It is the start of a school year unlike any other. Many schools, especially in large urban districts, are fully remote. In New York, school opening was announced and then delayed. Some schools have opened and then grappled with quarantine. Others started closed and are now opening. As this has been happening, I’ve been talking and writing a lot about school opening and the best ways for parents and educators to gauge the risks involved. My central message has been that we need to focus on the denominators.
What does that mean?
Much of the reporting on schools has focused on cases of Covid-19. There are several dashboards, including in the Times, which do an excellent job of collecting the available information on coronavirus cases in schools. That information is limited, but it has grown over time.
What these reports lack, though, is a sense of the size of the pool. Knowing that there are five cases associated with a school may be useful information, but it is difficult to interpret that information without knowing whether those cases occurred in a school of 15 students or a school of 1,500. One way to think about it: If there are 5 cases in a school of 15, then if your child interacts with other children randomly, there is a 35 percent chance that they interact with someone who has Covid-19. If there are 5 cases in a school of 1500, there is a 0.33 percent chance. That’s the denominator.
Denominators are part of the larger context, but they are not the only piece.
We also need to understand what schools are doing. Are they undertaking mitigation? Masking? Distancing? Are they open at all? Some reported cases come from districts which are operating fully remotely, or districts in which the cases occurred before school was open. In both of those situations, the cases have nothing to do with schools, they just happen to be among school-affiliated people. Without linking the cases to their context, it is very difficult to understand what the numbers mean.
Several weeks ago, I teamed up with a group to try to fix this data problem. This group included a technology company, Qualtrics, that collects and analyzes base-line and follow-up data, and a group of educators, from superintendents’ and principals’ associations, who had access to schools. The project is largely volunteer, although we have a small amount of foundation funding. (Our team is not monetizing the data, and identifying information on districts or schools will not be made public.)
Our goal was to start not with case counts of Covid-19 but with schools. We wanted to ask schools how they were opening (or not), what mitigation strategies they were using, and to describe their enrollment and staffing levels. And then we wanted to ask them about Covid-19 cases. But only once we had the context.
The highly decentralized, fragmented American school system makes this kind of data collection difficult, and it may explain why a coordinated Covid-19 response in schools has been so hard. Reporting requirements vary from district to district and from state to state. Even in states with detailed coronavirus school case dashboards, such as Tennessee, the group that creates the dashboard, usually a state Covid-19 response team, often does not have good access to underlying enrollment data. Private schools have little or no reporting requirements for coronavirus, but in many locations they are the only ones to open. These private schools are an opportunity to learn about what might happen when public schools open in these areas, but only if we have data.
This fragmentation means there is no centralized location to look for the context we need. But it also provides opportunities: Since there is so much individual decision-making, there is enormous variation across the country in school reopening plans. This variation provides an opportunity to learn about the effectiveness of different reopening strategies. But, again, only if we have the data.
We are starting to get the data in, and it’s available in a public dashboard, here. Our Qualtrics team is analyzing it, but it can also be analyzed directly, by anyone. We have data from about 400,000 children in more than 700 schools across 48 states, while the total K-12 population in the United States is about 56 million. So we have a way to go. About 123,000 of those children are in person on an average day, along with 47,000 staff members.
And we have information on Covid-19 cases, at least in the first weeks of school. So far, the numbers are small. In our data, as of Sunday, confirmed case rates in students are 0.073 percent and, in staff, 0.14 percent. That means, in a school of 1,350 students you’d expect one case every two weeks and, in a staff of 100, one case about every 14 weeks. These numbers are about three times as high if we include suspected cases.
The top-line numbers are usually what people ask about first, but by starting with the context we can look at all sorts of additional information. For example: In some school districts, staff are working in person and students are not in person. Staff suspected and confirmed case rates in these schools look similar to schools that have students in person (although all are low), which suggests that staff may be spreading the coronavirus to each other, or these cases may be the result of general community spread. Another simple finding: Private schools in our data have lower infection rates, which seems to reflect, at least in part, their demographics and the fact that they do more mitigation.
Data with more context can also reveal the relative frequency of coronavirus prevention policies (masks are the most common, while routine staff testing is very uncommon) and give information on the use of different learning models. According to our data, 13 percent of schools have changed their learning model since the start of the school year. As the data grows, it will allow researchers — us and others — to analyze the relationship between prevention and outcomes. In our early analysis, limiting group sizes to under 25 seems to be the mitigation practice that is linked most strongly with low infection rates. Time and more data will help us learn whether this holds up.
As a parent, or as a school administrator, this data may give you more context for your own choices and encourage you to ask informed questions. What should I expect in a school like mine, if we open? What do “best practices” look like in general, and in my area? Information like this may be crucial to parents’ choices about their children, and schools’ choices about reopening.
Emily Oster (@ProfEmilyOster), a professor of economics at Brown University, is the author of “Cribsheet: A Data-Driven Guide to Better, More Relaxed Parenting, From Birth to Preschool.”
The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips. And here’s our email: firstname.lastname@example.org.