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Kumar V, Singh A, Adhikary M, et al. Seasonality of tuberculosis in delhi, India: a time series analysis. Tuberc Res Treat. 2014;2014:514093doi: 10.1155/2014/514093.
Kumar, V., Singh, A., Adhikary, M., Daral, S., Khokhar, A., & Singh, S. (2014). Seasonality of tuberculosis in delhi, India: a time series analysis. Tuberculosis research and treatment, 2014514093. https://doi.org/10.1155/2014/514093
Kumar, Varun, et al. "Seasonality of tuberculosis in delhi, India: a time series analysis." Tuberculosis research and treatment vol. 2014 (2014): 514093. doi: https://doi.org/10.1155/2014/514093
Kumar V, Singh A, Adhikary M, Daral S, Khokhar A, Singh S. Seasonality of tuberculosis in delhi, India: a time series analysis. Tuberc Res Treat. 2014;2014:514093. doi: 10.1155/2014/514093. Epub 2014 Mar 23. PMID: 24778871; PMCID: PMC3981520.
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Tuberc Res Treat. 2014;2014:514093. doi: 10.1155/2014/514093. Epub 2014 Mar 23.

Seasonality of tuberculosis in delhi, India: a time series analysis.

Tuberculosis research and treatment

Varun Kumar, Abhay Singh, Mrinmoy Adhikary, Shailaja Daral, Anita Khokhar, Saudan Singh

Affiliations

  1. Department of Community Medicine, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi 110029, India.

PMID: 24778871 PMCID: PMC3981520 DOI: 10.1155/2014/514093

Abstract

Background. It is highly cost effective to detect a seasonal trend in tuberculosis in order to optimize disease control and intervention. Although seasonal variation of tuberculosis has been reported from different parts of the world, no definite and consistent pattern has been observed. Therefore, the study was designed to find the seasonal variation of tuberculosis in Delhi, India. Methods. Retrospective record based study was undertaken in a Directly Observed Treatment Short course (DOTS) centre located in the south district of Delhi. Six-year data from January 2007 to December 2012 was analyzed. Expert modeler of SPSS ver. 21 software was used to fit the best suitable model for the time series data. Results. Autocorrelation function (ACF) and partial autocorrelation function (PACF) at lag 12 show significant peak suggesting seasonal component of the TB series. Seasonal adjusted factor (SAF) showed peak seasonal variation from March to May. Univariate model by expert modeler in the SPSS showed that Winter's multiplicative model could best predict the time series data with 69.8% variability. The forecast shows declining trend with seasonality. Conclusion. A seasonal pattern and declining trend with variable amplitudes of fluctuation were observed in the incidence of tuberculosis.

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