Author Archives: Duncan Mahood

After the MPH, is a PhD next?

Category : PROspective

From Dr. Shakira Suglia, Associate Professor of Epidemiology and Director of Graduate Studies (DGS) for the PhD program in epidemiology: 

As the semester starts to wind down, many of us are figuring out what’s next and while a simple answer may be ‘Spring semester is next’ some of you may be considering what’s next beyond the Spring semester. If you have been considering continuing into the PhD as your next career step after the MPH there are a few things worth considering as you embark on this journey.


Should I apply?

When considering applying, think of what you want to do after the PhD, that should drive the reason for applying. Think of your career goals, do you enjoy leading research teams? Developing research projects? Teaching and mentoring? A PhD changes the types of jobs you are competitive for, you move into a lead role conceptualizing and leading research rather than carrying out the research. Think of organizations and positions you may enjoy working in after your PhD, do the people holding those positions have PhDs?

What is getting a PhD like?

Depending on the program and academic institution, the time from start to finish of a PhD can be between 4 and 6 years. Being a PhD student is a full time ‘job’ – in addition to coursework, there are often teaching and research expectations. Compared to a MPH program there is a lot of unstructured time in the PhD program as you work on your dissertation. Some institutions, but not all, provide stipends and may cover tuition and health insurance. It’s important to have a good understanding of what obtaining a PhD is like, so it is a good idea to talk to current doctoral students, postdoctoral fellows or recent grads to learn what their day to day is like.

How do I know where to apply?

Again, do some homework. Research programs websites, read up on the work being done in each institution – are there faculty that do work in the areas that you want to work on? While a perfect match is not necessary, you want to ensure there will be faculty that can mentor you in the work that you want to engage in. If you are interested in something that no one on faculty focuses on, that is not a good match. If you can, try to distinguish between primary faculty and adjuncts who are actively mentoring students. Understand what are the training priorities of each program and how do they align with your priorities. Again throughout the application process you should reach out to faculty, students and alumni of the programs you are considering.

A bit more on the Epi PhD program

The PhD in epidemiology in our institution is offered through Emory’s Laney Graduate School. This program trains students to become independent investigators and to obtain skills to be successful in PhD-level positions in academia, government, and the private sector. Typical time to degree is 5 years, and students typically spend the first 2 years doing coursework and 3 years for dissertation work. Tuition, health insurance and a stipend are provided for students.

You can find more information on our website and you can reach out to sphepidept [at] emory [dot] edu directly with your questions. The application deadline for Fall 2021 matriculation is December 1st, 2020. 


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Vote like an Epidemiologist, Vote for Public Health

Category : PROspective

With election day now just hours away, the Confounder invited Nellie Garlow and Lisa Chung, 2nd year Epi MPH students and founders of the Rollins Election Day Initiative (REDI), to talk about their motivations for creating the organization and how they view epidemiology and civic engagement as two sides of the same coin. 

Growing up with Civic Engagement

Nellie: When Dean Curran asked a group of students in January of this year, “what can we do right now to solve some of our greatest public health problems?” the first thing that came into my mind was cancelling classes on Election Day. To some, this may have seemed unrelated, but, for me, having grown up right outside of Washington, DC, politics were integral to my world view. From a young age, I attended protests on the national mall, shook the hands of congressmen and congresswomen, and listened in while politics were debated at the dinner table. My parents, who were both federal workers in different public health sectors, not only taught me about the connection between politics and human health, but also showed me how critical it was to engage civically no matter which party was in office.

Lisa: The earliest memory that I have of elections is walking to the polling site with my family of three generations. After voting, we would turn on the news as soon as we were back home and follow the election results as every single ballot was counted until well past midnight. To me, voting was always a family affair. It was only possible with my parents’ busy schedules because the Presidential Election Day is recognized as a national holiday in South Korea. From casting my first ever ballot in Korean Presidential Election to returning my mail-in ballots in Washington state, voting has always been as easy (if not easier!) as running an errand. It is unfathomable to me, to this date, that anyone would ever have to question whether to vote or work (or attend classes) or wait for hours in line. So, once Nellie shared her ideas for the Rollins Election Day Initiative, I knew that this would reduce an enormous potential burden to civic engagement within the Rollins community.

Starting REDI

When the opportunity arose to increase election and civic engagement at Rollins, we knew the fastest way our school could make a difference was to remove synchronous class content from Election Day so that public health students could vote and help others in the community to cast their ballots safely. Together, we would spend the next eight months standing up Rollin’s first non-partisan voting rights organization, the Rollins Election Day Initiative (REDI), in hopes of making a difference in public health. 

Civic Engagement and Epidemiology

As epidemiologists, we have an ethical obligation to act in the best interest of the public’s health and one key way we can do this is by ensuring all people have a say in which politicians make decisions that impact their health directly. When citizens face barriers to voting, they lose that representation on the local, state, and national levels. As with health, a wealth of evidence suggests that disparities in access to voting happen along socioeconomic and racial lines in the US. This is no coincidence, since disparities in both health and voting access are driven by the same structural mechanisms. As those responsible for both elucidating the causal structure of such inequalities and working to undo them, it is thus our responsibility as epidemiologist to also advocate for the elimination of the upstream causes of unequal access to voting.

Another critical reason we as epidemiologists must pay attention to elections and politics is because we hold elected officials to using the best evidence available when making public health decisions. It is not enough to simply produce the evidence of a causal mechanism and then rely on others to ensure its appropriate application – when we see politicians disregarding facts for political gains that negatively impact the public’s health and our own credibility as scientists, it is our obligation to speak up. Epidemiologists must hold our elected leaders accountable.

One thing we cannot compromise on when getting involved in politics is civility. We must remember that there are a wide range of political opinions, both across the US and at Rollins, and it is critical that we listen to one another and convey our opinions respectfully.  As is emphasized from the core of our department, we should approach these conversations with flexibility and empathy, but most importantly with respect to one another. Rather than silencing the views that oppose yours, think about the growth that can be experienced when listening and learning to from others, especially in the moments when it feels most challenging. We can move forward as a nation, as a department, as scientists and individuals, only if we allow ourselves to learn from each other in a civil, respectful manner. Only together, we can grow, flourish, and “redeem the soul of our nation.

Finally, engaging in politics and elections makes us establish good habits. As Emory professor Carol Anderson writes in her book, One Person No Vote, it is critical we get involved in elections and civic engagement early on and to not take our democracy for granted. This year’s presidential election may seem like a contentious one, but we’re betting it won’t be the last nail-bitter we see in our lifetimes. Furthermore, there will be countless local elections that will have an even greater impact on the public’s health and as public health professionals, we can’t forget those.

As we head into the election, we have a simple message for all of you: get involved on Election Day, stay involved in your local elections, and support your community in the process. To quote, late Congressman John Lewis, “when historians pick up their pens to write the story of the 21st century, let them say that it was your generation who laid down the heavy burdens of hate at last and that peace finally triumphed over violence, aggression and war.”


If you would like to get involved in REDI’s work or learn more about their efforts, check out their website where you can find all of the resources they have compiled, like a map of ballot drop boxes, nonpartisan fact sheets about what is on the ballot in local elections, and more! Also, be sure to follow REDI on Twitter and Instagram and like their Facebook page. A special thanks to Nellie and Lisa for sharing their stories and to the entire REDI team for the work they continue to do to improve civic engagement at Emory and throughout Georgia!

Goal Setting

Category : PROspective

From Dr. Lauren McCullough, Assistant Professor, Department of Epidemiology

I love the start of a new semester. As a kid, it meant new school supplies. In college, it was a fresh beginning. Now, it represents an opportunity to reflect on what is important to me. How much progress do I want to make on that research paper? What new skills do I want to learn? A new semester brings a fresh set of goals.

Goal setting is a helpful way to establish a marker for success and measure your progress. Yet, your journey may be inefficient or ill-conceived if your goal setting strategy is missing some crucial steps. Over the years, I have refined my strategy for developing and achieving my professional goals. (1) Who are you? (2) What do you want? (3) What is your plan? Below, I outline some goal setting techniques that are easy to implement and may be useful in your own journey towards success.

Who are you?

The goals and aspirations of your colleagues may be different than your own. Think about what brought you to public health and imagine your future self. This will serve as a guiding light. What are your passions, interests, and values? What skills do you have or want to gain? Staying keenly aware of who you are will allow you to forge a path that is uniquely yours while maximizing the opportunities Rollins and Emory have to offer.

What do you want?

See the long-term goal (the BIG picture) and develop specific short-term goals to get you there. For many of you, this may be a time to figure out the big picture, and that’s ok! During my MSPH, I spent a whole semester conducting informational interviews with professionals I respected and admired to better understand their path, perceived opportunities, and challenges. Guided by my passions, interests, and values, I ultimately figured out what I wanted… to improve cancer outcomes among African-Americans through research. That’s a HUGE goal, and I’m still working at it! So along the way, you should set some short-term SMART goals (Specific, Measurable, Attainable, Relevant, and Time-based) that will get you there. For me, that meant getting research experience—taking an unpaid internship with an epidemiologist at a major cancer center—and finding ways to connect with affected communities. Importantly, think in chapters. You can’t possibly do everything now. Maximize your current environment or opportunity to its fullest potential and know that some things will have to wait until the next chapter.

What is your plan?

The best goals are inconsequential if they can’t be executed, so I consider this last section the most important. Let me start by saying that strategic planning is a SKILL! It requires intentionality, practice, and repetition. Once you have a short-term goal in mind (i.e., reviewing the literature for a thesis project), the planning process can be accomplished in 4 easy stages.

  1. Map the steps—these are the specific tasks that are necessary to achieve the short term goal.
  2. Integrate into your calendar—allocate specific time to work on these tasks. Literally, put it on your calendar like you would a meeting!
  3. Create accountability—check-in with yourself or an accountability partner. Did you accomplish the task? If not, why? If so, find a way to celebrate!
  4. Refine and repeat.

Finally, for additional inspiration, take a look at this article from on SMART goal setting.


Dr. Lauren E. McCullough is Rollins Assistant Professor in the Department of Epidemiology at the Rollins School of Public Health. Her overarching research interests are in the life-course epidemiology of cancer (breast cancer and lymphoma), specifically the contributions of obesity and physical inactivity to the tumor epigenome and microenvironment, as well as disparities in cancer outcomes. 

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Sleep Epidemiology: Contributions of Social Determinants

Category : PROspective

from Assistant Professor, Dr. Dayna A. Johnson, PhD, MPH, MSW, MS:

The practice of epidemiology applies to many health outcomes (e.g., cardiovascular disease) and types of risk factors (e.g., social) that form the specific areas within epidemiology (e.g., social, environmental, genetic, etc.). In my research, I employ epidemiologic methods to study determinants and consequences of adverse sleep health and sleep disorders; therefore, I identify as a sleep epidemiologist. As defined in the book, The Social Epidemiology of Sleep, sleep epidemiology is “the study of the distribution and determinants of sleep, sleep-related symptoms, and sleep disorders and the application of this study to improve sleep health and sleep-health related conditions, including studies of how sleep influences health and disease”.1


Sleep involves a dynamic set of neurophysiological and behavioral states. What I find most interesting about sleep, is that it is a physiologic activity that is necessary for health and well-being – everyone must sleep. Healthy sleep is multidimensional involving adequate sleep duration, continuity or efficiency, appropriate and consistent sleep timing, alertness during wakefulness, and individual satisfaction.2  Sleep and sleep patterns are adapted to individual, social, and environmental demands. Similarly, our sleep is shaped by many factors including social, environmental, and genetic. In my research, I primarily study social and environmental determinants of adverse sleep health.


Racism and Sleep

The current climate in the world is truly affecting how we sleep. Individuals around the world have witnessed the heinous killings of George Floyd, Breonna Taylor, Ahmaud Arbery (which occurred in our home of Georgia) as well as many others. Witnessing such injustices, which are the result of racism – a fundamental cause of health inequities, can cause a state of vigilance, which is particularly salient for racial minorities. These brutal acts can be even more traumatic for the individuals who resemble the victims, which can cause one to ruminate over how that could have been them or their spouse, father, brother, sister, friend, etc. These are vicarious experiences of racism or discrimination, which are known to affect health, and sleep. These experiences can lead to stress, anxiety or depression, which can directly affect sleep and/or indirectly through rumination where someone repetitively go overs thoughts or problems, which can inhibit sleep onset or disturb sleep.


Location and Environment

Racism, discrimination, and stress are just a few of the contributing social factors to the high prevalence of sleep deficiencies among racial and sexual minorities as well as individuals of lower socioeconomic status (SES). Another important social factor to consider is where we live. Our household and neighborhood environments contain features such as light, noise, safety, density, cohesion that are associated with sleep health.3 Residential segregation based on race, immigration status, SES has largely determined the resources within neighborhoods. Historical discriminatory policies, such as redlining, unwarrantedly denied racial minorities (mainly Black/African American or Latinx) in urban areas mortgages to purchase a home or loans to renovate homes. Housing discrimination is considered one of the largest contributors to the wealth gap and these effects have lasted across generations. Additionally, these under-resourced environments often house manufacturing companies that emit pollution into the air as well as traffic which promotes noise and pollution. Air pollution is directly related to a common sleep disorder, sleep apnea.4 And, noise and light pollution are associated with less sleep and sleep difficulties.5 Emerging data suggests that the neighborhood environment partially explains racial disparities in sleep. It is also important to note, that there is evidence suggesting that racial disparities in sleep are minimized when Black and White individuals – for example live in similar environments; thus, underscoring the effect of place as opposed to race.


Sleep can be considered a privilege.

It is important to consider the person that works multiple jobs due to low wages, or lives in a neighborhood with noise, violence and/or a household with interpersonal violence… how will they sleep? Children exposed to high levels of screen time or those without a regular bedtime routine are placed on a trajectory of sleep deficiencies in adulthood, which is related to poor health outcomes such as obesity, diabetes, cancer, cardiovascular disease, cognitive decline and mortality. School start times are another factor that can affect sleep, particularly for the student who must take the bus across town to school who, therefore, has less opportunity for sleep. As seen during COVID-19, racial minorities and individuals of lower SES are more likely to be low wage essential workers without worker protection such as sick leave, thus leading to fear and anxiety and consequently sleep deficiencies. This is important because sleep is necessary for healing. In general, those of higher SES have better sleep health. However, higher SES racial minorities such as Black or African Americans tend to have worse sleep compared to their lower SES counterparts. It is hypothesized that stress may explain this unexpected gradient, but more research is needed to fully understand this association.


Sleep Equity

The social factors referenced above-racism, discrimination, stress, mood, household and neighborhood environment are all understudied determinants of sleep deficiencies. Sleep is socially patterned, therefore exploring and addressing these factors can help decrease the burden of adverse sleep health and sleep disorders as well as reduce health disparities. Targeting sleep may improve overall health, decrease accidents (occupational and motor vehicle), and improve performance (athletic and academic).


Sleep is critical and everyone deserves it! Therefore, as epidemiologists we can shed light on the social factors that are contributing to sleep disparities and inform the policies and interventions that may improve sleep for all individuals.


Sleep well!



  1. Duncan DT, Kawachi I and Redline S. The Social Epidemiology of Sleep: Oxford University Press; 2019.
  2. Buysse DJ. Sleep health: can we define it? Does it matter? Sleep. 2014;37:9-17.
  3. Johnson DA, Billings ME and Hale L. Environmental Determinants of Insufficient Sleep and Sleep Disorders: Implications for Population Health. Curr Epidemiol Rep. 2018;5:61-69.
  4. Billings ME, Hale L and Johnson DA. Physical and Social Environment Relationship With Sleep Health and Disorders. Chest. 2020;157:1304-1312.
  5. Billings ME, Gold D, Szpiro A, Aaron CP, Jorgensen N, Gassett A, Leary PJ, Kaufman JD and Redline SR. The Association of Ambient Air Pollution with Sleep Apnea: The Multi-Ethnic Study of Atherosclerosis. Ann Am Thorac Soc. 2018.



Dr. Dayna A. Johnson, PhD, MPH, MSW, MS is an Assistant Professor in the Department of Epidemiology. Her research is aimed at understanding the root causes of sleep health disparities and their impact on cardiovascular disease by 1) addressing the social and environmental determinants of sleep disorders and insufficient sleep; and 2) investigating the influence of modifiable factors such as sleep disorders and disturbances on disparities in cardiovascular outcomes.


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Exit Interviews and Winding Down Your APE

Category : PROspective

With the Fall quickly approaching, over the next couple of weeks many rising 2nd year MPH students will be transitioning from their Summer APE projects back to classes, homework, and thesis or capstone research. Throughout the Summer, we have been highlighting many of those exciting APE projects through our #InsideAPE segment in the Confounder, and couldn’t be more proud of the innovative and impactful work our students have been involved with over the last few months. Furthermore, as those APEs come to an end over the next couple weeks, there will be some opportunities for reflection and relationship building that you would be remiss to let pass. In the professional settings, this often takes place through an Exit Interview – and though you probably won’t be having the classic Exit Interview, it is a good exercise to think about some key takeaways from that process and how you can take advantage of those benefits towards the end of your APE.

Ask for feedback

Last year, alumna Elizabeth Hannapel (EPI MPH, 2012) wrote a spectacular PROspective article on Professional Feedback. She highlighted the fact that feedback in the workplace is very different than feedback in the classroom – it requires being proactive and committing to a growth mindset. The Exit Interview is no exception. If your supervisor hasn’t suggested having an Exit Interview, ask for one yourself! During the interview, maintain an open mind and view feedback as an opportunity to grow instead of as a personal affront. You should want to know where your weaknesses lie so that you can spend the next year working to fill those gaps in your skillset. Typically, Exit Interviews are also an opportunity to give feedback to your employer or supervisor directly – but be careful not to complain or vent – keep it focused on the positive and on items that can actually be improved. For more on exit interviews specifically, take a look at this article from Forbes on common pitfalls. 

Develop those relationships

If your APE was with an organization outside of Rollins, chances are that you met a lot of new co-workers and collaborators… virtually. Regardless, individuals you have been working with throughout the summer, including your direct supervisor and even department directors, represent a HUGE opportunity to develop your network. Not so long from now, you will be back in the job market looking for full-time post-graduate work and these individuals already have a good idea of your strengths, weaknesses, and accomplishments. In Getting to ‘Yes’, Dr. Lash talked about how the professional setting is an environment of reciprocation. Ask your supervisor if they would be willing to write recommendations for you in the future and make connections with your co-workers on LinkedIn – but make sure to offer something in return. Developing strong professional relationships takes time and commitment to reciprocating.

Next steps

Your APE doesn’t always end the day you log your 200th hour – often there is still a manuscript getting submitted for publication or maybe even an ongoing, uncontrolled global pandemic. Opportunities may still abound if you are willing (and have bandwidth) to continue with your team in a different capacity. Either way, it is a very good idea to discuss any outstanding action items and make a clear plan for the hand-over of your duties to the rest of the team. When your project ends, you want to leave your team with a good impression of you, and helping them take over your work seamlessly is a great, proactive way to do just that.

At the end of the day, your APE should be a learning opportunity. That includes learning how to apply those soft skills – asking for feedback, developing relationships, and managing transitions. A little bit of effort in these areas will definitely pay off down the road.

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Are you a (social) Epidemiologist?

Category : PROspective

From Dr. Michael Kramer, Associate Professor, Department of Epidemiology

Are you a (social) epidemiologist? Should you be?

I am. A social epidemiologist that is. I was actually that long before I even knew what the combination of those words – ‘social’ + ‘epidemiology’ – even meant! Just as the notoriety of ‘epidemiology’ has risen in recent pandemic-tainted months (even my stoner neighbor knows what an epidemiologist is now!), so has the discourse around social epidemiology. It seems that the idea of unjust or preventable differences in health outcomes across the social dimensions that shape so much of our modern life – race, ethnicity, class, gender, geography, sexual identity, etc, etc – is having its ‘five minutes of fame’. Pundits, talking heads, and social influencers are suddenly speaking about, and wondering why, communities of color are bearing a disproportionate burden of COVID-19 morbidity and mortality. Following the state-sponsored murder of George Floyd in Minneapolis, it also seems that wide swathes of (white) America are opening their eyes to the longstanding existence of institutionalized and structural racial injustice that has direct (e.g. murder) and less direct (e.g. over policing and mass incarceration, segregation, racism in employment, education, healthcare, etc) consequences for the health of Black and brown communities.

So I ask again, are you a Social Epidemiologist? Or should you be?

Don’t worry, I won’t guilt you into being a ‘Social Epidemiologist’ (with a capital ‘S’)! However I will argue that if you do epidemiology you must be a ‘social epidemiologist’ with a little ‘s’, or else risk making (and re-making) mistakes that have littered the history of epidemiology and public health, and have the potential to cause harm rather than help. To distinguish what I mean between capital-S versus lowercase-s social epidemiology, let’s start by defining our collective work as epidemiologists.

I like the definition of epidemiology from Modern Epidemiology (3rd edition, p 32): “Epidemiology is the study of the distribution and determinants of disease frequency in human populations” (emphasis added). This definition aligns nicely with John Snow, Cholera and our beloved Origin Story of epidemiology. Measuring the distribution of disease is about describing the who, where, when, and what of population health outcomes, whether infectious, chronic, behavioral or injury related. Describing the determinants of disease is about answering the questions of how and why disease varies between groups and over time. It is here we try to estimate causal effects of exposures or interventions.

Those of us who self-define as Social Epidemiologists are fundamentally epidemiologists. We work in pediatrics and geriatrics; in infectious and chronic disease; and in government, industry, or academic settings. While the health outcomes and occupational settings are diverse, the organizing principle of Social Epidemiology is exposure-oriented. We tailor the focus of our work to study of the social distribution and social determinants of disease. Describing social distributions of disease means intentionally conceptualizing, measuring and reporting disease occurrence along the social lines described above (e.g. race, ethnicity, class, gender, etc). Understanding the social determinants of health between and within populations also requires a shift in the exposures under consideration. Instead of individual behaviors, individual exposures, and inherited genes we might center our attention on social environments, racism & discrimination, political economy, social policy, and health policy as determinants of health overall and specifically of health inequities. 

While the lessons of John Snow – careful observation, shoe leather detective work, intentional contrasting of competing hypotheses – are just as important for Social Epidemiologists as any others, we might look to additional role models as well. Although formally a sociologist, W.E.B. DuBois is arguably the founding father of social epidemiology.1 In The Philadelphia Negro, DuBois2 used systematic quantitative analysis to characterize health and social outcomes as they varied in 19th Century Philadelphia by race, employment status, and neighborhood segregation level. The modern Social Epidemiologist builds on this early work by recognizing that socially patterned experiences that occur through interpersonal interactions, in the non-random allocation of opportunity or exposure across one’s life span, and even across generations, are literally embodied as altered biological and psychological function.3  Our bodies express the health that is shaped by their continuous and accumulated interaction with a social world. It’s pretty fascinating and important stuff!

But what if Social Epidemiology (with a capital-S) is not your thing? That’s ok. Public health and epidemiology benefit from the big tent under which we all work. However choosing not to center your interest on social determinants of health does not diminish your responsibility to learn about and understand the use and misuse of socially constructed measures in the conduct of epidemiologic analysis. Let’s take, for example, the use of ‘race’ as a variable in epidemiologic analysis. Its use as a ‘confounder’ or even an ‘exposure’ has been ubiquitous across a wide range of study areas for many decades, yet very often the interpretation and meaning imbued into results from such analyses are poorly communicated at best, and in worse circumstances may represent lazy thinking and biased assumptions of the investigator, ultimately causing harm to population health.

While it is common to acknowledge that race is a ‘social construct’, there is often confusion about the implications of this idea for epidemiology. Does the presence of a ‘racial disparity’ in a health outcome mean some people are just born less healthy? Or if we ‘adjust’ for socioeconomic status should we assume that any residual racial difference is suggestive of a genetic cause? Or perhaps if race is ‘socially constructed’ we shouldn’t even be using these variables. Each of these conclusions has been made frequently in epidemiologic research, but rarely are they justified or well-supported either empirically or theoretically. Most of us align ourselves with multiple identities along the lines of race, ethnicity, gender, sexual orientation, religion, etc. Few of us could honestly say that none of these dimensions have any influence whatsoever on our lives and health. Saying that these dimensions are ‘socially constructed’ does not mean they are not real in each of our lives; it simply means that they are not biologically essential, and therefore we would not inevitably expect differences in health simply because of these identities. So what do we make of a significant ‘effect’ of race from an epidemiologic model? That is a subject of ongoing discussion and debate, but one thing most social epidemiologists would agree with is that the interpretation is not simple or simplistic, as it has often been treated in epidemiologic research.

So even if you are defiantly not a Social Epidemiologist, I hope that you will take the initiative and opportunity to educate yourself on the obvious, and not so obvious, ways that population health and health inequities are generated. Learn about the debates about measurement and methods that concern social variation in health, and seek guidance when designing studies, selecting measures, conducting analyses, and interpreting results to reduce the chance that you unintentionally produce spurious or even harmful interpretations of results. At RSPH you can do this in many ways. There are elective courses explicitly in social epidemiology, but the issues of social drivers of the distribution and determinants of health are increasingly evident even in classes without the moniker of ‘social epi’. Talk with faculty, talk with other students, ask questions, but also listen closely. Perhaps we will not all choose to be Social Epidemiologists, but hopefully we can all agree that ‘social’ is critical to all of our work as epidemiologists.

1Sharon D. Jones-Eversley, Lorraine T. Dean. After 121 Years, It’s Time to Recognize W.E.B. Du Bois as a Founding Father of Social Epidemiology. The Journal of Negro Education. 2018;87(3):230-245. doi:10.7709/jnegroeducation.87.3.0230

2Du Bois W. The Philadelphia Negro: A Social Study. University of Pennsylvania; 1899.

3Krieger N. Epidemiology and the People’s Health: Theory and Context. Oxford University Press; 2011.

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Dr. Kramer is a social epidemiologist in the Department of Epidemiology with particular interest in maternal and child health populations and life course processes.  His current research and teaching interests fall into three areas, and often include the intersection of these areas: Social determinants of health, maternal and child health, and spatial analysis.


Category : #WeAreEmoryEPI

When the Confounder began almost 2 years ago, the editorial team decided from the beginning that we wanted to create a space to showcase what our amazing students, faculty, and staff were accomplishing both in their careers and in their lives outside of EPI. It is no wonder, then, that #IamEmoryEPI quickly became our most-read section of the Confounder – our community is an inspiring, innovative, and hard-working bunch, which makes for some exceptionally engaging reading.


Fast forward 2 years, and we have an update that, though mostly symbolic, is meant to place a special emphasis on our greatest quality: our community.


This week, our editorial team unanimously voted to re-brand #IamEmoryEPI as #WeAreEmoryEPI. This segment will continue to highlight students, faculty, and staff in the same way as it has in the past, but from now on will represent our shared journey and accomplishments in EPI as opposed to the individuals themselves whose stories we share. This also exemplifies our dedication at the Confounder to inclusion and diversity – our community includes epidemiologists and investigators of every color, nationality, and background. That diversity is our greatest strength and we look forward to sharing more stories that represent the true diversity of our community.

Thanks to our readers!

Editorial Team, The Confounder

If you are interested in being featured on #WeAreEmoryEPI, please complete the form below to be added to our highlight list!



Category : PROspective

As the semester comes to a close, we wanted to take a look back at some of our favorite PROspectives over the last few months. As the COVID situation developed from an isolated outbreak to a full-blown pandemic, we have gotten insights from faculty, staff, and alumni on topics both pandemic and non-pandemic related. Here are some of our favorite articles from Spring 2020:


1. In Keep Calm and…, Dr. Timothy Lash started off the semester with a discussion about performing under pressure, be it for school, work, or almost any other context. Given the pressure that many of us are feeling now to turn our skill sets towards the global crisis or maybe just to survive final exams, the strategies laid out in this article have probably never been more applicable. The quote:


“Learning to use stress to your advantage is healthy and will give you a competitive edge. Like many career skills, it requires introspection and a commitment to being intentional about the goal.”



2. Next, we heard from Dr. Jodie Guest about the value of reading outside the classroom in The EPI-Curious Society. The quote:


“Learning from our past and talking about our different perspectives is fundamental to doing good work.”



3. At the beginning of February, Dr. Lauren Christiansen-Lindquist helped us to think about APEs differently in her article, Internships: Not just about fulfilling the APE requirement. The quote:


“You may have heard this from me before, but my motivation for pursuing a career in public health was driven by wanting to make a difference. The reason why I love the APE so much is that it affords our students the opportunity to make their mark on public health even before graduation.”



4. In a 3 part series, we heard from alum Roice Fulton (GLEPI, 2014) on careers in global public health with multinational organizations. In Part 1, Roice shared his path from GLEPI student graduating at the height of the 2014 Ebola outbreak in West Africa (more relevant now than ever), to a full-blown career at an unexpected employer. In Part 2, the focus turned toward the nuts and bolts of the global NGO industry and how to navigate your own entry post-graduation. Finally, Part 3 uncovered the role of teamwork and leadership in public health. The quote:


“You may be faced with a call to lead from unexpected places and at unexpected times, especially as we reckon with a pandemic that touches every facet of our work. We’ve got to be ready for the call when it comes.”



5. Epidemiology is not just about the 2×2 tables and the regression coefficients – in our profession, we will also be called to translate science into policy and action. To do that, writes Dr. Lash, we will need to Tell Influential Stories. The quote:


“To be influential, one must change minds. To change minds, those minds must be open to change.”



6. With our schedules packed and mid-semester productivity waning, ADAP Farah Dharamshi introduced the term single-tasking in her article Juggling 101. The quote:


“Our days are filled with a constant barrage of distractions, unexpected challenges and increasing responsibilities. But, the science and experience are clear – by doing less all at once, you will likely be able to accomplish much more.”



7. As Emory, along with nearly every university and employer nationwide, transitioned to remote learning and working, I shared my how-to guide for executing that transition in my article WFH: New Challenges & New Opportunities. The quote:


“This experience is likely to teach you a lot about yourself, your ability to self-manage, your discipline, and your needs as an employee – knowledge that will help you better understand your own strengths and weaknesses going forward.”



8. This Spring, the word ‘epidemiologist’ entered the public domain and left a lot of people outside our profession itching to learn more about what exactly we do. In Dr. Guest’s article The ‘Rockstars’ of 2020, we gained a new way of thinking about our role in society. The quote: 


“In our work, the forgotten past and the unrealized outcomes are our principal indicators of success. Long, healthy lives, not fanfare, signal our victory.”



9. As the need for (and public misunderstanding of) COVID models increased throughout the Spring, Dr. Samuel Jenness provided us with a background on modeling and its application in infectious disease epidemiology in his article Modeling COVID-19. The quote:


“In one sense, models prove their utility in the absence of bad news if they stimulate public action towards prevention, which may have an effect on the shape of the future epidemic curve. In the short-term, public consumers of models may not be able to fully determine the technical quality of that research. But it is important to understand that priorities of newspapers and politicians, and what they find useful in some models, may differ substantially from strong scientific principles.”



10. As the economic impact of COVID became clearer, students wondered what the pandemic meant for their career opportunities. In her article, Job Hunting in the time of COVID, ADAP Noni Bourne gave us some of her insights into the current hiring atmosphere. The quote:


“Human connection is taking on a completely different role in our lives. More than ever, it will be critical to know the person behind the email and to forge relationships that might not have otherwise been so central to success in the workplace.”



11. In Work and Study Efficiency in Difficult Times, Dr. Lash helped us understand that all days are not created equal and, especially today, challenges with motivation and work efficiency are both normal, and acceptable. The quote:


“It might also be helpful to envision what success will look like for you in the long term. We will all one day tell the stories of what happened to us and what we did during the COVID-19 pandemic. You will want to say that you did your part and put your shoulder into it as best you could. Imagine your future self and the story you will want to tell, and then make it so.



12. In our final PROspective article of the semester, Dr. Christiansen-Lindquist helped trace a path towards identifying APE opportunities during the pandemic in her article APEs: The Best Laid Plans… of 1st-Year Spring. The quote:


“Although your APE might not look like what you had planned, I would encourage you to view this as a speed bump, rather than a roadblock. Our capacity for resiliency is far greater than any of us can comprehend, and these challenging times have the potential to bring out creativity that we didn’t know that we had.”



From all of us on the editorial team, thanks so much for reading PROspective and congratulations for completing another semester! As we transition to our Summer publishing schedule, keep an eye out for more from PROspective going forward.

What was your favorite article this semester? Tell us in the comments!


Featured image from:

Summer Schedule: Confounder goes Bi-Weekly

Category : News/Events

Going to miss the Confounder over the summer? Don’t sweat it! We will still be sending the Confounder every other week until the beginning of the Fall semester. As always, you can navigate to the full website at any time for the most up-to-date content in between email weeks.

Have a great summer!

Modeling COVID-19

Category : PROspective

From Dr. Samuel Jenness, Assistant Professor, Department of Epidemiology:

The global pandemic of COVID-19 has raised the profile of mathematical modeling, a core epidemiological approach to investigate the transmission dynamics of infectious diseases. Infectious disease modeling has been featured in routine briefings by the federal COVID task force, including projections of future COVID cases, hospitalizations, and deaths. Models have also been covered in the news, with stories on modeling research that has provided information into the burden of disease in the United States and globally. Along with this coverage has also come interest in and criticism of modeling, including common sources of data inputs and structural assumptions.


In this post, I describe the basics of mathematical modeling, how it has been used to understand COVID-19, and its impact on public health decision making. This summarizes the material I discussed extensively in a recent invited talk on modeling for COVID-19 global pandemic.


What Are Models?

Much of epidemiology (with many exceptions) is focused on the relationship between individual-level exposures (e.g., consumption of certain foods) and individual-level outcomes (e.g., incident cancers). Studying infectious diseases break many of these rules, due to the interest in quantifying not just disease acquisition but also disease transmission. Transmission involves understanding the effects of one’s exposures on the outcomes of other people. This happens because infectious diseases are contagious. Sir Ronald Ross, a British medical doctor and epidemiologist who characterized the transmission patterns of malaria in the early 20th century, called these “dependent happenings.”


Dependent happenings are driven by an epidemic feedback loop, whereby the individual risk of disease is a function of the current prevalence of disease. As prevalence increases, the probability of exposure to an infected person grows. And prevalence increases with incident infections, and this is driven by individual risk related to exposure.

These dependencies create non-linearities over time, as shown in the right panel above. At the beginning of an infectious disease outbreak, there is an exponential growth curve. This may be characterized based on the doubling time in cumulative case counts. Epidemic potential can also be quantified with R0, which average number of transmissions resulting an infected individual in a completely susceptible population. The 0 in R0 refers to the time 0 in an epidemic when this would be the case; colloquially, people also use R0 to discuss epidemic potential at later time points. Therefore, R0 might shrink over time as the susceptible population is depleted, or as different behavioral or biological interventions are implemented.


Mathematical models for epidemics take parameters like R0 as inputs. Models then construct the mechanisms to get from the micro-level (individual-level biology, behavior, and demography) to the macro-level (population disease incidence and prevalence). This construction depends heavily on theory, often supported by multiple fields of empirical science that provides insight into how the mechanisms (gears in the diagram below) fit together individually and together in the system.

Because of the complexity of these systems, and the wide range of mechanisms embedded, models typically synthesize multiple data streams from interdisciplinary scientific fields. Flexibility with data inputs is also important during disease outbreaks, when the availability of large cohort studies or clinical trials to explain the disease etiology or interventions with precision may be limited.


Fortunately, there are several statistical methods for evaluating the consistency of the hypothesized model against nature. Model calibration methods that test what model parameter values (e.g., values of R0) are more or less consistent with data (e.g., case surveillance of diagnosed cases). Sensitivity analyses quantify how much the final projections of a model (e.g., the effect of an infectious disease intervention) depend on the starting model inputs.

From Garnett et al, Lancet, 2011

Putting these pieces together, models provide a virtual laboratory to test different hypotheses about the often complex and counterintuitive relationships between inputs and outputs. This virtual laboratory not only allows for estimation of projected future outcomes, but also testing of counterfactual scenarios for which complete data may not be available.


How Are Models Built and Analyzed?

There are many classes of mathematical models used within epidemiology. Three broad categories are: deterministic compartmental models (DCMs), agent-based models (ABMs), and network models. DCMs divide the population into groups defined, at a minimum, by the possible disease states that one could be in over time. ABMs and network models represent and simulate individuals rather than groups, and they provide a number of advantages in representing the contact processes that generate disease exposures. DCMs are the foundation of mathematical epidemiology, and provide a straightforward introduction to how models are built.


Take the example in the figure below of an immunizing disease like influenza or measles, which can be characterized by the disease states of susceptible (compartment S), infected (compartment I), and recovered (compartment R). Persons start out in S at birth, then move to I, and then to R. The flow diagram, kind of like a DAG, defines the types of transition that are hypothesized to be possible (and by an omission of arrows, which are hypothesized not). Movement from S to I corresponds to disease transmission, and the movement from I to R corresponds to recovery. There may be additional exogenous in-flows and out-flows, like those shown in the diagram, that correspond to births and deaths.

The speed at which transmission and recovery occur over time is controlled by model parameters. These flow diagrams are translated into mathematical equations that formally define this model structure and the model parameters. The following set of equations that correspond to this figure. These are differential equations that specify, on the left-hand side, how fast the sizes of the compartments change (the numerators) over time (the denominator). On the right-hand side are the definition of the set of flows in and out of each compartment.

One flow, from the S to I compartment, includes the λ (lambda) parameter that defines the “force of infection.” This is the time-varying rate of disease transmission. It varies over time for the reasons shown in the epidemic feedback loop diagram, shown above, and formalized in the equation below. The rate of disease transmission per unit of time can be defined as the rate of contact per time, c, times the probability that each contact will lead to a transmission event, t, times the probability that any contact is with an infected person. The last term is another way of expressing the disease prevalence; this is the feature of the feedback loop that changes over time as the epidemic plays out.

The overall size of transitions is therefore a function of these model parameters and the total size of the compartments that the parameters apply to. In the case of disease transmission, the parameters apply to people who could become infected, or people in the S compartment. Once all the equations are built, they are programmed in a computer, such as the software tool for modeling that I built called EpiModel. To experiment with a simple DCM model, check out our Shiny app


More complex models build out the possible disease states, for example, by adding a latently infected but un-infectious stage (called SEIR models). Or they add another transition, by adding an arrow from R back to S in the case that immunity is temporary (called SIRS models). Or they add extra stratifications, such as age groups, when those strata are relevant to the disease transmission or recovery process. By adding these stratifications, different assumptions about the contact process are then possible; for example, by simulating a higher contact rate for younger persons or concentrating most of the contacts of young people with other young people. These additional model structures should be based on good theory, supported by empirical data.


How Have Models Been Used to Understand COVID-19?

Mathematical models have been used broadly in two ways in the current COVID-19 global pandemic: 1) understanding what has just happened to the world or what will soon happen; 2) figuring what to do about it.


In the first category, several models have estimated the burden of disease (cases, hospitalizations, deaths) against healthcare capacity. The most famous of these models is the “Imperial College” model, led by investigators at that institution, and published online on March 16. This is an agent-based model that first projected the numbers of deaths and hospitalizations of COVID in the U.K. and the U.S. against current critical care capacity under different scenarios. In the “do nothing” scenario, in which there were no changes to behavior, the model projected 2.2 million deaths would occur in the U.S. and over 500,000 in the U.K.

The model also included scenarios of large-scale behavioral change (an example of the second category of use, what to do about it), in which different case isolation and “social distancing” (a new addition to the lexicon) measures were imposed. Under these scenarios, we could potentially “flatten the curve,” which meant reducing the peak incidence of disease relative to the healthcare system capacity. These changes were implemented in the model by changing the model parameters related to the contact rates; in this case, the model structure and the contact rates were stratified by location of contacts (home, workplace, school, community) and age group.

After these models were released, the U.S. federal government substantially changed its recommendations related to social distancing nationally. There was subsequent discussion about how long these distancing measures needed to be implemented, because of the huge social and economic disruption that these changes entailed. One high-stakes policy question was whether these changes could be relaxed by Easter in mid-April or perhaps early Summer.


The Imperial College model suggests that as soon as the social distancing measures are relaxed (in the purple band) there will be a resurgence of new cases. This second wave of infection was driven by the fact that the outbreak would continue in the absence of any clinical therapy to either prevent the acquisition of disease (e.g., a vaccine) or reduce its severity (e.g., a therapeutic treatment). Particularly concerning with these incremental distancing policies would be if the second wave occurred during the winter months later this year, which would coincide with seasonal influenza.

An update to the Imperial College model was released on March 30. This model projected a much lower death toll in the U.K. (around 20,000 cases, compared to over 500,000 in the earlier model). This was interpreted by some news reports as an error in the earlier model. But instead, this revised model incorporated the massive social changes that were implemented in the U.K. and other European countries over the month of March, as shown in the figure below. Adherence to these policies were estimated to have prevented nearly 60,000 deaths during March.

This is just one of many mathematical models for COVID. Several other examples of interest are included in the resource list below. There has been an explosion of modeling research on COVID since the initial outbreak in Wuhan, China in early January. This has been facilitated by the easy sharing of pre-print papers, along with the relatively low threshold in building simple epidemic models. With this explosion of research, much of the world has become interested with modeling research as the model projections are very relevant to daily life, and fill the gap in the news coverage in advance with clinical advances in testing, treatment, and vaccine technologies. Because pre-prints have not been formally vetted in peer review, it can be challenging for non-modelers (including news reporters and public health policymakers) to evaluate the quality of modeling projections. We have seen several cases already where nuanced modeling findings have been misinterpreted or overinterpreted in the news.


As the adage by George Box goes: all models are wrong, but some are useful. This applies to mathematical models for epidemics too, including those for COVID-19. Useful models are informed by good data, and this data collection usually takes time. These data inputs for models may rapidly change as well, as was the case for the updated Imperial college model, so earlier model projections may be outdated. This does not mean that the earlier model was wrong. In one sense, models prove their utility in the absence of bad news if they stimulate public action towards prevention, which may have an effect on the shape of the future epidemic curve. In the short-term, public consumers of models may not be able to fully determine the technical quality of that research. But it is important to understand that priorities of newspapers and politicians, and what they find useful in some models, may differ substantially from strong scientific principles.



There are many resources for learning more about modeling, including my Spring Semester course at RSPH, EPI 570 (Infectious Disease Dynamics: Theory and Models). We use the textbook, An Introduction to Infectious Disease Modeling, by Emilia Vynnycky & Richard White, that provides an excellent overview of modeling basics. We also have open materials available for our summer workshop, Network Modeling for Epidemics, that focuses specifically on stochastic network models

In addition, here is a short list of interesting and well-done COVID modeling studies:


Samuel Jenness, PhD is an Assistant Professor in the Department of Epidemiology at the Rollins School of Public Health at Emory University. He is the Principal Investigator of the EpiModel Research Lab, where the research focuses on developing methods and software tools for modeling infectious diseases. Our primary applications are focused on understanding HIV and STI transmission in the United States and globally, as well as the intersection between infectious disease epidemiology and network science.