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

Details

• Journal Article:
  Preventing Chronic Disease (PCD)
• Personal Author:
  Song, Lin ; Mercer, Laina ; Wakefield, Jon ; Laurent, Amy ; Solet, David
• Description:
  Introduction
  
  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).
  
  Methods
  
  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.
  
  Results
  
  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.
  
  Conclusion
  
  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.
  
  
• Subjects:
  Adolescent Adult Aged Aged, 80 And Over Bayes Theorem Behavioral Risk Factor Surveillance System Censuses Female Humans Male Middle Aged Smoking Socioeconomic Factors Young Adult

  More +
• Source:
  Prev Chronic Dis. 13.
• DOI:
  http://dx.doi.org/10.5888/pcd13.150536
• ISSN:
  1545-1151
• Pubmed ID:
  27149070
• Pubmed Central ID:
  PMC4858449
• Document Type:
  Journal Article
• Funding:
  R01 CA095994/CA/NCI NIH HHS/United States ; T32 HD007543/HD/NICHD NIH HHS/United States
• Place as Subject:
  Washington
• Volume:
  13
• Collection(s):
  Preventing Chronic Disease
• Main Document Checksum:
  urn:sha-512:96fd55b0abc4414e37ef6f85353e04a839f9c25c78a33d78da3036b5d64539625cfc4f4fe6258bb4550f5ef381631e29b8ceaf95f5cfc08856d4d33c160a7d2d
• Download URL:
  https://stacks.cdc.gov/view/cdc/39723/cdc_39723_DS1.pdf
• File Type:
  [PDF - 437.05 KB ]