Impact of the COVID-19 pandemic on incidence of coronary heart disease in Bavaria, Germany: an analysis of health claims data
Bibliographic record
Abstract
BACKGROUND: Inconsistent findings about the impact of the COVID-19 pandemic on cardiovascular disease diagnosis and consultations have been reported internationally. The objective of this study was to analyse the impact of the pandemic period (2020-2021) on the incidence rate of coronary heart disease (CHD) compared with the pre-pandemic period (2012-2019) in Bavaria, Germany. METHODS: We used health claims data of around 9 million statutorily insured residents (≥20 years) of Bavaria, Germany. We calculated quarterly age-standardised incidence rates for men and women diagnosed with CHD using the European Standard Population 2013. Interrupted time series regression models were used to analyse possible pandemic effects on the CHD incidence rates. RESULTS: Overall, 797 074 new CHD cases (47% women) were diagnosed from 2012 to 2021. Both pre-pandemic and pandemic incidence rates for women were lower than for men. Regression models showed decreasing incidence rates in the pre-pandemic period in men (-5.2% per year (p.a.), 95% CI: -5.7% to -4.7%) and in women (-6.6% p.a., 95% CI: -7.3% to -6.0%) and seasonal effects (higher in quarter 4 compared with Q1-Q3). During the pandemic period, there was no clear evidence of a level change in the incidence rates both in women and men. However, there are indications of a smaller decline in the incidence during the pandemic compared with the pre-pandemic period, in particular in women (-0.7% p.a., 95% CI: -6.0% to 4.8%) and less prominent in men (-1.7% p.a., 95% CI: -6.0% to 2.8%). CONCLUSIONS: An overall decreasing CHD incidence rate was observed in men and women in the past decade but no clear impact of the pandemic was seen. These results show the importance of incidence monitoring beyond the pandemics to maintain chronic disease care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".