Genomics and Health Care Disparities: The Role of Statistical Discrimination

Genomics and Health Care Disparities: The Role of Statistical Discrimination

The Journal of the American Medical Association
Volume 308, Number 19 (2012-11-21)
pages 1979-1980
DOI: 10.1001/2012.jama.10820

Katrina Armstrong, MD, MSCE, Professor of Medicine
University of Pennsylvania School of Medicine

Ten years ago, 2 events occurred that have transformed biomedical research. In 2001, the draft sequence of the human genome was announced. One year later, the Institute of Medicine released “Unequal Treatment,” the first comprehensive report on racial and ethnic health care disparities in the United States.1 Although the report downplayed the contribution of genetics to disparities, enthusiasm about the human genome spread rapidly to disparities research, creating a new field focused on translating knowledge of human genetic variation into reductions in disparities in health and health care. This Viewpoint examines the potential contribution of 2 pathways in this field—the identification of genetic variation as a cause of disparities and the reduction of clinical uncertainty and statistical discrimination. The terms race and ethnicity are used to mean socially determined, generally self-reported, categories.

A common hypothesis is that advances in human genomics will reduce disparities by identifying genetic causes of disparities. In support of this hypothesis, racial and ethnic differences in genetic variant frequency have been demonstrated for many diseases. However, translating this evidence into reductions in disparities has proven challenging for several reasons. First, many variants identified have a small attributable risk and explain little of the disease burden in any group, either because of a weak association between variant and disease or because the variant is rare in the population. Second, far more genetic variation occurs within racial or ethnic groups than between groups,and disease-associated variation has no apparent predilection for the 4% to 8% of variation that can be linked to race or ethnicity. Thus, if genomic variation explains a minority of most diseases and is unlikely to be linked to a racial or ethnic group, it becomes unlikely that genomic variation between groups will be a substantial cause of disparities in most common diseases. Third, developing interventions based on this information is challenging. Although prenatal or even premarital genetic screening can reduce the burden of severe diseases if screening influences reproductive decision making, lack of acceptance of these approaches has limited their effectiveness. For other diseases, knowledge of genetic risk factors should increase the ability to target preventive interventions to high-risk individuals. However, the limited effect of genomics on risk prediction for many diseases combined with the relative paucity of effective preventive interventions for some diseases has limited the benefit of such an approach.

Another pathway by which genomics may reduce racial disparities that has received considerably less attention is its effect on clinical uncertainty and statistical discrimination. The need to make decisions under conditions of uncertainty is one of the hallmarks of medicine. This uncertainty arises on 2 levels. For many decisions, there is no credible and consistent evidence about risks and benefits of different interventions. Moreover, even when evidence exists, uncertainty arises about the effect of that evidence on the individual patient. The gap between the average effect in a population and the effect in a specific patient can be substantial, in part because of differences between patients in practice and trial participants and in part because the average effect in a trial masks substantial variation among trial participants…

Read or purchase the article here.

Tags: , ,