Incidence of CKD in Australian Primary Care: Analysis of a National Primary Care Dataset
Bibliographic record
Abstract
Background: Data on the incidence of chronic kidney disease (CKD) in Australia are limited and largely derived from studies focused on high-risk groups. This study aimed to estimate the incidence of CKD in Australia using a national general practice dataset inclusive of a broad patient population managed in contemporary primary care settings. Methods: We included adults who attended MedicineInsight-participating general practices between 2011 and 2020 and had eGFR≥60 mL/min/1.73m2 or ACR<3.5 mg/mmol (females) and <2.5 mg/mmol (males) at baseline. Incident CKD was defined as 2 consecutive eGFR measurements <60 mL/min/1.73m2 ≥90 days apart or eGFR ≥60 mL/min/1.73m2 with UACR ≥3.5 mg/mmol for females and ≥2.5 mg/mmol for males. Incidence rates were calculated per 1,000 person-years. Multivariable Cox models were constructed to identify baseline sociodemographic and clinical factors associated with incident CKD. Results: Among 2,103,945 individuals (58% female; mean age 46.2 years) followed for a median of 3.76 years, 121,499 individuals developed incident CKD (5.8%), with overall CKD incidence of 13.8 per 1,000 person-years. Incidence increased with older age (2.2 per 1,000 in aged 18–29 years to 71 per 1,000 in ≥80 years). CKD incidence was independently associated with older age (HR per 20-years increase: 2.36 [2.33–2.38]), greater socioeconomic disadvantage (most disadvantaged: HR 1.15 [1.13–1.17]; compared to least disadvantaged), and the presence of clinical comorbidities including T2DM (4.04 [3.99–4.08]), hypertension (1.98 [1.95–2.01]), heart failure (1.88 [1.85–1.92]), chronic liver disease (1.41 [1.34–1.48]), and cancer (1.07 [1.06–1.08]). Conclusion: CKD incidence was substantial and increased sharply with age. The presence of socioeconomic disadvantage and other comorbidities were associated with higher CKD incidence. These results confirm that CKD remains a significant burden across diverse patient groups in contemporary primary care. Continued prioritisation of higher-risk CKD patients within primary care is crucial for effective management and improved outcomes. Funding: Commercial Support - The Renal Division of The George Institute for Global Health has received sponsorship funding provided by Boehringer Ingelheim and Eli Lilly Alliance and is supported by the University of New South Wales Scientia Program. The design, analysis, interpretation or writing of this work was performed independent of all funding bodies.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".