Cancer survivors are at increased risk for comorbid conditions, and acceptance of healthy behaviors may reduce dysfunction and improve long-term health. Ultimately, opportunities exist for clinicians to promote lifestyle changes that may improve the length and quality of life of their patients.

The inverse association between socioeconomic status and smoking is well established, yet the mechanisms that drive this relationship are unclear. We developed and tested four theoretical models of the pathways that link socioeconomic status to current smoking prevalence using a structural equation modeling (SEM) approach. Using data from the 2013 National Health Interview Survey, we selected four indicator variables (poverty ratio, personal earnings, educational attainment, and employment status) that we hypothesize underlie a latent variable, socioeconomic status. We measured direct, indirect, and total effects of socioeconomic status on smoking on four pathways through four latent variables representing social cohesion, financial strain, sleep disturbance, and psychological distress. Results of the model indicated that the probability of being a smoker decreased by 26% of a standard deviation for every one standard deviation increase in socioeconomic status. The direct effects of socioeconomic status on smoking accounted for the majority of the total effects, but the overall model also included significant indirect effects. Of the four mediators, sleep disturbance and psychological distress had the largest total effects on current smoking. We explored the use of structural equation modeling in epidemiology to quantify effects of socioeconomic status on smoking through four social and psychological factors to identify potential targets for interventions. A better understanding of the complex relationship between socioeconomic status and smoking is critical as we continue to reduce the burden of tobacco and eliminate health disparities related to smoking.

Objective: The primary purpose of this study was to compare age-adjusted mortality rates before and after linkage with Indian Health Service records, adjusting for racial misclassification. We focused on differences in racial misclassification by gender, age, geographic differences, substate planning districts, and cause of death. Our secondary purpose was to evaluate time trends in misclassification from 1991 to 2015. Design: Retrospective, descriptive study. Setting: Oklahoma. Participants: Persons contained in the Oklahoma State Health Department Vital Records. Main Outcome Measures: To evaluate the age-adjusted mortality ratio pre– and post–Indian Health Service record linkage (misclassification rate ratio) and to evaluate the overall trend of racial misclassification on mortality records measured through annual percent change (APC) and average annual percent change (AAPC). Results: We identified 2 stable trends of racial misclassification upon death for American Indians/Alaska Natives (AI/ANs) from 1991 to 2001 (APC: −0.2%; 95% confidence interval: −1.4% to 1.0%) and from 2001 to 2005 (APC: −6.9%; 95% confidence interval: −13.7% to 0.4%). However, the trend identified from 2005 to 2015 decreased significantly (APC: −1.4%; 95% confidence interval: −2.5% to −0.2%). For the last 5 years available (2011-2015), the racial misclassification adjustment resulted in higher mortality rates for AI/ANs reflecting an increase from 1008 per 100 000 to 1305 per 100 000 with the linkage process. There were an estimated 3939 AI/ANs in Oklahoma who were misclassified as another race upon death in those 5 years, resulting in an underestimation of actual AI/AN deaths by nearly 29%. Conclusions: An important result of this study is that misclassification is improving; however, this effort needs to be maintained and further improved. Continued linkage efforts and public access to linked data are essential throughout the United States to better understand the burden of disease in the AI/AN population.