Predicting students' noncompliance with a smoke-free university campus policy

Preventive Medicine 114 (2018) 209–216

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Predicting students' noncompliance with a smoke-free university campus policy

T

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Marc T. Bravermana, , G. John Geldhofa, Lisa A. Hoogestegerb, Jessica A. Johnsonc a

School of Social and Behavioral Health Sciences, Oregon State University, Corvallis, OR 97331, USA Career Services, Linn-Benton Community College, Albany, OR 97321, USA c Southern Nevada Health District, Office of Chronic Disease Prevention and Health Promotion, 280 S. Decatur Blvd., Las Vegas, NV 89107, USA b

A R T I C LE I N FO

A B S T R A C T

Keywords: Universities smoke-free policy tobacco smoke pollution social norms tobacco products

The adoption of university campus smoke-free and tobacco-free policies has risen dramatically, but research on effective implementation is scant. Significant challenges exist regarding policy implementation, particularly enforcement. This study examined college students’ noncompliance with a recently implemented smoke-free campus policy at a public university. The sample included students who reported past-month smoking of tobacco or e-cigarettes in a 2013 web-based survey, 9 months after a smoke-free campus policy took effect. Ordinal logistic regression was used to examine predictors of students’ having smoked on campus since the policy began (n = 1055). Predictor variables included past-month use of cigarettes, e-cigarettes, smokeless tobacco, and noncigarette tobacco products, secondhand smoke (SHS) exposure, support for a smoke-free campus, tobacco-related social norms, use of strategies to deal with smoking urges, and other variables. In multivariate analysis, policy violation was positively associated with past-month use of cigarettes and non-cigarette combustible tobacco, SHS exposure on campus, living on campus, and use of nicotine gum/patches to handle urges. Violation was negatively associated with smoke-free campus support, age, estimates of student policy support and cigarette smoking, and self-reported absence of smoking urges. Results suggest that nicotine dependence may be an underlying influence on policy violation. Several recommendations are offered. First, upon policy adoption, campuses should ensure student smokers’ access to cessation support and assistance with dealing with nicotine cravings. Second, campus information campaigns should focus particularly on younger students and those living on campus. Third, campuses should establish strong anti-tobacco norms, monitor SHS exposure, and communicate levels of students’ policy support.

1. Introduction One significant trend in tobacco control within the past decade is the adoption by colleges and universities of policies making their campuses smoke-free or fully tobacco-free (American Nonsmokers' Rights Foundation, 2018; Bresnahan et al., 2016; Lee et al., 2010; Reindl et al., 2014; Sieben, 2011; Wang et al., 2018). In the United States, the American Nonsmokers’ Rights Foundation identified 2164 campuses with completely smoke-free policies as of April 2018, an increase of 345% from the 486 campuses identified in October 2010 (American Nonsmokers' Rights Foundation, 2018). Outside of the U.S., research studies on the implementation and impacts of smoke-free campus policies have appeared from universities in Australia (Burns et al., 2016; Jancey et al., 2014), Canada (Baillie et al., 2011; ProcterScherdtel and Collins, 2013; Wallar et al., 2013), China (Gong et al.,

2016), Japan (Ohmi et al., 2013), and the Middle East (Chaaya et al., 2013). Research on smoke-free policies has demonstrated their effectiveness in reducing smoking on these campuses. In surveys of student populations, smoke-free policies have been consistently associated with reductions in on-campus exposure to secondhand smoke (SHS; Bennett et al., 2017; Burns et al., 2016; Fallin et al., 2015; Lechner et al., 2012; Lupton and Townsend, 2015), and student smokers have reported reductions in their smoking levels following the adoption of smoke-free policies, in most cases (Lechner et al., 2012; Lupton and Townsend, 2015; Seo et al., 2011) though not all (Burns et al., 2016). However, as these studies demonstrate, smoke-free policies have not been able to eliminate smoking completely. Some campuses have supplemented the adoption of smoke-free policies with informational campaigns or other interventions, which have tended to increase policy effectiveness (Fallin

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Corresponding author at: School of Social and Behavioral Health Sciences, 105-G Ballard Hall, Oregon State University, Corvallis, OR 97331, USA. E-mail addresses: [email protected] (M.T. Braverman), [email protected] (G.J. Geldhof), [email protected] (L.A. Hoogesteger), [email protected] (J.A. Johnson). https://doi.org/10.1016/j.ypmed.2018.07.002 Received 19 January 2018; Received in revised form 29 June 2018; Accepted 2 July 2018 Available online 03 July 2018 0091-7435/ © 2018 Elsevier Inc. All rights reserved.

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2. Methods

et al., 2013; Hahn et al., 2012; Harris et al., 2009; Ickes et al., 2015; Lechner et al., 2012). The widespread adoption of smoke-free campus policies has been accompanied by a growing recognition of significant challenges in their implementation. Interview studies have highlighted concerns about inadequate policy communication, the safety of smokers who regularly leave campus to smoke, and the concentration of smoking at campus boundaries due to smokers' attempts to comply (Baillie et al., 2009, 2011; Burns et al., 2014; Procter-Scherdtel and Collins, 2013). The most daunting issue is the difficulty of policy enforcement. Enforcement strategies are often vague and ineffective, and at some campuses, students, faculty and administrators have expressed frustration with the apparent lack of sanctions for violators (Baillie et al., 2009, 2011; Fallin-Bennett et al., 2017; Fennell, 2012). Several approaches have been used to study the extent of compliance with partial or comprehensive campus smoking policies. The collection of smoking refuse and the direct observation of campus smoking behavior have been used to compare campuses with different kinds of policies (Fallin et al., 2012; Lee et al., 2013), to estimate changes in compliance over time (Fallin et al., 2013; Harris et al., 2009; Ickes et al., 2015), and to identify “hotspots” where smoking violations occur (Jancey et al., 2014; Pires et al., 2016; Seitz et al., 2012). Campus compliance levels have also been addressed through surveys of the student population that inquire about exposure to SHS on campus, before and after policy implementation (Burns et al., 2016; Gong et al., 2016; Lechner et al., 2012). In a handful of studies, student surveys have asked smokers directly whether they have violated the policy (Chaaya et al., 2013; Ohmi et al., 2013). However, beyond establishing the extent of policy noncompliance and the continued presence of SHS on campuses that ostensibly ban all smoking, there has been little research that attempts to understand the reasons for policy noncompliance or to identify its correlates. An understanding of patterns of policy violation can have significant value for campus administrators in developing enforcement strategies, informing design and delivery of interventions and communication campaigns, and, ultimately, ensuring the success of campus-wide tobacco control policies. The present study aimed to shed light on students’ policy compliance by identifying correlates of violation by student smokers at a large public university in the Pacific Northwest—Oregon State University—that had recently implemented a comprehensive smokefree policy. In attempting to understand the characteristics of students who reported having violated the policy, several dimensions were considered to be of theoretical interest based on prior research studies or anecdotal reports, including tobacco use (in various forms), exposure to SHS, students’ own attitudes toward smoke-free policies, their perceptions of tobacco-related campus norms, their strategies for dealing with cravings, and other variables. The policy, which took effect in September 2012, prohibits smoking of all combustible tobacco products, including e-cigarettes, in all indoor and outdoor spaces on campus, including vehicles. A variety of supporting activities accompanied the policy's implementation, guided by a smoke-free campus task force convened the previous year. These activities included generation of a campus communications and marketing plan; promotion of the policy in campus tours, orientations, and summer registration activities for parents and students; and communication to campus visitors, contractual workers, new hires, Athletics department contacts, and other audiences. Signage was placed at campus entry points, building entrances, and parking lots, and Student Health Services provided cessation support, including nicotine replacement therapy (NRT), for students.

2.1. Setting and participants In May–June 2013, students, faculty and staff were invited to participate in a web-based survey that assessed the campus community's adaptation and reactions to the new smoke-free policy. The survey addressed policy support, tobacco use prevalence, tobacco use norms, compliance by smokers, and other variables. Invitations for the survey were sent via e-mail to all students registered for an on-campus course of one credit or more or enrolled in the international study program (n = 22,141). After the original survey request plus three reminders spaced one week apart, complete responses were received from 5691 students (26% response rate). The study was approved by the university’s Institutional Review Board. Further details on the survey procedure, as well as analyses involving the full student sample (tobacco users and non-users), have been described previously (Braverman et al., 2015). Comparing the survey sample with the demographic profile of the student body during the spring 2013 term, the survey respondents identified themselves as 53.5% male, 45.5% female, and 0.9% other, compared to 52.8% male and 47.2% female reported for the full student population. With regard to age, 72.0% of the sample was below 25, compared to 71.1% of the student population (using the cutpoint reported by the university). The sample consisted of 78.6% undergraduates, 13.6% graduates, and 7.8% non-degree student status, compared to 82.8%, 14.9%, and 2.4%, respectively, for the campus population. With regard to race, 78.6% of the sample was white, compared to 82.8% of the population. Finally, 6.8% of the sample consisted of international students, compared to 9.8% of the population. For the present analysis focusing on behavior of student smokers, we included only those respondents from the full sample who reported any use within the past month of cigarettes, other combustible tobacco products, or e-cigarettes. From this group we omitted four participants because of inappropriate responses that raised concerns about data validity, and 11 participants who provided inconsistent smoking information (e.g., reported having never smoked while also reporting daily cigarette use). Removal of these cases resulted in a sample of 1182 students. 2.2. Measures 2.2.1. Frequency of violation of the smoke-free campus policy This was the outcome of primary theoretical interest. Students were asked, “Have you smoked on campus since the smoke-free campus policy went into effect in September 2012?” Five original response options were collapsed into a three-level ordinal variable consisting of: not at all, once or a few times, and many times. 2.2.2. Past-month tobacco use Students were asked the number of days within the past 30 they had used four types of tobacco product: cigarettes; smokeless tobacco; ecigarettes; or combustible “tobacco products other than standard cigarettes, such as cigars, pipes, hookahs, bidis, or clove cigarettes.” (Smokeless tobacco use, although one of the survey's tobacco-related behaviors, was not a criterion for sample selection because its use on campus did not constitute violation of the smoke-free policy.) For each product category, seven original response categories were combined into four: 0 days, 1–5 days, 6–19 days; 20 or more days. In addition, we created a dichotomous variable, polytobacco use, that indicated whether students had used more than one of the four types of tobacco product during the month, coded as 1 (more than one product) or 0 (only one product). Students who reported smoking cigarettes were also asked how many cigarettes they usually smoke on cigarette-smoking days, with six potential response categories. The wording and response categories for past-month tobacco use questions were adapted from the 210

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smoke”) or an unrelated response. A variable was created, Absence of smoking urges, to identify respondents who spontaneously commented that they did not use or need a strategy to handle cravings while on campus. Cases were coded 1 if such clear statements were present, and 0 otherwise.

Youth Risk Behavior Survey (Centers for Disease Control and Prevention, 2017). 2.2.3. Exposure to secondhand smoke We measured exposure to SHS within the past two weeks using two variables. The first, exposure on campus, was a combination of two items, “at an entrance to a campus building,” and “elsewhere on campus.” Seven original response options for these items were combined into three (not at all, once or twice, 3 times or more) and the higher response from the two original items was retained. The location for the second exposure variable was “near the campus boundary, including just outside campus,” with responses being condensed into the same three options. Exposure at the campus perimeter was treated separately because it does not necessarily represent policy violation, and indeed can be exacerbated by smoke-free campus policies (Braverman et al., 2015; Baillie et al., 2009).

2.4. Data analysis strategy Due to the noncontinuous nature of the 3-level dependent variable, frequency of policy violation, we analyzed the data using ordinal logistic regression with a proportional odds assumption. The ordered logistic regression uses maximum likelihood estimation but deletes cases listwise. Ten percent of the cases (n = 118) contained missing data on one or more variables. In addition, the “Other” option on gender was selected by only 9 students with complete data, none of whom appeared in the dependent variable’s middle category (smoked on campus once or a few times). This resulted in empty cells and computational estimation problems, and thus those cases were eliminated from the regression analysis, producing a final sample of 1055 cases for the analysis. Data were analyzed using SAS 9.4. Prior to fitting the target model, we examined the distribution of key predictors and noted the highly uneven distribution of the 3-level policy awareness variable, with 91% (n = 957) of students reporting they were fully aware of the policy and only 2% (n = 21) reporting they were unaware. In addition, the bivariate association between policy awareness and violation was far from statistical significance (χ2 (4) = 3.14, Monte Carlo p = .52). We therefore omitted the awareness variable from all regression models. Number of cigarettes smoked per day was also omitted from the regression models because it was missing for those students who smoked only non-cigarette products (37% of the sample). We next examined collinearity between predictors using ordinary least squares regression and determined that no collinearity existed. Last, we fitted a binary logistic regression (never vs. ever violated the policy) that allowed us to examine deviance residuals, leverage, and dfbetas in order to detect potential outliers. These statistics did not indicate any extreme outliers, and removing possible moderate outliers (e.g., producing a dfbeta less than 0.50) did not impact the overall pattern of results in our final models. We therefore did not omit any outliers.

2.2.4. Awareness of the policy Students were asked, “Before taking this survey, were you aware that the OSU campus is 100% smoke-free?” Response options (abbreviated here) were: not aware, somewhat aware but not sure about the details, and aware. 2.2.5. Support for a smoke-free campus Measured with the item, “Our campus should be 100% smoke-free.” Response options ranged from 1 (strongly disagree) to 7 (strongly agree). 2.2.6. Smoking-related perceptions and norms Three variables addressed students' perceptions related to smokingrelated norms. They were asked to estimate, from 0% to 100%, the percentages of their student peers who support the smoke-free campus policy, who smoke cigarettes, and who use tobacco products other than cigarettes. 2.2.7. Campus life variables Respondents were asked whether they lived on or off campus, and whether they belonged to a fraternity/sorority. 2.2.8. Demographics These included gender (male, female, other), age, race, and status as an international or domestic student. For race, six original categories were combined into two (white, nonwhite) because 74% of the sample was white.

3. Results

2.2.9. Handling cravings Students were asked, “How do you handle the urge or desire to smoke when you are on campus?” They could select one or more of several response options: “I use nicotine gum or patches,” “I use smokeless tobacco products or e-cigarettes,” “I go off campus to smoke,” “I take a walk or engage in physical activity,” “I distract myself in other ways,” and “Other” (with space to write a comment). Each of these variables was coded 1 if checked and 0 if unchecked.

3.1. Descriptive information and bivariate analyses Table 1 presents descriptive information on the sample and the variables in the analysis. Cell counts are presented for the categorical variables; means and standard deviations are presented for the pseudocontinuous variables. Nearly 30% (n = 336) reported having violated the policy at least “once or a few times,” including 7% (n = 80) who reported “many times or more.” More students reported using non-cigarette combustible products than cigarettes (64.2% vs. 60.2%). Almost 40% reported using multiple tobacco products. About 40% of the sample reported exposure to SHS on campus within the past two weeks, and 76.1% reported exposure at the campus perimeter, possibly due to smokers complying with the policy by going off-campus. Table 2 presents bivariate associations of the predictor variables with policy violation; significant bivariate associations were found for use of cigarettes, e-cigarettes and smokeless tobacco, polytobacco use, support for smoke-free campus, estimates of other students’ policy support and cigarette smoking, age, gender, campus residence, and use of several strategies for handling smoking urges including nicotine gum/patches, smokeless tobacco/e-cigarettes, leaving campus to smoke, and self-reported absence of smoking urges.

2.3. Coding of qualitative responses In describing how they handle the “urge or desire to smoke” while on campus, the “Other” option was selected by 474 students, of whom 444 added an explanatory comment. Many of these comments appeared to indicate that the respondent used no strategy, did not have urges, and/or was able to abstain without problem. To examine this more formally and incorporate it into the analysis, three members of the research team independently coded all of the comments, judging each one as either a clear instance of “No strategy needed” (examples: “I suck it up and don't smoke”; “Wait until I get home, it’s not that urgent”), or alternatively, an ambiguous response (examples: “Nothing”; “I don't 211

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Table 1 Descriptive statistics for the student sample (n = 1182, Spring 2013). Variable

na

Smoke-free policy violation Not at all Once or a few times Many times or more

%

na

Variable

%

Categorical variables

Pseudo-continuous variables Age Respondent's support for a smoke-free campusd Respondent's estimate of the percent of students who... ...Support the smoke-free campus policy ...Smoke cigarettes ...Smoke non-cigarette tobacco products

Violation of campus policy (smoked on campus) na

Residence location

813 70.8 Lives on campus 256 22.3 Lives off campus 80 7.0 Fraternity/sorority membership Cigarette use, past month Yes 0 days 418 39.8 No 1–5 days 312 29.4 International status 6–19 days 109 10.6 International 20 or more days 210 20.2 Domestic U.S. Non-cigarette combustible SHS exposure on campus, tobacco use, past month past 2 weeksb 0 days 422 35.8 None 1–5 days 616 52.2 1–2 times 6–19 days 108 9.2 3 or more times 20 or more days 33 2.8 SHS exposure at campus E-cigarette use, past month perimeter, past 2 weeksb 0 days 1007 85.2 None 1–5 days 118 10.0 1–2 times 6–19 days 36 3.0 3 or more times 20 or more days 16 1.4 Strategies for dealing with Smokeless tobacco use, nicotine urges or cravings past month Uses nicotine gum/patches 0 days 979 83.1 Yes 1–5 days 90 7.6 No 6–19 days 45 3.8 Uses smokeless tobacco 20 or more days 64 5.4 or e-cigarettes Polytobacco use Yes Yes 467 39.7 No No 709 60.3 Goes off campus to smoke Awareness of policy Yes Fully aware 1071 90.7 No Somewhat aware 86 7.3 Walk or physical activity Not aware 24 2.0 Yes Gender No Female 373 31.6 Other distraction Male 751 63.5 Yes Other 10 0.8 No Race Absence of smoking urges White 879 74.4 No urges Nonwhite 303 25.6 Did not comment on this

c

Table 2 Bivariate associations of predictors with policy violation (Spring 2013).

Mean

Not at all (%)

190 16.8 944 83.2 Cigarette use, past month 0 days 1–5 days 6–19 days 20 or more days Non-cigarette combustible tobacco use, past month 0 days 1–5 days 6–19 days 20 or more days E-cigarette use, past month 0 days 1–5 days 6–19 days 20 or more days Smokeless tobacco use, past month 0 days 1–5 days 6–19 days 20 or more days Polytobacco use Yes No Awareness of policy Fully aware Somewhat aware Not aware SHS exposure on campus, past 2 weeks None 1–2 times 3 or more times SHS exposure at campus perimeter, past 2 weeks None 1–2 times 3 or more times Residence location Lives on campus Lives off campus Fraternity/sorority membership Yes No International status International Domestic U.S. Gender Female Male Other Race White Nonwhite Strategies for dealing with nicotine urges or cravings Uses nicotine gum/ patches Yes No Uses smokeless tobacco or e-cigarettes Yes No

167 14.8 964 85.2 67 5.9 1068 94.1

631 53.9 305 26.1 234 20.0

281 23.9 305 26.0 588 50.1

21 1.8 1161 98.2

81 6.9 1101 93.1 358 30.3 824 69.7 103 8.7 1079 91.3 168 14.2 1014 85.8 270 22.8 912 77.2

S.D.

23.04 3.49

5.50 2.33

58.19 29.27 38.66

23.84 16.43 23.47

a

N's for each variable vary slightly because of missing data. SHS = Secondhand smoke. c Age was positively skewed (minimum = 18, maximum = 60, median = 21.0). d Support for smoke-free campus measured as 1 (Strongly disagree) to 7 (Strongly agree). b

3.2. Ordinal logistic regression Table 3 presents each predictor's overall statistical significance and Table 4 presents odds ratios for all statistically significant predictors. A test of the proportional odds assumption for the ordinal regression model supported that assumption (χ2 (33) = 45.13, p = .08), confirming that odds ratios can be considered equal when moving between any two adjacent levels of the outcome variable, and, thus, justifying the calculation of a single odds ratio for each predictor. All respondents in the regression analysis reported use of either cigarettes or non-cigarette combustible products within the past month. For the cigarette smoking frequency variable, the second level

Many times (%)

Once or a few times (%)

pb

448 341 123 235

89.5 % 72.1 53.7 42.1

9.2 % 23.5 33.3 40.0

1.3 % 4.4 13.0 17.9

< 0.001

403 605 107 33

65.5 77.9 63.6 30.3

27.5 17.4 27.1 30.3

6.9 4.8 9.3 39.4

0.896

992 107 35 12

73.6 57.9 40.0 50.0

20.7 30.8 40.0 25.0

5.7 11.2 20.0 25.0

< 0.001

952 87 44 63

72.9 59.8 63.6 60.3

21.2 27.6 25.0 28.6

5.9 12.6 11.4 11.4

0.001

461 685

58.1 79.4

30.2 16.9

11.7 3.6

< 0.001

1041 83 24

70.6 73.5 66.7

22.5 21.7 16.7

6.9 4.8 16.7

0.843

616 297 227

71.6 78.5 59.5

21.9 17.5 29.1

6.5 4.0 11.5

0.061

272 298 573

63.6 80.5 69.5

25.0 17.1 23.4

11.4 2.3 7.2

0.597

187 933

58.8 73.0

25.1 22.0

16.0 5.0

< 0.001

167 950

71.3 70.5

19.8 22.9

9.0 6.5

0.388

67 1054

61.2 71.3

32.8 21.8

6.0 6.9

0.112

366 744 10

77.9 67.3 50.0

18.3 24.7 0.0

3.8 7.9 50.0

< 0.001

870 279

72.0 67.0

21.6 24.4

6.4 8.6

0.238

21 1128

42.9 71.3

33.3 22.1

23.8 6.6

0.002

80 1069

55.0 71.9

37.5 21.1

7.5 6.9

0.002

(continued on next page)

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Table 2 (continued) Categorical variables

Table 3 Significance of individual predictor variables in ordinal logistic regression, predicting students' frequency of smoke-free policy violation (n = 1055, Spring 2013).

Violation of campus policy (smoked on campus) na

Not at all (%)

Once or a few times (%)

pb

Many times (%)

Wald χ2

Predictor variable a

Goes off campus to smoke Yes No Walk or physical activity Yes No Other distraction Yes No Absence of smoking urges No urges Did not comment on this

Pseudo-continuous variables

Age Support for smoke-free campusc Estimate of the percent of students who... ...Support the smoke-free campus policy ...Smoke cigarettes ...Smoke non-cigarette tobacco products

357 792

52.1 79.2

38.1 15.2

9.8 5.7

< 0.001

103 1046

74.8 70.4

17.5 22.8

7.8 6.9

0.465

168 981

63.7 72.0

28.6 21.2

7.7 6.8

0.080

268 881

84.7 66.5

14.2 24.7

1.1 8.7

< 0.001

Cigarette use, past month Non-cigarette combustible tobacco use, past montha E-cigarette use, past montha Smokeless tobacco use, past montha Polytobacco use SHS exposure on campus, past 2 weeksb SHS exposure at campus perimeter, past 2 weeksb Support for a smoke-free campus Estimated percent of students who support the smokefree campus policy Estimated percent of students who smoke cigarettes Estimated percent of students who smoke non-cigarette tobacco products Residence location Fraternity/sorority membership International status Age Gender Race Strategies for dealing with smoking urges: Uses nicotine gum/patches Uses smokeless tobacco/e-cigarettes Goes off campus to smoke Walk or physical activity Other distraction Absence of smoking urges

Violation of campus policy (smoked on campus) Not at all (mean)

Once or a few times (mean)

Many times (mean)

pb

23.03 4.11

23.15 2.21

22.37 1.30

0.541 < 0.001

63.59

49.07

33.14

< 0.001

28.44 38.93

29.88 36.99

35.43 43.51

0.001 0.094

df

p

66.42 18.30 3.27 2.18 0.78 7.82 5.82 32.30 16.50

3 3 3 3 1 2 2 1 1

< 0.001 < 0.001 0.35 0.54 0.38 0.02 0.054 < 0.001 < 0.001

4.09 1.11

1 1

20.57 0.00 3.13 9.09 1.23 0.16

1 1 1 1 1 1

< 0.001 0.96 0.08 0.003 0.27 0.69

9.10 0.06 1.10 0.00 0.05 15.48

1 1 1 1 1 1

0.003 0.81 0.29 0.96 0.83 < 0.001

0.04 0.29

a Past-month tobacco product use refers to the number of days in which those products were used. b SHS = secondhand smoke.

0–100 for the percentage estimates) and the odds ratios pertain only to a one-unit change.

a

N's for each contingency table vary slightly because of missing data. Tests of association: Chi-square (χ2) for nominal variables: Polytobacco use, Gender, Race, Residence location, Fraternity/sorority membership, International status, Strategies for dealing with nicotine urges. Gamma for ordinal variables: Cigarette use, Non-cigarette combustible tobacco use, E-cigarette use, SLT use, Awareness, SHS exposure at campus locations, SHS exposure at campus perimeter. Analysis of variance for pseudo-continuous variables. c Support for smoke-free campus measured as 1 (Strongly disagree) to 7 (Strongly agree). b

3.3. Distinctions between bivariate and multivariate analyses Several variables were significantly associated with policy violation in bivariate analyses but not when other variables were controlled in the full model, including gender, frequency of use of smokeless tobacco and e-cigarettes, polytobacco use, and several of the urge strategy variables. By contrast, SHS exposure on campus, non-cigarette combustible tobacco use, and age were significant predictors only in the multivariate model. International student status, race, and fraternity/ sorority membership were not associated with compliance in either bivariate or multivariate analyses.

(1–5 days in the past 30) rather than the lowest level (0 days) was selected as the referent category for computing odds ratios. This referent category was judged to provide a more meaningful comparison for the relationship with policy violation (i.e., heavier vs. lighter cigarette smoking, rather than heavier vs. no cigarette smoking), because violation by a non-cigarette smoker, using an alternative product, would probably be relatively rare. As Table 4 shows, the regression analysis found that student smokers who were more likely to violate the policy were those who smoked cigarettes and other combustible tobacco products with greater frequency, were exposed to SHS on campus, were younger, lived on campus, and used nicotine gum/patches to handle cravings. Students who were less likely to violate were those who supported a smoke-free campus, estimated higher levels of policy support from other students, estimated higher levels of other students' cigarette smoking, and reported that they did not have smoking urges. Many of the effect sizes were in the moderate to large range, e.g., odds ratios of 2.74 and 4.68, respectively, for the positive effects of living on campus and use of nicotine gum/patches, and 0.37 for the negative effect of absence of smoking urges. The negative effects for age, estimated percent of students supporting a smoke-free campus, and estimated percent of student cigarette smokers appear to be weaker (odds ratios of 0.95, 0.98, and 0.99, respectively), but these small absolute sizes are misleading because the variables have substantial ranges (18–60 for age,

4. Discussion 4.1. Policy violation and nicotine dependence The strongest predictor of students' noncompliance with the smokefree campus policy was past-month cigarette usage. The likelihood of policy violation increased with each successive level of number of days smoked. This may be a direct reflection of opportunity, since more frequent smoking produces more opportunities for policy violation, but it must also be considered that heavier smokers probably have a stronger level of nicotine dependence, compelling them to seek immediate practical outlets for their frequent smoking. This interpretation is supported by the high correlation of cigarette smoking frequency with number of cigarettes smoked per day (r = 0.505, n = 742, p < .001), which is a component of numerous nicotine dependence scales (Chabrol et al., 2005; Fagan et al., 2015; National Cancer Institute, 2012). Several other findings from the model bolster the interpretation that nicotine dependence influenced frequency of policy violation. First, 213

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is consistent with previous studies (Lazarus et al., 2009; Parks et al., 2009), which involved other populations and/or other types of policies. To our knowledge, the present study is the first that addresses this relationship specifically with regard to students’ smoking behavior on smoke-free campuses. These findings underscore the importance of offering cessation programs and resources for smokers when introducing a smoke-free or tobacco-free policy.

Table 4 Ordinal regression parameter estimates for significant predictors of students' frequency of smoke-free policy violation (n = 1055, Spring 2013). Predictor variablea

Cigarette use, past monthb 0 days 1–5 days 6–19 days 20 or more days Non-cigarette combustible tobacco use, past monthb 0 days 1–5 days 6–19 days 20 or more days SHS exposure on campus, past 2 weeks No exposure Once or twice 3 or more times Support for a smoke-free campus Estimated percent of students who... ...Support the smoke-free policy ...Smoke cigarettes Residence location Lives off campus Lives on campus Age Uses nicotine gum/patches No Yes Absence of smoking urges No (did not comment) Yes (absence of urges)

n

Odds ratio

95% C.I. Boundaries Lower

Upper

0.09

0.34

1.01 2.08

2.80 5.51

418 313 113 211

0.18 Ref 1.68 3.39

370 557 101 27

Ref 2.18 1.86 7.68

1.24 0.93 2.95

3.84 3.73 20.03

573 279 203

Ref 1.24 1.94 0.73

0.80 1.22 0.65

1.91 3.09 0.81

0.98 0.99

0.98 0.98

0.99 1.00

880 175

Ref 2.74 0.95

1.77 0.92

4.24 0.98

1037 18

Ref 4.68

1.72

12.78

799 256

Ref 0.37

0.23

0.61

4.2. Recommendations Our findings suggest several practical recommendations for promoting compliance with smoke-free campus policies. First, upon adoption of a policy, campuses should communicate strategies to assist smokers in dealing with nicotine cravings, and they should ensure that effective programs and adequate resources exist to provide cessation support for smokers. Although cessation support as an adjunct to policy implementation is recommended by the American College Health Association (2012), a review of 162 campuses with tobacco-free policies found that only 54% provide evidence-based cessation support services (Plaspohl et al., 2012). Further, with respect to non-addicted smokers, these results highlight the significant potential of smoke-free campus policies to interrupt the transition from social to addicted smoking, by blocking opportunities for frequent smoking during students’ daily routines. Second, given the findings that both younger students and those living on campus have a greater tendency to violate the policy, informational campaigns should devote particular attention to reaching and engaging these groups, e.g., through a focus on residence halls and residential life areas. It is not surprising that campus residents are more likely to smoke on campus, since they do not have a regular part of their daily routine in which they are off-campus. Nevertheless, it is important to establish evidence for this association between on-campus residence and policy violation, as a stimulus for focusing communication efforts. Third, given that students’ policy support and their estimates of other students’ support were associated with noncompliance, campus officials should strive to establish strong anti-tobacco norms on campus (Mead et al., 2014), and should monitor and communicate levels of students’ policy support. Similarly, they should place priority on monitoring and minimizing SHS exposure of students and other campus personnel. Following up from these findings, future research should examine the potential impacts that comprehensive campus tobacco policies may have on students' nicotine dependence and their patterns of using cigarettes and other tobacco products. Given the critical importance of the college years with respect to smoking transitions (Klein et al., 2013; White et al., 2009), policies that deter daily smoking can be pivotal in preventing tobacco dependence and, potentially, turning intermittent smokers into nonsmokers.

a

Support for a smoke-free campus, Age, and the two Estimated percent variables were entered in the model as continuous variables. All other variables are categorical. b All respondents reported past-month use of either cigarettes or non-cigarette combustible tobacco products.

students' self-generated written comments that they did not have smoking urges strongly predicted compliance. While it cannot be assumed that smokers' assessments of the strength of their nicotine dependence are entirely accurate and reliable, these spontaneous comments suggest that a large proportion of these student smokers, many of whom described themselves as social smokers who only smoked at weekend events, were, as yet, non-addicted. It is noteworthy that the absence of smoking urges variable is not simply a proxy for infrequent smoking, since cigarette smoking frequency was included in the model and the significant associations of these two variables with noncompliance were statistically independent. Second, students who had utilized nicotine replacement products—either gum or patches—were significantly more, rather than less, likely to have violated the smoke-free policy. We note that the number of students reporting use of NRT was small (18 in the regression model; Table 4), but the odds ratio for this variable, 4.68, was one of the strongest in the model. The use of NRT suggests a desire to curtail one's smoking, and the availability of these products provides an avenue to comply with smoke-free regulations. Therefore one might expect that NRT use would be negatively associated with violations. The surprising finding of a positive association suggests that these students smoked on campus even though they may have been trying to reduce their smoking. Thus, their violations may have been driven in part by dependence. However, the small number of students in this category suggests that additional research is needed to replicate this effect. The potential influence of nicotine dependence on policy violations

4.3. Study limitations Several limitations should be noted. First, students might not have been completely forthcoming or accurate in their responses, despite the anonymity of the response format; however, several past studies have found that bias due to social desirability and/or other factors is relatively low with self-administered tobacco use questionnaires (e.g., Crutzen and Göritz, 2010; Messeri et al., 2007; Molina et al., 2010; Wong et al., 2012). Second, this study took place on a single campus. Third, the cross-sectional nature of the study does not allow for identification of the causal mechanisms underlying the associations between predictors and the outcome variable. Fourth, the absence of smoking urges variable reflects a coding of qualitative responses. Because it was unanticipated, we did not ask participants directly about the urges they experienced, which would have resulted in a more comprehensive assessment. Given that this variable was a strong predictor of policy compliance despite the indirect measurement approach, it should be explored in future studies using more direct strategies. 214

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Finally, the survey's 26% response rate introduces the possibility of nonresponse bias. However, this response rate is comparable to, or even higher than, numerous other web-based surveys of college students, even in cases where response incentives are offered (Berg et al., 2011; Burns et al., 2013, 2016; Garg et al., 2011; Latimer et al., 2014; McDonald et al., 2017; Primack et al., 2013; Reed et al., 2007; Wolfson et al., 2009). The National College Health Assessment reported a mean response rate of 18% among participating campuses in 2013 and 19% in 2017 (American College Health Association, 2013, 2017). Braverman et al. (2015) provide a fuller discussion. The close match in demographics between the survey sample and the campus population suggests it is unlikely that the findings were impacted to a meaningful degree by nonresponse bias due to demographics. It is possible, however, that bias existed due to nonresponse patterns related to smoking status.

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