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IMPORTANCE: Approximately 24 million US individuals receive care at federally qualified health centers, which historically have low rates of colorectal cancer screening. The US Preventive Services Task Force recommends routine colorectal cancer screening for individuals aged 50 to 75 years. OBJECTIVE: To determine the effectiveness of an electronic health record (EHR)-embedded mailed fecal immunochemical test (FIT) outreach program implemented in health centers as part of standard care. DESIGN, SETTING, AND PARTICIPANTS: This cluster randomized pragmatic clinical trial was conducted in 26 federally qualified health center clinics, representing 8 health centers in Oregon and California, randomized to intervention (n = 13) or usual care (n = 13). All participants were overdue for colorectal cancer screening during the accrual interval (February 4, 2014 to February 3, 2015). INTERVENTIONS: Electronic health record-embedded tools to identify eligible adults and to facilitate implementation of a stepwise mailed intervention involving (1) an introductory letter, (2) a mailed FIT, and (3) a reminder letter; training, collaborative learning, and facilitation through a practice improvement process. MAIN OUTCOMES AND MEASURES: Effectiveness was measured as clinic-level proportions of adults who completed a FIT, and secondarily, any colorectal cancer screening within 12 months of accrual or by August 3, 2015. Implementation was measured as clinic-level proportions of adults who were mailed an introductory letter and ordered a FIT. RESULTS: Twenty-six clinics with 41 193 adults (mean [SD] age, 58.5 [6.3] years; 22 994 women) were randomized to receive the direct mail colorectal screening intervention (13 clinics; 21 134 patients) or usual care (13 clinics; 20 059 patients). Compared with usual care clinics, intervention clinics had significantly higher adjusted clinic-level proportion of participants who completed a FIT (13.9% vs 10.4%; difference, 3.4 percentage points; 95% CI, 0.1%-6.8%) and any colorectal cancer screening (18.3% vs 14.5%; difference, 3.8 percentage points; 95% CI, 0.6%-7.0%). We observed large variation across health centers in effectiveness (FIT completion differences range, −7.4 percentage points to 17.6 percentage points) and implementation (proportion who were mailed a FIT range, 6.5% to 68.2%). The number needed to mail to achieve a completed FIT was 4.8 overall, and 4.0 in clinics that mailed a FIT reminder. CONCLUSIONS AND RELEVANCE: An EHR-embedded mailed FIT outreach intervention significantly improved rates of FIT completion and rates of any colorectal cancer screening. Higher rates of colorectal cancer screening occurred in clinics that successfully implemented the mailed outreach program. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01742065

Multiple clinical practice guidelines exist for breast and cervical cancer screening, and differ in aggressiveness with respect to the recommended frequency and target populations for screening.

The population served by Federally Qualified Health Centers (FQHCs) has lower levels of cancer screening compared with the general population and suffers a disproportionate cancer burden. To address these disparities, 3 federal agencies and a primary care association established and tested the feasibility of a Regional Cancer Collaborative (RCC) in 2005.

Medical expenditures adjusted for price differences are a barometer of total resources devoted to patient care and thus may reflect treatment differentials.

Abstract Objective: We evaluated the association of pre-term delivery (PTD), low birth weight (LBW), and fetal death with breast density by age at mammogram and years since birth. Methods: Subjects were women aged =55 years who had a screening mammogram between 1 June 1996 and 1 August 1997 in Seattle, Washington, and whose records were linked to their previous state birth (1 January 1968 to 1 August 1997) or fetal death (1/1/1984–8/1/1997) records. We used unconditional logistic regression, adjusting for age at mammogram, body mass index, age at first birth, and menopausal status, to calculate the odds of dense (extremely or heterogeneously dense by BI-RADS) (n=3593) versus fatty breasts (scattered fibroglandular tissue or almost entirely fat) (n=2378) for women with a prior PTD (<34, 34–36 versus =37 weeks gestation), LBW (<2500 versus =2500 g), or fetal death (stillborn 20 weeks gestation versus live birth). Results: The odds for denser breasts increased among women with PTD at <34 weeks gestation who were =45 years at time of mammogram (odds ratio (OR) and 95 confidence interval (CI)=2.8 (1.3–6.1)) and for whom <10 years had elapsed since pregnancy (OR=8.8 (1.7–45.8)). We observed similar increases in density among women with LBW (OR=3.3 (1.3–8.2)) when <10 years had elapsed. Conclusions: PTD and LBW may have a transitory effect on breast density.

It is not well understood whether breast density is a marker of cumulative exposure to estrogen or a marker of recent exposure to estrogen. The authors examined the relationship between bone mineral density (BMD; a marker of lifetime estrogen exposure) and breast density.The authors conducted a cross‐sectional analysis among 1800 postmenopausal women ≥ 54 years. BMD data were taken from two population‐based studies conducted in 1992–1993 (n = 1055) and in 1998–1999 (n = 753). The authors linked BMD data with breast density information collected as part of a mammography screening program. They used linear regression to evaluate the density relationship, adjusted for age, hormone therapy use, body mass index (BMI), and reproductive covariates.There was a small but significant negative association between BMD and breast density. The negative correlation between density measures was not explained by hormone therapy or age, and BMI was the only covariate that notably influenced the relationship. Stratification by BMI only revealed the negative correlation between bone and breast densities in women with normal BMI. There was no relationship in overweight or obese women. The same relationship was seen for all women who had never used hormone therapy, but it was not significant once stratified by BMI.BMD and breast density were not positively associated although both are independently associated with estrogen exposure. It is likely that unique organ responses obscure the relationship between the two as indicators of cumulative estrogen exposure. Cancer 2004. © 2004 American Cancer Society.

Breast-conserving surgery (BCS) with radiation (BCSR) requires a multidisciplinary care approach between surgeons and radiation oncologists. This retrospective cohort study examined the use of preoperative radiation oncology consultation and whether use of or distance to this care was associated with treatment choice among 1188 women age ≥ 65 years who were diagnosed with local or regional breast carcinoma in Washington State in 1994 and 1995. Study outcomes included rates of BCSR; BCS alone; and mastectomy; and radiation therapy among women who underwent BCS. Only 29% of patients in the current study consulted with a radiation oncologist preoperatively, and less than half of the patients (46.6%) consulted with either a medical oncologist or a radiation oncologist. Among women who underwent either BCSR or mastectomy, the odds of undergoing BCSR among women who had a preoperative radiation oncology consultation were 6.7 times the odds of women who did not have the consultation (P ≤ 0.001). Similarly, the odds of receiving radiation therapy among women who underwent BCS and had a preoperative radiation oncology consultation were 5 times the odds of women who did not have the consultation (P &lt; 0.001). The 3.4% of women who lived > 50 miles from the radiation therapy center had the lowest BCSR rate (15.8%) and had the lowest radiation therapy rate among women who underwent BCS (54.5%), although these findings were not statistically significant in adjusted analyses. A preoperative visit with a radiation oncologist was associated strongly with BCSR use. More should be done to evaluate the role of multidisciplinary consultation in the decision to use BCSR. Cancer 2004;100:701–9. © 2004 American Cancer Society.

The costs to an insurer of a 10-year maternal serum alpha-feto protein (MSAFP) screening program were subtracted from future medical care costs avoided by the insurer (benefits) to examine whether such a program would be cost-justified from the perspective of a managed health care system (i.e., result in net costs ≥ 0). The analysis considered MSAFF screening for neural tube defects (NTDs) alone and then was repeated to consider screening for both NTDs and Down's syndrome. Using a 5 discount rate for future dollars, the costs to the insurer of a screening program for NTDs alone over 10 years exceeded costs avoided by 10.00 per person screened. Adding screening for Down's syndrome using the same MSAFP test increased the net cost by 22.00 to a total of -32.00 per screenee. The estimate of the cost to the insurer was sensitive to assumptions regarding the costs of medical care avoided, the expense of MSAFP, the proportion of screened women requiring a genetic amniocentesis, and the cost of that procedure. The conclusion that screening would not result in a cost savings to the insurer was not changed by reasonable assumptions regarding 1) the appropriate discount rate; 2) the costs of MSAFP; 3) the costs of genetic amniocentesis; 4) the sensitivity of MSAFP; 5) the proportion of the population requiring genetic amniocentesis; and 6) the costs of 10 years of medical care for someone affected by Down's syndrome or an NTD. Other analyses suggested that screening for NTDs or Down's syndrome would be cost-justified when viewed from the perspective of society. The present work suggests this conclusion does not hold when the perspective of the insurer is taken because avoided costs of care realized by society exceed those realized by the insurer.

This chapter has outlined some principles of tumor growth, test characteristics, and the evaluation of screening technologies. We have emphasized that test specificity is the critical parameter in the evaluation of technologies because it is the healthy people who will suffer the most from the adverse effects of screening. We have also emphasized that the efficacy of a test is best evaluated by examining mortality reductions in comparable populations. The purpose has been to assist clinicians with their interpretation of the literature.

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.

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.

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.

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.

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.

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: 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 ≥ 3cm 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.