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ORIGINAL ARTICLE
Year : 2021  |  Volume : 10  |  Issue : 3  |  Page : 234-242

Spatiotemporal analysis and seasonality of tuberculosis in Algeria


1 L’IFORCE, IFORCE, Faculty of Mathematics, University of Sciences and Technology Houari Boumediene, Algiers, Algeria
2 Beni Messous University Hospital Centre, Ministry of Health, Population and Hospital Reform, Algiers, Algeria

Correspondence Address:
Schehrazad Selmane
L'IFORCE, Faculty of Mathematics, University of Sciences and Technology Houari Boumediene, Algiers
Algeria
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijmy.ijmy_111_21

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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.


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