Sustained Improvement in Glycemic Control in Emerging Adults with Type 1 Diabetes 2 Years After the Start of the COVID-19 Pandemic
Bibliographic record
Abstract
Although there were initial concerns that the public health response to the COVID-19 pandemic would adversely affect glycemic control in people with type 1 diabetes, several early continuous glucose monitor (CGM) studies reported an unexpected slight improvement in glucose metrics. Early emerging adulthood (roughly spanning the ages of 18–24 years) is often a vulnerable time in the life of a person with type 1 diabetes. Here, we set out to determine how the care and glucose management of emerging adults with type 1 diabetes changed over a period of approximately 2 years from the start of the COVID-19 pandemic. This was a retrospective study of a tertiary referral, multidisciplinary young adult diabetes clinic, spanning before and after the 777-day state of emergency in the City of Toronto. Of 130 emerging adults with type 1 diabetes (80 male, 50 female; mean age 21.0 ± 2.1 years), baseline pre-pandemic HbA 1c values were available for 120 individuals. During 24.9 ± 5.4 months of follow-up before and after the start of the COVID-19 pandemic, HbA 1c fell from 8.5 ± 1.7% (69.3 ± 18.8 mmol/mol) to 8.1 ± 1.9% (65.2 ± 20.5 mmol/mol) ( P < 0.05), with change in HbA 1c from pre-lockdown levels being sustained throughout the second year of the pandemic. Over the same period, CGM use rose from 43% to 83%, primarily through increased uptake of intermittently scanned CGM, which is covered through the Ontario Drug Benefit program. Change in HbA 1c was most evident in Dexcom G6 real-time CGM users − 0.7 ± 1.2% (− 9.8 ± 17.1 mmol/mol) ( P < 0.01 vs. self-monitoring of blood glucose). Among emerging adults with type 1 diabetes attending a multidisciplinary clinic in a high-income country, glycated hemoglobin levels are on average 0.4% (4.1 mmol/mol) lower than they were before the pandemic. This reduction in HbA 1c is unlikely to be a consequence of early strict lockdowns given the length of time of follow-up. Rather, improved glycemic control coincided with increased utilization of wearable diabetes devices.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".