Evidence map›Paper›PMID 38764553›Full record

ArticleAerosol science and technology : the journal of the American Association for Aerosol Research2024

Air Change Rate and SARS-CoV-2 Exposure in Hospitals and Residences: A Meta-Analysis.

Yuetong Zhang, Sripriya Nannu Shankar, William B Vass, John A Lednicky, Z Hugh Fan, Duzgun Agdas, Robert Makuch, Chang-Yu Wu

Abstract read
In one paragraph

Article in Aerosol science and technology : the journal of the American Association for Aerosol Research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Article
  2. Development of a Platform forAerosol science and technology : the journal of the American Association for Aerosol Research · 2026
    Article
  3. Occupational exposure monitoring of airborne respiratory viruses in outpatient medical clinics.Aerosol science and technology : the journal of the American Association for Aerosol Research · 2024
    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

8 authors.

Yuetong ZhangDepartment of Environmental Engineering Sciences, University of Florida, Gainesville, Florida, USA.
Sripriya Nannu ShankarDepartment of Environmental Engineering Sciences, University of Florida, Gainesville, Florida, USA.
William B VassDepartment of Environmental Engineering Sciences, University of Florida, Gainesville, Florida, USA.
John A LednickyDepartment of Environmental and Global Health, University of Florida, Gainesville, Florida, USA.
Z Hugh FanMechanical and Aerospace Engineering, University of Florida, Gainesville, Florida, USA.
Duzgun AgdasEngineering School of Sustainable Infrastructure & Environment, University of Florida, Gainesville, Florida, USA.
Robert MakuchDepartment of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA.
Chang-Yu WuDepartment of Environmental Engineering Sciences, University of Florida, Gainesville, Florida, USA.

Funding

Using social networks to map and evaluate team science across CTSA hubsUL1TR001427 · NCATS · UNIVERSITY OF FLORIDA · PI MITCHELL, DUANE A. · 2015 to 2024
$37.2M
Together: Transforming and Translating Discovery to Improve HealthTL1TR001428 · NCATS · UNIVERSITY OF FLORIDA · PI MCCORMACK, WAYNE T. · 2015 to 2023
$3.9M
Multiplexed Airborne Virus Collection and Detection at the Point-of-CareR01AI158868 · NIAID · UNIVERSITY OF FLORIDA · PI FAN, Z. HUGH · 2021 to 2025
$1.6M
Occupational Inhalation Exposure to Infectious Respiratory Virus Aerosols in Medical FacilitiesR21OH012114 · OH · UNIVERSITY OF FLORIDA · PI LEDNICKY, JOHN A, WU, CHANG-YU · 2021 to 2022
$401k
New BioCascade System for Investigating the Airborne Transmission Modes of Respiratory VirusesR43AI157123 · NIAID · AEROSOL DYNAMICS, INC. · PI EIGUREN FERNANDEZ, ARANTZAZU · 2021 to 2021
$256k
NCATS NIH HHS TL1 TR001428NCATS NIH HHS UL1 TR001427NIAID NIH HHS R01 AI158868NIAID NIH HHS R43 AI157123NIOSH CDC HHS R21 OH012114
6 · The paper itself

Abstract

As SARS-CoV-2 swept across the globe, increased ventilation and implementation of air cleaning were emphasized by the US CDC and WHO as important strategies to reduce the risk of inhalation exposure to the virus. To assess whether higher ventilation and air cleaning rates lead to lower exposure risk to SARS-CoV-2, 1274 manuscripts published between April 2020 and September 2022 were screened using key words "airborne SARS-CoV-2 or "SARS-CoV-2 aerosol". Ninety-three studies involved air sampling at locations with known sources (hospitals and residences) were selected and associated data were compiled. Two metrics were used to assess exposure risk: SARS-CoV-2 concentration and SARS-CoV-2 detection rate in air samples. Locations were categorized by type (hospital or residence) and proximity to the sampling location housing the isolated/quarantined patient (primary or secondary). The results showed that hospital wards had lower airborne virus concentrations than residential isolation rooms. A negative correlation was found between airborne virus concentrations in primary-occupancy areas and air changes per hour (ACH). In hospital settings, sample positivity rates were significantly reduced in secondary-occupancy areas compared to primary-occupancy areas, but they were similar across sampling locations in residential settings. ACH and sample positivity rates were negatively correlated, though the effect was diminished when ACH values exceeded 8. While limitations associated with diverse sampling protocols exist, data considered by this meta-analysis support the notion that higher ACH may reduce exposure risks to the virus in ambient air.

Identifiers

PMID38764553
PMCPMC11101186

What OpenQuestion holds

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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.