Socioeconomic differences in nicotine exposure and dependence in adult daily smokers

The role of disadvantaged social class in tobacco use is increasingly being recognized as a critical factor in tobacco use behaviors. In the recent National Cancer Institute Monograph 22 “A Socioecological Approach to Addressing Tobacco-Related Health Disparities,” it was noted that historically, individuals with higher incomes and levels of education were more likely to be smokers [1]. This has changed dramatically in the past few decades where smoking is now a habit that occurs predominantly in persons with a high school degree or less. Indicators of socioeconomic status (SES) such as levels of education and income show that low levels of educational achievement and poverty are major determinants of tobacco use and lower smoking cessation rates [2‐6]. Yet another indicator of SES is occupation. As with education, tobacco use varies by occupation [4, 7], and is highest in mining, construction, vehicle mechanics and operations [8] [9]. Among the unemployed, smokers have longer-term unemployment and are paid less when obtaining jobs than unemployed non-smokers [10].

The reasons for these disparities may include early exposure to smoking, social pressure to smoke, lack of access or knowledge of inexpensive smoking cessation aids, stress, greater exposure to tobacco advertising and lack of efficacy of anti-tobacco messaging [11]. Smokers with low SES have higher smoking-related adverse health outcomes [4‐6, 12‐17]. Further, these health disparities have increased over the years [18]. While the need for smoking cessation in these individuals is a major public health goal, it has also been argued that decreasing smoking prevalence in these smokers should also be considered as a poverty reduction strategy [19, 20].

Collectively SES has been characterized in relation to tobacco use, age at initiation, and tobacco cessation. Individually, lower educational attainment has been shown to predict increasing number of cigarettes smoked in current smokers [21]^, [22]^, [23] and with years of smoking [24, 25]. Similar findings have been reported for income [24, 25].

However, very little is known whether SES affects levels of nicotine exposure and dependence in smokers. It is the nicotine in tobacco that is ultimately responsible for tobacco use being a leading cause of preventable mortality worldwide [26]. Lower education but not income was associated with a higher Fagerström Test for Nicotine Dependence score in current smokers of the Finnish FINRISK study [27]. Data from the National Health and Nutrition Examination Surveys (NHANES III and NHANES 1999–2000) and the Heath Survey for England (1993–1996, 1998, 2001) provide support that lower SES might increase nicotine exposure and dependence. The concentration of cotinine, the immediate metabolite of the addicting agent nicotine, was higher with lower individual educational attainment in both surveys, and with higher neighborhood deprivation in the NHANES surveys [28, 29]^, [30].

The current study’s main aim is to determine how SES affects levels of nicotine exposure and dependence. We hypothesized that lower SES (lower education and income) is associated with greater nicotine exposure and dependence. Additionally, since a wide range of SES variables beyond education and income were collected on each participant we used a principal components analysis to create a SES summarized variable.

The Pennsylvania Adult Smoking Study (PASS) is a cross-sectional study of 352 adult cigarette smokers, completed in 14 counties of central Pennsylvania [31]. The sample size was based on effect sizes for cotinine between high and low SES groups, based on previous findings of sample means and standard deviations for cotinine [32]. A sample size of 280 yielded 80% power at an alpha of 0.05 for moderate effect sizes. Eligible subjects were ages 18–65 who currently smoked daily for at least one or more years. Subjects were recruited from June 2012 to April 2014, using primary recruitment methods reliant on internet and social media, radio advertisements, posted flyers, and word of mouth. Eligible participants gave written consent and attended two study visits, and upon completion, were provided with compensation. This study received approval from the Penn State Hershey College of Medicine Institutional Review Board (Hershey, Pennsylvania, USA).

There is a vast literature on the role of SES and tobacco use, yet many aspects of this relationship remain underexplored. In the current study, lower SES was associated with a higher number of cigarettes smoked per day, which is consistent with previous studies. We also found that cigarettes smoked per day was highest among the unemployed. Little is known about the smoking habits of the unemployed. In the Framingham Heart Study Offspring Cohort, men but not women who smoked and became unemployed increased their daily cigarette consumption [42]. These data indicate that unemployment or the stressors and lack of support structures associated with unemployment has an effect on cigarette consumption independent of low income.

SES is a construct that measures the concept of social position or class, or the ability to obtain desired resources such as material goods or knowledge [43]. The common core elements widely used to measure SES are education, income (if available), and employment. Other approaches emphasize assets rather than income as a better measure of stable SES. Composite SES measures have been developed [3, 35, 37] but the validity and reliability are often not known [44]. Information on individuals may not be known and SES classification may be based on group level measurements such as neighborhood characteristics [35, 37, 40]. In our study, neighborhood characteristics (e.g. county income) did not add any predictive power of nicotine exposure above the individual level variables identified in the PCA. The wide variety of measures has led to inconsistencies in findings on SES and health disparities [45]. The best measure may depend upon the specific outcome and population under study [46].

The effects of SES on smoking dose or levels of tobacco consumption have been more difficult to understand for a number of reasons. First, characterizing SES is challenging since it is a construct that cannot be directly measured by a single indicator. Income is considered an important component of SES but is often not assessed or difficult to analyze in health research due to nonresponse [47]. Education has been used commonly as a proxy. We employed methods that have been developed to elicit higher response rates including initial broad income categories followed by more specific questions on exact income and additional assurances about the confidentiality of these questions and their importance. While a number of SES indicators were collected including family members’ level of education, and individual questions on pension plans, health insurance and savings account as well as county income data, household income data was the most strongly correlated variable in the SES PCA, and was individually related to key outcomes as well. There are few reports of smoking frequency by income at the individual level. In England, a composite scale of income, employment status and other SES indicators was used to show that lower SES was associated with more cigarettes per day than higher SES but the extent of differences also differed by geography [48]. In Australia, smokers who smoke more than 15 cigarettes per day and have difficulty quitting are more likely to live in disadvantaged geographic areas [17]. In PASS, it was observed that heavier smokers (> 20 CPD) had household incomes nearly $10,000 per year less than lighter smokers. Another challenge in assessing the relationship between SES and levels of smoke exposure is the lack of objective levels of smoke exposure [5]. Misreporting of smoking habits is one concern but variability in smoke intake per cigarette also contributes to significant variability in this measure. There is, for example, over a 20-fold difference in blood levels of cotinine in one pack per day smokers [32]. Cotinine levels also tend to plateau at around 15–20 cigarettes per day, and some smokers who smoke two packs per day have the same cotinine levels as smokers who take fewer than one half pack per day. In the current study we therefore used biochemical levels of nicotine intake to determine levels of exposure in relation to SES. We used not just cotinine but both major nicotine metabolites (cotinine +3HC) as outcome measures, where total metabolites accounts for inter-individual and genetic variation in the metabolism of cotinine to 3HC.

The income and other SES data were analyzed using principal components analysis. In the PCA, the factor loadings for PC1 were all positive, indicating that each variable (household income, home dwelling type [house vs. other] and number of people living in the household), has an increasing relationship with SES. The relationship with number of people living in the household likely reflects the advantages of shared resources, where for example, two household members can carpool to work for the same cost as a single household member [49]. It should be noted that the PASS population is not highly socioeconomically deprived. The mean family household income was $59.2 k. In Pennsylvania, the median income was $55.7 k in 2015. Household number can also be used as a measure of low SES, based on the extent to which children of the opposite sex may have to share a bedroom or adults and children share bedrooms [50]. We did not collect this information but the positive loading for this variable indicates that it likely represents a benefit of shared resources. After adjusting for known predictors of cotinine, the results showed that the SES PC1 was associated with cotinine and TSNM but that the findings varied by the main contributor to PC2 (e.g. occupation category). Lower SES was significantly associated with higher levels of TSNM in the unemployed. Lower SES was associated with higher levels of TSNM in blue-collar workers but the relationship was not statistically significant. Grouping jobs into 3 main employment categories is convenient and provides for statistical power to detect differences but these broad groupings may hide underlying relationships. It is possible that a significant relationship exists in subgroups of blue-collar jobs. The topography data is consistent with these findings where unemployed subjects had significantly higher number of daily puffs compared to white collar workers, and blue-collar workers had intermediate levels between the two other groups. Surprisingly, there was a positive relationship between SES and TSNM in white-collar workers. This relationship may be due compensatory smoking within this group as increasing SES was associated with higher levels of puff volume in white collar workers. This relationship was not found in the two other job groups. It is possible for example, that the higher income white collar earners smoke more intensely due to greater stress than the lower income white collar workers and/or this may occur for one sex but not the other. Further work would be needed to explore this relationship. It should be noted that educational level was not a significant predictor of outcomes using PCA whether scaled as a categorical or continuous variable. This may be because it is correlated with income in our data and likely in general but the unit increase in income explains more variability than education. Additional file 3 shows one of our univariate models of education categories in relation to the tobacco smoke biomarkers. The adjusted R2 values are lower than that for the PCA models.

This study is not the first to indicate a relationship between SES and a biochemical measure of nicotine exposure. Higher levels of education predicted lower serum thiocyanate levels in the 1992 Czech MONICA study, and lower plasma cotinine levels in the 2007 FINRISK study [27, 51]. Lower income was associated with higher cotinine in NHANES and the Health Survey for England [28‐30]. Consistent with these findings, we found that our SES PC1 measure was predictive of lower levels of cotinine, 3HC and TSNM. However, this relationship changed after adjustment for significant covariates and further stratification by occupation. It also depended somewhat on the specific exposure measure used (e.g. COT, 3HC, TSNM). The inverse association between SES and biochemical markers was only apparent in the unemployed and blue-collar groups.

As noted, while SES and tobacco outcomes have been studied extensively, less is known about its relationship with degree of tobacco exposure. Similarly, there has been a dearth of research on SES and nicotine dependence. In the International Tobacco Control Four Country Survey (ITC-4; 2002 data), income and education was associated with a lower Heaviness of Smoking Index in three countries. Education but not income was significant for U.S. subjects, although significant differences for income were found when comparing the highest vs. lowest income levels. The current study showed that lower household income was significantly associated with the FTND score, and further that the scores varied by occupation type from 5.1 in the unemployed, to 4.4 in blue-collar workers, and 3.8 in white collar workers. These FTND findings are consistent with the differences in the relationship between SES and the biomarkers between these groups.

There are several limitations of our study including relatively few racial and ethnic minority participants. We had a small proportion of non-whites (12%), which is representative of the population demographics of central Pennsylvania but limits our ability to generalize the findings to a non-white population. Secondly, the design is cross-sectional, and that limits our ability to understand the causal relationships that underlie the statistical associations. Psychosocial stress, less social support, greater exposure to tobacco advertising, and living with another smoker in the household may all contribute to increased tobacco smoke exposure. We cannot conclude, for example, that unemployed or blue collar workers smoke more due to stress or other factors associated with their job status or have more opportunities to smoke.

The PASS study was specifically designed to include a number of measures of SES, and we used principle components to create a summary indicator of SES. In this population and with regard to smoking exposure as an outcome, the study indicates that important SES measures for smoking are household income, number in household, type of dwelling and occupation. In general, lower SES smokers smoke more frequently, have higher levels of dependence and tobacco smoke exposure.