The natural history of college smoking: Trajectories of daily smoking during the freshman year

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1 Addictive Behaviors 31 (2006) The natural history of college smoking: Trajectories of daily smoking during the freshman year Craig R. Colder a, *, Elizabeth E. Lloyd-Richardson b, Brian P. Flaherty c, Donald Hedeker d, Eisuke Segawa d, Brian R. Flay e The Tobacco Etiology Research Network a University at Buffalo, State University of New York, USA b Brown Medical School and The Miriam Hospital, USA c University of Washington, USA d University of Illinois at Chicago, USA e Oregon State University, USA Abstract Although the initiation of cigarette use typically occurs prior to age 18, there is evidence for considerable change in smoking behavior after this age. College may be a particularly important period to study smoking because it is a time when adolescents transition into a new social context where substance use is normative. Using a longitudinal design, daily assessments of smoking were collected during the entire first year of college for a large cohort of freshman (N = 496). Findings suggested a weekly cycle of smoking such that the probability of smoking was much higher on weekends (Friday and Saturday) than on remaining days of the week. In addition to this weekly cycle, there was an overall trend for smoking to decline over the course of the year. Substantial individual variability in levels of smoking was observed. These findings provide new insights into college smoking, and have implications for assessment, policy, intervention, and future directions for research. D 2006 Elsevier Ltd. All rights reserved. Keywords: Smoking; College students; Trajectory * Corresponding author. Department of Psychology, Park Hall, University at Buffalo, Buffalo, NY , USA. Tel.: x235; fax: address: ccolder@buffalo.edu (C.R. Colder) /$ - see front matter D 2006 Elsevier Ltd. All rights reserved. doi: /j.addbeh

2 C.R. Colder et al. / Addictive Behaviors 31 (2006) Introduction Smoking continues to be an important public health concern among youth and there is a need for interventions to target smoking prior to initiation. For prevention efforts to be successful, it is necessary to understand the timing of initiation and escalation of cigarette use. In the current study, we examined trajectories of smoking during the freshman year of college. Although the initiation of cigarette use typically occurs prior to age 18 (Lynch & Bonnie, 1994), longitudinal research suggests that smoking is often initiated and escalates rapidly after age 18 (Chassin, Presson, Sherman, & Pitts, 2000; Chassin, Sherman, Presson, & Edwards, 1991; Orlando, Tucker, Ellickson, & Klein, 2004). Late onset smokers were characterized in adolescence by low exposure to smoking models, low levels of deviance, and negative beliefs about smoking and by college attendance. Chassin et al. (2000) speculated that late-onset smokers may be more likely to follow prohibitions about smoking during adolescence, but when they transition from adolescence into young adulthood, they may no longer view smoking as a deviant behavior. Moreover, after leaving home, perhaps to start college, they are no longer subject to parental rules and monitoring. These findings are notable because they suggest that the transition to young adulthood may be a period of vulnerability for escalation in smoking. College may be a particularly important context to study smoking. For many adolescents, college marks the beginning of an important transition from adolescence to emerging adulthood (Arnett, 2000). Upon entering college, many students experience a change in social context and increased freedom and independence (Schulenberg, O Malley, Bachman, Wadsworth, & Johnston, 1996). Prior inhibitions about a variety of risk behaviors may weaken with the transition to college, given declines in adult supervision and the perception that many risk behaviors are considered adult behaviors (e.g., cigarette and alcohol use, sexual behavior). Epidemiological studies of college smoking suggest that many students who abstained from smoking in high school are likely to experiment with cigarettes when they get to college, and those who were light occasional smokers in high school are likely to become more frequent, heavy smokers (Wechsler, Rigotti, Gledhill-Hoyt, Lee, 1998; Schorling, Gutgesell, Klas, Smith, & Keller, 1994). We could locate only one study that examined college smoking using a prospective design. Wetter et al. (2004) examined change in smoking among a sample of college students over a period of 4 years, and found considerable heterogeneity in the longitudinal progression of smoking, such that smoking increased for some students, and decreased or remained stable for others. The dearth of longitudinal research on college smoking is troublesome given the rise in prevalence rates of cigarette use observed in the 1990s on college campuses (Wechsler et al., 1998; Rigotti, Lee, & Wechsler, 2000) and the dynamic nature of smoking during this developmental period. Much of what we know about the correlates of initiation and escalation of tobacco use is based on adolescent samples prior to the college years. Risk and protective factors for smoking may vary across different developmental periods (Jamner et al., 2004), and accordingly, what we know about smoking prior to age 18 may not apply to college smoking. A more detailed examination of the natural history of college smoking with multiple repeated assessments is an important direction for research. The University Project of the Robert Wood Johnson Foundation Tobacco Etiology Research Network (UPTERN) was designed to fill this gap in the literature. We used a longitudinal design that integrated quantitative and qualitative methodologies, and gathered daily assessments of smoking in a large sample of college students during their freshman year. This micro-level assessment allowed us to model trajectories of smoking during the academic year, as well as to examine day to day variability during the first year of college, a period characterized by substantial change in multiple psychosocial domains.

3 2214 C.R. Colder et al. / Addictive Behaviors 31 (2006) Method 2.1. Participants The sample was selected to include students who smoked prior to freshman year because these students were expected to be at risk for smoking during their freshman year. Only students 18 years old or older were eligible to participate. A brief screening questionnaire was administered to incoming freshman during summer orientation to identify students who had at least one lifetime cigarette or at least one puff of a cigarette within the last year. Of 6560 incoming freshman, 4690 (71%) completed the screening questionnaire. Half of the students who completed the screener (50%) reported that they had ever smoked, 34% reported having smoked in the past year, and 21% reported having smoked in the past 30 days. Other studies of college smoking have reported higher prevalence rates of smoking, between 50% and 74% for lifetime smoking, 38% and 41% for past year smoking, and 28% and 29% for past 30 days smoking (Patterson, Lerman, Kaufmann, Neuner, & Audrain-McGovern, 2004). The lower prevalence of smoking in our screening sample may be attributable to the administration of the screening questionnaire prior to freshman year, and thus smoking initiated during the first year of college was not included in our prevalence estimates. The 2001 students who met our screening criteria were contacted to participate in the study; 912 agreed to participate. Comparison between eligible students and those who were successfully recruited into the study suggested statistically significant but small difference on gender (v 2 (1)=3.65, p b.06, phi =.02), such that females were slightly over-represented (46% vs. 42%) in the recruited sample compared to males (54% vs. 57%). A statistically significant, but small difference on minority status was also found (v 2 (1)=3.65, p b.05, phi=.03), such that White students were slightly over-represented in the recruited sample (93% vs. 89%) compared to minority students (7% vs. 11%). Students who were recruited into the study reported a lower number of lifetime cigarettes ( F(1, 4687)=1363.5, p b.01, g 2 =.23) and less recent smoking ( F(1, 4684)=1217.4, p b.01, g 2 =.21). Overall, students recruited into the study were less involved in smoking at the beginning of the freshman year, and were slightly less likely to be male and minority. There was a 96% retention rate over the 35weeks of the study. The goal of the current study was to model trajectories of smoking across the freshman year, and therefore, we excluded students who smoked fewer than three times during the year. This exclusionary criterion resulted in a sample of 496. This sample of 496 did not differ from the full sample of 912 on gender (v 2 (1)=.02, p b.89, phib.01) or minority status (v 2 (1)=.64, p b.45, phib.03). Demographic and smoking characteristics of the sample assessed prior to the start of the freshman year are presented in Table Procedures Weekly internet-based assessments started the first week of September and ended the first week of May, 2003, which provided 247 days of data. Information about smoking was gathered during these weekly assessments. The ethnographic component of the study involved 108 in-depth ethnographic interviews with 84 freshmen who showed a variety of patterns of smoking, as well as 15 focus groups conducted with members of the Fraternity/Sorority system and counselors in the residence halls. Qualitative data collection, coding, and analytic methods are described in detail elsewhere (Nichter et al., in press; Tiffany et al., 2005). Although qualitative data will not be formally presented in any

4 C.R. Colder et al. / Addictive Behaviors 31 (2006) Table 1 Demographic and smoking characteristics of the study sample (N = 496) Frequency Percentage Sex a Male Female Ethnicity White Asian 27 5 Other 5 1 Age first smoked even a puff of a cigarette 8 years old or younger years old years old years old years old years old Lifetime number of cigarettes smoked 500 or more to to to to to or more puffs, but less than a cigarette 14 3 Last time smoked a cigarette, even a puff a Past week to 29 days ago to 3 months ago to 6 months ago to 11 months ago 27 5 A year or more ago 37 7 Percentages do not necessarily sum to 100% due to rounding error. Missing data was present for one case. detail within this paper, the Discussion section highlights findings derived from the UPTERN qualitative research that facilitates interpretation of the longitudinal pattern that emerged from our quantitative analysis Measures Smoking Self-reported smoking was assessed using daily diaries administered on a weekly basis. The weekly online assessment included a question about whether the participant smoked in the past week. If participants reported smoking in the past week, then they were administered a daily diary, which asked them to report on the number of cigarettes they smoked on each day.

5 2216 C.R. Colder et al. / Addictive Behaviors 31 (2006) Party weekends We documented campus-wide social and academic events and activities, such as exam weeks, vacation schedules and holidays, home football games, and fraternity and sorority rush activities. As part of our qualitative research, students were asked to identify events having a high likelihood of bpartyingq and alcohol consumption. For purposes of this study, we were interested in evaluating the role of bparty weekendsq on students tobacco consumption. Thus, we selected for analysis two weekends that were consistently identified as having a very high likelihood of parties and substance use campus-wide. These weekends included campus wide social events that occurred in the fall (Halloween weekend) and spring (Alumni weekend, which includes a student sponsored racing competition). 3. Results 3.1. Descriptive results Average level of smoking for each day is presented in Panel A of Fig. 1, and the proportion of the sample that smoked on each day is presented in Panel B of Fig. 1. In both panels of Fig. 1, a weekly cycle is apparent, such that smoking is intermediate for one day, high on two days, and low for the Mean Number of Cigarettes Sample Proportion of Smoking Panel A: Average Daily Cigarette Use Day Panel B: Proportion of Daily Cigarette Use Day Fig. 1. Levels of smoking (Panel A) and proportion of the sample that smoked (Panel B) across 247days of freshman year.

6 C.R. Colder et al. / Addictive Behaviors 31 (2006) remainder of the week. This represents higher levels of smoking during Friday and Saturday, and intermediate levels of smoking on Thursday relative to Sunday through Wednesday. Consistent with these data, our qualitative interviews suggested that Friday and Saturday were high party nights, and so for the purposes of this paper, we refer to Friday and Saturday as weekends. Also apparent from Panel A of Fig. 1 is the low level of smoking in this sample. The highest average daily number of cigarettes smoked during the year was four, which occurred early in the fall semester. By the end of the year, average daily smoking levels hovered between one and two cigarettes. The high level of smoking on day 124 corresponds to New Year s Eve. Standard deviations for the number of cigarettes smoked ranged from 1.55 to Panel B of Fig. 1 shows that the highest proportion of the sample smoked on weekends early in the year, a peak of 53% early in the fall semester. The proportion of the sample that smoked on Friday and Saturday declined to between 20% and 25% by the end of the study. Rates of weekday smoking were generally low, ranging from a high of 36% in the beginning of the year to less than 20% by the end of the year. Fig. 1 also suggests a decline in smoking over the course of the year, and this decline is most rapid for weekend smoking. This is not surprising because weekday smoking was relatively low at the beginning of the year. Overall, average levels of smoking were low, and smoking was rare on weekdays and became increasingly less common on weekends (Friday and Saturday) as the year progressed Data analytic methods for random effects and marginal models We used both random-effects models (also known as multilevel and hierarchical linear models) and marginal models (also known as GEE models) for growth trajectories. These models are appropriate for nested data structures, which in the current application included repeated assessments of smoking nested within participants. The low levels of smoking, particularly at the end of the study, presented some data analytic challenges because the smoking variables were not normally distributed (skew ranged from 1.8 to 3.6 and kurtosis ranged from 2.47 to 21.7). To address this issue, we created categorical smoking variables (yes/no), and estimated a random-effects logistic regression model using the Hierarchical Linear Modeling (HLM) software version 5.05 (Raudenbush, Bryk, Cheong, & Congdon, 2001). Estimation of the models for categorical data is computationally very demanding for this sample because of the sparseness of the outcome (i.e., many subjects with a lot of zeros across time), the high intra-subject correlation of the longitudinal data, and the large numbers of repeated observations within each individual (247). Thus, the data were collapsed into three weekly periods rather than analyzing each day as a binary outcome. That is, we summed the number of smoking days from Sunday to Wednesday to represent a weekday smoking variable, and from Friday to Saturday to create a weekend smoking variable. Sundays were considered a weekday because smoking levels on Sunday were similar to those observed on Monday through Wednesday, and because students described social and academic activities similarly on Sunday through Wednesday. Thursdays were left as a separate repeated measure because students consistently characterized a unique pattern of academic and social activities for Thursdays compared to other weekdays and weekends. Indeed, the data in Fig. 1 suggest that levels of smoking on Thursdays were often between that of weekdays (Sunday to Wednesday) and weekends (Friday and Saturday). Accordingly, our analysis included 3 repeated measures (weekday, Thursday, and weekend) for each of 35weeks resulting in a total of 105 repeated measures of smoking. A binomial representation of the smoking outcome was employed in which a weighting variable, representing the number of days incorporated into each weekly period (4 for weekday, 1 for Thursday,

7 2218 C.R. Colder et al. / Addictive Behaviors 31 (2006) and 2 for weekend), was used so that our outcome of interest was the proportion of smoking responses in each of the three weekly periods. For presenting results, we focus on the marginal (population-averaged) estimates because they indicate how the population of subjects is changing over time, and they are more directly interpretable vis-à-vis the marginal outcome data presented in Figs. 1 and 2. An advantage of random-effects models is that they allow inclusion of cases with missing outcomes across time (here smoking). Although attrition was low (4%), missing data occurred because students did not provide data for a given week, or they did not respond to the smoking questions. Rates of missing data across the 35weeks ranged from 2% to 26% with an average of 12%. Weeks with higher rates of missingness correspond to winter and spring breaks, times when students were off campus and likely had more limited internet access. Random-effects and marginal models accommodate missing data on the dependent variable under the assumption that the data are missing at random (MAR) and missing completely at random (MCAR), respectively. Given that the level of missing data was modest in our sample and not necessarily related to smoking (i.e., it was more related to school vacations), and because the two models provided similar conclusions, we felt that missingness did not undermine our results. Our models included orthogonal polynomial contrasts (linear, quadratic, and cubic), and several other terms. The weekly cycle of higher smoking on Thursdays and weekends was modeled by including two dummy coded variables that contrasted Thursday with weekday smoking, and weekend with weekday smoking. These dummy variables were crossed with the polynomial contrasts to form multiplicative interactions because we expected weekend, and perhaps Thursday smoking, to show a different trajectory than weekday smoking. At the start of the spring semester the trajectory was relatively flat (see Fig. 1). To model the difference between the fall and spring semester, we included a dummy coded semester variable that contrasted the spring and fall semesters, and crossed this dummy variable with the polynomial contrasts and the weekday contrasts to form 3-way multiplicative interaction terms. We also included dummy coded variables that corresponded to winter and spring break because, during these breaks, students were away from campus, which may have disrupted their routine smoking behavior. Finally, we included a dummy variable to indicate high party weekends as identified by our qualitative research. We expected that such weekends would result in higher than usual rates of smoking. We estimated a random-intercept model that included the variables and interaction terms described above. Probability of Smoking Observed Model Implied Fall semester ends Spring semester begins Spring break Week Fig. 2. Probability of weekday (Sunday to Wednesday), Thursday, and weekend (Friday and Saturday) smoking across 35weeks.

8 C.R. Colder et al. / Addictive Behaviors 31 (2006) Results of random-effects and marginal models The random-effects model showed that the random intercept variance was statistically significant (v 2 (495) = 23271, p b.01) and the reliability estimate was.97, suggesting significant individual differences in probability of smoking. Population-averaged estimates from the final trimmed model are presented in Table 2. As shown in Table 2, the polynomial contrast, semester, and weekday contrast variables entered into complex interaction terms making the first-order effects of these variables difficult to interpret. The significant 2- and 3-way interaction terms involving these variables suggest that the rate of change in probability of smoking was different for Thursdays and weekends (Friday and Saturday) relative to weekdays (Sunday to Wednesday), and that these effects varied across the fall and spring semester. Also evident from Table 2 is the higher rates of smoking on party weekends, and the lower rates of smoking during spring break. The winter break by weekend and winter break by Thursday interaction terms suggest that the difference between weekend and Thursday smoking relative to weekday smoking was smaller during winter break than when school was in session. To facilitate interpretation of these higherorder terms, the model implied and observed growth trajectories are presented in Fig. 2. The segment of the trajectory corresponding to the first four weeks of the fall semester is presented in Fig. 3 to clarify the weekly cycle of smoking. As indicated in Fig. 2, the probability of smoking declines over the course of Table 2 Population-averaged estimates from final random-effects logistic regression model Term Coefficient Robust standard error p-value Intercept Thursday vs. weekday b0.01 Weekend vs. weekday Linear Quadratic Cubic Party weekend b0.01 Winter break Spring break b0.01 Semester Winter breakweekend vs. weekday Spring break weekend vs. weekday Winter breakthursday vs. weekday b0.01 Spring break Thursday vs. weekday Linear semester Quadratic semester Cubic semester Thursday vs. weekday semester b0.01 Weekend vs. weekday semester b0.01 LinearThursday vs. weekday b0.01 Linearweekend vs. weekday b0.01 Quadratic Thursday vs. weekend b0.01 Quadraticweekend vs. weekday Cubic Thursday Cubic weekend vs. weekday Linear weekend vs. weekday semester b0.01

9 2220 C.R. Colder et al. / Addictive Behaviors 31 (2006) Probability of Smoking Observed Model Implied Weekly Period: Wkday Th Wkend Wkday Th Wkend Wkday Th Wkend Wkday Th Wkend Week Fig. 3. Probability of weekday (Wkday; Sunday to Wednesday), Thursday (Th), and weekend (Wkend; Friday and Saturday) smoking for the first 4weeks of the fall semester. the year, and most of this decline occurs during the fall semester. There is a weekly cycle such that the probability of smoking is higher during weekends, but this difference is less apparent during the spring semester. This pattern suggests that the probability of smoking during weekdays is low, and that smoking is most likely on weekends during the fall semester, but by spring semester the probability of weekend smoking has declined such that there is only a small difference in the likelihood of smoking on weekdays and weekends. 4. Discussion The goal of this study was to describe changes in college smoking during the course of the freshman year. Our data collection method allowed us to examine short-term cycles of cigarette use as well as long-term trajectories. Considerable variability in smoking was found, not only as a function of day of the week, but also over the course of the first year of college. A weekly cycle was observed, such that the probability of smoking was low during the week, and increased on weekends. Our interviews suggested that students made a clear qualitative distinction between weekday and weekend smoking, with the latter being characterized by parties, which often included smoking and drinking. This suggests that social factors may have a major impact on smoking among college students. These findings also suggest the importance of differentiating between weekday and weekend smoking when designing a study of college smoking. Asking students to aggregate their level of smoking over longer periods of time (e.g., past 30days) will obscure these important smoking patterns. Laid over the weekly cycle was an overall decline in smoking during the freshman year, with most of this decline occurring during the fall semester. Although both weekend and weekday smoking decreased, weekend smoking declined more rapidly, resulting in convergence of weekend and weekday smoking over the course of the study. Perhaps newly acquired independence and the relative absence of adult supervision in a new social context where substance use is normative increases the likelihood of experimentation in the early weeks of the freshman year. Indeed, during qualitative interviews students described the initial weeks of the academic year as a time of freedom and

10 C.R. Colder et al. / Addictive Behaviors 31 (2006) exploration, with academic demands increasing as the year progressed, thus reducing the amount of socializing and partying. Alternatively, the possibility of reactivity to the weekly assessments cannot be ruled out. The UPTERN survey may have served as an intervention of sorts, leading students to reflect on their substance use. It is also possible that students learned over the course of the year that the length of the survey was directly related to how much smoking they reported. This seems less likely to have influenced smoking self-reports, as students were instructed to provide monthly saliva samples, which presumably increased the accuracy of responding. However, collection of saliva samples did not begin until the middle of the study, about the time when smoking levels began to stabilize. Therefore, we cannot rule out the possibility that motivation to reduce survey length accounts for the observed decline in smoking over the freshman year. Not surprisingly, smoking was higher on weekends that students identified as high risk for substance use. While most college and university officials are well aware of when bparty weekendsq take place on campus, our findings suggest the need for further research on prevention and intervention strategies that target these high-risk periods. Policies that support campus organizations to sponsor substance-free activities as alternatives to traditional social events may buffer the impact of party weekends. Our qualitative interviews conducted with residence hall counselors suggest that students may also benefit from hearing substance abuse prevention messages that identify high-risk contexts and come from multiple sources, ranging from university administration to residence hall counselors to peer leaders. We also found that smoking was lower during winter and spring breaks. Although it is possible that internet access for completing the web-based surveys was more difficult while away from campus, our qualitative interviews suggest that typical smoking patterns may be disrupted during breaks for several reasons, including going home to additional adult supervision (many students reported that their parents did not know they smoked) and thus more limited opportunities for smoking. Similarly, some students described returning home to a boyfriend or girlfriend who was a non-smoker, thus dissuading them from smoking around this person. Finally, the low levels of smoking in our sample and frequent assessment of smoking presented some challenges because conventional data analytic strategies are inappropriate for such data. Fortunately, recent advances in software have provided alternatives to conventional random effects modeling that can accommodate non-normally distributed data. Our data analytic strategy provides an illustration of trajectory analysis for researchers with similar kinds of data, which are likely to become more common as studies increasingly utilize experienced sampling and web-based data collection methods. In conclusion, our data collection method, which included web-based surveys and qualitative interviews, provided a unique and valuable opportunity to examine temporal variability in college smoking at both a micro- and macro-level. A substantial number of college students engaged in weekend smoking during the fall of their freshman year. Although smoking declined over the course of the year, there was considerable variability in probability of smoking. These findings provide new insights into college smoking, and have implications for policy, intervention, and future directions for research. Acknowledgments This study was supported by the Tobacco Etiology Research Network (TERN) of The Robert Wood Johnson Foundation. TERN members include: Richard Clayton (network chair), David Abrams,

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