Spatiotemporal analysis and seasonality of tuberculosis in Algeria

Abstract

Background: This study aimed to describe the spatiotemporal distribution, to build a forecasting model, and to determine the seasonal pattern of tuberculosis (TB) in Algeria.

Methods: The Box-Jenkins methodology was used to develop predictive models and GeoDa software was used to perform spatial autocorrelation.

Results: Between 1982 and 2019, the notification rate per 100,000 population of smear-positive pulmonary TB (SPPTB) has dropped 62.2%, while that of extrapulmonary TB (EPTB) has risen 91.3%. For the last decade, the mean detection rate of PTB was 82.6%. At around, 2% of PTB cases were yearly reported in children under 15 years old, a peak in notification rate was observed in the elderly aged 65 and over, and the sex ratio was in favor of men. Between 52% and 59% of EPTB cases were lymphadenitis TB and between 15% and 23% were pleural TB. About two-third of EPTB cases were females and around 10% were children under the age of 15. The time series analysis showed that (1,1, 2) × (1, 1, 0)4 (respectively (0, 1, 2) × (1, 1, 0)4, (3, 1, 0) × (1, 1, 0)4) offered the best forecasting model to quarterly TB (respectively EPTB, SPPTB) surveillance data. The most hit part was the Tell followed by high plateaus which accounted for 96.6% of notifications in 2017. Significant hot spots were identified in the central part for EPTB notification rate and in the northwestern part for SPPTB.

Conclusions: There is a need to reframe the set objectives in the state strategy to combat TB taking into account seasonality and spatial clustering to ensure improved TB management through targeted and effective interventions.

Keywords: Algeria; Box–Jenkins; seasonality; spatial autocorrelation analysis; spatial clustering; spatial distribution; tuberculosis.