Estimating ethnic differences in self‐reported new use of antidepressant medications: results from the Multi‐Ethnic Study of Atherosclerosis
Bibliographic record
Abstract
INTRODUCTION: There is evidence that the utilization of antidepressant medications (ADM) may vary between different ethnic groups in the United States population. METHODS: The Multi-Ethnic Study of Atherosclerosis (MESA) is a population-based prospective cohort study of 6814 US adults from 4 different ethnic groups. After excluding baseline users of ADM, we examined the relation between baseline depression and new use of ADM for 4 different ethnicities: African-Americans (n = 1822), Asians (n = 784) Caucasians (n = 2300), and Hispanics (n = 1405). Estimates of the association of ethnicity and ADM use were adjusted for age, study site, gender, Center for Epidemiologic Studies Depression Scale (CES-D), alcohol use, smoking, blood pressure, diabetes, education, and exercise. Non-random loss to follow-up was present and estimates were adjusted using inverse probability of censoring weighting (IPCW). RESULTS: Of the four ethnicities, Caucasian participants had the highest rate of ADM use (12%) compared with African-American (4%), Asian (2%), and Hispanic (6%) participants. After adjustment, non-Caucasian ethnicity was associated with reduced ADM use: African-American (HR: 0.42; 95% Confidence Interval (CI): 0.31-0.58), Asian (HR: 0.14; 95%CI: 0.08-0.26), and Hispanic (HR: 0.47; 95%CI: 0.31-0.65). Applying IPCW to correct for non-random loss to follow-up among the study participants weakened but did not eliminate these associations: African-American (HR: 0.48; 95%CI: 0.30-0.57), Asian (HR: 0.23; 95%CI: 0.13-0.37), and Hispanic (HR: 0.58; 95%CI: 0.47-0.67). CONCLUSION: Non-Caucasian ethnicity is associated with lower rates of new ADM use. After IPCW adjustment, the observed ethnicity differences in ADM use are smaller although still statistically significant.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".