Evidence map›Paper›PMID 33302253›Full record

ArticleJournal of medical Internet research2020

Reduction of COVID-19 Incidence and Nonpharmacologic Interventions: Analysis Using a US County-Level Policy Data Set.

Senan Ebrahim, Henry Ashworth, Cray Noah, Adesh Kadambi, Asmae Toumi, Jagpreet Chhatwal

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 5 pooled it
–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

20 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  3. Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023
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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

6 authors.

Senan Ebrahim *Harvard Medical School, Boston, MA, United States.ORCID 0000-0003-3901-7160
Henry Ashworth *Harvard Medical School, Boston, MA, United States.ORCID 0000-0002-8989-8560
Cray NoahHarvard Medical School, Boston, MA, United States.ORCID 0000-0002-6610-6831
Adesh KadambiHikma Health, San Jose, CA, United States.ORCID 0000-0001-5129-1985
Asmae ToumiMassachusetts General Hospital, Boston, MA, United States.ORCID 0000-0002-6547-4747
Jagpreet ChhatwalHarvard Medical School, Boston, MA, United States.ORCID 0000-0001-8741-4430

Funding

Medical Scientist Training ProgramT32GM007753 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI WALENSKY, LOREN DAVID · 1985 to 2021
$50.0M
PHD TRAINING IN NEUROSCIENCET32MH020017 · NIMH · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI JOHN ASSAD · 1997 to 2026
$15.8M
Seizure Prediction in a Rodent Model of Focal EpilepsyF31NS105161 · NINDS · HARVARD MEDICAL SCHOOL · PI EBRAHIM, SENAN · 2018 to 2018
$38k
NIGMS NIH HHS T32 GM007753NIMH NIH HHS T32 MH020017NINDS NIH HHS F31 NS105161
6 · The paper itself

Abstract

backgroundWorldwide, nonpharmacologic interventions (NPIs) have been the main tool used to mitigate the COVID-19 pandemic. This includes social distancing measures (closing businesses, closing schools, and quarantining symptomatic persons) and contact tracing (tracking and following exposed individuals). While preliminary research across the globe has shown these policies to be effective, there is currently a lack of information on the effectiveness of NPIs in the United States.

objectiveThe purpose of this study was to create a granular NPI data set at the county level and then analyze the relationship between NPI policies and changes in reported COVID-19 cases.

methodsUsing a standardized crowdsourcing methodology, we collected time-series data on 7 key NPIs for 1320 US counties.

resultsThis open-source data set is the largest and most comprehensive collection of county NPI policy data and meets the need for higher-resolution COVID-19 policy data. Our analysis revealed a wide variation in county-level policies both within and among states (P<.001). We identified a correlation between workplace closures and lower growth rates of COVID-19 cases (P=.004). We found weak correlations between shelter-in-place enforcement and measures of Democratic local voter proportion (R=0.21) and elected leadership (R=0.22).

conclusionsThis study is the first large-scale NPI analysis at the county level demonstrating a correlation between NPIs and decreased rates of COVID-19. Future work using this data set will explore the relationship between county-level policies and COVID-19 transmission to optimize real-time policy formulation.

Indexed as

Contact TracingCOVID-19Datasets as TopicHumansIncidencePhysical DistancingPolicySARS-CoV-2SchoolsUnited Statescommunicable diseasesCOVID-19datadata seteffectivenessincidenceinterventionpandemicpolicypublic healthtime series

Identifiers

PMID33302253
PMCPMC7755429

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.