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<p>Large scale data repositories like the <i>All of Us</i> Research Program are spurring new understanding of health and disease. <i>All of Us</i> aims to create a database of all Americans, addressing patterns of understudy of some groups in biomedical research. We study the representativeness (similarity to the U.S. population) and coverage (equality of proportion across U.S. Census demographic categories) of <i>All of Us</i> from 2017 to 2022, finding that <i>All of Us</i> recruited almost every understudied group at or above the group’s Census proportion. Building on the program’s successes, we propose a computational strategic recruitment method that optimizes multiple recruitment goals by allocating recruitment resources to sites and evaluate this method in recruitment simulation. We find that our methodology is indeed able to improve both cohort representativeness and coverage. Moreover, improvements in representativeness and coverage hold across numerous simulation conditions, supporting the promise of our recruitment techniques in real-world application.</p>

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Computational strategic recruitment for representation and coverage studied in the All of Us Research Program

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月3日

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  • Victor A. Borza,
  • Qingxia Chen,
  • Ellen W. Clayton,
  • Murat Kantarcioglu,
  • Lina Sulieman,
  • Yevgeniy Vorobeychik,
  • Bradley A. Malin

摘要

Large scale data repositories like the All of Us Research Program are spurring new understanding of health and disease. All of Us aims to create a database of all Americans, addressing patterns of understudy of some groups in biomedical research. We study the representativeness (similarity to the U.S. population) and coverage (equality of proportion across U.S. Census demographic categories) of All of Us from 2017 to 2022, finding that All of Us recruited almost every understudied group at or above the group’s Census proportion. Building on the program’s successes, we propose a computational strategic recruitment method that optimizes multiple recruitment goals by allocating recruitment resources to sites and evaluate this method in recruitment simulation. We find that our methodology is indeed able to improve both cohort representativeness and coverage. Moreover, improvements in representativeness and coverage hold across numerous simulation conditions, supporting the promise of our recruitment techniques in real-world application.