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Awarded Grant

American Cancer Society
Research Scholar Grant
PI: Ryan Suk, PhD
Our research project aims to address health inequity in cancer prevention by examining access to HPV vaccination among underserved populations in Texas. We will apply spatial accessibility measures, spatial modeling, and graph neural networks to assess geographic access to affordable HPV vaccination and utilization patterns. Additionally, we will conduct cognitive task analysis to understand how eligible families navigate access to free or low-cost HPV vaccines, informing the development of a user-centered navigation tool.
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DR. RYAN SUK (석리언)
She/They

"My research vision is,

Sustainable, Efficient & Inclusive Population Health Improvement"
 

I am an Assistant Professor in Health Economics who focuses on data-driven, community-engaged, and process-embedded research to advance health equity and improve healthcare efficiency. My research focuses on HPV-associated cancer prevention and control, and I am dedicated to tailoring efforts for under-recognized/underserved populations, including LGBTQIA+ communities, socio-economically disadvantaged children, and older adults (Project "Second Chance"; 27-45 years for HPV vaccination and 66+ years for cervical cancer prevention). I am especially interested in integrating economic evaluation, community engagement, and business process models to optimize resources and their allocation by developing and implementing various decision support or navigation tools to enhance HPV-associated cancer prevention and control.

  • Clinical topic: HPV-associated cancer prevention and control

  • Method expertise: Economic evaluation & decision modeling; quantitative data analysis

  • Population focus: Under-recognized and/or underserved populations, including LGBTQ+ individuals, socioeconomically disadvantaged children, and older adults outside the routine HPV vaccination or cervical cancer screening age groups.

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Suk's Decision Science Lab at Emory University

Data-driven, community-engaged, and process-embedded research for optimizing resources and allocations to improve cancer prevention and reduce cancer health inequity.

- Decision modeling - Economic evaluation - Machine learning - Deep learning

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