Seminal research at TDI has transformed our nation’s understanding of the causes and consequences of health care disparities. Building on Jack Wennberg’s original small area variation research and motivated by the Institute of Medicine’s 2003 report “Unequal Treatment,” Dartmouth researchers established that disparities in medical care drive much of this unequal treatment. This finding called attention to the fact that historical residential segregation by race in the United States has led to Black and White individuals accessing different health care delivery systems, with health systems serving Black patients tending to be lower quality.
As our nation reckons with its legacy of systemic racism and the associated maldistribution of wealth and additional social determinants of health, TDI made a concerted effort to shift our research focus from documenting health disparities to ameliorating health inequity. Since 2021, we have recruited new faculty dedicated to health equity research and education, instituted a series of programs to strengthen career development pathways for scientists interested in health equity research, established a health equity seminar series, and begun to reimagine the Dartmouth Atlas of Health Care as a Dartmouth Health Equity Atlas.
Health Equity and Advocacy Lab
The Health Equity and Advocacy Lab (HEAL) is a collaborative network of researchers, students, and community members committed to addressing inequities and their consequences for health. The founding faculty members and their labs span a broad array of equity-focused research, ranging from maternal health equity (Dr. Alka Dev), housing precarity and health (Dr. Terri Lewinson), reinforcement of bias through big data algorithms (Dr. Wesley Marrero), and health literacy and public risk communication in cancer prevention and screening (Dr. Christine Gunn). Many additional TDI faculty with an historical or emerging focus on health equity in their research are part of the HEAL collaborative, including Amber Barnato, Paul Barr, Nancy Birkmeyer, Inas Khayal, Matthew Mackwood, James O’Malley, and Karen Schifferdecker. For more information about faculty labs, see the directory of TDI community members.
Health Equity Research Pathways Programs
TDI offers a series of fellowships for health equity researchers at different stages of their careers. Faculty, pre-doctoral students, and Dartmouth undergraduates participate in mentored research and peer-supported learning activities to increase their engagement with and understanding of health equity research. The goal of the Pathways Programs is to expand the pipeline of researchers in the field.
W.E.B. DuBois Lecture
Established in 2022, TDI’s annual W.E.B. DuBois Lecture features prominent thought leaders and scholars in the field of health equity. The inaugural program featured a panel discussion with Typhanye Dyer, PhD, MPH (University of Maryland School of Public Health), and Andrew Anderson, PhD (Johns Hopkins Bloomberg School of Public Health). In 2023, we hosted Lee Pelton, PhD, President and CEO of the Boston Foundation. In 2024, we were pleased to host a lecture by Abre’ Conner, JD, Director of Environmental and Climate Justice at the NAACP and an assistant professor at the University of California-Davis.
Health Equity Resources
All TDI faculty members have been provided training in data equity. In the bibliography below, we share a list of useful papers for health services researchers interested in learning more about racial health equity research.
Adkins-Jackson, P. B., Legha, R. K., & Jones, K. A. (2021). How to measure racism in academic health centers. AMA Journal of Ethics, 23(2), 140-145.
Adkins-Jackson, P. B., Chantarat, T., Bailey, Z. D., & Ponce, N. A. (2022). Measuring structural racism: a guide for epidemiologists and other health researchers. American Journal of Epidemiology, 191(4), 539-547.
Allen, J., Mohatt, G. V., Fok, C. C. T., Henry, D., Burkett, R., & People Awakening Project. (2014). A protective factors model for alcohol abuse and suicide prevention among Alaska Native youth. /American Journal of Community Psychology, 54/, 125–139.
Aikenhead, G. S., & Ogawa, M. (2007). Indigenous knowledge and science revisited. /Cultural Studies of Science Education, 2/, 539–620.
Alvarez, C. H., & Evans, C. R. (2021). Intersectional environmental justice and population health inequalities: A novel approach. Social Science & Medicine, 269, 113559.
Bacchetti, P. (2010). Current sample size conventions: flaws, harms, and alternatives. BMC medicine, 8(1), 1-7.
Bailey, Z. D., Feldman, J. M., & Bassett, M. T. (2021). How structural racism works—racist policies as a root cause of US racial health inequities. New England Journal of Medicine, 384(8), 768-773.
Bauer, G. R., Churchill, S. M., Mahendran, M., Walwyn, C., Lizotte, D., & Villa-Rueda, A. A. (2021). Intersectionality in quantitative research: A systematic review of its emergence and applications of theory and methods. SSM-population health, 14, 100798.
Benegal, S., & Motta, M. (2022). Overconfident, resentful, and misinformed: How racial animus motivates confidence in false beliefs. Social Science Quarterly.
Bensimon, Estela Mara and Yolanda Watson Spiva. “The End of “Equity Gaps” in Higher Education?” Diverse Issues in Higher Education (2022)
Briggs, A. H. (2022). Healing the past, reimagining the present, investing in the future: What should be the role of race as a proxy covariate in health economics informed health care policy?. Health Economics, 31(10), 2115-2119.
Curran, P. J., & Hussong, A. M. (2009). Integrative data analysis: the simultaneous analysis of multiple data sets. Psychological methods, 14(2), 81.
Dean, L. T., & Thorpe Jr, R. J. (2022). What structural racism is (or is not) and how to measure it: clarity for public health and medical researchers. American journal of epidemiology, 191(9), 1521-1526.
Dwivedi, Alok Kumar, Indika Mallawaarachchi, and Luis A. Alvarado. "Analysis of small sample size studies using nonparametric bootstrap test with pooled resampling method." Statistics in medicine 36, no. 14 (2017): 2187-2205.
Fok CC, Henry D, Allen J. Maybe Small Is Too Small a Term: Introduction to Advancing Small Sample Prevention Science. Prev Sci. 2015 Oct;16(7):943-9.
Frohlich, K. L., & Potvin, L. (2008). Transcending the known in public health practice: The inequality paradox: The population approach and vulnerable populations. /American Journal of Public Health, 98/, 216–221.
Gollust, Sarah E., et al. "Americans' perceptions of health disparities over the first year of the COVID-19 pandemic: Results from three nationally-representative surveys." Preventive medicine 162 (2022): 107135.
Gone, J. P. (2012). Indigenous traditional knowledge and substance abuse treatment outcomes: The problem of efficacy evaluation. /American Journal of Drug and Alcohol Abuse, 38/, 493–497.
Hawe, P. (1994). Capturing the meaning of “community” in community intervention evaluation: Some contributions from community psychology. /Health Promotion International, 9/, 199–210.
He, Jingjing, Wei Wang, Min Huang, Shaohua Wang, and Xuefei Guan. "Bayesian inference under small sample sizes using general noninformative priors." Mathematics 9, no. 21 (2021): 2810.
Henry, D., Fok, C.C.T., Allen, J. (2015). Why small is too small a term: Prevention science for health disparities, culturally distinct groups, and community-level intervention. /Prevention Science.
Hoch, J. S., Trenaman, L., Hearney, S. M., & Dewa, C. S. (2021). How Economic Decision Modeling Can Facilitate Health Equity. AMA Journal of Ethics, 23(8), 624-630.
Hofman, J. M., Goldstein, D. G., & Hullman, J. (2020, April). How visualizing inferential uncertainty can mislead readers about treatment effects in scientific results. In Proceedings of the 2020 chi conference on human factors in computing systems (pp. 1-12).
Holder, Eli, and Cindy Xiong. "Dispersion vs Disparity: Hiding Variability Can Encourage Stereotyping When Visualizing Social Outcomes." IEEE Transactions on Visualization and Computer Graphics (2022).
Hofman, Jake M., Daniel G. Goldstein, and Jessica Hullman. "How visualizing inferential uncertainty can mislead readers about treatment effects in scientific results." In Proceedings of the 2020 chi conference on human factors in computing systems, pp. 1-12. 2020.
Hullman, J., & Diakopoulos, N. (2011). Visualization rhetoric: Framing effects in narrative visualization. IEEE transactions on visualization and computer graphics, 17(12), 2231-2240.
Keller, L., Lüdtke, O., Preckel, F., & Brunner, M. (2023). Educational Inequalities at the Intersection of Multiple Social Categories: A n Introduction and Systematic Review of the Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) Approach. Educational Psychology Review, 35(1), 31.
Manski, C. F., Mullahy, J., & Venkataramani, A. (2022). Using Measures of Race to Make Clinical Predictions: Decision Making, Patient Health, and Fairness (No. w30700). National Bureau of Economic Research.
McNeish, D. (2016). On using Bayesian methods to address small sample problems. Structural Equation Modeling: A Multidisciplinary Journal, 23(5), 750-773.
Miller, C. A., Wilkins, C. L., de Paula Couto, C., Farias, J., & Lisnek, J. A. (2023). Anti-Black attitudes predict decreased concern about COVID-19 among Whites in the US and Brazil. Social Science & Medicine, 320, 115712.
Nash, K., Trott, V., & Allen, W. (2022). The politics of data visualisation and policy making. Convergence, 28(1), 3–12. https://doi.org/10.1177/13548565221079156
Padilla, L., Kay, M., & Hullman, J. (2020, April 27). Uncertainty Visualization. https://doi.org/10.31234/osf.io/ebd6r
Pett, Marjorie A. Nonparametric statistics for health care research: Statistics for small samples and unusual distributions. Sage Publications, 2015.
Rubel, Laurie H., Maren Hall-Wieckert, and Vivian Y. Lim. "Making space for place: Mapping tools and practices to teach for spatial justice." Journal of the Learning Sciences 26, no. 4 (2017): 643-687.
Sen, Maya, and Omar Wasow. "Race as a bundle of sticks: Designs that estimate effects of seemingly immutable characteristics." Annual Review of Political Science 19 (2016): 499-522.
Skinner-Dorkenoo, A. L., Sarmal, A., Rogbeer, K. G., André, C. J., Patel, B., & Cha, L. (2022). Highlighting COVID-19 racial disparities can reduce support for safety precautions among White US residents. Social Science & Medicine, 301, 114951.
Srinivasan, S., Moser, R. P., Willis, G., Riley, W., Alexander, M., Berrigan, D., & Kobrin, S. (2015). Small is essential: Importance of subpopulation research in cancer control. /American Journal of Public Health, 105/, 371–373.
Stephens-Dougan, L. (2022). White Americans’ reactions to racial disparities in COVID-19. American Political Science Review, 1-8.
Vyas, Darshali A., Leo G. Eisenstein, and David S. Jones. "Hidden in plain sight—reconsidering the use of race correction in clinical algorithms." New England Journal of Medicine 383.9 (2020): 874-882.
Wihbey, John P., Sarah J. Jackson, Pedro M. Cruz, and Brooke Foucault Welles. "22. Visualizing diversity: Data deficiencies and semiotic strategies." Data Visualization in Society (2020): 369.
The Dartmouth Center for Advancement of Learning offers online resources and training programs to improve inclusive teaching.