R Top1, H Boshuizen, A Dekkers, H Korthals Altes. 1. National Institute for Public Health and the Environment, Epidemiology and Surveillance, Bilthoven, The Netherlands; ResultsinHealth, Leiderdorp, The Netherlands.
Abstract
BACKGROUND: The incidence of extra-pulmonary tuberculosis (EPTB) in the Netherlands shows a seasonal trend, with a peak in spring and a trough in autumn. Possible causes of this peak are winter crowding and a seasonal decrease in immune competence in spring. A third explanation may be a reporting bias. OBJECTIVE: To investigate the role of winter crowding by a time-series analysis of notification data. DNA fingerprinting clustering status can differentiate between recent and remote infections. Seasonality in clustered cases would reflect enhanced transmission in winter and/or seasonally lowered immunity, while seasonality in unique cases would only reflect seasonally lowered immunity. METHODS: We fitted (seasonal) auto-regressive moving average models to culture-positive TB notifications in the Netherlands (1993-2008) to assess seasonality. We then used seasonal trend Loess decompositions to derive the seasonal pattern, and compared the heights of the seasonal peaks. RESULTS: Clustered and unique EPTB notifications showed a seasonal trend that was absent in clustered and unique PTB notifications. The seasonal peak in clustered EPTB cases was not significantly higher than in unique EPTB cases. CONCLUSIONS: The similar timing and height of the seasonal peak of clustered and unique EPTB cases suggests that winter crowding is unlikely to cause the seasonal trend in notifications.
BACKGROUND: The incidence of extra-pulmonary tuberculosis (EPTB) in the Netherlands shows a seasonal trend, with a peak in spring and a trough in autumn. Possible causes of this peak are winter crowding and a seasonal decrease in immune competence in spring. A third explanation may be a reporting bias. OBJECTIVE: To investigate the role of winter crowding by a time-series analysis of notification data. DNA fingerprinting clustering status can differentiate between recent and remote infections. Seasonality in clustered cases would reflect enhanced transmission in winter and/or seasonally lowered immunity, while seasonality in unique cases would only reflect seasonally lowered immunity. METHODS: We fitted (seasonal) auto-regressive moving average models to culture-positive TB notifications in the Netherlands (1993-2008) to assess seasonality. We then used seasonal trend Loess decompositions to derive the seasonal pattern, and compared the heights of the seasonal peaks. RESULTS: Clustered and unique EPTB notifications showed a seasonal trend that was absent in clustered and unique PTB notifications. The seasonal peak in clustered EPTB cases was not significantly higher than in unique EPTB cases. CONCLUSIONS: The similar timing and height of the seasonal peak of clustered and unique EPTB cases suggests that winter crowding is unlikely to cause the seasonal trend in notifications.
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