ArticleBMC health services research2023
Cost-effectiveness analysis of diagnostic strategies for COVID-19 in Iran.
Article in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 7 citations in OpenAlex.
- Economic Analyses of COVID-19 Interventions: A Narrative Review of Global Evidence.Healthcare (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSince 2020, COVID-19 has become a global public health issue and has caused problems worldwide. This infection can lead to a fever and respiratory problems. Asymptomatic carriers of the virus are a significant part of the spread of the disease, so early screening and diagnosis of suspected cases of COVID-19 are essential. Generally, standard diagnostic methods include lung imaging (CT), polymerase chain reaction (PCR), and corona antibody (IgM&IgG) testing. However, the costs of the above tests for the healthcare system cannot be ignored, and evaluating the incremental costs against the additional benefit is necessary. Therefore, this study aimed to determine the cost-effectiveness of diagnostic methods for COVID-19 patients. MATERIALS AND
methodsIn this research, an economic evaluation analysis was conducted to reveal the cost-effectiveness of the diagnostic strategies for COVID-19 from the service provider's perspective. Basic information about the costs of CT, serology (IgG&IgM), and molecular (PCR) tests were collected from the Ministry of Health of Iran. The effectiveness data were calculated according to the sensitivity and specificity of the diagnostic tests for COVID-19. In this study, the incremental cost-effectiveness ratio (ICER) of the diagnostic strategies for COVID-19 was estimated, and the most cost-effective diagnostic strategy was determined. In calculating ICER and analyzing the sensitivity of the results, Treeage software was used.
resultsAccording to the calculated incremental effectiveness cost ratio for scenarios with 5, 10, and 50% prevalence of COVID-19 and according to the threshold defined by the World Health Organization, in the study, PCR, PCR, and IgG&IgM strategies are the most cost-effective diagnostic methods of the corona. Also, the results were not sensitive to the desired parameters based on the results of one-way sensitivity analysis.
conclusionNowadays there are various tests with different levels of accuracy in the diagnosis of COVID-19. In general, PCR tests are more cost-effective for low prevalence of Covid-19, while IgM&IgG tests are more cost-effective for high estimated prevalence. The results of this research can help policymakers and health system managers to validate the most accurate diagnostic method for COVID-19, considering the prevalence of the disease.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.