Logistic Regression Models and Classification Tree for Deaths and Recovered Patients Records of Covid-19 in the State of Minas Gerais, Brazil

The challenges for the construction of a pandemic confrontation agenda by COVID-19 in Brazil come up against social inequalities, as a reflection of the segregation of access to comprehensive basic sanitation services and public health assistance programs. The objective of this work is to analyze the profile of deaths and recoveries by COVID-19 in the state of Minas Gerais, based on socio-environmental predictors, using a logistic regression model and classification tree (CHAID). Data on recovered individuals and confirmed deaths for COVID-19 were obtained from the Minas Gerais State Department of Health, containing records of age, sex, race, comorbidity and municipality of residence. The data regarding municipal basic sanitation were obtained from Instituto Trata Brasil. The Minitab and SPSS software were used in the elaboration of the logistic regression models and classification tree, respectively. The probability of death from COVID-19 in the state is significantly higher in males, over the age of 60 years old, with some comorbidity, declared black and brown, living in municipalities located in the poorest macro-regions of the state, where classes prevail inadequate or inadequate basic sanitation. The classification tree for deaths by COVID-19, differentiates young blacks and browns without comorbidity, and the elderly with comorbidity not assisted by a comprehensive basic sanitation network. It is concluded that the worsening of the pandemic in the state is related to aspects of social vulnerability, and that the implementation of inclusive public policies is urgent.