Neighborhood attributes and cardiovascular disease risk in breast cancer survivors: The Pathways Study.

BACKGROUND: Breast cancer survivors are at a higher risk of cardiovascular disease (CVD) morbidity and mortality compared with the general population. The impact of objective social and built neighborhood attributes on CVD risk in a cohort of female breast cancer survivors was examined. METHODS: The 3975 participants came from the Pathways Study, a prospective cohort of women with invasive breast cancer from an integrated health care system in northern California. Women diagnosed with breast cancer from 2006 through 2013 were enrolled on average approximately 2 months after diagnosis. Their baseline addresses were geocoded and appended to neighborhood attributes for racial/ethnic composition, socioeconomic status (SES), population density, urbanization, crime, traffic density, street connectivity, parks, recreational facilities, and retail food environment. Incident CVD events included ischemic heart disease, heart failure, cardiomyopathy, or stroke. Cox proportional hazards models estimated associations of neighborhood attributes with CVD risk, which accounted for clustering by block groups. Fully adjusted models included sociodemographic, clinical, and behavioral factors. RESULTS: During follow-up through December 31, 2018, 340 participants (8.6%) had CVD events. A neighborhood racial/ethnic composition measure, percent of Asian American/Pacific Islander residents (lowest quintile hazard ratio [HR], 1.85; 95% CI, 1.03-3.33), and crime index (highest quartile HR, 1.48; 95% CI, 1.08-2.03) were associated with the risk of CVD events independent of individual SES, hormone receptor status, treatment, cardiometabolic comorbidities, body mass index, and physical activity. CONCLUSIONS: With the application of a socio-ecological framework, how residential environments shape health outcomes in women with breast cancer and affect CVD risk in this growing population can be understood.

Representativeness of breast cancer cases in an integrated health care delivery system.

BACKGROUND: Integrated health care delivery systems, with their comprehensive and integrated electronic medical records (EMR), are well-poised to conduct research that leverages the detailed clinical data within the EMRs. However, information regarding the representativeness of these clinical populations is limited, and thus the generalizability of research findings is uncertain. METHODS: Using data from the population-based California Cancer Registry, we compared age-adjusted distributions of patient and neighborhood characteristics for three groups of breast cancer patients: 1) those diagnosed within Kaiser Permanente Northern California (KPNC), 2) non-KPNC patients from NCI-designated cancer centers, and 3) those from all other hospitals. RESULTS: KPNC patients represented 32 % (N = 36,109); cancer center patients represented 7 % (N = 7805); and all other hospitals represented 61 % (N = 68,330) of the total breast cancer patients from this geographic area during 1996-2009. Compared with cases from all other hospitals, KPNC had slightly fewer non-Hispanic Whites (70.6 % versus 74.4 %) but more Blacks (8.1 % versus 5.0 %), slightly more patients in the 50-69 age range and fewer in the younger and older age groups, a slightly lower proportion of in situ but higher proportion of stage I disease (41.6 % versus 38.9 %), were slightly less likely to reside in the lowest (4.2 % versus 6.5 %) and highest (36.2 % versus 39.0 %) socioeconomic status neighborhoods, and more likely to live in suburban metropolitan areas and neighborhoods with more racial/ethnic minorities. Cancer center patients differed substantially from patients from KPNC and all other hospitals on all characteristics assessed. All differences were statistically significant (p < .001). CONCLUSIONS: Although much of clinical research discoveries are based in academic medical centers, patients from large, integrated medical centers are likely more representative of the underlying population, providing support for the generalizability of cancer research based on electronic data from these centers.