Examining the Influence of Pre-Existing Mental Health Conditions among End-Stage Kidney Disease Patients at Dialysis Initiation in Ontario
Bibliographic record
Abstract
This dissertation aims to address the knowledge gap regarding the prevalence of mental health conditions (MHCs) among end-stage kidney disease (ESKD) patients initiating dialysis, their clinical outcomes, and impact to the overall healthcare system. The cross-sectional study determined the prevalence of MHCs among ESKD patients who initiated dialysis from 2006 to 2017, compared baseline characteristics for patients with vs. without MHCs, and examined factors independently associated with MHCs. The cohort study compared ESKD patients with and without MHCs at dialysis initiation on the risk of mortality and the risk and rate of all-cause hospitalizations and unplanned emergency department (ED) visits. Lastly, the cost-of-illness study, from the third-party public payer’s perspective, compared the one-year mean healthcare costs incurred by patients with and without MHCs. Among ESKD patients initiating dialysis, 26.3% had MHCs, of which 13.6% of patients experienced psychiatric hospitalizations or ED visits three years prior to starting dialysis. Those with MHCs were more likely to be younger, female, White, urban dwellers, residing in the most residentially unstable neighbourhoods, less likely to receive peritoneal dialysis, and have slightly higher levels of comorbidity. Patients with MHCs had a modest increase in the risk of death and moderate increases in the risk and rate of all-cause hospitalizations and unplanned ED visits, respectively, in the first year following dialysis initiation. Lastly, the mean one-year healthcare costs for individuals with MHCs were approximately $6,900 higher compared to those without MHCs. Moderate increases in service-specific costs were observed for inpatient hospitalizations and slight increases in costs were seen for outpatient non-dialysis clinics, physician-related visits, ED visits, inpatient complex continuing care, and long-term care. The findings demonstrate that ESKD patients have a significant burden of MHCs at dialysis initiation, and experience slightly higher risk of mortality, hospitalizations, ED visits, and overall healthcare service utilization. Future research should explore the risk for mortality and healthcare service utilization for ESKD patients with and without MHCs as they progress through each stage of chronic kidney disease, assess the broader societal economic burden, and address how mental healthcare can be integrated throughout the continuum of kidney disease management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".