ArticleBMC health services research2022
Benefits, risks, and cost-effectiveness of COVID-19 self-tests from a consumer's perspective.
Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 3 of them syntheses that pooled it.
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
6 citing papers in PubMed, 3 syntheses or guidelines pooled it, 7 citations in OpenAlex.
- Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review.Systematic reviews · 2024Pooled it
- Diagnostics and treatments of COVID-19: two-year update to a living systematic review of economic evaluations.Frontiers in pharmacology · 2023Pooled it
- Cost-effectiveness of interventions for the prevention and control of COVID-19: Systematic review of 85 modelling studies.Journal of global health · 2022Pooled it
- Health economic evaluation of health promotion and disease prevention in Germany: a scoping review.Health economics review · 2026Review
- Acceptability of Covid-19 self-testing among Primary Health Care users.Revista de saude publica · 2026Article
- Evaluating the Usefulness of Population-Wide COVID-19 Testing in the Omicron Era: Insights from a German Model.Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany)) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe aim of this study is to quantify the health benefits, risks, and cost-effectiveness of COVID-19 self-tests from a consumer's perspective in Germany.
methodsThe analysis is based on a modelling approach using secondary data. The clinical endpoints considered in this analysis are avoided SARS-CoV-2 infections and secondary severe clinical events (death, intensive care unit (ICU) admission, and long COVID syndrome). The study determines the number of self-tests that need to be conducted under a 7-day incidence of 75 per 100,000 population to prevent one infection or severe clinical event. Furthermore, the study calculates the cost of testing per avoided clinical event and quality-adjusted life year (QALY) gained from a consumer perspective.
resultsDisregarding the rate of unreported COVID-19 cases, 4556 self-tests need to be conducted (over 12 years) in order to avoid one undesirable event (death, intensive care unit stay, or long COVID syndrome). Ninety percent of infections are not avoided among direct contacts but along the chain of infection. The costs per quality-adjusted life year gained from a consumer's perspective are €5870. This ratio is particularly sensitive to the 7-day incidence, effective reproduction number, and the age of contacts.
conclusionsThe benefits of self-testing in the general population at a 7-day incidence rate of 75 per 100,000 appear to be minor. Nevertheless, cost-effectiveness may still be acceptable in the presence of higher-risk contacts given the low costs of self-test kits in Germany.
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.