The use of intervention analysis of the mortality rates from breast cancer in assessing the Brazilian screening programme

Abstract

There is a need to develop methods to evaluate public health interventions. Therefore, this work proposed an intervention analysis on time series of breast cancer mortality rates to assess the effects of an action of the Brazilian Screening Programme. Methods: The analysed series was the monthly female breast cancer mortality rates from January 1996 to March 2016. The intervention was the establishment of the National Information System on Breast Cancer in June 2009. The Box-Tiao approach was used to build a Global Intervention Model (GIM) composed of a component that fits the series without the intervention, and a component that fits the effect with the intervention. The intervention's response time was estimated and used to define the length of the residual series to assess the predictive accuracy of the GIM, which was compared to a one-step-ahead forecasting approach. Results: The pre-intervention period was fitted to a SARIMA (0,1,2) (1,1,1)12 model and the intervention's effect to an ARIMA (1,1,0) model. The intervention led to an increase in the mortality rates, and its response time was 24 months. The forecast error (MAPE) for the GIM was 3.14%, and for the one-step-ahead forecast it was 2.15%. Conclusion: This work goes one step further in relation to the studies carried out to evaluate the Breast Cancer Screening Programme in Brazil, considering that it was possible to quantify the effects and the response time of the intervention, demonstrating the potential of the proposed method to be used to evaluate health interventions.

Demand forecast in the emergency department in Minas Gerais, Brazil

“…The analysis of the prediction errors allows to choose, among the applied models, the one that is most adequate to make predictions for the series under study (AFONSO et al, 2011). Thus, to fit the most adequate model to perform predictions of the number of green patients attended per month, tests were done with the two different models, comparing them based on the Mean Absolute Error (MAPE) (NOVAES et al, 2010;ROSALES-LÓPEZ et al, 2018). For the green patients, the ARIMA model (1,1,1) obtained a prediction error of 13.98%, since it was the lowest value, it uses it to make the predictions of the 12 months.…”

Aplicação do modelo ARIMA na previsão de atendimentos em pontos de atenção com alta demanda da Rede de Assistência à Saúde do município de Monte Carmelo, MG

“…Assim, realizou-se uma análise dos correlogramas da Função de Autocorrelação (FAC) (Gráfico 5) e Função de Autocorrelação Parcial (FACP) (Gráfico 6) da série original para averiguar os lags (defasagens) significativos. A análise do MAPE é usada para medir as precisões das previsões (Rosales-López et al, 2018). De tal modo, para o ajuste do modelo mais adequado para realizar as previsões do número de pacientes atendidos, por mês, foram feitos testes entre três modelos distintos, comparando-os baseado MAPE (Tabela 3).…”

“…Na concepção deEaves (2002), para comparar previsões de séries diferentes, deve ser utilizado o MAPE, que por ser uma unidade métrica livre, consegue relacionar o tamanho do erro para uma observação real proporcional. O MAPE é usado para medir a acurácia da previsão(Rosales-López et al, 2018). Edmundson e O'Connor (1995) também sugerem a utilização deste, pois é uma medida mais apropriada nos estudos empíricos, sendo uma medida que não é afetada por valores extremos, utiliza percentuais do erro e não depende da unidade dos dados.Portanto, tem-se que a estratégia para a construção do modelo será baseada em um ciclo interativo, no qual a estrutura do modelo é baseada nos próprios dados.…”