The influence of occupational stress factors on the nicotine dependence: a cross sectional study

  • Anna Schmidt1Email author,

    Affiliated with

    • Melanie Neumann2,

      Affiliated with

      • Markus Wirtz3,

        Affiliated with

        • Nicole Ernstmann1,

          Affiliated with

          • Andrea Staratschek-Jox4,

            Affiliated with

            • Erich Stoelben5,

              Affiliated with

              • Jürgen Wolf6 and

                Affiliated with

                • Holger Pfaff1

                  Affiliated with

                  Tobacco Induced Diseases20108:6

                  DOI: 10.1186/1617-9625-8-6

                  Received: 8 October 2009

                  Accepted: 13 April 2010

                  Published: 13 April 2010

                  Abstract

                  Objective

                  This study analyses the association between occupational stress factors and nicotine dependence. Our hypothesis is that occupational stress factors increase nicotine dependence.

                  Methods

                  Data were taken from the Cologne Smoking Study, a case-control study that examines which genetic/psychosocial factors lead to a higher risk for smokers to suffer from cardiac infarction, lung cancer and/or to become addicted to nicotine. Our sample consisted of N = 197 currently smoking and employed participants. Nicotine dependence was measured using the Fagerström Test for Nicotine Dependence (FTND). The extent of the stress factors experienced at work was assessed using the Effort-Reward Imbalance scale (ERI). Logistic regression was used for the statistical analysis.

                  Results

                  Contrary to our hypothesis, the results show that occupational stress factors are actually associated with lower levels of nicotine dependence (N = 197; adjusted OR = 0.439; p = .059).

                  Conclusions

                  One possible explanation for the study's findings is that the participants have a heavy workload and can only smoke in their spare time. Another reason may be workplace smoking bans. Furthermore, the Fagerström Test for Nicotine Dependence is unable to examine nicotine dependence during working hours.

                  Introduction

                  Tobacco use is a risk factor for six of the eight leading causes of death. In fact, tobacco kills a third to half of all its users. On average, every tobacco user loses 15 years of their life. The total number of tobacco-attributable deaths - from ischaemic heart disease, lung cancer and other diseases [1] - is projected to rise from 5.4 million in 2004 to 8.3 million in 2030 [2].

                  Nicotine dependence and the degree of that dependence are determined by individual, genetic and psychosocial factors as well as combinations of these factors [3]. Psychosocial factors, both occupational (e.g., work stress) and personal (e.g., poor quality of life), have an influence on the initiation and extent of smoking [4, 5]. For example, smoking is used as a coping strategy for dealing with work stress [6, 7]. The degree of cigarette consumption can therefore shed some light on potential stress at work. By using the "Fagerström Test for Nicotine Dependence" (FTND), it is possible to obtain more detailed information about survey participants' smoking behaviour than simply asking them to provide their smoking status. The FTND is an internationally recognized and statistically validated instrument for assessing the degree of nicotine dependence in smokers and has been tested in numerous empirical studies [8].

                  The effort-reward imbalance model provides a theoretical approach to assessing psychosocial stress experienced at work as measured by the Effort-Reward Imbalance (ERI) scale [9, 10]. This approach has been successfully tested and examined in many social epidemiological studies [11]. By measuring psychosocial stress at work, it is possible to identify a risk group and to then intervene using measures targeted at that particular group [12].

                  Originally developed to explain the adverse health effects of stressful work experiences, the ERI model posits that effort at work is exerted as part of a socially organized exchange process, to which society at large contributes through occupational rewards. These rewards are distributed by means of three transmitter systems: money, esteem and job security/career opportunities. The model claims that an imbalance between high efforts and low rewards may cause a state of emotional distress [13]. In addition to the two work-related dimensions of effort and reward, overcommitment at work acts as a personal risk factor. Separate and combined effects of these three dimensions on health are then postulated [13].

                  Only one other study has used the FTND to determine whether there is correlation between nicotine dependence and job stress factors measured using the Karasek model of job strain [14]. Other social epidemiological studies support the hypothesis of a correlation between job stress factors and nicotine dependence by using the ERI scale and data on smoking status [8, 13, 15, 16].

                  Two cross-sectional studies conducted by Ota and colleagues (2004) [17] and John (2006) [14] are the only studies that support a different thesis. These studies, which used the FTND, found that smoking is unrelated to job stress.

                  Our study aims to determine whether there is a correlation between the experiences of occupational stress, measured using the ERI scale, and nicotine dependence, measured with the FTND. A systematic search in PubMed in January 2008 (MeSH terms: disorder, imbalance, psychosocial factor(s), working stress, effort, reward, gratification crisis, worker, nicotine dependence and smoking) found no other studies that have investigated this research question using both of these same measures.

                  Methods

                  Study design and participants

                  Data for the study were taken from the Cologne Smoking Study (CoSmoS), a case-control study that examines which genetic/psychosocial factors lead to a higher risk for smokers to suffer a cardiac infarction, develop lung cancer and/or become addicted to nicotine. The study was approved by the Ethics Committee of the University Hospital of Cologne (UHC). Patients were included in the study after signing an informed consent form.

                  CoSmoS consisted of N = 524 participants. Of these participants, 457 (87.2%) were smokers/ex-smokers and 64 (12.8%) were non-smokers. The study's design required that primarily smokers be included in the study. 180 lung cancer patients and 170 myocardial infarction patients (acute myocardial infarction and/or a history of myocardial infarction) were recruited at the UHC and the Chest Clinic Merheim. 174 control patients, who had not been diagnosed with either condition and who had not been admitted with a diagnosis of cancer and/or a nicotine-related disease, were selected from the Orthopaedics and Dermatology departments. The participants were surveyed in hospital in face-to-face interviews.

                  Measures

                  Nicotine dependence was assessed using the FTND, a psychometrically evaluated instrument used to determine the degree of cigarette consumption and the inability to abstain from nicotine use [8, 18, 19].

                  The independent variable was measured using the German version of the ERI scale [13, 9], which consists of three subscales: "effort," "reward" and "overcommitment". To evaluate the effort-reward imbalance experienced by study participants, only the scores of the effort and reward scales were needed; an effort-reward ratio was then computed using a standardized syntax [13].

                  The six items of the "effort" scale measure extrinsic components of stressful experiences at work. The response options for the "effort" scale are: "Disagree," "Agree, but I am not at all distressed," "Agree, I am somewhat distressed," Agree, I am distressed," and "Agree, I am very distressed". The "reward" scale includes 11 items assessing the extrinsic components of occupational rewards and contains questions pertaining to opportunities for advancement, employee appreciation, salary and job security. Participants with no superiors or colleagues have the option to respond with "Not applicable". For seven of the items, participants can respond with "Agree," "Disagree, but I am not at all distressed," "Disagree, I am somewhat distressed," "Disagree, I am distressed," and "Disagree, I am very distressed". The response options for the other four items are the same as those of the "effort" scale. The reliability and validity of the "effort," "reward" and "overcommitment" subscales as well as of the "effort-reward ratio" have been demonstrated in many studies [for an overview, see [12]].

                  Statistical analysis

                  The individual items of the FTND were combined into a sum score for the multivariate analysis. Scores of one to three represent smokers with low nicotine dependence and scores of four to five represent smokers with a heavy dependence on nicotine [18, 19]. In order to compare workers with low nicotine dependence to those with heavy dependence, the FTND sum score was dichotomised at the value of 4 (highly dependent) for the logistic regression because the dependent variable was not normally distributed.

                  The ERI analysis was conducted as follows: If the participants disagreed with a statement, their response was assigned a value of 0. If the participants agreed with a statement but did not experience any stress, their response was assigned a value of 1. The greater the level of stress experienced, the greater the value up to 4. The ERI is, therefore, a five-point Likert scale. When interpreting the values, the higher the sum score of the "effort" and "reward" subscales, the greater the level of occupational stress. Values over 1 indicate an imbalance between effort and reward [20].

                  A logistic regression model was calculated using all sociodemographic variables. Statistical data were analysed using SPSS version 15.0 for Windows.

                  Results

                  Descriptive statistics

                  The study sample consisted of N = 197 currently smoking and employed participants, of which 70 were lung cancer patients, 53 were myocardial infarction patients and 74 were control patients. To prevent any memory-based distortions, 64 non-smokers and 263 unemployed and retired patients were excluded from the study. The resulting subsample is representative of the original total sample of 524 patients because the study participants are evenly distributed between the two case-study groups and the control group and because all of the patients were hospitalized at the time of the survey. The distribution of the sociodemographic characteristics is shown in Table 1.
                  Table 1

                  Sociodemographic characteristics of the sample (N = 197).

                  Variables

                  N

                  %

                  Sex

                    

                     male

                  133

                  67.5

                     female

                  64

                  32.5

                  Age

                    

                     > 53

                  99

                  50.3

                     < 53

                  98

                  49.7

                  Family status

                    

                     not married

                  53

                  26.9

                     married

                  144

                  73.1

                  Religion

                    

                     not religious

                  63

                  32.0

                     religious

                  134

                  68.0

                  Level of education

                    

                     low

                  125

                  63.5

                     high

                  72

                  36.5

                  Residence

                    

                     country

                  121

                  61.4

                     city

                  74

                  37.6

                  The following degrees of nicotine dependence were found among the study sample: 51 participants (25.9%) had a very low dependence on nicotine, 54 (27.4%) had a low dependence, 26 (13.2%) were moderately dependent, 45 (22.8%) were highly dependent and 21 (10.7%) were very highly dependent [21]. The mean value of the FTND scale was 2.65 (range: 1-5), which indicates a moderate level of dependence. After dichotomization, there were 131 workers with low nicotine dependence and 66 with high dependence.

                  Results of the ERI scale showed that 13.8% of participants do not experience an imbalance between effort and reward (up to a value of 0.99). 67.2% experience a low imbalance (values of 1 to 1.99), 16.9% experience a moderate imbalance (values of 2 to 2.99) and only 2.1% experience a high imbalance (values of 3 to 4). The mean value of the effort-reward ratio is 2.07 (range: 2-3).

                  Multivariate analysis

                  The results of the logistic regression are shown in Table 2.
                  Table 2

                  Results of the logistic regression model, nicotine dependence and ERI (N = 197).

                  Independent variable

                  Beta

                  SC

                  SD

                  p-value

                  OR

                  95% CI

                        

                  lower limit

                  higher limit

                  Sex (female/male*)

                  -0.460

                  1.567

                  .368

                  .211

                  0.631

                  0.307

                  1.298

                  Age (> 53/< 53*)

                  -0.443

                  1.735

                  .337

                  .188

                  0.642

                  0.332

                  1.241

                  Family status (married/not married*)

                  -0.727

                  3.980

                  .364

                  .046

                  0.483

                  0.237

                  0.987

                  Religion (religious/not religious*)

                  -0.755

                  4.681

                  .349

                  .030

                  0.470

                  0.237

                  0.931

                  Level of education (high/low*)

                  -0.896

                  5.544

                  .381

                  .019

                  0.408

                  0.193

                  0.861

                  Residence (city/country*)

                  0.475

                  1.947

                  .341

                  .163

                  1.609

                  0.825

                  3.137

                  ERI (no effort-reward imbalance/effort-reward imbalance*)

                  0.822

                  3.572

                  .435

                  .059

                  0.439

                  0.187

                  1.031

                  Cox and Snell pseudo-R2 = .109

                  Nagelkerke pseudo-R2 = .151

                  Mc Fadden pseudo-R2 = .10

                  Note: * = reference group, Beta = regression coefficient, SC = standardized effect coefficient, SD = standard deviation, CI = 95% confidence interval

                  For currently smoking and employed participants, a decrease (p = .059) in their likelihood to suffer from nicotine dependence was found to be associated with their experience of an effort-reward imbalance (adjusted OR = 0.439; CI = 0.187-1.031). The amount of explained variance in this model is 15.1% (Nagelkerke pseudo-R2; for the other coefficients, see Table 2, rows 6 and 8). The specificity of the model is 65.6% and the sensitivity is 57.6%.

                  The model also demonstrates that being religious, being married and having a higher level of education have a significant effect on the prevention of nicotine dependence (Table 2).

                  Conclusions

                  Main findings

                  Contrary to our hypothesis, the analysis indicates that the experience of occupational stress factors reduced the likelihood of nicotine dependence in currently smoking and employed participants. The study conducted by Ota and colleagues (2004) [16] and John (2006) [15], as mentioned above, supports our finding that smoking is unrelated to job stress. In scientific literature, both hypotheses have been discussed and debated. For example, contrary to the findings of our study, Kouvonen and colleagues found that Finnish public sector employees who experience an effort-reward imbalance at work are subject to an increased risk of regular tobacco consumption [7].

                  Given the marginal significance of the association found between nicotine dependence and occupational stress in our study, caution should be taken when drawing conclusions from its findings. Our analysis shows that heavy employee workload is associated with lower nicotine dependence. One possible explanation for this is that a heavy workload may drive employees to smoke in their spare time only. Another reason may be the growing number of workplace smoking bans leading participants to reduce their consumption [22]. A further possibility is that the Fagerström Test for Nicotine Dependence is not fully able to examine nicotine dependence during working hours.

                  The logistic regressions in our study also indicate that not being religious, not being married and having a lower level of education are significant risk factors for nicotine dependence. These findings correspond to those of Blay and colleagues (2008) [23], who found that evangelical affiliation reduced the odds of being a tobacco user by 51%. It is therefore possible to assert that religious affiliation is associated with a decrease in the frequency of tobacco usage [24].

                  The finding that a higher level of education is a protective factor against nicotine dependence may be explained by the fact that those with a higher level of education are aware of the risks of smoking and belong to the group of people among whom smoking is less common [25].

                  A possible explanation for the finding that being married is a protective factor against nicotine dependence is the fact that people who are in a relationship tend to take care of each other [26].

                  Limitations of the study

                  Due to the retrospective design of our study, there may be memory-based distortions in the participants' responses. In addition, the first author, who was responsible for interviewing participants in CoSmoS, noticed that the questions of the ERI scale evoked emotional reactions of denial and reticence in the participants, making it difficult for them to respond.

                  Further, because data were collected in face-to-face interviews, the presence of another individual at these interviews (e.g., patient, visitor) may have been enough to distort the results [27]. Social desirability also seemed to play a major role in the response behaviour of the participants. Because "social desirability bias" involves the systematic distortion of responses in a certain direction, contorted marginal distributions in the participants' responses must be considered when looking at the results [28].

                  Unlike the studies discussed above, the CoSmoS study surveyed severely ill participants. Interviews therefore had to be conducted within the hospital and were not anonymous. Also, since this was a correlative cross-sectional study, only associations could be examined. Furthermore, this retrospective survey was probably an underpowered substudy of a heterogeneous population.

                  Future research

                  Both the findings of previous studies as well as the findings of the present study indicate the need for further investigations. Future research should include prospective studies with larger samples of currently smoking and employed individuals from various professional fields. The FTND is not fully able to examine employee dependence during working hours. Future studies should aim to obtain a more precise assessment of employee smoking behaviour at work. The growing number of workplace smoking bans may be pushing employees to shift their smoking habits into their spare time. Items which take this shift into consideration may be a reasonable supplement to the evaluation instrument.

                  The sizes of the individual case-study groups in this study were too small for studying and comparing the experience of work stress among the lung cancer patients, patients with myocardial infarction and the control group. Due to the small sample size, the number of independent variables studied for their association with nicotine dependence had to be limited. An excess of parameters in comparison to the information content of the data, would have led to unstable regression coefficient estimates (i.e., "overfitting") [29]. In future studies, it would certainly be interesting to determine whether there is an association between work stress and nicotine dependence. However, a larger sample size would be needed.

                  Policy implications

                  The results of this study indicate that employees who experience stress at work are more likely to have a low dependence on nicotine. It, therefore, seems impossible to provide any policy implications because it cannot be said that employees who do not experience work stress have higher nicotine dependence or that greater stress at work results in lower nicotine dependence.

                  Although our study as well as that of Blay and colleagues (2008) [23] show that being religious, being married and having a higher level of education are protective factors against nicotine dependence, it is impossible to derive policy implications because these three factors cannot be influenced directly.

                  Declarations

                  Acknowledgements

                  This work was supported by the 'Helmholtz Association of German Research Centres' [grant number VH-VI-143]. The authors would like to thank all of the patients for their active participation as well as all of the cooperating clinics and institutes for their help in conducting the study. We would also like to thank the reviewers for their recommendations. We are grateful to Fawn Zarkov for her qualified support concerning our use of the English language.

                  Authors’ Affiliations

                  (1)
                  Institute for Medical Sociology, Health Services Research and Rehabilitation Science (IMVR), Faculty of Human Science and Faculty of Medicine, University of Cologne
                  (2)
                  Integrated Curriculum for Anthroposophic Medicine, private University of Witten/Herdecke
                  (3)
                  Institute for Psychology, University of Education Freiburg
                  (4)
                  LIMES (Life and Medical Sciences Bonn), Program Unit Molecular Immune & Cell Biology, Functional Genomics, University of Bonn
                  (5)
                  Hospital Merheim, chest clinic
                  (6)
                  First Department of Internal Medicine, Molecular Tumour Biology and Tumour Immunology & Centre for Integrated Oncology (CIO), University Hospital Cologne

                  References

                  1. Inoue T: Cigarette smoking as a risk factor of coronary artery disease and its effects on platelet function. Tobacco induces diseases 2004, 2:27–33.
                  2. WHO: World Health Statistics 2008. Part 1. Ten highlights in health statistics 2008.
                  3. Batra V, Patkar AA, Berrettini WH, Weinstein SP, Leone FT: The genetic determinants of smoking. Chest 2003, 123:1730–1739.View ArticlePubMed
                  4. Hourani LL, Yuan H, Bray RM, Vincus AA: Psychosocial correlates of nicotine dependence among men and women in the U.S. Naval Services. Addictive Behaviors 1999, 24:521–536.View ArticlePubMed
                  5. Schumann A, Hapke U, Rumpf HJ, Meyer C, John U: Gesundheitsverhalten von Rauchern - Ergebnisse der TACOS-Studie. [Health behavior of smokers - results of the TACOS (Transitions in Alcohol Consumption and Smoking) Study]. Gesundheitswesen 2000, 62:275–281.View ArticlePubMed
                  6. Schwarzer R: Psychologie des Gesundheitsverhaltens. [The Psychology of Health Behaviour]. 2nd edition. Göttingen: Hogrefe; 1996:326.
                  7. Kouvonen A, Kivimäki M, Virtanen M, Pentti J, Vahtera J: Work stress, smoking status, and smoking intensity: an observational study of 46,190 employees. Journal of Epidemiology and Community Health 2005, 59:63–69.View ArticlePubMed
                  8. Batra A: Tabakabhängigkeit. Biologische und psychosoziale Entstehungsmöglichkeiten und Therapiemöglichkeiten. [Biological and psychosocial determinants of tobacco dependence and potential treatment options.]. Darmstadt: Steinkopff; 2000:21.
                  9. Rödel A, Siegrist J, Hessel A, Brähler E: Fragebogen zur Messung beruflicher Gratifikationskrisen. Psychometrische Testung an einer repräsentativen deutschen Stichprobe. [Psychometric test of the questionnaire measuring effort-reward imbalance at work in a representative German sample.]. Zeitschrift für Differentielle und Diagnostische Psychologie 2004, 25:277–238.View Article
                  10. Siegrist J, Rödel A: Work stress and health risk behavior. Scandinavian Journal of Work, Environment & Health 2006, 32:473–481.
                  11. Siegrist J, Starke D, Chandola T, Godin I, Marmot M, Niedhammer I, Peter R: The measurement of effort-reward imbalance at work: European comparisons. Social Science & Medicine 2004, 58:1483–1499.View Article
                  12. Peter R: Berufliche Gratifikationskrisen und Gesundheit [Effort-reward imbalance and ill health]. Physiotherapeut 2002, 47:386–398.
                  13. Niedhammer I, Tek ML, Starke D, Siegrist J: Effort-reward imbalance model and self-reported health: cross-sectional and prospective findings from the GAZEL cohort. Social Science & Medicine 2004, 58:1531–1541.View Article
                  14. John U, Riedel J, Rumpf H-J, Hapke U, Meyer C: Associations of perceived work strain with nicotine dependence in a community sample. Occupational and Environmental Medicine 2006, 63:207–11.View ArticlePubMed
                  15. Kuper H, Singh-Manoux A, Siegrist J, Marmot M: When reciprocity fails: effort- reward imbalance in relation to coronary heart disease and health functioning within the Withehall II study. Occupational and Environmental Medicine 2002, 59:777–784.View ArticlePubMed
                  16. Peretti-Watel P, Constance J, Seror V, Beck F: Working Conditions, Job Dissatisfaction and Smoking Behaviours among French Clerks and Manual Workers. Journal of Occupational & Environmental Medicine 2009, 51:343–350.View Article
                  17. Ota A, Yasuda N, Okamoto Y, Kobayashi Y, Sugihara Y, Koda S, Kawakami N, Ohara H: Relationship of job stress with nicotine dependence of smokers - a cross sectional study of female nurses in a general hospital. Journal of Occupational Health 2004, 46:220–224.View ArticlePubMed
                  18. Radzius A, Gallo JJ, Epstein DH, Gorelick DA, Cadet JL, Uhl GE, Moolchan ET: A factor analysis of the Fagerström Test for Nicotine Dependence (FTND). Nicotine & Tobacco Research 2003, 5:255–260.View Article
                  19. Sledjeski EM, Dierker LC, Costello D, Shiffman S, Donny E, Flay BR: Predictive validity of four nicotine dependence measures in a college sample. Drug and Alcohol Dependence 2007, 87:10–19.View ArticlePubMed
                  20. Tsutsumi A, Kayaba K, Nagami M, Miki A, Kawano YO, Odagiri Y, Shimomitsu T: The Effort-reward Imbalance Model: Experience in Japanese working population. Journal of Occupational Health 2002, 44:398–407.View Article
                  21. Solomon C, Poole J, Palmer KT, Coggon D: Health-related job loss: findings from a community-based survey. Scandinavian Journal of Work, Environment & Health 2007, 64:144–149.
                  22. Etter JF, Vu Duc T, Perneger TV: Validity of the Fagerström test for nicotine dependence and of the heaviness of smoking index among relatively light smokers. Addiction 1999, 94:269–281.View ArticlePubMed
                  23. Blay SL, Batista AD, Andreoli SB, Gastal FL: The relationship between religiosity and tobacco, alcohol use, and depression in an elderly community population. American Journal of Geriatric Psychiatry 2008, 16:934–943.View ArticlePubMed
                  24. Koenig HG, George LK, Cohen HJ, Hays JC, Larson DB, Blazer DG: The relationship between religious activities and cigarette smoking in older adults. The International Journal of Psychiatry in Medicine 1998, 28:189–213.View Article
                  25. Finney Rutten LJ, Augustson EM, Moser RP, Burke Beckjord E, Hesse BW: Smoking knowledge and behaviour in the United States: Sociodemographic, smoking status, and geographic patterns. Nicotine & Tobacco Research 2008, 10:1559–1570.View Article
                  26. Broms U, Silventoinen K, Lahelma E, Koskenvuo M, Kaprio J: Smoking cessation by socioeconomic status and marital status: The contribution of smoking behaviour and family background. Nicotine & Tobacco Research 2003, 6:447–455.View Article
                  27. Lander B: Anwesenheitseffekte im Wandel. Eine Sekundäranalyse zur Anwesenheit des Partners im Interview anhand des ALLBUS 1980 bis 1998. [Changes in the effects of the presence of partners at interviews. A secondary analysis using data from ALLBUS 1980–1998]. Zeitschrift für Soziologie 2000, 29:227–239.
                  28. Stein LA, Colby SM, O'Leary TA, Monti PM, Rohsenow DJ, Spirito A, Riggs S, Barnett NP: Response distortion in adolescents who smoke: a pilot study. Journal of Drug Education 2002, 32:271–86.View ArticlePubMed
                  29. Muche R: Die logistische Regression - ein vielseitiges Analyseinstrument rehabilitationswissenschaftlicher Forschung. [Logistic regression: a useful tool in rehabilitation research]. Rehabilitation 2007, 46:1–7.View Article

                  Copyright

                  © Schmidt et al. 2010

                  This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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