Influence of Socioeconomic Status on Drug Selection for the Elderly in Canada
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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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".