Population Trends of New Prescriptions for Antihyperglycemics and Antihypertensives Between 2014 and 2022
Bibliographic record
Abstract
BACKGROUND: In the wake of pandemic-related health decline and health care disruptions, there are concerns that previous gains for cardiovascular risk factors may have stalled or reversed. Population-level excess burden of drug-treated diabetes and hypertension during the pandemic compared with baseline is not well characterized. We evaluated the change in incident prescription claims for antihyperglycemics and antihypertensives before versus during the pandemic. METHODS AND RESULTS: In this retrospective, serial, cross-sectional, population-based study, we used interrupted time series analyses to examine changes in the age- and sex-standardized monthly rate of incident prescriptions for antihyperglycemics and antihypertensives in patients aged ≥66 years in Ontario, Canada, before the pandemic (April 2014 to March 2020) compared with during the pandemic (July 2020 to November 2022). Incident claim was defined as the first prescription filled for any medication in these classes. The characteristics of patients with incident prescriptions of antihyperglycemics (n=151 888) or antihypertensives (n=368 123) before the pandemic were comparable with their pandemic counterparts (antihyperglycemics, n=97 015; antihypertensives, n=146 524). Before the pandemic, monthly rates of incident prescriptions were decreasing (-0.03 per 10 000 individuals [95% CI, -0.04 to -0.01] for antihyperglycemics; -0.14 [95% CI, -0.18 to -0.10] for antihypertensives). After July 2020, monthly rates increased (postinterruption trend 0.31 per 10 000 individuals [95% CI, 0.28-0.34] for antihyperglycemics; 0.19 [95% CI, 0.14-0.23] for antihypertensives). CONCLUSIONS: Population-level increases in new antihyperglycemic and antihypertensive prescriptions during the pandemic reversed prepandemic declines and were sustained for >2 years. Our findings are concerning for current and future cardiovascular health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".