Impact of eGFR reporting on health care utilization in Ontario
Bibliographic record
Abstract
The objective of this thesis was to determine the impact of eGFR reporting on health care utilization in Ontario. There were two main aims of this thesis 1) to assess the impact or eGFR reporting on nephrology consults and 2) to assess the impact of eGFR reporting on kidney protective medication usage.\nSome clinicians believe chronic kidney disease (CKD) is under-recognized in the community. As a result, many outpatient laboratories now report the estimated glomerular filtration rate (eGFR) in addition to serum creatinine as a measure of kidney function. In January 2006, all outpatient laboratories in the province of Ontario, Canada began reporting eGFR. We linked health administrative data for more than 8 million adults of age 25 years or older from January, 1999 to September, 2007. We conducted a population-based intervention analysis with seasonal time-series modeling to examine secular trends in the number and type of patients seen by nephrologists. Compared to the pre-eGFR period, the number of patients seen in consultation by nephrologists increased after eGFR reporting [percentage increase of 24% (95% Cl 16 - 31%); absolute increase of 2.9 consults per 100,000 adult population (95% Cl 2.5 - 3.4)]. This translated into an increase of about 23 consults per nephrologist per year. The greatest increases were seen in women (percentage increase of 39%, 95% Cl 28 - 51%) and the elderly, age > 80 years (percentage increase of 58%, 95% Cl 35 - 80%). eGFR reporting was associated with a sudden increase in the number of nephrology consults seen in Ontario. This increase was especially prominent amongst women and the elderly, populations who some believe are\nunder-recognized as having CKD.\nSome patients with chronic kidney disease (CKD) in whom angiotensin converting enzyme inhibitors or angiotensin-ll receptor blockers are recommended do not receive these medications (collectively referred to as RAAS-Blockers). We considered whether RAAS-Blocker use increases amongst CKD patients after the introduction of eGFR reporting. In January 2006, all outpatient laboratories in the province of Ontario, Canada began reporting eGFR. We performed a population-based intervention analysis with seasonal time-series modeling for the period of January 2003 to April 2008. We linked health administrative data for adults living in south western Ontario. For our primary outcome we considered RAAS-Blocker usage amongst 45 361 ambulatory residents with CKD (eGFR < 60 mL/min per 1.73m2). The introduction of eGFR reporting was associated with a significant increase in the use of RAAS-Blockers. Just prior to eGFR reporting the prescription rate was 571 per 1000 CKD patients; by early 2008 the rate had increased to 607 per 1000 CKD patients. According to the model, the increase in RAAS- Blocker use attributable to eGFR reporting was 19 per 1000 CKD patients (p=0.034). These\niii\nresults suggest eGFR reporting contributes to improved, guideline appropriate, care of patients with CKD. Estimating that 8% of the adult population has CKD, for every 10 million adults this means about 15 200 new patients are treated with RAAS Blockers by one year after the introduction of eGFR reporting in community laboratories.\nIn summary, in Ontario eGFR reporting was associated with an increase in consults, particularly among elderly and female patients. Also, it was associated with more CKD patients using renal- protective medications. Although these two finding suggest that there may be benefit to its introduction, further studies are need to determine if these changes actually result in clinical improvements.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".