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Published in final edited form as: Prev Med. 2025 Apr 14;195:108291. doi: 10.1016/j.ypmed.2025.108291 Search in PMC Search in PubMed View in NLM Catalog Add to search Associations between State Tobacco Control Measures and Cigarette Purchases by U.S. Households, 2015–2021 Rishika Chakraborty Rishika Chakraborty , PhD 1 Center for Health Policy and Media Engagement, School of Nursing, George Washington University, Washington D.C., USA Find articles by Rishika Chakraborty 1, * , Yan Li Yan Li , PhD 2 Department of Epidemiology and Biostatistics and Joint Program in Survey Methodology, University of Maryland-College Park, Maryland, USA Find articles by Yan Li 2 , Yan Wang Yan Wang , MD, DrPH 3 Department of Prevention and Community Health, Milken Institute School of Public Health, George Washington University, Washington D.C., USA 4 George Washington Cancer Center, George Washington University, Washington D.C., USA Find articles by Yan Wang 3, 4 , Carla Berg Carla Berg , PhD 3 Department of Prevention and Community Health, Milken Institute School of Public Health, George Washington University, Washington D.C., USA 4 George Washington Cancer Center, George Washington University, Washington D.C., USA Find articles by Carla Berg 3, 4 , Sabrina Zhang Sabrina Zhang , MSc 5 Joint Program in Survey Methodology, College of Behavioral and Social Science, University of Maryland-College Park, Maryland, USA Find articles by Sabrina Zhang 5 , Debra Bernat Debra Bernat , PhD 6 Department of Epidemiology, Milken Institute School of Public Health, George Washington University, Washington D.C., USA Find articles by Debra Bernat 6 , Y Tony Yang Y Tony Yang , ScD 1 Center for Health Policy and Media Engagement, School of Nursing, George Washington University, Washington D.C., USA 4 George Washington Cancer Center, George Washington University, Washington D.C., USA Find articles by Y Tony Yang 1, 4 Author information Article notes Copyright and License information 1 Center for Health Policy and Media Engagement, School of Nursing, George Washington University, Washington D.C., USA 2 Department of Epidemiology and Biostatistics and Joint Program in Survey Methodology, University of Maryland-College Park, Maryland, USA 3 Department of Prevention and Community Health, Milken Institute School of Public Health, George Washington University, Washington D.C., USA 4 George Washington Cancer Center, George Washington University, Washington D.C., USA 5 Joint Program in Survey Methodology, College of Behavioral and Social Science, University of Maryland-College Park, Maryland, USA 6 Department of Epidemiology, Milken Institute School of Public Health, George Washington University, Washington D.C., USA * Corresponding Author: Rishika Chakraborty, [email protected] , Center for Health Policy and Media Engagement, School of Nursing, George Washington University, Washington D.C., USA Issue date 2025 Jun. PMC Copyright notice PMCID: PMC12919606 NIHMSID: NIHMS2075153 PMID: 40239897 The publisher's version of this article is available at Prev Med Abstract Objective: While effects of key tobacco control policies are well-documented, limited research has explored their varying associations across different policy contexts over time. This is crucial given the diverse and evolving tobacco control contexts across states and over time. We evaluated the association between state-level tobacco control measures and cigarette purchases in the US from 2015–2021. Methods: We analyzed NielsenIQ Consumer Panel data from 10,187 households that purchased cigarettes in 2015–2021. State-level tobacco control policy scores for smoke-free laws, taxes, prevention/control funding, and cessation services were obtained from the American Lung Association’s State of Tobacco Control reports. Censored regression models, reporting adjusted beta estimates and 95% confidence intervals (CI), estimated the associations between each tobacco control measure and annual household cigarette purchases, adjusting for sociodemographics (household composition, marital status, age, education, race/ethnicity, annual income, and internet connection) and accounting for clustering within households and states. Results: Higher scores for smoke-free laws (adjusted beta =-1.00, 95% CI=−1.73, −0.27), taxes (adjusted beta =−1.23, 95% CI=−1.88, −0.58), and prevention/control funding (adjusted beta =−0.22, 95% CI=−0.38, −0.06) were associated with fewer cigarette purchases over time. In the model considering all four measures together, higher tax score was associated with fewer cigarette purchases over time (adjusted beta=−0.96, 95% CI=−1.73, −0.87). Conclusions: Smoke-free laws, taxation, and prevention/control funding play critical roles in lowering cigarette purchases, while access to cessation services alone may not drive behavioral change. These findings highlight the need for comprehensive tobacco control efforts and renewed policy action to curb cigarette use. Keywords: cigarette purchases, tobacco prevention and control, health policy, longitudinal analysis, smoking 1. Introduction The prevalence of cigarette smoking among adults in the United States (US) has dramatically declined over the years, e.g., from 20.9% in 2005 to 11.5% in 2021. 1 , 2 However, cigarette smoking continues to be the predominant cause of preventable disease and death in the US, responsible for nearly one in five deaths. 3 Despite the general progress made in reducing smoking prevalence, nearly 28.3 million US adults reported current cigarette use in 2021. 1 Furthermore, some subpopulations remain disproportionately affected by cigarette smoking, including those representing groups with lower income, 2 , 4 with lower education, 2 , 4 living in rural areas, 4 , 5 or identifying as sexual minorities 4 , 6 and certain racial/ethnic groups (e.g., American Indians or Alaska Natives). 4 , 7 Thus, it is crucial to implement and strengthen evidence-based tobacco control measures to reduce smoking prevalence, particularly among those disproportionately affected. Comprehensive state measures such as smoke-free air laws, tobacco taxes, tobacco prevention programs (e.g., prevention campaigns), and cessation programs have played key roles in decreasing smoking prevalence in the US. 7 , 8 Despite a couple of studies reporting null 9 , 10 effects of smoke-free air laws and cigarette taxes on smoking-related outcomes, several prior studies have documented that smoke-free air laws 11 – 15 and cigarette taxes 6 , 11 , 12 , 14 , 16 contribute to reductions in cigarette expenditures and smoking prevalence, as well as improved cessation rates. Moreover, implementing prevention strategies, such as anti-smoking campaigns and community awareness programs, and increasing access to cessation services – alongside strong smoke-free air laws and tobacco taxation – may optimize tobacco control efforts and yield the greatest reduction in smoking prevalence. 8 , 17 Despite the promise of tobacco control and prevention policies in curbing smoking and improving cessation outcomes in the US, there are gaps in existing systems that hinder further progress in decreasing population-level tobacco use and its related burden. 7 For instance, regular surveillance and evaluation of state and local policies is required to help identify challenges in implementation and can inform the design of more comprehensive measures. 7 This is particularly crucial given the diverse and evolving tobacco control contexts across states and over time, 5 , 7 as well as the possibility that tobacco control laws may not yield the same level of benefit for all subpopulations, which is important given ongoing tobacco-related disparities. Prior research has been limited in several ways, such as focusing on only one tobacco control effort, 6 , 10 , 15 use of repeated cross-sectional designs which limit the extent to which behavior change can be examined, 6 , 13 , 15 , 17 assessment over only a single timepoint or a short period which limits the ability to detect longer-term impact, 10 , 12 , 14 or use of national survey data representing snapshots of annual smoking prevalence, often not taking into account individuals’ tobacco use histories. 11 , 12 Only a few studies have assessed the effects of multiple key tobacco control strategies simultaneously, examined effects specifically on those who use cigarettes, and/or analyzed longitudinal data over longer time periods. 18 , 19 Such studies are crucial for examining distinct impacts of such tobacco control efforts on behavior change, particularly among groups who use cigarettes and may suffer the greatest tobacco-related burdens. Given the limitations to the existing research, particularly regarding lack of longitudinal studies and addressing multiple tobacco policy efforts, the current study examined the association of four key tobacco control measures with cigarette purchases over time. Specifically, we assessed state smoke-free air laws, cigarette excise taxes, tobacco prevention and control funding, and access to cessation services in relation to cigarette purchasing behavior over a seven-year period (between 2015 to 2021) among US households that had purchased cigarettes during this timeframe. 2. Methods Study design and sample This study analyzed longitudinal data from two sources: 1) the American Lung Association’s (ALA) State of Tobacco Control reports, 20 which evaluates the strength of state tobacco control measures; and 2) cigarette purchasing data from the NielsenIQ Consumer Panel (NCP), a nationally representative sample of approximately 60,000 US households (excluding Alaska and Hawaii). This study was approved by the George Washington University Institutional Review Board. The NCP collects data, reported by households, through barcode scans or mobile phone apps, on all household purchases, including cigarettes. Panelist retention is ~80% from one year to the next. 21 The analytic sample for the current study was restricted to NCP households that 1) purchased cigarettes at least once from 2015 to 2021; and 2) participated in the panel in 2015 and at least one subsequent year, ensuring both baseline and longitudinal data. Of the 113,878 households that participated in the NCP during this period, 21,139 households (18.6%) purchased cigarettes, of those, 11,602 (54.9%) participated in 2015, with 10,187 households (87.8% of those who participated in 2015) included in the final analysis, accounting for 56,916 household-year observations (see Supplementary eFigure 1 ). Measures Exposure variables: Four continuous exposure variables, indicating each state’s strength of a measure for each year, were drawn from the ALA State of Tobacco Control regarding: a) smoke-free laws (0–40/44, based on comprehensiveness across settings and enforcement, and possible bonus points), b) cigarette taxes (0–30, with possible bonus points [up to 10] for taxes on other tobacco products), c) tobacco control and prevention funding (as a percentage of the US Centers for Disease Control and Prevention [CDC] recommendation), and d) access to cessation services (0–70, based on Medicaid, state health insurance, and quitline coverage). Higher scores indicate stronger tobacco control measures. To ensure comparability, we restricted the timeframe to 2015–2021, as the ALA revised its evaluation methodology in 2015, which has been published elsewhere. 20 Outcome variable: The outcome was the recorded annual number of cigarette packs purchased per household between 2015 and 2021. About 0.25% (143 observations out of 56,916) had cigarette purchase quantity values higher than the 99.5th percentile value. To minimize the influence of outliers, we capped these extreme values at the 99.5 th percentile (992), resulting in an outcome variable range of 0 – 992. Covariates: Sociodemographic variables included: household composition (single female, single male, multiple kin or non-kin adults), marital status (married, widowed, divorced/separated, single), age of male and female head of the households (<55 years, ≥55 years), household head education (male or female head ≥high school, male or female head <high school), race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, other race/ethnicity), and annual household income (<$20,000, $20,000–39,999, $40,000–59,999, $60,000–99,999, ≥$100,000). To account for potential online cigarette purchases, we also included household internet connection status as a covariate. 16 Statistical analysis We merged ALA tobacco control scores with NCP data by US state and year. Descriptive analyses were conducted to summarize household sociodemographics, cigarette purchases, and tobacco control measures by year. Four separate multilevel censored regressions were performed to estimate associations between each tobacco control measure and household cigarette purchases over time, adjusting for sociodemographics, state, and categorical survey year and accounting for state-level stratification and clustering of repeated measure within households. An additional model included all four tobacco control measures to assess their independent contributions when accounting for the other tobacco control measures. Results from the censored regression model are interpreted in a similar manner as results from linear regression, reporting beta coefficients ( ß ) and 95% confidence intervals (CIs). We chose this model since several households did not report any cigarette purchases (the dependent variable) in some of the years (a lower threshold of zero) while the higher threshold was 992. Weighted analyses were also conducted and showed similar patterns compared to the unweighted analysis. However, this study aimed to examine associations within this restricted sample of households purchasing cigarettes in the NCP, so we report findings from the unweighted covariate-adjusted analysis, accounting for clustering of repeated measures within households and stratification by state. We used R statistical software (V4.4.0) and STATA for analysis. 3. Results As shown in Table 1 , most households (76.1%) were composed of multiple kin or non-kin adults, with 68.1% of households reporting married heads. Over half (62.9%) of the male and female heads of households were aged 55 years or older, and 54.7% had completed at least a high school education. The majority of households identified as non-Hispanic White (78.5%), and 65.7% of the households reported an annual income of less than $60,000. Additionally, 95.1% of households had internet access, which was included as a control variable for potential online cigarette purchases. Table 1: Baseline (2015) sociodemographic characteristics of analytic sample of US households in the NielsenIQ Consumer Panel that purchased cigarettes at least once in 2015–2021 * , N=10,187. Variable N (%) Household composition Single female 1676 (16.5) Single male 756 (7.4) Multiple adults (kin or non-kin) 7755 (76.1) Marital status Married 6291 (61.8) Widowed 782 (7.7) Divorced/Separated 1841(18.0) Single 1273 (12.5) Household head age Early- to mid-life adults (<55 years) 3780(37.1) Later-life adults (≥55 years) 6407 (62.9) Household head education Male and female head ≥high school 5577 (54.7) Male or female head <high school 4610(45.3) Race/Ethnicity Non-Hispanic White 8000 (78.5) Non-Hispanic Black 1143 (11.2) Hispanic 589 (5.8) Other race/ethnicity 455 (4.5) Annual household income <$20,000 1376 (13.5) $20,000 - $39,999 2852 (30.0) $40,000 - $59,999 2261 (22.2) $60,000- $99,999 2525 (24.8) ≥$100,000 1173 (11.5) Household internet connection Yes 9687 (95.1) No 500 (4.9) Open in a new tab Notes : * Criteria also included participation in 2015 and one assessment in a subsequent year. There were no missing sociodemographic data. During the study period, state tobacco control scores varied widely across the four measures ( Table 2 and Supplementary eFigure 2 ). For smoke-free laws, scores ranged from 0 to 47, while tax scores ranged from 8 to 38. Table 2: Average American Lung Association scores for US states for tobacco control measures by year, 2015–2021. Smoke-free laws Tobacco taxes Prevention and control funding * Access to cessation services Year Mean (SD) Mean (SD) Mean (SD) Mean (SD) 2015 33.4 (12.2) 19.9 (6.9) 17.2 (15.4) 35.7 (8.1) 2016 33.4 (12.2) 20.4 (6.7) 17.1 (15.2) 36.8 (8.9) 2017 33.8 (12.4) 21.4 (7.6) 17.1 (14.6) 38.1 (7.9) 2018 33.8 (12.4) 21.1 (7.4) 22.2 (24.7) 42.9 (9.7) 2019 32.4 (12.5) 20.9 (6.9) 20.5 (19.7) 43.6 (10.3) 2020 32.6 (12.5) 20.3 (7.5) 22.1 (24.1) 43.9 (10.7) 2021 32.5 (12.6) 20.7 (7.5) 20.0 (19.8) 44.6 (10.9) Open in a new tab Notes : State ranges for these variables during the study period varied (0–47 for smoke-free laws, 8–38 for taxes, 2%-112% for prevention and control, and 15–69 for cessation services). * % of US Centers for Disease Control and Prevention recommendation. Tobacco prevention and control funding ranged from 2% to 112% of CDC-recommended funding, and scores regarding access to cessation services ranged from 15 to 69. State average scores fluctuated over time: smoke-free laws dropped slightly from 33.8 in 2017–2018 to 32.5 in 2021, and tobacco taxes peaked at 21.4 in 2017 before decreasing to 20.7 in 2021. Prevention and control funding reached 22.1 in 2020 before declining, while access to cessation services steadily increased from 35.7 in 2015 to 44.6 in 2021. Notably, only California saw consistent improvement in all four measures, while Tennessee experienced declines. All states saw a change in scores for at least one measure; however, in six states, the scores did not change for two or three measures. The average number of cigarette packs purchased by study households declined significantly, in a linear trend, from 68.9 packs in 2015 to 33.9 packs in 2021 ( Figure 1 ). A parallel decrease was observed in the percentage of households reporting cigarette purchases, dropping from 65% in 2015 to 32% in 2021. Figure 1: Cigarette pack purchases among US households in the NielsenIQ Consumer Panel analytic sample, 2015–2021. Open in a new tab Table 3 displays findings from the censored regression analyses assessing the association between state tobacco control measures and household cigarette purchases in the US from 2015 to 2021, adjusting for covariates. When examined individually, a 1-unit increase in smoke-free law scores, tobacco taxes, and prevention/control funding was significantly associated with lower cigarette purchases by 1 unit (95% CI: −1.73, −0.27), 1.23 units (95% CI: −1.88, −0.58), and 0.22 units (95% CI: −0.38, −0.06), respectively. In contrast, access to cessation services showed no significant association with cigarette purchases ( ß =−0.19, 95% CI: −0.46, 0.08). In an adjusted model including all measures, tobacco taxes remained the strongest predictor of lower cigarette purchases ( ß =−0.96, 95% CI: −1.73, −0.87) while other measures were no longer significantly associated with the outcome. Table 3: Censored regression analyses examining each tobacco control measure, separately and adjusted, in relation to household cigarette purchases, US, 2015–2021 † . Adjusted model Tobacco control measure ß (95% CI) Models testing each measure separately Smoke-free laws −1.00 (−1.73, −0.27) Tobacco taxes −1.23 (−1.88, −0.58) Prevention/control funding −0.22 (−0.38, −0.06) Access to cessation services −0.19 (−0.46, 0.08) Model adjusting for all measures Smoke-free laws − 0.22 ( − 1.11, 0.68) Tobacco taxes −0.96 (−1.73, −0.87) Prevention/control funding − 0.08 ( − 0.24, 0.09) Access to cessation services − 0.04 ( − 0.32, 0.24) Open in a new tab Notes : † Models account for clustering within households and US state stratification and adjust for year, household composition, marital status, age, education, race/ethnicity, annual income, US state, and internet connection. Left censor =0, right censor = 992. Italicized values significant (p < 0.05) ß =beta coefficient All censored regression models accounted for clustering within households and stratification based on states, and adjusted for the covariates including year, household composition, marital status, age, education, race/ethnicity, annual income, US state, and internet connection. Sociodemographic factors associated with fewer cigarette purchases included younger age, higher education, higher annual income, and identification as non-Hispanic Black or Hispanic (vs. non-Hispanic White), and single female households (vs. single male households). 4. Discussion This study documents significant associations between stronger state tobacco control measures—specifically smoke-free laws, tobacco taxation, and prevention/control funding—and fewer cigarette purchases among US households over a seven-year period (2015–2021). These findings align with previous research indicating associations between taxes and smoke-free laws with lower cigarette expenditure, 18 smoking initiation, and use 19 and between tobacco control funding and lower odds of smoking. 22 Furthermore, the synergistic associations of multiple tobacco control measures suggest that a comprehensive approach is crucial for achieving greater reductions in cigarette consumption. 23 , 24 One unexpected finding was the lack of association between access to cessation services and cigarette purchases, which mirrors similar null results in prior studies. 11 , 12 This is particularly noteworthy given that the current sample consisted of households that had purchased cigarettes and that access to cessation services such as counseling, medication, and quitlines have been linked to successful smoking cessation. 7 , 8 One reason for this null finding may be that the ALA scores reflect the extent of accessible cessation services – but not the extent of service utilization – and despite higher cessation access scores in most states during this study period, services may have still been under-resourced and under-utilized. 25 Notably, only six states received an “A” grade from ALA for access to cessation services in 2021. Moreover, some research suggests that only about one-third of those who want to quit smoking use approved cessation treatments. 26 , 27 This under-utilization may reflect ongoing barriers to cessation services, 7 , 25 – 27 such as resource constraints impacting state services (e.g., quitline funding), clinical settings (e.g., limited capacity to make referrals for cessation services or provide prescriptions for cessation pharmacotherapy), and insurance coverage (e.g., copays for cessation products, limits on the number of covered quit attempts per year). 7 , 26 , 27 When assessing all four tobacco control measures simultaneously, tobacco taxation had the strongest association with fewer cigarette purchases, which also aligns with some prior studies, 12 – 14 , 17 , 18 , 23 including a couple of studies that used NCP data. 16 , 28 However, states with high tobacco taxes also have stronger smoke-free laws and more funds allocated to prevention and control, on average. Thus, it would be an overstatement to suggest that tobacco taxation should be the focal point of state tobacco control efforts. 23 , 29 Rather, it is important for states to implement a combination of tobacco control efforts in order to realize the largest reductions in tobacco use. 24 In interpreting the study findings, it is critical to consider the tobacco control contexts across states over time. In general, several states lag in implementing strong tobacco control efforts across multiple measures. The average tobacco tax increased from $1.54 per pack in 2015 30 to $1.88 in 2021, 20 which represents a 6.8% increase (after considering inflation) over the seven-year period. Thus, state legislative activity continues to be slow in this area since 2010. 31 Moreover, the majority of states have yet to adopt comprehensive smoke-free laws. From 2012 to 2021, no state has approved comprehensive smoke-free laws eliminating smoking in all public and workplaces. 20 As efforts to promote comprehensive smoke-free laws advance, it is crucial that legislation is carefully crafted. For example, it is critical to advocate for passing comprehensive state laws from the start instead of initial partial laws, in the hopes of later strengthening them, 5 , 32 as the literature suggests that, once states pass a smoke-free law, the law is rarely changed over time. 32 Additionally, states should resist industry lobbying efforts promoting state preemption of local tobacco control ordinances, as preemption may further impede tobacco control efforts and widen tobacco related health disparities. 33 Furthermore, the majority of states fail to allocate even half of the recommended funding for prevention and control or effectively promote access to and utilization of cessation services. In short, these data echo prior concerns regarding stalled progress in implementing strong tobacco control efforts, 12 , 31 which may hinder achievement of any Healthy People 2030 general tobacco use targets. 34 These findings should be interpreted with caution due to several limitations. First, the cigarette purchase data from the NCP is recorded at the household level and only from brick-and-mortar retailers, making it difficult to determine whether all cigarette purchases were assessed, whether all purchased cigarettes were consumed, who within (or outside of) the household used them, and whether individual behavior changed over time. Additionally, the NCP provides a limited set of sociodemographic data at the household level, constraining our ability to fully characterize the households in our sample as well as the individuals. Subgroups such as young adults and those with higher socioeconomic status are also underrepresented, potentially affecting the generalizability of the results. Moreover, the analysis focuses on state-level tobacco control policies and does not account for local laws, which could influence cigarette purchasing behavior in regions with stricter or more lenient policies. We also lacked data on other factors that may affect cigarette purchases, such as changes in social norms, thus leading to unmeasured confounding and limiting our ability to establish causality. These limitations suggest that while our study provides valuable insights, further research is needed to explore these dynamics in greater depth, particularly at the local level and across underrepresented populations. 5. Conclusion This study provides critical insights into the association of four key state-level tobacco control measures and cigarette purchasing behaviors over seven years. Our findings reaffirm the importance of smoke-free laws, taxation, and prevention/control funding as critical tobacco control and prevention strategies, while highlighting the current challenges of cessation services. The lack of significant association with cessation services suggests that availability of cessation services alone may not be sufficient, and that more attention is needed on increasing utilization and addressing barriers to access. Despite progress, our analysis reveals a concerning stagnation in recent tobacco control efforts across many states, particularly in advancing smoke-free laws, taxation, and prevention funding. To meet national tobacco reduction goals, states must implement a coordinated, multi-pronged approach, combining taxes, smoke-free environments, prevention programs, and accessible cessation services. Renewed efforts are essential to drive further reductions in smoking rates and address ongoing disparities in tobacco use. Supplementary Material 1 NIHMS2075153-supplement-1.docx (1MB, docx) Highlights. Smoke-free laws, taxes, & prevention/control funding help lower cigarette purchases Higher tobacco taxation is strongly associated with fewer cigarette purchases Overall tobacco control efforts have stagnated across many US states in recent years 7. Funding: This work was supported by the National Cancer Institute (R01CA275066, MPIs: Yang, Berg). Footnotes 8. CRediT Roles: Rishika Chakraborty: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing -original draft, Writing – review and editing. Yan Li: Methodology, Validation, Writing -original draft, Writing – review and editing. Yan Wang: Methodology, Writing -original draft, Writing – review and editing. Carla Berg: Conceptualization, Data Curation, Funding Acquisition, Methodology, Writing -original draft, Writing – review and editing. Sabrina Zhang: Formal Analysis, Validation, Writing – review and editing. Debra Bernat: Conceptualization, Methodology, Writing – review and editing. Y. Tony Yang: Conceptualization, Data Curation, Funding Acquisition, Writing -original draft, Writing – review and editing. Declaration of Interest Statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 6. Conflicts of Interest: None. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. 9. 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[ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials 1 NIHMS2075153-supplement-1.docx (1MB, docx) Data Availability Statement The ALA state of tobacco control scores were requested from the American Lung Association, available at www.Lung.org/sotc . The NielsenIQ Consumer Panel data were requested from https://www.chicagobooth.edu/research/kilts/research-data/nielseniq . Researchers' own analyses calculated (or derived) based in part on data from Nielsen Consumer LLC and marketing databases provided through the NielsenIQ Datasets at the Kilts Center for Marketing Data Center at The University of Chicago Booth School of Business. The conclusions drawn from the NielsenIQ data are those of the researchers and do not reflect the views of NielsenIQ. NielsenIQ is not responsible for, had no role in, and was not involved in analyzing and preparing the results reported herein. ACTIONS View on publisher site PDF (232.7 KB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top