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Bayesian spatial modeling of COVID-19 case-fatality rate inequalities
Gina Polo1, Diego Soler-Tovar1, Luis Carlos Villamil Jimenez1
1Grupo de Investigación en Epidemiología y Salud Pública, Universidad de La Salle, Bogotá, Colombia.
Spatial and Spatio-Temporal Epidemiology
|June 12, 2022
Summary
COVID-19 mortality risk is higher in poorer regions of Colombia. Socioeconomic factors like housing, education, and health deprivations significantly increase the risk of dying from the disease.
Area of Science:
- Public Health
- Epidemiology
- Health Economics
Background:
- The COVID-19 pandemic has exposed global health inequalities.
- Preventive measures show spatially heterogeneous effectiveness, highlighting disparities.
Purpose of the Study:
- To identify the spatial association between socioeconomic factors and COVID-19 mortality risk in Colombia.
- To analyze the heterogeneous distribution of COVID-19 case-fatality rates and poverty.
Main Methods:
- Bayesian-based Markov chain Monte Carlo simulations were employed.
- Spatial analysis was used to assess the relationship between socioeconomic deprivations and mortality risk.
Main Results:
- A heterogeneous spatial distribution of COVID-19 case-fatality rates and the multidimensional poverty index was confirmed.
- Increased risk of dying from COVID-19 was observed in areas with higher proportions of people experiencing dwelling, educational, childhood/youth, and health deprivations.
Conclusions:
- Disadvantaged populations are more vulnerable to dying during pandemics.
- Findings support spatial planning for targeted preventive strategies in vulnerable communities.
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