Antiretroviral Drug Costs and Prescription Patterns in British Columbia, Canada
Bibliographic record
Abstract
INTRODUCTION: Treatment options and therapeutic guidelines have evolved substantially since highly active antiretroviral treatment (HAART) became the standard of HIV care in 1996. We conducted the present population-based analysis to characterize the determinants of direct costs of HAART over time in British Columbia, Canada. METHODS: We considered individuals ever receiving HAART in British Columbia from 1996 to 2011. Linear mixed-effects regression models were constructed to determine the effects of demographic indicators, clinical stage, and treatment characteristics on quarterly costs of HAART (in 2010$CDN) among individuals initiating in different temporal periods. The least-square mean values were estimated by CD4 category and over time for each temporal cohort. RESULTS: Longitudinal data on HAART recipients (N = 9601, 17.6% female, mean age at initiation = 40.5) were analyzed. Multiple regression analyses identified demographics, treatment adherence, and pharmacological class to be independently associated with quarterly HAART costs. Higher CD4 cell counts were associated with modestly lower costs among pre-HAART initiators [least-square means (95% confidence interval), CD4 > 500: 4674 (4632-4716); CD4: 350-499: 4765 (4721-4809) CD4: 200-349: 4826 (4780-4871); CD4 <200: 4809 (4759-4859)]; however these differences were not significant among post-2003 HAART initiators. Population-level mean costs increased through 2006 and stabilized post-2003 HAART initiators incurred quarterly costs up to 23% lower than pre-2000 HAART initiators in 2010. CONCLUSIONS: Our results highlight the magnitude of the temporal changes in HAART costs, and disparities between recent and pre-HAART initiators. This methodology can improve the precision of economic modeling efforts by using detailed cost functions for annual, population-level medication costs according to the distribution of clients by clinical stage and era of treatment initiation.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".