Racial Differences and Disparities in Osteoporosis-related Bone Health
Bibliographic record
Abstract
BACKGROUND: Determining whether observed differences in health care can be called disparities requires persistence of differences after adjustment for relevant patient, provider, and health system factors. We examined whether providing dual-energy x-ray absorptiometry (DXA) test results directly to patients might reduce or eliminate racial differences in osteoporosis-related health care. DESIGN, SUBJECTS, AND MEASURES: We analyzed data from 3484 white and 1041 black women who underwent DXA testing at 2 health systems participating in the Patient Activation after DXA Result Notification (PAADRN) pragmatic clinical trial (ClinicalTrials.gov NCT-01507662) between February 2012 and August 2014. We examined 7 outcomes related to bone health at 12 weeks and 52 weeks post-DXA: (1) whether the patient correctly identified their DXA baseline results; (2) whether the patient was on guideline-concordant osteoporosis pharmacotherapy; (3) osteoporosis-related satisfaction; (4) osteoporosis knowledge; (5 and 6) osteoporosis self-efficacy for exercise and for diet; and (7) patient activation. We examined whether unadjusted differences in outcomes between whites and blacks persisted after adjusting for patient, provider, and health system factors. RESULTS: Mean age was 66.5 years and 29% were black. At baseline black women had less education, poorer health status, and were less likely to report a history of osteoporosis (P<0.001 for all). In unadjusted analyses black women were less likely to correctly identify their actual DXA results, more likely to be on guideline-concordant therapy, and had similar patient activation. After adjustment for patient demographics, baseline health status and other factors, black women were still less likely to know their actual DXA result and less likely to be on guideline-concordant therapy, but black women had greater patient activation. CONCLUSIONS: Adjustment for patient and provider level factors can change how racial differences are viewed, unmasking new disparities, and providing explanations for others.
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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) | 0.001 | 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 | 0.001 |
| 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".