Impact of the COVID-19 Pandemic on Antihyperglycemic Prescriptions for Adults With Type 2 Diabetes in Canada: A Cross-sectional Study
Bibliographic record
Abstract
OBJECTIVES: Diabetes is a major public health problem in Canada and requires multifactorial, consistent clinical management. The COVID-19 pandemic has increased challenges in the management of many chronic ailments, including diabetes. Diabetes was associated with a higher risk of severe illness in the context of COVID-19. Pandemic restrictions also impacted diabetes care continuity, which may have contributed to an increased risk of diabetes-related complications and mortality. METHODS: This was a retrospective cross-sectional study of prescription patterns of antihyperglycemic medications claimed by individuals with type 2 diabetes (T2D) before and during the COVID-19 pandemic using the IQVIA Canada Longitudinal Prescription Claims database. The study period was from March 1, 2018, to February 28, 2021. The study outcomes are described on a monthly, quarterly, and yearly basis and overall, and by medication, medication class, and insurance coverage type. "New-to-molecule" patients were defined as those claiming a medication during the analysis period that they had no history of claiming in the database. Adults with at least 1 year of prescription history available and claiming their first prescription for an antihyperglycemic drug during the analysis period were classified as newly diagnosed with T2D. RESULTS: A similar number of people had at least 1 non-insulin antihyperglycemic prescription during the baseline, prepandemic, and pandemic periods in Canada (1,778,155, 1,822,403, and 1,797,272, respectively). However, the number of people initiating newer antihyperglycemic medications decreased at the beginning of the pandemic, in contrast to older medications, which remained consistent across the pandemic period. The number of people diagnosed with T2D decreased in the early months of the pandemic but recovered by October 2020. CONCLUSION: The COVID-19 epidemic in Canada impacted clinical care for at-risk Canadians, with fewer being prescribed newer antihyperglycemic drugs and a reduction in the number of diagnoses of T2D.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".