Curve fitting and autoregressive models in admissions data for respiratory diseases
DOI:
https://doi.org/10.5433/1679-0375.2025.v46.53584Keywords:
hospital expenses, respiratory diseases, curve fitting, time series, ARIMA modelAbstract
This study investigated hospitalization costs for respiratory diseases in the state of São Paulo between 2002 and 2025, analyzing the dynamics of these expenses within the Brazilian Unified Health System (SUS) and contextualizing the challenges of financing and managing public health in Brazil. The objective of this work was to identify historical patterns and understand expenditures by analyzing data from the DATASUS Hospital Information System. Using polynomial, logistic, and trigonometric curve fitting, as well as statistical time series models, with emphasis on the seasonal ARIMA model, seasonal trends and patterns in hospital expenditures were identified. This allowed for the capture of seasonal behaviors related to the amounts paid, specifically regarding the increase in hospitalizations for respiratory diseases during the transition between fall and winter. Finally, in the graphs associated with fluctuations around the average trend, two level transitions were observed in relation to the values paid: one associated with COVID-19 (mid-2020) and the other associated with H1N1 (in 2009). These levels correspond to the values of K=22.65 million, referring to the pre-COVID logistics model, and to K=35 million (logistics model that includes the COVID-19 pandemic period).
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Copyright (c) 2025 Raphael de Oliveira Garcia, Graciele Paraguaia Silveira, Bruna Santos Silva
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