Jaeyoung Kim1, Soong Nang Jang. 1. Department of Environmental Health, Harvard School of Public Health, USA.
Abstract
OBJECTIVES: This study describes trends in the socioeconomic disparities in breast cancer screening among US women aged 40 or over, from 2000 to 2005. We assessed 1) the disparities in each socioeconomic dimension; 2) the changes in screening mammography rates over time according to income, education, and race; and 3) the sizes and trends of the disparities over time. METHODS: Using data from the Behavioral Risk Factor Surveillance System (BRFSS) from 2000 to 2005, we calculated the age-adjusted screening rate according to relative household income, education level, health insurance, and race. Odds ratios and the relative inequality index (RII) were also calculated, controlling for age. RESULTS: Women in their 40s and those with lower relative incomes were less likely to undergo screening mammography. The disparity based on relative income was greater than that based on education or race (the RII among low-income women across the survey years was 3.00 to 3.48). The overall participation rate and absolute differences among socioeconomic groups changed little or decreased slightly across the survey years. However, the degree of each socioeconomic disparity and the relative inequality among socioeconomic positions remained quite consistent. CONCLUSIONS: These findings suggest that the trend of the disparity in breast cancer screening varied by socioeconomic dimension. Continued differences in breast cancer screening rates related to income level should be considered in future efforts to decrease the disparities in breast cancer among socioeconomic groups. More focused interventions, as well as the monitoring of trends in cancer screening participation by income and education, are needed in different social settings.
OBJECTIVES: This study describes trends in the socioeconomic disparities in breast cancer screening among US women aged 40 or over, from 2000 to 2005. We assessed 1) the disparities in each socioeconomic dimension; 2) the changes in screening mammography rates over time according to income, education, and race; and 3) the sizes and trends of the disparities over time. METHODS: Using data from the Behavioral Risk Factor Surveillance System (BRFSS) from 2000 to 2005, we calculated the age-adjusted screening rate according to relative household income, education level, health insurance, and race. Odds ratios and the relative inequality index (RII) were also calculated, controlling for age. RESULTS:Women in their 40s and those with lower relative incomes were less likely to undergo screening mammography. The disparity based on relative income was greater than that based on education or race (the RII among low-income women across the survey years was 3.00 to 3.48). The overall participation rate and absolute differences among socioeconomic groups changed little or decreased slightly across the survey years. However, the degree of each socioeconomic disparity and the relative inequality among socioeconomic positions remained quite consistent. CONCLUSIONS: These findings suggest that the trend of the disparity in breast cancer screening varied by socioeconomic dimension. Continued differences in breast cancer screening rates related to income level should be considered in future efforts to decrease the disparities in breast cancer among socioeconomic groups. More focused interventions, as well as the monitoring of trends in cancer screening participation by income and education, are needed in different social settings.
Authors: Phyllis Brawarsky; Bridget A Neville; Garrett M Fitzmaurice; Michael J Hassett; Jennifer S Haas Journal: J Gen Intern Med Date: 2011-10-18 Impact factor: 5.128
Authors: Kevin A Henry; Kaila McDonald; Recinda Sherman; Anita Y Kinney; Antoinette M Stroup Journal: J Womens Health (Larchmt) Date: 2014-05-27 Impact factor: 2.681
Authors: Kristen J Wells; John S Luque; Branko Miladinovic; Natalia Vargas; Yasmin Asvat; Richard G Roetzheim; Ambuj Kumar Journal: Cancer Epidemiol Biomarkers Prev Date: 2011-06-08 Impact factor: 4.254
Authors: Antoinette M Stroup; Kimberly A Herget; Heidi A Hanson; Diana Lane Reed; Jared T Butler; Kevin A Henry; C Janna Harrell; Carol Sweeney; Ken R Smith Journal: Cancer Epidemiol Biomarkers Prev Date: 2016-09-21 Impact factor: 4.254
Authors: Barbara A Berman; Angela Jo; William G Cumberland; Heidi Booth; Jon Britt; Carolyn Stern; Philip Zazove; Gary Kaufman; Georgia Robins Sadler; Roshan Bastani Journal: Disabil Health J Date: 2013-06-30 Impact factor: 2.554
Authors: Jennifer S Haas; Deirdre A Hill; Robert D Wellman; Rebecca A Hubbard; Christoph I Lee; Karen J Wernli; Natasha K Stout; Anna N A Tosteson; Louise M Henderson; Jennifer A Alford-Teaster; Tracy L Onega Journal: Cancer Date: 2015-12-28 Impact factor: 6.860
Authors: Elena B Elkin; J Paige Nobles; Laura C Pinheiro; Coral L Atoria; Deborah Schrag Journal: Cancer Causes Control Date: 2013-03-07 Impact factor: 2.506
Authors: Jada G Hamilton; Nancy Breen; Carrie N Klabunde; Richard P Moser; Bryan Leyva; Erica S Breslau; Sarah C Kobrin Journal: Cancer Epidemiol Biomarkers Prev Date: 2014-10-09 Impact factor: 4.254