Estimating the Use of Potentially Inappropriate Medications Among Older Adults in the United States
Bibliographic record
Abstract
OBJECTIVES: Inappropriate prescribing of medications is common in health care, and is an important safety concern, especially for older adults, who have a high burden of comorbidity and are at greater risk for medication-related adverse events. This study aims to estimate the extent and cost of potentially inappropriate prescribing of medications to older adults in the United States. DESIGN: A cross-sectional study. SETTING: Medicare Part D Prescription Drug Program data set (2014-2018). PARTICIPANTS: Older adults who were enrolled in Medicare Part D Prescription Drug Program between 2014 and 2018. MEASUREMENTS: Potentially inappropriate medications were identified using the 2019 American Geriatrics Society Beers Criteria®. RESULTS: In 2018, 7.3 billion doses of potentially inappropriate medications were dispensed. The most common medications by number of doses dispensed were proton pump inhibitors, benzodiazepines, and tricyclic antidepressants, and the top five unique medications by reported spending were dexlansoprazole, esomeprazole, omeprazole, dronedarone, and conjugated estrogens. From 2014 to 2018, 43 billion doses of potentially inappropriate medications were dispensed, with a reported spending of $25.2 billion. CONCLUSION: Potentially inappropriate medication use among older adults is both common and costly. Careful attention to potentially inappropriate medication use and deprescribing when clinically appropriate could reduce costs and potentially improve outcomes among older adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".