Evidence map›Paper›PMID 39595701›Full record

ArticleInternational journal of environmental research and public health2024

Cost-Utility Analysis of Supervised Inspiratory Muscle Training Added to Post-COVID Rehabilitation Program in the Public Health System of Brazil.

Guilherme Pacheco Modesto, Aline Loschi Soria, Luis V F Oliveira, Everton Nunes da Silva, Graziella F B Cipriano, Gerson Cipriano, Vinicius Maldaner

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Article in International journal of environmental research and public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Guilherme Pacheco ModestoHuman Movement and Rehabilitation Graduate Program, UniEvangelica, Anapolis 75083-515, Brazil.
Aline Loschi SoriaHealth Sciences Program, Escola Superior de Ciências da Saúde, Brasilia 70710-907, Brazil.ORCID 0000-0003-2963-5029
Luis V F OliveiraHuman Movement and Rehabilitation Graduate Program, UniEvangelica, Anapolis 75083-515, Brazil.ORCID 0000-0002-3852-9415
Everton Nunes da SilvaSciences and Technology in Health Program, Universidade de Brasilia, Brasilia 72220-275, Brazil.
Graziella F B CiprianoSciences and Technology in Health Program, Universidade de Brasilia, Brasilia 72220-275, Brazil.
Gerson CiprianoHuman Movement and Rehabilitation Graduate Program, UniEvangelica, Anapolis 75083-515, Brazil.ORCID 0000-0001-6323-7003
Vinicius MaldanerHuman Movement and Rehabilitation Graduate Program, UniEvangelica, Anapolis 75083-515, Brazil.ORCID 0000-0002-7804-7517

Funding

Fundação de Ensino e Pesquisa em Ciências da Saúde FEPECS 02/2021National Council for Scientific and Technological Development 09/2020
6 · The paper itself

Abstract

objectivesThis study aims to provide model-based cost-utility estimates for the addition of inspiratory muscle training (IMT) in COVID-19 pulmonary rehabilitation (PR).

methodsA cohort model comparing IMT with PR (intervention group) to IMT with only PR (control group) was used. The payer perspective from the Unified Health System in Brazil was adopted. Effectiveness parameters: Effectiveness was measured in quality-adjusted life years (QALYs). Probabilistic sensitivity analyses were performed using 1000 Monte Carlo simulations. A beta probability distribution was assumed for utilities, and a gamma distribution was applied to the costs. A cost-effectiveness threshold of BRL 40.000/QALYs was applied.

resultsAs the threshold of BRL 40.000/QALYs, we obtained 512 (51.2%) simulations that can be considered cost-effective to IMT added in PR programs. IMT added in PR treatment was more expensive (USD 317.73 versus USD 293.93) and more effective (incremental utility of 0.03 to INT group) than PR alone. The incremental cost-effectiveness ratio (ICER) was 793.93 USD/QALY.

conclusionsIMT added to PR is a cost-effective alternative compared with PR for post-COVID-19 patients. This strategy may result in net cost savings and improvements in the QALYs for these patients.

Indexed as

Cost-Benefit AnalysisCOVID-19BrazilBreathing ExercisesCohort StudiesHumansPublic HealthQuality-Adjusted Life YearsRespiratory MusclesSARS-CoV-2cost-effectiveness analysisCOVID-19health-related quality of liferehabilitationrespiratory muscle training

Identifiers

PMID39595701
PMCPMC11593497

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.