Health insurance affects the use of disease-modifying therapy in multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between health insurance coverage and disease-modifying therapy (DMT) use for multiple sclerosis (MS). METHODS: In 2014, we surveyed participants in the North American Research Committee on MS registry regarding health insurance coverage. We investigated associations between negative insurance change and (1) the type of insurance, (2) DMT use, (3) use of free/discounted drug programs, and (4) insurance challenges using multivariable logistic regressions. RESULTS: Of 6,662 respondents included in the analysis, 6,562 (98.5%) had health insurance, but 1,472 (22.1%) reported negative insurance change compared with 12 months earlier. Respondents with private insurance were more likely to report negative insurance change than any other insurance. Among respondents not taking DMTs, 6.1% cited insurance/financial concerns as the sole reason. Of respondents taking DMTs, 24.7% partially or completely relied on support from free/discounted drug programs. Of respondents obtaining DMTs through insurance, 3.3% experienced initial insurance denial of DMT use, 2.3% encountered insurance denial of DMT switches, and 1.6% skipped or split doses because of increased copay. For respondents with relapsing-remitting MS, negative insurance change increased their odds of not taking DMTs (odds ratio [OR] 1.50; 1.16-1.93), using free/discounted drug programs for DMTs (OR 1.89; 1.40-2.57), and encountering insurance challenges (OR 2.48; 1.64-3.76). CONCLUSIONS: Insurance coverage affects DMT use for persons with MS, and use of free/discounted drug programs is substantial and makes economic analysis that ignores these supplements potentially inaccurate. The rising costs of drugs and changing insurance coverage adversely affect access to treatment for 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 | 0.001 |
| 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".