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A Profile in Mentorship: Dr. Thomas Pearson

Every scientist, even those who are particularly good at hiding away in their offices, will have an impact on others. Most of the time, this impact is a byproduct of our everyday work. We don’t give it much thought – it just happens. But what if that impact was not accidental, and instead was a deliberate, strategic path of choices that build up those around us – even those who disagree with us, compete with us, and threaten us? That is the path that has been trodden by Dr. Thomas Pearson who was awarded the 2019 AHA Council on Epidemiology and Prevention Mentoring Award.

Dr. Pearson has an impeccable academic pedigree and an enviable career. After an early start at the University of Wisconsin, he earned his Bachelor of Arts, MD, MPH, and PhD all from the Johns Hopkins University. He has achieved the goal of every early career scientist – over 35 years of continuous NIH support and is a Fellow of the American Heart Association, American College of Cardiology, the American College of Preventive Medicine, and the American College of Physicians. But the degrees, grants, and accolades are a byproduct of a man driven to service for the love of science.

Dr. Pearson’s own mentors reflected his insatiable curiosity. As a student, he drew from a broad mentoring team that left lifelong impressions of the qualities of good mentor. While excellent teaching was important, more so was the “utterly frank” assessment and advice they provided him. He states, “from them I learned that the primary role of a mentor is to provide an honest, encouraging perspective on the mentee’s ideas, plans and experiences. While some mentors may be tempted to acquiesce or tell mentees what they want to hear- that is abrogation of their responsibility of a mentor.” Such frankness can be tough in today’s academic environment, so to help cultivate this skill, Dr. Pearson’s University of Florida developed the Mentorship Academy. Equally important to learning how to deliver a frank assessment of the mentee is helping the mentee learn how to receive and act upon such advice without taking umbrage to it.

Additionally, Dr. Pearson offered this advice on how early stage professionals can intentionally become effective mentors, including:

  • Be a good communicator. Communication is the basis of mentoring. Good communication should include developing shared expectations of the goals, responsibilities, and processes of the mentor-mentee relationship. Many of the problems that occur in the mentoring relationship result from a misalignment of expectations and reality. An honest conversation, with both parties being active listeners, about the mentor and mentee’s strengths, weaknesses and goals early on in the relationship can set both parties up for success.
  • Broaden your network. Every day we hear about new grants, interesting conferences, and visiting professors. Yet because they are just starting their career, mentees may not hear about the same opportunities. Part of being a good mentor, Dr. Pearson suggests, “is to continually be looking out for opportunities for your mentee and actively encouraging them to pursue them”. This includes inviting a mentee to a lecture and offering to introduce her to the speaker. Opening this door can help a mentee broaden her professional network and embolden her to pursue new opportunities.
  1. Focus on the mentee. The mentor must recognize the mentee is not “hers.” Rather the mentor should focus on the mentee’s needs and goals and, if she finds another investigator can provide a better opportunity for the mentee, help to arrange it. Dr. Pearson states, “Mentoring and selfishness are like oil and water- they don’t mix.”
  2. Stay curious. In his acceptance speech, Dr. Pearson talked about how much he learned from each of his mentees. They taught and inspired him as much as he did them. But that can only happen by accepting that mentoring is a partnership in which each person has a lot to offer. Good mentors need to be curious about their mentees and excited about learning from them.

Dr. Pearson has mentored over 60 people during his career. Many have gone to have equally enviable careers where their impact reverberates into every corner of our profession. He told me, “You never really stop mentoring a mentee. People I mentored still call me and talk about their career, their family. At some point, they start being friends.”

Mentors – true mentors – view their work not as a requisite service but as a thread that weaves together the knowledge of the current and past generations to the next. Mentors are the foundation upon which scientific progress is made; and their impact is the greatest when their mentoring is done with humility, enthusiasm, compassion, curiosity, and an infallible sense of optimism. These are the traits Dr. Pearson embodies; and are the ones that all who seek to see further should strive to emulate.

 

 

 

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3 Tips for Live Tweeting a Conference

What is Live Tweeting?

Live tweeting is when you tweet about an event while you’re there.

You can harness conference hashtags, like #EpiLifestyle19 for the upcoming Epi | Lifestyle Scientific Sessions in Houston, or this past year’s #AHA18 Scientific Sessions in Chicago, to group your tweets with others and help people follow along.

Live tweeting doesn’t mean typing out every word a speaker is saying.

Tweet the name of the presentation and the speaker, the energy of the room, or your big takeaway.

What’s the “so what?” behind the presentation? What did you find most interesting?

You also don’t have to tweet in the moment.

Write down some of your thoughts, and after the session, write up your summary tweet.

 

Why Live Tweet?

Tweeting short comments at a conference presentation or seminar let’s your followers tune in, like they’re sitting there with you.

In an article for PLOS Blogs, Atif Kukaswadia (@DrEpid) shares an impressive example from the 2011 2nd National Obesity Summit in Montreal.

The conference had 800 attendees, and only a handful of people tweeted.

But those handful of people produced 500 tweets with the conference hashtag, and those 500 tweets reached 80,000 people.

80,000 people.

Can you imagine how many people we’d reach at a bigger AHA conference with meeting reporters live tweeting from nearly every session?

Not only does live tweeting make followers feel like they’re there, but it stimulates discussion as people comment, asking questions, offering their own thoughts, and connecting to other science resources.

In his article “The Challenges of Conference Blogging”, Daniel MacArthur reminded us of the purpose of presenting science at conferences.

Why do we do it?

To promote discussion about our science.

To expand our own influence for future job opportunities and collaborations.

Live tweeting at conferences achieves these things – with the added benefit of concise science communication that expands both the reach of the science but also the understanding.

 

Tips for Live Tweeting

  1. Live tweeting doesn’t have to be a play-by-play of the talk. Don’t worry about tweeting every single word. Instead, think about what theme or finding resonates the most with you. Tweet about that!
  2. Visuals make any tweet that much more engaging. Use high quality, free stock photos from unsplash.com or www.rawpixel.com along with your post, or search online for a corresponding paper or faculty webpage to link in. Many people snap a pic of the slides or the speaker on stage – just be sure to check with conference policies before posting photos.
  3. Search for the speaker on Twitter so you can tag them with their handle (preceded by @). One of the best ways to do this is to use a search engine with their full name, and “Twitter”. If nothing comes up, try tagging their institution. Many schools of medicine, hospital departments, and universities have Twitter accounts. If you know you’ll be reporting on a session in advance, you can look up these handles beforehand.

 

For examples of live tweets, search previous conference hashtags on Twitter, like #AHA18, #EpiLifestyle18, #QCOR18, or your council’s Scientific Sessions hashtag.

To learn more about using social media for science communication, with more tips for tweeting and blogging, be sure to come to the Epi Early Career session on Friday March 8th, 7 – 8:30 am in the Galleria Ballroom, Westin Galleria, Houston, TX at Epi | Lifestyles Scientific Sessions 2019.

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3 Tips for Incorporating Coauthor Feedback

When we review a paper, we often forget how we feel in the role of author. Along the same lines, when we read over drafts coauthors’ send us, we forget how we act in the role of editor.

Suggested changes seem personal.

 

Tip #1: Get your head right

We often have coauthors at different institutions and finalize manuscripts via email. Receiving criticism, constructive or otherwise, is never pleasant but receiving criticism over email opens the situation up to miscommunication.

Why?

When we write or read an email, our current mood influences how we perceive it.

Incorporating coauthor feedback is a key step in the science writing process, and seeing it as an integral part of the final product and not a burden to be borne can help you orient yourself. Approach the process constructively and with an open mind.

“You cannot grow if you are not willing to change, to accept new perspectives on life or to change your habits.” – Steven Aitchison

 

Tip #2: Consider the type of feedback

So, you’ve taken a deep breath and opened up the document with coauthor feedback. Plan to make several sweeps through the document. If you have comments from several people in one document, consider isolating each author. If you’re using Microsoft Word, you can do this by going to the Review pane, selecting “Show Markup”,
and then “Specific People”.

As you read through feedback, I’d like you to think about them as 4 types of feedback.

  1. Clarifying content
  2. Modifying style elements
  3. Correcting grammar
  4. Changing wording

Simplifying wording often leads to clarifying content. Modifying style elements, such as use of “First,…” “Second,…”, “…; however…”, or by changing passive to active voice, often modify writing style but may also increase or decrease writing clarity.

Kristin Sainini teaches the online Coursera course “Writing in the Sciences” and uses assignments like “Give a short word that means the same thing as “utilize””. Here’s a table of words that can be simplified.

Instead of… Use…
Accordingly, So
Address Discuss
Afford the opportunity Allow
Advantageous Helpful
Due to the fact that Due to, since
Determine Decide, figure, find
Demonstrate Show
Evident Clear
Evidenced Showed
In lieu of Instead
In regard to About, concerning
Magnitude Size
Notwithstanding In spite of, still
Numerous Many
Preclude Prevent
Provided that If
Provides guidance for Guides
Represents Is
Similar to Like
Subsequent Next
Subsequently Then
Sufficient Enough
Therefore So
Utilize Use
Viable Practical
Warrant Call for

CAPTION: Not all of them involve fewer words. Some are just more common in spoken language, and so better understood. [Source]

Correcting grammar is often straight forward, but may be stylistic. Double check with a style guide or online grammar guru like Grammarly. If you’re both right, feel free to keep your own. Consistency throughout the manuscript – both in writing style and grammar choices – is important.

If they are suggesting a change in wording, think about why. Is it a simpler word? Is it more correct?

If you write with the same coauthors frequently, you may find certain people predominantly provide a certain type of feedback.

Then, think about the purpose of an edit. Why do you think the coauthor made that suggestion?

If the suggestion is a stylistic change that doesn’t clarify content or simplify wording, then it’s an edit that shifts your writing style towards their own. These are edits that, in my opinion, you are not “required” to make. But if you know your writing could benefit from some streamlining, look at what these edits are. Are they moving the main subject from the end to the beginning of the sentence? Adding transitions? Cutting out unnecessary words? These are changes you can make in your own writing style to improve communication.

However, keep in mind when you are providing feedback on someone else’s writing that these comments aren’t always helpful. Instead of trying to mold someone’s words into your own, assess writing for clarity and purpose.

If a coauthor changes a word that changes the meaning of a sentence – that’s a big problem. Definitely don’t change anything that results in a false statement, but acknowledge that if someone on the project didn’t understand what you were trying to say, a reader definitely won’t. That sentence needs to be clarified. Spend time on these edits to make sure what you’re trying to say is coming across.

 

Tip #3: Stand up for yourself

Hopefully you have a great group of mentors, and at least one is in your coauthor group.

Don’t hesitate to have a frank conversation with your mentor about manuscript editing. They have much more experience than you, and have encountered many different frustrating situations. Some of the best advice I’ve gotten is to “choose your battles”. Not only does that mean to let some things go, but think about what you’d like to stand up for.

Being able to edit someone’s writing without replacing their writing style is difficult. Not all coauthors, no matter how senior, are able to do so.

Recently, a colleague in my lab group asked for advice on how to handle conflicting coauthor feedback. My mentor brought up a good point: many times, in academia we don’t feel like we are “doing our job” if we don’t come up with a suggestion. As the “commenter”, we feel satisfied with ourselves for contributing. But as the recipient, we consider all feedback and suggestions as changes to make.

Having straight forward discussions about what changes benefit the paper as a whole not only improve your communication skills, but your independence.

What challenges have you encountered in this area?

 

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5 Tips for Science Writing

Among the many responsibilities you have, writing is probably the one that gets pushed to the bottom of your to-do list again and again.

During the #EpiWritingChallenge last November, many public health researchers, trainees, scientists, and clinicians shared their biggest barriers to achieving their writing goals.

My next few posts will summarize some of the discussions and writing tips that emerged from the 20 day writing challenge. Each post will be dedicated to one topic: writing, editing, and incorporating coauthor feedback.

 

Tip 1: Make time and space for writing

If you’re like me, you’re juggling several research projects among other work duties, and while you think about working on your manuscripts often, it seems like you never get to them. Unless there is an abstract deadline, it seems like the writing process stretches on and on.

Many #EpiWritingChallenge participants set goals aimed at writing more often, with daily or weekly goals.

Hopefully you’ve heard of SMART goals, but if you haven’t, they stand for specific, measurable, achievable, relevant, and time-bound.

First, if you want to change your writing habits, telling yourself to “write more” likely won’t cut it. It takes at least a month to form a new habit, and to maximize your success I suggest breaking down your overarching goal into manageable chunks (that are also SMART).

Second, reflect on when and where you write best. Are you a morning person or a night owl? Do you need complete silence or the bustle of a busy café? Thinking about these aspects of writing and how an ideal writing session can fit into your schedule will set you up for success. You might block off time on your work calendar as busy (to avoid meetings being scheduled during that time), and shut your office door. You might wake up an hour or two earlier to enjoy the quiet of your office as you type away. If you work best in a group, you might organize a Writing Accountability Group for even better accountability.

 

Tip 2: Focus on writing clearly

Writing clearly is something we all strive for (hopefully) but is harder than it sounds. As Ernest Hemingway said, “prose is architecture, not interior decoration.”

Two rules of thumb are 1) write shorter sentences and 2) choose simpler words if it doesn’t change the meaning.

Dallas Murphy, a book author and writing workshop instructor, gave a great example of typical scientific writing transformed into clear scientific writing, in “How to write a first-class paper” published on the Nature blog last year.

ORIGINAL: “Though not inclusive, this paper provides a useful review of the well-known methods of physical oceanography using as examples various research that illustrates the methodological challenges that give rise to successful solutions to the difficulties inherent in oceanographic research.”

This writing is defensive and scared to make confident statements. The language is ornate, and lists caveats, fending off criticism that hasn’t yet been made.

REWRITE: “We review methods of oceanographic research with examples that reveal specific challenges and solutions.”

Much better!

You might even explore voice-to-text apps for clear writing. We often express ideas more clearly in speech than in writing. In that same Nature article, Stacy Konkiel of Altmetric encouraged readers to make their point “in non-specialist language” if possible. “If you write in a way that is accessible to non-specialists, you…open yourself up to citations by experts in other fields and make your writing available to laypeople.”

 

Tip 3: Keep a “great writing” folder

What we read strongly influences how we write. In other words, “you write what you read”. Keeping up with the literature is a whole other blog post in itself, but reading other science writing not only expands your content knowledge but your writing abilities.

Whenever you come across a paper that makes you think “wow, that is great writing” tuck it away in a “Great Writing Folder”. When you sit down to write, marinate your brain in that concise science writing before putting pen to paper.

 

Tip 4: Create an elevator pitch for your paper

We typically talk about elevator pitches in relation to networking and job interviews. In fact, at last year’s AHA EPI | Lifestyles Scientific Sessions, one of the Connection Corners was focused on crafting an effective elevator pitch. Just as you summarize the key parts of what you do and why, and what you research, you can adapt that to a specific paper or project.

Create different ways of explaining your project in terms of what you did and why. Keep that list nearby when you write to help you stay on point and stay clear throughout your paper. Every main point should be coming back to that elevator pitch. That list is great to review at the beginning of each writing session to get you in the right mindset, too.

 

Tip 5: Prioritize topic sentences

Topic sentences are just as important now, in your science writing, as they were in your high school English class. Make sure you have topic sentences for each section of your manuscript. If you create an outline beforehand, those main ideas should morph into your topic sentence. After the topic sentence, every bit of that paragraph should connect back or move the argument forward. If it doesn’t contribute, cut it or move it.

In the tips for editing post we’ll be talking about using a Reverse Outline, a method with topic sentences as its backbone, to strengthen your argument.

 

 

In sum, science writing is a complex task for us to tackle. Whether a clinician-scientist, full-time researcher, trainee, or professor, it’s something on all of our to-do lists.

What is your biggest writing challenge?

 

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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.

Generalizability

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.

Logistics

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

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.

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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.

 

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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.

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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.

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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.