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Background: Women with elevated mammographic density have an increased risk of developing breast cancer. However, among women diagnosed with breast cancer, it is unclear whether higher density portends reduced survival, independent of other factors., Methods: We evaluated relationships between mammographic density and risk of death from breast cancer and all causes within the US Breast Cancer Surveillance Consortium. We studied 9232 women diagnosed with primary invasive breast carcinoma during 1996-2005, with a mean follow-up of 6.6 years. Mammographic density was assessed using the Breast Imaging Reporting and Data System (BI-RADS) density classification. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated by Cox proportional hazards regression; women with scattered fibroglandular densities (BI-RADS 2) were the referent group. All statistical tests were two-sided., Results: A total of 1795 women died, of whom 889 died of breast cancer. In multivariable analyses (adjusted for site, age at and year of diagnosis, American Joint Committee on Cancer stage, body mass index, mode of detection, treatment, and income), high density (BI-RADS 4) was not related to risk of death from breast cancer (HR = 0.92, 95% CI = 0.71 to 1.19) or death from all causes (HR = 0.83, 95% CI = 0.68 to 1.02). Analyses stratified by stage and other prognostic factors yielded similar results, except for an increased risk of breast cancer death among women with low density (BI-RADS 1) who were either obese (HR = 2.02, 95% CI = 1.37 to 2.97) or had tumors of at least 2.0 cm (HR = 1.55, 95% CI = 1.14 to 2.09)., Conclusions: High mammographic breast density was not associated with risk of death from breast cancer or death from any cause after accounting for other patient and tumor characteristics. Thus, risk factors for the development of breast cancer may not necessarily be the same as factors influencing the risk of death after breast cancer has developed.

Health care in the United States is notoriously expensive while often failing to deliver the care recommended in published guidelines. There is, therefore, a need to consider our approach to health-care delivery. Cancer care is a good example for consideration because it spans the continuum of health-care issues from primary prevention through long-term survival and end-of-life care. In this monograph, we emphasize that health-care delivery occurs in a multilevel system that includes organizations, teams, and individuals. To achieve health-care delivery consistent with the Institute of Medicine's six quality aims (safety, effectiveness, timeliness, efficiency, patient-centeredness, and equity), we must influence multiple levels of that multilevel system. The notion that multiple levels of contextual influence affect behaviors through interdependent interactions is a well-established ecological view. This view has been used to analyze health-care delivery and health disparities. However, experience considering multilevel interventions in health care is much less robust. This monograph includes 13 chapters relevant to expanding the foundation of research for multilevel interventions in health-care delivery. Subjects include clinical cases of multilevel thinking in health-care delivery, the state of knowledge regarding multilevel interventions, study design and measurement considerations, methods for combining interventions, time as a consideration in the evaluation of effects, measurement of effects, simulations, application of multilevel thinking to health-care systems and disparities, and implementation of the Affordable Care Act of 2010. Our goal is to outline an agenda to proceed with multilevel intervention research, not because it guarantees improvement in our current approach to health care, but because ignoring the complexity of the multilevel environment in which care occurs has not achieved the desired improvements in care quality outlined by the Institute of Medicine at the turn of the millennium.

The complex environmental context must be considered as we move forward to improve cancer care and, ultimately, patient and population outcomes. The cancer care continuum represents several care types, each of which includes multiple technical and communication steps and interfaces among patients, providers, and organizations. We use two case scenarios to 1) illustrate the variability, diversity, and interaction of factors from multiple levels that affect care quality and 2) discuss research implications and provide hypothetical examples of multilevel interventions. Each scenario includes a targeted literature review to illustrate contextual influences upon care and sets the stage for theory-informed interventions. The screening case highlights access issues in older women, and the survivorship case illustrates the multiple transition challenges faced by patients, families, and organizations. Example interventions show the potential gains of implementing intervention strategies that work synergistically at multiple levels. While research examining multilevel intervention is a priority, it presents numerous study design, measurement, and analytic challenges.

This summary reflects on this monograph regarding multilevel intervention (MLI) research to 1) assess its added value; 2) discuss what has been learned to date about its challenges in cancer care delivery; and 3) identify specific ways to improve its scientific soundness, feasibility, policy relevance, and research agenda. The 12 submitted chapters, and discussion of them at the March 2011 multilevel meeting, were reviewed and discussed among the authors to elicit key findings and results addressing the questions raised at the outset of this effort. MLI research is underrepresented as an explicit focus in the cancer literature but may improve implementation of studies of cancer care delivery if they assess contextual, organizational, and environmental factors important to understanding behavioral and/or system-level interventions. The field lacks a single unifying theory, although several psychological or biological theories are useful, and an ecological model helps conceptualize and communicate interventions. MLI research designs are often complex, involving nonlinear and nonhierarchical relationships that may not be optimally studied in randomized designs. Simulation modeling and pilot studies may be necessary to evaluate MLI interventions. Measurement and evaluation of team and organizational interventions are especially needed in cancer care, as are attention to the context of health-care reform, eHealth technology, and genomics-based medicine. Future progress in MLI research requires greater attention to developing and supporting relevant metrics of level effects and interactions and evaluating MLI interventions. MLI research holds an unrealized promise for understanding how to improve cancer care delivery.

Multilevel interventions, implemented at the individual, physician, clinic, health-care organization, and/or community level, increasingly are proposed and used in the belief that they will lead to more substantial and sustained changes in behaviors related to cancer prevention, detection, and treatment than would single-level interventions. It is important to understand how intervention components are related to patient outcomes and identify barriers to implementation. Designs that permit such assessments are uncommon, however. Thus, an important way of expanding our knowledge about multilevel interventions would be to assess the impact of interventions at different levels on patients as well as the independent and synergistic effects of influences from different levels. It also would be useful to assess the impact of interventions on outcomes at different levels. Multilevel interventions are much more expensive and complicated to implement and evaluate than are single-level interventions. Given how little evidence there is about the value of multilevel interventions, however, it is incumbent upon those arguing for this approach to do multilevel research that explicates the contributions that interventions at different levels make to the desired outcomes. Only then will we know whether multilevel interventions are better than more focused interventions and gain greater insights into the kinds of interventions that can be implemented effectively and efficiently to improve health and health care for individuals with cancer. This chapter reviews designs for assessing multilevel interventions and analytic ways of controlling for potentially confounding variables that can account for the complex structure of multilevel data.

Background: Computer-aided detection (CAD) is applied during screening mammography for millions of US women annually, although it is uncertain whether CAD improves breast cancer detection when used by community radiologists., Methods: We investigated the association between CAD use during film-screen screening mammography and specificity, sensitivity, positive predictive value, cancer detection rates, and prognostic characteristics of breast cancers (stage, size, and node involvement). Records from 684 956 women who received more than 1.6 million film-screen mammograms at Breast Cancer Surveillance Consortium facilities in seven states in the United States from 1998 to 2006 were analyzed. We used random-effects logistic regression to estimate associations between CAD and specificity (true-negative examinations among women without breast cancer), sensitivity (true-positive examinations among women with breast cancer diagnosed within 1 year of mammography), and positive predictive value (breast cancer diagnosed after positive mammograms) while adjusting for mammography registry, patient age, time since previous mammography, breast density, use of hormone replacement therapy, and year of examination (1998-2002 vs 2003-2006). All statistical tests were two-sided., Results: Of 90 total facilities, 25 (27.8%) adopted CAD and used it for an average of 27.5 study months. In adjusted analyses, CAD use was associated with statistically significantly lower specificity (OR = 0.87, 95% confidence interval [CI] = 0.85 to 0.89, P < .001) and positive predictive value (OR = 0.89, 95% CI = 0.80 to 0.99, P = .03). A non-statistically significant increase in overall sensitivity with CAD (OR = 1.06, 95% CI = 0.84 to 1.33, P = .62) was attributed to increased sensitivity for ductal carcinoma in situ (OR = 1.55, 95% CI = 0.83 to 2.91; P = .17), although sensitivity for invasive cancer was similar with or without CAD (OR = 0.96, 95% CI = 0.75 to 1.24; P = .77). CAD was not associated with higher breast cancer detection rates or more favorable stage, size, or lymph node status of invasive breast cancer., Conclusion: CAD use during film-screen screening mammography in the United States is associated with decreased specificity but not with improvement in the detection rate or prognostic characteristics of invasive breast cancer.

Background: Most breast biopsies will be negative for cancer. Benign breast biopsy can cause changes in the breast tissue, but whether such changes affect the interpretive performance of future screening mammography is not known., Methods: We prospectively evaluated whether self-reported benign breast biopsy was associated with reduced subsequent screening mammography performance using examination data from the mammography registries of the Breast Cancer Surveillance Consortium from January 2, 1996, through December 31, 2005. A positive interpretation was defined as a recommendation for any additional evaluation. Cancer was defined as any invasive breast cancer or ductal carcinoma in situ diagnosed within 1 year of mammography screening. Measures of mammography performance (sensitivity, specificity, and positive predictive value 1 [PPV1]) were compared both at woman level and breast level in the presence and absence of self-reported benign biopsy history. Referral to biopsy was considered a positive interpretation to calculate positive predictive value 2 (PPV2). Multivariable analysis of a correct interpretation on each performance measure was conducted after adjusting for registry, year of examination, patient characteristics, months since last mammogram, and availability of comparison film. Accuracy of the mammogram interpretation was measured using area under the receiver operating characteristic curve (AUC). All statistical tests were two-sided., Results: A total of 2,007,381 screening mammograms were identified among 799,613 women, of which 14.6% mammograms were associated with self-reported previous breast biopsy. Multivariable adjusted models for mammography performance showed reduced specificity (odds ratio [OR] = 0.74, 95% confidence interval [CI] = 0.73 to 0.75, P < .001), PPV2 (OR = 0.85, 95% CI = 0.79 to 0.92, P < .001), and AUC (AUC 0.892 vs 0.925, P < .001) among women with self-reported benign biopsy. There was no difference in sensitivity or PPV1 in the same adjusted models, although unadjusted differences in both were found. Specificity was lowest among women with documented fine needle aspiration-the least invasive biopsy technique (OR = 0.58, 95% CI = 0.55 to 0.61, P < .001). Repeating the analysis among women with documented biopsy history, unilateral biopsy history, or restricted to invasive cancers did not change the results., Conclusions: Self-reported benign breast biopsy history was associated with statistically significantly reduced mammography performance. The difference in performance was likely because of tissue characteristics rather than the biopsy itself.

Cancer screening is a process of care consisting of several steps and interfaces. This article reviews what is known about the association between organizational factors and cancer screening rates and examines how organizational strategies can address the steps and interfaces of cancer screening in the context of both intraorganizational and interorganizational processes. We reviewed 79 studies assessing the relationship between organizational factors and cancer screening. Screening rates are largely driven by strategies to 1) limit the number of interfaces across organizational boundaries; 2) recruit patients, promote referrals, and facilitate appointment scheduling; and 3) promote continuous patient care. Optimal screening rates can be achieved when health-care organizations tailor strategies to the steps and interfaces in the cancer screening process that are most critical for their organizations, the providers who work within them, and the patients they serve.

To achieve the benefit of cancer screening, appropriate follow-up of abnormal screening test results must occur. Such follow-up requires traversing the transition between screening detection and diagnosis, including several steps and interfaces in care. This article reviews factors and interventions associated with follow-up of abnormal tests for cervical, breast and colorectal cancers. We synthesized 12 reviews of descriptive and intervention studies published between 1980 and 2008. There was wide variability in definition of follow-up, setting, study population, and reported prevalence rates. Correlates of follow-up included patient characteristics (eg, knowledge and age), social support, provider characteristics, practice (eg, having reminders systems), community and professional norms (eg, quality measures), and policy (eg, federal programs). Effective interventions included patient education and support; delivery systems design changes, such as navigation; and information system changes, most notably patient tracking and physician reminders. Few studies focused explicitly on interfaces and steps of care, such as communication between primary care and specialists, or simultaneously targeted the multilevel factors that affect care. Future practice and research priorities should include development of clear operational definitions of the steps and interfaces related to patients, providers, and organizations; reflect evolving guidelines and new technologies; determine priorities for intervention testing; and improve measures and apply appropriate study designs.

Background: Interpretive performance of screening mammography varies substantially by facility, but performance of diagnostic interpretation has not been studied., Methods: Facilities performing diagnostic mammography within three registries of the Breast Cancer Surveillance Consortium were surveyed about their structure, organization, and interpretive processes. Performance measurements (false-positive rate, sensitivity, and likelihood of cancer among women referred for biopsy [positive predictive value of biopsy recommendation {PPV2}]) from January 1, 1998, through December 31, 2005, were prospectively measured. Logistic regression and receiver operating characteristic (ROC) curve analyses, adjusted for patient and radiologist characteristics, were used to assess the association between facility characteristics and interpretive performance. All statistical tests were two-sided., Results: Forty-five of the 53 facilities completed a facility survey (85% response rate), and 32 of the 45 facilities performed diagnostic mammography. The analyses included 28 100 diagnostic mammograms performed as an evaluation of a breast problem, and data were available for 118 radiologists who interpreted diagnostic mammograms at the facilities. Performance measurements demonstrated statistically significant interpretive variability among facilities (sensitivity, P = .006; false-positive rate, P < .001; and PPV2, P < .001) in unadjusted analyses. However, after adjustment for patient and radiologist characteristics, only false-positive rate variation remained statistically significant and facility traits associated with performance measures changed (false-positive rate = 6.5%, 95% confidence interval [CI] = 5.5% to 7.4%; sensitivity = 73.5%, 95% CI = 67.1% to 79.9%; and PPV2 = 33.8%, 95% CI = 29.1% to 38.5%). Facilities reporting that concern about malpractice had moderately or greatly increased diagnostic examination recommendations at the facility had a higher false-positive rate (odds ratio [OR] = 1.48, 95% CI = 1.09 to 2.01) and a non-statistically significantly higher sensitivity (OR = 1.74, 95% CI = 0.94 to 3.23). Facilities offering specialized interventional services had a non-statistically significantly higher false-positive rate (OR = 1.97, 95% CI = 0.94 to 4.1). No characteristics were associated with overall accuracy by ROC curve analyses., Conclusions: Variation in diagnostic mammography interpretation exists across facilities. Failure to adjust for patient characteristics when comparing facility performance could lead to erroneous conclusions. Malpractice concerns are associated with interpretive performance.

Background: Black-white disparities exist in receipt of recommended medical care, including colorectal cancer treatment. This retrospective cohort study examines the degree to which health systems (e.g., physician, hospital) factors explain black-white disparities in colon cancer care., Methods: Data from the Surveillance, Epidemiology, and End Results program; Medicare claims; the American Medical Association Masterfile; and hospital surveys were linked to examine chemotherapy receipt after stage III colon cancer resection among 5294 elderly (> or = 66 years of age) black and white Medicare-insured patients. Logistic regression analysis was used to identify factors associated with black-white differences in chemotherapy use. All statistical tests were two-sided., Results: Black and white patients were equally likely to consult with a medical oncologist, but among patients who had such a consultation, black patients were less likely than white patients (59.3% versus 70.4%, difference = 10.9%, 95% confidence interval [CI] = 5.1% to 16.4%, P < .001) to receive chemotherapy. This black-white disparity was highest among patients aged 66-70 years (black patients 65.7%, white patients 86.3%, difference = 20.6%, 95% CI = 10.7% to 30.4%, P < .001) and decreased with age. The disparity among patients aged 66-70 years also remained statistically significant in the regression analysis. Overall, patient, physician, hospital, and environmental factors accounted for approximately 50% of the disparity in chemotherapy receipt among patients aged 66-70 years; surgical length of stay and neighborhood socioeconomic status accounted for approximately 27% of the disparity in this age group, and health systems factors accounted for 12%., Conclusions: Black and white Medicare-insured colon cancer patients have an equal opportunity to learn about adjuvant chemotherapy from a medical oncologist but do not receive chemotherapy equally. Little disparity was explained by health systems; more was explained by illness severity, social support, and environment. Further qualitative research is needed to understand the factors that influence the lower receipt of chemotherapy by black patients.

Background: Invasive cervical cancer is highly preventable, yet it continues to occur, even among women who have access to cancer screening and treatment services. To reduce cervical cancer among such women, reasons for its occurrence must be better understood. We examined factors associated with the diagnosis of cervical cancer among women enrolled in health plans., Methods: We identified all cases of invasive cervical cancer (n = 833) diagnosed from January 1, 1995, through December 31, 2000, among women who were long-term members of seven prepaid comprehensive health plans and reviewed each woman's medical records for the 3 years prior to her cancer diagnosis. Women were classified into one of three categories based on Pap test histories 4-36 months before diagnosis: failure to screen with a Pap test, failure in detection by a Pap test, or failure in follow-up of an abnormal test result., Results: The majority of cases (n = 464; 56%) were in women who had no Pap tests during the period 4-36 months prior to diagnosis. Of the remaining cases, 263 (32%) were attributed to Pap test detection failure and 106 (13%) to follow-up failure. Being older (odds ratio [OR] = 6.48, 95% confidence interval [CI] = 3.89 to 10.79) or living in an area of higher poverty (OR = 1.72, 95% CI = 1.11 to 2.67) or having a lower education level (OR= 1.52; 95% CI = 1.07 to 2.16) was associated with the likelihood of being assigned to the failure to screen category versus either of the other two categories. A total of 375 (81%) of the 464 patients who had not had Pap screening had had at least one outpatient visit 4-36 months prior to cancer diagnosis. The cancer diagnostic process was triggered by a routine screening examination in 44% of patients, whereas 53% of the patients presented with symptoms consistent with cervical cancer; the remaining 3% were identified fortuitously during the course of receiving noncervical care., Conclusions: To reduce the incidence of invasive cervical cancer among women with access to screening and treatment, Pap screening adherence should be increased. In addition, strategies to improve the accuracy of Pap screening could afford earlier detection of cervical cancer.

Background: Delay in diagnosis of breast cancer can occur at several points on the diagnostic pathway. We examined characteristics of women with breast cancer who before diagnosis actively refused recommended follow-up of tests or symptoms suggestive of breast cancer., Methods: We identified women aged 50 years or older diagnosed with late-stage (metastatic disease or tumors > or = 3 cm at diagnosis) and a matched sample of women with early-stage (tumors < 3 cm) breast cancer from 1995 to 1999. Using medical records, we investigated clinical characteristics, use of health care, and documentation of care refusal during the 3 years before diagnosis. We used logistic regression models to compare refusers to nonrefusers., Results: Of the 2694 women studied, 7.2% refused provider follow-up advice during the 3 years. These women were more likely to have late-stage breast cancer at diagnosis than were nonrefusers (odds ratio [OR] = 1.9, 95% confidence interval [CI] = 1.4 to 2.6). They were more likely to be aged 75 years or older (OR = 1.9, 95% CI = 1.4 to 2.7 compared with age 50-64) or to have six or more children (OR = 2.3, 95% CI = 1.3 to 4.2 compared to women with one to two children). Clinical factors associated with refusal included low use of mammography, high use of clinical breast exam, and missed appointments. A minority of women who refused had a reason documented in the medical record; the most frequent reasons were avoidance-denial-fatalism, fear of diagnostic tests, and fear of surgery or disfigurement., Conclusions: Our results suggest that certain demographic and clinical characteristics are associated with women's refusal of diagnostic testing for breast cancer. Further study is needed on refusers' characteristics and on how such refusals affect outcomes. Efforts aimed at identifying and counseling women with abnormal results who refuse follow-up are warranted.

Critical questions about cancer prevention, care, and outcomes increasingly require research involving large patient populations and their care delivery organizations. The Cancer Research Network (CRN) includes 11 integrated health systems funded by the National Cancer Institute (NCI) to conduct collaborative cancer research. This article describes the challenges of constructing a productive consortium of large health systems, and explores the CRN's responses. The CRN was initially funded through an NCI cooperative agreement in 1999 and has since received a second 4-year grant. Leadership and policy development are provided through a steering committee, subcommittees, and an external advisory committee. The CRN includes integral and affiliated research projects supported by a Scientific and Data Resources Core. Three characteristics of the CRN intensified the general challenges of consortium research: 1) its members are large health systems with legitimate concerns about confidentiality of data about enrollees, providers, and the organization; 2) CRN research projects often generate highly sensitive data about quality of care; and therefore 3) each participating organization wants a strong voice in CRN direction. CRN experience to date confirms that a consortium of health systems with internal research capacity can address a range of important cancer research questions that would be difficult to study in other venues. The advantages and challenges of consortium research are explored, with suggestions for the development, execution, and management of multisystem population laboratories.

Background: Radiologists differ in their ability to interpret screening mammograms accurately. We investigated the relationship of radiologist characteristics to actual performance from 1996 to 2001., Methods: Screening mammograms (n = 469,512) interpreted by 124 radiologists were linked to cancer outcome data. The radiologists completed a survey that included questions on demographics, malpractice concerns, years of experience interpreting mammograms, and the number of mammograms read annually. We used receiver operating characteristics (ROC) analysis to analyze variables associated with sensitivity, specificity, and the combination of the two, adjusting for patient variables that affect performance. All P values are two-sided., Results: Within 1 year of the mammogram, 2402 breast cancers were identified. Relative to low annual interpretive volume (< or =1000 mammograms), greater interpretive volume was associated with higher sensitivity (P = .001; odds ratio [OR] for moderate volume [1001-2000] = 1.68, 95% CI = 1.18 to 2.39; OR for high volume [>2000] = 1.89, 95% CI = 1.36 to 2.63). Specificity decreased with volume (OR for 1001-2000 = 0.65, 95% CI = 0.52 to 0.83; OR for more than 2000 = 0.76, 95% CI = 0.60 to 0.96), compared with 1000 or less (P = .002). Greater number of years of experience interpreting mammograms was associated with lower sensitivity (P = .001), but higher specificity (P = .003). ROC analysis using the ordinal BI-RADS interpretation showed an association between accuracy and both previous mammographic history (P = .012) and breast density (P<.001). No association was observed between accuracy and years interpreting mammograms (P = .34) or mammography volume (P = .94), after adjusting for variables that affect the threshold for calling a mammogram positive., Conclusions: We found no evidence that greater volume or experience at interpreting mammograms is associated with better performance. However, they may affect sensitivity and specificity, possibly by determining the threshold for calling a mammogram positive. Increasing volume requirements is unlikely to improve overall mammography performance.

Background: Mammography screening may reduce breast cancer mortality by detecting cancers at an earlier stage. However, certain questions remain, including the ideal interval between mammograms., Methods: We conducted an observational study using information collected by seven mammography registries across the United States to investigate whether women diagnosed with breast cancer after having screening mammograms separated by a 2-year interval (n = 2440) are more likely to be diagnosed with late-stage disease (positive lymph nodes or metastases) than women diagnosed with breast cancer after having screening mammograms separated by a 1-year interval (n = 5400). Analyses were stratified by age and breast density to clarify whether groups that have the poorest mammography sensitivity (i.e., women under age 50 years and those with mammographically dense breasts) would benefit most from annual screening. The subjects were women diagnosed with breast cancer between 1996 and 2001 who were 40-89 years old at their index mammographic examination (i.e., the most recent screen at or before breast cancer diagnosis). Data were analyzed by logistic regression, adjusting for race, ethnicity, family history of breast cancer, and mammography registry., Results: Among women age 40-49 years at the index mammogram, those with a 2-year screening interval were more likely to have late-stage disease at diagnosis than those with a 1-year screening interval (28% versus 21%; odds ratio [OR] = 1.35, 95% confidence interval [CI] = 1.01 to 1.81). There was no increase in late-stage disease for women 50 years or older with a 2-year versus a 1-year screening interval (women age 50-59 years at index mammogram: OR = 0.97, 95% CI = 0.75 to 1.25; women age 60-69 years at index mammogram: OR = 0.99, 95% CI = 0.72 to 1.35; women age 70 years or older at index mammogram: OR = 0.88, 95% CI = 0.64 to 1.19). There was no indication that women with dense breasts would benefit more from a 1-year versus 2-year screening interval than women with fatty breasts., Conclusion: These findings may be useful for policy decisions about appropriate screening intervals and for use in statistical models that estimate the costs and benefits of mammography by age and screening interval.

Background: Mammography screening increases the detection of early-stage breast cancers. Therefore, implementing screening should reduce the percentage of women who are diagnosed with late-stage disease. However, despite high national mammography screening rates, late-stage breast cancers still occur, possibly because of failures in screening implementation., Methods: Using data from seven health care plans that included 1.5 million women aged 50 years or older, we conducted retrospective reviews of chart and automated data for 3 years before 1995-1999 diagnoses of late-stage (metastatic and/or tumor size > or =3 cm; case subjects, n = 1347) and early-stage breast cancers (control subjects, n = 1347). We categorized the earliest screening mammogram during the period 13-36 months before diagnosis as none (absence of screening), negative (absence of detection), or positive (potential breakdown in follow-up). We compared the proportion of case and control subjects in each category of screening implementation and estimated the likelihood (odds ratio [OR] with 95% confidence intervals [CIs]) of late-stage breast cancer. We also evaluated demographic characteristics associated with absence of screening in women with late-stage disease. All statistical tests were two-sided., Results: Absence of screening, absence of detection, and potential breakdown in follow-up were distributed differently among case (52.1%, 39.5%, and 8.4%, respectively) and control subjects (34.4%, 56.9%, and 8.8%, respectively) (P = .03). Among all women, the odds of having late-stage cancer were higher among women with an absence of screening (OR = 2.17, 95% CI = 1.84 to 2.56; P<.001). Among case patients, women were more likely to be in the absence-of-screening group if they were aged 75 years or older (OR = 2.77, 95% CI = 2.10 to 3.65), unmarried (OR = 1.78, 95% CI = 1.41 to 2.24), or without a family history of breast cancer (OR = 1.84, 95% CI = 1.45 to 2.34). A higher proportion of women from census blocks with less education (58.5% versus 49.4%; P = .003) or lower median annual income (54.4% versus 42.9%; P = .004) were in the absence-of-screening category compared with the proportion for the other two categories combined., Conclusions: To reduce late-stage breast cancer occurrence, reaching unscreened women, including elderly, unmarried, low-income, and less educated women, should be made a top priority for screening implementation.

Background: Younger women (40-49 years) have lower mammographic sensitivity (i.e., greater proportion of cancers detected after a negative mammogram) than older women (> or =50 years). We explored the effect of tumor growth rate, breast density, mammographic image quality, and breast cancer risk factors on mammographic sensitivity in younger and older women., Methods: We studied 576 women (n = 73 aged 40-49 years and n = 503 aged 50 years or older) who were diagnosed with invasive breast cancer between 1988 and 1993. Interval cancers were defined as those diagnosed within 12 or 24 months after a negative screening mammogram and before a subsequent mammogram. Tumor growth rate was assessed by mitotic figure count and Ki-67 positivity. The main outcome measures were percentage of women with interval cancer (1 -mammographic sensitivity) by age, odds ratio (OR) of interval cancer by age, and excess odds (i.e., the percentage of the odds ratio for age that was explained by individual covariates)., Results: Interval cancers occurred in 27.7% of younger women and 13.9% of older women within 12 months (OR = 2.36, 95% confidence interval [CI] = 1.14 to 4.77) and in 52.1% of younger women and 24.7% of older women within 24 months (OR = 3.58, 95% CI = 2.15 to 5.97). Greater breast density explained 67.6% of the decreased mammographic sensitivity in younger women at 12 months, whereas rapid tumor growth explained 30.6% and breast density explained 37.6% of the decreased sensitivity in younger women at 24 months., Conclusions: Breast density largely explained decreased mammographic sensitivity at 12 months, whereas rapid tumor growth contributed to decreased mammographic sensitivity at 24 months. A 12-month versus a 24-month mammography screening interval may therefore reduce the adverse impact of faster growing tumors on mammographic sensitivity in younger women.

Background: With the large number of women having mammography-an estimated 28.4 million U.S. women aged 40 years and older in 1998-the percentage of cancers detected as ductal carcinoma in situ (DCIS), which has an uncertain prognosis, has increased. We pooled data from seven regional mammography registries to determine the percentage of mammographically detected cancers that are DCIS and the rate of DCIS per 1000 mammograms., Methods: We analyzed data on 653 833 mammograms from 540 738 women between 40 and 84 years of age who underwent screening mammography at facilities participating in the National Cancer Institute's Breast Cancer Surveillance Consortium (BCSC) throughout 1996 and 1997. Mammography results were linked to population-based cancer and pathology registries. We calculated the percentage of screen-detected breast cancers that were DCIS, the rate of screen-detected DCIS per 1000 mammograms by age and by previous mammography status, and the sensitivity of screening mammography. Statistical tests were two-sided., Results: A total of 3266 cases of breast cancer were identified, 591 DCIS and 2675 invasive breast cancer. The percentage of screen-detected breast cancers that were DCIS decreased with age (from 28.2% [95% confidence interval (CI) = 23.9% to 32.5%] for women aged 40-49 years to 16.0% [95% CI = 13.3% to 18.7%] for women aged 70-84 years). However, the rate of screen-detected DCIS cases per 1000 mammograms increased with age (from 0.56 [95% CI = 0.41 to 0.70] for women aged 40-49 years to 1.07 [95% CI = 0.87 to 1.27] for women aged 70-84 years). Sensitivity of screening mammography in all age groups combined was higher for detecting DCIS (86.0% [95% CI = 83.2% to 88.8%]) than it was for detecting invasive breast cancer (75.1% [95% CI = 73.5% to 76.8%])., Conclusions: Overall, approximately 1 in every 1300 screening mammography examinations leads to a diagnosis of DCIS. Given uncertainty about the natural history of DCIS, the clinical significance of screen-detected DCIS needs further investigation.

Background: The performance of diagnostic mammography for women with signs or symptoms of breast cancer has not been well studied. We evaluated whether age, breast density, self-reported breast lump, and previous mammography influence the performance of diagnostic mammography., Methods: From January 1996 through March 1998, prospective diagnostic mammography data from women aged 25-89 years with no previous breast cancer were linked to cancer outcomes data in six mammography registries participating in the Breast Cancer Surveillance Consortium. We used the final mammographic assessment at the end of the imaging work-up to determine abnormal mammographic examination rate, positive predictive value (PPV), sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve. We used age, breast density, prior mammogram, and self-reported breast lump jointly as predictors of performance. All statistical tests were two-sided., Results: Of 41 427 diagnostic mammograms, 6279 (15.2%) were judged abnormal. The overall PPV was 21.8%, sensitivity was 85.8%, and specificity was 87.7%. Multivariate analysis showed that sensitivity and specificity generally declined as breast density increased (P =.007 and P<.001, respectively), that previous mammography decreased sensitivity (odds ratio [OR] = 0.52, 95% confidence interval [CI] = 0.36 to 0.74; P<.001) but increased specificity (OR = 1.43, 95% CI = 1.31 to 1.57; P<.001), and that a self-reported breast lump increased sensitivity (OR = 1.64, 95% CI = 1.13 to 2.38; P =.013) but decreased specificity (OR = 0.54, 95% CI = 0.49 to 0.59; P<.001). ROC analysis showed that higher breast density and previous mammography were negatively related to accuracy (P<.001 for both)., Conclusions: Diagnostic mammography in women with signs or symptoms of breast cancer shows higher sensitivity and lower specificity than screening mammography does. Higher breast density and previous mammographic examination appear to impair performance.