Impact of COVID-19 Pandemic on Dispensing of Cardiovascular Drugs in Norway: An Interrupted Time Series Study
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic resulted in changes in prescription patterns and fillings for certain medications, but little is known about its impact on the dispensing of cardiovascular drugs. METHODS: Trends in dispensing of cardiovascular drugs before and during the pandemic were examined using a population-based cohort in Norway. Using interrupted time series analyses and considering March 1, 2020 as the interruption point, the impact of the pandemic on defined daily dose dispensing of prescribed cardiovascular drugs was estimated in a population of adults with and without pre-existing cardiovascular disease from January 2018 to December 2021. All data were analyzed in 2023. RESULTS: In a total of 4,053,957 adults, 690,910 (17.0%) had pre-existing cardiovascular disease. Prior to the pandemic, there was a significant monthly increase in any cardiovascular drug dispensing among those with pre-existing cardiovascular disease (0.30 defined daily dose per month per adult), including prescription of diuretics, calcium channel blockers, and lipid-modifying agents. After controlling for preinterruption trends, there was a slight decrease in level change immediately after the start of the pandemic (2.5 defined daily dose per month per adult) but an increase in the postinterruption trend (0.06 defined daily dose per month per adult) for dispensing of cardiovascular prescriptions, although these changes were not significant. CONCLUSIONS: Although the COVID-19 pandemic did not appear to result in significant changes in patterns of cardiovascular drug dispensing in Norway, continued access to cardiovascular drugs remains important to prevent further related morbidity.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".