Time Series Analysis of Tuberculosis in Medea Province in Algeria

Yıl 2019, Cilt: 4 Sayı: 2, 85 - 94, 31.08.2019

Mohamed L'hadj Schehrazad Selmane

https://doi.org/10.30931/jetas.595549

Öz

Despite Algeria has been able to join the group of countries with moderate tuberculosis (TB) prevalence since the 1980s, the disease remains one of the major public health issues in the country. Over the past decade, the annual incidence rate has hovered around 55 per 100 000 people. The incidence rate remains, however, very high in some provinces. The aim of this study was to describe the temporal patterns of TB in Médéa province which records the highest incidence rate in the country. In this retrospective study, the monthly pulmonary TB (PTB) and extrapulmonary TB (EPTB) data from 2008 to 2017, extracted from the national surveillance system, were analyzed and seasonal fluctuation was examined. The Box-Jenkins approach to fit seasonal autoregressive integrated moving average (SARIMA) model to the monthly PTB and EPTB notification data from 2008 to 2016 was performed. The models were used to predict the monthly cases of PTB and EPTB for the year 2017. The models were found to be adequate. Our findings indicate that SARIMA models are useful tools for monitoring and for predicting trends of TB incidence in Médea province.

Anahtar Kelimeler

Algeria, biostatistics, Box-Jenkins, seasonal autoregressive integrated moving average, times series analysis, tuberculosis

Kaynakça

• [1] World Health Organization. Global tuberculosis. 2017 Report. Geneva; 2017.
• [2] Manuel de la lutte antituberculeuse à l’usage des personnels médicaux. Programme National de Lutte Contre la Tuberculose-MSP, Direction de la prévention-INSP. INSP, édition 2007.
• [3] Selmane, S., “The impact of isolation of identified active Tuberculosis cases on the number of latently infected individuals”, ICCSA 5 (2011) : 527-536.
• [4] Manuel de la lutte antituberculeuse à l’usage des personnels médicaux. Programme National de Lutte Contre la Tuberculose- MSP, Direction de la prévention-INSP. Edition & Publications-ANDS 2012.
• [5] Office national des statistiques. URL : http://www.ons.dz
• [6] Allard R., “Use of time-series analysis in infectious disease surveillance”, Bull World Health Organ 76 (1998) : 327-333.
• [7] Box G.E.P., Jenkins G.M., “Time series analysis, forecasting and control”, Holden-Day, San Francisco (1976).
• [8] Selmane, S., L’hadj, M., “Forecasting and prediction of scorpion sting cases in Biskra Province, Algeria, using a seasonal autoregressive integrated moving average model”, Epidemiol Health 8 (2016) : 1-8.
• [9] IHS EViews. EViews 9. Https://www.eviews.com/EViews9/ev9main.html.
• [10] Solovic, I., Jonsson, J., Korzeniewska- Koseła, M., Chiotan, D.I., Pace-Asciak, A., Slump, E., Rumetshofer, R., Abubakar, I., Kos, S., Svetina-Sorli, P., Haas, W., Bauer, T., Sandgren, A., van der Werf, M.J., “Challenges in diagnosing extrapulmonary tuberculosis in the European Union, 2011”, Euro Surveill 18(12) (2013) : 1-9.
• [11] Kruijshaar M.E., Abubakar I., “Increase in extrapulmonary tuberculosis in England and Wales 1999-2006”, Thorax 64(12) (2009) : 1090-5.
• [12] Ates Guler, S., Bozkus, F., Inci M.F., et al., “Evaluation of pulmonary and extrapulmonary Tuberculosis in immunocompetent adults: A retrospective case series analysis”, Med Princ Pract 24 (2015) : 75-79.
• [13] Gomes T., Reis-Santos B., Bertolde A., et al., “Epidemiology of extrapulmonary tuberculosis in Brazil: a hierarchical model”, BMC Infectious Diseases 14 (2014) : 9.
• [14] Fernanda Monteiro de Castro, F., Eder de Souza, M., Daniella Melo Arnaud Sampaio, P., Maria do Socorro Nantua, E., “Relationship between climatic factors and air quality with tuberculosis in the Federal District, Brazil, 2003–2012”, The Brazilian Journal of Infectious Diseases 21(4) (2017) : 369-375.