A Within-Trial Economic Evaluation of a Patient Navigator Program in Children With CKD
Bibliographic record
Abstract
Introduction The NAVKIDS2 trial was a patient navigation program for children and their caregivers living with chronic kidney disease (CKD) in Australia. We conducted a within-trial economic evaluation to describe the cost-effectiveness of patient navigation compared to standard care. Methods Cost and resource utilization data were prospectively collected over 6 months, from a healthcare funder perspective. Costs are reported in Australian dollars. Quality of life data was collected from 0 to 6 months. Incremental cost-effectiveness ratios (ICERs) were reported as the additional cost per quality-adjusted life years (QALYs) gained. Results Over the 6-month period, total per patient costs were higher in the patient navigation group compared to those in the standard care group ($10,249 vs $9,368 respectively, p<0.001). There was no significant difference in mean healthcare costs between the two groups ($9,848 vs $9,368 respectively, p=0.98), but the intervention group incurred an additional cost of $1,075 per person for the patient navigator. There was no significant difference in total QALYs over 6 months between patient navigation and standard care groups (0.33 vs 0.30 respectively, p=0.11). The ICER for the intervention compared to usual care was $41,960/QALY gained with a wide 95% confidence interval (-$300,123 to +$769,958) indicating substantial uncertainty. Conclusion The economic evaluation found that the cost of patient navigators is relatively low compared to total healthcare costs but there is considerable uncertainty regarding the cost-effectiveness of the intervention. Further research is needed to evaluate long-term cost-effectiveness as well as potential impacts on health outcomes and healthcare utilization.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".