Prescribing Trends and Associated Outcomes of Antiepileptic Drugs and Other Psychotropic Medications in US Nursing Homes: Proposal for a Mixed Methods Investigation
Bibliographic record
Abstract
BACKGROUND: Pilot data suggest that off-label, unmonitored antiepileptic drug prescribing for behavioral and psychological symptoms of dementia is increasing, replacing other psychotropic medications targeted by purposeful reduction efforts. This trend accelerated during the COVID-19 pandemic. Although adverse outcomes related to this trend remain unknown, preliminary results hint that harms may be increasing and concentrated in vulnerable populations. OBJECTIVE: Using a mixed methods approach including both a retrospective secondary data analysis and a national clinician survey, this study aims to describe appropriate and potentially inappropriate antiepileptic and other psychoactive drug prescribing in US nursing homes (NHs), characteristics and patient-oriented outcomes associated with this prescribing, and how these phenomena may be changing under the combined stressors of the COVID-19 pandemic and the pressure of reduction initiatives. METHODS: To accomplish the objective, resident-level, mixed-effects regression models and interrupted time-series analyses will draw on cohort elements linked at an individual level from the Centers for Medicare and Medicaid Services' (CMS) Minimum Data Set, Medicare Part D, Medicare Provider Analysis and Review, and Outpatient and Public Use Files. Quarterly cohorts of NH residents (2009-2021) will incorporate individual-level data, including demographics; health status; disease variables; psychotropic medication claims; comprehensive NH health outcomes; hospital and emergency department adverse events; and NH details, including staffing resources and COVID-19 statistics. To help explain and validate findings, we will conduct a national qualitative survey of NH prescribers regarding their knowledge and beliefs surrounding changing approaches to dementia care and associated outcomes. RESULTS: Funding was obtained in September 2022. Institutional review board exemption approval was obtained in January 2023. The CMS Data Use Agreement was submitted in May 2023 and signed in March 2024. Data access was obtained in June 2024. Cohort creation is anticipated by January 2025, with crosswalks finalized by July 2025. The first survey was fielded in October 2023 and published in July 2024. The second survey was fielded in March 2024. The results are in review as of July 2024. Iterative survey cycles will continue biannually until December 2026. Multidisciplinary dissemination of survey analysis results began in July 2023, and dissemination of secondary data findings is anticipated to begin January 2025. These processes are ongoing, with investigation to wrap up by June 2027. CONCLUSIONS: This study will detail appropriate and inappropriate antiepileptic drug use and related outcomes in NHs and describe disparities in long-stay subpopulations treated or not treated with psychotropics. It will delineate the impact of the pandemic in combination with national policies on dementia management and outcomes. We believe this mixed methods approach, including processes that link multiple CMS data sets at an individual level and survey-relevant stakeholders, can be replicated and applied to evaluate a variety of patient-oriented questions in diverse clinical populations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64446.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.329 | 0.274 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.017 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".