Modelling the impact of health-related variables, age, migration, and socio-economic factors in the geographical distribution of early tested case-fatality risks associated with COVID-19 in Mexico

ABSTRACT

COVID-19 is a respiratory disease caused by SARS-CoV-2, which has significantly impacted economic and public healthcare systems world-wide. SARS-CoV-2 is highly lethal in older adults (>65 years old) and in cases with underlying medical conditions including chronic respiratory diseases, immunosuppression, and cardio-metabolic diseases including severe obesity, diabetes, and hypertension. The course of the COVID-19 pandemic in Mexico has led to many fatal cases in younger patients attributable to cardio-metabolic conditions. Here, we aimed to perform an early spatial epidemiological analysis for the COVID-19 outbreak in Mexico to evaluate how tested case-fatality risks (t-CFRs) are geographically distributed and to explore spatial predictors of early t-CFRs considering the variation of their impact on COVID-19 fatality across different states in Mexico, controlling for the severity of the disease. As results, considering health related variables; diabetes and obesity were highly associated with COVID-19 fatality. We identified that both external and internal migration had an important impact over early COVID-19 risks in Mexico, with external migration having the second highest impact when analyzing Mexico as a whole. Physicians-to-population ratio, as a representation of urbanity, population density, and overcrowding households, has the highest impact on t-CFRs, whereas the age group of 10 to 39 years was associated with lower risks. Geographically, the states of Quintana Roo, Baja California, Chihuahua, and Tabasco had higher t-CFRs and relative risks comparing with a national standard, suggesting that risks in these states were above of what was nationally expected; additionally, the strength of the association between some spatial predictors and the COVID-19 fatality risks variates by zone depending on the predictor.