Saleh M M Rahman1, Mark B Dignan, Brent J Shelton. 1. Department of Public and Allied Health, Bowling Green State University, Bowling Green, Ohio 43403, USA. srahman@bgnet.bgsu.edu
Abstract
OBJECTIVE: To explore patterns of adherence to guidelines for screening mammography among participants in the Colorado Mammography Project (CMAP) surveillance database. METHODS: An algorithm was developed to assess factors associated with adherence to mammography screening guidelines. RESULTS: Of the 27,778 women ranging from 40-90 years of age included in the analysis, 41.4% were adherent with mammography screening guidelines. According to the model tested in this study, race/ethnicity (Black vs White, OR=0.76, 95% CI=0.64-0.91); educational attainment (high school vs <high school, OR=1.10, 95% CI=1.04-1.18, college graduate vs <high school OR=1.33, 95% CI=1.25-1.42); insurance status, (any coverage vs no coverage, OR=1.62, 95% CI=1.25-2.12); and community economic status as defined by median income by zip code of residence (15,000 dollars-24,999 dollars vs <15,000 dollars, OR=0.84, 95% CI=0.76-0.94, >55,000 dollars vs <15,000 dollars, OR 1.14, 95% CI=1.03-1.26) were statistically significant predictors of adherence to guidelines. A significant interaction between age and family history of breast cancer (BC) was also found. Younger females with a family history of BC were less likely to be adherent than their counterparts without a family history (OR=0.93, 95% CI=0.90-0.96). In general, elderly women were more likely to be adherent compared with the youngest group in this cohort (OR=1.21, 95% CI=1.11-1.33). Inclusion or exclusion of women aged 70 years and older did not change the outcome of the analysis. CONCLUSION: Adherence with screening mammography guidelines was found to be associated with women's personal characteristics including race/ethnicity, age, and family history of BC. In addition, socioeconomic status, as measured by educational level and community economic status, are important predictors of adherence. Efforts to increase adherence may need to be specific to race/ethnic group and age, but the effect of age is mediated by family history of BC and vice versa.
OBJECTIVE: To explore patterns of adherence to guidelines for screening mammography among participants in the Colorado Mammography Project (CMAP) surveillance database. METHODS: An algorithm was developed to assess factors associated with adherence to mammography screening guidelines. RESULTS: Of the 27,778 women ranging from 40-90 years of age included in the analysis, 41.4% were adherent with mammography screening guidelines. According to the model tested in this study, race/ethnicity (Black vs White, OR=0.76, 95% CI=0.64-0.91); educational attainment (high school vs <high school, OR=1.10, 95% CI=1.04-1.18, college graduate vs <high school OR=1.33, 95% CI=1.25-1.42); insurance status, (any coverage vs no coverage, OR=1.62, 95% CI=1.25-2.12); and community economic status as defined by median income by zip code of residence (15,000 dollars-24,999 dollars vs <15,000 dollars, OR=0.84, 95% CI=0.76-0.94, >55,000 dollars vs <15,000 dollars, OR 1.14, 95% CI=1.03-1.26) were statistically significant predictors of adherence to guidelines. A significant interaction between age and family history of breast cancer (BC) was also found. Younger females with a family history of BC were less likely to be adherent than their counterparts without a family history (OR=0.93, 95% CI=0.90-0.96). In general, elderly women were more likely to be adherent compared with the youngest group in this cohort (OR=1.21, 95% CI=1.11-1.33). Inclusion or exclusion of women aged 70 years and older did not change the outcome of the analysis. CONCLUSION: Adherence with screening mammography guidelines was found to be associated with women's personal characteristics including race/ethnicity, age, and family history of BC. In addition, socioeconomic status, as measured by educational level and community economic status, are important predictors of adherence. Efforts to increase adherence may need to be specific to race/ethnic group and age, but the effect of age is mediated by family history of BC and vice versa.
Authors: Yelena Bird; John Moraros; Matthew P Banegas; Sasha King; Surasri Prapasiri; Beti Thompson Journal: J Health Care Poor Underserved Date: 2010-05
Authors: Kenyon C Bolton; John L Mace; Pamela M Vacek; Sally D Herschorn; Ted A James; Jeffrey A Tice; Karla Kerlikowske; Berta M Geller; Donald L Weaver; Brian L Sprague Journal: J Natl Cancer Inst Date: 2014-06-23 Impact factor: 13.506
Authors: Ami Vyas; Suresh Madhavan; Traci LeMasters; Elvonna Atkins; Sara Gainor; Stephenie Kennedy; Kimberly Kelly; Linda Vona-Davis; Scot Remick Journal: J Community Health Date: 2012-06
Authors: Tracy Onega; Tor D Tosteson; Julie Weiss; Jennifer S Haas; Martha Goodrich; Roberta DiFlorio; Charles Brackett; Cheryl Clark; Kimberly Harris; Anna N A Tosteson Journal: J Gen Intern Med Date: 2018-08-03 Impact factor: 5.128
Authors: Sandi L Pruitt; Matthew J Shim; Patricia Dolan Mullen; Sally W Vernon; Benjamin C Amick Journal: Cancer Epidemiol Biomarkers Prev Date: 2009-10 Impact factor: 4.254
Authors: Jennifer M Gierisch; Suzanne C O'Neill; Barbara K Rimer; Jessica T DeFrank; J Michael Bowling; Celette Sugg Skinner Journal: Cancer Epidemiol Date: 2009-05-29 Impact factor: 2.984