The Impact of Homelessness on Kidney Outcomes Among Adults With Diabetes
Bibliographic record
Abstract
Introduction: People with diabetes experiencing homelessness face barriers to self-management, contributing to suboptimal glycemia and reduced screening for microvascular complications. The objective of the study was to assess whether a history of homelessness is associated with kidney-related outcomes among people with diabetes. Methods: A propensity-matched cohort study using administrative health data from Ontario, Canada, was conducted, including residents with diabetes who had ≥1 hospital encounter during the study period (2008-2020). Having a history of homelessness was identified using a validated algorithm. Outcomes of interest included nephrologist visits, reduction in estimated glomerular filtration rate (eGFR), initiation of renal replacement therapy, and acute care visits for kidney-related ambulatory care-sensitive conditions. Negative binomial regression and Cox proportional hazard models were used to assess outcomes. Results: Of 659,877 eligible people with diabetes living in Ontario, 3366 had a history of homelessness, with 2650 successfully matched to non-homeless controls. People with a history of homelessness had similar rates of nephrologist visits compared with those with no history of homelessness (rate ratio [RR] = 1.27; 95% confidence interval [CI]: 0.82-1.97), but had higher rates of hospitalization for chronic kidney disease (CKD)-related conditions, including volume overload (RR = 3.13; 95% CI: 1.62-6.04), hyperkalemia (RR = 3.01; 95% CI: 2.07-4.39), and heart failure (RR = 2.06; 95% CI: 1.62-2.63). They had a higher hazard of eGFR decline (hazard ratio [HR] = 1.71; 95% CI: 1.56-1.88), and renal replacement therapy (HR = 1.65; 95% CI: 1.04-2.60) compared with nonhomeless controls. Conclusion: Homelessness is associated with higher rates of kidney-related adverse events in people living with diabetes, supporting the need for tailored approaches that reduce barriers to accessing diabetes care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".