Evidence map›Paper›PMID 39148747›Full record

ArticleSSM - population health2024

Formative reasons for state-to-state influences on firearm acquisition in the U.S.

Xu Wang, James Macinko, Maurizio Porfiri, Rifat Sipahi

Abstract read
In one paragraph

Article in SSM - population health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Xu WangDepartment of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, 02115, USA.
James MacinkoDepartment of Health Policy and Management, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, 90095, USA.
Maurizio PorfiriCenter for Urban Science and Progress, Tandon School of Engineering, New York University, Brooklyn, NY, 11201, USA.
Rifat SipahiDepartment of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, 02115, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Firearm-related crimes and self-inflicted harms pose a significant threat to the safety and well-being of Americans. Investigation of firearm prevalence in the United States (U.S.) has therefore been a center of attention. A critical aspect in this endeavor is to explain whether there are identifiable patterns in firearm acquisition. Methods: We view firearm acquisition patterning as a spatio-temporal dynamical system distributed across U.S. states that co-evolves with crime rates, political ideology, income levels, population, and the legal environment. We leverage transfer entropy and exponential random graph models along with publicly available data, to statistically reveal the formative factors in how each state's temporal patterning of firearm acquisition influences other states. Results: Results help to explain how and why U.S. states influence each other in their firearm acquisition. We establish that state-to-state influences, or lack thereof, in firearm acquisition patterning are explained by states' percent of gun homicide, firearm law strictness, geographic neighborhood, and citizen ideology. Network-based characteristics, namely, mutuality and transitivity, are also important to explain such influence. Conclusions: Results suggest that state policies or programs that reduce gun homicides will also help suppress that state's influence on the patterning of firearm acquisition in other states. Furthermore, states with stricter firearm laws are more likely to influence firearm acquisition in other states, but are themselves shielded from the effects of other states' firearm acquisition patterns. These results inform future research in public health, criminology, and policy making.

Indexed as

Causal influenceExponential-family random graph modelsfirearm acquisitionState-to-state networksTransfer entropy

Identifiers

PMID39148747
PMCPMC11325079

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.