Analysing the seasonality of active TB in the UK, 2000 to 2017
PHE ePoster Library. Glaser L. Apr 9, 2019; 257525; 15450
Ms. Lisa Glaser
Ms. Lisa Glaser
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Abstract Background:
Seasonal variation in TB incidence has been reported in many countries. In the UK, several studies have shown a peak during the spring/summer and a trough in the winter months. Possible explanations include increased transmission during the winter leading to a delay in diagnosis during the summer, or biological factors such as a lowered immune response and Vitamin D levels increasing the risk of infection, which after the incubation period could again cause a peak in diagnoses during the summer. Understanding the seasonality of TB in the UK could provide a vital insight of host-pathogen interactions and help identify the major risk factors involved in the spread of the disease. Method: We conducted a time-series analysis on all 136,590 TB cases notified in the UK between 2000 and 2017 to identify and analyse trend and seasonality. Cases were aggregated into months by notification date, and seasonality assessed using spectral analysis and applying appropriate 'peak' months to the model. Demographic and clinical characteristics will then be analysed to assess the factors contributing to the seasonality in the model..
: The number of TB cases increased between 2000 and 2011, and decreased between 2012 and 2017. Overall, during this time period, there was strong evidence for seasonality; 24% more cases were notified in the summer (May-July: 28.1%) compared with winter months (December-February: 22.6%).
TB notifications in the UK show clear seasonality, being highest in the summer and lowest in the winter months. Further analyses will incorporate data on ethnicity, migration and latitude to explore the effect of Vitamin D deficiency, and the impact of diagnostic delays on seasonal variation. Our results will improve the accuracy of TB incidence forecasting, helping plan strategies for disease prevention during periods of increased transmission, and improving resource allocation during periods of increased diagnoses.
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