Predicting State-Level Firearm Suicide Rates: A Machine Learning Approach Using Public Policy Data.

Journal: American journal of preventive medicine
PMID: 38908723

Abstract

INTRODUCTION: Over 40,000 people die by suicide annually in the U.S., and firearms are the most lethal suicide method. There is limited evidence on the effectiveness of many state-level policies on reducing firearm suicide. The objective of this study was to identify public policies that best predict state-level firearm suicide rates.

Authors

  • Evan V Goldstein
    Department of Population Health Sciences, Spencer Fox Eccles School of Medicine, The University of Utah, Salt Lake City, Utah. Electronic address: evan.goldstein@hsc.utah.edu.
  • Fernando A Wilson
    Matheson Center for Health Care Studies, University of Utah, Salt Lake City.

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