Frequency of Autoantibody-Negative Type 1 Diabetes in Children, Adolescents, and Young Adults During the First Wave of the COVID-19 Pandemic in Germany
Bibliographic record
Abstract
OBJECTIVE The aim of this study was to investigate the frequency of newly diagnosed type 1 diabetes without evidence of autoimmunity and the respective frequencies of ketoacidosis in children, adolescents, and young adults during the coronavirus disease 2019 (COVID-19) pandemic in Germany compared with the previous decade. RESEARCH DESIGN AND METHODS Based on data from the German Diabetes Prospective Follow-up Registry (DPV), we compared data from 715 children, adolescents, and young adults, newly diagnosed with type 1 diabetes during the COVID-19 pandemic in Germany between 1 March and 30 June 2020, with data from 5,428 children, adolescents, and young adults of the same periods from 2011 to 2019. Adjusted differences and relative risks (RRs) of negative β-cell autoantibody test results and diabetic ketoacidosis were estimated using multivariable log-binomial regression analysis. An upper noninferiority test (margin 1%) was applied to evaluate whether the autoantibody-negativity rate in 2020 was not higher than that in 2011 to 2019. RESULTS The estimated frequencies of autoantibody negativity in 2020 and 2011–2019 were 6.6% (95% CI 5.1–8.4) and 7.2% (95% CI 6.5–8.0), respectively, with an absolute difference of −0.68% (90% CI −2.07 to 0.71; Pupper noninferiority = 0.023). The increase of the estimated frequency of diabetic ketoacidosis during the COVID-19 pandemic was similar between autoantibody-negative and -positive type 1 diabetes (adjusted RRs 1.28 [95% CI 0.80–2.05] and 1.57 [1.41–1.75], respectively). CONCLUSIONS This study found no evidence that the COVID-19 pandemic leads to a significantly increased number of new cases with autoantibody-negative type 1 diabetes in children, adolescents, and young adults. In addition, autoantibody-negative type 1 diabetes showed no particular susceptibility to ketoacidosis, neither before nor during the pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".