Capturing Demographic, Health-Related, and Psychosocial Variables in a Standardized Manner: Towards Improving Cancer Screening Adherence.

Yannan Lin, Ruiwen Ding, Ashley E Prosper, Denise R Aberle, Alex A T Bui, William Hsu
Author Information
  1. Yannan Lin: Department of Bioengineering, University of California, Los Angeles, CA, USA.
  2. Ruiwen Ding: Department of Bioengineering, University of California, Los Angeles, CA, USA.
  3. Ashley E Prosper: Medical & Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
  4. Denise R Aberle: Department of Bioengineering, University of California, Los Angeles, CA, USA.
  5. Alex A T Bui: Department of Bioengineering, University of California, Los Angeles, CA, USA.
  6. William Hsu: Department of Bioengineering, University of California, Los Angeles, CA, USA.

Abstract

Determining factors influencing patient participation in and adherence to cancer screening recommendations is key to successful cancer screening programs. However, the collection of variables necessary to anticipate patient behavior in cancer screening has not been systematically examined. Using lung cancer screening as a representative example, we conducted an exploratory analysis to characterize the current representations of 18 demographic, health-related, and psychosocial variables collected as part of a conceptual model to understand factors for lung cancer screening participation and adherence. Our analysis revealed a lack of standardization in controlled terminologies and common data elements for these variables. For example, only eight (44%) demographic and health-related variables were recorded consistently in the electronic health record. Multiple survey instruments could collect the remaining variables but were highly inconsistent in how variables were represented. This analysis suggests opportunities to establish standardized data formats for psychological, cognitive, social, and environmental variables to improve data collection.

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Grants

  1. R01 CA226079/NCI NIH HHS

MeSH Term

Humans
Early Detection of Cancer
Lung Neoplasms
Data Collection
Patient Participation
Demography

Word Cloud

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