Evidence map›Paper›PMID 41922173›Full record

ArticleTobacco control2026

Modelling how age-restricted location policies would impact tobacco retailer density in four US states: California, Connecticut, North Carolina and Ohio.

Peter F Craigmile, Emerson Webb, Joseph G L Lee, Meghan E Morean, Grace Kong, Jessica Barrington-Trimis, Rachel Carmen Ceasar, Vitoria Borges Spinola, Micah L Berman, Megan E Roberts

Abstract read
In one paragraph

Article in Tobacco control, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Peter F CraigmileDepartment of Mathematics and Statistics, Hunter College, CUNY, New York, NY, USA.
Emerson WebbDepartment of Statistics, The Ohio State University, Columbus, OH, USA.
Joseph G L LeeDepartment of Implementation Science, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA.ORCID http://orcid.org/0000-0001-9698-649X
Meghan E MoreanDepartment of Psychiatry, Yale School of Medicine, New Haven, CT, USA.ORCID http://orcid.org/0000-0003-4865-1155
Grace KongDepartment of Psychiatry, Yale School of Medicine, New Haven, CT, USA.ORCID http://orcid.org/0000-0002-9269-3435
Jessica Barrington-TrimisKeck School of Medicine, University of Southern California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-3331-0326
Rachel Carmen CeasarKeck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Vitoria Borges SpinolaCollege of Public Health, The Ohio State University, Columbus, OH, USA.
Micah L BermanCollege of Public Health, The Ohio State University, Columbus, OH, USA.ORCID http://orcid.org/0000-0002-0336-1317
Megan E RobertsCollege of Public Health, The Ohio State University, Columbus, OH, USA roberts.1558@osu.edu.ORCID http://orcid.org/0000-0003-4743-2145

Funding

Modeling the impact of age restricted in-person location policies for youth tobacco useR01CA273206 · NCI · OHIO STATE UNIVERSITY · PI Megan Eleanor Roberts · 2023 to 2026
$2.5M
NCI NIH HHS R01 CA273206
6 · The paper itself

Abstract

backgroundAge-restricted location (ARL) policies are a novel tobacco control strategy that restrict in-person tobacco sales to locations that do not allow underage buyers on their premises, such as tobacco shops. This study aimed to examine how ARL policies might impact different US communities.

methodsPreliminary work geocoded the locations of all tobacco retailers (collected 2023-2024) across four states (California, Connecticut, North Carolina and Ohio) and calculated tobacco retailer density (TRD) at the census tract level. We then simulated an ARL policy by removing all retailers that would no longer be allowed to sell tobacco. Finally, using statistical methods that accounted for varying spatial distributions over census tracts for each state, we conducted pre-post analyses to determine how an ARL policy would reduce TRD overall, and by neighbourhood characteristics (census tract-level poverty, race and ethnicity and rurality).

resultsThe percentage reduction in tobacco retailers was highest for North Carolina (86.4% reduction in TRD), followed by Ohio (85.8%), California (74.4%) and Connecticut (62.4%). Results also indicated that an ARL policy would generally be equity-neutral, meaning that it would not exacerbate the current disparities that exist in TRD. And, in some cases, the policy would be equity-enhancing, such as in the case of reducing existing rural TRD disparities.

conclusionsThe degree of TRD reduction estimated by these simulation models for an ARL policy is more powerful than what has been found for other retailer-focused strategies (eg, prohibiting tobacco retailers close to schools). ARL policies could be a promising and potentially powerful strategy for reducing TRD in the USA.

Indexed as

DisparitiesEnd gameTobacco industry

Identifiers

PMID41922173
PMCPMC13264472

What OpenQuestion holds

Textmetadata
LicenceTDM
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.