MétaCan
Menu
Retour à la cohorte
Enregistrement W7056859941

Evaluating the Fiscal Impact of Antiretroviral Therapy for the Management of HIV in the United States 1987–2023

2025· article· en· W7056859941 sur OpenAlexaff

Notice bibliographique

RevueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Langueen
DomaineEngineering
ThématiqueParticle accelerators and beam dynamics
Établissements canadiensInstitute of Health Economics
Organismes subventionnairesnon disponible
Mots-clésEpidemiologyPublic healthHuman immunodeficiency virus (HIV)Government (linguistics)Antiretroviral therapyTransmission (telecommunications)RevenueHealth care
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,013
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,117
Score d'incertitude au seuil0,232

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,013
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,252
Tête enseignante GPT0,558
Écart entre enseignants0,306 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueDOAJ (DOAJ: Directory of Open Access Journals)Même sujetParticle accelerators and beam dynamicsTravaux en français237 207