The HIV Epidemic in the United States – Epidemiological Projections and Public Economic Impact of Achieving Zero Transmission Goals
Bibliographic record
Abstract
Cillian Copeland,1,2 Rui Martins,1,3 Ryan Thaliffdeen,4 Nikos Kotsopoulos,1,5 James Jarrett,6 Paresh Chaudhari,4 Uche Mordi,4 Maarten J Postma3,7– 9 1Global Market Access Solutions Sarl, Chardonne, Switzerland; 2J.E. Cairnes School of Business and Economics, University of Galway, Galway, Ireland; 3University Medical Center Groningen, Department of Health Sciences, Unit of Global Health, University of Groningen, Groningen, Netherlands; 4Gilead Sciences, Inc, Foster City, CA, USA; 5Department of Economics, University of Athens MBA, University of Athens, Athens, Greece; 6Gilead Sciences Europe Ltd., Uxbridge, UK; 7Department of Economics, Econometrics & Finance, University of Groningen, Faculty of Economics & Business, Groningen, Netherlands; 8Center of Excellence for Pharmaceutical Care Innovation, Universitas Padjadjaran, Bandung, Indonesia; 9Division of Pharmacology & Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, IndonesiaCorrespondence: Cillian Copeland, Global Market Access Solutions Sarl, Chardonne, Switzerland, Email cillian@gmasoln.comBackground: The United States (US) HIV/AIDS strategy has targeted a 90% reduction in HIV infections by 2030. Whilst progress has been made in US HIV policy, the persistence of nearly 32,000 new infections annually highlights substantial barriers that still hinder effective treatment and the achievement of national targets. While the humanistic burden of HIV is well documented, there are broader economic effects on employment, disability and retirement. The objective of this study is to evaluate these economic gains when improving various HIV policies.Methodology: This analysis adapts a published Markov model assessing the role of six key policy parameters related to diagnosis, pre-exposure prophylaxis (PrEP) uptake, treatment initiation, and treatment as prevention (TasP) on the HIV epidemic in the US. Improvements in these parameters were explored to estimate averted HIV infections over 50 years and, subsequently, the feasibility of achieving the 2030 target of a 90% reduction in HIV incidence.A fiscal economic framework was also applied, linking HIV cases and policy targets to productivity, tax revenue, transfer benefits, and healthcare costs incurred by the US government.Results: Results were calculated for the general US population and for men who have sex with men (MSM), a subgroup experiencing a high HIV burden. For the general US population, policy improvements led to an average of 5,324 averted HIV infections annually over the 50-year horizon, corresponding to a total net annual fiscal gain of $397 million or $74,511 per averted infection. For the MSM subgroup, 911 infections were averted annually resulting in a net fiscal gain of $96 million, or $105,031 per averted infection.Conclusion: This analysis demonstrates that the benefits of HIV policy are not limited to the healthcare setting and show how initial investments provide long-term benefits by preventing HIV transmission and the associated impact on individuals’ labor market outcomes. While relying on assumptions and projections that may not capture all real-word complexities, fiscal analyses can provide a useful tool for policy evaluation that facilitate a holistic assessment of the wider costs and benefits that fall on governments as well as benefits of achieving policy targets.Keywords: HIV/AIDS, burden of disease, public costs, pre-exposure prophylaxis, treatment-as-prevention
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".