The effects of Covid-19 on the indigenous population of Mexico. A Bayesian spatio-temporal analysis

• Autores: Núñez Medina, Gerardo, Uribe Salas, Felipe
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Pontificia Universidad Católica del Perú
• Repositorio: Revistas - Pontificia Universidad Católica del Perú
• Lenguaje: español
• OAI Identifier: oai:revistaspuc:article/26997
• Enlace del recurso: http://revistas.pucp.edu.pe/index.php/Kawsaypacha/article/view/26997
• Nivel de acceso: acceso abierto
• Materia: Covid-19; Incidence Rates; INLA; Indigenous Population; Bayesian Hierarchical Model; Spatio-Temporal Analysis; Mexico; Tasas de incidencia; Población indígena; Modelo jerárquico bayesiano. Análisis espacio tempora; Modelo jerárquico bayesiano; Análisis espacio temporal; México

The aim of this work is to analyze the impact of Covid-19 on indigenous populations in municipalities of Mexico. To analyze this relationship, Bayesian spatio-temporal models are used to capture the complex dynamics of epidemiological transmission in terms of spatial, temporal and joint spatio-temporal dependence. These models have the ability to include covariates, such as the percentage of indigenous population, which makes it possible to quantify the effect that the covariate has on the evolution of the epidemic. Likewise, the models allow us to identify spatio-temporal clusters with high and low incidence rates, showing health inequalities based on the proportion of the indigenous population residing in specific municipalities. Contrary to expectations, the results showed a protective effect on the incidence rate of COVID-19 for the indigenous population. Furthermore, a wide heterogeneity was observed in the distribution of COVID-19 incidence rates by municipality, with significant fluctuations over time. The incidence rates of COVID-19 in indigenous populations were low, which may be due to the fact that the indigenous population predominates in municipalities with low population density, less access to health services, and greater social marginalization. However, it is important to interpret these results with caution due to the high level of observed underreporting of COVID-19 cases found in indigenous populations.