An exploratory analysis of the impact of the COVID-19 pandemic on pediatric type 1 diabetes mellitus patient outcomes: A single-center study
Bibliographic record
Abstract
Background The COVID-19 pandemic led to substantial shifts in pediatric diabetes care delivery to virtual and hybrid models. It is unclear if these changes in care delivery impacted short-term patient outcomes. Objectives We aimed to explore glycemic control and other diabetes-related outcomes in children living with Type 1 Diabetes Mellitus (T1DM) during the first year of the COVID-19 pandemic at a tertiary pediatric academic center in Canada. Subjects Patients <18 years of age with a confirmed diagnosis of T1DM for at least one year were included. Methods This was a retrospective chart review. We compared data from two years pre-pandemic (March 15, 2018–March 14, 2020) to the first year of the pandemic (March 15, 2020–March 14, 2021). The data assessed included glycemic control [Hemoglobin A1c (HbA1c)], diabetic ketoacidosis (DKA), hospital attendance and hospitalizations, hypoglycemia, and hyperglycemia. The generalized estimating equation (GEE) analysis was used to model potential factors affecting the HbA1c and diabetes-related morbidities. Multiple imputations were conducted as a sensitivity analysis. Results There were 346 eligible patients included in the study. The HbA1c remained stable during the pandemic compared to the pre-pandemic phase (MD-0.14, 95% CI, −0.28, 0.01;p = 0.058). The pandemic saw an increase in the number of newly diagnosed patients (X2 = 16.52,p < 0.001) and a higher number of newly diagnosed patients presenting in DKA (X2 = 12.94,p < 0.001). In patients with established diabetes, there was an increase in hyperglycemia (OR1.38, 95% CI, 1.12,1.71;p = 0.003) and reduced DKA (OR 0.30, 95% CI, 0.12,0.73;p = 0.009) during the pandemic compared to the pre-pandemic phase. Stable rates of hospitalization (OR0.57, 95% CI, 0.31,1.04,p = 0.068) and hypoglycemia (OR1.11, 95% CI, 0.83,1.49;p = 0.484) were noted. These results were retained in the sensitivity analysis. Conclusions Glycemic control in children with T1DM remained stable during the first year of the pandemic. There were more newly diagnosed patients during the pandemic compared to the pre-pandemic phase, and more of these new patients presented in DKA. The latter presentation was reduced in those with established diabetes during the same period. Further studies are needed to assess the ongoing impact of the COVID-19 pandemic on T1DM care pathways and outcomes to allow children, families, and diabetes teams to personalize choices of care models.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".