Geospatial Variability in Excess Death Rates during the COVID-19 Pandemic in Mexico: Examining Socio Demographic, Climate and Population Health Characteristics
Sushma Dahal, Ruiyan Luo, Monica H. Swahn, Gerardo Chowell
Abstract
OBJECTIVES: This study examined how socio-demographic, climate and population health characteristics shaped the geospatial variability in excess mortality patterns during the COVID-19 pandemic in Mexico. METHODS: We used Serfling regression models to estimate all-cause excess mortality rates for all 32 Mexican states. The association between socio-demographic, climate, health indicators and excess mortality rates were determined using multiple linear regression analyses. Functional data analysis characterized clusters of states with distinct excess mortality growth rate curves. RESULTS: =77%). We identified four distinct clusters with qualitatively similar excess mortality curves. CONCLUSION: Central states exhibited the highest excess mortality rates, whereas the distribution of aging index, marginalization index, and average household size explained the variability in excess mortality rates across Mexico.