Article

Looking to the future: incorporating genomic information into disparities research to reduce measurement error and selection bias.

Harvard/MGH Center for Genomics, Vulnerable Populations and Health Disparities, and Mongan Institute for Health Policy, Massachusetts General Hospital, Boston, MA, USA.
Health Services Research (Impact Factor: 2.49). 04/2012; 47(3 Pt 2):1387-410. DOI: 10.1111/j.1475-6773.2012.01413.x
Source: PubMed

ABSTRACT To extend recent conceptual and methodological advances in disparities research to include the incorporation of genomic information in analyses of racial/ethnic disparities in health care and health outcomes.
Published literature on human genetic variation, the role of genetics in disease and response to treatment, and methodological developments in disparities research.
We present a conceptual framework for incorporating genomic information into the Institute of Medicine definition of racial/ethnic disparities in health care, identify key concepts used in disparities research that can be informed by genomics research, and illustrate the incorporation of genomic information into current methods using the example of HER-2 mutations guiding care for breast cancer.
Genomic information has not yet been incorporated into disparities research, though it has direct relevance to concepts of race/ethnicity, health status, appropriate care, and socioeconomic status. The HER-2 example demonstrates how available genetic information can be incorporated into current disparities methods to reduce selection bias and measurement error. Advances in health information infrastructure may soon make standardized genetic information more available to health services researchers.
Genomic information can refine measurement of racial/ethnic disparities in health care and health outcomes and should be included wherever possible in disparities research.

0 Bookmarks
 · 
76 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Unprecedented change in the US health care system is being driven by the rapid uptake of health information technology and national investments in multi-institution research networks comprising academic centers, health care delivery systems, and other health system components. An example of this changing landscape is Optum Labs, a novel network "node" that is bringing together new partners, data, and analytic techniques to implement research findings in health care practice. Optum Labs was founded in early 2013 by Mayo Clinic and Optum, a commercial data, infrastructure services, and care organization that is part of UnitedHealth Group. Optum Labs now has eleven collaborators and a database of deidentified information on more than 150 million people that is compliant with the Health Insurance Portability and Accountability Act (HIPAA) of 1996. This article describes the early progress of Optum Labs. The combination of the diverse collaborator perspectives with rich data, including deep patient and provider information, is intended to reveal new insights about diseases, treatments, and patients' behavior to guide changes in practice. Practitioners' involvement in agenda setting and translation of findings into practical care innovations accelerates the implementation of research results. Furthermore, feedback loops from the clinic help Optum Labs expand on successes and give quick attention to challenges as they emerge.
    Health Affairs 07/2014; 33(7):1187-94. · 4.32 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Biobanking research seeks to improve the diversity, availability, and quality of human specimens critical for translational research, including biospecimen collections from disadvantaged minorities. American rural whites are seldom represented in such initiatives as geographic isolation makes obtaining informed consent challenging. We report a case series of 83 newly diagnosed cancer patients, attending a rural community medical center, who consented to participate in cancer research. To enable pooling with population studies, we created a BioGeoBank using 2007 NCI and ISBER Best Practices, after a protocol approval by Eastern Maine Medical Center (EMMC) IRB and OHP HRPO. Informed consent forms were at Flesch-Kincaid 8th Grade reading level, supplemented by NCI educational brochures. Of 108 patients identified, 85 were eligible. Of these, 83 patients (49 lung cancer, 21 breast cancer, and 13 other cancers) consented to donate data, blood, and tissue specimens for future research, and maintained eligibility. Two years later, we executed a legacy protocol to transfer specimens to NCI's biorepository. Of the 69 surviving patients, 9 patients could not be contacted. All those contacted (60) agreed to provide additional data on environmental risks, and consented to specimen transfer. Self-organizing map analyses showed no evidence that age, education, income, familial susceptibility, or lifestyle factors were associated with consent to donate data or biospecimens. Cancer cases reported 1-3 co-morbid chronic diseases (mostly cardiovascular), near lifetime smoking and/or alcohol consumption; familial cancer risks, and many had a prior cancer history. Anecdotally, willingness to consent was based on altruistic hopes that research would generate knowledge to reduce cancer incidence. Our study shows that cancer patients from disadvantaged white rural communities with health disparities associated with geographic isolation are motivated to consent to participate and support biobank research.
    Biopreservation and Biobanking 04/2013; 11(2):107-14. · 1.50 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: To illustrate the complex patterns that emerge when race/ethnicity, socioeconomic status (SES), and gender are considered simultaneously in health care disparities research and to outline the needed research to understand them by using disparities in lung cancer risks, treatment, and outcomes as an example. SES, gender, and race/ethnicity are social categories that are robust predictors of variations in health and health services utilization. These are usually considered separately, but intersectionality theory indicates that the impact of each depends on the others. Each reflects historically and culturally contingent variations in social, economic, and political status. Distinct patterns of risk and resilience emerge at the intersections of multiple social categories and shape the experience of health, health care access, utilization, quality, and outcomes where these categories intersect. Intersectional approaches call for greater attention to understand social processes at multiple levels of society and require the collection of relevant data and utilization of appropriate analytic approaches to understand how multiple risk factors and resources combine to affect the distribution of disease and its management. Understanding how race/ethnicity, gender, and SES are interactive, interdependent, and social identities can provide new knowledge to enhance our efforts to effectively address health disparities.
    Health Services Research 06/2012; 47(3 Pt 2):1255-77. · 2.49 Impact Factor

Full-text (2 Sources)

Download
7 Downloads
Available from
Jun 16, 2014