Evidence map›Paper›PMID 34985588›Full record

ArticleGeroScience2022

Modeling of nursing care-associated airborne transmission of SARS-CoV-2 in a real-world hospital setting.

Attila Nagy, Alpár Horváth, Árpád Farkas, Péter Füri, Tamás Erdélyi, Balázs G Madas, Aladár Czitrovszky, Béla Merkely, Attila Szabó, Zoltán Ungvári and 1 more

Open access · hybridAbstract read
In one paragraph

Article in GeroScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 26% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
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  3. Review
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  5. Review
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

11 authors at 4 institutions in 2 countries.

Attila NagyDepartment of Applied and Nonlinear Optics, Wigner Research Centre for Physics, Konkoly-Thege Miklós st. 29-33, Budapest, Hungary.ORCID 0000-0003-4595-3625
Alpár HorváthDepartment of Pulmonology, Semmelweis University, Budapest, Hungary.
Árpád FarkasEnvironmental Physics Department, Centre for Energy Research, Budapest, Hungary.
Péter FüriEnvironmental Physics Department, Centre for Energy Research, Budapest, Hungary.
Tamás ErdélyiDepartment of Pulmonology, Semmelweis University, Budapest, Hungary.
Balázs G MadasEnvironmental Physics Department, Centre for Energy Research, Budapest, Hungary.
Aladár CzitrovszkyDepartment of Applied and Nonlinear Optics, Wigner Research Centre for Physics, Konkoly-Thege Miklós st. 29-33, Budapest, Hungary.
Béla MerkelyHeart and Vascular Centre, Semmelweis University, Budapest, Hungary.
Attila Szabó1st Department of Pediatrics Semmelweis University, Budapest, Hungary.
Zoltán UngváriVascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, Department of Biochemistry & Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 731042, USA.
Veronika MüllerDepartment of Pulmonology, Semmelweis University, Budapest, Hungary.
Semmelweis University · HUHUN-REN Centre for Energy Research · HUHUN-REN Wigner Research Centre for Physics · HUVi Technology (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Respiratory transmission of SARS-CoV-2 from one older patient to another by airborne mechanisms in hospital and nursing home settings represents an important health challenge during the COVID-19 pandemic. However, the factors that influence the concentration of respiratory droplets and aerosols that potentially contribute to hospital- and nursing care-associated transmission of SARS-CoV-2 are not well understood. To assess the effect of health care professional (HCP) and patient activity on size and concentration of airborne particles, an optical particle counter was placed (for 24 h) in the head position of an empty bed in the hospital room of a patient admitted from the nursing home with confirmed COVID-19. The type and duration of the activity, as well as the number of HCPs providing patient care, were recorded. Concentration changes associated with specific activities were determined, and airway deposition modeling was performed using these data. Thirty-one activities were recorded, and six representative ones were selected for deposition modeling, including patient's activities (coughing, movements, etc.), diagnostic and therapeutic interventions (e.g., diagnostic tests and drug administration), as well as nursing patient care (e.g., bedding and hygiene). The increase in particle concentration of all sizes was sensitive to the type of activity. Increases in supermicron particle concentration were associated with the number of HCPs (r = 0.66; p < 0.05) and the duration of activity (r = 0.82; p < 0.05), while submicron particles increased with all activities, mainly during the daytime. Based on simulations, the number of particles deposited in unit time was the highest in the acinar region, while deposition density rate (number/cm

Indexed as

COVID-19SARS-CoV-2AerosolsHospitalsHumansPandemicsAerosolsAerosol dispersionAerosol measurementAirborne transmissionElderlyLung depositionNursingSARS-CoV-2

Identifiers

PMID34985588
PMCPMC8729098
OpenAlexW4206503737

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.