Prevalence and Demographics of CKD in Canadian Primary Care Practices: A Cross-sectional Study
Bibliographic record
Abstract
Introduction Surveillance systems enable optimal care delivery and appropriate resource allocation, yet Canada lacks a dedicated surveillance system for chronic kidney disease (CKD). Using data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), a national chronic disease surveillance system, this study describes the geographic, sociodemographic, and clinical variations in CKD prevalence in the Canadian primary care context. Methods This cross-sectional study included 559,745 adults in primary care in 5 provinces across Canada from 2010 through 2015. Data were analyzed by geographic (urban or rural residence), sociodemographic (age, sex, deprivation index), and clinical (medications prescribed, comorbid conditions) factors, using data from CPCSSN and the Canadian Deprivation Index. CKD stage 3 or higher was defined as 2 estimated glomerular filtration rate (eGFR) values of <60 ml/min per 1.73 m 2 more than 90 days apart as of January 1, 2015. Results Prevalence of CKD was 71.9 per 1000 individuals and varied by geography, with the highest prevalence in rural settings compared with urban settings (86.2 vs. 68.4 per 1000). CKD was highly prevalent among individuals with 3 or more other chronic diseases (281.7 per 1000). Period prevalence of CKD indicated a slight decline over the study duration, from 53.4 per 1000 in 2010 to 46.5 per 1000 in 2014. Conclusion This is the first study to estimate the prevalence of CKD in primary care in Canada at a national level. Results may facilitate further research, prioritization of care, and quality improvement activities to identify gaps and improvement in CKD care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".