Temporal and regional trends in Canada for the epidemiology and management of diabetic foot ulcers
Bibliographic record
Abstract
OBJECTIVE: Diabetic foot ulcers (DFUs) are a common downstream consequence of diabetes and pose significant concern to the health of affected individuals. There are currently limited data available that detail the epidemiology and inpatient burden of DFUs in Canada. This study aims to provide updated data on the epidemiological and economic burden of DFUs in Canada between 2015 and 2019. METHOD: Using the Canadian Institute for Health Information Patient Cost Estimator the authors estimated, as the primary outcome, the number of DFU inpatient cases in Canada, the associated financial burden of these admissions on the Canadian healthcare system, physician compensation, and average patient length of hospitalisation. This analysis covered 12 Canadian jurisdictions and was stratified by age, sex, province and geographical region. The secondary outcome was to highlight temporal trends in the public health burden of DFUs by computing the average annual percentage change (AAPC; the weighted average of several annual percentage changes over multiple years) using Joinpoint (Surveillance Research Program National Cancer Institute, US) regression analysis. RESULTS: The total number of cases, average length of hospitalisation and physician costs across Canada were highest for patients over ≥60 years of age. By region, the total number of cases and standardised physician costs were highest in Central Canada, followed by Western Canada, and subsequently Eastern and Northern Canada. In 2019, there were >1800 patients with DFUs admitted to Canadian acute care hospitals. Despite having the lowest number of inpatient admissions, Northern Canada had the highest associated inpatient costs, followed by Central, then Western and, lastly, Eastern Canada. Overall, mean inpatient costs remained stable over time across all age groups (AAPC 0.61; 95% confidence interval: -1.87-3.15), with an average cost of >$10,000 CAD per case. Average physician cost across all jurisdictions was approximately $1000 CAD per case, with the mean hospitalisation time being nine days. CONCLUSION: The findings of this study emphasise the dynamic nature of the economic and epidemiological DFU burden in Canada, underscoring the need for targeted interventions, multidisciplinary care and evidence-based resource allocation for the optimal management of diabetes and DFUs.
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) | 0.000 | 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".