Background: Stigma is the deleterious, structural force that devalues members of groups that hold undesirable characteristics. Since stigma is created and reinforced by society—through in-person and online social interactions—referencing the novel coronavirus as the “Chinese virus” or “China virus” has the potential to create and perpetuate stigma. Objective: The aim of this study was to assess if there was an increase in the prevalence and frequency of the phrases “Chinese virus” and “China virus” on Twitter after the March 16, 2020, US presidential reference of this term. Methods: Using the Sysomos software (Sysomos, Inc), we extracted tweets from the United States using a list of keywords that were derivatives of “Chinese virus.” We compared tweets at the national and state levels posted between March 9 and March 15 (preperiod) with those posted between March 19 and March 25 (postperiod). We used Stata 16 (StataCorp) for quantitative analysis, and Python (Python Software Foundation) to plot a state-level heat map. Results: A total of 16,535 “Chinese virus” or “China virus” tweets were identified in the preperiod, and 177,327 tweets were identified in the postperiod, illustrating a nearly ten-fold increase at the national level. All 50 states witnessed an increase in the number of tweets exclusively mentioning “Chinese virus” or “China virus” instead of coronavirus disease (COVID-19) or coronavirus. On average, 0.38 tweets referencing “Chinese virus” or “China virus” were posted per 10,000 people at the state level in the preperiod, and 4.08 of these stigmatizing tweets were posted in the postperiod, also indicating a ten-fold increase. The 5 states with the highest number of postperiod “Chinese virus” tweets were Pennsylvania (n=5249), New York (n=11,754), Florida (n=13,070), Texas (n=14,861), and California (n=19,442). Adjusting for population size, the 5 states with the highest prevalence of postperiod “Chinese virus” tweets were Arizona (5.85), New York (6.04), Florida (6.09), Nevada (7.72), and Wyoming (8.76). The 5 states with the largest increase in pre- to postperiod “Chinese virus” tweets were Kansas (n=697/58, 1202%), South Dakota (n=185/15, 1233%), Mississippi (n=749/54, 1387%), New Hampshire (n=582/41, 1420%), and Idaho (n=670/46, 1457%). Conclusions: The rise in tweets referencing “Chinese virus” or “China virus,” along with the content of these tweets, indicate that knowledge translation may be occurring online and COVID-19 stigma is likely being perpetuated on Twitter.

Governments worldwide are implementing mass vaccination programs in an effort to end the novel coronavirus (COVID-19) pandemic. Here, we evaluated the effectiveness of the COVID-19 vaccination program in its early stage and predicted the path to herd immunity in the U.S. By early March 2021, we estimated that vaccination reduced the total number of new cases by 4.4 million (from 33.0 to 28.6 million), prevented approximately 0.12 million hospitalizations (from 0.89 to 0.78 million), and decreased the population infection rate by 1.34 percentage points (from 10.10 to 8.76%). We built a Susceptible-Infected-Recovered (SIR) model with vaccination to predict herd immunity, following the trends from the early-stage vaccination program. Herd immunity could be achieved earlier with a faster vaccination pace, lower vaccine hesitancy, and higher vaccine effectiveness. The Delta variant has substantially postponed the predicted herd immunity date, through a combination of reduced vaccine effectiveness, lowered recovery rate, and increased infection and death rates. These findings improve our understanding of the COVID-19 vaccination and can inform future public health policies.

In the last 2 decades, cigarette smoking has decreased steadily among US adolescents.^1,2 In 2020, 1.6% of middle school students and 4.6% of high school students reported use of cigarettes in the past 30 days, compared with 12.8% of middle school students and 34.8% of high school students in 1999.^3-5 In contrast, the popularity of e-cigarettes has increased markedly among adolescents in recent years.^6,7 Since 2014, e-cigarettes have become the most commonly used nicotine or tobacco product among middle school and high school students.^8,9 From 2017 to 2018, current use of e-cigarettes, defined as having vaped in the past 30 days, increased from 11.7% to 20.8% among high school students and from 3.3% to 4.9% among middle school students.³ The rates grew even higher in 2019, at 27.5% among high school students and 10.5% among middle school students.¹⁰ Vaping decreased substantially in 2020 to 19.6% of high school students and 4.7% of middle school students.¹¹

Since 2014, electronic cigarettes (e-cigarettes) have become the most commonly used nicotine-containing product among US middle school and high school students.^1,2 According to the National Youth Tobacco Survey, the prevalence of past 30-day e-cigarette use increased from 11.7% to 27.5% among high school students from 2017 to 2019.^3,4 For middle school students, the prevalence of past 30-day e-cigarette use increased from 3.3% to 10.5% from 2017 to 2019. e-Cigarette use decreased significantly among middle school and high school students in 2020 and 2021; 11.3% of high school students and 2.8% of middle school students reported past 30-day e-cigarette use in 2021.^5,6

Introduction: Prospective studies have consistently reported a strong association between e-cigarette use and subsequent cigarette smoking, but many failed to adjust for important risk factors. Methods: Using longitudinal data from the Population Assessment of Tobacco and Health (PATH) Study, we employed multivariable logistic regressions to assess the adolescent vaping-to-smoking relationship, with four regressions (Models 1–4) sequentially adding more risk factors. Our sample included all waves (waves 1–5) of the PATH Study. Results: The association between ever e-cigarette use and subsequent cigarette smoking decreased substantially in magnitude when adding more control variables, including respondents’ sociodemographic characteristics, exposure to tobacco users, cigarette susceptibility, and behavioral risk factors. Using the most recent data (waves 4–4.5 and waves 4.5–5), this association was not significant in the most complete model (Model 4). Using wave 4.5–5 data, the adjusted odds ratio (aOR) for ever e-cigarette use at initial wave and subsequent past 12-month smoking declined from 4.07 (95% confidence interval [CI, 2.86−5.81) in Model 1, adjusting only for sociodemographic characteristics, to 1.35 (95% CI, 0.84−2.16) in Model 4, adjusting for all potential risk factors. Similarly, the aOR of ever e-cigarette use and past 30-day smoking at wave 5 decreased from 3.26 (95% CI, 1.81−5.86) in Model 1 to 1.21 (95% CI, 0.59−2.48) with all covariates (Model 4). Conclusions: Among adolescent never cigarette smokers, those who had ever used e-cigarettes at baseline, compared with never e-cigarette users, exhibited modest or non-significant increases in subsequent past 12-month or past 30-day smoking when adjusting for behavioral risk factors.

Since 2014, electronic cigarettes (e-cigarettes) have become the most popular nicotine or tobacco product among adolescents in the US.^1-4 After the peak in 2019, the prevalence of current e-cigarette use (any use in the past 30 days) in high school students who completed the National Youth Tobacco Survey decreased to 14.1% in 2022.^2-5