Geospatial evaluation of COVID-19 mortality: Influence of socio-economic status and underlying health conditions in contiguous USA

Akinola S Akinwumiju; Olawale Oluwafemi; Yahaya D Mohammed; Jacob W Mobolaji

doi:10.1016/j.apgeog.2022.102671

Geospatial evaluation of COVID-19 mortality: Influence of socio-economic status and underlying health conditions in contiguous USA

Appl Geogr. 2022 Apr:141:102671. doi: 10.1016/j.apgeog.2022.102671. Epub 2022 Mar 3.

Authors

Akinola S Akinwumiju¹, Olawale Oluwafemi², Yahaya D Mohammed³, Jacob W Mobolaji⁴

Affiliations

¹ Department of Remote Sensing and GIS, Federal University of Technology, Akure, Ondo State, Nigeria.
² Spatially Integrated Social Science Program, Department of Geography and Planning, University of Toledo, Toledo, OH, USA.
³ Centre for Geodesy and Geodynamics, Toro, Bauchi State, Nigeria.
⁴ Department of Demography and Social Statistics, Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria.

Abstract

Since its outbreak, COVID-19 disease has claimed over one hundred thousand lives in the United States, resulting to multiple and complex nation-wide challenges. In this study, we employ global and local regression models to assess the influence of socio-economic and health conditions on COVID-19 mortality in contiguous USA. For a start, stepwise and exploratory regression models were employed to isolate the main explanatory variables for COVID-19 mortality from the ensemble 33 socio-economic and health parameters between January 1st and 16th of September 2020. Preliminary results showed that only five out of the examined variables (case fatality rate, vulnerable population, poverty, percentage of adults that report no leisure-time physical activity, and percentage of the population with access to places for physical activity) can explain the variability of COVID-19 mortality across the Counties of contiguous USA within the study period. Consequently, we employ three global and two local regression algorithms to model the relationship between COVID-19 and the isolated socio-economic and health variables. The outcomes of the regression analyses show that the adopted models can explain 61%-81% of COVID-19 mortality across the contiguous USA within the study period. However, MGWR yielded the highest R² (0.81) and lowest AICc values (4031), emphasizing that it is the most efficient among the adopted regression models. The computed average adjusted R² values show that local regression models (mean adj. R² = 0.80) outperformed the global regression models (mean adj. R² = 0.64), indicating that the former is ideal for modeling spatial causal relationships. The GIS-based optimized cluster analyses results show that hotspots for COVID-19 mortality as well as socioeconomic variables are mostly delineated in the South, Mid-West and Northeast of contiguous USA. COVID-19 mortality exhibited positive and significant association with black race (0.51), minority (0.48) and poverty (0.34). Whereas, the percentage of persons that attended college was negatively associated with poverty (-0.51), obesity (-0.50) and diabetes (-0.45). Results show that education is crucial to improve socio-economic and health conditions of the Americans. We conclude that investing in people's standard of living would reduce the vulnerability of an entire population.

Keywords: COVID-19 mortality; Case fatality rate; Poverty.