Identifying pre-existing dementia in older adults diagnosed with cancer using a national claims database.

Abstract

e18678 Background: Older adults over the age of 65 represent the majority of patients diagnosed with (60%), among them, 15-30% have a pre-existing Alzheimer’s disease or related dementia (ADRD) that puts them at higher risk for over and under treatment. Studying the role of pre-existing ADRD in cancer patients is vital to understanding treatment planning behavior, patterns of health care utilization, and adverse treatment outcomes. Massive administrative datasets, or “big data” represent the information rich environment that is useful for this endeavor. Methods: Our study utilized a clinically validated algorithm to assess the prevalence of pre-existing ADRD and cancer across six cancer types. We utilized the SEER-Medicare dataset for analyzing the study years 2004-2015 (N = 337 932). We extracted ICD-9 codes to identify ADRD using the Centers for Medicaid Services Chronic Conditions Warehouse (CCW) algorithm. In sensitivity analysis we compared the prevalence of ADRD+Cancer using the NCI (2014) and CCW algorithms. Results: We found a significant difference between the two algorithms (p < .0001) and a higher overall prevalence of comorbid ADRD+Cancer using the CCW (6.6%). Additionally, we found ADRD+Cancer prevalence was significantly higher among racial and ethnic subgroups compared to White and unstaged tumors compared with any numbered American Joint Committee on Cancer (AJCC) stages (p < .0001). Conclusions: Using a clinically validated algorithm we were able to identify more cases of ADRD+Cancer in big data. This figure remains underestimated for ADRD+cancer compared to clinically-validated studies. Further research into the validation approach and codes that are used for ADRD classification can improve how we identify ADRD in massive administrative data. This is critical given the growing population of diverse older adults in the U.S.