Introduction

Tobacco use is the leading preventable cause of death in the US, killing over 480,000 people each year [1]. Most of these deaths occur among cigarette smokers, 80% of whom begin smoking by age 18 [2] and 99% of whom begin by age 25 [3]. The transition from experimentation to established smoking generally occurs in the late teens and early 20s [4, 5].

Tobacco use patterns vary within the population, with some people never smoking, some remaining occasional users, and others progressing to daily use or quitting. Existing research has identified 4–6 trajectories of smoking, typically classified as never-smokers, experimenters/occasional users, reducers or quitters, those who start smoking young and quickly become daily smokers, and those who start smoking as young adults and become daily smokers [6–12]. Studies of smoking trajectories have primarily focused on identifying risk factors at the individual or family level [7, 8, 10–17], determining associations between trajectory type and health outcomes [6, 9, 18], and assessing links between trajectories and use of other products [19]. There has been little attention to how tobacco control policies affect these trajectories. Understanding the factors that influence smoking initiation and the transition to regular smoking is critical to developing tobacco control interventions that improve public health [8]. The identification of distinct trajectories of smoking behavior, which arise from different determinants, offers a more accurate way of describing and understanding behavior and makes it possible to design interventions for specific groups instead of assuming that interventions have comparable effects across the entire population. This is similar to the market segmentation that the tobacco industry employs in designing its products and marketing strategies [20, 21]. Tobacco control policies may reduce smoking at the population level by reducing the risk of initiation, encouraging users to quit once they have begun smoking, discouraging ex-smokers from relapsing, or some combination of these effects [22, 23].

Population-based studies have found that in addition to protecting people from secondhand smoke, smoke-free laws stimulate quit attempts, support smoking cessation, and contribute to reducing cigarette smoking among adolescents and young adults [24, 25]. One study used 11 years of data from the National Longitudinal Survey of Youth 1997 (NLSY97) to assess the effects of smoking restrictions on respondents’ smoking behavior and found laws for smoke-free workplaces, but not bars, were associated with reduced smoking initiation; smoke-free bar laws were associated with lower odds of current smoking and fewer days of smoking among current smokers [26]. However, literature on the effects of smoke-free laws on smoking trajectories is limited, reflecting in part the difficulty of collecting longitudinal data on tobacco use and policies (which are complicated by substantial differences in smoke-free laws between localities and states), as well as the issue that many growth models do not accommodate time-varying covariates.

Tax increases reduce smoking and increase quit attempts at the population level [27, 28], but research seeking to assess their effects on established smokers has identified inconsistent effects [29–32]. Popular media reports on tax increases typically assume that established smokers will not quit and instead will switch to discount brands and seek out lower-taxed or illicit tobacco [33, 34]. However youth and young adults who are still forming smoking habits are highly price-sensitive [35], suggesting that tax increases could reduce initiation or discourage the transition to established smoking; higher prices could make starting smoking, or continuing to smoke, too costly. A previous study based on 11 years of NLSY97 data found that taxes were associated with a lower risk of initiation into smoking but did not affect current smoking [26], but this study treated all respondents as a single group and did not account for smoking trajectories. A cross-sectional latent class analysis in Minnesota found that a cigarette tax increase was associated with less smoking across classes [27]. Despite literature suggesting that taxes deter smoking, multiple studies have found that daily smokers use price minimization strategies, such as discount brands, coupons, and purchasing in lower tax jurisdictions, to offset increases in excise taxes [36–41]. The tobacco industry has also lobbied for changes in tax calculations and redesigned marketing to focus on discounts and coupons to undercut the effects of tobacco tax increases [42, 43]. In response, policies have attempted to establish counter-measures such as tobacco minimum floor prices [40, 42, 44].

Little research exists on the role that changes in taxes over time play in the transition from adolescent smoking initiation to established use [7, 27]. Even less exists on associations between policies and smoking behavior within and across smoking trajectories. While one study considered the role of media campaigns on smoking behavior over time, we were unable to identify any previous trajectory-based research assessing smoke-free laws [24, 45]. Examining the influence of policies on smoking trajectories has the potential to identify heterogeneous effects of existing policies and the need for tailored smoking prevention and cessation approaches.

Our current study addressed this gap by assessing the effects of tobacco control policies known to be effective at the population level on trajectories of use for adolescents as they become young adults. We anticipated that smoke-free laws would reduce the risk of tobacco initiation and use across all smoking trajectories and that tax increases would reduce the risk of initiation among never smokers but not reduce use among current established smokers. The authors previously published research that identified trajectories of smoking using NLSY97 data; this earlier paper did not consider the impact of the policy environment (smoke-free laws and taxes) [8]. Adding these variables, which changed over time, is the important new contribution of this paper. This new analysis provides guidance for policy makers seeking to reduce tobacco initiation and use across the population and reduce relapse to use among those who have quit.

Methods

Using longitudinal data collected over 15 years, we identified associations between smoking patterns, local smoke-free laws, and tobacco tax rates for young people in multiple smoking trajectories while controlling for known individual risk factors.

We used weighted data from the NLSY97, a nationally representative sample of individuals born between 1980 and 1984 [46]. The NLSY97 dataset is collected by the US Bureau of Labor Statistics (BLS). The initial sample was drawn using the National Opinion Research Center’s (NORC) 1990 master probability US sample. Data collection began in 1997 when participants were 12 to 16 years old and continued with annual follow-ups through 2011 (data collection thereafter was biennial). Surveys were completed using computer-assisted in-person and telephone interviews. Flowcharts for participants’ progress through the survey instruments in each year of data collection are provided at the U.S. Bureau of Labor Statistics National Longitudinal Surveys website [47]. We extracted variables from the NLSY97 at the NLS Investigator site by selecting variables describing participant attitudes, behavior, and demographics (detailed below), as well as sampling weights, from the complete list provided by NLSY97 and downloading them for analysis. The public use variables included in this analysis can be obtained through NLS Investigator (https://www.nlsinfo.org/investigator/pages/login) and geographical data by making a request to BLS for access to data restricted under the Confidential Information Protection and Statistical Efficiency Act (CIPSEA) (https://www.bls.gov/rda/home.htm). The NLSY97 panel began with 8,984 participants, and by the 15^th wave in 2011, had experienced 17.4% attrition (n = 7,423). We used participant geocodes (states and counties) from the BLS restricted-use dataset to link smoke-free law coverage and tobacco taxes to participants’ survey data.

Results

The group-based trajectory analysis identified trajectories (Fig 1) consistent with the results of the previous work [8]. The first trajectory consisted of never smokers (50.8%). Given that we identified similar trajectories as in past research, we used the same naming convention for the other four trajectories: experimenters (12.5%), late escalators (9.8%) quitters (9.4%), and early established smokers (17.5%). A classification as “experimenter” was associated with less than 10 smoking days per month in every year of data collection. “Quitters’” consumption peaked in early adulthood, between ages 18–22 years, and then declined. “Late escalators” peaked with respect to days smoked per month at ages 22–26 years, in contrast to “early escalators,” who did so at ages 19–23 years.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 1. Five identified trajectories of smoking behavior in the National Longitudinal Survey of Youth (NLSY97).

https://doi.org/10.1371/journal.pone.0246321.g001

Regarding the effects of policies on the trajectories (i.e., days of smoking within each trajectory), as the probability of being covered by comprehensive smoke-free law increased, predicted days of use in a month decreased in all trajectories other than experimenters, where coverage by comprehensive smoke-free laws was associated with more days of smoking (Table 1). The effect was most substantial for quitters (-1.99±0.04 [SE] days/month) and never smokers (-0.36±0.10 days/month) and was also associated with reduced days of smoking in a month for late escalators (-0.13±0.01 days/month) and early established smokers (-0.05±0.01 days/month). Experimenters (+0.81±0.03 days/month) were an exception.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 1. Policies and risk factors associated with smoking in different trajectories (n = 4,192; significant associations in bold).

https://doi.org/10.1371/journal.pone.0246321.t001

As tax rates increased, risk of initiation and days of smoking per month decreased in less established users. The effects were most substantial for experimenters (-0.32±0.01 days/month/(ln unit)), never smokers (-0.15±0.02 days/month/(ln unit)) and quitters (-0.02±0.01 days/month/(ln unit)). In contrast, days of use increased for both late escalators (0.83±0.01 days/month/(ln unit) and early established smokers (0.08±0.002 days/month/(ln unit)).

When computing the tax equivalent of a smoke-free law, the largest effect was on quitters, where the smoke-free law was equivalent to a tax well over $1,000, reflecting the fact that the estimated effect of taxes after quitting was so small. The next largest comparative effect was among never smokers, where a smoke-free law was equivalent to a $11.06 tax. The tax-equivalent effects of smoke-laws were more modest among late escalators ($0.85) and early established smokers ($0.52). The equivalent tax effect of smoke-free laws on experimenters was much smaller ($0.08).

Regarding the effects of the sociodemographic risk factors on the probability of membership in each trajectory, compared to never smokers, experimenters were more likely to be younger in 1997; to report being male; having ever used alcohol; having ever used cannabis; higher levels of depression, peer smoking, and a history of rule breaking; and having at least one child (Table 1). Relative to never smokers, late escalators were more likely to report being male; ever using alcohol; ever using cannabis; being younger; higher levels of depression, peer smoking, and a history of rule breaking; and having at least one child. They were less likely to be Hispanic, employed and in school, or have ever been married. Compared to never smokers, quitters were more likely to report being male; being employed and not in school or neither in school nor employed; living in a non-two parent family; ever using alcohol; ever using cannabis; higher levels of depression, peer smoking, and rule breaking; and having at least one child. Quitters were less likely to be non-Hispanic Black or Hispanic, or to have ever been married. Relative to never smokers, early established smokers were more likely to report being male; being employed but not in school or neither in school nor employed; living in a non-two parent family; ever using alcohol; ever using cannabis; higher levels of depression, peer smoking, and rule breaking; and having at least one child. Early established smokers were less likely to be non-Hispanic Black or Hispanic, or to have ever married.

Once respondents had been classified into different groups based on the trajectory analysis, we reviewed the characteristics of the respondents assigned to each trajectory to identify between-trajectory differences (Table 2). We reviewed the characteristics of members of each trajectory; dichotomous variables are reported as percentages, and ordinal variables are reported with means and confidence intervals. Participants identifying as males were more likely to have established smoking habits at a younger age; while less than half of never smokers, experimenters, and late escalators were male, 59% of quitters and 72% of early established smokers were male. While 31% of never smokers reported ever drinking alcohol at baseline, 39% of late escalators, 48% of experimenters, 59% of quitters, and 66% of early established smokers reported ever drinking alcohol. Similarly, 8% of never smokers reported cannabis use at baseline relative to 25% of late escalators, 20% of experimenters, 33% of quitters, and 42% of early established smokers. For the depression, peer smoking, and rule breaking scales, the differences between never smokers and early established smokers ranged between 0.10 and 1.85 on a 5-point scale.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 2. Individual level characteristics by smoking trajectory.

https://doi.org/10.1371/journal.pone.0246321.t002

We compared the means and confidence intervals for all variables in the entire NLSY cohort and those in the subset included in the trajectories analysis to assess potential bias in the sample due to missing data. We found that, among the sociodemographic indicators, the subset of observations included in the trajectories analysis had a larger share of respondents identifying as non-Hispanic White and as being both enrolled in school and employed, while a smaller share identified as Hispanic, reported that they were not living with both biological parents and had a mother with less than a GED/high school diploma.

Discussion

Overall, as anticipated, we identified significant associations between smoking trajectories, tobacco control policy interventions and known risk factors for progression to established smoking. Our findings were consistent with and expand on results from prior research by adding the time-varying effects of two important tobacco policy interventions, smoke-free laws and taxes [8]. In addition, our results demonstrate the effects of socio-demographic variables on patterns of smoking. Our results suggest that policy has different influences on the patterns of smoking behavior of different types of smokers. Our analysis also demonstrated the stronger effects of smoke-free laws on frequency of smoking than tobacco taxes.

Our findings with respect to risk factors for smoking frequency were generally consistent with previous research [7, 8, 10–17], which suggested that white men were more likely to be daily smokers; smoking is associated with alcohol and drug use, peer smoking, and a history of rule breaking; established smoking is associated with lower socioeconomic status; and depression and anxiety are associated with smoking. A previous trajectory analysis using NLSY97 data that did not include time-varying covariates and relied on latent class growth analysis (LCGA) identified roughly comparable shares of experimenters and quitters, smaller shares of never smokers (34.1% versus 50.8% in this study), and larger shares of late escalators (52.0% versus 9.8%) and early established smokers (39.0% versus 17.5%) [8]. With respect to risk factors, confidence intervals in this updated analysis were narrower, identifying significant associations for additional variables in one or more trajectories, including male, being employed and not in school, ever using cannabis, age, depression/anxiety, peer smoking, rule breaking, and having at least one child. In this analysis, non-Hispanic Black participants were also more likely to be experimenters, and Hispanic participants were less likely to be early established smokers.

Our analysis expands upon the existing literature on tobacco control policies and smoking behavior, which focuses on measures of smoking behavior at specific time points, such as initiation, smoking status, and cessation. This is the first analysis to examine the relationship between tobacco control policies and patterns of smoking behavior over time. Our finding that policies have differential effects on smoking trajectories establishes that smokers are heterogeneous, meaning not all smokers follow the same progression to smoking. It may be necessary to tailor cessation interventions to different types of smokers to increase the efficacy of these approaches.

The results also support the importance of tobacco control policy interventions in modifying smoking behavior across all trajectories of use. Comprehensive smoke-free laws were associated with decreased risk of initiation, decreased use, and reduced likelihood of return to use across four out of five trajectories. The effects were greatest for never smokers and quitters, while still evident among established smokers, whether they began smoking as adolescents or as young adults. The only trajectory that did not reduce its exposure to tobacco as a result of coverage by comprehensive smoke-free laws was experimenters.

Our finding that smoke-free laws were not associated with patterns of use among experimenters is consistent with previous literature that established varying effects of smoke-free laws across different patterns of smoking behavior [26, 70]. Siegel et al found that strong smoke-free restaurant laws were associated with lower odds of transitioning from experimentation to established smoking, but not of transitioning from nonsmoking to experimentation [70]. Song et al found that smoke-free laws had a different relationship with smoking initiation, smoking status, and days smoked [26]. Specifically, Song et al found that smoke-free bar laws were associated with lower odds of being a current smoker and fewer days of smoking but not lower odds of smoking initiation [26]. Our findings are also consistent with the intention of smoke-free laws not to prevent experimentation, but to prevent progression from experimentation to established smoking, in addition to protecting nonsmokers from secondhand smoke exposure [71].

The knowledge that experimenters are more likely to have counterintuitive responses to smoke-free laws has the potential to influence tobacco cessation efforts. When a state or locality improves its smoke-free law coverage, it may wish to supplement these changes with smoking prevention and cessation targeting experimenters to ensure that no group fails to benefit from these policy improvements. The analysis that this one builds upon revealed that, compared to never smokers, experimenters were more likely to be neither in school nor working [8]. This finding suggests that school-based tobacco control efforts are less likely to be effective for experimenters than some other types of smokers. Tobacco control programs targeting these youth should be tailored to their use patterns by promoting complete cessation and elimination of occasional or social smoking. These efforts should be placed in locations likely to be frequented by youth who are neither in school nor employed, such as community centers and athletic courts.

While increased tax rates were associated with reduced risk of initiation among never smokers (i.e., a higher cost makes it less appealing to start smoking), reduced days of smoking among experimenters, and reduced likelihood of return to use among quitters, they were associated with increased days of smoking among early established users and late escalators. The finding that established users increase smoking after tax increases is contrary to the intended effect of tobacco tax increases.

In general, because cigarettes are addictive, the relationship between changes in the price and consumption of cigarettes tends to be different from that of many other goods [72]. In addition, previous research suggests that, when tax increases occur, smokers increasingly engage in price minimization strategies such as coupons, bulk purchasing, and switching to discount brands to maintain their prior levels of use [36–41]. In addition, tobacco manufacturer use price promotions to reduce the post-tax consumer prices of cigarettes to levels below the pre-tax prices [43]. Because these changes result in smokers purchasing cigarettes in larger quantities (e.g., carton instead of pack), they also have the potential to result in increased availability, and therefore, increased use. In addition, the use of price minimization strategies and coverage by tobacco-free policies [73] tend to vary by socioeconomic status (SES) [74], and we found differences in some indicators of SES across classes. Policy interventions such as tobacco minimum floor prices or sudden, large tax increases might circumvent the price-minimization strategies likely to be used by established users and late escalators [40, 42, 44, 60].

Future research could consider the effectiveness of these policies by considering changes in smoking trajectories in years beyond 2011, after the introduction of substantial state level annual tax increases (e.g., the 2013 Minnesota tax increase of $1.75 [75]) and local tobacco minimum floor prices (e.g., $7 in Sonoma County, CA in 2016 [76]). In addition, to ensure that all youth benefit from tax increases, states and localities planning tax increases could supplement these increases with cessation methods targeting early established smokers and late escalators. These were the only two trajectories that were significantly more likely to report having frequently broken rules in school compared to never smokers in a previous analysis [8]. School-based efforts and tobacco educational campaigns targeting youth who self-report higher rates of rule-breaking would be most likely to be effective for these types of smokers. In addition, because late escalators do not become established smokers until late adolescence or early adulthood, these programs should extend their reach beyond youth to include young adults by utilizing not only school-based, but also community- and higher education-based smoking prevention and cessation approaches.

Acknowledgments

This research was conducted with restricted access to Bureau of Labor Statistics (BLS) data. The views expressed are those of the authors and do not reflect the views of the BLS. We gratefully acknowledge the assistance of Jing Cheng in analyzing the trajectory model.