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Cancer Epidemiol Biomarkers Prev. 2016 Oct;25(10):1402-1410.

Multilevel Small-Area Estimation of Multiple Cigarette Smoking Status Categories Using the 2012 Behavioral Risk Factor Surveillance System.

Author information

1
National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC), Chamblee, Georgia. zab3@cdc.gov.
2
National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC), Chamblee, Georgia.

Abstract

BACKGROUND:

Smoking is the leading preventable cause of death; however, small-area estimates for detailed smoking status are limited. We developed multilevel small-area estimate mixed models to generate county-level estimates for six smoking status categories: current, some days, every day, former, ever, and never.

METHOD:

Using 2012 Behavioral Risk Factor Surveillance System (BRFSS) data (our sample size = 405,233 persons), we constructed and fitted a series of multilevel logistic regression models and applied them to the U.S. Census population to generate county-level prevalence estimates. We mapped the estimates by sex and aggregated them into state and national estimates. We conducted comparisons for internal consistency with BRFSS states' estimates using Pearson correlation coefficients, and external validation with the 2012 National Health Interview Survey current smoking prevalence.

RESULTS:

Correlation coefficients ranged from 0.908 to 0.982, indicating high internal consistency. External validation indicated complete agreement (prevalence = 18.06%). We found large variations in current and former smoking status between and within states and by sex. County prevalence of former smokers was highest among men in the Northeast, North, and West. Utah consistently had the lowest smoking prevalence.

CONCLUSIONS:

Our models, which include demographic and geographic characteristics, provide reliable estimates that can be applied to multiple category outcomes and any demographic group. County and state estimates may help understand the variation in smoking prevalence in the United States and provide information for control and prevention.

IMPACT:

Detailed county and state smoking category estimates can help identify areas in need of tobacco control and prevention and potentially allow planning for health care. Cancer Epidemiol Biomarkers Prev; 25(10); 1402-10. ©2016 AACR.

PMID:
27697795
DOI:
10.1158/1055-9965.EPI-16-0244
[Indexed for MEDLINE]
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