Estimating breast cancer recurrence in a population-based registry in Georgia, US
Chrystelle Kiang, Micah Streiff, Rebecca Nash, Robert H. Lyles, Deirdre Cronin-Fenton, Anke Huels, Timothy L. Lash, Kevin C. Ward

TL;DR
This study estimates breast cancer recurrence rates in Georgia using imputation methods, revealing disparities by race, ethnicity, and tumor characteristics, and highlights the need for targeted surveillance in survivors.
Contribution
It introduces a novel application of missing data imputation to estimate recurrence rates in a large population-based registry, providing new epidemiological insights.
Findings
Estimated 7.2% recurrence rate within 5 years post-diagnosis
Disparities observed in recurrence risk by race and ethnicity
Higher recurrence risk associated with advanced tumor stage and grade
Abstract
Although the descriptive epidemiology of primary breast cancer is well characterized in the US, breast cancer recurrence rates have not been measured in an unselected population. The number of breast cancer survivors at risk for recurrence is growing each year, so recurrence surveillance is a pressing need. We used missing data methods to impute breast cancer recurrence and estimate the risk of recurrence in the Cancer Recurrence Information and Surveillance Program (CRISP) cohort in the Georgia Cancer Registry. The imputation model was based on an internal validation substudy and indicators recorded in the registry (e.g., pathology reports, imaging claims), prognostic variables (e.g., stage at diagnosis), and characteristics associated with missing data (e.g., insurance coverage). We pooled hazard ratios (HR) and 95% Confidence Intervals (CI) across 1000 imputed datasets, adjusted for…
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