Community-Led research, quantitative research, and MAIHDA, with Clare Evans, ScD

Описание: Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) or I-MAIHDA (where the I stands for intersectional).  If you’re passionate about quantitative intersectionality research, you likely already know about MAIHDA.  MAIHDA is an innovative quantitative approach for examining intersectional health, socioeconomic, psychosocial, disease inequalities and inequities.

Clare Evans, ScD, our guest for the February 2026 salon, invented MAIHDA for her 2015 Harvard T. Chan School of Public Health dissertation research proposal.  Yep, you read that correctly: she invented it! MAIHDA is now the “gold standard” for intersectional research in social epidemiology and is rapidly growing with the other social sciences.

Since then, Dr. Evans has written prolifically about the benefits and limitations of MAIHDA.  Her 2019 Social Science and Medicine article, “Adding Interactions to Models of Intersectional Health Inequalities: Comparing Multilevel and Conventional Methods” has more than 100 Google Scholar citations.  Her 2024, co-authored MAIHDA tutorial, published in SSM-Population Health is mandatory reading for anyone interested in this method.  

Joining Lisa as co-hosts of this special salon are Ariel Beccia, PhD and Dougie Zubizaretta, MS who also hail from the Harvard T. Chan School of Public Health, are ISI 2022 cohort members, and are intersectionality researchers who’ve published several peer-reviewed articles on MAIHDA.

Intersectionality Research Salons: https://www.intersectionalitytraining...

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The original paper - before MAIHDA was called MAIHDA (2018): https://www.sciencedirect.com/science...

Chat log from the Salon:

Have there been evaluations assessing how well predicted values align with observed (actual) values? In other words, how has the accuracy of the model’s predictions been formally assessed? 

See: https://academic.oup.com/eurpub/artic... great piece that speaks to the second point: https://doi.org/10.1016/j.socscimed.2...

The gap between. Singleton and twins as it arose from Dr. Evan’s lived experience: Evans, C. R., Nieves, C. I., Erickson, N., & Borrell, L. N. (2023). Intersectional inequities in the birthweight gap between twin and singleton births: A random effects MAIHDA analysis of 2012–2018 New York City birth data. Social Science & Medicine, 331, 116063. https://doi.org/https://doi.org/10.10...

Would be still be possible to apply MAIHDA when you are limited to neighborhood-level measures? 

This might be relevant (and great question) - https://pubmed.ncbi.nlm.nih.gov/41625...

For cross-national research with lots of countries, what is your opinion on the best way to treat country effects- would country be an additional level and what is the interpretation, or what other options are there? 

An approach that Dougie and Ariel recently used in a study that was looking at participants nested in different schools was to treat this as a cross-classified random intercepts structure (and I could imagine this being used to address cross-nesting in countries, although there are certainly other approaches): https://pubmed.ncbi.nlm.nih.gov/41016...

a really good paper (qualitative methodology) regarding the specific perceptions and definitions of identities across contexts that also addresses the criticism that intersectionality implies a transhistoric and universalist view of identity: https://www.researchgate.net/publicat...

Excellent book re racism and methods: Bonilla-Silva, E., & Zuberi, T. (Eds.). (2008). White logic, white methods: Racism and methodology. Rowman and Littlefield.

This paper has a great discussion of how / why sampling weights might not be appropriate in the context of estimating inequities with trans populations: https://pubmed.ncbi.nlm.nih.gov/34757...

Fine, M., Weis, L., Weseen, S., & Wong, L. (2000). For whom? Qualitative research, representations, and social responsibilities. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (2nd ed., pp. 107–131). Sage.