Impact of the COVID-19 Pandemic on Consultations and Diagnoses in Gastroenterology Practices in Germany
Bibliographic record
Abstract
The COVID-19 pandemic has been a major burden for healthcare systems worldwide and has caused multiple changes and problems in outpatient care. The aim of this study was to investigate the impact of the COVID-19 pandemic on consultations and diagnoses in gastroenterology practices in Germany. To this end, we retrospectively analyzed data from the Disease Analyzer database (IQVIA) using the International Classification of Diseases, 10th revision (ICD-10). We included all patients aged ≥18 years with at least one visit to one of 48 gastroenterology practices in Germany between April and September 2019 and April and September 2020. A total of 63,914 patients in the 2nd quarter of 2019, 63,701 in the 3rd quarter of 2019, 55,769 in the 2nd quarter of 2020, and 60,446 in the 3rd quarter of 2020 were included. Overall, a clear downward trend in the number of visits to gastroenterologists was observed in the 2nd quarter of 2020 compared to 2019 (−13%, p = 0.228). The decrease in consultations was particularly pronounced in patients >70 years of age (−17%, p = 0.096). This trend was evident for all gastrointestinal diagnoses except for tumors. Most notably, rates of gastrointestinal infections (−19%) or ulcers (−43%) were significantly lower in this period than in the same quarter of 2019. Reflecting the course of the pandemic, the differences between the 3rd quarter of 2020 and that of 2019 were less pronounced (−5%, p = 0.560). Our data show that the pandemic changed patients' behavior with respect to the health care system. Using the example of German gastroenterology practices, we show that the number of consultations as well as the number and range of diagnoses have changed compared to the same period in 2019.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".