Prevalence, expenditures, utilization, and payment for persons with MS in insured populations
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence, expenditures, and utilization of enrollees with MS relative to all enrollees in privately insured, Medicare, and Medicaid populations. METHODS: The authors used insurer administrative billing data to identify persons with MS, their insured medical expenditures and utilization, and benchmark general insured population expenditures and utilization. Three samples of insurer billing data were analyzed: nationally representative samples for the privately insured (1994 through 1995) and Medicare (1996 though 1997) populations, and Medicaid data for disabled (1991 through 1996) populations from six states. RESULTS: Using 2 years of diagnoses on claims, the prevalence of MS in the privately insured population was 24 per 10,000, 36 per 10,000 in the Medicare population, and 71 per 10,000 in the Medicaid disabled population. Annual insured expenditures were $7,677 per privately insured enrollee with MS vs $2,394 for all privately insured enrollees, $13,048 per Medicare beneficiary with MS compared with $6,006 for all Medicare beneficiaries, and $7,352 per Medicaid disabled recipient with MS vs $4,088 per disabled recipient without MS. Home health expenditures were very high for Medicare beneficiaries with MS and nursing facility expenditures were very high for Medicaid disabled recipients with MS. A small proportion of enrollees with MS accounted for most expenditures. CONCLUSIONS: Insured enrollees with MS are two to three times more expensive than average insured enrollees. If the premiums that employers or governments pay health insurers and the capitation amounts that insurers pay health care providers do not account for these higher costs, a disincentive is created for the enrollment and care of persons with MS.
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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 | 0.000 | 0.000 |
| 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".