Evidence map›Paper›PMID 38429552›Full record

ArticleCommunications medicine2024

State variation in neighborhood COVID-19 burden across the United States.

Grace A Noppert, Philippa Clarke, Andrew Hoover, John Kubale, Robert Melendez, Kate Duchowny, Sonia T Hegde

Abstract read
In one paragraph

Article in Communications medicine, 2024. 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

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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

5 · Who and what money

Authors and funding

7 authors.

Grace A NoppertInstitute for Social Research, University of Michigan, Ann Arbor, USA. gnoppert@umich.edu.ORCID http://orcid.org/0000-0002-2040-960X
Philippa ClarkeInstitute for Social Research, University of Michigan, Ann Arbor, USA.
Andrew HooverInstitute for Social Research, University of Michigan, Ann Arbor, USA.ORCID http://orcid.org/0009-0005-1568-1296
John KubaleInstitute for Social Research, University of Michigan, Ann Arbor, USA.ORCID http://orcid.org/0000-0001-5571-9774
Robert MelendezInstitute for Social Research, University of Michigan, Ann Arbor, USA.
Kate DuchownyInstitute for Social Research, University of Michigan, Ann Arbor, USA.
Sonia T HegdeDepartment of Epidemiology, Johns Hopkins University, Baltimore, USA.ORCID http://orcid.org/0000-0001-6426-0096

Funding

Research Education CoreP30AG024824 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Lona Mody, RAYMOND L YUNG · 2004 to 2026
$29.2M
Consortium Coordinating Center for Social, Behavioral, and Economic Research on Public Health Emergencies Supplement #2U24AG076462 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI John Kubale, Margaret Catherine Levenstein · 2021 to 2026
$5.9M
Network on Life Course Health Dynamics and Disparities in 21st Century America RenewalR24AG045061 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI JENNIFER A AILSHIRE, Sarah A. Burgard · 2014 to 2026
$5.2M
A National Neighborhood Data Resource to Understand the Health and Socioeconomic Impacts of the 2020 Pandemic in the United StatesU01NR020556 · NINR · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Philippa Clarke, Grace A Noppert · 2022 to 2026
$3.2M
Examining the role of the neighborhood environment on muscle function in older adultsR00AG066846 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DUCHOWNY, CATHERINE · 2023 to 2025
$742k
NIA NIH HHS P30 AG024824NIA NIH HHS R00 AG066846NIA NIH HHS R24 AG045061NIA NIH HHS U24 AG076462NINR NIH HHS U01 NR020556
6 · The paper itself

Abstract

backgroundA lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 infection burden has been distributed across neighborhoods, a key determinant of both risk and resilience. Without more spatially resolute data, efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities will remain difficult to quantify and intervene on.

methodsWe leveraged spatially-referenced data from 21 states collated through the COVID Neighborhood Project to examine the distribution of COVID-19 cases across neighborhoods and states in the U.S. We also linked the COVID-19 case data with data on the neighborhood social environment from the National Neighborhood Data Archive. We then estimated correlations between neighborhood COVID-19 burden and features of the neighborhood social environment.

resultsWe find that the distribution of COVID-19 at the neighborhood-level varies within and between states. The median case count per neighborhood (coefficient of variation (CV)) in Wisconsin is 3078.52 (0.17) per 10,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (CV) is 810.98 (0.84) per 10,000 population. We also find that correlations between features of the neighborhood social environment and burden vary in magnitude and direction by state.

conclusionsOur findings underscore the importance that local contexts may play when addressing the long-term social and economic fallout communities will face from COVID-19.

Identifiers

PMID38429552
PMCPMC10907669

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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.