Jessica L. Roberts

Limiting Occupational Medical Evaluations Under the Americans with Disabilities Act and the Genetic Information Nondiscrimination Act

Although medical care delivery by ones personal physician is the paradigmatic American healthcare arrangement, in the workplace setting, many Americans undergo medical evaluations to assess their fitness for duty or degree of impairment. This Article explores the complex and evolving legal status of occupational medical evaluations. Beginning with the legal and ethical frameworks of occupational medical practice, the Article then examines the effects of increasingly detailed legal regulation under the Americans with Disabilities Act and the Genetic Information Nondiscrimination Act on employees, employers, and physicians.

Should you profit from your genome

VOLUME 35 NUMBER 1 JANUARY 2017 NATURE BIOTECHNOLOGY the EGFP expression plasmid (pEGFP-N1) driven by a cytomegalovirus (CMV) promoter. As the expression of NgAgo modified at its N terminus with a nuclear localization signal (NLS; NLS-NgAgo) is also driven by a CMV promoter, the Ekker lab used EGFP expression as a surrogate marker of NgAgo in these cells. At 4, 12, and 24 h after lipofection, HeLa cells and HEK293 cells were fixed with paraformaldehyde and the coverslips mounted on slides. Strong red fluorescence corresponding to Alexa Fluor 594 was observed inside the cells by 4 h and persisted for at least 24 h (Supplementary Fig. 2b). The distribution of red fluorescence was diffuse within cells at 4 h, but later became more punctated. As expected, some EGFP fluorescence was observed by 4 h, with a much stronger signal detected at 12 and 24 h after transfection. Delivery of NgAgo plasmid was also confirmed by PCR assays (Supplementary Fig. 2c). To confirm expression of NgAgo in transfected cells, the extent of RNA or protein expression was determined either by qualitative reverse transcription PCR (RT-PCR; Supplementary Fig. 1e), immunoblot analysis of hemagglutinin (HA)-tagged versions of NgAgo (Supplementary Figs. 1f and 3n), or flow cytometry of a DsRed-tagged version of NgAgo (Supplementary Fig. 1f). Taken together, these data suggest that plasmid DNA and gDNA are efficiently delivered to the human cell lines used here and that all the tested cells are capable of expressing NgAgo. On the basis of the above data, we conclude that in conditions designed to replicate those in Gao et al.3, co-delivery of plasmid DNA encoding NgAgo and a 5ʹ-phosphorylated single-strand gDNA alone is insufficient to induce gene editing at the indel frequencies in cultured human cells reported in the original study.

Stigmatizing the Unhealthy

Stigma can lead to poor health outcomes. At the same time, people who are perceived as unhealthy may experience stigma as the result of that perception. As part of a larger project examining discrimination on the basis of health status or “healthism,” we explore the role of stigma in producing disadvantage based on health status. Specifically, we look to the principles of health equality and health justice. An intervention violates health equality when it is driven by animus, which can be the result of stigma. Additionally, laws and policies offend health justice when they worsen health outcomes or they create or deepen health disparities. An intervention that produces stigma — whether intentionally or unintentionally — may offend health justice by making people worse off, in absolute or in comparative terms. Stigma-related health laws and policies can therefore be healthist in at least two ways. We therefore conclude that stigma should neither be the basis, nor the product, of efforts to improve health.

Protecting Privacy to Prevent Discrimination

Traditionally, laws that protect privacy and laws that prohibit discrimination have been considered distinct kinds of legal protections. This Essay challenges that binary on both practical and theoretical grounds. Using the Genetic Information Nondiscrimination Act (GINA) as a case study, it argues that lawmakers can use privacy law to further antidiscrimination goals. GINA, which prohibits geneticinformation discrimination in health insurance and employment, does more than simply outlaw discriminatory conduct. It also prohibits employers from requiring—or even requesting—their employees’ genetic information. While GINA’s privacy and antidiscrimination protections have previously been viewed as discrete, this Essay reads them in concert, arguing that restricting access to information about protected status can thwart future discrimination by denying potential discriminators the very information they would use to discriminate. Informed by this perspective, the Essay explores the advantages and disadvantages of using privacy law as a tool for antidiscrimination, in the context of genetic information and beyond. Finally, the Essay concludes that the weaknesses endemic to privacy law might be addressed by adopting an explicit antidiscrimination purpose. Thus, just as advocates of antidiscrimination may look to privacy law, advocates of privacy protections can look to antidiscrimination. * Assistant Professor of Law, University of Houston Law Center. Thank you to Aaron Bruhl, Brad Areheart, Jessica Clarke, Dave Fagundes, and Mark Rothstein for reading and commenting on drafts. I presented early versions of this Essay during the 3rd Annual National Conference on Genetics, Ethics, & the Law at the University of Virginia Law School and the 2013 ASU Legal Scholars Conference, so my gratitude likewise goes to the participants of those workshops. Many thanks to Chelsea Averill and Zachary White for research assistance and Emily Lawson for library help. ii PRIVACY 1 August 2013

Healthism and the Law of Employment Discrimination

Recently, several employers around the country announced they would no longer hire applicants who use nicotine, even off the clock. Just last year, one entity adopted a policy that it would not employ individuals classified as severely obese. Read together, nicotine and obesity bans can be understood as employer practices that intentionally screen out unhealthy individuals.Yet should these employer practices constitute legally actionable discrimination? That question is the central inquiry of this Article. It begins by identifying those recently adopted policies as discrimination on the basis of employee health. It then analyzes this novel brand of employment discrimination by comparing employer bans on nicotine and obesity to the employment actions forbidden by the current federal statutes that cover health-related information, mainly the Rehabilitation and Americans with Disabilities Acts, the Genetic Information Nondiscrimination Act, and the Health Insurance Portability and Accountability and Affordable Care Acts. The Article distinguishes between discrimination on the basis of health-related traits and discrimination on the basis of health-related conduct. Because the existing laws are uniformly trait-based, prohibiting employment policies related to nicotine use and weight requires a different kind of antidiscrimination statute. The Article then surveys existing state legislation that limits an employer’s ability to discriminate on the basis of unhealthy behavior. It ends by proposing that well-structured legislation could reconcile the concerns surrounding this contentious issue, simultaneously shielding the interests of employers while offering workers protection.

Negotiating Commercial Interests in Biospecimens

Proposed changes to the Common Rule would require publicly funded researchers to disclose whether a subjects biospecimens could be used for commercial profit and whether the subject will share in those proceeds. Disclosing commercial interests will inform research participants that their tissue may have commercial value, a possibility that those individuals might not have previously considered. The proposed changes may then provide people with an opportunity to negotiate commercial rights in their biospecimens despite the well-accepted legal precedent that individuals maintain no interests in their excised tissue.

The Legality of Biometric Screening of Professional Athletes

In their thoughtful article, “Tracking U.S. Professional Athletes: The Ethics of Biometric Technologies,” Katrina Karkazis and Jennifer Fishman do an excellent job of outlining the concerns associa...

What to Expect When [Your Employer Suspects] You’re Expecting

Employers are increasingly contracting with firms that use big data to predict and promote employee health. By aggregating claims data, browser searches, and demographic information, these firms can foresee whether employees are at risk for health issues—and their associated costs—and target preventive resources accordingly. However, a recent report1 indicates that this analytic capability is being extended to predict not only diabetes or the need for back surgery but also whether an employee is or is considering becoming pregnant. This trend raises troubling questions about the appropriate limits of wellness program analytics. When is an employer’s use of big data a legitimate prevention strategy, and when is it an impermissible intrusion into employee privacy? From the employer’s perspective, pregnancy shares obvious parallels to other conditions targeted by wellness programs. Pregnancy, like obesity or tobacco use, increases health care costs, which, in the American model of employer-sponsored health insurance, presents financial implications for employers. Similarly, pregnancy may affect workplace efficiency via productivity and absenteeism, including missed time for prenatal appointments and maternity leave. Earlier knowledge of employee pregnancy can therefore benefit employers, enabling them to more accurately project workforce capacity and health-related costs and to plan accordingly. It may also promote employee health, providing women with diverse resources, ranging from guidance on selecting an obstetrician to discounts on prenatal vitamins. Nevertheless, several features of pregnancy make it a problematic subject for predictive analytics. First, reproductive decisions are among the most intimate choices a person makes in her lifetime. Inquiries into these decisions—explicitly or via analytics—intrude into a space many would reasonably prefer to keep private. Second, unlike obesity or diabetes, pregnancy is not an illness but a normal—and often socially encouraged— condition. Third, only women can become pregnant. Although this last point is obvious, it is also critical. Because pregnancy is exclusive to women, efforts to predict employee pregnancy risk exacerbating longstanding patterns of employment-based discrimination. Moreover, an intended pregnancy is not subject to legal protections afforded to other conditions, such as by the Genetic Information Nondiscrimination Act or the Americans with Disabilities Act, which protect against discrimination on the basis of genetic test results, family medical history, or manifested diseases, such as multiple sclerosis and diabetes. Consequently, new strategies are needed to manage the risks of data-driven prediction of employee pregnancy. The Pregnancy Discrimination Act (PDA) of 1978 made outright discrimination on the basis of pregnancy illegal. To make a successful claim of discrimination under the PDA, a woman must demonstrate the following: (1) she is or was pregnant, (2) she was qualified for her job, (3) she was subjected to an adverse employment decision, and (4) there is a nexus between her pregnancy and the challenged decision. The employer, in turn, must provide a legitimate, nondiscriminatory reason for the decision; thereafter, the burden of proof is on the employee to prove that the employer’s articulated reason is a pretext for intentional discrimination.2 Although the PDA has reduced explicit employment discrimination, pregnancy-based discrimination persists. A 2013 survey3 of American women who had recently given birth found that more than a quarter who had resumed work reported experiencing bias because of perceptions of their “desire, ability, or commitment” to their jobs. The same year, government agencies received 5370 allegations of pregnancy-based workplace discrimination; the most common complaint was that employers fired them because they became pregnant.4 Data from the Equal Employment Opportunity Commission and state and local Fair Employment Practices Agencies offer further evidence of systematic employment discrimination based on pregnancy. In 2012, an Equal Employment Opportunity Commission investigation into Bayou City, a restaurant chain in Houston, Texas, determined that the chain fired at least 8 women based on a written policy that required firing pregnant women after their first trimester.5 Ironically, even companies that cater to pregnant women are not immune. In 2007, Motherhood Maternity, a clothing retailer, paid

‘Good soldiers are made, not born’⊥: the dangers of medicalizing ability in the military use of genetics

375 000 to settle a discrimination suit after they allegedly refused to hire 3 qualified applicants because they were pregnant.5 Data-driven pregnancy predictions threaten to reinforce this pattern of discrimination in at least 2 ways. First, although employers receive only aggregate-level data on employee health from analytic firms, this information may nevertheless enable employers to identify individual employees. Such identification extends the potential period for discrimination, signaling employers to an employee’s pregnancy weeks or even months before she would otherwise disclose it. Although this risk of identifiability exists for other conditions targeted by wellness programs, it is heightened for pregnancy given that sex and age substantially narrow the potentially affected group. Second, it may extend the risk of discrimination beyond those who are or wish to become pregnant. Substantial empirical evidence indicates that individuals hold VIEWPOINT

Rethinking Employment Discrimination Harms

Advances in genetic and genomic science are of particular interest to the United States military. Responding to Maxwell J. Mehlmans and Tracy Yeheng Lis article Ethical, Legal, Social, and Policy Issues in the Use of Genomic Technology by the U.S. Military, this Commentary explores the social consequences of medicalizing what it means to be a good soldier. It begins by reviewing the well-documented consequences of medicalization in the contexts of the eugenics movement and modern genetic and genomic science. It then applies that analysis to the military use of genetics and genomics, focusing on the ways in which genetic or genomic accounts of military ability could entrench existing gender and racial disparities.