Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2021 Jan 4.
Published in final edited form as: Drug Alcohol Depend. 2017 Oct 12;181:177–185. doi: 10.1016/j.drugalcdep.2017.09.015

Improving tobacco dependence treatment outcomes for smokers of lower socioeconomic status: A randomized clinical trial*

Christine E Sheffer 1, Warren K Bickel 2, Christopher T Franck 3, Luana Panissidi 1, Jami Pittman 1, Helen Stayna 1, Shenell Evans 1
PMCID: PMC7780926  NIHMSID: NIHMS913072  PMID: 29065390

Abstract

Introduction

Evidence-based treatments for tobacco dependence are significantly less effective for smokers of lower socioeconomic status which contributes to socioeconomic disparities in smoking prevalence rates and health. We aimed to reduce the socioeconomic gradient in treatment outcomes by systematically adapting evidence-based, cognitive-behavioral treatment for tobacco dependence for diverse lower socioeconomic smokers.

Methods

Participants were randomized to adapted or standard treatment, received six 1-hour group treatment sessions, and were followed for six months. We examined the effectiveness of the adapted treatment to improve treatment outcomes.

Results

Participants (n=227) were ethnically, racially, and socioeconomically diverse. The adapted treatment significantly reduced the risk ratio for days to relapse for the two lowest socioeconomic groups: SES1: RR =0.63 95% CI, 0.45, 0.88, p = 0.0013 (M=76.6 (SD 72.9) vs. 38.3 (SD 60.1) days to relapse); SES2: RR = 0.57 95% CI, 0.18, 0.70, p=0.0024 (M=88.2 (SD 67.3) vs. 40.1 (SD 62.6) days to relapse). Interactions between socioeconomic status and condition were significant for initial abstinence (OR= 1.26, 95% CI 1.09, 1.46, p=.002), approached significance for 3-month abstinence (OR= .90, 95% CI .80, 1.01, p < .071), and were not significant for 6-month abstinence (OR= .99 95% CI .88, 1.10, p=.795). No significant differences in long-term abstinence were observed.

Conclusion

Systematic adaption of evidence-based treatment for tobacco dependence can significantly improve initial and short-term treatment outcomes for diverse lower socioeconomic smokers and reduce inequities in days to relapse. Novel methods of providing targeted extended support are needed to improve long-term outcomes.

Keywords: Smoking cessation, treatment for tobacco dependence, lower socioeconomic status, Black or African American, relapse, disparities

1. Introduction

The prevalence of cigarette smoking among lower socioeconomic status (SES) groups in the US remains extraordinarily high (Jamal et al., 2016) and contributes significantly to smoking-related socioeconomic health inequities and costs (Kanjilal et al., 2006; Harper and Lynch 2007; Smith et al., 2009; Trinidad et al., 2011; Bosdriesz et al., 2015; Singh et al., 2015; Bosdriesz et al., 2016; Singh and Jemal 2017). At present, nearly 30% of adults with Medicaid, a low-income government-sponsored health insurance program, smoke compared with 15% of the population (Jamal et al., 2016) and 15% of all Medicaid costs are smoking-related (Xu et al., 2015). Few socioeconomic differences are observed in attempts to quit smoking (Kotz and West 2009; Reid et al., 2010; Christiansen et al., 2012); however, there is a significant socioeconomic gradient in cessation that is associated with a variety of social, clinical, environmental, and treatment-related factors (Hiscock et al., 2012; Sheffer et al., 2012b; Hiscock et al., 2013; Varghese et al., 2014; Hiscock et al., 2015).

Evidence-based treatment (EBT) for tobacco dependence greatly improves the odds of cessation (Fiore et al., 2008); however, lower SES groups do not benefit equally from EBT (Judge et al., 2005; Foulds et al., 2006; Fiore et al., 2008; Robles et al., 2008; Burgess et al., 2009; Sheffer et al., 2009; Hiscock et al., 2012; Sheffer et al., 2012b; Varghese et al., 2014; Nollen et al., 2017). Given the same EBT, lower SES smokers are significantly less likely to achieve short-term (ST) (Businelle et al., 2011; Hiscock et al., 2013) and long-term (LT) abstinence than higher SES smokers (Kotz and West 2009; Sheffer et al., 2012b; Varghese et al., 2014; Hiscock et al., 2015).

LT abstinence, defined as abstinence ≥ 6 months after the quit date (Hughes 2003), is an important milestone; but alone masks the processes required to achieve this milestone (Shiffman et al., 2006). Initial abstinence, defined as 24 hours of continuous abstinence (Hughes 2003; Shiffman et al., 2006) must be followed by avoiding the progression from lapse to relapse during ST abstinence, broadly defined as periods of abstinence > 24 hours and < 6 months (Hughes 2003). The UK smoking treatment services uses 4-week abstinence as a ST milestone (Judge et al., 2005; Hiscock et al., 2013). The number of days to relapse, called latency to relapse, provides incremental abstinence information from initial to LT abstinence (Hughes 2003; Shiffman et al., 2006).

SES is an index of social and economic position (Galobardes et al., 2006a; 2006b) and are racially and ethnically diverse in the US; however, Black Americans are substantially over-represented (Bureau 2010; Macartney et al., 2013) and racial differences in cessation are sometimes found after making statistical adjustments for SES (Trinidad et al., 2011; Kulak et al., 2016). Cognitive-behavioral EBT for tobacco dependence adapted for Black Americans significantly improves ST abstinence rates for Black smokers (Webb Hooper et al., 2017); however, improvements in LT abstinence rates were not observed and the socioeconomic gradient in outcomes was not reported. Given the magnitude of smoking-related socioeconomic disparities and the impact of these disparities on public health, simply establishing socioeconomic equity in EBT outcomes would represent progress and provide significant public health benefits.

In this study, we compared the effects of cognitive-behavioral EBT adapted for diverse lower SES smokers with standard cognitive-behavioral EBT using multiple abstinence milestones among socioeconomically, racially, and ethnically diverse smokers. We hypothesized that the adapted treatment would increase the latency to relapse and show significantly greater initial, ST, and LT abstinence rates among the lowest SES smokers with little effect on the highest SES smokers. Thus, the interaction between condition and SES on abstinence milestones were of primary interest. Increased efficacy among lower SES smokers was expected to result in improved overall efficacy.

2. Methods

2.1 Participants

Participants (n=227) were recruited in New York City by word of mouth, fliers in the community, and newspaper advertisements. Participants were eligible if they were ≥ 18 years of age, smoked daily, were ready to quit in 30 days, were able to engage in group treatment, had no regular use of other tobacco products, had reliable telephonic communication, had no contra-indications for nicotine patch use, were not currently using cessation medications, screened negative for drugs of abuse, drank < 20 alcoholic drinks per week, and attended at least one group treatment session. A socioeconomically, racially, and ethnically diverse sample was sought to enable socioeconomic comparisons and support external validity.

2.2 Materials

2.2.1 Standard Treatment (StdT)

The StdT was a well-established, multi-component, manual-driven cognitive-behavioral EBT for tobacco dependence with 6 weekly 1-hour group sessions used in numerous programs and studies (Schmitz et al., 1993; Smith et al., 2003; Payne et al., 2006; Sheffer et al., 2009; Sheffer et al., 2012a; Sheffer et al., 2012b; Sheffer et al., 2013; Varghese et al., 2014). StdT components included understanding and applying the cue-urge-smoking cycle, developing individualized strategies for managing cues and urges, self-monitoring, guided scheduled rate reduction, goal setting, stress management, problem-solving, conflict management, tobacco refusal training, relapse prevention, enhancing social support, and education about medication and the health effects of tobacco. The StdT participant workbook included treatment session content and psychoeducational materials.

2.2.2 Adapted Treatment (AdT)

The AdT was developed from the StdT with the goals of addressing treatment outcome disparities and the needs, experiences, and perspectives of diverse lower SES smokers in 6 weekly 1-hour group sessions. We used an established framework for adapting EBTs that included four broad steps:

  1. Information Gathering: Identify modifiable factors that have theoretical and/or empirical support for reducing treatment outcome disparities;

  2. Preliminary Adaptation Design: Incorporate data from Step 1 into a clinical and cultural adaptation;

  3. Preliminary Adaptation Tests: Pilot test the preliminary adaptation from Step 2, obtain community and treatment provider feedback; and

  4. Adaptation Refinement: Incorporate feedback from Step 3 into the final treatment manual (Barrera and Castro 2006; Lau 2006). See Evans et al., 2015 (Evans et al., 2015) for details.

2.2.3 Information Gathering

The team reviewed conceptual models of health disparities. Theoretical and empirical evidence indicated that health disparities emerge from complex reciprocal social, psychological, environmental, and biological determinants across the lifespan (Bandura 2001; Adler and Newman 2002; Baranowski et al., 2002; Gallo and Matthews 2003; Ghaed and Gallo 2007; Moolchan et al., 2007; Adler and Rehkopf 2008; Adler and Stewart 2010; Kawachi et al., 2010). The Adler et al. (2010) framework of health disparities was selected as the most comprehensive and applicable model (Adler and Stewart 2010). Factors empirically associated with socioeconomic disparities in cessation were mapped onto the Adler framework (Stronks et al., 1997; Gallo and Matthews 2003; Ferguson et al., 2005; Honjo et al., 2006; Siahpush et al., 2006a; Siahpush et al., 2006b; Fernander et al., 2007; Manfredi et al., 2007; Siahpush et al., 2007a; 2007; Kendzor et al., 2009; Siahpush et al., 2009; Businelle et al., 2011; Hiscock et al., 2012; Sheffer et al., 2012a; Sheffer et al., 2012b; Businelle et al., 2013; Kaplan et al., 2013; Bickel et al., 2014; Varghese et al., 2014). The factors determined to be modifiable included: Stress, negative affect, smoking in response to negative affect, delay discounting, locus of control, impulsiveness, smoking policies in the home, and treatment utilization.

2.2.4 Preliminary Adaptation Design

We adapted the treatment by including and/or emphasizing interventions to address the modifiable factors identified in the Step 1. We modified the clinical adaptation by systematically incorporating community values and perceptions. Values associated with many lower SES groups, such as greater sensitivity to social context, other-oriented emotional focus, and increased value on pro-social behaviors (Cote et al., 2011; Kraus et al., 2011) overlapped wholly with perspectives endorsed in the PEN-3 Model (Airhihenbuwa 1990; Airhihenbuwa 1992), a model for incorporating Black perspectives into health interventions. We cross-referenced each intervention component in each session with the elements of the PEN-3 Model (e.g., perceptions, enablers, nurturers). Community partners reviewed the resulting matrix and provided structured feedback. Community partners also recommended a participant “Toolkit” (e.g., workbook) and this was developed accordingly.

2.2.5 Preliminary Adaption Tests

We pilot-tested the AdT with diverse smokers in two treatment groups (n=12, n=13) followed by two focus groups led by community consultants using a democratic deliberative approach.

2.2.6 Adaptation Refinement

We incorporated feedback from Step 3 into the final AdT treatment manual.

The final AdT incorporated new components (e.g., interventions to reduce delay discounting and impulsive decision-making), increased the emphasis on other components (e.g., stress management, treatment utilization), eliminated components (e.g., health effects of tobacco use), and incorporated greater sensitivity to social/family context, other-oriented emotional focus, and pro-social behaviors. Language, tailored options, and activities were designed to foster an internal locus of control and resonate with the experiences of lower SES smokers. For example, we emphasized stress management by including at least one stress management intervention in every treatment session and proactively discussing relevant sources of stress (e.g., financial stress, micro-aggressions, discrimination). We included a thematic emphasis on internal locus of control and self-determination by emphasizing the use of “tools” and “personal control,” and discussing internal locus of control in the management of stress, negative affect, and impulsive actions. See Treatment Manual and Participant Toolkit in Supplemental Materials1.

2.3 Procedures

This study was approved by the City University of New York Institutional Review Board and conducted on the City College of New York campus from July 2013 to June 2015. Informed consent was obtained from all participants. Participants completed baseline assessment and were assigned to a treatment group. Groups were randomized by simple randomization in a 50/50 ratio by the study coordinator immediately prior to the first group treatment session. Participants were blind to condition. Daily smoking was assessed weekly during treatment, monthly by telephone afterward, and in-person during outcome assessments 3 and 6 months after the quit day.

Participants received 6 weekly 1-hour group treatment sessions of either the AdT or the StdT delivered by tobacco treatment specialists (TTSs) trained by accredited TTS training programs (www.ctttp.org). The third treatment session was the quit day. Participants were given 8 weeks of 24-hour nicotine patches (21mg/4 weeks, 14mg/2 weeks, 7mg/2 weeks). Patch use was initiated the morning of the quit day. All sessions were recorded. Treatment fidelity was addressed with 1) manual-driven sessions, 2) post-session treatment component checklists (TSCLs) completed by TTSs, and independent review of recorded sessions by team members who also completed TSCLs. A 60-minute session time limit was enforced.

2.4 Measures

Demographic, clinical, tobacco use, and psychosocial measures were administered. See Table 1 and Participant Characteristics Supplemental Materials2. A composite index for SES incorporated educational level and household income consistent with previous studies (Sheffer et al., 2012b; Varghese et al., 2014). Composite measures adjust for the limitations of single indicators (Galobardes et al., 2006a; 2006b). Household income was assessed with the six income categories (Bureau 2012). Educational level was grouped into 4 categories. Values assigned to income level (lowest=1 to highest=6) and educational category (lowest=1 to highest=4) were combined resulting in a discrete analogue SES scale (range=2–10). This scale was blocked into 3 equal SES levels SES1 (2–4), SES2 (5–7), and SES3 (8–10) which are observed to differentiate along pertinent socioeconomic dimensions (Sheffer et al., 2012b).

Table 1.

Demographic, environmental, clinical and psychosocial characteristics of participants (n=227)

Variable Category, or Range Percent (n) or
Mean (SD)
Demographic characteristics
Age, y 21–73 48.2 (9.0)
Sex Male 72.2 (164)
Partnered status Partnered1 19.4 (44)
Race White or Caucasian 18.9 (43)
African American or Black 66.1 (150)
Other 15.0 (34)
Ethnicity Hispanic 20.7 (47)
Work status Full time 10.6 (24)
Part time 13.7 (31)
Retired 3.5 (8)
Disabled 14.1 (32)
Unemployed 56.8 (129)
Homemaker 1.3 (3)
Socioeconomic status 2–10 4.1 (2.1)
Categories SES 1 64.3 (146)
SES 2 25.6 (58)
SES 3 10.1 (23)
Household income ≤ $10,000 56.8 (129)
$10,000 – $14,999 17.2 (39)
$15,000 – $24,999 8.4 (19)
$25,000 – $34,999 6.6 (15)
$35,000 – $49,999 6.2 (14)
≥ $50,000 4.8 (11)
Education, y 7–20 12.1 (2.2)
Categories < 12 years 33.5 (76)
12 years 37.0 (84)
13–14 years 15.4 (35)
> 15 years 14.1 (32)
Health insurance status Medicaid and/or Medicare 89.9 (204)
None 6.2 (14)
Private 4.0 (9)
Deprivation of basic needs 0–10 6.8 (3.0)
Tobacco-related clinical characteristics
Cigarettes per day 2–40 13.8 (7.4)
Categories ≤10 42.3 (96)
11–20 43.2 (98)
21–30 10.6 (24)
≥31 4.0 (9)
Fagerstrom Test for Nicotine Dependence 0–9 4.5 (2.2)
Smokes menthol cigarettes Yes 88.1 (200)
Age started smoking, y 7–53 16.7 (5.6)
Duration of regular smoking, y 2–50 26.6 (11.0)
Partner smoking status2 Partner smokes 45.5 (66)
Smoke free indoor policy Work3 83.3 (100)
Home 56.8 (129)
Timing of last quit attempt Never 11.9 (27)
< 6 months ago 32.6 (74)
6–12 months ago 10.1 (23)
> 12 months ago 45.4 (103)
Duration of longest quit attempt, weeks 0–1032 55.5 (115.5)
Readiness to quit Already stopped 1.3 (3)
Next 30 days 67.8 (154)
Next 6 months 26.0 (59)
Not in next 6 months 3.1 (7)
No plans to stop 0.4 (1)
Sought help to quit in past Yes 17.2 (39)
Motivation to quit 0–10 8.8 (1.9)
Self-efficacy for quitting 1–10 7.6 (2.3)
Alcoholic drinks per week 0–14 .87 (2.1)
Substance use history4 In recovery Yes 55.6 (100)
Duration current recovery5 ≤12 months 38.0 (38)
> 12 months 62.0 (62)
Duration longest recovery5 ≤12 months 10.0 (10)
> 12 months 90.0 (90)
Nicotine patch adherence Can use all patches as directed Not at all or very little 16.7 (38)
Somewhat or extremely 83.3 (189)
Believe patches have a positive effect Not at all or very little 24.7 (56)
Somewhat or extremely 75.3 (171)
Believe relapse if non-adherent with patches Not at all or very little 40.1 (91)
Somewhat or extremely 59.9 (136)
Treatment utilization characteristics
Number of sessions attended 1–6 5.6 (1.0)
Patch use (%,n) Yes 93.1 (202)
Number of patches used7 1–146 41.2 (22.5)
Open in a new tab
1

Married or living with significant other

2

Of partnered respondents

3

Of employed respondents

4

Of respondents (n=180)

5

Of those in recovery from SUDs (n=100)

6

In the past month other than regular job.

7

Of those who used patches (n=202). Some participants purchased or were given more patches than provided by the study.

Note: SUDs= Substance use disorders; Other race = Asian/Pacific islander, American Indian/American Native, Multi-ethic, or more than one race and non-specified.

SES 1, SES 2, SES 3: Values assigned to income level (lowest|=|1 to highest|=|6) and educational category (lowest|=|1 to highest|=|4) were combined resulting in a discrete analogue SES scale (range|=|2–10). This scale was collapsed into 3 SES levels: SES1 (2–4), SES2 (5–7), and SES3 (8–10).

Baseline assessment included cigarettes per day, partner smoking status, timing/duration of last quit attempt, use of mentholated cigarettes, age began smoking, years of regular smoking, and number of alcoholic drinks per week. Motivation, self-efficacy, and concern about weight gain were assessed on a 0–10 scale with 0= “not at all” and 10= “most ever” and the questions: “How much do you want to quit smoking?”, “How confident are you that you can quit using tobacco and stay quit for good?” and “How concerned are you about gaining weight after you quit?” (Perkins et al., 2001; Moolchan et al., 2003; McKee et al., 2005; McCarthy et al., 2010) Nicotine dependence was assessed with the Fagerström Test for Nicotine Dependence. Higher scores (range 0–10) indicate greater dependence (Heatherton et al., 1991; Fagerstrom et al., 1996). Smoking policies were assessed with: a) no smoking anywhere inside or outside, b) no smoking inside, but smoking is allowed outside, c) smoking is allowed in certain areas inside, or d) smoking allowed anywhere inside (Messer et al., 2008). Treatment utilization was assessed with number of treatment sessions attended, use of nicotine patches (yes/no), number of patches used, and perceived effectiveness of patches. Daily patch use was assessed weekly during treatment and monthly after treatment using the timeline follow-back procedure (Sobell and Sobell 1992).

2.5 Outcome Measures

Primary outcome measures were 1) latency to relapse, 2) initial abstinence, and 3) biochemically confirmed 7-day point prevalence abstinence 3 and 6 months after the quit date. Daily cigarette smoking was assessed using the timeline follow-back procedure shown to be accurate and reliable for past 30 days (Sobell and Sobell 1992; Toll et al., 2005). Relapse was defined as any smoking for seven consecutive days (Hughes et al., 2003). Point prevalence abstinence was confirmed with exhaled carbon monoxide levels < 8ppm (verification 2002).

2.6 Statistical Analysis

Sample size was estimated from the slope of the effect of SES on LT StdT abstinence rates in community samples (Sheffer et al., 2009; Sheffer et al., 2012a; Sheffer et al., 2012b). The slope of SES (βST) was 0.14 (95% CI:0.10, 0.19) (Sheffer et al., 2012b). With projected StdT LT abstinence rates of 22%, we expected .80 power to detect a hazard ratio of 1.46 (a medium effect size) using a 1-tailed test (alpha = 0.05) with n=220 participants. To estimate the power required to detect an elimination of the effect of SES (βRT = 0), we simulated the SES distribution with confidence limits of 0.10 and 0.19. With n=220 participants, 1-sided tests to detect βRT = 0 were estimated to have a power of 0.42, 0.75, and 0.93 when the βST=0.10, 0.14, and 0.19, respectively. We estimated 15% of those enrolled would not attend treatment. Thus enrollment was discontinued at n=253.

Descriptive analyses were conducted to characterize the sample. Cox Proportional Hazards (CPH) survival analyses were used to examine the effects of condition and SES on latency to relapse. Separate CPH models were used to examine the effects of condition on each SES level. The CPH analyses included all participants with cigarettes per day data beyond the quit date. Logistic regression was used to examine interactions between condition and SES and the main effect of condition on initial, 3, and 6 month abstinence rates using simple contrasts. Missing point prevalence data was imputed as smoking. We explored the effect of race and ethnicity by repeating these analyses with race and ethnicity as covariates.

3. Results

3.1 Participants

Participants (n=227) were primarily middle aged and identified as a racial and/or ethnic minority; three-quarters reported household incomes <$15,000. About one-third did not complete high school. Over half were unemployed and nearly all had Medicare and/or Medicaid. About two-thirds were classified as SES1, one-quarter as SES2, and 10.1% as SES3. Over two-thirds reported that their basic needs were not being met to some degree. Participants were highly nicotine dependent, most smoked menthol cigarettes, and most were ambivalent about the effectiveness of nicotine patches. See Table 1.

Half of participants received AdT (50.2%) and half StdT (49.8%). No differences between the AdT and StdT were found in session attendance (M =5.6 (SD 1.0) vs. M =5.6 (1.0); F (1,225) =0.05, p=.82) and number of participants per group (M= 4.9 (SD .9) vs. M=4.8 (SD 1.1); F(1,49)=.19, p=.66); however the AdT sessions were about 2 min longer than the StdT sessions (M=60.7 (SD 4.5) vs. M=58.2 (SD 4.5), F (1, 299) <.01). TTSs reported that they covered a smaller proportion of the AdT content (M=98.6% (SD 2.9%) vs. M=99.4% (SD 1.9%), F= (1, 297)=8.3, p <.01); however, independent reviews (n=95) reported no difference in amount of content covered (AdT M=95.2% (SD 5.5%) vs. StdT 93.2% (SD 6.0%), F (1, 93) = 2.8, p=.10). Differences between AdT and StdT in patch use (95.5% vs. 90.7%; χ2=1.94, df=1, p=.16) and the number of patches used (M =42.4 (SD 22.2) vs. M =39.8 (SD 22.9); F (1,200) =0.67, p=.41) were not significant. Daily cigarette use data was available for 95.6% of participants. Biochemical validation of 3 and 6-month abstinence was available for 87.2% of participants. See Figure 1.

Figure 1.

Figure 1

Open in a new tab

CONSORT Diagram. The number of participants retained for the Cox Proportional Hazard analyses included all those with some cigarettes per day data beyond the quit date (n=217). All eligible participants (n=227) were included in the logistic regression; those with missing three and six month data (n=14 standard, n=15 adpated) were imputed as smoking.

3.2 Primary Outcomes

Latency to relapse. The CPH models revealed the interaction between SES and condition (Likelihood ratio χ2 with 2 df = 2.76, p= 0.25) and the effect of SES (Likelihood ratio χ2 with 2 df = 0.1041, p = 0.95) were not significant; however, AdT significantly increased the number of days to relapse for the two lowest SES groups. For SES1, AdT participants maintained abstinence for a mean of 76.6 days (SD 72.9) compared with 38.3 days (SD 60.1) for StdT (χ2 with 1 df = 10.41, RR =0.49 95% CI, 0.32, 0.76, p = 0.001). For SES2, AdT participants maintained abstinence for a mean of 88.2 days (SD 67.3) compared with 40.1 days (SD 62.6) for StdT (χ2 with 1 df = 9.21, RR =0.35 95% CI, 0.18, 0.70, p=0.0024). For SES3, AdT participants maintained abstinence for 66.5 days (SD 64.6) compared with 68.6 days (SD 73.5) for StdT (χ2 with 1 df = .014, RR=1.06, 95% CI, .38, 3.05, p=.907). See Table 2.

Table 2.

Differences in days to relapse between the adapted and the standard treatment by socioeconomic status

Adapted
Treatment		 Standard
Treatment		 Relative Risk
(95% Confidence
Interval)		 p-value
SES1 76.6 (72.9) 38.3 (60.1) 0.63 (0.45, 0.88) 0.0013
SES2 88.2 (SD 67.3) 40.1 (SD 62.6) 0.57 (0.18, 0.70) 0.0024
SES3 66.5 (SD 64.6) 68.6 (SD 73.5) 1.06 (0.38, 3.05) 0.907
Across SES levels 78.7 (SD 70.4) 41.9 (SD 62.2) 0.57 (0.37, 0.87) 0.01
Open in a new tab

SES 1, SES 2, SES 3: Values assigned to income level (lowest|=|1 to highest|=|6) and educational category (lowest|=|1 to highest|=|4) were combined resulting in a discrete analogue SES scale (range|=|2–10). This scale was collapsed into 3 SES levels: SES1 (2–4), SES2 (5–7), and SES3 (8–10).

The effect of condition on overall latency to relapse was highly significant (Likelihood ratio χ2 with 2 df = 6.51, p=0.01). The mean days to relapse for AdT was 78.7 (SD 70.4) days and for StdT was 41.9 (SD 62.2) days (RR = 0.57 95% CI, 0.37, 0.87). The median latency to relapse for AdT was 66.0 (IQR 7 to 168) days; StdT was 8 (IQR 0 to 49) days. See Figure 2.

Figure 2.

Figure 2

Open in a new tab

The raw data reflect no socioeconomic gradient in the number of days to relapse for the Adapted Treatment while the Standard Treatment reflects the familiar socioeconomic gradient. Socioeconomic status (SES) was assessed with a composite index composed of education and income levels, range 2–10.

Point prevalence abstinence rates. The interaction between SES and condition was significant for initial abstinence (Wald χ2== 9.41, df=1, OR= 1.26, 95% CI 1.09, 1.46, p=.002), approached significance for 3-month abstinence (Wald χ2=3.26, df =1, OR= 1.11, 95% CI .99, 1.25, p < .071), and was not significant for 6-month abstinence (Wald χ2 = .068, df =1, OR= 1.02 95% CI .91, 1.14, p=.795). See Figure 3.

Figure 3.

Figure 3

Open in a new tab

Significant interactions between condition and socioeconomic status were found for short-term treatment outcomes. For initial abstinence, the interaction between SES and condition was significant (Wald χ2 with 1 df = 9.41, OR= 1.26, 95% CI 1.09, 1.46, p=.002). For 3-month abstinence, the interaction approached significance (Wald χ2 with 1 df = 3.26, OR= .90, 95% CI .80, 1.01, p < .07). For 6-month abstinence, the interaction was not significant (Wald χ2 with 1 df = .07, OR= .99 95% CI .88, 1.10, p=.80). The AdT yielded a higher proportion of participants who achieved initial abstinence than ST (87.7% vs. 69.0%; χ2 =11.72, df=1, p=.001). No significant differences were found between conditions in 3-month (48.2% vs. 37.2%; χ2 =2.846, df=1, p=.092) and 6-month (44.7% vs. 44.2%; χ2 =.005, df=1, p=.941) abstinence. Socioeconomic status was assessed with a composite index composed of education and income levels, range 2–10, SES1=2–4, SES2=5–7, SES3=8–10.

Percent abstinent by socioeconomic condition, socioeconomic level, and abstinence milestones. Lower SES participants were more likely to achieve short-term abstinence if they were in the adapted treatment. For SES1, significant differences between conditions were found in initial abstinence (FET, p=.039) (blue lines), but not for 3-month (FET, p=.249) (red lines) and 6-month (FET, p=.500) (yellow lines) abstinence. For SES2, significant differences between conditions were found for initial abstinence (FET, p=.001) (blue lines), marginal significance was found for 3-month abstinence (FET, p=.057) (red lines), and no significance was found for 6-month abstinence (p=.50) (yellow lines). For SES3, no significant differences were found between conditions for initial (p=.466), 3-month (p=.593), and 6-month (p=.579) abstinence. Socioeconomic status was assessed with a composite index composed of education and income levels (range 2–10, SES1=2–4, SES2=5–7, SES3=8–10).

The main effect of condition was significant for initial, but not for LT abstinence. A higher proportion of AdT participants achieved initial abstinence than StdT (87.7% vs. 69.0%; Wald χ2 =11.05, df=1, OR=3.21, 95% CI 1.61, 6.37, p=.001). No significant differences were found between conditions in 3-month (48.2% vs. 37.2%; Wald χ2 =2.83, df=1, OR = 1.57, CI .93, 2.68, p=.09) and 6-month (44.7% vs. 44.2%; Wald χ2 =.005, df=1, OR = 1.02, CI .60, 1.72, p=.941) abstinence. Neither race nor ethnicity were statistically significant in any of these analyses. See Supplemental Race and Ethnicity Results3.

4. Discussion

This is the first study to examine the efficacy of EBT adapted for diverse lower SES smokers. The findings indicate that systematic adaption can incrementally increase latency to relapse among socioeconomically, racially, and ethnically diverse smokers by increasing initial and ST abstinence rates. The AdT reduced inequities in treatment outcomes by eliminating the socioeconomic gradient in days to relapse (see Figure 2). Among AdT participants, the lowest SES group was abstinent 10 more days than the highest SES group. Among StdT participants, SES1 were abstinent 30 days fewer days than the highest SES group (see Table 2). The socioeconomic gradient in treatment outcomes is a critical barrier to achieving equity in smoking prevalence rates and health outcomes. While no significant differences in LT outcomes were observed, these findings identify therapeutic targets for improving LT abstinence in this large, diverse, disparate population. These findings extend the work of Webb Hooper et al. (2017) by demonstrating that EBT adapted for socioeconomic factors and diverse perspectives obtains similar results with socioeconomically, racially, and ethnically diverse smokers without adversely affecting higher SES smokers.

Achieving initial and ST abstinence are essential steps to achieving LT abstinence (Shiffman et al., 2006). While the advantages of the AdT dissipated over time, specific therapeutic targets for improving LT abstinence rates for lower SES groups might include interventions specifically focused on avoiding lapses and the progression from lapse to relapse during ST abstinence. Future research also might examine factors differentially associated with achieving specific milestones among lower SES smokers (Shiffman et al., 2006) (e.g., the availability of loosies). Many lower SES smokers have little experience living as non-smoking adults and face a multitude of challenges in exceptionally difficult environments. Skills required to maintain abstinence, such as the management of stress and negative mood, are known to require extensive practice and support. Thus, achieving LT abstinence might require novel methods of providing targeted extended support after participating in AdT. AdT, however, will ensure diverse lower SES smokers can achieve initial and ST abstinence.

The strengths of this study include a rigorous comparative effectiveness design that controlled for contact time; the use of multiple methods and milestones to assess abstinence; biochemical validation of point prevalence abstinence; a strong theoretical and methodological approach; a socioeconomically, racially and ethnically diverse sample that increases the external validity of the findings; and the ability to compare abstinence between socioeconomic groups within and between conditions. The setting facilitated the recruitment and retention of lower SES and minority smokers.

Limitations include potential bias due to inability to blind treatment providers. We speculate that the unusually high abstinence rates for the StdT condition might be due to an indirect influence from the AdT. Additionally, the TTSs were highly skilled which contributed to high abstinence rates, but might decrease the generalizability of findings. Our setting did not facilitate recruitment of higher SES smokers which resulted in only 10% of participants classified as SES3 and limited power to detect a significant interaction between SES and condition in the higher SES range.

5. Conclusion

Systematic adaption of EBT for tobacco dependence for diverse lower SES smokers can incrementally improve abstinence rates among racially, ethnically, and socioeconomically diverse lower SES smokers and reduce socioeconomic disparities in treatment outcomes.

Supplementary Material

1
2
3

Highlights.

  • Treatments for tobacco dependence are less effective for lower socioeconomic groups

  • Adapted treatment eliminates the socioeconomic gradient in outcomes

  • Adapted treatment improves short-term outcomes for lower socioeconomic groups

  • No long-term outcome differences are found between adapted and standard treatment

  • Extended support might help sustain short-term outcomes

Acknowledgments

This research was supported by a grant from the National Institute on Minority Health and Health Disparities (R01 MD007054 PI: Sheffer) and the National Cancer Institute (P20 CA192993 PI: Sheffer).

Role of Funding

This research was supported by a grant from the National Institute on Minority Health and Health Disparities (R01 MD007054 PI: Sheffer) and the National Cancer Institute (P20 CA192993 PI: Sheffer). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The National Institutes of Health had no further role in study design; collection, analysis, and interpretation of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

*

Supplementary material can be found by accessing the online version of this paper at http://dx.doi.org and by entering doi:…

1

* Supplementary material can be found by accessing the online version of this paper at http://dx.doi.org and by entering doi:…

2

* Supplementary material can be found by accessing the online version of this paper at http://dx.doi.org and by entering doi:…

3

* Supplementary material can be found by accessing the online version of this paper at http://dx.doi.org and by entering doi:…

Conflict of Interest

All authors declare that they have no conflicts of interest.

Contributors

All authors made substantial contributions to the conception, design, data acquisition, data analysis, and/or interpretation of data for this work as well as contributed to drafting the work and revising the work. Dr. Sheffer led this project and is responsible for the overall content as guarantor. Drs. Sheffer, Bickel, and Franck contributed to the overall conception and design. Drs. Sheffer, Stayna, and Evans and Ms. Panissidi and Ms. Pittman made substantial contributions to data acquisition. Drs. Sheffer, Franck and Ms. Pittman contributed to the data analysis. All authors provided final approval of this submission and agree to be accountable for all aspects of the work.

References

  1. Adler NE, Newman K. Socioeconomic disparities in health: Pathways and policies. Health Aff. (Millwood) 2002;21:60–76. doi: 10.1377/hlthaff.21.2.60. [DOI] [PubMed] [Google Scholar]
  2. Adler NE, Rehkopf DH. U.S. disparities in health: Descriptions, causes, and mechanisms. Annu. Rev. Public Health. 2008;29:235–252. doi: 10.1146/annurev.publhealth.29.020907.090852. [DOI] [PubMed] [Google Scholar]
  3. Adler NE, Stewart J. Health disparities across the lifespan: meaning, methods, and mechanisms. Ann. N.Y. Acad. Sci. 2010;1186:5–23. doi: 10.1111/j.1749-6632.2009.05337.x. [DOI] [PubMed] [Google Scholar]
  4. Airhihenbuwa CO. A conceptual model for culturally appropriate health education programs in developing countries. Int. Q. Community Health Educ. 1990;11:53–62. doi: 10.2190/LPKH-PMPJ-DBW9-FP6X. [DOI] [PubMed] [Google Scholar]
  5. Airhihenbuwa CO. Health promotion and disease prevention strategies for african americans: a conceptual model. health issues in the black community. In: Braithwaite RL, Taylor SE, editors. Health Issues in the Black Community. Jossey-Bass; San Francisco, California: 1992. pp. 267–280. [Google Scholar]
  6. Bandura A. Social cognitive theory: An agentic perspective. Annu. Rev. Psychol. 2001;52:1–26. doi: 10.1146/annurev.psych.52.1.1. [DOI] [PubMed] [Google Scholar]
  7. Baranowski T, Perry CL, Parcel GS. How individuals, environments, and health behavior interact. In: Glanz KR, Lewis FM, editors. Health Behavior and Health Education Theory Research and Practice. 3. Jossey-Bass; San Francisco, California: 2002. pp. 165–184. [Google Scholar]
  8. Barrera M, Castro FG. A heuristic framework for the cultural adaptation of interventions. Clin. Psychol. 2006;13:311–316. [Google Scholar]
  9. Bickel WK, Moody L, Quisenberry AJ, Ramey CT, Sheffer CE. A competing neurobehavioral decision systems model of SES-related health and behavioral disparities. Prev. Med. 2014;68:37–43. doi: 10.1016/j.ypmed.2014.06.032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bosdriesz JR, Willemsen MC, Stronks K, Kunst AE. Socioeconomic inequalities in smoking cessation in 11 European countries from 1987 to 2012. J. Epidemiol. Community Health. 2015;69:886–892. doi: 10.1136/jech-2014-205171. [DOI] [PubMed] [Google Scholar]
  11. Bosdriesz JR, Willemsen MC, Stronks K, Kunst AE. Tobacco control policy and socio-economic inequalities in smoking in 27 European countries. Drug Alcohol Depend. 2016;165:79–86. doi: 10.1016/j.drugalcdep.2016.05.020. [DOI] [PubMed] [Google Scholar]
  12. Burgess DJ, Fu SS, Noorbaloochi S, Clothier BA, Ricards J, Widome R, van Ryn M. Employment, gender, and smoking cessation outcomes in low-income smokers using nicotine replacement therapy. Nicotine Tob. Res. 2009;11:1439–1447. doi: 10.1093/ntr/ntp158. [DOI] [PubMed] [Google Scholar]
  13. Businelle MS, Kendzor DE, Reitzel LR, Costello TJ, Cofta-Woerpel L, Li Y, Mazas CA, Vidrine JI, Cinciripini PM, Greisinger AJ, Wetter DW. Mechanisms linking socioeconomic status to smoking cessation: a structural equation modeling approach. Health Psychol. 2011;29:262–273. doi: 10.1037/a0019285. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Businelle MS, Kendzor DE, Reitzel LR, Vidrine JI, Castro Y, Mullen PD, Velasquez MM, Cofta-Woerpel L, Cinciripini PM, Greisinger AJ, Wetter DW. Pathways linking socioeconomic status and postpartum smoking relapse. Ann. Behav. Med. 2013;45:180–191. doi: 10.1007/s12160-012-9434-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Christiansen B, Reeder K, Hill M, Baker TB, Fiore MC. Barriers to effective tobacco-dependence treatment for the very poor. J. Stud. Alcohol Drugs. 2012;73:874–884. doi: 10.15288/jsad.2012.73.874. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Cote S, Kraus MW, Cheng BH, Oveis C, van der Lowe I, Lian H, Keltner D. Social power facilitates the effect of prosocial orientation on empathic accuracy. J. Pers. Soc. Psychol. 2011;101:217–232. doi: 10.1037/a0023171. [DOI] [PubMed] [Google Scholar]
  17. Evans SD, Sheffer CE, Bickel WK, Cottoms N, Olson M, Panissidi L, Austin T, Stayna H. The process of adapting the evidence-based treatment for tobacco dependence for smokers of lower socioeconomic status. J. Addict. Res. Ther. 2015;6:219, 10. doi: 10.4172/2155-6105.1000219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Fagerstrom KO, Kunze M, Schoberberger R, Breslau N, Hughes JR, Hurt RD, Puska P, Ramstrom L, Zatonski W. Nicotine dependence versus smoking prevalence: Comparisons among countries and categories of smokers. Tob. Control. 1996;5:52–56. doi: 10.1136/tc.5.1.52. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Ferguson J, Bauld L, Chesterman J, Judge K. The English smoking treatment services: One-year outcomes. Addiction. 2005;100(Suppl 2):59–69. doi: 10.1111/j.1360-0443.2005.01028.x. [DOI] [PubMed] [Google Scholar]
  20. Fernander AF, Shavers VL, Hammons GJ. A biopsychosocial approach to examining tobacco-related health disparities among racially classified social groups. Addiction. 2007;102(Suppl 2):43–57. doi: 10.1111/j.1360-0443.2007.01954.x. [DOI] [PubMed] [Google Scholar]
  21. Fiore MC, Jaén CR, Baker TB, Bailey WC, Benowitz NL, Curry SJ, Dorfman SF, Froelicher EF, Goldstein MG, Healton CG, Henderson PN, Heyman RB, Koh HK, Kottke TE, Lando HA, Mecklenburg RE, Mermelstein RM, Mullen PD, Orleans CT, Robinson L, Stitzer M, Tommasello AC, Villejo L, Wewers ME. Clinical practice guideline. U.S. Department of Health and Human Services, Public Health Service; Rockville, MD; 2008. Treating tobacco use and dependence: 2008 update. [Google Scholar]
  22. Foulds J, Gandhi KK, Steinberg MB, Richardson DL, Williams JM, Burke MV, Rhoads GG. Factors associated with quitting smoking at a tobacco dependence treatment clinic. Am. J. Health Behav. 2006;30:400–412. doi: 10.5555/ajhb.2006.30.4.400. [DOI] [PubMed] [Google Scholar]
  23. Gallo LC, Matthews KA. Understanding the association between socioeconomic status and physical health: Do negative emotions play a role? Psychol. Bull. 2003;129:10–51. doi: 10.1037/0033-2909.129.1.10. [DOI] [PubMed] [Google Scholar]
  24. Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey Smith G. Indicators of socioeconomic position (part 1) J. Epidemiol. Community Health. 2006a;60:7–12. doi: 10.1136/jech.2004.023531. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey Smith G. Indicators of socioeconomic position (part 2) J Epidemiol. Community Health. 2006b;60:95–101. doi: 10.1136/jech.2004.028092. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Ghaed SG, Gallo LC. Subjective social status, objective socioeconomic status, and cardiovascular risk in women. Health Psychol. 2007;26:668–674. doi: 10.1037/0278-6133.26.6.668. [DOI] [PubMed] [Google Scholar]
  27. Harper S, Lynch J. Trends in socioeconomic inequalities in adult health behaviors among U.S. states, 1990–2004. Public Health Rep. 2007;122:177–189. doi: 10.1177/003335490712200207. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO. The Fagerstrom Test for Nicotine Dependence: A revision of the Fagerstrom Tolerance Questionnaire. Br. J. Addict. 1991;86:1119–1127. doi: 10.1111/j.1360-0443.1991.tb01879.x. [DOI] [PubMed] [Google Scholar]
  29. Hiscock R, Bauld L, Amos A, Fidler JA, Munafo M. Socioeconomic status and smoking: A review. Ann. N.Y. Acad. Sci. 2012;1248:107–123. doi: 10.1111/j.1749-6632.2011.06202.x. [DOI] [PubMed] [Google Scholar]
  30. Hiscock R, Dobbie F, Bauld L. Smoking cessation and socioeconomic status: An update of existing evidence from a national evaluation of english stop smoking services. BioMed Res. Int. 2015;274056:10. doi: 10.1155/2015/274056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Hiscock R, Murray S, Brose LS, McEwen A, Bee JL, Dobbie F, Bauld L. Behavioural therapy for smoking cessation: The effectiveness of different intervention types for disadvantaged and affluent smokers. Addict. Behav. 2013;38:2787–2796. doi: 10.1016/j.addbeh.2013.07.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Honjo K, Tsutsumi A, Kawachi I, Kawakami N. What accounts for the relationship between social class and smoking cessation? Results of a path analysis. Soc. Sci. Med. 2006;62:317–328. doi: 10.1016/j.socscimed.2005.06.011. [DOI] [PubMed] [Google Scholar]
  33. Hughes JR, Keely JP, Niaura RS, Ossip-Klein DJ, Richmond RL, Swan GE. Measures of abstinence in clinical trials: Issues and recommendations. Nicotine Tob. Res. 2003;5:13–25. [PubMed] [Google Scholar]
  34. Jamal A, King BA, Neff LJ, Whitmill J, Babb SD, Graffunder CM. Current cigarette smoking among adults – U.S., 2005–2015. MMWR Morb. Mortal. Wkly. Rep. 2016;65:1205–1211. doi: 10.15585/mmwr.mm6544a2. [DOI] [PubMed] [Google Scholar]
  35. Judge K, Bauld L, Chesterman J. The English smoking treatment services: short-term outcomes. Addiction. 2005;100(Suppl 2):46–58. doi: 10.1111/j.1360-0443.2005.01027.x. [DOI] [PubMed] [Google Scholar]
  36. Kanjilal S, Gregg EW, Cheng YJ, Zhang P, Nelson DE, Mensah G, Beckles GL. Socioeconomic status and trends in disparities in 4 major risk factors for cardiovascular disease among U.S. adults, 1971–2002. Arch. Intern. Med. 2006;166:2348–2355. doi: 10.1001/archinte.166.21.2348. [DOI] [PubMed] [Google Scholar]
  37. Kaplan SA, Madden VP, Mijanovich T, Purcaro E. The perception of stress and its impact on health in poor communities. J. Community Health. 2013;38:142–149. doi: 10.1007/s10900-012-9593-5. [DOI] [PubMed] [Google Scholar]
  38. Kawachi I, Adler NE, Dow WH. Money, schooling, and health: Mechanisms and causal evidence. Ann. N.Y. Acad. Sci. 2010;1186:56–68. doi: 10.1111/j.1749-6632.2009.05340.x. [DOI] [PubMed] [Google Scholar]
  39. Kendzor DE, Businelle MS, Mazas CA, Cofta-Woerpel LM, Reitzel LR, Vidrine JI, Li Y, Costello TJ, Cinciripini PM, Ahluwalia J, Wetter DW. Pathways between socioeconomic status and modifiable risk factors among African American smokers. J. Behav. Med. 2009;32:545–557. doi: 10.1007/s10865-009-9226-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Kotz D, West R. Explaining the social gradient in smoking cessation: It's not in the trying, but in the succeeding. Tob. Control. 2009;18:43–46. doi: 10.1136/tc.2008.025981. [DOI] [PubMed] [Google Scholar]
  41. Kraus MW, Piff PK, Keltner D. Social class as culture: The convergence of resources and rank in the social realm. Curr. Dir. Psychol. Sci. 2011;20:246–250. [Google Scholar]
  42. Kulak JA, Cornelius ME, Fong GT, Giovino GA. Differences in quit attempts and cigarette smoking abstinence between whites and African Americans in the U.S.: Literature review and results from the international tobacco control us survey. Nicotine Tob. Res. 2016;18(Suppl. 1):S79–87. doi: 10.1093/ntr/ntv228. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Lau AS. Making the case for selective and directed cultural adaptations of evidence-based treatments: Examples from parent training. Clin. Psychol. 2006;13:295–310. [Google Scholar]
  44. Macartney S, Bishaw A, Fontenot KA. U.S. Department of Commerce, Economics and Statistics Administration. U.S. Census Bureau; Washington, DC: 2013. Poverty Rates for Selected Detailed Race and Hispanic Groups by State and Place: 2007–2011 American Community Survey Briefs. [Google Scholar]
  45. Manfredi C, Cho YI, Crittenden KS, Dolecek TA. A path model of smoking cessation in women smokers of low socio-economic status. Health Educ. Res. 2007;22:747–756. doi: 10.1093/her/cyl155. [DOI] [PubMed] [Google Scholar]
  46. McCarthy DE, Piasecki TM, Jorenby DE, Lawrence DL, Shiffman S, Baker TB. A multi-level analysis of non-significant counseling effects in a randomized smoking cessation trial. Addiction. 2010;105:2195–2208. doi: 10.1111/j.1360-0443.2010.03089.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. McKee SA, O'Malley SS, Salovey P, Krishnan-Sarin S, Mazure CM. Perceived risks and benefits of smoking cessation: Gender-specific predictors of motivation and treatment outcome. Addict. Behav. 2005;30:423–435. doi: 10.1016/j.addbeh.2004.05.027. [DOI] [PubMed] [Google Scholar]
  48. Messer K, Mills AL, White MM, Pierce JP. The effect of smoke-free homes on smoking behavior in the U.S. Am. J. Prev. Med. 2008;35:210–216. doi: 10.1016/j.amepre.2008.05.023. [DOI] [PubMed] [Google Scholar]
  49. Moolchan ET, Berlin I, Robinson ML, Cadet JL. Characteristics of African American teenage smokers who request cessation treatment: Implications for addressing health disparities. Arch. Pediatr. Adolesc. Med. 2003;157:533–538. doi: 10.1001/archpedi.157.6.533. [DOI] [PubMed] [Google Scholar]
  50. Moolchan ET, Fagan P, Fernander AF, Velicer WF, Hayward MD, King G, Clayton RR. Addressing tobacco-related health disparities. Addiction. 2007;102(Suppl 2):30–42. doi: 10.1111/j.1360-0443.2007.01953.x. [DOI] [PubMed] [Google Scholar]
  51. Nollen NL, Sanderson Cox L, Mayo M, Ellerbeck EF, Scheuermann TS, Tyndale R, Benowitz N, Ahluwalia J. Varenicline is less effective in African Americans compared to caucasian American smokers: Results from a proposective stratified clinical trial; Society for Research on Nicotine and Tobacco; March 8–12; Florence, Italy. 2017. [Google Scholar]
  52. Payne TJ, Smith PO, Adams SG, Diefenbach L. Pretreatment cue reactivity predicts end-of-treatment smoking. Addict. Behav. 2006;31:702–710. doi: 10.1016/j.addbeh.2005.05.053. [DOI] [PubMed] [Google Scholar]
  53. Perkins KA, Marcus MD, Levine MD, D'Amico D, Miller A, Broge M, Ashcom J, Shiffman S. Cognitive-behavioral therapy to reduce weight concerns improves smoking cessation outcome in weight-concerned women. J. Consult. Clin. Psychol. 2001;69:604–613. [PubMed] [Google Scholar]
  54. Reid JL, Hammond D, Boudreau C, Fong GT, Siahpush M. Socioeconomic disparities in quit intentions, quit attempts, and smoking abstinence among smokers in four western countries: findings from the International Tobacco Control Four Country Survey. Nicotine Tob. Res. 2010;12(Suppl):S20–33. doi: 10.1093/ntr/ntq051. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Robles GI, Singh-Franco D, Ghin HL. A review of the efficacy of smoking-cessation pharmacotherapies in nonwhite populations. Clin.Ther. 2008;30:800–812. doi: 10.1016/j.clinthera.2008.05.010. [DOI] [PubMed] [Google Scholar]
  56. Schmitz JM, Rosenfarb IS, Payne TJ. Cognitive and affective responses to successful coping during smoking cessation. J. Subst. Abuse. 1993;5:61–72. doi: 10.1016/0899-3289(93)90123-s. [DOI] [PubMed] [Google Scholar]
  57. Sheffer C, Stitzer M, Landes R, Brackman SL, Munn T. In-person and telephone treatment of tobacco dependence: A comparison of treatment outcomes and participant characteristics. Am. J. Public Health. 2013;103:e74–82. doi: 10.2105/AJPH.2012.301144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Sheffer CE, Mackillop J, McGeary J, Landes R, Carter L, Yi R, Jones B, Christensen D, Stitzer M, Jackson L, Bickel WK. Delay discounting, locus of control, and cognitive impulsiveness independently predict tobacco dependence treatment outcomes in a highly dependent, lower socioeconomic group of smokers. Am. J. Addict. 2012a;21:221–232. doi: 10.1111/j.1521-0391.2012.00224.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Sheffer CE, Stitzer M, Landes R, Brackman SL, Munn T, Moore PM. Socioeconomic disparities in community-based treatment of tobacco dependence. Am. J. Public Health. 2012b;102:e8–16. doi: 10.2105/AJPH.2011.300519. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Sheffer CE, Stitzer M, Payne TJ, Applegate BW, Bourne D, Wheeler JG. Treatment for tobacco dependence for rural, lower-income smokers: Outcomes, predictors, and measurement considerations. Am. J. Public Health. 2009;23:328–338. doi: 10.4278/ajhp.06031933. [DOI] [PubMed] [Google Scholar]
  61. Shiffman S, Scharf DM, Shadel WG, Gwaltney CJ, Dang Q, Paton SM, Clark DB. Analyzing milestones in smoking cessation: Illustration in a nicotine patch trial in adult smokers. J. Consult. Clin. Psychol. 2006;74:276–285. doi: 10.1037/0022-006X.74.2.276. [DOI] [PubMed] [Google Scholar]
  62. Siahpush M, McNeill A, Borland R, Fong GT. Socioeconomic variations in nicotine dependence, self-efficacy, and intention to quit across four countries: findings from the International Tobacco Control (ITC) Four Country Survey. Tob. Control. 2006a;15(Suppl):iii71–75. doi: 10.1136/tc.2004.008763. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Siahpush M, McNeill A, Hammond D, Fong GT. Socioeconomic and country variations in knowledge of health risks of tobacco smoking and toxic constituents of smoke: results from the 2002 International Tobacco Control (ITC) Four Country Survey. Tob. Control. 2006b;15(Suppl 3):iii65–70. doi: 10.1136/tc.2005.013276. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Siahpush M, Spittal M, Singh G. Association of smoking cessation with financial stress and material well-being: Results from a prospective study of a population-based national survey. Am. J. Public Health. 2007a;97:2281–2287. doi: 10.2105/AJPH.2006.103580. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Siahpush M, Spittal M, Singh G. Smoking cessation and financial stress. J. Public Health. 2007b;29:338–342. doi: 10.1093/pubmed/fdm070. [DOI] [PubMed] [Google Scholar]
  66. Siahpush M, Yong HH, Borland R, Reid JL, Hammond D. Smokers with financial stress are more likely to want to quit but less likely to try or succeed: Findings from the International Tobacco Control (ITC) Four Country Survey. Addiction. 2009;104:1382–1390. doi: 10.1111/j.1360-0443.2009.02599.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Singh GK, Jemal A. Socioeconomic and racial/ethnic disparities in cancer mortality, incidence, and survival in the United States, 1950–2014: Over six decades of changing patterns and widening inequalities. J. Environ. Public Health. 2017;2819372 doi: 10.1155/2017/2819372. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Singh GK, Siahpush M, Azuine RE, Williams SD. Widening socioeconomic and racial disparities in cardiovascular disease mortality in the United States, 1969–2013. Int. J. MCH AIDS. 2015;3:106–118. [PMC free article] [PubMed] [Google Scholar]
  69. Smith P, Frank J, Mustard CJ. Trends in educational inequalities in smoking and physical activity in Canada: 1974–2005. J. Epidemiol. Community Health. 2009;63:317–323. doi: 10.1136/jech.2008.078204. [DOI] [PubMed] [Google Scholar]
  70. Smith PO, Sheffer CE, Payne TJ, Applegate BW, Crews K. Smoking cessation research in primary care treatment centers: The SCRIPT-MS project. AJMS. 2003;326:238–241. doi: 10.1097/00000441-200310000-00017. [DOI] [PubMed] [Google Scholar]
  71. Sobell LC, Sobell MB. Timeline follow-back: A technique for assessing self-consumpton. In: Allen J, Litten RZ, editors. Measuring Alcohol Consumption: Psychosocial and Biological Methods. Humana Press: Totowa, New Jersey; 1992. pp. 41–72. [Google Scholar]
  72. Stronks K, van de Mheen HD, Looman CW, Mackenbach JP. Cultural, material, and psychosocial correlates of the socioeconomic gradient in smoking behavior among adults. Prev. Med. 1997;26:754–766. doi: 10.1006/pmed.1997.0174. [DOI] [PubMed] [Google Scholar]
  73. Toll BA, Cooney NL, McKee SA, O'Malley SS. Do daily interactive voice response reports of smoking behavior correspond with retrospective reports? Psychol. Addict. Behav. 2005;19:291–295. doi: 10.1037/0893-164X.19.3.291. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Trinidad DR, Perez-Stable EJ, White MM, Emery SL, Messer K. A nationwide analysis of us racial/ethnic disparities in smoking behaviors, smoking cessation, and cessation-related factors. Am. J. Public Health. 2011;101:699–706. doi: 10.2105/AJPH.2010.191668. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. U.S. Census Bureau. US Census Profile of General Demographic Characteristics. US Census Bureau; Washington, DC: 2010. [Google Scholar]
  76. U.S. Census Bureau. The 2012 statistical abstract: the national data book. U.S. Department of Commerce, U.S, Census Bureau; Washington, DC: 2012. Income, expenditures, poverty, and wealth; pp. 431–470. [Google Scholar]
  77. Varghese M, Sheffer CE, Stitzer M, Landes R, Brackman SL, Munn TC. Socioeconomic disparities in telephone-based treatment of tobacco dependence. Am. J. Public Health. 2014;104:e76–84. doi: 10.2105/AJPH.2014.301951. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. SRNT Subcommittee on Biochemical Verification. Biochemical verification of tobacco use and cessation. Nicotine Tob. Res. 2002;4:149–159. doi: 10.1080/14622200210123581. [DOI] [PubMed] [Google Scholar]
  79. Webb Hooper M, Antoni MH, Okuyemi K, Dietz NA, Resnicow KM. Randomized controlled trial of group-based culturally specific cognitive behavioral therapy among African American smokers. Nicotine Tob. Res. 2017;19:333–341. doi: 10.1093/ntr/ntw181. [DOI] [PubMed] [Google Scholar]
  80. Xu X, Bishop EE, Kennedy SM, Simpson SA, Pechacek TF. Annual healthcare spending attributable to cigarette smoking: an update. Am. J. Prev. Med. 2015;48:326–333. doi: 10.1016/j.amepre.2014.10.012. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS913072-supplement-1.docx (31.6KB, docx)
2
NIHMS913072-supplement-2.docx (74.7KB, docx)
3
NIHMS913072-supplement-3.docx (19.6KB, docx)

RESOURCES