Skip to main content

Opening of Large Institutions of Higher Education and County-Level COVID-19 Incidence — United States, July 6–September 17, 2020

Our take —

In this study, the authors compared COVID-19 trends among US counties with large universities (20,000+ students) that reopened with in-person classes, those with large universities that reopened with virtual instruction, and those without large universities. Comparing the 21-day periods before and after the start of classes, counties with in-person instruction exhibited substantially larger increases in COVID-19 incidence and SARS-CoV-2 test positivity relative to other counties. However, the use of data aggregated to the county level, inattention to COVID-19 mitigation measures implemented during the study period, and handling of university instructional format as fixed over time limit the attribution of these trends in county-level COVID-19 incidence to university re-openings alone.

Study design

Ecological

Study population and setting

The investigators aggregated US county-level COVID-19 incidence between July and September 2020. Counties were classified into three groups based on the presence or absence of large (20,000+ enrolled students) not-for-profit universities and the instructional modality (i.e., in-person or virtual classes) used in the first days of the academic year. County-level COVID-19 incidence, SARS-CoV-2 test positivity rates, and “hotspot” status (defined by both absolute numbers of new cases and recent trends in incidence) were compared across counties (1) with universities offering in-person instruction, (2) with universities offering virtual instruction, and (3) without large universities. Investigators also conducted a separate analysis, in which counties with universities offering in-person classes were matched with non-university counties based on population size, the % of urban residents, and proximity (i.e., within 500 miles of each other).

Summary of Main Findings

Counties with universities offering in-person instruction (n = 79) experienced a substantial average increase (56.2%) in COVID-19 incidence comparing the 21-day periods before and after classes started. In contrast, counties with universities offering virtual instruction (n = 22) and counties without large universities (n = 3,009) experienced COVID-19 incidence reductions of 17.9% and 5.9%, respectively, over the same time period. Mean SARS-CoV-2 test positivity followed similar patterns, with increases of 1.1% in counties with universities offering in-person classes and reductions of 1.8% and 0.6%, respectively, in counties with universities offering virtual classes and non-university counties. Although detection of COVID-19 hotspots increased across counties during the study period, the increase was substantially larger in counties with in-person university instruction (30.4%) relative to that observed in counties where universities offered virtual classes (9.1%) and non-university counties (1.5%), respectively. Results were roughly comparable in a sensitivity analysis where 68 counties with universities offering in-person classes were compared to 68 counties with non-university counties: in-person university counties were associated with an increase of 10.6 cases per 100,000 residents relative to non-university counties.

Study Strengths

The investigators used a difference-in-differences method, which can be useful in comparing groups differentially exposed to a non-randomized policy. The investigators also conducted a sensitivity analysis through which counties were matched on demographic and geographic factors, demonstrating the robustness of their findings.

Limitations

Counties without and with large universities, offering either in-person or virtual learning in summer 2020, may have differed in unmeasured ways (e.g., local mask mandates, political affiliation, presence or absence of large congregate residences) that affected COVID-19 outcomes; this likelihood limits the ability to attribute observed changes in incidence to university re-openings. Additionally, data aggregated to the county level weakens the plausibility of university re-openings causing these secular changes because the source of COVID-19 infections (i.e., university campuses or in community settings) was not ascertained. Because the investigators treated instructional format (in-person or virtual) as a static, non-time-varying measure in their analysis, effect estimates may have been biased due to misclassification of counties with universities switching instructional modalities (i.e., from in-person to virtual or vice versa) during the study period. Counties with multiple universities were assigned the instructional format of the university with the earliest start date, which may have led to misclassification. Finally, the selection of 20,000 enrolled students as the population size threshold for counties with large universities limits inferences that can be drawn about the association between university re-openings and county-level COVID-19 transmission in settings with smaller (<20,000) universities. Sensitivity analyses using smaller thresholds for classification of counties into university or non-university groups may have permitted comparison of COVID-19 incidence patterns across counties with universities of variable population sizes.

Value added

This is the first study to compare secular trends in COVID-19 incidence and transmission dynamics at the county level and to attribute these changes to university re-openings in late summer 2020.

This review was posted on: 22 January 2021