Jéssica Alves Gomes1, José Elias Bezerra Barros1, André Luis Oliveira do Nascimento1, Carlos Alberto de Oliveira Rocha1, João Paulo Oliveira de Almeida1, Gibson Barros de Almeida Santana1, Divanise Suruagy Correia2, Márcio Bezerra Santos3, Rodrigo Feliciano do Carmo4,5, Carlos Dornels Freire de Souza1,6
J Bras Pneumol.2022;48(3):e20210434
Objective: To assess the temporal trends of hospitalizations for pulmonary embolism (PE) in Brazil, its regions, and states between 2008 and 2019. Methods: An ecological and time series study was conducted. Data were obtained from the Hospital Information System (SIH) of the Brazilian Ministry of Health. The inflection point regression model was applied for temporal trend analyses. Trends were classified as increasing, decreasing, or stationary according to the slope of the regression line. The Annual Percent Charge (APC) and the Average Annual Percent Change (AAPC) were calculated considering a confidence interval of 95% and p-value <0.05. Furthermore, spatial distribution maps of epidemiological indicators related to PE in Brazil were elaborated. Results: There was an increasing trend in the hospitalization rate for PE in Brazil, ranging from 2.57 in 2008 to 4.44/100,000 in 2019 (AAPC=5.6%; p<0.001). Total and average hospitalizations costs also showed increasing trend in the country (AAPC=9.2% and 3.0%, respectively). Still, there was a decrease in the in-hospital mortality rate (from 21.21% to 17.11%; AAPC=-1.9%; p<0.001). Similar trends were observed in most regions. The average hospitalization time in Brazil showed a stationary trend. The hospitalization rate has also increased in 18 states (66.67%). Seven states showed a decrease in the mortality rate (25.93%), except for Roraima, which showed an increasing trend. Conclusion: Hospitalizations for PE represent a serious public health problem in Brazil and the temporal patterns observed herein demonstrate an increasing trend in all regions and states of the country.
Keywords: Pulmonary embolism; Epidemiology; Ecological studies; Time series.