The association between cannabis vaping and other substance use.

Introduction: The popularity of cannabis vaping has increased rapidly, especially among adolescents and young adults. We posit some possible explanations and, to evaluate them, examine whether cannabis vapers differ from non-vaping cannabis users in other substance use. Methods: Using nationally representative data from the Population Assessment of Tobacco and Health (PATH) Study wave 5 (Dec. 2018-Nov. 2019), we assessed the association between cannabis vaping and other substance use. A total of 1,689 adolescents and 10,620 adults who reported cannabis use in the past 12 months were included in the study. We employed multivariable logistic regressions to assess the association between cannabis vaping and other substance use. Results: Among past 12-month cannabis users, compared with those who do not vape cannabis, participants who vape cannabis had higher risks of using alcohol (adjusted relative risk [aRR] = 1.04, 95 % CI, 1.01-1.07), cigarettes (aRR = 1.09, 95 % CI, 1.02-1.15), cigars (aRR = 1.17, 95 % CI, 1.06-1.30), other tobacco products (aRR = 1.29, 95 % CI, 1.14-1.45), electronic nicotine products (aRR = 4.64, 95 % CI, 4.32-4.99), other illicit drugs (aRR = 1.53, 95 % CI, 1.29-1.80), and misuse of prescription drugs (aRR = 1.43, 95 % CI, 1.19-1.72). Compared to older cannabis vapers, younger cannabis vapers were at risk of using more other substances. Cannabis vaping was associated with all seven measures of substance use among young adults. Conclusions: Compared to non-vaping cannabis users, cannabis vapers have higher likelihood of using other substances. Research is needed to understand why, as well as the implications of the association.

Examining Racial/Ethnic and Income Disparities on Tobacco Product Use Among US Adults Within Wave 5 of the PATH Study.

OBJECTIVE: The aim of this study was to examine the interactions between race/ethnicity and income across different types of tobacco products. METHODS: The prevalence of past 30-day use of cigarettes, traditional cigars, cigarillos, filtered little cigars, and electronic nicotine delivery systems (ENDS) among adults was examined by race/ethnicity and income levels based on wave 5 (2018-2019) data of the Population Assessment of Tobacco and Health study. RESULTS: Multivariate analysis across race/ethnicity and income showed that, although non-Hispanic Blacks (NHBs) were significantly more than likely to smoke cigarettes than non-Hispanic Whites (NHWs) at low- and high-income levels, such disparity only applied to low-income Hispanics compared with low-income NHWs. NHBs were significantly more likely to smoke traditional cigars, cigarillos, and filtered little cigars than NHWs at low and high incomes. No differences were found between Hispanics and NHWs with regard to traditional cigars and cigarillos. However, low-income Hispanics were significantly less likely to smoke filtered little cigars than NHWs, whereas high-income Hispanics were more likely to do so than NHWs. With regard to ENDS, significant differences were only found at the low-income bracket with NHBs and Hispanics being less likely to smoke these products than NHWs. CONCLUSIONS: Our findings highlight significant interactions between race/ethnicity and income in the use of tobacco products, suggesting that income should be taken into account when designing interventions targeting different racial/ethnic groups.

Impacts of COVID-19 on mental health in the US: evidence from a national survey.

BACKGROUND: Studies have reported substantial effects of the COVID-19 pandemic on mental health, but little is known whether the impacts of COVID on individuals, such as being tested for COVID or experiencing disruptions to healthcare utilization, would affect their mental health differently. AIMS: To examine the impacts of COVID-19 on depression and anxiety disorders among US adults. METHODS: We included 8098 adults with no prior mental health problems using data from the National Health Interview Survey (2019-2020). We examined two outcomes: current depression and anxiety; and three COVID-related impact measures: ever COVID test, delayed medical care, and no medical care due to COVID. Multinomial logistic regressions were conducted. RESULTS: Delayed or no medical care were significantly associated with current depression, with adjusted relative risks (aRRs) of 2.17 (95% CI, 1.48-2.85) and 1.85 (95% CI, 1.33-2.38). All three COVID-related impact measures were significantly associated with current anxiety. The aRRs were 1.16 (95% CI, 1.01-1.32) for ever COVID test, 1.94 (95% CI, 1.64-2.24) for no medical care, and 1.90 (95% CI, 1.63-2.18) for delayed medical care. CONCLUSIONS: Individuals who were affected by COVID were more likely to experience depression or anxiety disorders. Mental health services need to prioritize these high-risk groups.

The Association Between Cannabis Use and Subsequent Nicotine Electronic Cigarette Use Among US Adolescents.

PURPOSE: The current study assessed the association between cannabis use among youth never e-cigarette users and subsequent e-cigarette use. METHODS: The Population Assessment of Tobacco and Health Study is a nationally representative cohort study. Participants aged 12 years and older were selected using a 4-stage, stratified probability sample design from the US civilian, noninstitutionalized population. We included adolescents who participated in both wave 4.5 (2017-2018) and wave 5 (2018-2019) of Population Assessment of Tobacco and Health, and were never e-cigarette users at baseline (N = 9,925). Through multivariable logistic regressions, we examined the prospective association between cannabis use and subsequent e-cigarette use. RESULTS: E-cigarette use at wave five was significantly more common among youth cannabis users at wave 4.5. The adjusted relative risks between ever cannabis use and subsequent past 12-month, past 30-day, and frequent e-cigarette use (≥20 days per month) were 1.53 (95% CI, 1.26-1.81), 1.70 (95% CI, 1.25-2.15), and 2.10 (95% CI, 1.17-3.03), respectively. The adjusted relative risks between past 30-day cannabis use and subsequent past 12-month, past 30-day, and frequent e-cigarette use were 1.54 (95% CI, 1.04-2.28), 2.01 (95% CI, 1.23-3.29), and 2.87 (95% CI, 1.44-5.71), respectively. We also found significant associations between ever cannabis vaping with subsequent e-cigarette use. DISCUSSION: While previous research associates e-cigarette use with subsequent onset of cannabis use, we identify a reverse directional effect, where adolescent cannabis use is associated with increased likelihood of future e-cigarette use.

Recurrent Atherosclerotic Cardiovascular Disease Events Potentially Prevented with Guideline-Recommended Cholesterol-Lowering Therapy following Myocardial Infarction.

PURPOSE: Many adults with atherosclerotic cardiovascular disease (ASCVD) who are recommended to take a statin, ezetimibe and/or a proprotein convertase subtilisin/kexin type 9 inhibitor (PCSK9i) by the 2018 American Heart Association/American College of Cardiology cholesterol guideline do not receive these medications. We estimated the percentage of recurrent ASCVD events potentially prevented with guideline-recommended cholesterol-lowering therapy following a myocardial infarction (MI) hospitalization. METHODS: We conducted simulations using data from US adults with government health insurance through Medicare or commercial health insurance in the MarketScan database. We used data from patients with an MI hospitalization in 2018-2019 to estimate the percentage receiving guideline-recommended therapy. We used data from patients with an MI hospitalization in 2013-2016 to estimate the 3-year cumulative incidence of recurrent ASCVD events (i.e., MI, coronary revascularization or ischemic stroke). The low-density lipoprotein cholesterol (LDL-C) reduction with guideline-recommended therapy was derived from trials of statins, ezetimibe and PCSK9i, and the associated ASCVD risk reduction was estimated from a meta-analysis by the Cholesterol-Lowering Treatment Trialists Collaboration. RESULTS: Among 279,395 patients with an MI hospitalization in 2018-2019 (mean age 75 years, mean LDL-C 92 mg/dL), 27.3% were receiving guideline-recommended cholesterol-lowering therapy. With current cholesterol-lowering therapy use, 25.3% (95%CI: 25.2%-25.4%) of patients had an ASCVD event over 3 years. If all patients were to receive guideline-recommended therapy, 19.8% (95%CI: 19.5%-19.9%) were estimated to have an ASCVD event over 3 years, representing a 21.6% (95%CI: 20.5%-23.6%) relative risk reduction. CONCLUSION: Implementation of guideline-recommended cholesterol-lowering therapy could prevent a substantial percentage of recurrent ASCVD events.

Association of Electronic Cigarette Use by US Adolescents With Subsequent Persistent Cigarette Smoking.

Importance: Many studies have reported a positive association of youth electronic cigarette (e-cigarette) use with subsequent cigarette smoking initiation, but it remains unclear whether e-cigarette use is associated with continued cigarette smoking after initiation. Objective: To assess the association of youth baseline e-cigarette use with their continued cigarette smoking 2 years after initiation. Design, Setting, and Participants: The Population Assessment of Tobacco and Health (PATH) Study is a national longitudinal cohort study. This sample consisted of youth who participated in waves 3, 4, and 5 of the study (wave 3 was from October 2015 to October 2016, wave 4 was from December 2016 to January 2018, and wave 5 was from December 2018 to November 2019) and had never used cigarettes (cigarette-naive) by wave 3. The current analysis used multivariable logistic regressions in August 2022 to assess the association between e-cigarette use among cigarette-naive adolescents aged 12 to 17 years in 2015 and 2016 and subsequent continued cigarette smoking. PATH uses audio computer-assisted self-interviewing and computer-assisted personal interviewing to collect data. Exposures: Ever and current (past 30-day) use of e-cigarettes in wave 3. Main Outcomes and Measures: Continued cigarette smoking in wave 5 after initiating smoking in wave 4. Results: The current sample included 8671 adolescents who were cigarette naive in wave 3 and also participated in waves 4 and 5; 4823 of the participants (55.4%) were aged 12 to 14 years, 4454 (51.1%) were male, and 3763 (51.0%) were non-Hispanic White. Overall, regardless of e-cigarette use, few adolescents (362 adolescents [4.1%]) initiated cigarette smoking at wave 4, and even fewer (218 participants [2.5%]) continued smoking at wave 5. Controlling for multiple covariates, the adjusted odds ratio of baseline ever e-cigarette use, compared with never e-cigarette use, was 1.81 (95% CI, 1.03 to 3.18) for continued smoking measured as past 30-day smoking at wave 5. However, the adjusted risk difference (aRD) was small and not significant. The aRD was 0.88 percentage point (95% CI, -0.13 to 1.89 percentage points) for continued smoking, with the absolute risk being 1.19% (95% CI, 0.79% to 1.59%) for never e-cigarette users and 2.07% (95% CI, 1.01% to 3.13%) for ever e-cigarette users. Similar results were found using an alternative measure of continued smoking (lifetime ≥100 cigarettes and current smoking at wave 5) and using baseline current e-cigarette use as the exposure measure. Conclusions and Relevance: In this cohort study, absolute and relative measures of risks yielded findings suggesting very different interpretations of the association. Although there were statistically significant odds ratios of continued smoking comparing baseline e-cigarette users with nonusers, the minor risk differences between them, along with the small absolute risks, suggest that few adolescents are likely to continue smoking after initiation regardless of baseline e-cigarette use.

Negative sentiments toward novel coronavirus (COVID-19) vaccines.

Addressing negative vaccine sentiments is paramount to COVID-19 prevention efforts. However, assessing population sentiments is challenging due to the desirability bias that can emerge when directly asking respondents for their opinions on vaccination. Social media data, containing people's unfiltered thoughts, have the potential to offer valuable insights that could guide vaccine promotion messaging. We extracted one week's (4/5-4/11, 2020) worth of COVID-19 vaccine posts on Twitter (tweets) from the U.S. (N = 208,973) and segmented tweets with negative sentiments toward COVID-19 vaccines (n = 14,794). We imputed location based on Twitter users' self-reported state of residence. We found that states in the South had significantly higher prevalence of negative tweets compared to states in other parts of the country, and higher-income states reported lower prevalence of negative tweets. Our findings suggest the existence of negative vaccine sentiments and geographic variability in these opinions, warranting tailored vaccine promotion efforts, particularly for the southern U.S.

Use of Electronic Cigarettes Among Cannabis-Naive Adolescents and Its Association With Future Cannabis Use.

Importance: Electronic cigarette (e-cigarette) use has been reported to increase the likelihood of future cigarette smoking among adolescents. The prospective association between e-cigarette use and cannabis use has been less clear, especially in recent years. Objective: To examine the association between e-cigarette use among cannabis-naive adolescents and cannabis use 1 year later. Design, Setting, and Participants: The Population Assessment of Tobacco and Health (PATH) Study, a nationally representative cohort study, uses a 4-stage, stratified probability sample design to select participants aged 12 years or older from the US civilian, noninstitutionalized population. This study sample included 9828 cannabis-naive adolescents at the baseline survey who participated in both wave 4.5 (2017-2018) and wave 5 (2018-2019) of PATH. Exposures: e-Cigarette use, assessed by ever use, past 12-month use, and past 30-day use. Main Outcomes and Measures: Cannabis use in wave 5, assessed by past 12-month and past 30-day use. Multivariable logistic regressions assessed the association between e-cigarette use and cannabis use 1 year later. Results were weighted to produce nationally representative findings. Results: Of the 9828 adolescents included in the analysis, 5361 (57.3%) were aged 12 to 14 years, 5056 (50.7%) were male, and 4481 (53.0%) were non-Hispanic White. After adjustment for sociodemographic characteristics, environmental factors, other substance use, and sensation seeking, e-cigarette use among cannabis-naive adolescents was associated with increased likelihoods of both self-reported past 12-month and past 30-day cannabis use 1 year later. The adjusted relative risks (aRRs) of subsequent past 12-month cannabis use with ever use of e-cigarettes was 2.57 (95% CI, 2.04-3.09), with past 12-month use of e-cigarettes was 2.62 (95% CI, 2.10-3.15), and with past 30-day use of e-cigarettes was 2.18 (95% CI, 1.50-2.85). The aRRs of subsequent past 30-day cannabis use with ever use of e-cigarettes was 3.20 (95% CI, 2.10-4.31), with past 12-month use of e-cigarettes was 3.40 (95% CI, 2.17-4.63), and with past 30-day use of e-cigarettes was 2.96 (95% CI, 1.52-4.40). Conclusions and Relevance: This cohort study's findings suggest a strong association between adolescent e-cigarette use and subsequent cannabis use. However, despite the strong association at the individual level, e-cigarette use seems to have had a minimal association with the prevalence of youth cannabis use at the population level.

Perceived Stigma in Health Care Settings Mediates the Relationships Between Depression, Diabetes, and Hypertension.

Stigma is one of the most harmful forces affecting population health. When stigma exists in clinical settings, environments that should be pro-patient and stigma-free, stigma may become internalized and affect patients' well-being. Informed by prior stigma research and the Intergroup Contact Theory, the authors elucidate statistical relationships between patients' perceptions of clinic-based stigma and stigma's impact on health among New York City's diverse residents. The authors hypothesize that perceiving stigma in clinical settings would mediate the relationships between depression, general health, diabetes, and hypertension; they tested this through multiple logistic regressions conducted on pooled data from the New York City Community Health Survey (N = 18,596, 2016-2017). Among women, depression was associated with stigma (α = 4.07, P < 0.01), hypertension (γ = 2.31, P < 0.01), diabetes (γ = 2.18, P < 0.01), and poor general health (γ = 6.34, P < 0.01). Among men, depression was associated with stigma (α = 3.7, P < 0.01), hypertension (γ = 2.35, P < 0.01), diabetes (γ = 1.86, P < 0.01), and poor general health (γ = 5.14, P < 0.01). Overall, perceived stigma in clinics significantly increased adjusted odds of self-reporting poor general health (adjusted ORs [AOR] = 1.87 men; AOR = 2.05 women). Findings contribute to the literature on the Intergroup Contact Theory, which suggests that stigma should be low in diverse communities; findings indicate that stigma may be a mediator, justifying inclusion in epidemiological and health services research. In addition, study outcomes suggest that depression may be associated with clinic-based stigma, and this stigma has deleterious effects on physical health. Thus, clinicians should emphasize stigma reduction in their facilities, potentially through the adoption of trauma-informed approaches or delivery of care using non-stigmatizing communication strategies, such as Motivational Interviewing.

Can PATH Study susceptibility measures predict e-cigarette and cigarette use among American youth 1 year later?

AIMS: To investigate whether e-cigarette and cigarette susceptibility predict e-cigarette and cigarette use among American youth 1 year later. DESIGN AND SETTING: Longitudinal data from the Population Assessment of Tobacco and Health (PATH) Study-a four-stage, stratified probability cohort study of youth (12-17 years old) sampled from the United States civilian, non-institutionalized population. Multivariable logistic regression was used to estimate the association between initial product-specific susceptibility and subsequent cigarette smoking and e-cigarette use while controlling for sociodemographic characteristics, exposure to nicotine users, and behavioral risk factors. PARTICIPANTS: The sample included 8841 adolescent never nicotine users at initial survey who participated in both wave 4 (2016-2017) and wave 4.5 (2017-2018) of PATH. MEASUREMENTS: We measured cigarette and e-cigarette susceptibility (defined as a lack of a firm commitment to not use cigarettes or e-cigarettes) among never nicotine users at baseline (wave 4) as well as cigarette and e-cigarette use at 12-month follow-up (wave 4.5). FINDINGS: Youth e-cigarette susceptibility was statistically significantly (P < 0.05) associated with e-cigarette use 1 year later, for both past 12-month (adjusted odds ratio [aOR], 2.99; 95% CI, 2.29-3.90) and past 30-day e-cigarette use (aOR, 2.73; 95% CI, 1.78-4.16), but not with cigarette smoking (aOR, 1.05; 95% CI, 0.64-1.73 for past 12-month smoking and aOR, 0.65; 95% CI, 0.29-1.45 for past 30-day smoking. Smoking susceptibility predicted subsequent smoking in the past 12 months (aOR, 1.82; 95% CI, 1.09-3.03) and past 30 days (aOR, 3.32; 95% CI (1.33-8.29), but not e-cigarette use in the past 12 months (aOR, 0.96; 95% CI, 0.77-1.19) or past 30 days (aOR, 1.11; 95% CI, 0.82-1.51). CONCLUSION: E-cigarette and cigarette susceptibility measures appear to predict product-specific use among youth 1 year later.

Impact of vaccination on the COVID-19 pandemic in U.S. states.

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.

Is Adolescent E-Cigarette Use Associated With Subsequent Smoking? A New Look.

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.

Trends in Nicotine Product Use Among US Adolescents, 1999-2020.

Importance: With increasing e-cigarette use among US adolescents and decreasing use of other tobacco products, it is unclear how total use of nicotine products, and its long-term health risks, have changed. The Centers for Disease Control and Prevention's standard measure-any tobacco product use in the past 30 days-considers neither frequency of use nor product risk implications. Objective: To investigate how nicotine product use, including frequency of use, and its associated risks have changed among middle school and high school students since 1999. Design, Setting, and Participants: This cross-sectional study used data from the 1999-2020 National Youth Tobacco Survey, an in-school survey of a nationally representative sample of students in grades 6 through 12; each survey recruited between 15 000 and 36 000 participants. Exposures: Nicotine product use in the past 30 days. Main Outcomes and Measures: Use of nicotine products assessed by nicotine product days (NPDs), the number of days that the average student consumed these products in the past 30 days. Risk-adjusted NPDs account for differential long-term health risks of various products. Results: This study included 16 years of cross-sectional survey data. Each survey recruited between 15 000 and 36 000 participants in grades 6 through 12 (male students: mean, 50.4% [minimum, 48.5%; maximum, 58.4%]; mean age, 14.5 years [minimum, 14.0 years; maximum, 14.7 years]). Nationally representative cross-sectional data for high school students showed that NPDs decreased steadily from 5.6 days per month in 1999 (95% CI, 5.0-6.2 days per month) to 2.2 days per month in 2017 (95% CI, 1.9-2.6 days per month), increased to 4.6 days per month in 2019 (95% CI, 4.1-5.1 days per month), and then decreased to 3.6 days per month in 2020 (95% CI, 3.0-4.1 days per month). For a risk weight of 0.1 for e-cigarettes, compared with combustible products, risk-adjusted NPDs decreased from 2.5 days per month in 2013 (95% CI, 2.2-2.9 days per month) (prior to the popularity of e-cigarettes) to 2.0 days per month in 2019 (95% CI, 1.6-2.5 days per month) and 1.4 days per month in 2020 (95% CI, 1.0-1.8 days per month). However, with a risk weight of 1.0 for e-cigarettes (identical to that of combustible products), risk-adjusted NPDs increased to 5.3 days per month in 2019 (95% CI, 4.4-6.2 days per month) and 3.9 days per month in 2020 (95% CI, 3.1-4.7 days per month). Similar trends were found for middle school students. Conclusions and Relevance: This study suggests that NPDs represent an improvement, albeit an imperfect one, compared with any 30-day tobacco product use by incorporating the frequency of use of various products. By distinguishing products, NPDs permit consideration of the health consequences associated with different mixes of products over time. Health risks of adolescent nicotine product use could have decreased during vaping's popularity if assessment of the long-term risks associated with vaping compared with those of smoking is low. There is a need to closely monitor youth nicotine and tobacco product use patterns.

Creating COVID-19 Stigma by Referencing the Novel Coronavirus as the "Chinese virus" on Twitter: Quantitative Analysis of Social Media Data.

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.

Initiation versus Cessation Control Policies: Deriving Optimal Resource Allocation Strategies to Decrease Smoking Prevalence Under a Fixed Budget.

Background. Over several decades the tobacco control community has recommended and implemented smoking initiation and cessation interventions to reduce the smoking toll. It is necessary to study the combined effect of these interventions to allocate resources optimally. However, there is a paucity of studies that address the right combination of initiation and cessation policies over time to reduce smoking prevalence. Objective. To derive optimal trajectories of initiation and cessation interventions that minimize overall smoking prevalence over a specified period while satisfying a budget constraint. Methods. Using an established dynamic model of smoking prevalence, we employ an optimal control formulation to minimize overall smoking prevalence within a specified time period. The budget constraint is handled through an iterative application of a penalty function on above-budget expenditures. We further derive the optimal cost ratio of initiation versus cessation programs over time. To parameterize our model, we use results from two empirical interventions. The demographic data are from the National Health Interview Survey in the United States. Results. For our example, our results show that the optimal cost ratio (initiation over cessation) starts around 2.02 and gradually increases to 5.28 in 30 years. Smoking prevalence decreases significantly compared with the status quo, 8.54% in 30 years with no interventions versus the estimated 6.43% with interventions. In addition, the optimal units of initiation and cessation interventions increase over time. Conclusions. Our model provides a general framework to incorporate policy details in determining the optimal mix of smoking interventions.

Finding the optimal mix of smoking initiation and cessation interventions to reduce smoking prevalence.

There are more than one billion smokers globally according to the World Health Organization (WHO) report in 2017. Every year tobacco use causes nearly 6 million deaths worldwide. To deal with the smoking epidemic, society needs to invest resources efficiently. In this paper we introduce an optimal control model to determine the optimal mix of smoking initiation and cessation interventions to reduce smoking. We construct the model to reach a smoking prevalence target within a specific time horizon while minimizing cost. Our performance measure captures the cost of policy implementation over time, adjusting for inflation and social discounting. The analytical solutions to the model are presented in forms of ordinary differential equations (ODE). We then conduct several numerical simulations using data from the National Health Interview Survey (NHIS) and empirical studies. We first present analytical solutions for our model to solve for the optimal mix of smoking interventions. Then we simulate a public health policy to achieve 5% smoking prevalence in the US by 2030 using different combinations of real-life interventions. We examine the optimal trajectories, allocative efficiency and annual total cost of smoking cessation and initiation interventions. We find consistent results across all simulations. Our specific example reveals that the most efficient way to reach stated goal is by targeting cessation interventions first, and then gradually shifting resources to initiation interventions over time. While our numerical results are specific to the intervention we selected, our framework can be easily expanded to consider other potential interventions. We discuss the implications of our approach for the formulation of dynamic public health policies.

An application of the Continuous Opinions and Discrete Actions (CODA) model to adolescent smoking initiation.

We investigated the impact of peers' opinions on the smoking initiation process among adolescents. We applied the Continuous Opinions and Discrete Actions (CODA) model to study how social interactions change adolescents' opinions and behaviors about smoking. Through agent-based modeling (ABM), we simulated a population of 2500 adolescents and compared smoking prevalence to data from 9 cohorts of adolescents in the National Survey on Drug Use and Health (NSDUH) from year 2001 till 2014. Our model adjusts well for NSDUH data according to pseudo R2 values, which are at least 96%. Optimal parameter values indicate that adolescents exhibit imitator characteristics with regard to smoking opinions. The imitator characteristics suggests that teenagers tend to update their opinions consistently according to what others do, and these opinions later translate into smoking behaviors. As a result, peer influence from social networks plays a big role in the smoking initiation process and should be an important driver in policy formulation.