Jemar R. Bather, Janice Johnson Dias, and Melody S. Goodman

The global pandemic increased the public’s demand for information. Almost daily, media and policymakers present numbers on testing, deaths, hospitalization rates, and vaccine distribution. This information often includes data visualizations and tables viewers must interpret to better understand the pandemic’s scope and its impact on local communities and countries worldwide. As a result, there is a need for a new vocabulary (e.g., flattening the curve, epidemiology, public health, pandemic) around public health and data literacy.

This daily influx of data exposure happens at a time when the average person in the United States has difficulty comprehending the data’s meaning or practical implications. In the most recent iteration of the Programme for International Student Assessment, a global assessment of 15-year-olds, the United States ranked 37th in math out of 79 countries and economies. The US had a score of 478, while China (591) and Singapore (569) had the highest [scores]. This study assesses whether 15-year-olds possess the necessary skills for full participation in social and economic life. The findings demonstrate US residents lag their global counterparts in quantitative literacy.

Increasing quantitative literacy training will bolster the knowledge of people in the United States and equip them with tools to become healthier now and in the future. Additionally, data literacy will provide US residents with information about how specific racial and ethnic groups experience health disparities. According to data from the Centers for Disease Control and Prevention, COVID-19 is yet another example of an infectious disease in which Native Americans, African Americans, and Hispanics are more likely than whites to be hospitalized or die because of the virus. There are many other diseases that provide similar data (e.g., chronic, sexually transmitted), but the average person in the United States may not grasp the depth of these inequities and, furthermore, may not understand phrases commonly associated with these diseases (e.g., risk factor, confounder). This lack of understanding is likely to stymie their ability to be empathetic and advocate for structural changes, as well as inhibit their ability to make informed decisions.

To address the gap in both quantitative literacy and public health disparities, we developed the Quantitative Public Health Data Literacy (QPHDL) course during the pandemic (summer 2020). QPHDL introduces students to the foundations of public health and teaches data literacy, statistical analysis software, and data ethics.

We received 695 applications for the first cohort of the QPHDL in 12 days (June 26 – July 8, 2020), with no money spent on advertising. Most of our applicants were beginners in quantitative data literacy, but they were all eager to learn.

Originally intended to serve Black girls in high school and college, this program quickly grew in popularity across ages, genders, and races. As expected, most of the applicants were girls/women (74 percent), African Americans (43 percent), and current students (80 percent). Of the 695 applicants, 46 percent were under the age of 20, 35 percent were between the ages of 21 and 30, and 20 percent were over the age of 30. In terms of race, 43 percent identified as African American, 30 percent as Asian, 12 percent as Latinx, 7 percent as white, and 6 percent as multiracial/other.

We differentiated our course offerings due to the range of ages and education levels. In summer 2020, we offered Track 1 to 55 high-school students and 188 college undergraduates, with representation from each level (high-school freshman to college seniors). Most of this cohort (73 percent) were girls/women, with an average age of 21. The majority of Track 1 students (44 percent) identified as African American, followed by those who identified as Asian (30 percent), Latinx (17 percent), white (4 percent), or multiracial/other (4 percent).

The second cohort (Track 2) trained during Black History Month (February 2021). The application was live for 13 days (January 9 – 22, 2021) and received more than 300 submissions in the first three days. Like Track 1, the majority of applicants were girls/women (83 percent) and African Americans (54 percent). Track 2’s average age was 32 years old.

With 858 total applications and the course initially designed for general audiences, it was striking to see a substantial percentage of our applicants were in school (49 percent; in high school, college, or graduate school) or worked as a public health professional (37 percent). Clearly, there is a population seeking to increase their data literacy outside the classroom and workplace. Additionally, we received nearly 200 international applications, suggesting a global demand for data literacy training.

The results from the evaluation of our program are forthcoming, but the growing number of applications for future iterations of the program demonstrates the added value we provide to people all over the world. As the world adapts to the current and future pandemics, we will continue to train people of all ages, genders, races, and ethnicities to synthesize quantitative data and translate it for their communities. As a result, disadvantaged populations will be better prepared to engage in public health research as partners, rather than participants. We implore nonprofit organizations, educational institutions, and private companies to join in this effort by pooling resources and developing programs like the QPHDL.