SEARCH

SEARCH BY CITATION

Keywords:

  • Blue-collar workers;
  • menthol;
  • quitting behaviors;
  • service industry workers;
  • smoking

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. Conflicts of interest
  9. References

Aim  This exploratory study sought to examine the relationships among occupational status, menthol smoking preference and employer-sponsored smoking cessation programs and policies on quitting behaviors.

Design  Data for this cross-sectional study were obtained from the 2006 Tobacco Use Supplement to the Current Population Survey (TUS CPS), a large national survey representative of the civilian population, containing approximately 240 000 respondents. The total sample for the current study was 30 176.

Measurements  The TUS CPS regularly collects data on cigarette prevalence, quitting behaviors, smoking history and consumption patterns. We performed a logistic regression with ‘life-time quitting smoking for 1 day or longer because they were trying to quit’ as outcome variable. Independent variables included type of occupation, employer-sponsored cessation programs and policies and menthol status.

Findings  When controlling for occupational status and work-place policies, there were no differences for menthol versus non-menthol smokers on quitting behaviors [odds ratio (OR) = 0.98; 95% confidence interval (CI) = 0.83, 1.15]. Service workers were less likely to quit compared with white-collar workers (OR = 0.80; 95% CI = 0.69, 0.94), and those with no employer-sponsored cessation program were less likely to quit (OR = 0.70; 95% CI = 0.60, 0.83). White-collar workers, compared with blue-collar and service workers, were more likely to have a smoking policy in the work area (93% versus 86% versus 88%, respectively).

Conclusions  When occupational status and work-place smoking policies are controlled for, smokers of menthol cigarettes in the United States appear to have similar self-reported life-time rates of attempts to stop smoking to non-menthol smokers.


INTRODUCTION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. Conflicts of interest
  9. References

Smoking, as an occupational hazard, has been studied extensively since the 1970s [1–9]. Overall, blue-collar workers, those who typically work in occupations such as construction, maintenance and assembly lines, have a higher prevalence of smoking. Moreover, blue-collar workers have experienced less of a decline in smoking prevalence, and are more likely to work in settings with job-related exposure to environmental hazards compared with white-collar workers [1–8]. Service workers are more likely to smoke than white-collar workers, and they are often employed in settings such as bars, restaurants and casinos, with some of the highest rates of second-hand smoke exposure [9,10]. By contrast, white-collar workers, those who work in retail, office administration or are professionals, have experienced the greatest overall decline in smoking, have limited or no work-related exposures to second-hand smoke and are more likely to be influenced positively by anti-smoking messages [9–11].

Menthol smoking may also be an important part of the equation when examining occupational hazards for blue-collar, service workers and white-collar workers. Menthol smoking has been linked to differential risk of lung cancer in African Americans [12], masking symptoms of respiratory disease [13] and increased nicotine dependence [14]. Menthol smoking is also disproportionately prevalent among females [15], low-income and low socio-economic status (SES) populations [16,17]. Additionally, menthol is the only flavored cigarette that benefits, in the way of loyal and new users, from extensive targeted marketing to attract youth, women and minorities to make menthol their brand of choice [18–25].

To date, the link between menthol smoking and occupational status has not been studied extensively. Given the well-established socio-economic differences, including race, education and gender among white-collar, blue-collar and service workers in the United States [1–9], we sought to examine the relationship of menthol smoking, especially quitting, in conjunction with other work-place variables of interest. In order to understand specific differences in risk exposure for menthol and non-menthol smokers we examined whether or not blue-collar and service workers are protected by tobacco work-place policies and work-place smoking cessation programs at the same level as their white-collar counterparts [9,10]. Work-place smoking bans are important because they limit exposure to the environmental hazards of second-hand smoke [10,11], and bans have been found to change social norms and attitudes about smoking as well as influencing others' intentions to quit [10].

It is equally important to examine white-collar, blue-collar and service industry workers' access to work-place smoking cessation programs. Employer-sponsored smoking cessation programs have been found to be important because of the health protection benefit to the worker [10] and the reduced costs to the employer, such as less utilization of benefits for tobacco-related illnesses and clean-up associated with removal of cigarette butts [7].

It is plausible to suggest that the higher rates of mentholated cigarette preference among specific populations detailed in this introduction and the over-representation of these subpopulations in the blue-collar and service industry [26] will result in a potential difference between menthol and non-menthol smokers' success with quitting. Specifically, we expect that menthol smokers will experience fewer quit attempts, have less access to smoking cessation programs at work and be less likely to work where there are smoking bans. Therefore, we analyzed population-level data to understand the influence of occupational status, menthol status and work-place policies and restrictions on quitting behaviors.

METHODS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. Conflicts of interest
  9. References

The data for this analysis were obtained from the 2006/07 Tobacco Use Supplement to the Current Population Survey (TUS CPS). The TUS CPS is a survey of tobacco use that has been administered as part of the US Census Bureau's CPS survey. The TUS CPS survey has been co-sponsored by the National Cancer Institute (NCI) and the Centers for Disease Control and Prevention (CDC) since 2001. TUS CPS is a large, nationally representative sample that contains approximately 240 000 respondents, aged 15 years and older, from the civilian, non-institutionalized population. It collects data periodically on the prevalence of cigarette smoking, cigar, pipe, chewing tobacco and snuff use, smoking history and cigarette consumption patterns (current and past). It also collects data on smoking quit attempts, intentions to quit, medical and dental advice to quit, home and work-place smoking rules/policies, attitudes towards smoking in public places, attitudes towards advertising and promotion of tobacco and opinions about the degree of youth access to tobacco in the community.

Because surveys in previous years did not include the question about work-place cessation programs, we used only the 2006/07 data.

Study sample

In the 2006/07 TUS CPS data set, there was a total of 172 023 self-respondents. Respondents eligible for inclusion in this analysis were TUS CPS current smokers (every day or some days) aged 18 years or older. There was a total of 31 501 eligible self-respondents. Of these, 1325 were excluded due to missing information on cigarette brand type (non-menthol or menthol). The total sample size was 30 176 respondents.

Demographic characteristics

Demographic characteristics used in the analysis were age (years), education (<high school, high school, some college and college +), marital status (married, divorce/widowed/separated and never married), race/ethnicity (black and non-black), sex, region (Northeast, Midwest, South and West) and smoking status (every day and some days). All variables except age were defined as categorical in the models.

Occupation status

The variable occupation status was defined as having three categories, blue-collar, white-collar and service, using the 2000 Standard Occupational Classification (SOC) [27] provided in the TUS CPS data set. Blue-collar workers were defined as those individuals working in: farming, fishing and forestry (SOC 45); construction and extraction (SOC 47); installation, maintenance and repair (SOC 49); production (SOC 51); and transportation and material moving (SOC 53). White-collar workers were defined as those individuals in: management, business and finance (SOC 11–13); professional (SOC 15–29); sales (SOC 41); and office and administrative support (SOC 43). Finally, service workers comprised those individuals working in service occupations (SOC 31–39).

Quitting behaviors

One dichotomous and two continuous quitting behavior variables were used in this study, corresponding to the following questions in the TUS CPS survey: (1) ‘Have you ever stopped smoking for 1 day or longer because you were trying to quit smoking (yes/no)?’; (2) ‘How many times during the past 12 months have you stopped smoking because you were trying to quit?’; and (3) ‘What is the longest length of time you stopped smoking (months)?’.

Quitting

The outcome variable for the logistic regression defined as quitting was taken from the quitting behavior question: ‘Have you ever stopped smoking for one day or longer because you were trying to quit smoking (yes/no)?’.

Work-place policies and smoking cessation programs

Work-place policies and smoking cessation programs were assessed from the following questions: (1) ‘Which of these best describes your place of work's smoking policy for indoor public or common areas (not allowed, allowed in some, allowed in all)?’; (2) ‘Which of these best describes your place of work's smoking policy for work areas (not allowed, allowed in some, allowed in all)?’; (3) ‘Has anyone smoked in the area in which you worked (yes/no)?’; (4) ‘Does your place of work have an official policy that restricts smoking in any way (yes/no)?’; and (5) ‘Within the past 12 months, has your employer offered any help to employees who want to quit smoking (yes/no)?’.

Data analysis

Because the TUS CPS is a complex stratified design, SUDAAN[28] software was used to perform all statistical analysis in order to calculate valid standard errors (SEs) and confidence intervals (CIs). These calculations were performed using the balanced repeated replication (BRR) method. Although the replicate weights are not distributed with the public TUS CPS file, we obtained them from NCI. These 160 weights account for the survey design as well as the response rates. For variance estimation, we used Fay's method [29] and specified ADJFAY = 4 in our models.

PROC CROSSTAB and PROC DESCRIPT were used for the bivariate analysis of categorical and continuous variables, respectively. Means, proportions, standard deviations (SDs) and CIs are reported as appropriate. The significance between menthol and non-menthol smokers was assessed using analysis of variance (ANOVA) and χ2 tests with alpha = 0.05. For the three occupation groups, we compared the demographic characteristics, cessation behaviors and work-place policies of adults that smoke menthol and non-menthol cigarettes in the three occupation categories.

To ascertain the effects of menthol smoking, occupational status and work-place policies and restrictions on quitting, we performed a multiple logistic regression analysis controlling for age, race, sex, education and region. We performed this analysis with PROC RLOGIST and modeled the probability of quitting at ‘yes’.

RESULTS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. Conflicts of interest
  9. References

Demographic characteristics and cessation behaviors for menthol and non-menthol smokers

Table 1 presents the comparisons of menthol and non-menthol smokers. Menthol smokers were younger (41.2 years versus 42.1 years, P = 0.001), a higher proportion were black (30.2% versus 4.4%, P ≤ 0.001), from the northeast region (21% versus 15%, P ≤ 0.001) and more often service employees (23% versus 18%, P ≤ 0.001) compared with non-menthol smokers. Also, menthol smokers were more likely to stop smoking in the past 12 months because they were trying to quit compared with non-menthol smokers (55% versus 50%, P ≤ 0.001).

Table 1.  Demographic characteristics and cessation behaviors of smokers by type.
VariablesMentholNon-mentholP-value
% (n)(95% CI)% (n)(95% CI)
  1. CI: confidence interval; SE: standard error.

Age (years), mean (SE)0.2 (41.2)(40.8, 41.7)0.1 (42.1)(41.9, 42.3)<0.001
Education     
 <High school20.2 (1478)(19.1, 21.5)17.9 (3 796)(17.2, 18.7)<0.001
 High school40.7 (3106)(39.4, 42.1)39.1 (8 931)(38.3, 39.9)
 Some college29.1 (2320)(27.9, 30.3)29.6 (6 723)(28.9, 30.4)
 College +10.0 (814)(9.2, 10.8)13.3 (3 008)(12.8, 13.9)
Marital status     
 Married36.8 (3051)(35.5, 38.2)44.7 (10 324)(43.7, 45.7)<0.001
 Divorce/widowed27.7 (2320)(26.5, 28.9)26.9 (6 635)(26.1, 27.7)
 Never married35.5 (2347)(29.7, 31.0)28.4 (5 499)(27.6, 29.2)
Race/ethnicity     
 White/other69.8 (5480)(68.5, 71.0)95.6 (20 900)(95.2, 96.0)<0.001
 Black30.2 (1801)(29.0, 31.5)4.4 (682)(4.0, 4.8)
Sex     
 Male44.9 (2967)(43.6, 46.1)56.6 (11 377)(55.9, 57.2)<0.001
 Female55.1 (4751)(53.9, 56.4)43.4 (11 081)(42.8, 44.1)
Region     
 Northwest21.1 (1564)(19.9, 22.4)15.4 (3 879)(14.7, 16.1)<0.001
 Midwest27.6 (2137)(26.2, 29.0)25.5 (6 153)(24.7, 26.3)
 South39.1 (2813)(37.6, 40.5)38.3 (7 212)(37.4, 39.2)
 West12.2 (1204)(11.3, 13.1)20.8 (5 214)(20.2, 21.5)
Smoking status     
 Every day79.0 (6183)(77.8, 80.1)81.2 (18 407)(80.7, 81.8)<0.001
 Some days21.0 (1535)(19.9, 22.2)18.76 (4 051)(18.2, 19.3)
Employment     
 White-collar46.4 (2689)(44.9, 48.1)46.7 (7 887)(45.7, 47.7)<0.001
 Blue-collar30.4 (1486)(28.8, 32.0)34.9 (5 327)(33.9, 35.9)
 Service23.2 (1298)(21.7, 24.7)18.4 (3 037)(17.7, 19.2)
Cessation behaviors     
 Stopped smoking for one day or longer?
  Yes70.9 (5033)(69.5, 72.2)69.5 (14 593)(68.7, 70.4)0.09
  No29.1 (1955)(27.8, 30.5)30.5 (5 780)(29.7, 31.3)
 Stopped smoking for one day or longer in the past 12 months?
  Yes55.0 (2636)(53.3, 56.8)50.3 (7 148)(49.4, 51.3)<0.001
  No45.0 (2384)(43.2, 46.7)49.7 (7 414)(48.7, 50.6)
 How many times during the past 12 months have you stopped smoking? Mean (SE)4.0 (0.2)(3.6, 4.4)3.8 (0.1)(3.6, 3.9) 
 What is the longest length of time you stopped smoking? Mean (SE)2.0 (0.2)(1.7, 2.3)2.2 (0.2)(1.9, 2.5) 

Demographic characteristics and cessation behaviors for menthol and non-menthol smokers by occupation status

Table 2 presents similar results as Table 1 by occupation status. For all three occupational status groups (white-collar, blue-collar and service), there were more black menthol smokers (22%, 30%, 38% vs. 3%, 3%, 6%, respectively, P < 0.001), more menthol smokers from the Northeast (22%, 20%, 20% vs. 17%, 13%, 16%, respectively, P < 0.001), and menthol smokers were significantly more likely to have never married (33%, 38%, 47% versus 30%, 30%, 39%, respectively, P < 0.001). Additionally, for blue-collar workers, menthol smokers compared with non-menthol smokers were more likely to stop smoking for 1 day or longer (71% versus 65%, P = 0.0008). For service workers, menthol smokers were less likely to stop smoking for 1 day or longer compared with non-menthol smokers (65% versus 71%, P = 0.001).

Table 2.  Demographic characteristics and cessation behaviors of brand type by occupation status.
VariablesWhite-collarBlue-collarService
MentholNon-mentholTotalP-valueMentholNon-mentholTotalP-valueMentholNon-mentholTotalP-value
% (n)% (n)% (n)% (n)% (n)% (n)% (n)% (n)% (n)
  1. SE: standard error.

Age mean (SE)40.2 (0.3)39.9 (0.2)40.0 (0.14) 38.3 (0.4)39.1 (0.2)38.9 (0.17) 37.0 (0.5)37.0 (0.3)37.0 (0.27) 
Education
 <High school7.3 (184)6.1 (442)6.4 (626)<0.00124.0 (343)22.3 (1075)23.9 (343)0.0624.4 (283)19.6 (580)21.1 (863)<0.001
 High school34.1 (896)30.3 (2476)31.3 (3372)49.5 (733)49.9 (2689)49.5 (733)46.8 (608)41.2 (1299)43.0 (1907)
 Some college38.0 (1058)36.9 (2894)37.2 (3952)23.7 (359)23.6 (1343)23.7 (359)26.1 (365)32.0 (957)30.1 (1322)
 College +20.7 (551)26.7 (2075)25.1 (2626)2.8 (51)4.2 (220)2.8 (51)2.7 (42)7.2 (201)5.8 (243)
Marital status
 Married39.7 (1144)44.6 (3633)43.3 (4777)0.00237.7 (604)46.7 (2553)44.5 (3157)<0.00128.4 (399)36.9 (1192)34.2 (1591)<0.001
 Divorced27.4 (787)25.1 (2161)25.7 (2948)24.7 (384)23.0 (1342)23.4 (1726)24.8 (358)24.1 (829)24.3 (1187)
 Never married32.8 (758)30.3 (2093)31.0 (2851)37.6 (498)30.3 (1432)32.1 (1930)46.7 (541)39.0 (1016)41.5 (1557)
Race/ethnicity
 White/other77.9 (2089)96.6 (7414)91.6 (9503)<0.00170.1 (1064)97.2 (5019)90.6 (6083)<0.00161.8 (830)94.5 (2787)84.1 (3617)<0.001
 Black22.1 (447)3.4 (175)8.4 (622)29.9 (341)2.8 (109)9.4 (450)38.2 (386)5.5 (110)15.9 (496)
Sex
 Male33.1 (717)46.9 (3216)43.2 (3933)<0.00181.1 (1144)88.9 (4564)87.0 (5708)<0.00134.9 (388)45.8 (1156)42.3 (1544)<0.001
 Female66.9 (1972)53.1 (4671)56.8 (6643)18.9 (342)11.1 (763)13.0 (1105)65.1 (910)54.2 (1881)57.7 (2791)
Region
 Northeast22.6 (566)16.9 (1419)18.4 (1985)<0.00119.5 (288)13.4 (859)14.9 (1147)<0.00120.3 (260)16.0 (521)17.4 (781)<0.001
 Midwest27.2 (751)25.0 (2188)25.6 (2939)26.8 (427)28.1 (1624)27.8 (2051)29.0 (371)26.0 (859)27.0 (1230)
 South35.7 (913)35.6 (2384)35.6 (3297)43.8 (570)40.0 (1693)40.9 (2263)39.3 (467)36.5 (899)37.4 (1366)
 West14.5 (459)22.5 (1896)20.3 (2355)9.9 (201)18.5 (1151)16.4 (1352)11.4 (200)21.5 (758)18.3 (958)
Smoking status
 Every day77.7 (2123)76.6 (6062)76.9 (8185)0.3380.7 (1222)84.1 (4529)83.3 (5751)0.0179.6 (1048)82.0 (2536)81.2 (3584)0.10
 Some days22.3 (566)23.5 (1825)23.1 (2391)19.3 (264)15.9 (798)16.7 (1062)20.4 (250)18.0 (501)18.8 (751)
Stopped smoking for one day or longer?
 Yes74.1 (1823)75.0 (5322)74.7 (7145)0.5470.6 (956)65.1 (3344)66.4 (4300)<0.00165.1 (808)70.5 (2000)68.8 (2808)0.007
 No25.9 (590)25.0 (1621)25.3 (2211)29.4 (403)34.9 (1574)33.6 (1977)34.9 (371)30.0 (784)31.2 (1155)
Stopped smoking for one day or longer in the past 12 months?
 Yes52.3 (924)51.4 (2668)51.6 (3592)0.5856.2 (501)49.0 (1586)50.8 (2087)0.00258.7 (431)54.2 (1050)55.5 (1481)0.07
 No47.7 (895)48.6 (2646)48.4 (3541)43.8 (452)51.1 (1752)49.2 (2204)41.3 (373)45.8 (946)44.5 (1319)
How many times during the past 12 months have you stopped smoking? Mean (se)3.9 (0.3)3.8 (0.2)3.81 (0.12) 3.9 (0.4)3.7 (0.2)3.74 (0.19) 3.8 (0.3)3.4 (0.2)3.50 (0.16) 
What is the longest length of time you stopped smoking? Mean (se)2.2 (0.4)1.9 (0.2)2.0 (0.16) 2.0 (0.4)2.1 (0.3)2.1 (0.24) 1.5 (0.2)2.1 (0.3)1.9 (0.21) 

Work-place policies and smoking cessation programs

Table 3 describes work-place smoking policies and the existence of smoking cessation programs sponsored by employers for each occupation. White-collar workers were more likely to have a smoking policy in place that did not allow smoking in any of the work areas compared with blue-collar and service employees (93% versus 86% versus 88%, P = 0.003) (data not shown).

Table 3.  Work-place policies and smoking cessation programs by employment status.
VariablesWhite-collarBlue-collarService
MentholNon-mentholTotalP-valueMentholNon-mentholTotalP-valueMentholNon-mentholTotalP-value
% (n)% (n)% (n)% (n)% (n)% (n)% (n)% (n)% (n)
Smokers in your work area?
 Yes9.2 (170)10.1 (528)9.8 (698)0.3519.0 (99)21.1 (407)20.6 (506)0.3218.5 (131)20.0 (340)19.5 (471)0.51
 No90.9 (1763)88.9 (5012)90.2 (6775)81.0 (513)78.9 (1604)79.4 (2117)81.5 (633)80.1 (1416)80.5 (2049)
Smoking policy in work area?
 Not allowed93.7 (1576)92.1 (4447)92.6 (6023)0.3584.8 (445)86.1 (1434)85.7 (1879)0.3288.0 (576)87.7 (1276)87.8 (1852)0.51
 Allowed in some5.6 (90)6.4 (284)6.2 (374)14.0 (66)11.7 (174)12.3 (240)10.6 (576)10.8 (146)10.7 (209)
 Allowed in all0.6 (14)1.4 (61)1.2 (75)1.3 (6)2.3 (39)2.0 (45)1.4 (11)1.5 (22)1.5 (33)
Smoking policy indoors at work?
 Not allowed82.4 (1408)84.0 (4074)83.5 (5482)0.4472.1 (388)74.7 (1252)74.1 (1640)0.1479.9 (530)77.9 (1145)78.5 (1675)0.72
 Allowed in some15.8 (246)14.3 (632)14.7 (878)26.4 (121)22.7 (349)23.7 (470)18.2 (111)19.8 (270)19.3 (381)
 Allowed in all1.7 (25)1.7 (75)1.7 (100)1.4 (7)2.6 (42)2.3 (49)1.9 (9)2.3 (31)2.2 (40)
Does your place of work have an official policy that restricts smoking in any way?
 Yes86.1 (1685)86.2 (4804)86.2 (6489)0.9881.3 (519)80.0 (1651)80.4 (2170)0.5984.9 (654)81.8 (1448)82.8 (2102)0.14
 No13.9 (251)13.8 (748)13.8 (999)18.7 (96)20.0 (374)19.6 (470)15.1 (111)18.2 (316)17.2 (427)
Employer-sponsored smoking cessation
 Yes20.1 (472)19.6 (1264)19.8 (1736)0.6815.0 (171)13.1 (576)13.5 (747)0.212.2 (116)9.9 (233)10.6 (349)0.19
 No79.9 (1606)80.4 (4788)80.2 (6394)85.0 (914)86.9 (3283)86.5 (4197)87.8 (758)90.1 (1862)89.4 (2620)

Multiple logistic regression analysis for quitting

In the logistic regression model (Table 4), occupational status and work-place policies and restrictions had a significant effect on quitting while controlling for age, race, sex, education and income; however, there was no difference between menthol and non-menthol smokers [odds ratio (OR) = 0.98; 95% confidence interval (CI) = 0.83, 1.15]. For occupational status, service workers were less likely to stop smoking for 1 day or longer compared with white-collar workers (OR = 0.80; 95% CI = 0.69, 0.94). With regard to work-place policies and restrictions and their effect on quitting, this model suggests that employees who did not have anyone smoke in the area in which they worked were less likely to stop smoking for 1 day or longer compared with those who have had someone smoke in the area in which they worked (OR = 0.77; 95% CI = 0.60, 0.97). It was also found that employees who do not have sponsored smoking cessation programs offered by their employers are significantly less likely to stop smoking for 1 day or longer compared with those employees who do have sponsored smoking cessation programs (OR = 0.70; 95% CI = 0.60, 0.83).

Table 4.  Multiple logistic regression model for occupation status predicting quitting.
 Ever stopped Smoking for one day or longer?
Estimated odds ratio95% CIP-value
Age1.01(1.00, 1.01)0.009
Smoker status
 Every day0.62(0.49, 0.78)<0.001
 Some daysRef  
Sex
 Male0.87(0.77, 1.00)0.042
 FemaleRef  
Region
 Northeast1.04(0.84, 1.28)0.739
 Midwest0.97(0.79, 1.20)0.806
 South0.69(0.57, 0.85)<0.001
 WestRef  
Education
 <High school0.60(0.47,0.76)<0.001
 High school0.75(0.62,0.90)0.003
 Some college1.00(0.83,1.21)0.987
 College +Ref  
Occupation
 Blue-collar0.87(0.73, 1.03)0.113
 Service0.80(0.69,0.94)0.006
 White-collarRef  
Income
 <$25 0000.90(0.73, 1.10)0.298
 $25 000–49 9990.87(0.74, 1.03)0.106
 $50 000–74 9990.94(0.77, 1.14)0.526
 >$75 000Ref  
Race
 White/other1.11(0.91, 1.37)0.415
 BlackRef  
Marital status
 Married1.23(1.04, 1.46)0.017
 Divorced1.22(1.01, 1.48)0.044
 Never marriedRef  
Menthol
 Yes0.98(0.83, 1.15)0.778
 NoRef  
Smoking policy indoors at work?
 Not allowedRef  
 Allowed in some1.11(0.93, 1.33)0.249
 Allowed in all0.79(0.45, 1.37)0.396
Smoking policy in work areas?
 Not allowedRef  
 Allowed in some0.89(0.70, 1.13)0.332
 Allowed in all1.27(0.68, 2.37)0.456
Smokers in your work area?
 YesRef  
 No0.77(0.60, 0.97)0.028
Employer-sponsored smoking cessation
 YesRef  
 No0.70(0.60, 0.83)<0.001

DISCUSSION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. Conflicts of interest
  9. References

Menthol and non-menthol smokers in occupational categories

To date, no published studies have examined the role of occupational status on the quitting behaviors of menthol versus non-menthol smokers. This exploratory study sought to examine the synergistic effects of menthol smoking, occupational status, work-place environments and work-place polices on smoking cessation behaviors. Blue-collar and service worker occupational categories are described in the scientific literature as having a higher smoking prevalence, more occupational hazards and more involuntary exposure to tobacco [1–9]. In the unadjusted analysis, we found differences for menthol versus non-menthol blue-collar and service workers with respect to a quit attempt. It was anticipated that because of the social, psychological and contextual variables which distinguish menthol and non-menthol smokers [21,30–33] and the influences of socio-economic position and smoking patterns among occupational groups in the United States [34,35], there would be a significant difference for menthol and non-menthol smokers on quitting. However, in our study this statistical significant difference disappeared after controlling for race, gender and education. In a smoking prevalence study, Barbeau et al. [36], looked at the complexities of class along with race/ethnicity and gender while studying the link between occupation and smoking. They found differences in the prevalence of smoking by occupational status when comparing classifications used in the United States versus those utilized in the United Kingdom. The latter method takes social gradients into consideration when determining occupational status, thereby creating a finer ‘resolution’ of smoking patterns than those that rely simply upon skill-based categories (such as those utilized in the United States). In the aforementioned study the differences in smoking between white-collar and service workers was highlighted using the more refined method of occupational classification. Perhaps this type of method applied to the current investigation would yield different results for menthol smokers in the context of occupational categories. Future studies might benefit from comparing the difference in results utilizing the two methods. Additionally, the lack of a positive relationship for menthol status on quitting in the present study could be attributed to the particular characteristics of the data set used for the analysis. Sterling & Weinkam [37] suggest that the confounding influences of occupation and smoking make it difficult to adjust for the relative effects of these inexplicably intertwined variables. In the future, it may be important to construct studies designed to better understand the confounding among occupation, SES and menthol smoking. This may result in a better understanding of the role of menthol smoking and cessation among service and blue-collar industry workers.

In our study, service workers were less likely than blue-collar or white-collar workers to report that they quit smoking. For the purposes of the current study this finding may shed some light upon the context and environment of smoking at work. Service workers, those employed in the restaurant, bar, casino and the hospitality industry, are less likely to be protected by smoking policies at work [7–9], and often experience a lack of compliance with policies even with states that have restrictions and laws that promote smoke-free work-places [5–8]. Among service workers, in particular, this vulnerability is extended to adolescents and young adults as well. Of the 2.5 million youth employed in the United States, almost 40% are in the service or hospitality industry [38]. The relevance to the current study findings is that young smokers tend to prefer menthol as an initiation brand [39], are more likely to report being addicted to menthol cigarettes [40–42] and have quitting behaviors that are influenced by tobacco home and work-place restrictions [43–45]. The service industry is also a probable employer of low SES women and girls [17]. This group is also less likely to be protected by smoking policies at work and simultaneously more likely to be targeted by aggressive marketing of the menthol brand. Although the relationship for menthol and occupational status on quitting was not borne out by our investigation, there may be enough similarities between the characteristics of menthol smokers and those in the service industry to warrant future studies.

In addition to occupational differences and quitting behavior the current analysis yielded significant results for employer-sponsored quitting programs and quitting. Our study results support the findings of Sorenson [10], Gerlach [46] and Shopland [47], respectively, who also reported disparaging differences in employee-offered tobacco programs and policies for blue-collar and service workers. Together with other important efforts in this field, our work sheds light on some potential differences between white-collar, blue-collar and service workers. One unexpected finding was that employees who do not have anyone smoking in their area at work were not influenced in terms of quitting. While smoking habits are perpetuated at work, this finding may represent the fact that most employers have federal and state level occupational safety regulations that do not permit smoking in the work area itself. The authors interpret the current finding as one that suggests that the smoker's habit may be influenced more by the referent group that joins them at a designated smoking area on the work-site as opposed to smokers barred from doing so in a work area. These results contribute to other studies that investigate attitudes about smoking policies at work and the influence of work-site and occupational norms [34,35]. Taken in the context of other occupational studies, this exploratory work may provide insight on important differences, such as quitting behaviors within occupational groups for smokers when planning work-site and occupational health interventions. The salient characteristics of both the worker and the organizational culture where work occurs may be significant for constructing smoking cessation messages or motivating smokers to quit. Although menthol use by occupational status was not included in her study, Sorenson suggested ‘tailoring interventions to individual workers whenever possible’[34]. Changing attitudes and norms at work regarding smoking and smoking behavior could help to minimize the occupational gaps in quitting among worker classifications in the United States.

CONCLUSIONS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. Conflicts of interest
  9. References

In this exploratory study, menthol versus non-menthol smoking as a factor in quitting was not significant. However, quitting behaviors were influenced by occupational class. The present study results may have implications for service industry smokers. These could include the importance of smoking bans in the work-place to aid in quitting, and the salient importance of education messages in the work-place about smoking cessation and programs designed to reinforce quitting. Furthering our knowledge about even subtle differences in tobacco use among white-collar, blue-collar and service workers may be an important factor in reducing the burden of tobacco-related disease. This is especially true for our nation's most vulnerable populations by creating policies that will protect them in the places they work and providing opportunities to stop smoking permanently.

Strengths and limitations

Because of the cross-sectional nature of the survey and self-report, the data are subject to recall bias and cannot necessarily infer causality. Menthol use, whether or not survey participants switched brands during or after any quit attempts, and exposure to menthol content in cigarettes cannot be validated. It is also possible that a limitation of the current study is in the measure of quitting used in the analysis. For those who respond ‘yes’, the question ‘Have you ever stopped smoking for one day or longer because you were trying to quit smoking’ includes responses from those that quit for short and long periods of time (i.e. 1 day versus 3 months). The length of abstinence from cigarette smoking may be an important distinction for those attempting to replicate these findings with other populations. However, the benefit of the TUS CPS data for this study is its usefulness in capturing trends as a part of a large national representative sample of working adults. While we acknowledge the limitations, information yielded from the current study provides a contextual framework for future research to add to our knowledge of menthol smoking and occupational status.

References

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. Conflicts of interest
  9. References
  • 1
    Sterling T. D., Weinkam J. J. Smoking characteristics by type of employment. J Occup Environ Med 1976; 18: 74354.
  • 2
    Nelson D. E., Emont S. L., Brackbill R. M., Cameron L. R., Peddicord J., Fiore M. C. Cigarette smoking prevalence by occupation in the United States: a comparison between 1978 to 1980 and 1987 to 1990. J Occup Med 1994; 36: 51625.
  • 3
    Weinkam J. J., Sterling T. D. Changes in smoking characteristics by type of employment from 1979 to 1979/80. Am J Ind Med 1987; 11: 53961.
  • 4
    US Department of Health and Human Services, Substance Abuse and Mental Health Services Administration, Office of Applied Studies. Results from the 2008 National Survey on Drug Use and Health: National Findings. Cigarette use among adults employed full time, by occupational category. DHHS Publication no. SMA 09-4434, NSDUH Series H-36. 2008. Department of Health and Human Services Rockville MD.
  • 5
    Brackbill R., Frazier T., Shilling S. Smoking characteristics of US workers, 1978–1980. Am J Ind Med 1988; 13: 541.
  • 6
    Davis R. M. Exposure to environmental tobacco smoke. JAMA 1998; 280: 19479.
  • 7
    Stark M. J., Rohde K., Maher J. E., Pizzacani B. A., Dent C. W., Bard R. et al. The impact of clean indoor air exemptions and preemption policies on the prevalence of a tobacco-specific lung carcinogen among nonsmoking bar and restaurant workers. Am J Public Health 2007; 97: 145763.
  • 8
    Howard J. Smoking is an occupational hazard. Am J Ind Med 2004; 46: 1619.
  • 9
    Lee D. J., LeBlanc W., Fleming L. E., Gómez-Marin O., Pittman T. Trends in US smoking rates in occupational groups: the National Health Interview Survey 1987–1994. J Occup Environ Med 2004; 46: 53848.
  • 10
    Sorensen G. Worksite tobacco control programs: the role of occupational health. Respir Physiol 2001; 128: 89102.
  • 11
    Shopland D. R., Anderson C. M., Burns D. M., Gerlach K. K. Disparities in smoke-free workplace policies among food service workers. J Occup Environ Med 2004; 46: 34756.
  • 12
    Abidoye O., Ferguson M. K., Salgia R. Lung carcinoma in African-Americans. Nat Clin Pract Oncol 2007; 4: 11829.
  • 13
    Garten S., Falkner R. V. Continual smoking of mentholated cigarettes may mask the early warning symptoms of respiratory disease. Prev Med 2003; 37: 2916.
  • 14
    Benowitz N. L., Herrera B., Jacob P. 3rd. Mentholated cigarette smoking inhibits nicotine metabolism. J Pharmacol Exp Ther 2004; 310: 120815.
  • 15
    Caraballo R. S. Menthol and Demographics, FDA Tobacco Products Scientific Advisory Committee, 30 March 2010. Accessed 25 July 2010 http://www.fds.gov/downloads/advisorycommittee/UCM207153.pdf
  • 16
    White V. M., White M. M. Cigarette promotional offers: who takes advantage? Am J Prev Med 2006; 30: 22531.
  • 17
    Shavers V. L., Fagan P., Alexander L. A., Clayton R., Doucet J., Baezconde-Garbanati L. Workplace and home smoking restrictions and racial/ethnic variation in the prevalence and intensity of current cigarette smoking among women by poverty status, TUS-CPS 1998–1999 and 2001–2002. J Epidemiol Commun Health 2006; 60: ii3443.
  • 18
    Yerger V. B., Przewoznik J., Malone R. E. Racialized geography, corporate activity, and health disparities: tobacco industry targeting of inner cities. J Health Care Poor Underserved 2007; 18: 1038.
  • 19
    Kreslake J. M., Wayne G. F., Alpert H. R., Koh H. K., Connolly G. N. Tobacco industry control of menthol in cigarettes and targeting of adolescents and young adults. Am J Public Health 2008; 98: 168592.
  • 20
    Cummings K. M., Giovino G., Mendicino A. J. Cigarette advertising and black–white differences in brand preference. Public Health Rep 1987; 102: 698701.
  • 21
    Gardiner P. S. The African Americanization of menthol cigarette use in the United States. Nicotine Tob Res 2004; 6: S5565.
  • 22
    Landrine H., Klonoff E. A., Fernandez S., Hickman N., Kashima K., Parekh B. et al. Cigarette advertising in Black, Latino, and White magazines 1998–2002: an exploratory investigation. Ethn Dis 2005; 15: 637.
  • 23
    Mackay J., Amos A. Women and tobacco. Respirology 2003; 8: 12330.
  • 24
    Ferris W. G., Connolly G. N. Application, function, and effects of menthol in cigarettes: a survey of tobacco industry documents. Nicotine Tob Res 2004; 6: S4354.
  • 25
    Sutton C. D., Robinson R. G. The marketing of menthol cigarettes in the United States: populations, messages, and channels. Nicotine Tob Res 2004; 6: S8391.
  • 26
    United States Department of Labor. Labor Force Characteristics by Race and Ethnicity. 2008, US Department of Labor, US Bureau of Labor Statistics, Washington, DC. November 2009, Report 1020.
  • 27
    Standard Occupational Classification Codes. Bureau of Labor Statistics, 2010. United States Department of Labor, US Bureau of Labor Statistics, Washington, DC. Available at: http://www.bls.gov/SOC/#classification2010 (accessed 25 July 2010).
  • 28
    Siller A. B., Tompkins L. The big four: analyzing complex sample survey data using SAS®, SPSS®, STATA®, and SUDAAN®. National Center for Health Statistics, Centers for Disease Control and Prevention, US Department of Health and Human Services, Rockville, Maryland, Paper 172-31. Available at: http://www2.sas.com/proceedings/sugi31/172-31.pdf (accessed 1 July 2009).
  • 29
    Dippo C. S., Fay R. E., Morganstein D. H. Computing variances from complex samples with replicate weights. Proc Survey Res Meth Sect Am Stat Assoc. Washington, DC; American Statistical Association; 1984.
  • 30
    Clark P. I., Gardiner P. S., Djordjevic M. V., Leischow S. J., Robinson R. G. Menthol cigarettes: setting the research agenda. In Swan GE, Balfour DJK, editors. Nicotine Tob Res 2004; 6: S59.
  • 31
    Henningfield J. E., Djordjevic M. V. Menthol cigarettes: research needs and challenges. In Swan GE, Balfour DJK, editors. Nicotine Tob Res 2004; 6: S116.
  • 32
    Castro F. G. Physiological, psychological, social, and cultural influences on the use of menthol cigarettes among Blacks and Hispanics. In Swan GE, Balfour DJK, editors. Nicotine Tob Res 2004; 6: S2941.
  • 33
    Wayne G. F., Connolly G. N. Application, function, and effects of menthol in cigarettes: a survey of tobacco industry documents. In Swan GE, Balfour DJK, editors. Nicotine Tob Res 2004; 6: S4354.
  • 34
    Sorenson G., Barbeau E., Hunt M. K., Emmons K. Reducing social disparities in tobacco use: a social-contextual model for reducing tobacco use among blue-collar workers. Am J Public Health 2004; 94: 2309.
  • 35
    Sorenson G., Pechacek T., Pallonen U. Occupational and worksite norms and attitudes about smoking cessation. Am J Public Health 1986; 76: 5449.
  • 36
    Barbeau E. M., Krieger N., Soobader M. Working class matters: socioeconomic disadvantage, race/ethnicity, gender, and smoking in NHIS 2000. Am J Public Health 2004; 94: 26978.
  • 37
    Sterling T., Weinkam J. The confounding of occupation and smoking and its consequences. Soc Sci Med 1990; 30: 45767.
  • 38
    Youth Employment. Bureau of Labor Statistic's Current Population Survey, NIOSH, CDC. Available at: http://www.cdc.gov/niosh/topics/youth/chartpackage.html (accessed 1 July 2010).
  • 39
    Hersey J. C., Ng S. W., Nonnemaker J. M., Mowery P., Thomas K. Y., Vilsaint M. C. et al. Are menthol cigarettes a starter product for youth? Nicotine Tob Res 2006; 8: 40313.
  • 40
    Collins C. C., Moolchan E. T. Shorter time to first cigarette of the day in menthol adolescent cigarette smokers. Addict Behav 2006; 31: 14604.
  • 41
    Moolchan E. T., Franken F. H., Jaszyna-Gasior M. Adolescent nicotine metabolism: ethnoracial differences among dependent smokers. Ethn Dis 2006; 16: 23943.
  • 42
    Wackowski O., Delnevo C. D. Menthol cigarettes and indicators of tobacco dependence among adolescents. Addict Behav 2007; 32: 19649.
  • 43
    Farkas A. J., Gilpin E. A., White M. M., Pierce J. P. Association between household and workplace smoking restrictions and adolescent smoking. JAMA 2000; 284: 71722.
  • 44
    Siegel M., Albers A. B., Cheng D. M., Hamilton W. L., Biener L. Local restaurant smoking regulations and the adolescent smoking initiation process: results of a multilevel contextual analysis among Massachusetts youth. Arch Pediatr Adolesc Med 2008; 162: 47783.
  • 45
    Muilenburg J. L., Legge J. S. African-American adolescents and menthol cigarettes: smoking behavior among secondary school students. J Adolesc Health 2008; 43: 5705.
  • 46
    Gerlach K. K., Shopland D. R., Hartman A. M. Workplace smoking policies in the United States: results from a national survey of more than 100 000 workers. Tob Control 1997; 6: 199206.
  • 47
    Shopland D. R., Gerlach K. K., Burns D. M., Hartman A. M., Gibson J. T. State-specific trends in smoke free workplace policy coverage: the current population survey tobacco use supplement, 1993 to 1999. J Occup Environ Med 2001; 43: 6806.