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Background: People with multiple sclerosis (pwMS) are at risk of concurrently using multiple central nervous system (CNS)-active drugs, yet the prevalence of CNS-active polypharmacy remains unmeasured in pwMS. Objective: The objective is to measure the prevalence of CNS-active polypharmacy in pwMS. Methods: This serial, cross-sectional study measured CNS-active polypharmacy in people with MS in the United States from 2008 to 2021 using insurance claims data. CNS-active polypharmacy was defined as the concurrent prescription of ⩾3 CNS-active drugs for >30 continuous days. CNS-active drugs included antidepressants, antiepileptics, antipsychotics, benzodiazepines, nonbenzodiazepine benzodiazepine receptor agonist hypnotics, opioids, and skeletal muscle relaxants. Results: The number of subjects included at each time point ranged from 23,917 subjects in 2008 to 55,797 subjects in 2021. In 2021, subjects with CNS-active polypharmacy were more likely to be 46–65 years of age and have CNS-related comorbidities compared to those without CNS-active polypharmacy. From 2008 to 2021, the age-adjusted prevalence of CNS-active polypharmacy among female subjects increased from 19.8% (95% confidence interval (CI) = 19.1–20.4) to 26.4% (95% CI = 25.9–26.8) versus 15.9% (95% CI = 14.8–17.0) to 18.6% (95% CI = 17.9–19.2) in male subjects. Conclusion: The prevalence of CNS-active polypharmacy has increased among people with MS with a growing disparity by sex. [ABSTRACT FROM AUTHOR]

Background: People with multiple sclerosis (pwMS) are at risk of concurrently using multiple central nervous system (CNS)-active drugs, yet the prevalence of CNS-active polypharmacy remains unmeasured in pwMS., Objective: The objective is to measure the prevalence of CNS-active polypharmacy in pwMS., Methods: This serial, cross-sectional study measured CNS-active polypharmacy in people with MS in the United States from 2008 to 2021 using insurance claims data. CNS-active polypharmacy was defined as the concurrent prescription of ⩾3 CNS-active drugs for >30 continuous days. CNS-active drugs included antidepressants, antiepileptics, antipsychotics, benzodiazepines, nonbenzodiazepine benzodiazepine receptor agonist hypnotics, opioids, and skeletal muscle relaxants., Results: The number of subjects included at each time point ranged from 23,917 subjects in 2008 to 55,797 subjects in 2021. In 2021, subjects with CNS-active polypharmacy were more likely to be 46-65 years of age and have CNS-related comorbidities compared to those without CNS-active polypharmacy. From 2008 to 2021, the age-adjusted prevalence of CNS-active polypharmacy among female subjects increased from 19.8% (95% confidence interval (CI) = 19.1-20.4) to 26.4% (95% CI = 25.9-26.8) versus 15.9% (95% CI = 14.8-17.0) to 18.6% (95% CI = 17.9-19.2) in male subjects., Conclusion: The prevalence of CNS-active polypharmacy has increased among people with MS with a growing disparity by sex., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: H.N., J.W., T.M.K., and E.H. have nothing to disclose. L.F. has received fees for consultancy and/or advisory board participation from Hoffmann-La Roche, Genentech, Novartis, EMD Serono, Sanofi, and Horizon Therapeutics; has received honorarium for participation in educational programs from Medscape, Inc and the MS Association of America; has received speaking fees from Sanofi, EMD Serono, and the MS Association of America; and has received program sponsorship from EMD Serono and grant support from NIH/NINDS, PCORI, Genentech, and EMD Serono through her institution.

Objectives: This study explored the contributions of social determinants of health (SDOH) to measures of population health-specifically cost, hospitalization rates, rate of emergency department utilization, and health status-in Texas., Study Design: The study associated common SDOH metrics from public data sources (county specific) with health plan enrollment data (including demographics, counties, and zip codes) and medical and pharmaceutical annual claims data., Methods: Following correlation analyses to reduce variables, the contribution of each SDOH individually and by category to the health outcomes was evaluated. Separate matrices for age populations (under age 19, general population [all ages], and ≥ 65 years) were created with assigned weights of influence for categories and the factors within each category., Results: The contributions of the categories varied by population, confirming that different SDOH influence populations to varying degrees. This was reflected in each model. The largest contributor to cost for the general population and for the group 65 years and older was factors grouped as health outcomes (such as perceived health), at 43.5% contribution and 37.7% contribution, respectively. Yet for the population younger than 19 years, the largest contributor to cost was socioeconomic factors (such as unemployment rate), at 40.2%. The other performance measures also varied by population and the mix and weight of determinants., Conclusions: This study and the developed population-based matrices can provide a valuable framework for reporting the impact of SDOH on health care quality. The variation suggests the need for further research on how age groups react to the social environment.

This research evaluated the 2013 published physician reimbursement rates for Medicare and Medicaid in Texas and compared the rates with the mean fees from private carriers. Physician claims data were extracted from the Truven MarketScan Commercial Claims Databases. The average allowed amounts per unit per procedure code were compiled. The 2013 Medicare physician fee schedule was obtained and filtered to Texas. The 2013 Texas Medicaid physician fee schedule was obtained. The mean commercial allowed amounts were compared with those of Medicare and Medicaid on a per-unit rate. Comparison ratios were derived for each code. The CPT© procedure codes were then grouped into the categories assigned by the American Medical Association. The ratios of private/Medicare and private/Medicaid varied greatly by procedure type and locality, with the Texas Medicaid fees well below both private and Medicare fees. The discrepancy in payment amounts demonstrates the variation in payment rates among payer sources. The practical implications demonstrate the provider challenges in managing patient mix to maintain a viable practice.