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

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          Abstract

          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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          The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ)

          Background Health literacy has become an increasingly important concept in public health. We sought to develop a comprehensive measure of health literacy capable of diagnosing health literacy needs across individuals and organisations by utilizing perspectives from the general population, patients, practitioners and policymakers. Methods Using a validity-driven approach we undertook grounded consultations (workshops and interviews) to identify broad conceptually distinct domains. Questionnaire items were developed directly from the consultation data following a strict process aiming to capture the full range of experiences of people currently engaged in healthcare through to people in the general population. Psychometric analyses included confirmatory factor analysis (CFA) and item response theory. Cognitive interviews were used to ensure questions were understood as intended. Items were initially tested in a calibration sample from community health, home care and hospital settings (N=634) and then in a replication sample (N=405) comprising recent emergency department attendees. Results Initially 91 items were generated across 6 scales with agree/disagree response options and 5 scales with difficulty in undertaking tasks response options. Cognitive testing revealed that most items were well understood and only some minor re-wording was required. Psychometric testing of the calibration sample identified 34 poorly performing or conceptually redundant items and they were removed resulting in 10 scales. These were then tested in a replication sample and refined to yield 9 final scales comprising 44 items. A 9-factor CFA model was fitted to these items with no cross-loadings or correlated residuals allowed. Given the very restricted nature of the model, the fit was quite satisfactory: χ 2 WLSMV(866 d.f.) = 2927, p<0.000, CFI = 0.936, TLI = 0.930, RMSEA = 0.076, and WRMR = 1.698. Final scales included: Feeling understood and supported by healthcare providers; Having sufficient information to manage my health; Actively managing my health; Social support for health; Appraisal of health information; Ability to actively engage with healthcare providers; Navigating the healthcare system; Ability to find good health information; and Understand health information well enough to know what to do. Conclusions The HLQ covers 9 conceptually distinct areas of health literacy to assess the needs and challenges of a wide range of people and organisations. Given the validity-driven approach, the HLQ is likely to be useful in surveys, intervention evaluation, and studies of the needs and capabilities of individuals.
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            The role of numeracy in understanding the benefit of screening mammography.

            Quantitative information about risks and benefits may be meaningful only to patients who have some facility with basic probability and numerical concepts, a construct called numeracy. To assess the relation between numeracy and the ability to make use of typical risk reduction expressions about the benefit of screening mammography. Randomized, cross-sectional survey. A simple random sample of 500 female veterans drawn from a New England registry. One of four questionnaires, which differed only in how the same information on average risk reduction with mammography was presented. Numeracy was scored as the total number of correct responses to three simple tasks. Participants estimated their risk for death from breast cancer with and without mammography. Accuracy was judged as each woman's ability to adjust her perceived risk in accordance with the risk reduction data presented. 61% of eligible women completed the questionnaire. The median age of these women was 68 years (range, 27 to 88 years), and 96% were high school graduates. Both accuracy in applying risk reduction information and numeracy were poor (one third of respondents thought that 1000 flips of a fair coin would result in < 300 heads). Accuracy was strongly related to numeracy: The accuracy rate was 5.8% (95% CI, 0.8% to 10.7%) for a numeracy score of 0, 8.9% (CI, 2.5% to 15.3%) for a score of 1, 23.7% (CI, 13.9% to 33.5%) for a score of 2, and 40% (CI, 25.1% to 54.9%) for a score of 3. Regardless of how information was presented, numeracy was strongly related to accurately gauging the benefit of mammography. More effective formats are needed to communicate quantitative information about risks and benefits.
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              Patient-Centered Precision Health In A Learning Health Care System: Geisinger’s Genomic Medicine Experience

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                Author and article information

                Contributors
                gail_henderson@med.unc.edu
                Journal
                Eur J Hum Genet
                Eur J Hum Genet
                European Journal of Human Genetics
                Springer International Publishing (Cham )
                1018-4813
                1476-5438
                26 May 2020
                October 2020
                : 28
                : 10
                : 1394-1402
                Affiliations
                [1 ]GRID grid.10698.36, ISNI 0000000122483208, University of North Carolina at Chapel Hill, ; Chapel Hill, NC USA
                [2 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Duke University, ; Durham, NC USA
                [3 ]GRID grid.16753.36, ISNI 0000 0001 2299 3507, Northwestern University, ; Evanston, IL USA
                Author information
                http://orcid.org/0000-0002-4968-7064
                Article
                657
                10.1038/s41431-020-0657-1
                7608237
                32457518
                f4429c8b-46b4-4261-ad06-a1599067d78e
                © The Author(s), under exclusive licence to European Society of Human Genetics 2020

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 27 December 2019
                : 3 April 2020
                : 28 April 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000051, U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI);
                Award ID: P50HG004488
                Award ID: P50HG004488
                Award ID: P50HG004488
                Award ID: P50HG004488
                Award ID: P50HG004488
                Award ID: P50HG004488
                Award ID: P50HG004488
                Award ID: P50HG004488
                Award ID: P50HG004488
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
                Funded by: U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
                Funded by: U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
                Funded by: U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
                Funded by: U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
                Funded by: U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
                Funded by: U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
                Funded by: U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
                Categories
                Article
                Custom metadata
                © European Society of Human Genetics 2020

                Genetics
                ethics,population screening
                Genetics
                ethics, population screening

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