Evidence map›Paper›PMID 33502000›Full record

SynthesisThe Cochrane database of systematic reviews2020

Digital contact tracing technologies in epidemics: a rapid review.

Andrew Anglemyer, Theresa Hm Moore, Lisa Parker, Timothy Chambers, Alice Grady, Kellia Chiu, Matthew Parry, Magdalena Wilczynska, Ella Flemyng, Lisa Bero

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in The Cochrane database of systematic reviews, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
106citing papers in PubMed, 9 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

106 citing papers in PubMed, 9 syntheses or guidelines pooled it.

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  6. The Role of Digital Technology in Curbing COVID-19.International journal of environmental research and public health · 2022
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  9. Digital contact tracing technologies in epidemics: a rapid review.The Cochrane database of systematic reviews · 2020
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46 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Andrew AnglemyerDepartment of Preventive and Social Medicine, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.
Theresa Hm MoorePopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Lisa ParkerSydney School of Pharmacy, The University of Sydney, Sydney, Australia.
Timothy ChambersDepartment of Public Health, University of Otago, Wellington, Wellington, New Zealand.
Alice GradySchool of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Callaghan, Australia.
Kellia ChiuCharles Perkins Centre and School of Pharmacy, The University of Sydney, Sydney, Australia.
Matthew ParryDepartment of Mathematics and Statistics, University of Otago, Dunedin, New Zealand.
Magdalena WilczynskaSchool of Medicine and Public Health, University of Newcastle, Callaghan, Australia.
Ella FlemyngEditorial and Methods Department, Cochrane, London, UK.
Lisa BeroCharles Perkins Centre and School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Camperdown, Sydney, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundReducing the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global priority. Contact tracing identifies people who were recently in contact with an infected individual, in order to isolate them and reduce further transmission. Digital technology could be implemented to augment and accelerate manual contact tracing. Digital tools for contact tracing may be grouped into three areas: 1) outbreak response; 2) proximity tracing; and 3) symptom tracking. We conducted a rapid review on the effectiveness of digital solutions to contact tracing during infectious disease outbreaks.

objectivesTo assess the benefits, harms, and acceptability of personal digital contact tracing solutions for identifying contacts of an identified positive case of an infectious disease. SEARCH

methodsAn information specialist searched the literature from 1 January 2000 to 5 May 2020 in CENTRAL, MEDLINE, and Embase. Additionally, we screened the Cochrane COVID-19 Study Register. SELECTION CRITERIA: We included randomised controlled trials (RCTs), cluster-RCTs, quasi-RCTs, cohort studies, cross-sectional studies and modelling studies, in general populations. We preferentially included studies of contact tracing during infectious disease outbreaks (including COVID-19, Ebola, tuberculosis, severe acute respiratory syndrome virus, and Middle East respiratory syndrome) as direct evidence, but considered comparative studies of contact tracing outside an outbreak as indirect evidence. The digital solutions varied but typically included software (or firmware) for users to install on their devices or to be uploaded to devices provided by governments or third parties. Control measures included traditional or manual contact tracing, self-reported diaries and surveys, interviews, other standard methods for determining close contacts, and other technologies compared to digital solutions (e.g. electronic medical records). DATA COLLECTION AND ANALYSIS: Two review authors independently screened records and all potentially relevant full-text publications. One review author extracted data for 50% of the included studies, another extracted data for the remaining 50%; the second review author checked all the extracted data. One review author assessed quality of included studies and a second checked the assessments. Our outcomes were identification of secondary cases and close contacts, time to complete contact tracing, acceptability and accessibility issues, privacy and safety concerns, and any other ethical issue identified. Though modelling studies will predict estimates of the effects of different contact tracing solutions on outcomes of interest, cohort studies provide empirically measured estimates of the effects of different contact tracing solutions on outcomes of interest. We used GRADE-CERQual to describe certainty of evidence from qualitative data and GRADE for modelling and cohort studies. MAIN

resultsWe identified six cohort studies reporting quantitative data and six modelling studies reporting simulations of digital solutions for contact tracing. Two cohort studies also provided qualitative data. Three cohort studies looked at contact tracing during an outbreak, whilst three emulated an outbreak in non-outbreak settings (schools). Of the six modelling studies, four evaluated digital solutions for contact tracing in simulated COVID-19 scenarios, while two simulated close contacts in non-specific outbreak settings. Modelling studies Two modelling studies provided low-certainty evidence of a reduction in secondary cases using digital contact tracing (measured as average number of secondary cases per index case - effective reproductive number (R AUTHORS'

conclusionsThe effectiveness of digital solutions is largely unproven as there are very few published data in real-world outbreak settings. Modelling studies provide low-certainty evidence of a reduction in secondary cases if digital contact tracing is used together with other public health measures such as self-isolation. Cohort studies provide very low-certainty evidence that digital contact tracing may produce more reliable counts of contacts and reduce time to complete contact tracing. Digital solutions may have equity implications for at-risk populations with poor internet access and poor access to digital technology. Stronger primary research on the effectiveness of contact tracing technologies is needed, including research into use of digital solutions in conjunction with manual systems, as digital solutions are unlikely to be used alone in real-world settings. Future studies should consider access to and acceptability of digital solutions, and the resultant impact on equity. Studies should also make acceptability and uptake a primary research question, as privacy concerns can prevent uptake and effectiveness of these technologies.

Indexed as

BotswanaCohort StudiesContact TracingCoronavirus InfectionsCOVID-19Disease OutbreaksHemorrhagic Fever, EbolaHumansMobile ApplicationsModels, TheoreticalPatient IsolationPrivacyQuarantineSecondary PreventionSierra LeoneTuberculosis

Identifiers

PMID33502000
PMCPMC8241885

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.