Methods to assess seasonal effects in epidemiological studies of infectious diseases—exemplified by application to the occurrence of meningococcal disease

Abstract

Seasonal variation in occurrence is a common feature of many diseases, especially those of infectious origin. Studies of seasonal variation contribute to healthcare planning and to the understanding of the aetiology of infections. In this article, we provide an overview of statistical methods for the assessment and quantification of seasonality of infectious diseases, as exemplified by their application to meningococcal disease in Denmark in 1995-2011. Additionally, we discuss the conditions under which seasonality should be considered as a covariate in studies of infectious diseases. The methods considered range from the simplest comparison of disease occurrence between the extremes of summer and winter, through modelling of the intensity of seasonal patterns by use of a sine curve, to more advanced generalized linear models. All three classes of method have advantages and disadvantages. The choice among analytical approaches should ideally reflect the research question of interest. Simple methods are compelling, but may overlook important seasonal peaks that would have been identified if more advanced methods had been applied. For most studies, we suggest the use of methods that allow estimation of the magnitude and timing of seasonal peaks and valleys, ideally with a measure of the intensity of seasonality, such as the peak-to-low ratio. Seasonality may be a confounder in studies of infectious disease occurrence when it fulfils the three primary criteria for being a confounder, i.e. when both the disease occurrence and the exposure vary seasonally without seasonality being a step in the causal pathway. In these situations, confounding by seasonality should be controlled as for any confounder.

Incidence and seasonality of respiratory viruses causing acute respiratory infections in the Northern United Arab Emirates

“…The peak‐to‐low ratio was interpreted as a measure of relative risk that compares the month with the highest incidence (peak) with the month with the lowest incidence (low or trough) . The positivity rates for respiratory viruses during the discrete peak and low periods were compared using a direct method ( χ2 ‐test) to analyze statistical significance . We applied the Chi‐squared ( χ2 ) test and Fisher's exact test to paired nominal data.…”

Seasonality in infectious diseases: does it exist for all pathogens?

“…Christiansen et al. [10] provide helpful guidance on the conduct and appropriate analysis of such studies. The bias introduced by simplistic analyses of aggregate data (e.g.…”

“…Common to all of the reviews included in this issue is a concern with the methods of analysis of seasonal trends in the primary studies describing the seasonality of infectious diseases. Christiansen et al [10] provide helpful guidance on the conduct and appropriate analysis of such studies. The bias introduced by simplistic analyses of aggregate data (e.g.…”

Temporal patterns of human and canine Giardia infection in the United States: 2003–2009

“…These methods are commonly used in fields such as econometrics, but their application to veterinary medical data has been limited (Benschop et al, 2008;Sanchez-Vazquez et al, 2012). A recent paper (Christiansen et al, 2012) that reviewed methods used to assess seasonality in epidemiological studies of human infectious disease did not include STL and ARIMA/SARIMA. The STL decomposition procedure provides an effective tool to visualize and explore time-series events by dividing them into trend, seasonal, and remainders components that best fit the data (Cleveland et al, 1990).…”

“…The STL decomposition procedure provides an effective tool to visualize and explore time-series events by dividing them into trend, seasonal, and remainders components that best fit the data (Cleveland et al, 1990). Other methods used to analyze epidemiological data collected over time include generalized linear models (GLM) focusing on evaluating change-point of time parameters rather than decomposing and describing its elements (Christiansen et al, 2012). The SARIMA approach is another equally effective time-series analysis method (Jiang et al, 2010) and was used here to provide some contrast and to verify results obtained from descriptive analyses, i.e.…”