Cost-Effectiveness of Anti-retroviral Adherence Interventions for People Living with HIV: A Systematic Review of Decision Analytical Models.

Autor: Ahmed A; School of Pharmacy, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Subang Jaya, Selangor, Malaysia. ali.ahmed@monash.edu., Dujaili JA; School of Pharmacy, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Subang Jaya, Selangor, Malaysia.; Swansea University Medical School, Singleton Campus, Swansea University, Wales, UK., Chuah LH; School of Pharmacy, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Subang Jaya, Selangor, Malaysia., Hashmi FK; University College of Pharmacy, University of Punjab, Allama Iqbal Campus, Lahore, 54000, Pakistan., Le LK; Monash University Health Economics Group (MUHEG), School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia., Khanal S; Health Economics Consulting, University of East Anglia, Coventry, UK., Awaisu A; Department of Clinical Pharmacy and Practice, College of Pharmacy, QU Health, Qatar University, Doha, Qatar., Chaiyakunapruk N; College of Pharmacy, University of Utah, Salt Lake City, UT, USA.; IDEAS Center, Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA.
Jazyk: angličtina
Zdroj: Applied health economics and health policy [Appl Health Econ Health Policy] 2023 Sep; Vol. 21 (5), pp. 731-750. Date of Electronic Publication: 2023 Jun 30.
DOI: 10.1007/s40258-023-00818-4
Abstrakt: Background: Although safe and effective anti-retrovirals (ARVs) are readily available, non-adherence to ARVs is highly prevalent among people living with human immunodeficiency virus/acquired immunodeficiency syndrome (PLWHA). Different adherence-improving interventions have been developed and examined through decision analytic model-based health technology assessments. This systematic review aimed to review and appraise the decision analytical economic models developed to assess ARV adherence-improvement interventions.
Methods: The review protocol was registered on PROSPERO (CRD42022270039), and reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Relevant studies were identified through searches in six generic and specialized bibliographic databases, i.e. PubMed, Embase, NHS Economic Evaluation Database, PsycINFO, Health Economic Evaluations Database, tufts CEA registry and EconLit, from their inception to 23 October 2022. The cost-effectiveness of adherence interventions is represented by the incremental cost-effectiveness ratio (ICER). The quality of studies was assessed using the quality of the health economics studies (QHES) instrument. Data were narratively synthesized in the form of tables and texts. Due to the heterogeneity of the data, a permutation matrix was used for quantitative data synthesis rather than a meta-analysis.
Results: Fifteen studies, mostly conducted in North America (8/15 studies), were included in the review. The time horizon ranged from a year to a lifetime. Ten out of 15 studies used a micro-simulation, 4/15 studies employed Markov and 1/15 employed a dynamic model. The most commonly used interventions reported include technology based (5/15), nurse involved (2/15), directly observed therapy (2/15), case manager involved (1/15) and others that involved multi-component interventions (5/15). In 1/15 studies, interventions gained higher quality-adjusted life years (QALYs) with cost savings. The interventions in 14/15 studies were more effective but at a higher cost, and the overall ICER was well below the acceptable threshold mentioned in each study, indicating the interventions could potentially be implemented after careful interpretation. The studies were graded as high quality (13/15) or fair quality (2/15), with some methodological inconsistencies reported.
Conclusion: Counselling and smartphone-based interventions are cost-effective, and they have the potential to reduce the chronic adherence problem significantly. The quality of decision models can be improved by addressing inconsistencies in model selection, data inputs incorporated into models and uncertainty assessment methods.
(© 2023. The Author(s).)
Databáze: MEDLINE
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