Racial and ethnic variation in health resource use and cost for prostate cancer
Bibliographic record
Abstract
OBJECTIVE: To analyse the racial and ethnic variation in health resource use (HRU) and direct medical care (DMC) cost in elderly men with prostate cancer. PATIENTS AND METHODS: This was a retrospective case-control study using the linked Surveillance, Epidemiology, and End Results Medicare database. Patients with prostate cancer diagnosed between 1995 and 1998 (50 147 men) were identified and followed retrospectively for 1 year before and 5 years after the diagnosis. Phase-specific HRU and DMC costs were compared between racial and ethnic groups using parametric and nonparametric analysis. To compute the incremental cost of prostate cancer, a matched non-cancer control group was extracted from Medicare database. Poisson and general linear models (log-link) were used to identify the association of race and ethnicity with HRU and DMC cost, after controlling for potentially influential clinical and demographic covariates. RESULTS: The African-American group was more likely to have emergency-room visits (odds ratio 1.19, 95% confidence interval 1.12-1.28) and less likely to have outpatient visits (0.96, 0.96-0.97) than whites. However, the Hispanic group was more likely to have inpatient and outpatient visits (odds ratio 0.88, 0.83-0.91; and 0.93, 0.91-0.95) than whites. Adjusted DMC cost showed racial and ethnic variation in all phases except the treatment and terminal phases. Factors associated with DMC cost varied among racial and ethnic groups. CONCLUSION: The incremental burden of prostate cancer remains significant in the long term. Overall, the cost of prostate cancer care was higher among African-American men than white and Hispanic men. This indicates the need for further research on care-level factors to comprehend the racial and ethnic disparity in HRU and cost.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".