The Impact of a Change in Medicare Reimbursement Policy and HEDIS Measures on Stage at Diagnosis Among Medicare HMO and Fee-For-Service Female Breast Cancer Patients
Bibliographic record
Abstract
OBJECTIVE: To examine the effects of health plan enrollment [health maintenance organizations (HMO) or fee-for-service (FFS)], a change in Medicare reimbursement policy which allowed for annual rather than biennial mammograms, and Health Plan Employer Data Information Set (HEDIS) measures on stage at diagnosis among older women with breast cancer. METHODS: We used the population-based Surveillance Epidemiology and End Results (SEER)-Medicare database to identify all elderly women age 65-74 who were diagnosed with breast cancer from 1994 to 2002. We compared stage at diagnosis, demographic characteristics, and tumor characteristics for FFS or HMO enrollment in the periods before and after the 1998 policy change. We compared the effect of women age 65-69 whose mammography use in the HMO system is measured by HEDIS and those who are older (age 70-74). RESULTS: We identified 20,106 women enrolled in FFS Medicare, and 10,751 women enrolled in an HMO. Women ages 65-74 who were enrolled in a Medicare HMO were more likely to be diagnosed at an early stage both before and after the policy change, but the disparity decreased from 4.7% to 2.3%, a relative change of 51.1%. The disparity was not specific to the ages included in the HEDIS measure. CONCLUSIONS: A decrease of 51.1% in the HMO-FFS disparity in breast cancer stage at diagnosis coincided with the 1998 change in Medicare mammography reimbursement policy. The existence of HEDIS measures for HMOs does not create a disparity in stage at diagnosis between those whose mammograms are measured by HEDIS (younger women) and those whose are not (older women).
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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) | 0.000 | 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".