Evidence map›Paper›PMID 35353857›Full record

ArticlePloS one2022

Association between city-wide lockdown and COVID-19 hospitalization rates in multigenerational households in New York City.

Arnab K Ghosh, Sara Venkatraman, Evgeniya Reshetnyak, Mangala Rajan, Anjile An, John K Chae, Mark A Unruh, David Abramson, Charles DiMaggio, Nathaniel Hupert

Open access · goldAbstract read
In one paragraph

Article in PloS one, 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
0.5field-weighted citation impact, top 37% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  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

10 authors at 2 institutions in 1 country.

Arnab K GhoshDepartment of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, United States of America.ORCID 0000-0002-5887-3301
Sara VenkatramanDepartment of Statistics and Data Science, Cornell University, Ithaca, New York, United States of America.
Evgeniya ReshetnyakDepartment of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, United States of America.
Mangala RajanDepartment of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, United States of America.
Anjile AnDepartment of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, New York, United States of America.
John K ChaeDepartment of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, United States of America.ORCID 0000-0001-9998-9100
Mark A UnruhDepartment of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, New York, United States of America.
David AbramsonSchool of Global Public Health, New York University, New York, New York, United States of America.
Charles DiMaggioDepartment of Surgery, New York University School of Medicine, New York, New York, United States of America.
Nathaniel HupertDepartment of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, New York, United States of America.
Cornell University · USNew York University · US

Funding

Disparities in COVID Disease Severity and Outcomes in New York CityUL1TR002384 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI JULIANNE L IMPERATO-MCGINLEY · 2017 to 2026
$86.2M
Institutional Career Development CoreKL2TR002385 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI YUAN-SHAN ZHU · 2017 to 2026
$13.3M
NCATS NIH HHS KL2 TR002385NCATS NIH HHS UL1 TR002384
6 · The paper itself

Abstract

backgroundCity-wide lockdowns and school closures have demonstrably impacted COVID-19 transmission. However, simulation studies have suggested an increased risk of COVID-19 related morbidity for older individuals inoculated by house-bound children. This study examines whether the March 2020 lockdown in New York City (NYC) was associated with higher COVID-19 hospitalization rates in neighborhoods with larger proportions of multigenerational households.

methodsWe obtained daily age-segmented COVID-19 hospitalization counts in each of 166 ZIP code tabulation areas (ZCTAs) in NYC. Using Bayesian Poisson regression models that account for spatiotemporal dependencies between ZCTAs, as well as socioeconomic risk factors, we conducted a difference-in-differences study amongst ZCTA-level hospitalization rates from February 23 to May 2, 2020. We compared ZCTAs in the lowest quartile of multigenerational housing to other quartiles before and after the lockdown.

findingsAmong individuals over 55 years, the lockdown was associated with higher COVID-19 hospitalization rates in ZCTAs with more multigenerational households. The greatest difference occurred three weeks after lockdown: Q2 vs. Q1: 54% increase (95% Bayesian credible intervals: 22-96%); Q3 vs. Q1: 48% (17-89%); Q4 vs. Q1: 66% (30-211%). After accounting for pandemic-related population shifts, a significant difference was observed only in Q4 ZCTAs: 37% (7-76%).

interpretationBy increasing house-bound mixing across older and younger age groups, city-wide lockdown mandates imposed during the growth of COVID-19 cases may have inadvertently, but transiently, contributed to increased transmission in multigenerational households.

Indexed as

COVID-19Bayes TheoremChildCommunicable Disease ControlHospitalizationHumansNew York CitySARS-CoV-2

Identifiers

PMID35353857
PMCPMC8967012
OpenAlexW4225713200

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.