News

In our increasingly digital and connected world, data is a key component of many fields of work. At St. Olaf, robust course offerings and research opportunities in statistics and data science prepare students to pursue a variety of careers that utilize data analysis.

Nicole Novak ’08 is one Ole using data to solve big problems. An epidemiologist and public health researcher at the University of Iowa, Novak conducts research projects and analyzes data in order to improve community health for rural, Latinx, and immigrant communities. As knowledge on health disparities across the United States grows, Novak’s work is providing crucial insights on the deeply-rooted causes of health inequity.

At St. Olaf, Novak majored in Environmental Studies, Spanish, and Hispanic Studies with a concentration in Statistics, ran on the track and cross country teams, and participated in the Center for Interdisciplinary Research, which matches groups of statistics students with faculty conducting research in a variety of departments. She also studied abroad for a semester in Guatemala, El Salvador, and Nicaragua in order to understand their history and politics, an experience that improved her Spanish language skills and gave her a better understanding of the home countries of many people she now meets and partners with in Iowa. And during her senior year at St. Olaf, Novak was awarded a Rhodes Scholarship, which allows students to pursue two years of fully funded graduate studies at Oxford University, where Novak completed her Master of Science degrees in Medical Anthropology and Global Health Science.

Here, Novak shares about her work and research interests in epidemiology and how St. Olaf influenced her career path. 

Can you describe your current role?
I work at the University of Iowa College of Public Health, where I work on research projects funded by external agencies like the Centers for Disease Control and Prevention, the National Institutes of Health, and the National Science Foundation. I work independently and in teams to design research studies, collect and analyze data, and disseminate research findings related to rural health, Latinx health, and immigrant health. In many cases this research is conducted with input or partnership from community stakeholders, such as advocacy groups or advisory panels of community members. I have also taught courses for undergraduate and graduate students.

 What past experiences led you to this position?
I grew up in Iowa and first became passionate about public health when I was working at mobile clinics for migrant farmworkers in rural Iowa. I began collaborating with colleagues at the University of Iowa in 2015, while I was finishing my Ph.D. at the University of Michigan. I’ve been very happy to come back and join the College of Public Health in my home state. The College has a focus on rural health and community engagement, both of which align with my passion for health equity in the rural Midwest. I treasure the opportunity to build relationships with people inside and outside the university to shed light on the factors affecting people’s health. 

How does your background in epidemiology and data science aid you in your work on health disparities? How do you approach data when applying it to research on systemic and policy issues?
Epidemiology is a powerful set of tools to describe and analyze the health of populations. It has many applications, but I use epidemiological methods to study inequalities in health, particularly for rural, Latinx and/or immigrant communities in the upper Midwest. I have used datasets gathered by the government, such as birth certificate data or Census data, datasets collected by teams of researchers such as cohort studies or cross-sectional surveys, and data I have collected myself. Depending on the research question, I use these data sources to describe patterns in health in a population, or to identify specific causal factors that are leading to poor health for some people.

While quantitative data can be very powerful, I try to approach each dataset carefully and critically, considering who created it, who may not be represented, and what perspectives and experiences are missing. Although it is powerful to see patterns in data about groups of people, I never want to lose sight of the fact that each person is unique with their own stories and perspectives. That’s why I increasingly complement quantitative analysis with interviews, or partner with community representatives to make sure I am asking the right questions of the data.

What are some of the research projects you have recently worked on?
As a Ph.D. student, I kept thinking about the Postville immigration raid, a major federal immigration raid that happened when I was a young person working at health clinics for migrant farmworkers in Iowa: in May 2008, over 900 officers came to a small town in Iowa and arrested 400 immigrant workers at a meatpacking plant. At the time, it was the largest single-site immigration raid in US history. Meatpacking companies had been recruiting immigrant workers to Iowa since the 1990s, after coordinated breakdowns of meatpacking unions led to wages dropping substantially throughout the 1980s. The workers in Postville had come to Iowa fleeing violence and poverty in their home countries, and despite enduring workplace abuses were generally living pretty peaceful lives with their families in rural Iowa. The raid separated hundreds of families and decimated the town’s economy and social fabric.

I kept thinking about the fear that a raid like that sends through communities, and wanted to look at the ways it mattered for public health. I got birth certificate data for the state of Iowa before and after the raid, and worked with a professor at the University of Michigan to test whether infant health in Iowa changed after the raid. We found that infants born to Latina moms (whether they were immigrants or born in the US) were 24 percent more likely to be born at a low birthweight after the raid compared to before the raid, whereas infants born to white moms had no change in low birthweight. This corresponds to the sorts of changes in infant health that you might see after other large population-level stressors such as earthquakes or even mass violence. It also underscores the ways that stressors like immigration raids matter not just for immigrants but also for other people — US-born Latina women experienced an increased risk of low birthweight, too.

More recently, as part of my work with the University of Iowa Prevention Research Center for Rural Health, I’ve been conducting in-depth interviews and analyzing survey data from a different rural Iowa community that is centered around a meatpacking plant. Rural communities have seen a lot of changes to their economy and their social fabric, especially since the Great Recession, and these shifts affect people’s access to resources and their ability to keep themselves and their loved ones healthy.

How did your time at St. Olaf influence your interests and career path?
Epidemiology is necessarily a multidisciplinary endeavor, especially when you apply it to social questions such as health inequities. Even if you have the best epidemiologic methods, if you ask the wrong research question you won’t get a useful answer! In addition to hard skills like statistical analysis or speaking Spanish, St. Olaf gave me the space to explore a wide range of topics — from Latin American history to environmental ethics — that help me think carefully about social context, power, and the moral dimensions of my work.