Impact of Direct-to-Consumer Genomic Testing
Impact of Direct-to-Consumer Genomic Testing
The SGHI was approved by the Scripps Office for the Protection of Research Subjects and Institutional Review Boards. Informed consent was obtained electronically from each participant. The details of our methods have been published previously and will only be briefly summarised; additional information is included in the online supplementary material.
The SGHI was initiated in October 2008. Participants were adults recruited primarily from health and technology companies, and procedures pertaining to enrolment have been previously described. Participants were administered web based health assessments at baseline, as well as 3 months (referred to as 'short term' follow-up) and 1 year (referred to as 'long term' follow-up) after receiving their genomic test results. The primary analyses presented here are based on data from the long term follow-up. Secondary analyses include linear mixed effects model analyses that leverage data from both time points.
Procedures for administration of the 1 year follow-up were such that once 365 days had elapsed from the time the participant received their genomic testing results, an email was sent to the participant with a link to the web based survey utility asking them to complete the assessment. Importantly, participants who were considered lost to short term follow-up were still contacted and asked to complete the long term assessment. A total of three email requests were sent within a 6 week time frame after which point, if the individual still had not completed the long term follow-up assessment, they were considered lost to long term follow-up.
We examined the impact of DTC genomic risk testing with the Navigenics Health Compass, at that time, a commercially available genomic test, which was developed based on previously reported genetic risk loci disease associations (see online supplementary figure S1). Information regarding the test and its content as initially released to SGHI participants can be found in a previous publication and in the online supplementary material. During the long term follow-up period of the SGHI, Navigenics developed and eventually released risk results for five new conditions, including deep vein thrombosis, melanoma, sarcoidosis, haemochromatosis, and lactose intolerance. In this report, we have included risk estimates for these new conditions in analyses evaluating psychological, behavioural, and clinical screening response to DTC genomic risk testing.
As part of the study, participants agreed to provide us with access to their genomic risk reports. This enabled us to assess the overall impact of genomic testing, as well as the impact of the personalised risk estimates disclosed. Study participants were also offered, at no charge, genetic counselling provided by Navigenics' staff of board certified genetic counsellors. In addition, as previously described, Navigenics conducted proactive outreach to subgroups of their customers (which included study participants) based on the customer's genomic risk results.
Baseline assessment included items to assess demographics, family health-span history, personal/individual health-span history, and attitudes about genetic testing. Both the baseline and follow-up assessments additionally included measures of anxiety, fat intake, and exercise behaviour to enable us to assess change in these areas. Situational anxiety was assessed with the state anxiety subscale of the Spielberger State-Trait Anxiety Inventory (STAI), fat intake with the Block Fat Screener, and exercise behaviour with the Godin Leisure-Time Exercise Questionnaire (GLTEQ). Both short and long term follow-up assessments included evaluation of test related distress using the Impact of Events Scale-Revised (IES-R). In addition, 13 health screening behaviours were assessed by asking about actual completion of a given screening test, as well as self-reported intention to complete the screening test with greater frequency since undergoing genome-wide disease risk testing. These 13 tests included the following: thyroid test, skin exam, ophthalmic exam, glucose exam, electrocardiogram, colonoscopy, cholesterol test, chest x-ray, cardiac stress test, blood test, self-breast exam, mammogram, and prostate specific antigen test.
Participants were also asked at both follow-ups whether they had shared their results with their own physician or healthcare provider and/or whether they had spoken with a Navigenics genetic counsellor about their results. Finally, unique to the 1 year assessment were items to gauge literacy (ie, 'Do you understand your test results?'), perceived consistency with family health history (ie, 'Do you feel your genetic test results are generally consistent with your previous health history and your family health history?'), and perceived utility of the genomic test (ie, 'In general, do you feel your genetic test results are useful to you?').
Primary outcomes were changes from baseline in subjects' anxiety symptoms, fat intake, and exercise behaviour, as well as levels of test related distress and self-reported completion and intention to complete health screening behaviours at long term follow-up.
All statistical analyses were conducted using the statistical software packages SPSS 14.0, R 2.13.2, and VasserStats web utility for computing z-tests and confidence intervals for proportions. Two-sided t tests, Mann-Whitney U tests or χ tests were used to compare baseline variables between individuals who completed follow-up versus those who were lost to follow-up (Table 1). Repeated measures analysis of variance and the within subjects effect of time controlling for eight covariates (age, sex, education, ethnicity (Caucasian yes/no), income, health related occupation, long term follow-up interval in days, and completion or non-completion of short term follow-up) was used to assess the extent to which baseline and long term follow-up scores on the STAI, Fat Screener, and GLTEQ differed. A similar analysis was done comparing IES-R scores, total screening tests completed, and total screening tests with intended increased frequency between short and long term follow-up. Percentages were also used to qualitatively describe scores on the IES-R at long term follow-up.
Associations between our outcomes and the genetic risk estimates disclosed were evaluated using linear regression controlling for the eight covariates described above and baseline scores on each measure. Specifically, follow-up STAI, Fat Screener, and GLTEQ scores were regressed on (1) average estimated lifetime risk (ELTR) across all of the conditions for which genetic results were viewed, (2) proportion of conditions colour coded orange indicating increased genetic risk (again, of those viewed), and (3) ELTR and colour coded risk for each of the 28 condition specific risk estimates. See a previous publication for a description of the composite genetic risk variables constructed for analysis (ie, items 1 and 2 above). This was similarly done for IES-R scores (without a baseline score). Each screening test was matched with its corresponding condition(s) and tested using logistic regression with the screening test (either actual or intended completion) as the dependent variable and our eight covariates plus the condition specific risk estimate as the independent variable.
We also assessed the proportion of participants who reported discussing their results with a Navigenics genetic counsellor or their own physician or healthcare provider, as well as the extent to which these factors were associated with study outcomes. Association between condition specific genomic risk estimates, sharing results with a physician, and their interaction was evaluated using a logistic regression model with the completion of a corresponding screening test as the dependent variable. We performed similar analyses for self-reported perceptions of understanding, utility, and consistency of genomic results.
Finally, longitudinal analyses were conducted to evaluate relationships with the condition specific risk estimates and our outcomes of interest across both short and long term follow-ups. Because of the longitudinal nature of the study and the fact that the data consist of non-uniform numbers of repeated measurements at non-uniform follow-up intervals, linear mixed-effects model analysis was used in which a random effect associated with the individual study participants was assumed. Each of the above statistical models included our eight covariates, as well as the baseline score (when applicable).
Materials and Methods
The SGHI was approved by the Scripps Office for the Protection of Research Subjects and Institutional Review Boards. Informed consent was obtained electronically from each participant. The details of our methods have been published previously and will only be briefly summarised; additional information is included in the online supplementary material.
Study Sample and Design
The SGHI was initiated in October 2008. Participants were adults recruited primarily from health and technology companies, and procedures pertaining to enrolment have been previously described. Participants were administered web based health assessments at baseline, as well as 3 months (referred to as 'short term' follow-up) and 1 year (referred to as 'long term' follow-up) after receiving their genomic test results. The primary analyses presented here are based on data from the long term follow-up. Secondary analyses include linear mixed effects model analyses that leverage data from both time points.
Procedures for administration of the 1 year follow-up were such that once 365 days had elapsed from the time the participant received their genomic testing results, an email was sent to the participant with a link to the web based survey utility asking them to complete the assessment. Importantly, participants who were considered lost to short term follow-up were still contacted and asked to complete the long term assessment. A total of three email requests were sent within a 6 week time frame after which point, if the individual still had not completed the long term follow-up assessment, they were considered lost to long term follow-up.
Intervention
We examined the impact of DTC genomic risk testing with the Navigenics Health Compass, at that time, a commercially available genomic test, which was developed based on previously reported genetic risk loci disease associations (see online supplementary figure S1). Information regarding the test and its content as initially released to SGHI participants can be found in a previous publication and in the online supplementary material. During the long term follow-up period of the SGHI, Navigenics developed and eventually released risk results for five new conditions, including deep vein thrombosis, melanoma, sarcoidosis, haemochromatosis, and lactose intolerance. In this report, we have included risk estimates for these new conditions in analyses evaluating psychological, behavioural, and clinical screening response to DTC genomic risk testing.
As part of the study, participants agreed to provide us with access to their genomic risk reports. This enabled us to assess the overall impact of genomic testing, as well as the impact of the personalised risk estimates disclosed. Study participants were also offered, at no charge, genetic counselling provided by Navigenics' staff of board certified genetic counsellors. In addition, as previously described, Navigenics conducted proactive outreach to subgroups of their customers (which included study participants) based on the customer's genomic risk results.
Instruments
Baseline assessment included items to assess demographics, family health-span history, personal/individual health-span history, and attitudes about genetic testing. Both the baseline and follow-up assessments additionally included measures of anxiety, fat intake, and exercise behaviour to enable us to assess change in these areas. Situational anxiety was assessed with the state anxiety subscale of the Spielberger State-Trait Anxiety Inventory (STAI), fat intake with the Block Fat Screener, and exercise behaviour with the Godin Leisure-Time Exercise Questionnaire (GLTEQ). Both short and long term follow-up assessments included evaluation of test related distress using the Impact of Events Scale-Revised (IES-R). In addition, 13 health screening behaviours were assessed by asking about actual completion of a given screening test, as well as self-reported intention to complete the screening test with greater frequency since undergoing genome-wide disease risk testing. These 13 tests included the following: thyroid test, skin exam, ophthalmic exam, glucose exam, electrocardiogram, colonoscopy, cholesterol test, chest x-ray, cardiac stress test, blood test, self-breast exam, mammogram, and prostate specific antigen test.
Participants were also asked at both follow-ups whether they had shared their results with their own physician or healthcare provider and/or whether they had spoken with a Navigenics genetic counsellor about their results. Finally, unique to the 1 year assessment were items to gauge literacy (ie, 'Do you understand your test results?'), perceived consistency with family health history (ie, 'Do you feel your genetic test results are generally consistent with your previous health history and your family health history?'), and perceived utility of the genomic test (ie, 'In general, do you feel your genetic test results are useful to you?').
Outcome Measures
Primary outcomes were changes from baseline in subjects' anxiety symptoms, fat intake, and exercise behaviour, as well as levels of test related distress and self-reported completion and intention to complete health screening behaviours at long term follow-up.
Statistical Analysis
All statistical analyses were conducted using the statistical software packages SPSS 14.0, R 2.13.2, and VasserStats web utility for computing z-tests and confidence intervals for proportions. Two-sided t tests, Mann-Whitney U tests or χ tests were used to compare baseline variables between individuals who completed follow-up versus those who were lost to follow-up (Table 1). Repeated measures analysis of variance and the within subjects effect of time controlling for eight covariates (age, sex, education, ethnicity (Caucasian yes/no), income, health related occupation, long term follow-up interval in days, and completion or non-completion of short term follow-up) was used to assess the extent to which baseline and long term follow-up scores on the STAI, Fat Screener, and GLTEQ differed. A similar analysis was done comparing IES-R scores, total screening tests completed, and total screening tests with intended increased frequency between short and long term follow-up. Percentages were also used to qualitatively describe scores on the IES-R at long term follow-up.
Associations between our outcomes and the genetic risk estimates disclosed were evaluated using linear regression controlling for the eight covariates described above and baseline scores on each measure. Specifically, follow-up STAI, Fat Screener, and GLTEQ scores were regressed on (1) average estimated lifetime risk (ELTR) across all of the conditions for which genetic results were viewed, (2) proportion of conditions colour coded orange indicating increased genetic risk (again, of those viewed), and (3) ELTR and colour coded risk for each of the 28 condition specific risk estimates. See a previous publication for a description of the composite genetic risk variables constructed for analysis (ie, items 1 and 2 above). This was similarly done for IES-R scores (without a baseline score). Each screening test was matched with its corresponding condition(s) and tested using logistic regression with the screening test (either actual or intended completion) as the dependent variable and our eight covariates plus the condition specific risk estimate as the independent variable.
We also assessed the proportion of participants who reported discussing their results with a Navigenics genetic counsellor or their own physician or healthcare provider, as well as the extent to which these factors were associated with study outcomes. Association between condition specific genomic risk estimates, sharing results with a physician, and their interaction was evaluated using a logistic regression model with the completion of a corresponding screening test as the dependent variable. We performed similar analyses for self-reported perceptions of understanding, utility, and consistency of genomic results.
Finally, longitudinal analyses were conducted to evaluate relationships with the condition specific risk estimates and our outcomes of interest across both short and long term follow-ups. Because of the longitudinal nature of the study and the fact that the data consist of non-uniform numbers of repeated measurements at non-uniform follow-up intervals, linear mixed-effects model analysis was used in which a random effect associated with the individual study participants was assumed. Each of the above statistical models included our eight covariates, as well as the baseline score (when applicable).
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