Examining the Survey Setting Effect on Current E-Cigarette Use Estimates among High School Students in the 2021 National Youth Tobacco Survey
Dados Bibliográficos
| ID | 15510620 |
|---|---|
| Autores | Julia Chen‐Sankey (0000-0002-1797-5248, Rutgers, the State University of New Jersey, autor correspondente), Michelle T B Manderski (0000-0003-0000-221X, Rutgers, the State University of New Jersey), William J Young (0000-0002-8051-6327, Rutgers, the State University of New Jersey), Cristine D Delnevo (0000-0001-9597-4307, Rutgers, the State University of New Jersey) |
| Ano | 2022 |
| Volume | 19 |
| Fascículo | 11 |
| Páginas | 6468-6468 |
| Data de publicação | 2022-05-26 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | International Journal of Environmental Research and Public Health (JOURNAL) |
| Identificadores do periódico | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Editora | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph19116468 |
| PMID | 35682059 |
| OpenAlex | W4281932805 |
| Idioma | EN |
| Citações recebidas | 5 |
| Referências citadas | 31 |
The 2021 National Youth Tobacco Survey (NYTS) was completed by youth online during class time, either in school or at home due to the COVID-19 pandemic. Given the role of NYTS data in tobacco regulatory science, it is vital to understand the effect of survey settings (home, school) on tobacco-use estimates. We used a series of multivariable logistic regressions to examine whether survey settings (home vs. school) predicted current e-cigarette use among high school students, controlling for other known predictors of e-cigarette use as well as the pandemic learning model that was dominant in students' counties (e.g., nearly all at-home, majority in school). We observed a significant survey setting effect. Those who completed the survey in school had higher odds of current e-cigarette use than those who completed the same survey at home (AOR = 1.74); this effect was attenuated when we controlled for the pandemic learning model (AOR = 1.38). Moreover, e-cigarette use was independently associated with students' learning model; students whose schools were nearly entirely in-person had the highest odds of e-cigarette use compared to students whose learning model was nearly all at-home (AOR = 1.65). Survey setting is a methodological artifact in the 2021 NYTS. Perceived privacy and peer effects can potentially explain this artifact
Coronavirus disease 2019 (COVID-19 · Environmental health · Logistic regression · Odds · Pandemic · Poison control · Sociology · Suicide prevention · Survey data collection · Tobacco use · Youth Risk Behavior Survey · Air Quality and Health Impacts · Behavioral Health and Interventions · Demography · Medicine · Psychology · Smoking Behavior and Cessation
Engagement, Mental Health, and Substance Use Under In‐Person or Remote School Instruction During the Covid ‐19 Pandemic
Racial/Ethnic Differences in Vaping Product Use among Youth
A Pilot Feasibility Study of an Online Youth Tobacco Survey Administration among High School Students
Trends in vaping and nicotine product use among youth in Canada, England and the USA between 2017 and 2022
What are the harms of vaping in young people who have never smoked
Assessment of factors affecting the validity of self-reported health-risk behavior among adolescents
Tobacco Product Use and Associated Factors Among Middle and High School Students — National Youth Tobacco Survey, United States, 2021
Notes from the Field
E-cigarette Use Among Middle and High School Students — United States, 2020
Cem
Collecting Sensitive Self-Report Data With Laptop Computers
A Comparison of Computer-Assisted and Paper-and-Pencil Self-Administered Questionnaires in a Survey on Smoking, Alcohol, and Drug Use
Does Telephone Audio Computer-Assisted Self-Interviewing Improve the Accuracy of Prevalence Estimates of Youth Smoking
The Association of Survey Setting and Mode with Self-Reported Health Risk Behaviors among High School Students
Why Propensity Scores Should Not Be Used for Matching
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
| Obras citantes distintas | 5 |
|---|---|
| Citações por ano | 1,25 |
| Intervalo de citações | 2022 - 2024 (3) |
| Velocidade de citação | recent |
| Altamente citado | Não |
| Tipos de citação | Neutras: 5 |