Research finds county-level differences in COVID outcomes based on college policy

By Dr. Ramya Dwivedi, Ph.D.Reviewed by Benedette Cuffari, M.Sc.Oct 12 2021

A recent study published on the preprint server medRxiv* analyzes the number of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and related deaths in education campuses. In their study, the authors specifically focused on the consequences of online as compared to in-person education and on-campus testing to screen for the infection. The researchers found that the outcomes were worse during in-person classes and when no testing was performed.

Study: Higher education responses to COVID-19 in the United States: Evidence for the impacts of university policy. Image Credit: FamVeld / Shutterstock.com

This news article was a review of a preliminary scientific report that had not undergone peer-review at the time of publication. Since its initial publication, the scientific report has now been peer reviewed and accepted for publication in a Scientific Journal. Links to the preliminary and peer-reviewed reports are available in the Sources section at the bottom of this article. View Sources

Background

Institutes of higher education (IHEs), of which include colleges, universities, and trade schools, are active centers where students attend classes, live in dorms, and participate in sports and other large gatherings. IHEs are, therefore, hotspots for transmissible pathogens, such as the SARS-CoV-2, which is the etiological agent of coronavirus disease 2019 (COVID-19).

With the help of vaccines against SARS-CoV-2, all institutions are slowly reopening, bringing back students and faculty for on-campus, in-person education. While this may contribute to or exacerbate large regional outbreaks from an IHE, it is also important to open these institutions in order to avoid economic and social hardships in the communities where these IHEs form foundational ground in the society.

The current study was aimed at understanding the impact of certain nonpharmaceutical measures to prevent virus mitigation and compare their success among online, in-person, and hybrid methods of education.

The study

The researchers introduced a dataset of testing and case counts from over 1,400 IHEs in the United States. Data was acquired from the Campus COVID Dataset from COVID dashboards through web-scraping, manual data entry, and communication with administrators at IHEs.

For the analysis, the researchers used a matching procedure designed to create groups of counties that are aligned along with age, race, income, population, and urban/rural categories. These socio-demographic variables were included in their analysis, as they have been shown to be correlated with COVID-19 outcomes.

The two study groups in this study included IHEs where students returned to fully or primarily in-person education and IHEs where students remained fully or primarily online. In their analyses, the researchers did not include “hybrid” IHEs, as there is a great deal of heterogeneity in what constitutes a “hybrid” reopening. This heterogeneity can depend on the professor’s choice, simultaneous teaching, as well as various teaching plans.

An important factor to be considered is the disproportionate impact of COVID-19 on older populations, as seen in the high hospitalization and death rates in regions with congregate senior living and long-term care facilities. Therefore, the researchers matched groups of counties with as similar distributions of demographics.

When examining the fall 2020 reopening status of in-person as compared to online education, the average new cases and new deaths per 100,000 people between the two groups of counties was almost identical between July and August. However, by the end of August, as the Fall 2020 semester begins, college counties with primarily in-person enrollment reported more new cases per 100,000 until the end of the semester. After which, the new cases are similar to the other group. The researchers observed a similar trend when comparing the average new deaths per 100,000 between the two groups of counties.

Counties with IHEs categorized as in-person vs. online for Fall 2020. Here, we compare the average (a) new cases and (b) new deaths per 100,000 in counties with IHEs that were categorized as “primarily in-person” vs. “primarily online” for the Fall 2020 semester. IHEs classified with “hybrid” reopening strategy were not included in this comparison as there is a great deal of heterogeneity in what constitutes a “hybrid” reopening. IHE reopening data is from [23]. Ribbons: 95% confidence interval. Insets: Cumulative differences between counties primarily in-person/online IHEs. Cumulative cases per 100,000 among primarily online counties during the study period (August - December, 2020) = 4,196.3, 95% CI: [4,042.9 – 4,347.6]; primarily in-person counties = 5,415.3, 95% CI: [5,310.6 – 5,552.0]. Cumulative deaths per 100,000 among primarily online counties = 44.5, 95% CI: [45.4 – 48.1]; primarily in-person counties = 67.2, 95% CI: [64.1 – 72.0].

COVID-19 testing varied substantially, as some schools employed their limited resources in testing only symptomatic students, whereas others did strict, massive testing that may have included frequent (weekly) asymptomatic testing. To quantify the benefits of IHE-affiliated testing, the researchers grouped IHEs that conduct any COVID-19 tests to those that conducted none.

Comparing counties with IHEs that reported any vs. zero COVID-19 tests. As in Figure 2, we compare the average (a) new cases and (b) new deaths per 100,000 in counties with IHEs reported conducting any COVID-19 tests vs. counties with IHEs that reported no tests. Note: if there are multiple IHEs in a single county, we sum together the total number of tests between all IHEs. Ribbons: 95% confidence interval (CI). Insets: Cumulative differences between counties with/without testing IHEs. Cumulative cases per 100,000 among testing counties during the study period = 5,235.7, 95% CI: [5,138.5 – 5,315.6]; non-testing counties = 5,264.1, 95% CI: [4961.0 – 6,009.7]. Cumulative deaths per 100,000 among testing counties = 56.0, 95% CI: [54.6 – 60.9]; non-testing counties = 70.2, 95% CI: [64.4 – 81.0].

To this end, a significant increase in the number of cases among counties with IHEs that did report testing was observed. However, the same increase in reported cases among counties with IHEs that do not report testing was not observed. Furthermore, fewer deaths in counties with IHEs reporting any on-campus testing compared to those that reported none were noted.

Based on these findings, the researchers supported the United States Center for Disease Control and Prevention (CD) recommendation that recommends the implementation of universal entry screening before the beginning of each semester and serial screening testing when capacity is sufficient.

Case Study: COVID-19 in Massachusetts college cities

Notably, the researchers analyzed Massachusetts as a case study about the role that the IHE-affiliated testing may play in a community’s response to COVID-19. In this experiment, the analysis was found to complement the observations from this study.

The case study strengthens the importance of testing at IHEs for the benefit of the broader community. Thus, regular campus testing can itself be a mitigation policy against the spread of COVID-19 in the general population.

Case Study: COVID-19 in Massachusetts college cities. Top: High-lighting testing and case counts in and around Amherst, Massachusetts. (a) Map of Massachusetts cities; in this map, the city of Amherst is red and the surrounding cities are colored blue. (b) Time series of weekly new cases per 1,000 in: Amherst, the surrounding cities, and the rest of Massachusetts. (c) Time series of weekly new tests per 1,000 in: Amherst, the surrounding cities, and the rest of Massachusetts. Bottom: Comparing outcomes of cities and IHEs with more/less IHE-affiliated testing. (d) City-level average weekly new cases per 1,000, grouped by cities with IHEs that test students on average fewer than once a month, between one and three times a month, and over three times a month (Note: we sought out city-level data for COVID-19 deaths, but the state does not report these). (e) IHE-level average weekly new cases, grouped by IHEs that test students on average fewer than once a month, between one and three times a month, and over three times a month.

Conclusion

The current study highlights clear differences in COVID-19 outcomes based on IHE reopening policy. Since large numbers of young adults who are likely to have increased mobility and less likely to practice COVID-19 mitigation behaviors are enrolled in HIEs, the researchers concluded that campus testing in counties with a large student population can play a relevant role in mitigating the number of COVID-19 outbreaks in the surrounding population.

This news article was a review of a preliminary scientific report that had not undergone peer-review at the time of publication. Since its initial publication, the scientific report has now been peer reviewed and accepted for publication in a Scientific Journal. Links to the preliminary and peer-reviewed reports are available in the Sources section at the bottom of this article. View Sources