Our analyses of individual centre results confirmed the recent slowing of incidence rate increases in some high-incidence areas such as Finland [7] and Norway [8], but using only data from Stockholm County we were unable to detect the same pattern that had previously been reported from Sweden [6]. Two of the three centres from the UK, another country with high rates, also showed reducing rates of increase, although these seemed to have begun a few years earlier than in Scandinavia.
Our pooled estimates suggest that, despite some high-risk countries showing some slowing in the rate of increase in recent years, the overall pattern is still one of an approximately 3% per annum increase, although with a possible temporary slowing in the 2004–2008 period. As previously noted in our 15 year analysis, the rate of increase in girls aged 10–14 years is less marked than in other age/sex subgroups [1].
Our analysis shows that, in the majority of centres, a steady log-linear increase in rates with time provided a good description of the temporal changes, with only a few (mainly high-incidence) areas showing some evidence of non-uniformity. The cyclical pattern in incidence observed in four of our 26 centres is consistent with the earliest report of a 4 year cyclical incidence pattern [10], but subsequent reports have described 5 year or 6 year periodicities [11,12,13], for which we found little support in our data.
No clear rationale for periodicity has yet been proposed and, to the authors’ knowledge, no climatological factor [18], viral infection [19] or other environmental exposure has yet been firmly established that exhibits such a cyclical pattern. Since autoimmunity and progressive beta cell destruction typically start long before the clinical diagnosis of type 1 diabetes, the periodicity in diagnosis could be indicative of cycles of infectious disease that accelerate the diagnosis rather than initiate the disease. Regular cycles of infectious diseases are well known from classic work done before population-wide vaccination for measles, an extremely contagious viral disease of childhood; this research showed that, in an otherwise stable population, epidemic cyclicity depends on community size [20].
It is also unclear why only a small proportion of the 26 centres showed this periodicity and, although we acknowledge that power may be limited in smaller centres, it was not apparent in many of the largest centres that might be expected to have had a high power to detect it. This could perhaps suggest that it may have more localised origins. What determines this localisation remains enigmatic, as cyclical patterns were absent in Austria, Czechia and Germany–Baden-Württemberg, three large registers each with neighbouring areas where pronounced cyclical patterns were noted. It is possible that not only the size of the population, but also its spatial structure (i.e. the size of the communities, and their mutual links) may play an important role in the ability of the hypothetical infectious accelerator to be transmitted [21].
To our knowledge, among autoimmune conditions, only incidence cycles in juvenile idiopathic arthritis have been correlated to cycles of serologically confirmed microbial agents—in a Canadian study, peak incidences of arthritis were concurrent with peaks of Mycoplasma pneumoniae, whereas no such phenomenon was noted for the incidence of seronegative (i.e. non-immune mediated) spondyloarthropathies [22]. The recent report of a twofold risk of type 1 diabetes diagnosed by the age of 30 years among those with laboratory-confirmed pandemic influenza A (H1N1) [23] may stimulate interest in less consistent patterns of incidence peaks in type 1 diabetes since localised seasonal influenza epidemics (as opposed to much rarer pandemics) can occur at irregular intervals [24].
Most of the participating registers have maintained their completeness of coverage at levels in excess of 90% in the most recent 5 year period, but these estimates of completeness rely on an assumption of independence in the primary and secondary sources that is very difficult to verify. As more sophisticated information systems for drug prescribing and clinical management become available, it seems likely that the traditional approach based on notification of individual new diagnoses will give way to more automated approaches that take advantage of these information systems.
Although it could be argued that the diagnosis of type 1 diabetes should ideally be confirmed by the presence of one or more specific autoimmune markers [25], this is seldom done in clinical practice, and we have therefore continued to use a pragmatic definition of type 1 diabetes based on clinical judgement. A UK study found that all but 8 (3%) of 256 clinically diagnosed cases of type 1 diabetes in individuals aged 20 years or younger were positive for one or more of four antibodies [26], but the case for routine antibody testing at diagnosis is not compelling [27]. Individuals diagnosed before 6 months of age now tend to be routinely investigated for monogenic forms of the disease [28], but the number of such cases is very small. Findings in the literature on whether or not type 2 diabetes is becoming more common in children and adolescents are inconsistent [29,30,31], but the distinction between the two types of diabetes is generally not difficult in the paediatric age group. Furthermore, European studies [30,31,32,33] confirm that the rate of type 2 diabetes is a small fraction of that of type 1 diabetes, and we do not therefore feel that misclassification of type 2 diabetes represents a serious challenge to the validity of our findings.
The use of mixed effects Poisson regression, in which age group and sex are considered as fixed effects but centre is treated as a random effect, gives similar estimates of the increase in incidence rate to the more conventional fixed effects analysis that we have used in previous analyses; however, confidence limits for the mixed effects model tend to be rather wider and should give a fairer reflection of uncertainty in the estimates of incidence rate increase. Taking into account the uncertainty associated with our overall incidence rate increase of 3.4% (95% CI 2.8%, 3.9%), we may expect to see a doubling in European incidence in between 18 and 25 years if the trends evident in the last 25 years are maintained.
The steadily increasing number of children being diagnosed with this chronic disease, which is associated with well-documented, life-long increases in morbidity and mortality, has important implications for those planning and delivering healthcare. The limited success in identifying either environmental causes or gene–environment interactions that could eventually lead to disease prevention means that efforts must continue to improve quality of care to help reduce long-term complications and diabetes-related deaths. Key to this is the improvement in glycaemic control that will be achieved not only by more sophisticated methods of insulin delivery, but also by an increased investment in services to support well-trained and dedicated care teams in sufficient numbers to meet the growing needs of this group of children and their families.
The EURODIAB childhood type 1 diabetes registers, with their wide, population-based coverage of European regions of differing incidence, and their high levels of case ascertainment, will continue to provide a valuable source of data for monitoring the future incidence of childhood type 1 diabetes.