Examining Disparities in Current E-Cigarette Use among U.S. Adults before and after the WHO Declaration of the COVID-19 Pandemic in March 2020.
Hadii M MamuduDavid AdzragoOluwabunmi DadaEmmanuel A OdameManik AhujaManul AwasthiFlorence M WeierbachFaustine WilliamsDavid W StewartTimir K PaulPublished in: International journal of environmental research and public health (2023)
This paper aims to estimate the prevalence of e-cigarette use before and after the COVID-19 pandemic declaration and to delineate disparities in use across subpopulations. Data were derived from the 2020 Health Information National Trends Survey ( N = 3865) to conduct weighted multivariable logistic regression and marginal analyses. The overall prevalence of current e-cigarette use increased from 4.79% to 8.63% after the COVID-19 pandemic declaration. Furthermore, non-Hispanic Black people and Hispanic people had lower odds of current e-cigarette use than non-Hispanic White people, but no significant differences were observed between groups before the pandemic. Compared to heterosexual participants, sexual minority (SM) participants had higher odds of current e-cigarette use after the declaration, with insignificant differences before. People who had cardiovascular disease conditions, relative to those without, had higher odds of current e-cigarette use after the declaration, but no group differences were found before the declaration. The marginal analyses showed that before and after the pandemic declaration, SM individuals had a significantly higher probability of using e-cigarettes compared to heterosexual individuals. These findings suggest the importance of adopting a subpopulation approach to understand and develop initiatives to address substance use, such as e-cigarettes, during pandemics and other public health emergencies.
Keyphrases
- smoking cessation
- health information
- cardiovascular disease
- public health
- sars cov
- coronavirus disease
- risk factors
- replacement therapy
- social media
- african american
- type diabetes
- healthcare
- computed tomography
- mental health
- machine learning
- electronic health record
- magnetic resonance
- cross sectional
- network analysis