NERCRD COVID-19 Issues Brief No. 2020-8, by Zheng Tian, Stephan J. Goetz, NERCRD and Penn State University; and L. Goetz-Weiss. May 5, 2020
A printer-friendly PDF is available here.
On June 23, 2020 we issued an update to the data shown here. Get the update here.
New COVID-19 cases are generally growing more rapidly in nonmetro than in metro areas at this time, but there is an important distinction. In all three non-metro county types, categorized by population sizes (20,000 population or more; 2,500-20,000; and 2,500 or smaller urban populations), the caseload growth is higher in counties that are adjacent to metropolitan areas compared to counties that are not adjacent (Fig. 1).
Figure 1. Daily new confirmed cases of COVID-19 by county type.
Data source: New York Times, Economic Research Service, U.S. Dept. of Agriculture, and authors' calculations.
Non-metro counties that are adjacent to metro areas tend to benefit from this proximity both in terms of access to employment as well as urban amenities and other services. At this time it appears that this same proximity to metro areas is also hurting them, through greater exposure to the coronavirus. These lines and our interpretation is subject to the usual caveats about the data (e.g., many infected cases likely go unreported). There is also considerable day-to-day variation (noise) in the data, even with three-day moving averages.
Even so, the data suggest that in all of the non-metros, the non-adjacent counties mostly had smaller and more gradual increases in cases, while the adjacent smaller counties especially saw more rapid increases around mid-April, which have since declined again. In the smallest-sized category of non-metro, cases were rising at almost similar rates in both adjacent and non-adjacent county types until about April 20, and since then they have converged again.
New daily cases of death show distinct differences in the growth patterns for larger and mid-sized non-metro counties, whereas in the smaller non-metro counties these lines crisscross one another and the fitted cubic-spline function shows similar trends for adjacent and non-adjacent counties (Fig. 2). It is noteworthy the mid-sized non-metros have higher death cases per capita than either of the other two types of non-metros.
Figure 2. Daily new death cases of COVID-19 by county type.
Data source: New York Times, Economic Research Service, U.S. Dept. of Agriculture, and authors' calculations.
Despite widely reported early concerns[1] that rural areas would be hit especially hard by the coronavirus, the case-fatality ratio - the number of deaths divided by the number confirmed cases - is generally lower in non-metro than metro areas (Fig. 3, Table 1). These early concerns were based on a more elderly and thus more vulnerable rural population as well as on proportionately lower medical care capacity (EMS, hospitals, doctors per capita). Of the four county types shown, the largest non-metros appear to be doing best in terms of bringing the curves down, while in the smallest the movement is more sideways.
Figure 3. Case-fatality rates of COVID-19 by county type.
Data source: New York Times, Economic Research Service, U.S. Department of Agriculture, and authors' calculations.
| Table 1. Confirmed and death cases and case-fatality rate on May 1 by county type | |||
| County types | Confirmed cases (3-day moving average) | Death cases (3-day moving average) | Case-fatality rate |
| RUCC 1-3 | 1,003,196 | 54,837 | 5.47% |
| RUCC 4 | 19,867 | 699 | 3.52% |
| RUCC 5 | 7,112 | 214 | 3.01% |
| RUCC 6 | 18,782 | 929 | 4.95% |
| RUCC 7 | 9,005 | 328 | 3.64% |
| RUCC 8 | 3,060 | 72 | 2.36% |
| RUCC 9 | 1,830 | 55 | 3.01% |
Data source: New York Times, Economic Research Service, U.S. Department of Agriculture, and authors' calculations.
It remains to be seen whether this conclusion will be robust over time, but at present it appears that the higher density in metropolitan U.S. areas is associated with a greater case-fatality ratios, and thus lethality of the coronavirus.
Note: A map of counties classified according to the rural urban continuum code (RUCC) is available here.
About the Authors
Zheng is a postdoctoral scholar at the Northeast Regional Center for Rural Development; Goetz is Director of NERCRD and Penn State Professor of Agricultural and Regional Economics. Contact: sgoetz@psu.edu
About this series
These issues briefs are designed to provide information quickly or stimulate discussion, and they have not undergone regular peer review. NERCRD receives core funds from the U.S. Department of Agriculture's National Institute of Food and Agriculture (award #2018-51150-28696) as well as from Multistate/Regional Research and/or Extension Appropriations (project #NE1749), the Northeastern Regional Association of State Agricultural Experiment Station Directors, and the Pennsylvania State University, College of Agricultural Sciences. Any opinions are solely those of the authors.
[1] Shannon Monnat, "Research Update: Why Coronavirus Could Hit Rural Areas Harder," The Daily Yonder, March 24, 2020, available at https://www.dailyyonder.com/research-update-why-coronavirus-could-hit-rural-areas-harder/2020/03/24/; Lois Parshley, "The coronavirus may hit rural Americans later - and harder," Vox, March 28, 2020, available at https://www.vox.com/2020/3/28/21197421/usa-coronavirus-covid-19-rural-america.
