Evaluating the Fiscal Impact of Antiretroviral Therapy for the Management of HIV in the United States 1987–2023
Bibliographic record
Abstract
Ana Teresa Paquete,1 Uche Mordi,2 James Jarrett,3 Ryan Thaliffdeen,2 Paresh Chaudhari,2 Mark P Connolly,1,4 Nikos Kotsopoulos,1,5 Patrick S Sullivan6 1Department of Health Economics, Global Market Access Solutions LLC, Mooresville, North Carolina, USA; 2Gilead Sciences, Inc, Foster City, California, USA; 3Gilead Sciences Europe Ltd., Uxbridge, UK; 4Health Economics Outcomes Research, Global Health, University Medical Center Groningen, Groningen, Netherlands; 5Department of Economics, University of Athens MBA, University of Athens, Athens, Greece; 6Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USACorrespondence: Mark P Connolly, Email mark@gmasoln.comPurpose: Investments in antiretroviral therapy (ART) have shown to improve outcomes for those living with human immunodeficiency virus (HIV) and reduce exposure to and transmission of the virus. In the current work, we assess the impact of ART on government public accounts since its introduction in 1987.Methods: National HIV epidemiological data from 1987 to 2023 were compared to a hypothetical no ART treatment scenario. This scenario was based on time series analysis, and on a transmission equation based on the effectiveness of ART. In the absence of historical epidemiological data, trend extrapolations were considered. The model assumes that individuals on ART are virally suppressed and, conservatively, excludes the impact of pre-exposure prophylaxis. The resulting differences in the number of HIV infections, acquired immunodeficiency syndrome (AIDS) cases and HIV-related deaths per year, were then considered to evaluate the impact on the labor market and on healthcare costs, based on the literature. The impact on employment was then used to estimate tax revenue and social benefits transfers. Results are presented separately with and without longevity effects.Results: The investment in ART from 1987 to 2023 was estimated to prevent millions of new infections and AIDS cases and to avoid HIV-related deaths. This investment was estimated to provide a return of US$2.11 trillion from 1987 to 2023; each US$1 spent on ART was estimated to create a revenue of US$4.3 to the public sector in the USA. Results remained positive when longevity effects were included. One-way sensitivity analysis showed results were robust.Conclusion: The analysis illustrates the broader economic benefits to the government attributed to public and private investments to develop and make ART available. The fiscal analysis of investing in ART shows a fourfold gain for the US government. This broader analysis is crucial to help shape health policy and funding decisions.Plain Language Summary: Treatments for human immunodeficiency virus (HIV) infection cost money to provide, but also can have positive economic effects by keeping people healthy, avoiding healthcare costs from HIV-related illnesses, and allowing people living with HIV to stay healthy and contribute to the workforce. To describe the overall impact of HIV treatments on the US public economy, we used historical data since antiretroviral therapy (ART) was first made available in the United States (US) for the treatment of HIV and compared it with a scenario in which ART was not available (1987– 2023). The aims were to consider the benefits of ART use for health outcomes and in the labor market and to quantify the overall costs and benefits to the US government. The impact of ART use in reducing the number of HIV infections, AIDS (acquired immunodeficiency syndrome) cases, HIV-related deaths and on improving the outcomes of those living with HIV was analyzed in terms of economic transfers to the government. More workers in the labor market attributed to using ART stopped the loss of tax revenue and the need for governmental payments, such as unemployment insurance and disability benefits. The reduction of healthcare costs for HIV care financed by the government was also considered. Our model suggests that public investment in the development of progressively more effective ART resulted in a net financial gain for the government since the mid 1990s, with a current gain of US$ 4.3 per every US$ 1 spent on ART and a net present value of US$ 2.11 trillion from 1987 to 2023. An analysis of the impact of healthcare public spending in broader economic sectors is essential to improve the evidence available to public decision makers.Keywords: antiretroviral therapy, HIV, economics, employment, viral suppression, innovation
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".