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Development and validation of a measure of comprehension of genomic screening-negative results (CoG-NR).

Gail E HendersonMolly EwingKristine J KuczynskiR Jean CadiganMargaret WaltzRita M ButterfieldChristine RiniKaren WeckJonathan S BergTeresa P Edwards
Published in: European journal of human genetics : EJHG (2020)
To realize the promise of population genomic screening for rare medically actionable conditions, critical challenges in the return of normal/negative results must be understood and overcome. Our study objective was to assess the functioning of a new 13-item measure (CoG-NR) of understanding of and knowledge about normal/negative genomic screening results for three highly actionable conditions: Lynch Syndrome, Hereditary Breast and Ovarian Cancer, and Familial Hypercholesterolemia. Based on our prior research and expert review, we developed CoG-NR and tested how well it functioned using hypothetical scenarios in three Qualtrics surveys. We report on its psychometric properties and performance across the three different conditions. The measure performed similarly for the three conditions. Examinations of item difficulty, internal reliability, and differential item functioning indicate that the items perform well, with statistically significant positive correlations with genomic knowledge, health literacy, and objective numeracy. CoG-NR assesses understanding of normal/negative results for each of the conditions. The next step is to examine its performance among individuals who have actually undergone such tests, and subsequent use in clinical or research situations. The CoG-NR measure holds great promise as a tool to enhance benefits of population genomic screening by bringing to light the prevalence of incorrect interpretation of negative results.
Keyphrases
  • psychometric properties
  • copy number
  • healthcare
  • climate change
  • big data
  • risk factors
  • machine learning
  • gene expression
  • health information
  • deep learning
  • social media
  • clinical practice