1424-P: The Impact of Homelessness on Diabetes-Related Chronic Kidney Disease (CKD)—A Propensity Score-Matched Analysis of CKD Risks and Outcomes in Ontario, Canada
Bibliographic record
Abstract
Introduction and Objective: People experiencing homelessness face barriers to accessing and adhering to preventive care measures, leading to poorer diabetes outcomes, and a higher risk of diabetes-related complications compared to the general population. The objective of the study was to determine whether individuals with diabetes and a history of homelessness have higher rates of adverse kidney-related outcomes compared to those who had no history of homelessness in Ontario, Canada. Methods: We conducted a propensity score-matched cohort study using administrative health data (2006-2019), including patients with diabetes and at least one hospital encounter, excluding those with prior renal disease. The exposure variable was a history of homelessness documented during the hospital encounter, and outcomes included nephrologist visits, reduction in kidney function (eGFR), and acute care visits for kidney-related avoidable causes. Rate ratios were used to compare rates per person-year, and cause-specific hazard models were used to assess eGFR decline and renal replacement therapy initiation. Results: Of the 660,999 eligible individuals with diabetes, 3366 had a history of homelessness, of which 2650 were matched to controls without a history of homelessness. After matching, the two groups were similar with respect to their baseline characteristics (e.g., age, diabetes type, or diabetes duration). The rate of nephrologist visits did not differ between the two groups (RR 1.27, 95% CI: 0.82 - 1.97), but those with a history of homelessness had higher rates of hospitalization for CKD-related conditions (RR 2.26, 95% CI: 1.82 - 2.80). Compared to non-homeless controls, the hazard ratio for eGFR decline was 1.71 (95% CI: 1.56 - 1.88), and 1.65 (95% CI: 1.04 - 2.60) for requiring renal replacement therapy. Conclusion: A history of homelessness among patients with diabetes is associated with higher rates of CKD-related adverse events compared to those without a history of homelessness. Disclosure T. Reed: None. K. Wiens: None. S. Tariq: None. L. Bai: None. P.E. Ronksley: None. S.W. Hwang: None. P. Austin: None. G.L. Booth: None. E. Spackman: None. D.J. Campbell: Research Support; Siemens Healthcare Diagnostics. Speaker's Bureau; Siemens Healthcare Diagnostics. Funding This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".