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 . 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. 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.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. 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,pot for growing marijuana 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 NLSY97data that did not include time-varying covariates and relied on latent class growth analysis identified roughly comparable shares of experimenters and quitters, smaller shares of never smokers , and larger shares of late escalators and early established smokers. 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. 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. Song et al found that smoke-free laws had a different relationship with smoking initiation, smoking status, and days smoked.
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. 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. The knowledge that experimenters are more likely to have counter intuitive 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. 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 , reduced days of smoking among experimenters,container for growing weed 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. 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. In addition, tobacco manufacturer use price promotions to reduce the post-tax consumer prices of cigarettes to levels below the pre-tax prices.
Because these changes result in smokers purchasing cigarettes in larger quantities , 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 tend to vary by socioeconomic status, 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. 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 and local tobacco minimum floor prices . 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. 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.Our study has limitations. Our analysis considered annual changes and does not consider policy changes after 2011 when NLSY97 data collection became biennial because the analytic method could not support missing years of data. Because we did not analyze NLSY97 data beyond 2011, we were unable to assess potential interactions between combustible cigarette use and new products such as e-cigarettes and possible complementary use of other substances such as cannabis, which has been increasingly legalized for medical and recreational use. Research using data from the CDC National Youth Tobacco Survey showed that the advent of e-cigarettes had not affected the rate of decline in youth cigarette use from 2004 through 2014 , but that e-cigarettes were adding to nicotine product use. The market for new tobacco products has continued to change, and caution is warranted in attempting to apply these findings to the current market. Our analysis did not include data on Tobacco 21 laws due to similar limitations. Although biennial NLSY datasets were available through 2018 at the time of writing, the only strong state T21 law in effect before 2019 was California, and organizations that code the strength of Tobacco 21 laws were unable to supply data on local Tobacco 21 laws for any time period. In addition, the switch to a biennial analysis would increase the share of missing data. Our analysis did not include data on state tobacco control funding, for at least two reasons: first, there are multiple differences between states relating to population and focus and quality of programs that make it unclear how to normalize a measure; second, there is likely collinearity between program funding and enactment of smoke free laws and tax increases, given that stimulating such policy change is often among the goals of state tobacco control programs. We used only a subset of the entire sample due in part to missing geographic identifiers in the underlying data and in part due to incomplete risk factor data ; it is unclear whether or how observations excluded due to missing geocodes or incomplete reporting might affect estimates. We relied on list wise deletion as a strategy to handle missing data given that this method is linked to loss of statistical power rather than to biased estimates. The fact that we identified statistically significant determinants of the trajectories suggests that this loss in power did not compromise the overall analysis. Another consequence of missing data is that we were unable to include a variable indicating ever use of cocaine/hard drugs, which dropped out of the analysis entirely . Another limitation of the analysis is the composition of the sample. A large proportion of the sample was non-Hispanic white and both enrolled in school and employed; a small proportion was Hispanic, not living with both biological parents, and had a mother with less than a GED/high school diploma. As a result, the analysis may not have identified some associations among respondents with underrepresented characteristics. Future research should consider questions left unanswered by this analysis, including further analysis of the identified increase in smoking days among experimenters under comprehensive smoke-free laws, and among early established and late escalators under higher excise taxes.Fragile X syndrome is an X-linked dominant disorder caused by the expansion of a trinucleotide repeat n within the first exon of the fragile X mental retardation 1 gene, which silences the expression of the fragile X mental retardation protein .The absence of FMRP, an important regulator of translation of many messenger RNAs involved in synaptic plasticity,2 leads to substantial intellectual deficits.