Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Sep;25(9):100899.
doi: 10.1016/j.gim.2023.100899. Epub 2023 May 19.

Information-seeking preferences in diverse patients receiving a genetic testing result in the Clinical Sequencing Evidence-Generating Research (CSER) study

Affiliations

Information-seeking preferences in diverse patients receiving a genetic testing result in the Clinical Sequencing Evidence-Generating Research (CSER) study

Anne Slavotinek et al. Genet Med. 2023 Sep.

Abstract

Purpose: Accurate and understandable information after genetic testing is critical for patients, family members, and professionals alike.

Methods: As part of a cross-site study from the Clinical Sequencing Evidence-Generating Research consortium, we investigated the information-seeking practices among patients and family members at 5 to 7 months after genetic testing results disclosure, assessing the perceived utility of a variety of information sources, such as family and friends, health care providers, support groups, and the internet.

Results: We found that individuals placed a high value on information obtained from genetics professionals and health care workers, independent of genetic testing result case classifications as positive, inconclusive, or negative. The internet was also highly utilized and ranked. Study participants rated some information sources as more useful for positive results compared with inconclusive or negative outcomes, emphasizing that it may be difficult to identify helpful information for individuals receiving an uncertain or negative result. There were few data from non-English speakers, highlighting the need to develop strategies to reach this population.

Conclusion: Our study emphasizes the need for clinicians to provide accurate and comprehensible information to individuals from diverse populations after genetic testing.

Keywords: Diverse populations; Exome sequencing; Genetic testing; Genome sequencing; Information-seeking preferences.

PubMed Disclaimer

Conflict of interest statement

Conflict of Interest The authors declare no conflicts of interest.

Figures

Figure 1.
Figure 1.. Bar graphs showing information sources consulted after receiving a genetic testing result for participants enrolled in the Clinical Sequencing Evidence-Generating Research (CSER) consortium.
Each bar graph shows the number of survey respondents who used the following information sources - ‘Family and Friends’, ‘Facebook’, ‘Support groups’, ‘My/my child’s other healthcare providers’, ‘Internet’, ‘Books and printed media’, ‘My/my child’s genetics provider’, and ‘Other’. Data from Program in Prenatal and Pediatric Genomic Sequencing (P3EGS) project, University of California San Francisco, is shown in dark blue, data from NYCKidSeq project, Icahn School of Medicine at Mount Sinai, is shown in orange, data from SouthSeq project, HudsonAlpha Institute for Biotechnology, is shown in grey, data from NCGENES 2, University of North Carolina, Chapel Hill, is shown in yellow, and data from KidsCanSeq project, Baylor College of Medicine, is shown in light blue.
Figure 2.
Figure 2.. Perceived usefulness of information sources consulted by participants enrolled in the Clinical Sequencing Evidence-Generating Research (CSER) study after receiving a genetic testing result.
Each dot represents the mean of data derived from a Likert scale ranging from 1 to 5 on the Y-axis, with 5 representing ‘very useful’ to 1 representing ‘not useful at all’. The data from all five CSER sites (P3EGS, NYCKidSeq, SouthSeq, NCGENES 2 and KidsCanSeq) are shown for the information sources ‘Family and Friends’, ‘Facebook’, ‘Support groups’, ‘My/my child’s other healthcare providers’, ‘Internet’, ‘Books and printed media’, and ‘My/my child’s genetics provider who ordered the test’. Data from Program in Prenatal and Pediatric Genomic Sequencing (P3EGS) project, University of California San Francisco, is shown in dark blue, data from NYCKidSeq project, Icahn School of Medicine at Mount Sinai, is shown in orange, data from SouthSeq project, HudsonAlpha Institute for Biotechnology, is shown in grey, data from NCGENES 2, University of North Carolina, Chapel Hill, is shown in yellow, and data from KidsCanSeq project, Baylor College of Medicine, is shown in light blue. The mean for all sites combined is represented in green and these dots are connected for better visibility, although the data were not continuous. The dots demonstrate high perceived utility for ‘My/my child’s other healthcare providers’ and ‘My/my child’s genetics provider who ordered the test’.
Figure 3.
Figure 3.. Perceived utility of information sources and result type for participants enrolled in the Clinical Sequencing Evidence-Generating Research (CSER) study.
The graph shows the mean perceived utility for data from three project sites - P3EGS, NYCKidSeq, and SouthSeq. Perceived utility is shown with a Likert scale on the Y-axis ranging from 1 (‘not useful at all’) to 5 (‘highly useful’) for each information source. Three different result types (positive, inconclusive, and negative) are shown, with the means for positive results shown in blue, the means for inconclusive results shown in orange, and the means for negative results shown in grey. Significance is marked with * (P < 0.05), ** (P < 0.01), and *** (P < 0.001).
Figure 4.
Figure 4.. Income and result type for participants enrolled in the Clinical Sequencing Evidence-Generating Research (CSER) study.
The graphs show the mean income converted to scale (see Supplementary data for methodology) for result type (positive, inconclusive, and negative) for three project sites - P3EGS, NYCKidSeq, and SouthSeq. The numbers on the Y axis corresponds to household income as follows: 0-$19,999, $20,000-$39,999, $40,000-$59,999, $60,000-$79,999, and $80,000-$99,999 (see Supplementary data). The means for household income for positive results are shown in blue, the means for inconclusive results are shown in orange, and the means for negative results are shown in grey. Significance is marked with * (P < 0.05), ** (P < 0.01), and *** (P < 0.001).
Figure 5.
Figure 5.. Educational experience and result type for participants enrolled in the Clinical Sequencing Evidence-Generating Research (CSER) study.
The graphs show the mean educational experience converted to scale for result type (positive, inconclusive, and negative) for three project sites - P3EGS, NYCKidSeq, and SouthSeq. The numbers on the Y axis corresponds to educational experience as follows: 1 = Less than high school; 2 = Attended high school but did not receive a diploma; 3 = High school diploma/GED; 4 = Some post high school education; 5 = Associate college degree, occupational, technical, or vocational program, degree, or certificate; 6 = Bachelor’s degree; 7 = Graduate or professional degree, for example, Master’s degree, doctoral degree, MD and other (see Supplementary data). The means for educational experience for positive results are shown in blue, the means for inconclusive results are shown in orange, and the means for negative results are shown in grey. Significance is marked with * (P < 0.05), ** (P < 0.01), and *** (P < 0.001).

References

    1. Stark Z, Tan TY, Chong B, Brett GR, Yap P, Walsh M, et al. A prospective evaluation of whole-exome sequencing as a first-tier molecular test in infants with suspected monogenic disorders. Genet Med. 2016;18:1090–1096. - PubMed
    1. Shah M, Selvanathan A, Baynam G, Berman Y, Boughtwood T, Freckmann ML, Parasivam G, White SM, Grainger N, Kirk EP, Ma AS, Sachdev R. Paediatric genomic testing: Navigating genomic reports for the general paediatrician. J Paediatr Child Health. 2022. Jan;58(1):8–15. - PMC - PubMed
    1. Zimmermann BM, Fanderl J, Koné I, Rabaglio M, Bürki N, Shaw D, Elger B. Examining information-seeking behavior in genetic testing for cancer predisposition: A qualitative interview study. Patient Educ Couns. 2021. Feb;104(2):257–264. - PubMed
    1. Jacobs W, Amuta AO, Chan Jeon K. Health information seeking in the digital age: An analysis of health information seeking behavior among US adults. Cogent Social Sciences 2017: 3: 1302785.
    1. Chavarria EA, Christy SM, Feng H, Miao H, Abdulla R, Gutierrez L, Lopez D, Sanchez J, Gwede CK, Meade CD. Online Health Information Seeking and eHealth Literacy Among Spanish Language-Dominant Latino Adults Receiving Care in a Community Clinic: Secondary Analysis of Pilot Randomized Controlled Trial Data. JMIR Form Res. 2022. Oct 13;6(10):e37687. doi: 10.2196/37687. - DOI - PMC - PubMed

Publication types