A population-based approach to assessing diabetes management during COVID-19: insights from population data in Ontario, Canada.
Bibliographic record
Abstract
ObjectiveDiabetes management requires ongoing monitoring of diabetes care from primary care, specialist care and laboratory testing. COVID-19 led to changes in access to in-person care. The purpose of this research was to assess changes in the management of diabetes during the COVID-19 pandemic using population-linked datasets and population segmentation.
 ApproachWe identified over 1.4 Million Ontarians with diabetes (approximately 10% of the population) with valid health insurance as of April 1, 2019 and April 1, 2020. We measured 11 indicators of diabetes management including laboratory testing for HbA1c and LDL (highlighted in this abstract). With screening indicators, we examined changes in the proportion of the population up-to-date with screening at the end of each fiscal year (March 31, 2020 and March 31, 2021) overall and according to population segments created using linked health data from primary care, home care, long term care and hospitals.
 ResultsOverall screening rates that required laboratory testing for HbA1c and LDL fell substantially from 54% to 40% and 68% to 59% overall. Comparing across population segments, residents in Long Term Care facilities had the smallest changes in screening rates; individuals with low, medium and high complexity chronic conditions and end-of-life conditions had the largest changes; maternity, cancer, mental health and frail populations were in the middle. Differences according the primary care enrolment models (capitation vs fee-for-service) were relatively minor but patients who were not rostered to a primary care physician had the largest reductions in laboratory screening. Results for all 11 indicators will be shared in the presentation.
 ConclusionCOVID-19 was associated with substantial reductions in laboratory-based diabetes screening. Poor diabetes management is one of the strongest risk-factors for adverse outcomes in COVID-19. Rates of diabetes management declined most for at risk patient populations amplifying the need to differentially connect with patients to ensure ongoing care during the pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".