Evidence map›Paper›PMID 40239897›Full record

ArticlePreventive medicine2025

Associations between state tobacco control measures and cigarette purchases by U.S. households, 2015-2021.

Rishika Chakraborty, Yan Li, Yan Wang, Carla Berg, Sabrina Zhang, Debra Bernat, Y Tony Yang

Abstract read
In one paragraph

Article in Preventive medicine, 2025. 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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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

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5 · Who and what money

Authors and funding

7 authors.

Rishika ChakrabortyCenter for Health Policy and Media Engagement, School of Nursing, George Washington University, Washington DC, USA. Electronic address: rishikac@gwu.edu.
Yan LiDepartment of Epidemiology and Biostatistics and Joint Program in Survey Methodology, University of Maryland - College Park, MD, USA.
Yan WangDepartment of Prevention and Community Health, Milken Institute School of Public Health, George Washington University, Washington DC, USA; George Washington Cancer Center, George Washington University, Washington DC, USA.
Carla BergDepartment of Prevention and Community Health, Milken Institute School of Public Health, George Washington University, Washington DC, USA; George Washington Cancer Center, George Washington University, Washington DC, USA.
Sabrina ZhangJoint Program in Survey Methodology, College of Behavioral and Social Science, University of Maryland- College Park, MD, USA.
Debra BernatDepartment of Epidemiology, Milken Institute School of Public Health, George Washington University, Washington DC, USA.
Y Tony YangCenter for Health Policy and Media Engagement, School of Nursing, George Washington University, Washington DC, USA; George Washington Cancer Center, George Washington University, Washington DC, USA.

Funding

Effects of State Preemption of Local Tobacco Control Legislation on Disparities in Tobacco Use, Exposure and RetailR01CA275066 · NCI · GEORGE WASHINGTON UNIVERSITY · PI Carla J Berg, Y. Tony Yang · 2023 to 2026
$2.0M
NCI NIH HHS R01 CA275066
6 · The paper itself

Abstract

objectiveWhile effects of key tobacco control policies are well-documented, limited research has explored their varying associations across different policy contexts over time. This is crucial given the diverse and evolving tobacco control contexts across states and over time. We evaluated the association between state-level tobacco control measures and cigarette purchases in the US from 2015 to 2021.

methodsWe analyzed NielsenIQ Consumer Panel data from 10,187 households that purchased cigarettes in 2015-2021. State-level tobacco control policy scores for smoke-free laws, taxes, prevention/control funding, and cessation services were obtained from the American Lung Association's State of Tobacco Control reports. Censored regression models, reporting adjusted beta estimates and 95 % confidence intervals (CI), estimated the associations between each tobacco control measure and annual household cigarette purchases, adjusting for sociodemographics (household composition, marital status, age, education, race/ethnicity, annual income, and internet connection) and accounting for clustering within households and states.

resultsHigher scores for smoke-free laws (adjusted beta = -1.00, 95 % CI = -1.73, -0.27), taxes (adjusted beta = -1.23, 95 % CI = -1.88, -0.58), and prevention/control funding (adjusted beta = -0.22, 95 % CI = -0.38, -0.06) were associated with fewer cigarette purchases over time. In the model considering all four measures together, higher tax score was associated with fewer cigarette purchases over time (adjusted beta = -0.96, 95 % CI = -1.73, -0.87).

conclusionsSmoke-free laws, taxation, and prevention/control funding play critical roles in lowering cigarette purchases, while access to cessation services alone may not drive behavioral change. These findings highlight the need for comprehensive tobacco control efforts and renewed policy action to curb cigarette use.

Indexed as

CommerceConsumer BehaviorFamily CharacteristicsSmoke-Free PolicyTobacco ProductsAdultFemaleHumansMaleMiddle AgedTaxesTobacco ControlUnited StatesCigarette purchasesHealth policyLongitudinal analysisSmokingTobacco prevention and control

Identifiers

PMID40239897
PMCPMC12919606

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