Excess natural-cause mortality in US counties and its association with reported COVID-19 deaths.

In the United States, estimates of excess deaths attributable to the COVID-19 pandemic have consistently surpassed reported COVID-19 death counts. Excess deaths reported to non-COVID-19 natural causes may represent unrecognized COVID-19 deaths, deaths caused by pandemic health care interruptions, and/or deaths from the pandemic's socioeconomic impacts. The geographic and temporal distribution of these deaths may help to evaluate which explanation is most plausible. We developed a Bayesian hierarchical model to produce monthly estimates of excess natural-cause mortality for US counties over the first 30 mo of the pandemic. From March 2020 through August 2022, 1,194,610 excess natural-cause deaths occurred nationally [90% PI (Posterior Interval): 1,046,000 to 1,340,204]. A total of 162,886 of these excess natural-cause deaths (90% PI: 14,276 to 308,480) were not reported to COVID-19. Overall, 15.8 excess deaths were reported to non-COVID-19 natural causes for every 100 reported COVID-19 deaths. This number was greater in nonmetropolitan counties (36.0 deaths), the West (Rocky Mountain states: 31.6 deaths; Pacific states: 25.5 deaths), and the South (East South Central states: 26.0 deaths; South Atlantic states: 25.0 deaths; West South Central states: 24.2 deaths). In contrast, reported COVID-19 death counts surpassed estimates of excess natural-cause deaths in metropolitan counties in the New England and Middle Atlantic states. Increases in reported COVID-19 deaths correlated temporally with increases in excess deaths reported to non-COVID-19 natural causes in the same and/or prior month. This suggests that many excess deaths reported to non-COVID-19 natural causes during the first 30 mo of the pandemic in the United States were unrecognized COVID-19 deaths.

Dynamics of racial disparities in all-cause mortality during the COVID-19 pandemic.

As research documenting disparate impacts of COVID-19 by race and ethnicity grows, little attention has been given to dynamics in mortality disparities during the pandemic and whether changes in disparities persist. We estimate age-standardized monthly all-cause mortality in the United States from January 2018 through February 2022 for seven racial/ethnic populations. Using joinpoint regression, we quantify trends in race-specific rate ratios relative to non-Hispanic White mortality to examine the magnitude of pandemic-related shifts in mortality disparities. Prepandemic disparities were stable from January 2018 through February 2020. With the start of the pandemic, relative mortality disadvantages increased for American Indian or Alaska Native (AIAN), Native Hawaiian or other Pacific Islander (NHOPI), and Black individuals, and relative mortality advantages decreased for Asian and Hispanic groups. Rate ratios generally increased during COVID-19 surges, with different patterns in the summer 2021 and winter 2021/2022 surges, when disparities approached prepandemic levels for Asian and Black individuals. However, two populations below age 65 fared worse than White individuals during these surges. For AIAN people, the observed rate ratio reached 2.25 (95% CI = 2.14, 2.37) in October 2021 vs. a prepandemic mean of 1.74 (95% CI = 1.62, 1.86), and for NHOPI people, the observed rate ratio reached 2.12 (95% CI = 1.92, 2.33) in August 2021 vs. a prepandemic mean of 1.31 (95% CI = 1.13, 1.49). Our results highlight the dynamic nature of racial/ethnic disparities in mortality and raise alarm about the exacerbation of mortality inequities for Indigenous groups due to the pandemic.

Association of cigarette and electronic cigarette use patterns with all-cause mortality: A national cohort study of 145,390 US adults.

OBJECTIVE: While e-cigarette use is associated with adverse cardiopulmonary health effects, the mortality risks associated with e-cigarette use alone and combined with smoking remain unexamined. METHODS: Data between 2014 and 2018 were obtained from the National Health Interview Survey (NHIS), an annual cross-sectional survey of US adults. All-cause mortality and date of death were obtained via linkage of the NHIS to the National Death Index through December 31, 2019. A 6-category composite cigarette (never, former, current) and e-cigarette (current, non-current) exposure variable was created. We examined the association of cigarette and e-cigarette use patterns with all-cause mortality using adjusted Cox models. RESULTS: Among 145,390 participants (79,294 women [51.5%]; 60,560 aged 18-44 [47.4%]), 5220 deaths were observed over a median follow-up of 3.5 years (508,545 total person-years). Dual use of cigarettes and e-cigarettes was associated with higher mortality risk compared with non-current e-cigarette use in combination with never smoking (hazard ratio [HR] 2.44; 95% CI, 1.90-3.13) and had a risk that did not differ from current exclusive smoking (HR, 1.06; 95% CI, 0.83-1.37). Current e-cigarette use in combination with former smoking was associated with a lower mortality risk than current exclusive cigarette smoking (HR 0.64; 95% CI, 0.41-0.99). CONCLUSIONS: The addition of e-cigarette use to smoking does not reduce mortality risk compared with exclusive smoking. However, transitioning completely from cigarettes to e-cigarettes may be associated with mortality risk reduction. Further research is needed to verify these findings in larger cohorts and over longer periods of follow-up.

Association of Electronic Cigarette Use with Respiratory Symptom Development among U.S. Young Adults.

Rationale: Electronic cigarette (e-cigarette) use is highly prevalent among young adults. However, longitudinal data assessing the association between e-cigarette use and respiratory symptoms are lacking. Objectives: To determine whether e-cigarette use is associated with the development of respiratory symptoms in young adults. Methods: Data are derived from the PATH (Population Assessment of Tobacco and Health) study waves 2 (2014-2015), 3 (2015-2016), 4 (2016-2018), and 5 (2018-2019). Young adults aged 18-24 years at baseline with no prevalent respiratory disease or symptoms were included in the analyses. Binary logistic regression models with a generalized estimating equation were used to estimate time-varying and time-lagged associations of e-cigarette use during waves 2-4, with respiratory symptom development approximately 12 months later at waves 3-5. Measurements and Main Results: The per-wave prevalence of former and current e-cigarette use was 15.2% and 5.6%, respectively. Former e-cigarette use was associated with higher odds of developing any respiratory symptom (adjusted odds ratio [aOR], 1.20; 95% confidence interval [CI], 1.04-1.39) and wheezing in the chest (aOR, 1.41; 95% CI, 1.08-1.83) in multivariable adjusted models. Current e-cigarette use was associated with higher odds for any respiratory symptom (aOR, 1.32; 95% CI, 1.06-1.65) and wheezing in the chest (aOR, 1.51; 95% CI, 1.06-2.14). Associations persisted among participants who never smoked combustible cigarettes. Conclusions: In this nationally representative cohort of young adults, former and current e-cigarette use was associated with higher odds of developing wheezing-related respiratory symptoms, after accounting for cigarette smoking and other combustible tobacco product use.

Psychosocial stressors and current e-cigarette use in the youth risk behavior survey.

BACKGROUND: This study explores the association between psychosocial stressors and current e-cigarette use among adolescents in the United States. METHODS: We used data from 12,767 participants in the 2019 National Youth Risk Behavioral Survey to examine the association between psychosocial stressors (bullying, sexual assault, safety-related absence from school, depressive symptoms, suicidal ideation, physical altercation, and weapon threats) and past-30-day e-cigarette use using multivariable-adjusted logistic regression models. We examined the association for each stressor and then as a burden score (0-7). To compare the strength of the association between stressors and current e-cigarette use to current combustible cigarette use, we additionally examined the association between each stressor and current combustible cigarette use. RESULTS: Approximately 32.7% reported current e-cigarette use. The weighted prevalence of current e-cigarette use was higher among individuals who experienced stressors than those who did not. For example, bullying (43.9% vs. 29.0%). Similar prevalence patterns were seen among other stressors. Individuals who experienced stressors had significantly higher adjusted odds of current e-cigarette use than those who did not (OR [Odds Ratio] range: 1.47-1.75). Similarly, individuals with higher burden scores had a higher prevalence (zero [20.5%], one [32.8%], two [41.4%], three [49.6%], four to seven [60.9%]) and higher odds of current e-cigarette use (OR range: 1.43-2.73) than those with a score of zero. The strength of the association between the stressors and e-cigarette use was similar to that between the stressors and combustible cigarette use. CONCLUSION: The study demonstrates a significant association between psychosocial stressors and adolescent e-cigarette use, highlighting the potential importance of interventions, such as targeted school-based programs that address stressors and promote stress management, as possible means of reducing adolescent e-cigarette use. Future research directions include exploring underlying mechanisms linking stressors to e-cigarette use and evaluating the effectiveness of interventions addressing stressors in reducing adolescent e-cigarette use.

Gaps in the type 2 diabetes care cascade: a national perspective using South Africa's National Health Laboratory Service (NHLS) database.

BACKGROUND: Research out of South Africa estimates the total unmet need for care for those with type 2 diabetes mellitus (diabetes) at 80%. We evaluated the care cascade using South Africa's National Health Laboratory Service (NHLS) database and assessed if HIV infection impacts progression through its stages. METHODS: The cohort includes patients from government facilities with their first glycated hemoglobin A1c (HbA1c) or plasma glucose (fasting (FPG); random (RPG)) measured between January 2012 to March 2015 in the NHLS. Lab-diagnosed diabetes was defined as HbA1c ≥ 6.5%, FPG ≥ 7.0mmol/l, or RPG ≥ 11.1mmol/l. Cascade stages post diagnosis were retention-in-care and glycaemic control (defined as an HbA1c < 7.0% or FPG < 8.0mmol/l or RPG < 10.0mmol/l) over 24-months. We estimated gaps at each stage nationally and by people living with HIV (PLWH) and without (PLWOH). RESULTS: Of the 373,889 patients tested for diabetes, 43.2% had an HbA1c or blood glucose measure indicating a diabetes diagnosis. Amongst those with lab-diagnosed diabetes, 30.9% were retained-in-care (based on diabetes labs) and 8.7% reached glycaemic control by 24-months. Prevalence of lab-diagnosed diabetes in PLWH was 28.6% versus 47.3% in PLWOH. Among those with lab-diagnosed diabetes, 34.3% of PLWH were retained-in-care versus 30.3% PLWOH. Among people retained-in-care, 33.8% of PLWH reached glycaemic control over 24-months versus 28.6% of PLWOH. CONCLUSIONS: In our analysis of South Africa's NHLS database, we observed that 70% of patients diagnosed with diabetes did not maintain in consistent diabetes care, with fewer than 10% reaching glycemic control within 24 months. We noted a disparity in diabetes prevalence between PLWH and PLWOH, potentially linked to different screening methods. These differences underscore the intricacies in care but also emphasize how HIV care practices could guide better management of chronic diseases like diabetes. Our results underscore the imperative for specialized strategies to bolster diabetes care in South Africa.

Maximum and Time-Dependent Body Mass Index and Breast Cancer Incidence Among Postmenopausal Women in the Black Women's Health Study.

While excess weight is an established risk factor for postmenopausal breast cancer, consideration of maximum body mass index (maxBMI; BMI is calculated as weight (kg)/height (m)2) or BMI at a point in time relevant for breast carcinogenesis may offer new insights. We prospectively evaluated maxBMI and time-dependent BMI in relation to breast cancer incidence among 31,028 postmenopausal women in the Black Women's Health Study. During 1995-2015, a total of 1,384 diagnoses occurred, including 787 estrogen-receptor (ER)-positive (ER+) cases and 310 ER-negative (ER-) cases. BMI was assessed at baseline and 2, 4, 6, and 8 years before diagnosis. Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Compared with women with BMI <25, those with BMI ≥35 had increased risk of ER+ breast cancer but not ER- breast cancer. For BMI assessed 2 years before diagnosis, the HRs for ER+ breast cancer associated with maxBMI ≥35 and time-dependent BMI ≥35 were 1.42 (95% confidence interval (CI): 1.10, 1.84) and 1.63 (95% CI: 1.25, 2.13), respectively. The corresponding HR for time-dependent BMI assessed 6 years before diagnosis was 1.95 (95% CI: 1.45, 2.62). These findings suggest strong associations of BMI with risk of ER+ breast cancer in postmenopausal women, regardless of timing of BMI assessment.

Body-mass index and diabetes risk in 57 low-income and middle-income countries: a cross-sectional study of nationally representative, individual-level data in 685 616 adults.

BACKGROUND: The prevalence of overweight, obesity, and diabetes is rising rapidly in low-income and middle-income countries (LMICs), but there are scant empirical data on the association between body-mass index (BMI) and diabetes in these settings. METHODS: In this cross-sectional study, we pooled individual-level data from nationally representative surveys across 57 LMICs. We identified all countries in which a WHO Stepwise Approach to Surveillance (STEPS) survey had been done during a year in which the country fell into an eligible World Bank income group category. For LMICs that did not have a STEPS survey, did not have valid contact information, or declined our request for data, we did a systematic search for survey datasets. Eligible surveys were done during or after 2008; had individual-level data; were done in a low-income, lower-middle-income, or upper-middle-income country; were nationally representative; had a response rate of 50% or higher; contained a diabetes biomarker (either a blood glucose measurement or glycated haemoglobin [HbA1c]); and contained data on height and weight. Diabetes was defined biologically as a fasting plasma glucose concentration of 7·0 mmol/L (126·0 mg/dL) or higher; a random plasma glucose concentration of 11·1 mmol/L (200·0 mg/dL) or higher; or a HbA1c of 6·5% (48·0 mmol/mol) or higher, or by self-reported use of diabetes medication. We included individuals aged 25 years or older with complete data on diabetes status, BMI (defined as normal [18·5-22·9 kg/m2], upper-normal [23·0-24·9 kg/m2], overweight [25·0-29·9 kg/m2], or obese [≥30·0 kg/m2]), sex, and age. Countries were categorised into six geographical regions: Latin America and the Caribbean, Europe and central Asia, east, south, and southeast Asia, sub-Saharan Africa, Middle East and north Africa, and Oceania. We estimated the association between BMI and diabetes risk by multivariable Poisson regression and receiver operating curve analyses, stratified by sex and geographical region. FINDINGS: Our pooled dataset from 58 nationally representative surveys in 57 LMICs included 685 616 individuals. The overall prevalence of overweight was 27·2% (95% CI 26·6-27·8), of obesity was 21·0% (19·6-22·5), and of diabetes was 9·3% (8·4-10·2). In the pooled analysis, a higher risk of diabetes was observed at a BMI of 23 kg/m2 or higher, with a 43% greater risk of diabetes for men and a 41% greater risk for women compared with a BMI of 18·5-22·9 kg/m2. Diabetes risk also increased steeply in individuals aged 35-44 years and in men aged 25-34 years in sub-Saharan Africa. In the stratified analyses, there was considerable regional variability in this association. Optimal BMI thresholds for diabetes screening ranged from 23·8 kg/m2 among men in east, south, and southeast Asia to 28·3 kg/m2 among women in the Middle East and north Africa and in Latin America and the Caribbean. INTERPRETATION: The association between BMI and diabetes risk in LMICs is subject to substantial regional variability. Diabetes risk is greater at lower BMI thresholds and at younger ages than reflected in currently used BMI cutoffs for assessing diabetes risk. These findings offer an important insight to inform context-specific diabetes screening guidelines. FUNDING: Harvard T H Chan School of Public Health McLennan Fund: Dean's Challenge Grant Program.

e-Cigarette Use and Combustible Cigarette Smoking Initiation Among Youth: Accounting for Time-Varying Exposure and Time-Dependent Confounding.

BACKGROUND: Youth e-cigarette use is associated with the initiation of combustible cigarette smoking, but prior studies have rarely accounted for time-varying measures of e-cigarette exposure or time-dependent confounding of e-cigarette use and smoking initiation. METHODS: Using five waves of the Population Assessment of Tobacco and Health (2013-2019), we estimated marginal structural models with inverse probability of treatment and censoring weights to examine the association between time-varying e-cigarette initiation and subsequent cigarette smoking initiation among e-cigarette- and cigarette-naïve youth (12-17 years) at baseline. Time-dependent confounders used as predictors in inverse probability weights included tobacco-related attitudes or beliefs, mental health symptoms, substance use, and tobacco-marketing exposure. RESULTS: Among 9,584 youth at baseline, those who initiated e-cigarettes were 2.4 times as likely to subsequently initiate cigarette smoking as youth who did not initiate e-cigarettes (risk ratio = 2.4, 95% confidence interval [CI] = 2.1, 2.7), after accounting for time-dependent confounding and selection bias. Among youth who initiated e-cigarettes, more frequent vaping was associated with greater risk of smoking initiation (risk ratio ≥3 days/month = 1.8, 95% CI = 1.4, 2.2; 1-2 days/month = 1.2; 95% CI = 0.93, 1.6 vs. 0 days/month). Weighted marginal structural model estimates were moderately attenuated compared with unweighted estimates adjusted for baseline-only confounders. At the US population level, we estimated over half a million youth initiated cigarette smoking because of prior e-cigarette use over follow-up. CONCLUSIONS: The association between youth vaping and combustible cigarette smoking persisted after accounting for time-dependent confounding. We estimate that e-cigarette use accounts for a considerable share of cigarette initiation among US youth. See video abstract at, http://links.lww.com/EDE/B937.

Associations of Smokeless Tobacco Use With Cardiovascular Disease Risk: Insights From the Population Assessment of Tobacco and Health Study.

INTRODUCTION: Cigarette smoking is strongly associated with the development of cardiovascular disease (CVD). However, evidence is limited as to whether smokeless tobacco (ST) use is associated with CVD. AIMS AND METHODS: Using data from 4347 adults in the Population Assessment of Tobacco and Health Study (2013-2014), we compared geometric mean concentrations of CVD-related harm biomarkers and biomarkers of exposure among exclusive ST users and exclusive cigarette smokers-in relation to recent nicotine exposure-and never tobacco users, adjusting for age, sex, race/ethnicity, income, body mass index, and CVD. Biomarker levels among exclusive ST users who were former established cigarette smokers were compared with exclusive cigarette smokers. RESULTS: Compared with cigarette smokers, ST users had significantly higher concentrations of total nicotine equivalents (TNE) but lower concentrations of inflammatory (high-sensitivity C-reactive protein, interleukin-6, intercellular adhesion molecule, fibrinogen) and oxidative stress (8-isoprostane) biomarkers (all p < .05). Biomarker levels among ST users were similar to never smokers. ST users who were former cigarette smokers had lower levels of inflammatory and oxidative stress biomarkers and biomarkers of exposure (cadmium, lead, 1-hydroxypyrene, acrylonitrile, and acrolein), compared with cigarettes smokers (p < .05), despite having higher TNE levels (p < .05). Among cigarette smokers, but not among ST users, inflammatory biomarkers and TNE were highly correlated. CONCLUSIONS: ST use is not associated with increases in biomarkers of CVD-related harm and exposure, compared with never smokers, despite exposure to nicotine at levels higher than those observed among cigarette smokers. These findings support the concept that increases in CVD risk among cigarette smokers is caused primarily by constituents of tobacco smoke other than nicotine. IMPLICATIONS: Despite having higher levels of nicotine and compared with exclusive cigarette smokers, exclusive ST users (including those who were former cigarette smokers) had significantly lower concentrations of inflammatory and oxidative stress biomarkers, comparable to levels observed among never tobacco users. These findings suggest that increases in CVD risk among cigarette smokers is caused primarily by tobacco constituents other than nicotine and that switching to ST is likely associated with lower CVD risk.

Increased all-cause mortality following occupational injury: a comparison of two states.

OBJECTIVES: To measure the impact of lost-time occupational injuries on all-cause mortality in Washington State and, using the same data elements and study design, to determine whether the estimated impact was similar to previous estimates for New Mexico. METHODS: We linked injuries in the Washington workers' compensation system with Social Security Administration data on earnings and mortality. We estimated Cox survival models of mortality for women and men with lost-time compared with medical-only injuries, adjusting for age, pre-injury earnings and industry. We used quantitative bias analysis to account for confounding by pre-injury smoking and obesity. RESULTS: The estimated mortality HR was 1.24 for women (95% CI 1.21 to 1.28) and 1.22 for men (95% CI 1.20 to 1.24). After adjusting for unmeasured pre-injury smoking and obesity, the estimated HR for women was 1.10, 95% simulation interval (SI) 1.00 to 1.21; for men, it was 1.15, 95% SI 1.04 to 1.27. CONCLUSIONS: All-cause mortality for Washington workers with lost-time injuries was higher than for those with medical-only injuries. Estimated HRs for Washington were consistent with those previously estimated for New Mexico, a less populous state with lower median wages and a different workers' compensation insurance mechanism. This suggests that the relationship between workplace injury and long-term mortality may be generalisable to other US states. These findings support greater efforts to enhance safety and to investigate factors that improve postinjury employment opportunities and long-term health. This association should be examined in additional locations, with different study conditions, or using additional data on pre-injury risk factors.

Association of device type, flavours and vaping behaviour with tobacco product transitions among adult electronic cigarette users in the USA.

BACKGROUND: Few studies assess whether electronic cigarette (e-cigarette) device characteristics or flavours impact longitudinal patterns of cigarette and e-cigarette use. DESIGN: We examined data from waves 2-4 of the Population Assessment of Tobacco and Health Study (2014-2018). Among adult (≥18 years) current e-cigarette users at wave 2 who were current smokers (dual users; n=1759) and former smokers (exclusive e-cigarette users; n=470), we classified participants into four use patterns at wave 3 (~12 months later) and wave 4 (~24 months later): (1) dual use of e-cigarettes and cigarettes; (2) exclusive cigarette smoking; (3) exclusive e-cigarette use; (4) non-use of both products. We used multinomial logistic regression to assess correlates of changing use patterns at 24 months, relative to no change, adjusting for sociodemographic factors. RESULTS: At 24 months, 26.5% of baseline exclusive e-cigarette users, and 9% of baseline dual users, abstained from both vaping and smoking. Participants who vaped non-tobacco flavours (vs tobacco flavours), and used refillable tank or modifiable devices (vs disposable, cartridges and other devices) were less likely to transition to non-use of both products and to exclusive cigarette smoking. Baseline daily vaping (vs non-daily) was positively associated with exclusive e-cigarette use at 24 months for baseline daily cigarette smokers, but negatively associated with exclusive e-cigarette use and non-use of both products at 24 months for baseline non-daily smokers. CONCLUSIONS: Non-tobacco flavours, daily vaping and modifiable e-cigarette devices may help some smokers abstain from cigarette smoking via transitioning to exclusive e-cigarette use, but are also associated with ongoing exclusive e-cigarette use.

COVID-19 and excess mortality in the United States: A county-level analysis.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) excess deaths refer to increases in mortality over what would normally have been expected in the absence of the COVID-19 pandemic. Several prior studies have calculated excess deaths in the United States but were limited to the national or state level, precluding an examination of area-level variation in excess mortality and excess deaths not assigned to COVID-19. In this study, we take advantage of county-level variation in COVID-19 mortality to estimate excess deaths associated with the pandemic and examine how the extent of excess mortality not assigned to COVID-19 varies across subsets of counties defined by sociodemographic and health characteristics. METHODS AND FINDINGS: In this ecological, cross-sectional study, we made use of provisional National Center for Health Statistics (NCHS) data on direct COVID-19 and all-cause mortality occurring in US counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a 10-week time lag between the final day that deaths occurred and the last day that deaths could be reported to improve the completeness of data. Our sample included 2,096 counties with 20 or more COVID-19 deaths. The total number of residents living in these counties was 319.1 million. On average, the counties were 18.7% Hispanic, 12.7% non-Hispanic Black, and 59.6% non-Hispanic White. A total of 15.9% of the population was older than 65 years. We first modeled the relationship between 2020 all-cause mortality and COVID-19 mortality across all counties and then produced fully stratified models to explore differences in this relationship among strata of sociodemographic and health factors. Overall, we found that for every 100 deaths assigned to COVID-19, 120 all-cause deaths occurred (95% CI, 116 to 124), implying that 17% (95% CI, 14% to 19%) of excess deaths were ascribed to causes of death other than COVID-19 itself. Our stratified models revealed that the percentage of excess deaths not assigned to COVID-19 was substantially higher among counties with lower median household incomes and less formal education, counties with poorer health and more diabetes, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of COVID-19 death based on direct counts, also reported higher percentages of excess deaths not assigned to COVID-19. Study limitations include the use of provisional data that may be incomplete and the lack of disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics. CONCLUSIONS: In this study, we found that direct COVID-19 death counts in the US in 2020 substantially underestimated total excess mortality attributable to COVID-19. Racial and socioeconomic inequities in COVID-19 mortality also increased when excess deaths not assigned to COVID-19 were considered. Our results highlight the importance of considering health equity in the policy response to the pandemic.

Cannabis vaping among adults in the United States: Prevalence, trends, and association with high-risk behaviors and adverse respiratory conditions.

The e-cigarette or vaping product-use-associated lung injury outbreak in the United States has raised concerns about the potential health effects of cannabis vaping, a method of cannabis use that is becoming increasingly popular. We used 2017-2019 Behavioral Risk Factor Surveillance System data to estimate yearly prevalence and trends of past-30-day cannabis use and vaping among US adults. We used multivariable logistic regression to evaluate the associations of cannabis vaping with high-risk behaviors, asthma, and other respiratory symptoms. Our sample size was 160,209 (53,945-2017; 55,475-2018; and 50,789-2019). Past-30-day cannabis use prevalence increased from 10.0% (95% CI, 9.4%-10.7%) in 2017 to 13.4% (12.8%-12.0%) in 2019. Similarly, past-30-day cannabis vaping prevalence increased from 1.0% (0.8%-1.2%) to 2.0% (1.7%-2.2%) over the same period, with the greatest increase, 1.2% to 3.9%, observed among young adults (18-24 years). Individuals who vaped cannabis were more likely to concurrently vape nicotine. Cannabis vaping was associated with increased odds of heavy alcohol use (aOR, 1.95; 95% CI, 1.45-2.63), binge drinking (aOR, 2.82; 95% CI, 2.25-3.54), and other high-risk behaviors (aOR, 2.47; 95% CI, 1.89-3.24). In analyses adjusting for sociodemographic characteristics and body mass index, cannabis vaping was not associated with asthma (aOR, 1.03; 95% CI, 0.64-1.64) or other respiratory symptoms (aOR, 1.08; 95% CI, 0.44-2.63). Adjusting for nicotine vaping did not substantively alter these associations. The prevalence of past-30-day cannabis vaping has increased, particularly among young adults, and was associated with high-risk behaviors. Although there was no association between cannabis vaping and asthma or other respiratory symptoms, the increasing trends of cannabis vaping, particularly among young adults, raise concern and underscore the need for continued surveillance.

Lipid profiles in users of combustible and electronic cigarettes.

Electronic cigarette use has especially risen among adolescents and young adults. The aim of this study was to investigate fasting blood glucose and lipid profiles in chronic combustible cigarette and electronic cigarette users. We evaluated participants aged 21 to 45 (n = 525, mean age 31 ± 7 years, 45% women) without established cardiovascular disease or risk factors who were combustible cigarette users (n = 290), electronic cigarette users (n = 131; 65 sole users and 66 dual users), or never users (n = 104). In the first wave of enrollment (2014-2017), electronic cigarette users reported their products as first, second and third generation devices (e-cig users) and were all largely current (i.e., dual) or former (sole) combustible cigarette users, whereas in the second wave of enrollment (2019-2020), electronic cigarette users all reported pod-based device use (pod users) and included more sole users who were never smokers. In multivariable-adjusted analyses comparing to never users, both sole e-cig users and combustible cigarette users had higher glucose and triglycerides and lower high-density lipoprotein (HDL) cholesterol levels. Dual e-cig users showed higher triglycerides and very-low-density lipoprotein cholesterol, and lower HDL cholesterol compared to never users. In contrast, pod users (both sole and dual) had lipid profiles and glucose levels similar to never users. Overall, users of early generation electronic cigarettes display adverse metabolic profiles. In contrast, pod-based electronic cigarette users have similar lipid profiles to never users. Future studies are needed to understand the cumulative effects of electronic cigarette use on cardiometabolic health.

Racial/Ethnic Differences in Associations of Non-cigarette Tobacco Product Use With Subsequent Initiation of Cigarettes in US Youths.

INTRODUCTION: Understanding which non-cigarette tobacco products precede smoking in youth across different racial/ethnic groups can inform policies that consider tobacco-related health disparities. METHODS: We used nationally representative, longitudinal data from the Population Assessment of Tobacco and Health Study waves 1-4. The sample was a dynamic cohort of cigarette-naïve youth aged 12-17 years. Mixed-effects models were used to assess non-cigarette product (e-cigarette, cigar product, or other product) use with cigarette use over 1-year intervals. RESULTS: Of the 28 788 observations pooled across waves 1-4, respondents were 48.7% non-Hispanic white, 13.9% non-Hispanic black, and 23.1% Hispanic. Odds of cigarette initiation over 1-year follow-up were higher among youth with prior use of e-cigarettes (odds ratio [OR], 2.76; 95% confidence interval [CI], 2.21-3.45), cigars (OR, 2.00; 95% CI, 1.42-2.80), or other products (OR, 1.66; 95% CI, 1.28-2.14) compared to never users. At the population level, 20.6% of cigarette initiation was attributable to e-cigarette use among white youth and 21.6% among Hispanic youth, while only 3.5% of cigarette initiation was attributable to e-cigarette use among black youth. In contrast, 9.1% of cigarette initiation for black youth was attributable to cigar use compared to only 3.9% for both white and Hispanic youth. CONCLUSIONS: Prior use of e-cigarettes, cigars, and other non-cigarette products were all associated with subsequent cigarette initiation. However, white and Hispanic youth were more likely to initiate cigarettes through e-cigarette use (vs. cigar or other product use), while black youth were more likely to initiate cigarettes through cigar use (vs. e-cigarette or other product use). IMPLICATIONS: Our findings suggest that previous studies on effects of non-cigarette tobacco products may overlook the critical role of cigar products as a pathway into cigarette smoking among US youth, particularly black youth. While our data support the importance of e-cigarette use as a pathway into smoking, regulatory actions aimed at addressing youth e-cigarette use alone may contribute to disparities in black versus white tobacco use and further exacerbate inequities in tobacco-related disease. Thus, contemporary policy development and discourse about the effects of non-cigarette tobacco products on cigarette initiation should consider cigar and other non-cigarette products as well as e-cigarettes.