Evidence map›Paper›PMID 33299072›Full record

ArticleNPJ digital medicine2020

Internet search patterns reveal firearm sales, policies, and deaths.

John S Brownstein, Adam D Nahari, Ben Y Reis

Abstract read
In one paragraph

Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Online Searches for Gun-Related Harm.JAMA network open · 2026
    Article
  2. Article
  3. Article
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

3 authors.

John S BrownsteinComputational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA.
Adam D NahariPredictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0003-0634-1108
Ben Y ReisHarvard Medical School, Boston, MA, USA. Ben_Reis@Harvard.edu.ORCID http://orcid.org/0000-0001-9908-5523

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Firearm-related violence is a leading cause of morbidity and mortality and is at the center of a major public health and policy debate in the United States. Despite the critical role of data in informing this debate, accurate and comprehensive data on firearm sales and ownership is not readily available. In this study, we evaluate the potential of using firearm-related internet search queries as a complementary, freely available, and near-real-time data source for tracking firearm sales and ownership that enables analysis at finer geographic and temporal scales. (Here, we examine data by state and by month to compare with other data sources, but search engine volume can be analyzed by city and by the week or by day). We validate search query volume against available data on background checks in all 50 US states, and find that they are highly correlated over time (Pearson's r = 0.96, Spearman's ρ = 0.94) and space (Pearson's r = 0.78, Spearman's ρ = 0.76). We find that stratifying this analysis by gun type (long-gun vs. handgun) increases this correlation dramatically, across both time and space. We also find a positive association between firearm-related search query volume and firearm-related mortality (Pearson's r = 0.87, Spearman's ρ = 0.90), and a negative association with the strength of state-level firearm control policies (Pearson's r = -0.82, Spearman's ρ = -0.83). Based on these findings, we propose a framework for prospective surveillance that incorporates firearm-related internet search volume as a useful complementary data source to inform the public health policy debate on this issue.

Identifiers

PMID33299072
PMCPMC7679459

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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.