How Much Progress Have We Made? Trends in Disparities in Tobacco Use

Cigarettes and e-cigarettes use among US adults with multimorbidity

Background

Tobacco use leads to multiple illnesses. Yet, the effects of different categories of tobacco use on multimorbidity remain understudied. We investigated the associations between tobacco use categories and multimorbidity and the potential moderating effects of age, sex, or race/ethnicity among adults in the United States.

Methods

We conducted a cross-sectional analysis using pooled data from the Behavioral Risk Factor Surveillance System for the years 2020-2022. Multimorbidity was ascertained through self-reported ≥2 chronic health conditions. We categorized tobacco use into nine derived from nonuse (did not use e-cigarettes or cigarettes), former cigarette or e-cigarette use, current (used on some days/everyday) cigarette use or e-cigarette use, or both (dual use). We used multinomial logistic regression to investigate the associations while accounting for potential confounding factors.

Results

Within the sample (N=1,080,257), 28.2% reported multimorbidity. For the categories examined (former exclusive e-cigarette, exclusive e-cigarette, former exclusive cigarette, former dual, former cigarette/current e-cigarette, exclusive cigarette, current cigarette/former e-cigarette and dual use), all reported higher odds of having multimorbidity compared to those who reported nonuse of both e-cigarettes and cigarettes. We found significant interactions for age, sex and race/ethnicity with the tobacco use categories for multimorbidity (p<0.01), where stronger associations were observed among younger adults, females and non-Hispanic Multiracial for current dual use (p<0.05).

Conclusions

The use of cigarettes, e-cigarettes, or both was associated with multimorbidity among adults, which was more pronounced among younger adults, females and non-Hispanic Multiracial. These findings underscore the importance of implementing targeted public health interventions to mitigate the health risks associated with using both products, particularly among specific demographics, to reduce the prevalence of multimorbidity.

US Cigarette Smoking Disparities by Race and Ethnicity - Keep Going and Going!

INTRODUCTION

Although current cigarette smoking among US adults decreased from 42.4% in 1965 to 12.5% in 2020, prevalence is higher among certain racial and ethnic groups, including non-Hispanic American Indian and Alaska Native (AIAN) adults.

METHODS

We examined trends in current cigarette smoking prevalence, population estimates, and relative disparity among US adults (aged ≥18 y) between 2011 and 2020 by using data from the National Health Interview Survey. SAS-callable SUDAAN was used to obtain prevalence and population estimates, and relative disparity was calculated on the basis of findings in the literature. Trends were significant at P < .05.

RESULTS

From 2011 to 2020, linear decreases in prevalence and population estimates were observed for non-Hispanic White (20.6% to 13.3%; 32.1 million to 20.7 million), non-Hispanic Black (19.4% to 14.4%; 5.1 million to 4.0 million), and Hispanic (12.9% to 8.0%; 4.2 million to 3.3 million) adults. For non-Hispanic AIAN adults, prevalence remained around 27%, and a linear increase in the population estimate was observed from 400,000 to 510,000. Relative disparity did not change across racial and ethnic categories.

CONCLUSION

Linear decreases have occurred between 2011 and 2020 for non-Hispanic White, non-Hispanic Black, and Hispanic adults who smoke, but the number of non-Hispanic AIAN adults who currently smoke has increased by 110,000, and relative disparities persist. To reduce racial and ethnic disparities in smoking, understanding how factors at multiple socioecologic levels impact smoking and helping to inform paths to equitable reach and implementation of tobacco control interventions for all population groups are needed.

Educating Substance Use Treatment Center Providers on Tobacco Use Treatments Is Associated with Increased Provision of Counseling and Medication to Patients Who Use Tobacco

Tobacco use is the leading preventable cause of death in America and is elevated among patients with non-tobacco substance use disorders. Substance use treatment centers (SUTCs) do not commonly address their patients' tobacco use. Lack of knowledge on treating tobacco use with counseling and medication may be a barrier that underlies this inaction. A multi-component tobacco-free workplace program implemented in Texas SUTCs educated providers on treating tobacco use with evidence-based medication (or referral) and counseling. This study examined how center-level changes in knowledge from pre- to post-implementation (i.e., over time) affected center-level behavioral changes in providers' provision of tobacco use treatment over time. Providers from 15 SUTCs completed pre- and post-implementation surveys (pre N = 259; post N = 194) assessing (1) perceived barriers to treating tobacco use, specifically, a lack of knowledge on treating tobacco use with counseling or medication; (2) receipt of past-year education on treating tobacco use with counseling or medication; and (3) their intervention practices, specifically, the self-reported regular use of (a) counseling or (b) medication intervention or referral with patients who use tobacco. Generalized linear mixed models explored associations between provider-reported knowledge barriers, education receipt, and intervention practices over time. Overall, recent counseling education receipt was endorsed by 32.00% versus 70.21% of providers from pre- to post-implementation; the regular use of counseling to treat tobacco use was endorsed by 19.31% versus 28.87% from pre- to post-implementation. Recent medication education receipt was endorsed by 20.46% versus 71.88% of providers from pre- to post-implementation; the regular use of medication to treat tobacco use was endorsed by 31.66% versus 55.15% from pre- to post-implementation. All changes were statistically significant (ps < 0.05). High versus low reductions in the provider-reported barrier of "lack of knowledge on pharmacotherapy treatment" over time were a significant moderator of effects, such that SUTCs with high reductions in this barrier were more likely to report greater increases in both medication education receipt and medication treatment/referral for patients who use tobacco over time. In conclusion, a tobacco-free workplace program implementation strategy that included SUTC provider education improved knowledge and resulted in increased delivery of evidence-based treatment of tobacco use at SUTCs; however, treatment provision rates-in particular, offering tobacco cessation counseling-remained less than desirable, suggesting that barriers beyond lack of knowledge may be important to address to improve tobacco use care in SUTCs. Moderation results suggest (1) differences in the mechanisms underlying uptake of counseling education versus medication education and (2) that the relative difficulty of providing counseling versus providing medication persists regardless of knowledge gains.

Evaluating a Tobacco Treatment Program's Transition to Telehealth Using a Social Determinants of Health Lens

INTRODUCTION

While strong associations exist between social determinants of health (SDOH), socioeconomic status, and smoking, these factors are not routinely assessed in tobacco treatment programs (TTP). This study addresses this gap by evaluating a composite metric of SDOH and a measure of access to care to determine program reach before and after the implementation of telehealth tobacco treatment delivery.

AIMS AND METHODS

We examined inpatient data from a large TTP during two comparable time periods from April 1, 2019 to September 30, 2019 (pre-telehealth) and from April 1, 2020 to September 30, 2020 (telehealth). The populations were compared using point-of-care data, including 5-digit zip codes mapped to the CDC's Social Vulnerability Index (SVI) and driving distance (in 60-min increments) to the study hospital. Chi-square tests for homogeneity were performed for SVI and driving distance comparisons.

RESULTS

While distance distributions were significantly different between the pre-telehealth and telehealth populations (χ 2 = 13.5 (df = 3, N = 3234), p = .004, no significant differences existed in the proportion of SVI categories between the two populations (χ 2 = 5.8 (df = 3, N = 3234), p = .12). In the telehealth population, patients with the highest SVI vulnerability had the greatest proportions living >1 h from the hospital.

CONCLUSIONS

This study offers a novel evaluation of tobacco treatment in relation to an SDOH metric (SVI) and care access (distance to the hospital) for inpatient populations. Patient reach, including to those with high vulnerabilities, remained consistent in a transition to telehealth. These methods can inform future reach and engagement of patients who use tobacco products, including patients with high vulnerability or who reside at greater distances from treatment programs.

IMPLICATIONS

This study provides the first analysis of inpatient tobacco use treatment (TUT) transition to telehealth delivery of care during the COVID-19 pandemic using the CDC's SVI metric and patient distance to the hospital. The transition resulted in consistent reach to patients at the highest vulnerability. These findings can inform efforts to evaluate SDOH measures and improve reach, engagement, and research on telehealth delivery of inpatient TUT.

New Zealand Smokers' Perceptions of Tobacco Endgame Measures: A Qualitative Analysis

INTRODUCTION

New Zealand's equity-focused endgame goal (Smokefree 2025) aims to reduce smoking prevalence to minimal levels (ie, <5%) in all population groups by 2025. Inadequate progress has stimulated discussion of innovative measures to reduce prevalence; because few studies have explored how marginalized groups perceive these measures, we addressed this knowledge gap.

AIMS AND METHODS

In November and December 2020, we conducted 20 in-depth interviews with people who smoked daily, were aged between 21 and 53, earned less than the median income (NZD33 900), and had marginal or inadequate income sufficiency. We explored participants' smoking history and used an elicitation exercise to probe their views on smokefree policies, including potential endgame measures. We used qualitative descriptive analysis and reflexive thematic analysis to interpret the data.

RESULTS

Participants favored increasing personal support to quit and reducing nicotine levels in cigarettes, but generally opposed tobacco excise tax increases and paying people to quit. While many privileged their right to "choose," some recognized that stronger policies could restore the loss of agency addiction caused. Participants felt smoking's powerful addictiveness remained poorly understood, and called for smoking to be recognized and treated as an addiction.

CONCLUSIONS

Several participants supported intensifying existing measures or introducing new measures. However, their use of tobacco industry rhetoric to frame smoking as a choice they had made could inadvertently reinforce the stigma they experienced. Reframing cigarettes as an addictive product engineered by a deceptive industry, may make it easier for participants to access the expanded support and compassion they sought.

IMPLICATIONS

Policy measures, such as reducing the nicotine level in cigarettes, could support endgame goals; however, greater public understanding of addiction is needed to reduce stigma, support self-efficacy, and foster smoking cessation. Industry denormalization campaigns could challenge views of smoking as a personal choice, decrease self-blame among people who smoke, and present endgame goals as likely to enhance agency.

Mobile Health Tobacco Cessation Interventions to Promote Health Equity: Current Perspectives

Although US tobacco use trends show overall improvement, social disadvantage continues to drive significant disparities. Traditional tobacco cessation interventions and public policy initiatives have failed to equitably benefit socially-disadvantaged populations. Advancements in mobile digital technologies have created new opportunities to develop resource-efficient mobile health (mHealth) interventions that, relative to traditional approaches, have greater reach while still maintaining comparable or greater efficacy. Their potential for affordability, scalability, and efficiency gives mHealth tobacco cessation interventions potential as tools to help redress tobacco use disparities. We discuss our perspectives on the state of the science surrounding mHealth tobacco cessation interventions for use by socially-disadvantaged populations. In doing so, we outline existing models of health disparities and social determinants of health (SDOH) and discuss potential ways that mHealth interventions might be optimized to offset or address the impact of social determinants of tobacco use. Because smokers from socially-disadvantaged backgrounds face multi-level barriers that can dynamically heighten the risks of tobacco use, we discuss cutting-edge mHealth interventions that adapt dynamically based on context. We also consider complications and pitfalls that could emerge when designing, evaluating, and implementing mHealth tobacco cessation interventions for socially-disadvantaged populations. Altogether, this perspective article provides a conceptual foundation for optimizing mHealth tobacco cessation interventions for the socially-disadvantaged populations in greatest need.

Use of Geographic Information System Technology to Evaluate Health Disparities in Smoking Cessation Class Accessibility for Patients in Louisiana Public Hospitals

Research has shown cigarette smoking is a major risk factors for many type of cancer or cancer prognosis. Tobacco related health disparities were addressed continually in cancer screening, diagnosis, treatment, prevention and control. The present study evaluated the health disparities in attendance of smoking cessation counseling classes for 4,826 patients scheduled to attend between 2005 and 2007. Of 3,781 (78.4%) patients with records to calculate the distance from their home domicile to counseling sites using Geographic Information System technology, 1,435 (38%) of smokers who attended counseling had shorter travel distances to counseling sites (11.6 miles, SD = 11.29) compared to non-attendees (13.4 miles, SD = 16.72). When the travel distance was >20 miles, the estimated odds of attending decreased with greater travel distance. Smokers who actually attended were more likely to be older, female, White, living in urban areas, and receiving free healthcare. After controlling for other socio-demographic factors, shorter distances were associated with greater class attendance, and individuals more likely to attend included those that lived closer to the counseling site and in urban settings, were female, White, commercially insured, and older than their counterparts. These findings have the potential to provide important insights for reducing health disparities for cancer prevention and control, and to improve shared decision making between providers and smokers.