Abstract
This critical commentary examines the challenges of leveraging data and technology to confront health care inequities, given growing public attention to health disparities amidst the rapid expansion of large-scale data sources. Grasso et al. (2019) recently published the first year of HRSA Uniform Data System reporting on patient sexual orientation and gender identity (SOGI), recounting three-quarters and two-thirds missing data respectively across all participating US health centers. Given broad investment in data-driven strategies with the rise of Electronic Health Records, scholars and advocates explain the observed missing data as an issue of poor implementation within sites of care. Such an explanation, however, deflects attention away from the upstream conditions that powerfully shape gender and sexual minority health inequities within biomedicine itself, including the content and breadth of clinical training, the location and scope of sites of care, and the diversity of the health professions workforce. Given these existing forms of biomedical stratification, data-driven strategies that prioritize counting patients over redressing structural conditions are unlikely to result in substantive social change. To advance health equity, we must do more than merely collect data: we must shift our analytic to consider the broader context in which health inequalities materialize.
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06 May 2021
A Correction to this paper has been published: https://doi.org/10.1007/s13178-021-00552-3
References
Bakhtiari, E., Olafsdottir, S., & Beckfield, J. (2018). Institutions, incorporation, and inequality: the case of minority health inequalities in Europe. Journal of Health and Social Behavior, 59(2), 248–267.
Braveman, P. (2014). What are health disparities and health equity? We need to be clear. Public Health Records, 129(Suppl 2), 5–8.
Braveman, P., Egerter, S., & Williams, D. R. (2011). The social determinants of health: coming of age. Annual Review of Public Health, 32, 381–398.
Cahill, S., & Makadon, H. (2013). Sexual Orientation and Gender Identity Data Collection in Clinical Settings and in Electronic Health Records: A Key to Ending LGBT Health Disparities. LGBT Health, 1(1), 1–8.
Carabez, R., Pellegrini, M., Mankovitz, A., Eliason, M., Ciano, M., & Scott, M. (2015). “Never in All My Years...”: nurses’ education about LGBT health. Journal of Professional Nursing, 31(4), 323–329.
Clarke, A. E., Shim, J. K., Mamo, L., Fosket, J. R., Fishman, J. R. (2003). Biomedicalization: Technoscientific Transformations of Health, Illness, and U.S. Biomedicine. Am Sociol Rev. 68(2), 161-194.
Cruz, T. M. (2020). Perils of Data-Driven Equity: Safety-Net Care and Big Data’s Elusive Grasp on Health Inequality. Big Data & Society, Jan-Jun:1–14.
Donald, C. A., DasGupta, S., Metzl, J. M., & Eckstrand, K. L. (2017). Queer frontiers in medicine: a structural competency approach. Academic Medicine, 92(3), 345–350.
Douglas, M. D., Dawes, D. E., Holden, K. B., & Mack, D. (2015). Missed policy opportunities to advance health equity by recording demographic data in electronic health records. American Journal of Public Health, 105(Suppl3), S380–S388.
Dubbin, L. A., Chang, J. S., & Shim, J. K. (2013). Cultural health capital and the interactional dynamics of patient-centered care. Social Science and Medicine, 93, 113–120.
Fenway Institute. (2015a). Collecting Sexual Orientation and Gender Identity Data in Electronic Health Records: Taking the Next Steps. https://www.lgbthealtheducation.org/wp-content/uploads/Collecting-Sexual-Orientation-and-Gender-Identity-Data-in-EHRs-2016.pdf. Accessed 6 May 2020.
Fenway Institute. (2015b). Landmark Decision by Department of Health and Human Services Will Reduce Health Disparities Experienced by LGBT People. http://fenwayfocus.org/2015/10/landmark-decision-by-dept-of-health-and-human-services-will-reduce-health-disparities-experienced-by-lgbt-people/. Accessed 6 May 2020.
Grasso, C., Goldhammer, H., Funk, D., et al. (2019). Required sexual orientation and gender identity reporting by US Health Centers: first-year data. American Journal of Public Health, 109(8), 1111–1118.
Health Resources and Services Administration. (2020). Program Assistance Letter 2016–02: Approved Uniform Data System Changes for Calendar Year 2016. https://bphc.hrsa.gov/sites/default/files/bphc/datareporting/reporting/program-assistance-letter-2016-02.pdf. Accessed 18 May 2020.
Institute of Medicine. (2014). Capturing Social and Behavioral Domains and Measures in Electronic Health Records: Phase 2. Washington, D.C.: National Academies Press. https://www.nap.edu/catalog/18951/capturing-social-and-behavioral-domains-and-measures-in-electronic-health-records.
Institute of Medicine. (2013). Collecting Sexual Orientation and Gender Identity in Electronic Health Records: Workshop Summary. Washington, D.C.: National Academies Press. https://www.nap.edu/catalog/18260/collecting-sexual-orientation-and-gender-identity-data-in-electronic-health-records.
Link, B. G., & Phelan, J. (1995). Social conditions as fundamental causes of disease. Journal of Health and Social Behavior, 35, 80–94.
Link, B. G., & Phelan, J. C. (2010). Social conditions as fundamental causes of health inequalities: theory, evidence, and policy implications. Journal of Health and Social Behavior, 51(1 Suppl), S28-40.
Mansh, M., Garcia, G., & Lunn, M. R. (2015a). From patients to providers: changing the culture in medicine toward sexual and gender minorities. Academic Medicine, 90(5), 574–580.
Mansh, M., White, W., Gee-Tong, L., et al. (2015b). Sexual and gender minority identity disclosure during undergraduate medical education: “in the closet” in medical school. Academic Medicine, 90(5), 1–11.
McKinlay, J. B. (1975). A Case for Refocusing Upstream: The Political Economy of Illness. In: Applying Behavioral Science to Cardiovascular Risk. Dallas, TX: American Heart Association; 9–25.
McKinlay, J. B., & Marceau, L. D. (1999). A Tale of 3 Tails. American Journal of Public Health, 89(3), 295–298.
Obedin-Maliver, J., Goldsmith, E. S., Stewart, L., et al. (2011). Lesbian, gay, bisexual, and transgender-related content in undergraduate medical education. Journal of the American Medical Association, 306(9), 971–977.
Onukwugha, E., Duru, O. K., & Peprah, E. (2017). Big data and its application in health disparities research. Ethnicity and Disease, 27(2), 69–72.
Shim, J. K. (2010). Cultural health capital: a theoretical approach to understanding health care interactions and the dynamics of unequal treatment. Journal of Health and Social Behavior, 51(1), 1–15.
Spencer, K. L., & Grace, M. (2016). Social foundations of health care inequality and treatment bias. Annual Review of Sociology, 42, 101–120.
Wasserman, J., Palmer, R. C., Gomez, M. M., Berzon, R., Ibrahim, S. A., & Ayanian, J. Z. (2019). Advancing health services research to eliminate health care disparities. American Journal of Public Health, 109(Suppl1), S64–S69.
Acknowledgements
The author would like to thank Edwin Lopez, Edward Ramirez, Sheridan Smith, and Ariana Thompson-Lastad for providing helpful feedback on an early manuscript draft. Previous versions of this work were presented at the Gay and Lesbian Medical Association 2019, American Sociological Association 2020, and the American Public Health Association 2020.
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The original version of this article contained a mistake in the abstract section.
The second sentence in the Abstract section should be changed to Grasso et al. (2019) recently published the first year of HRSA Uniform Data System reporting on patient sexual orientation and gender identity (SOGI), recounting three-quarters and two-thirds missing data respectively across all participating US health centers.
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Cruz, T.M. Shifting Analytics within US Biomedicine: From Patient Data to the Institutional Conditions of Health Care Inequalities. Sex Res Soc Policy 19, 287–293 (2022). https://doi.org/10.1007/s13178-021-00541-6
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DOI: https://doi.org/10.1007/s13178-021-00541-6