Influence of Socioeconomic Status on Drug Selection for the Elderly in Canada
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To examine the association between socioeconomic status, as indicated by neighborhood median income levels, and physician drug selection between older, less expensive generic drugs and newer, more expensive brand-name drugs for elderly patients initiating drug therapy in a universal healthcare system. METHODS: We conducted a population-based, retrospective, cross-sectional study. Using healthcare administrative databases, we assessed the medication profiles of 128 314 patients from more than 1.4 million residents of Ontario > or =65 years old initiating antipsychotic, hydroxymethylglutaryl-coenzyme A reductase inhibitor (statin), or ocular beta-blocker drug therapy from January 1, 1998, through December 31, 1999. We examined the selection of older generic drugs relative to newer brand-name agents for patients in each of 5 income quintiles. RESULTS: Overall, brand-name drug prescribing modestly increased with increasing income quintile after adjusting for patient age and gender (61.2% in the lowest income quintile vs. 64.1% in the highest income quintile; p value for trend < 0.001). Significant risk ratios comparing the highest with the lowest income-quintile patients were observed for selection of newer, brand-name antipsychotics (RR 1.14; 95% CI 1.06 to 1.23), older generic statins (RR 0.86; 95% CI 0.77 to 0.95), and newer, brand-name ocular beta-blockers (RR 1.13; 95% CI 1.02 to 1.25). CONCLUSIONS: This study suggests that income-related differences in treatment selection by physicians may exist. The reasons for these differences and subsequent impact on health outcomes warrant further investigation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it