Evidence map›Paper›PMID 39850507›Full record

ArticleStats2024

Investigating Risk Factors for Racial Disparity in E-Cigarette Use with PATH Study.

Amy Liu, Kennedy Dorsey, Almetra Granger, Ty-Runet Bryant, Tung-Sung Tseng, Michael Celestin, Qingzhao Yu

Abstract read
In one paragraph

Article in Stats, 2024. 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

7 authors.

Amy LiuStatistics and Computer Science, Duke University, Trinity College of Arts and Sciences, Durham, NC 27708, USA.
Kennedy DorseyBehavioral and Community Health Sciences, Louisiana State University Health Sciences Center School of Public Health, New Orleans, LA 70112, USA.
Almetra GrangerBehavioral and Community Health Sciences, Louisiana State University Health Sciences Center School of Public Health, New Orleans, LA 70112, USA.
Ty-Runet BryantBehavioral and Community Health Sciences, Louisiana State University Health Sciences Center School of Public Health, New Orleans, LA 70112, USA.
Tung-Sung TsengBehavioral and Community Health Sciences, Louisiana State University Health Sciences Center School of Public Health, New Orleans, LA 70112, USA.ORCID 0000-0001-6266-9891
Michael CelestinBehavioral and Community Health Sciences, Louisiana State University Health Sciences Center School of Public Health, New Orleans, LA 70112, USA.ORCID 0000-0001-9619-9461
Qingzhao YuBiostatistics and Data Sciences, Louisiana State University Health Sciences Center School of Public Health, New Orleans, LA 70122, USA.ORCID 0000-0001-8194-0798

Funding

Role of microbiota in increased influenza severity due to particulate matter exposureP42ES013648 · NIEHS · LOUISIANA STATE UNIV A&M COL BATON ROUGE · PI MERCANTE, DONALD E · 2009 to 2024
$27.6M
Interactions between ES-miRNAs and environmental risk factors are responsible for TNBC progression and associated racial health disparities: a novel analysis with multilevel moderation inferencesR01CA275089 · NCI · LSU HEALTH SCIENCES CENTER · PI Yaguang Xi, Qingzhao Yu · 2023 to 2026
$1.3M
Trends of disparities in breast cancer progression and health care considering multilevel risk factorsR15MD012387 · NIMHD · LSU HEALTH SCIENCES CENTER · PI YU, QINGZHAO · 2017 to 2023
$1.1M
NCI NIH HHS R01 CA275089NIEHS NIH HHS P42 ES013648NIMHD NIH HHS R15 MD012387
6 · The paper itself

Abstract

Background: Previous research has identified differences in e-cigarette use and socioeconomic factors between different racial groups However, there is little research examining specific risk factors contributing to the racial differences. Objective: This study sought to identify racial disparities in e-cigarette use and to determine risk factors that help explain these differences. Methods: We used Wave 5 (2018-2019) of the Adult Population Assessment of Tobacco and Health (PATH) Study. First, we conducted descriptive statistics of e-smoking across our risk factor variables. Next, we used multiple logistic regression to check the risk effects by adjusting all covariates. Finally, we conducted a mediation analysis to determine whether identified factors showed evidence of influencing the association between race and e-cigarette use. All analyses were performed in R or SAS. The R package mma was used for the mediation analysis. Results: Between Hispanic and non-Hispanic White populations, our potential risk factors collectively explain 17.5% of the racial difference, former cigarette smoking explains 7.6%, receiving e-cigarette advertising 2.6%, and perception of e-cigarette harm explains 27.8% of the racial difference. Between non-Hispanic Black and non-Hispanic White populations, former cigarette smoking, receiving e-cigarette advertising, and perception of e-cigarette harm explain 5.2%, 1.8%, and 6.8% of the racial difference, respectively. E-cigarette use is most prevalent in the non-Hispanic White population compared to non-Hispanic Black and Hispanic populations, which may be explained by former cigarette smoking, exposure to e-cigarette advertising, and e-cigarette harm perception. Conclusions: These findings suggest that racial differences in e-cigarette use may be reduced by increasing knowledge of the dangers associated with e-cigarette use and reducing exposure to e-cigarette advertisements. This comprehensive analysis of risk factors can be used to significantly guide smoking cessation efforts and address potential health burden disparities arising from differences in e-cigarette usage.

Indexed as

disparitiese-cigarettessmoking cessation

Identifiers

PMID39850507
PMCPMC11756910

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.