Economic and health implications from earlier detection of HIV infection ă in the United Kingdom
Bibliographic record
Abstract
Vladimir Zah,1,2 Mondher Toumi1 1Ecole Doctoral Interdisciplinaire Sciences-Santé (EDISS), University of Lyon, Lyon, France; 2ZRx Outcomes Research Inc., Mississauga, Canada Purpose: To model the budget and survival impact of implementing interventions to increase the proportion of HIV infections detected early in a given UK population. Patients and methods: A Microsoft Excel decision model was designed to generate a set of outcomes for a defined population. Survival was modeled on the Collaboration of Observational HIV Epidemiological Research Europe (COHERE) study extrapolated to a 5-year horizon as a constant hazard. Hazard rates were specific to age, sex, and whether detection was early or late. The primary outcomes for each year up to 5 years were: annual costs, numbers of infected cases, hospital admissions, and surviving cases. Three locations in the UK were chosen to model outcomes across a range of HIV prevalence areas: Lambeth, Southwark, and Lewisham (LSL), Greater Manchester Cluster (GMC), and Kent and Medway (K&M). Results: In LSL, the projected cumulative cost savings over 5 years were £3,210,206 or £5,290,206 when including the value of the 104 life-years saved. Savings were insensitive to transmission rates, but sensitive in direct proportion to the percentage shift from late to early detection. In GMC, savings were in a similar proportion to LSL, but the magnitude was smaller, as a consequence of the lower base-case HIV prevalence. In K&M, with a smaller population and lower HIV prevalence than GMC, savings were commensurately smaller (£733,202 cumulatively over 5 years). Conclusion: The results strengthen the rationale for implementing increased testing in high prevalence areas. However, in areas of low prevalence, it is unlikely that costs will be returned over a 5-year period. Keywords: HIV, testing, costs, savings, model
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".