Ethnic differences in the use of prescription drugs: a cross-sectional analysis of linked survey and administrative data.
Bibliographic record
Abstract
BACKGROUND: Evidence from the United States and Europe suggests that the use of prescription drugs may vary by ethnicity. In Canada, ethnic disparities in prescription drug use have not been as well documented as disparities in the use of medical and hospital care. We conducted a cross-sectional analysis of survey and administrative data to examine needs-adjusted rates of prescription drug use by people of different ethnic groups. METHODS: For 19 370 non-Aboriginal people living in urban areas of British Columbia, we linked data on self-identified ethnicity from the Canadian Community Health Survey with administrative data describing all filled prescriptions and use of medical services in 2005. We used sex-stratified multivariable logistic regression analysis to measure differences in the likelihood of filling prescriptions by drug class (antihypertensives, oral antibiotics, antidepressants, statins, respiratory drugs and nonsteroidal anti-inflammatory drugs [NSAIDs]). Models were adjusted for age, general health status, treatment-specific health status, socio-economic factors and recent immigration (within 10 years). RESULTS: We found evidence of significant needs-adjusted variation in prescription drug use by ethnicity. Compared with women and men who identified themselves as white, those who were South Asian or of mixed ethnicity were almost as likely to fill prescriptions for most types of medicines studied; moreover, South Asian men were more likely than white men to fill prescriptions for antibiotics and NSAIDs. The clearest pattern of use emerged among Chinese participants: Chinese women were significantly less likely to fill prescriptions for antihypertensives, antibiotics, antidepressants and respiratory drugs, and Chinese men for antidepressant drugs and statins. INTERPRETATION: We found some disparities in prescription drug use in the study population according to ethnic group. The nature of some of these variations suggest that ethnic differences in beliefs about pharmaceuticals may generate differences in prescription drug use; other variations suggest that there may be clinically important disparities in treatment use.
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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 | 0.000 |
| 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".