Claims for disease-modifying therapy by Alberta non-insured health benefits clients
Bibliographic record
Abstract
BACKGROUND: Uncontrolled disease activity in inflammatory diseases of the joints, skin and bowel leads to morbidity and disability. Disease-modifying therapies are widely used to suppress this disease activity, but cost-coverage is variable. For Treaty First Nations and Inuit people in Canada without alternative private or public health insurance, cost-coverage for disease-modifying therapy is provided through Non-Insured Health Benefits (NIHB). Our objective was to describe the prevalence and patterns of treatment with disease-modifying therapy for the NIHB claimant population, and also examine adjuvant therapy (analgesics, non-steroidal anti-inflammatory drugs (NSAIDs), corticosteroids) use. METHODS: Cases (n = 2512) were defined by ≥1 claim for a disease-modifying anti-rheumatic drug (DMARD) or biologic between 1999 and 2012 in the NIHB pharmacy claim database. The proportion of the population with claims for individual agents and drug classes annually was calculated to estimate annual incidence and prevalence rates for use of disease-modifying therapy, and the prevalence of use of individual DMARDs, biologics and adjuvants. Differences in the proportion accessing adjuvant therapies and median doses in the 6 months following initiation of disease-modifying therapies was estimated. RESULTS: The incidence rate of treatment was calculated at an average of 127.5 cases per 100,000 population between 2001 and 2012, and the cumulative prevalence, accounting for patients lost to the database, increased and then stabilized at 1.3 % in the last three years of the study. Annual dispensation of methotrexate, combination DMARD therapy and biologic therapy approached 35 %, 19 %, and 10 % of the cohort respectively. A declining prevalence of claims for acetaminophen (28 % to 15 %) and anti-inflammatories (73 % to 63 %) occurred from 2000 to 2012, however corticosteroid (32 %) and opioid (65 %) dispensation remained stable. The proportion of patients with claims for NSAIDs (69.9 % to 61.1 %, p = 0.002), oral corticosteroids (45.4 % to 33.6 %, p < 0.001) and parenteral corticosteroids (16.2 % to 8.3 %, p = 0.002) decreased in the 6 months following biologic initiation. CONCLUSIONS: The proportion of NIHB clients with active claims for disease-modifying therapy is lower than expected based on existing epidemiologic knowledge of the prevalence of inflammatory conditions in the First Nations and Inuit populations. These findings should be further explored in order to optimize treatment outcomes for NIHB claimants with inflammatory disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".