An Analysis COVID-19 in Mexico: a Prediction of Severity

BACKGROUND: Coronavirus disease 2019 (COVID-19) causes a mild illness in most cases; forecasting COVID-19-associated mortality and the demand for hospital beds and ventilators are crucial for rationing countries’ resources. OBJECTIVE: To evaluate factors associated with the severity of COVID-19 in Mexico and to develop and validate a score to predict severity in patients with COVID-19 infection in Mexico. DESIGN: Retrospective cohort. PARTICIPANTS: We included 1,435,316 patients with COVID-19 included before the first vaccine application in Mexico; 725,289 (50.5%) were men; patient’s mean age (standard deviation (SD)) was 43.9 (16.9) years; 21.7% of patients were considered severe COVID-19 because they were hospitalized, died or both. MAIN MEASURES: We assessed demographic variables, smoking status, pregnancy, and comorbidities. Backward selection of variables was used to derive and validate a model to predict the severity of COVID-19. KEY RESULTS: We developed a logistic regression model with 14 main variables, splines, and interactions that may predict the probability of COVID-19 severity (area under the curve for the validation cohort = 82.4%). CONCLUSIONS: We developed a new model able to predict the severity of COVID-19 in Mexican patients. This model could be helpful in epidemiology and medical decisions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11606-021-07235-0.