Clinic accessibility and clinic-level predictors of the geographic variation in 2009 pandemic influenza vaccine coverage in Montreal, Canada.
Identifieur interne : 000300 ( Main/Exploration ); précédent : 000299; suivant : 000301Clinic accessibility and clinic-level predictors of the geographic variation in 2009 pandemic influenza vaccine coverage in Montreal, Canada.
Auteurs : Katia M. Charland [États-Unis] ; Luc De Montigny ; John S. Brownstein ; David L. BuckeridgeSource :
- Influenza and other respiratory viruses [ 1750-2659 ] ; 2014.
Descripteurs français
- KwdFr :
- Accessibilité des services de santé, Adolescent, Adulte, Adulte d'âge moyen, Canada (épidémiologie), Caractéristiques de l'habitat (), Enfant, Enfant d'âge préscolaire, Femelle, Grippe humaine (), Grippe humaine (épidémiologie), Géographie, Humains, Jeune adulte, Mâle, Nourrisson, Pandémies, Sous-type H1N1 du virus de la grippe A (immunologie), Sujet âgé, Sujet âgé de 80 ans ou plus, Vaccination, Vaccins antigrippaux (administration et posologie), Établissements de soins ambulatoires ().
- MESH :
- administration et posologie : Vaccins antigrippaux.
- immunologie : Sous-type H1N1 du virus de la grippe A.
- épidémiologie : Canada, Grippe humaine.
- Accessibilité des services de santé, Adolescent, Adulte, Adulte d'âge moyen, Caractéristiques de l'habitat, Enfant, Enfant d'âge préscolaire, Femelle, Grippe humaine, Géographie, Humains, Jeune adulte, Mâle, Nourrisson, Pandémies, Sujet âgé, Sujet âgé de 80 ans ou plus, Vaccination, Établissements de soins ambulatoires.
- Wicri :
- geographic : Canada.
English descriptors
- KwdEn :
- Adolescent, Adult, Aged, Aged, 80 and over, Ambulatory Care Facilities (statistics & numerical data), Canada (epidemiology), Child, Child, Preschool, Female, Geography, Health Services Accessibility, Humans, Infant, Influenza A Virus, H1N1 Subtype (immunology), Influenza Vaccines (administration & dosage), Influenza, Human (epidemiology), Influenza, Human (prevention & control), Male, Middle Aged, Pandemics, Residence Characteristics (statistics & numerical data), Vaccination, Young Adult.
- MESH :
- chemical , administration & dosage : Influenza Vaccines.
- geographic , epidemiology : Canada.
- epidemiology : Influenza, Human.
- immunology : Influenza A Virus, H1N1 Subtype.
- prevention & control : Influenza, Human.
- statistics & numerical data : Ambulatory Care Facilities, Residence Characteristics.
- Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Female, Geography, Health Services Accessibility, Humans, Infant, Male, Middle Aged, Pandemics, Vaccination, Young Adult.
Abstract
BACKGROUND
Nineteen mass vaccination clinics were established in Montreal, Canada, as part of the 2009 influenza A/H1N1p vaccination campaign. Although approximately 50% of the population was vaccinated, there was a considerable variation in clinic performance and community vaccine coverage.
OBJECTIVE
To identify community- and clinic-level predictors of vaccine uptake, while accounting for the accessibility of clinics from the community of residence.
METHODS
All records of influenza A/H1N1p vaccinations administered in Montreal were obtained from a vaccine registry. Multivariable regression models, specifically Bayesian gravity models, were used to assess the relationship between vaccination rates and clinic accessibility, clinic-level factors, and community-level factors.
RESULTS
Relative risks compare the vaccination rates at the variable's upper quartile to the lower quartile. All else being equal, clinics in areas with high violent crime rates, high residential density, and high levels of material deprivation tended to perform poorly (adjusted relative risk [ARR]: 0·917, 95% CI [credible interval]: 0·915, 0·918; ARR: 0·663, 95% CI: 0·660, 0·666, ARR: 0·649, 95% CI: 0·645, 0·654, respectively). Even after controlling for accessibility and clinic-level predictors, communities with a greater proportion of new immigrants and families living below the poverty level tended to have lower rates (ARR: 0·936, 95% CI: 0·913, 0·959; ARR: 0·918, 95% CI: 0·893, 0·946, respectively), while communities with a higher proportion speaking English or French tended to have higher rates (ARR: 1·034, 95% CI: 1·012, 1·059).
CONCLUSION
In planning future mass vaccination campaigns, the gravity model could be used to compare expected vaccine uptake for different clinic location strategies.
DOI: 10.1111/irv.12227
PubMed: 24382000
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Although approximately 50% of the population was vaccinated, there was a considerable variation in clinic performance and community vaccine coverage.</p></div><div type="abstract" xml:lang="en"><p><b>OBJECTIVE</b></p><p>To identify community- and clinic-level predictors of vaccine uptake, while accounting for the accessibility of clinics from the community of residence.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>All records of influenza A/H1N1p vaccinations administered in Montreal were obtained from a vaccine registry. Multivariable regression models, specifically Bayesian gravity models, were used to assess the relationship between vaccination rates and clinic accessibility, clinic-level factors, and community-level factors.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>Relative risks compare the vaccination rates at the variable's upper quartile to the lower quartile. All else being equal, clinics in areas with high violent crime rates, high residential density, and high levels of material deprivation tended to perform poorly (adjusted relative risk [ARR]: 0·917, 95% CI [credible interval]: 0·915, 0·918; ARR: 0·663, 95% CI: 0·660, 0·666, ARR: 0·649, 95% CI: 0·645, 0·654, respectively). Even after controlling for accessibility and clinic-level predictors, communities with a greater proportion of new immigrants and families living below the poverty level tended to have lower rates (ARR: 0·936, 95% CI: 0·913, 0·959; ARR: 0·918, 95% CI: 0·893, 0·946, respectively), while communities with a higher proportion speaking English or French tended to have higher rates (ARR: 1·034, 95% CI: 1·012, 1·059).</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSION</b></p><p>In planning future mass vaccination campaigns, the gravity model could be used to compare expected vaccine uptake for different clinic location strategies.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">24382000</PMID><DateCompleted><Year>2014</Year><Month>12</Month><Day>01</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1750-2659</ISSN><JournalIssue CitedMedium="Internet"><Volume>8</Volume><Issue>3</Issue><PubDate><Year>2014</Year><Month>May</Month></PubDate></JournalIssue><Title>Influenza and other respiratory viruses</Title><ISOAbbreviation>Influenza Other Respir Viruses</ISOAbbreviation></Journal><ArticleTitle>Clinic accessibility and clinic-level predictors of the geographic variation in 2009 pandemic influenza vaccine coverage in Montreal, Canada.</ArticleTitle><Pagination><MedlinePgn>317-28</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1111/irv.12227</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Nineteen mass vaccination clinics were established in Montreal, Canada, as part of the 2009 influenza A/H1N1p vaccination campaign. Although approximately 50% of the population was vaccinated, there was a considerable variation in clinic performance and community vaccine coverage.</AbstractText><AbstractText Label="OBJECTIVE" NlmCategory="OBJECTIVE">To identify community- and clinic-level predictors of vaccine uptake, while accounting for the accessibility of clinics from the community of residence.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">All records of influenza A/H1N1p vaccinations administered in Montreal were obtained from a vaccine registry. Multivariable regression models, specifically Bayesian gravity models, were used to assess the relationship between vaccination rates and clinic accessibility, clinic-level factors, and community-level factors.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Relative risks compare the vaccination rates at the variable's upper quartile to the lower quartile. All else being equal, clinics in areas with high violent crime rates, high residential density, and high levels of material deprivation tended to perform poorly (adjusted relative risk [ARR]: 0·917, 95% CI [credible interval]: 0·915, 0·918; ARR: 0·663, 95% CI: 0·660, 0·666, ARR: 0·649, 95% CI: 0·645, 0·654, respectively). Even after controlling for accessibility and clinic-level predictors, communities with a greater proportion of new immigrants and families living below the poverty level tended to have lower rates (ARR: 0·936, 95% CI: 0·913, 0·959; ARR: 0·918, 95% CI: 0·893, 0·946, respectively), while communities with a higher proportion speaking English or French tended to have higher rates (ARR: 1·034, 95% CI: 1·012, 1·059).</AbstractText><AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">In planning future mass vaccination campaigns, the gravity model could be used to compare expected vaccine uptake for different clinic location strategies.</AbstractText><CopyrightInformation>© 2014 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Charland</LastName><ForeName>Katia M</ForeName><Initials>KM</Initials><AffiliationInfo><Affiliation>Surveillance Lab, McGill Clinical and Health Informatics, McGill University, Montreal, QC, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada; Children's Hospital Informatics Program, Children's Hospital Boston, Boston, MA, USA; Division of General Pediatrics, Department of Medicine, Children's Hospital Boston, Harvard Medical School, Boston, MA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>de Montigny</LastName><ForeName>Luc</ForeName><Initials>L</Initials></Author><Author ValidYN="Y"><LastName>Brownstein</LastName><ForeName>John S</ForeName><Initials>JS</Initials></Author><Author ValidYN="Y"><LastName>Buckeridge</LastName><ForeName>David L</ForeName><Initials>DL</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>01</Month><Day>02</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Influenza Other Respir Viruses</MedlineTA><NlmUniqueID>101304007</NlmUniqueID><ISSNLinking>1750-2640</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D007252">Influenza Vaccines</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000293" MajorTopicYN="N">Adolescent</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000369" MajorTopicYN="N">Aged, 80 and over</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000554" MajorTopicYN="N">Ambulatory Care Facilities</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D002170" MajorTopicYN="N" Type="Geographic">Canada</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D002648" MajorTopicYN="N">Child</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002675" MajorTopicYN="N">Child, Preschool</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005843" MajorTopicYN="N">Geography</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006297" MajorTopicYN="Y">Health Services Accessibility</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007223" MajorTopicYN="N">Infant</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D053118" MajorTopicYN="N">Influenza A Virus, H1N1 Subtype</DescriptorName><QualifierName UI="Q000276" MajorTopicYN="N">immunology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D007252" MajorTopicYN="N">Influenza Vaccines</DescriptorName><QualifierName UI="Q000008" MajorTopicYN="Y">administration & dosage</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000517" MajorTopicYN="Y">prevention & control</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012111" MajorTopicYN="N">Residence Characteristics</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014611" MajorTopicYN="N">Vaccination</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Influenza vaccine</Keyword><Keyword MajorTopicYN="N">mass vaccination</Keyword><Keyword MajorTopicYN="N">public 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sort="Brownstein, John S" uniqKey="Brownstein J" first="John S" last="Brownstein">John S. Brownstein</name><name sortKey="Buckeridge, David L" sort="Buckeridge, David L" uniqKey="Buckeridge D" first="David L" last="Buckeridge">David L. Buckeridge</name><name sortKey="De Montigny, Luc" sort="De Montigny, Luc" uniqKey="De Montigny L" first="Luc" last="De Montigny">Luc De Montigny</name></noCountry><country name="États-Unis"><region name="Massachusetts"><name sortKey="Charland, Katia M" sort="Charland, Katia M" uniqKey="Charland K" first="Katia M" last="Charland">Katia M. Charland</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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