Evidence map›Paper›PMID 38380060›Full record

ArticlePNAS nexus2024

The deadliest local police departments kill 6.91 times more frequently than the least deadly departments, net of risk, in the United States.

Josh Leung-Gagné

Open access · goldAbstract read
In one paragraph

Article in PNAS nexus, 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
0cells of the map it votes in
0citing papers in PubMed
2.2field-weighted citation impact, top 14% of its field
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, 1 citations in OpenAlex.

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

1 author at 1 institution in 1 country.

Josh Leung-GagnéCenter on Poverty & Inequality, Stanford University, Stanford, CA 94305, USA.ORCID https://orcid.org/0000-0002-1859-1326
Stanford University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

I use data linking counts of homicides by police to police department (PD) and jurisdiction characteristics to estimate benchmarked (i.e. risk-adjusted) police homicide rates in 2008-2017 among the 711 local PDs serving 50,000 or more residents, a sample with demographics resembling all mid-to-large Census places. The benchmarked rate estimates capture PD deadliness by comparing PDs to peers whose officers face similar risks while adjusting for access to trauma care centers to account for differential mortality from deadly force. Compared to existing estimates, differences in benchmarked estimates are more plausibly attributable to policing differences, speaking to whether the force currently used is necessary to maintain safety and public order. I find that the deadliest PDs kill at 6.91 times the benchmarked rate of the least deadly PDs. If the PDs with above-average deadliness instead killed at average rates for a PD facing similar risks, police homicides would decrease by 34.44%. Reducing deadliness to the lowest observed levels would decrease them by 70.04%. These estimates also indicate the percentage of excess police homicides-those unnecessary for maintaining safety-if the baseline agency is assumed to be optimally deadly. Moreover, PD deadliness has a strong, robust association with White/Black segregation and Western regions. Additionally, Black, Hispanic, foreign-born, lower income, and less educated people are disproportionately exposed to deadlier PDs due to the jurisdictions they reside in. Police violence is an important public health concern that is distributed unevenly across US places, contributing to social disparities that disproportionately harm already marginalized communities.

Indexed as

criminologypublic healthsocial demographyspatial inequality

Identifiers

PMID38380060
PMCPMC10878363
OpenAlexW4394760853

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.