Patterns, Policy and Appropriateness: A 12-Year Utilization Review of Blood Glucose Test Strip Use in Insulin Users
Bibliographic record
Abstract
OBJECTIVES: Considerable attention has been paid to the rising costs of the use of blood glucose test strips (BGTS). Insulin users have generally been treated as a single homogeneous group, resulting in policies that cap usage (8.2 strips/day) in provincial drug insurance programs. The objective of this study was to conduct a utilization review of BGTS by insulin users and to evaluate use patterns against current insulin use patterns and BGTS policy. METHODS: BGTS usage was examined in a cohort of insulin users with type 1 and type 2 diabetes over a 12-year period (2001 to 2013) using the population-based administrative data in Manitoba, Canada. RESULTS: Total BGTS strip use increased by 121%, from $4.3 to $9.5 million. However, the number of insulin users also increased by 115%. Use has been stable at 1.5 strips per day per person since 2004 by insulin users with type 2 diabetes but has risen from 1.9 to 3.0 strips per day per person in those with type 1 diabetes. Mean daily test strip use was below the number of daily tests recommended for patients using insulin as per the current Canadian guidelines, with 11% and 15% of insulin users with type 1 and type 2 diabetes not claiming any BGTS use and a further 15% (type 1) and 28% (type 2) using fewer than 1 strip per day. CONCLUSIONS: BGTS use per insulin user has been stable for most of the past decade, and the vast majority of use falls well below provincial insurance caps. The amount of low-level testing (0 to <1 strip/day) suggests that greater attention should be directed to ensuring a safe level of testing by all insulin users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".