The Delta-Omicron wave of the COVID-19 pandemic (Wave 4) in the United States occurred in Fall of 2021 through Spring of 2022. Although vaccinations were widely available, this was the deadliest period to date in the U.S., and the toll was especially high in rural areas, exacerbating an existing rural mortality penalty.

This paper uses county-level multilevel regression models and publicly available data at the county level for 47 U.S. states and the District of Columbia. We describe differences in COVID-19 case and mortality rates across the rural-urban continuum during Wave 4 of the COVID-19 pandemic. A progressive modeling approach evaluates the relative contribution of a range of explanatory factors for the rural disadvantage we observe, including: pre-pandemic population health composition, vaccination rates, political partisanship, socioeconomic composition, access to broadband internet rate, and primary care physicians per capita.

Results show that rural counties had higher observed burdens of cases and deaths in Wave 4 compared to more urban counties. The most remote rural counties had a Wave 4 COVID-19 mortality rate that was 52% higher than the most urban counties.

Older age composition, worse pre-pandemic population health, lower vaccination rates, higher share of votes cast for Donald Trump in the 2020 Presidential election, and lower socioeconomic composition completely explained the rural disadvantage in reported COVID-19 case rates in Wave 4, and accounting for these factors reversed the observed rural disadvantage in COVID-19 mortality. In models of mortality rate, Trump vote share had the largest effect size, followed by the percentage of the population age 50 or older, the poverty rate, the pre-pandemic mortality rate, the share of residents with a 4-year college degree, and the vaccination rate.

These findings add to a growing literature describing the disproportionate toll of the COVID-19 pandemic on rural America, highlighting the combined effect of multiple sources of rural disadvantage.