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Grant Details

Grant Number: 1R01CA260891-01A1 Interpret this number
Primary Investigator: Hashibe, Mia
Organization: University Of Utah
Project Title: Identifying Cancer Recurrence with Novel Data Linkages with a Cancer Registry
Fiscal Year: 2022


ABSTRACT For the estimated 17 million cancer survivors in the US today, fear of recurrence is a substantial source of stress and an issue that drives survivorship care. Understanding the scope of recurrence among cancer survivors can inform clinical practice, improve patient health, and allow for real-world assessment of treatment effectiveness. Population-level data on cancer recurrence are difficult to capture, and require evaluation of multiple data sources to accurately identify cancer recurrences. The Utah Cancer Registry (UCR), a SEER registry since 1973, is strongly positioned to identify recurrences in a population-based setting. The registry data are linked to the Utah Population Database (UPDB), which includes electronic medical records (EMR), statewide healthcare facility data (SHFD; inpatient, ambulatory surgery and emergency department), and claims data (All Payer Claims Database (APCD), Medicare). We propose to assess the utility of using data sources common across all state cancer registries and to investigate the added value of novel data linkages available at the Utah Cancer Registry. We also propose to extend and validate a recently-developed algorithm to identify individual level breast cancer recurrence to identify recurrence for other cancer types to estimate the population-level burden of recurrence. Our specific aims are as follows: 1) Determine the predictive performance to identify recurrence using data currently available to cancer registries for breast and prostate cancer. These would include Commission on Cancer recurrence variables, electronic pathology reports, and death certificates. 2) Estimate the improvements in predictive performance to identify recurrence by inclusion of novel administrative data linkage for breast and prostate cancer. 3) Evaluate the scalability and transportability of recurrence identifying algorithms across settings and populations for research. We will validate the algorithms’ predictive performance by estimating positive and negative predictive values among a racially and ethnically diverse collection of cancer cases from the Seattle- Puget Sound SEER registry, including comparisons of performance across race/ethnicity, age, stage, and rural/urban status. In addition, we will validate the breast recurrence identification algorithm recently developed in the Seattle registry in the Utah breast cancer population. No algorithms currently exist to evaluate the data sources individually and combined to identify recurrence events based on cancer registry and administrative data. Our results will inform the predictive performance for routinely available data and the value added of administrative data sources, which may be differentially complete and/or costly to procure. Our work will establish a path forward for population-level tracking of cancer recurrence and facilitate prioritization of data generation efforts and algorithms that can be customized based on the data available in different situations. 1



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