title: Clinic accessibility and clinic-level predictors of the geographic variation in 2009 pandemic influenza vaccine coverage in Montreal, Canada.
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).
  
  
  CONCLUSIONS
  In planning future mass vaccination campaigns, the gravity model could be used to compare expected vaccine uptake for different clinic location strategies.
authors.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.
authors.forename: Katia M
authors.initials: KM
authors.lastname: Charland
authors.forename: Luc
authors.initials: L
authors.lastname: de Montigny
authors.forename: John S
authors.initials: JS
authors.lastname: Brownstein
authors.forename: David L
authors.initials: DL
authors.lastname: Buckeridge
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000554:Ambulatory Care Facilities / Q000706:statistics & numerical data; D002170:Canada / Q000453:epidemiology; D002648:Child; D002675:Child, Preschool; D005260:Female; D005843:Geography; D006297:Health Services Accessibility; D006801:Humans; D007223:Infant; D053118:Influenza A Virus, H1N1 Subtype / Q000276:immunology; D007252:Influenza Vaccines / Q000008:administration & dosage*; D007251:Influenza, Human / Q000453:epidemiology / Q000517:prevention & control*; D008297:Male; D008875:Middle Aged; D058873:Pandemics; D012111:Residence Characteristics / Q000706:statistics & numerical data; D014611:Vaccination; D055815:Young Adult
chemical_list: D007252:Influenza Vaccines
keywords: Influenza vaccine; mass vaccination; public health