In honor of the Society for Epidemiologic Research 2020 Meeting, the hosts of four epidemiology podcasts came together to record the first ever “crossover event” to talk about their experiences recording our shows and what podcasting can bring to the table for the field of epidemiology. Join the hosts of Epidemiology Counts (Bryan James), SERiousEPi (Matt Fox, Hailey Banack), Casual Inference (Lucy D’Agostino McGowan), and Shiny Epi People (Lisa Bodnar) as they engage in a fun and informative (we hope!) conversation of the burgeoning field of epidemiology podcasting, emceed by Geetika Kalloo. The audio podcast will be released on some of our pod feeds, and the video recording will be available to watch on the SER website.

Episode Title: The need for theory in epidemiology with Dr. Nancy Krieger

This episode features an interview with Dr. Nancy Krieger, Professor of Social Epidemiology at the T.H. Chan School of Public Health and author of Epidemiology and the People’s Health: Theory and Context. Dr. Krieger discusses the importance of using conceptual frameworks to improve people’s health and the role of population-level determinants of health (including social determinants) in population health research.  We discuss a range of topics, including the differences between biomedical and analytics driven approaches to population health research and theory driven research, as well as the importance of descriptive epidemiology.

What puts the quasi in quasi-experimental designs? What makes a quasi-experimental study different than a “real” experiment? Ever wondered about the difference between regression discontinuity, difference-in-differences, and synthetic control methods? Dr. Tarik Benmarnhia joins us on this episode of SERious Epidemiology to talk us through a range of quasi-experimental designs. He makes a strong case for why we should integrate these designs in a variety of settings in epidemiology ranging from public health policy to clinical epidemiology

After listening to this podcast, if you are interested in learning more about quasi-experimental designs, you can check out some of the resources below:

Abadie A, Diamond A, Hainmueller J. (2010) Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program, Journal of the American Statistical Association, 105:490, 493-505, DOI: 10.1198/jasa.2009.ap08746

Chen H, Li Q, Kaufman JS, Wang J, Copes R, Su Y, Benmarhnia T. Effect of air quality alerts on human health: a regression discontinuity analysis in Toronto, Canada. Lancet Planet Health. 2018 Jan;2(1):e19-e26. doi: 10.1016/S2542-5196(17)30185-7. Epub 2018 Jan 9. PMID: 29615204.

Auger N, Kuehne E, Goneau M, Daniel M. Preterm birth during an extreme weather event in Québec, Canada: a “natural experiment”. Matern Child Health J. 2011 Oct;15(7):1088-96. doi: 10.1007/s10995-010-0645-0. PMID: 20640493.

Hernán MA, Robins JM. Instruments for causal inference: an epidemiologist’s dream? Epidemiology. 2006 Jul;17(4):360-72. doi: 10.1097/01.ede.0000222409.00878.37. Erratum in: Epidemiology. 2014 Jan;25(1):164. PMID: 16755261.

Courtemanche, C., Marton, J., Ukert, B., Yelowitz, A. and Zapata, D. (2017), Early Impacts of the Affordable Care Act on Health Insurance Coverage in Medicaid Expansion and Non‐Expansion States. J. Pol. Anal. Manage., 36: 178-210. https://doi.org/10.1002/pam.21961

Bor J, Fox MP, Rosen S, Venkataramani A, Tanser F, Pillay D, Bärnighausen T. Treatment eligibility and retention in clinical HIV care: A regression discontinuity study in South Africa. PLoS Med. 2017 Nov 28;14(11):e1002463. doi: 10.1371/journal.pmed.1002463. PMID: 29182641; PMCID: PMC5705070.

Bor J, Moscoe E, Mutevedzi P, Newell ML, Bärnighausen T. Regression discontinuity designs in epidemiology: causal inference without randomized trials. Epidemiology. 2014 Sep;25(5):729-37. doi: 10.1097/EDE.0000000000000138. PMID: 25061922; PMCID: PMC4162343.

Elder TE. The importance of relative standards in ADHD diagnoses: evidence based on exact birth dates. J Health Econ. 2010;29(5):641-656. doi:10.1016/j.jhealeco.2010.06.003

Smith LM, Kaufman JS, Strumpf EC, Lévesque LE. Effect of human papillomavirus (HPV) vaccination on clinical indicators of sexual behaviour among adolescent girls: the Ontario Grade 8 HPV Vaccine Cohort Study. CMAJ. 2015;187(2):E74-E81. doi:10.1503/cmaj.140900

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.

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:

Terris, M. Approaches to an Epidemiology of Health. Am J Public Health. 1975; 65(10)

Additional info on agent-based modeling:

In this episode we interview Dr. Ghassan Hamra and talk about all things Bayesian. If you’re like us, you have likely been trained in traditional, frequentist approaches to statistics and have always wondered what the big deal is about Bayesian approaches. Well, have no fear, Dr. Hamra is here to explain it all. In this episode we cover a range of topics introducing Bayesian analyses, including how Bayesian and frequentist statistics differ, the concept of integrating a prior into your analyses, and whether Bayesian statistics are really a “subjective” approach (**spoiler alert: they’re not).

After listening to this podcast, if you’re interested in learning more about Bayesian analyses some links are included below:

https://doi.org/10.1007/s40471-014-0019-z

doi:10.1097/EDE.0b013e318280db1d

http://ghassanbhamra-phd.org/publications

http://ghassanbhamra-phd.org/presentations-and-such

Sander Greenland, Bayesian perspectives for epidemiological research: I. Foundations and basic methods, International Journal of Epidemiology, Volume 35, Issue 3, June 2006, Pages 765–775, https://doi.org/10.1093/ije/dyi312

Sander Greenland, Bayesian perspectives for epidemiological research. II. Regression analysis, International Journal of Epidemiology, Volume 36, Issue 1, February 2007, Pages 195–202, https://doi.org/10.1093/ije/dyl289

Sander Greenland, Bayesian perspectives for epidemiologic research: III. Bias analysis via missing-data methods, International Journal of Epidemiology, Volume 38, Issue 6, December 2009, Pages 1662–1673, https://doi.org/10.1093/ije/dyp278

Why is it so important to ask good study questions? Why is it so hard to develop good study questions? Do all study questions need to be directly relevant for public health policy?  In this episode of SERious Epidemiology, we talk with Dr. Maria Glymour about what it means to ask a good study question and how we can get better at asking questions that will make a meaningful contribution to public health.

After listening to this podcast, if you’re interested in learning more about some of the topics we discussed, here are links for you to check out:

https://doi.org/10.2105/AJPH.2018.304901

doi:10.1097/EDE.0b013e3181875e61

https://doi.org/10.2105/AJPH.2018.304383

doi: 10.1097/EDE.0b013e31826d078d.

https://doi.org/10.1093/aje/kwt172

Is all epidemiology social epidemiology? If I am someone who studies cancer, or obesity, or infectious disease, or any other branch of epidemiology, should I be considering topics related to social epidemiology in my own work? In this episode of SERious Epidemiology, Dr. Whitney Robinson joins us to explain key concepts in social epidemiology.

After listening to this podcast, if you are interested in learning more about social epidemiology or some of the resources mentioned are included below:

https://www.amazon.com/Methods-Social-Epidemiology-Public-Biostatistics/dp/111850559X

Ford, Chandra L, and Collins O Airhihenbuwa. “Critical Race Theory, race equity, and public health: toward antiracism praxis.” American journal of public health vol. 100 Suppl 1,Suppl 1 (2010): S30-5. doi:10.2105/AJPH.2009.171058

Ford CL, Airhihenbuwa CO. The public health critical race methodology: Praxis for antiracism research. Social Science & Medicine. 2010;71:1390-1398.

In this journal club episode, we discuss one of our top 10 favourite epidemiology papers: “Does obesity shorten life? The importance of well-defined interventions to answer causal questions” by Miguel Hernán and Sarah Taubman. We talk about the consistency assumption in causal inference, why we think measurement error needs to be added to the list of assumptions for causal inference, and invent a new word (“statisticalize”) to dismiss the notion that fancy methods can always solve our problems.

References:

Hernán MA, Taubman SL. Does obesity shorten life? The importance of well-defined interventions to answer causal questions. Int J Obes. 2008;32:s8-s14.

Cole S, Frangakis C. The consistency statement in causal inference: a definition or an assumption? Epidemiology. 2009; 20:3-5.

Causal inference and the potential outcomes model are now both commonly taught in graduate programs in epidemiology. However, I think we can all agree that counterfactual thinking can be a bit mind-bending at times and it is really easy to get lost deep in the weeds when trying to think through the potential for unobserved comparison groups or outcomes. In this episode of SERious Epi, we speak to Dr. Daniel Westreich about counterfactuals, the difference between causal inference and causal effect estimation, and assumptions required to estimate causal effects from observational data.

After listening to this podcast, if you’re interested in learning more about the potential outcomes model or checking out any of the resources mentioned on this podcast, links are included below:

– Rose, G. Sick individuals and sick populations. International Journal of Epidemiology 1985; 14:32–38.

– Greenland, S. For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates. Eur J Epidemiol 32, 3–20 (2017). https://doi.org/10.1007/s10654-017-0230-6

– Morabia, Alfredo. “On the Origin of Hill’s Causal Criteria.” Epidemiology 2, no. 5 (1991): 367-69. Accessed August 13, 2020. www.jstor.org/stable/20065702.

– Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.

-Westreich, D. (2020). Epidemiology by Design: A Causal Approach to the Health Sciences. https://global.oup.com/academic/product/epidemiology-by-design-9780190665760?cc=us&lang=en&

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

– Hernán MA, Taubman SL. Does obesity shorten life? The importance of well-defined interventions to answer causal questions. Int J Obes. 2008;32:s8-s14.

-Neyman, J (1923) http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.473.4367&rep=rep1&type=pdf

– Donald B Rubin (2005) Causal Inference Using Potential Outcomes, Journal of the American Statistical Association, 100:469, 322-331, DOI: 10.1198/016214504000001880

– Edwards JK, Cole SR, Westreich D. All your data are always missing: incorporating bias due to measurement error into the potential outcomes framework. Int J Epidemiol. 2015;44(4):1452-1459. doi:10.1093/ije/dyu272

– Westreich D, Edwards JK, Cole SR, Platt RW, Mumford SL, Schisterman EF. Imputation approaches for potential outcomes in causal inference. Int J Epidemiol. 2015;44(5):1731-1737. doi:10.1093/ije/dyv135