Quantifying the Risk of Infectious Diseases for People With Diabetes
Bibliographic record
Abstract
OBJECTIVE: In vitro evidence shows that immune function is compromised in people with diabetes. Although certain rare infections are more common and infection-related mortality is higher, the risk of acquiring an infectious disease for diabetic patients has never been quantified. RESEARCH DESIGN AND METHODS: A retrospective cohort study using administrative data compared all people with diabetes in Ontario, Canada, on 1 April 1999 to matched nondiabetic people (n = 513,749 in each group). The risk ratios of having an infectious disease and of death attributable to infectious disease between those with and without diabetes were calculated. Secondary analysis individually examined common infectious diseases. The study was repeated using a second pair of cohorts defined in 1996 to confirm stability of the estimates. RESULTS: Nearly half of all people with diabetes had at least one hospitalization or physician claim for an infectious disease in each cohort year. The risk ratio for diabetic versus nondiabetic people was 1.21 (99% CI 1.20-1.22) in both cohort years. The risk ratio for infectious disease-related hospitalization was up to 2.17 (99% CI 2.10-2.23). The risk ratio for death attributable to infection was up to 1.92 (1.79-2.05). Many individual infections were more common in people with diabetes, especially serious bacterial infections. CONCLUSIONS: Diabetes confers an increased risk of developing and dying from an infectious disease, corroborating both in vitro evidence and commonly held clinical belief. In addition to microvascular and macrovascular sequelae, clinicians should consider infection a complication of diabetes.
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 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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".