ArticlePublic health2021
A scoping review of the experience of implementing population testing for SARS-CoV-2.
Article in Public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- Rapid reviews methods series: guidance on rapid scoping, mapping and evidence and gap map ('Big Picture Reviews').BMJ evidence-based medicine · 2025Article
- Mapping reviews, scoping reviews, and evidence and gap maps (EGMs): the same but different- the "Big Picture" review family.Systematic reviews · 2023Article
- Mobile SARS‑CoV‑2 screening facilities for rapid deployment and university-based diagnostic laboratory.Engineering in life sciences · 2023Article
- Article
- Impact of Mass Workplace COVID-19 Rapid Testing on Health and Healthcare Resource Savings.International journal of environmental research and public health · 2021Article
- A Cost-Benefit Analysis of the COVID-19 Asymptomatic Mass Testing Strategy in the North Metropolitan Area of Barcelona.International journal of environmental research and public health · 2021Article
- Self-Scheduling Process Efficiency and Utilization of Online Self-Scheduling of Lab Tests: A Retrospective Analysis of Self-Scheduled Appointments for COVID Testing.Health services research and managerial epidemiologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) - also known as the coronavirus disease (COVID-19) - pandemic has led to the swift introduction of population testing programmes in many countries across the world, using testing modalities such as drive-through, walk-through, mobile and home visiting programmes. Here, we provide an overview of the literature describing the experience of implementing population testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). STUDY
designScoping review.
methodsWe conducted a scoping review using Embase, Medline and the Cochrane library in addition to a grey literature search. We identified indicators relevant to process, quality and resource outcomes related to each testing modality.
resultsIn total, 2999 titles were identified from the academic literature and the grey literature search, of which 22 were relevant. Most studies were from the USA and the Republic of Korea. Drive-through testing centres were the most common testing modality evaluated and these provided a rapid method of testing whilst minimising resource use.
conclusionsThe evidence base for population testing lacks high quality studies, however, the literature provides evaluations of the advantages and limitations of different testing modalities. There is a need for robust evidence in this area to ensure that testing is deployed in a safe and effective manner in response to the COVID-19 pandemic.
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.