Evidence map›Paper›PMID 35618714›Full record

ArticleNature communications2022

Combining rapid antigen testing and syndromic surveillance improves community-based COVID-19 detection in a low-income country.

Fergus J Chadwick, Jessica Clark, Shayan Chowdhury, Tasnuva Chowdhury, David J Pascall, Yacob Haddou, Joanna Andrecka, Mikolaj Kundegorski, Craig Wilkie, Eric Brum and 14 more

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.3field-weighted citation impact, top 19% 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

9 citing papers in PubMed, 13 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
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

24 authors at 4 institutions in 3 countries.

Fergus J ChadwickInstitute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK. fergusjchadwick@gmail.com.ORCID 0000-0001-8650-1938
Jessica ClarkInstitute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK.ORCID 0000-0003-1692-899X
Shayan Chowdhurya2i, United Nations Development Program, ICT Ministry, Dhaka, Bangladesh.ORCID 0000-0001-5153-9055
Tasnuva ChowdhuryInstitute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK.ORCID 0000-0003-0660-9784
David J PascallMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.ORCID 0000-0002-7543-0860
Yacob HaddouInstitute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK.
Joanna AndreckaFood and Agriculture Organisation of the United Nations in support of the UN Interagency Support Team, Dhaka, Bangladesh.
Mikolaj KundegorskiCOVID-19 in LMICs Research Group, University of Glasgow, Glasgow, UK.ORCID 0000-0002-0982-9371
Craig WilkieCOVID-19 in LMICs Research Group, University of Glasgow, Glasgow, UK.ORCID 0000-0003-0805-0195
Eric BrumFood and Agriculture Organisation of the United Nations in support of the UN Interagency Support Team, Dhaka, Bangladesh.ORCID 0000-0002-0244-7178
Tahmina ShirinInstitute of Epidemiology, Disease Control and Research, Ministry of Health, Dhaka, Bangladesh.
A S M AlamgirInstitute of Epidemiology, Disease Control and Research, Ministry of Health, Dhaka, Bangladesh.
Mahbubur RahmanInstitute of Epidemiology, Disease Control and Research, Ministry of Health, Dhaka, Bangladesh.ORCID 0000-0001-8577-8281
Ahmed Nawsher AlamInstitute of Epidemiology, Disease Control and Research, Ministry of Health, Dhaka, Bangladesh.ORCID 0000-0002-7962-0725
Farzana KhanInstitute of Epidemiology, Disease Control and Research, Ministry of Health, Dhaka, Bangladesh.
Ben SwallowCOVID-19 in LMICs Research Group, University of Glasgow, Glasgow, UK.ORCID 0000-0002-0227-2160
Frances S MairGeneral Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK.
Janine IllianCOVID-19 in LMICs Research Group, University of Glasgow, Glasgow, UK.ORCID 0000-0002-6130-2796
Caroline L TrotterDepartments of Pathology and Veterinary Medicine, University of Cambridge, Cambridge, UK.ORCID 0000-0003-4000-2708
Davina L HillInstitute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK.ORCID 0000-0001-9085-6192
Dirk HusmeierSchool of Mathematics and Statistics, University of Glasgow, Glasgow, UK.ORCID 0000-0003-1673-7413
Jason MatthiopoulosInstitute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK.ORCID 0000-0003-3639-8172
Katie HampsonInstitute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK.ORCID 0000-0001-5392-6884
Ayesha SaniaDivision of Developmental Neuroscience, Department of Psychiatry, Columbia University, New York, NY, USA.
University of Glasgow · GBInstitute of Epidemiology, Disease Control and Research · BDUniversity of Cambridge · GBColumbia University · US

Funding

Medical Research Council MR/V038613/1Wellcome TrustWellcome Trust 207569/Z/17/Z
6 · The paper itself

Abstract

Diagnostics for COVID-19 detection are limited in many settings. Syndromic surveillance is often the only means to identify cases but lacks specificity. Rapid antigen testing is inexpensive and easy-to-deploy but can lack sensitivity. We examine how combining these approaches can improve surveillance for guiding interventions in low-income communities in Dhaka, Bangladesh. Rapid-antigen-testing with PCR validation was performed on 1172 symptomatically-identified individuals in their homes. Statistical models were fitted to predict PCR-status using rapid-antigen-test results, syndromic data, and their combination. Under contrasting epidemiological scenarios, the models' predictive and classification performance was evaluated. Models combining rapid-antigen-testing and syndromic data yielded equal-to-better performance to rapid-antigen-test-only models across all scenarios with their best performance in the epidemic growth scenario. These results show that drawing on complementary strengths across rapid diagnostics, improves COVID-19 detection, and reduces false-positive and -negative diagnoses to match local requirements; improvements achievable without additional expense, or changes for patients or practitioners.

Indexed as

COVID-19EpidemicsBangladeshHumansModels, StatisticalSentinel Surveillance

Identifiers

PMID35618714
PMCPMC9135686
OpenAlexW4281564235

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.