title: Using Small-Area Estimation to Calculate the Prevalence of Smoking by Subcounty Geographic Areas in King County, Washington, Behavioral Risk Factor Surveillance System, 2009-2013.
abstract: King County, Washington, fares well overall in many health indicators. However, county-level data mask disparities among subcounty areas. For disparity-focused assessment, a demand exists for examining health data at subcounty levels such as census tracts and King County health reporting areas (HRAs).
  
  
  
  We added a "nearest intersection" question to the Behavioral Risk Factor Surveillance System (BRFSS) and geocoded the data for subcounty geographic areas, including census tracts. To overcome small sample size at the census tract level, we used hierarchical Bayesian models to obtain smoothed estimates in cigarette smoking rates at the census tract and HRA levels. We also used multiple imputation to adjust for missing values in census tracts.
  
  
  
  Direct estimation of adult smoking rates at the census tract level ranged from 0% to 56% with a median of 10%. The 90% confidence interval (CI) half-width for census tract with nonzero rates ranged from 1 percentage point to 37 percentage points with a median of 13 percentage points. The smoothed-multiple-imputation rates ranged from 5% to 28% with a median of 12%. The 90% CI half-width ranged from 4 percentage points to 13 percentage points with a median of 8 percentage points.
  
  
  
  The nearest intersection question in the BRFSS provided geocoded data at subcounty levels. The Bayesian model provided estimation with improved precision at the census tract and HRA levels. Multiple imputation can be used to account for missing geographic data. Small-area estimation, which has been used for King County public health programs, has increasingly become a useful tool to meet the demand of presenting data at more granular levels.
authors.affiliation: Public Health - Seattle & King County, 501 5th Ave, Ste 1300, Seattle, WA 98104. Email: lin.song@kingcounty.gov.
authors.forename: Lin
authors.initials: L
authors.lastname: Song
authors.affiliation: Department of Statistics, University of Washington, Seattle, Washington.
authors.forename: Laina
authors.initials: L
authors.lastname: Mercer
authors.affiliation: Department of Statistics and Department of Biostatistics, University of Washington, Seattle, Washington.
authors.forename: Jon
authors.initials: J
authors.lastname: Wakefield
authors.affiliation: Public Health - Seattle & King County, Seattle, Washington.
authors.forename: Amy
authors.initials: A
authors.lastname: Laurent
authors.affiliation: Public Health - Seattle & King County, Seattle, Washington.
authors.forename: David
authors.initials: D
authors.lastname: Solet
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D040343:Behavioral Risk Factor Surveillance System; D019352:Censuses; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012907:Smoking / Q000453:epidemiology*; D012959:Socioeconomic Factors; D014861:Washington / Q000453:epidemiology; D055815:Young Adult