Abstract
IntroductionUnderstanding the temporal patterns in disease occurrence is valuable for formulating effective disease preventive programs. Cohort studies present a unique opportunity to explore complex interactions associated with emergence of seasonal patterns of infectious diseases.MethodsWe used data from 452 children participating in a birth cohort study to assess the seasonal patterns of rotavirus diarrhea by creating a weekly time series of rotavirus incidence and fitting a Poisson harmonic regression with biannual peaks. Age and cohort effects were adjusted for by including the weekly counts of number of children in the study and the median age of cohort in a given week. Weekly average temperature, humidity and an interaction term to reflect the joint effect of temperature and humidity were included to consider the effects of meteorological variables.ResultsIn the overall rotavirus time series, two significant peaks within a single year were observed – one in winter and the other in summer. The effect of age was found to be the most significant contributor for rotavirus incidence, showing a strong negative association. Seasonality remained a significant factor, even after adjusting for meteorological parameters, and the age and cohort effects.ConclusionsThe methodology for assessing seasonality in cohort studies is not yet developed. This is the first attempt to explore seasonal patterns in a cohort study with a dynamic denominator and rapidly changing immune response on individual and group levels, and provides a highly promising approach for a better understanding of the seasonal patterns of infectious diseases, tracking emergence of pathogenic strains and evaluating the efficacy of intervention programs.
Highlights
Understanding the temporal patterns in disease occurrence is valuable for formulating effective disease preventive programs
We examined the joint effect of seasonality and age in three major genotypes of rotavirus, building on earlier published work where we demonstrated the epidemiology of rotavirus in a well-established birth cohort [13] and emphasized the importance of time-referenced covariates in quasi-experimental studies [21]
Ethics Statement This paper presents the results of a secondary data analysis from a birth cohort study in southern India, which was approved by the Institutional Review Board of Christian Medical College, Vellore, India
Summary
Understanding the temporal patterns in disease occurrence is valuable for formulating effective disease preventive programs. A deep understanding of temporal patterns in disease occurrence and its governing principles is valuable for designing preventive programs for disease control, tracking effectiveness of public health programs, and allocating scarce resources. Seasonality can be characterized by the magnitude, timing, and duration of a seasonal increase. It may differ by pathogen and its strain virulence [3,4], and may change from year to year due to shift/drift in antigenic strain and change in immunity of a naıve and exposed population [5]. Introduction of a vaccine and/or successful coverage expansion [8,9,10] may dramatically alter the seasonal fluctuations in infectious disease occurrence
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