Does concordance between survey responses and administrative records differ by ethnicity for prescription medication?
Bibliographic record
Abstract
BACKGROUND: Self-reported prescription medication use data is often used to measure differences across ethnic groups, but its accuracy may differ across ethnic groups. OBJECTIVE: We compared ethnic groups' self-reported medication use to their administrative records for respondents with diabetes, hypertension, and asthma. METHODS: We linked the Canadian Community Health Survey to administrative prescription drug records for 17,191 respondents in British Columbia, Canada. We evaluated the concordance between self-reported medication use and prescription drug records using positive predictive value, negative predictive value, sensitivity, specificity, and kappa statistic for self-identified Whites, Chinese, South Asians, and Southeast Asians/Filipinos. The concordance was calculated using prescription drug records as the reference standard. We also estimated the odds of disagreement (either a false positive or negative) in medication use with logistic regressions for each ethnic group, and compared them using the Blinder-Oaxaca method. RESULTS: We found that Chinese had the worst positive predictive value for asthma medication use at 0.41, while South Asians had the worst sensitivity for hypertension medication use at 0.60. The difference in reporting an error between ethnic groups was likely explained by differences in respondent characteristics. Particularly, if White respondents had the same characteristics as South Asians, then White respondents would have had 1.031 (95% CI: 1.020-1.041) higher odds of disagreement for hypertension medication use than with their own characteristics. CONCLUSION: Self-reported medication use may be a valid measure of ethnic groups' medication use if ethnic differences in characteristics, like household income are held constant. However, an important determinant of validity for all ethnic groups is whether medications are used routinely, or for a specific episode.
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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 | 0.034 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".