The analysis of the provided model yields several noteworthy conclusions. Firstly, the prevalence of diabetes (DB) is found to be extremely significant with a p-value of 0.00, and it is identified as the most influential predictor of mortality rate, in line with Corona et al. (2021). Counties with high diabetes rates also exhibit excess mortality not attributed to COVID-19, as per Stokes et al. (2020). Secondly, the unemployment rate (UN) has a significant impact, with a positive estimate and a p-value of 0.00, indicating that mortality rate increases in cities with higher unemployment rates, consistent with the findings of Paul et al. (2021) and Mirahmadizadeh et al. (2022). Thirdly, life expectancy (LE) is significant at the 1% level with a positive estimate, suggesting that mortality rates are slightly higher in cities with higher life expectancy, consistent with the observations of Notari and Torrieri (2022) and Wang et al. (2020), who demonstrated a positive correlation between life expectancy and the initial transmission growth rate of COVID-19.