PRIVATE INSURANCE VERSUS MEDICAID AND ADHERENCE TO MEDICATION IN OLDER ADULTS WITH FIBROMYALGIA
Bibliographic record
Abstract
Background: Fibromyalgia, defined as chronic, wide-spread musculoskeletal pain, affects 4 to 10 million Americans and up to 6% of the world population. Medication nonadherence results in $100 to $300 billion in US health expenditures annually. Previous studies have examined medication adherence in commercial health plans or public health plans, but relatively few have compared both populations. The purpose of this study was to estimate the effect of type of insurance on adherence to medication for older adults with fibromyalgia. Methods: The retrospective cohort study analyzed medical claims of fibromyalgia patients collected between January 1, 2005 to June 30, 2011 from the Blue Cross Blue Shield South Carolina State Health Plan (BCBS) and Medicaid data. Older adults age 60 and older were included if they were prescribed duloxetine, milnacipran, or pregabalin (N=3,187). The primary outcome, medication adherence, was defined as having a medication possession ratio (MPR) of ≥ 80%. Independent variables included health insurance, FMS medication, selected comorbidities (FMS-related, musculoskeletal pain, or neuropathic pain), gender, age, and the interaction between health insurance type and treatment. Results: Logistic regression showed older adults with fibromyalgia on Medicaid were over 3 times more likely to be adherent when compared to BCBS in both unadjusted (OR: 3.21, p<0.0001) and adjusted models (OR: 3.74, p<0.0001). Conclusion: Most states do not require a Medicaid prescription co-pay; whereas, private insurers, like Blue Cross Blue Shield, require more out-of-pocket costs. Our study suggests that the co-pays for medications in private plans may present a barrier to patient adherence.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".