Evidence map›Paper›PMID 41766686›Full record

ArticleAmerican journal of epidemiology2026

An approach to estimating how effective and well targeted Extreme Risk Protection Orders have been with respect to suicide prevention.

Sonja A Swanson, Jessica Handy, Veronica A Pear, Yifan Zhang, David Studdert, Hyunseung Kang, Elizabeth Diemer, Matthew Miller

Abstract read
In one paragraph

Article in American journal of epidemiology, 2026. 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
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

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

8 authors.

Sonja A SwansonDepartment of Epidemiology, University of Pittsburgh, Pittsburgh, PA, United States.
Jessica HandyDepartment of Epidemiology, University of Pittsburgh, Pittsburgh, PA, United States.
Veronica A PearCenters for Violence Prevention, University of California, Davis, Sacramento, CA, United States.ORCID 0000-0002-2462-3785
Yifan ZhangDepartment of Health Policy, Stanford University School of Medicine, Stanford, CA, United States.
David StuddertDepartment of Health Policy, Stanford University School of Medicine, Stanford, CA, United States.
Hyunseung KangDepartment of Statistics, University of Wisconsin-Madison, Madison, WI, United States.
Elizabeth DiemerDepartment of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Matthew MillerDepartment of Public Health and Health Science, Northeastern University, Boston, MA, United States.

Funding

National Collaborative for Gun Violence Research
6 · The paper itself

Abstract

Extreme Risk Protection Orders (ERPOs) are state laws intended to prevent gun violence by preemptively and temporarily removing firearms from individuals determined to be at risk of self-harm or violence against others. Here, we propose a framework for tackling questions about how well ERPOs have been targeted to those at the highest risk of suicide and how effective ERPOs are at preventing suicide among those who were targeted. This framework makes use of novel causal inference approaches using data on ERPOs issued and suicide deaths that could be reasonably obtained in many states with ERPO policies. More specifically, we formalize estimands related to effectiveness and risk-targeting and describe the conditions under which these estimands can be identified or bounded with available data. We provide adaptable R code for implementing these approaches and highlight key considerations for using these methods in practice.

Indexed as

FirearmsGun ViolenceSuicide PreventionHumansSuicideUnited StatesExtreme Risk Protection Ordersinstrumental variablepartial identificationsuicide

Identifiers

PMID41766686
PMCPMC13231853

What OpenQuestion holds

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LicenceCC BY-NC
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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.