Evidence map›Paper›PMID 33287821›Full record

ArticleBMC medicine2020

Optimizing COVID-19 surveillance in long-term care facilities: a modelling study.

David R M Smith, Audrey Duval, Koen B Pouwels, Didier Guillemot, Jérôme Fernandes, Bich-Tram Huynh, Laura Temime, Lulla Opatowski, AP-HP/Universities/Inserm COVID-19 research collaboration

Abstract read
In one paragraph

Article in BMC medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 1 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

46 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. The COVID-19 Pandemic and Responses in Nursing Homes: A Cross-Sectional Study in Four European Countries.International journal of environmental research and public health · 2022
    Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

9 authors.

David R M Smith *Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France. david.smith@pasteur.fr.ORCID http://orcid.org/0000-0002-7330-4262
Audrey Duval *Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France.
Koen B PouwelsHealth Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Didier GuillemotInstitut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France.
Jérôme FernandesClinique de soins de suite et réadaptation, Choisy-Le-Roi, France.
Bich-Tram HuynhInstitut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France.
Laura Temime *Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire national des arts et métiers, Paris, France.
Lulla Opatowski *Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France.
AP-HP/Universities/Inserm COVID-19 research collaboration

Funding

Agence Nationale de la Recherche ANR-10-LABX-62-IBEIDAgence Nationale de la Recherche SPHINX-17-CE36-0008-01CIHR DFSA 164263National Institute for Health Research NIHR200915
6 · The paper itself

Abstract

backgroundLong-term care facilities (LTCFs) are vulnerable to outbreaks of coronavirus disease 2019 (COVID-19). Timely epidemiological surveillance is essential for outbreak response, but is complicated by a high proportion of silent (non-symptomatic) infections and limited testing resources.

methodsWe used a stochastic, individual-based model to simulate transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) along detailed inter-individual contact networks describing patient-staff interactions in a real LTCF setting. We simulated distribution of nasopharyngeal swabs and reverse transcriptase polymerase chain reaction (RT-PCR) tests using clinical and demographic indications and evaluated the efficacy and resource-efficiency of a range of surveillance strategies, including group testing (sample pooling) and testing cascades, which couple (i) testing for multiple indications (symptoms, admission) with (ii) random daily testing.

resultsIn the baseline scenario, randomly introducing a silent SARS-CoV-2 infection into a 170-bed LTCF led to large outbreaks, with a cumulative 86 (95% uncertainty interval 6-224) infections after 3 weeks of unmitigated transmission. Efficacy of symptom-based screening was limited by lags to symptom onset and silent asymptomatic and pre-symptomatic transmission. Across scenarios, testing upon admission detected just 34-66% of patients infected upon LTCF entry, and also missed potential introductions from staff. Random daily testing was more effective when targeting patients than staff, but was overall an inefficient use of limited resources. At high testing capacity (> 10 tests/100 beds/day), cascades were most effective, with a 19-36% probability of detecting outbreaks prior to any nosocomial transmission, and 26-46% prior to first onset of COVID-19 symptoms. Conversely, at low capacity (< 2 tests/100 beds/day), group testing strategies detected outbreaks earliest. Pooling randomly selected patients in a daily group test was most likely to detect outbreaks prior to first symptom onset (16-27%), while pooling patients and staff expressing any COVID-like symptoms was the most efficient means to improve surveillance given resource limitations, compared to the reference requiring only 6-9 additional tests and 11-28 additional swabs to detect outbreaks 1-6 days earlier, prior to an additional 11-22 infections.

conclusionsCOVID-19 surveillance is challenged by delayed or absent clinical symptoms and imperfect diagnostic sensitivity of standard RT-PCR tests. In our analysis, group testing was the most effective and efficient COVID-19 surveillance strategy for resource-limited LTCFs. Testing cascades were even more effective given ample testing resources. Increasing testing capacity and updating surveillance protocols accordingly could facilitate earlier detection of emerging outbreaks, informing a need for urgent intervention in settings with ongoing nosocomial transmission.

Indexed as

Coronavirus InfectionsCOVID-19FemaleHumansLong-Term CareMaleMass ScreeningPneumonia, ViralPractice Guidelines as TopicPublic Health SurveillanceSARS-CoV-2Computational modellingContact networkCOVID-19Infectious disease surveillanceLong-term careMathematical modellingPublic healthSARS-CoV-2TestingTransmission dynamics

Identifiers

PMID33287821
PMCPMC7721547

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.