The relationship between type of drug therapy and blood glucose self-monitoring test strips claimed by beneficiaries of the Seniors' Pharmacare Program in Nova Scotia, Canada
Bibliographic record
Abstract
BACKGROUND: The healthcare expenditure on self-monitoring of blood glucose (SMBG) test strips under the Nova Scotia Seniors' Pharmacare Program (NSSPP) has increased significantly in recent years. The objective of this study was to identify the frequency and cost of claims for blood glucose monitoring test strips by NSSPP beneficiaries in the fiscal year 2005/06 and to explore the variation in the use of test strips by type of treatment, age and sex. METHODS: Retrospective analysis was conducted using pharmacy administrative claims data for NSSPP beneficiaries. Study subjects were aged > or = 65 years on October 1, 2004, received SMBG test strips in the 110 days prior to April 1, 2005, and were alive throughout the twelve month study period. Subjects were categorized into four groups: insulin only, oral antihyperglycemic agents (OAA) only, both OAA and insulin; and no reimbursed diabetes medications. Statistical analysis was performed to identify differences in expenditure by medication group and in frequency of SMBG test strips claimed by medication group, age, and sex. RESULTS: Of 13,564 included beneficiaries, 13.2% were categorized as insulin only, 53.5% OAA only, 7.2% both OAA and insulin, and 26.0% no reimbursed diabetes medications. Over half (58.7%) were femle. The insulin only category had the highest mean (+/- SD) number of SMBG test strips claimed per day (2.0 +/- 1.5) with a mean annual total cost of $615 +/- $441/beneficiary. Beneficiaries aged 80 years and above claimed fewer test strips than beneficiaries below 80 years. CONCLUSION: This population based study shows that in Nova Scotia the SMBG test strips claimed by the majority of seniors were within Canadian guidelines. However, a small proportion of beneficiaries claimed for SMBG test strips infrequently or too frequently, which suggests areas for improvement. The provincial drug plan covers the majority of the costs of test strip utilization, suggesting that the majority of test strips claimed did not exceed the maximum allowable cost (MAC) established in the program's MAC policy. Drug insurance programs need to work with healthcare providers to determine if patients are using test strips optimally; and to determine their impact on patient outcomes. In addition, they need to determine the cost-effectiveness of their SMBG test strip reimbursement policies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".