The Role of Observational Studies in Epidemic Responses
Emily Ricotta, PhD, MSc, discusses how observational studies shape early decisions and address challenges in infectious disease outbreaks
The significance of observational studies in clinical research and public health responses during epidemics is important work. These studies provide critical data that inform early decisions, but they also come with challenges in ensuring the data’s accuracy and relevance. The paper stresses the importance of focusing on key research questions, study design, data management, and community engagement to make the most of observational studies. Despite their value, challenges remain in how these studies are conducted, designed, and communicated.1
In an exclusive interview, Emily Ricotta, PhD, MSc, infectious disease epidemiologist at the Uniformed Services University of the Health Sciences, shared her insights on the role of observational studies in understanding epidemics, particularly in the early stages of outbreaks. The views expressed are her own and do not represent those of the US government or her employer.
Ricotta explained the fundamentals of observational studies: “Observational studies are those where the investigator or the research team is not controlling any of the variables or any treatments that are received or any outcomes that happen during a study or to a population. We are just literally observing what is happening.”
“We have to rely on observational studies for a lot of the early evidence that we get when an infectious disease emerges,” Ricotta noted. She pointed to how these studies provided crucial information during the COVID-19 pandemic, when questions about infection patterns, symptoms, and at-risk groups needed quick answers. “It’s usually pretty important. Especially think during COVID—it was this new virus that we hadn’t seen before this exact one, and we needed to know: Who did it infect? What were the symptoms? Who was most at risk?”
As Ricotta explained, “None of those questions could be answered with randomized control trials or the more structured, rigorous types of study designs that we use. So, we have to rely on observational studies to get this kind of information that is going to help us inform early policy, inform early treatment decisions, and help actually inform those more rigorous clinical trials later on down the line.”
Conducting observational studies, especially in epidemic settings, involves complex logistics and challenges. Ricotta elaborated, “To do any kind of study, it takes a lot to do well. We have to know the questions that we want to answer. We don’t generally just throw out a net and fish for whatever might come up. We want to make sure that we have our question identified, so we know what sorts of information we need to collect and the types of people we need to talk to. All of that takes time, and if you haven’t set it all up ahead of time, it becomes more difficult.”
Accessing the right people and collecting data in the field can also be difficult. As Ricotta explained, “Just getting into the field and finding the right people can be challenging. As scientists, we’re not always great at community engagement or talking to the people we’re going to interview. Often, we just drop into a place, say, ‘Here are some questions we’re going to ask you, we’re going to take some samples, and then we’ll leave.’” These barriers are prominent in disaster settings, such as those that accompany many infectious disease outbreaks.
Moreover, the lack of resources in certain settings can make data collection even more difficult. “It’s hard to collect data when you don’t necessarily have the high-tech equipment you might want, or easy access to a cloud database, or even basic resources like internet and electricity,” she said. “These factors can come together to make data collection very challenging.”
Ricotta also pointed out the ethical and logistical hurdles that complicate the conduct of observational studies during outbreaks. “Zika cases spread so quickly through South America that by the time a study was designed and approved, there weren’t enough new cases to study. Everyone had already been infected.”
Similarly, during Ebola outbreaks, there was a lack of sufficient treatments, which raised ethical questions regarding how limited resources should be distributed. “We had some early treatments that maybe worked, like monoclonal antibodies and early vaccines, but we didn’t have enough of them to give to everybody,” Ricotta explained. “The ethical question arose: should we give any of the limited treatments to some people, or should we just treat people with supportive care since there wasn’t any other treatment available?”
These challenges illustrate why randomized control trials may not always be feasible or appropriate, especially during fast-moving outbreaks. Instead, observational studies become a vital tool for researchers to gather early data.
Another key issue with observational studies is the inconsistency in data collection. Ricotta discussed how even basic variables like age can be recorded in different ways, which complicates analysis. “Age is a variable that gets surprisingly collected in many different ways. You can have age in months, age in years, or age categories. I’ve been asked to do an analysis looking at the age of people who had Ebola, but the data was broken down into ‘over 65’ and ‘under 65.’ I asked, ‘How am I supposed to do an age analysis if I don’t have the data properly categorized?’”
The challenge of inconsistent data collection is compounded by the inherent biases and confounding variables in observational studies. Ricotta explained that these factors can distort the relationship between exposures, such as infectious diseases, and outcomes, making it harder to draw accurate conclusions. “There are additional factors that could mess up our understanding of that outcome-exposure relationship,” she noted. While randomized control trials can better control for these biases, they are not always available or practical in epidemic settings.
Listen to Part 2 of our interview with Ricotta here: Strengthening Observational Studies: Community Engagement and Data Practices
