ArticlePreventing chronic disease2016
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.
Article in Preventing chronic disease, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Impact of the use of small-area models on estimation of attributable mortality at a regional level.European journal of public health · 2024Article
- Examining select sociodemographic characteristics of sub-county geographies for public health surveillance.Population health metrics · 2024Article
- State-Level Firearm Laws and Firearm Homicide in US Cities: Heterogenous Associations by City Characteristics.Journal of urban health : bulletin of the New York Academy of Medicine · 2024Article
- Re-scaling and small area estimation of behavioral risk survey guided by social vulnerability data.BMC public health · 2023Article
- A reanalysis: Do hog farms cause disease in North Carolina neighborhoods?Frontiers in veterinary science · 2022Article
- From the clinic to the community: Can health system data accurately estimate population obesity prevalence?Obesity (Silver Spring, Md.) · 2021Article
- Small-Area Estimation of Smoke-Free Workplace Policies and Home Rules in US Counties.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2021Article
- Mapping changes in district level prevalence of childhood stunting in India 1998-2016: An application of small area estimation techniques.SSM - population health · 2021Article
- Health and Health Determinant Metrics for Cities: A Comparison of County and City-Level Data.Preventing chronic disease · 2020Article
- Developing a surveillance system of sub-county data: Finding suitable population thresholds for geographic aggregations.Spatial and spatio-temporal epidemiology · 2020Article
- Use of Small-Area Estimates to Describe County-Level Geographic Variation in Prevalence of Extreme Obesity Among US Adults.JAMA network open · 2020Article
- Reweighting national survey data for small area behaviour estimates: modelling alcohol consumption in Local Authorities in England.Population health metrics · 2020Article
- Small Area Estimation of Cancer Risk Factors and Screening Behaviors in US Counties by Combining Two Large National Health Surveys.Preventing chronic disease · 2019Article
- Building a Data Platform for Cross-Country Urban Health Studies: the SALURBAL Study.Journal of urban health : bulletin of the New York Academy of Medicine · 2019Article
- Social media captures demographic and regional physical activity.BMJ open sport & exercise medicine · 2019Article
- Estimating missing values in China's official socioeconomic statistics using progressive spatiotemporal Bayesian hierarchical modeling.Scientific reports · 2018Article
- Sub-County Life Expectancy: A Tool to Improve Community Health and Advance Health Equity.Preventing chronic disease · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
introductionKing 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).
methodsWe 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.
resultsDirect 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.
conclusionThe 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.
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