Health policy and community pharmacy provision of oncology medications: an assessment of the effect of a changing health policy environment on the medication adherence, cost and safety of oncology medications provided by community pharmacies
Bibliographic record
Abstract
Introduction: Oral cancer therapy has more than doubled in the past 10 years and all indications are that the pipeline of new and emerging cancer therapies continues to include a higher percentage of oral drugs. With the increased availability of oral oncology products, patients can have access to their medications in community pharmacies and avoid the burden of travel and waiting time in cancer clinics or hospitals. However, this shift in care may also pose some challenges such as adherence to the treatment, safe use of the oncology products and increased costs to patients and the health care system. This study examined the impact of this shift to greater use of oral chemotherapy, looking at adherence, drug interaction management and cost through a series of health policy changes. Methods: Administrative health data between April 1, 2000 and March 31, 2015 from the Manitoba Centre for Health Policy Repository were used for this study. Dispensation records were used to assess drug utilization, measure adherence, analyze drug interactions and calculate medication costs. Results: Adherence was examined using imatinib as the case example. High levels of adherence (>90%) were found throughout the study period regardless of insurance coverage. The clinically important drug interaction between tyrosine kinase inhibitors and proton pump inhibitors was assessed for medication provided by community pharmacies. Over 1/3 of patients received these interacting drugs together with little evidence of interventions to manage this interaction. During the study period, the cost of oral oncology medications rose more than 7 fold from $2,682,805 to $21,311,652, with increases in the rate of prescribing (20 to 29 per 1000 people) and increases in the cost of oncology prescriptions ($113 to $549 per prescription). Conclusion: Although oral therapy provides advantages and greater flexibility for cancer patients which is reflected in a high degree of adherence, additional policy reform and support need to be given to ensure that these medications can be used safely and effectively in the community setting.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".