A crash course on modeling infection inequities

talk
inequity
COVID-19
Author

Jon Zelner

Published

May 22, 2023

This talk is an effort to distill some of the challenges associated with making infectious disease models more useful for accounting for infection inequity. The goal is to briefly highlight why I think we didn’t quite get it right during the COVID-19 pandemic, discuss important progress towards these goals in the wake of the pandemic, and highlight remaining challenges.

Slides

A brief (and incomplete!) annotated bibliography of resources on modeling acute infection inequities

Structural Racism and Infection

  • (Adkins-Jackson et al. 2021) provides a very useful guide for thinking about how epidemiological models and modelers should account for the impacts of racism on disease outcomes. This piece has specific recommendations for analysts around model construction and accounting for multiple, intersecting identities.

  • (Noppert, Hegde, and Kubale 2022) articulate a framework based on fundamental cause theory for accounting for and addressing inequities in infection risk. (Zelner, Naraharisetti, and Zelner 2023) is an invited commentary that builds on some of the ideas in the original piece.

  • (Chowkwanyun and Reed 2020) is an excellent piece from early in the COVID-19 pandemic which highlights many of the challenges and pitfalls associated with a focus on racial health disparities without adequate attention to their material causes.

Modeling for Equity

  • (Zelner et al. 2022) and (Abuelezam et al. 2023) attempt to articulate frameworks for doing a better job of using epidemic models to capture the dynamics of inequity in infection and disease outcomes.

  • (Ma et al. 2021) is an excellent data-driven exercise in modeling the linkages between race/ethnicity, essential work and inequity in disease outcomes.

  • (Nande et al. 2021) is a data-informed simulation model of the potential impact of ending the CDC eviction moratorium on population-wide infection risks and group-specific disparities.

  • This is a hands-on example we put together that translates the framework in (Acevedo-Garcia 2000) into a simple dynamic model of the relationship between residential segregation and infection.

Methodological challenges

  • (Zelner et al. 2020) is an analysis examining the relative impacts of case-fatality vs. incidence of infection in inequities in COVID-19 mortality in Michigan.

  • (Trangucci, Chen, and Zelner 2022) presents a computationally efficient Bayesian method for accounting for non-random missingness of race-ethnicity data without making restrictive assumptions and/or using surname coding.

  • This is a hands-on comparison of different metrics of spatial segregation applied to the same simulated dataset.

(If you’d like a bibtex file w/these resources, you can find it here)

References

Abuelezam, Nadia N., Isaacson Michel, Brandon DL Marshall, and Sandro Galea. 2023. “Accounting for Historical Injustices in Mathematical Models of Infectious Disease Transmission: An Analytic Overview.” Epidemics, March, 100679. https://doi.org/10.1016/j.epidem.2023.100679.
Acevedo-Garcia, Dolores. 2000. “Residential Segregation and the Epidemiology of Infectious Diseases.” Social Science & Medicine 51 (8): 1143–61. https://doi.org/10.1016/S0277-9536(00)00016-2.
Adkins-Jackson, Paris B, Tongtan Chantarat, Zinzi D Bailey, and Ninez A Ponce. 2021. “Measuring Structural Racism: A Guide for Epidemiologists and Other Health Researchers.” American Journal of Epidemiology, September, kwab239. https://doi.org/10.1093/aje/kwab239.
Chowkwanyun, Merlin, and Adolph L. Reed. 2020. “Racial Health Disparities and Covid-19Caution and Context.” New England Journal of Medicine 383 (3): 201–3. https://doi.org/10.1056/NEJMp2012910.
Ma, Kevin C, Tigist F Menkir, Stephen M Kissler, Yonatan H Grad, and Marc Lipsitch. 2021. “Modeling the Impact of Racial and Ethnic Disparities on COVID-19 Epidemic Dynamics.” Edited by Joshua T Schiffer. eLife 10 (May): e66601. https://doi.org/10.7554/eLife.66601.
Nande, Anjalika, Justin Sheen, Emma L. Walters, Brennan Klein, Matteo Chinazzi, Andrei H. Gheorghe, Ben Adlam, et al. 2021. “The Effect of Eviction Moratoria on the Transmission of SARS-CoV-2.” Nature Communications 12 (1, 1): 2274. https://doi.org/10.1038/s41467-021-22521-5.
Noppert, Grace A, Sonia T Hegde, and John T Kubale. 2022. “Exposure, Susceptibility, and Recovery: A Framework for Examining the Intersection of the Social and Physical Environment and Infectious Disease Risk.” American Journal of Epidemiology, October, kwac186. https://doi.org/10.1093/aje/kwac186.
Trangucci, Rob, Yang Chen, and Jon Zelner. 2022. “Modeling Racial/Ethnic Differences in COVID-19 Incidence with Covariates Subject to Non-Random Missingness.” August 16, 2022. https://doi.org/10.48550/arXiv.2206.08161.
Zelner, Jon, Nina B. Masters, Ramya Naraharisetti, Sanyu A. Mojola, Merlin Chowkwanyun, and Ryan Malosh. 2022. “There Are No Equal Opportunity Infectors: Epidemiological Modelers Must Rethink Our Approach to Inequality in Infection Risk.” PLOS Computational Biology 18 (2): e1009795. https://doi.org/10.1371/journal.pcbi.1009795.
Zelner, Jon, Ramya Naraharisetti, and Sarah Zelner. 2023. “To Make Long-Term Gains Against Infection Inequity, Infectious Disease Epidemiology Needs to Develop a More Sociological Imagination.” American Journal of Epidemiology, February, kwad044. https://doi.org/10.1093/aje/kwad044.
Zelner, Jon, Rob Trangucci, Ramya Naraharisetti, Alex Cao, Ryan Malosh, Kelly Broen, Nina Masters, and Paul Delamater. 2020. “Racial Disparities in COVID-19 Mortality Are Driven by Unequal Infection Risks.” Clinical Infectious Diseases 72 (5). https://doi.org/10.1093/cid/ciaa1723.

Citation

BibTeX citation:
@online{zelner2023,
  author = {Jon Zelner},
  title = {A Crash Course on Modeling Infection Inequities},
  date = {2023-05-22},
  url = {https://zelnotes.io/posts/idm-symposium},
  langid = {en}
}
For attribution, please cite this work as:
Jon Zelner. 2023. “A Crash Course on Modeling Infection Inequities.” May 22, 2023. https://zelnotes.io/posts/idm-symposium.