Use of Inappropriate Prescription Drugs by Older People
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence and predictors of inappropriate drug prescribing defined by expert national consensus panel drug utilization review criteria for community-dwelling older people. DESIGN: Survey. SETTING: Five adjacent urban and rural counties in the Piedmont area of North Carolina. PARTICIPANTS: A stratified random sample of participants from the fourth (n = 3,234) and seventh (n = 2,508) waves of the Duke Established Populations for Epidemiological Studies of the Elderly. MEASUREMENTS: The prescribing appropriateness for digoxin, calcium channel blockers, angiotensin-converting enzyme inhibitors, histamine(2) receptor antagonists, nonsteroidal antiinflammatory drugs (NSAIDs), benzodiazepines, antipsychotics, and antidepressants as determined by explicit criteria (through Health Care Financing Administration expert consensus panel drug utilization review criteria for dosage, duplication, drug-drug interactions and duration, and U.S. and Canadian expert consensus panel criteria for drug-disease interactions). Multivariable analyses, using weighted data adjusted for sampling design, were conducted to assess the association between inappropriate prescribing and demographic, health-status, and access-to-healthcare factors cross-sectionally and longitudinally. RESULTS: We found that 21.0 of the fourth wave and 19.2 of the seventh wave participants who used one or more agents from the eight drug classes had one or more elements identified as inappropriate. The therapeutic classes with the most problems were benzodiazepines and NSAIDs. The most common problems were with drug-disease interactions and duration of use. Longitudinal multivariable analyses found that participants who were white (adjusted odds ratio (AOR) = 1.67, 95 confidence interval (CI) = 1.28-2.17), were married (AOR = 1.40, 95% CI = 1.01-1.93), had arthritis (AOR = 1.74, 95% CI = 1.27-2.38), had one or more physical function disabilities (AOR = 1.42, 95% CI = 1.02-1.96), and had inappropriate drugs prescribed at wave 4 (AOR = 6.87, 95% CI = 5.11-9.22) were more likely to have inappropriate prescribing at wave 7. CONCLUSION: These results indicate that inappropriate prescribing is common among community-dwelling older people and persists over time. Longitudinal studies in older people are needed to examine the impact of inappropriate drug prescribing on health-related outcomes.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".