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. Author manuscript; available in PMC: 2024 Jun 19.
Published in final edited form as: Am J Prev Med. 2023 Jan 16;64(4 Suppl 1):S42–S52. doi: 10.1016/j.amepre.2022.08.018

Smoking Histories by State in the U.S.

Theodore R Holford 1,*, Lisa McKay 2, Jihyoun Jeon 3, Jamie Tam 2, Pianpian Cao 3, Nancy L Fleischer 3, David T Levy 4, Rafael Meza 3
PMCID: PMC11186469  NIHMSID: NIHMS1999938  PMID: 36653233

Abstract

Introduction:

Smoking rates across US states have declined at different rates over time, as some states have progressive tobacco control policies while others have yet to adopt them. Therefore, each state has its own unique historical experience of smoking initiation, cessation, and prevalence. This study characterizes smoking histories for each US state by birth cohort.

Methods:

Using 1965-2018 National Health Interview Survey and 1992-2019 Tobacco Use Supplement to the Current Population Survey data, statistical methods applied an age-period-cohort modeling framework to reconstruct population-level smoking histories for each state. Smoking initiation, cessation, and intensity by age, gender, and cohort were estimated for each state. These were used to construct state-specific trends in prevalence of current, former, and never smoking, as well as mean smoking duration and pack-years. Analysis was conducted from 2017-2022.

Results:

California and Kentucky, respectively, are exemplar states of more and less aggressive tobacco control. Initiation probabilities were consistently lower in California than in Kentucky and cessation probabilities were higher. Hence, the smoking prevalence derived from these parameters is higher in Kentucky. The intensity of cigarette smoking was higher in Kentucky than in California, yielding considerably higher estimated pack-years when used with the other parameters. Summaries of smoking trends are given for all states.

Conclusions:

Smoking initiation, cessation, and intensity trends vary substantially across states, resulting in major differences in estimated smoking prevalence, duration, and pack-years. Some states show improvements in smoking metrics over time with more recent birth cohorts, but others have shown very little.

Introduction

Smoking rates vary substantially across the US.1 In 2019, Utah and California had the nation’s lowest smoking prevalence, with 7.9% and 10.0% of adults smoking, respectively, while nearly a quarter of adults still smoke in Kentucky (23.6%) and West Virginia (23.8%). In general, states in the Northeast and West Census regions have lower smoking prevalence than in the South and Midwest.2 These geographic variations in smoking ultimately translate into state-level disparities in the total burden of smoking-related death and disease.3

Smoking prevalence is a function of smoking initiation and cessation4—the latter being more difficult among people who smoke with higher intensity (cigarettes smoked per day). State smoking variations reflect a combination of differences in patterns of smoking uptake among youth and young adults, the level of smoking intensity, and rates of successful quitting.5 State and local tobacco control interventions can be designed to target each of these behaviors, thereby reducing smoking and related health harms. Direct historical estimates of smoking patterns could facilitate a more precise understanding of each state’s smoking experience, capture the effects of factors that contribute to those behaviors, and guide future policymaking at the state and national levels.

The Cancer Intervention and Surveillance Modeling Network lung working group (CISNET-LWG) developed a smoking history generator (SHG) using data from the National Health Interview Survey (NHIS) to quantify the effects of cigarette smoking on lung cancer mortality6,7 and all-cause mortality for the US population.8 The SHG summarized the smoking history of birth cohorts by providing estimates of ever smoker prevalence, initiation and cessation probabilities, and smoking intensity, which was used to estimate current, former, and never smoking prevalence.9 These analyses use age-period-cohort (APC) models as a framework for estimating gender- and birth cohort-specific smoking histories and projecting them forward to provide a tool to evaluate future trends under status quo and other policy scenarios.10

NHIS has collected US smoking data since 1965, following the publication of the first Surgeon General’s Report on Smoking and Health in 1964.4,8,11 The 50+ year time span covered is a major strength for estimating temporal trends, but NHIS lacks the sample size and spatial detail necessary for analyzing smoking histories by state. Tobacco Use Supplement to the Current Population Survey (TUS-CPS) provides larger, state-representative samples that can be used for state-specific analyses. These surveys began collecting data in 1992, giving a history of just 26 years, making it more challenging to identify temporal trends within a cohort. The analysis presented here marries the temporal strengths of NHIS with the spatial strengths of TUS-CPS to estimate state-specific smoking histories. This work provides estimates for a comprehensive SHG that can be used to model the impact of smoking for each state and to explore the effects of health policies within states in more detail.

Methods

This analysis brings together data from two surveys to estimate smoking transition parameters that characterize populations in each state. We provide an overview of the approach here, and the Supplement gives further details.

Study Sample

NHIS is a nationally representative survey that collected self-reported smoking behavior among adult (ages 18+) civilian, non-institutionalized US residents from 1965-2018, with 7,000-53,000 (interquartile range 18,000-32,000) adults in each round (response rate ~70%).12 TUS-CPS has been administered by the US Census Bureau as part of the Current Population Survey and includes detailed smoking and other tobacco data for the adult population. The survey was administered every 3-4 years since 1992, with waves in 1992-93, 1995-96, 1998-99, 2000, 2001-02, 2003, 2006-07, 2010-11, 2014-15, 2018-19, and approximately 250,000 respondents per wave (74% response rate). Behavioral Risk Factor Surveillance System (BRFSS) data offered snapshots of each state’s smoking prevalence since 1993 but did not include questions about smoking initiation—critical for understanding smoking histories. Therefore, BRFSS data were not used for this analysis.3,13-15

Figure 1 shows the time spanned by NHIS and TUS-CPS, with years of the population surveyed (x-axis) and age (y-axis). Both surveys were cross-sectional, so they only captured responses at the time of the survey. However, some surveys asked for detailed retrospective information such as age at initiation or cessation, which lengthens the time horizon by providing information on the earlier experience of individuals in a birth cohort, as shown by the diagonal lines in Figure 1.

Figure 1.

Figure 1.

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Ranges for available data on cigarette use from the National Health Interview Survey (NHIS), the Tobacco Use Supplement to the Current Population Survey (TUS-CPS) and estimated from the analysis.

NHIS provided smoking initiation and cessation information for cohorts born as early as 1890 from subjects older than 75 in the earliest survey in 1965.4,16 However, calibration was required to adjust for higher mortality among individuals who smoke, resulting in selection bias for the survey sample. The later date at which TUS-CPS started limited the earliest cohort that was represented, shown to be about 1910 in Figure 1. Smoking initiation and cessation probabilities were estimated beginning with the 1908 cohort in each state. Data for the 1908 cohort were provided by individuals aged 85 at the survey in 1993, providing initiation and cessation information for most of the cohort's life.

Measures

The analysis used an established smoking model where individuals who have never smoked may transition to current smoking and then may quit smoking to become former smokers.4,8,9,16 The state-specific smoking history measures estimated by age for each gender and birth cohort include:

  • prevalence of ever smoking—proportion of individuals who have smoked at least 100 cigarettes in their lifetime (current or former smokers)

  • initiation probability—conditional probability of transition to ever smoking by the end of a year for an individual who had never smoked at the beginning of that year

  • cessation probability—conditional probability of quitting smoking by the end of a year for an individual who was smoking at the beginning of that year. (Because of high relapse rates among those who recently quit, an individual was not classified as formerly smoked until having quit for at least two years. Relapses were censored in the year just before interview.)

  • smoking intensity probability— cigarettes smoked (CPD) per day was classified into six categories (approximate mean CPD of each category): CPD≤5 (3); 5< CPD≤15 (10); 15<CPD≤25 (20); 25<CPD≤35 (30); 35<CPD≤45 (40); and 45<CPD (60).

  • duration— length of exposure to smoking based on age at initiation and cessation

  • pack-years—calculated by multiplying cigarette packs smoked per day with smoking duration in years

Statistical Analysis

First, the prevalence of ever smoking after age 30 was estimated directly from the NHIS survey data. This would capture the effect of differential mortality among those who smoke as a cohort ages because initiation after age 30 was rare.8,9,16 Excess risk of death among smokers may lead to a declining prevalence of ever smoking as a cohort gets older. Still, a decline could also result from individuals forgetting or denying having smoked when they were younger. Estimating age trends required a relatively long span for each cohort. Because TUS-CPS had a history of only 26 years, estimated age trends were unstable. Instead, NHIS, with its 53-year history, was used to estimate the age effect for ever smoking prevalence after age 30 in the US, and this was assumed to be the same for each state. Similarly, when trying to simultaneously estimate period and cohort effects using TUS-CPS data, the cohort had a much longer time span than the period. It was assumed that the cohort had a more substantial temporal effect, so the period was excluded from the model.16 Hence, the model for prevalence of ever smoking for each state included the age effect estimated from NHIS as an offset parameter, and the cohort effect was estimated using TUS-CPS data from each state.

In some TUS-CPS surveys, age at initiation was reported, which was used to obtain a crude estimate of the conditional initiation probability for each state using an APC model. To adjust for mortality differences caused by smoking, crude initiation probabilities were calibrated so that the cumulative initiation probability at age 30 was equal to the prevalence estimate at age 30 obtained from the analysis of ever smoking.

TUS-CPS data on self-reported age at smoking cessation were also used to estimate cessation probabilities for each state. The conditional probability of a smoker at the beginning of the year quitting during that year was estimated using an APC model. The exception was for an individual quitting in the year just before the interview. These individuals were censored in the final year but coded as continuing to smoke in previous years. Estimates from the APC model were used to estimate cessation probabilities up to 2018, the last year of available data. The most recent estimate for a given age was then carried forward for future years.

For smoking intensity in each state, data from TUS-CPS on the number of cigarettes smoked per day were used.

Smoking initiation and cessation probabilities were used to estimate age-specific prevalence for current, former, and never smoking, the distribution of smoking duration, and thus the mean duration of smoking exposure for each cohort. Assuming independence of duration and intensity provided an estimate of the joint distribution or pack-years. This yielded estimates of mean pack-years by age for each cohort.

For illustration purposes, California and Kentucky were compared and presented as exemplar states of more and less aggressive tobacco control policies. California had been more aggressive than many states in controlling smoking through public health policies, including smoke-free air laws. In contrast, Kentucky has been a tobacco-producing state that has introduced few policies to control cigarette smoking. See supplement for details on all states. Users can examine and compare the smoking patterns of different states at the website: https://apps.cisnetsmokingparameters.org/states/.

Analyses were conducted from 2017-2022.

Results

Comparison of California and Kentucky

Figures 2a and 2b show birth-cohort trends in age-specific smoking initiation and cessation probabilities for females and males. Initiation probabilities in Kentucky were consistently higher than in California over the entire time considered; however, probabilities reached their highest levels for females born in the 1940s and males born in the 1920s or 1930s and declined after that. This was especially true in California, but Kentucky has maintained considerably higher initiation probabilities. Before 2018, cessation increases with age for each cohort, and declines subsequently arise from carrying forward estimates from earlier cohorts for individuals who reach a given age after 2018. For cohorts born after 1950, cessation probabilities are considerably higher for California.

Figure 2.

Figure 2.

Figure 2.

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Smoking history parameters by birth cohort for California and Kentucky. An interactive version of this figure's data can be found at: https://apps.cisnetsmokingparameters.org/states/

Initiation and cessation probability estimates with adjustment for differential mortality yield current smoker prevalence trends by cohort (Figure 2c). Higher initiation and lower cessation for Kentucky resulted in consistently higher prevalence estimates than in California. An exception occurs for the 1910 cohort when Kentucky’s prevalence becomes lower than California’s in older ages because cessation probabilities in this cohort increase more rapidly in Kentucky than in California (Figure 2b). In subsequent cohorts, the prevalence difference becomes more pronounced, a result of Kentucky’s consistently lower cessation probabilities.

Mean cigarettes smoked per day consistently decline with age (Supplement, Figure 2s). Before 1960-1970, levels were similar for California and Kentucky, but more recently, California has considerably lower mean cigarettes smoked per day. See Supplement Figures 3s and 4s for estimates of mean duration of smoking and pack-years, respectively.

Comparisons for all States

Full details for all 50 states and DC are conveniently displayed for user-selected comparison at the website https://apps.cisnetsmokingparameters.org/states/. Estimates are provided by single years of age (0-99) and by birth cohort (1908-2065).

Cumulative initiation probabilities at age 25, when most initiation will have occurred, standardized for the 2000 US population, provide a summary of initiation for each cohort. Figure 3 shows trends in initiation up to age 25 by birth cohort and gender, with a tile for each state. Female initiation is much lower than males for early cohorts, although recent differences are smaller. The pattern for females is like males in some states, e.g., California, but the peak is lower. However, in Kentucky, the temporal variation by cohort is much smaller for females. This difference is apparent in several states with a recent higher prevalence of cigarette smoking.

Figure 3.

Figure 3.

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Cumulative smoking initiation probability at age 25 by gender and cohort, and overall adult current smoker prevalence in year 2019 (background color) by state. The background color for each tile signifies the prevalence of current adult smoking in 2019, standardized for the 2000 US population.

NOTE: y-axis values are uniform across states and range from 0.11 to 0.85; x-axis values represent birth cohorts from 1908 through 2030.

Figure 4 shows cumulative cessation probabilities for each cohort standardized for the 2000 US population at age 60 when the harmful impacts of smoking begin to have a substantial effect on personal health. These probabilities have increased substantially, and for most states, the probabilities are similar for males and females. For earlier cohorts, many states have lower cessation probabilities for females; however, a few states also have lower probabilities for females in recent cohorts, e.g., Alabama, Rhode Island, and West Virginia. A careful study of Figures 3 and 4 provides insight into the history of initiation and cessation by the cohort that has resulted in the current prevalence of smoking in each state. See Supplement Figure 5s for current smoker prevalence estimates for all states.

Figure 4.

Figure 4.

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Cumulative smoking cessation probability at age 60 by gender and cohort, and overall adult current smoker prevalence in year 2019 (background color) by state. The background color for each tile signifies the prevalence of current adult smoking in 2019, standardized for the 2000 US population.

NOTE: y-axis values are uniform across states and range from 0.22 to 0.96; x-axis values represent birth cohorts from 1908 through 2030.

Figure 5 shows estimates of the mean duration among adults standardized for the 2000 population of the US by calendar year. Estimates for California are low compared to Kentucky and are projected to continue decreasing. On the other hand, in South Dakota and West Virginia, duration estimates are high and are projected to change very little. Standardized average pack-years are shown by calendar year for each state in Figure 6, which shows substantial differences in the spatial patterns across states and over time. Trends decline with time; for most states, recent and projected values are similar by gender. However, some states have substantial gender differences, e.g., Kentucky and Oklahoma.

Figure 5.

Figure 5.

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Mean lifetime smoking duration by gender and period, and overall adult current smoker prevalence in the year 2019 (background color) by state. The background color for each tile signifies the prevalence of current adult smoking in 2019, standardized for the 2000 US population.

NOTE: y-axis values are uniform across states and with a range from 148 to 1363; x-axis values represent periods from 1993 through 2065.

Figure 6.

Figure 6.

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Mean pack-years by gender and period, and overall adult current smoker prevalence in the year 2019 (background color) by state. The background color for each tile signifies the prevalence of current adult smoking in 2019, standardized for the 2000 US population.

NOTE: y-axis values are uniform across states and with a range from 42 to 2105; x-axis values represent periods from 1993 through 2065.

Discussion

This study presents a comprehensive analysis of historical smoking experiences within each of the 50 US states and DC. These detailed smoking behavior profiles are useful for understanding the health consequences expected to flow from each state’s smoking history. In California and Kentucky, initiation probabilities in males began to decline with the 1940 birth cohort and continued to steadily decline until the current birth cohorts. California females had a similar pattern, but the decline was somewhat later for Kentucky females. The Surgeon General’s Report11 on smoking and health appeared in 1964, when the 1940 birth cohort was 25 years of age, slightly later than the peak age for smoking initiation. The effect of this report appears to have been more consistently seen in males, but the impact on females is more complex. These results are consistent with results from earlier analyses for the whole US8, which noted that some of the earliest studies on the health effects of smoking appeared well before 1964, i.e., public awareness had already begun to affect smoking behavior by the time the Report was published.8 However, the decline in initiation probabilities varies among the states.

Limitations

This analysis showed a rapid rise in cessation probabilities in recent cohorts compared to increases seen at comparable ages in older cohorts. In some cases, this yields a peak in the age trend for more recent cohorts (e.g., California in Figure 2b). However, cessation probabilities for recent birth cohorts are based on limited information (data available only for young people) and may therefore be less reliable. Recent cessation trends are challenging to estimate because the dynamics of smoking histories have changed greatly,7,17 due in part to tobacco policies and the emergence of e-cigarettes in the tobacco product landscape.18,19 Another limitation is that both NHIS and TUS-CPS rely on self-reported smoking behavior, which may be subject to bias. Lastly, the age effect estimated from the NHIS was assumed to be the same for each state, but it is possible that age effects could differ by state.

Public health implications

There is wide variability in tobacco control policies, with some states and localities enacting aggressive interventions that raise cigarette taxes, fund educational media campaigns,20 or restrict smoking in public places while other states have yet to implement them. Georgia has the lowest average state cigarette tax at $0.37, while DC and New York boast an average of $4.50 and $4.35 per cigarette pack, respectively (Georgia and New York have not increased their tax since 2012).21 Today, 28 states and Washington DC have comprehensive smoke-free air laws in place, yet 38% of the US population remains without protection from clean air in workplaces, restaurants, and bars.22 This study provides detailed smoking histories for each state, allowing future research to explore the impact of these policy differences on geographic smoking disparities. These data also offer a starting point for assessing the potential impact of future state-level tobacco control efforts on public health.

Public health policies are not the only factors affecting the smoking behavior of individuals in a state. Differences in demographics and rurality can also influence smoking behavior, e.g., overall smoking prevalence could be skewed downwards by a high proportion of Hispanics in the population due to their lower-than-average smoking rates or skewed upwards if the population is disproportionately rural given higher-than-average rural smoking rates.23 Furthermore, social and cultural differences across the country may contribute to different attitudes towards tobacco use and the acceptability of tobacco control policies. For example, North Carolina, Kentucky, and Virginia are major tobacco-producing states.24 These issues highlight the need for the state-level estimates of smoking behaviors provided by this study.

Conclusions

This study describes temporal patterns by state, showing ongoing geographic disparities in smoking behaviors that have downstream health consequences. The burden of lung cancer differs by state,25 and the pack-year estimates produced by this study could be useful for states in determining low-dose computed tomography eligibility and cancer control plans.26

The estimates can also inform tobacco simulation models to analyze the potential impact of tobacco control efforts—past, present, and future—without discounting differences in state experiences that would otherwise be masked by a country-level analysis.27-30 Tam et al.31 describe a web tool that considers the potential impact of specific state-level policy changes; this work used estimates that summarize smoking histories for the entire country,4 inferring parameters for a state based on policies that have been in place. Future extensions of these types of tools could rely on the parameters generated by this study. Decision-makers and researchers who aim to evaluate the impact of programs and policies on population health can use these estimates to better address the harmful consequences of tobacco use in their states.

Supplementary Material

Supplement

Conflict of interest statement:

This project was funded through National Cancer Institute (NCI) grants U01CA199284 & U01CA253858. The study sponsor had no role in the study design; collection, analysis, and interpretation of data; writing the report; or the decision to submit the report for publication.

Footnotes

Financial disclosure: No financial disclosures were reported by the authors of this paper.

References

  • 1.Centers for Disease Control and Prevention. Map of Current Cigarette Use among Adults. U.S. Department of Health & Human Services. Accessed January 5, 2021, 2021. www.cdc.gov/statesystem/cigaretteuseadult.html [Google Scholar]
  • 2.Cornelius ME, Wang TW, Jamal A, Loretan CG, Neff LJ. Tobacco Product Use Among Adults — United States, 2019. Morbidity and Mortality Weekly Report. 2020;69(46):1736–1742. doi: 10.15585/mmwr.mm6946a4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Lortet-Tieulent J, Sauer AG, Siegel RL. State-level cancer mortality attibutable to cigarette smoking in the United States. JAMA Intern Med. 2016;176(12):1792–1798. doi: 10.1001/jamainternmed.2016.6530 [DOI] [PubMed] [Google Scholar]
  • 4.Holford TR, Levy DT, McKay LA, et al. Patterns of Birth Cohort–Specific Smoking Histories, 1965–2009. Am J Prev Med. 2014;46(2):e31–e37. doi:doi: 10.1016/j.amepre.2013.10.022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.U.S. Department of Health and Human Services. Smoking Cessation. A Report of the Surgeon General. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2020. [Google Scholar]
  • 6.Moolgavkar SH, Holford TR, Levy DT, et al. Impact of the reduction in tobacco smoking on lung cancer mortality in the U.S. during the period 1975–2000. J Natl Cancer Inst. 2012;104:541–548. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Jeon J, Holford TR, Levy DT, et al. Smoking and Lung Cancer Mortality in the United States from 2015 to 2065. Ann Intern Med. 2018;169(10):684–693. doi: 10.7326/M18-1250 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Holford TR, Meza R, Warner KE, et al. Tobacco control and the reduction in smoking-related premature deaths in the United States, 1964-2012. J Am Med Assoc. 2014;311(2):164–171. doi:doi: 10.1001/jama.2013.285112 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Anderson CM, Burns DM, Dodd KW, Feuer EJ. Birth-cohort-specific estimates of smoking behaviors for the U.S. population. Risk Anal. 2012;32(S1):S14–S24. doi:doi: 10.1111/j.1539-6924.2011.01703.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Holford TR, Clarke LD. Development of the counterfactual smoking histories used to assess the effects of tobacco control. Risk Anal. 2012;32:S39–S50. doi:doi: 10.1111/j.1539-6924.2011.01759.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Surgeon General's Advisory Committee on Smoking and Health. Smoking and Health: Report of the Advisory Committee to the Surgeon General of the Public Health Service. U.S. Department of Health, Education, and Welfare, Public Health Service, Office of the Surgeon General; 1964: [Google Scholar]
  • 12.Carlson SA, Densmore D, F JE, Yore MM, Kohl HW. Differences in physical activity prevalence and trends from 3 U.S. surveillance systems: NHIS, NHANES, and BRFSS. Journal of Physical Activity and Health. 2009;6(1):S18–S27. [DOI] [PubMed] [Google Scholar]
  • 13.Centers for Disease Control and Prevention. BRFSS Prevalence & Trends Data. National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health. Accessed June 8, 2022, 2022. https://www.cdc.gov/brfss/brfssprevalence/index.html [Google Scholar]
  • 14.Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System, Annual Survey Data. National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health. Accessed June 8, 2022, 2022. https://www.cdc.gov/brfss/annual_data/annual_data.htm [Google Scholar]
  • 15.Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System, CDC-List of States Conducting Surveillance by Year. National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health. Accessed June 8, 2022, 2022. https://www.cdc.gov/brfss/annual_data/all_years/states_data.htm [Google Scholar]
  • 16.Holford TR, Levy DT, Meza R. Comparison of Smoking History Patterns Among African American and White Cohorts in the United States Born 1890 to 1990. Nicotine & Tobacco Research. Apr 2016;18 Suppl 1:S16–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Mendez D, Tam J, Giovino GA, Tsodikov A, Warner KE. Has smoking cessation increased? An examination of the US Adult Smoking Cessation Rate 1990-2014. Nicotine & Tobacco Research. 2017;19(12):1418–1424. doi: 10.1093/ntr/ntw239 [DOI] [PubMed] [Google Scholar]
  • 18.Brouwer AF, Jeon J, Cook SF, et al. The impact of menthol cigarette flavor in the U.S.: Cigarette and ENDS transitions by sociodemographic group. Am J Prev Med. 2022;62(2):243–251. doi: 10.1016/j.amepre.2021.08.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Gentzke A, Creamer M, Cullen K, et al. Vital Signs: Tobacco Product Use Among Middle and High School Students — United States. MMWR Morb Mortal Wkly Rep. 2019;68:157–164. doi: 10.15585/mmwr.mm6806e1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Colston DC, Xie Y, Thrasher JF, et al. Examining Truth and State-Sponsored Media Campaigns as a Means of Decreasing Youth Smoking and Related Disparities in the United States. Nicotine & Tobacco Research. 2021;doi: 10.1093/ntr/ntab226 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Campaign for Tobacco-Free Kids. Key State-Specific Tobacco Related Data & Rankings. Accessed January 11, 2022, 2022. https://www.tobaccofreekids.org/assets/factsheets/0176.pdf [Google Scholar]
  • 22.American Nonsmokers' Rights Foundation. Summary of 100% Smokefree State Laws and Population Protected by 100% U.S. Smokefree Laws. American Nonsmokers' Rights Foundation. 2022. https://no-smoke.org/wp-content/uploads/pdf/SummaryUSPopList.pdf [Google Scholar]
  • 23.Tam J, Warner KE, Gillespie BW, Gillespie JA. Impact of changing U.S. demographics on the decline in smoking prevalence, 1980-2010. Nicotine & Tobacco Research. 2014;16(6):864–866. doi: 10.1093/ntr/ntt223 [DOI] [PubMed] [Google Scholar]
  • 24.Shahbandeh M. Major U.S. states in tobacco production 2015-2020. 2022. https://www.statista.com/statistics/192022/top-10-tobacco-producing-us-states/ [Google Scholar]
  • 25.Yu-Che L, Calderon-Caandelario RA, Holt GE, Campos MA, Mirsaeidi M. State-level disparity in lung cancer survival in the United States. Front Oncol. 2020;101449. doi: 10.3389/fonc.2020.01449 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Fedewa SA, Kazerooni EA, Studts JL, et al. State varition in low-dose computed tomography scanning for lung cancer screening in the United States. J Natl Cancer Inst. 2020;113(8):1044–1052. doi: 10.1093/jnci/djaa170 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Levy DT, Huang AT, Havumaki JS, Meza R. The role of public policies in reducing smoking prevalence: results from the Michigan SimSmoke tobacco policy simulation model. Cancer Causes Control. May 2016;27(5):615–25. doi: 10.1007/s10552-016-0735-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Levy DT, Yuan Z, Li Y, St Claire AW, Schillo BA. The Minnesota SimSmoke Tobacco Control Policy Model of Smokeless Tobacco and Cigarette Use. Am J Prev Med. Oct 2019;57(4):e103–e115. doi: 10.1016/j.amepre.2019.06.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Sánchez-Romero LM, Yuan Z, Li Y, Levy DT. The Kentucky SimSmoke Tobacco Control Policy Model of Smokeless Tobacco and Cigarette Use. Int J Health Policy Manag. Oct 27 2020;doi: 10.34172/ijhpm.2020.187 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Levy DT, Hyland A, Higbee C, Remer L, Compton C. The role of public policies in reducing smoking prevalence in California: results from the California tobacco policy simulation model. Health Policy. Jul 2007;82(2):167–85. doi: 10.1016/j.healthpol.2006.09.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Tam J, Levy DT, Jeon J, et al. Projecting the effects of tobacco control policies in the USA through microsimulation: a study protocol. BMJ Open. 2018;8:e019169. doi: 10.1136/bmjopen-2017-0119169 [DOI] [PMC free article] [PubMed] [Google Scholar]

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Supplementary Materials

Supplement
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