Evidence map›Paper›PMID 35970068›Full record

ArticleHealth & place2022

Connected in health: Place-to-place commuting networks and COVID-19 spillovers.

Christopher H Seto, Corina Graif, Aria Khademi, Vasant G Honavar, Claire E Kelling

Abstract read
In one paragraph

Article in Health & place, 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
–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

5 citing papers in PubMed.

  1. Article
  2. A Bayesian mixture model for Poisson network autoregression.Social network analysis and mining · 2025
    Article
  3. Article
  4. Review
  5. 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

5 authors.

Christopher H SetoDepartment of Sociology and Criminology, Pennsylvania State University, University Park, PA, USA; Population Research Institute, Pennsylvania State University, University Park, PA, USA. Electronic address: chs37@psu.edu.
Corina GraifDepartment of Sociology and Criminology, Pennsylvania State University, University Park, PA, USA; Population Research Institute, Pennsylvania State University, University Park, PA, USA. Electronic address: corina.graif@psu.edu.
Aria KhademiCollege of Information Science and Technology, Pennsylvania State University, University Park, PA, USA.
Vasant G HonavarCollege of Information Science and Technology, Pennsylvania State University, University Park, PA, USA; Center for Big Data Analytics and Discovery Informatics, Pennsylvania State University, University Park, PA, USA; Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA, USA.
Claire E KellingDepartment of Statistics, Pennsylvania State University, University Park, PA, USA.

Funding

Penn State Clinical and Translational Science InstituteUL1TR002014 · NCATS · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI KRASCHNEWSKI, JENNIFER L. · 2016 to 2025
$33.7M
The Population Research Institute: Development CoreP2CHD041025 · NICHD · PENNSYLVANIA STATE UNIVERSITY, THE · PI Brian Thiede · 2016 to 2026
$9.0M
Big Data and Network Analysis of Children's HealthK01HD093863 · NICHD · PENNSYLVANIA STATE UNIVERSITY, THE · PI GRAIF, CORINA · 2017 to 2021
$692k
NCATS NIH HHS UL1 TR002014NICHD NIH HHS K01 HD093863NICHD NIH HHS P2C HD041025
6 · The paper itself

Abstract

Biweekly county COVID-19 data were linked with Longitudinal Employer-Household Dynamics data to analyze population risk exposures enabled by pre-pandemic, country-wide commuter networks. Results from fixed-effects, spatial, and computational statistical approaches showed that commuting network exposure to COVID-19 predicted an area's COVID-19 cases and deaths, indicating spillovers. Commuting spillovers between counties were independent from geographic contiguity, pandemic-time mobility, or social media ties. Results suggest that commuting connections form enduring social linkages with effects on health that can withstand mobility disruptions. Findings contribute to a growing relational view of health and place, with implications for neighborhood effects research and place-based policies.

Indexed as

COVID-19Social MediaHumansPandemicsResidence CharacteristicsTransportationCommuting networksComputational statisticsCOVID-19Fixed-effectsMobility dataSpatial models

Identifiers

PMID35970068
PMCPMC9365871

What OpenQuestion holds

Textmetadata
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.