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 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".