The Association of Homelessness With Rates of Diabetes Complications: A Population-Based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To estimate the rates of diabetes complications and revascularization procedures among people with diabetes who have experienced homelessness compared with a matched cohort of nonhomeless control subjects. RESEARCH DESIGN AND METHODS: A propensity-matched cohort study was conducted using administrative health data from Ontario, Canada. Inclusion criteria included a diagnosis of diabetes and at least one hospital encounter between April 2006 and March 2019. Homeless status was identified using a validated administrative data algorithm. Eligible people with a history of homelessness were matched to nonhomeless control subjects with similar sociodemographic and clinical characteristics. Rate ratios (RRs) for macrovascular complications, revascularization procedures, acute glycemic emergencies, skin/soft tissue infections, and amputation were calculated using generalized linear models with negative binomial distribution and robust SEs. RESULTS: Of 1,076,437 people who were eligible for inclusion in the study, 6,944 were identified as homeless. A suitable nonhomeless match was found for 5,219 individuals. The rate of macrovascular complications was higher for people with a history of homelessness compared with nonhomeless control subjects (RR 1.85, 95% CI 1.64-2.07), as were rates of hospitalization for glycemia (RR 5.64, 95% CI 4.07-7.81) and skin/soft tissue infections (RR 3.78, 95% CI 3.31-4.32). By contrast, the rates of coronary revascularization procedures were lower for people with a history of homelessness (RR 0.76, 95% CI 0.62-0.94). CONCLUSIONS: These findings contribute to our understanding of the impact of homelessness on long-term diabetes outcomes. The higher rates of complications among people with a history of homelessness present an opportunity for tailored interventions to mitigate these disparities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".