Rurality and COVID-19 Outcomes: Unraveling the Impact of Nursing Home Residency Using Bayesian Analysis

Background and Objectives: Many studies have analyzed the impact of rurality on the incidence and consequences of COVID-19 infection. However, these studies have not considered the impact of different numbers of nursing homes in rural, semi-urban, or urban areas. Our objective was to analyze the effect of the factor of rurality on the incidence and mortality of COVID-19 while accounting for the impact of the variable of nursing home residency. In addition, we performed a comparative analysis of the infected population in semi-urban and rural areas. Methods: We first analyzed COVID-19 infection in all populations in the Balaguer Primary Health Care Area before examining the impact of rurality using Bayesian logistic regression analysis, specifically excluding the population living in nursing homes. We also performed an epidemiological and clinical analysis comparing rural and semi-urban areas. Results: We found higher incidence of and higher relative and absolute mortality from COVID-19 infection in semi-urban areas than in rural areas. After excluding nursing home residents from our sample, the Bayesian analysis indicated that rurality was not protective against COVID-19 infection or mortality. The incidence rates, specific mortality rates, and case fatality rates were similar in semi-urban and rural areas. All comorbidities, except chronic obstructive pulmonary disease, were associated with higher mortality, while no symptoms were associated with higher mortality. Conclusions: Excluding the population residing in nursing homes from the analysis, we found that rurality was not a protective factor against either infection or mortality during the first COVID-19 wave. Our Bayesian model analysis confirmed that rurality alone did not enhance survival among residents of rural areas.