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. 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. 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. One study used 11 years of data from the National Longitudinal Survey of Youth 1997 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. 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 , 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, but research seeking to assess their effects on established smokers has identified inconsistent effects. 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.
However youth and young adults who are still forming smoking habits are highly price-sensitive, suggesting that tax increases could reduce initiation or discourage the transition to established smoking; higher prices could make starting smoking,grow cannabis in containers 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, 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. 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.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. In response, policies have attempted to establish counter-measures such as tobacco minimum floor prices. Little research exists on the role that changes in taxes over time play in the transition from adolescent smoking initiation to established use. 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. 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. 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.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. The NLSY97 dataset is collected by the US Bureau of Labor Statistics . The initial sample was drawn using the National Opinion Research Center’s 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 . 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. We extracted variables from the NLSY97 at the NLS Investigator site by selecting variables describing participant attitudes, behavior, and demographics , 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 and geographical data by making a request to BLS for access to data restricted under the Confidential Information Protection and Statistical Efficiency Act . The NLSY97 panel began with 8,984 participants, and by the 15th wave in 2011, had experienced 17.4% attrition . We used participant geocodes from the BLS restricted-use dataset to link smoke-free law coverage and tobacco taxes to participants’ survey data.Our primary outcome measure was days smoked per month. There is no widely accepted measure of smoking for trajectory models addressing smoking among youth and young adults.Previous studies have compared four different measures of smoking: mean cigarettes smoked per day, cigarettes per day on days smoked, days smoked per month, and total cigarettes per month; of these, days smoked per month provided maximum differentiation between trajectories, captured smoking progression over time, uniquely described smoking behavior, and avoided problems of instability over time created by the use of measures such as cigarettes per day or month.We included two types of policy interventions as time varying covariates, measured annually: smoke-free laws and tax rates. NLSY97 geocodes provide Federal Information Processing Standards codes that identify the county of each respondent, which made it possible for us to match policy variables with survey responses. Smoke-free laws. The smoke-free law variable accounted for both state and local laws because state-level smoke-free laws are often weaker than local ordinances. RTI International, which has collected information on smoke-free laws by locality throughout the US since the 1990s, provided data on state and local smoke-free law coverage. RTI’s database is quarterly and calculates smoke-free law coverage by combining smoke-free law data from the American Nonsmokers’ Rights Foundation database with annual population data from the U.S. Census Bureau. The database was created by entering smoke-free laws individually by effective date and using statistical code to estimate the fraction of the population covered by smoke-free laws at the state, county, and place level for workplaces,pot for cannabis restaurants, and bars. This figure can also be interpreted as the probability that a given individual in a given locality will be covered by smoke-free laws. The database only includes 100% smoke-free laws, which are defined by the American Nonsmokers’ Rights Foundation as smoke-free laws with few or no exceptions. This study used 100% smoke-free law coverage by workplace, restaurant, and bar laws as our measure, consistent with US Centers for Disease Control and Prevention guidelines. When 100% of the county’s population was covered by smoke-free laws , the county was assigned a 1. For counties not covered by any state, county, or local smoke-free law, the county was assigned a 0. For counties with no state or county laws but with local laws, the county was assigned a quantity between 0 and 1 that represented the proportion of the county’s population covered by smoke-free laws . We lagged smoke-free coverage by a year to ensure smoke-free laws had been fully implemented and had time to affect behavior before each year of NLSY97 data collection. Tobacco taxes. Taxes are a more accurate measure of tobacco control policy than prices.
Tax data were drawn from Tax Burden on Tobacco, an annually updated resource archived by the US Centers for Disease Control and Prevention that lists statewide tobacco tax rates in every year since 1970. Nominal taxes were converted to 2011 dollars using average Consumer Price Index data for all goods and services. We did not include local tax rates; very few US localities can impose tobacco excise taxes. Changes in taxes were assumed to take effect the same year, and the amount of the tax in each year, in dollars, was natural log-transformed due to skew.To assess young adult characteristics, we included two categorical variables, ever married and having one or more children, measured at age 26 given their low probability or illegality at baseline. Household income was coded on a 4-point scale relative to the previous year’s federal poverty line into four quartiles from below poverty line , up to 199%, 200–299%, and 300%+ .We created a group-based trajectory model using the Stata version 15 “traj” plugin with a zero-inflated Poisson model due to the large numbers of zeroes in the data. Within each trajectory, smoking was modeled as a function of time. The group-based trajectory model clumps individual trajectories into distinctive clusters to permit identification of the characteristics of individuals in these clusters. This model allows investigation of differences across groups within a population and assessment of patterns of shifting behavior over time using maximum likelihood methods to estimate the parameters of the model. The group-based trajectory model requires making a preliminary decision about the number of assumed trajectories. We specified five trajectories based on a previous latent class growth analysis of the same years of the NLSY97 data that modeled days smoked in the past 30 days. We used traj’s built-in capacity to calculate the effect of time-varying covariates, in this case, policies , on the probability of membership in each trajectory. Traj conducts these calculations by generalizing the specification of the polynomial function of time, which defines the shape of the trajectory, to include covariates. We also used traj’s builtin ability to calculate the effect of time-invariant covariates, in this case, the socio-demographic risk factors, on the trajectory itself. Traj uses a generalized logistic function for these calculations. We assessed robustness of the final model by verifying that the directionality of covariates remained the same across all steps of stepwise deletion of risk factor variables and when policy variables were log-transformed and/or lagged. Traj uses listwise deletion for missing data. Missing data led to the exclusion of 43.5% of the 2011 sample. Approximately half of these exclusions represented individuals for which NLSY97 did not report a geocode, and the remainder were for participants with incomplete risk factor data. The group-based trajectory analysis identified trajectories consistent with the results of the previous work. The first trajectory consisted of never smokers . Given that we identified similar trajectories as in past research, we used the same naming convention for the other four trajectories: experimenters , late escalators quitters , and early established smokers . 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. Regarding the effects of policies on the trajectories , 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 . The effect was most substantial for quitters and never smokers and was also associated with reduced days of smoking in a month for late escalators and early established smokers . Experimenters were an exception.