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ORIGINAL ARTICLE
Year : 2013  |  Volume : 2  |  Issue : 2  |  Page : 95-98

Seasonality of tuberculosis in rural West Bengal: A time series analysis


1 Department of Community Medicine, RG Kar Medical College and Hospital, Kolkata, India
2 Department of Community Medicine, Vardhaman Mahavir Medical College and Safdarjung Hospital, New Delhi, India

Correspondence Address:
Ranadip Chowdhury
Department of Community Medicine, 3rd Floor Academic Building, RG Kar Medical College and Hospital, 1, Kshudiram Bose Sarani, Kolkata - 700 004
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2278-344X.115684

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Background: There is a recent concern about the global climatic change that is expected to have broad health impacts. The health effects of extreme weather events include a spectrum of wide variety of impacts. According to a study in Northern India, tuberculosis (TB) diagnosis peaked between April and June, and it reached a nadir between October and December. However, no seasonality was reported from South India. Aims: This study is aimed to assess the seasonality of TB in rural West Bengal and to develop a univariate time series model. Settings and Design: Retrospective record-based study was carried out at Amdanga tuberculosis unit (TU), North 24-parganas, West Bengal. Materials and Methods: A total of 1507 new TB cases were registered in the TB register of the TU during January-2008 to December-2011 period were taken for this study. Statistical Analysis: Seasonal adjusted factor (SAF), autocorrelation function (ACF), partial autocorrelation function (PACF), and seasonal autoregressive integrated moving average (SARIMA) methods were applied by using the SPSS 16.0 version. Results: ACF and PACF at lag 12 shows significant pick suggesting seasonal component of the TB series. SAF showed peak seasonal variation from March to June and nadir from October to December in additive model. Univariate model by expert-modeler in the SPSS showed SARIMA ((0,0,0)(1,2,0) 12 could best predict the model with 54.3% variability. Conclusion: A seasonal pattern of TB was observed. This information would be usefulfor administration and managers to take extra care to arrange and provide extra facilities during the peak seasons.


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