMA Intern. Med. 177, 326–333 (2017).

  10. 10.

    Cooper, C., Eslinger, D. M. & Stolley, P. D. Hospital-based violence intervention programs work. J. Trauma Acute Care Surg. 61, 534–540 (2006).

  11. 11.

    Purtle, J., Rich, J. A., Fein, J. A., James, T. & Corbin, T. J. Hospital-based violence prevention: progress and opportunities. Ann. Intern. Med. 163, 715–717 (2015).

  12. 12.

    Butts, J. A., Roman, C. G., Bostwick, L. & Porter, J. R. Cure violence: a public health model to reduce gun violence. Annu. Rev. Public Health 36, 39–53 (2015).

  13. 13.

    Whitehill, J. M., Webster, D. W., Frattaroli, S. & Parker, E. M. Interrupting violence: how the ceasefire program prevents imminent gun violence through conflict mediation. J. Urban Health 91, 84–95 (1994).

  14. 14.

    Crandall, V. & Wong, S. L. Group Violence Reduction Strategy: Call-in Preparation and Execution (The Office of Community Oriented Policing Strategies, 2012).

  15. 15.

    Braga, A. A. & Weisburd, D. The effects of focused deterrence strategies on crime: a systematic review and meta-analysis of the empirical evidence. J. Res. Crime Delinq. 49, 323–358 (2012).

  16. 16.

    Braga, A. A., Papachristos, A. V. & Hureau, D. The effects of hot spots policing on crime: an updated systematic review and meta-analysis. Justice Q. 31, 633–663 (2014).

  17. 17.

    Braga, A. A., Weisburd, D. & Turchan, B. Focused deterrence strategies and crime control: an updated systematic review and meta-analysis of the empirical evidence. Criminol. Public Policy 17, 205–250 (2018).

  18. 18.

    Heckman, J. J. The scientific model of causality. Sociol. Methodol. 35, 1–97 (2006).

  19. 19.

    Durlauf, S. N., Navarro, S. & Rivers, D. A. Understanding aggregate crime regressions. J. Econ. 158, 306–317 (2010).

  20. 20.

    Gravel, J. & Tita, G. E. With great methods comes great responsibilities. Criminol. Public Policy 14, 559–572 (2015).

  21. 21.

    Valente, T. W. Network interventions. Science 337, 49–53 (2012).

  22. 22.

    Proestakis, A. et al. Network interventions for changing physical activity behaviour in preadolescents. Nat. Hum. Behav. 2, 778–787 (2018).

  23. 23.

    Kennedy, D. M., Braga, A. A. & Piehl, A. M. The (un)known yniverse: mapping gangs and gang violence in Boston. in Crime Mapping and Crime Prevention (ed. Weisburd, D. & McEwan, T.) 219–262 (Criminal Justice Press, 1997).

  24. 24.

    Papachristos, A. V. & Kirk, D. S. Changing the street dynamic: evaluating Chicago’s group violence reduction strategy. Criminol. Public Policy 14, 525–558 (2015).

  25. 25.

    National Network for Safe Communities. Group Violence Intervention: An Implementation Guide (US Department of Justice, Office of Community Oriented Policing Services, 2016).

  26. 26.

    Broockman, D. & Kalla, J. Durably reducing transphobia: a field experiment on door-to-door canvassing. Science 352, 220–224 (2016).

  27. 27.

    Clarke, R. V. & Weisburd, D. Diffusion of crime control benefits: observations on the reverse of displacement. Crime Prev. Stud. 2, 165–184 (1994).

  28. 28.

    Kennedy, D. M., Piehl, A. M. & Braga, A. A. Youth violence in Boston: gun markets, serious youth offenders, and a use-reduction strategy. Law Contemp. Probl. 59, 147–196 (1996).

  29. 29.

    Braga, A. A. & Weisburd, D. Focused deterrence and the prevention of violent gun injuries: practice, theoretical principles, and scientific evidence. Annu. Rev. Public Health 36, 55–68 (2015).

  30. 30.

    Charette, Y. & Papachristos, A. V. The network dynamics of co-offending careers. Soc. Netw. 51, 3–13 (2017).

  31. 31.

    Paluck, E. L., Shepherd, H. & Aronow, P. M. Changing climates of conflict: a social network experiment in 56 schools. Proc. Natl Acad. Sci. USA 113, 566–571 (2016).

  32. 32.

    Aronow, P. M. & Samii, C. Estimating average causal effects under general interference, with application to a social network experiment. Ann. Appl. Stat. 11, 1912–1947 (2017).

  33. 33.

    Hill, J. L. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20, 217–240 (2011).

  34. 34.

    Chipman, H. A., George, E. I. & McCulloch, R. E. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4, 266–298 (2010).

  35. 35.

    Green, D. P. & Kern, H. L. Modeling heterogenous treatment effects in survey experiments with Bayesian additive regression trees. Public Opin. Q. 76, 491–511 (2012).

  36. 36.

    Hainmueller, J. Entropy balancing for causal effects: a multivariate reweighting method to produce balanced samples in observational studies. Polit. Anal. 20, 25–46 (2012).

  37. 37.

    Palinkas, L. A. et al. Influence network linkages across implementation strategy conditions in a randomized controlled trial of two strategies for scaling up evidence-based practices in public youth-serving systems. Implement. Sci. 8, 133 (2013).

  38. 38.

    Christakis, N. A. & Fowler, J. H. Social network sensors for early detection of contagious outbreaks. PLoS One 5, e12948 (2010).

  39. 39.

    Centola, D. The spread of behavior in an online social network experiment. Science 329, 1194–1197 (2010).

  40. 40.

    Kim, D. A. et al. Social network targeting to maximise population behavior change: a cluster randomised controlled trial. Lancet 386, 145–153 (2015).

  41. 41.

    Shalizi, C. R. & Thomas, A. C. Homophily and contagion are generically confounded in observational social network studies. Sociol. Methods Res. 40, 211–239 (2011).

  42. 42.

    Legewie, J. & Fagan, J. Aggressive policing and the educational performance of minority youth. Am. Sociol. Rev. 84, 220–247 (2019).

  43. 43.

    Valente, T. W. Social network thresholds in the diffusion of innovations. Soc. Netw. 18, 69–89 (1996).

  44. 44.

    Rogers, E. M. Diffusion of preventative innovations. Addict. Behav. 27, 989–993 (2002).

  45. 45.

    Forastiere, L., Airoldi, E. M. & Mealli, F. Identification and estimation of treatment and interference effects in observational studies on networks. Preprint at arXiv https://arxiv.org/abs/1609.06245 (2018).

  46. 46.

    Hamilton, B., Rosenfeld, R. & Levin, A. Opting out of treatment: self-selection bias in a randomized controlled study of a focused deterrence notification meeting. J. Exp. Criminol. 14, 1–17 (2018).

  47. 47.

    Rosenbaum, P. R. Choice as an alternative to control in observational studies. Stat. Sci. 14, 259–304 (1999).

  48. 48.

    Rubin, D. B. Bayesian inference for causal effects: the role of randomization. Ann. Stat. 6, 34–58 (1978).

  49. 49.

    Pearl, J. Causal inference in statistics: an overview. Stat. Surv. 3, 96–146 (2009).

  50. 50.

    Steinman, K. J. & Zimmerman, M. A. Episodic and persistent gun-carrying among urban African-American adolescents. J. Adolesc. Health 35, 356–364 (2003).

  51. 51.

    Cook, P. J. & Laub, J. H. After the epidemic: recent trends in youth violence in the united states. Crime Justice 29, 1–37 (2002).

  52. 52.

    Peterson, R. D. & Krivo, L. J. Macrostructural analyses of race, ethnicity, and violent crime: recent lessons and new directions for research. Annu. Rev. Sociol. 31, 331–356 (2005).

  53. 53.

    Jones-Webb, R. & Wall, M. Neighborhood racial/ethnic concentration, social disadvantage, and homicide risk: an ecological analysis of 10 U.S. cities. J. Urban Health 8, 662–676 (2008).

  54. 54.

    Papachristos, A. V., Braga, A. A., Piza, E. & Grossman, L. S. The company you keep? The spillover effects of gang membership on individual gunshot victimization in a co-offending network. Criminology 53, 624–649 (2015).

  55. 55.

    Fewell, Z., Davey Smith, G. & Sterne, J. A. C. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Am. J. Epidemiol. 166, 646–655 (2007).

  56. 56.

    Groenwold, R. H. H. et al. Sensitivity analysis for the effects of multiple unmeasured confounders. Ann. Epidemiol. 26, 601–605 (2016).

Download references

Acknowledgements

This research was supported by a CAREER award (No. SES-1151449) from the Sociology and Law and Social Science Programs at the National Science Foundation. We thank Y. Charette and D. Kirk for providing valuable feedback. The funder had no role in the conceptualization, design, data collection, analysis, decision to publish or preparation of the manuscript.

Author information

G.W. and A.V.P. designed research. A.V.P. obtained data. G.W. performed research and analysed data. G.W. and A.V.P. wrote the paper.

Correspondence to Andrew V. Papachristos.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Primary Handling Editor: Aisha Bradshaw.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark