265-OR: Impact of the COVID-Pandemic on Diabetes Screening from 20to 2021 in Ontario, Canada
Bibliographic record
Abstract
Diabetes incidence is expected to increase following the COVID-pandemic due to widespread changes in physical activity, diet, and access to health care services. We used administrative health care databases from Ontario, Canada to examine monthly changes in diabetes screening during the pandemic (Mar 2020-Feb 2021) compared to the pre-pandemic period (Mar 2019-Feb 2020) among adults aged 20-85 without prior diabetes. The eligible population was 9,599,079 in Mar 20 and 9,941,336 in Feb 2021. Overall, the number of people screened for diabetes was 25.3% lower in the pandemic (N=4,060,348) versus pre-pandemic (N=5,437,284) period. However, the number of people screened each month declined by 65.6% between February and April 2020 (Figure 1; 1.53 vs. 4.44 per 100, -2.91 per 100) . Screening rates recovered by July 2020 (3.88 per 100) but remained 15.6% lower than in the pre-pandemic period. Similar patterns were observed in all age groups but declines in screening rates between February and April 2020 were greatest in adults aged 35-49 (-69.4%) and 50-64 (-69.5%) . Findings were also consistent across income groups. In summary, we observed a sudden decline in diabetes screening in Ontario, Canada, where laboratory tests and other health care services are universally insured. This may lead to delays in prediabetes and diabetes diagnosis, resulting in missed opportunities for diabetes prevention and early management. Disclosure G.S.Fazli: None. R.Moineddin: None. V.Ling: None. G.L.Booth: None. Funding Canadian Institute for Health Research
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".