SimSmoke simulation models distinguished by race/ethnicity: past and future trends and the potential role of policy

David T Levy; James H Buszkiewicz; Zhe Yuan; Yameng Li; Rafael Meza; Nancy L Fleischer

doi:10.1093/jncimonographs/lgaf016

. 2025 Aug 11;2025(70):253–265. doi: 10.1093/jncimonographs/lgaf016

SimSmoke simulation models distinguished by race/ethnicity: past and future trends and the potential role of policy

David T Levy ^1,^✉, James H Buszkiewicz ², Zhe Yuan ³, Yameng Li ⁴, Rafael Meza ^5,⁶, Nancy L Fleischer ⁷

PMCID: PMC12342921 PMID: 40795912

Abstract

Introduction

Policy interventions to reduce racial/ethnic cigarette smoking and related health disparities are needed to improve health equity. Simulation models can be useful in gauging the impact of tobacco control policies on trends in smoking-related outcomes, but few have systematically analyzed the impact of tobacco control policies across racial/ethnic groups.

Methods

We developed 3 separate SimSmoke models for the non-Hispanic White (NHW), non-Hispanic Black (NHB), and Hispanic populations. Following a first-order Markov process, population projections evolve through net immigration and death rates, and smoking prevalence evolves through initiation, cessation, and relapse. The models incorporate policies implemented from 2011 to 2023 and are used to consider trends in NHW, NHB, and Hispanic smoking prevalence and smoking-attributable death and the impact of policies on those trends.

Results

The models indicate major differences in smoking trends and smoking-attributable deaths (SADs) among NHW, NHB, and Hispanic adults, with NHB males experiencing the smallest smoking decline through 2023 and having the highest 2023 smoking prevalence. The models predict major differences in the impact of tobacco control policies, especially the greater effect of cigarette taxes on NHB and Hispanic adults than NHW adults and the reduced impact of T21 laws on NHB compared to NHW and Hispanic adults.

Discussion

The models predict large differences in levels and rates of decline in NHW, NHB, and Hispanic smoking prevalence, leading to widening health disparities between racial/ethnic groups. Further study is needed on differential race/ethnicity impacts of tobacco control policies and the role of cigars, e-cigarettes, and other product use.

Introduction

Despite decreasing cigarette use in the United States since the mid-1900s,¹ smoking disparities are common.² Disparities by race/ethnicity in cigarette smoking prevalence, smoking initiation and cessation, and downstream health consequences are complex. For example, American Indian and Alaskan Native (AI/AN) populations consistently have the highest smoking prevalence, with non-Hispanic Black (NHB) and non-Hispanic White (NHW) adults smoking cigarettes at similar rates.^2-4 Cigarette smoking initiation is highest among AI/AN populations, and cessation is lowest among AI/AN and NHB populations.²^,³^,⁵ From 2016 to 2020, cancer mortality was highest among the AI/AN followed by NHB, NHW, and Hispanic populations, while cancer incidence was highest for Black males.⁶ Identifying policy interventions to reduce racial/ethnic disparities in cigarette smoking and related health outcomes is needed to improve health equity.

Tobacco control policies have been effective tools in reducing cigarette smoking outcomes in the broader population.^7-17 Most studies find that minimum legal sales age laws, such as Tobacco 21 (T21),^18-25 cigarette taxes,^26-43 smoke-free air laws,²⁶^,³⁵^,³⁷^,³⁸^,^43-55 and media campaigns,⁴⁵^,^56-65 reduce cigarette smoking prevalence, initiation, and cessation. While studies call for consideration of the equity impact of tobacco control policies,⁸^,⁹^,¹²^,^14-17^,⁶⁶ the limited evidence indicates that the policy impacts on persistent racial/ethnic smoking disparities are largely ineffective.¹⁹^,²⁰^,²⁴^,²⁶^,³³^,³⁴^,³⁹^,⁴¹^,⁵²^,⁵⁸^,⁶¹^,⁶⁴^,⁶⁵^,^67-81 The richest literature on cigarette tax policies does indicate that higher taxes may be more effective in curbing smoking outcomes among NHB and Hispanic adults than NHW adults.²⁶^,³³^,³⁴^,³⁹^,^70-73 Heterogeneity in settings, context, measures, and populations complicates the conclusions drawn from evaluations of the impact of smoke-free air laws and media campaigns on racial/ethnic smoking disparities.¹⁷

Simulation models, such as SimSmoke,^82-87 can be useful in gauging the impact of past and potential future tobacco control policies on variations in racial/ethnic smoking-related outcomes.⁸⁸ For example, menthol cigarette use among the NHB population may explain their later smoking initiation and lower smoking intensity but similar or higher smoking rates and higher tobacco-related mortality at older ages.⁸⁹ Simulations of the impact of menthol cigarette bans have found that menthol cigarette use was overrepresented among NHB adults, and menthol use was partially responsible for tobacco-related smoking disparities.^90-93 A menthol cigarette ban is projected to reduce smoking prevalence by 9.7% and avert 633 252 smoking-attributable deaths (SADs) in the overall population by 2050,⁹⁴ but reduce smoking prevalence by 24.8% and avert 237 317 SADs among NHB adults alone.⁹² Otherwise, simulation models have not been used to estimate the role of policies in reducing racial/ethnic smoking and health disparities.⁸⁸^,⁹⁵^,⁹⁶

We develop separate US SimSmoke tobacco control simulation models⁸⁵^,⁸⁷^,^97-99 for NHW, NHB, and Hispanic adults to project trends in smoking prevalence and smoking-attributable mortality among these groups and the impact of policies on these trends. We compare model outcomes to recent smoking trends. The difference between the model and survey estimators from 2014 to 2018 is used to explain the implicit impact of ENDS and other omitted factors, a method previously employed using US,⁸⁷ England,¹⁰⁰ and Canada¹⁰¹ SimSmoke models. Building on prior work,⁸⁷ we also distinguish the impact of tobacco control policies for each racial/ethnic group on smoking prevalence and SADs.

Methods

Because data are limited for AI/AN and Asian populations, we consider only the NHW, NHB, and Hispanic populations. We develop 3 separate SimSmoke models for these populations, each with individual components for population, cigarette smoking prevalence, tobacco control policies, and SADs.

The models start in 2006, a year of relative stability for tobacco control policies and smoking prevalence. Starting in 2006 also provided sufficient years for model calibration and validation. Populations are distinguished by age and sex and further differentiated by current-, former-, and never-smoking status. Assuming a first-order Markov process, population projections evolve through net immigration and death rates, and smoking prevalence evolves through initiation, cessation, and relapse as impacted by policies implemented through 2023.

Data

Using demographic data from the US Census Bureau and the Centers for Disease Control and Prevention (see Appendix S1),¹⁰² we started with the population in 2006, and in future years applied mortality rates in 2006-2060, and estimated net immigration rates by age, sex, and race/ethnicity to predict population in 2007-2060. Mortality rates were distinguished by age, sex, and race/ethnicity using actual rates for 1999-2019 and projected rates for 2020-2060. Mortality rates of NHW, NHB and Hispanic adults were further distinguished by smoking status using yearly data from CISNET (see Appendix S1).^103-105 Net immigration rates were estimated by the ratio of net immigrants to the total population for NHW, Black alone (NHB rates were not available from the US Census Bureau), and Hispanic by age and sex. This ratio was averaged over 2017-2019 by age, sex, and racial/ethnic group and applied to all years. The models well approximate population trends (see Appendix S2).

The model distinguishes the 2006 baseline year population for each racial/ethnic group into never, current, and former smoking status. Smoking prevalence by race/ethnicity, 7 age groups (18-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75+), and sex were obtained from the 2006/7 Tobacco Use Supplement (TUS) of the Current Population Survey (CPS), a large-scale, nationally-representative survey. Current smoking status is defined as smoking more than 100 cigarettes lifetime and currently smoking either daily or some days. Former smoking status is defined as having smoked more than 100 cigarettes but not currently smoking, and is distinguished into 17 subgroups as “quit less than 1 year,” “quit at least 1 year but less than 2 years,” through “quit more than 15 years.”

The 2006/7 TUS-CPS smoking prevalence by 7 age groups and sex were first linearly interpolated to single ages and smoothed using a moving average (MA-3), yielding differences by age and sex group between the initial survey and 2006 prevalence estimates. We further adjusted by race/ethnicity, sex, and 4 age groups (18-24, 25-44, 45-64 and 65+, but not all adults age 18 and above) using the ratio of the 2006 TUS-CPS to the initial 2006 model prevalence. Thereby, the model starts at the same prevalence by sex and age group as the 2006 TUS-CPS.

SimSmoke models generally set the last age of male and female smoking initiation at 1 year before reaching the maximum smoking prevalence in 2006. SimSmoke applies smoking cessation rates for after the last age of net initiation. Cessation rates are measured as the percentage that quit smoking within the last 3-12 months among those who smoked 12 months ago, where those quitting in the last 3 months offset former smokers who quit in the previous 3-12 months but subsequently relapse.^82-86 Cessation rates are distinguished by racial/ethnic group as suggested by recent studies.^106-108 After cessation in the first year, relapse occurs among adults who quit smoking at least 12 months ago. The model applies the same (non-race specific) relapse rates by age, sex, and years since quit to each racial/ethnic group due to mixed evidence of differential relapse rates after the first year quit.^109-112 The formulas and the measure of net initiation are provided in Appendix S3.

Policy effects

The SimSmoke model includes separate modules for cigarette prices (taxes), smoke-free air laws, mass media campaigns, marketing restrictions, cessation treatments, and youth access (T21) policies. Policy effects are estimated in terms of the percentage change in smoking rates relative to initial rates [(post-policy rate—initial rate)/initial rate]. When a policy change is implemented, the immediate PC effect is applied to smoking prevalence in year t. In the following years, SimSmoke applies a percentage reduction to the initiation rate and a percentage increase to the cessation rate. When more than one policy is implemented, multiplicative effects are applied, ie, (1+PC_i) *(1+PC_j) for policies i and j. Table 1 provides policy descriptions and effect sizes.

Table 1.

Tobacco control policies, specifications, and effect sizes applied in SimSmoke race models.

Policy	Description	Policy effect size
Cigarette excise taxes
Cigarette price/tax	The effect of taxes is directly incorporated through the average price after tax. The price elasticity is used to convert the price changes (%) into effect sizes.	Elasticities
		−0.4 for ages 14-17
		−0.3 for ages 18-24
		−0.2 for ages 25-34
		−0.1 for ages 35-64
		−0.2 for ages 65+
Smoke-free air laws
Worksite smoking ban	Ban in all indoor worksites, with strong enforcement of laws (reduced 1/3 if allowed in ventilated areas and 2/3 if allowed in common areas)	−6% prevalence and initiation, +6% cessation
Restaurant smoking ban	Ban in all indoor restaurants (scaled for lower coverage), with strong enforcement of laws	−2% prevalence and initiation, +2% cessation
Pubs and bars smoking ban	Ban in all indoor in pubs and bars (scaled for lower coverage), with strong enforcement of laws	−1% prevalence and initiation, +1% cessation
Other place bans	Ban in 3 out of 4 government buildings (scaled for lower coverage), retail stores, public transportation, and elevators, with strong enforcement of laws	−1% prevalence and initiation, +1% cessation
Enforcement and publicity	Government agency enforces the laws and publicity via tobacco control campaigns	Effects reduced 50% absent publicity and enforcement
Media campaigns
High level	Campaign publicized heavily with state and local programs with strong funding (>$0.50 USD)	−6.5% prevalence and initiation, +6.5% cessation
Medium level	Campaign publicized with funding of at least $0.10 USD per capita	−3.25% prevalence and initiation, +3.25% cessation
Low-level media	Campaign publicized only sporadically with minimal funding (<$0.10 USD per capita)	−1.63% prevalence and initiation, +1.63% cessation
Marketing restrictions
Comprehensive	Ban on all forms of direct advertising including point of sale and indirect marketing	−5% prevalence,
		−8% initiation,
		+4% cessation
Moderate	Ban on broadcast media, newspapers, and billboards marketing and at least some indirect marketing (sponsorship, branding, giveaways)	−3% prevalence,
		−4% initiation,
		+2% cessation
Minimal	Ban on broadcast media advertising	−1% prevalence and −1% initiation only
Enforcement	Government agency enforces the laws	Reduced 50% absent enforcement
Cessation treatment policies
Availability of pharmacotherapies	Legality of nicotine replacement therapy and/or Bupropion and Varenicline	−1% prevalence,
Availability of pharmacotherapies		+4% cessation
Cessation treatment financial coverage	Payments to cover pharmacotherapy and behavioral cessation treatment with high publicity (reduced 12.5% for moderate and 18.75% for low publicity)	−2.25% prevalence,
Cessation treatment financial coverage		+8% cessation
Quitline	Three quit line types: passive, proactive, and active with follow-up. (Effect size reduced by 1/3 if quit line is proactive, reduced by 2/3 if quit line passive.)	−1% prevalence,
Quitline		+6% cessation
Brief interventions	Advice by healthcare provider to quit and advice on treatment	−1% prevalence,
Brief interventions		+6% cessation
All cessation policies combined	Complete availability and reimbursement of pharmaco- and behavioral treatments, quitlines, and brief interventions	−5.68% prevalence,
All cessation policies combined		+29.4% cessation
Youth access policies
Strong enforcement, well publicized	Compliance checks conducted 4 times per year per outlet, penalties are potent and enforced with heavy publicity	−16% for ages 16-17 and −24% for ages 10-15
Moderate enforcement with some publicity	Compliance checks conducted regularly, penalties are potent, and publicity and merchant training are included	−8% initiation and prevalence for ages 16-17 and −12% for ages 10-15
Law enforcement	Compliance checks are conducted sporadically, penalties are weak	−2% initiation and prevalence for ages 16-17 and −3% ages 10-15
Tobacco 21 policy
T21 laws coverage	State-level laws coverage with moderate compliance checks	−30% prevalence for ages 18-20 and −15% for ages 16-17, and −30% initiation for all ages under 21

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Unless otherwise indicated, the effects are in terms of the reduction in prevalence during the first year with continuing impacts on initiation and cessation rates in future years. Only the effects of cigarette prices differed by race/ethnicity in the best estimation, with a 1:2:2 ratio for NHW/NHB/Hispanic, implying multipliers of 0.76/1.52/1.52 (see Appendix S4).

Policy effects from previous US SimSmoke models⁸⁵^,^87-99 are applied to each racial/ethnic model. Based on previous reviews¹⁰^,¹²^,¹⁷ and our literature review of more recent evaluations (see Appendix S4), differential policy effects by race/ethnicity were not well distinguished for Tobacco 21, cessation treatment, health warnings, mass media campaigns, and smoke-free-air laws. Only cigarette price was estimated to have differential effects by race/ethnicity, with a 1:2:2 ratio for NHW/NHB/Hispanic populations applied to the cigarette price elasticities in Table 1 (see Appendix S4).

Calibration

Each racial/ethnic subgroup model was calibrated by age and sex by adjusting initiation, cessation, and relapse rates based on differences in the model projected 2006-2010 relative reduction in smoking prevalence compared to the TUS-CPS estimated 2006-2010 relative reduction, eg, if the SimSmoke relative reduction is greater than the TUS-CPS relative reduction, we increased net initiation rates at younger ages or decreased cessation rates at older ages. Since the Hispanic model after the calibration by 2010 exhibited an increasing pattern for male prevalence at ages 18-24 (contradicting our expectation and the survey decreasing pattern in 2010-2014 as well) and an unexpectedly large reduction for female prevalence at ages 65 and above for future years, we re-calibrated the Hispanic model only for those 2 age and sex groups. We re-calibrated the model by considering the relative reduction in 2006-2014 (the next available TUS-CPS survey after 2010, but before significant ENDS use) so that the relative reduction in 2006-2010 smoking prevalence was near the average relative reduction from the 2006-2010 TUS-CPS and half the relative reduction in 2006-2014. A more detailed description of the methods and results is provided in Appendix S5.

Analysis of outcomes

Four types of analysis were conducted. First, we predicted short-term trends in smoking prevalence by age, sex, and race/ethnicity in 2006-2023. Second, we compared the relative changes in smoking prevalence by age, sex, and race/ethnicity from SimSmoke to that of 2006-2018 TUS-CPS. A third analysis estimated the impact of cigarette-oriented policies from 2006 to 2023 in reducing NHW, NHB, and Hispanic smoking prevalence, where the relative changes in smoking prevalence incorporating specific policies are compared to the relative changes in smoking prevalence under a counterfactual with policies maintained at 2006 levels.

Our final analysis pertains to SADs and life year lost (LYLs) health impacts by race/ethnicity. SADs by age, sex, and race/ethnicity are calculated by first multiplying the number of adults who currently smoke at each age by their excess (no. currently smoke−no. never smoke) mortality risks. We applied the same procedure to adults who formerly smoked and summed over the number of adults who currently and formerly smoked to obtain total SADs.⁸⁵ We calculated LYLs as the product of SADs and life expectancy.¹⁰² LYLs show a greater loss among younger than older adults.⁸⁷ To gauge disparities, we calculated per capita SADs and LYLs. Since SADs occur almost exclusively after age 40, we measured per capita SADs and LYLs as 2023 SADs and LYLs for each race/ethnicity divided by their respective 2022 population at ages 40+.

Results

Smoking levels and trends projected by SimSmoke

Tables 2-4 show SimSmoke results for adult NHW, NHB, and Hispanic smoking prevalence by age and sex from 2006 to 2023. For ages 18 and above, the 2006 male (female) smoking prevalence was 21.9% (18.7%) for NHW, 21.2% (14.9%) for NHB, and 16.2% (9.1%) for Hispanic adults, with relative reductions from 2006 to 2023 of 37.5% (36.2%) for NHW, 25.2% (35.8%) for NHB, and 42.6% (41.8%) for Hispanic adults.

Table 2.

Smoking prevalence of NHW adults and percent changes in SimSmoke and TUS-CPS, by age and sex, 2006-2023.

Sex	Ages	Source	2006	2010	2018	2023	% change 2006-2010	% change 2010-2018	Difference % change 2010-2018	% change 2006-2023
Male	18 and above	SimSmoke	21.9%	19.1%	15.6%	13.7%	−12.9%	−18.4%	10.2%	−37.5%
	18 and above	TUS-CPS	21.5%	18.9%	13.5%		−12.3%	−28.6%
	18-24	SimSmoke	27.1%	23.3%	21.2%	18.1%	−13.9%	−8.9%	48.2%	−33.0%
	18-24	TUS-CPS	27.1%	23.3%	10.0%		−13.9%	−57.1%
	25-44	SimSmoke	25.7%	22.6%	18.5%	16.7%	−12.3%	−18.2%	10.5%	−35.2%
	25-44	TUS-CPS	25.7%	22.6%	16.1%		−12.3%	−28.7%
	45-64	SimSmoke	22.5%	19.8%	15.7%	13.7%	−12.2%	−20.7%	0.2%	−39.1%
	45-64	TUS-CPS	22.5%	19.8%	15.6%		−12.2%	−20.9%
	65 and above	SimSmoke	9.4%	8.9%	8.9%	8.4%	−5.2%	0.1%	7.3%	−9.9%
	65 and above	TUS-CPS	9.4%	8.9%	8.3%		−5.1%	−7.2%
Female	18 and above	SimSmoke	18.7%	16.4%	13.5%	12.0%	−12.5%	−17.6%	9.3%	−36.2%
	18 and above	TUS-CPS	18.6%	16.2%	11.9%		−12.8%	−26.8%
	18-24	SimSmoke	24.2%	19.2%	16.7%	14.2%	−20.5%	−13.2%	42.9%	−41.4%
	18-24	TUS-CPS	24.2%	19.3%	8.4%		−20.3%	−56.2%
	25-44	SimSmoke	23.3%	19.5%	13.9%	11.9%	−16.4%	−28.5%	1.3%	−48.9%
	25-44	TUS-CPS	23.3%	19.5%	13.7%		−16.3%	−29.8%
	45-64	SimSmoke	19.3%	18.3%	16.3%	14.6%	−5.3%	−10.5%	5.0%	−24.2%
	45-64	TUS-CPS	19.3%	18.3%	15.4%		−5.3%	−15.5%
	65 and above	SimSmoke	8.3%	7.8%	8.3%	8.5%	−6.6%	6.0%	20.5%	2.2%
	65 and above	TUS-CPS	8.3%	7.8%	6.7%		−6.5%	−14.5%

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Table 3.

Smoking prevalence of NHB adults and percent changes in SimSmoke and TUS-CPS, by age and sex, 2006-2023.

Sex	Ages	Sources	2006	2010	2018	2023	% change 2006-2010	% change 2010-2018	Difference % change 2010- 2018	% change 2006-2023
Male	18 and above	SimSmoke	21.2%	19.4%	17.2%	15.9%	−8.5%	−11.3%	10.4%	−25.2%
	18 and above	TUS-CPS	21.3%	19.5%	15.3%		−8.4%	−21.7%
	18-24	SimSmoke	18.5%	13.8%	11.9%	10.1%	−25.3%	−13.7%	15.2%	−45.3%
	18-24	TUS-CPS	18.5%	13.8%	9.8%		−25.3%	−28.9%
	25-44	SimSmoke	20.4%	19.2%	17.3%	16.2%	−6.2%	−9.9%	8.1%	−20.7%
	25-44	TUS-CPS	20.4%	19.1%	15.7%		−6.3%	−18.0%
	45-64	SimSmoke	26.2%	25.1%	21.3%	19.3%	−4.0%	−15.3%	14.7%	−26.3%
	45-64	TUS-CPS	26.2%	25.1%	17.6%		−3.9%	−30.0%
	65 and above	SimSmoke	13.8%	12.5%	13.8%	13.8%	−9.2%	10.6%	8.4%	0.5%
	65 and above	TUS-CPS	13.8%	12.5%	12.8%		−9.2%	2.2%
Female	18 and above	SimSmoke	14.9%	13.0%	10.7%	9.6%	−12.8%	−17.6%	1.3%	−35.8%
	18 and above	TUS-CPS	15.0%	13.0%	10.6%		−12.9%	−18.9%
	18-24	SimSmoke	12.1%	8.1%	6.6%	5.6%	−32.6%	−18.6%	14.4%	−53.6%
	18-24	TUS-CPS	12.1%	8.1%	5.4%		−32.9%	−33.0%
	25-44	SimSmoke	14.6%	13.7%	11.2%	10.1%	−6.4%	−17.8%	6.3%	−30.9%
	25-44	TUS-CPS	14.6%	13.7%	10.4%		−6.4%	−24.1%
	45-64	SimSmoke	19.9%	16.8%	12.7%	10.7%	−15.5%	−24.2%	−3.8%	−46.0%
	45-64	TUS-CPS	19.9%	16.8%	13.4%		−15.6%	−20.4%
	65 and above	SimSmoke	7.2%	7.5%	8.9%	9.2%	3.9%	17.7%	0.6%	27.2%
	65 and above	TUS-CPS	7.2%	7.5%	8.8%		3.9%	17.0%

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Table 4.

Smoking prevalence of Hispanic adults and percent changes in SimSmoke and TUS-CPS, by age and sex, 2006-2023.

Sex	Ages	Sources	2006	2010	2018	2023	% change 2006-2010	% change 2010-2018	Difference % change 2010-2018	% change 2006-2023
Male	18 and above	SimSmoke	16.2%	13.6%	10.7%	9.3%	−16.0%	−21.1%	2.1%	−42.6%
	18 and above	TUS-CPS	16.3%	13.9%	10.7%		−14.4%	−23.2%
	18-24	SimSmoke	14.5%	12.3%	11.3%	9.7%	−14.9%	−7.9%	42.8%	−33.2%
	18-24	TUS-CPS	14.5%	14.0%	6.9%		−3.0%	−50.6%
	25-44	SimSmoke	16.9%	14.0%	10.7%	9.5%	−17.3%	−23.4%	−4.9%	−43.8%
	25-44	TUS-CPS	16.9%	14.0%	11.4%		−17.3%	−18.5%
	45-64	SimSmoke	18.2%	15.3%	11.0%	9.2%	−16.4%	−27.7%	−13.3%	−49.5%
	45-64	TUS-CPS	18.2%	15.2%	13.1%		−16.4%	−14.3%
	65 and above	SimSmoke	9.1%	8.7%	8.9%	8.3%	−4.7%	2.5%	23.3%	−8.8%
	65 and above	TUS-CPS	9.1%	8.7%	6.9%		−4.6%	−20.7%
Female	18 and above	SimSmoke	9.1%	7.9%	6.2%	5.3%	−13.0%	−21.9%	20.5%	−41.8%
	18 and above	TUS-CPS	9.1%	7.8%	4.5%		−13.9%	−42.4%
	18-24	SimSmoke	8.1%	6.8%	5.7%	4.9%	−15.4%	−15.7%	51.6%	−39.0%
	18-24	TUS-CPS	8.1%	6.8%	2.2%		−15.4%	−67.3%
	25-44	SimSmoke	9.0%	8.0%	6.1%	5.2%	−11.4%	−23.9%	16.2%	−42.6%
	25-44	TUS-CPS	9.0%	8.0%	4.8%		−11.3%	−40.2%
	45-64	SimSmoke	11.4%	9.7%	7.3%	6.1%	−14.9%	−24.7%	15.0%	−46.0%
	45-64	TUS-CPS	11.4%	9.7%	5.8%		−15.0%	−39.6%
	65 and above	SimSmoke	5.2%	4.6%	4.4%	4.3%	−12.0%	−3.5%	5.4%	−17.7%
	65 and above	TUS-CPS	5.2%	3.9%	3.6%		−23.8%	−8.9%

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For ages 18-24, 2006 male (female) smoking prevalence was 27.1% (24.2%) NHW, 18.5% (12.1%) for NHB, and 14.5% (8.1%) Hispanic adults, with respective 2006-2023 smoking relative reductions of 33.0% (41.4%), 45.3% (53.6%), and 33.2% (39.0%). For ages 25-44, 2006 smoking prevalence was 25.7% (23.3%) for NHW, 20.4% (14.6%) for NHB, and 16.9% (14.9%) for Hispanic adults, with respective relative reductions of 35.2% (48.9%), 20.6% (30.9%), and 43.8% (42.6%). For ages 45-64, NHB males and females had the highest 2006 prevalence, while NHB males had the least decline, but females showed a rapid decline by 2023. However, NHB females ages 65+ had mid-level prevalence and a relative increase of 27.2%.

Thus, for most age groups, NHB males had a lower initial prevalence than NHW males but showed the least decline from 2006 to 2023, while Hispanic males had the lowest prevalence and greatest decline. NHB females also generally had a lower 2006 prevalence but similar declines as NHW females, while Hispanic females generally had the lowest prevalence and similar declines to NHW females.

Comparison of relative reductions in smoking prevalence from SimSmoke and TUS-CPS

Tables 2-4 also compare SimSmoke smoking prevalence projections and relative changes from 2010 to 2018 by age, sex, and race/ethnicity to TUS-CPS estimates.

For age 18+ NHW males (females), TUS-CPS estimates projected a 10.2% (9.3%) greater absolute decline in actual prevalence than predicted by SimSmoke. For NHB males (females) age 18+, TUS-CPS showed a 10.4% (1.3%) greater absolute decline compared to SimSmoke. Hispanic smoking prevalence showed a 2.1% (20.5%) greater absolute decline than SimSmoke.

For ages 18-24, the difference in smoking prevalence relative reductions between Sim-Smoke and TUS-CPS was 48.2% (42.8%) for NHW, compared to 15.2% (14.4%) for NHB, and 42.8% (51.6%) for Hispanic males (females). For ages 25-44, the difference between SimSmoke and TUS-CPS was 10.5% (1.3%) for NHW, 8.1% (6.3%) for NHB, and −4.9% (16.2%) for Hispanic males (females). For ages 45-64, the difference was 0.2% (5.0%) NHW, 14.7% (−3.8%) for NHB, and −13.3% (15.0%) for Hispanic males (females). For ages 65+, the difference was 7.3% (20.5%) for NHW, 8.4% (0.5%) for NHB, and 23.3% (5.4%) for Hispanic males (females).

SimSmoke generally projected smaller relative reductions in smoking prevalence than TUS-CPS, especially for ages 18-24. The relative differences varied at other ages. Notably, NHB adults generally showed less relative reduction than NHW and Hispanic adults.

The effect of policies implemented through 2023

As shown in Tables 5-7, the impact of a particular policy or group of policies was estimated by comparing smoking prevalence with policies implemented to a counterfactual with no new policies implemented after 2006.

Table 5.

Smoking prevalence, smoking-attributable deaths, and averted deaths for both sexes predicted by SimSmoke for NHW adults in different policy scenarios, 2006-2060.

Adult smoking prevalence	2006	2023	2060	Relative change by 2023 ^a	Relative change by 2060 ^a
No policies	20.3%	15.2%	11.0%	–	–
All policies implemented	20.3%	12.8%	7.2%	−15.4%	−34.4%
Price only	20.3%	14.2%	9.7%	−6.5%	−11.4%
Smoke-free air only	20.3%	14.4%	10.3%	−4.7%	−5.9%
Media campaign only	20.3%	15.2%	11.0%	0.1%	0.4%
Cessation treatment only	20.3%	14.9%	10.8%	−1.6%	−2.0%
Marketing restrictions only	20.3%	15.1%	10.9%	−0.7%	−0.9%
Youth access ban only	20.3%	15.2%	11.0%	−0.1%	−0.2%
T21 only	20.3%	14.7%	8.9%	−3.1%	−18.9%
Total smoking-attributable deaths	2006	2023	2060	Cumulative 2006-2023 ^b	Cumulative 2006-2060 ^b
No policies	371 732	286 241	160 991	5 517 332	14 354 062
All policies implemented	371 732	274 344	140 058	5 430 437	13 574 311
Price only	371 732	281 961	151 994	5 483 577	14 055 561
Smoke-free air only	371 732	280 464	154 497	5 476 595	14 030 814
Media campaign only	371 732	286 347	161 315	5 518 063	14 361 011
Cessation treatment only	371 732	284 498	158 135	5 506 678	14 220 789
Marketing restrictions only	371 732	285 459	160 081	5 511 613	14 310 590
Youth access ban only	371 732	286 241	160 921	5 517 332	14 353 356
T21 only	371 732	286 241	157 239	5 517 332	14 322 332
Total SADs averted deaths	2006	2023	2060	Cumulative 2006-2023 ^b	Cumulative 2006-2060 ^b
All policies implemented	0	11 896	20 933	86 895	779 751
Price only	0	4279	8996	33 756	298 501
Smoke-free air only	0	5777	6493	40 737	323 248
Media campaign only	0	−107	−325	−731	−6950
Cessation treatment only	0	1742	2855	10 655	133 273
Marketing restrictions only	0	782	909	5719	43 472
Youth access ban only	0	0	69	0	706
T21 only	0	0	3751	0	31 730

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Relative changes in smoking prevalence by sex in 2023 and 2060 from scenario with overall/individual policy changes compared with a scenario with policies remaining constant at 2006 levels.

Sum of smoking-attributable deaths/sum of averted deaths for both sexes in 2006-2023 and 2006-2060.

Table 6.

Smoking prevalence, smoking-attributable deaths, and averted deaths for both sexes predicted by SimSmoke for NHB adults in different policy scenarios, 2006-2060.

Adult smoking prevalence	2006	2023	2060	Relative change by 2023 ^a	Relative change by 2060 ^a
No policies	18.1%	15.0%	11.2%	–	–
All policies implemented	18.1%	12.4%	7.1%	−17.3%	−37.2%
Price only	18.1%	13.4%	9.1%	−10.9%	−19.1%
Smoke-free air only	18.1%	14.4%	10.8%	−3.9%	−4.4%
Media campaign only	18.1%	15.0%	11.3%	0.0%	0.3%
Cessation treatment only	18.1%	14.9%	11.2%	−0.8%	−0.5%
Marketing restrictions only	18.1%	14.9%	11.2%	−0.6%	−0.7%
Youth access ban age 18	18.1%	15.0%	11.2%	−0.1%	−0.2%
T21 only	18.1%	14.7%	9.3%	−2.4%	−17.6%
Total smoking-attributable deaths	2006	2023	2060	Cumulative 2006-2023 ^b	Cumulative 2006-2060 ^b
No policies	55 959	52 245	37 248	920 315	2 625 783
All policies implemented	55 959	49 134	29 545	896 934	2 399 262
Price only	55 959	50 594	32 357	907 246	2 491 033
Smoke-free air only	55 959	51 056	35 759	911 930	2 560 011
Media campaign only	55 959	52 238	37 292	920 325	2 625 234
Cessation treatment only	55 959	51 965	36 851	918 531	2 606 908
Marketing restrictions only	55 959	52 082	37 031	919 112	2 616 813
Youth access ban only	55 959	52 245	37 224	920 315	2 625 539
T21 only	55 959	52 245	35 844	920 315	2 614 164
Total averted deaths	2006	2023	2060	Cumulative 2006-2023 ^b	Cumulative 2006-2060 ^b
All policies implemented	0	3111	7703	23 381	226 522
Price only	0	1651	4891	13 069	134 750
Smoke-free air only	0	1189	1489	8385	65 772
Media campaign only	0	7	−44	−10	549
Cessation treatment only	0	280	398	1784	18 876
Marketing restrictions only	0	163	217	1203	8971
Youth access ban only	0	0	24	0	244
T21 only	0	0	1404	0	11 620

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Relative changes in smoking prevalence by sex in 2023 and 2060 from scenario with overall/individual policy changes compared with a scenario with policies remaining constant at 2006 levels.

Sum of smoking-attributable deaths/sum of averted deaths for both sexes in 2006-2023 and 2006-2060.

Table 7.

Smoking prevalence, smoking-attributable deaths, and averted deaths for both sexes predicted by SimSmoke for Hispanic adults in different policy scenarios, 2006-2060.

Adult smoking prevalence	2006	2023	2060	Relative change by 2023 ^a	Relative change by 2060 ^a
No policies	12.6%	9.4%	6.8%	–	–
All policies implemented	12.6%	7.3%	3.8%	−22.6%	−43.4%
Price only	12.6%	8.1%	5.3%	−14.1%	−22.2%
Smoke-free air only	12.6%	9.0%	6.4%	−4.6%	−5.5%
Media campaign only	12.6%	9.4%	6.8%	0.2%	0.4%
Cessation treatment only	12.6%	9.3%	6.7%	−1.4%	−1.5%
Marketing restrictions only	12.6%	9.4%	6.7%	−0.7%	−0.9%
Youth access ban only	12.6%	9.4%	6.8%	−0.1%	−0.3%
T21 only	12.6%	9.1%	5.3%	−3.9%	−21.0%
Total smoking-attributable deaths	2006	2023	2060	Cumulative 2006-2023 ^b	Cumulative 2006-2060 ^b
No policies	21 645	27 429	31 601	403 898	1 612 497
All policies implemented	21 645	25 953	25 481	394 321	1 463 351
Price only	21 645	26 643	27 895	398 520	1 526 936
Smoke-free air only	21 645	26 886	30 287	400 570	1 569 594
Media campaign only	21 645	27 440	31 673	403 963	1 613 804
Cessation treatment only	21 645	27 261	31 037	402 990	1 593 951
Marketing restrictions only	21 645	27 355	31 413	403 427	1 606 669
Youth access ban only	21 645	27 429	31 574	403 898	1 612 239
T21 only	21 645	27 429	30 497	403 898	1 603 419
Total averted deaths	2006	2023	2060	Cumulative 2006-2023 ^b	Cumulative 2006-2060 ^b
All policies implemented	0	1476	6120	9577	149 146
Price only	0	787	3706	5378	85 561
Smoke-free air only	0	543	1313	3328	42 902
Media campaign only	0	−11	−72	−65	−1307
Cessation treatment only	0	168	564	908	18 545
Marketing restrictions only	0	74	188	471	5828
Youth access ban only	0	0	26	0	258
T21 only	0	0	1104	0	9078

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Relative changes in smoking prevalence by sex in 2023 and 2060 from scenario with overall/individual policy changes compared with a scenario with policies remaining constant at 2006 levels.

Sum of smoking-attributable deaths/sum of averted deaths for both sexes in 2006-2023 and 2006-2060.

Compared with the counterfactual with no new policies implemented after 2006, SimSmoke projected that 2023 smoking rates with all policies were lower by 15.4% in relative terms for NHW, 17.3% for NHB, and 22.6% for Hispanic adults. By 2060, the relative impact on smoking prevalence increased to a 34.4%, 37.2%, and 43.4% decline for each respective group, since policies continued to reduce smoking rates through increased cessation and reduced initiation.

Much of the reduction in smoking prevalence was due to inflation-adjusted price increases ($4.95 in 2006 to $6.64 in 2023), which alone were predicted to reduce NHW, NHB, and Hispanic smoking prevalence in relative terms by 6%, 11%, and 14% by 2023 increasing to 11%, 19%, and 22% by 2060, averting 298,501, 134,750 and 85,561 SADs from 2006 to 2060. The differential impacts were primarily due to different price elasticities by racial/ethnic group (see Appendix S6 for impacts when assuming the same price elasticities).

Among other policies, most restrictions were implemented since 2006 (except media campaigns with negligible impacts) with the smallest impacts on NHB relative to NHW and Hispanic adults. Relative reductions by 2023 (2026) from Tobacco 21 were 2.4%-3.9% (17.6%-21.0%), from smoke-free air laws were 3.9%-4.7% (4.4%-5.9%), from cessation treatment were 0.8%-1.6% (0.5%-2.0%), and from marketing restrictions were 0.6%-0.7% (0.7%-0.9%).

Projected SADs and LYLs

Tables 5-7 also show SADS. With the policies implemented from 2006 through 2023 and maintained through 2060, SADs for both sexes combined decreased from 371,732 in 2006 to 274,344 in 2023 and 140,058 in 2060 for NHW adults, and from 55,959 in 2006 to 49,134 in 2023 and 29,545 in 2060 for NHB adults. However, SADs among Hispanic adults increased from 21,645 in 2006 to 25,953 in 2023 and then increased through 2039 with subsequent declines to 25,481 in 2060. From 2006 to 2060, SADs were 13.6 million for NHW, 2.4 million for NHB, and 1.5 million for Hispanic adults.

As a result of all policies, the total SADs averted from 2006 to 2060 were 779,751 for NHW, 226,522 for NHB, and 149,146 for Hispanic adults. As shown per thousand adults in Table 8, NHB adults faced the greatest health impact (2.7 SADs and 44.8 LYLs), followed by NHW (2.5 SADs and 38.9 LYLs) and Hispanic (1.1 SADs and 20.5 LYLs) adults.

Table 8.

Smoking-attributable deaths (SADs) and life-years lost (LYL) per thousand people at ages 40+ among NHW, NHB, and Hispanic populations, both male and females, in 2022.

	NHW male and female	NHB male and female	Hispanic male and female
2023 Smoking-attributable deaths in thousands	274.3 (165.2, 109.1)	49.5 (32.1, 17.4)	26.0 (17.9, 8.0)
2023 Life-years lost (LYLs) in thousands	4284 (2577, 1708)	834 (534, 300)	482 (341, 141)
SADs per thousand people (with SADs in thousands and age 40+ population in millions)	2.5 (3.1, 1.9)	2.7 (3.9, 1.7)	1.1 (1.5, 0.7)
LYLs per thousand people (with LYLs in thousands and age 40+ population in millions)	38.9 (48.6, 29.9)	44.8 (64.3, 29.1)	20.5 (29.1, 11.9)

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Discussion

The separate US SimSmoke models for NHW, NHB, and Hispanic adults indicate major differences in smoking trends and SADs among NHW, NHB, and Hispanic adults, with NHB males having the highest smoking prevalence in 2023 and experiencing the smallest decline in smoking prevalence from 2006 to 2023. The results also indicate important differences in the impact of tobacco control policies, with large disparities among NHB compared to NHW and Hispanic adults. These findings suggest the need for policies that target specific racial/ethnic groups, particularly the higher NHB smoking prevalence and the associated health impacts. Past policy impacts indicate that higher taxes have been particularly effective.

Our results are broadly consistent with age-period cohort analyses of racial/ethnic groups by Meza et al.³ They found that NHB initiation historically has been lower than that of NHWs, with more rapid decreases since the 1970 birth cohort. Similarly, our study indicates that NHB initiation rates are about 40%-70% for males and 20%-60% for females compared to NHW initiation rates. Like our study, Meza et al. also found that cessation probabilities were similar among NHW and Hispanic adults but lower for NHB adults. Across cohorts, they observed that smoking prevalence among NHB populations, particularly males, was lower than among NHW populations at younger ages but higher at older ages. An important difference is that Meza et al. allow for cohort differences among recent generations, thereby incorporating potential recent trends, while our analysis explicitly incorporates recent policies.

Our results for recent years are more tentative due to differences in the 2018 model predictions and the 2018 TUS-CPS estimates. However, as previously applied to the United States,⁸⁷ England,¹⁰⁰ and Canada,¹⁰¹ the variation in predictions shows the potential impact of changes in ENDS and other omitted factors. While our results indicate a relatively large impact of these factors, especially for male and female NHW adult smoking prevalence, the results varied considerably by age, sex, and race/ethnicity. These findings suggest the need to better understand the impact of non-cigarette use and related policies in recent years, particularly for adults ages 18-24.¹¹³^,¹¹⁴

In addition to ENDS, the role of smokeless tobacco¹¹⁵^,¹¹⁶ and non-premium cigar use, including blunts (especially by NHB adults)⁵^,¹¹⁷^,¹¹⁸ merit consideration. Studies exploring the reactions of adults who use cigars to a hypothetical cigar flavor ban suggest that adults who use blunts may be less likely to quit cigars (blunts), and those who quit tend to switch to cannabis products.¹¹⁷^,¹¹⁸ Consequently, blunt use may depend on local cannabis laws; for example, the market share of a popular Swisher cigarillo brand used for blunts was higher in areas that legalized cannabis in the United States.¹¹⁹^,¹²⁰ Further research is needed on racial/ethnic variations in cigar use and the impact of cannabis laws in conjunction with tobacco control laws.

Our results are subject to additional limitations. First, the projected racial/ethnic differences depend on the structure of the model itself, which depends on the initiation and cessation rates by race/ethnicity and failure to incorporate changes in behavior by recent cohorts, eg, due to changing product availability and use patterns. Second, the models do not consider deaths due to second-hand smoke or the impact of the COVID-19 pandemic. While studies indicate immediate impacts of COVID-19 on smoking behaviors by race/ethnicity,¹²¹^,¹²² information on long-term impacts is limited, especially as they apply to racial/ethnic groups. We implicitly assume that the pandemic’s short-term impacts are temporary and trends revert to their pre-pandemic levels, but further research is warranted on long-term impacts by race/ethnicity.

Third, while having the greatest policy impact, price/tax is the only policy modeled with differential impacts by racial/ethnic group. Due to the limited information on differential racial/ethnic impacts, we did not distinguish racial/ethnic policy effects for smoke-free air laws, mass media campaigns, marketing restrictions, cessation treatments, and youth access policies. While we did not find that other policies had well-supported differential effects by race/ethnicity, one review¹⁷ indicated evidence of different impacts of cessation treatment policy due to the more limited access to treatments and a lack of targeting treatments to minority populations.¹⁰⁶^,¹⁰⁸^,¹⁰⁹^,¹²³ In addition, studies suggest that Tobacco 21 laws are less well enforced in areas with minority populations.^124-126 Further study is needed on differential tobacco-control policy impacts by race/ethnicity.

Fourth, trends in smoking prevalence and policy impacts may differ by socioeconomic status, geographic location within the United States, and variations within the NHW, NHB and Hispanic populations. In focusing on racial/ethnic variations, we do not distinguish the role of socioeconomic status or geography (eg, different locations in the United States) on smoking trends and policy impacts in order to reduce the complexity of the model. However, socioeconomic status (eg, the separate vs. intersectional impacts of socioeconomic status with race/ethnicity)⁶⁶^,¹¹⁶ and geographical location¹²⁷^,¹²⁸ may differentially affect smoking trends and the impact of policies on racial/ethnic minority groups. Smoking prevalence and policy impacts may also vary within different segments of NHW, NHB and especially the Hispanic (eg, differences in Mexican vs. Puerto Rican smoking patterns)^129-131 populations, but sample size restrictions and the lack of information on differential policy impacts limit the ability to distinguish any differences among these populations. Further research is warranted on differential impacts of socioeconomic relative to racial/ethnic, geographic, and sub-population variations.

Finally, while we have focused on policy impacts implemented via government, the cigarette industry may influence smoking trends and the implementation and impact of policies through lobbying¹³² and product marketing (via differential pricing and promotion through media and other outlets) to different racial/ethnic groups.¹³³^,¹³⁴ These pathways of influence are complex and not incorporated in the model but merit further research.

Our paper provides the first estimates of expected future trends in smoking and health outcomes and the impact of multiple tobacco control policies on racial/ethnic disparities in smoking and SADs. Following Occam’s razor, important simplifications were made in developing the model, in order to focus on race/ethnicity using measures and policy effect sizes that are currently robust. Thereby, the models provide a systematic approach to identifying the information needed to understand the complexities associated with health inequities. Viewing modeling as an ongoing process, future extensions of the model¹³⁵ will focus on the limitations highlighted above, especially the interactions between socioeconomic status and racial/ethnic identity, and the role of cigar and ENDS use by race/ethnicity. As we gain a better understanding of these and other factors, each of the racial/ethnic models will be updated, calibrated, and validated to reflect changes in use patterns and trends as influenced by policies.

Conclusions

Our analyses suggest large differences in the levels and rates of decline in NHW, NHB, and Hispanic smoking prevalence, indicating a widening of smoking-related health disparities. The variation in trends was partially related to the differential impacts of policies on racial/ethnic groups. This information can be used to inform additional tobacco control efforts to reduce disparities and improve health equity.

Supplementary Material

lgaf016_Supplementary_Data

lgaf016_supplementary_data.docx^{(171.9KB, docx)}

Contributor Information

David T Levy, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, United States.

James H Buszkiewicz, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109, United States.

Zhe Yuan, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, United States.

Yameng Li, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, United States.

Rafael Meza, Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada; School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.

Nancy L Fleischer, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109, United States.

Author contributions

David T. Levy (Conceptualization, Formal analysis, Validation, Writing—review & editing), James H. Buszkiewicz (Methodology, Writing—original draft, Writing—review & editing), Zhe Yuan (Methodology, Writing—review & editing), Yameng Li (Methodology, Writing—original draft, Writing—review & editing), Rafael Meza (Methodology, Writing—original draft, Writing—review & editing), and Nancy L. Fleischer (Funding acquisition, Methodology, Writing—original draft, Writing—review & editing).

Supplementary material

Supplementary material is available at Journal of the National Cancer Institute Monographs online.

Funding

This research was supported by National Cancer Institute of the National Institutes of Health (grants nos. R37CA214787 and 5U01CA152956). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Some of the authors were paid contributors to a Surgeon General’s Report and the corresponding author received an honorarium for their contribution to this special issue.

Monograph sponsorship

This article appears as part of the monograph, “Continued Impact of the Use of Commercial Tobacco Products on Health Disparities in the U.S.,” sponsored by the Robert Wood Johnson Foundation. The views expressed here do not necessarily reflect the views of the Foundation.

Conflicts of interest

The authors declare no conflicts of interest.

Data availability

All data used in this study are publicly available and will be provided to others upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

lgaf016_Supplementary_Data

lgaf016_supplementary_data.docx^{(171.9KB, docx)}

Data Availability Statement

All data used in this study are publicly available and will be provided to others upon reasonable request.

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PERMALINK

SimSmoke simulation models distinguished by race/ethnicity: past and future trends and the potential role of policy

David T Levy, PhD

James H Buszkiewicz, PhD

Zhe Yuan, MSc

Yameng Li, MSc

Rafael Meza, PhD

Nancy L Fleischer, PhD

Roles

Abstract

Introduction

Methods

Results

Discussion

Introduction

Methods

Data

Policy effects

Table 1.

Calibration

Analysis of outcomes

Results

Smoking levels and trends projected by SimSmoke

Table 2.

Table 3.

Table 4.

Comparison of relative reductions in smoking prevalence from SimSmoke and TUS-CPS

The effect of policies implemented through 2023

Table 5.

Table 6.

Table 7.

Projected SADs and LYLs

Table 8.

Discussion

Conclusions

Supplementary Material

Contributor Information

Author contributions

Supplementary material

Funding

Monograph sponsorship

Conflicts of interest

Data availability

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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