Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2013 May 1.
Published in final edited form as: J Addict Nurs. 2012 Apr 4;23(2):132–136. doi: 10.3109/10884602.2012.669421

Smokeless Tobacco Use Among Operating Engineers

Devon Noonan 1,✉, Sonia A Duffy 2
PMCID: PMC3366014  NIHMSID: NIHMS364067  PMID: 22471777

Abstract

Purpose

Workers in blue collar occupations have been shown to have higher rates of smokeless tobacco (ST) use compared to other occupational groups. Guided by the Health Promotion Model, the purpose of this study was to understand various factors that predict ST use in Operating Engineers.

Methods

A cross-sectional design was used to determine variables related to ST use among Operating Engineers. Engineers (N=498) were recruited during their three-day apprentice certification course to participate in the study. Logistic regression was used to assess the associations between personal, psychological and behavioral characteristics associated with ST use.

Results

Past month ST use was reported among 13% of operating engineers surveyed. Multivariate analysis showed that younger age and lower rates of past month cigarette use were significantly associated with ST use, while higher rates of problem drinking were marginally associated with ST use.

Conclusions

Operating Engineers, are at high risk for using ST products with rates in this sample well over the national average. Work site interventions, which have shown promise in other studies, may be useful in decreasing ST use among this population.

Keywords: Smokeless Tobacco, Addiction, Tobacco, Health Behaviors

Introduction

Tobacco use is the leading preventable cause of death in the United States (U.S.) and is responsible for an estimated 438,000 deaths, or 1 out of 5 deaths annually (Centers for Disease Control and prevention [CDC], 2005, 2006). Smokeless tobacco (ST) use carries many risks similar to cigarette smoking including nicotine addiction, exposure to a large array of toxic chemicals, risk for heart diseases and oral cancers (Henely, Thum, Connell & Calle, 2005; Lee, 2007; Muwonge et al., 2008; Piano et al., 2010). An estimated 8.1 million people in the United States use ST (Substance Abuse and Mental Health Services {SAMHSA}, 2008). Unlike cigarette use which has been declining (Tomar 2010a; Piano et al., 2010), the use of ST products, including snuff and chewing tobacco, have remained relatively stable over the past ten years at about 4.4% in 2000 and 4.3% in 2005 (Rodu & Cole, 2009).

Workers in blue collar occupations in particular have been shown to have higher rates of ST use compared to other occupational groups, such as white collar workers (SAMPSA, 2008; Lee et al., 2007). In 2005, the prevalence of ST among blue collar workers was 5.6% compared to 1.6% in white collar occupations (Lee et al., 2007). Although some studies have focused on ST use in blue collar workers, very few have focused specifically on Operating Engineers (those responsible for operating heavy equipment such as bulldozers and cranes). Using the Health Promotion Model (HPM) as a guide (Pender, Murdaugh & Parsons, 2010), this study will report personal, psychological and behavioral characteristics that explain ST use among Operating Engineers. The major concepts of the HPM focus on personal (biological, socio-cultural), psychological and behavioral characteristics (past related behaviors) that explain behavior (Pender, Murdaugh & Parsons, 2010). The personal (age, race, marital status, and educational level), psychological (depression) and behavioral factors (cigarette use, alcohol use, physical activity, BMI and sleep) that may influence ST use are presented below.

Personal Factors and Psychological Factor Related to Smokeless Tobacco Use

Age, sex, race/ethnicity, marital status, educational level and area of residence have all been shown to be associated with ST use. Adult ST users are more likely to be younger, male, white, have lower education levels, work in blue collar occupations, live in the South or Midwest, live in small metro areas, and live in rural areas (SAMHSA, 2008; Lee et al., 2007; Nelson et al., 2006; Rodu & Cole, 2009). Depression, a psychological factor has been associated with ST use, with ST users reporting higher rates of depressive symptoms (Smith, Ferucci, Dillard & Lanier, 2010; Coogan, Geller & Adams, 2000).

Behavioral Factors Related to Smokeless Tobacco Use

Smokeless tobacco use has also been associated with cigarette use (Backinger et al., 2008; Rodu & Cole, 2009; Tomar, Alpert & Connelly, 2010). Daily ST users have the lowest prevalence of daily smoking (Tomar et al., 2010). Furthermore, cigarette smoking prevalence among these men increased with decreased ST use. Rodu and Cole (2009) reported similar results with one-third of ST users also using cigarettes, and dual users (both cigarette and ST users) consuming significantly fewer cigarettes on average than those that used cigarettes exclusively.

Smokeless tobacco use has also been associated with risky alcohol use (Accort, Waterbor, Beall & Howard, 2002; Ary et al., 1989; Engstorm, Magnusson & Galanti, 2010; Kao, Schnider & Hoffman, 2000; Padãro, Silva Matos, Damasceno & Lumet, 2010). Falk and colleagues (2006) examined the relationship between tobacco use (cigarettes, ST and cigars) and alcohol use and found that approximately 20% of U.S. adults use both alcohol and tobacco. Engstorm and colleagues (2010) found an association between risky alcohol consumption, binge drinking and ST use.

Cigarette smoking has been linked to physical inactivity (Engstorm et al., 2010; Kaczynski, Manske, Mannell, & Grewal, 2008), although interestingly, many studies have reported no association between physical inactivity and ST use (Engstorm et al., 2010; Henely et al, 2005; Lando et al., 1999). This may be related to measurement differences as well as occupational differences (Engstorm et al., 2010; Henely et al., 2005). Furthermore, studies have reported inconsistent results examining the relationship of ST use and BMI readings, with ST use being associated with both higher and lower BMI readings (Engstorm et al., 2010; Henely et al., 2005; Shukla, Gupta, Mehta, & Hebert, 2002; Vander Weg, Klesges & DeBon, 2005). Finally, sleep disturbances have been associated with cigarette use and poor health behavior although few studies have examined the association of sleep disturbances and ST use (Phillips and Danner, 1995). Overall, the relationship between health behaviors and ST use has not been studied among Operating Engineers.

Methods

Design, Sample and Procedure

A cross-sectional design was used to determine variables related to ST use among Operating Engineers. The dependent variable was past month smokeless tobacco use. The explanatory variables included personal factors (age, sex, marital status, race and educational level), one physiological factor (depression), and behavioral factors (cigarette use, alcohol intake, BMI, physical activity and sleep). Human subject approval was obtained from the University of Michigan. Consent was implied with reading the provided information and returning the survey.

The sampling frame included approximately 12,000 Operating Engineers in the state of Michigan participating in either a three-day apprentice certification course for new Operating Engineers or the 8-hour Hazardous Materials (Hazmat) refresher course. All courses were provided by the Operating Engineers Local 324 during the winter of 2008. Engineers were recruited from the classes to participate until a quota of 500 participants was obtained. The sample size quota of 500 participants was for feasibility purposes because data collection only occurred over one winter as well as to assure adequate power for less prevalent risk behaviors in this population. The instructors of the courses explained the study to the participants, passed out the study packets and collected completed surveys in a sealed envelope. Participation was voluntary and engineers received a $10 gasoline gift card for completing the survey.

Almost all, (90%) of the Operating Engineers who were asked to participate agreed and returned the survey. Two of the surveys returned were incomplete and dropped from the analysis, resulting in a final sample of 498. The sample, described in detail elsewhere, (Duffy et al., 2010) had a mean age of 41, was predominately male and Caucasian.

Measures

Dependent Variable

Self- report smokeless tobacco use was determined based on use in the past thirty days. Participants were first asked if they had used tobacco products in the past month. Participants were then asked to check the type to tobacco used in the past month from a list including cigarette use, snuff/chewing tobacco use, cigar use, pipe use, and cigarillo use. All those that selected ST use were assumed to have used in the past month.

Independent Variables

Personal Factors and Psychological Factors

Demographic information including age, sex, race/ethnicity, marital status and educational level were collected from all participants. The well validated Center for Epidemiologic Studies Depression Scale (CES-D), a 20 item self-report scale that measured various symptoms of depression in community populations was used to measure depression in this study (Radloff, 1977). Scores range from zero to 60 with a cut-off score of 16 indicating probable depression (Radloff, 1977). Internal consistency ranged from .84 to .87 across ethnicities (Crockett et al., 2005; Radloff, 1977).

Behavioral Factors

Self-report cigarette use was determined the same way past month smokeless tobacco use was determined. All participants that had used tobacco in the past month were asked to check the type to tobacco used in the past month from a list including cigarette use, snuff/chewing tobacco use, cigar use, pipe use, and cigarillo use. All those that selected cigarette use were assumed to have used in the past month. All those that selected cigarette use were assumed to have used in the past month and were coded as yes, all others as no. Participants were also asked to report in whole numbers the number of cigarettes smoked per day.

The Alcohol Use Disorder Identification Test (AUDIT), a 10 item self-report scale that measures amount of alcohol use, frequency of alcohol use, alcohol dependence and alcohol related problems were used to measure risky drinking behavior (Babor, Higgins-Biddle, Saunders & Monteiro, 2001). The possible range of scores was from zero to 40 with a cut-off point of eight indicating problem drinking (Babor et al., 2001). High internal consistency of the measure has been reported in studies examining various populations (Babor et al., 2001).

The Physical Activity Questionnaire, a six item survey that assesses time spent, duration and intensity of various activities including occupational activity was used to measure physical activity (Norman, Bellocco, Bergstm, & Wolk, 2001). A physical activity score is based on the duration and intensity of the activities reported over a one year time period. The test-retest reliability was .65 in a group of over 100 men, and criterion validity has been reported to be high (Norman et al., 2001). Body Mass Index (BMI), was calculated based on dividing self-reported weight by self-reported height squared multiplied by the conversion factor of 703 (CDC, 2011). Finally sleep was measured by the well validated Medical Outcomes Study (MOS) sleep scale, a six item questionnaire measuring quality and quantity of sleep (Hayes et al., 2005). Scores range from zero to 100 with higher scores indicating better sleep quality. The internal consistency of the scales ranged from .63 to .73 (Hayes et al., 2005).

Data Analysis

Data was analyzed using Statistical Package for Social Sciences, PAWS version 18. Descriptive statistics, chi-square tests and independent t-tests were used to determine the bivariate relationships. Multivariate, logistic regression was used to determine the associations between the independent variables and ST use; due to power constraints, only those variables that showed promise in the bivariate analyses were included in the multivariate analysis. The multivariate analysis was also examined accounting for possible clustering among the classes that were used for recruiting. There were no differences in the analysis results so the clustering variable was not included in the final model. Values of p <0.05 were considered to be significant.

Results

Forty-four percent (n=206) of the sample reported current tobacco use. Twenty-nine percent (n=142) reported current cigarette use and 13% (n=66) reported current ST use. Fifteen percent (n=10) of smokeless tobacco users also used cigarettes The bivariate associations between the independent variables and ST use are presented in Table 1. Compared to non-smokeless tobacco users, users were more likely to be younger (p=.002), be male (p=.012), be less likely to have smoked cigarettes in the past month (p=.010) and had a lower mean number of cigarettes smoked per day (p=.027). Smokeless tobacco users had higher alcohol intake compared to non-smokeless tobacco users although this difference was not significant (41% vs. 31%). Although not significant, depression rates were high among both ST users and non-users (48.4% vs. 46.6%).

Table 1.

Bivariate Associations of Independent Variables and Smokeless Tobacco Use Status

Variable Past Month ST Users
(N=66)
Frequency (%)		 ST Non-users
(N=432)
Frequency (%)		 P-value
Sex
  Male 66 (100%) 379 (91.1%) .012*
  Female 0 (0%) 37 (8.9%)
Race
  White 62 (96.9%) 374 (91.7%) .144
  Non-white 2 (3.1%) 34 (8.3%)
Educational Level
  High school or less 44 (66.7%) 251 (58.1%) .296
  College or more 22 (33.3%) 168 (38.9%)
Married
  Yes 45 (68.2%) 284 (65.7%) .948
  No 21 (31.8%) 135 (31.3%)
Past month cigarette use
  Yes 10 (15.2%) 132 (30.6%) .010*
  No 56 (84.8%) 300 (69.4%)
AUDIT (Problem Drinking)
Yes 27 (41.5%) 129 (31.4%) .105
No 38 (58.5%) 282 (68.6%)
CES-D (Depression)
Yes 31 (48.4) 189 (46.6) .779
No 33 (51.6) 217 (53.4)
Variable Past Month ST Users
(N=66)
Mean (SD)		 ST Non-users
(N=432)
Mean (SD)		 P-value
Age 39.6 (9.5) 43.4 (9.2) .002*
Number of Cigarettes Smoked a Day 11.4 (7.3) 20.6 (12.8) .027*
MOS Sleep Scale 69.9 (20.3) 70.3 (16.8) .853
Physical Activity Score 42.3 (5.1) 42.6 (5.3) .416
BMI 30.4 (5.4) 30.2 (5.8) .763
Open in a new tab
*

p <0.05

Data do not always sum to total sample size because of missing data

Percentages are based on total for each category and may not always total 100 due to rounding

The multivariate associations between the independent variables and ST use are presented in Table 2. Age was significantly associated with ST use, with each year increase in age, Operating Engineers were 5% less likely to use ST (OR=.951, 95% CI= .925–.982, p=.002). Sex was not significant probably due to lack of power, but in the expected direction as none of the 37 women in the study used ST. Past month cigarette use was also significant; those who had smoked cigarettes in the past month were 60% less likely to use ST (OR=.402, 95% CI= .191–.849, p=.017). Problem drinking approached significance; those who screened positive for problem drinking were 1.6 times more likely to use ST (OR= 1.67, 95% CI= .937–2.98, p=.082).

Table 2.

Multivariate Analysis of Independent Variables Associated with Smokeless Tobacco Status

Variable Odds Ratio 95% CI P-Value
Age .951 .922–.981 .002*
Sex 5.06 .660–38.75 .119
(Male =1)
Past Month Cigarette Use		 .402 .191–.849 .017*
(Yes = 1)
AUDIT ( ETOH Problem)		 1.67 .937–2.98 .082
Race
(White = 1)		 1.78 .398–8.03 .448
Education
(High school or less= 1)		 1.44 .797–2.62 .224
Open in a new tab
*

= p<0.05

Discussion

Thirteen percent of the entire sample of Operating Engineers reported past month smokeless tobacco use. This is over three times the national average, and more than double the use rate of other blue collar workers reported in the literature (Lee et al., 2007; Rodu & Cole, 2009). Similar to other studies, the smokeless tobacco users in this study were more likely to be younger and male (Nelson et al., 2006; Lee et al., 2007; Rodu & Cole, 2009; SAMPSA, 2007). Educational level was not associated with ST use. Smokeless tobacco use rates have been disproportionately higher in lower educated populations (Nelson et al., 2006; Rodu & Cole, 2009), although these studies did not analyze only blue collar workers, who on the whole, have lower educational levels.

Past month cigarette use was associated with decreased smokeless tobacco use. Many studies have also reported a similar relationship between ST use and cigarette use (Rodu & Cole, 2009; Tomar et al., 2010). Other studies have demonstrated an increase in poly-tobacco use (concurrent ST use and cigarette use) among young adults. This poly-tobacco use should be explored further, because ST users are more likely to be younger and therefore may be more at risk for polytobacco use (Backinger et al., 2008; Tomar et al., 2010).

While the association between problem drinking and ST use only approached significance in this study, other studies have supported the link between ST use and problem drinking (Engstorm et al., 2010; Padãro et al., 2010). The combination of alcohol and tobacco use increases the risk of head and neck cancers (Hashibe et al., 2009) which are common among Operating Engineers (Boffetta et al., 2003). Moreover, those with problem drinking have a harder time quitting tobacco use (Leeman et al., 2008). Coupling smokeless tobacco prevention/cessation initiatives with interventions geared towards decreasing alcohol consumption may be efficacious in this population.

Research has reported the association of smokeless tobacco use and depression with ST users reporting higher depressive symptoms (Smith et al., 2010). Yet, there were no differences in depression rates among ST users and non-users. Depression rates among both the ST users and non-users in this study were over 40%, this is almost double population norms reported in the literature which are approximately 21% (Radloff, 1977). Depression often impedes behavior change (Onyike et al., 2003; Roberts et al., 2003) and treatment for depression among those that are ST users may increase motivation to stop using ST.

Limitations

This was cross-sectional study, causality between age, cigarette use, and problem drinking and the dependent variable of ST use cannot be assumed. While the sample was relatively large, the sample was recruited from convenience sample of primarily Caucasian Operating Engineers in Michigan which limits generalizability. Finally, although self-report smoking status is reliable, biological endpoints including serum cotinine is the superior way to measure tobacco use behavior (Gorber, Schofield-Hurwitz, Hardt, Levasserur & Tremblay, 2009).

Conclusion

The rates of ST use among Operating Engineers in Michigan are about triple population norms. Those who are younger, male, and screen positive for drinking appear to be at higher risk for using ST. Work site interventions, which have been shown to have promise in other studies for health behavior change (Harely et al., 2010; Sorensen et al., 2007), may be useful in decreasing ST use among operating engineers.

Footnotes

Declaration of Interest

None to declare.

Contributor Information

Devon Noonan, Health Promotion/Risk Reduction Interventions with Vulnerable Populations, University of Michigan School of Nursing, 400 North Ingalls, Ann Arbor, MI, 48109-5482, dnoonan@umich.edu, Phone: 617-543-5759, FAX: 734-647-9325.

Sonia A. Duffy, University of Michigan, Nursing, Otolaryngology, Psychiatry Ann Arbor VA Health Care System, 400 North Ingalls Building Ann Arbor, MI 48109-0482 bump@umich.edu Telephone: (734) 395-0613.

References

  1. Accortt NA, John W, Waterbor JW, Beall C, Howard G. Chronic disease mortality in a cohort of smokeless tobacco users. American Journal of Epidemiology. 2002;156(8):730–737. doi: 10.1093/aje/kwf106. [DOI] [PubMed] [Google Scholar]
  2. Babor TF, Higgins-Biddle JC, Saunders JB, Monteiro MG. The alcohol use disorders identification test guidelines for use in primary care. 2001 Retrieved from http://www.who.int/substance_abuse/publications/alcohol/en/
  3. Backinger CL, Fagan P, O’Connell Grana R, Lawrence D, Bishop JA, Gibson JT. Use of other tobacco products among US adult cigarette smokers: prevalence, trends and correlates. Addictive Behaviors. 2008;33:472–489. doi: 10.1016/j.addbeh.2007.10.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Boffetta P, Richiardi L, Berrino F, Esteve J, Pisani P, Crosignani P, Merletti F. Occupation and larynx and hypolarynx cancer: an international case-control study in France, Italy, Spain and Switzerland. Cancer Causes and Control. 2003;14:203–212. doi: 10.1023/a:1023699717598. [DOI] [PubMed] [Google Scholar]
  5. CDC. Cigarette smoking among adults-United States, 2004. Morbidity and Mortality Weekly Report. 2005;54(44):1121–1124. [PubMed] [Google Scholar]
  6. CDC. Tobacco use among adults: United States, 2005. Morbidity and Mortality Weekly Report. 2006;55(42):1145–1148. [PubMed] [Google Scholar]
  7. CDC. About BMI for adults. 2011 Retrieved from http://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html#Interpreted.
  8. Coogan PF, Geller A, Adams M. Prevalence and correlates of smokeless tobacco use in a sample of Connecticut students. Journal of Adolescence. 2000;2392:129–135. doi: 10.1006/jado.2000.0303. [DOI] [PubMed] [Google Scholar]
  9. Duffy SA. Health behaviors risk factors among operating engineers. 2010 Under Review. [Google Scholar]
  10. Engstorm K, Magnusson C, Galanti MR. Socio-demographics among snus users and dual tobacco users in Stockholm County, Sweden. BMC Public Health. 2010;10(619) doi: 10.1186/1471-2458-10-619. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Falk DE, Yi H, Hiller-Strumhöfel S. An epidemiologic survey of co-occurring alcohol and tobacco use disorders. Alcohol Research & Health. 2006;29(3):161–172. [PMC free article] [PubMed] [Google Scholar]
  12. Gorber SC, Schofield-Hurwitz S, Hardt J, Levasseur G, Tremblay M. The accuracy of self-reported smoking: A systematic review of the relationship between self-reported and cotinine-assessed smoking status. Nicotine and Tobacco Research. 2009;11(1):12–24. doi: 10.1093/ntr/ntn010. [DOI] [PubMed] [Google Scholar]
  13. Harley AE, Devine CM, Beard B, Stoddard AM, Hunt MK, Sorensen G. Multiple health behavior changes in a cancer prevention intervention for construction workers, 2001–2003. Preventing Chronic Disease. 2010;7(3):1–12. [PMC free article] [PubMed] [Google Scholar]
  14. Hashibe M, Brennan P, Chuang S, Boccia S, Castellsague X, Chen C, Boffetta P. Interaction between tobacco and alcohol use and the risk of head and neck cancer: pooled analysis in the international head and neck cancer epidemiology consortium. Cancer epidemiology, Biomarker and Prevention. 2009;18(2):541–549. doi: 10.1158/1055-9965.EPI-08-0347. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hays R, Martin S, Sesti A, Spritzer K. Psychometric properties of the medical outcomes study sleep measure. Sleep Medicine. 2005;6(1):41–44. doi: 10.1016/j.sleep.2004.07.006. [DOI] [PubMed] [Google Scholar]
  16. Henely SJ, Thum MJ, Connell C, Calle EE. Two large prospective studies of mortality among men who use snuff or chewing tobacco (United States) Cancer Causes and Control. 2005;16:347–358. doi: 10.1007/s10552-004-5519-6. [DOI] [PubMed] [Google Scholar]
  17. Kaczynski AT, Manske SR, Mannell RC, Grewal K. Smoking and physical activity. American Journal of Health Behavior. 2008;32(1):93–110. doi: 10.5555/ajhb.2008.32.1.93. [DOI] [PubMed] [Google Scholar]
  18. Kao T, Schneider SJ, Hoffman KJ. Co-occurrence of alcohol, smokeless tobacco, cigarette, and illicit drug use by lower ranking military personal. Addictive Behaviors. 2000;25(2):253–262. doi: 10.1016/s0306-4603(99)00062-3. [DOI] [PubMed] [Google Scholar]
  19. Lee DJ, Flemming L, Dietz N, LeBlanc B, Chung-Bridges K, Christ S, McCollister K. Trends in smokeless tobacco use in the US workforce: 1987–2005. 2007 doi: 10.1186/1617-9625-9-6. Retrieved from http://ncth.confex.com/ncth/2007/techprogram/P15274.HTM. [DOI] [PMC free article] [PubMed]
  20. Leeman R, McKee S, Toll B, Krishnan-Sarin S, Cooney J, Makuch R, O’Malley SS. Risk factors for treatment failure in smokers: Relationship to alcohol use and to lifetime history of an alcohol use disorder. Nicotine and Tobacco Research. 2008;10(12):1793–1809. doi: 10.1080/14622200802443742. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Mejia AB, Ling PM. Tobacco industry consumer research on smokeless tobacco users and product development. American Journal of Public Health. 2010;100(1):78–87. doi: 10.2105/AJPH.2008.152603. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Muwonge R, Ramadas K, Sankila R, Thara S, Thomas G, Vinoda J, Sankaranarayanan R. Role of tobacco smoking, chewing and alcohol drinking in the risk of oral cancer in Trivandrum, India: a nested case-control design using incident cancer cases. Oral Oncology. 2008;44:446–454. doi: 10.1016/j.oraloncology.2007.06.002. [DOI] [PubMed] [Google Scholar]
  23. Nelson DE, Mowery P, Tomar S, marcus S, Giovino G, Zhao L. Trends in smokeless tobacco use among adults and adolescents in the United States. American Journal of Public Health. 2006;96(5):897–905. doi: 10.2105/AJPH.2004.061580. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Norman A, Bellocco R, Bergstrm A, Wolk A. Validity and reproducibility of self-reported total physical activity--differences by relative weight. International Journal of Obesity. 2001;25(5):682–688. doi: 10.1038/sj.ijo.0801597. [DOI] [PubMed] [Google Scholar]
  25. Onyike CV, Crum RM, Hochang BL, Lyketsos CG, Eaton WW. Is obesity associated with major depression? Results from the third national health and nutrition examination survey. American Journal of Epidemiology. 2003;158:1139–1147. doi: 10.1093/aje/kwg275. [DOI] [PubMed] [Google Scholar]
  26. Padãro P, Silva Matos C, Damasceno A, Lumet N. Association between tobacco consumption and alcohol, vegetable and fruit intake across urban and rural areas in Mozambique. Journal of Epidemiology and Community Health. 2010 doi: 10.1136/jech.2009.099440. [DOI] [PubMed] [Google Scholar]
  27. Pender NJ, Murdaugh CL, Parsons MA. Health promotion in nursing practice. 6th Ed. Upper Saddle River, NJ: Pearson; 2010. [Google Scholar]
  28. Piano MR, Benowitz N, Fitzgerald GA, Corbridge S, Heath J, Hahn E, Pechaek TF, Howard G. Impact of smokeless tobacco products on cardiovascular disease: implications for policy, prevention, and treatment: a policy statement from the American Heart Association. Circulation. 2010;122:1520–1544. doi: 10.1161/CIR.0b013e3181f432c3. [DOI] [PubMed] [Google Scholar]
  29. Radloff LS. The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement. 1977;1(3):385–401. [Google Scholar]
  30. Roberts RE, Deleger S, Strawbridge WJ, Kaplan GA. Prospective association between obesity and depression: Evidence from the Alameda County study. International Journal of Obesity and Related Metabolic Disorders. 2003;27:514–526. doi: 10.1038/sj.ijo.0802204. [DOI] [PubMed] [Google Scholar]
  31. Rodu B, Cole P. Smokeless tobacco use among men in the United States, 2000 and 2005. Journal of Oral Pathology Medicine. 2009;38:545–550. doi: 10.1111/j.1600-0714.2009.00780.x. [DOI] [PubMed] [Google Scholar]
  32. Schueller RA, Albrecht JA, Young L. Taste perceptions and dietary intakes of smokeless tobacco users and nontobacco users. Journal of Sensory Studies. 2005;20(3):200–216. [Google Scholar]
  33. Shukla HC, Gupta PC, Mehta HC, Herbert JR. Descriptive epidemiology of body mass index of an urban adult population in western India. Journal of Epidemiology Community Health. 2002;56:876–880. doi: 10.1136/jech.56.11.876. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Smith JJ, Ferucci ED, Dillard DA, Lanier AP. Tobacco use among Alaska native people in the EARTH study. Nicotine and Tobacco Research. 2010;12(8):839–844. doi: 10.1093/ntr/ntq091. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Sorensen G, Barbeau EM, Stoddard AM, Hunt MK, Goldman R, Smith A, Wallace L. Tools for health: The efficacy of a tailored intervention targeted for construction laborers. Cancer Causes Control. 2007;18(1):51–59. doi: 10.1007/s10552-006-0076-9. [DOI] [PubMed] [Google Scholar]
  36. Substance Abuse and Mental Health Services Administration. Rockville, MD: 2008. Results from the 2008 National Survey on Drug Use and Health: National Findings (Office of Applied Studies, NSDUH Series H-36, HHS Publication No. SMA 09-4434) Retrieved from http://oas.samhsa.gov/nsduh/2k8nsduh/2k8Results.cfm. [Google Scholar]
  37. Tomar SL. Trends and patterns of tobacco use in the United States. The American Journal of the Medical Sciences. 2003;326(4):248–254. doi: 10.1097/00000441-200310000-00019. [DOI] [PubMed] [Google Scholar]
  38. Tomar SL. The prevalence of smokeless tobacco use remained low and unchanged among US men in 2000–2005 while smoking declined. Journal of Evidenced Based Dental Practice. 2010a;10(3):183–185. doi: 10.1016/j.jebdp.2010.05.001. [DOI] [PubMed] [Google Scholar]
  39. Tomar SL, Alpert HR, Connolly GN. Patterns of dual use of cigarettes and smokeless tobacco among US males: findings from national surveys. Tobacco Control. 2010b;19:104–109. doi: 10.1136/tc.2009.031070. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Vander Weg MW, Klesges RC, DeBon M. Relationship between smokeless tobacco and body weight in young adult military recruits. Nicotine & Tobacco Research. 2005;7:301–305. doi: 10.1080/14622200500056317. [DOI] [PubMed] [Google Scholar]
  41. Willett W, Sampson L, Stampfer MJ, Rosner B, Bain C, Witschi J, Speizer FE. Reproducibility and validity of a semiquantitative food frequency questionnaire. American Journal of Epidemiology. 1985;122(1):51–65. doi: 10.1093/oxfordjournals.aje.a114086. [DOI] [PubMed] [Google Scholar]

RESOURCES