Evidence map›Paper›PMID 36718200›Full record

ArticleRegional science policy & practice2022

COVID-19 and income profile: How communities in the United States responded to mobility restrictions in the pandemic's early stages.

Qianqian Sun, Weiyi Zhou, Aliakbar Kabiri, Aref Darzi, Songhua Hu, Hannah Younes, Lei Zhang

Abstract read
In one paragraph

Article in Regional science policy & practice, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Qianqian SunMaryland Transportation Institute (MTI), Department of Civil and Environmental Engineering University of Maryland 8228 Paint Branch Dr College Park MD 20742 USA.ORCID https://orcid.org/0000-0003-3684-4603
Weiyi ZhouMaryland Transportation Institute (MTI), Department of Civil and Environmental Engineering University of Maryland 8228 Paint Branch Dr College Park MD 20742 USA.
Aliakbar KabiriMaryland Transportation Institute (MTI), Department of Civil and Environmental Engineering University of Maryland 8228 Paint Branch Dr College Park MD 20742 USA.ORCID https://orcid.org/0000-0003-2119-007X
Aref DarziMaryland Transportation Institute (MTI), Department of Civil and Environmental Engineering University of Maryland 8228 Paint Branch Dr College Park MD 20742 USA.
Songhua HuMaryland Transportation Institute (MTI), Department of Civil and Environmental Engineering University of Maryland 8228 Paint Branch Dr College Park MD 20742 USA.
Hannah YounesMaryland Transportation Institute (MTI), Department of Civil and Environmental Engineering University of Maryland 8228 Paint Branch Dr College Park MD 20742 USA.
Lei ZhangMaryland Transportation Institute (MTI), Department of Civil and Environmental Engineering University of Maryland 8228 Paint Branch Dr College Park MD 20742 USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mobility interventions in communities play a critical role in containing a pandemic at an early stage. The real-world practice of social distancing can enlighten policymakers and help them implement more efficient and effective control measures. A lack of such research using real-world observations initiates this article. We analyzed the social distancing performance of 66,149 census tracts from 3,142 counties in the United States with a specific focus on income profile. Six daily mobility metrics, including a social distancing index, stay-at-home percentage, miles traveled per person, trip rate, work trip rate, and non-work trip rate, were produced for each census tract using the location data from over 100 million anonymous devices on a monthly basis. Each mobility metric was further tabulated by three perspectives of social distancing performance: "best performance," "effort," and "consistency." We found that for all 18 indicators, high-income communities demonstrated better social distancing performance. Such disparities between communities of different income levels are presented in detail in this article. The comparisons across scenarios also raise other concerns for low-income communities, such as employment status, working conditions, and accessibility to basic needs. This article lays out a series of facts extracted from real-world data and offers compelling perspectives for future discussions.

Indexed as

causal impact analysis of incomecommunity differencesCOVID‐19 pandemicMobile location datasocial distancing behavior

Identifiers

PMID36718200
PMCPMC9877830

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.