Effect of the Women's Health Initiative on Osteoporosis Therapy and Expenditure in Medicaid
Bibliographic record
Abstract
UNLABELLED: Decreasing HRT use among postmenopausal women may have a reciprocal impact on other osteoporosis therapy. Time series analysis of prescribing trends for millions of Medicaid beneficiaries revealed a 57% decline in HRT without augmenting the pace of bisphosphonate use. Prescribing changes dramatically increased Medicaid spending on osteoporosis therapy over the last decade and requires further evaluation of cost effectiveness. INTRODUCTION: Hormone replacement therapy (HRT) has been commonly prescribed to postmenopausal women, but its use is decreasing because adverse cardiac outcomes were reported by the Women's Health Initiative (WHI) in July 2002. The reciprocal impact of the WHI on other osteoporosis medications use and expenditure is unknown. MATERIALS AND METHODS: We conducted a time series analysis on prescription data from 50 state Medicaid programs between 1995 and 2004. Five medication categories were used: HRT, bisphosphonates, calcium, calcitonin, and raloxifene. RESULTS: HRT was increasing before publication of the WHI, reaching 5 million prescriptions per year by mid-2002 (136 prescriptions per 1000 beneficiaries). Bisphosphonate prescribing rose in parallel until mid-2002. WHI publication was associated with a rapid reduction in HRT use, declining 57% by mid-2004 to an average of 59 prescriptions per 1000 beneficiaries (p = 0.01). WHI publication did not augment bisphosphonates' nearly linear rate of rise (p = 0.43) as their prescribing pace continued, whereas HRT declined. Medicaid spending on osteoporosis therapy also changed dramatically during the last decade, as yearly expenditure increased 664% from 1465 US dollars to 9742 US dollars per 1000 beneficiaries. Over this period, a significant shift from daily to weekly bisphosphonates also occurred. CONCLUSIONS: A dramatic decline in HRT and continued rise in bisphosphonate prescribing has occurred since the publication of the WHI. During this time, there have also been substantial increases in osteoporosis medication spending within Medicaid. Determining whether these trends are clinically appropriate and cost effective for osteoporosis therapy will have important implications for the development of future drug reimbursement programs, especially for elderly patients.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".