The Impact of Changing the CMS Nursing Home Compare 5-Star Ratings on the Use of Antipsychotics
Bibliographic record
Abstract
Abstract Due to their potential to increase falls and death in people with dementia, antipsychotic medications (APMs) have been the subject of several federal efforts to reduce their use in nursing homes (NHs). In 2015, the Centers for Medicare & Medicaid Services added inappropriate APM use to their NH 5-star quality ratings. We examined the impact of this policy decision on NH residents with dementia by race/ethnicity. Using a quasi-experimental study design and Minimum Data Set (MDS) 3.0 assessments, we examined long-stay NH residents with dementia. We examined changes in APM use quarterly (2013-2016) using interrupted time series analyses, stratified by race/ethnicity. There were about 1 million NH residents per quarter. Baseline use of APMs among persons with dementia was 29.1% for Whites, 29.2% for Blacks, and 33.7% for Hispanics. All three races experienced significant declines in APM use prior to the addition of AP use into the quality rating (p<0.001). During the first quarter of rating system changes, there were significant declines in APM use for all three races: Blacks, 0.48%; Hispanics, 1.0%; Whites, 0.49%. Subsequent rates of decrease in APM use did not differ from the baseline rate of decline (p> 0.5). The policy change did result in a one-time, significant drop in APM use, but did not alter the rate of decline already in place, presumably stemming from the National Partnership instituted in 2012. Hispanics started with the highest rate of APM use and experienced the greatest decreases over time and with the new star rating measure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".