Trends, seasonality and forecasts of Pulmonary Tuberculosis in Portugal

Abstract

SETTING: Tuberculosis (TB) is a global public health concern. Surveillance programs 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 biggest challenge in Public Health. OBJECTIVES: To characterize, model and predict PTB monthly incidence rates in Portugal, and disaggregated by high/low incidence areas, sex and age groups. DESIGN: PTB monthly incidence rates were estimated using PTB cases diagnosed in 2000-2010, disaggregated by population and geographic characteristics. A Seasonal-Trend Loess (STL) decomposition was employed to model trend and seasonality. SARIMA models were fit in order to characterize the series behavior and forecast PTB monthly incidence rates. RESULTS: Overall, the time series showed a downward trend 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 (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 were concurring and accurate. Endemic PTB seems to be slowly declining and case diagnosis is likely seasonal, which can be expected to persist if past conditions persevere in future.