Limitations in US Cancer Screening Rate Data and Opportunities to Improve Precision Prevention.
5 affiliations
- 1Prevent Cancer Foundation, 333 John Carlyle Street, Suite 635, Alexandria, VA 22314, USA.
- 2AstraZeneca, Wilmington, DE 19803, USA.
- 3Personalized Medicine Coalition, 1710 Rhode Island Ave NW, Washington, DC 20036, USA.
- 4Health Advances LLC, 275 Grove Street, Newton, MA 02466, USA.
- 5The Ohio State University Wexner Medical Center, 410 West Tenth Avenue, Columbus, OH 43210, USA.
Abstract
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.
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BibTeX
@article{hoyos2026limitations,
title = {{Limitations in US Cancer Screening Rate Data and Opportunities to Improve Precision Prevention.}},
author = {Jody Hoyos and Elissa Quinn and Daryl Pritchard and Gary Gustavsen and Owen Fahey and Arushi Agarwal and Chyke Doubeni},
journal = {Diagnostics (Basel, Switzerland)},
year = {2026},
month = sep,
doi = {10.3390/diagnostics16193089},
url = {https://europepmc.org/article/MED/42882244},
note = {CC BY},
}
RIS
TY - JOUR
TI - Limitations in US Cancer Screening Rate Data and Opportunities to Improve Precision Prevention.
AU - Jody Hoyos
AU - Elissa Quinn
AU - Daryl Pritchard
AU - Gary Gustavsen
AU - Owen Fahey
AU - Arushi Agarwal
AU - Chyke Doubeni
PY - 2026
DA - 2026/09/24
JO - Diagnostics (Basel, Switzerland)
DO - 10.3390/diagnostics16193089
UR - https://europepmc.org/article/MED/42882244
AB - 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.
SN - 2075-4418
ER -