COVID-19 lockdown and fatal motor vehicle collisions due to speed-related traffic violations in Japan: a time-series study.
Identifieur interne : 000588 ( Main/Exploration ); précédent : 000587; suivant : 000589COVID-19 lockdown and fatal motor vehicle collisions due to speed-related traffic violations in Japan: a time-series study.
Auteurs : Haruhiko Inada [Japon] ; Lamisa Ashraf [États-Unis] ; Sachalee Campbell [États-Unis]Source :
- Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention [ 1475-5785 ] ; 2021.
Descripteurs français
- KwdFr :
- Accidents de la route (législation et jurisprudence), Accidents de la route (mortalité), Accidents de la route (tendances), Accélération (effets indésirables), Adulte (MeSH), Adulte d'âge moyen (MeSH), Analyse de série chronologique interrompue (MeSH), Conduite automobile (législation et jurisprudence), Conduite automobile (statistiques et données numériques), Humains (MeSH), Japon (épidémiologie), Jeune adulte (MeSH), Modèles statistiques (MeSH), Police (MeSH), Sujet âgé (MeSH), Sécurité (MeSH), Véhicules motorisés (statistiques et données numériques).
- MESH :
- effets indésirables : Accélération.
- législation et jurisprudence : Accidents de la route, Conduite automobile.
- mortalité : Accidents de la route.
- statistiques et données numériques : Conduite automobile, Véhicules motorisés.
- tendances : Accidents de la route.
- épidémiologie : Japon.
- Adulte, Adulte d'âge moyen, Analyse de série chronologique interrompue, Humains, Jeune adulte, Modèles statistiques, Police, Sujet âgé, Sécurité.
- Wicri :
- geographic : Japon.
English descriptors
- KwdEn :
- Acceleration (adverse effects), Accidents, Traffic (legislation & jurisprudence), Accidents, Traffic (mortality), Accidents, Traffic (trends), Adult (MeSH), Aged (MeSH), Automobile Driving (legislation & jurisprudence), Automobile Driving (statistics & numerical data), COVID-19 (epidemiology), Humans (MeSH), Interrupted Time Series Analysis (MeSH), Japan (epidemiology), Middle Aged (MeSH), Models, Statistical (MeSH), Motor Vehicles (statistics & numerical data), Police (MeSH), SARS-CoV-2 (MeSH), Safety (MeSH), Young Adult (MeSH).
- MESH :
- geographic , epidemiology : Japan.
- adverse effects : Acceleration.
- epidemiology : COVID-19.
- legislation & jurisprudence : Accidents, Traffic, Automobile Driving.
- mortality : Accidents, Traffic.
- statistics & numerical data : Automobile Driving, Motor Vehicles.
- trends : Accidents, Traffic.
- Adult, Aged, Humans, Interrupted Time Series Analysis, Middle Aged, Models, Statistical, Police, SARS-CoV-2, Safety, Young Adult.
Abstract
Between March and May 2020, Japan experienced a lockdown due to the COVID-19 crisis. Empty roads possibly triggered speed-related traffic violations that caused fatal motor vehicle collisions (MVCs). Using police data on the monthly number of fatal MVCs between January 2010 and February 2020 in which motor vehicle drivers were at fault, we forecasted the numbers of fatal MVCs due to the speed-related violations during the lockdown and compared these with those observed. We also compared the observed to forecasted using the ratio of the number of speed-related fatal MVCs to that of non-speed related fatal MVCs. The observed numbers of speed-related fatal MVCs were within the 95% CIs of the forecasted numbers. The observed ratio was higher than the forecasted ratio in April (p=0.016). In the second month of the lockdown, drivers were more likely to commit speed-related violations that caused fatal MVCs than before the lockdown.
DOI: 10.1136/injuryprev-2020-043947
PubMed: 33067222
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Traffic (trends)</term><term>Adult (MeSH)</term><term>Aged (MeSH)</term><term>Automobile Driving (legislation & jurisprudence)</term><term>Automobile Driving (statistics & numerical data)</term><term>COVID-19 (epidemiology)</term><term>Humans (MeSH)</term><term>Interrupted Time Series Analysis (MeSH)</term><term>Japan (epidemiology)</term><term>Middle Aged (MeSH)</term><term>Models, Statistical (MeSH)</term><term>Motor Vehicles (statistics & numerical data)</term><term>Police (MeSH)</term><term>SARS-CoV-2 (MeSH)</term><term>Safety (MeSH)</term><term>Young Adult (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Accidents de la route (législation et jurisprudence)</term><term>Accidents de la route (mortalité)</term><term>Accidents de la route (tendances)</term><term>Accélération (effets indésirables)</term><term>Adulte (MeSH)</term><term>Adulte d'âge moyen (MeSH)</term><term>Analyse de série chronologique interrompue (MeSH)</term><term>Conduite automobile (législation 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xml:lang="en"><term>Adult</term><term>Aged</term><term>Humans</term><term>Interrupted Time Series Analysis</term><term>Middle Aged</term><term>Models, Statistical</term><term>Police</term><term>SARS-CoV-2</term><term>Safety</term><term>Young Adult</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte</term><term>Adulte d'âge moyen</term><term>Analyse de série chronologique interrompue</term><term>Humains</term><term>Jeune adulte</term><term>Modèles statistiques</term><term>Police</term><term>Sujet âgé</term><term>Sécurité</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Japon</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Between March and May 2020, Japan experienced a lockdown due to the COVID-19 crisis. Empty roads possibly triggered speed-related traffic violations that caused fatal motor vehicle collisions (MVCs). Using police data on the monthly number of fatal MVCs between January 2010 and February 2020 in which motor vehicle drivers were at fault, we forecasted the numbers of fatal MVCs due to the speed-related violations during the lockdown and compared these with those observed. We also compared the observed to forecasted using the ratio of the number of speed-related fatal MVCs to that of non-speed related fatal MVCs. The observed numbers of speed-related fatal MVCs were within the 95% CIs of the forecasted numbers. The observed ratio was higher than the forecasted ratio in April (p=0.016). In the second month of the lockdown, drivers were more likely to commit speed-related violations that caused fatal MVCs than before the lockdown.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">33067222</PMID><DateCompleted><Year>2021</Year><Month>01</Month><Day>27</Day></DateCompleted><DateRevised><Year>2021</Year><Month>01</Month><Day>27</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1475-5785</ISSN><JournalIssue CitedMedium="Internet"><Volume>27</Volume><Issue>1</Issue><PubDate><Year>2021</Year><Month>02</Month></PubDate></JournalIssue><Title>Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention</Title><ISOAbbreviation>Inj Prev</ISOAbbreviation></Journal><ArticleTitle>COVID-19 lockdown and fatal motor vehicle collisions due to speed-related traffic violations in Japan: a time-series study.</ArticleTitle><Pagination><MedlinePgn>98-100</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1136/injuryprev-2020-043947</ELocationID><Abstract><AbstractText>Between March and May 2020, Japan experienced a lockdown due to the COVID-19 crisis. Empty roads possibly triggered speed-related traffic violations that caused fatal motor vehicle collisions (MVCs). Using police data on the monthly number of fatal MVCs between January 2010 and February 2020 in which motor vehicle drivers were at fault, we forecasted the numbers of fatal MVCs due to the speed-related violations during the lockdown and compared these with those observed. We also compared the observed to forecasted using the ratio of the number of speed-related fatal MVCs to that of non-speed related fatal MVCs. The observed numbers of speed-related fatal MVCs were within the 95% CIs of the forecasted numbers. The observed ratio was higher than the forecasted ratio in April (p=0.016). In the second month of the lockdown, drivers were more likely to commit speed-related violations that caused fatal MVCs than before the lockdown.</AbstractText><CopyrightInformation>© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Inada</LastName><ForeName>Haruhiko</ForeName><Initials>H</Initials><Identifier Source="ORCID">0000-0002-4743-3028</Identifier><AffiliationInfo><Affiliation>Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA hinada-tky@umin.ac.jp.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ashraf</LastName><ForeName>Lamisa</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Campbell</LastName><ForeName>Sachalee</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>10</Month><Day>16</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Inj Prev</MedlineTA><NlmUniqueID>9510056</NlmUniqueID><ISSNLinking>1353-8047</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000054" MajorTopicYN="N">Acceleration</DescriptorName><QualifierName UI="Q000009" MajorTopicYN="N">adverse effects</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000063" MajorTopicYN="N">Accidents, Traffic</DescriptorName><QualifierName UI="Q000331" MajorTopicYN="N">legislation & jurisprudence</QualifierName><QualifierName UI="Q000401" MajorTopicYN="Y">mortality</QualifierName><QualifierName UI="Q000639" MajorTopicYN="N">trends</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001334" MajorTopicYN="N">Automobile Driving</DescriptorName><QualifierName UI="Q000331" MajorTopicYN="Y">legislation & jurisprudence</QualifierName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000086382" MajorTopicYN="N">COVID-19</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D065186" MajorTopicYN="N">Interrupted Time Series Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007564" MajorTopicYN="N" Type="Geographic">Japan</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015233" MajorTopicYN="N">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018986" MajorTopicYN="N">Motor Vehicles</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016495" MajorTopicYN="N">Police</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086402" MajorTopicYN="N">SARS-CoV-2</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012449" MajorTopicYN="N">Safety</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">behaviour</Keyword><Keyword MajorTopicYN="Y">mortality</Keyword><Keyword MajorTopicYN="Y">motor vehicle � occupant</Keyword><Keyword MajorTopicYN="Y">speed</Keyword><Keyword MajorTopicYN="Y">time series</Keyword></KeywordList><CoiStatement>Competing interests: None declared.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>08</Month><Day>05</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>09</Month><Day>30</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>10</Month><Day>01</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>10</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2021</Year><Month>1</Month><Day>28</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>10</Month><Day>17</Day><Hour>5</Hour><Minute>24</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">33067222</ArticleId><ArticleId IdType="pii">injuryprev-2020-043947</ArticleId><ArticleId IdType="doi">10.1136/injuryprev-2020-043947</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Japon</li><li>États-Unis</li></country><region><li>Maryland</li></region></list><tree><country name="Japon"><noRegion><name sortKey="Inada, Haruhiko" sort="Inada, Haruhiko" uniqKey="Inada H" first="Haruhiko" last="Inada">Haruhiko Inada</name></noRegion></country><country name="États-Unis"><region name="Maryland"><name sortKey="Ashraf, Lamisa" sort="Ashraf, Lamisa" uniqKey="Ashraf L" first="Lamisa" last="Ashraf">Lamisa Ashraf</name></region><name sortKey="Campbell, Sachalee" sort="Campbell, Sachalee" uniqKey="Campbell S" first="Sachalee" last="Campbell">Sachalee 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