Expanding access to HAART: a cost-effective approach for treating and preventing HIV
Bibliographic record
Abstract
OBJECTIVE: HIV continues to present a substantial global health burden. Given the high direct medical costs associated with the disease, prevention of new transmission is an important element in limiting economic burden. In addition to providing therapeutic benefit, treatment with HAART has potential to prevent transmission of HIV. The objective in this study was to perform an economic evaluation of the incremental net benefit associated with an intervention to expand treatment with HAART in British Columbia, Canada. DESIGN: A mathematical model describing transmission of HIV, integrated with a microsimulation model describing the clinical and economic course of HIV. METHODS: The primary outcome was the incremental net benefit of expanding treatment with HAART from 50 to 75% of clinically eligible individuals in British Columbia, assuming a willingness-to-pay threshold of US$ 50,000 per quality-adjusted life year. Direct medical costs included were antiretroviral and nonantiretroviral medications, hospitalizations, physician visits, and laboratory tests. The mathematical and microsimulation models were based on patient characteristics observed in British Columbia. Longitudinal data described health services utilization, clinical progression, and survival for all individuals receiving treatment for HIV in British Columbia. RESULTS: Over 30 years, the HAART expansion scenario was associated with a net benefit of US$ 900 million (95% confidence interval US$ 493 million to 1.45 billion). CONCLUSION: Increasing the HAART treatment rate from 50 to 75% of clinically eligible individuals in British Columbia appears to be a cost-effective strategy based on this model. These cost-effectiveness results are consistent with public health objectives: all individuals who are eligible for an established life-saving treatment should receive it.
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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) | 0.000 | 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".