Designation, diligence and drift: understanding laboratory expenditure increases in British Columbia, 1996/97 to 2005/06
Bibliographic record
Abstract
Background: Laboratory testing is one of the fastest growing areas of health services spending in Canada. We examine the extent to which increases in laboratory expenditures might be explained by testing that is consistent with guidelines for the management of chronic conditions, by analyzing fee-for-service physician payment data in British Columbia from 1996/97 and 2005/06. Method We used direct standardization to quantify the effect on laboratory expenditures from changes in: fee levels; population growth; population aging; treatment prevalence; expenditure on recommended tests for those conditions; and expenditure on other tests. The chronic conditions selected were those with guidelines containing laboratory recommendations developed by the BC Guidelines and Protocol Advisory Committee: diabetes, hypertension, congestive heart failure, renal failure, liver disease, rheumatoid arthritis, osteoarthritis and dementia. Result Laboratory service expenditures increased by $98 million in 2005/06 compared to 1996/97, or 3.6% per year after controlling for population growth and aging. Testing consistent with guideline-recommended care for chronic conditions explained one-third (1.2% per year) of this growth. Changes in treatment prevalence were just as important, contributing 1.5% per year. Hypertension was the most common condition, but renal failure and dementia showed the largest changes in prevalence over time. Changes in other laboratory expenditure including for those without chronic conditions accounted for the remaining 0.9% growth per year. Conclusion Increases in treatment prevalence were the largest driver of laboratory cost increases between 1996/97 and 2005/06. There are several possible contributors to increasing treatment prevalence, all of which can be expected to continue to put pressure on health care expenditures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 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".