{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:04:26Z","timestamp":1790240666588,"version":"4.1.0"},"reference-count":21,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AstraZeneca US Medical Affairs"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Cancer screening is central to cancer control and an increasingly important component of personalized medicine, enabling risk-adaptive prevention, earlier detection, and more tailored intervention strategies. However, screening participation varies widely across tumor types, populations, and geographic regions in the United States. Although screening rate data are widely used to guide clinical recommendations, policy, advocacy, and early detection innovation, the underlying data ecosystem remains fragmented and poorly characterized. Screening rates are commonly derived from surveys, electronic health records (EHRs), and claims data, each with limitations that may misrepresent true screening participation and hinder efforts to improve early detection. This study evaluated the current cancer screening data ecosystem to characterize key limitations and identify actionable solutions. Methods: Primary qualitative interviews were conducted with cancer screening data users (n = 8), including academic researchers and patient advocacy organizations, and data aggregators (n = 2) from national public health organizations. Secondary research included a structured review of publicly available survey-, EHR-, and claims-based screening data sources and relevant literature. Findings were synthesized and evaluated in a multistakeholder workshop with 20 participants representing data aggregators, researchers, clinicians, advocates, and industry experts to identify and prioritize solutions based on feasibility and potential impact. Results: Four major categories of limitations were identified across data sources: access, accuracy, consistency, and completeness. Data fragmentation, variable transparency, technical complexity, and cost limit accessibility and comparability. Accuracy is affected by recall bias, documentation and coding errors, and imperfect correction methods. Inconsistent tumor coverage, survey changes, and irregular reporting cycles hinder longitudinal analyses. Gaps in underserved population capture, risk factors, follow-up, and outcomes limit assessment of disparities and real-world impact. Stakeholders prioritized near-term solutions, including improved data education, standardized reporting, cross-source validation, and expanded data sharing, alongside longer-term investments in registries and longitudinal infrastructure. Conclusions: Structural limitations in cancer screening data constrain efforts to optimize screening, address inequities, and advance early detection. Targeted improvements and longer-term system investments are needed to strengthen data quality, usability, and population-level impact.<\/jats:p>","DOI":"10.3390\/diagnostics16193089","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T08:06:37Z","timestamp":1790237197000},"page":"3089","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Limitations in US Cancer Screening Rate Data and Opportunities to Improve Precision Prevention"],"prefix":"10.3390","volume":"16","author":[{"given":"Jody","family":"Hoyos","sequence":"first","affiliation":[{"name":"Prevent Cancer Foundation, 333 John Carlyle Street, Suite 635, Alexandria, VA 22314, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elissa","family":"Quinn","sequence":"additional","affiliation":[{"name":"AstraZeneca, Wilmington, DE 19803, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2675-0371","authenticated-orcid":false,"given":"Daryl","family":"Pritchard","sequence":"additional","affiliation":[{"name":"Personalized Medicine Coalition, 1710 Rhode Island Ave NW, Washington, DC 20036, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0382-6062","authenticated-orcid":false,"given":"Gary","family":"Gustavsen","sequence":"additional","affiliation":[{"name":"Health Advances LLC, 275 Grove Street, Newton, MA 02466, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0148-9856","authenticated-orcid":false,"given":"Owen","family":"Fahey","sequence":"additional","affiliation":[{"name":"Health Advances LLC, 275 Grove Street, Newton, MA 02466, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arushi","family":"Agarwal","sequence":"additional","affiliation":[{"name":"Health Advances LLC, 275 Grove Street, Newton, MA 02466, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chyke","family":"Doubeni","sequence":"additional","affiliation":[{"name":"The Ohio State University Wexner Medical Center, 410 West Tenth Avenue, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Aguiar-Ib\u00e1\u00f1ez, R., Mbous, Y., Sharma, S., Chakali, R., and Chawla, E. (2025). Barriers to cancer screening uptake and approaches to overcome them: A systematic literature review. Front. Oncol., 15.","DOI":"10.3389\/fonc.2025.1575820"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e2537905","DOI":"10.1001\/jamanetworkopen.2025.37905","article-title":"Geographic and Temporal Patterns of Screening for Breast, Cervical, and Colorectal Cancer in the US, 1997\u20132019","volume":"8","author":"Pradhan","year":"2025","journal-title":"JAMA Netw. Open"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1158\/1055-9965.EPI-24-1436","article-title":"Examining Lung Cancer Screening Uptake in the United States: Recent Research and Limitations of Public-Use Data","volume":"34","author":"Maki","year":"2025","journal-title":"Cancer Epidemiol. Biomark. Prev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1093\/jnci\/djy228","article-title":"Evaluating Lung Cancer Screening Uptake, Outcomes, and Costs in the United States: Challenges With Existing Data and Recommendations for Improvement","volume":"111","author":"Rai","year":"2019","journal-title":"J. Natl. Cancer Inst."},{"key":"ref_5","unstructured":"ACS (2025). Cancer Surveillance Programs and Data Tracking, American Cancer Society. Available online: https:\/\/www.cancer.org\/cancer\/preparing-for-treatment\/cancer-surveillance-programs-and-data-tracking.html."},{"key":"ref_6","first-page":"287","article-title":"An overview of real-world data sources for oncology and considerations for research","volume":"72","author":"Penberthy","year":"2022","journal-title":"CA Cancer J. Clin."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"kae113","DOI":"10.1093\/jncics\/pkae113","article-title":"Rural-urban disparities and trends in cancer screening: An analysis of Behavioral Risk Factor Surveillance System data (2018\u20132022)","volume":"8","author":"Benavidez","year":"2024","journal-title":"JNCI Cancer Spectr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e2451827","DOI":"10.1001\/jamanetworkopen.2024.51827","article-title":"Disparities and Gaps in Breast Cancer Screening for Women Aged 40 to 49 Years","volume":"7","author":"Gu","year":"2024","journal-title":"JAMA Netw. Open"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"211","DOI":"10.2147\/JMDH.S104807","article-title":"Information bias in health research: Definition, pitfalls, and adjustment methods","volume":"9","author":"Althubaiti","year":"2016","journal-title":"J. Multidiscip. Healthc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e001491","DOI":"10.1136\/bmjoq-2021-001491","article-title":"Understanding challenges of using routinely collected health data to address clinical care gaps: A case study in Alberta, Canada","volume":"11","author":"McGuckin","year":"2022","journal-title":"BMJ Open Qual."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1007\/s11136-021-03003-z","article-title":"Perceived benefits and limitations of using patient-reported outcome measures in clinical practice with individual patients: A systematic review of qualitative studies","volume":"31","author":"Campbell","year":"2022","journal-title":"Qual. Life Res."},{"key":"ref_12","unstructured":"Division of Cancer Prevention and Control (2025, October 15). Measuring Breast, Cervical, and Colorectal Cancer Screening Rates in Health System Clinics, Available online: https:\/\/www.google.com\/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiqzabah8CRAxWFFlkFHWdeFkcQFnoECBkQAQ&url=https%3A%2F%2Fstacks.cdc.gov%2Fview%2Fcdc%2F43398%2Fcdc_43398_DS1.pdf%3F&usg=AOvVaw3VTPpXinV8uoupVb6cHOzK&opi=89978449."},{"key":"ref_13","unstructured":"American Cancer Society (2025, October 15). Quality Improvement Implementation Manual: Cancer Prevention and Screening. Available online: https:\/\/www.mysocietysource.org\/Resource\/2025%20ACS%20QI%20Implementation%20Manual.pdf."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ghalavand, H., Shirshahi, S., Rahimi, A., Zarrinabadi, Z., and Amani, F. (2024). Common data quality elements for health information systems: A systematic review. BMC Med. Inform. Decis. Mak., 24.","DOI":"10.1186\/s12911-024-02644-7"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e57615","DOI":"10.2196\/57615","article-title":"Data Quality-Driven Improvement in Health Care: Systematic Literature Review","volume":"26","author":"Lighterness","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_16","unstructured":"Goldman, D., Lakdawalla, D., and Van Nuys, K. (2025, October 15). Cancer-Related Technologies Have Changed a Lot. So Should Cancer Screening. USC Schaeffer Institute for Public Policy & Government Service. Available online: https:\/\/schaeffer.usc.edu\/wp-content\/uploads\/2024\/10\/MCED_White_Paper.pdf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1093\/jncimonographs\/lgad025","article-title":"Data gaps and opportunities for modeling cancer health equity","volume":"2023","author":"Corley","year":"2023","journal-title":"J. Natl. Cancer Institute. Monogr."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e2242354","DOI":"10.1001\/jamanetworkopen.2022.42354","article-title":"Evaluation of a National Quality Improvement Collaborative for Improving Cancer Screening","volume":"5","author":"Joung","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"ref_19","unstructured":"IEEE DataPort (2025, October 15). Strategies for Improving Data Accessibility in Academic Institutions. Strategies for Improving Data Accessibility in Academic Institutions | IEEE DataPort. Available online: https:\/\/ieee-dataport.org\/news\/strategies-improving-data-accessibility-academic-institutions."},{"key":"ref_20","unstructured":"(2025, October 15). National Cancer Institute\u2014Surveillance, Epidemiology, and End Results Program. Process\u2014SEER Quality Improvement. SEER, Available online: https:\/\/seer.cancer.gov\/qi\/process.html."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1177\/1094428119836485","article-title":"Best practices in data collection and preparation: Recommendations for reviewers, editors, and authors","volume":"24","author":"Aguinis","year":"2021","journal-title":"Organ. Res. Methods"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/19\/3089\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T08:24:33Z","timestamp":1790238273000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/19\/3089"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,24]]},"references-count":21,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["diagnostics16193089"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16193089","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,24]]}}}