Evidence map›Paper›PMID 33006944›Full record

ArticleJMIR mHealth and uHealth2020

Performance of Digital Contact Tracing Tools for COVID-19 Response in Singapore: Cross-Sectional Study.

Zhilian Huang, Huiling Guo, Yee-Mun Lee, Eu Chin Ho, Hou Ang, Angela Chow

Open access · goldAbstract readComparative Study
In one paragraph

Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 2 pooled it
19.2field-weighted citation impact, top 1% of its field
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

40 citing papers in PubMed, 2 syntheses or guidelines pooled it, 80 citations in OpenAlex.

  1. Digital Contact Tracing Applications against COVID-19: A Systematic Review.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2022
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  12. Nurse, Give Me the News! Understanding Support for and Opposition to a COVID-19 Health Screening System.International journal of environmental research and public health · 2023
    Article
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  14. Educate to transform: An innovative experience for faculty training.Education and information technologies · 2023
    Article
  15. The promise of digital healthcare technologies.Frontiers in public health · 2023
    Review
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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

6 authors at 2 institutions in 1 country.

Zhilian HuangDepartment of Clinical Epidemiology, Office of Clinical Epidemiology, Analytics, and Knowledge, Tan Tock Seng Hospital, Singapore, Singapore.ORCID 0000-0002-4445-1669
Huiling GuoDepartment of Clinical Epidemiology, Office of Clinical Epidemiology, Analytics, and Knowledge, Tan Tock Seng Hospital, Singapore, Singapore.ORCID 0000-0002-8730-6349
Yee-Mun LeeDepartment of Urology, Tan Tock Seng Hospital, Singapore, Singapore.ORCID 0000-0001-9233-0207
Eu Chin HoDepartment of Ear, Nose, and Throat, Tan Tock Seng Hospital, Singapore, Singapore.ORCID 0000-0002-4869-9493
Hou AngDepartment of Emergency Medicine, Tan Tock Seng Hospital, Singapore, Singapore.ORCID 0000-0002-4681-0610
Angela ChowDepartment of Clinical Epidemiology, Office of Clinical Epidemiology, Analytics, and Knowledge, Tan Tock Seng Hospital, Singapore, Singapore.ORCID 0000-0002-4063-736X
Tan Tock Seng Hospital · SGNanyang Technological University · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEffective contact tracing is labor intensive and time sensitive during the COVID-19 pandemic, but also essential in the absence of effective treatment and vaccines. Singapore launched the first Bluetooth-based contact tracing app-TraceTogether-in March 2020 to augment Singapore's contact tracing capabilities.

objectiveThis study aims to compare the performance of the contact tracing app-TraceTogether-with that of a wearable tag-based real-time locating system (RTLS) and to validate them against the electronic medical records at the National Centre for Infectious Diseases (NCID), the national referral center for COVID-19 screening.

methodsAll patients and physicians in the NCID screening center were issued RTLS tags (CADI Scientific) for contact tracing. In total, 18 physicians were deployed to the NCID screening center from May 10 to May 20, 2020. The physicians activated the TraceTogether app (version 1.6; GovTech) on their smartphones during shifts and urged their patients to use the app. We compared patient contacts identified by TraceTogether and those identified by RTLS tags within the NCID vicinity during physicians' 10-day posting. We also validated both digital contact tracing tools by verifying the physician-patient contacts with the electronic medical records of 156 patients who attended the NCID screening center over a 24-hour time frame within the study period.

resultsRTLS tags had a high sensitivity of 95.3% for detecting patient contacts identified either by the system or TraceTogether while TraceTogether had an overall sensitivity of 6.5% and performed significantly better on Android phones than iPhones (Android: 9.7%, iPhone: 2.7%; P<.001). When validated against the electronic medical records, RTLS tags had a sensitivity of 96.9% and specificity of 83.1%, while TraceTogether only detected 2 patient contacts with physicians who did not attend to them.

conclusionsTraceTogether had a much lower sensitivity than RTLS tags for identifying patient contacts in a clinical setting. Although the tag-based RTLS performed well for contact tracing in a clinical setting, its implementation in the community would be more challenging than TraceTogether. Given the uncertainty of the adoption and capabilities of contact tracing apps, policy makers should be cautioned against overreliance on such apps for contact tracing. Nonetheless, leveraging technology to augment conventional manual contact tracing is a necessary move for returning some normalcy to life during the long haul of the COVID-19 pandemic.

Indexed as

Computer SystemsMobile ApplicationsWearable Electronic DevicesClinical Laboratory TechniquesContact TracingCoronavirus InfectionsCOVID-19COVID-19 TestingCross-Sectional StudiesElectronic Health RecordsHumansPandemicsPhysician-Patient RelationsPneumonia, ViralReproducibility of ResultsSingaporecontact tracingCOVID-19electronic medical recordsinfectious diseasepublic healthreal-time locating systems

Identifiers

PMID33006944
PMCPMC7599064
OpenAlexW3090717363

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.