High Prevalence of Inappropriate Benzodiazepine and Sedative Hypnotic Prescriptions among Hospitalized Older Adults
Bibliographic record
Abstract
BACKGROUND: Benzodiazepines and sedative hypnotics are commonly used to treat insomnia and agitation in older adults despite significant risk. A clear understanding of the extent of the problem and its contributors is required to implement effective interventions. OBJECTIVE: To determine the proportion of hospitalized older adults who are inappropriately prescribed benzodiazepines or sedative hypnotics, and to identify patient and prescriber factors associated with increased prescriptions. DESIGN: Single-center retrospective observational study. SETTING: Urban academic medical center. PARTICIPANTS: Medical-surgical inpatients aged 65 or older who were newly prescribed a benzodiazepine or zopiclone. MEASUREMENTS: Our primary outcome was the proportion of patients who were prescribed a potentially inappropriate benzodiazepine or sedative hypnotic. Potentially inappropriate indications included new prescriptions for insomnia or agitation/anxiety. We used a multivariable random-intercept logistic regression model to identify patient- and prescriber-level variables that were associated with potentially inappropriate prescriptions. RESULTS: Of 1308 patients, 208 (15.9%) received a potentially inappropriate prescription. The majority of prescriptions, 254 (77.4%), were potentially inappropriate. Of these, most were prescribed for insomnia (222; 87.4%) and during overnight hours (159; 62.3%). Admission to a surgical or specialty service was associated with significantly increased odds of potentially inappropriate prescription compared to the general internal medicine service (odds ratio [OR], 6.61; 95% confidence interval [CI], 2.70-16.17). Prescription by an attending physician or fellow was associated with significantly fewer prescriptions compared to first-year trainees (OR, 0.28; 95% CI, 0.08-0.93). Nighttime prescriptions did not reach significance in initial bivariate analyses but were associated with increased odds of potentially inappropriate prescription in our regression model (OR, 4.48; 95% CI, 2.21-9.06). CONCLUSIONS: The majority of newly prescribed benzodiazepines and sedative hypnotics were potentially inappropriate and were primarily prescribed as sleep aids. Future interventions should focus on the development of safe sleep protocols and education targeted at first-year trainees.Journal of Hospital Medicine 2017;12:310-316.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".