64 Progression of COVID-19 from Urban to Rural Areas in the Southwest: A Spatiotemporal Analysis of Prevalence Rates

Ashurst, J.; Berg, E.; Santarelli, A.

Background: COVID-19 emerged in the United States on January 20, 2020 and has rapidly proliferated across the nation affecting more than 15 million individuals. Case incidence rates vary at the local, county, and state levels and it is unclear if a difference exists in the incidence of new COVID-19 cases among urban or rural counties. Due to county population density and geographic progression of the virus over time, COVID-19 may demonstrate variable incidence rates among urban and rural population centers. Study Objective: The present study examines whether temporal differences in the incidence rate of diagnosed cases of COVID-19 exist between urban and rural counties in the Southwest United States. Methods: Daily COVID-19 cases from Arizona, California, Oklahoma, Texas, and Utah were retrospectively tabulated on a county basis using publicly accessible state health department data from March 1, 2020 to November 28, 2020. Counties were classified as rural or urban using the current classification by the Federal Office of Rural Health. Daily incident cases and prevalence per 100,000 persons were analyzed as continuous variables. Linear regression was used to assess the temporal relationships between new cases and county type. Descriptive statistics summarized the data. Results: A total of 400 counties were analyzed with the majority of counties being rural (n=262, 65.5%). No difference was detected in the prevalence of COVID-19 cases per 100,000 people between rural and urban counties (3616.4 Rural vs 3387.6 Urban;p=0.117) but there was a linear increase in total cases per 100,000 over the calendar year (p<.001). Rural counties demonstrated a significantly higher COVID-19 incidence rate in October (587.2 Rural vs 414.4 Urban;p<0.001) and November (919.0 Rural vs 771.6 Urban;p<0.001) than urban counties (Figure). However, no difference was observed in the incidence rates for March through September (p>0.05). Conclusions: Temporal data from this epidemiologic study show that the largest increases in COVID-19 cases during the “second wave” were attributed to rural counties. Despite its limitations as a geographic and population-based survey, this data indicates that continued efforts to prevent the rural spread of COVID-19 are warranted. [Formula presented]