Time Trends and Geographic Disparities in Acute Complications of Diabetes in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: This study examines whether acute diabetes complication rates have fallen in recent years and whether geographic factors influence these trends. RESEARCH DESIGN AND METHODS: A population-based time-trend analysis of acute complications of diabetes was conducted using linked administrative and census data from Ontario, Canada. The study population included all adults identified through a province-wide electronic diabetes registry between 1994 and 1999 (n = 577,659). The primary outcome was hospitalizations for hyper- and hypoglycemia and emergency department visits for diabetes. RESULTS: Between 1994 and 1999, rates of hospitalization for hyper- and hypoglycemic emergencies decreased by 32.5 and 76.9%, respectively; emergency department visits for diabetes fell by 23.9%. On multivariate analysis, fiscal year was an independent predictor of acute diabetes complications, with individuals in our cohort experiencing a decline in risk of approximately 6% per year for either being hospitalized with hyper- or hypoglycemia or requiring an emergency department visit for diabetes. After accounting for variation in physician service use, diabetic individuals living in rural areas or Aboriginal communities were nearly twice as likely to have an acute complication, whereas those residing in remote areas of the province were nearly three times as likely to experience an event. CONCLUSIONS: Although our findings suggest an overall improvement in diabetes care in Ontario, certain subgroups of the population continue to experience higher rates of complications that are potentially preventable through good ambulatory care. Measures to improve access to timely and effective outpatient care may further reduce rates of acute complications among the diabetic population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".