Dr. Odis Johnson Jr. discusses the need for detailed metrics on students in order to best provide resources and determine school opening policies that support all aspects of a child’s learning: health, safety, and education.
Dr. Odis Johnson Jr. is a Bloomberg Distinguished Professor and Director of the Johns Hopkins Center for Safe and Healthy Schools. As founder of the Institute in Critical Quantitative, Computational, and Mixed Methodologies, Dr. Johnson is an expert on data science and discusses the intersection between public health data and education that we explored in this week’s Pandemic Data Outlook blog.
We have no clue how our decisions and on-the-spot changes supported healthy outcomes for kids. We will not know how much growth rates and learning trajectories have changed during the pandemic, and what school resources and programs were actually impactful. I can't say that states and school systems have ever been consistent and uniform in collecting data related to those outcomes. We would have benefited from federal leadership on accountability measures for learning and data collection.
Along with that, there are vulnerable populations that need additional support and we have no data to show what school systems did to support those high-need populations and what impact they may have had. For instance, since COVID-19 undermined homeownership for many families, we are not clear on how many children were homeless throughout this pandemic. School doors were closed so where were they learning? We don’t have data on students whose families do not speak English as a primary language. When schools went hybrid or switched to distance learning, we relied more on those families to supervise instruction and be partners in their kids’ learning. We have no clue how well that worked and what schools did to support that challenging expectation.
We're very impressed with Baltimore City Public Schools, which has been a thought leader on the impacts of COVID-19 on education. They actually surveyed families to answer some of these questions, broadening the arena of metrics that they might collect related to their students’ well-being. It’s not typical of school systems to collect data on metrics, and some may think they are not even relevant. We at the Center for Safe and Healthy Schools have been talking to school officials about how the lessons learned from the pandemic’s challenges suggest schools should reform and what resources may be important in the future. Baltimore City has been actively engaged in trying to make sense of this opportunity.
Details such as urbanicity, neighborhood determinants, and structural and built environment factors were definitely related to our ability to manage the pandemic and secure effective learning environments for kids. At the height of the pandemic it would have been great to have epidemiological rates of infection that were informed by structural determinants of transmission.
Within urban space, we know that there are different environments and population densities that then relate to schooling and how successful mitigation techniques are likely to be. We know that in urban neighborhoods family size might be a little bit larger than average, population density tends to be higher, and with larger families and smaller living quarters it also becomes hard to implement mitigation strategies. We don’t have any of that data or how it correlates to COVID-19, which would have been a great aid to schools and local governments during this pandemic.
Within the educational space, demographic data is critical because we find that families are differently situated to support the learning of their kids. Because of that we might see that some segments of the population actually didn't experience a setback in learning during the pandemic, whereas other populations that have relied on schools more probably experienced a greater loss. It’s really important then for us to have that specific dis-aggregated data in order to understand these disparities and also to then extrapolate back to how this pandemic differed for populations given some of these things such as segregation, differential access to care, difference in co-morbidities, or the correlates of infection, hospitalization, and death.
There’s a lot that makes basing policy decisions on the data a little challenging. For instance, the rate of vaccination is important for educators to know, but it is a sensitive topic. It definitely is important for us to have those data, but I question how actionable the data will be for health policy and educational policy decision making because of two major complications. One is trust; not only trust in the vaccine, but teachers trusting that their school systems and policy leaders have their health and wellbeing in mind. Second, there are those who have religious exemptions and health-related factors that exempt them from vaccination, which complicates policy making.
There were a lot of decision makers this past year. At the ground level you have families and children making decisions based on their employment situation, childcare logistics, and a policy ecosystem that is rarely in agreement. Families are often confused by all of the messages. They hear the governor, the mayor, and the CDC, then they're conflicted in their decision making. Where they get their advice and whose advice they’re following when they make these decisions are things that data will need to clarify to best inform our strategies for communicating with families in the future.