Posters describing the study were placed in waiting areas and women attending mammographic breast screening or diagnostic procedures at the Joanne Knight Breast Health Center, St Louis, Missouri, were approached to participate, all of them completing extended data collection for breast cancer risk estimation. The Joanne Knight Breast Health Center, at Siteman Cancer Center at Washington University School of Medicine (St. Louis, MO) screens approximately 25,000 women and does high risk and diagnostic screening for another 15,000 women per year . Women aged 18 and older attending the Breast Health Center were eligible to enroll. Less than 50% of eligible women attending for screening mammograms refused to enroll. Males were excluded, as were women with self-reported blood transfusion within the past 4 months, and self-reported HIV+, Hepatitis B or C+.
The variables needed for the Rosner-Colditz breast cancer risk prediction model (see measures below) have been routinely collected since 2010, and risk estimates are incorporated into reporting from breast health screening mammograms. Those invited to the study and agreeing were consented and then proceeded to blood draw. 20 mls of blood were drawn and aliquoted for storage at -80oC in the Siteman Tissue Procurement Core liquid nitrogen freezer system. Aliquots of white blood cells and separately plasma are stored in cryotubes.
Cohort participants consented to (1) retrospective and prospective review of medical records (including radiologic images, pathology reports, etc.); (2) one-time 20 ml blood draw; (3) access to tissue not required for clinical care (e.g., breast biopsy tissue following conclusive clinical pathology assays); and (4) optional future contact for the purposes of long-term follow-up and/or to recruit for other related research projects. Record linkage identifies new mammograms, biopsies, and other visits to BJC Health Care facilities. BJC is a nonprofit health care organization serving metro St Louis, mid-Missouri and Southern Illinois.
Enrollment from November 2008 to April 2012 included 12,153 women who provided blood and risk factor data. A survey of 158 women who opted not to enroll over a two-week period in October 2009 showed most women who didn’t participate cited a lack of time to give the blood sample (30.4%). The next largest group (19.0%) wanted more time to think about participation. The remaining reasons for not participating included not wanting to give a blood sample (8.9%), not wanting researchers to have access to their medical records (8.9%), and (13.9%) provided no answer. Women were attending for screening mammogram or a subset for diagnostic follow-up (5.4% of total cohort). Of these enrolled women, 1,672 had a history of cancer at enrolment leaving 10,481 were free from cancer at baseline.
How are they followed up?
Despite COVID adversely impacting routine medical care over many months in 2020, follow-up of cohort participants was: 78% seen in 2019 or 2020; a further 4.4% seen most recently in 2018 and a further 2.4% in 2017. All women remain under surveillance for return to follow-up mammography. Follow-up is passive through medical record linkages, tumor registry searches, and mortality searches. This results in over 80% active follow-up for women seen within the last 36 months. The average person-years of follow-up through most recent contact is 9.2 person-years.
What exposures have been measured?
At enrollment a baseline questionnaire, blood draw and mammogram were obtained along with address for follow-up and for geocoding for measures of structural inequality. Baseline blood samples taken with DNA extraction (3 aliquots of 1ml); plasma aliquots of 1 ml (6 per participant) placed into cryovials and stored at -80C in LN2 freezers.
Women self-reported breast cancer risk factors on entry to the cohort. These are drawn from established and validated measures  include: height, weight at age 18, current weight and weight at menopause; age at menarche; age at first birth, age at each subsequent birth, parity; menses ceased (yes/no), age at menopause and surgical removal of uterus, with or without removal of ovaries, and age at hysterectomy; family history of breast cancer (mother and/or sister); Ashkenazi Jewish heritage; history of benign breast biopsy; current use of hormone therapy (yes / no, and type of hormone therapy); current use of oral contraceptives (yes / no); current alcohol intake; current smoking status, cigarettes per day.
Mammograms: a screening mammogram 12 to 24 months prior to baseline; at baseline; and subsequent follow-up screening have been identified and stored. These images are stored along with BI-RADS density report recorded (a=almost entirely fat, b=scattered areas of fibroglandular density, c=heterogeneously dense, d=extremely dense). Routine screening mammograms were obtained using Hologic machines.
County-level measures of structural inequality: We summarize multiple measures of county-level structural inequality that were included for relevance to population health and health disparities. First, we include five latent factors representing multiple domains of structural inequality. The data were publicly available and previously compiled by the Health Inequality Project . They were derived using exploratory factor analysis (EFA) in SAS 9.4 (SAS Institute Inc., Cary, NC) and theory-driven choices. The factors each consist of four or five variables clustering around the following themes: racial and economic segregation; population change; opportunity for socioeconomic advancement; economic environment; and population and housing characteristics. We also include the Index of Concentration at the Extremes (ICE) for race, income, and income and race combined. These measures describe the distribution of extreme privilege and deprivation for these indicators across a specified area. Finally, we include measures of area-level debt delinquency for any debt and for medical debt since area-level indebtedness has been shown to impact household finances as well as available neighborhood-level services which has implications for neighborhood stability and subsequent health. All variables were appended to participant’s geocoded county of residence at the time of enrollment for a total of 224 unique counties.