Collaborative Studies: Are They Worth It?

In a small evening session at the 51st annual Society for Epidemiologic Research meeting in Baltimore, MD, a group of epidemiologists from Johns Hopkins Bloomberg School of Public Health discussed the how and why of collaborative studies. Dr. Bryan Lau, Dr. Keri Althoff, Dr. Josef Coresh, Dr. Jessie Buckley, and Dr. Lisa Jacobson each presented on a key aspect of conducting successful collaborative studies, from getting investigators on board, to various data methods to address the inherent challenges of such heterogenous data.

Even after a long day of workshops, this session piqued my interest. The discussions – both candid and practical – addressed sides of collaborative science I had never thought of.

Note: This post will be 1 of 3 (or more!) on collaborative studies. Scroll to the end to see other topics I plan to address and leave a comment or Tweet us with feedback or questions.

Not All Collaborative Studies Are the Same

Let’s start with what we’re talking about when we say “collaborative study”. Dr. Lau presented this working definition: a collection of multiple independent studies collaborating together for a scientific goal.

This broad definition groups together many different types of collaborative studies, from multi-site randomized trials with standardized protocols to pooled data from several different cohort studies. What these different examples have in common, however, is at least some overlapping data elements and buy-in from leaders of each participating study. I learned that these two ends of the collaborative science spectrum are often driven by common disease versus common population.

the spectrum of collaborative studies

Common Disease

Studies of the same disease area typically have extensive overlap of data elements that are key in analyzing the condition. Dr. Lau gave the example of HIV, with the North American AIDS Cohort Collaboration of Research and Design (NA-ACCORD), and CD4 count, viral load, and other measures almost always collected in HIV/AIDS research.

How could we apply that to cardiovascular and chronic disease research? It would be nearly unheard of to explore a heart disease question in a data set lacking medical history of diabetes, stroke, MI, hypertension; clinical measures such as total cholesterol, LDL-cholesterol, triglycerides, and troponin in acute care questions.

Common Population

In contrast, if we want to examine childhood predictors of cardiovascular disease, we may combine different cohorts that start following participants at a young age. Unfortunately, these cohorts may be centered around different research questions – environmental exposures, asthma, developmental disorders – and may lack the research elements we want for cardiovascular risk, like basic lipid panels. Alternatively, some cohorts may have half of the data elements we want, but the other cohorts have the other half, and there’s nothing overlapping between them. How would we approach our analyses? We’ll talk about that in my post next month.

For now, let’s wrap up with a summary of the pros and cons of even conducting a collaborative study. With the picture I’ve painted so far, it seems like it can be frustrating, challenging, and perhaps not even doable.

Want to dig in more? Check out this paperCollaborative, pooled and harmonized study designs for epidemiologic research: challenges and opportunitiespublished earlier this year in the International Journal of Epidemiology, by Drs. Catherine Lesko, Lisa Jacobson, Keri Althoff, Alison Abraham, Stephen Gange, Richard Moore, Sharada Modur, and Bryan Lau.

Why Should You Conduct a Collaborative Study?

The two main reasons we often put together collaborative studies is to increase sample size and try to increase generalizability.

Sample Size

Often a collaborative study can address research questions that aren’t answerable in the independent contributing studies – due to a lack of statistical power.

If you’re studying a rare outcome or exposure, or want to conduct subgroup analyses, you need numbers.


With increased sample size, you might think we have a better chance at generalizability. That’s a common misconception too large to address today, but you’re line of thinking isn’t completely wrong. 

By combining different study populations, we’re getting closer to emulating a target population (if that is your target population), and that is why we have the potential for increased generalizability.

Note that I said potential – this segues into our discussion of the cons (or as I like to call them, challenges to overcome) in conducting collaborative studies.

Bigger Not Always Better

Increased sample size does not guarantee generalizability, as I outlined above. Similarly, all of that data coming in from each individual study may be subpar in data quality, and then you can’t combine it for your rare disease analysis or subgroup analyses. What will you do then? (Hint: in the next post on analyses strategies for collaborative studies, we’ll talk about how to optimize your meta data methods).

What else? Like I mentioned before, you may have all of your data elements measured in your contributing studies, but with no overlap. That can lead to unbalanced confounders. Let’s say all of your clinically measured hypertension variables are from two large studies out of the ten you’re combining. Are those two studies representative of the others? A similar issue is overall data harmonization, which can be thought of as a form of complete case analysis. Do you conduct your analyses with the lowest common denominator data elements – those that all studies have in common? We’ll talk more about meta data visualization and individual pooled analyses in the next post.


How do you get buy-in from each study? Can you imagine the egos and the bureaucracy? Dr. Joe Coresh had some great advice from his work with Morgan Grams and the CK-EPID collaboration on how to smooth over logistical issues, from data use agreements, computational infrastructure, and transparency in procedures. Dr Lisa Jacobson had great advice, too – how to involve everyone in analyses, give primary authorship to contributing study investigators, and other tips and tricks for a successful collaboration. We’ll talk about that in post 3 of 3. Looking forward to it – hope you are!

Upcoming Posts:

  • Analytical strategies for harmonizing data in collaborative studies
  • How to conduct a successful collaborative study: the nitty gritty
  • Interested in something else specific? Leave a comment or tweet us @BaileyDeBarmore and @AHAMeetings to let us know!

Follow Drs. Althoff and Lau on Twitter for great EPI methods tweets

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Bailey DeBarmore is a cardiovascular epidemiology PhD student at the University of North Carolina at Chapel Hill. Her research focuses on diabetes, stroke, and heart failure. She tweets @BaileyDeBarmore and blogs at baileydebarmore.com. Find her on LinkedIn and Facebook.


Shared Decision Making In Cardiac Care

Illustration of seniors with life insurance

Shared decision making (SDM) is an approach both patients and clinicians can use to improve patient education and discussion in decision making. Decision aids are tools that promote SDM by improving patient-clinician communication about all treatment options and how the risks and benefits fit with their personal values and preferences. Today, we’ll chat about SDM in the context of cardiac care, with examples from atrial fibrillation and heart failure, as well as the entire CAD spectrum.

A Chronological Look at SDM
In 2012, Karen Sepucha reflected on shared decision-making and patient decision aids in an editorial for Circulation: Cardiovascular Quality and Outcomes. Two studies published in the same issue evaluated the impact of decision aids in urgent management of acute coronary syndrome (The Chest Pain Choice Decision Aid) and vascular access choice for coronary angiogram procedures. These studies reflect scenarios that are not traditionally thought to be amenable to SDM, but the results show that SDM in the emergency department and regarding technical treatment decisions, such as femoral versus radial access, had a positive impact on patient knowledge and decisional conflict. Sepucha’s concluding statement still rings true today: “Despite considerable evidence from many studies of decision aids, few of these tools are used routinely in practice.”

There are several challenges to using decision tools in practice. Decision aids should be brief and easily incorporated into the workflow of clinical practice, as well as easily accessible in the public domain. In 2015, CMS implemented a policy around healthcare for atrial fibrillation patients requiring all non-implanting physicians to use evidence-based decision tools and SDM practices. A 2017 article by Megan Coylewright and David Holmes in Circulation caution against this CMS mandate for SDM for patients with atrial fibrillation. The authors cite reasons reminiscent of Sepucha’s conclusions in 2012. Coylewright and Holmes point out that there is limited guidance on navigating SDM for patients with atrial fibrillation at risk for stroke. Specifically, while research consistently shows that “decision aids improve patient outcomes including knowledge, engagement, and satisfaction”, SDM has yet to become an integral part of clinical practice for a variety of reasons, both on the patient side and the clinician side. For example, barriers to use of SDM by physicians include insufficient training in the skillsets necessary to implement SDM, such as “inadequate assessment of patient preferences,” in addition to logistic challenges to implementation, whether at the institutional level, the lack of publicly available decision aids, or the time availability during patient-clinician interactions to thoroughly discuss patient preferences.

Atrial Fibrillation
Atrial fibrillation presents a particularly challenging case for optimizing treatment, due to low prescription, low adherence due to side effects and frequent testing, as well as increased risk of negative reactions due to polypharmacy. Thomson et al. found that atrial fibrillation patients at risk of stroke may deicide to forgo treatment with anticoagulants to avoid the medications’ adverse effects, with the knowledge that their stroke risk later on increased. However, professional guidelines on treatment of atrial fibrillation promote SDM and use of evidence-based decision tools while recognizing that inviting patients to participate in care decisions can be challenging to physicians.

A recent systematic review, “Availability of Patient Decision Aids for Stroke Prevention in Atrial Fibrillation” by O’Neill and colleagues summarize the current state of decision aids for patients with atrial fibrillation at risk for stroke and their treating physicians. The current landscape of pharmacologic therapy for stroke reduction includes multiple DOACs, implantable LAA closure devices, aspirin, and warfarin – each treatment with benefits and trade-offs that must be considered “in the context of individual values and preferences and willingness to adhere to therapy”. Many of the decision aids in the studies reviewed by O’Neill et al. did not display all therapeutic choices, were delivered to patients primarily by nonphysicians, and were not publicly available.

Depression and CAD
SDM and decision tools pair nicely with assessment of depression in heart disease patients. In a recent paper, “Identifying and Managing Depression in Patients with CAD,” Aimee Salzer Pragle and Susan Salashor discuss the epidemiology and risk factors, clinical presentation, assessment tools, and treatment options for depression in patients with heart disease. In a 2011 review, Christopher Celano and Jeff Huffman estimated that 20-40% of patients with CAD may suffer from depression, and despite the availability of screening tools brief enough for clinical practice (such as the Patient Health Questionnaire-9 or -2), depression often goes unrecognized in cardiac care. Depression in patients with CAD is associated with progression of heart disease, poor quality of life and physical functioning, repeat cardiac events, and 2-2.5 fold increased risk of mortality [link]. Risk factors for depression in patients with CAD include “younger age, female sex, a history of depression, social isolation, previous cardiac events, and diabetes” and depression can increase risk of suicide, homicide, and substance abuse (which can exacerbate cardiac symptoms and promote further degeneration).

Innovative Frameworks – A Segue to Implementing SDM?
From the clinician side, disease-specific evaluation frameworks inclusive of all relevant aspects of a patient’s health provide another avenue towards optimizing patient-clinician communication. For example, in a recent paper, Gorodeski et al. present a multi-domain framework for managing heart failure and the associated questionnaires and tools that go with each task.

Gorodeski image
These tools include the Mini Nutritional Assessment Short Form (MNA-SF), the Mini-Cog and PHQ-2 (Patient Health Questionnaire) for mental and emotional state, gait speed, timed Up and Go test, FRAIL questionnaire, and ADL/IADL discussion for assessing physical function, as well as inquiries to social support at home, adaptable and safe environment plans, access to nutrition and transportation, as well as abilities and support in medication management (Table 1, Gorodeski et al).

 Does your department or institution use shared decision making or decision aids in practice?

 What barriers to implementation do you see in incorporating these tools into everyday patient interactions?

SDM and Decision Aids Resources
The Ottawa Hospital Research Institute hosts an up-to-date A to Z inventory of patient decision aids, and includes tools for angina, atrial fibrillation, CABG and other cardiac procedures, blood pressure medications, peripheral artery disease surgery, type 2 diabetes, depression, kidney disease and dialysis, and many others. Many of the tools geared towards cardiac care were in the form of informative articles for patients to read, or interactive tutorial-style pages for patients. None that I found were visual or brief enough to be used in clinical practice. Additionally, purely text-based “decision aids” are not useful for low-literacy populations, and online-only resources are not easily accessible for patients who are less tech-savvy. However, the Heart to Heart tool developed by the University of North Carolina General Internal Medicine department provides a user-friendly interface, even for the less technologically-savvy patients, that guides the user through several slides on how to join the discussion of their own treatment with their doctor. Similarly, the AHA Rise above Heart Failure initiative has two resources heart failure patients and physicians might find particularly useful: a guide to overcoming barriers to shared-decision making and a list of questions to ask the doctor. If you are a physician, reviewing these materials provides you with the guidance of what questions and topics to address during the clinic visit.

Quick links to select decision aids related to heart disease:


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Bailey DeBarmore is a cardiovascular epidemiology PhD student at the University of North Carolina at Chapel Hill. Her research focuses on diabetes, stroke, and heart failure. She tweets @BaileyDeBarmore and blogs at baileydebarmore.com. Find her on LinkedIn and Facebook.


Storytelling In Science: A Different Way To Communicate

Does it bother you when a news article misinterprets a study finding?

An article published in the British Medical Journal in 2014 described an association between exaggeration in academic press releases and subsequent health-related science news pieces. Exaggerations included “explicit advice not indicated in the journal article” and use of stronger causal language. But don’t bury your head in the sand just yet. In a post about the media and your research, Science Magazine makes a good point: if you don’t tell the story of your science, someone else will – and they won’t do it well. From a more optimistic perspective, engaging the media about your research will “help educate the general public, and promote a more positive attitude towards research.” With that in mind, let’s talk about how to communicate with the media, so that your science gets communicated.

A survey of scientists found that many “worry about being misquoted and find journalists unpredictable.” The lack of formal media training in most scientific programs means when you get a call from a reporter, it’s sink-or-swim. The surge in science communication online brings scientists closer to reporters and closer to the public. Whether you’re interviewed by a journalist, or crafting your own message for the public, learning to communicate your research findings succinctly can help you avoid 3 errors often found in science journalism: errors by simplification, errors by omission, and false statements.

These errors stem from a lack of understanding by both parties – journalists of the research topic, and scientists of the purpose of the article. Simplification is necessary to communicate key findings, and the purpose of science news article is to convey that big idea and the ‘So What?’ implications to the general public. Errors by omission follows, as many details crucial to a research paper just aren’t necessary in the media counterpart. In my last post, I wrote about tips for constructing effective elevator speeches, with the focus on narrowing a project down to the main idea. Being at the center of your research means we’re often surrounded by the details and see them all as important. But distilling the main idea down into that ‘So What?’ implication, and then adding on how you did it, is a key skill that everyone can develop with a little work.

Storytelling in Science

Robin Smith, PhD and science writer, shared her tips for telling a story with your science, at a talk with the Science Writing and Communication club at University of North Carolina at Chapel Hill. Beyond communicating the main idea of your research, telling a story with your science is more effective than our traditional logical format. Don’t believe me? Reading narrative stories engages more regions of your brain compared to reading expository writing. The more parts of your brain engaged, the more you understand, and the more you remember. Michael Dahlstrom says it well in his 2014 paper “Using Narratives and Storytelling to Communicate Science with Nonexpert Audiences:” when we move to data collection to science communication,

AHA ECR May2018 Storytelling in Science

narratives are not only more appropriate but potentially more important.

Here’s Dr. Robin Smith’s 3 tips on telling a story with your science.

1. Lead with the Back Story
Ask yourself how you came up with this idea. It’s a great prompt for interviews with journalists or discussion at a family reunion. When we grab on to a research idea, we develop it, we do it, we publish it, and we continue to think forward. But it’s often our previous work that spurs our future work. Back pedal for a moment and start with a brief explanation of what you did, but then elaborate on why you did it. It may seem overly simple, but I guarantee you’ll replace blank stares with nodding heads.
2. Begin in the Middle
This tip is one of my favorites. When you’re reading a journal article, it’s up to you where you stop and start. But when you’re listening to a talk, you’re stuck. Expository writing provides all the information in that same logical order, but that doesn’t mean it’s the most interesting order. If your research involves a wacky method, or your results were particularly surprising, start there – catch your reader’s interest – and then go back to fill in the earlier parts. I think her example involved camping in a blizzard to study penguin behavior. Pretty wacky, right?
3. Pick a Data Point
Dr. Smith illustrated this last tip with (you guessed it) a story. She spoke about a man who lost his leg in a work accident, and felt limited by his prosthesis in simple tasks, like standing up from a chair. Our natural limbs, connected to our brain via nerves, anticipate our next movement. But many prostheses lag behind.

Why was she talking about prostheses? A researcher at North Carolina State University, Dr. Helen Huang, uses science to develop advanced control of adaptive, efficient, and safe robotic prostheses.

Picking a data point means describing a person, a place, or an event, that illustrates the problem your research studies or the impact of a solution it points to. Can you think of a ‘data point’ in your research?

Effective Communication = Clinical Impact

Does the idea of storytelling in science still make you cringe? Worried about your work being distorted into a sensationalizing headline? Or that you’ll end up writing to entertain, not inform? Or maybe you barely interact with the media and have no desire to change that in the future. Narrative science writing and story telling may have a bigger role in your science future than you think. Whether you write a guest post on your hospital’s blog, develop new patient communication materials, or you’re presenting at this week’s M&M, engaging your reader will make your science understood, and that’s what you want, right?

Communicating science effectively has direct clinical implications: it can help a patient overcome barriers to seeking treatment; it can provide a role model for behavior change through an anecdote; and the effective delivery of a science-based message via narrative writing will create patient attitudes strengthened by both a cognitive and emotional basis.

How do you communicate science?

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Bailey DeBarmore is a cardiovascular epidemiology PhD student at the University of North Carolina at Chapel Hill. Her research focuses on diabetes, stroke, and heart failure. She tweets @BaileyDeBarmore and blogs at baileydebarmore.com. Find her on LinkedIn and Facebook.



Connecting At Conferences: Networking At Its Best

This March in New Orleans, the Council on Epidemiology and Prevention with the Council on Lifestyle and Cardiometabolic Health hosted the 2018 EPI Lifestyle Scientific Sessions. A great part of the early career programming this year were the Connection Corners. Small roundtable discussions led by two established investigators drew early career attendees to learn about grant writing, crafting effective elevator speeches, and improving their curriculum vitae (CV). I was lucky enough to listen in (while snapping photos and live tweeting the conversation) and have some highlights to share.

 Drs Brooke Aggarawal and Mercedes Carnethon talk with early career investigators about crafting your elevator speech at AHA Epi Lifestyles 2018 in New Orleans. 
Drs Brooke Aggarawal and Mercedes Carnethon talk with early career investigators about crafting your elevator speech at AHA Epi Lifestyles 2018 in New Orleans. 

Practice your Elevator Speech

Dr. Brooke Aggarawal and Dr. Mercedes Carnethon

An elevator speech is a 30-second “pitch” or summary named for when you find yourself in an elevator with a stranger, and they ask “What do you do?” Of course, you could encounter that question in many scenarios that necessitate an engaging, brief, and understandable response.
Dr. Brooke Aggarawal and Dr. Mercedes Carnethon had a variety of advice on how to develop your pitch, as well as some unique ideas. Dr. Carnethon’s biggest takeaway was to have several elevator speeches – one for each research topic, as well as one for various scenarios.

  • Brainstorm situations you may need an elevator speech
    • Family gatherings and dinners
    • Social gatherings with new friends
    • New colleagues at a conference
    • On an interview
    • In an elevator
  • Outline your research topics – maybe you study cardiovascular disease, but with a focus on sleep, or diabetes, or congenital abnormalities. You probably have a project or two that goes along with each of those topics.

The first step is to write down your speech and then simplify the details into one message per pitch. That means all the details and objectives and methods you might explain to your mentor or at a poster presentation won’t have a place in this elevator speech.

Dr. Brooke Aggarawal had some great advice. Avoid using jargon and be concise while still answering the question “So what?”. And the answer to “So what?” will be different depending on who you’re talking to.

When simplifying your project into a single method, you may try to:

  • Develop analogies that paint a picture for your listener
  • Open and/or close with a question that piques their interest

And lastly, practice practice practice! Especially if you’re preparing for an interview, this answer to “tell us about yourself” should roll of the tongue and demonstrate your passion for your research.

Drs.Christopher Imes and John Wilkins talk to early career investigators about “Boosting Your CV” at AHA Epi Lifestyle 2018 Connection corner.
Drs.Christopher Imes and John Wilkins talk to early career investigators about “Boosting Your CV” at AHA Epi Lifestyle 2018 Connection corner.

Boosting Your CV

Dr. Christopher Imes and Dr. John Wilkins

This Connection Corner was all about how to get your message across quickly and concisely in your curriculum vitae. Just like with resumes, your CV is typically skimmed by the reader, and despite the traditional format, there are some steps you can take to stand out.

Dr. John Wilkins pointed out that you should utilize the librarians – experts in information sciences – at your institution to summarize your “research impact”. They can do searches that summarize your publications, citations, etc. that you can include in a biosketch intro that precedes your CV. Plus, a biosketch is the ultimate way to make sure the first thing the reader sees is the message you want them to!

The laptops came out and Dr. Imes and Dr. Wilkins read and provided feedback on CVs for early investigators at various levels – PhDs, post-docs, and early career professors. The last point they drove home was that while it may seem inconsequential, pristine formatting and grammar is imperative in your final draft. Typos and bad spelling leave a bad impression that can move your CV to the “no” pile before they even finish reading it.

Drs. Norrina Allen and Deepak Gupta field questions at AHA EPI Lifestyle 2018 about grant writing.

Specific Aims

Dr. Norrina Allen and Dr. Deepak Gupta

The group around the table at the grant writing Connection Corner was an accomplished one! With 4 K-awards submitted or about to be between them, they had plenty of questions for Dr. Norrina Allen from Northwestern and Dr. Deepak Gupta from Vanderbilt.

The two main points of the discussion were to craft your career development plan in a way that conveys responsibility, innovation, and prospect to the grant readers, as well as:

Mentoring questions were a big part of this discussion. One person asked, “Should your K-award mentor be someone you’ve worked with before, or someone in the field you’re aiming to get new training in?” Drs. Allen and Gupta answered – definitely someone you have a connection with. They explained that the primary mentor on your K-award is for the career development, while the training goals you set will be met with the network of content and methods experts you put together. Similarly, they noted that the aims for your K grant should match the training domains you want to achieve.

Drs. Allen and Gupta emphasized that for career development grants, it’s important to be specific in your career trajectory section about how that grant is a stepping stone for your future goals. They mentioned being particularly impressed when grant writers state how that K-award (for example) will set them up to submit an R01 on topic “<enter Title>” in year # of their career development award. While you don’t need that R01 grant written up in your head, having an idea of the title and the timeline shows planning and promise.

Lastly, the classic question: How many aims should you have? The answer, like many things, is that it depends. Too many aims, or aims that are too diverse, will water down the focus of your overall proposal. The aims you craft need to be achievable during the time period, concise and straight forward, and should not be interdependent on one another, whether the success of one depends on the success of another.

If you couldn’t be there in person, I hope these pearls of wisdom were the next best thing! What’s on your to do list for career development this month?

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Bailey DeBarmore is a cardiovascular epidemiology PhD student at the University of North Carolina at Chapel Hill. Her research focuses on diabetes, stroke, and heart failure. She tweets @BaileyDeBarmore and blogs at baileydebarmore.com. Find her on LinkedIn and Facebook.



Shift Your Perspective To Get The Most Out Of Mentoring

The AHA Epidemiology and Prevention and Lifestyle and Cardiometabolic Health Scientific Sessions is quite different from AHA Scientific Sessions. Smaller in size and more focused, with few concurrent sessions and ample coffee breaks, I enjoyed attending the numerous Early Career sessions. They varied in topic and format: “Connection Corners” were short round-table discussions twice a day with focused conversations on ‘beefing up your CV’, the grant writing process, developing a catchy elevator speech, and leveraging non-NIH funding. Both the EPI-Prevention and Lifestyle councils had lunchtime panels at the end of their annual business lunches, and had the audience asking questions about avoiding burnout in academia and global collaboration in cardiovascular research.

To end the week, the Early Career Council outdid themselves with the early morning ‘fire-side’ chat with Drs. Emelia Benjamin, MD ScM from Boston University School of Medicine, Norrina Allen, PhD from Northwestern Medicine, Jean-Pierre Després, PhD from Laval University in Quebec, Chiadi Ndumele, MD MHS from Johns Hopkins Medicine, and Lenny Lopez, MD MDiv MPH from UC San Francisco.

 Drs. Emelia Benjamin, Jean-Pierre Depres, Chiadi Ndumele, Lenny Lopez, and Norrina Allen (left to right) provide eye-opening mentoring advice to early career investigators at the EPI Lifestyles Scientific Sessions 2018 in New Orleans, LA
Drs. Emelia Benjamin, Jean-Pierre Depres, Chiadi Ndumele, Lenny Lopez, and Norrina Allen (left to right) provide eye-opening mentoring advice to early career investigators at the EPI Lifestyles Scientific Sessions 2018 in New Orleans, LA.

If you missed this morning session, no worries! I have you covered. The panel conversation, led by Dr. Emelia Benjamin, started with finding your niche as an early career investigator, and developed into a great discussion on building a mentoring team and planning your own path.

Using Sli.do to anonymously ask questions allowed for an unbiased view of what the audience was thinking. And overwhelmingly were questions along the lines of:

  • What do you do if your mentor selects a niche for you that doesn’t excite you?
  • How do you separate your niche from your mentor?
  • What can you do to fix a fall-out with your mentor?

I found these questions concerning! To me, they reflect a mentee perspective that 1) once you’re assigned a mentor, you’re stuck with them; 2) your mentor is the be-all-end-all guide in your career path; and 3) you must do everything your mentor tells you.

My first response to this perspective is: we’ve got to shift this mindset! If your relationship with your mentor is that of a duckling and mother goose, something has got to change. A mentor that “assigns” a research niche to you is either a Tormentor or is responding to your lack of initiative. If the former, you should find a new mentor. Your institution will have a number of resources including a faculty affairs office or an ombudsman and possibly a mentoring program that will help you find a mentor that best fits with your needs.

If the latter, you’ve got some work to do! But the career panel provided some great advice on how to get started. (So do Vineet Chopra, MD MSc, Vineet M. Arora, MD MAPP, and Sanjay Saint, MD MPH in an article titled “Will you be my mentor?” published in JAMA last year).

Make the most of your time

Mentors have a number of responsibilities and how they have made their own career path and achieve work life balance is a great indicator if you will be a good fit. Do you aspire to a career like theirs? Do you admire their work-life balance? They might make a great life or career mentor for you.

Just as you expect your mentor to give you their full attention when discussing your goals, you must respect their time as well! That means giving thought to your research goals, planning the steps to get there, and using their expertise and experience to provide direction and improve your process.

Set up a meeting with your mentor and prepare an agenda beforehand. Know the topics you’d like to cover, whether their input on goals and milestones, plans for research projects, or ideas to brainstorm on. Preparing an agenda shows respect for both of your times and keeps you on track for a productive meeting. Jot down action items and follow-up after the meeting.

Judy T. Zerzan, MD MPH and coauthors discuss “managing up” and how to take responsibility for your half of the mentor-mentee relationship in “Making the Most of Mentors: A Guide for Mentees.”

Earlier this year, Dr. David Werho wrote about sponsorship versus mentoring in his 2-part article “When Mentoring Isn’t Enough”. Read Part 1 and Part 2 to learn about why dependability pays off, how to diversify and be the protégé you want, and why it’s worth it to do your homework.’

One is the Loneliest Number

Another solution to mentor woes is creating a mentor network. Over and over, the panel expounded on the advantages of having both a primary mentor and a mentoring group. This structure is explicit in career development grants, where the primary mentor supports your career development initiatives, and the content and methods experts support your training goals. Content and methods mentors in your network can also help you explore different areas in your field as you work to identify your research niche.

A mentor network means different researchers with different backgrounds and different perspectives. Bouncing your research ideas off them results in contrasting views, some that will jive more with you, and some that will make you think. Instead of being molded into a “mini”-me mentee, a mentor network helps you build the scaffolding upon which you’ll grow into your own independent researcher.

I’ll touch more on this idea later, but here’s a great read from Yan Shen, Richard D. Cotton, and Kathy Kram for the MIT Sloan Management Review. Even if you are post-tenure, you still benefit from a strong mentoring network! Read more from Kerry Ann Rockquemore in “Posttenure Mentoring Networks.”

Identifying a Niche

The pre-established theme of the Friday morning early career session was how to “Identify Your Niche”. While much of the discussion centered around mentoring and its supportive role in finding your niche, there was also focused advice on how to find your way.

The panel emphasized that as an early career investigator, it’s imperative to utilize this time to identify and achieve the additional training you see as important to your overall career goals. While this may be in the form of a post-doctoral position or a K-award, it can also be informal in the research projects you pursue and the skills you acquire.

Dr. Emelia Benjamin, who provides mentoring support to early career faculty at Boston University, gave us 2 homework assignments to help us plan our way.

First, reflect on where you’ve been and where you’re going. A 1-page personal statement makes a powerful addition to your CV, and the journey to this final product will help you learn to tell your story as a connected arc, rather than a zig-zag path jumping from topic to topic. The evolution of your research niche from project to project is hardly evident in your publication list, but through narration and self-reflection you can illustrate your approach to the scientific process and summarize where you might go next. Not only will you provide a picture of your research goals and personality to anyone reading your CV, but you will likely have a few “Aha!” moments discovering connections between projects you hadn’t seen before.

Second, diagram your mentoring network. It’s important to visualize this – are all of your mentors above you? Below you? Horizontal to you? Peers? Are they in the same division, institution, or all distance? A mixture is key, but the components of that mixture depends on your research and career goals. Dr. Chiadi Ndumele from Johns Hopkins Medicine shared his take on 5 valuable types of mentors to have:

  1. Methodological mentors are those you go to for questions and feedback about approach.
  2. Content or clinical mentors are those you go to about patient care of content expertise.
  3. Life mentors are those whose work-life balance is one you admire.
  4. Career mentors help you step back and see the big picture, particularly the asks you should say no to.
  5. A brainstorm mentor plays devils advocate and is a great sounding board to bounce ideas off of that also bounces back.

5 Valuable MentorsDr. Emelia Benjamin utilizes the theories from Kathy Kram, Monica Higgins, and David Thomas on “Creating Developmental Networks” and “Reconceptualizing Mentoring” with her early career faculty at BU. Take a cue from her, and use this worksheet, Define your Developmental Network, to identify the gaps in your mentoring network, and take the first step to filling them.

Bailey DeBarmore Headshot
Bailey DeBarmore is a cardiovascular epidemiology PhD student at the University of North Carolina at Chapel Hill. Her research focuses on diabetes, stroke, and heart failure. She tweets @BaileyDeBarmore and blogs at baileydebarmore.com. Find her on LinkedIn and Facebook.


AHA EPI | Lifestyle 2018 – Health Promotion: Risk Prediction To Risk Prevention

“Epidemiology is the study of the causes and distributions of diseases in human populations so that we may identify ways to prevent and control disease.”

(JM Last, A Dictionary of Epidemiology)

In a 2013 commentary, Sandro Galea reminds us of the definition of epidemiology [above] and notes that it “neatly communicates 2 central actions for the field:

  1. we identify causes so that
  2. we may intervene….

However, in practice, academic epidemiology now spends most of its time concerned with identifying the causes and distributions of disease in human populations and far less of its time and imagination asking how we might improve population health…”

In a seminal paper in 1985, Geoffrey Rose showed that populations are not the sum of their individuals, highlighting the difference between epidemiology for public health and individual-based medicine. In a recent paper, Dr. Rogawski and coauthors speak to this, pointing out that individual level risk factors identified in population based studies “do not always inform public health interventions since targeting of interventions occurs when individuals present to the healthcare system,” or “medical epidemiology.”

AHA EPI | Lifestyle Scientific Sessions – March 20-23, 2018 (New Orleans, Louisiana)

Later this month, AHA Epidemiology and Lifestyle Councils travel to New Orleans for the annual specialty conference. The theme? Health Promotion: Risk Prediction to Risk Prevention. The 4-day conference will feature 11 sessions, 3 poster sessions, 6 Early Career events, and more. Last year in Portland, Oregon, the conference focused on “Location, Location, Location: Improving Individual and Community Health,” and in 2015 in Baltimore, Maryland “From Precision Medicine to a Culture of Health.” The past 3 years parallel the surge of interest in consequentialist epidemiology, with noted efforts into precision medicine through mHealth interventions as well as theoretical interventions of moving population-wide blood pressure by 1 mmHg.

Drs. Daniel Rodríguez, Wayne Rosamond, and Robert Ross answer questions at Opening Sessions, AHA EPI I Lifestyles 2017 in Portland, Oregon.

Drs. Daniel Rodríguez, Wayne Rosamond, and Robert Ross answer questions at Opening Sessions, AHA EPI I Lifestyles 2017 in Portland, Oregon.

Drs. Darwin Labarthe, David Goff, and Donald Lloyd-Jones catch up before opening session in Portland, OR at AHA EPI | Lifestyle 2017. Make sure to get your Life’s Simple Seven pin at your next AHA conference!

Drs. Darwin Labarthe, David Goff, and Donald Lloyd-Jones catch up before opening session in Portland, OR at AHA EPI | Lifestyle 2017. Make sure to get your Life’s Simple Seven pin at your next AHA conference!
Early Career Events at AHA EPI | Lifestyle

Over this past year, I’ve become more active in the American Heart Association than I have in any other member organization and it’s all due to being an Early Career Blogger. After attending Early Career events at AHA Scientific Sessions in November 2017 – from luncheons to networking to panel sessions – I keep my eyes peeled for similar events at all conferences I attend. The focus for Early Career Events at EPI | Lifestyle this year will be on international collaboration in cardiovascular epidemiology through a “speed dating” format session on Thursday, and a roundtable luncheon on Friday. In addition, the Lifestyle Council will host a 3 Minute Thesis (3MT) Competition at their early career lunch, and early Friday morning is “Lost or Found?  Identifying your Niche in Academic Research.”

Don’t Miss Out!

Between the coffee breaks, be sure to catch these notable epidemiologists and scientists who will be speaking throughout the week in New Orleans. I think their research and background paint the perfect picture for a conference focused on health promotion.

I’ve included their Twitter handle when I can – so be sure to tweet them your questions, and tag #EPILifestyle18 so we can follow, too!

Health Promotion: Risk Prediction to Risk Prediction, Opening Remarks (Session 1)

Alfredo Morabia, MD, PhD, MPH, MSc is a professor of clinical epidemiology at Columbia University Mailman School of Public Health. His research spans from history of epidemiology and health ethics to urban health projects, such as health of first responders following 9/11. Tweet him @AlfredoMorabia.

Angela Odoms-Young, PhD is an associate professor at the University of Illinois at Chicago and a fellow of the Institute of Health Research and Policy, which aims to advance health practice and policy through collaborative research. Her current research projects at the Illinois Prevention Research Center include policy research and evaluation on environmental change related to nutrition and obesity. Tweet her @OdomsYoung.

Mintu Turakhia, MD, MAS, FAHA is an associate professor of cardiovascular medicine at the Palo Alto VA and Executive Director of Stanford University’s new Center for Digital Health. His research focuses on heart rhythm disorders through outcomes research and clinical practice. Tweet him @LeftBundle.

Hypertension: Guidelines and Prevention, Rapid Fire Oral Presentations (Session 2)

Paul Whelton, MD MSc will recap the new Hypertension Guidelines unveiled at #AHA17 and orient them within the guise of population health and disease prevention.

David Kritchevsky Memorial Lecture (Session 5)

Barry M. Popkin, PhD established the Division of Nutrition Epidemiology at University of North Carolina at Chapel Hill as well as the NIH funded UNC interdisciplinary Obesity Center. He developed the Nutrition Transition theory and studies these dynamic shifts in dietary intake and physical activity around obesity on a national and global scale.

Richard D. Remington Methodology Lecture (Session 9)

Joel Kaufman, MD, MPH is a physician epidemiologist and interim dean at the School of Public Health at the University of Washington. His research focuses on environmental factors in cardiovascular and respiratory disease, and is a PI on MESA Air.

William B. Kannel MD Memorial Lectureship in Preventative Cardiology

Emelia J. Benjamin, MD, ScM, FAHA is a professor at the Boston University School of Medicine and longtime researcher on the Framingham Heart Study. She focuses on the intersection of genetic, epidemiology, and prognosis of cardiovascular conditions and biomarkers, particularly atrial fibrillation. Tweet her @EmeliaBenjamin.

 Bailey DeBarmore Headshot
Bailey DeBarmore is a cardiovascular epidemiology PhD student at the University of North Carolina at Chapel Hill. Her research focuses on diabetes, stroke, and heart failure. She tweets @BaileyDeBarmore and blogs at baileydebarmore.com. Find her on LinkedIn and Facebook.



An Interview With A Physician-Epidemiologist

Many of my fellow bloggers here at AHA Early Career Voice are clinicians, and we’re all busy, and we all see the value in research. I wanted this post to speak to everyone who feels they’re spinning the plates of patient care, research, personal life, and having something to show for it all (besides broken china). Figuring answers from someone who makes it look easy would be a good place to start, I shot my colleague, Stephen Juraschek, MD PhD, an email.

Balancing Act

And you thought juggling was hard…

“Are you going to AHA EPI?”, I ask him on an afternoon phone call. “Yeah! Have you been to New Orleans before?” No, I reply. He hasn’t been either. We’re both excited to reconnect with colleagues in epidemiology and lifestyle prevention at the annual specialty conference held in March. This year it’s in New Orleans. Next year is Houston.

Connected by our time at the Johns Hopkins Bloomberg School of Public Health Welch Center for Prevention, Epidemiology and Clinical Research, we talk about what distinguishes a clinical investigator from an epidemiologist, and how he straddles both worlds. An internal medicine doc at Beth Israel Deaconess Medical Center, Stephen sees patients twice a week, and spends the rest of his time analyzing data, writing papers, and collaborating with colleagues.

I thought of Stephen for this feature post because I’m continuously impressed by the volume (and quality) of publications he produces. PubMed notes 64 articles authored by Stephen since 2008. A dual MD-PhD, he’s also got around 1,136 citations for his papers on diabetes, hypertension, the DASH diet, ARIC, and more. At AHA Scientific Sessions last November in Anaheim, California, Stephen presented his recent research on the DASH diet (read more in my post “Incorporating Scientific Sessions into Everyday Life”). In the few months since, he’s had 3 more first author papers go to print.

When asked how he balances work as a clinician and his research, he had some good pointers. While having protected research time from his K-award certainly helps schedule wise, his desire for his “research to be complimentary to what [he does] with patients in clinic” makes the straddle more seamless. While the topic, like blood pressure, may exist in both his worlds, the skills used are very different. “In clinic,” he starts, “you’re focused on that one patient, assessing priorities for that one visit. When you’re doing research, it’s macro, it’s population based.” The question that seems to drive Stephen is the desire to “understand diseases on a larger scale” and doing research to “move the needle of health towards benefitting more people”.

Switching gears, I launch into my next question.

“What do you feel are the keys to success as an early career investigator, whether from the clinical perspective or the research perspective?”

Without skipping a beat, he responds: “It depends on how you want to define success.” 

 Definition of Success
The key to success depends on how you define it.
And that is so true. We’ve all read an article or two about work-life balance, setting goals, planning out your career, and the like. But Stephen lays it out simply: “Reflect on what makes you happy,” he recommends, “and think about what gets you excited.” Personally, as a doctoral student, the task of finding a dissertation topic (or that I don’t have one yet) is daunting. It’s easy to push it to the back of my mind and focus on coursework and current research projects. I don’t kick myself for doing that, though, because I have a plan. I choose my research projects and experiences carefully, with the goal of exposing myself to many different advisors, working styles, topics, and methods. An older student reflected on how she came upon her dissertation topic – when she tabulated all the projects she had worked on up to that point, they all centered around one topic. But she didn’t see the pattern until she sat down and thought it through.

Stephen identified his passion earlier as making a lasting impact on others in a positive way. Expanding on this idea seen in many researchers, clinicians, and public health professionals, he notes that “for some people, that is excellence in patient care…and [for] other people it’s policy and implementation and integration of scientific discovery…and for others, it’s doing the science.” His current role as physician-epidemiologist is clear in his passion: “I strive to include clinical excellence in my professional trajectory, and at the same time, incorporating scholarship and generating novel insights from data to guide our care.”

He notes that, like many of us, his trajectory hasn’t been a straight shot. Our conversation morphs into one of mentorship, as he describes his strategy as finding projects he feels passionate about, and then finding other people passionate about that, too. But when he first started as a trainee, that strategy often meant finding a mentor passionate about something, and then trying that passion on for size. Working with different people on a number of projects helped him identify what worked for him, what didn’t, and let him refine his writing process because, quoting a colleague Joe Coresh at the Welch Center: “to write a good paper, you have to write a lot of papers.”

After going through my first peer-review publication process just recently, I was heartened to hear Stephen admit that despite his now-refined writing process, his first paper did not go so smoothly. A math major in undergrad, Stephen relates that while his quantitative skills were great, it took him his first few papers to get the hang of the scientific writing process.

“Research is a very humbling process,” he tells me. “There are always going to be people who find issues with your work, or think there is a better way to say something. It can be discouraging. I remember being a trainee and just wanting to throw in the towel sometimes. But persevering through the process, knowing that the process is meant to make the product better, is a key mentality to have in research. Every time I’ve written a paper, I feel I have a better sense of what the message should be.”

He brings up a larger issue. When we see a successful person, we don’t often see the struggle behind the success. Learning from difficult experiences is often what catapults someone into success, and you can learn from their experience, too. You just have to ask.

Last month I wrote about “Making Epidemiology Make Sense for Clinicians”. Along the same lines, we wrap up our phone call with a final thought – how can clinicians and epidemiologists come together?  

“Clinicians should feel empowered to make observations and ask practical questions.” Often, researchers are entrenched in data, not the day-to-day aspect of medicine, and “it can hamper the research process to not ask the right questions.”

When clinicians and epidemiologists partner together, they can leverage data to answer questions in a way that is very useful in the practice of medicine.

What plates are you spinning? I know I’ve got a few on board.

Bailey DeBarmore Headshot

Bailey DeBarmore is a cardiovascular epidemiology PhD student at the University of North Carolina at Chapel Hill. Her research focuses on diabetes, stroke, and heart failure. She tweets @BaileyDeBarmore and blogs at baileydebarmore.com. Find her on LinkedIn and Facebook.


Making Epidemiology Make Sense For Clinicians

I discovered epidemiology through an interest in evidence-based practice and clinical research. Seeing patients brought up research questions, and I wanted to be able to answer those with numbers. What I learned is that our results differed from the few published studies that crafted the informal, “word on the street” guidelines we abided by, not because their research was flawed, but because our patient populations were different. Had the situation been two Table 1’s side by side, we would’ve seen the clear demographic differences.

Hannaford and Owen-Smith did a proof-of-concept literature search in 1998 to see how many population studies (epidemiologic studies) provided relevant data to answer their specific clinical question. There are a few points of comparison between epidemiology and clinical practice here:

Clinical vs Epidemiologic Research

So, this is where adjustment versus stratification comes in. Multivariable adjustment is a statistical method that attempts to isolate the effect of our exposure (oral contraceptives) on the outcome (cardiovascular risk). We often adjust for factors related to both, because we don’t want a relationship such as age (younger women more likely to be on oral contraceptives and are at decreased risk of cardiovascular events compared to older women) to secretly be explaining a statistical association. Specifically, Hannaford and Owen-Smith note that “in effect, these adjustments level the epidemiological playing field so that the real effects of combined oral contraceptives can be determined, but at a cost of losing information about the effects of the adjusting factor (in this case smoking) among contraceptive users.” There are many other ways to control for confounding, such as randomization, restriction, matching, stratification, and of course, adjustment. But more often than not in epidemiology, we use adjustment because it’s answering our question.

The clinical mind searches for subgroup analysis as the most efficient way to answer the question “What about my patient?” Such as – “What about men? Women? Comorbidities?” without having to calculate a beta coefficient (given the authors even provided it). In other words, instead of smoothing out the data over all possible groups (smokers, those with diabetes, etc.), we want to plot individual points on the graph.

Hannaford and Owen didn’t find many epidemiologic studies that answered their very specific clinical question in 1998 – hopefully the odds would be higher 20 years later. But, compromising epidemiologic methods or clinical methods isn’t the answer to meet in the middle. So, what can we do? Epidemiology provides methods to systematically think about patterns and causes of disease for the clinician. Many of my colleagues are physicians seeking additional research training. They compare anesthesia protocols for outcomes after colon cancer surgeries, while my academic colleagues look at cumulative environmental exposures and lifetime risk of colon cancer. The overarching topics are similar, but the questions and resulting methods are incredibly different.

How do we make population studies more relevant to clinicians? There are many ways, and I’d love to hear your thoughts, but some to get us started include: interdisciplinary teams of epidemiologist and clinicians when designing studies and analyses; utilizing different but valid methods such as stratification with or instead of adjustment (and powering our studies for subgroup analysis), and…what else?

Bailey DeBarmore Headshot
Bailey DeBarmore is a cardiovascular epidemiology PhD student at the University of North Carolina at Chapel Hill. Her research focuses on diabetes, stroke, and heart failure. She tweets @BaileyDeBarmore and blogs at baileydebarmore.com. Find her on LinkedIn and Facebook.


Back To Reality: Incorporating Scientific Sessions Into Everyday Life

Nearly 2 weeks after AHA Scientific Sessions 2017, I’m back at home, sipping coffee on a chilly Sunday morning and thinking about Anaheim. The larger-than-life convention center, the numerous and packed sessions, and the built-in-a-day pharma fueled exhibit halls.

Working backwards, I remember fitting in a lunch sponsored by Amgen, given by Dr. Alan Brown, Director of Cardiology at Advocate Lutheran General Hospital. It boasted boxed lunches but lacked elbow room, but by the end of the hour, I was impressed.

As a trained dietitian, I’m aware of at least some of the challenges in providing patient care. With new lipid guidelines, new blood pressure guidelines, new everything guidelines, up until now, the ease of popping a pill has seemed to rise above the effectiveness of lifestyle changes.

For the first time, I heard physicians calling on one another to sit face-to-face and eye-to-eye with their patients, and ask them about their physical activity. And their eating habits. In Dr. Brown’s words, by asking about these topics, you communicate to your patients that they are important.

Vegetables on the kitchen counter

(The DASH Diet stands for Dietary Approaches to Stop Hypertension and is rich in fruit, vegetables, low-fat dairy while reduced in saturated fat and cholesterol. Content Provider: CDC/Amanda Mills. 2011)

At this same conference, Dr. Stephen Juraschek presented his results using the DASH diet – “The Effects of Sodium Reduction and the DASH Diet in Relation to Baseline Blood Pressure,” published just a few weeks ago. The investigators randomized adults with pre- or stage 1 hypertension (and not using blood pressure lowering medications) to DASH diet or control diet. Then in random order, over 4 weeks with 5-day breaks, participants were fed at 3 sodium levels: 50, 100, 150 mmol/day at 2,100 kcal. And what did they find? Adopting the DASH diet in combination with reduced sodium intake achieved “progressively greater reductions at higher levels of baseline SBP [≥150 mmHg]”.

So why am I talking about lifestyle modifications in a post about incorporating conference learnings back into your everyday reality at work? Well, a big announcement that came out of AHA17 was the new hypertension guidelines. I noticed recurrent statements and questions about these guidelines, in presentations, on social media, and from my peers when I returned home. 

At our first peer led research meeting back from AHA17, I printed off a few copies of the Top Ten Things To Know (PDF) about the 2017 hypertension guidelines. We touched on the implications of new classification categories – more treatment, higher prevalence, changes in comparisons over time in our epidemiologic studies. 

Connie Alfred (left), of the National Center for Infectious Diseases (NCID), was shown having her blood pressure taken by Robyn Morgan, of the National Center for Chronic Disease Prevention and Health Promotion

(Connie Alfred (left), of the National Center for Infectious Diseases (NCID), was shown having her blood pressure taken by Robyn Morgan, of the National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP), during a free blood pressure screening event that was held on all CDC campuses in 2005. Content Provider: CDC/CDC Connects. 2005.)

We were happy to see the focus on accurate measurement of BP, ensuring adequate rest time and taking averages of measurements, a technique we use in epidemiologic studies to minimize measurement error. Those of us particularly interested in physical activity and nutrition epidemiology rejoiced at the lifestyle modification efforts. We closed the discussion with an acknowledgement of conflicting and numerous other guidelines, the reality of putting them into practice – from primary care to cardiology clinics – as well as misinformation in the media coverage of the guidelines, such as misquoting the relaxed recommendations for older adults. 

With so much to chew on, I closed the discussion encouraging everyone around the table to think more on the implications of new guidelines, and our role in developing them, implementing them, and evaluating them.

Bailey DeBarmore Headshot

Bailey DeBarmore is a cardiovascular epidemiology PhD student at the University of North Carolina at Chapel Hill. Her research focuses on diabetes, stroke, and heart failure. She tweets @BaileyDeBarmore and blogs at baileydebarmore.com. Find her on LinkedIn and Facebook.



Closing The Gap On Cardiovascular Health Disparities

Kicking off the #AHA17 session on Closing the Gap on Disparities: Practical Strategies and Implementation, Dr. Michelle Albert out of UCSF fits an astonishingly large amount of information into a succinct 15-minute talk on Improving Cardiovascular Risk in African Americans. She alludes to her research on psychological stress in the context of cardiovascular well-being being a function of adversity and resilience, divided by wealth, and cautions against interpreting wealth as income.

A feature article by the UCSF Cardiology department quotes her well as she explains that “some forms of adversity” are similar to post-traumatic stress disorder, and that while “…stress is a normal part of life…chronic, persistent stress…accompanied by a lack of control [of that stress]…is associated with hypertension, obesity, [and] inflammation.”

I hope that quote makes you think of the term “microaggressions”, a concept that has received note by many social media groups such as Buzzfeed, and a brief online search returns an article from Psychology Today in 2010 both defining the term, and providing examples.

She adds in today’s presentation that sleep disturbances disproportionately afflict African Americans, who are 5 times more likely to experience shorter sleep times compared to whites (adjusted for sex, age, and site).

Her closing call to action gave life to thoughts I’ve had the past few months as a doctoral epidemiology student.

“Epidemiologists are accustomed to describing things but we need to move on to taking those associations and putting them into practice, whether designing trials or conducting community based research for interventions”.

In the epidemiologic world of causal inference, I’m glad I am not the only one who’s asking when we will move from associations to interventions.

 Bailey DeBarmore Headshot
Bailey DeBarmore is a cardiovascular epidemiology PhD student at the University of North Carolina at Chapel Hill. Her research focuses on diabetes, stroke, and heart failure. She tweets @BaileyDeBarmore and blogs at baileydebarmore.com. Find her on LinkedIn and Facebook.