Evidence map›Paper›PMID 38911235›Full record

ArticleIJID regions2024

Automated local lockdowns for SARS-CoV-2 epidemic control-assessment of effect by controlled interrupted time series analysis.

Laura Espenhain, Steen Ethelberg, Laust Hvas Mortensen, Lasse Engbo Christiansen

Abstract read
In one paragraph

Article in IJID regions, 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

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

4 authors.

Laura EspenhainDepartment of Infectious Disease epidemiology and Prevention, Statens Serum Institut, Copenhagen, Denmark.
Steen EthelbergDepartment of Infectious Disease epidemiology and Prevention, Statens Serum Institut, Copenhagen, Denmark.
Laust Hvas MortensenDepartment of Public Health, University of Copenhagen, Copenhagen, Denmark.
Lasse Engbo ChristiansenDepartment of Epidemiology research, Statens Serum Institut, Copenhagen, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: During the COVID-19 pandemic, broad non-pharmaceutical interventions such as national lockdowns were effective but had significant drawbacks, prompting targeted approaches, such as Denmark's localized lockdowns, based on specific epidemiological criteria. This study evaluates the effect of Denmark's automated local lockdown strategy on epidemic control to inform future response. Methods: This was a register-based controlled interrupted time series analysis, examining SARS-CoV-2 infection rates in Danish parishes from March to September 2021. The matching of control parishes was based on location, time, and pre-lockdown infection trends, with the lockdown's start defined as the day after a parish exceeded the lockdown criteria. Follow-up included 3-week pre-lockdown and 2-week post-lockdown. Results: A total of 30 parishes were mandated to lockdown, approximately 3.5% of the population of Denmark. A total of 94 control parishes were used as 109 controls. The decrease in the incidence during the 2-week follow-up period after the initiation of the lockdown was 13% points higher in case parishes: in case parishes, the incidence was reduced by 78% compared with 65% in control parishes. Conclusions: Our findings demonstrate that local lockdowns did have a positive effect in mitigating the spread of the SARS-CoV-2 virus, making them valuable in the fight against the COVID-19 pandemic and an important alternative to national lockdowns.

Indexed as

Containment strategiesEpidemic controlEpidemiologyPublic healthRegional lockdownSARS-CoV-2

Identifiers

PMID38911235
PMCPMC11186853

What OpenQuestion holds

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Registered trials

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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.