What have we learned from the time trend of mass shootings in the U.S.?

PLoS One. 2018 Oct 18;13(10):e0204722. doi: 10.1371/journal.pone.0204722. eCollection 2018.

Abstract

Little is known regarding the time trend of mass shootings and associated risk factors. In the current study, we intended to explore the time trend and relevant risk factors for mass shootings in the U.S. We attempted to identify factors associated with incidence rates of mass shootings at the population level. We evaluated if state-level gun ownership rate, serious mental illness rate, poverty percentage, and gun law permissiveness could predict the state-level mass shooting rate, using the Bayesian zero-inflated Poisson regression model. We also tested if the nationwide incidence rate of mass shootings increased over the past three decades using the non-homogenous Poisson regression model. We further examined if the frequency of online media coverage and online search interest levels correlated with the interval between two consecutive incidents. The results suggest an increasing trend of mass shooting incidences over time (p < 0.001). However, none of the state-level variables could predict the mass shooting rate. Interestingly, we have found inverse correlations between the interval between consecutive shootings and the frequency of on-line related reports as well as on-line search interests, respectively (p < 0.001). Therefore, our findings suggest that online media might correlate with the increasing incidence rate of mass shootings. Future research is warranted to continue monitoring if the incidence rates of mass shootings change with any population-level factors in order to inform us of possible prevention strategies.

Publication types

  • Historical Article

MeSH terms

  • Communications Media / history
  • Communications Media / trends
  • Firearms / legislation & jurisprudence
  • History, 20th Century
  • History, 21st Century
  • Humans
  • Incidence
  • Mass Casualty Incidents / history*
  • Mass Casualty Incidents / prevention & control
  • Mass Casualty Incidents / statistics & numerical data
  • Regression Analysis
  • Risk Factors
  • Time Factors
  • United States / epidemiology
  • Wounds, Gunshot / history*

Grant support

The authors received no specific funding for this work.