As the COVID-19 pandemic emerged in early 2020, people were forced to make important decisions concerning their health. A new study by Indiana University sociologists Elaine Hernandez and Jessica Calarco examines how people confront health controversies, which occur when there is widespread questioning of medical recommendations. They found that when people experience health controversies, such as whether to drink alcohol during pregnancy or to get a COVID-19 vaccine, the social networks they belong to, as well as their individual socioeconomic status, can play a major role in decision-making. Their findings, Hernandez says, help explain how social norms and social status can influence whether people question or even ignore medical recommendations. The study looked at first-time, pregnant women and the question of light or moderate prenatal alcohol consumption. People rely on their networks when making such decisions, Hernandez says, but the findings show that people’s experiences are unequal -- in the study, higher-status people faced fewer consequences for resisting medical guidelines. Individuals with bachelor’s or advanced degrees felt empowered to question medical recommendations both in formal and informal settings. Individuals from higher social positions tend to have social ties with physicians, reducing their perceived social distance and empowering them to question advice, Hernandez says. Among individuals who had not completed four-year college degrees, the study found that when others in the woman’s network agreed about how to behave, she followed this agreed-upon social norm. As the COVID-19 pandemic has made clear, health behaviors are easily politicized, leading to misinformation and inconsistent behavior, Hernandez says. The findings also emphasize the need for clear, effective health guidelines, and for health care professionals to avoid unequal policing of lower-status patients and to consider the influence of network norms, particularly when it comes to controversial health risks, she says.
In other news, the COVID-19 pandemic took many people by surprise. But what if there was a way to forecast the probability that such a virus would result in a pandemic? IUPUI professor George Mohler, funded through a new National Science Foundation grant, is working to develop new algorithms for modeling and forecasting emerging infectious diseases. Mohler says new diseases are discovered every day. He wants to know what the risk is that a local outbreak will subside on its own or continue to spread. The models in development, he says, can be used to estimate the reproduction number of a virus, the serial interval between infections, and for forecasting short-term transmission to inform public health decisions. Mohler will use COVID-19 data collected by state and county governments, along with historical datasets and emerging data coming from the Program for Monitoring Emerging Diseases. This will be coupled with census data and Google mobility data to model how the local spread of diseases is linked to variations in geography, demographics and human behavior. Mohler says the models of contagion processes developed through the project will be applicable beyond epidemiology, including areas such social media, criminology and seismology.