Annie Gjelsvik1, Michelle L Rogers2, Melissa A Clark3, Hernando C Ombao4, William Rakowski5. 1. Brown University School of Public Health, Department of Epidemiology, Providence, RI, USA. annie_gjelsvik@brown.edu. 2. Brown University School of Public Health, Center for Population Health and Clinical Epidemiology, Providence, RI, USA. 3. Brown University School of Public Health, Department of Epidemiology, Providence, RI, USA. 4. University of California at Irvine, Department of Statistics, Irvine, CA, USA. 5. Brown University School of Public Health, Department of Behavioral and Social Sciences, Providence, RI, USA.
Abstract
OBJECTIVES: To identify women with low mammography utilization. METHODS: We used Classification Tree Analysis among women aged 42-80 from the 2008 Behavioral Risk Factor Surveillance System (N = 169,427) to identify sub-groups along a continuum of screening. RESULTS: Women with neither a primary care provider nor health insurance had the lowest utilization (33.9%) and were 2.8% of the sample. Non-smoking women aged 55-80, with a primary care provider, health insurance, and income of $75,000 or more had the highest utilization (90.7%) and comprised 5% of the sample. CONCLUSION: As access to primary care providers and health insurance increases with the Affordable Care act, classification tree analyses may help to identify women of high priority for intervention.
OBJECTIVES: To identify women with low mammography utilization. METHODS: We used Classification Tree Analysis among women aged 42-80 from the 2008 Behavioral Risk Factor Surveillance System (N = 169,427) to identify sub-groups along a continuum of screening. RESULTS:Women with neither a primary care provider nor health insurance had the lowest utilization (33.9%) and were 2.8% of the sample. Non-smoking women aged 55-80, with a primary care provider, health insurance, and income of $75,000 or more had the highest utilization (90.7%) and comprised 5% of the sample. CONCLUSION: As access to primary care providers and health insurance increases with the Affordable Care act, classification tree analyses may help to identify women of high priority for intervention.
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