BALANCING PRIVACY AND USABILITY IN REAL-WORLD DATA FOR OLDER ADULTS
Author(s)
Amy Price, PhD1, Dena Jaffe, PhD2.
1Oracle Health, Kansas City, MO, USA, 2Oracle Health, Alon Shvut, Israel.
1Oracle Health, Kansas City, MO, USA, 2Oracle Health, Alon Shvut, Israel.
OBJECTIVES: Older adults represent a growing and important segment of the U.S. population, accounting for a disproportionate share of healthcare utilization and expenditures due to their unique and often complex needs. Generating real-world evidence (RWE) to better support healthy aging relies on real-world data (RWD) that reflect the diversity and clinical profiles of older individuals. However, curating fit-for-purpose RWD for this population is challenged by privacy-driven data modifications, which can limit fidelity. This study examines the impact of age top coding on the relevance of RWD for evidence generation in older adults.
METHODS: The Oracle Health RWD electronic health record (OHRWD EHR) dataset (7/2024-7/2025) and U.S. Census Bureau data (2024) were used to describe adults aged ≥65 years. Data in the OHRWD EHR are top coded (aggregated) at 89 years.
RESULTS: In the OHRWD and U.S. Census populations, 25.2% and 16.9% of adults were aged ≥65 years, respectively. Demographic characteristics varied across older age sub-groups. The percentage of female adults aged ≥89 years was 61.7% (OHRWD) and 67.9% (U.S. Census). Using U.S. Census non-aggregated age data, the percentage of females increased from 63.4% to 81.1% from ages 89 to 100. For race, the percentage of Black and Asian adults aged ≥89 years was 4.6% and 2.5% (OHRWD) and 8.3% and 6.1% (U.S. Census), respectively. Using U.S. non-aggregated age data, these percentages remained similar among elderly Black and increased among elderly Asian adults from 5.6% to 10.3% from ages 89 to 100.
CONCLUSIONS: The heterogeneity of demographic characteristics in older age groups indicates that aggregation into a single, broad age category may bias risk estimates and mask within-category heterogeneity. Study designs that avoid this potential bias by using less restrictive age inclusion criteria can limit generalizability. Transparency about privacy-protecting techniques and awareness of data limitations are essential for producing relevant and reliable RWE.
METHODS: The Oracle Health RWD electronic health record (OHRWD EHR) dataset (7/2024-7/2025) and U.S. Census Bureau data (2024) were used to describe adults aged ≥65 years. Data in the OHRWD EHR are top coded (aggregated) at 89 years.
RESULTS: In the OHRWD and U.S. Census populations, 25.2% and 16.9% of adults were aged ≥65 years, respectively. Demographic characteristics varied across older age sub-groups. The percentage of female adults aged ≥89 years was 61.7% (OHRWD) and 67.9% (U.S. Census). Using U.S. Census non-aggregated age data, the percentage of females increased from 63.4% to 81.1% from ages 89 to 100. For race, the percentage of Black and Asian adults aged ≥89 years was 4.6% and 2.5% (OHRWD) and 8.3% and 6.1% (U.S. Census), respectively. Using U.S. non-aggregated age data, these percentages remained similar among elderly Black and increased among elderly Asian adults from 5.6% to 10.3% from ages 89 to 100.
CONCLUSIONS: The heterogeneity of demographic characteristics in older age groups indicates that aggregation into a single, broad age category may bias risk estimates and mask within-category heterogeneity. Study designs that avoid this potential bias by using less restrictive age inclusion criteria can limit generalizability. Transparency about privacy-protecting techniques and awareness of data limitations are essential for producing relevant and reliable RWE.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
RWD168
Topic
Real World Data & Information Systems
Topic Subcategory
Data Protection, Integrity, & Quality Assurance
Disease
No Additional Disease & Conditions/Specialized Treatment Areas