Multilevel regression for small-area estimation of mammography use in the United States, 2014

Abstract

Background: The U.S. Preventive Services Task Force recommends biennial screening mammography for average-risk women aged 50–74 years. County-level information on population measures of mammography use can inform targeted intervention to reduce geographic disparities in mammography use. County-level estimates for mammography use nationwide are rarely presented. Methods: We used data from the 2014 Behavioral Risk Factor Surveillance System (BRFSS; n ¼ 130,289 women), linked it to the American Community Survey poverty data, and fitted multilevel logistic regression models with two outcomes: mammography within the past 2 years (up-to-date), and most recent mammography 5 or more years ago or never (rarely/never). We poststratified the data with U.S. Census population counts to run Monte Carlo simulations. We generated county-level estimates nationally and by urban–rural county classifications. County-level prevalence estimates were aggregated into state and national estimates. We validated internal consistency between our model-based state-specific estimates and urban–rural estimates with BRFSS direct estimates using Spearman correlation coefficients and mean absolute differences. Results: Correlation coefficients were 0.94 or larger. Mean absolute differences for the two outcomes ranged from 0.79 to 1.03. Although 78.45% (95% confidence interval, 77.95%–78.92%) of women nationally were up-to-date with mammography, more than half of the states had counties with >15% of women rarely/never using a mammogram, many in rural areas. Conclusions: We provided estimates for all U.S. counties and identified marked variations in mammography use. Many states and counties were far from the 2020 target (81.1%). Impact: Our results suggest a need for planning and resource allocation on a local level to increase mammography uptake.