tetano
Editor, Senior Moderator
Am J Infect Control. 2015 Jan 27. pii: S0196-6553(14)01364-9. doi: 10.1016/j.ajic.2014.11.022. [Epub ahead of print]
[h=1]Geospatial patterns in influenza vaccination: Evidence from uninsured and publicly insured children in North Carolina.[/h] Trogdon JG[SUP]1[/SUP], Ahn T[SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]BACKGROUND:[/h] The purpose of this study was to explore geospatial patterns in influenza vaccination.
[h=4]METHODS:[/h] We conducted an ecological analysis of publicly funded influenza vaccinations at the ZIP code tabulation area (ZCTA) level using secondary data for publicly funded influenza vaccinations among eligible school-aged children (age range, 5-17 years) for the 2010-2011 and 2011-2012 influenza seasons from the North Carolina Immunization Registry (NCIR). NCIR data were merged by ZCTA with other publicly available data. We tested for spatial autocorrelation in unadjusted influenza vaccination rates using choropleth maps and Moran's I. We estimated nonspatial and spatial negative binomial models with spatially correlated random effects adjusted for demographic, economic, and health care variables. The study was conducted at the University of North Carolina at Chapel Hill in the spring of 2014.
[h=4]RESULTS:[/h] The NCIR demonstrated spatial autocorrelation in publicly funded influenza vaccinations among uninsured and means-tested, publicly insured school-aged children; ZCTAs tended to have influenza vaccination rates that were similar to their neighbors. This result was partially explained by included ZCTA characteristics, but not wholly.
[h=4]CONCLUSION:[/h] To the extent that the geospatial clustering of vaccination rates is the result of social influences, targeting interventions to increase influenza vaccination among school-aged children in one area could also lead to increases in neighboring areas.
Copyright ? 2015 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.
[h=4]KEYWORDS:[/h] Influenza; Spatial analysis; Vaccination
PMID: 25637432 [PubMed - as supplied by publisher]
[h=1]Geospatial patterns in influenza vaccination: Evidence from uninsured and publicly insured children in North Carolina.[/h] Trogdon JG[SUP]1[/SUP], Ahn T[SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]BACKGROUND:[/h] The purpose of this study was to explore geospatial patterns in influenza vaccination.
[h=4]METHODS:[/h] We conducted an ecological analysis of publicly funded influenza vaccinations at the ZIP code tabulation area (ZCTA) level using secondary data for publicly funded influenza vaccinations among eligible school-aged children (age range, 5-17 years) for the 2010-2011 and 2011-2012 influenza seasons from the North Carolina Immunization Registry (NCIR). NCIR data were merged by ZCTA with other publicly available data. We tested for spatial autocorrelation in unadjusted influenza vaccination rates using choropleth maps and Moran's I. We estimated nonspatial and spatial negative binomial models with spatially correlated random effects adjusted for demographic, economic, and health care variables. The study was conducted at the University of North Carolina at Chapel Hill in the spring of 2014.
[h=4]RESULTS:[/h] The NCIR demonstrated spatial autocorrelation in publicly funded influenza vaccinations among uninsured and means-tested, publicly insured school-aged children; ZCTAs tended to have influenza vaccination rates that were similar to their neighbors. This result was partially explained by included ZCTA characteristics, but not wholly.
[h=4]CONCLUSION:[/h] To the extent that the geospatial clustering of vaccination rates is the result of social influences, targeting interventions to increase influenza vaccination among school-aged children in one area could also lead to increases in neighboring areas.
Copyright ? 2015 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.
[h=4]KEYWORDS:[/h] Influenza; Spatial analysis; Vaccination
PMID: 25637432 [PubMed - as supplied by publisher]