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Socioeconomic Disparities in Community-Based Treatment of Tobacco Dependence [American Journal of Public Health](American Journal of Public Health Via Acquire Media NewsEdge) Objectives. We examined socioeconomic disparities in a community-based tobacco dependence treatment program. Methods. We provided cognitive-behavioral treatment and nicotine patches to 2739 smokers. We examined treatment use, clinical and environmental, and treatment outcome differences by socioeconomic status (SES). We used logistic regressions to model end-of-treatment and 3- and 6-month treatment outcomes. Results. The probability of abstinence 3 months after treatment was 55% greater for the highest-SES than for the lowest-SES (adjusted odds ratio [AOR]= 1.55; 95% confidence interval [CI]=1.03, 2.33) smokers and increased to 2.5 times greater for the highest-SES than for the lowest-SES smokers 6 months after treatment (AOR=2.47; 95% CI=1.62, 3.77). Lower-SES participants received less treatment content and had fewer resources and environmental supports to manage a greater number of clinical and environmental challenges to abstinence. Conclusions. Targets for enhancing therapeutic approaches for lower socioeconomic groups should include efforts to ensure that lower-SES groups receive more treatment content, strategies to address specific clinical and environmental challenges associated with treatment outcomes for lower-SES smokers (i.e., higher dependence and stress levels and exposure to other smokers), and strategies to provide longer-term support. (Am J Public Health. 2012;102:e8-e16. doi:10.2105/AJPH.2011.300519) Tobacco use is a leading contributor to socioeconomic health disparities in the United States.1-5 Americans with household incomes of $15000 or less smoke at nearly 3 times the rate of those with incomes of $50000 or greater.6 Quit attempts show no socioeconomic gradient, but successful cessation is associated with a considerable socioeconomic disparity7-15 that appears to be increasing.4,16,17 Community-based tobacco dependence treatment programs can reduce these disparities by providing all smokers with needed assistance; however, smokers of lower socioeconomic status (SES) often have poorer treatment outcomes.18-23 Examination of factors related to socioeconomic disparities in treatment outcomes may identify targets for enhancing therapeutic approaches for lowersocioeconomic groups. In health research, SES is a broad construct describing relative access to basic resources required to achieve and maintain good health.24,25 Common measures of SES (e.g., educational achievement, income, occupation, wealth) assess different, albeit related aspects of the construct but are generally limited by a lack of precision and difficulty classifying all groups. Educational achievement is considered a basic element of SES, captures important aspects of lifestyle and behavior, and is perhaps the most widely used proxy for SES because of its influence on future occupational opportunities and earning potential; however, household income is considered the best measure of available material resources, especially for those who are not primary wage earners in families.24,26,27 Composite measures incorporate and, therefore, adjust for different aspects of SES.28 In the United States, minority ethnic status often affects access to basic resources, but the magnitude of socioeconomic disparities is often greater than that between minority and majority ethnic groups in the United States, and the effects of minority ethnic status on smoking cessation are often reduced or eliminated after socioeconomic factors are taken into account.29-31 Nonetheless, in the United States, ethnic groups tend to live in different social and physical environments, and minority ethnic status often includes a constellation of stressors separate from and additive to SES.29 Moreover, lower SES coupled with tobacco use and minority ethnic status might lead to a unique set of multiple and cumulative disparities.29 Conceptual models propose that health disparities emerge because of higher levels of stress, less access to physical and environmental resources, greater environmental constraints, fewer affective and cognitive resources, and poorer health behaviors.26,32 Consistent with these models, SES is empirically related to smoking cessation through complex reciprocal relations among numerous clinical and environmental factors, including stress, coping resources, psychological factors, exposure to other smokers, and use of treatment resources.33-38 Cognitive-behavioral treatment of tobacco dependence can potentially address many of these factors, but little is known about the role these factors play when lower-SES smokers are provided with treatment. Disparities are prominent among numerous factors important to cessation and treatment of tobacco dependence. In the United States, minority ethnic groups such as African Americans and Hispanic Americans as well as lower- SES groups are less likely to receive advice about and assistance with smoking cessation from health care providers.39-41 Although minority ethnic groups are no less likely to quit when provided with nicotine replacement,42 they and lower-SES groups are less likely to have accurate information about nicotine replacement and less likely to use evidence-based tobacco dependence treatments.43-47 Lower-SES groups are less likely to be covered by smoking bans in the workplace and at home,48-50 and some lower-SES groups are more highly nicotine dependent than higher-SES groups.51 The role of many of these disparities in the treatment of tobacco dependence remains relatively unexamined. We examined socioeconomic disparities in a community-based tobacco dependence treatment program in Arkansas. We used statistical modeling of treatment outcomes to examine the independent contributions of SES, ethnicity, and other factors. Consistent with conceptual models and previous studies, we hypothesized that the lowest-SES participants would have less treatment use and the greatest clinical and environmental challenges to achieving and maintaining abstinence from tobacco use. METHODS All English-speaking smokers (n=2739) who attended treatment for tobacco dependence at 14 sites from 2005 to 2008 were included. Sites included 10 area health education centers, 2 regional medical centers, 1 women's health clinic, and 1 medical center primary care clinic. Tobacco Dependence Treatment Behavioral treatment consisted of 6 weekly closed-group 60-minute sessions of manualdriven, multicomponent cognitive-behavioral therapy consistent with the Public Health Service Clinical Practice Guideline.18,23,52-54 A lay overview of the biopsychosocial underpinnings of tobacco dependence was addressed, including physiological components (i.e., tolerance and withdrawal), learning components (i.e., triggers or cues, tobacco use as reinforcement), and use of tobacco to cope with nicotine-related (i.e., lowered plasma nicotine levels) and nicotine-unrelated (i.e., managing stress) events. Flexibility in establishing the quit date was permitted. Treatment components were delivered as per written treatment protocols and included scheduled gradual rate reduction, self-monitoring, stimulus control, problem-solving, conflict management, cigarette refusal training, enhancing social support, goal setting, relapse prevention, and stress management. Groups consisted of 5 to 10 participants. Individual sessions were offered when participants were unable to attend group sessions. We provided free nicotine patches in 2-week increments with a prescription and treatment attendance and encouraged 12 weeks of patch use. Structured treatment protocols, standardized quarterly site reviews, and weekly supervision meetings addressed treatment fidelity. Procedures This study was approved by the institutional review board at the University of Arkansas for Medical Sciences and was part of a state-sponsored treatment and referral system that used quitline media promotion, health care provider training, a smoke-free workplace program, and a faxed referral program to recruit participants into 2 treatment programs (in-person and telephone quitline). The Arkansas Department of Health managed the promotion for the quitline. The first author (C. E. S.) managed all other programs. Protocols dictated that all participants be offered both quitline and inperson treatment on initial contact. All staff scheduled participants for both treatment programs. Both treatment programs provided the same manual-driven treatment protocols and used the same centralized electronic treatment record database in which all participant contacts were recorded. Tobacco treatment specialists certified by the ACT Center University of Mississippi Medical Center training program delivered treatment. Tobacco treatment specialists had multiple responsibilities, including helping community health care providers to identify, treat, and refer tobacco users; responding to on-site providers' requests to discuss tobacco use with patients; conducting intakes and delivering group and individual treatment; entering intake and treatment data into the database; and devoting 10% to 50% of their time to delivering quitline treatment as needed. Measures We collected standard demographic, tobacco use, and clinical information during the intake interview. Socioeconomic status. Household income was assessed with 6 categories used by the US Census Bureau (<$10000, $10000-$14999, $15000-$24 999, $25000-$34 999, $35000-$49999, [double dagger]$50000). Use of these categories allowed the program to make direct comparisons with population data sets that used the same categories. Educational level was assessed with years of completed education, a continuous variable that was later grouped into 4 categories (<12 years of education, 12 years of education, 13-14 years of education, [double dagger]15 years of education). A composite index for SES was created that incorporated both household income and educational level. Values were assigned to income level (lowest= 1 to highest=6) and educational category (lowest=1 to highest=4). Adding the income and educational level values resulted in a discrete analogue SES scale (range=2-10). This SES scale also was collapsed into 3 SES levels: SES1 (2-4), SES2 (5-7), and SES3 (8-10). Fagerström Test for Nicotine Dependence. Scores from 5 to 10 indicate higher nicotine dependence levels.55,56 Perceived Stress Scale-4. Higher scores on this 4-item questionnaire indicate greater stress. Mean national levels range from 4.2 to 4.7. Mean levels for smokers range from 4.8 to 5.9.57,58 Motivation, self-efficacy, and concern about weight gain. Motivation, self-efficacy, and concern about weight gain were measured on a scale of 0 to 10, with 0="not at all" and 10= "the most ever" in response to the following questions: "How much do you want to quit smoking?"; "How confident are you that you can quit using tobacco and stay quit for good?"59-61; and "How concerned are you about gaining weight after you quit?"62 Smoking policy at work and at home. The smoking policies in participants' workplaces and homes were assessed with the following options: (1) no smoking anywhere inside or outside; (2) no smoking inside, but smoking is allowed outside; (3) smoking is allowed in certain areas inside; or (4) smoking is allowed anywhere inside. Treatment use. We measured use of treatment with 1. number of treatment contacts, 2. amount of treatment content (sessions 1-6), 3. whether patches were dispensed, 4. number of nicotine patches dispensed, 5. whether the behavioral treatment was completed, and 6. treatment modality (individual, group, or both). Treatment completion was defined as completing 5 of the 6 sessions of content. Outcome Assessment Smoking status at the last treatment contact was used as the end-of-treatment outcome. Three and 6 months after the end of treatment, a specially trained follow-up interviewer attempted contact with all participants by telephone and asked, "How many cigarettes are you smoking on a usual day?," followed by "Have you smoked any cigarettes in the past 7 days?" Participants were considered abstinent if they answered "zero" and "no," respectively. Seven-day point prevalence is an appropriate, valid, and reliable method for assessing abstinence in this type of program.63,64 Data Analysis We analyzed data with PASW Version 18 (SPSS Inc, Chicago, IL). We calculated descriptive statistics and conducted analysis of variance and v2 analyses to examine differences among participants contacted and lost to follow- up 6 months after treatment and to examine differences among the 3 SES levels (a=0.05). Bonferroni correction was used to manage familywise error in multiple comparisons.65 Standardized residuals (R= {observed-expected}/[square root of expected]) were used to identify sources of significant differences (standardized residual>2.00) for categorical variables.66 Abstinence rates were calculated for end-oftreatment and 3- and 6-month treatment outcomes by SES with 2 methods to accommodate missing data: (1) intention to treat, imputing all participants lost to follow-up as smoking, and (2) complete-case analysis, eliminating participants lost to follow-up from the analysis.67,68 We used logistic regressions to model abstinence rates at the end of treatment and 3 and 6 months after treatment, accounting for demographic, treatment use, and clinical or environmental factors, with SES as the primary focus. A backward conditional process eliminated variables with significance levels greater than .1 in a stepwise manner. All variables found to differ significantly by SES level (see Table 3) were included in preliminary analyses in which demographic, treatment use, and clinical or environmental factors were analyzed in blocks. RESULTS Participants were primarily middle-aged (mid 40s) and lower SES (Table 1). Nearly 1 in 5 were members of a minority ethnic group (African American, multiethnic, American Indian, Hispanic, Asian or Pacific Islander, or other); about 10% had the equivalent of a bachelor's degree or greater; and just 42.7% had private health insurance. Participants were highly motivated and moderately confident about quitting smoking but also were highly dependent and smoked, on average, more than a pack a day, starting in midadolescence, and had smoked for nearly 3 decades. Although most were ready to make a quit date at intake, most had not made a quit attempt recently. Levels of stress were relatively high.57 Of those who were employed, about 80% reported that their efforts to quit were supported by their workplaces and that they worked in smoke-free indoor environments, but more than half of those who were partnered had smoking partners, and nearly two thirds allowed smoking inside their homes. Most participants were referred by health care providers (46.6%), followed by word-of-mouth (25.5%); workplaces (9.7%); print, brochures, and Web site or other (9.1%); and television and radio (8.9%). Note that no television and radio promotion for inperson treatment was used. Those referred by television or radio were scheduled for inperson treatment by quitline intake staff. Treatment Use The treatment completion rate was 40.8%. Participants completed a mean of 3.6 (SD=2.0) sessions of content in a mean of 4.4 (SD=3.3) treatment contacts. Nicotine patches were dispensed to 39.5% of the participants, with a mean of 15.4 (SD=28.2) patches dispensed per participant. Almost half of the participants (47.7%) were treated entirely in groups; 37.7% attended only individual sessions; and 14.6% attended both group and individual sessions. Differences Between Participants Contacted and Lost to Follow-Up Those contacted were more likely to be retired, older, and of higher SES and to have smoked for more years and started smoking at a later age compared with those lost to followup 6 months after treatment. They had lower stress levels and were more likely to have a nonsmoking partner and less likely to have no health insurance; they were more likely to have never made a quit attempt and to have previously sought professional help with quitting than were those lost to follow-up. Those contacted were less likely to be referred by workplaces and less likely to be unemployed than were those lost to follow-up. Greater treatment use was associated with a greater likelihood of being contacted (Table 2). Treatment Outcomes Treatment outcomes were available for 100% of the participants at end of treatment, 67.4% at 3 months after treatment, and 62.0% at 6 months after treatment. The end-of-treatment tobacco use abstinence rate was 37.7%. The intention-to-treat abstinence rates were 19.0% at 3 months and 16.9% at 6 months after treatment. The complete-case analysis abstinence rates were 28.2% at 3 months and 27.3% at 6 months after treatment. Socioeconomic Disparities The composite SES measure differentiated SES groups along appropriate dimensions. SES1 individuals were the least likely to be employed and to have private health insurance and the most likely to be disabled, to be covered by Medicaid and/or Medicare, or to have no health insurance. SES1 composed 35.4% of the sample, SES2 40.5%, and SES3 24.1%. Treatment outcomes. Significant differences in abstinence rates across SES levels were found at the end of treatment and 3 and 6 months after treatment (Figure 1). Consistent with our expectations, SES1 was the least likely to be abstinent at end of treatment (v2 2=7.33; P=.03) and 3 months (v2 2=11.00; P<.01) and 6 months (v2 2=31.07; P<.01) after treatment. Six-month differences were maintained with complete-case analysis abstinence rates (23.5%, 25.8%, and 34.0%; v2 2=14.52; P<.01), suggesting that the findings were not an artifact of a greater proportion of lower-SES participants being lost to follow-up and thus imputed as smoking. Treatment use. Contrary to expectations, the SES1 group was the most likely to receive nicotine patches, although no differences were found in the mean number of patches dispensed. Although there were no differences in the mean number of treatment contacts, the lowest-SES groups received the least amount of treatment content and were the least likely to complete treatment. SES1 patients were the most likely to be treated entirely with individual sessions (Table 3). Clinical and environmental factors. Consistent with expectations, lower-SES groups smoked more cigarettes per day, had higher Fagerström Test for Nicotine Dependence scores, started smoking at an earlier age, and smoked more years than did SES3 individuals. SES1 individuals were the least likely to have sought professional assistance in the past; however, SES1 individuals were significantly more motivated to quit and confident about quitting than were SES2 and SES3 individuals. SES1 individuals reported the highest Perceived Stress Scale-4 scores and had the highest proportion of current psychiatric diagnoses. SES1 individuals were the least likely to report that their workplace supported quitting, the most likely to report that smoking was allowed inside their workplaces, the most likely to have smoking partners, and the most likely to allow smoking inside their homes. SES1 participants were the most likely to be referred by health care providers, the least likely to be referred by television or radio advertisements, and the least likely to be referred by workplaces. SES3 participants were the least likely to be referred by word of mouth (Table 3) Models predicting treatment outcomes. SES was not significantly associated with end-oftreatment outcomes but was significantly associated with 3- and 6-month treatment outcomes. With all other factors accounted for, the odds of achieving abstinence 3 months after treatment increased by a factor of 1.06 (95% CI=1.00, 1.11) for every 1-unit increase in the SES scale (range=2-10); the odds of achieving abstinence 6 months after treatment increased to 1.12 (95% CI=1.06, 1.18) for every 1-unit increase in the SES scale. The probability of abstinence 3 months after treatment was 55% greater for the highest-SES participants (SES=10) than for those with the lowest SES (SES=2; AOR=1.55; 95% CI= 1.03, 2.33) and increased to 2.5 times greater for the highest-SES participants 6 months after treatment (AOR=2.47; 95% CI=1.62, 3.77). The final model of 6-month treatment outcomes included 8 variables: SES, amount of treatment content, treatment modality, cigarettes per day, confidence level, Perceived Stress Scale- 4, smoking policy in the home, and referral source (Table 4). The odds of abstinence increased for those who received more treatment content and were more confident about quitting and decreased as the number of cigarettes smoked per day and the level of stress increased. Those with home smoking bans were more likely to be abstinent than were those who were referred from sources other than workplaces. Those who were treated entirely in groups were less likely to be abstinent than those who were treated with both individual and group sessions and those treated with individual sessions only. Ethnicity was not significantly associated with treatment outcomes at any point; nonetheless, minority ethnic smokers were disproportionately represented in the socioeconomic gradient: 14% of those with an SES score of 10 and 33% with an SES score of 2 were members of a minority ethnic group. DISCUSSION Significant socioeconomic disparities were found in this community-based tobacco dependence treatment program. These disparities affected treatment outcomes and increased in magnitude as time elapsed from end of treatment to 6 months after treatment (Figure 1). Consistent with conceptual and empirical models, lower-SES participants received less treatment content and had fewer resources and environmental supports to manage a greater number of clinical and environmental challenges. These findings suggest that targets for enhancing therapeutic approaches for lower- SES groups should include efforts to ensure that lower-SES groups receive more treatment content, strategies that address the specific clinical and environmental characteristics that affect treatment outcomes for lower-SES smokers, and strategies to manage long-term clinical and environmental challenges presented earlier in the treatment protocol. These strategies should include a more proactive approach to problem-solving barriers to establishing smoking bans in the home and implementing more intensive and tailored approaches to managing stress and bolstering cognitive and affective resources. Consistent with other studies, these findings support efforts to provide more intensive pharmacotherapy for lower-SES groups to help minimize treatment outcome disparities associated with higher dependence levels and heavier smoking69; however, these findings indicate that methods to enhance medication use in community treatment programs are also needed. Given that the magnitude of the disparities increased as the time from treatment end increased, innovative methods to provide continued support in the months after treatment also should be considered. Although the effects of ethnicity were not significant in our models, a greater proportion of minority ethnic group participants were of lower SES, suggesting that when considering the needs of lower-SES groups, the perspectives of minority ethnic smokers who might constitute a significant proportion of that group need to be considered. This study contributes to the few studies that examined community-based in-person treatment programs.70 Outcomes were comparable to those found in more controlled settings, even though overall participant characteristics were indicative of poorer outcomes.19,53 Behavioral treatment use was relatively high for communitybased treatment, but nicotine patch use was relatively low. Although the prescription requirement might have posed a barrier, prescriptions often were provided without additional visits, and most participants were referred by health care providers. With a mean of more than 4 treatment contacts, attendance did not limit patch dispensation. Given the lack of third-party coverage for cessation medications at the time in Arkansas, the likelihood that participants received medications fromother sources was small. Consistent with previous studies and tobacco treatment specialist reports (not shown), nonuse of nicotine patches might have been related to the desire to quit without medication.71 More research is needed to examine systematic and attitudinal barriers to use of free nicotine replacement in treatment programs for lower-SES groups. The lowest-SES smokers were the most frequently referred by health care providers. These findings contradict previous findings but show that health care providers, if cultivated, can be an important referral source for lower- SES smokers. In this study, the site agreement included annual 1-hour trainings in brief interventions. Tobacco treatment specialists were located in the health care environment, developed relationships with providers, and encouraged the use of fax referrals. These results were consistent with the notion that providers who are familiar with treatment programs and have training are more likely to refer.72,73 Higher-SES smokers were referred more frequently by television or radio advertisements for the quitline, supporting previous contentions that quitline television and radio advertisements enhance socioeconomic disparities.74,75 Future research is needed to examine the efficacy of new approaches to more effectively treat lower-SES smokers and reduce treatment outcome disparities as well as to ascertain whether these findings are applicable to quitlines. Although numerous states provide inperson treatment, telephone treatment is offered in every state and uniquely purported to access and treat lower-SES groups.70,76,77 We used a uniquely rich data set for exploring outcome associations from a regionally diverse sample of smokers. 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Tobacco cessation quitlines in North America: a descriptive study. Tob Control. 2007;16(suppl 1):i9-i15. 78. Tomson T, Bjornstrom C, Gilljam H, Helgason A. Are non-responders in a quitline evaluation more likely to be smokers? BMC Public Health. 2005;5:1-6. Christine E. Sheffer, PhD, Maxine Stitzer, PhD, Reid Landes, PhD, S. Laney Brackman, MPH, Tiffany Munn, BA, and Page Moore, PhD About the Authors Christine E. Sheffer, S. Laney Brackman, and Tiffany Munn are with the Department of Health Behavior and Health Education, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock. Maxine Stitzer is with Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD. Reid Landes and Page Moore are with the Department of Biostatistics, Fay W. Boozman College of Public Health. Correspondence should be sent to Christine E. Sheffer, PhD, City College of New York, Sophie Davis School of Biomedical Education, Department of Community Health and Social Medicine, Townsend Harris Hall Ste 400, 160 Convent Ave., New York, NY 10031 (e-mail: csheffer@ med.cuny.edu). Reprints can be ordered at http://www.ajph. org by clicking the "Reprints/Eprints" link. This article was accepted September 12, 2011. Contributors C. E. Sheffer developed the idea and the methods for the data collection, conducted several of the analyses, and wrote the article. M. Stitzer operationalized the idea and contributed to the writing of the article. R. Landes helped to shape the analyses, developed methods to present the data, and contributed to the writing of the article. S. L. Brackman contributed to the data collection methods and the writing of the article. T. Munn conducted some of the data analyses and contributed to the writing of the article. P. Moore contributed to the data analyses. Acknowledgments The programs mentioned in this study were funded by contracts from the Arkansas Department of Health. The data analysis and preparation of this manuscript were funded by a grant from the National Institutes of Health National Cancer Institute (1 R03 CA141995-01A1) and National Center for Research Resources (RR 020146). Human Participant Protection Institutional review board approval was obtained from Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock. (c) 2012 American Public Health Association |