Investigating Disparities in Smoking Cessation Treatment for Veterans with Multiple Sclerosis: A National Analysis

Abstract: Background and aims: Smoking is a risk factor for multiple sclerosis (MS) development, symptom burden, decreased medication efficacy, and increased disease-related mortality. Veterans with MS (VwMS) smoke at critically high rates; however, treatment rates and possible disparities are unknown. To promote equitable treatment, we aim to investigate smoking cessation prescription practices for VwMS across social determinant factors. Methods: We extracted data from the national Veterans Health Administration electronic health records between October 1, 2017, and September 30, 2018. To derive marginal estimates of the association of MS with receipt of smoking-cessation pharmacotherapy, we used propensity score matching through the extreme gradient boosting machine learning model. VwMS who smoke were matched with veterans without MS who smoke on factors including age, race, depression, and healthcare visits. To assess the marginal association of MS with different cessation treatments, we used logistic regression and conducted stratified analyses by sex, race, and ethnicity. Results: The matched sample achieved a good balance across most covariates, compared to the pre-match sample. VwMS (n = 3320) had decreased odds of receiving prescriptions for nicotine patches ([Odds Ratio]OR = 0.86, p < .01), non-patch nicotine replacement therapy (NRT; OR = 0.81, p < .001), and standard practice dual NRT (OR = 0.77, p < .01), compared to matches without MS (n = 13,280). Men with MS had lower odds of receiving prescriptions for nicotine patches (OR = 0.88, p = .05), non-patch NRT (OR = 0.77, p < .001), and dual NRT (OR = 0.72, p < .001). Similarly, Black VwMS had lower odds of receiving prescriptions for patches (OR = 0.62, p < .001), non-patch NRT (OR = 0.75, p < .05), and dual NRT (OR = 0.52, p < .01). The odds of receiving prescriptions for bupropion or varenicline did not differ between VwMS and matches without MS. Conclusion: VwMS received significantly less smoking cessation treatment, compared to matched controls without MS, showing a critical gap in health services as VwMS are not receiving dual NRT as the standard of care. Prescription rates were especially lower for male and Black VwMS, suggesting that under-represented demographic groups outside of the white female category, most often considered as the "traditional MS" group, could be under-treated regarding smoking cessation support. This foundational work will help inform future work to promote equitable treatment and implementation of cessation interventions for people living with MS.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.

    Mental Health Peer reviewed