ABSTRACT
Background
This study explored employees' decision‐making processes and perspectives on workplace genetic testing (wGT), comparing those who had not participated (Non‐Testers) with those who had participated (Testers) in wGT.
Methods
Employees at a large US healthcare system offering wGT were recruited via email and onsite advertising and surveyed. The web survey assessed respondent demographics, information‐seeking activities, and perceived benefits and risks of wGT. Descriptive statistics, Chi‐square analyses, and multivariable logistic regression compared the similarities and differences between Non‐Testers and Testers on key study outcomes.
Results
Survey respondents included 358 Non‐Testers (80.7% female; 69.8% White, non‐Hispanic) and 418 Testers (88.3% female; 80.6% White, non‐Hispanic). Demographics associated with lower odds of being a Tester included self‐reporting as Black, non‐Hispanic (p = 0.001), male (p = 0.004), and less than one year of employment (p < 0.001). Non‐Testers endorsed more perceived risks regarding wGT (e.g., privacy, discrimination) than Testers, while Testers more commonly endorsed perceived benefits, including lack of cost and potential healthcare benefits. Non‐Testers engaged in fewer information‐seeking activities than Testers. Non‐Testers' most common decision‐making activity was talking with family (28.9%), and Testers' most common activity was engaging with the testing company's information (50.0%).
Conclusion
This is the first study to examine wGT decision‐making in a real‐world setting. To address the perceived risks of Non‐Testers and ensure equitable access, it may be necessary to develop educational materials that review current legal protections and limitations, as well as potential health benefits of genetic testing. Outreach strategies providing such education must address different types of information seeking to ensure perceived risks are addressed.
Keywords: decision‐making, direct‐to‐consumer (DTC) genetic testing, ethical issues, information seeking, workplace genetic testing (wGT), workplace wellness programs
Employees who are offered workplace genetic testing differ in their engagement in information‐seeking activities and in their views and concerns about workplace genetic testing (wGT).
1. Introduction
Some employers now offer genetic testing (workplace genetic testing—wGT) to their employees through workplace wellness programs, providing individuals with access to genetic testing at low or no cost, regardless of their family history or health status (Cohn et al. 2023; Sanghavi et al. 2022; Briscoe et al. 2024). wGT programs typically include analysis of genes associated with hereditary cancer risk, heart disease risk, and medication response (Briscoe et al. 2024; Charnysh et al. 2024), generally with limited or optional pretest education and without clinician involvement. wGT has the potential to widen access to genetic testing by removing logistical and cost barriers, but only if employee concerns are properly addressed (e.g., privacy) (Sanghavi et al. 2022; Charnysh et al. 2024; McDonald et al. 2020).
wGT builds on the strong public interest in direct‐to‐consumer (DTC) genetic testing, which occurs outside clinical settings (Covolo et al. 2015; Caulfield and McGuire 2012). Millions have utilized DTC genetic testing to learn about their ancestry and receive personalized health assessments (Henry 2021; Rosenberg 2025). The integration of genetic testing into the employment setting occurring in wGT, however, raises specific ethical, legal, and social implications (ELSI) regarding genetic privacy and the potential for employment and insurance discrimination (Sanghavi et al. 2022; Deverka et al. 2020). The privacy of personal information and the potential for de‐identified data to be re‐identified are consistently noted as concerns surrounding genetic testing (Clayton et al. 2018). Additionally, discrimination in the form of harm based on others' use of their genetic data is another common concern; however, few studies have examined these concerns in the context of current antidiscrimination policies, including perceptions of their efficacy (Clayton et al. 2018). These are, in fact, general concerns around discrimination that persist for genetic testing despite federal legal protections under the Genetic Information Nondiscrimination Act (GINA) (Clayton et al. 2018; Lenartz et al. 2021; Willard et al. 2024; US EEOC 2008). ELSI scholarship has found public interest in obtaining genetic testing via wGT (Briscoe et al. 2024, 2023; Sanghavi et al. 2021), which increases with particular program design features such as the ability to delete one's genetic data (Briscoe et al. 2024) and obtain result interpretation assistance from a genetics professional (Sanghavi et al. 2021). However, concerns about data privacy are often noted as reasons for declining genetic testing (Lenartz et al. 2021) and genetic research (Clayton et al. 2018).
The NIH‐funded INSIGHT @ Work (Interdisciplinary National Study Investigating Genetics & Health Testing) Study has investigated employees' views of the ELSI of wGT. Our team's previous analyses of the health impact of employee participation in wGT found that wGT promoted behavior changes (e.g., diet, exercise) and healthcare utilization (e.g., screenings, medical appointments) (Charnysh et al. 2024), with infrequent reports of psychosocial harm (Charnysh, McCain, et al. 2025); however, for health impacts to be fully realized, it is critical to understand the decision‐making factors that influence which employees participate in testing. In this analysis, we compare the decision‐making considerations and information seeking among those who did and did not undergo wGT, aiming to (1) describe the demographic similarities and differences between employees who had not initiated wGT and employees who had initiated wGT and (2) describe the decision‐making considerations and information‐seeking activities that influenced employees' wGT participation.
To our knowledge, this is the first study to comparatively examine employees' decision‐making processes in a real‐world wGT program at a single large US healthcare system, where barriers of cost and access are minimized (Shen et al. 2022; White et al. 2018). This unique setting presents an opportunity to further understand employee perspectives on wGT and compare the views of individuals who do or do not participate in a genetic testing program with few barriers (e.g., cost, clinician access, appointment wait times). To better understand the factors that inform wGT participation, a comparative analysis of the information‐seeking and decision‐making factors among those who participate and those who do not is critical and has implications for understanding genetic testing decisions more broadly.
2. Materials and Methods
2.1. Study Site
The survey took place at a large US regional health care system with an ongoing wGT program. The wGT program offered employees genetic testing at no cost through a third‐party genetic testing company. The primary genetic tests offered to employees were for hereditary cancer risk, heart disease risk, and medication response. Employees were offered a variety of wGT educational materials from the testing company, including options to engage in genetic counseling sessions, read a brochure, peruse the testing company's website (link provided in the survey invitation email), watch a video (link sent with email invitation), or attend group education sessions. For greater details, see Charnysh et al. (2024).
2.2. Participants and Recruitment
Data collection for this survey took place between December 2021 and May 2022 through the third‐party survey firm, SoundRocket (Ann Arbor, MI). Through nonpurposive sampling, participants were recruited via various approaches coordinated by the healthcare institution, including posting invitations on the internal employee website, electronic and print advertisements throughout the healthcare system and all affiliated campuses, and disseminating study announcements in employee meetings and newsletters (Charnysh et al. 2024). At the time of the survey, the healthcare system had approximately 30,000 employees, all of whom were eligible for wGT.
Employees were eligible to participate in the survey if they were 18 years or older and employed at the institution for at least 60 days. Excluded individuals included those who indicated they had ordered a test kit but had not yet returned it and those who had returned a test kit but had not yet received their test results. Before beginning the survey, participants read an information sheet and provided implied consent by submitting a survey response. Participants who completed the survey received a $25 electronic gift card. This study was reviewed by the University of Michigan Institutional Review Board and determined to be exempt.
2.3. Survey
The survey items for this analysis were selected from a broader instrument developed for the INSIGHT @ Work Study. An interdisciplinary team with expertise in genetic counseling, health law, ELSI research, survey design, and public health designed the broader instrument, adapting survey items from team members' prior survey instruments (e.g., the Impact of Personal Genomics Study (Carere et al. 2014)) and developing novel questions informed by a literature review. Survey items for this analysis used newly created measures to assess employees' perceptions of and experiences with wGT. The survey included multiple‐choice (select all that apply), Likert‐scale, and open‐ended response questions. The survey was piloted twice by colleagues external to the study, then iteratively revised.
2.3.1. Demographics and Participant Characteristics
Self‐reported demographics, including age, sex, race and ethnicity, marital status, number of children, education, and years of employment, were ascertained (Table 1). In addition, physical and mental health status, personal and family history of cancer and heart disease, and prior genetic testing (e.g., clinical, DTC, research study) experience were collected. Those who had not yet participated were also asked to indicate their likelihood of future wGT participation on a five‐point scale, ranging from 1 = very unlikely to 5 = very likely.
TABLE 1.
Demographics and participant characteristics (N = 776).
| Characteristic | Non‐Testers (n = 358) N (%) | Testers (n = 418) N (%) | p‐Value* proportional Z test |
|---|---|---|---|
|
Age (years) Mean (range) |
43.1 (22–74) | 43.8 (20–74) |
0.3 — |
| Sex | 0.003 | ||
| Female | 289 (80.7) | 369 (88.3) | — |
| Male | 69 (19.3) | 49 (11.7) | — |
| Race and ethnicity | < 0.001 | ||
| White, non‐Hispanic | 250 (69.8) | 337 (80.6) | < 0.001 b |
| Black, non‐Hispanic | 54 (15.1) | 24 (5.7) | < 0.001 b |
| Asian, non‐Hispanic | 21 (5.9) | 27 (6.5) | 0.7 |
| Hispanic | 13 (3.6) | 12 (2.9) | 0.6 |
| Other, not listed | 5 (1.4) | 5 (1.2) | 0.8 |
| Multiracial | 15 (4.2) | 13 (3.1) | 0.4 |
| Years of employment | < 0.001 | ||
| < 1 year | 60 (16.8) | 26 (6.2) | < 0.001 b |
| 1–2 years | 64 (17.9) | 44 (10.5) | 0.003 b |
| 3–5 years | 72 (20.1) | 108 (25.8) | 0.1 b |
| 6–10 years | 44 (12.3) | 77 (18.4) | 0.02 b |
| > 10 years | 118 (33.0) | 163 (39.0) | 0.1 b |
| Marital status | 0.2 | ||
| Married/In a committed relationship | 246 (68.9) | 320 (76.6) | — |
| Never married | 76 (21.3) | 61 (14.6) | — |
| Divorced | 24 (6.7) | 26 (6.2) | — |
| Separated | 6 (1.7) | 4 (1.0) | — |
| Widowed | 3 (0.8) | 5 (1.2) | — |
| Other | 2 (0.6) | 2 (0.5) | — |
| Missing | 1 | 0 | — |
| Children | 0.6 | ||
| None | 139 (39.0) | 161 (38.5) | — |
| Biological children | 193 (54.2) | 214 (51.0) | — |
| Adopted children | 5 (1.4) | 6 (1.4) | — |
| Stepchildren | 6 (1.7) | 12 (2.9) | — |
| 2+ children | 12 (3.4) | 23 (5.5) | — |
| Other | 1 (0.3) | 2 (0.5) | — |
| Missing | 2 | 0 | — |
| Education | 0.3 | ||
| High school/Equivalent | 16 (4.5) | 15 (3.6) | — |
| Some college/Technical/Associate's degree | 105 (29.3) | 102 (24.4) | — |
| Bachelor's degree | 126 (35.2) | 150 (35.9) | — |
| Professional degree a | 111 (31.0) | 151 (26.1) | — |
| Personal history of cancer | 0.2 | ||
| Yes | 29 (8.7) | 46 (11.8) | — |
| No | 306 (91.3) | 343 (88.2) | — |
| Missing | 23 | 29 | — |
| Personal history of heart disease | 0.03 | ||
| Yes | 36 (10.7) | 64 (16.4) | — |
| No | 300 (89.3) | 327 (83.6) | — |
| Missing | 22 | 27 | — |
| Family history of cancer | 0.1 | ||
| None | 81 (24.7) | 76 (19.5) | — |
| 1 relative | 86 (26.2) | 85 (21.9) | — |
| 2+ relatives | 144 (43.9) | 210 (54.0) | — |
| Don't know | 17 (5.2) | 18 (4.6) | — |
| Missing | 30 | 29 | — |
| Family history of heart disease | 0.7 | ||
| None | 66 (20.0) | 71 (18.4) | — |
| 1 Relative | 90 (27.4) | 97 (25.1) | — |
| 2+ relatives | 149 (45.3) | 192 (49.7) | — |
| Don't know | 24 (7.3) | 26 (6.7) | — |
| Missing | 29 | 32 | — |
| Health status | 0.5 | ||
| Excellent | 55 (16.4) | 70 (17.8) | — |
| Very good | 155 (46.1) | 189 (48.1) | — |
| Good | 95 (28.3) | 111 (28.2) | — |
| Fair | 29 (8.6) | 21 (5.3) | — |
| Poor | 2 (0.6) | 2 (0.5) | — |
| Missing | 22 | 25 | — |
| Mental health status | 0.8 | ||
| Excellent | 79 (24.0) | 88 (22.0) | — |
| Very good | 125 (37.0) | 129 (35.0) | — |
| Good | 97 (29.0) | 117 (30.0) | — |
| Fair | 29 (8.6) | 36 (9.2) | — |
| Poor | 6 (1.8) | 12 (3.1) | — |
| Missing | 22 | 26 | — |
| Prior genetic testing | 0.03 | ||
| Yes | 70 (20.8) | 103 (26.2) | — |
| No | 288 (79.2) | 315 (73.8) | — |
| Future wGT participation | — | ||
| Very likely | 53 (14.8) | — | — |
| Somewhat likely | 89 (24.9) | — | — |
| Somewhat unlikely | 53 (14.8) | — | — |
| Very unlikely | 80 (22.3) | — | — |
| Undecided | 83 (23.2) | — | — |
Note: Analyses used Pearson's Chi‐squared test; Fisher's exact test: significant if p < 0.05. Proportional Z‐tests were used to compare proportions.
Abbreviations: N, total number; wGT, workplace genetic testing.
Professional degree: Master's degree, PhD, and MD.
Denotes the proportional Z‐test p‐values, comparing Non‐Testers and Testers within statistically significant variable categories.
p‐Values denote the statistical significance between the overall demographic categories of Non‐Testers and Testers.
2.3.2. wGT Decision‐Making and Preferences
To assess wGT decision‐making processes and preferences, participants were asked to indicate which of 10 activities (plus Other) they had engaged in before deciding whether to participate in wGT (Figure 1). Participants also reported their preferences for how employers should offer genetic testing to their employees by selecting one of four options (the current option, [Healthcare System]‐operated clinic, Primary Care Provider (PCP), or a Provider I choose on my own). Participants were asked to rate the importance of 16 potential motivations for deciding whether to participate in wGT on a three‐point scale (1 = not at all important/not applicable to 3 = very important) (Table 2). In addition, all participants were asked to list other factors they considered when deciding whether to pursue wGT via open‐ended responses (Table S1).
FIGURE 1.
Workplace genetic testing (wGT) information‐seeking activities. (a) Number of activities engaged in. Notes: All p < 0.001; Missing responses: Non‐Testers n = 9, Testers n = 4. (b) Informational activity/resource. Notes: Missing responses: Non‐Testers n = 9, Testers n = 4. (c) Engagement in only one informational activity/resource. n = 0 Non‐Testers and Testers reported talking to HR or a supervisor/manager. All analyses used Pearson's chi‐squared test. If the cell size < 5%, then Fisher's exact test was used, with significance at p < 0.05. Abbreviations: HCP, healthcare provider; HR, human resources; N, total number; PCP, primary care provider. *p < 0.001; **p = 0.02; ***p = 0.04.
TABLE 2.
Perceived benefits and risks of wGT participation.
| Perceived benefit/risk | Non‐Testers N (%) | Testers N (%) | Adjusted p‐value a |
|---|---|---|---|
| What I may learn about my risk of cancer | N = 355 | N = 416 | < 0.001 |
| Very important | 210 (59.2) | 328 (78.8) | |
| Somewhat important | 106 (29.9) | 83 (20.0) | |
| Not at all important/not applicable | 39 (11.0) | 5 (1.2) | |
| Missing | 3 | 2 | |
| What I may learn about my risk of heart disease | N = 355 | N = 416 | < 0.001 |
| Very important | 206 (58.0) | 295 (70.9) | |
| Somewhat important | 109 (30.7) | 114 (27.4) | |
| Not at all important/not applicable | 40 (11.3) | 7 (1.7) | |
| Missing | 3 | 2 | |
| The results may inform my health behavior or lifestyle choices (e.g., diet, exercise) | N = 355 | N = 416 | < 0.001 |
| Very important | 205 (57.7) | 277 (66.6) | |
| Somewhat important | 108 (30.4) | 127 (30.5) | |
| Not at all important/not applicable | 42 (11.8) | 12 (2.9) | |
| Missing | 3 | 2 | |
| My family health history | N = 352 | N = 412 | < 0.001 |
| Very important | 202 (57.4) | 318 (77.2) | |
| Somewhat important | 120 (34.1) | 84 (20.4) | |
| Not at all important/not applicable | 30 (8.5) | 10 (2.4) | |
| Missing | 6 | 6 | |
| Concerns over who might have access to my genetic information | N = 351 | N = 410 | < 0.001 |
| Very important | 192 (54.7) | 130 (31.7) | |
| Somewhat important | 82 (23.4) | 134 (32.7) | |
| Not at all important/not applicable | 77 (21.9) | 146 (35.6) | |
| Missing | 7 | 8 | |
| The results may inform my medical care decisions | N = 355 | N = 415 | < 0.001 |
| Very important | 194 (54.6) | 279 (67.2) | |
| Somewhat important | 126 (35.5) | 129 (31.1) | |
| Not at all important/not applicable | 35 (9.9) | 7 (1.7) | |
| Missing | 3 | 3 | |
| My personal health history | N = 352 | N = 411 | < 0.001 |
| Very important | 188 (53.4) | 289 (70.3) | |
| Somewhat important | 125 (35.5) | 109 (26.5) | |
| Not at all important/not applicable | 29 (11.1) | 13 (3.2) | |
| Missing | 6 | 7 | |
| The results may help me plan for the future | N = 355 | N = 416 | < 0.001 |
| Very important | 189 (53.2) | 292 (70.2) | |
| Somewhat important | 124 (34.9) | 114 (27.4) | |
| Not at all important/not applicable | 42 (11.8) | 10 (2.4) | |
| Missing | 3 | 2 | |
| How results might impact family members | N = 349 | N = 410 | 0.004 |
| Very important | 178 (51.0) | 250 (61.0) | |
| Somewhat important | 115 (33.0) | 122 (29.8) | |
| Not at all important/not applicable | 56 (16.0) | 38 (9.3) | |
| Missing | 9 | 8 | |
| Testing would be at no cost | N = 352 | N = 412 | < 0.001 |
| Very important | 175 (49.7) | 260 (63.1) | |
| Somewhat important | 102 (29.0) | 120 (29.1) | |
| Not at all important/not applicable | 75 (21.3) | 32 (7.8) | |
| Missing | 6 | 6 | |
| Curiosity about my genetic makeup in general | N = 355 | N = 416 | < 0.001 |
| Very important | 163 (45.9) | 270 (64.9) | |
| Somewhat important | 125 (35.2) | 125 (30.0) | |
| Not at all important/not applicable | 67 (18.9) | 21 (5.0) | |
| Missing | 3 | 2 | |
| What I may learn about my personal response to medications | N = 355 | N = 415 | < 0.001 |
| Very important | 147 (41.4) | 197 (47.5) | |
| Somewhat important | 132 (37.2) | 178 (42.9) | |
| Not at all important/not applicable | 76 (21.4) | 40 (9.6) | |
| Missing | 3 | 3 | |
| The results may cause me worry/distress | N = 355 | N = 416 | < 0.001 |
| Very important | 129 (36.3) | 89 (21.4) | |
| Somewhat important | 147 (41.4) | 181 (43.5) | |
| Not at all important/not applicable | 79 (22.3) | 146 (35.1) | |
| Missing | 3 | 2 | |
| Testing would go through a service outside of my employer | N = 351 | N = 409 | 0.3 |
| Very important | 103 (29.3) | 107 (26.2) | |
| Somewhat important | 120 (34.2) | 129 (31.5) | |
| Not at all important/not applicable | 128 (36.5) | 173 (42.3) | |
| Missing | 7 | 9 | |
| The time it would take to complete testing | N = 352 | N = 410 | 0.2 |
| Very important | 74 (21.0) | 68 (16.6) | |
| Somewhat important | 118 (33.5) | 130 (31.7) | |
| Not at all important/not applicable | 160 (45.5) | 212 (51.7) | |
| Missing | 6 | 8 | |
| My co‐worker's experiences with this type of testing | N = 352 | N = 410 | 0.2 |
| Very important | 52 (14.8) | 45 (11.0) | |
| Somewhat important | 65 (18.5) | 68 (16.6) | |
| Not at all important/not applicable | 235 (66.8) | 297 (72.4) | |
| Missing | 6 | 8 |
Note: Missing values are due to nonresponse.
Abbreviations: N, total number; wGT, workplace genetic testing.
Significant level of Fisher's exact test is p < 0.05; adjusted p‐value with Bonferroni correction—significant adjusted p‐values have been bolded.
2.3.3. wGT Perspectives
Participants were asked about their views on wGT program‐related issues by indicating their level of agreement (Agree, Disagree, Not Sure) with 13 statements. Survey items addressed views on potential wGT benefits and concerns, as well as attitudes toward potential policy options regarding wGT (Table S2).
2.4. Data Analysis
Data analyses examined participants' perceived benefits and risks of wGT participation, as well as their decision‐making processes. Descriptive statistics characterized respondents' characteristics and wGT perspectives and decision‐making processes, including comparisons between Non‐Testers and Testers. Here, we refer to individuals who have not initiated or participated in testing as “Non‐Testers,” and those who have initiated testing as “Testers.” Non‐Testers, however, are a diverse group that includes undecided individuals and those open to pursuing wGT in the future. Given that employees can opt into wGT at any point in their employment if the program is still offered, current Non‐Testers may include future Testers.
Statistical analyses employed Chi‐squared tests with Bonferroni correction to test for significant differences between Non‐Testers and Testers across demographics, information‐seeking activities, motivations and concerns, and wGT perspectives. Fisher's exact test was used for answer choices with fewer than five participant responses. Additionally, a multivariable logistic regression examining employee demographics (e.g., race and ethnicity, sex, and years of employment) assessed potential predictors associated with being a Non‐Tester or Tester.
Open‐ended responses gathered information regarding additional factors considered during the wGT decision‐making process. These responses (Non‐Testers: 133; Testers: 111) were qualitatively coded following inductive thematic analysis by two researchers: a qualitatively trained social scientist (K.H.) and a genetic counselor (A.V.). Researchers collaboratively developed codes after reviewing responses (Braun et al. 2012). The codes were then applied to all the responses by both researchers through the following iterative process: independent coding of responses, collaborative review of coding and resolution of differences, and refinement of code definitions. The Codebook is available in Supporting Information S3.
3. Results
3.1. Demographics and Participant Characteristics
3.1.1. Descriptives and Chi‐Square Analyses
The survey was completed by 776 employees: 358 Non‐Testers and 418 Testers (Table 1). Non‐Testers were, on average, 43 years old (range: 22–74), 80.7% female, and 69.8% White, non‐Hispanic. Testers were, on average, 44 years old (range: 20–74), 88.3% female, and 80.6% White, non‐Hispanic. Non‐Testers and Testers reported being in good physical health, with only 9.2% of Non‐Testers and 5.8% of Testers rating their health as fair or poor. Both groups were majority female, but Non‐Testers were significantly less likely to be female (p = 0.003) and White, non‐Hispanic (p < 0.001). Non‐Testers were significantly more likely to be Black, non‐Hispanic (Non‐Testers: 15.1%; Testers: 5.7%; p < 0.001) and employed less than one year (Non‐Testers: 16.8%; Testers: 6.2%; p < 0.001) or one to two years (Non‐Testers: 17.9%; Testers: 10.5%; p = 0.003) compared to Testers.
Non‐Testers, compared to Testers, were less likely to have a personal history of heart disease (10.7% vs. 16.4%; p = 0.03). Holding all other factors constant, Non‐Testers were less likely to have had prior genetic testing (20.8% vs. 26.2%; p = 0.03) compared to Testers (Table 1). Overall, approximately 40% of Non‐Testers said they would be somewhat or very likely to participate in wGT in the future. There was no significant difference between Non‐Testers and Testers regarding their marital status, number of children, education, personal history of cancer, family history of cancer/heart disease, mental health, or physical health.
3.1.2. Multivariable Logistic Regression
The multivariable logistic regression examining potential predictors associated with being a Non‐Tester or Tester indicated that employees who identified as Black, non‐Hispanic were less likely to be a Tester compared to employees who identified as White, non‐Hispanic [adjusted odds ratio (aOR): 0.39; 95% CI: 0.21–0.68; p = 0.001]. Additionally, male employees were less likely to be Testers than females (aOR: 0.53; 95% CI: 0.35–0.82; p = 0.004). Finally, employees with less than one year of employment at the healthcare system were less likely to be a Tester than employees who had been employed for more than ten years (aOR: 0.34; 95% CI: 0.18–0.61; p < 0.001) (Table 3). Demographics that did not statistically differ between Non‐Testers and Testers included age, education, and prior genetic testing.
TABLE 3.
Multivariable logistic regression: predictors of being a Non‐Tester or Tester.
| Variable | Testers vs. Non‐Testers (N = 776) | ||
|---|---|---|---|
| aOR | (95% CI) | p‐Value* | |
| Age (years) | |||
| < 35 | 1.10 | 0.68, 1.77 | 0.71 |
| 35–54 | 1.16 | 0.79, 1.72 | 0.44 |
| 55+ | 1.00 | ||
| Race and ethnicity | |||
| Black, non‐Hispanic | 0.39 | 0.21, 0.68 | 0.001 |
| Asian, non‐Hispanic | 0.97 | 0.50, 1.92 | 0.93 |
| Hispanic | 0.66 | 0.28, 1.55 | 0.34 |
| Multiracial | 0.67 | 0.29, 1.49 | 0.32 |
| Other, not listed | 0.99 | 0.25, 4.25 | 0.99 |
| White, non‐Hispanic | 1.00 | ||
| Sex | |||
| Male | 0.53 | 0.35, 0.82 | 0.004 |
| Female | 1.00 | ||
| Education | |||
| High School/Equivalent | 0.69 | 0.29, 1.59 | 0.38 |
| Some College, Technical, Associate's degree | 0.87 | 0.58, 1.32 | 0.52 |
| Bachelor's degree | 0.89 | 0.61, 1.28 | 0.52 |
| Professional degree | 1.00 | ||
| Years of employment | |||
| < 1 | 0.34 | 0.18, 0.61 | < 0.001 |
| 1–2 | 0.63 | 0.37, 1.08 | 0.09 |
| 3–5 | 1.07 | 0.68, 1.69 | 0.76 |
| 6–10 | 1.22 | 0.75, 1.98 | 0.43 |
| > 10 | 1.00 | ||
| Prior genetic testing | |||
| Yes | 1.38 | 0.96, 1.99 | 0.09 |
| No | 1.00 | ||
Note: Non‐Testers (N = 358); Testers (n = 418).
Abbreviations: aOR, adjusted odds ratio; CI, confidence interval.
Significant p‐values are bolded.
3.2. wGT Decision‐Making
3.2.1. wGT Information Seeking
Non‐Testers and Testers differed in the number and types of information‐seeking activities they reported (Figure 1a). Non‐Testers more often reported not engaging in any activities compared to Testers (43.3% vs. 20.1%, respectively; p < 0.001). Less than 15% of Non‐Testers engaged in three or more activities. The most common activities reported by Non‐Testers were talking with family/loved ones (28.9%), reading information provided by the testing company (e.g., brochure, website) (19.5%), and talking with other employees (18.1%) (Figure 1b). Non‐Testers and Testers reported similar rates of consulting their PCP or other healthcare provider (HCP) (10.3% vs. 13.0%, respectively). Testers were more likely to talk with other employees (25.1% vs. 18.1%; p = 0.02) than Non‐Testers. Very few Non‐Testers or Testers spoke with a supervisor/manager (0.9% vs. 1.2%) or someone from HR (Human Resources) (0.6% vs. 0.5%) (Figure 1b). For participants who indicated engaging in only one activity before making a testing decision, the most common activity reported by Non‐Testers was talking with their family/loved ones (31.7%), compared with reading the information provided by the testing company for Testers (45.1%) (Figure 1c). If participants engaged in only one information‐seeking activity, Non‐Testers were significantly more likely to talk with other employees (19.5% vs. 8.3%; p = 0.02) or search for relevant information online (9.8% vs. 3.0%; p = 0.04) compared to Testers. Alternatively, Testers were significantly more likely than Non‐Testers to read the information provided by the testing company (45.1% vs. 19.5%; p < 0.001) if they only engaged in one information‐seeking activity (Figure 1c). Non‐Testers and Testers differed on their preferences for how their employer should offer genetic testing. Non‐Testers, compared to Testers, were significantly more likely to prefer testing outside the current option (i.e., through a third‐party company) (34.0% vs. 70.1%; p < 0.001).
3.2.2. Perceived Benefits and Risks of wGT Participation
Non‐Testers and Testers endorsed different perceived benefits and risks as important (very/somewhat) regarding their wGT participation (Table 2). Non‐Testers were significantly more likely to endorse perceived risks over who might have access to their genetic information (Non‐Testers: 78.1% vs. Testers: 64.4%), and that the test results may cause worry or distress (Non‐Testers: 77.7% vs. Testers: 64.9%) (both p < 0.001). Non‐Testers were significantly less likely than Testers to endorse learning about their risk of heart disease as a perceived benefit for getting tested (88.7% vs. 98.3%) and learning about their risk for cancer (89.1% vs. 98.8%), and how it may inform medical decisions (90.1% vs. 98.3%) (all p < 0.001).
Testers, compared to Non‐Testers, were significantly more likely to endorse the following perceived benefits as very or somewhat important in their decision‐making process: their personal (96.8% vs. 88.9%) and family health histories (97.6% vs. 91.5%), using the results for future planning (97.6% vs. 88.1%), curiosity about their genetic makeup (94.9% vs. 81.1%), and testing would be at no cost (92.2% vs. 78.7%) (all p < 0.001). Testers were also significantly more likely to endorse other potential benefits of wGT as perceived benefits of participation, including what they may learn about their response to medications (90.4% vs. 78.6%; p < 0.001), how the results may inform their health behaviors or lifestyle (97.1% vs. 88.1%; p < 0.001), and how the results may impact their family (90.8% vs. 84.9%; p = 0.004).
3.2.3. Open‐Ended Responses: Additional Decision‐Making Considerations
Non‐Testers and Testers differed in their open‐ended responses about other wGT decision‐making considerations (Table S1). The most prominent considerations among Non‐Testers were insurance concerns (e.g., changes in coverage based on results, results impacting the cost of coverage) and potential distress concerns (e.g., concern about results creating worry/anxiety). Non‐Testers also mentioned privacy‐related concerns (e.g., result privacy, employer access, secondary use of data) and logistical concerns (e.g., time, lack of knowledge about the program). In contrast, Testers' most prominent themes were health‐related considerations (e.g., family health history concerns, improving personal health, risk management), as well as cost‐savings factors and curiosity about their genetics. Both Non‐Testers and Testers endorsed perceived privacy risks; however, these were more prominent among Non‐Testers.
3.3. Perspectives on wGT Programs
3.3.1. General Employee Perspectives
Table S2 summarizes participants' level of agreement on various statements about wGT. Non‐Testers were more likely to endorse perceived risks about wGT, including employers using employees' genetic information against them in hiring and promotion decisions (27.8% vs. 13.0%; p < 0.001). Testers were more likely to endorse perceived benefits of wGT, such as improving employees' health (71.3% vs. 42.7%; p < 0.001).
3.3.2. Employee Perspectives on Employer Motivations
Table S2 describes employees' perspectives on employer motivations for offering wGT. Non‐Testers were more likely than Testers to believe employers would charge higher insurance premiums to riskier employees (27.2% vs. 12.2%; p < 0.001) and to use wGT in hiring and promotion decisions (19.3% vs. 11.2%; p < 0.001). Non‐Testers were less likely than Testers to believe that employers offer wGT to improve employee health (66.3% vs. 87.2%; p < 0.001) or to attract and keep their employees (34.9% vs. 48.6%; p < 0.001).
4. Discussion
To our knowledge, this is the first study to examine the decision‐making processes, preferences, and perspectives of employees offered wGT. Our real‐world findings align with those of studies on hypothetical wGT programs, which demonstrate that for some employees, the lack of cost and interest in genetic testing were key perceived benefits, whereas for others, concerns about privacy, discrimination, and emotional distress prevent them from participating (Briscoe et al. 2024; Sanghavi et al. 2021).
Non‐Testers were more likely to endorse perceived risks of wGT participation as very important. These risks ranged from concerns about the privacy of their genetic information, potential employment or insurance discrimination, and the fact that results may cause worry or distress. Given that Non‐Testers were more likely to be Black, these perceived risks may reflect well‐documented concerns about genetic testing and research participation in Black communities (Fisher et al. 2020), as well as equitable access to genetic technologies (Fisher et al. 2020). wGT removes disparities related to low referral rates to genetic testing among minority groups (Gold et al. 2026; Muller et al. 2018), but that alone did not remove the differences in wGT uptake among racial groups. This finding aligns with studies indicating low uptake of genetic testing and limited participation in clinical research among Black, Asian, and Hispanic individuals (Saulsberry and Terry 2013). Privacy concerns and fears about potential insurance and employment discrimination are more likely in these individuals when offered genetic testing (Clayton et al. 2018). Disparities in uptake of genetic technologies may be informed by trust and historical concerns surrounding genetics. For example, concerns about the use of genetic data may fuel distrust of the medical community (Saulsberry and Terry 2013), which often stems from historical abuses and inequities in medicine, particularly in genetics (Saulsberry and Terry 2013).
Perceived risks regarding potential psychological distress are also reported in studies of individuals who decline clinical testing (Zimmermann et al. 2021; Keogh et al. 2017; Callahan et al. 2024). Although most literature indicates limited negative psychological outcomes after genetic testing, literature also shows that, if any group experiences such psychosocial consequences, it is more likely to be racial and ethnic minorities, including those identifying as Black, non‐Hispanic (Charnysh, McCain, et al. 2025). Such perceived risks may have dissuaded some Non‐Testers from pursuing and/or learning more about the wGT program offered by their employer.
Lack of knowledge about antidiscrimination policies (e.g., GINA) may shape employees' perceived risks. Non‐Testers' views of their employer's motivations for offering wGT aligned with their concerns about participating in wGT. For example, they were more likely to endorse perceived risks that their employer was offering wGT to make employment or insurance decisions. These findings mirror the literature, which has consistently shown public fear of genetic discrimination (Wauters and Van Hoyweghen 2016; Prince et al. 2021). Although evidence of such discrimination is minimal (Areheart and Roberts 2019) and GINA provides some protection against employment and health insurance discrimination (US EEOC 2008), possible implications for nonhealth insurance, such as life or disability insurance (Anderson et al. 2021), remain, as GINA does not cover life, disability, or other insurances (US EEOC 2008). Given prior studies showing a lack of public awareness of GINA (Lenartz et al. 2021; Willard et al. 2024), employees who expressed concerns that are illegal under GINA may likely have been unaware of the law, its protections/limitations, and the rarity of genetic discrimination cases. Education about GINA's protections and limitations is important for facilitating employees' access to relevant information for informed wGT decision‐making.
The fact that testing was offered at no cost was a stronger perceived benefit for Testers than Non‐Testers. This finding supports one of the key benefits of wGT, as cost is a documented reason why individuals do not participate in clinical (White et al. 2018; Kahn et al. 2023) and DTC genetic testing (Hazel et al. 2021). Learning about their cancer and heart disease risks and informing medical decisions were reported by the vast majority (88%) of Testers as perceived benefits of participating in wGT. This finding is consistent with a study of individuals offered elective genetic testing—clinical genetic testing offered to healthy individuals without a family history of a disease—which found that patients who underwent testing expected the information to help with medication management, prevent future disease, and provide risk information to their family (Zoltick et al. 2024).
Interestingly, despite not initiating or undergoing wGT, almost a third of Non‐Testers preferred the current testing option (i.e., through a third‐party company offered by their employer via a wellness benefit) over alternatives, including through outside providers (e.g., PCP, HCP, provider chosen by the employee). This finding aligns with some research (Sanghavi et al. 2021) but contrasts with other studies, which generally find that people prefer undergoing genetic testing through an HCP or their PCP (Briscoe et al. 2024, 2023; Schaper et al. 2019), with minimal preference for employer‐offered genetic testing (Briscoe et al. 2024); however, this is only a recent option and not widely available.
Our survey found a significant gap in information‐seeking: close to half of Non‐Testers did not engage in any information‐seeking activities, whereas nearly four in five Testers engaged in at least one. This gap in information seeking may reflect Non‐Testers' preexisting concerns about wGT risks and limitations (e.g., related to genetic privacy or distress from results) or general lack of test interest, such that they did not feel a need to learn more about the wGT program offered by their employer or discuss it with others. Alternatively, it could indicate that Non‐Testers were at an earlier stage (e.g., precontemplation) in their decision‐making process; a majority of Non‐Testers reported being either undecided or likely (somewhat/highly) to pursue genetic testing in the future. Non‐Testers were significantly more likely to have been employed for less than two years (i.e., newer employees), and open‐ended responses indicated that some Non‐Testers were unaware of the wGT program. Thus, some Non‐Testers may be considered “conditional decliners” (Keogh et al. 2017), who are open to testing in the future.
Family members were a key information source in both groups, as approximately a third of Non‐Testers and Testers consulted with family members in their wGT decision‐making, which supports findings from other studies in clinical settings (Zimmermann et al. 2021; Slavotinek et al. 2023). When Non‐Testers engaged in information‐seeking activities, they most commonly spoke with friends or family, which aligns with findings from a clinical genetic testing study showing that individuals who unequivocally declined testing consulted family rather than clinicians or genetic counselors (Kanga‐Parabia et al. 2018). In contrast, Testers were most likely to read information from the testing company; however, this could be an artifact of the testing process, which may have necessitated engagement with the testing company's website or informational materials. A quarter of Testers talked with fellow employees, and almost one‐fifth of Non‐Testers did too. These findings highlight that employee‐to‐employee information sharing is a component of wGT decision‐making. Notably, testing was more often discussed with employee peers than with individuals in oversight roles (e.g., supervisor/manager or HR). While the numbers are small, when employees engaged in just one activity, Non‐Testers were significantly more likely than Testers to talk with other employees; however, it is not known whether the other employees were Non‐Testers or Testers.
4.1. Implications and Future Research
These results can help inform wGT program development, broader clinical practice, and future studies on information seeking. Employers interested in implementing wGT should consider partnering with trusted third parties to develop educational materials that ensure equitable education about the health implications and legal protections for genetic testing. Materials should provide balanced education on the benefits and limitations of legal protections and wGT in general to maximize potential benefits and minimize possible harms, thereby improving employee trust (Charnysh, Sanghavi, et al. 2025).
Development of educational materials and communication about genetic testing should ensure that perceived risks, particularly regarding employment and insurance discrimination, are addressed, with current protections and coverage gaps clearly conveyed. For example, providing information about GINA as part of outreach around wGT may benefit both Non‐Testers and Testers by ensuring that all employees are educated about its protections and limitations. In addition, given that Non‐Testers expressed perceived risks regarding their employer and the testing companies, Non‐Testers and Testers may benefit from information provided by neutral third parties (e.g., governmental or professional medical organizations) to supplement materials offered by the employer or testing company. These materials could include information on the clinical and psychological impacts of genetic testing. Future research is needed to examine which informational sources best reduce racial disparities in genetic testing uptake (Table 3). In addition, research using more robust genetic literacy measures can help determine the effectiveness of such wGT resources on program understanding. In addition, the complex relationship between information‐seeking and participation in wGT warrants further research examining how information seeking and decision‐making are related to better understand their reciprocal influence. While our study found differences in both the type and number of sources that Testers and Non‐Testers consulted (Figure 1), it is not clear if the information seeking shaped the concerns or was an outcome of those concerns. Qualitative research is needed to better understand why individuals seek different informational sources, how those influence their decisions, and the role of demographic factors (e.g., race, gender, education) in shaping information seeking.
4.2. Strengths and Limitations
This study has several strengths. Firstly, although this was a nonpurposive sample, it included employees from a large US healthcare system that was diverse in terms of age, education, and race. There are few studies on wGT, and to our knowledge, this is the first to investigate information‐seeking and decision‐making processes in a real‐world wGT program. Furthermore, our analysis focuses on the unique perspectives of Non‐Testers and adds to the wGT literature, which is mainly focused on Testers.
The strengths of this study must be considered in light of its limitations. While we used real‐world data rather than hypothetical scenarios, we analyzed a single healthcare system, with only a fraction of employees participating in wGT; their responses may not be representative of other health system employees or employment sectors, and therefore, the results may not be generalizable to the entire healthcare system workforce due to this selection bias. Although physical health was included in the regression, it was not significant, so we hypothesize that the healthy employee effect was not that influential. More influential may be that the employees most likely to engage in the survey were those with the strongest impression of and/or positive experiences with genetic testing, compared to those who were less likely or held neutral feelings about the topic; a finding consistent with the literature (Saylor et al. 2019; Likhanov et al. 2023). This analysis captured the extreme positions on this—those who did and did not engage in wGT. Our exploration of open‐ended responses supplements our findings, but this was not a formal qualitative study; therefore, some responses lacked depth and context. Similarly, some questions lacked the detail of formal clinical assessments (e.g., self‐reported family health history, specific type of prior genetic testing), so we could not glean more insight into the extent to which engagement in or the temporal sequence of information‐seeking activities influenced employees' decisions to pursue wGT, a limitation inherent to cross‐sectional studies. Finally, some questions were novel and therefore, not validated.
5. Conclusion
This study contributes to the limited literature on wGT and provides insights into the decision‐making process and perspectives of wGT‐eligible employees. We highlight the differences between Non‐Testers and Testers to gain insight into the number and types of resources utilized before making a testing decision. Understanding the differences in decision‐making processes between Non‐Testers and Testers can benefit and inform the development of wGT programs and the types of resources provided to employees. Beyond wGT, these results also have applicability for individuals considering genetic testing through clinical practice and other nonclinical testing models.
Author Contributions
Katherine Hendy: writing – original draft, formal analysis, writing – review and editing, conceptualization, methodology, visualization, investigation. Rebecca Ferber: writing – original draft, formal analysis, writing – review and editing, conceptualization, methodology, visualization, project administration, investigation. Subhamoy Pal: writing – review and editing, formal analysis, writing – original draft, software, data curation, visualization. Sarah McCain: project administration, writing – original draft, writing – review and editing, visualization. Alyx Vogle: formal analysis, writing – review and editing, investigation. Jonathan M. Reader: formal analysis, software, data curation, visualization. Elizabeth Charnysh: writing – review and editing, project administration. W. Gregory Feero: writing – review and editing, conceptualization, methodology, investigation. Anya E. R. Prince: writing – review and editing, conceptualization, methodology, investigation. Kunal Sanghavi: writing – review and editing, conceptualization, methodology, funding acquisition, investigation, project administration. Alexandra Truhlar: writing – review and editing. Drew Blasco: conceptualization, methodology, writing – review and editing, investigation. Veda N. Giri: writing – review and editing, conceptualization, methodology, resources, investigation. Charles Lee: writing – review and editing, conceptualization, methodology, funding acquisition, investigation. J. Scott Roberts: writing – original draft, writing – review and editing, conceptualization, methodology, funding acquisition, validation, investigation, supervision. Wendy R. Uhlmann: writing – original draft, writing – review and editing, methodology, conceptualization, funding acquisition, validation, supervision, investigation.
Funding
This work was supported by the National Human Genome Research Institute of the National Institutes of Health (R01‐HG010679) (to C.L. and J.S.R.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Ethics Statement
Institutional review board (IRB) approval was obtained from the University of Michigan for this study (HUM00206346). All procedures followed were in accordance with the ethical standards of the University of Michigan's IRB. Before accessing the web survey, participants reviewed an information sheet. Written informed consent to participate in the study was not directly obtained but inferred by completion of the web‐based survey (following online education).
Conflicts of Interest
C.L. is a scientific advisor for Nabsys LLC. The other authors declare no conflicts of interest.
Supporting information
Table S1: Open‐ended responses: additional decision‐making considerations regarding wGT.
Table S2: Employee perspectives on wGT programs.
File S3: Codebook.
Acknowledgments
The authors acknowledge the contributions from the staff of the healthcare institution. The authors also thank Scott D. Crawford and the SoundRocket (Ann Arbor, MI) team for their expert assistance with survey planning and programming. The INSIGHT @ Work Consortium consists of the following core team members, in addition to the named authors: Betty Cohn, Nicole Crumpler, Amy Leader, Debra Mathews, Kerry Ryan, and Kayte Spector‐Bagdady. The consortium is supported by the advisory board members Kyle Brothers, Ellen Wright Clayton, Patricia Deverka, Thomas Ellis, Aaron Goldenberg, Susan Mockus, Cynthia Casson Morton, Jens Rueter, and Brett Witham, along with stakeholder workgroup members Ethan Bessey, Erynn Gordon, LaTasha Lee, Jessica Roberts, and Fatima Saidi.
Hendy, K. , Ferber R., Pal S., et al. 2026. “To Test or Not to Test: A Real‐World Study of Employee Views and Decision‐Making for Genetic Testing in the Workplace.” Molecular Genetics & Genomic Medicine 14, no. 10: e70311. 10.1002/mgg3.70311.
For a complete list of the INSIGHT @ Work Consortium Investigators, see the Acknowledgments section.
Wendy R. Uhlmann should be considered the senior author.
Data Availability Statement
Currently, the data that support the findings of this study are available upon reasonable request from the corresponding author (K.H.). In the future, the survey data supporting these findings will be made available on the Inter‐university Consortium for Political and Social Research (ICPSR) website, hosted by the University of Michigan.
References
- Anderson, J. , Lewis A., and Prince A.. 2021. “The Problems With Patchwork: State Approaches to Regulating Insurer Use of Genetic Information.” DePaul Journal of Health Care Law 22, no. 1: 1–26. [Google Scholar]
- Areheart, B. A. , and Roberts J. L.. 2019. “GINA, Big Data, and the Future of Employee Privacy.” Yale Law Journal 128, no. 3: 544–871. [Google Scholar]
- Braun, V. , Clarke V., and Terry G.. 2012. “Chapter 4: Thematic Analysis.” In APA Handbook of Research Methods in Psychology, Vol. 2. Research Designs: Quantitative, Qualitative, Neuropsychological, and Biological, edited by Cooper H., Camic P. M., Long D. L., et al., 57–71. American Psychological Association. 10.1037/13620-004. [DOI] [Google Scholar]
- Briscoe, F. , Ajunwa I., Bourgoin A., and Maxwell J.. 2023. “Racial Differences in Perceptions of Genetic Wellness Programs.” American Journal of Health Promotion 37, no. 7: 940–952. 10.1177/08901171231184360.16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Briscoe, F. , Maxwell J. H., and Bourgoin A.. 2024. “Workplace Genetic Testing: Which Employees Are Likely to Participate, What Are Their Concerns With Employer Sponsorship, and Which Design Features Could Reduce Barriers and Increase Participation?” Frontiers in Genetics 15: 1496900. 10.3389/fgene.2024.1496900. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Callahan, K. P. , Mueller R., Crew K., et al. 2024. “Frequency and Reasons That Parents Decline Genetic Testing for Critically Ill Neonates.” Genetics in Medicine Open 2: 101896. 10.1016/j.gimo.2024.101896. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Carere, D. A. , Couper M. P., Crawford S. D., et al. 2014. “Design, Methods, and Participant Characteristics of the Impact of Personal Genomics (PGen) Study, A Prospective Cohort Study of Direct‐to‐Consumer Personal Genomic Testing Customers.” Genome Medicine 6: 96. 10.1186/s13073-014-0096-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Caulfield, T. , and McGuire A. L.. 2012. “Direct‐to‐Consumer Genetic Testing: Perceptions, Problems, and Policy Responses.” Annual Review of Medicine 63: 23–33. 10.1146/annurev-med-062110-123753. [DOI] [PubMed] [Google Scholar]
- Charnysh, E. , McCain S., Truhlar A., et al. 2025. “Perceived Understanding and Psychosocial Outcomes: Employees' Responses to Learning Results of Workplace Genetic Testing.” Personalized Medicine 22, no. 4: 211–221. 10.1080/17410541.2025.2515003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Charnysh, E. , Pal S., Reader J. M., et al. 2024. “Health Care Utilization and Behavior Changes After Workplace Genetic Testing at a Large US Health Care System.” Genetics in Medicine 26, no. 8: 101160. 10.1016/j.gim.2024.101160. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Charnysh, E. , Sanghavi K., Ryan K. A., et al. 2025. “Multidisciplinary Stakeholder‐Informed Identification of Key Characteristics for Implementation of Workplace Genetic Testing.” HGG Advances 6, no. 3: 100458. 10.1016/j.xhgg.2025.100458. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Clayton, E. W. , Halverson C. M., Sathe N. A., and Malin B. A.. 2018. “A Systematic Literature Review of Individuals' Perspectives on Privacy and Genetic Information in the United States.” PLoS One 13, no. 10: e0204417. 10.1371/journal.pone.0204417. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cohn, B. , Ryan K. A., Hendy K., et al. 2023. “Genomic Testing in Voluntary Workplace Wellness Programs in the US: Evidence and Challenges.” Molecular Genetics & Genomic Medicine 11, no. 11: e2245. 10.1002/mgg3.2245. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Covolo, L. , Rubinelli S., Ceretti E., and Gelatti U.. 2015. “Internet‐Based Direct‐to‐Consumer Genetic Testing: A Systematic Review.” Journal of Medical Internet Research 17, no. 12: e279. 10.2196/jmir.4378. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Deverka, P. A. , Goehringer J., McDonald W., et al. 2020. Stakeholders Assessing Genetics With Employers (SAGE). NHGR Institute. https://www.genome.gov/sites/default/files/media/files/2020‐12/SAGE_White_Paper_final_0.pdf. [Google Scholar]
- Fisher, E. R. , Pratt R., Esch R., et al. 2020. “The Role of Race and Ethnicity in Views Toward and Participation in Genetic Studies and Precision Medicine Research in the United States: A Systematic Review of Qualitative and Quantitative Studies.” Molecular Genetics & Genomic Medicine 8, no. 2: e1099. 10.1002/mgg3.1099. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gold, J. I. , Elkaim Y., Gold N. B., et al. 2026. “Racial and Socioeconomic Disparities in Genetic Evaluation and Testing in the Adult Patient Population.” American Journal of Human Genetics 113, no. 1: 29–40. 10.1016/j.ajhg.2025.11.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hazel, J. W. , Hammack‐Aviran C., Brelsford K. M., et al. 2021. “Direct‐to‐Consumer Genetic Testing: Prospective Users' Attitudes Toward Information About Ancestry and Biological Relationships.” PLoS One 16, no. 11: e0260340. 10.1371/journal.pone.0260340. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Henry, T. A. 2021. “Protect Sensitive Individual Data at Risk From DTC Genetic Tests.” https://www.ama‐assn.org/practice‐management/hipaa/protect‐sensitive‐individual‐data‐risk‐dtc‐genetic‐tests.
- Kahn, R. M. , Ahsan M. D., Chapman‐Davis E., et al. 2023. “Barriers to Completion of Cascade Genetic Testing: How Can We Improve the Uptake of Testing for Hereditary Breast and Ovarian Cancer Syndrome?” Familial Cancer 22, no. 2: 127–133. 10.1007/s10689-022-00316-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kanga‐Parabia, A. , Gaff C., Flander L., Jenkins M., and Keogh L. A.. 2018. “Discussions About Predictive Genetic Testing for Lynch Syndrome: The Role of Health Professionals and Families in Decisions to Decline.” Familial Cancer 17, no. 4: 547–555. 10.1007/s10689-018-0078-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Keogh, L. A. , Niven H., Rutstein A., et al. 2017. “Choosing Not to Undergo Predictive Genetic Testing for Hereditary Colorectal Cancer Syndromes: Expanding Our Understanding of Decliners and Declining.” Journal of Behavioral Medicine 40, no. 4: 583–594. 10.1007/s10865-016-9820-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lenartz, A. , Scherer A. M., Uhlmann W. R., et al. 2021. “The Persistent Lack of Knowledge and Misunderstanding of the Genetic Information Nondiscrimination Act (GINA) More Than a Decade After Passage.” Genetics in Medicine 23, no. 12: 2324–2334. 10.1038/s41436-021-01268-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Likhanov, M. , Zakharov I., Awofala A., et al. 2023. “Attitudes Towards Genetic Testing: The Role of Genetic Literacy, Motivated Cognition, and Socio‐Demographic Characteristics.” PLoS One 18, no. 11: e0293187. 10.1371/journal.pone.0293187. [DOI] [PMC free article] [PubMed] [Google Scholar]
- McDonald, W. S. , Wagner J. K., Deverka P. A., et al. 2020. “Genetic Testing and Employer‐Sponsored Wellness Programs: An Overview of Current Vendors, Products, and Practices.” Molecular Genetics & Genomic Medicine 8, no. 10: e1414. 10.1002/mgg3.1414. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Muller, C. , Lee S. M., Barge W., et al. 2018. “Low Referral Rate for Genetic Testing in Racially and Ethnically Diverse Patients Despite Universal Colorectal Cancer Screening.” Clinical Gastroenterology and Hepatology 16, no. 12: 1911–1918.e2. 10.1016/j.cgh.2018.08.038. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Prince, A. E. R. , Uhlmann W. R., Suter S. M., and Scherer A. M.. 2021. “Genetic Testing and Insurance Implications: Surveying the US General Population About Discrimination Concerns and Knowledge of the Genetic Information Nondiscrimination Act (GINA).” Risk Management and Insurance Review 24, no. 4: 341–365. 10.1111/rmir.12195. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rosenberg, J. 2025. “As DTC Genetic Testing Grows Among Consumers, Insurers Are Beginning to Get on Board.” [Internet]. https://www.ajmc.com/view/as‐dtc‐genetic‐testing‐grows‐among‐consumers‐insurers‐are‐beginning‐to‐get‐on‐board.
- Sanghavi, K. , Cohn B., Prince A. E. R., et al. 2022. “Voluntary Workplace Genomic Testing: Wellness Benefit or Pandora's Box?” NPJ Genomic Medicine 7, no. 5: 1–6. 10.1038/s41525-021-00276-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sanghavi, K. , Feero W. G., Mathews D. J. H., et al. 2021. “Employees' Views and Ethical, Legal, and Social Implications Assessment of Voluntary Workplace Genomic Testing.” Frontiers in Genetics 12: 643304. 10.3389/fgene.2021.643304. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Saulsberry, K. , and Terry S. F.. 2013. “The Need to Build Trust: A Perspective on Disparities in Genetic Testing.” Genetic Testing and Molecular Biomarkers 17, no. 9: 647–648. 10.1089/gtmb.2013.1548. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Saylor, K. W. , Ekunwe L., Antoine‐LaVigne D., et al. 2019. “Attitudes Toward Genetics and Genetic Testing Among Participants in the Jackson and Framingham Heart Studies.” Journal of Empirical Research on Human Research Ethics 14, no. 3: 262–273. 10.1177/1556264619844851. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schaper, M. , Wöhlke S., and Schicktanz S.. 2019. “I Would Rather Have It Done by a Doctor”—Laypeople's Perceptions of Direct‐to‐Consumer Genetic Testing (DTC GT) and Its Ethical Implications.” Medicine, Health Care, and Philosophy 22: 31–40. 10.1007/s11019-018-9837-y. [DOI] [PubMed] [Google Scholar]
- Shen, E. C. , Srinivasan S., Passero L. E., et al. 2022. “Barriers and Facilitators for Population Genetic Screening in Healthy Populations: A Systematic Review.” Frontiers in Genetics 13: 865384. 10.3389/fgene.2022.865384. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Slavotinek, A. , Prasad H., Outram S., et al. 2023. “Information‐Seeking Preferences in Diverse Patients Receiving a Genetic Testing Result in the Clinical Sequencing Evidence‐Generating Research (CSER) Study.” Genetics in Medicine 25, no. 9: 100899. 10.1016/j.gim.2023.100899. [DOI] [PMC free article] [PubMed] [Google Scholar]
- US EEOC . 2008. Genetic Information Nondiscrimination Act of 2008. US EEOC. https://www.eeoc.gov/statutes/genetic‐information‐nondiscrimination‐act‐2008. [Google Scholar]
- Wauters, A. , and Van Hoyweghen I.. 2016. “Global Trends on Fears and Concerns of Genetic Discrimination: A Systematic Literature Review.” Journal of Human Genetics 61, no. 4: 275–282. 10.1038/jhg.2015.151. [DOI] [PubMed] [Google Scholar]
- White, V. B. , Walsh K. K., Foss K. S., et al. 2018. “Genetic Testing for Hereditary Breast Cancer: The Decision to Decline.” American Surgeon 84, no. 1: 154–160. 10.1177/000313481808400139. [DOI] [PubMed] [Google Scholar]
- Willard, L. , Uhlmann W., Prince A. E. R., et al. 2024. “The Genetic Information Nondiscrimination Act and Workplace Genetic Testing: Knowledge and Perceptions of Employed Adults in the United States.” Journal of Genetic Counseling 34, no. 2: e1945. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zimmermann, B. M. , Fanderl J., Koné I., et al. 2021. “Examining Information‐Seeking Behavior in Genetic Testing for Cancer Predisposition: A Qualitative Interview Study.” Patient Education and Counseling 104, no. 2: 257–264. 10.1016/j.pec.2020.09.019. [DOI] [PubMed] [Google Scholar]
- Zoltick, E. S. , Bell M., Hickingbotham M. R., et al. 2024. “Attitudes, Knowledge, and Risk Perceptions of Patients Who Received Elective Genomic Testing as a Clinical Service.” Genetics in Medicine 26, no. 10: 101200. 10.1016/j.gim.2024.101200. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1: Open‐ended responses: additional decision‐making considerations regarding wGT.
Table S2: Employee perspectives on wGT programs.
File S3: Codebook.
Data Availability Statement
Currently, the data that support the findings of this study are available upon reasonable request from the corresponding author (K.H.). In the future, the survey data supporting these findings will be made available on the Inter‐university Consortium for Political and Social Research (ICPSR) website, hosted by the University of Michigan.