Evidence map›Paper›PMID 34316553›Full record

ArticleInfection prevention in practice2021

Spring 2020 COVID-19 community transmission behaviours around New York City medical facilities.

S-A Kingsbury Lee, D F Laefer

Abstract read
In one paragraph

Article in Infection prevention in practice, 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
–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. Article
  3. 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

2 authors.

S-A Kingsbury LeeCenter for Urban Science + Progress, New York University, Brooklyn, NY, USA.
D F LaeferCenter for Urban Science + Progress, New York University, Brooklyn, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEpidemiological studies have long been used for infection transmission prevention, but exact patterns of touch behaviours and transportation choices [contributors to community spread of coronavirus disease 2019 (COVID-19)] were previously unknown.

aimTo investigate individual risk behaviour levels with respect to local COVID-19 infection levels.

methodsA longitudinal field study recorded behaviours of individuals leaving medical facilities following the New York State's PAUSE order. A subset of those data was analysed herein (4793 records, 16 facilities, 23

findingsIn Week 1, 64.1% of subjects touched at least one environmental object [such as a building door handle (21.8%); traffic light, railing or parking meter (5.6%)]; shared object [such as a vehicle door handle (19.7%)]; personal object [such as a cell phone (4.2%)]; or themselves (0.4%). By Week 8, <35% of subjects touched at least one object, where the greatest reduction was in touching environmental objects. The frequency of touching increased slightly during the observation period for some personal objects such as cell phones. The use of public transportation remained steady (approximately 20%) throughout the study period; for-hire vehicle usage increased from 0% in Week 1 to 7% in Week 8, mirroring a 7% decrease in the use of personal vehicles (from 34% to 27%). Touching and transportation patterns varied significantly by facility.

conclusionsWhile this study observed a decline in touch patterns and use of shared modes of transportation, the persistence of many risk-related behaviours suggests that more effective public health policies, including cleaning regimens for public environmental objects and the removal or relocation of frequently touched objects, could help limit the spread of COVID-19.

Indexed as

Community transmissionCOVID-19New York CityPublic transportationTouch behaviour

Identifiers

PMID34316553
PMCPMC8233409

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LicenceCC BY-NC-ND
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Registered trials

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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.