Cancer diagnosis is one quarter lower than the expected cancer incidence in the first year of COVID‐19 pandemic in Germany: A retrospective register‐based cohort study
Bibliographic record
Abstract
Cancer diagnosis is one quarter lower than the expected cancer incidence in the first year of COVID-19 pandemic in Germany: A retrospective register-based cohort studyDear Editor,The coronavirus disease of 2019 (COVID-19) pandemic has necessitated that semi-urgent and elective medical cases, including cancer screenings, are postponed in hospitals worldwide to accommodate the increasing number of patients with severe COVID-19 symptoms [1-4].Consequently, several reports from the first COVID-19 wave in spring 2020 showed a significant decrease in new cancer diagnoses as well as cancer therapy and screenings [5,6].We aimed to investigate this further by performing a novel registry-based analysis of insurance claim data from BARMER, Germany's second largest health insurance provider.Detailed descriptions of the study can be found in the Supplementary Materials and Methods.A total of 33,810,604 person-years were analyzed.This included a reference period from January 2017 to December 2019 covering 25,614,919 person-years and the first year of COVID-19 from April 2020 to March 2021, covering 8,195,685 person-years.One hundred sixty-one thousand and fifty individual patients with newly encoded malignant diagnoses (ICD-GM codes: C00-C26, C30-41, C43-58, C60-97) or benign neoplasms (ICD-GM codes: D00-48) were identified (Supplementary Table S1).The median age was 66 years, and 58.6% were female.At the time of data analysis, there were 696 patients with incomplete information (0.4% of all cases).These cases were not considered.The overall incidence of benign and malignant neoplasms encoded by ICD C00 to D48 decreased by 23.06% (-11.63[95%CI:-11.13 to -12.14] per 10,000 insurance holders [IH]; P < 0.001).The most prominent decrease in benign and malignant neoplasms was in the age group 18-49 years with 26.98% (95%CI: -5.69[-5.15 to -6.23] per 10,000 IH; P <
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".