Clinical Outcomes and Healthcare Utilization in Patients Receiving Maintenance Dialysis After the Onset of the COVID-19 Pandemic in Ontario, Canada
Bibliographic record
Abstract
Background: The impact of the COVID-19 pandemic on clinical outcomes and healthcare utilization in patients receiving maintenance dialysis is unclear. Objective: To compare the rates of clinical outcomes and healthcare utilization in patients receiving maintenance dialysis (in-center and home modalities) before and during the COVID-19 pandemic. Design: Population-based, repeated cross-sectional study. Setting: Linked administrative healthcare databases from Ontario, Canada. Patients: Adults receiving maintenance dialysis from March 15, 2017, to March 14, 2020 (pre-COVID-19 pandemic period) and from March 15, 2020, to March 14, 2023 (COVID-19 pandemic period). Measurements: Our primary outcome was all-cause mortality. Our secondary outcomes included non-COVID-19-related mortality, all-cause hospitalizations (excluding elective surgeries), emergency room visits, intensive care unit admissions, and hospital admissions with mechanical ventilation. We also examined cardiovascular-related hospitalizations, kidney-related outcomes, and ambulatory visits. Methods: We used Poisson generalized estimating equations to model pre-COVID outcome trends and used these to predict post-COVID outcomes and to estimate the relative change (i.e., the ratio of the observed to the expected rate). Results: In 31 900 individuals receiving maintenance dialysis during the study period, the crude incidence rate (per 1000 person-years) of all-cause mortality was 165.0 in the pre-COVID-19 period, compared to 173.2 during the first year of the pandemic and 171.7 during the first 36 months of the pandemic. After adjustment, there was a statistically significant increase in all-cause mortality in 14 out of the 36 months of the COVID-19 period compared to the pre-COVID-19 period, with 494 recorded COVID-19-related deaths. However, when examining the overall all-cause mortality across the months, the adjusted relative rate (aRR) comparing the observed to expected all-cause mortality rate was not statistically significant in the first year of the pandemic (1.08, 95% CI: 1.00, 1.16) and the first 36 months of the pandemic (1.08, 95% CI: 0.99, 1.18) compared to the pre-pandemic period. The crude incidence rate of non-COVID-19-related mortality was 165.0 in the pre-COVID-19 period, compared to 163.3 during the first year of the pandemic and 157.7 during the first 36 months. After adjustment, there was no substantial change in the rate of non-COVID-19-related deaths in the first year of the pandemic (aRR 1.01, 95% CI: 0.94, 1.09), but there was a substantial decrease in all-cause hospitalization, with an aRR of 0.92 (95% CI: 0.88, 0.97), and a substantial decrease in emergency room visits and intensive care unit admissions; findings were consistent 36 months into the pandemic. Limitations: External generalizability to other jurisdictions may be limited, with each region experiencing different COVID-19 rates and implementing different mitigation strategies. Conclusions: In the maintenance dialysis population, all-cause mortality was significantly higher during several months of the pandemic; however, the overall rate of all-cause mortality was not substantially higher than expected in the first 36 months of the COVID-19 pandemic. There was no substantial increase in non-COVID-19-related mortality despite a substantial decrease in acute healthcare utilization. Ongoing monitoring of the dialysis population will offer further insights into the long-term effects of the pandemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".