Travel-related control measures to contain the COVID-19 pandemic: an evidence map.


Journal

BMJ open
ISSN: 2044-6055
Titre abrégé: BMJ Open
Pays: England
ID NLM: 101552874

Informations de publication

Date de publication:
09 04 2021
Historique:
entrez: 10 4 2021
pubmed: 11 4 2021
medline: 16 4 2021
Statut: epublish

Résumé

To comprehensively map the existing evidence assessing the impact of travel-related control measures for containment of the SARS-CoV-2/COVID-19 pandemic. Rapid evidence map. MEDLINE, Embase and Web of Science, and COVID-19 specific databases offered by the US Centers for Disease Control and Prevention and the WHO. We included studies in human populations susceptible to SARS-CoV-2/COVID-19, SARS-CoV-1/severe acute respiratory syndrome, Middle East respiratory syndrome coronavirus/Middle East respiratory syndrome or influenza. Interventions of interest were travel-related control measures affecting travel across national or subnational borders. Outcomes of interest included infectious disease, screening, other health, economic and social outcomes. We considered all empirical studies that quantitatively evaluate impact available in Armenian, English, French, German, Italian and Russian based on the team's language capacities. We extracted data from included studies in a standardised manner and mapped them to a priori and (one) post hoc defined categories. We included 122 studies assessing travel-related control measures. These studies were undertaken across the globe, most in the Western Pacific region (n=71). A large proportion of studies focused on COVID-19 (n=59), but a number of studies also examined SARS, MERS and influenza. We identified studies on border closures (n=3), entry/exit screening (n=31), travel-related quarantine (n=6), travel bans (n=8) and travel restrictions (n=25). Many addressed a bundle of travel-related control measures (n=49). Most studies assessed infectious disease (n=98) and/or screening-related (n=25) outcomes; we found only limited evidence on economic and social outcomes. Studies applied numerous methods, both inferential and descriptive in nature, ranging from simple observational methods to complex modelling techniques. We identified a heterogeneous and complex evidence base on travel-related control measures. While this map is not sufficient to assess the effectiveness of different measures, it outlines aspects regarding interventions and outcomes, as well as study methodology and reporting that could inform future research and evidence synthesis.

Identifiants

pubmed: 33837093
pii: bmjopen-2020-041619
doi: 10.1136/bmjopen-2020-041619
pmc: PMC8042592
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e041619

Informations de copyright

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: None declared.

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Auteurs

Ani Movsisyan (A)

Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilians University Munich, Munich, Germany ani.movsisyan@ibe.med.uni-muenchen.de.
Pettenkofer School of Public Health, Ludwig Maximilians University Munich, Munich, Germany.

Jacob Burns (J)

Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilians University Munich, Munich, Germany.
Pettenkofer School of Public Health, Ludwig Maximilians University Munich, Munich, Germany.

Renke Biallas (R)

Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilians University Munich, Munich, Germany.
Pettenkofer School of Public Health, Ludwig Maximilians University Munich, Munich, Germany.

Michaela Coenen (M)

Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilians University Munich, Munich, Germany.
Pettenkofer School of Public Health, Ludwig Maximilians University Munich, Munich, Germany.

Karin Geffert (K)

Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilians University Munich, Munich, Germany.
Pettenkofer School of Public Health, Ludwig Maximilians University Munich, Munich, Germany.

Olaf Horstick (O)

Heidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany.

Irma Klerings (I)

Department for Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Austria.

Lisa Maria Pfadenhauer (LM)

Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilians University Munich, Munich, Germany.
Pettenkofer School of Public Health, Ludwig Maximilians University Munich, Munich, Germany.

Peter von Philipsborn (P)

Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilians University Munich, Munich, Germany.
Pettenkofer School of Public Health, Ludwig Maximilians University Munich, Munich, Germany.

Kerstin Sell (K)

Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilians University Munich, Munich, Germany.
Pettenkofer School of Public Health, Ludwig Maximilians University Munich, Munich, Germany.

Brigitte Strahwald (B)

Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilians University Munich, Munich, Germany.
Pettenkofer School of Public Health, Ludwig Maximilians University Munich, Munich, Germany.

Jan M Stratil (JM)

Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilians University Munich, Munich, Germany.
Pettenkofer School of Public Health, Ludwig Maximilians University Munich, Munich, Germany.

Stephan Voss (S)

Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilians University Munich, Munich, Germany.
Pettenkofer School of Public Health, Ludwig Maximilians University Munich, Munich, Germany.

Eva Rehfuess (E)

Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilians University Munich, Munich, Germany.
Pettenkofer School of Public Health, Ludwig Maximilians University Munich, Munich, Germany.

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