Impact of the COVID-19 Pandemic on Diabetes Care for Adults With Type 2 Diabetes in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: The COVID-19 pandemic and related public health prevention measures have led to a disruption of the delivery of routine care and may have had an impact on the quality of diabetes care. Our aim in this study was to evaluate the extent to which structure, process and outcome quality measures in diabetes care changed in the first 6 months of the pandemic compared with previous periods. METHODS: A before-and-after observational study was conducted of all community-living Ontario residents >20 years of age and living with diabetes. The patients were divided into 3 cohorts: a pandemic cohort, alive March to September 2020 (n=1,393,404); reference cohort 1, alive March to September 2019 (n=1,415,490); and reference cohort 2, alive September 2019 to February 2020 (n=1,444,000). Outcome measures were in-person/virtual visits to general practitioners and specialists, eye examinations, glycated hemoglobin (A1C) and low-density lipoprotein (LDL) testing, filled prescriptions, and admissions to emergency departments (EDs) and hospitals for acute and chronic diabetes complications. RESULTS: The probability of an in-person visit to a general practitioner decreased by 47% (95% confidence interval [CI], 47% to 47%) in the pandemic period compared with both previous periods. The probability of having an eye exam was lower by 43% (95% CI, 44% to 43%), an A1C test by 28% (95% CI, 29% to 28%) and an LDL test by 31% (95% CI, 31% to 31%) in the pandemic period compared with the same 6-month period the year before. There were very small decreases in drug prescriptions and decreases of 18% and 16% in ED and hospital visits for complications. CONCLUSIONS: We observed disruptions to both structure and process measures of diabetes care in Ontario during the first wave of the pandemic.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".