| Abstract: | Objectives. We examined socioeconomic disparities in tobacco dependence treatment outcomes from a free, proactive telephone counseling quitline.Methods. We delivered cognitive–behavioral treatment and nicotine patches to 6626 smokers and examined socioeconomic differences in demographic, clinical, environmental, and treatment use factors. We used logistic regressions and generalized estimating equations (GEE) to model abstinence and account for socioeconomic differences in the models.Results. The odds of achieving long-term abstinence differed by socioeconomic status (SES). In the GEE model, the odds of abstinence for the highest SES participants were 1.75 times those of the lowest SES participants. Logistic regression models revealed no treatment outcome disparity at the end of treatment, but significant disparities 3 and 6 months after treatment.Conclusions. Although quitlines often increase access to treatment for some lower SES smokers, significant socioeconomic disparities in treatment outcomes raise questions about whether current approaches are contributing to tobacco-related socioeconomic health disparities. Strategies to improve treatment outcomes for lower SES smokers might include novel methods to address multiple factors associated with socioeconomic disparities.In the United States, the prevalence of daily smoking among lower socioeconomic status (SES) groups is 3 to 4 times higher than that of higher SES groups and a leading contributor to socioeconomic health disparities.1–5 Comprehensive tobacco control programs can reduce these disparities by providing all smokers with effective treatment for tobacco dependence; however, significant socioeconomic disparities in treatment outcomes are observed in many treatment settings, raising concerns about contributing to or at least maintaining existing disparities with these approaches.6–14 Treatment delivered through telephone quitlines has become widely available in the United States and the United Kingdom.15 Proactive quitlines attract a large proportion of lower SES smokers16–18 and smokers with different demographic and clinical characteristics than in-person, community-based treatments.16,19,20 Because of their ubiquitous nature and because they appear to be especially accessible and attractive to lower SES smokers,16,17,18 quitlines have the potential to attenuate tobacco-related disparities; however, if quitlines also demonstrate socioeconomic disparities in treatment outcomes, then this would strengthen concerns about current approaches contributing to or maintaining these disparities.SES ideally incorporates the social and economic factors that influence what position individuals or groups hold in a societal structure.21,22 In health research, SES is a broad construct describing relative access to basic resources required to achieve or maintain good health.23,24 Consistent with leading conceptual models of health disparities,23–26 SES is empirically related to smoking cessation through complex reciprocal relations among clinical, environmental, and treatment utilization factors including stress, coping resources, psychological factors, exposure to other smokers, and use of treatment resources.6,27–32Cognitive–behavioral treatment (CBT) provided through proactive quitlines is a practical innovation that attracts a promising number of lower SES smokers.15–17,33 Although not targeted to or tailored for lower SES groups, CBT, when delivered appropriately, addresses individuals’ treatment-related clinical characteristics (e.g., stress, coping, dependence level, motivation, self-efficacy, environmental challenges). Nonetheless, significant disparities have been found in CBT treatment outcomes in many tobacco treatment settings.6–14 Quitlines treat smokers with different characteristics than in-person treatment,16,19,20 however, and thus might not demonstrate the same disparities as in-person CBT treatment.6–13We investigated socioeconomic disparities in tobacco dependence treatment outcomes using data from a proactive quitline in Arkansas in operation from 2005 to 2008. We used statistical modeling of abstinence at the end of treatment (EOT) and 3 and 6 months after treatment to examine the independent contribution of SES to treatment outcomes controlling for other factors. Consistent with findings from community-based treatment, we hypothesized that after accounting for demographic, clinical, environmental, and treatment utilization factors, the lowest SES participants would be least likely to achieve long-term abstinence. |
