Citation

Barak-Ventura R, Marín MR, Porfiri M. Patterns (N Y) 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, Cell Press)

DOI

10.1016/j.patter.2022.100546

PMID

unavailable

Abstract

Firearm injury is a major public health crisis in the United States, where more than 200 people sustain a nonfatal firearm injury and more than 100 people die from it every day. To formulate policy that minimizes firearm-related harms, legislators must have access to spatially resolved firearm possession rates. Here, we create a spatiotemporal econometric model that estimates monthly state-level firearm ownership from two cogent proxies (background checks per capita and fraction of suicides committed with a firearm). From calibration on yearly survey data that assess ownership, we find that both proxies have predictive value in estimation of firearm ownership and that interactions between states cannot be neglected. We demonstrate use of the model in the study of relationships between media coverage, mass shootings, and firearm ownership, uncovering causal associations that are masked by the use of the proxies individually.

• A spatiotemporal model of firearm prevalence in the United States is created
• The econometric model predicts firearm ownership in every state for every month
• Information theory is used to detail causal links related to firearm prevalence
• The media can influence firearm prevalence, which in turn moderates mass shootings

Firearm violence is a major public health crisis in the United States, where more than 200 people sustain a nonfatal firearm injury and more than 100 people die from it every day. Despite these unsettling figures, scientific research on firearm-related harm significantly lags behind because spatially and temporally resolved data on firearm ownership are unavailable. This paper presents a spatiotemporal model that predicts firearm prevalence at the resolutions of one state and one month from the numbers of background checks and suicides committed with a firearm. Drawing on principles from econometrics, the model also accounts for interactions between states. The model's output is challenged in causal analysis, which uncovers unprecedented associations between firearm prevalence, media output on firearm regulations, and mass shootings.

Language: en

Keywords

DSML3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems; firearm ownership; firearm violence; spatial econometrics; time series