Evaluating the Effectiveness and Economic Viability of a Novel Population-Based Colorectal Cancer Screening Strategy

Evaluating the Effectiveness and Economic Viability of a Novel Population-Based Colorectal Cancer Screening Strategy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating the Effectiveness and Economic Viability of a Novel Population-Based Colorectal Cancer Screening Strategy Christopher Grivas, Daohai Yu, Abraham Ifrah, Manasa Vallabhaneni, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6542743/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Colorectal cancer (CRC) remains the second leading cause of cancer-related deaths in the United States, with disparities in screening disproportionately affecting socioeconomically disadvantaged and underrepresented populations, particularly in urban areas. Previous research has shown that proactive outreach strategies such as Fecal Immunochemical Test(s) (FIT) and colonoscopy significantly enhance CRC screening although these efforts typically have targeted patients already engaged with primary care and exclude those without recent primary care visits in the past year. The ACCESS (Advancing Colorectal Cancer Equity through Systematic Screening) initiative, offers a unique and financially sustainable method to address discrepancies in CRC screening. Objective: To evaluate the effectiveness and financial sustainability of the ACCESS initiative, a community-based approach launched by Temple University Hospital (TUH) in Philadelphia, Pennsylvania, which targets CRC screening disparities by circumventing the primary care health system and distributing FIT directly to patients. Methods: The ACCESS program distributed FIT in non-traditional, high-traffic community settings to average risk individuals aged 45-75, per United States Preventive Services and Task Force (USPSTF) guidelines. This method circumvents traditional healthcare touchpoints, such as primary care referrals, reaching those who typically lack regular healthcare engagement. Results: Among the 799 FIT distributed, 293 results were reported (response rate: 36.7%), with 48 positive FIT (positivity rate: 16.4%). Notably, individuals who had not visited a primary care provider in the past year exhibited a higher positivity rate (26.5%) compared to those who had (15.1%) (p=.064). More interestingly, men who saw a primary care doctor within the past year had a positivity rate of 16.0% compared to a 41.4% positivity rate amongst men who did not see a primary care doctor within the past year (p=.056). Follow-up colonoscopy was completed in 29.2% of cases with positive FIT results at Temple University Hospital while the other individuals chose to follow up outside of the Temple Health system. Financial analysis revealed that the majority of follow-up colonoscopies were charged using diagnostic current procedural terminology (CPT) codes as opposed screening CPT codes and that the average reimbursement per CPT charged to a FIT-prompted colonoscopy to be $1,171.62 compared to $1084.60 for non-FIT test prompted colonoscopies. Conclusion: The ACCESS initiative successfully extends CRC screening to underserved populations not previously outlined in other literature describing population-based CRC screening efforts. This initiative demonstrated higher positivity rates among those less engaged in traditional primary care systems and offers insight into the financial sustainability of population-based screening initiatives. Introduction Colorectal cancer (CRC) is the third most commonly diagnosed cancer in the world and the second leading cause of cancer-related deaths in the United States, despite its high preventability through timely screening and early intervention. 1 – 5 Unfortunately, significant disparities in CRC screening persist, particularly in socioeconomically disadvantaged and underrepresented populations. 6 , 7 These disparities are most pronounced in urban settings, where systemic barriers—such as limited access to healthcare, low health literacy, distrust in medical institutions, and a lack of primary care—compound to delay screening. This results in disproportionately higher CRC morbidity and mortality rates in low-income and minority communities compared to the general population. 8 – 11 . In addition, data suggests that there are racial differences in incidence of CRC diagnosis showing that for every 100 CRC diagnosis in white Americans, there are 113 CRC diagnosis in Black Americans. Moreover, data shows there are mortality differences as well with every 100 CRC deaths in white Americans, there are 132 CRC deaths among Black Americans. 12 Previous studies have explored outreach strategies to improve CRC screening rates, specifically demonstrating that both FIT and colonoscopy outreach significantly improved screening rates over usual care in the primary care setting, with FIT outreach showing the highest impact in underserved populations due to its accessibility and convenience. 13 – 16 While demonstrating the effectiveness of proactive outreach efforts, these efforts have largely focused on patients already within the primary care system and who have reliable follow up with their primary care doctor. Many of these studies excluded patients who have not seen a primary care doctor within the past year. Moreover, there have been additional ongoing efforts to improve colorectal cancer screening rates among vulnerable populations. For instance, the Centers for Disease Control and Prevention's (CDC) Colorectal Cancer Control Program (CRCCP) partnered with state health departments to boost screening in underserved communities. Each participating organization implemented strategies such as patient reminders, provider alerts, provider performance reviews, staff incentives, and patient navigation support showing increased CRC screening rates. 17 – 20 However, these efforts also relied on patients already within the primary care system. The reliance on existing healthcare touchpoints, such as primary care referrals, continues to leave many vulnerable individuals underserved. The ACCESS (Advancing Colorectal Cancer Equity through Systematic Screening) initiative, launched by Temple University Hospital in 2023, addresses this critical gap with a pioneering community-based approach. Temple University Hospital, located in Philadelphia, Pennsylvania, mirrors the demographic and financial challenges faced by other large safety-net hospitals across the United States, with over 87% of its patient population enrolled in Medicaid or Medicare, and a significant portion identifying as Black (38.6%) or Hispanic (16.1%). This predominance of patients reliant on government insurance, coupled with the area's high levels of poverty and healthcare inequity, creates substantial barriers to CRC screening. Traditional healthcare approaches, which depend heavily on primary care referrals and follow-up, often fail to adequately serve these communities, contributing to delayed diagnosis and worse CRC outcomes. ACCESS is unique in its strategy, removing the need for prior healthcare engagement by directly reaching underserved populations where they live, work, and gather. Instead of waiting for patients to visit a primary care provider, ACCESS distributes FIT in high-traffic community locations such as hospital lobbies, churches, grocery stores, and health fairs. This proactive and accessible model makes CRC screening more feasible for individuals who would otherwise face significant barriers to healthcare. ACCESS directly reaches out to the participants to report FIT results and, if positive, offers a colonoscopy at Temple University Hospital. Moreover, while other programs have demonstrated the effectiveness of FIT outreach, many do not highlight the crucial follow-up steps, such as colonoscopy completion after a positive FIT result. ACCESS not only emphasizes screening uptake but also ensures a clear pathway to follow-up care, directly addressing these critical gaps. By focusing on both engagement and follow-up, the ACCESS initiative represents a novel approach to CRC screening for vulnerable populations—potentially serving as a model for other large health systems. In this article, we will analyze the ACCESS strategy by providing evidence showing the novelty and effectiveness of this initiative compared to other previous CRC screening uptake efforts, report on the financial sustainability of this initiative, and advantages and disadvantages of this strategy to help other safety net hospitals optimize their CRC outreach. Methods Average risk participants aged 45–75 years were eligible for FIT based on the USPSTF guidelines. Participants were engaged by a team of residents and medical students at various distribution events with the exception of 94 participants calling into Temple University Hospital after watching a local news segment highlighting the initiative (Supplemental Fig. 1) . Participants then underwent a screening process involving a series of questions to assess their average risk status (Supplemental Fig. 2). Screening results were recorded using REDCap (Research Electronic Data Capture), a web-based, Health Insurance Portability and Accountability Act (HIPAA) compliant software platform designed to support data capture for research studies, on iPads that were brought to the events. Survey responses were recorded in real time. Those meeting the criteria for average risk were offered FIT and provided with instructions for its proper use at home. The FIT used was the second-generation FIT manufactured by Pinnacle Biolabs. Participants were requested to provide their phone number, acknowledging that they would be contacted for follow-up regarding the FIT results. A community health worker was hired for this initiative and contacted participants by texts once per week and by phone call during the third week to obtain results. After three unsuccessful attempts, participants were categorized as "incomplete." In the event of a negative result, relevant data was recorded, and participants were informed about the necessity of annual follow-up tests with FIT to be compliant with CRC screening guidelines. Conversely, if the result was positive, participants were offered a colonoscopy and gastroenterology (GI) follow-up at Temple University Hospital. Those who opted to follow up with a non-Temple University Hospital GI doctor were counseled on the importance of following up their positive result. Participant colonoscopy outcomes were assessed through electronic medical records. All data was recorded REDcap. Notes were written in the electronic medical records of all outcomes so other providers would be aware. Participants who were given FIT were also assessed to see if they had seen an internal medicine physician, family medicine physician, gastroenterologist, or medical oncologist within the past year from when they were given a FIT. For the ease of this writing, internal medicine, family medicine, gastroenterologist or medical oncologist will be referred to as primary care physician as these physicians are most likely to engage in colorectal cancer screening for their participants. Each participant was searched using Care Everywhere to assess if they had seen a primary physician in the past year from when they received a FIT. Care Everywhere is an interoperability feature within Epic Systems, a widely used electronic medical record (EMR) platform. It enables the secure exchange of patient health information between different healthcare organizations, even if they use different instances of Epic or other EMR systems that are connected to the Carequality interoperability framework. Descriptive statistics were used to summarize the study population. Continuous variables, such as age, were reported as medians with interquartile ranges (IQR), while categorical variables, including race and gender, were summarized using frequencies and percentages. FIT completion rate was calculated as the proportion of FIT that were returned with a result (either positive or negative) out of all FIT distributed. The FIT positivity rate was defined as the proportion of positive results among all returned FIT. The colonoscopy conversion rate was calculated as the proportion of individuals who, after returning a FIT result, proceeded to undergo a follow-up colonoscopy at Temple University Hospital. Comparisons of FIT completion, FIT positivity, and colonoscopy conversion rates were conducted across various groups, including event setting, gender, race, and age categories. The Fisher’s Exact Mid p-value test was used for comparisons involving categorical variables with two levels, such as gender, while the Chi-square test was applied for variables with three or more levels, such as race. FIT completion and positivity rates were also compared between individuals who had a primary care visit within the past year and those who did not. Additionally, these comparisons were stratified by gender to assess differences within male and female subgroups based on primary care utilization. A p-value of less than 0.05 was considered statistically significant. Due to the exploratory nature of the study, adjustments for multiple comparisons were not made. All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC). To evaluate financial sustainability, we analyzed colonoscopy billing data (CPT codes) from a total of 27,169 codes charged at Temple University Hospital between 2017 and 2022. 16 individual colonoscopy CPT codes were categorized into two groups—screening and diagnostic. Using Medicare reimbursement rates, we determined the reimbursement amounts for each screening and diagnostic CPT code and calculated the overall average reimbursement for a screening CPT code and diagnostic CPT code (Supplemental Fig. 3). The historical annual amount and proportion of screening and diagnostic CPT codes charged was then calculated along with average reimbursement for a screening and diagnostic CPT code that were associated with non-FIT prompted colonoscopy (Supplemental Fig. 4). For FIT-prompted colonoscopies, the proportion of screening and diagnostic CPT codes was analyzed and the average reimbursement for a screening and diagnostic CPT code associated with a FIT-prompted colonoscopy was calculated. Results Study Population Overall, FIT were distributed to 799 individuals at 55 different distribution events either on the Temple University Hospital campus or community distribution events with an average of 14.5 FIT being distributed per event. Of the 799 individuals, the median age was 60.3 years old and 62.2% identified as women with 5.4% not reporting their gender. 47.8% of the 799 individuals reported their race as Black and 22.9% identified as white. A total of 23 events were held at different locations at Temple University Hospital campus with a total of 414 FIT distributed and an average of 18.0 FIT distributed per event. The median age at the hospital lobby events was 58.7 and 58.9% identified as female with 8.9% not reporting their gender. 44.4% of participants identified as Black while 20.5% identified as white. A total of 32 distribution events were held at various community events with a total of 385 FIT being distributed and an average of 12.0 FIT distributed per event. The median age at community events was 61.9 and 65.7% identified as female with 1.6% not reporting their gender. 51.4% of participants identified as Black and 25.5% identified as white. ( Table 1 ) FIT Completion Rates, Response Rates and Conversion to Colonoscopy at Home Institution A total of 799 FIT were distributed at all distribution events with 293 FIT results being reported back to the ACCESS team equating to a 36.7% FIT completion rate and 48 were returned as positive equating to a positivity rate of 16.4%. Of the 48 positive participants, 14 opted receive follow-up colonoscopy at Temple University Hospital for a conversion rate from positive FIT to colonoscopy of 29.2%. Hospital distribution events and community events has completion rates of 34.8% and 38.7%, respectively (p = .26). Hospital distribution events and community events has positivity rates of 16.7% and 16.1%, respectively (p = .94). Hospital distribution events and community events had conversion rates from positive FIT to colonoscopy at Temple University Hospital of 41.7% and 16.7%, respectively (p = .068). Males and females had completion rates of 34.4% and 37.4%, respectively (p = .40). Male and females had positivity rates of 21.3%% and 15.6%, respectively (p = .21). Males and females had conversion rates from positive FIT to colonoscopy at Temple University Hospital of 26.3% and 31.0%%, respectively (p = .76). Black, white, and Hispanic/other had completion rates of 34.6%, 42.6%, 29.8%, respectively (p = 0.045). Black, white, and Hispanic/other had positivity rates of 19.7%, 12.8%, 21.4%, respectively (p = 0.36). Black, white, and Hispanic/other had conversion rates from positive FIT to colonoscopy at Temple University Hospital of 34.6%, 0.0%, 55.6% respectively (p = 0.028). Participants that were ages 45–54, 55–64, and 65–75 had completion rates of 37.4%, 33.3%, and 40.5% (p = .21), positivity rates of 18.0%, 17.6%, and 13.5%, respectively (p = 0.65), and conversion rates to colonoscopy at TUH of 31.3%, 42.1%, 7.7% respectively (p = .11). ( Table 2 ) Primary Care Visits within the past year and completion rates and positivity rates Of the 799 participants who have received a FIT, 520 had data available in Care Everywhere to determine if they saw a primary care physician within the past year from receiving their FIT. Of the 520 participants, 241 saw a primary care physician within the past year since the day they received their FIT. Participants who saw a primary care doctor within the past year had a completion rate of 38.6% and participants who did not see a primary care doctor within the past year had a completion rate of 35.1% (p = 0.44). Participants who saw a primary care doctor within the past year had a positivity rate of 15.1% and participants who did not see a primary care doctor within the past year had a positivity rate of 26.5% (p = 0.064). The remaining 279 did not have any data available in Care Everywhere to determine if they saw a primary care physician in the past year. Regarding the male participants, 61 saw a primary care physician within the past year since the day they received the test. Men who saw a primary care doctor within the past year had a completion rate of 41.0% and men who did not see a primary care doctor within the past year had a completion rate of 32.6% (p = 0.26). Men who saw a primary care doctor within the past year had a positivity rate of 16.0% and men who did not see a primary care doctor within the past year had a positivity rate of 41.4% (p = 0.056). Regarding the female participants, 167 saw a primary care physician within the past year since the day they received the test. Women who saw a primary care doctor within the past year had a completion rate of 37.1% and women who did not see a primary care doctor within the past year had a completion rate of 37.1% (p = 0.95). Women who saw a primary care doctor within the past year had a positivity rate of 16.1% and women who did not see a primary care doctor within the past year had a positivity rate of 22.6% (p = 0.38) ( Table 3 ). Types of Colonoscopies Performed for FIT Positive Participants. Temple University Hospital charges on average 4,453 colonoscopy associated CPT codes per year in the outpatient setting with 34.0% of the CPT codes being charged categorized as screening and 66.0% being categorized as diagnostic. The distribution of CPT codes charged for FIT-prompted colonoscopy were found to be 7.10% screening and 92.9% diagnostic. The average Medicare reimbursement rate for screening and diagnostic CPT code is $ 870.81 and $ 1,194.61, respectively. As a result, the average reimbursement per non-FIT-prompted CPT code done at Temple historically is $ 1084.60 and the average reimbursement per FIT-prompted CPT code is $ 1171.62 which is a difference $ 87.02 ( Table 4 ). Discussion Developing creative and data-driven CRC screening strategies that are also financially viable for hospitals is critical for assisting vulnerable patients in receiving lifesaving CRC screening. To our best knowledge, no other initiative has implemented a strategy to improve screening for patients by circumventing the primary care system. It is our hope that the ACCESS initiative can serve as a model for other large health systems in urban areas to create and improve their CRC screening initiatives to effectively serve their vulnerable populations in a financially sustainable way. Our data shows that completion rates of FIT were similar regardless of location, age, and gender. However, our data does show the FIT completion rates were significantly different among race with white participants having the highest completion rate of 42.6% followed by Black and Hispanic/other having lower completion rates of 34.2% and 29.8%, respectively, suggesting that greater efforts need to be made in encouraging non-white participants to complete their FIT. In addition, positivity rates among distribution event location, age, race, and gender were non-statistically different which suggests that discrepancies in CRC incidence among different racial groups may be contributed, in part, to a lack of completing these tests. It is important to note that completion rates of FIT in the ACCESS initiative were comparable to, if not better than, other randomize control trials exploring FIT as a viable CRC screening strategy. In a 2018 JAMA randomized control trial, Coronado et al mailed 21,134 people FIT and the completion rate was 13.9%. In addition, in 2013 JAMA article, Gupta et al explored mailing uninsured patients who have been seen by a primary physician in the past 8 months had completion rates of FIT of 40.7%. The ACCESS initiative had a completion rate of 36.7% which is in line with completion rate of the current literature and addresses a unique subset of patients that these other studies do not address but need to be made to better through data driven strategies. Possible reasons completion rates are not higher may include participants losing the test after receiving it, concerns about a positive result, or not understanding the importance of CRC screening. Further research needs to be completed to elucidate ways to increase completion rates. In addition, our initiative shows how other studies demonstrating the efficacies of FIT screening strategies can fall short and neglect a portion of patients who are vulnerable to not receiving adequate CRC screening. The lack of high touch points within the primary care system can lead to clinically significant discrepancies in positivity rates as demonstrated by our initiative. Our study shows participants who have not seen a primary care doctor in the past year are about 1.8 times more likely to have a positive FIT result compared to those who have seen a primary care doctor in the past year. This is even more pronounced in men where men who have not seen a primary care doctor in the past year are 2.6 times more likely to have a positive FIT compared to men who have seen a primary care doctor in the past year. Women who have not seen a primary doctor in the past year were only 1.4 times more likely to test positive compared to women. Response rates among men and women were similar regardless of whether participants have seen a primary care physician within the past year. This data demonstrates there is a subset of participants who do not have access to primary care and as result are at higher risk for a positive FIT and ultimately colorectal pathology and should strongly be considered when developing CRC screening strategies to ensure these populations are getting the screening they need. Creating a financially sustainability CRC initiative is critical to ensuring longitudinal success of initiatives like ACCESS. Our data, shows the majority of FIT positive participants had CPT codes assigned to their colonoscopy that were diagnostic as opposed to screening. The leads to an increase in revenue of $ 87.02 on average when a FIT prompted CPT code is charged. Assuming a fixed number of colonoscopies per year, if a hospital were able to triage 500 FIT prompted colonoscopies per year, they would recognize an additional $ 43,510 that can be reinvested into ACCESS to help financial sustain this initiative either through purchasing more FIT to screen more people or rescreen the following year. Although this revenue increase is modest, it shows the potential for initiatives like these to be financially self-sustainable and target patients with low primary care access who are at higher risk for positive FIT. In addition, it is important to note that five of the fourteen patients who underwent colonoscopy also underwent EGD due to also experiencing upper GI symptoms that were identified during the GI office visit following a positive FIT. This could offer additional revenue to be reinvested to CRC initiatives like ACCESS. Moreover, the conversion rate from positive FIT to colonoscopy at TUH was 29.2%. Expanding efforts to increase the conversion rate will both ensure that patients follow their positive FIT results and also provide additional revenue to fund initiatives like ACCESS. Lastly, these data should emphasize the need programs like Medicare/Medicaid to increase investment in screening initiatives that circumvent the primary care system. It should also be addressed that ACCESS faced logistical challenges regarding the distribution of FIT at events. 55 events were held over the study period with an average of 14.5 tests being distributed per event. Frequency of events were limited by the availability of medical students and resident physicians to distribute the FIT. These challenges can be addressed by hiring dedicated personnel to staff distribution events to achieve more FIT distributed per event and higher frequency of distribution events. In conclusion, the ACCESS initiative underscores the urgent need to address gaps in colorectal cancer screening for populations traditionally underserved by the primary care system. By implementing novel strategies such as the ACCESS initiative, which circumvents traditional primary care pathways, we can significantly improve CRC screening uptake and potentially reduce the incidence and mortality associated with this disease. Our findings highlight that targeting high-risk populations who are less likely to visit primary care providers can lead to earlier detection of CRC through increased FIT positivity rates. Among the 14 colonoscopies performed through the initiative, we successfully identified a patient with colorectal cancer and subsequently underwent treatment. It should be noted that this patient is male and did see a primary care doctor within the past year from the date of receiving a FIT. This approach not only has implications for improving health outcomes but also underscores the necessity for large urban hospitals like Temple University Hospital to adapt and invest in more accessible and equitable screening processes. Our initiative offers a promising model that, with appropriate financial support and scalability, could be a cornerstone in efforts to close the CRC screening gap and save lives. Conclusion The ACCESS initiative demonstrates that reaching patients outside of traditional primary care settings can effectively expand colorectal cancer screening access to underserved populations, identifying individuals at higher risk who might otherwise remain undiagnosed. With promising clinical and financial outcomes, this model offers a scalable and sustainable framework for other urban health systems working to close gaps in preventive cancer care. Abbreviations CRC Colorectal Cancer FIT Fecal Immunochemical Test(s) ACCESS Advancing Colorectal Cancer Equity through Systematic Screening TUH Temple University Hospital USPSTF United States Preventive Services Task Force CPT Code Current Procedural Terminology Code CDC Centers for Disease Control and Prevention's CRCCP Colorectal Cancer Control Program REDCap Research Electronic Data Capture HIPAA Health Insurance Portability and Accountability Act GI Gastroenterology EMR Electronic Medical Record IQR Interquartile Ranges (IQR) Declarations Ethics Approval and Consent to Participate This project was classified as IRB-exempt by the Lewis Katz School of Medicine IRB at Temple University, as it involved the delivery of evidence-based usual care consistent with established colorectal cancer screening guidelines. The requirement for informed consent was waived given the minimal risk to participants and the use of procedures ordinarily conducted in routine clinical practice. Competing Interests The authors declare no competing interests Availability of Data and Materials The authors are committed to transparency and will make data, analytic methods, and study materials related to this article available to other researchers upon request, provided they are not used for commercial or profit-driven purposes. Interested parties may contact the corresponding author via email, including a rationale for the request. Relevant materials will be shared following appropriate discussion and approval. Funding This work was supported by the 2023 Independence Blue Cross Clinical Care Innovation Grant that was award to Temple University Hospital. The funders played no part in the data collection, management, or analysis. The funders did not have a role in the manuscript preparation or in the choice of journal to which the manuscript was submitted. Author Contributions C.G.: Conceptualized and designed the study, led data collection and statistical analysis, interpreted results, drafted the manuscript, and provided critical revisions for intellectual content. D.Y.: Conducted the primary statistical analysis and contributed significant revisions to the manuscript. A.I.: Assisted with study design, was integral to data collection, and contributed to manuscript revisions. D.K., M.V., B.G.: Facilitated recruitment of medical student and resident volunteers for FIT kit distribution events and supported data analysis. G.G., S.M., N.K.: Actively participated in FIT kit distribution and contributed to data collection and analysis. D.C.: Provided additional statistical analysis support and manuscript feedback. R.K.: Co-authored the Independence Blue Cross Clinical Care Innovation Grant that funded this initiative at Temple University Hospital; played a key role in developing the infrastructure for FIT kit distribution and follow-up of positive results with colonoscopy. C.R.: Co-authored the Independence Blue Cross Clinical Care Innovation Grant, was instrumental in designing the operational framework for FIT distribution and follow-up, and provided mentorship and guidance throughout the development and execution of the project. Acknowledgements We sincerely thank community members of North Philadelphia who participated in the ACCESS initiative and contributed to its success. We also acknowledge the dedicated medical students, residents, and faculty from Temple University Hospital who supported FIT distribution, data collection, and patient follow-up. Their commitment was instrumental in advancing colorectal cancer screening access for underserved populations. 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JAMA Intern Med . 2013;173(18):1725-1732. doi:10.1001/jamainternmed.2013.9294 Singal AG, Gupta S, Skinner CS, et al. Effect of Colonoscopy Outreach vs Fecal Immunochemical Test Outreach on Colorectal Cancer Screening Completion: A Randomized Clinical Trial. JAMA . 2017;318(9):806-815. doi:10.1001/jama.2017.11389 Somsouk M, Rachocki C, Mannalithara A, et al. Effectiveness and Cost of Organized Outreach for Colorectal Cancer Screening: A Randomized, Controlled Trial. J Natl Cancer Inst . 2020;112(3):305-313. doi:10.1093/jnci/djz110 Dougherty MK, Brenner AT, Crockett SD, et al. Evaluation of Interventions Intended to Increase Colorectal Cancer Screening Rates in the United States: A Systematic Review and Meta-analysis. JAMA Intern Med . 2018;178(12):1645-1658. doi:10.1001/jamainternmed.2018.4637 Kim KE, Tangka FKL, Jayaprakash M, et al. Effectiveness and Cost of Implementing Evidence-Based Interventions to Increase Colorectal Cancer Screening Among an Underserved Population in Chicago. Health Promot Pract . 2020;21(6):884-890. doi:10.1177/1524839920954162 Barajas M, Tangka FKL, Schultz J, et al. Examining the Effectiveness of Provider Incentives to Increase CRC Screening Uptake in Neighborhood Healthcare: A California Federally Qualified Health Center. Health Promot Pract . 2020;21(6):898-904. doi:10.1177/1524839920954166 Conn ME, Kennedy-Rea S, Subramanian S, et al. Cost and Effectiveness of Reminders to Promote Colorectal Cancer Screening Uptake in Rural Federally Qualified Health Centers in West Virginia. Health Promot Pract . 2020;21(6):891-897. doi:10.1177/1524839920954164 Coronado GD, Petrik AF, Vollmer WM, et al. Effectiveness of a Mailed Colorectal Cancer Screening Outreach Program in Community Health Clinics: The STOP CRC Cluster Randomized Clinical Trial. JAMA Intern Med . 2018;178(9):1174-1181. doi:10.1001/jamainternmed.2018.3629 Tables Table 1: Demographic Summary of All Subjects Who Received FIT and FIT Distribution Event Type (n=799) Variable Overall Hospital Events Community Events Age: Median (IQR) 60.3 (53.5, 66.2) 58.7 (52.4, 64.2) 61.9 (55.6, 68.0) Age Group 45-54 238 (29.8%) 149 (36.0%) 89 (23.1%) 55-64 324 (40.6%) 175 (42.3%) 149 (38.7%) 65-75 237 (29.7%) 90 (21.7%) 147 (38.2%) Gender Male 259 (32.4%) 133 (32.1%) 126 (32.7%) Female 497 (62.2%) 244 (58.9%) 253 (65.7%) Not-Reported/Unknown 43 (5.4%) 37 (8.9%) 6 (1.6%) Race Black 382 (47.8%) 184 (44.4%) 198 (51.4%) White 183 (22.9%) 85 (20.5%) 98 (25.5%) Other 141 (17.6%) 90 (21.7%) 51 (13.2%) Not-reported 93 (11.6%) 55 (13.3%) 38 (9.9%) Average FIT Distributed/Event 14.5 (799/55) 18.0 (414/23) 12.0 (385/32) Table 2: Completion, Positivity, and Conversion to Colonoscopy at TUH Event Type, Sex, Race, and Age Groups N FIT Completion p-value FIT Positivity p-value Conversion To Colonoscopy p-value Overall 799 293 (36.7%) 48 (16.4%) 14 (29.2%) FIT Event Type 0.26 0.94 0.068 Hospital Lobby 414 144 (34.8%) 24 (16.7%) 10 (41.7%) Community 385 149 (38.7%) 24 (16.1%) 4 (16.7%) Sex 0.40 0.21 0.76 Male 259 89 (34.4%) 19 (21.3%) 5 (26.3%) Female 497 186 (37.4%) 29 (15.6%) 9 (31.0%) Race 0.045 0.36 0.028 Black 382 132 (34.6%) 26 (19.7%) 9 (34.6%) White 183 78 (42.6%) 10 (12.8%) 0 (0.0%) Hispanic/Other 141 42 (29.8%) 9 (21.4%) 5 (55.6%) Age Group 0.21 0.65 0.11 45-54 238 89 (37.4%) 16 (18.0%) 5 (31.3%) 55-64 324 108 (33.3%) 19 (17.6%) 8 (42.1%) 65-75 237 96 (40.5%) 13 (13.5%) 1 (7.7%) Table 3: Comparison of FIT Completion and Positivity Rates Between Individuals With and Without a Primary Care Visit in the Year Prior to Receiving FIT Primary Care in the last Year Yes No p-value Overall 241 279 FIT Completed (Completion Rate) 93 (38.6%) 98 (35.1%) 0.44 FIT Positive (Positivity Rate) 14 (15.1%) 26 (26.5%) 0.064 Male 61 89 FIT Completed (Completion Rate) 25 (41.0%) 29 (32.6%) 0.26 FIT Positive (Positivity Rate) 4 (16.0%) 12 (41.4%) 0.056 Female 167 167 FIT Completed (Completion Rate) 62 (37.1%) 62 (37.1%) 0.95 FIT Positivity (Positivity Rate) 10 (16.1%) 14 (22.6%) 0.38 §Primary care data was available on 520 participants. Furthermore,13 participants who did not report their sex were not included in the stratified Sex analyses. Table 4: Comparison of CPT Code Distribution and Reimbursement for Non-FIT vs. FIT-Prompted Colonoscopies CPT Code Type Reimbursement Proportion Average Reimbursement Non-FIT Prompted Colonoscopy Screening CPT Code $870.81 34.0% $1,084.60 Diagnostic CPT Code $1,194.61 66.0% FIT Prompted Colonoscopy Screening CPT Code $870.81 7.10% $1,171.62 Diagnostic CPT Code $1,194.61 92.9% Additional Declarations No competing interests reported. Supplementary Files SupplementalFigures.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6542743","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467578536,"identity":"9110a63e-3e7a-4468-93f4-8d9fb278cdd4","order_by":0,"name":"Christopher Grivas","email":"data:image/png;base64,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","orcid":"","institution":"Temple University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Grivas","suffix":""},{"id":467578537,"identity":"675a0000-5529-4581-b542-da97ca1fe88e","order_by":1,"name":"Daohai Yu","email":"","orcid":"","institution":"Center for Biostatistics \u0026 Epidemiology at Lewis Katz School of 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University","correspondingAuthor":false,"prefix":"","firstName":"Darina","middleName":"","lastName":"Chudnovskaya","suffix":""},{"id":467578556,"identity":"aa79f36c-a925-4b17-b8fb-ab3a58097cf4","order_by":11,"name":"Rishabh Khatri","email":"","orcid":"","institution":"Temple University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rishabh","middleName":"","lastName":"Khatri","suffix":""},{"id":467578557,"identity":"c3995828-3871-42c0-8a7a-f51090cf5658","order_by":12,"name":"Claire Raab","email":"","orcid":"","institution":"Temple University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Claire","middleName":"","lastName":"Raab","suffix":""}],"badges":[],"createdAt":"2025-04-28 00:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6542743/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6542743/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101847226,"identity":"843f4cf0-e66b-45d9-ba8d-0e467fed7259","added_by":"auto","created_at":"2026-02-04 09:28:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1037313,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6542743/v1/8a8732f0-d262-4608-8f9f-1c254c0906c2.pdf"},{"id":84273678,"identity":"099a9b5a-692c-4c9f-97c3-3745bc42833e","added_by":"auto","created_at":"2025-06-10 05:10:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22181,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-6542743/v1/5bd5baa23afc3238616c1adf.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating the Effectiveness and Economic Viability of a Novel Population-Based Colorectal Cancer Screening Strategy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is the third most commonly diagnosed cancer in the world and the second leading cause of cancer-related deaths in the United States, despite its high preventability through timely screening and early intervention.\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Unfortunately, significant disparities in CRC screening persist, particularly in socioeconomically disadvantaged and underrepresented populations.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e These disparities are most pronounced in urban settings, where systemic barriers\u0026mdash;such as limited access to healthcare, low health literacy, distrust in medical institutions, and a lack of primary care\u0026mdash;compound to delay screening. This results in disproportionately higher CRC morbidity and mortality rates in low-income and minority communities compared to the general population.\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In addition, data suggests that there are racial differences in incidence of CRC diagnosis showing that for every 100 CRC diagnosis in white Americans, there are 113 CRC diagnosis in Black Americans. Moreover, data shows there are mortality differences as well with every 100 CRC deaths in white Americans, there are 132 CRC deaths among Black Americans.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePrevious studies have explored outreach strategies to improve CRC screening rates, specifically demonstrating that both FIT and colonoscopy outreach significantly improved screening rates over usual care in the primary care setting, with FIT outreach showing the highest impact in underserved populations due to its accessibility and convenience.\u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e While demonstrating the effectiveness of proactive outreach efforts, these efforts have largely focused on patients already within the primary care system and who have reliable follow up with their primary care doctor. Many of these studies excluded patients who have not seen a primary care doctor within the past year. Moreover, there have been additional ongoing efforts to improve colorectal cancer screening rates among vulnerable populations. For instance, the Centers for Disease Control and Prevention's (CDC) Colorectal Cancer Control Program (CRCCP) partnered with state health departments to boost screening in underserved communities. Each participating organization implemented strategies such as patient reminders, provider alerts, provider performance reviews, staff incentives, and patient navigation support showing increased CRC screening rates.\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e However, these efforts also relied on patients already within the primary care system. The reliance on existing healthcare touchpoints, such as primary care referrals, continues to leave many vulnerable individuals underserved.\u003c/p\u003e \u003cp\u003eThe ACCESS (Advancing Colorectal Cancer Equity through Systematic Screening) initiative, launched by Temple University Hospital in 2023, addresses this critical gap with a pioneering community-based approach. Temple University Hospital, located in Philadelphia, Pennsylvania, mirrors the demographic and financial challenges faced by other large safety-net hospitals across the United States, with over 87% of its patient population enrolled in Medicaid or Medicare, and a significant portion identifying as Black (38.6%) or Hispanic (16.1%). This predominance of patients reliant on government insurance, coupled with the area's high levels of poverty and healthcare inequity, creates substantial barriers to CRC screening. Traditional healthcare approaches, which depend heavily on primary care referrals and follow-up, often fail to adequately serve these communities, contributing to delayed diagnosis and worse CRC outcomes.\u003c/p\u003e \u003cp\u003eACCESS is unique in its strategy, removing the need for prior healthcare engagement by directly reaching underserved populations where they live, work, and gather. Instead of waiting for patients to visit a primary care provider, ACCESS distributes FIT in high-traffic community locations such as hospital lobbies, churches, grocery stores, and health fairs. This proactive and accessible model makes CRC screening more feasible for individuals who would otherwise face significant barriers to healthcare. ACCESS directly reaches out to the participants to report FIT results and, if positive, offers a colonoscopy at Temple University Hospital.\u003c/p\u003e \u003cp\u003eMoreover, while other programs have demonstrated the effectiveness of FIT outreach, many do not highlight the crucial follow-up steps, such as colonoscopy completion after a positive FIT result. ACCESS not only emphasizes screening uptake but also ensures a clear pathway to follow-up care, directly addressing these critical gaps. By focusing on both engagement and follow-up, the ACCESS initiative represents a novel approach to CRC screening for vulnerable populations\u0026mdash;potentially serving as a model for other large health systems.\u003c/p\u003e \u003cp\u003eIn this article, we will analyze the ACCESS strategy by providing evidence showing the novelty and effectiveness of this initiative compared to other previous CRC screening uptake efforts, report on the financial sustainability of this initiative, and advantages and disadvantages of this strategy to help other safety net hospitals optimize their CRC outreach.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e Average risk participants aged 45\u0026ndash;75 years were eligible for FIT based on the USPSTF guidelines. Participants were engaged by a team of residents and medical students at various distribution events with the exception of 94 participants calling into Temple University Hospital after watching a local news segment highlighting the initiative \u003cb\u003e(Supplemental Fig.\u0026nbsp;1)\u003c/b\u003e. Participants then underwent a screening process involving a series of questions to assess their average risk status \u003cb\u003e(Supplemental Fig.\u0026nbsp;2).\u003c/b\u003e Screening results were recorded using REDCap (Research Electronic Data Capture), a web-based, Health Insurance Portability and Accountability Act (HIPAA) compliant software platform designed to support data capture for research studies, on iPads that were brought to the events. Survey responses were recorded in real time. Those meeting the criteria for average risk were offered FIT and provided with instructions for its proper use at home. The FIT used was the second-generation FIT manufactured by Pinnacle Biolabs. Participants were requested to provide their phone number, acknowledging that they would be contacted for follow-up regarding the FIT results. A community health worker was hired for this initiative and contacted participants by texts once per week and by phone call during the third week to obtain results. After three unsuccessful attempts, participants were categorized as \"incomplete.\"\u003c/p\u003e \u003cp\u003e In the event of a negative result, relevant data was recorded, and participants were informed about the necessity of annual follow-up tests with FIT to be compliant with CRC screening guidelines. Conversely, if the result was positive, participants were offered a colonoscopy and gastroenterology (GI) follow-up at Temple University Hospital. Those who opted to follow up with a non-Temple University Hospital GI doctor were counseled on the importance of following up their positive result. Participant colonoscopy outcomes were assessed through electronic medical records. All data was recorded REDcap. Notes were written in the electronic medical records of all outcomes so other providers would be aware.\u003c/p\u003e \u003cp\u003eParticipants who were given FIT were also assessed to see if they had seen an internal medicine physician, family medicine physician, gastroenterologist, or medical oncologist within the past year from when they were given a FIT. For the ease of this writing, internal medicine, family medicine, gastroenterologist or medical oncologist will be referred to as primary care physician as these physicians are most likely to engage in colorectal cancer screening for their participants. Each participant was searched using Care Everywhere to assess if they had seen a primary physician in the past year from when they received a FIT. Care Everywhere is an interoperability feature within Epic Systems, a widely used electronic medical record (EMR) platform. It enables the secure exchange of patient health information between different healthcare organizations, even if they use different instances of Epic or other EMR systems that are connected to the Carequality interoperability framework.\u003c/p\u003e \u003cp\u003eDescriptive statistics were used to summarize the study population. Continuous variables, such as age, were reported as medians with interquartile ranges (IQR), while categorical variables, including race and gender, were summarized using frequencies and percentages. FIT completion rate was calculated as the proportion of FIT that were returned with a result (either positive or negative) out of all FIT distributed. The FIT positivity rate was defined as the proportion of positive results among all returned FIT. The colonoscopy conversion rate was calculated as the proportion of individuals who, after returning a FIT result, proceeded to undergo a follow-up colonoscopy at Temple University Hospital.\u003c/p\u003e \u003cp\u003eComparisons of FIT completion, FIT positivity, and colonoscopy conversion rates were conducted across various groups, including event setting, gender, race, and age categories. The Fisher\u0026rsquo;s Exact Mid p-value test was used for comparisons involving categorical variables with two levels, such as gender, while the Chi-square test was applied for variables with three or more levels, such as race. FIT completion and positivity rates were also compared between individuals who had a primary care visit within the past year and those who did not. Additionally, these comparisons were stratified by gender to assess differences within male and female subgroups based on primary care utilization. A p-value of less than 0.05 was considered statistically significant. Due to the exploratory nature of the study, adjustments for multiple comparisons were not made. All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC).\u003c/p\u003e \u003cp\u003eTo evaluate financial sustainability, we analyzed colonoscopy billing data (CPT codes) from a total of 27,169 codes charged at Temple University Hospital between 2017 and 2022. 16 individual colonoscopy CPT codes were categorized into two groups\u0026mdash;screening and diagnostic. Using Medicare reimbursement rates, we determined the reimbursement amounts for each screening and diagnostic CPT code and calculated the overall average reimbursement for a screening CPT code and diagnostic CPT code \u003cb\u003e(Supplemental Fig.\u0026nbsp;3).\u003c/b\u003e The historical annual amount and proportion of screening and diagnostic CPT codes charged was then calculated along with average reimbursement for a screening and diagnostic CPT code that were associated with non-FIT prompted colonoscopy \u003cb\u003e(Supplemental Fig.\u0026nbsp;4).\u003c/b\u003e For FIT-prompted colonoscopies, the proportion of screening and diagnostic CPT codes was analyzed and the average reimbursement for a screening and diagnostic CPT code associated with a FIT-prompted colonoscopy was calculated.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eOverall, FIT were distributed to 799 individuals at 55 different distribution events either on the Temple University Hospital campus or community distribution events with an average of 14.5 FIT being distributed per event. Of the 799 individuals, the median age was 60.3 years old and 62.2% identified as women with 5.4% not reporting their gender. 47.8% of the 799 individuals reported their race as Black and 22.9% identified as white.\u003c/p\u003e \u003cp\u003eA total of 23 events were held at different locations at Temple University Hospital campus with a total of 414 FIT distributed and an average of 18.0 FIT distributed per event. The median age at the hospital lobby events was 58.7 and 58.9% identified as female with 8.9% not reporting their gender. 44.4% of participants identified as Black while 20.5% identified as white.\u003c/p\u003e \u003cp\u003eA total of 32 distribution events were held at various community events with a total of 385 FIT being distributed and an average of 12.0 FIT distributed per event. The median age at community events was 61.9 and 65.7% identified as female with 1.6% not reporting their gender. 51.4% of participants identified as Black and 25.5% identified as white. \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFIT Completion Rates, Response Rates and Conversion to Colonoscopy at Home Institution\u003c/h3\u003e\n\u003cp\u003eA total of 799 FIT were distributed at all distribution events with 293 FIT results being reported back to the ACCESS team equating to a 36.7% FIT completion rate and 48 were returned as positive equating to a positivity rate of 16.4%. Of the 48 positive participants, 14 opted receive follow-up colonoscopy at Temple University Hospital for a conversion rate from positive FIT to colonoscopy of 29.2%.\u003c/p\u003e \u003cp\u003eHospital distribution events and community events has completion rates of 34.8% and 38.7%, respectively (p\u0026thinsp;=\u0026thinsp;.26). Hospital distribution events and community events has positivity rates of 16.7% and 16.1%, respectively (p\u0026thinsp;=\u0026thinsp;.94). Hospital distribution events and community events had conversion rates from positive FIT to colonoscopy at Temple University Hospital of 41.7% and 16.7%, respectively (p\u0026thinsp;=\u0026thinsp;.068).\u003c/p\u003e \u003cp\u003eMales and females had completion rates of 34.4% and 37.4%, respectively (p\u0026thinsp;=\u0026thinsp;.40). Male and females had positivity rates of 21.3%% and 15.6%, respectively (p\u0026thinsp;=\u0026thinsp;.21). Males and females had conversion rates from positive FIT to colonoscopy at Temple University Hospital of 26.3% and 31.0%%, respectively (p\u0026thinsp;=\u0026thinsp;.76).\u003c/p\u003e \u003cp\u003eBlack, white, and Hispanic/other had completion rates of 34.6%, 42.6%, 29.8%, respectively (p\u0026thinsp;=\u0026thinsp;0.045). Black, white, and Hispanic/other had positivity rates of 19.7%, 12.8%, 21.4%, respectively (p\u0026thinsp;=\u0026thinsp;0.36). Black, white, and Hispanic/other had conversion rates from positive FIT to colonoscopy at Temple University Hospital of 34.6%, 0.0%, 55.6% respectively (p\u0026thinsp;=\u0026thinsp;0.028).\u003c/p\u003e \u003cp\u003eParticipants that were ages 45\u0026ndash;54, 55\u0026ndash;64, and 65\u0026ndash;75 had completion rates of 37.4%, 33.3%, and 40.5% (p\u0026thinsp;=\u0026thinsp;.21), positivity rates of 18.0%, 17.6%, and 13.5%, respectively (p\u0026thinsp;=\u0026thinsp;0.65), and conversion rates to colonoscopy at TUH of 31.3%, 42.1%, 7.7% respectively (p\u0026thinsp;=\u0026thinsp;.11). \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003ePrimary Care Visits within the past year and completion rates and positivity rates\u003c/h3\u003e\n\u003cp\u003eOf the 799 participants who have received a FIT, 520 had data available in Care Everywhere to determine if they saw a primary care physician within the past year from receiving their FIT. Of the 520 participants, 241 saw a primary care physician within the past year since the day they received their FIT. Participants who saw a primary care doctor within the past year had a completion rate of 38.6% and participants who did not see a primary care doctor within the past year had a completion rate of 35.1% (p\u0026thinsp;=\u0026thinsp;0.44). Participants who saw a primary care doctor within the past year had a positivity rate of 15.1% and participants who did not see a primary care doctor within the past year had a positivity rate of 26.5% (p\u0026thinsp;=\u0026thinsp;0.064). The remaining 279 did not have any data available in Care Everywhere to determine if they saw a primary care physician in the past year.\u003c/p\u003e \u003cp\u003eRegarding the male participants, 61 saw a primary care physician within the past year since the day they received the test. Men who saw a primary care doctor within the past year had a completion rate of 41.0% and men who did not see a primary care doctor within the past year had a completion rate of 32.6% (p\u0026thinsp;=\u0026thinsp;0.26). Men who saw a primary care doctor within the past year had a positivity rate of 16.0% and men who did not see a primary care doctor within the past year had a positivity rate of 41.4% (p\u0026thinsp;=\u0026thinsp;0.056).\u003c/p\u003e \u003cp\u003eRegarding the female participants, 167 saw a primary care physician within the past year since the day they received the test. Women who saw a primary care doctor within the past year had a completion rate of 37.1% and women who did not see a primary care doctor within the past year had a completion rate of 37.1% (p\u0026thinsp;=\u0026thinsp;0.95). Women who saw a primary care doctor within the past year had a positivity rate of 16.1% and women who did not see a primary care doctor within the past year had a positivity rate of 22.6% (p\u0026thinsp;=\u0026thinsp;0.38) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eTypes of Colonoscopies Performed for FIT Positive Participants.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTemple University Hospital charges on average 4,453 colonoscopy associated CPT codes per year in the outpatient setting with 34.0% of the CPT codes being charged categorized as screening and 66.0% being categorized as diagnostic. The distribution of CPT codes charged for FIT-prompted colonoscopy were found to be 7.10% screening and 92.9% diagnostic. The average Medicare reimbursement rate for screening and diagnostic CPT code is \u003cspan\u003e$\u003c/span\u003e870.81 and \u003cspan\u003e$\u003c/span\u003e1,194.61, respectively. As a result, the average reimbursement per non-FIT-prompted CPT code done at Temple historically is \u003cspan\u003e$\u003c/span\u003e1084.60 and the average reimbursement per FIT-prompted CPT code is \u003cspan\u003e$\u003c/span\u003e1171.62 which is a difference \u003cspan\u003e$\u003c/span\u003e87.02 \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003e).\u003c/b\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDeveloping creative and data-driven CRC screening strategies that are also financially viable for hospitals is critical for assisting vulnerable patients in receiving lifesaving CRC screening. To our best knowledge, no other initiative has implemented a strategy to improve screening for patients by circumventing the primary care system. It is our hope that the ACCESS initiative can serve as a model for other large health systems in urban areas to create and improve their CRC screening initiatives to effectively serve their vulnerable populations in a financially sustainable way.\u003c/p\u003e \u003cp\u003eOur data shows that completion rates of FIT were similar regardless of location, age, and gender. However, our data does show the FIT completion rates were significantly different among race with white participants having the highest completion rate of 42.6% followed by Black and Hispanic/other having lower completion rates of 34.2% and 29.8%, respectively, suggesting that greater efforts need to be made in encouraging non-white participants to complete their FIT. In addition, positivity rates among distribution event location, age, race, and gender were non-statistically different which suggests that discrepancies in CRC incidence among different racial groups may be contributed, in part, to a lack of completing these tests.\u003c/p\u003e \u003cp\u003eIt is important to note that completion rates of FIT in the ACCESS initiative were comparable to, if not better than, other randomize control trials exploring FIT as a viable CRC screening strategy. In a 2018 JAMA randomized control trial, Coronado et al mailed 21,134 people FIT and the completion rate was 13.9%. In addition, in 2013 JAMA article, Gupta et al explored mailing uninsured patients who have been seen by a primary physician in the past 8 months had completion rates of FIT of 40.7%. The ACCESS initiative had a completion rate of 36.7% which is in line with completion rate of the current literature and addresses a unique subset of patients that these other studies do not address but need to be made to better through data driven strategies. Possible reasons completion rates are not higher may include participants losing the test after receiving it, concerns about a positive result, or not understanding the importance of CRC screening. Further research needs to be completed to elucidate ways to increase completion rates.\u003c/p\u003e \u003cp\u003eIn addition, our initiative shows how other studies demonstrating the efficacies of FIT screening strategies can fall short and neglect a portion of patients who are vulnerable to not receiving adequate CRC screening. The lack of high touch points within the primary care system can lead to clinically significant discrepancies in positivity rates as demonstrated by our initiative. Our study shows participants who have not seen a primary care doctor in the past year are about 1.8 times more likely to have a positive FIT result compared to those who have seen a primary care doctor in the past year. This is even more pronounced in men where men who have not seen a primary care doctor in the past year are 2.6 times more likely to have a positive FIT compared to men who have seen a primary care doctor in the past year. Women who have not seen a primary doctor in the past year were only 1.4 times more likely to test positive compared to women. Response rates among men and women were similar regardless of whether participants have seen a primary care physician within the past year. This data demonstrates there is a subset of participants who do not have access to primary care and as result are at higher risk for a positive FIT and ultimately colorectal pathology and should strongly be considered when developing CRC screening strategies to ensure these populations are getting the screening they need.\u003c/p\u003e \u003cp\u003eCreating a financially sustainability CRC initiative is critical to ensuring longitudinal success of initiatives like ACCESS. Our data, shows the majority of FIT positive participants had CPT codes assigned to their colonoscopy that were diagnostic as opposed to screening. The leads to an increase in revenue of \u003cspan\u003e$\u003c/span\u003e87.02 on average when a FIT prompted CPT code is charged. Assuming a fixed number of colonoscopies per year, if a hospital were able to triage 500 FIT prompted colonoscopies per year, they would recognize an additional \u003cspan\u003e$\u003c/span\u003e43,510 that can be reinvested into ACCESS to help financial sustain this initiative either through purchasing more FIT to screen more people or rescreen the following year. Although this revenue increase is modest, it shows the potential for initiatives like these to be financially self-sustainable and target patients with low primary care access who are at higher risk for positive FIT. In addition, it is important to note that five of the fourteen patients who underwent colonoscopy also underwent EGD due to also experiencing upper GI symptoms that were identified during the GI office visit following a positive FIT. This could offer additional revenue to be reinvested to CRC initiatives like ACCESS. Moreover, the conversion rate from positive FIT to colonoscopy at TUH was 29.2%. Expanding efforts to increase the conversion rate will both ensure that patients follow their positive FIT results and also provide additional revenue to fund initiatives like ACCESS. Lastly, these data should emphasize the need programs like Medicare/Medicaid to increase investment in screening initiatives that circumvent the primary care system.\u003c/p\u003e \u003cp\u003eIt should also be addressed that ACCESS faced logistical challenges regarding the distribution of FIT at events. 55 events were held over the study period with an average of 14.5 tests being distributed per event. Frequency of events were limited by the availability of medical students and resident physicians to distribute the FIT. These challenges can be addressed by hiring dedicated personnel to staff distribution events to achieve more FIT distributed per event and higher frequency of distribution events.\u003c/p\u003e \u003cp\u003eIn conclusion, the ACCESS initiative underscores the urgent need to address gaps in colorectal cancer screening for populations traditionally underserved by the primary care system. By implementing novel strategies such as the ACCESS initiative, which circumvents traditional primary care pathways, we can significantly improve CRC screening uptake and potentially reduce the incidence and mortality associated with this disease. Our findings highlight that targeting high-risk populations who are less likely to visit primary care providers can lead to earlier detection of CRC through increased FIT positivity rates. Among the 14 colonoscopies performed through the initiative, we successfully identified a patient with colorectal cancer and subsequently underwent treatment. It should be noted that this patient is male and did see a primary care doctor within the past year from the date of receiving a FIT. This approach not only has implications for improving health outcomes but also underscores the necessity for large urban hospitals like Temple University Hospital to adapt and invest in more accessible and equitable screening processes. Our initiative offers a promising model that, with appropriate financial support and scalability, could be a cornerstone in efforts to close the CRC screening gap and save lives.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe ACCESS initiative demonstrates that reaching patients outside of traditional primary care settings can effectively expand colorectal cancer screening access to underserved populations, identifying individuals at higher risk who might otherwise remain undiagnosed. With promising clinical and financial outcomes, this model offers a scalable and sustainable framework for other urban health systems working to close gaps in preventive cancer care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eColorectal Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFIT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFecal Immunochemical Test(s)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACCESS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdvancing Colorectal Cancer Equity through Systematic Screening\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTUH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTemple University Hospital\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUSPSTF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited States Preventive Services Task Force\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPT Code\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCurrent Procedural Terminology Code\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCenters for Disease Control and Prevention's\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRCCP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eColorectal Cancer Control Program\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eREDCap\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eResearch Electronic Data Capture\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHIPAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth Insurance Portability and Accountability Act\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGastroenterology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectronic Medical Record\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile Ranges (IQR)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was classified as IRB-exempt by the Lewis Katz School of Medicine IRB at Temple University, as it involved the delivery of evidence-based usual care consistent with established colorectal cancer screening guidelines. The requirement for informed consent was waived given the minimal risk to participants and the use of procedures ordinarily conducted in routine clinical practice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are committed to transparency and will make data, analytic methods, and study materials related to this article available to other researchers upon request, provided they are not used for commercial or profit-driven purposes. Interested parties may contact the corresponding author via email, including a rationale for the request. Relevant materials will be shared following appropriate discussion and approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the 2023 \u003cstrong\u003eIndependence Blue Cross Clinical Care Innovation Grant that was award to Temple University Hospital. The funders played no part in the data collection, management, or analysis. The funders did not have a role in the manuscript preparation or in the choice of journal to which the manuscript was submitted.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.G.: Conceptualized and designed the study, led data collection and statistical analysis, interpreted results, drafted the manuscript, and provided critical revisions for intellectual content.\u003c/p\u003e\n\u003cp\u003eD.Y.: Conducted the primary statistical analysis and contributed significant revisions to the manuscript.\u003c/p\u003e\n\u003cp\u003eA.I.: Assisted with study design, was integral to data collection, and contributed to manuscript revisions.\u003c/p\u003e\n\u003cp\u003eD.K., M.V., B.G.: Facilitated recruitment of medical student and resident volunteers for FIT kit distribution events and supported data analysis.\u003c/p\u003e\n\u003cp\u003eG.G., S.M., N.K.: Actively participated in FIT kit distribution and contributed to data collection and analysis.\u003c/p\u003e\n\u003cp\u003eD.C.: Provided additional statistical analysis support and manuscript feedback.\u003c/p\u003e\n\u003cp\u003eR.K.: Co-authored the Independence Blue Cross Clinical Care Innovation Grant that funded this initiative at Temple University Hospital; played a key role in developing the infrastructure for FIT kit distribution and follow-up of positive results with colonoscopy.\u003c/p\u003e\n\u003cp\u003eC.R.: Co-authored the Independence Blue Cross Clinical Care Innovation Grant, was instrumental in designing the operational framework for FIT distribution and follow-up, and provided mentorship and guidance throughout the development and execution of the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank community members of North Philadelphia who participated in the ACCESS initiative and contributed to its success. We also acknowledge the dedicated medical students, residents, and faculty from Temple University Hospital who supported FIT distribution, data collection, and patient follow-up. Their commitment was instrumental in advancing colorectal cancer screening access for underserved populations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Wagle NS, Cercek A, Smith RA, Jemal A. Colorectal cancer statistics, 2023. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e. 2023;73(3):233-254. doi:10.3322/caac.21772\u003c/li\u003e\n\u003cli\u003eBrenner H, Kloor M, Pox CP. Colorectal cancer. \u003cem\u003eLancet\u003c/em\u003e. 2014;383(9927):1490-1502. doi:10.1016/S0140-6736(13)61649-9\u003c/li\u003e\n\u003cli\u003eRocca A, Cipriani F, Belli G, et al. The Italian Consensus on minimally invasive simultaneous resections for synchronous liver metastasis and primary colorectal cancer: A Delphi methodology. \u003cem\u003eUpdates Surg\u003c/em\u003e. 2021;73(4):1247-1265. doi:10.1007/s13304-021-01100-9\u003c/li\u003e\n\u003cli\u003eAvella P, Vaschetti R, Cappuccio M, et al. The role of liver surgery in simultaneous synchronous colorectal liver metastases and colorectal cancer resections: a literature review of 1730 patients underwent open and minimally invasive surgery. \u003cem\u003eMinerva Surg\u003c/em\u003e. 2022;77(6):582-590. doi:10.23736/S2724-5691.22.09716-7\u003c/li\u003e\n\u003cli\u003eCarbone F, Chee Y, Rasheed S, et al. Which surgical strategy for colorectal cancer with synchronous hepatic metastases provides the best outcome? A comparison between primary first, liver first and simultaneous approach. \u003cem\u003eUpdates Surg\u003c/em\u003e. 2022;74(2):451-465. doi:10.1007/s13304-021-01234-w\u003c/li\u003e\n\u003cli\u003eEarl V, Beasley D, Ye C, et al. Barriers and Facilitators to Colorectal Cancer Screening in African-American Men. \u003cem\u003eDig Dis Sci\u003c/em\u003e. 2022;67(2):463-472. doi:10.1007/s10620-021-06960-0\u003c/li\u003e\n\u003cli\u003eMojica CM, Vargas N, Bradley S, Parra-Medina D. Barriers and Facilitators of Colonoscopy Screening Among Latino Men in a Colorectal Cancer Screening Promotion Program. \u003cem\u003eAm J Mens Health\u003c/em\u003e. 2023;17(3):15579883231179325. doi:10.1177/15579883231179325\u003c/li\u003e\n\u003cli\u003eDoubeni CA, Selby K, Gupta S. 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Effect of Colonoscopy Outreach vs Fecal Immunochemical Test Outreach on Colorectal Cancer Screening Completion: A Randomized Clinical Trial. \u003cem\u003eJAMA\u003c/em\u003e. 2017;318(9):806-815. doi:10.1001/jama.2017.11389\u003c/li\u003e\n\u003cli\u003eSomsouk M, Rachocki C, Mannalithara A, et al. Effectiveness and Cost of Organized Outreach for Colorectal Cancer Screening: A Randomized, Controlled Trial. \u003cem\u003eJ Natl Cancer Inst\u003c/em\u003e. 2020;112(3):305-313. doi:10.1093/jnci/djz110\u003c/li\u003e\n\u003cli\u003eDougherty MK, Brenner AT, Crockett SD, et al. Evaluation of Interventions Intended to Increase Colorectal Cancer Screening Rates in the United States: A Systematic Review and Meta-analysis. \u003cem\u003eJAMA Intern Med\u003c/em\u003e. 2018;178(12):1645-1658. doi:10.1001/jamainternmed.2018.4637\u003c/li\u003e\n\u003cli\u003eKim KE, Tangka FKL, Jayaprakash M, et al. Effectiveness and Cost of Implementing Evidence-Based Interventions to Increase Colorectal Cancer Screening Among an Underserved Population in Chicago. \u003cem\u003eHealth Promot Pract\u003c/em\u003e. 2020;21(6):884-890. doi:10.1177/1524839920954162\u003c/li\u003e\n\u003cli\u003eBarajas M, Tangka FKL, Schultz J, et al. Examining the Effectiveness of Provider Incentives to Increase CRC Screening Uptake in Neighborhood Healthcare: A California Federally Qualified Health Center. \u003cem\u003eHealth Promot Pract\u003c/em\u003e. 2020;21(6):898-904. doi:10.1177/1524839920954166\u003c/li\u003e\n\u003cli\u003eConn ME, Kennedy-Rea S, Subramanian S, et al. Cost and Effectiveness of Reminders to Promote Colorectal Cancer Screening Uptake in Rural Federally Qualified Health Centers in West Virginia. \u003cem\u003eHealth Promot Pract\u003c/em\u003e. 2020;21(6):891-897. doi:10.1177/1524839920954164\u003c/li\u003e\n\u003cli\u003eCoronado GD, Petrik AF, Vollmer WM, et al. Effectiveness of a Mailed Colorectal Cancer Screening Outreach Program in Community Health Clinics: The STOP CRC Cluster Randomized Clinical Trial. \u003cem\u003eJAMA Intern Med\u003c/em\u003e. 2018;178(9):1174-1181. doi:10.1001/jamainternmed.2018.3629\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Demographic Summary of All Subjects Who Received FIT and \u0026nbsp;FIT Distribution Event Type (n=799)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eOverall\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eHospital Events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eCommunity Events\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eAge: Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e60.3 (53.5, 66.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e58.7 (52.4, 64.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e61.9 (55.6, 68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eAge Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e238 (29.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e149 (36.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e89 (23.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e55-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e324 (40.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e175 (42.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e149 (38.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e65-75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e237 (29.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e90 (21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e147 (38.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e259 (32.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e133 (32.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e126 (32.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e497 (62.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e244 (58.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e253 (65.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eNot-Reported/Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e43 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e37 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e6 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e382 (47.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e184 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e198 (51.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e183 (22.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e85 (20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e98 (25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e141 (17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e90 (21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e51 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eNot-reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e93 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e55 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e38 (9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eAverage FIT Distributed/Event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e14.5 (799/55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e18.0 (414/23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e12.0 (385/32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Completion, Positivity, and Conversion to Colonoscopy at TUH Event Type, Sex, Race, and Age Groups\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"714\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFIT Completion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eFIT Positivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eConversion To Colonoscopy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e293 (36.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e48 (16.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e14 (29.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eFIT Event Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Hospital Lobby\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e144 (34.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e24 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e10 (41.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Community\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e149 (38.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e24 (16.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e4 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e89 (34.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e19 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e5 (26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e186 (37.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e29 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e9 (31.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e132 (34.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e26 (19.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e9 (34.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e78 (42.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e10 (12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eHispanic/Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e42 (29.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e9 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e5 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eAge Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e89 (37.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e16 (18.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e5 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e55-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e108 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e19 (17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e8 (42.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e65-75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e96 (40.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e13 (13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Comparison of FIT Completion and Positivity Rates Between Individuals With and Without a Primary Care Visit in the Year Prior to Receiving FIT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003ePrimary Care in the last Year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eFIT Completed (Completion Rate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e93 (38.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e98 (35.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eFIT Positive (Positivity Rate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e14 (15.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e26 (26.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eFIT Completed (Completion Rate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e25 (41.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e29 (32.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eFIT Positive (Positivity Rate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e12 (41.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eFIT Completed (Completion Rate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e62 (37.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e62 (37.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eFIT Positivity (Positivity Rate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e10 (16.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e14 (22.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026sect;Primary care data was available on 520 participants. Furthermore,13 participants who did not report their sex were not included in the stratified Sex analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Comparison of CPT Code Distribution and Reimbursement for Non-FIT vs. FIT-Prompted Colonoscopies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eCPT Code Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eReimbursement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eProportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eAverage Reimbursement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNon-FIT Prompted Colonoscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eScreening CPT Code\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e$870.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e34.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e$1,084.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eDiagnostic CPT Code\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e$1,194.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e66.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eFIT Prompted Colonoscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eScreening CPT Code\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e$870.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e7.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e$1,171.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eDiagnostic CPT Code\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e$1,194.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e92.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6542743/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6542743/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Colorectal cancer (CRC) remains the second leading cause of cancer-related deaths in the United States, with disparities in screening disproportionately affecting socioeconomically disadvantaged and underrepresented populations, particularly in urban areas. Previous research has shown that proactive outreach strategies such as Fecal Immunochemical Test(s) (FIT) and colonoscopy significantly enhance CRC screening although these efforts typically have targeted patients already engaged with primary care and exclude those without recent primary care visits in the past year. The ACCESS (Advancing Colorectal Cancer Equity through Systematic Screening) initiative, offers a unique and financially sustainable method to address discrepancies in CRC screening.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To evaluate the effectiveness and financial sustainability of the ACCESS initiative, a community-based approach launched by Temple University Hospital (TUH) in Philadelphia, Pennsylvania, which targets CRC screening disparities by circumventing the primary care health system and distributing FIT directly to patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The ACCESS program distributed FIT in non-traditional, high-traffic community settings to average risk individuals aged 45-75, per United States Preventive Services and Task Force (USPSTF) guidelines. This method circumvents traditional healthcare touchpoints, such as primary care referrals, reaching those who typically lack regular healthcare engagement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among the 799 FIT distributed, 293 results were reported (response rate: 36.7%), with 48 positive FIT (positivity rate: 16.4%). Notably, individuals who had not visited a primary care provider in the past year exhibited a higher positivity rate (26.5%) compared to those who had (15.1%) (p=.064). More interestingly, men who saw a primary care doctor within the past year had a positivity rate of 16.0% compared to a 41.4% positivity rate amongst men who did not see a primary care doctor within the past year (p=.056). Follow-up colonoscopy was completed in 29.2% of cases with positive FIT results at Temple University Hospital while the other individuals chose to follow up outside of the Temple Health system. Financial analysis revealed that the majority of follow-up colonoscopies were charged using diagnostic current procedural terminology (CPT) codes as opposed screening CPT codes and that the average reimbursement per CPT charged to a FIT-prompted colonoscopy to be $1,171.62 compared to $1084.60 for non-FIT test prompted colonoscopies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The ACCESS initiative successfully extends CRC screening to underserved populations not previously outlined in other literature describing population-based CRC screening efforts. This initiative demonstrated higher positivity rates among those less engaged in traditional primary care systems and offers insight into the financial sustainability of population-based screening initiatives.\u003c/p\u003e","manuscriptTitle":"Evaluating the Effectiveness and Economic Viability of a Novel Population-Based Colorectal Cancer Screening Strategy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 05:09:59","doi":"10.21203/rs.3.rs-6542743/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"21533a28-962d-409b-ac21-52ebf1e7b751","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-04T09:26:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 05:09:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6542743","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6542743","identity":"rs-6542743","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}