Monthly Archives: November 2020

1.9. When Epidemiologists and Variables Collide: with Elizabeth Rose Mayeda

In most introductory epidemiology courses, students are taught about three categories of bias: confounding, information bias, and selection bias. On this episode of the podcast, we talk to Dr. Elizabeth Rose Mayeda about where collider stratification bias fits in to the framework of biases in epidemiology. Is collider stratification bias the same as selection bias? Why is collider bias so hard to understand, conceptually and empirically? Does collider stratification bias even matter? Listen in for some great conversation explaining these topics and others.

After listening to this podcast, if you are interested in learning more about selection bias and collider stratification bias some resources are included below:

Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15:615-625.

Howe CJ, Cole SR, Lau B, Napravnik S, Eron JJJ. Selection Bias Due to Loss to Follow Up in Cohort Studies. Epidemiology. 2016;27:91-97.

Hernán MA. Invited Commentary: Selection Bias Without Colliders. American journal of epidemiology. 2017;185:1048-1050.

Greenland S. Response and follow-up bias in cohort studies. Am J Epidemiol. 1977 Sep;106(3):184-7. doi: 10.1093/oxfordjournals.aje.a112451.

Kleinbaum D, Morgenstern H, Kupper L. Selection bias in epidemiologic studies. Am J Epidemiol. 1981;113:452-463.

Greenland S, Pearl J, Robins JM. Causal Diagrams for Epidemiologic Research. Epidemiology. 1999;10:37-48.

Mayeda ER, Banack HR, Bibbins-Domingo K, Zeki Al Hazzouri A, Marden JR, Whitmer RA, et al. Can Survival Bias Explain the Age Attenuation of Racial Inequalities in Stroke Incidence?: A Simulation Study. Epidemiology. 2018;29:525-532.

1.8. The Discipline Olympics: Epidemiology vs. Public Health with Dr. Laura Rosella

Given the COVID-19 pandemic there is an urgent need for us to better understand how scientific evidence generated in epidemiologic research gets translated into information that can be used to create public health policy. In this episode of SERious Epidemiology, we talk with Dr. Laura Rosella about data driven public health, the role of epidemiology in public health, and more broadly, the importance of knowledge translation for epidemiologists.

After listening to this podcast, if you are interested in learning more about the intersection of epidemiology and public health some resources are included below:

  1. How’s my flattening: A centralized data analytics and visualization hub monitoring Ontario’s response to COVID-19
  1. Definitions of epidemiology, including references to the definition Dr. Rosella mentioned from McMahon and Pugh’s epidemiology textbook (1970):
    Frérot M, Lefebvre A, Aho S, Callier P, Astruc K, Aho Glélé LS. What is epidemiology? Changing definitions of epidemiology 1978-2017. PLoS One. 2018;13(12):e0208442.

    Terris, M. Approaches to an Epidemiology of Health. Am J Public Health. 1975; 65(10),.%221I%20This%20definition%20repre%2D

  1. The use of scientific evidence for public health decision making:
    Rosella LC, Wilson K, Crowcroft NS, Chu A, Upshur R, Willison D, Deeks SL, Schwartz B, Tustin J, Sider D, Goel V. Pandemic H1N1 in Canada and the use of evidence in developing public health policies–a policy analysis. Soc Sci Med. 2013 Apr;83:1-9.
    doi: 10.1016/j.socscimed.2013.02.009.
  1. Agent-based modeling
    Tracy M, Cerdá M, Keyes KM. Agent-Based Modeling in Public Health: Current Applications and Future Directions. Annu Rev Public Health. 2018 Apr 1;39:77-94.
    doi: 10.1146/annurev-publhealth-040617-014317.

Additional info on agent-based modeling: