Journal of Preventive Medicine

Keywords

Tobacco use; Occupational health and safety; Workers health; Tobacco policies; Disparities; India

Introduction

Tobacco use is one of the most important preventable causes of death and disease globally. Tobacco kills nearly 6 million people each year globally [1] of those around 5.4 million are a direct result of tobacco use while around 0.6 million are a result of exposure to second-hand smoke. Unless urgent action is taken, the annual global death toll could rise to more than 8 million by 2030.

India, being the second most populous country in the world with over one third of the population using tobacco, [2] makes significant contributions to the global burden of disease attributable to tobacco [3-6]. Tobacco use annually kills over one million Indians [3]. However, quitting is not a common practice in India due to a lack of widely available cessation support resources and few social norms to support quitting [2,4,7-14].

Even though smoking rates are declining in many countries, the disparity among occupational groups still exists. Over the past several decades, blue-collar workers have been identified as a high-risk group for smoking [15]. Although a few studies in India have documented the association between occupation and tobacco use [16-21], with higher rates for unskilled workers, the factors contributing to this disparity are not clear. Furthermore, most global research on the association between occupation and tobacco use relates to smoking [22-24], a prevalent form in most countries. But in India, tobacco is consumed more in smokeless forms than smoking forms [4]. Therefore the purpose of this study is to further elucidate the patterns of smoking and smokeless tobacco use among production and non-production workers and explore the potential role of the work context, including tobacco policies and co-worker tobacco use patterns, which may help explain this disparity.

This study was conducted as part of the Mumbai Worksite Tobacco Control Study (MWTCS); a cluster randomized controlled trial, which tested an intervention aimed at reducing workers’ tobacco use and increasing the adoption, implementation and enforcement of worksite tobacco control policies. Data presented here were collected as a part of a baseline survey conducted in 20 manufacturing worksites in the greater Mumbai area. This paper describes the patterns of tobacco use (in smoking or smokeless forms) among these workers, and examines factors that may be associated with workers’ current and/or past tobacco use.

Methods

Study sample

A total of 20 manufacturing worksites from chemical, metal, textile, printing, food and beverages, pharmacy, electricity production, and petroleum industries employing 200 to 500 workers were recruited in Mumbai District and two surrounding periphery districts (Thane and Raigad) on a rolling basis. Additional eligibility criteria included worksites employing either at least 60% of their workforce as production workers or at least 200 production workers; ability to make autonomous decisions; and willingness to allow us to conduct study activities at their worksite, to provide current employee rosters, and to be randomly assigned to either an intervention or delayed intervention control condition. More details on worksite recruitment were published earlier [25]. Study methods and materials were approved by institutional review boards at the Harvard T.H.Chan School of Public Health in the United States and the Healis-Sekhsaria Institute for Public Health in India, as well as by the Indian Council of Medical Research. This study has been registered with ClinicalTrials.gov and the Clinical Trial Registry of India.

The baseline data were collected through a survey of individual workers in each worksite immediately after recruitment (July 2012-July 2013). Within worksites, all workers listed on the employee roster were eligible to participate in the baseline survey, and included all full- and part-time workers employed directly by the worksite. Of the total 6880 surveyed workers, 554 workers who were eligible but not listed on an employee roster were added to the roster on the day of the survey after we confirmed their eligibility with the respective worksite management. Workers who reported 12 or more years of formal education were eligible to be assigned to complete a self-administered survey (SAS), and workers who reported less than 12 years of formal education were eligible to be assigned to an interviewer-administered survey (IAS), which was conducted by trained study staff. The survey was conducted in Marathi, Hindi or English, depending on respondent preference. On average, the SAS and IAS took around 20 minutes, although IAS took a little more time than SAS.

Measures

Tobacco use was measured by self-report using separate questions to assess use of smokeless (this includes applying tobacco such as mishri, snuff, dentobac, etc., chewing tobacco such as gutka or zarda, khaini, pan with tobacco or tobacco Tobacco use was measured by self-report using separate questions to assess use of smokeless (this includes applying tobacco such as mishri, snuff, dentobac, etc., chewing tobacco such as gutka or zarda, khaini, pan with tobacco or tobaccolime- supari mixtures, tobacco-lime mixtures without supari, etc.) and smoked (this includes bidi, cigarettes, hukka, cigar, hukali, cheelim, chirut, or pipes,etc.) forms of tobacco. Questions assessed current, past or never use of tobacco. Sample characteristics assessed by the survey included age (categorized as <35/35-45/45-55/≥55 years), gender (male/ female), education (none to 6th standard/7th to 10th standard/12th standard or diploma/graduation or above), marital status (currently married/currently not married), economic wellbeing index (have both refrigerator and vehicle/ either refrigerator or vehicle/none), and health status (excellent/either good or fair or poor). We also assessed the number of co-workers (people who work with the respondent) using tobacco (0/1 to 2/≥3), and workers’ reports on whether their workplace had a policy or rule prohibiting tobacco use (yes/no). In addition, we obtained employee’s job title using information available from the worksite; we used the department and job title from employee rosters to categorize workers as either production or non-production workers.

Statistical analysis

We conducted descriptive analyses to examine difference in the prevalence of tobacco use behaviours between production and non-production workers. Multivariate logistic regression analysis was used to assess the association of each participant characteristic with current smoking and smokeless tobacco use compared to no tobacco use, and with past tobacco use compared to current tobacco use, controlling for age, gender and education. We classified current tobacco users as workers who reported either currently smoking or using smokeless forms of tobacco during last 30 days. SPSS version 20.0 was used to conduct data analysis. Associations between predictor variables and outcomes are reported as odds ratios (ORs) along with 95% confidence intervals (CIs).

Results

The average response rate for the 20 worksites (that employed 7633 workers) for the baseline survey was 90% (ranges from 78% to 94%). A total of 6880 participants (SAS=4192; IAS=2688) were surveyed. Only those workers reporting their tobacco use status and having information about their job title available (N=6760) were included in analyses reported here.

Of the total 6760 workers (Table 1), 79% were production and 21% were non-production workers. Current tobacco use was higher among production (23.5%; of which 15.5% were smokeless and 8% were smokers) than non-production (19.2%; of which 10.2% were smokeless and 9% were smokers) workers. In contrast, past tobacco use was somewhat lower among production (6.2%) than non-production (8.4%) workers. Over 90% of workers were males, ~70% were currently married, ~50% reported one or more of their co-workers using tobacco and ~75% reported their workplace had a policy or rule prohibiting tobacco use. Even though less than 10% of production (8%) and non-production (7%) workers reported their education as 6 years or below, twice as many production workers (30%) compared to than non-production workers (15%) were from the lowest economic wellbeing index.

Note: + smokers may include the few workers who both smoke and use smokeless tobacco, * total may not add due to respective missing cases

Table 1: Participant characteristics by type of worker and tobacco use status

Although there were very few female workers in the sample (~4%), male workers were more likely to be current tobacco users compared to female workers (Table 2). Increasing age increased the odds for production workers to be a current tobacco user. In contrast, decreasing education (a component of socio-economic status, SES) increased the odds for both production and non-production workers to be a current tobacco user. Similarly, decreasing economic wellbeing index (another component of SES) was associated with increased odds for production workers to be current smokeless tobacco users and decreased odds to be current smokers. Co-workers’ tobacco use increased the odds for both production and nonproduction workers current tobacco use. Production workers who were current smokeless tobacco users were twice as likely to report their workplaces did not have a policy or rule prohibiting tobacco use than production workers who were smokers or non-tobacco users.

Note: ORs are adjusted for age, gender and education, + smokers may include the few workers who both smoke and use smokeless tobacco

Table 2: Odds ratios (ORs) and 95% confidence intervals (CIs) for the association between participant characteristics and tobacco use status by type of worker

The prevalence of past tobacco use compared to current use (Table 3) was associated with workers’ education, economic wellbeing index, and number of co-workers using tobacco.

Table 3: Odds ratios (ORs) and 95% confidence intervals (CIs) for the association between participant characteristics and tobacco use status

Discussion

Substantial socio-economic inequalities exist related to health. Inequities in tobacco consumption across social determinants [16,17] are well recognized and widespread. Although community smoking rates are declining in many countries, the fact remains that smoking rates are not evenly distributed across certain occupation categories, such as bluecollar workers (referred as production worker henceforth) continuing to use tobacco at higher rates compared to their white-collar (referred as non-production worker henceforth) counterparts [15]. For example, in Australia the gap between non-production- and production workers smoking rates was 16% in 1976, which increased to 22% in 1995.

In line with previous studies, the current study re-confirmed that the prevalence of tobacco use is higher among production workers (23.5%) than non-production workers (19.2%). Similar findings were reported from other nationally representative surveys [15,26,27] (from Australia, USA, and UK), in which smoking rates were higher among production (includes bluecollar/ manual) workers than non-production (includes whitecollar/ non-manual) workers. A large (~100,000 individuals) cross-sectional survey conducted in the city of Mumbai [16] also reported that the prevalence of tobacco use varied across occupational categories, with the highest tobacco use observed among unskilled workers (76%) and the lowest among professionals (45%). Studies in other Indian manufacturing worksites also reported high (ranges from 39% to 86%) prevalence of tobacco use among production workers [17-21]. Taken together, these findings indicate that tobacco use prevalence among production workers varies across India and is mostly higher than the prevalence among nonproduction workers or the general population [2]. Production workers are also exposed to occupational hazards and hence, are at dual health risk if they use tobacco, due to the possible synergistic effect between the exposures and tobacco use. Tobacco use initiation usually occurs before people enter the workforce but some organizational factors that may influence tobacco use are job stress, hazardous working conditions, pace of work, and the functional meaning of tobacco use among workers [28]. Smokers are known to have greater absences from work, more sick days per year, and higher health care cost than never smokers [15]. Therefore, tobacco control strategies that are targeted at production workers and implemented at workplaces will be beneficial for employer and employee in India as it was beneficial in HICs [15].

Studies in India from worksites [18-21] and from the general population [16,17,29-32] have reported an inverse association between SES (in terms of education and economic index) and tobacco use, with the highest tobacco use among those with lowest SES. In this study, in addition to an increase in current tobacco-use with decreasing education, past tobacco use decreased with decreasing education. Similar to a large crosssectional survey in Mumbai, [32] our study also shows that the proportion of quitters was more than double among workers with higher education (12 or more years of formal education) than workers with lower education. Education is also strongly correlated with career choices, income, and health behaviors. While most professional jobs have intrinsic educational requirements, the same does not always apply to many manufacturing occupations. In our study, even though less than 10% of production and non-production workers reported their education as 6 years or below, twice as many production workers (30%) compared to non-production (15%) workers were from the lowest economic index. Thus, disparities in tobacco use and tobacco use cessation across occupational categories reflect not only occupational differences, but also the larger structural forces which shape workers’ lives, beyond the work environment. Therefore, more studies might help to understand the exact reasons why certain occupational categories such as production workers continue to use tobacco at higher rates than their non-production counterparts.

As tobacco use is the most important preventable risk factor for non-communicable diseases (NCDs: the total burden of NCDs in India [33] is expected to rise from 40% in 1990 to about 75% by 2030), through knowledge of related work context factors may be useful for prevention of NCDs in workers. NCDs also place a significant financial burden on the family as only about 10% of the Indian population is covered by some form of health insurance, resulting in a greater proportion of out-of-pocket expenditures [34,35]. The Indian economy had an estimated loss of nine billion dollars in 2005 which is expected to rise to 54 billion dollars by 2015 due to loss of labour supplies and savings attributable to NCD causes. 36 In India, over 90% of 500 million workers work in unincorporated, unorganized sectors mostly earning less than US $ 1.25 a day [36,37], currently there is no comprehensive legislation for occupational health, safety and health promotion that covers employees in all economic sectors [36]. This becomes highly relevant when we observe from our study that the production workers who used tobacco were more likely to report their own health status as not being excellent compared to production workers who did not use tobacco. The problem gets compounded because of inadequacies in the health care infrastructure [36]. The occupational disparities in tobacco use behaviors observed in our study and other studies [15-17,26,28] point toward opportunities to target high-risk populations, such as production workers through worksite tobacco control programs. Through such programs, it is possible to influence large groups of people (e.g. 500 million workers in India [36]) and provide supports for tobacco use cessation [38,39]. Effectively addressing health inequalities requires new sets of interventions, which account for disparities in social determinants of health. Worksites also provide an opportunity for sustained peer group support for quitting tobacco and modifications in social norms toward support for not using tobacco, thereby helping workers stay tobacco free, including the convenience of on-site cessation programs for employees [21,38,39].

Previous studies found that workers in worksites with smoke-free policies were more likely quit smoking than workers in worksites that had no such policy [26,38]. Prior research also highlighted the importance of co-workers’ support in quitting smoking [28,39]. In India, tobacco is consumed in both smoking and smokeless forms, but tobacco control policies are mostly focused on smoking forms only. This was supported by the observation from this study that production workers who use smokeless tobacco were more likely to report their workplaces did not have policies prohibiting tobacco use compared to corresponding workers who smoke or do not use tobacco. Additionally, the prevalence of current and past tobacco use was associated with coworkers’ tobacco use. A possible explanation for this finding might be that workers generally spend much of the day with their co-workers, and co-workers’ behavior influences an individual’s behavior. Other studies in India have also reported similar associations of an individual’s tobacco use with their friend’s or their co-workers’ tobacco use [17-21,39]. Thus, the current study underscores the importance of the role of coworkers; tobacco use patterns and worksite policies on tobacco control. The findings from this study point towards the importance of having worksite tobacco control policies that cover for both smoking and smokeless forms, in order to support reductions in tobacco use among workers.

It is important to note several limitations to this study. This was a cross-sectional survey and findings rely on self-reported tobacco use without any biochemical validation. Biochemical validation of tobacco use was not feasible mainly because the appearance of drug testing would likely be resisted in a manufacturing worksite setting and also due to low sensitivity in detecting use of smokeless tobacco use reported from previous Indian studies [40,41]. We took numerous precautions to minimize reporting bias. We used anonymous surveys and assured confidentiality by informing workers that the results would be communicated in aggregate and no individual information collected as a part of this study would be provided to the employer. This helped and is reflected in the high participation rate in the survey; on average, 9 out of 10 workers participated in the baseline survey.

Conclusion

The high prevalence of tobacco use among production workers in India underscores the need for targeted cessation efforts especially for production workers. Production workers are less educated, are mostly from lowest economic group and are more likely to use tobacco and report their health status as poor. Creating a work environment supportive of tobacco control, including through well-enforced tobacco control policies for all forms of tobacco and through involving coworkers is very important. This can be achieved through programs that provide guidance to users on quitting their tobacco use and to non-users to support users to quit tobacco use, which may ultimately reduce tobacco-related morbidity and mortality. Future worksite interventions need to take into account the social determinants that increase the risk of tobacco use among workers. The final results of the MWTCS may provide insights as well as direction to guide tobacco control efforts among manufacturing workers in India and potentially in other low- and middle-income counties.

Acknowledgements

The authors wish to thank the numerous investigators and staff members in India and USA who contributed to this study, including from the US, Linnea Benson-Whelan, Laura Shulman Cordeira, and Lorraine Wallace; and from India, Mayuri Sawant, Juhi Gautam, Bharti Prabhakar, and V. N. Prabhu. In addition, this work could not have been completed without the participation of the 22 worksites in Mumbai, participants in the formative research conducted to plan this study, the study’s scientific advisory board, and the staff of the Healis- Sekhsaria Institute for Public Health.

Author Contributions

Mangesh S. Pednekar was a Co-Principal Investigator for the Mumbai Tobacco Control Study in India. He conceptualized this paper and took lead on writing the paper.

Eve Nagler was a Co-Investigator for the Mumbai Tobacco Control Study. She contributed to the writing and finalizing the manuscript.

Pratibha Pawar was a Research Scientist for the Mumbai Tobacco Control Study. She contributed to the writing the manuscript.

Glorian Sorensen was the Principal Investigator for the Mumbai Tobacco Control Study. She contributed to writing and finalizing the manuscript.

Sameer Narake was a statistician for the Mumbai Tobacco Control Study. He contributed to the data analysis and writing the manuscript.

Anne M. Stoddard was a Co-investigator for Mumbai Tobacco Control Study. She contributed to the writing and finalizing the manuscript.

Prakash C. Gupta was Co-Principal Investigator for the Mumbai Tobacco Control Study in India. He contributed to writing and finalizing the manuscript.

Funding

This work was funded by the National Cancer Institute, Grant number R01 CA140304.

References

1. WHO Report on the Global Tobacco Epidemic (2008) The MPOWER Package. Geneva, World Health Organization.
2. https://www.who.int/tobacco/surveillance/en_tfi_india_gats_fact_sheet.pdf
3. Jha P, Jacob B, Gajalakshmi V, Gupta PC, Dhingra N, et al. (2008) A nationally representative case-control study of smoking and death in India. N Engl J Med 358: 1137-1147.
4. Reddy KS, Gupta PC (2004) Report on Tobacco Control in India. New Delhi, India: Ministry of Health and Family Welfare, Government of India.
5. Gupta PC, Pednekar MS, Parkin DM, Sankaranarayanan R (2005) Tobacco associated mortality in Mumbai (Bombay) India. Results of the Bombay Cohort Study. Int J Epidemiol 34: 1395-1402.
6. Pednekar MS, Gupta PC (2007) Prospective study of smoking and tuberculosis in India. Prev Med 44: 496-498.
7. Murthy P, Saddichha S (2010) Tobacco cessation services in India: recent developments and the need for expansion.Indian J Cancer 47 Suppl 1: 69-74.
8. Varghese C, Kaur J, Desai NG (2012) Initiating tobacco cessation services in India: challenges and opportunities. WHO South-East Asia J Public Health 1: 159-168.
9. Tripathy JP (2013) Quitting smoking: challenges and the way forward in the Indian scenario. Current Science 104:1006-1007.
10. Goenka S, Tewari A, Arora M, Stigler MH, Perry CL, et al. (2010) Process evaluation of a tobacco prevention program in Indian schools--methods, results and lessons learnt. Health Educ Res 25: 917-935.
11. Jayakrishnan R, Mathew A, Uutela A (2013) Multiple Approaches and Participation Rate for a Community Based Smoking Cessation Intervention Trial in Rural Kerala, India. Asian Pacific J Cancer Prev 14: 2891-2896.
12. Kumar MS, Sarma PS, Thankappan KR (2012) Community-based group intervention for tobacco cessation in rural Tamil Nadu, India: a cluster randomized trial. J Subst Abuse Treat 43: 53-60.
13. Gupta PC, Mehta FS, Pindborg JJ, Aghi MB, Bhonsle RB, et al. (1986) Intervention study for primary prevention of oral cancer among 36 000 Indian tobacco users. Lancet 1: 1235-1239.
14. Gupta PC, Mehta FS, Pindborg JJ, Bhonsle RB, Murti PR, et al. (1992) Primary prevention trial of oral cancer in india: a 10-year follow-up study. J Oral Pathol Med 21: 433-439.
15. Smith DR (2008) Tobacco smoking by occupation in Australia and the United States: a review of national surveys conducted between 1970 and 2005. Ind Health 46: 77-89.
16. Sorensen G, Gupta PC, Pednekar MS (2005) Social disparities in tobacco use in Mumbai, India: the roles of occupation, education, and gender. Am J Public Health 95: 1003-1008.
17. Prabhakar B, Narake SS, Pednekar MS (2012) Social disparities in tobacco use in India: the roles of occupation, education and gender. Indian J Cancer 49: 401-409.
18. Ansari ZA, Bano SN, Zulkifle M (2010) Prevalence of tobacco use among power loom workers - a cross-sectional study. Indian J Community Med 35: 34-39.
19. Panigrahi A, Das BC, Panigrahi M (2012) Tobacco use among daily wage laborers in the city of Bhubaneswar, Odisha, India. J Public Health 21: 57-61.
20. Bharadva NA, Gamit CL, Mansuri BM, Chaudhari VP, Kantharia SL (2014) Prevalence of Various forms of Tobacco Use and Factors Related with it in Diamond Cutting and Polishing Workers of Surat City, India. International Journal of Interdisciplinary and Multidisciplinary Studies 1: 27-32.
21. Pimple S, Pednekar M, Majmudar P, Ingole N, Goswami S, et al. (2012) An integrated approach to worksite tobacco use prevention and oral cancer screening among factory workers in Mumbai, India. Asian Pac J Cancer Prev 13: 527-532.
22. Fingerhut M, Nelson DI, Driscoll T, Concha-Barrientos M, Steenland K, et al. (2006) The contribution of occupational risks to the global burden of disease: summary and next steps. Med Lav 97: 313-321.
23. Steenland K, Burnett C, Lalich N, Ward E, Hurrell J, et al. (2003) Dying for work: The magnitude of US mortality from selected causes of death associated with occupation. Am J Ind Med 43: 461-482.
24. Sorensen G, Stoddard A, Hammond SK, Hebert JR, Avrunin JS, et al. (1996) Double jeopardy: workplace hazards and behavioral risks for craftspersons and laborers. Am J Health Promot 10: 355-363.
25. Cordeira LS, Pednekar MS, Nagler EM, Gautam J, Wallace L, et al. (2015) Experiences recruiting Indian worksites for an integrated health protection and health promotion randomized control trial in Maharashtra, India. Health Educ Res. Jun 30: 412-421.
26. Ham DC, Przybeck T, Strickland JR, Luke DA, Bierut LJ, et al. (2011) Occupation and workplace policies predict smoking behaviors: analysis of national data from the current population survey. J Occup Environ Med 53: 1337-1345.
27. Jefferis BJ, Power C, Graham H, Manor O (2004) Changing social gradients in cigarette smoking and cessation over two decades of adult follow-up in a British birth cohort. J Public Health (Oxf) 26: 13-18.
28. Sorensen G, Barbeau E, Hunt MK, Emmons K (2004) Reducing social disparities in tobacco use: a social-contextual model for reducing tobacco use among blue-collar workers.Am J Public Health 94: 230-239.
29. Gupta PC, Ray CS (2007) Tobacco, education & health. Indian J Med Res 126: 289-299.
30. Rani M, Bonu S, Jha P, Nguyen SN, Jamjoum L, et al. (2003) Tobacco use in India: prevalence and predictors of smoking and chewing in a national cross sectional household survey. Tob Control 12: e4.
31. Thakur JS, Prinja S, Bhatnagar N, Rana S, Sinha DN, et al. (2013) Socioeconomic inequality in the prevalence of smoking and smokeless tobacco use in India. Asian Pac J Cancer Prev 14: 6965-6969.
32. Gupta PC (1996) Survey of sociodemographic characteristics of tobacco use among 99,598 individuals in Bombay, India using handheld computers. Tob Control 5: 114-120.
33. Lopez AD, Murray CC (1998) The global burden of disease, 1990-2020. Nat Med 4: 1241-1243.
34. Patel V, Chatterji S, Chisholm D, Ebrahim S, Gopalakrishna G, et al. (2011) Chronic diseases and injuries in India. Lancet 377: 413-428.
35. World Health Organization Country Cooperation Strategy: India.
36. Babu AS, Madan K, Veluswamy SK, Mehra R, Maiya AG, et al. (2014) Worksite health and wellness programs in India. Prog Cardiovasc Dis 56: 501-507.
37. Central Intelligence Agency. The world factbook: India.
38. Cahill K, Moher M, Lancaster T (2014) Workplace Interventions for Smoking cessation. Cochrane Database Syst Rev. 2: CD003440.
39. Nagler EM, Sinha DN, Pednekar MS, Stoddard AM, Gupta PC, et al. (2015) Social contextual factors and tobacco use among Indian teachers: insights from the Bihar School Teachers' Study. Prev Med 74: 24-30.
40. Balhara YP, Jain R, Sundar AS, Sagar R (2012) Use of cotinine urinalysis to verify self-reported tobacco use among male psychiatric out-patients. Lung India 29: 217-220.
41. Dhavan P, Bassi S, Stigler MH, Arora M, Gupta VK, et al. (2011) Using salivary cotinine to validate self-reports of tobacco use by Indian youth living in low-income neighborhoods. Asian Pac J Cancer Prev 12: 2551-2554.