This national, population-based study has revealed a sinusoidal cyclical pattern in the temporal trend of childhood type 1 diabetes mellitus in Australia, corroborating similar findings previously observed in the state of Western Australia . Interestingly, the sinusoidal pattern was observed in both boys and girls and in all age groups, suggesting that factors influencing the incidence of childhood type 1 diabetes mellitus in Australia are similar across all these subgroups.
Despite consistency in incidence registry definitions and statistical methods, temporal trends in the incidence of childhood type 1 diabetes mellitus continue to be highly variable across different populations and studies. For example, an analysis of temporal trends in childhood type 1 diabetes mellitus across Europe between 1989 and 2008 showed an average annual increase in incidence of 3%, with specific countries showing periods of less or more rapid increase over the study period .
In addition to the linear temporal trends in the incidence of childhood type 1 diabetes that have been described in many populations, non-linear trends have also been observed in some studies. For example, similar to this study, a cyclical incidence pattern has been reported in north-east England, where peaks and troughs in incidence were observed to occur in 5–6 year intervals . A study from the US Virgin Islands reported an ‘epidemic-like’ spike in incidence in 2006, when the incidence increased threefold compared with the previous year . In Finland, a steeper rise in incidence in the early 1990s contributed a significant non-linear component to the incidence rate trend, resulting in a curvilinear, rather than cyclical, temporal trend between 1980 and 2005 . Other studies have observed a non-linear temporal trend of levelling off, or plateauing, in the incidence of childhood type 1 diabetes mellitus in recent years [2–4].
Differences in the temporal trends of childhood type 1 diabetes mellitus probably reflect changes in environmental factors within the different study populations. Type 1 diabetes mellitus is generally thought to be the result of both genetic and environmental risk factors, although the underlying factors are not yet understood. Transient or rapid changes in incidence are most likely a result of changes in environmental factors, rather than changes in the more stable population gene pool.
Non-linear temporal trends of a cyclical nature, as observed in this study, or those with peaks/epidemics of incidence, may indicate a role for environmental factors such as viral infections or climatic factors, which display similar temporal trends. Viral infections, in particular enteroviruses, have long been implicated in the aetiology of childhood type 1 diabetes mellitus. Climatic factors may influence other factors associated with childhood diabetes mellitus, such as diet, physical activity and exposure to sunlight and vitamin D, as well as to viruses and other infectious agents. Another possible explanation is that peaks in incidence are followed by troughs due to a reduction in the pool of individuals at risk of the disease. However, although this might explain peaks and troughs that occur randomly over time, it would not account for the regular 5-yearly peaks and troughs in incidence observed in this study.
This study reports the interesting observation of a regular cyclical pattern in the incidence of childhood type 1 diabetes mellitus in Australia, similar to that observed over an extended period of time in Western Australia. This suggests that caution should be applied in modelling and predicting future incidences of childhood type 1 diabetes mellitus based on only linear trends.
The strengths of this study are its use of population-based data that are more than 97% complete and the application of standard statistical methods, enabling comparison with other studies. A limitation of this study is the relatively short duration of 12 years; however, all available data have been analysed. Furthermore, no national population-level data were available on environmental factors such as viral infections for comparative analyses.
There is much yet to be understood regarding the aetiology, natural history and critical windows in the development of childhood type 1 diabetes mellitus. In the future, determining different phenotypes or subgroups of type 1 diabetes mellitus  and understanding more about the timescale and natural history of diabetes will help researchers to identify factors relevant to different phases of the natural history and the likely multiple causal pathways of this complex disease. Meanwhile, worldwide population-based registers will continue to provide invaluable resources for monitoring the incidence of childhood type 1 diabetes mellitus.