Development of a novel cancer survivorship database to describe health care utilization patterns for Coloradans who have completed primary cancer treatment

Development of a novel cancer survivorship database to describe health care utilization patterns for Coloradans who have completed primary cancer treatment | 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 Development of a novel cancer survivorship database to describe health care utilization patterns for Coloradans who have completed primary cancer treatment Carlin Callaway, Elizabeth Molina, Linda Overholser, Santi Das This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3346259/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Dec, 2023 Read the published version in Journal of Cancer Survivorship → Version 1 posted 7 You are reading this latest preprint version Abstract Purpose Electronic health records (EHR) and data warehouses hold promise for population health management. Specific aims were: (a) to build a comprehensive database representing adult Coloradans who completed cancer treatment within a health care system, and (b) to conduct a secondary analysis of this database. Methods A survivorship database (HDC-SD) was built from the Health Data Compass (HDC) warehouse, which includes diagnostic and treatment information and incorporates all-payer data to identify individuals with histories of cancer who received treatment at the University of Colorado Cancer Center (UCCC) between January 1, 2020 and December 31, 2021. Data was analyzed using Chi-square tests to compare sociodemographic characteristics, disease characteristics, and health maintenance between urban and rural settings. Results The HDC-SD includes 1933 records representing 13 categories of cancers. The majority live in an urban setting (89.8%). Patients in the database living in urban areas had statistically (higher/lower) rates of completing colorectal screening, mammography, flu shots, and COVID-19 vaccination. Emergency department visits occurred at a statistically significant (higher/lower) level for those living in urban areas. Conclusions Creating a database of individuals who have completed active cancer treatment, while incorporating longitudinal health utilization data, may help prepare for systematic population management for cancer survivors. Cancer survivorship Treatment summary care plan Survivorship care plan Electronic health record Cancer data base Clinical outcomes Figures Figure 1 Figure 2 Introduction Cancer, a complex and often chronic condition, impacts millions. In 2023, the American Cancer Society estimates that 1.9 million new cancer cases will be diagnosed [1]. Nationally, Bluethmann predicted that 26.1 million survivors will be living in 2040 and that 47% will live more than 10 years after their diagnosis [2]. The Colorado Department of Public Health and Environment estimates that 360,000 people with histories of cancer were currently living in Colorado as of April 2023 ( https://cdphe.colorado.gov/center-for-health-and-environmental-data/registries-and-vital-statistics/colorado-central-cancer ). Despite detection and treatment advances, people with histories of cancer experience unique health challenges including treatment-related late effects. People with histories of cancer are also more likely to develop additional cancers. According to SEER data, of 765,843 incident cancers diagnosed between 2009 and 2013, approximately one-fourth of adults aged 65 and older and just more than one-tenth of younger adults aged 20 to 64 years were experiencing their second or higher cancer. Most of these new cancers (termed second primary cancers) were diagnosed in different anatomic locations [3]. Coordinated health care following active cancer treatment between primary care and oncology care is crucial; it is one of the six standards for survivorship care as defined by the National Comprehensive Cancer Network (Version 1.2023). Additional standards include: (a) surveillance for recurrence and screening for new cancers; (b) monitoring for late effects; (c) preventing and detecting late effects; (d) evaluating cancer-related syndromes; and (e) planning for ongoing care. Ongoing care includes general preventive health screenings, management of co-morbid conditions, promotion of healthy behaviors, and psychosocial support ( https://www.nccn.org/professionals/physician_gls/pdf/survivorship.pdf ). Gaps in information exchange, limited care coordination activities, and lack of clarity of provider roles have all been cited as reasons for the well-known fragmentation in cancer survivorship care across providers and especially during the transition following completion of active cancer treatment [4] [5]. A previous study conducted in Colorado to evaluate a cancer survivorship educational intervention for rural primary care practices revealed that one barrier for these practices to care for cancer survivors was a limited ability to identify people with past histories of cancer within their practices. Risendal and her team identified he need for additional research to better understand the patterns of care that Coloradans with past histories of cancer receive in multiple settings [6]. Although the literature is conflicted, many organizations recommend that treatment summary care plans or survivorship care plans (SCPs) be provided to people who complete curative treatment for malignancies. Treatment summary care plans may play an important role in communicating the need for preventive care following cancer treatment. However, additional research is needed to understand the impact of SCPs on health care outcomes and health care utilization. Nonetheless, the presence of SCPs may be useful to identify individuals who have completed primary oncology treatment. The two specific aims of this project were: (a) to build a comprehensive database representing adult Coloradans who completed cancer treatment within the Metro Denver University of Colorado Health system, and (b) to conduct a secondary analysis of this database to describe demographic characteristics and health care utilization patterns for these individuals. The HDC-SD database provides descriptions of HDC-SD was to describe socio-demographic characteristics. Such characteristics include urban vs rural residence, cancer diagnostic and treatment information, and oncology and non-oncology health care utilization patterns (primary care preventative services and emergency care). Such information will enable a systematic approach to population management for cancer survivorship care, provide a foundation for collaborative outreach to help reduce disparities in care, and potentially improve health outcomes throughout Colorado. It was hypothesized that a considerable number of Coloradans who have been treated with curative intent for cancer did not receive recommended cancer follow-up or screenings. It was also hypothesized that these levels fell below those reported in the literature. Finally, it was hypothesized that individuals who lived in rural areas had lower rates of receipt of preventive care tests than those who lived in urban areas. Methods Data source Health Data Compass (HDC) Enterprise Health Data Warehouse is hosted by Google Cloud, and integrates patient clinical data and relevant billing information from the EPIC electronic health record used throughout the University of Colorado Health system (Epic Systems Corporation, Verona, Wisconsin). Additionally, HDC may link data from other sources, including the Center for Improving Value in Health Care’s (CIVHC) Colorado All Payers Claims Database (APCD) and the University of Colorado Cancer Registry. Data are available from 2011 to present and are updated monthly. Available variables include patient demographics, medical encounters and visits, diagnoses (including cancer diagnosis), health history (including personal, family, and social), medications, procedures, labs, billing codes, payers, and provider queries and notes. HDC may be used to generate de-identified data sets, limited data sets, and full protected health information (PHI) data sets by request. This data then flows into the Observational Medical Outcomes Partnership (OMOP) Common Data Model, which then flows into the Eureka Virtual Machine. Such flow enables secure data sharing and delivery to national networks (Fig. 1). Additional information is available at https://www.healthdatacompass.org/home . Inclusion and Exclusion in HDC-SD Patients were included in the HDC-SD data set who were 18–85 years of age, had a UCHealth medical record number, and were diagnosed with a non-hematologic malignancy specifically leukemia or multiple myeloma Additionally, patients were identified based on their receipt of a completed treatment summary care plan (TSCP) delivered between January 1, 2020 to December 31, 2021. UCHealth TSCPs are available within the EPIC Electronic Health Record Problem List. The UCHealth custom-built TSCP template specifies the treatment team including the patient’s primary care provider, diagnostic and staging information including detailed pathology findings, treatment details, follow-up recommendations as guided by the National Comprehensive Cancer Network (NCCN) and one’s oncology team, and overall wellness information. Wellness information includes reasons for patients to contact their oncology and primary care teams, possible late effects, mental health recommendations, health screening and immunizations recommendations, advance care planning, and available health system resources including sexual health and fertility services. Within the Metro Denver region, TSCPs are generated from reviews of monthly positive pathology reports provided by the Tumor Registry (which have since been replaced with surgical reports), as well as anti-cancer therapy discontinuation reports and radiation end-of-treatment summary reports designed by an in-house EPIC analyst. The data represented in the HDC-SD were pulled from HDC on October, 4, 2022. The process of generating and delivering TSCPs has been an evolving process influenced by the Institute for Healthcare Improvement Plan-Do-Study-Act methodology ( https://www.ihi.org/resources/Pages/HowtoImprove/default.aspx ). Variables of interest The objective was to use HDC-SD to identify rates of U.S. Preventive Services Task Force (USPSTF) recommended screening procedures, immunizations, and health care utilization among the sample. Screening procedures were identified using a combination of procedure labels and Current Procedural Terminology (CPT) codes. Receipt of immunizations were identified using immunization and procedure labels. Heath care utilization were defined as primary care visits, oncology visits, and health-system specific emergency department visits. Such data were identified based on encounter data, department type, and clinician specialty. Demographic information of interest included urban and rural residence as defined by the U.S. Department of Agriculture Economic Research Service (ERS) 2013 Urban Influence Codes ( https://www.ers.usda.gov/data-products/urban-influence-codes/ ). Additional information of interested included patient demographics (sex, age, and race/ethnicity), primary cancer site, and payer types. Record review for data validation A total of 20 records were randomly selected to validate the HDC-SD derived data with the EPIC derived data. Validated data points of interest included date of birth, type of cancer, date of diagnosis, age at diagnosis, smoking history, date of TSCP completion, initial and subsequent oncology visits thereafter, initial and subsequent primary care visits thereafter, dates of recommended screening procedures, and dates of immunizations. Statistical analysis Chi-square tests were used to compare patient demographics, disease characteristics, socioeconomic characteristics, and health maintenance between urban and rural settings. All tests were 2-sided and performed in SAS Version 9.4 (SAS Institute Inc., Cary, NC) using a statistical significance level of p < 0.05. The study protocol was determined to be exempt by the Colorado Multiple Institutional Review Board. Results Dataset validation During the chart review process, several discrepancies were identified between the HDC-SD and individualized records within the EPIC EHR. Of the 20 patients reviewed, 10 patients (50%) within the HDC-SD had conflicting dates of diagnosis versus dates determined through the EPIC EHR. In some cases, these differences stretched from months to years. Upon further review, it was discovered that the HDC-SD diagnosis date was defined as the first encounter date associated with an oncology-specific ICD-10 code, even if the code corresponded to an unspecified tumor. To resolve this issue, the date of diagnosis was subsequently defined as the first date identifying a diagnosis code associated with a specific tumor. This adjustment resulted in the HDC-SD diagnosis dates more closely reflecting the dates identified within the EPIC EHR. Additionally, many completed screening procedures as identified through the EPIC EHR Media tab (where information and documents from outside sources can be scanned in for information purposed and become a part of the medical record) were not identified by the HDC-SD. As a specific example, 9 (45%) patients had record of completed colorectal cancer screening in their EHR, but only 2 (10%) patients had a confirmed colorectal cancer screening procedure captured by HDC-SD. Documents uploaded into the EPIC EHR Media tab do not translate into a discrete field detectable by HDC thus, services and procedures completed outside the UCHealth system may not be fully discovered by Health Data Compass (Table 1). Descriptive analysis The HDC-SD contains 1,933 patients who completed curative-intent primary treatment between January 1, 2020 and December 31, 2021 for diagnoses of cancer. The top three cancers included breast (24%), male reproductive (23%), and cutaneous (13%; mostly early stage melanoma). Of those included within the HDC-SD, 50.5% were women and 79% were white non-Hispanic. The majority of patients were aged 55 years and older (68%) (Table 2). All 21 Colorado Health Statistic Regions were represented within this database (Figs. 2a and 2b). According to the United States 2020 Census ( https://www.census.gov/library/stories/state-by-state/colorado-population-change-between-census-decade.html ), approximately 84% of Colorado’s population resides in Health Statistic Regions 2 (Larimer County), 3 (Douglas County), 4 (El Paso), 12 (Garfield, Pitkin, Eagle, Summit, and Grand Counties), 14 (Adams County), 15 (Arapahoe Counties), 16 (Boulder and Broomfield Counties), 18 (Weld County), 20 (Denver County), and 21 (Jefferson County). This was well represented in HDC-SC, with 90% of patients living in these same highly-populated regions. Urban vs. rural findings The majority of patients represented in the HDC-SD lived in an urban setting (89.8%), and there was a higher percentage of females in the urban setting compared to rural (51.8% vs. 39.9%, p = 0.0010). In terms of diagnosis, the urban sample had a higher percentage of cutaneous malignancies (14.0% vs. 6.6%, p < .0001) and breast tumors (25.0% vs. 14.6%, p < .0001). However, the urban sample had a lower percentage of bladder and urologic diagnoses (7.7% vs. 18.2%, p < .0001). The majority of patients had insurance coverage (84.8% of urban patients with commercial, Medicare, and Tricare coverage vs. 87.8% of rural patients with commercial, Medicare, and Tricare coverage) at the time of diagnosis. The urban sample contained more current commercial insurance enrollees than the rural sample (48.8% vs. 38.9%, p = 0.0123), while the rural sample contained more Medicare enrollees (48.0% vs. 35.4%, p = 0.0123). Finally, the urban sample had a smaller percentage of White Non-Hispanic patients than the rural sample (78.2% vs. 85.4%, p = 0.0024). In terms of health maintenance, there were some statistically significant findings between patients living in urban areas and rural areas. A greater percentage of eligible people aged 45 years and older within the urban sample were up to date with their colorectal cancer screening (6.2% vs. 0.6%, p = 0.0020). A greater percentage of men aged 50 years and above within the urban sample had a PSA test within the past 2 years (48.9% vs. 22.7%, p < 0.0001). Additionally, a greater percentage of women aged 45 years and above within the urban sample a mammogram in the previous 2 years (25.7% vs. 10.6%, p = 0.0009). Finally, a greater percentage of adults within the urban sample received a flu shot (41.7% vs. 13.6%, p < 0.0001), received one or more COVID-19 vaccines (37.8% vs. 9.6%; p < 0.0001), and had an emergency department visit within the UCHealth system (7.4% vs. 3%, p = 0.0225). There were no statistically significant findings between the urban and rural samples for pap tests (5.3% vs. 5.8%, p = 0.8896), primary care visits (88.4% vs. 89.4%, p = 0.6653), and oncology visits (67.6% vs 62.1%, p = 0.1238). Discussion This is the first study to examine socio-demographics and health outcomes of people who have received TSCPs using a novel regional database that combines cancer treatment variables with health care utilization. Overall, these efforts increase the capacity to describe longitudinal care patterns of a specific population within and across health care systems. Such a database could also be a potential first step towards panel management as part of a primary care medical home model of care, which is in alignment with management of other chronic health conditions such as diabetes or hypertension. Doing so could encourage the development of more proactive approaches for cancer survivorship specific care. Data from the HDC-SD indicate that although almost 90% of people living in urban and rural counties in Colorado who received TSCPs are seeking medical care following primary treatment for past cancer diagnoses, many screening exams or preventive services are not being completed in accordance with either US Preventive Services Taskforce (USPSTF) guidelines or National Comprehensive Cancer Network (NCCN) and American Cancer Society (ACS) guidelines. Disparities in preventive care services for cancer survivors in Colorado appear to exist across the urban-rural spectrum. These results suggest the need to refine handoffs between oncology and primary care professionals. Several lessons were learned in terms of defining, requesting, receiving, and analyzing such data over a two-year period during a pandemic. Initially, data was obtained from reviewing lists of procedures with the goal of being inclusive. Upon analysis, standardized codes provided more accuracy. If novel databases are to be used for research and quality improvement, then the processes for obtaining data should continue to be refined. While doing so, some degree of manual review may be necessary to ensure accurate data. For example, category assignment by provider type— individual physicians and advanced practice providers–were manually reviewed to ensure they were correctly categorized as either oncology or primary care clinicians. Data and lessons learned should subsequently be shared with individuals providing the clinical care as well as with the communities and health care systems involved. If TSCPs are to be used for research and quality improvement, then they should be designed to facilitate data collection and analysis. The helpful record review provided clarity and insight into the need to use specific data fields. In terms of formatting TSCPs, it would be ideal to have specific fields for dates of diagnoses as indicated by dates specified on pathology reports in addition to the already existing field for end of treatment date. Outcomes should be publicized to support individuals impacted by cancer diagnoses and community/state organizations. Outcomes should also be publicized to clinicians, administrators, and researchers committed to improvement and discovery. Such publicity may help to support and fund long-term investments in outcomes-based data analysis. Study limitations Several limitations were noted. First, this database explored health care utilization during the COVID-19 pandemic. The pandemic contributed to screening and treatment delays, so the utilization and receipt of health care services may be underrepresented in the database. Second, the lists of CPT codes used for colorectal cancer and lung cancer screening procedures were not complete when the data were pulled from HDC. As a result, the actual rates of colorectal screening may be higher than what was captured in HDC-SD and will be corrected moving forward. Additionally, lung cancer screening was omitted from this analysis due to the inability to accurately capture the appropriate CPT screening codes as well as largely missing data on smoking history. Another significant limitation is that data uploaded from outside health systems within the EPIC Media tab cannot be captured in HDC. Consequently, we are unable to potentially capture some health care utilization, immunizations, or procedures completed outside of the UCHealth system unless these were available in CORHIO (as is the case for many immunizations for example). Finally, emergency department visits in this first iteration were limited to the UCHealth system, which does have sites of emergency care in locations throughout the state. Future research and development The initial creation of the HDC-SD will continue to serve as a foundation for further inquiry. Data collection processes will continue to be refined. The research team would like to further explore outcomes associated with race/ethnicity variables across the urban-rural spectrum, disease type (primary cancer and co-morbid health conditions), other relevant health maintenance variables including behavioral health care, and insurance/payor type. By leveraging the capabilities of the EHR and loco-regionally available data warehouses, granular data that is more relevant to local communities and health systems may be captured and evaluated. Such information may be used to systematically improve health maintenance behaviors for individuals with a history of cancer. Implications for cancer care clinicians Overall, these efforts increased available knowledge about a specific population. These results indicate that almost 90% of people living in urban and rural counties in Colorado who received TSCPs are seeking medical care following primary treatment for past cancer diagnoses. However, many screening exams are not being completed in accordance with either US Preventive Services Taskforce guidelines or National Comprehensive Cancer Network and American Cancer Society guidelines. These results suggest the need to refine handoffs between oncology and primary care professionals. Additional attention should be directed towards improving the overall number of screening tests performed. These results signal a call to action for clinicians, public health officials, and policy makers. Clinicians understand that the goals of screening are to detect new malignancies and conditions early. They also understand that the goal of follow-up is to catch reoccurrences early and intervene timely. Within the rural population, the lack of documented colorectal cancer screening and mammograms highlights opportunities for improvement. Additional interventions and partnerships are needed to improve outcomes identified by this data. The authors look forward to partnering with non-profit organizations to improve health outcomes. Such organizations may include the Colorado Cancer Coalition, American Cancer Society, University of Colorado Cancer Center Office of Community Outreach, Cancer Prevention and Control Research Network at the Colorado School of Public Health, High Plains Research Network (HPRN), Colorado Rural Health Center, and State Network of Ambulatory Care Practices (SNOCAP) Furthermore, oncology and primary care clinicians should receive education about specific health maintenance features within their electronic health records. Thereafter, they should design workflows and strategies to incorporate these features while minimizing the burden on clinicians. Going forward, it is the ultimate hope and intent of this team to show the utilization of such database to help describe the value of diverse survivorship programs. Declarations This work was supported by Paul R. O’Hara Seed Grant Funds. The University of Colorado Cancer Center Population Health Shared Resource is supported by NCI grant P30CA046934. “All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Carlin Callaway, Elizabeth Molina, Linda Overholser, and Santi Das. The first draft of the manuscript was written by Carlin Callaway and Elizabeth Molina. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.” “The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.” This project was approved for exemption through the Colorado Multiple Institutional Review Board of the University of Colorado. The authors are grateful to Melissa Haendel, PhD, FACMI and Julie McMurry, MPH for Figure 1. References A. C. Society, "Cancer Facts & Figures 2023," American Cancer Society, Atlanta, 2023. S. M. Bluethmann, A. B. Mariotto and J. H. Rowland, "Anticipating the 'silver tsunami': Prevalence trajectories and co-morbidity burden among older cancer survivors in the United States," Cancer Epidemiol Biomarkers Prev, vol. 25, no. 7, pp. 1029-1036, 2016. C. C. Murphy, D. E. Gerber and S. L. Pruitt, "Prevalence of prior cancer among persons newly diagnosed with cancer: An initial report from the Surveillance, Epidemiology, and End Results Program," JAMA Oncology, vol. 4, no. 6, pp. 832-836, 2018. L. Grassi, D. Spiegel and M. Riba, "Advancing psychosocial care in cancer patients," F1000Research, vol. 6, pp. 1-9, 2017. R. A. Hoekstra, M. J. Heins and J. C. Korevaar, "Health care needs of cancer survivors in general practice: a systematic review," BMC Family Practice, vol. 15, no. 94, p. 19, 2014. B. Risendal, J. M. Westfall, C. Zittleman and et al., "Impact of cancer survivorship care training on rural primary care practice teams: A mixed methods approach," Journal of Cancer Education, vol. 37, pp. 71-80, 2022. Tables Tables 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables129.11.2023.xlsx Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2023 Read the published version in Journal of Cancer Survivorship → Version 1 posted Editorial decision: Major revision 07 Oct, 2023 Reviews received at journal 07 Oct, 2023 Reviewers agreed at journal 16 Sep, 2023 Reviewers invited by journal 16 Sep, 2023 Submission checks completed at journal 16 Sep, 2023 Editor assigned by journal 16 Sep, 2023 First submitted to journal 11 Sep, 2023 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. We do this by developing innovative software and high quality services for the global research community. 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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-3346259","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":234001739,"identity":"739f8121-b789-4978-b1ee-b431e634badb","order_by":0,"name":"Carlin Callaway","email":"data:image/png;base64,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","orcid":"","institution":"University of Colorado Cancer Center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Carlin","middleName":"","lastName":"Callaway","suffix":""},{"id":234001740,"identity":"bd7b5dc0-e4db-48af-8afc-cb2db5abb865","order_by":1,"name":"Elizabeth Molina","email":"","orcid":"","institution":"University of Colorado Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Molina","suffix":""},{"id":234001741,"identity":"98e5157e-7e90-4662-a54b-1078583d5023","order_by":2,"name":"Linda Overholser","email":"","orcid":"","institution":"University of Colorado Division of General Internal Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Linda","middleName":"","lastName":"Overholser","suffix":""},{"id":234001742,"identity":"730411bb-6382-4bd5-8098-01a646071032","order_by":3,"name":"Santi Das","email":"","orcid":"","institution":"University of Colorado Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Santi","middleName":"","lastName":"Das","suffix":""}],"badges":[],"createdAt":"2023-09-11 21:29:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3346259/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3346259/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11764-023-01506-x","type":"published","date":"2023-12-23T15:00:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":43554482,"identity":"35cc62ac-c843-482e-93af-8c69c329ffd7","added_by":"auto","created_at":"2023-09-22 19:32:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":649313,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3346259/v1/eb9613dc4123dc2d5e6ac6b8.jpg"},{"id":43554484,"identity":"f547917f-5e78-444b-a840-65a6c7196796","added_by":"auto","created_at":"2023-09-22 19:32:31","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1134684,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3346259/v1/972947e3249b0560cbe73e05.jpg"},{"id":48776957,"identity":"11a43bbb-d6a3-4260-ba00-69817311dc18","added_by":"auto","created_at":"2023-12-25 15:09:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":504041,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3346259/v1/4928581a-014b-49ca-a7af-2bfaa4b783bc.pdf"},{"id":43554485,"identity":"38abf0c4-a191-4149-b8ee-7145c2874671","added_by":"auto","created_at":"2023-09-22 19:32:32","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31081,"visible":true,"origin":"","legend":"","description":"","filename":"Tables129.11.2023.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3346259/v1/407ed2034a655269e914767b.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a novel cancer survivorship database to describe health care utilization patterns for Coloradans who have completed primary cancer treatment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer, a complex and often chronic condition, impacts millions. In 2023, the American Cancer Society estimates that 1.9\u0026nbsp;million new cancer cases will be diagnosed [1]. Nationally, Bluethmann predicted that 26.1\u0026nbsp;million survivors will be living in 2040 and that 47% will live more than 10 years after their diagnosis [2]. The Colorado Department of Public Health and Environment estimates that 360,000 people with histories of cancer were currently living in Colorado as of April 2023 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cdphe.colorado.gov/center-for-health-and-environmental-data/registries-and-vital-statistics/colorado-central-cancer\u003c/span\u003e\u003cspan address=\"https://cdphe.colorado.gov/center-for-health-and-environmental-data/registries-and-vital-statistics/colorado-central-cancer\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite detection and treatment advances, people with histories of cancer experience unique health challenges including treatment-related late effects. People with histories of cancer are also more likely to develop additional cancers. According to SEER data, of 765,843 incident cancers diagnosed between 2009 and 2013, approximately one-fourth of adults aged 65 and older and just more than one-tenth of younger adults aged 20 to 64 years were experiencing their second or higher cancer. Most of these new cancers (termed second primary cancers) were diagnosed in different anatomic locations [3].\u003c/p\u003e \u003cp\u003eCoordinated health care following active cancer treatment between primary care and oncology care is crucial; it is one of the six standards for survivorship care as defined by the National Comprehensive Cancer Network (Version 1.2023). Additional standards include: (a) surveillance for recurrence and screening for new cancers; (b) monitoring for late effects; (c) preventing and detecting late effects; (d) evaluating cancer-related syndromes; and (e) planning for ongoing care. Ongoing care includes general preventive health screenings, management of co-morbid conditions, promotion of healthy behaviors, and psychosocial support (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nccn.org/professionals/physician_gls/pdf/survivorship.pdf\u003c/span\u003e\u003cspan address=\"https://www.nccn.org/professionals/physician_gls/pdf/survivorship.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGaps in information exchange, limited care coordination activities, and lack of clarity of provider roles have all been cited as reasons for the well-known fragmentation in cancer survivorship care across providers and especially during the transition following completion of active cancer treatment [4] [5].\u003c/p\u003e \u003cp\u003eA previous study conducted in Colorado to evaluate a cancer survivorship educational intervention for rural primary care practices revealed that one barrier for these practices to care for cancer survivors was a limited ability to identify people with past histories of cancer within their practices. Risendal and her team identified he need for additional research to better understand the patterns of care that Coloradans with past histories of cancer receive in multiple settings [6].\u003c/p\u003e \u003cp\u003eAlthough the literature is conflicted, many organizations recommend that treatment summary care plans or survivorship care plans (SCPs) be provided to people who complete curative treatment for malignancies. Treatment summary care plans may play an important role in communicating the need for preventive care following cancer treatment. However, additional research is needed to understand the impact of SCPs on health care outcomes and health care utilization. Nonetheless, the presence of SCPs may be useful to identify individuals who have completed primary oncology treatment.\u003c/p\u003e \u003cp\u003eThe two specific aims of this project were: (a) to build a comprehensive database representing adult Coloradans who completed cancer treatment within the Metro Denver University of Colorado Health system, and (b) to conduct a secondary analysis of this database to describe demographic characteristics and health care utilization patterns for these individuals. The HDC-SD database provides descriptions of HDC-SD was to describe socio-demographic characteristics. Such characteristics include urban vs rural residence, cancer diagnostic and treatment information, and oncology and non-oncology health care utilization patterns (primary care preventative services and emergency care). Such information will enable a systematic approach to population management for cancer survivorship care, provide a foundation for collaborative outreach to help reduce disparities in care, and potentially improve health outcomes throughout Colorado.\u003c/p\u003e \u003cp\u003eIt was hypothesized that a considerable number of Coloradans who have been treated with curative intent for cancer did not receive recommended cancer follow-up or screenings. It was also hypothesized that these levels fell below those reported in the literature. Finally, it was hypothesized that individuals who lived in rural areas had lower rates of receipt of preventive care tests than those who lived in urban areas.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eHealth Data Compass (HDC) Enterprise Health Data Warehouse is hosted by Google Cloud, and integrates patient clinical data and relevant billing information from the EPIC electronic health record used throughout the University of Colorado Health system (Epic Systems Corporation, Verona, Wisconsin). Additionally, HDC may link data from other sources, including the Center for Improving Value in Health Care\u0026rsquo;s (CIVHC) Colorado All Payers Claims Database (APCD) and the University of Colorado Cancer Registry. Data are available from 2011 to present and are updated monthly. Available variables include patient demographics, medical encounters and visits, diagnoses (including cancer diagnosis), health history (including personal, family, and social), medications, procedures, labs, billing codes, payers, and provider queries and notes. HDC may be used to generate de-identified data sets, limited data sets, and full protected health information (PHI) data sets by request. This data then flows into the Observational Medical Outcomes Partnership (OMOP) Common Data Model, which then flows into the Eureka Virtual Machine. Such flow enables secure data sharing and delivery to national networks (Fig.\u0026nbsp;1). Additional information is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.healthdatacompass.org/home\u003c/span\u003e\u003cspan address=\"https://www.healthdatacompass.org/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and Exclusion in HDC-SD\u003c/h2\u003e \u003cp\u003ePatients were included in the HDC-SD data set who were 18\u0026ndash;85 years of age, had a UCHealth medical record number, and were diagnosed with a non-hematologic malignancy specifically leukemia or multiple myeloma Additionally, patients were identified based on their receipt of a completed treatment summary care plan (TSCP) delivered between January 1, 2020 to December 31, 2021.\u003c/p\u003e \u003cp\u003eUCHealth TSCPs are available within the EPIC Electronic Health Record Problem List. The UCHealth custom-built TSCP template specifies the treatment team including the patient\u0026rsquo;s primary care provider, diagnostic and staging information including detailed pathology findings, treatment details, follow-up recommendations as guided by the National Comprehensive Cancer Network (NCCN) and one\u0026rsquo;s oncology team, and overall wellness information. Wellness information includes reasons for patients to contact their oncology and primary care teams, possible late effects, mental health recommendations, health screening and immunizations recommendations, advance care planning, and available health system resources including sexual health and fertility services.\u003c/p\u003e \u003cp\u003e Within the Metro Denver region, TSCPs are generated from reviews of monthly positive pathology reports provided by the Tumor Registry (which have since been replaced with surgical reports), as well as anti-cancer therapy discontinuation reports and radiation end-of-treatment summary reports designed by an in-house EPIC analyst. The data represented in the HDC-SD were pulled from HDC on October, 4, 2022. The process of generating and delivering TSCPs has been an evolving process influenced by the Institute for Healthcare Improvement Plan-Do-Study-Act methodology (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ihi.org/resources/Pages/HowtoImprove/default.aspx\u003c/span\u003e\u003cspan address=\"https://www.ihi.org/resources/Pages/HowtoImprove/default.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eVariables of interest\u003c/h2\u003e \u003cp\u003eThe objective was to use HDC-SD to identify rates of U.S. Preventive Services Task Force (USPSTF) recommended screening procedures, immunizations, and health care utilization among the sample. Screening procedures were identified using a combination of procedure labels and Current Procedural Terminology (CPT) codes. Receipt of immunizations were identified using immunization and procedure labels. Heath care utilization were defined as primary care visits, oncology visits, and health-system specific emergency department visits. Such data were identified based on encounter data, department type, and clinician specialty. Demographic information of interest included urban and rural residence as defined by the U.S. Department of Agriculture Economic Research Service (ERS) 2013 Urban Influence Codes (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ers.usda.gov/data-products/urban-influence-codes/\u003c/span\u003e\u003cspan address=\"https://www.ers.usda.gov/data-products/urban-influence-codes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Additional information of interested included patient demographics (sex, age, and race/ethnicity), primary cancer site, and payer types.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eRecord review for data validation\u003c/h2\u003e \u003cp\u003eA total of 20 records were randomly selected to validate the HDC-SD derived data with the EPIC derived data. Validated data points of interest included date of birth, type of cancer, date of diagnosis, age at diagnosis, smoking history, date of TSCP completion, initial and subsequent oncology visits thereafter, initial and subsequent primary care visits thereafter, dates of recommended screening procedures, and dates of immunizations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eChi-square tests were used to compare patient demographics, disease characteristics, socioeconomic characteristics, and health maintenance between urban and rural settings. All tests were 2-sided and performed in SAS Version 9.4 (SAS Institute Inc., Cary, NC) using a statistical significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The study protocol was determined to be exempt by the Colorado Multiple Institutional Review Board.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDataset validation\u003c/h2\u003e \u003cp\u003eDuring the chart review process, several discrepancies were identified between the HDC-SD and individualized records within the EPIC EHR. Of the 20 patients reviewed, 10 patients (50%) within the HDC-SD had conflicting dates of diagnosis versus dates determined through the EPIC EHR. In some cases, these differences stretched from months to years. Upon further review, it was discovered that the HDC-SD diagnosis date was defined as the first encounter date associated with an oncology-specific ICD-10 code, even if the code corresponded to an unspecified tumor. To resolve this issue, the date of diagnosis was subsequently defined as the first date identifying a diagnosis code associated with a specific tumor. This adjustment resulted in the HDC-SD diagnosis dates more closely reflecting the dates identified within the EPIC EHR.\u003c/p\u003e \u003cp\u003eAdditionally, many completed screening procedures as identified through the EPIC EHR Media tab (where information and documents from outside sources can be scanned in for information purposed and become a part of the medical record) were not identified by the HDC-SD. As a specific example, 9 (45%) patients had record of completed colorectal cancer screening in their EHR, but only 2 (10%) patients had a confirmed colorectal cancer screening procedure captured by HDC-SD. Documents uploaded into the EPIC EHR Media tab do not translate into a discrete field detectable by HDC thus, services and procedures completed outside the UCHealth system may not be fully discovered by Health Data Compass (Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive analysis\u003c/h2\u003e \u003cp\u003eThe HDC-SD contains 1,933 patients who completed curative-intent primary treatment between January 1, 2020 and December 31, 2021 for diagnoses of cancer. The top three cancers included breast (24%), male reproductive (23%), and cutaneous (13%; mostly early stage melanoma). Of those included within the HDC-SD, 50.5% were women and 79% were white non-Hispanic. The majority of patients were aged 55 years and older (68%) (Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eAll 21 Colorado Health Statistic Regions were represented within this database (Figs.\u0026nbsp;2a and 2b). According to the United States 2020 Census (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.census.gov/library/stories/state-by-state/colorado-population-change-between-census-decade.html\u003c/span\u003e\u003cspan address=\"https://www.census.gov/library/stories/state-by-state/colorado-population-change-between-census-decade.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), approximately 84% of Colorado\u0026rsquo;s population resides in Health Statistic Regions 2 (Larimer County), 3 (Douglas County), 4 (El Paso), 12 (Garfield, Pitkin, Eagle, Summit, and Grand Counties), 14 (Adams County), 15 (Arapahoe Counties), 16 (Boulder and Broomfield Counties), 18 (Weld County), 20 (Denver County), and 21 (Jefferson County). This was well represented in HDC-SC, with 90% of patients living in these same highly-populated regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eUrban vs. rural findings\u003c/h2\u003e \u003cp\u003eThe majority of patients represented in the HDC-SD lived in an urban setting (89.8%), and there was a higher percentage of females in the urban setting compared to rural (51.8% vs. 39.9%, p\u0026thinsp;=\u0026thinsp;0.0010). In terms of diagnosis, the urban sample had a higher percentage of cutaneous malignancies (14.0% vs. 6.6%, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001) and breast tumors (25.0% vs. 14.6%, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). However, the urban sample had a lower percentage of bladder and urologic diagnoses (7.7% vs. 18.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). The majority of patients had insurance coverage (84.8% of urban patients with commercial, Medicare, and Tricare coverage vs. 87.8% of rural patients with commercial, Medicare, and Tricare coverage) at the time of diagnosis. The urban sample contained more current commercial insurance enrollees than the rural sample (48.8% vs. 38.9%, p\u0026thinsp;=\u0026thinsp;0.0123), while the rural sample contained more Medicare enrollees (48.0% vs. 35.4%, p\u0026thinsp;=\u0026thinsp;0.0123). Finally, the urban sample had a smaller percentage of White Non-Hispanic patients than the rural sample (78.2% vs. 85.4%, p\u0026thinsp;=\u0026thinsp;0.0024).\u003c/p\u003e \u003cp\u003eIn terms of health maintenance, there were some statistically significant findings between patients living in urban areas and rural areas. A greater percentage of eligible people aged 45 years and older within the urban sample were up to date with their colorectal cancer screening (6.2% vs. 0.6%, p\u0026thinsp;=\u0026thinsp;0.0020). A greater percentage of men aged 50 years and above within the urban sample had a PSA test within the past 2 years (48.9% vs. 22.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Additionally, a greater percentage of women aged 45 years and above within the urban sample a mammogram in the previous 2 years (25.7% vs. 10.6%, p\u0026thinsp;=\u0026thinsp;0.0009). Finally, a greater percentage of adults within the urban sample received a flu shot (41.7% vs. 13.6%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), received one or more COVID-19 vaccines (37.8% vs. 9.6%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and had an emergency department visit within the UCHealth system (7.4% vs. 3%, p\u0026thinsp;=\u0026thinsp;0.0225). There were no statistically significant findings between the urban and rural samples for pap tests (5.3% vs. 5.8%, p\u0026thinsp;=\u0026thinsp;0.8896), primary care visits (88.4% vs. 89.4%, p\u0026thinsp;=\u0026thinsp;0.6653), and oncology visits (67.6% vs 62.1%, p\u0026thinsp;=\u0026thinsp;0.1238).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study to examine socio-demographics and health outcomes of people who have received TSCPs using a novel regional database that combines cancer treatment variables with health care utilization. Overall, these efforts increase the capacity to describe longitudinal care patterns of a specific population within and across health care systems. Such a database could also be a potential first step towards panel management as part of a primary care medical home model of care, which is in alignment with management of other chronic health conditions such as diabetes or hypertension. Doing so could encourage the development of more proactive approaches for cancer survivorship specific care. Data from the HDC-SD indicate that although almost 90% of people living in urban and rural counties in Colorado who received TSCPs are seeking medical care following primary treatment for past cancer diagnoses, many screening exams or preventive services are not being completed in accordance with either US Preventive Services Taskforce (USPSTF) guidelines or National Comprehensive Cancer Network (NCCN) and American Cancer Society (ACS) guidelines. Disparities in preventive care services for cancer survivors in Colorado appear to exist across the urban-rural spectrum. These results suggest the need to refine handoffs between oncology and primary care professionals.\u003c/p\u003e\n\u003cp\u003eSeveral lessons were learned in terms of defining, requesting, receiving, and analyzing such data over a two-year period during a pandemic. Initially, data was obtained from reviewing lists of procedures with the goal of being inclusive. Upon analysis, standardized codes provided more accuracy.\u003c/p\u003e\n\u003cp\u003eIf novel databases are to be used for research and quality improvement, then the processes for obtaining data should continue to be refined. While doing so, some degree of manual review may be necessary to ensure accurate data. For example, category assignment by provider type\u0026mdash; individual physicians and advanced practice providers\u0026ndash;were manually reviewed to ensure they were correctly categorized as either oncology or primary care clinicians. Data and lessons learned should subsequently be shared with individuals providing the clinical care as well as with the communities and health care systems involved.\u003c/p\u003e\n\u003cp\u003eIf TSCPs are to be used for research and quality improvement, then they should be designed to facilitate data collection and analysis. The helpful record review provided clarity and insight into the need to use specific data fields. In terms of formatting TSCPs, it would be ideal to have specific fields for dates of diagnoses as indicated by dates specified on pathology reports in addition to the already existing field for end of treatment date. Outcomes should be publicized to support individuals impacted by cancer diagnoses and community/state organizations. Outcomes should also be publicized to clinicians, administrators, and researchers committed to improvement and discovery. Such publicity may help to support and fund long-term investments in outcomes-based data analysis.\u003c/p\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy limitations\u003c/h2\u003e\n\u003cp\u003eSeveral limitations were noted. First, this database explored health care utilization during the COVID-19 pandemic. The pandemic contributed to screening and treatment delays, so the utilization and receipt of health care services may be underrepresented in the database. Second, the lists of CPT codes used for colorectal cancer and lung cancer screening procedures were not complete when the data were pulled from HDC. As a result, the actual rates of colorectal screening may be higher than what was captured in HDC-SD and will be corrected moving forward. Additionally, lung cancer screening was omitted from this analysis due to the inability to accurately capture the appropriate CPT screening codes as well as largely missing data on smoking history. Another significant limitation is that data uploaded from outside health systems within the EPIC Media tab cannot be captured in HDC. Consequently, we are unable to potentially capture some health care utilization, immunizations, or procedures completed outside of the UCHealth system unless these were available in CORHIO (as is the case for many immunizations for example). Finally, emergency department visits in this first iteration were limited to the UCHealth system, which does have sites of emergency care in locations throughout the state.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture research and development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe initial creation of the HDC-SD will continue to serve as a foundation for further inquiry. Data collection processes will continue to be refined. The research team would like to further explore outcomes associated with race/ethnicity variables across the urban-rural spectrum, disease type (primary cancer and co-morbid health conditions), other relevant health maintenance variables including behavioral health care, and insurance/payor type. By leveraging the capabilities of the EHR and loco-regionally available data warehouses, granular data that is more relevant to local communities and health systems may be captured and evaluated. Such information may be used to systematically improve health maintenance behaviors for individuals with a history of cancer.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eImplications for cancer care clinicians\u003c/h2\u003e\n\u003cp\u003eOverall, these efforts increased available knowledge about a specific population. These results indicate that almost 90% of people living in urban and rural counties in Colorado who received TSCPs are seeking medical care following primary treatment for past cancer diagnoses. However, many screening exams are not being completed in accordance with either US Preventive Services Taskforce guidelines or National Comprehensive Cancer Network and American Cancer Society guidelines. These results suggest the need to refine handoffs between oncology and primary care professionals.\u003c/p\u003e\n\u003cp\u003eAdditional attention should be directed towards improving the overall number of screening tests performed. These results signal a call to action for clinicians, public health officials, and policy makers. Clinicians understand that the goals of screening are to detect new malignancies and conditions early. They also understand that the goal of follow-up is to catch reoccurrences early and intervene timely.\u003c/p\u003e\n\u003cp\u003eWithin the rural population, the lack of documented colorectal cancer screening and mammograms highlights opportunities for improvement. Additional interventions and partnerships are needed to improve outcomes identified by this data. The authors look forward to partnering with non-profit organizations to improve health outcomes. Such organizations may include the Colorado Cancer Coalition, American Cancer Society, University of Colorado Cancer Center Office of Community Outreach, Cancer Prevention and Control Research Network at the Colorado School of Public Health, High Plains Research Network (HPRN), Colorado Rural Health Center, and State Network of Ambulatory Care Practices (SNOCAP)\u003c/p\u003e\n\u003cp\u003eFurthermore, oncology and primary care clinicians should receive education about specific health maintenance features within their electronic health records. Thereafter, they should design workflows and strategies to incorporate these features while minimizing the burden on clinicians.\u003c/p\u003e\n\u003cp\u003eGoing forward, it is the ultimate hope and intent of this team to show the utilization of such database to help describe the value of diverse survivorship programs.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis work was supported by Paul R. O\u0026rsquo;Hara Seed Grant Funds. The University of Colorado Cancer Center Population Health Shared Resource is supported by NCI grant P30CA046934.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Carlin Callaway, Elizabeth Molina, Linda Overholser, and Santi Das. The first draft of the manuscript was written by Carlin Callaway and Elizabeth Molina. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThis project was approved for exemption through the Colorado Multiple Institutional Review Board of the University of Colorado.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to Melissa Haendel, PhD, FACMI and Julie McMurry, MPH for Figure 1.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eA. C. Society, \u0026quot;Cancer Facts \u0026amp; Figures 2023,\u0026quot; American Cancer Society, Atlanta, 2023.\u003c/li\u003e\n\u003cli\u003eS. M. Bluethmann, A. B. Mariotto and J. H. Rowland, \u0026quot;Anticipating the \u0026apos;silver tsunami\u0026apos;: Prevalence trajectories and co-morbidity burden among older cancer survivors in the United States,\u0026quot; \u003cem\u003eCancer Epidemiol Biomarkers Prev, \u003c/em\u003evol. 25, no. 7, pp. 1029-1036, 2016. \u003c/li\u003e\n\u003cli\u003eC. C. Murphy, D. E. Gerber and S. L. Pruitt, \u0026quot;Prevalence of prior cancer among persons newly diagnosed with cancer: An initial report from the Surveillance, Epidemiology, and End Results Program,\u0026quot; \u003cem\u003eJAMA Oncology, \u003c/em\u003evol. 4, no. 6, pp. 832-836, 2018. \u003c/li\u003e\n\u003cli\u003eL. Grassi, D. Spiegel and M. Riba, \u0026quot;Advancing psychosocial care in cancer patients,\u0026quot; \u003cem\u003eF1000Research, \u003c/em\u003evol. 6, pp. 1-9, 2017. \u003c/li\u003e\n\u003cli\u003eR. A. Hoekstra, M. J. Heins and J. C. Korevaar, \u0026quot;Health care needs of cancer survivors in general practice: a systematic review,\u0026quot; \u003cem\u003eBMC Family Practice, \u003c/em\u003evol. 15, no. 94, p. 19, 2014. \u003c/li\u003e\n\u003cli\u003eB. Risendal, J. M. Westfall, C. Zittleman and et al., \u0026quot;Impact of cancer survivorship care training on rural primary care practice teams: A mixed methods approach,\u0026quot; \u003cem\u003eJournal of Cancer Education, \u003c/em\u003evol. 37, pp. 71-80, 2022. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-survivorship","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcsu","sideBox":"Learn more about [Journal of Cancer Survivorship](https://www.springer.com/journal/11764)","snPcode":"11764","submissionUrl":"https://submission.nature.com/new-submission/11764/3","title":"Journal of Cancer Survivorship","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cancer survivorship, Treatment summary care plan, Survivorship care plan, Electronic health record, Cancer data base, Clinical outcomes","lastPublishedDoi":"10.21203/rs.3.rs-3346259/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3346259/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003ePurpose\u003c/b\u003e\u003c/p\u003e \u003cp\u003eElectronic health records (EHR) and data warehouses hold promise for population health management. Specific aims were: (a) to build a comprehensive database representing adult Coloradans who completed cancer treatment within a health care system, and (b) to conduct a secondary analysis of this database.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA survivorship database (HDC-SD) was built from the Health Data Compass (HDC) warehouse, which includes diagnostic and treatment information and incorporates all-payer data to identify individuals with histories of cancer who received treatment at the University of Colorado Cancer Center (UCCC) between January 1, 2020 and December 31, 2021. Data was analyzed using Chi-square tests to compare sociodemographic characteristics, disease characteristics, and health maintenance between urban and rural settings.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe HDC-SD includes 1933 records representing 13 categories of cancers. The majority live in an urban setting (89.8%). Patients in the database living in urban areas had statistically (higher/lower) rates of completing colorectal screening, mammography, flu shots, and COVID-19 vaccination. Emergency department visits occurred at a statistically significant (higher/lower) level for those living in urban areas.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCreating a database of individuals who have completed active cancer treatment, while incorporating longitudinal health utilization data, may help prepare for systematic population management for cancer survivors.\u003c/p\u003e","manuscriptTitle":"Development of a novel cancer survivorship database to describe health care utilization patterns for Coloradans who have completed primary cancer treatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-22 19:32:27","doi":"10.21203/rs.3.rs-3346259/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-10-08T01:18:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-10-07T22:38:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2d6938b4-2f15-4518-8ada-b87768adebd0","date":"2023-09-16T13:43:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-09-16T13:29:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-09-16T07:13:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-09-16T07:13:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cancer Survivorship","date":"2023-09-11T21:25:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-survivorship","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcsu","sideBox":"Learn more about [Journal of Cancer Survivorship](https://www.springer.com/journal/11764)","snPcode":"11764","submissionUrl":"https://submission.nature.com/new-submission/11764/3","title":"Journal of Cancer Survivorship","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"24fbe787-f64f-4422-b80c-403f44ef1b5a","owner":[],"postedDate":"September 22nd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-12-25T15:07:29+00:00","versionOfRecord":{"articleIdentity":"rs-3346259","link":"https://doi.org/10.1007/s11764-023-01506-x","journal":{"identity":"journal-of-cancer-survivorship","isVorOnly":false,"title":"Journal of Cancer Survivorship"},"publishedOn":"2023-12-23 15:00:42","publishedOnDateReadable":"December 23rd, 2023"},"versionCreatedAt":"2023-09-22 19:32:27","video":"","vorDoi":"10.1007/s11764-023-01506-x","vorDoiUrl":"https://doi.org/10.1007/s11764-023-01506-x","workflowStages":[]},"version":"v1","identity":"rs-3346259","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3346259","identity":"rs-3346259","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}