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The CRC screening process in community settings: A conceptual model for the Population-based Research Optimizing Screening through Personalized Regimens Consortium

. Author manuscript; available in PMC: 2015 Jul 1.

Reducing colorectal cancer (CRC) mortality by promoting screening has been a national goal for two decades. The National Cancer Institute's Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) consortium is the first federal initiative to foster coordinated, transdisciplinary research evaluating the entire cancer screening process in community settings. PROSPR is creating a central data repository to facilitate research evaluating the breast, cervical, and CRC screening process across different patient populations, provider types, and delivery systems. Data are being collected and organized at the multiple levels in which individuals are nested (e.g., healthcare systems, facilities, providers, patients). Here, we describe a conceptual model of the CRC screening process guiding data collection and highlight critical research questions that will be addressed through pooled data. We also describe the three Research Centers focused on CRC screening with respect to study populations, practice settings, and screening policies. PROSPR comprehensively elucidates the complex screening process through observational study, and has potential to improve care delivery beyond the healthcare systems studied. Findings will inform intervention designs and policies to optimize CRC screening delivery and advance the Institute of Medicine's goals of effective, efficient, coordinated, timely, and safe health care with respect to evidence-based cancer screening.

Keywords: colorectal neoplasms, mass screening, delivery of health care, model/framework

In the U.S., colorectal cancer (CRC) is the second leading cause of cancer deaths despite availability of screening tests that could greatly reduce both CRC incidence and mortality (1-3). Guidelines recommend three screening strategies: guaiac-based fecal occult blood testing (gFOBT) or fecal immunochemical testing (FIT) every year, flexible sigmoidoscopy every 5 years with gFOBT/FIT every 3 years, or colonoscopy every 10 years (4-6). Most intervention research has focused on one-time participation in guideline-recommended CRC screening (7, 8). However, the CRC screening process is complex and involves delivery of several steps of care beyond one-time screening, including diagnostic evaluation following an abnormal test result, treatment of detected lesions, surveillance after abnormal results or—for those with normal results—repeat screening at guideline-appropriate intervals. Completion of the CRC screening process requires coordination of care and transfer of responsibilities among healthcare teams (physicians, nurses, and staff) in primary care, gastroenterology, and oncology (9). For screening to achieve greater impact on CRC incidence and mortality, teams and delivery systems must implement efficient, effective interventions that address the full CRC screening process (10, 11).

There are gaps in our knowledge about effective delivery of CRC screening including the best ways to measure breakdowns in the screening process and the impact of implementing evidence-based interventions on screening outcomes in community settings (7, 12). Given the complexity of the CRC screening process, more research is needed on the multiple factors acting at the patient-, provider-, and system-levels that dynamically influence optimal screening test use. This knowledge will inform future interventions and policies to improve CRC screening in real-world settings.

To address these gaps, the National Cancer Institute established in 2011 the Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) consortium. PROSPR's overall aim is to promote multi-site, coordinated, transdisciplinary research that evaluates and improves breast, cervical, and colorectal cancer screening in community settings(13). A key PROSPR goal is to create a central data repository on the entire cancer screening process. Recognizing the numerous factors influencing delivery, PROSPR is collecting and organizing data at the multiple levels in which individuals are nested (e.g., healthcare systems, facilities, providers, patients) for a large, geographically and demographically diverse patient cohort. Common data elements, measured at the patient-, provider-, facility-, and system-level, include information on tests and procedures ordered and performed, cancer outcomes, and characteristics empirically associated with the screening process (e.g., patient sociodemographics and healthcare coverage, provider specialty and patient volume, facility and systems policies and incentives to promote screening). Prior to annual data submission, investigators refine operational definitions and expand the list of required elements to broaden comprehensiveness and robustness of the dataset. Data pooled across participating sites (described below) will facilitate research comparing screening performance across different patient populations, provider types, and delivery systems.

The PROSPR consortium has established seven Research Centers and one Statistical Coordinating Center. Three Research Centers are focused on studying and improving the CRC screening process–Group Health Research Institute (Group Health), Kaiser Foundation Research Institute [Kaiser Permanente Northern California (KPNC) and Kaiser Permanente Southern California (KPSC)], and the Parkland Health & Hospital System/University of Texas-Southwestern Medical Center (Parkland-UTSW).

Here, we: 1) describe the conceptual model depicting the CRC screening process that is guiding data collection; 2) highlight CRC-specific research questions that will be addressed using pooled data and some of the methodologic challenges to these analyses; and 3) provide an overview of the Research Centers with respect to study populations, settings, and policies, procedures, and programs to facilitate completion of the CRC screening process.

Each CRC Research Center has two principal aims. The first aim is to execute three research projects around a central theme that addresses key factors affecting the CRC screening process. Group Health, an integrated healthcare system located in Seattle, is assessing the effectiveness of different CRC screening strategies and identifying low-risk groups for whom a less intensive screening approach might be appropriate. KPNC/KPSC is examining screening delivery among its enrollees, with the aim of optimizing FIT and colonoscopy screening strategies. Parkland-UTSW is evaluating screening delivery within an integrated safety-net system, including use of personalized approaches and whether these approaches improve outcomes in a low-income, uninsured population.

The second aim is to contribute data to the central repository and collaborate on analyses both specific to CRC and across cancer types: breast, cervical, and colorectal. Research using pooled data requires establishing a common conceptual and methodological model (e.g., similar operational definitions), so that data can be validly integrated for comparative studies of the screening process.

Our CRC Screening Process Model (Figure 1) expands on the general continuum of cancer care developed by Taplin and Zapka (10, 11, 14-17). The model illustrates challenges specific to delivering CRC screening, including the multiple test modalities involved, the many activities that require coordination between primary and specialty care teams, and the need to identify patients with symptoms who enter the process at the diagnostic step. This model is intended to represent the screening process for patients at average CRC risk because it allows for completion of the process with any of the three guideline-recommended screening modalities. Patients at elevated CRC risk due to personal history (e.g., adenoma) and/or family history (e.g., first-degree relative diagnosed with CRC) should follow surveillance guidelines recommending colonoscopy at more frequent intervals and/or an earlier starting age (18, 19).

We used Taplin and Zapka's typology for classifying aspects of the CRC screening process (9). Figure 1 highlights in grey the four types of care that are critical to accomplishing key goals of the CRC screening process – risk assessment, detection, diagnostic evaluation, and treatment. Steps, represented by boxes, are medical encounters or actions required of patients, providers or clinical staff. Steps with dashed borders depict interfaces where information and/or responsibility must be transferred among different healthcare teams (e.g., primary care and specialist) to progress to the next step. Transitions are sets of multiple steps and interfaces necessary to move from one type to the next (e.g., detection to diagnosis).

Our model depicts all steps that may occur during one screening episode. The process can begin in three ways: 1) a provider recommends screening to a patient during a clinical encounter; 2) the healthcare organization systematically invites eligible patients to screen; or 3) a patient requests screening (Figure 1, Transition #1). Which screening test modality is used depends on patient and provider preferences, resources of the patient, and policies of the organization. If results are normal, the process for this episode ends and guideline-based recommendations for repeat screening are communicated to the patient and relevant providers. Patients are referred for diagnostic colonoscopy if the screening process began with other tests (e.g., gFOBT, FIT, sigmoidoscopy, or CT colonography) with abnormal findings. Symptomatic patients (e.g., iron deficiency anemia, gastrointestinal bleeding) enter the process at referral for diagnostic colonoscopy (Figure 1, Transition #2). Because colonoscopy can be performed for screening or diagnostic purposes, defining and documenting the exam's indication is particularly important, yet challenging (20-24). Also, critically important is reporting of colonoscopy results, to help providers and organizations identify patients at elevated risk who may require a more frequent screening or surveillance regimen. There are several interfaces within each type of care and during care transitions that require coordination between primary care and specialty providers; these interfaces may be particularly vulnerable to breakdown (10, 15). Unless cancer is detected or surveillance is recommended at the end of the screening episode, the process begins again and the patient re-enters the model based on guidelines and patient and provider preferences. PROSPR uniquely positioned to address questions raised by our conceptual model.

Pooled PROSPR data offer unique opportunities to assess the CRC screening process in diverse, community settings. PROSPR CRC investigators intend to examine a wide range of clinical and policy-relevant research questions addressing:

• What are the best strategies to promote appropriate use of CRC screening and follow-up, in terms of patient preference, patient adherence, local healthcare systems, and specific populations (25)? For example, Holden et al.(7) found strong evidence that elimination of structural barriers, one-on-one interactions, patient reminders, and system-level interventions improved screening rates; however, what is the impact of deploying these evidence-based strategies in real-world settings? What contextual factors hamper their effectiveness (e.g., characteristics of the patient population, providers, and/or facilities)?

• What steps in the screening process (Figure 1) are vulnerable to failure (26, 27)? What types of patients are at particular risk for failures (8, 28) and what are the factors at the provider-, facility-, and system levels that contribute to failures (11, 15, 29-33)?

• What are the performance characteristics (sensitivity, specificity, positive predictive value) of CRC screening tests conducted in the diverse health systems represented in PROSPR (34, 35)?

• To what extent do underuse, misuse, and overuse of CRC screening occur (7)? For example, how often are FITs given to patients who had a recent colonoscopy (less than 10 years) with normal results? How often do physicians recommend a FIT to patients who are in colonoscopy surveillance program due to prior adenomas? What strategies ameliorate misuse and overuse patterns (7)?

• How does CRC risk change following screening (36, 37)? Can a risk-based management strategy be used for screening and surveillance (38, 39)?

• What is the comparative effectiveness (i.e., balance of benefits and harms) of FIT versus colonoscopy screening regimens in the diverse health systems and patient populations represented in PROSPR, and the generalizability of these organized screening approaches to resource-limited settings (40-42)?

Findings will inform policy and clinical recommendations issued by experts, as well as design and implementation of population-based screening programs such as those supported by the Centers for Disease Control and Prevention (43).

To generate pooled data, CRC PROSPR investigators are establishing operational definitions for a wide array of common data elements characterizing the CRC screening process (e.g., benefits and harms of screening, colonoscopy indication) and are harnessing relevant clinical information systems including the EMR and local clinical and administrative databases. Research Centers submit data on an annual basis and each year the required list of common data elements is reviewed, refined, and expanded to ensure the comprehensiveness and robustness of the pooled dataset. Research Centers are linking screening process data with cancer outcomes available from regional and local cancer registries and considering linkages with pertinent contextual data to derive measures of community-level health status, resources, and utilization (e.g., US Census, Health Resources and Services Administration's Area Resource File) (44-46).

While pooled PROSPR data provide a unique opportunity to address important questions about CRC screening, they also pose several challenges for analysis. Accounting for potential confounders and heterogeneity across systems are two key challenges. Screening rates and performance characteristics are likely to vary across Research Centers due to differences in preferred test modality, approaches to facilitate screening, and characteristics of the screen-eligible patient populations. To minimize bias, investigators are utilizing methods to account for confounding when combining information across the Research Centers. For example, instrumental variable analysis (47, 48) or propensity score adjustment (49, 50) are approaches used to minimize selection bias (51, 52). Also, system-level variability can be addressed using multilevel modeling (53). Another challenge is accurate measurement of patient-level risk factors for prediction of short- and long-term outcomes, because risk factor information may change at any step in the screening process. Accuracy of information affect whether risk prediction algorithms can be used to develop personalized screening regimens and improve population-level screening outcomes. We are also assessing patient comorbidity to examine competing risks. Although conducting comparative effectiveness analyses of different CRC screening strategies with PROSPR's observational data is complex, validity can be greatly improved by carefully formulating research questions, implementing appropriate study designs, and applying sophisticated statistical methods.

The three CRC Research Centers are uniquely positioned to answer the key research questions described above. The Research Centers are comprised of four U.S. integrated health care systems covering four geographic regions. Integrated systems have been recognized as ideal settings for assessing the entire screening process in contrast to loosely affiliated or unaffiliated primary care practices that refer to specialty providers. This is because integrated systems can systematically track data at multiple levels through a comprehensive electronic medical record (EMR) (54, 55) and care is delivered through a single, coordinated organization of primary and specialty practices (56-59).

Group Health is a mixed-model, non-profit, health plan serving nearly 625,000 members in Washington State. The Kaiser Permanente Research Center is comprised of two healthcare systems delivering care to about 7 million members in Northern (KPNC) and Southern (KPSC) California. Parkland-UTSW is the sole safety-net provider for under-insured and uninsured Dallas County residents living at ≤200% of federal poverty level; approximately 64,000adults each year use Parkland's eleven primary care clinics. These four systems have a combined population of over 7.7 million people from which a pooled, dynamic CRC cohort has been derived.

We have developed cohort definitions and eligibility criteria to facilitate pooling of the screening process data (Table 1). The Group Health and Kaiser Permanente cohorts consist of health plan members ages 50 to 89 years on or after January 1, 2010 with new cohort members added as they become eligible for CRC screening (i.e., enters cohort on 50^th birthday). For the Kaiser Permanente cohort, one year of prior membership is also required for cohort inclusion. Membership eligibility criteria were used to provide new enrolled Kaiser Permanente patients with a sufficient period of time for providers to document CRC screening history, determine screening eligibility, and offer screening services. For Parkland-UTSW, Dallas County residents ages 50-64 with a visit at one of Parkland's eleven primary care clinics on or after January 1, 2010 are entered into the cohort on the date of their visit. These latter eligibility criteria were applied for two reasons: 1) access to CRC screening services at Parkland is limited to patients seen in primary care (as opposed to those seen only in the emergency department or at a specialty clinic); and 2) Parkland is obligated to care for all uninsured Dallas county residents but residents who become Medicare-eligible at age 65 may opt to receive care outside the Parkland system. All Research Centers excluded patients diagnosed with CRC or with a partial or total colectomy prior to cohort entry date.

Table 2 describes sociodemographic characteristics and screening process outcomes of cohort members who entered in 2010 for each of the four systems. Age distributions of screen-eligible patients at Group Health, KPNC, and KPSC are very similar, with approximately 75% of patients in the fifth and sixth decade of life and 25% in the seventh and eighth decades. Because of the restricted age range of the Parkland-UTSW cohort, almost 72% of the patients are in their fifties. More than half of the patients are female, with the highest proportion of females at Parkland-UTSW (64%). There is substantial racial and ethnic variation across the systems. Group Health members are predominantly white (72.1%) and, although the proportions are lower at KPNC (58.4%) and KPSC (45.7%), non-Hispanic whites still comprise the majority. Nearly a quarter of the KPSC cohort is Hispanic, compared with 11.6% at KPNC and 3.3% at GH. Only 18.4% of Parkland-UTSW patients are white; most are either non-Hispanic black (38.9%) or Hispanic (36.1%). Of the four systems, KPNC has the highest proportion of Asians and Pacific Islanders (14.0%).

Almost all patients across the four systems live in metropolitan areas (60, 61). Group Health, KPNC, and KPSC are very similar in insurance coverage distribution— most patients are privately insured (54.9-66.6%) or have Medicare (32.4-44.3%), and very small proportion have Medicaid or are dual-eligible for Medicare and Medicaid. In contrast, 68.4% of Parkland-UTSW patients are uninsured; their medical care costs are covered by Dallas County's medical assistance program financed by property taxes. About 9% of Parkland-UTSW patients have Medicare coverage; 7% are covered by Medicaid. Overall, significant heterogeneity of the pooled cohort with respect to race/ethnicity, age, and insurance coverage is evident.

FIT comprised the vast majority of stool-based tests performed in 2010 at KPNC, KPSC, and Parkland-UTSW. In contrast, Group Health used gFOBT during 2010 and transitioned to FIT in late 2011. More than 30,000 abnormal stool tests required diagnostic evaluation within the four systems during 2010. Across the four systems, colonoscopy was the most common endoscopy procedure. We are currently cross-validating methods to define indication as none of the systems' EMR has a discrete field for identifying reason for colonoscopy; this effort will enable definition of a screening episode and identification of misuse and overuse patterns. In 2010, more than 2,300 CRC cases were identified by the systems.

Table 3 describes the four systems' healthcare delivery resources. There is substantial range in number of primary care physicians who care for adult patients (total > 3,700) and number of endoscopy providers (total >250). Group Health and Parkland-UTSW use mid-level providers such as nurse practitioners and physician assistants in primary care. KPSC and Parkland-UTSW are teaching institutions that employ gastroenterology fellows; therefore, attending physicians and fellows perform endoscopy procedures. To meet increasing capacity needs over a wide geographic area, Group Health contracts with outside endoscopy providers to perform some colonoscopies; the contracted providers use various clinical information systems for storing data and are not all universally linked to Group Health members' EMR.

Endoscopy procedures are performed in three distinct facility types—ambulatory surgical centers, medical offices, and hospitals. These types are subject to different regulatory requirements (62, 63). These facilities may be owned and managed by the healthcare system or may have a formal contract with the system to provide services. The nature of the relationship may affect interfaces (i.e., transfer of clinical information and responsibility) between primary care and endoscopy providers and increase difficulty of extracting endoscopy results because scanned reports are stored as images and require more resources to extract than text information stored in the EMR. We are also cross-validating natural language processing and algorithm-based methods to systematically extract text data from endoscopy reports given the wide variation in how results are documented.

All four healthcare systems employ a common and comprehensive EPIC® platform-based EMR. The EPIC EMR can provide information on electronic orders, referrals, scheduling, test results, treatment, and billing for both inpatient and outpatient care. KPNC, KPSC and Parkland-UTSW have a Cerner-based clinical information system for reporting pathology results; Group Health uses Progeny. Group Health, KPNC, and KPSC carry out centralized processing of stool-based tests. In contrast, each of the eleven Parkland-UTSW primary-care clinics process FITs at local on-site laboratories. Pathology processing for endoscopy specimens is centralized for Parkland-UTSW and Group Health-performed procedures; for KPNC and KPSC and some contracted providers for Group Health, pathology processing is local to each endoscopy facility. The heterogeneous nature of the three CRC Research Centers not only contributes to the robust nature of the data collected in the PROSPR consortium but will also make findings from PROSPR studies more generalizable across populations and delivery settings.

All CRC Research Centers have clinic- and system-level policies procedures, and programs targeted toward asymptomatic, average-risk individuals to facilitate completion of screening process steps, transitions between types of care, and interfaces among providers (Table 4) (9, 15, 16).

To initiate the screening process, Group Health, KPNC, and KPSC have organized programs that systematically invite all average-risk members aged 50-75 to participate in CRC screening. Electronic clinical information systems identify and generate lists of eligible patients. Invitation letters are mailed and timing of delivery may be based on birth month or anniversary of prior screening. Stool blood testing is the preferred screening modality, but endoscopy is also available as an option based on patient/provider preference. At KPNC and KPSC, the FIT kit is mailed with the invitation letter; if endoscopy is chosen, a primary care provider must place an order or request a consult. At Group Health patients can self-refer for colonoscopy. Response to invitations is monitored and either a reminder is sent to non-adherent individuals or a patient's primary care team is notified. Timing and delivery mode of reminders varies across the systems. We are currently working to obtain data from these clinical information systems and expand our common data elements to identify moderators of program effectiveness.

At Parkland-UTSW, screening is available to all primary care patients 50 years of age or older opportunistically during in-person clinic visits. The screening modality offered is based on provider and patient preferences.

Across all four systems, screening results are reported to primary care providers via EMR in-basket. The systems have at least three methods for notifying patients about results (e.g., information is posted in a secure website, letter is mailed, or a provider calls) and delivery mode depends on the test modality and findings. Group Health, KPNC, and KPSC also have system- and practice-level strategies to encourage patients with abnormal test results to complete diagnostic evaluation. Finally, all four systems generate population-level reports based on data stored in their clinical information systems to monitor adherence to CRC screening guidelines.

Collaboration among the PROSPR CRC Research Centers will advance knowledge about the screening process by providing detailed, longitudinal, multi-level, population-based data about steps, transitions, and interfaces along the CRC screening process. Background information about the cohort, integrated healthcare systems, and CRC-specific policies and procedures illustrates that PROSPR is poised to comprehensively compare and systematically evaluate the CRC screening process in diverse metropolitan community settings.

There are a few limitations to acknowledge. Our main data source for the pooled dataset is the EMR. Because each system's EMR differs in its documentation of CRC screening process, we have simplified some of our common data elements. We refine operational definitions each year and expand the list of required elements to broaden the comprehensiveness and robustness of the pooled dataset. Also, the EMR does not systematically record patients' and providers' beliefs, intentions, or motivation for CRC screening; thus, we will not have information about those important variables. There are two limitations that impact generalizability of PROSPR CRC pooled dataset. We will have little data from patients living in rural areas. Data are from integrated systems and their ability to coordinate services along the screening process make differ substantially from other practice models. Therefore, we encourage researchers with access to rural populations and different practice settings to use our conceptual model to collect similar data addressing research questions detailed above. PROSPR CRC investigators are interested in collaborating to broaden our collective understanding of CRC screening delivery. In accordance with the National Institutes of Health's data sharing policy, we are currently creating a web portal to receive proposals from researchers interested in collaborating with PROSPR investigators and use the pooled data.

Analysis of patient-, provider-, practice-, and system-level factors that influence screening process outcomes is critical to inform the design and evaluation of multi-level interventions and policies to optimize delivery of CRC screening in healthcare systems serving diverse populations. The ultimate goal of the PROSPR consortium is to optimize screening delivery so that it is consistent with the Institute of Medicine's goals of effective, efficient, coordinated, timely, and safe health care (64).

The authors acknowledge the assistance of Dr. Ann Geiger, Dr. Virginia Quinn, Dr. Nirupa Ghai, Dr. Christopher Jensen, Dr. Noel Santini, Ms. Katharine McCallister, Ms. Winifred Apraku, Mr. Adam Loewen, Mr. Arvind Ramaprasan, and Ms. Susan Carol Bradford.

Financial Support: This research funded by the National Cancer Institute. J.A. Tiro, C.S. Skinner, E.A. Halm, and S. Gupta were supported by grant 5U54CA163308. A. Kamineni, C.M. Rutter, J. Chubak, and K.J. Wernli were supported by 5U54CA163261. T.R. Levin, Joanne S. Schottinger, D.A. Corley, and C.A. Doubeni were supported by 5U54CA163262. Y.Zheng was supported by 5U01CA163304.

ASC

ambulatory surgery centers

COL

colonoscopy

CRC

colorectal cancer

EMR

electronic medical record

FIT

fecal immunochemical test

gFOBT

guaiac-based fecal occult blood test

KPNC

Kaiser Permanente Northern California

KPSC

Kaiser Permanente Southern California

MOB

medical office buildings

Parkland-UTSW

Parkland Health & Hospital System – University of Texas Southwestern Medical Center

PCP

primary care provider

PROSPR

Population-based Research Optimizing Screening through Personalized Regimens

SIG

flexible sigmoidoscopy

Conflict of Interest Statement: Samir Gupta has received prior research support from Polymedco in the form of donated fecal immunochemical tests.

• 1.American Cancer Society. Cancer Facts & Figures 2013. Atlanta, GA: American Cancer Society; 2013. [Google Scholar]
• 2.Shapiro JA, Klabunde CN, Thompson TD, Nadel MR, Seeff LC, White A. Patterns of colorectal cancer test use, including CT colonography, in the 2010 National Health Interview Survey. Cancer Epidemiol Biomarkers Prev. 2012;21:895–904. doi: 10.1158/1055-9965.EPI-12-0192. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 3.Klabunde CN, Cronin KA, Breen N, Waldron WR, Ambs AH, Nadel MR. Trends in colorectal cancer test use among vulnerable populations in the United States. Cancer Epidemiol Biomarkers Prev. 2011;20:1611–1621. doi: 10.1158/1055-9965.EPI-11-0220. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 4.U.S. Preventive Services Task Force. Screening for Colorectal Cancer: U.S. Preventive Services Task Force Recommendation Statement. Ann Intern Med. 2008;149:627–638. doi: 10.7326/0003-4819-149-9-200811040-00243. [DOI] [PubMed] [Google Scholar]
• 5.Levin B, Lieberman DA, McFarland B, Andrews KS, Brooks D, Bond J, et al. Screening and surveillance for the early detection of colorectal cancer and adenomatous polyps, 2008: a joint guideline from the American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology. Gastroenterology. 2008;134:1570–1595. doi: 10.1053/j.gastro.2008.02.002. [DOI] [PubMed] [Google Scholar]
• 6.McFarland EG, Levin B, Lieberman DA, Pickhardt PJ, Johnson CD, Glick SN, et al. Revised colorectal screening guidelines: joint effort of the American Cancer Society, U.S. Multisociety Task Force on Colorectal Cancer, and American College of Radiology. Radiology. 2008;248:717–720. doi: 10.1148/radiol.2483080842. [DOI] [PubMed] [Google Scholar]
• 7.Holden DJ, Jonas DE, Porterfield DS, Reuland D, Harris R. Systematic review: enhancing the use and quality of colorectal cancer screening. Ann Intern Med. 2010;152:668–676. doi: 10.7326/0003-4819-152-10-201005180-00239. [DOI] [PubMed] [Google Scholar]
• 8.Naylor K, Ward J, Polite BN. Interventions to improve care related to colorectal cancer among racial and ethnic minorities: a systematic review. J Gen Intern Med. 2012;27:1033–1046. doi: 10.1007/s11606-012-2044-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 9.Taplin SH, Rodgers AB. Toward improving the quality of cancer care: addressing the interfaces of primary and oncology-related subspecialty care. J Natl Cancer Inst Monogr. 2010;2010:3–10. doi: 10.1093/jncimonographs/lgq006. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 10.Taplin SH, Clauser S, Rodgers AB, Breslau E, Rayson D. Interfaces across the cancer continuum offer opportunities to improve the process of care. J Natl Cancer Inst Monogr. 2010;2010:104–110. doi: 10.1093/jncimonographs/lgq012. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 11.Taplin SH, Anhang PR, Edwards HM, Foster MK, Breslau ES, Chollette V, et al. Introduction: Understanding and influencing multilevel factors across the cancer care continuum. J Natl Cancer Inst Monogr. 2012;2012:2–10. doi: 10.1093/jncimonographs/lgs008. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 12.Yano EM, Green LW, Glanz K, Ayanian JZ, Mittman BS, Chollette V, et al. Implementation and Spread of Interventions Into the Multilevel Context of Routine Practice and Policy: Implications for the Cancer Care Continuum. JNCI Monographs. 2012;2012:86–99. doi: 10.1093/jncimonographs/lgs004. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 13.Population-based Research Optimizing Screening through Personalized Regimens (PROSPR; RFA-CA-11-003) National Cancer Institute; [updated 2011 Oct 27; cited 2013 Jun 1]. Internet. Available from: http://appliedresearch.cancer.gov/networks/prospr. [Google Scholar]
• 14.Zapka JG, Taplin SH, Solberg LI, Manos MM. A framework for improving the quality of cancer care: the case of breast and cervical cancer screening. Cancer Epidemiol Biomarkers Prev. 2003;12:4–13. [PubMed] [Google Scholar]
• 15.Anhang PR, Zapka J, Edwards H, Taplin SH. Organizational factors and the cancer screening process. J Natl Cancer Inst Monogr. 2010;2010:38–57. doi: 10.1093/jncimonographs/lgq008. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 16.Zapka J, Taplin SH, Price RA, Cranos C, Yabroff R. Factors in quality care--the case of follow-up to abnormal cancer screening tests--problems in the steps and interfaces of care. J Natl Cancer Inst Monogr. 2010;2010:58–71. doi: 10.1093/jncimonographs/lgq009. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 17.Nekhlyudov L, Latosinsky S. The interface of primary and oncology specialty care: from symptoms to diagnosis. J Natl Cancer Inst Monogr. 2010;2010:11–17. doi: 10.1093/jncimonographs/lgq001. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 18.Lieberman DA, Rex DK, Winawer SJ, Giardiello FM, Johnson DA, Levin TR. Guidelines for colonoscopy surveillance after screening and polypectomy: a consensus update by the US Multi-Society Task Force on Colorectal Cancer. Gastroenterology. 2012;143:844–857. doi: 10.1053/j.gastro.2012.06.001. [DOI] [PubMed] [Google Scholar]
• 19.Lindor NM, Petersen GM, Hadley DW, Kinney AY, Miesfeldt S, Lu KH, et al. Recommendations for the care of individuals with an inherited predisposition to Lynch syndrome: a systematic review. JAMA. 2006;296:1507–1517. doi: 10.1001/jama.296.12.1507. [DOI] [PubMed] [Google Scholar]
• 20.Sewitch MJ, Jiang M, Joseph L, Hilsden RJ, Bitton A. Developing model-based algorithms to identify screening colonoscopies using administrative health databases. BMC Med Inform Decis Mak. 2013;13:45. doi: 10.1186/1472-6947-13-45. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 21.Sewitch MJ, Stein D, Joseph L, Bitton A, Hilsden RJ, Rabeneck L, et al. Comparing patient and endoscopist perceptions of the colonoscopy indication. Can J Gastroenterol. 2010;24:656–660. doi: 10.1155/2010/328178. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 22.Fisher DA, Grubber JM, Castor JM, Coffman CJ. Ascertainment of colonoscopy indication using administrative data. Dig Dis Sci. 2010;55:1721–1725. doi: 10.1007/s10620-010-1200-y. [DOI] [PubMed] [Google Scholar]
• 23.Weiss NS. Application of the case-control method in the evaluation of screening. Epidemiol Rev. 1994;16:102–108. doi: 10.1093/oxfordjournals.epirev.a036136. [DOI] [PubMed] [Google Scholar]
• 24.Weiss NS. Analysis of case-control studies of the efficacy of screening for cancer: How should we deal with tests done in persons with symptoms? Am J Epidemiol. 1998;147:1099–1102. doi: 10.1093/oxfordjournals.aje.a009407. [DOI] [PubMed] [Google Scholar]
• 25.Church TR. Screening for colorectal cancer--which strategy is the best? J Natl Cancer Inst. 2011;103:1282–1283. doi: 10.1093/jnci/djr300. [DOI] [PubMed] [Google Scholar]
• 26.Taplin SH, Ichikawa L, Yood MU, Manos MM, Geiger AM, Weinmann S, et al. Reason for late-stage breast cancer: absence of screening or detection, or breakdown in follow-up? J Natl Cancer Inst. 2004;96:1518–1527. doi: 10.1093/jnci/djh284. [DOI] [PubMed] [Google Scholar]
• 27.Leyden WA, Manos MM, Geiger AM, Weinmann S, Mouchawar J, Bischoff K, et al. Cervical cancer in women with comprehensive health care access: attributable factors in the screening process. J Natl Cancer Inst. 2005;97:675–683. doi: 10.1093/jnci/dji115. [DOI] [PubMed] [Google Scholar]
• 28.Fiscella K, Humiston S, Hendren S, Winters P, Jean-Pierre P, Idris A, et al. Eliminating disparities in cancer screening and follow-up of abnormal results: what will it take? J Health Care Poor Underserved. 2011;22:83–100. doi: 10.1353/hpu.2011.0023. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 29.Zapka J. Innovative provider- and health system-directed approaches to improving colorectal cancer screening delivery. Med Care. 2008;46:S62–S67. doi: 10.1097/MLR.0b013e31817fdf57. [DOI] [PubMed] [Google Scholar]
• 30.Zapka J, Taplin SH, Ganz P, Grunfeld E, Sterba K. Multilevel factors affecting quality: examples from the cancer care continuum. J Natl Cancer Inst Monogr. 2012;2012:11–19. doi: 10.1093/jncimonographs/lgs005. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 31.Zapka JM, Klabunde CN, Arora NK, Yuan G, Smith JL, Kobrin SC. Physicians' colorectal cancer screening discussion and recommendation patterns. Cancer Epidemiol Biomarkers Prev. 2011;20:509–521. doi: 10.1158/1055-9965.EPI-10-0749. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 32.Klabunde CN, Lanier D, Breslau ES, Zapka JG, Fletcher RH, Ransohoff DF, et al. Improving colorectal cancer screening in primary care practice: innovative strategies and future directions. J Gen Intern Med. 2007;22:1195–1205. doi: 10.1007/s11606-007-0231-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 33.Arroyave AM, Penaranda EK, Lewis CL. Organizational change: a way to increase colon, breast and cervical cancer screening in primary care practices. J Community Health. 2011;36:281–288. doi: 10.1007/s10900-010-9309-7. [DOI] [PubMed] [Google Scholar]
• 34.Haug U, Brenner H. A simulation model for colorectal cancer screening: potential of stool tests with various performance characteristics compared with screening colonoscopy. Cancer Epidemiol Biomarkers Prev. 2005;14:422–428. doi: 10.1158/1055-9965.EPI-04-0411. [DOI] [PubMed] [Google Scholar]
• 35.Haug U, Brenner H. New stool tests for colorectal cancer screening: a systematic review focusing on performance characteristics and practicalness. Int J Cancer. 2005;117:169–176. doi: 10.1002/ijc.21016. [DOI] [PubMed] [Google Scholar]
• 36.Edwards BK, Ward E, Kohler BA, Eheman C, Zauber AG, Anderson RN, et al. Annual report to the nation on the status of cancer, 1975-2006, featuring colorectal cancer trends and impact of interventions (risk factors, screening, and treatment) to reduce future rates. Cancer. 2010;116:544–573. doi: 10.1002/cncr.24760. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 37.Zauber AG, Lansdorp-Vogelaar I. Changes in risk factors and increases in screening contribute to the decline in colorectal cancer mortality, 1975 to 2000. Gastroenterology. 2010;139:698. doi: 10.1053/j.gastro.2010.06.011. [DOI] [PubMed] [Google Scholar]
• 38.van Ballegooijen M, Boer R, Zauber AG. Simulation of colorectal cancer screening: What we do and do not know and does it matter? Best Practice & Research Clinical Gastroenterology. 2010;24:427–437. doi: 10.1016/j.bpg.2010.07.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 39.Zauber AG, Lansdorp-Vogelaar I, Knudsen AB, Wilschut J, van BM, Kuntz KM. Evaluating test strategies for colorectal cancer screening: a decision analysis for the U.S. Preventive Services Task Force. Ann Intern Med. 2008;149:659–669. doi: 10.7326/0003-4819-149-9-200811040-00244. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 40.Levin TR, Jamieson L, Burley DA, Reyes J, Oehrli M, Caldwell C. Organized colorectal cancer screening in integrated health care systems. Epidemiol Rev. 2011;33:101–110. doi: 10.1093/epirev/mxr007. [DOI] [PubMed] [Google Scholar]
• 41.Green BB, Wang CY, Anderson ML, Chubak J, Meenan RT, Vernon SW, et al. An automated intervention with stepped increases in support to increase uptake of colorectal cancer screening: a randomized trial. Ann Intern Med. 2013;158:301–311. doi: 10.7326/0003-4819-158-5-201303050-00002. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 42.Gupta S, Halm EA, Rockey DC, Hammons M, Koch M, Carter E, et al. Comparative effectiveness of fecal immunochemical test outreach, colonoscopy outreach, and usual care for boosting colorectal cancer screening among the underserved: A randomized clinical trial. JAMA Intern Med. 2013;173:1725–1732. doi: 10.1001/jamainternmed.2013.9294. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 43.Colorectal Cancer Control Program. Centers for Disease Control and Prevention (CDC); [updated 2013 Nov 5; cited 2014 Feb 28]. Internet. Available from: http://www.cdc.gov/cancer/crccp/ [Google Scholar]
• 44.State and County QuickFacts. U.S. Census Bureau; [updated 2009 Feb 20; cited 2014 Feb 28]. Internet. Available from: http://quickfacts.census.gov/qfd/states/48000.html. [Google Scholar]
• 45.Area Resource File (ARF): National County-level Health Resource Information Database. Health Resources and Services Administration; [updated 2013 Jan 1; cited 2013 Oct 7]. Internet. Available from: http://arf.hrsa.gov/ [Google Scholar]
• 46.Health Professional Shortage Areas (HPSA) U.S. Department of Health and Human Services; [updated 2011 May 9; cited 2014 Feb 28]. Internet. Available from: http://hpsafind.hrsa.gov/ [Google Scholar]
• 47.Newhouse JP, McClellan M. Econometrics in outcomes research: the use of instrumental variables. Annu Rev Public Health. 1998;19:17–34. doi: 10.1146/annurev.publhealth.19.1.17. [DOI] [PubMed] [Google Scholar]
• 48.Martens EP, Pestman WR, de BA, Belitser SV, Klungel OH. Instrumental variables: application and limitations. Epidemiology. 2006;17:260–267. doi: 10.1097/01.ede.0000215160.88317.cb. [DOI] [PubMed] [Google Scholar]
• 49.Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:55. [Google Scholar]
• 50.Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46:399–424. doi: 10.1080/00273171.2011.568786. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 51.McIntosh MW. Instrumental variables when evaluating screening trials: estimating the benefit of detecting cancer by screening. Stat Med. 1999;18:2775–2794. doi: 10.1002/(sici)1097-0258(19991030)18:20<2775::aid-sim196>3.0.co;2-i. [DOI] [PubMed] [Google Scholar]
• 52.Jacob BJ, Sutradhar R, Moineddin R, Baxter NN, Urbach DR. Methodological approaches to population based research of screening procedures in the presence of selection bias and exposure measurement error: colonoscopy and colorectal cancer outcomes in Ontario. BMC Med Res Methodol. 2013;13:59. doi: 10.1186/1471-2288-13-59. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 53.Kreft I, De Leeuw J. Introducing multilevel modeling. Los Angeles, CA: Sage Publications; 1998. [Google Scholar]
• 54.Nekhlyudov L, Greene SM, Chubak J, Rabin B, Tuzzio L, Rolnick S, et al. Cancer research network: using integrated healthcare delivery systems as platforms for cancer survivorship research. J Cancer Surviv. 2013;7:55–62. doi: 10.1007/s11764-012-0244-8. [DOI] [PubMed] [Google Scholar]
• 55.Steinwachs D, Allen JD, Barlow WE, Duncan RP, Egede LE, Friedman LS, et al. National Institutes of Health state-of-the-science conference statement: Enhancing use and quality of colorectal cancer screening. Ann Intern Med. 2010;152:663–667. doi: 10.7326/0003-4819-152-10-201005180-00237. [DOI] [PubMed] [Google Scholar]
• 56.Anderson RJ, Amarasingham R, Pickens SS. The quest for quality: perspectives from the safety net. Frontiers of Health Services Management. 2007;23:15–28. [PubMed] [Google Scholar]
• 57.Pickens S, Boumbulian P, Anderson RJ, Ross S, Phillips S. Community-oriented primary care in action: a Dallas story. Am J Public Health. 2002;92:1728–1732. doi: 10.2105/ajph.92.11.1728. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 58.Katz MH, Brigham TM. Transforming A Traditional Safety Net Into A Coordinated Care System: Lessons From Healthy San Francisco. 2011;30:237–245. doi: 10.1377/hlthaff.2010.0003. [DOI] [PubMed] [Google Scholar]
• 59.Ku L, Regenstein M, Shin P, Mead H, Levy A, Buchanan K, Byrne F. Coordinating and Integrating Care for Safety Net Patients: Lessons from Six Communities. George Washington University; 2012. [Google Scholar]
• 60.Rural-Urban Community Area Codes (RUCAs) WWAMI Rural Health Research Center; [updated 2013 Jan 1 cited 2013 Oct 7]. Internet. Available from: http://depts.washington.edu/uwruca/index.php. [Google Scholar]
• 61.Rural-Urban Community Area Codes. US Department of Agriculture, Economic Research Service; [updated 2013 Jul 8; cited 2013 Oct 7]. Internet. Available from: http://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes.aspx. [Google Scholar]
• 62.Report to Congress: Medicare Ambulatory Surgical Center Value-Based Purchasing Implementation Plan. U.S. Department of Health and Human Services; [updated 2013 Apr 11; cited 2013 Oct 7]. Internet. Available from: https://www.cms/gov/Medicare/Medicare-Fee-for-Service-Payment/ASCPayment/Downloads/C_ASC_RTC-2011.pd. [Google Scholar]
• 63.Conditions for Coverage & Conditions of Participations. Centers for Medicare & Medicaid Services (CMS); [updated 2013 May 11; cited 2013 Oct 7]. Internet. Available from: http://www.cms.gov/Regulations-and-Guidance/Legislation/CFCsAndCoPs/index.html. [Google Scholar]
• 64.Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press; 2001. [PubMed] [Google Scholar]

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