Geographic Disparities in the Rate of Major Adverse Kidney Events Among Patients with CKD in Alberta
Bibliographic record
Abstract
Background: Individuals in rural communities in Alberta disproportionately experience adverse health outcomes. Characterizing temporal trends in the incidence of major adverse kidney events (MAKE) by geographic characteristics could identify inequities in the management of CKD that can guide policy action. Methods: We quantified the annual incidence of MAKE in Alberta from 2003-2019, overall and by geographic characteristics of patients' residential zip codes, using routine healthcare data available in the Alberta Kidney Disease Network (AKDN) database. CKD status for cohort eligibility was ascertained based on outpatient eGFR and defined as 2 eGFR values <60 ml/min/1.73m2 at least 60 days apart; patients entered the cohort on the date of the second qualifying eGFR measurement. MAKE was defined as all-cause death or kidney failure (i.e., chronic dialysis, transplant or sustained eGFR <15 ml/min/1.73m2) and assessed as the annual proportion of patients with the event from April 1 to March 31. Temporal trends in the rate of change of MAKE were estimated using linear regression. Results: Among 262,392 patients (median age 75 years; 56% female), the overall incidence rate of MAKE decreased from 7.3% between 2003 and 2004 to 6.5% between 2018 and 2019; and the corresponding rate of change in the annual incidence of MAKE was -0.08 (95% confidence interval -0.10 to -0.06). There was an excess incidence rate of MAKE in rural vs urban locations between 2003 and 2004 (8.7% vs 7.4%); however, this excess rate was attenuated between 2018 and 2019 (6.7% vs 6.5%). Likewise, the excess incidence rate of MAKE in residential locations >100 km vs ≤50 km from the nearest nephrology center between 2003 and 2004 (9.3% vs 6.8%) was substantially reduced between 2018 and 2019 (6.5% vs 6.4%). Conclusions: Disparities in incident MAKE by geographic characteristics of patients in Alberta have improved over the last 2 decades. Future studies exploring factors (e.g., CKD care indicators, population mix) that might have contributed to these noticeable improvements are needed for development of policy interventions to optimize CKD outcomes equitably for all patients. Funding: Government Support - Non-U.S.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".