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Spatial Survival Model for COVID-19 in México.
Eduardo Pérez-Castro1, María Guzmán-Martínez2, Flaviano Godínez-Jaimes2
1Unidad de Investigación de Salud en el Trabajo, Centro Médico Nacional Siglo XXI, Ciudad de México 06720, Mexico.
Healthcare (Basel, Switzerland)
|February 10, 2024
Summary
Spatial survival analysis in Mexico reveals that factors like age, gender, comorbidities, and pneumonia significantly impact COVID-19 patient survival. The spatial model proved superior in identifying these survival influences.
Area of Science:
- Epidemiology
- Biostatistics
- Spatial Analysis
Background:
- COVID-19 has presented significant public health challenges globally and in Mexico.
- Understanding regional variations in patient survival is crucial for targeted interventions.
- Previous analyses may not have fully captured the spatial heterogeneity of risk factors.
Purpose of the Study:
- To identify factors influencing COVID-19 patient survival in Guerrero, Mexico, and Chihuahua.
- To assess the impact of spatial effects on COVID-19 survival.
- To compare the performance of spatial and non-spatial survival models.
Main Methods:
- Spatial survival analysis using Cox proportional hazards frailty and Cox proportional hazards models.
- Bayesian approach for parameter estimation.
- Model comparison using DIC, WAIC, and LPML criteria.
Main Results:
- The spatial model demonstrated superior fit compared to the non-spatial model.
- Significant spatial effects were identified, influencing COVID-19 patient survival times.
- Key risk factors identified include age, gender, comorbidities, and pneumonia development.
Conclusions:
- Spatial analysis is essential for understanding geographically varying COVID-19 survival patterns.
- Age, gender, comorbidities, and pneumonia are critical predictors of mortality.
- Findings highlight the need for state-specific public health strategies to mitigate COVID-19 mortality.
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