268-OR: Impact of the COVID-Pandemic on Antihyperglycemic Prescription Patterns in Canada
Bibliographic record
Abstract
Background: COVID-public health measures may have impacted diabetes care through delayed care and reduced medication access. This study describes antihyperglycemic medication prescription patterns among adults with type 2 diabetes (T2D) before and during the COVID-pandemic in Canada. Methods: Using IQVIA’s longitudinal pharmacy based prescription data, antihyperglycemic prescriptions from March 1, 2018 to February 28, 2021 were analyzed among adults who had ≥1 prescription for a non-insulin antihyperglycemic drug. The number of people who: 1) had antihyperglycemic prescriptions,2) were newly started on antihyperglycemic drugs, and3) were newly diagnosed with T2D (inferred from prescriptions) were reported. Results: The number of people who had ≥1 antihyperglycemic prescription was comparable in the year of the COVID-pandemic (March 2020 to February 2021) and the year prior (March 20to February 2020) . The number of people who newly initiated a GLP-1RA, SGLT2i or second-generation basal insulin analogue decreased for the first few months of the pandemic (April to September 2020) with recovery thereafter. The number of people who were newly diagnosed with T2D decreased by 7% in the COVID-year. Conclusion: Fewer people initiated newer antihyperglycemic medications and fewer people were newly diagnosed with T2D in the first few months for the pandemic which may reflect reduced health care access. Disclosure A.Y.Cheng: Advisory Panel; Abbott, AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Dexcom, Inc., Eli Lilly and Company, HLS Theraoeutics, Insulet Corporation, Janssen Pharmaceuticals, Inc., Medtronic, Novo Nordisk, Sanofi, Board Member; Type 1 Diabetes Think Tank Network, Other Relationship; Diabetes Canada, Speaker's Bureau; Bausch Health, Canada, Merck & Co., Inc. R.Goldenberg: Consultant; IQVIA Inc., Speaker's Bureau; Amgen Canada, AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Canada Inc., Sanofi. I.E.Krawchenko: Speaker's Bureau; Janssen Pharmaceuticals, Inc. R.Tytus: Other Relationship; Banty , Boehringer Ingelheim International GmbH, Canadian Health Research Company, Merck & Co., Inc., Novo Nordisk, Pfizer Inc. J.Hahn: Employee; Novo Nordisk Canada Inc. A.R.Liu: Employee; Novo Nordisk A/S, Novo Nordisk Canada Inc. T.Lan: Other Relationship; Novo Nordisk Canada Inc. B.Millson: Other Relationship; Novo Nordisk Canada Inc. S.B.Harris: Consultant; Abbott, AstraZeneca, Eli Lilly and Company, Novo Nordisk, Sanofi, Other Relationship; Abbott, AstraZeneca, Bayer Inc., Dexcom, Eli Lilly and Company, HLS Therapeutics, Janssen Pharmaceuticals, Inc., Novo Nordisk, Sanofi, Research Support; Applied Therapeutics Inc., AstraZeneca, Canadian Institutes of Health Research, Juvenile Diabetes Research Foundation (JDRF) , Novo Nordisk, Sanofi, The Lawson Foundation.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".