A174 TESTING FOR AND INCIDENCE OF CELIAC DISEASE AUTOIMMUNITY SAW SIGNIFICANT BUT TEMPORARY DECLINES DURING COVID-19 PANDEMIC
Bibliographic record
Abstract
Abstract Background Celiac disease (CD) is an autoimmune disorder with various complications and long-term health risks. The COVID-19 pandemic affected the entire healthcare system—including screening for and diagnosing non-infectious conditions. Aims Determine how the COVID-19 pandemic affected the testing for, and incidence of, CD autoimmunity. Methods Using the population-based Alberta Medical Laboratory database, all tissue transglutaminase antibody tests (tTG-IgA) were identified in Alberta (April 2012 to March 2023). Incident cases of CD autoimmunity comprised of individuals newly positive for tTG-IgA between April 2015 and March 2023. Prevalent cases of CD were excluded based on previous tTG-IgA positivity and/or CD diagnosis codes in outpatient or inpatient settings. Average monthly percent changes and inflection points for testing and incidence rates were estimated using Joinpoint regression. Autoregressive integrated moving average models were performed to forecast rates if the pandemic had not occurred. Incidence rate ratios (IRRs) were estimated to compare a pre-pandemic era (April 2017 to March 2020) and a pandemic era (April 2020 to March 2023) overall and across subgroups. Results In the pandemic era, 311,971 tTG-IgA tests were performed on 284,882 unique individuals (21.1 per 1000 person-years (Table 1)). Testing decreased by 23.4% per month from February 2020 until May 2020 (Figure 1A). From June 2020 to August 2020, testing rates increased by 22.3% per month and then remained stable until the end of the study period. There were 4907 new cases of CD autoimmunity in the pandemic era, with an incidence of 36.3 per 100,000 person-years (Table 1). Incidence rates decreased by 14.1% per month from January 2020 until April 2020 (Figure 1B). Subsequently, incidence continued to rise by 2.0% per month from May 2020 until the end of the study period. Conclusions The COVID-19 pandemic appears to have had a minor impact on identifying potential CD. Future surveillance is needed to determine if the pandemic affected the diagnostic pathway to biopsy-confirmed CD through upper endoscopy procedures and any adverse outcomes individuals may have had from delayed diagnosis. Table 1: Testing and incidence rates for CD autoimmunity in Alberta in pre-pandemic era (April 2017 to March 2020) and pandemic era (April 2020 to March 2023) IRR=incidence rate ratio; CI=confidence interval Figure 1: Testing (A) and incidence (B) rates for CD autoimmunity before and after the start of the COVID-19 pandemic Funding Agencies CAG, CIHRhttps://triangleprogram.org
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".