Abstract

S E T T I N G : Tuberculosis (TB) is a global public health concern. Surveillance programmes present invaluable epidemiological information regarding its temporal evolution, particularly for pulmonary tuberculosis (PTB), the most common form of TB and the one that presents the greatest challenge in public health. OBJECTIVES : To characterise, model and predict monthly incidence rates for PTB in Portugal disaggregated by high/low-incidence areas, sex and age groups. DESIGN : PTB monthly incidence rates were estimated based on PTB cases diagnosed in 2000–2010, disaggregated by population and geographic characteristics. Seasonal-trend LOESS (STL) decomposition was employed to model trend and seasonality. Seasonal autoregressive integrated moving average (SARIMA) models were fit to characterise series behaviour and forecast PTB monthly incidence rates. RESULT S : Overall, the time series showed a downward trend in and seasonality of PTB diagnosis, with a peak in March and a trough in December. The mean seasonal amplitude was consistently higher in high-incidence areas, in males and in adults aged 25–54 years. SARIMA models were found to adequately fit and forecast the time series, thus predicting trend and seasonal persistence. CONCLUSIONS: STL and SARIMA findings concurred and were accurate. Endemic PTB seems to be slowly declining and case diagnosis is likely seasonal, which can be expected to persist if past conditions continue.