Estimating county-level tobacco use and exposure in South Carolina: a spatial model-based small area estimation approach

Abstract Background The workplace and home are sources of exposure to secondhand smoke (SHS), a serious health hazard for nonsmoking adults and children. Smoke-free workplace policies and home rules protect nonsmoking individuals from SHS and help individuals who smoke to quit smoking. However, estimated population coverages of smoke-free workplace policies and home rules are not typically available at small geographic levels such as counties. Model-based small area estimation techniques are needed to produce such estimates. Methods Self-reported smoke-free workplace policies and home rules data came from the 2014-2015 Tobacco Use Supplement to the Current Population Survey. County-level design-based estimates of the two measures were computed and linked to county-level relevant covariates obtained from external sources. Hierarchical Bayesian models were then built and implemented through Markov Chain Monte Carlo methods. Results Model-based estimates of smoke-free workplace policies and home rules were produced for 3,134 (out of 3,143) U.S. counties. In 2014-2015, nearly 80% of U.S. adult workers were covered by smoke-free workplace policies, and more than 85% of U.S. adults were covered by smoke-free home rules. We found large variations within and between states in the coverage of smoke-free workplace policies and home rules. Conclusions The small-area modeling approach efficiently reduced the variability that was attributable to small sample size in the direct estimates for counties with data and predicted estimates for counties without data by borrowing strength from covariates and other counties with similar profiles. The county-level modeled estimates can serve as a useful resource for tobacco control research and intervention. Implications Detailed county- and state-level estimates of smoke-free workplace policies and home rules can help identify coverage disparities and differential impact of smoke-free legislation and related social norms. Moreover, this estimation framework can be useful for modeling different tobacco control variables and applied elsewhere, e.g., to other behavioral, policy, or health related topics.

Download Full-text

Estimation of Provincial Tobacco Use in Thailand Using Multilevel Small Area Estimation from a National Probability Sample.

International Journal of Epidemiology ◽

10.1093/ije/dyv096.244 ◽

2015 ◽

Vol 44 (suppl_1) ◽

pp. i173-i173

Author(s):

J. Hsia ◽

X. Zhang ◽

H. Puckcharern

Keyword(s):

Tobacco Use ◽

Small Area ◽

Small Area Estimation ◽

Area Estimation ◽

Probability Sample ◽

National Probability Sample

Download Full-text

Logit-Normal Spatial Model for Small Area Estimation: Case Study of Poverty in Bengkulu

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/187/1/012051 ◽

2018 ◽

Vol 187 ◽

pp. 012051

Author(s):

E Sunandi

Keyword(s):

Spatial Model ◽

Small Area ◽

Small Area Estimation ◽

Area Estimation

Download Full-text

Hierarchical Bayesian models for small area estimation of county-level private forest landowner population

Canadian Journal of Forest Research ◽

10.1139/cjfr-2017-0154 ◽

2017 ◽

Vol 47 (12) ◽

pp. 1577-1589 ◽

Cited By ~ 1

Author(s):

Neil R. Ver Planck ◽

Andrew O. Finley ◽

Emily S. Huff

Keyword(s):

Population Size ◽

Small Area ◽

Mean Squared Error ◽

Small Area Estimation ◽

Model Performance ◽

Remote Sensing Data ◽

Level Population ◽

County Level ◽

Private Forest ◽

Area Estimation

The National Woodland Owner Survey (NWOS), administered by the USDA Forest Service, provides estimates of private forest ownership characteristics and owners’ attitudes and behaviors at a national, regional, and state levels. Due to sample sizes prescribed for inference at the state level, there are insufficient data to support county-level estimates. However, county-level estimates of NWOS variables are desired because ownership programs and education initiatives often occur at the county level and such information could help tailor these efforts to better match county-specific needs and demographics. Here, we present and assess methods to estimate the number of private forest ownerships at the county level for two states, Montana and New Jersey. To assess model performance, true population parameters were derived from cadastral and remote sensing data. Two small area estimation (SAE) models, the Fay-Herriot (FH) and the FH with conditional autoregressive random effects (FHCAR), improved estimated county-level population mean squared error (MSE) over that achieved by direct estimates. The proposed SAE models use covariates to improve accuracy and precision of county-level estimates. Results show total forest area, and 2010 decennial census population density covariates explained a significant portion of variability in county-level population size. These and other results suggest that the proposed SAE methods yield a statistically robust approach to deliver reliable estimates of private ownership population size and could be extended to additional important NWOS variables at the county level.

Download Full-text

Small Area Model-Based Estimators Using Big Data Sources

Journal of Official Statistics ◽

10.1515/jos-2015-0017 ◽

2015 ◽

Vol 31 (2) ◽

pp. 263-281 ◽

Cited By ~ 31

Author(s):

Stefano Marchetti ◽

Caterina Giusti ◽

Monica Pratesi ◽

Nicola Salvati ◽

Fosca Giannotti ◽

...

Keyword(s):

Data Mining ◽

Big Data ◽

Small Area ◽

Small Area Estimation ◽

The Body ◽

Statistical Research ◽

Social Data ◽

Area Estimation ◽

Model Based ◽

Social Data Mining

Abstract The timely, accurate monitoring of social indicators, such as poverty or inequality, on a finegrained spatial and temporal scale is a crucial tool for understanding social phenomena and policymaking, but poses a great challenge to official statistics. This article argues that an interdisciplinary approach, combining the body of statistical research in small area estimation with the body of research in social data mining based on Big Data, can provide novel means to tackle this problem successfully. Big Data derived from the digital crumbs that humans leave behind in their daily activities are in fact providing ever more accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential to provide a novel microscope through which to view and understand social complexity. This article suggests three ways to use Big Data together with small area estimation techniques, and shows how Big Data has the potential to mirror aspects of well-being and other socioeconomic phenomena.

Download Full-text