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


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.

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Fig. 1: The CoG-NR Measure: scenario, items, and correct answers.


  1. 1.

    Williams MS, Buchanan AH, Davis FD, Faucett WA, Hallquist MLG, Leader JB, et al. Patient-centered precision health in a learning health care system: geisinger’s genomic medicine experience. Health Aff. 2018;37:757–64.

    Article  Google Scholar 

  2. 2.

    May T, Nakano-Okuno M, Kelley WV, East K, Moss IP, Sodeke S, et al. Return of raw data in genomic testing and research: ownership, partnership, and risk-benefit. Genet Med. 2020;22:12–4.

    Article  Google Scholar 

  3. 3.

    National Institutes of Health. 2019.

  4. 4.

    Evans JP, Powell BC, Berg JS. Finding the rare pathogenic variants in a human genome. JAMA 2017;317:1904–5.

    Article  Google Scholar 

  5. 5.

    Feero WG, Wicklund CA, Veenstra D. Precision medicine, genome sequencing, and improved population health. JAMA 2018;319:1979–80.

    Article  Google Scholar 

  6. 6.

    Murray MF, Evans JP, Angrist M, Chan K, Uhlmann W, Doyle DL, et al. A Proposed Approach for Implementing Genomics-Based Screening Programs for Healthy Adults. NAM Perspectives. Discussion Paper, National Academy of Medicine, Washington, DC. 2018.

  7. 7.

    Nightingale BM, Hovick SR, Brock P, Callahan E, Jordan E, Roggenbuck J, et al. Hypertrophic cardiomyopathy genetic test reports: A qualitative study of patient understanding of uninformative genetic test results. J Genet Couns. 2019;28:1087–97.

    Article  Google Scholar 

  8. 8.

    Guan Y, Condit CM, Escoffery C, Bellcross CA, McBride CM. Do women who receive a negative BRCA1/2 risk result understand the implications for breast cancer risk? Public Health Genom. 2019;22:102–9.

    Article  Google Scholar 

  9. 9.

    Wittman AT, Hashmi SS, Mendez-Figueroa H, Nassef S, Stevens B, Singletary CN. Patient perception of negative noninvasive prenatal testing results. AJP Rep. 2016;6:e391–e406.

    Article  Google Scholar 

  10. 10.

    Michie S, Smith JA, Senior V, Marteau TM. Understanding why negative genetic test results sometimes fail to reassure. Am J Med Genet Part A. 2003;119a:340–7.

    Article  Google Scholar 

  11. 11.

    Butterfield RM, Evans JP, Rini C, Kuczynski KJ, Waltz M, Cadigan RJ, et al. Returning negative results to individuals in a genomic screening program: lessons learned. Genet Med 2019;21:409–16.

    Article  Google Scholar 

  12. 12.

    Qualtrics. 2019 ed. (Qualtrics, Provo, UT). 2019.

  13. 13.

    Langer MM, Roche MI, Brewer NT, Berg JS, Khan CM, Leos C, et al. Development and validation of a genomic knowledge scale to advance informed decision making research in genomic sequencing. MDM Policy Pract. 2017;2:2381468317692582.

    Article  PubMed Central  Google Scholar 

  14. 14.

    Osborne RH, Batterham RW, Elsworth GR, Hawkins M, Buchbinder R. The grounded psychometric development and initial validation of the health literacy questionnaire (HLQ). BMC Public Health. 2013;13:658.

    Article  Google Scholar 

  15. 15.

    Schwartz LM, Woloshin S, Black WC, Welch HG. The role of numeracy in understanding the benefit of screening mammography. Ann Intern Med. 1997;127:966–72.

    CAS  Article  Google Scholar 

  16. 16.

    Streiner D, Norman G, Cairney J. Health measurement scales: a practical guide to their development and use. Oxford, United Kingdon: Oxford University Press; 2015. p. 399.

  17. 17.

    Clauser B, Mazor K. Using statistical procedures to identify differentially functioning test items. Educ Meas Issues Pract. 2005;17:31–44.

    Article  Google Scholar 

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The authors wish to acknowledge: James P. Evans, MD, PhD, Professor Emeritus, UNC School of Medicine, for his significant contributions to this work, and Gregory Cizek, PhD, Professor of Educational Measurement and Evaluation, UNC School of Education, for his methodological guidance.


This work was supported by the ELSI Research Program of the National Human Genome Research Institute of the National Institutes of Health, Grant Number P50HG004488.

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Correspondence to Gail E. Henderson.

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Henderson, G.E., Ewing, M., Kuczynski, K.J. et al. Development and validation of a measure of comprehension of genomic screening—negative results (CoG-NR). Eur J Hum Genet 28, 1394–1402 (2020).

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