Statistical modeling of hospital admissions for pneumonia in Campo Grande

Abstract

Justification and Objectives: Brazil lacks consistent epidemiological data on the respiratory morbidity of children and older adults, which makes it difficult to plan and execute effective preventive and health promotion actions. The objective of this study was to analyze the adjustments of distributions (Weibull, Normal, Gamma, Logistic) of historical series of hospitalizations for respiratory diseases (total hospitalizations), from 2011 to 2015, in Campo Grande, Mato Grosso do Sul. Methods: to determine the statistical models, four statistical indicators (coefficient of determination, mean root square error, mean absolute error and mean absolute percentage error) were performed from 2011 to 2015. Parameter estimates are obtained for the models adopted in the study, with and without a regression structure. Results: the results showed that Weibull, Gamma, Normal and Logistic distributions, applied to the series of hospitalizations for respiratory diseases in Campo Grande, were satisfactory in determining the shape and scale parameters, and the statistical indicators R2, MAE, RSME and MAPE confirmed the data goodness-of-fit, and the graphical analysis indicated a satisfactory distribution fit. Conclusion: the analysis of monthly values indicates that Gamma is the best of the four distributions based on those selected. The regression model can be adjusted to the data and used as an alternative distribution that describes the hospitalization data considered in Campo Grande, Brazil.