Evidence map›Paper›PMID 34727895›Full record

ArticleBMC public health2021

Survival analysis of factors affecting the timing of COVID-19 non-pharmaceutical interventions by U.S. universities.

Kevin E Cevasco, Amira A Roess, Hayley M North, Sheryne A Zeitoun, Rachel N Wofford, Graham A Matulis, Abigail F Gregory, Maha H Hassan, Aya D Abdo, Michael E von Fricken

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2021. 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
1.0field-weighted citation impact, top 21% 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

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

10 authors at 1 institution in 1 country.

Kevin E CevascoDepartment of Global and Community Health, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA.
Amira A RoessDepartment of Global and Community Health, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA.
Hayley M NorthDepartment of Global and Community Health, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA.
Sheryne A ZeitounDepartment of Global and Community Health, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA.
Rachel N WoffordDepartment of Global and Community Health, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA.
Graham A MatulisDepartment of Biology, College of Science, George Mason University, Fairfax, VA, USA.
Abigail F GregoryDepartment of Global and Community Health, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA.
Maha H HassanDepartment of Global and Community Health, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA.
Aya D AbdoDepartment of Global and Community Health, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA.
Michael E von FrickenDepartment of Global and Community Health, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA. mvonfric@gmu.edu.
George Mason University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDuring March of 2020 the Centers for Disease Control and Prevention (CDC) announced non-pharmaceutical intervention (NPI) guidance as the primary mitigation strategy against growing COVID-19 community spread due to the absence of a vaccine or effective treatment at that time. CDC guidance states that NPIs are most effective when instituted in an early, targeted, and layered fashion. NPIs are effective in slowing spread, and measures should be custom-tailored to each population. This study examines factors associated with implementation and timing of NPI interventions across large public and private U.S. universities at the onset of the COVID-19 pandemic.

methodsNPI decisions of interest include when U.S. universities canceled international travel, shifted to online learning, moved faculty/staff to remote work, limited campus housing, and closed campus for all non-essential personnel. Cox proportional hazard analyses of retrospective data were conducted to assess the time to NPI events. Hazard ratios were calculated for university governance, campus setting, religious affiliation, health infrastructure, faculty diversity, and student demographics. The methods control for variance inflation factors, COVID case prevalence, and time varying covariates of spring break and states' state of emergency (SOE) orders. This study captures NPI decisions at 575 U.S. universities during spring of 2020 which affected the movement of seven million students and two million employees.

resultsUniversities located in districts represented by Democratic party congressional members reported earlier NPI implementation than Republican (Cox proportional hazard ratio (HR) range 0.61-0.80). University religious affiliation was not associated with the timing any of the NPI decisions. Universities with more diverse faculty showed an association with earlier NPI implementation (HR range 0.65-0.76). The existence of university-affiliated health infrastructure was not associated with NPI timing.

conclusionUniversity NPI implementation was largely driven by local COVID-19 epidemiology, culture and political concerns. The timing of university NPI decisions varied by regional politics, faculty demographics, university governance, campus setting, and foreign student prevalence adjusting for COVID-19 state case prevalence and spring break timing. Religious affiliation and presence of university health infrastructure were not associated with timing.

Indexed as

COVID-19PandemicsHumansRetrospective StudiesSARS-CoV-2Survival AnalysisUniversitiesCOVID-19Non-pharmaceutical interventionsPandemic responseTimingU.S. universities

Identifiers

PMID34727895
PMCPMC8562371
OpenAlexW3208675930

What OpenQuestion holds

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LicenceCC BY
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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.