We examined the factors associated with screening mammography adherence behaviors and influencing factors on women's attitudes toward mammography in non-adherent women. Design-based logistic regression models were developed to characterize the influencing factors, including socio-demographic, health related, behavioral characteristics, and knowledge of breast cancer/mammography, on women's compliance with and attitudes toward mammography using the 2003 Health Information National Trends Survey data. Findings indicate significant associations among adherence to mammography and marital status, income, health coverage, being advised by a doctor to have a mammogram, having had Pap smear before, perception of chance of getting breast cancer, and knowledge of mammography (frequency of doing mammogram) in both women younger than 65 and women aged 65 and older. However, number of visits to a healthcare provider per year and lifetime number of smoked cigarettes are only significant for women younger than 65. Factors significantly associated with attitudes toward mammography in non-adherent women are age, being advised by a doctor to have a mammogram, and seeking cancer information. To enhance adherence to mammography programs, physicians need to continue to advise their patients to obtain mammograms. In addition, increasing women's knowledge about the frequency and starting age for screening mammography may improve women's adherence. Financially related factors such as income and insurance are also shown to be significant factors. Hence, healthcare policies aimed at providing breast cancer screening services to underserved women will likely enhance mammography participation.

The objective of this paper is to model the impact of comorbidity on breast cancer patient outcomes (e.g., length of stay and disposition). Previous studies suggest that comorbidities may significantly affect mortality risks for breast cancer patients. The 2006 AHRQ Nationwide Inpatient Sample (NIS) is used to analyze the relationships among comorbidities (e.g., hypertension, diabetes, obesity, and mental disorder), total charges, length of stay, and patient disposition as a function of age and race. A multifaceted approach is used to quantify these relationships. A causal study is performed to explore the effect of various comorbidities on patient outcomes. Least squares regression models are developed to evaluate and compare significant factors that influence total charges and length of stay. Logistic regression is used to study the factors that may cause patient mortality or transferring. In addition, different survival models are developed to study the impact of comorbidity on length of stay with censoring information. This study shows the interactions and relationship among various comorbidities and breast cancer. It shows that certain hypertension may not increase length of stay and total charges; diabetes behaves differently among general population and breast cancer patients; mental disorder has an impact on patient disposition that affects true length of stay and charges, and obesity may have limited effect on patient outcomes. Moreover, this study will help to better understand the expenditure patterns for population subgroups with several chronic conditions and to quantify the impact of comorbidities on patient outcomes. Lastly, it also provides insight for breast cancer patients with comorbidities as a function of age and race.

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The effect of breast density on survival outcomes for American women who participate in screening remains unknown. We studied the role of breast density on both breast cancer and other cause of mortality in screened women. Data for women with breast cancer, identified from the community-based Carolina Mammography Registry, were linked with the North Carolina cancer registry and NC death tapes for this study. Cause-specific Cox proportional hazards models were developed to analyze the effect of several covariates on breast cancer mortality-namely, age, race (African American/White), cancer stage at diagnosis (in situ, local, regional, and distant), and breast density (BI-RADS( ® ) 1-4). Two stratified Cox models were considered controlling for (1) age and race, and (2) age and cancer stage, respectively, to further study the effect of density. The cumulative incidence function with confidence interval approximation was used to quantify mortality probabilities over time. For this study, 22,597 screened women were identified as having breast cancer. The non-stratified and stratified Cox models showed no significant statistical difference in mortality between dense tissue and fatty tissue, while controlling for other covariate effects (p value = 0.1242, 0.0717, and 0.0619 for the non-stratified, race-stratified, and cancer stage-stratified models, respectively). The cumulative mortality probability estimates showed that women with dense breast tissues did not have significantly different breast cancer mortality than women with fatty breast tissue, regardless of age (e.g., 10-year confidence interval of mortality probabilities for whites aged 60-69 white: 0.056-0.090 vs. 0.054-0.083). Aging, African American race, and advanced cancer stage were found to be significant risk factors for breast cancer mortality (hazard ratio >1.0). After controlling for cancer incidence, there was not a significant association between mammographic breast density and mortality, adjusting for the effects of age, race, and cancer stage.