Evidence map›Paper›PMID 32732730›Full record

ArticleJournal of public health management and practice : JPHMP

State Tobacco Excise Taxation, Comprehensive Smoke-free Air Laws, and Tobacco Control Appropriations as Predictors of Smoking Cessation Success in the United States.

Jennifer Dahne, Georges J Nahhas, Amy E Wahlquist, K Michael Cummings, Matthew J Carpenter

Abstract read
In one paragraph

Article in Journal of public health management and practice : JPHMP. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

5 authors.

Jennifer DahneDepartment of Psychiatry and Behavioral Sciences (Drs Dahne, Nahhas, Cummings, and Carpenter), Hollings Cancer Center (Drs Dahne, Cummings, and Carpenter and Ms Wahlquist), and Department of Public Health Sciences (Ms Wahlquist and Drs Cummings and Carpenter), Medical University of South Carolina, Charleston, South Carolina.
Georges J Nahhas
Amy E Wahlquist
K Michael Cummings
Matthew J Carpenter

Funding

Development and Testing of a Depression-Specific Behavioral Activation Mobile App Paired with Nicotine Replacement Therapy Sampling for Smoking Cessation Treatment Via Primary CareK23DA045766 · NIDA · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI DAHNE, JENNIFER RENEE · 2018 to 2022
$1.0M
NIDA NIH HHS K23 DA045766
6 · The paper itself

Abstract

The purpose of the present study was (1) to examine demographic differences between smokers who successfully quit (n = 1809), who relapsed (n = 6548), and who did not attempt to quit (n = 11 102) within the last year, and (2) to examine state-level tobacco policies/programs as predictors of quit success. Data were utilized from the 2014-2015 Tobacco Use Supplement to the Current Population Survey, which were paired with 2014 data on taxation, appropriations, and smoke-free air laws. As compared with smokers who relapsed, those who successfully quit were more likely to be white, married, more highly educated, of higher income, and heavier smokers. Compared with those who did not attempt to quit, those who attempted to quit, regardless of success, were younger and more likely to be Hispanic. State comprehensive smoke-free air laws and tobacco excise taxation significantly predicted quit success. Thus, expansions of these policies should be considered to promote successful quitting.

Indexed as

Smoking CessationTobacco UseHumansNicotianaSmokersSocial Control, FormalTaxesUnited States

Identifiers

PMID32732730
PMCPMC7394937

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.