Covid-19 Per Capita Fatality Rate: A Path Analysis Model

Penuliar, M. S.; Clark, C.; Curti, D.; Carrasco, M.; Hudson, C.; Philips, B.

Objectives: Various individual factors have been shown to influence Covid-19 mortalities, but these factors do not exist in isolation. Unique to this study is a multivariate approach that has yet to be fully explored by previous research. Using an interconnected multifactor model, this work investigated social determinant, geographic, prior health, and political behavioral factors likely to influence Covid-19 per capita fatalities in Texas. Methods: County-level income, rurality, insurance, health status, 2020 presidential vote percentage, and fatality rate data were collected and analyzed in a path analysis model with Covid-19 per capita fatalities as the key variable of interest. Results: The analysis found strong support for the proposed model structure (R2 = 37.6%). The strongest overall effects on the Covid-19 per capita fatality rate came from income levels and voting behaviors. Conclusion: The model explained a substantial amount of variability in mortalities attributed to Covid-19. Socioeconomic and political factors provided the strongest contribution to the per-capita Covid-19 death rate, controlling for the other variables studied. The Covid-19 pandemic was highly politicized by various leaders and media outlets. The current analysis showed that political trends were one of the key overall factors related to Covid-19 mortality. The strongest overall factor was median income. Income is used to enhance one's current health or acquire adequate treatment which may safeguard people from the most severe effects of Covid-19. Counties with lower income levels had higher rates of Covid-19 per capita fatalities.