With recent events being what they are, epidemiology has come into the spotlight. What do epidemiologists do and how does data shape their everyday experience? Sitara and Mee-a from “Donuts and Data” fill us in.    

Ginette: I’m Ginette,

Curtis: and I’m Curtis,

Ginette: and you are listening to Data Crunch,

Curtis: a podcast about how applied data science, machine learning, and artificial intelligence are changing the world.

Ginette: Data crunch is produced by the Data Crunch Corporation, an analytics training and consulting company.

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Now onto the show.

Curtis: I’d like to welcome Sitara and Mee-a from the Instagram account Donuts and Data to talk to us today. I guess let’s just have you guys introduce yourselves, as opposed to me trying to introduce you cause you know what you do better than I do. So maybe we just have some introductions.

Sitara: So I’m Sitara one half of Donuts and Data. I’m a PhD student in epidemiology at the University of Texas Health Science Center. I’m also a research assistant in a lab that I work in.

Mee-a: And I’m Mee-a. I am an infectious disease epidemiologist that works in the public sector. I actually met Sitara through the lab that she’s currently working in.

Curtis: Nice. And I’m excited to have you guys on. I just, I think epidemiology is a really interesting space, especially with what, you know, with what’s going on now with COVID. I think it’s more pertinent than it ever has been. Not that it ever hasn’t been pertinent, but maybe it’s more top of mind for people. So I’d love maybe just to have you guys level set with everybody, like what is epidemiology. There’s probably some confusion about what that is and maybe how you guys got into it. And then we can get into what your day to day is and, and what it’s all about.

Sitara: So, epidemiology, I think everyone’s kind of understanding is setting patterns of disease in the, in the human population. And so in that sense, what Mee-a and I do are the same, but instead of studying infectious diseases or the natural science part of epidemiology, what I focus on is how human behavior contributes to those patterns of disease. So I look for patterns in data associated like demographics or just behaviors, diet, nutrition, and how that contributes to getting diseases.

Mee-a: For me in the public sector, it’s going to be a lot of looking at incidents, rates of infectious diseases. It . . . primarily with COVID-19 right now, and just different ways that we can try to possibly implement infection prevention measures. So we are dealing a little bit more with, I don’t want to say the medical side of it because we aren’t clinicians, but we are dealing more with the medical side of, of the infectious disease than we are with, with the data compared to when I was in academia, at least.

Curtis: So take us through maybe the end goal, right? So what you guys are working on. You’re hoping to come out with, I think, some recommendations for people to, to take maybe a better understanding of how the disease spreads, so we get in front of it. What does that look like?

Mee-a: I always thought that epidemiology’s gold standard of what we try to achieve is probably smoking cessation. So, you know, when at least growing up for me, I felt like cigarettes and smoking were very, very pervasive and widespread. And as we grew up and we started seeing more of these campaigns showing just how unhealthy smoking was and how much it can really, really be such a detriment to your health, it became a thing where now as adults, our generation looks down upon smoking. And so that’s something that I feel like epidemiology and public health in general has helped to implement that view. And so for the public sector of things, our ideal goal is to really implement infection prevention measures. So that’s going to be in light of COVID-19, that would be making masking a normal thing, making sure social distancing is the new norm, making sure that we are washing our hands for the appropriate amount of time, making sure that when you do disinfect something that you’re disinfecting it properly.

If we are in large congregate settings, that we’re trying to do everything that we can to make sure that we don’t create a hotbed of COVID cases. So that’s all the stuff that we’re trying to do right now. That would be, if everything goes correctly, ideally we would be getting to the point where we could either (1) control COVID or (2) completely eradicate it. So that’s, that would be our goal in the public sector.

Sitara: And I think, going off of that, things like seatbelts were once seen as a radical change, but that was a public health measure. That was something that epidemiologists put people in the public health world, they looked at the data of car crashes and they decided that wearing a seatbelt was a safety measure that they could implement. And a lot of people were against it, but now that’s obviously the norm that’s in it’s own every car.

So I think similar to that, we hope that mask wearing becomes the norm and it becomes okay. And it’s not, it’s not scary. It’s not . . . there’s no . . . there shouldn’t be any stigma on wearing a mask. But in terms of academia, I think what we want is for people to be able to read our research and, and know that that a lot of work went into it. And a lot of, you know, the scientific method, it’s evidence-based, and we’ve done these tests over and over again, this is real science. So I think in the end, we want people to read our research and take something away from it and, and be able to live a healthier lifestyle.

Mee-a: The work that Sitara does in the academic field is what we build off in the public field. So we implement the measures that she proves in her research, if that makes sense.

Curtis: Yeah, no, that’s awesome. And I’d like to maybe dig into that a little bit. Sitara, can you talk to us and maybe you can just pick one or, or however you want to go about it, but I’m curious, I’d like to give people a sense for how you approach a research problem like this, how you make sure it’s rigorous, how you go about collecting the data and analyzing it. All of that would be really interesting just to kind of hear from your perspective.

Sitara: Yeah. So, okay. So for example, with COVID, we can talk about COVID, one of the faculty in the lab that I work in, we had a question of, you know, what is the shelter and policies? What are they doing to people’s behaviors? How is that affecting people’s behaviors? And we had these questions, like, are people working out more? Are they working out less? Are they eating more, are they eating less? And so we formulated a survey, we wrote questions. We took, we didn’t write the questions. That’s important. We took the questions from previously validated surveys. So these are, these are questionnaires that have been validated by other scientists that they’re good measures of asking these questions and getting the information that you want. And so we created this long survey that asks questions about physical activity, diet, drug use, sleep habits, and it’s this long survey.

And then we just disseminated it on the internet. We shared it on our social media. We shared it in emails to the faculty at school, to students at the school. And then we just asked everyone, you know, could you share this with your friends, your family? And in the end, we ended up getting, I think, over 4,000 responses. And so what we’re doing with that data is then. So that this, the survey was on a data management website. We specifically used Red Cap and then that data was pulled from Red Cap, downloaded into an Excel file and plugged into a statistical software. So I think we used Stata for the specific one, and stata is what I most commonly used for data analysis, and then we just run tests on that data. So we do like T tests, Chi square test, cross tabulations, regression. That’s the type of tests that we do to see if there’s any pattern in that data to see if there’s any association.

And then we take those results, and we write a manuscript, we write a paper, an introduction, a methods, results, conclusion, and then we to publish that. And then once that’s published, we hope that people read that we either hope that policymakers are reading that and they’re seeing these are the effects in shelter and policies. How can we change it to make it better? Or we hope that the public reads it or, or that the news, the media catches on and, and writes an articles, studies find that people are working out less during shelter and policies. So that’s kind of, you know, in a, in a, like in a nutshell, what the process is of coming up with a question and then getting that data and publishing it, there’s so many different ways of doing it though. There are different types of studies that we can do that are more longitudinal. They follow people over time. There also vaccine studies that I think the public is getting to know more about what a vaccine study is like, but in the end, you know, all of it is collecting data from humans and then plugging that data into a statistical software and seeing what we get.

Curtis: And then Mia, do you then pick up where Sitara leaves off and try to help the public sector understand and implement?

Mee-a: Exactly. So, I mean, I end up picking up where Sitara leaves off in her research, where after she’s published it, we then take that publication. We read it, we after, it’s peer reviewed, obviously, and then we try to disseminate that information to the public using those best practices. So it’s important, I think to, to note that Sitara and I’s target demographic of audience is going to be a little bit different. So Sitara with her research is trying to target the scientists, the politicians, the policy makers, and then we’re going to clinicians and the clinicians you’re right. And then we’re trying to target the public. We’re trying to make sure that we take this data and we make it to where it’s easy to understand and interpret for everyone because we need to make sure that they understand the reasoning for why we’re trying to implement the measures we’re trying to implement.

Curtis: Is that more sort of public relations type, type work and explaining, I don’t know, maybe hard scientific concepts to the public in an explainable easy way?

Mee-a: Yeah. I mean, I definitely think there are two separate types. Not separate. I don’t want to say separate, but there are definitely two specialties within my office, it seems like. There are the epidemiologists that are working a little bit more face to face with people. And there are the epidemiologists that work a little bit more face to face with the data. And right now, my day to day is looking a little bit more like I’m talking to a lot more people. We’re focusing, at least for me, I’m focusing a lot on facility outbreaks because that’s something that we are seeing a lot with COVID-19. And so it is a lot of me trying to track down these facilities and then try to get the data from them from their positive cases or their probable cases. And trying to get information out into a place where we be able to run some analysis on it.

So we ask them for basic stuff like where their positive cases live. What are their ages? What are their ethnicities? When did they test positive? When was their last day of work? What if they’re working in a facility? Like what line do they work on? Because we want to see if there is a particular place that we can pinpoint where this infection is stemming from.

Sitara: I do want to add something real quick. Sorry. I just remember. I think it’s important to say, so when we develop these surveys, I feel like I need to clarify. They are approved by an IRB, which is like an ethics review board. And so we can’t just create surveys like willy nilly and just text them to people. We do have to approve the method that they’re well, we do, you have to prove, first of all, the questions that we’ve included and then the method that they’re disseminated, this all has to be approved by an ethics board. So just want to make sure that that’s clear.

Curtis: Yeah, no, that’s good. This is really pertinent. Right. And I’m so curious to this process, which thank you for being so clear in helping us understand how this works. And that’s really interesting. Have you been frustrated by how people have started to implement some of these insights? Do you feel like we’re making progress or have you felt like people are responding well or, you know, I’m just curious how that feels to you guys?

Mee-a: I don’t know the politically correct way to answer this.

Sitara: I don’t know if this is appropriate to say. I think that this issue has kind of taken two political sides. And I think that might be the most disappointing thing, at least for me personally, because this is not political at all. There’s no political part. We don’t, we’re not biased in any way in this research. We are just looking at the data, we’re just looking at the numbers. And so I think if anything, it’s, it’s unfortunate that some people think that there’s a political aspect to it because there’s not at all.

Mee-a: Yeah. I think Sitara hit the nail right on the head when she said that it is sad to see that a lot of people are placing a political shade or shadow on this when it really shouldn’t be. That being said, I do think it just kind of depends on person to person.

You know. I mean, there are some people who are very close minded as to trying to, trying to prevent the infection from spreading, but then there are other people who have been so helpful and they might be experiencing an outbreak, not because of any type of malintent, it’s just, they don’t know. It’s a lack of information. And so I think that’s why, what we’re trying to do is so important. It’s pushing out information so that everybody knows, because I do think a lot of, I think it’s with a lot of different problems. Like I think the fact that people are misinformed or just don’t have any information in general is the reason why we’re having these issues.

Sitara: And I think also, I think that’s also why I want people to read the research that we publish with an open, not an open mind, but just understanding that this isn’t twisted or biased or political in any way. This is just the research that we’ve published.

These are the results that we found. And so I think that, that if people were able to read that and understand it, they would be able to have that information. They would be able to understand the severity of the issue. And they would be able to live their life in a healthier way, whether that’s wearing a mask.

Mee-a: Yeah, I totally agree. Actually, one of the classes is to Tara and I went to the same grad school. So we have a lot of the similar classes. I don’t know if she actually took this one. I took a nutritional epi class and while I’m not in nutritional research, the one thing that I really pulled from that class was just being wary of what you read because a lot of times you get these amazing research articles that are published and then a reputable news anchor, like a news website, will try to take that information and break it down into something that’s a little simpler.

And by simplifying it like that, it gets to the point where (1) they can input their own like personal inflection into, into their findings. But (2) they, they do over simplified to the point where I think I’m trying to give an example here, I guess like if they say red wine is good for your health, that depends right. You don’t want to just tell people, Oh, red wine is great for your health. People are going to be chugging bottles of wine. Like it’s crazy. And I can tell you from personal practice that that’s not actually true. Um, and so, you know, what you don’t see is when you read the full article there, when they say it’s statistically significant, that might not necessarily be what your mind thinks of as statistically significant.

Sitara: There’s a huge difference between statistical significance and clinical significance. And that’s what is really emphasized in our program. Statistical significance might mean that statistically, the P value is significant, but when it’s translated into the clinical practice, you might see slightly different results. And that’s also one huge part of vaccine trials is they test it in a perfect setting and then they test it in a clinical setting to see what the differences are. And so I mentioned earlier that it’s great when our studies get picked up by the media, but it actually ends up hurting us a lot when the media just takes, you know, one line of the results in the abstract. And they just kind of put that as a headline and that can be, or they simplify that, that one sentence and they put that as a headline. And that can be really harmful to the data that we’ve, that we’ve looked at. Because if you actually read the methods and the results, you’ll see slightly, slightly different result.

Mee-a: Yeah. You’re so right. Oh, that frustrates me when that happens.

Curtis: That’s fair. And it’s sort of a different incentive there, right. Sometimes for, for what they’re trying to do. So this is a good point, right? And I’m curious what your, your advice would be for people who they’re not epidemiologists, but maybe they are concerned and they want to do, they want to understand what the research says, what the truth is and take appropriate action to maybe help other people do the same thing. What do you recommend that they do? Or what are some good sources that they can, they can lean on so they maybe avoid this kind of bias?

Mee-a: Yeah. For me, I would suggest that they read reputable sources such as like the CDC guidance, Dallas County, or like county guidance, state health services, guidance. What I wouldn’t say that, you know, before going into epidemiology and before properly learning what quote unquote good sources are. I used to think that a lot of news sources were really, really reputable. So, I mean, I’m not trying to come out for anybody here, but like say NBC posts something about research and I’m like, “Oh my gosh, it must be true because NBC reported on it and NBC is a major network, so I have to believe it.” But then afterwards learning what I know now and reading those articles, I’m like, “Oh, well, I mean, technically, not exactly true, because if you read the limitations in the paper, they say X, Y, and Z, or if you read the study design, you’ll realize that maybe their study population wasn’t as strong as it should have been,” you know, stuff like that. “Or maybe they had a lot of loss to follow up. And so maybe some of the answers are a little skewed.” And so I would definitely suggest just making sure that (1) you either read the article itself because they almost always link the original article or (2) they should be reading stuff that is published from the CDC, because there are very, very skilled and talented epidemiologists that are constructing that guidance.

Curtis: Yeah. That’s fair. That’s fair. Okay. Well, I mean, this is good stuff. This is great. You have a channel doughnuts and data? And I think that’s just an Instagram account and a podcast, right? What do you do on that show? Do you help people understand these kinds of concepts? Is that, is that what you’re focus there? Or what do you do on that one?

Sitara: So we . . .

Mee-a: Go ahead, Sitara, you go.

Sitara: So we, we met actually, well, we met in our master’s program. We didn’t know we were in a few classes together.

Mee-a: We were actually on one of the, what was it? It was intro to data science. We’re the same team.

Sitara: Yeah. We were in the same group and we did the entire course together. And then we were in another class at Epi3, our final epidemiology class in our master’s program. And Mee-a actually, she was in Austin, but she moved to Dallas and then came to our study group for that class. And even then we were still not, you know, we knew each other, but not. And when I finally joined the lab that she got a job at, we finally were like, Oh. And we immediately. Go ahead.

Mee-a: Yeah. Like I just remember that. I just remember being like, Oh my gosh, I finally remember you.” And then I remember that study group because that study group was crazy but fun.

Sitara: Yeah. And we immediately just clicked, we found so many similarities that we both had. We just had similar childhoods. And then we wrote, realize you were both laughing about how when people ask us what we do, we say epidemiology, they’re like, “Oh, are you a skin doctor? And I have to say, I have to say, when I first got into public health way back in college,” I also didn’t know what epidemiology was. And I took a foundations of epidemiology class and I, the first day I remember I walked in the class and was like, “skin? Maybe? Is that what we’re going to talk about?” I had no idea. So totally fair. I totally understand where everyone’s coming from.

But anyway, we were just laughing about it. And we were like, we noticed that on Instagram, you know, there’s these influencer accounts for medical students, for doctors, for chemists and people in the lab. And we noticed that there was a significant gap. There’s nobody that represented public health professionals, people who work in public health research and in epidemiology. So we created this account to fill that gap, to kind of get us out there to show that representation of, public health representation. And we kind of just try to tell people what the work that we do is and what it means to be an epidemiologist and what it means to work in public health, like on the ground, in the field.

Mee-a: Yeah. I mean, I think the layman’s term for epidemiology is typically that people assume that we’re kind of like a disease detective of some kind. And in a sense we are, but I will say that if you use those words, there are two sets of imagery that kind of show up. Whenever I say a disease detective, I don’t want people thinking that I’m running around with like a magnifying glass in my hand, looking at tiny little germs everywhere and trying to figure out where they came from. Um, and I’m also not in a wet lab where I’m running different types of Eliza tests or like Western blots or anything like that. Like I’m just . . .

Sitara: There are epidemiologists who do that, though. Just to do that though,

Mee-a: That’s true. That’s true. So, I mean, it’s just the field of epidemiology. The field of public health is so expansive and so different from one sector to another that I think we just really wanted to show people what our little niche academic setting was. And now that I’ve moved on to the public sector, it’s been nice because now it’s two different settings where we can show people what our day to day looks like.

I just think it was kind of difficult. ‘Cause I mean, I got my undergrad in pure mathematics, not really knowing what I was going to do, not knowing how I was going to apply my skills. And I mean, I did have professor suggest epidemiology to me and whenever, you know, I mean, I’m a millennial. The first thing I do is I go on social media to see what the heck is an epidemiologist and I see nothing. And there is no clear cut definition of, “Oh, an epidemiology does this.” Whereas like, if you tell somebody that you’re a chemical engineer, you can go and look online and you know, what a chemical engineer is going to do. Yes. Maybe their day to day looks slightly different from one job to another. But you know, the general jist of it and same thing with like a doctor or another type of clinician, like a nurse, you’ll see what they’re doing.

Yes. Maybe a neonatal nurse is going to be different than a labor and delivery nurse, but they’re still practicing medicine. You still see their general day to day. So we wanted that in public health. We wanted that in epidemiology where we could show you like, this is what we do. This is what we try to do. This is what we intend to do, you know? And that way people get a little bit of a better idea as to what epidemiology is. And hopefully we can bring more people into the field because it’s fun. Yeah.

Sitara: We also, we also wanted to provide, like Mee-a said, we also wanted to provide undergrad and graduate students with, ’cause we are all young and all on social media. We wanted to provide them with like a fun platform where they can learn about what, what constitutes an MPH program.

How do you get into graduate school? What are, what are some fun graduate school tips? So we wanted to also show that graduate school side of things as well for getting a degree in public health, what does that mean? What can you do with it? So kind of just a resource for people to learn about the field.

Mee-a: Yeah. And I liked that. Like Sitara and I both, while we both started the program at the same time, we started them at different points in our life. Where Sitara jumped from her undergrad straight into her graduate degree because she knew exactly what she wanted to do. I went from my undergraduate degree, took four years teaching and working and then went back and got my graduate degree and so different points in our lives. But I just like how we were both able to come together, and we learned the same stuff and we were able to build off of each other and it was, it was really just awesome seeing that.

Sitara: Yeah.

Curtis: That’s awesome. Do you feel like it’s been a positive experience?

Sitara: Yeah, definitely. I think we have a lot of people who are really grateful for the resources, for the graduate school side of things. We have a lot of people that really appreciate the advice that we give and how to get, how to get into graduate . . . or not how to get into graduate school, but you know how to create your application, like that sort of thing. And we also have a lot of people who like when we explain what writing a research paper is like, so yeah, we have people that like all sides of it, and it’s been pretty successful so far.

Mee-a: And also because Sitara loves her data. I just think it’s really funny because we originally started this as a personal, um, not intending to do anything with it. Um, and then she switched it to a professional account, and I cannot tell you how funny it was watching her be like, “Oh my God, do you know the level of data we can pull from this?”

And like, just like watching her like look at the metrics and be like, “look at this, look at this.” And then, I mean, at one point she was running T test on it to see what posts were statistically significant. And I could not stop laughing. I thought it was the funniest thing ever.

Sitara: I don’t know. I don’t know how many people realize when you switch your account to a business account, you get so many metrics. Like how many people viewed your post? How many people liked it, how many people shared it, how many people like bookmarked it? And they’re just, there’s so many different little statistics that you can do that you can plug into Stata and do stuff with. I don’t know if other people are doing this, but I love it.

Mee-a: I know. She does it all the time, and she’ll like message me and be like, guess what I just found that statistically significant. And it’ll be like 3:00 a.m. in the morning and I’ll be like, ma’am, ah, like . . .

Curtis: Right. I mean, what better time to do statistics, right?

Sitara: I just, I think that if I had to tell people, I even say that like, I would love to turn my own, my own personal account into a business account, just so I can have these metrics, but I won’t. But I just think that if, if I could say one thing about Instagram, everybody’s account should be a business account because you can get so many cool statistics from it. And it’s just so much fun.

Curtis: Fair enough. Well, what’s maybe one of the most interesting insights you’ve pulled from your Instagram statistical journey?

Sitara: Just the, the numbers wise, I think we found that like when we stayed on brand so to speak. So when we posted things that were public health or epidemiology, or, or even biostatistics related, it got more traction. It got more shares, likes, comments versus when we just did like an influencer type posts. So we are called Donuts and Data because we love donuts and we love data. And so we would do posts that were, before quarantine when we actually saw each other, we would go get donuts and we’d do like fun donuts posts. And while that was really fun and popular and everyone loved it. We noticed that when we gave people advice, people shared that post a little bit more. So in terms of the numbers, that’s what we saw as like more popular.

Mee-a: Yeah, we saw a lot more engagement with that.

Curtis: So data is more popular than donuts is what you telling me.

Mee-a: Yes, currently, right now data is more popular than donuts.

Curtis: That’s great. You know, that’s a good audience, you know. That’s awesome.

Ginette: A big thank you to Mee-a and Sitara from Donuts and Data for being on the show. As always go to datacrunchpodcast.com for our transcript and attributions.



“Loopster” Kevin MacLeod (incompetech.com)

Licensed under Creative Commons: By Attribution 3.0 License