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Discrepancy statistics between aggregated state average from county estimates and state composite estimates
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... Questions were added to the BRFSS within two weeks, and reports were generated on a weekly basis for same-week data collection. Hence, the BRFSS allowed the CDC to examine adherence to its policy (provision of reserve vaccines to individuals at high risk) and to change the policy in mid season as data showed the availability of vaccines in December (10,20,34). Moreover, the BRFSS can now respond to emergencies and natural or man-made disasters by providing much needed data to plan for adequate responses (15,18). ...
The Behavioral Risk Factor Surveillance System (BRFSS) is a large state-based telephone survey. BRFSS is designed to monitor the leading risk factors for morbidity and mortality in the United States at the local, state, and national levels. The BRFSS has proven to be a powerful tool for building heath-promotion activities. However, the use of telephone-based, random-digit-dial (RDD) methods in public health surveys and surveillance is at a crossroads. Rapid changes in telecommunication, declines in participation rates, increases in the required level of effort and associated costs are becoming key challenges for BRFSS. To maintain the highest data quality and service to the local and state health departments, BRFSS has adopted an ongoing effort to improve coverage and response to the survey. This article provides an overview of the issues faced by BRFSS and the strategies in place to address them.
The purpose of this study was to explore geospatial patterns in influenza vaccination.
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.
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.
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.
We compared prevalence estimates of self-rated health (SRH) derived indirectly using four different small area estimation methods for the Vadu (small) area from the national Study on Global AGEing (SAGE) survey with estimates derived directly from the Vadu SAGE survey. The indirect synthetic estimate for Vadu was 24% whereas the model based estimates were 45.6% and 45.7% with smaller prediction errors and comparable to the direct survey estimate of 50%. The model based techniques were better suited to estimate the prevalence of SRH than the indirect synthetic method. We conclude that a simplified mixed effects regression model can produce valid small area estimates of SRH.
