de Assis TSM, Freire ML, Carvalho JDP, Rabello A, Cota G. Cost-effectiveness of anti-SARS-CoV-2 antibody diagnostic tests in Brazil.
PLoS One 2022;
17:e0264159. [PMID:
35213578 PMCID:
PMC8880880 DOI:
10.1371/journal.pone.0264159]
[Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/07/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND
Although serologic tests for COVID-19 diagnosis are rarely indicated nowadays, they remain commercially available and widely used in Brazil. The objective of this study was to evaluate the cost-effectiveness of anti-SARS-CoV-2antibody diagnostic tests for COVID-19 in Brazil.
METHODS
Eleven commercially available diagnostic tests, comprising five lateral-flow immunochromatographic assays (LFAs) and six immunoenzymatic assays (ELISA) were analyzed from the perspective of the Brazilian Unified Health System.
RESULTS
The direct costs of LFAs ranged from US$ 11.42 to US$ 17.41and of ELISAs, from US$ 6.59 to US$ 10.31. Considering an estimated disease prevalence between 5% and 10%, the anti-SARS-CoV-2 ELISA (IgG) was the most cost-effective test, followed by the rapid One Step COVID-19 Test, at an incremental cost-effectiveness ratio of US$ 2.52 and US$ 1.26 per properly diagnosed case, respectively. Considering only the LFAs, at the same prevalence estimates, two tests, the COVID-19 IgG/IgM and the One Step COVID-19 Test, showed high effectiveness at similar costs. For situations where the estimated probability of disease is 50%, the LFAs are more costly and less effective alternatives.
CONCLUSIONS
Nowadays there are few indications for the use of serologic tests in the diagnosis of COVID-19 and numerous commercially available tests, with marked differences are observed among them. In general, LFA tests are more cost-effective for estimated low-COVID-19-prevalences, while ELISAs are more cost-effective for high-pretest-probability scenarios.
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