A comparison between different patient groups for diabetes management during phases of the COVID-19 pandemic: a retrospective cohort study in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: With the onset of the COVID-19 pandemic and the large uptake in virtual care in primary care in Canada, the care of patients with type 2 diabetes has been greatly affected. This includes decreased in-person visits, laboratory testing and in-person assessments such as blood pressure (BP). No studies have investigated if these changes persisted with pandemic progression, and it is unclear if shifts impacted patient groups uniformly. The purpose of this paper was to examine changes in diabetes care pre, early, and later pandemic across different patient groups. METHODS: A repeated cross-sectional design with an open cohort was used to investigate diabetes care in adults with type 2 diabetes for a 6-month interval from March 14 to September 13 over three consecutive years: 2019 (pre-pandemic period), 2020 (early pandemic period), and 2021 (later pandemic period). Data for this study were abstracted from the University of Toronto Practice-Based Research Network (UTOPIAN) Data Safe Haven, a primary care electronic medical records database in Ontario, Canada. Changes in diabetes care, which included primary care total visits, in-person visits, hemoglobin A1c (HbA1c) testing, and BP measurements were evaluated across the phases of the pandemic. Difference in diabetes care across patient groups, including age, sex, income quintile, prior HbA1c levels, and prior BP levels, were assessed. RESULTS: A total of 39,401 adults with type 2 diabetes were included in the study. Compared to the 6-month pre-pandemic period, having any in-person visits decreased significantly early pandemic (OR = 0.079 (0.076-0.082)), with a partial recovery later pandemic (OR = 0.162 (95% CI: 0.157-0.169). Compared to the pre-pandemic period, there was a significant decrease early pandemic for total visits (OR = 0.486 (95% CI: 0.470-0.503)), HbA1c testing (OR = 0.401 (95% CI: 0.389-0.413)), and BP measurement (OR = 0.121 (95% CI: 0.116-0.125)), with partial recovery later pandemic. CONCLUSIONS: All measures of diabetes care were substantially decreased early pandemic, with a partial recovery later pandemic across all patient groups. With the increase in virtual care due to the COVID-19 pandemic, diabetes care has been negatively impacted over 1-year after pandemic onset.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".