Space-time clustering of childhood Type 1 diabetes in Devon and Cornwall, England

Spatiotemporal trends and influence factors of global diabetes prevalence in recent years

Diabetes is one of the most widespread global epidemics and has become the main component of the global disease burden. Based on data regarding the prevalence of diabetes in 203 countries and territories from 2013 to 2017, we employed the Bayesian space-time model to investigate the spatiotemporal trends in the global diabetes prevalence. The factors influencing the diabetes prevalence were assessed by the Bayesian LASSO regression model. We identified 77 (37.9%) hotspots with a higher diabetes prevalence than the global average, 10 (0.4%) warm spots with global average level and 116 (57.1%) cold spots with lower level than global average. Of the 203 countries and territories, 68 (33.5%), including 31 hotspots, 5 warm spots and 32 cold spots, exhibited an increasing trend. Of these, 60 experienced an annual increase of more than 0.25%, and 8 showed an increasing trend. Three populous countries, namely China, the USA and Mexico, exhibited a high prevalence and an increasing trend simultaneously. Three socioeconomic factors, body mass index (BMI), urbanization rate (UR) and gross domestic product per capita (GDP-PC), and PM_2.5 pollution were found to significantly influence the prevalence of diabetes. BMI was the strongest factor; for every 1% increase in BMI, the prevalence of diabetes increased by 2.371% (95% confidence interval (95% CI): 0.957%, 3.890%) in 2013 and by 3.045% (95% CI: 1.803%, 4.397%) in 2015 and 2017. PM_2.5 pollution could be a risk factor, and its influencing magnitude gradually increased as well. With an annual PM_2.5 concentrations increase of 1.0% in a country, the prevalence of diabetes increased by 0.196% (95% CI: 0.020%, 0.356%). The UR, on the other hand, was found to be inversely associated with the prevalence of diabetes; with each UR increase of 1%, the prevalence of diabetes decreased by 0.006% (95% CI: 0.001%, 0.011%).

A stochastic epigenetic Mendelian oligogenic disease model for type 1 diabetes

The incidence of type 1 diabetes (T1D) and some other complex diseases is increasing. The cause has been attributed to an undefined changing environment. We examine the role of the environment (or any changing non-genetic mechanism) in causing the rising incidence, and find much evidence against it: 1) Dizygotic twin T1D concordance is the same as siblings of patients in general; 2) If the environment is responsible for both the discordance among identical twins of patients with T1D and its rising incidence, the twin concordance rate should be rising, but it is not; 3) Migrants from high-to low-incidence countries continue to have high-incidence children; 4) TID incidence among the offspring of two T1D parents is identical to the monozygotic twin rate. On the other hand, genetic association studies of T1D have revealed strong susceptibility in the major histocompatibility complex and many optional additive genes of small effect throughout the human genome increasing T1D risk. We have, from an analysis of previously published family studies, developed a stochastic epigenetic Mendelian oligogenic (SEMO) model consistent with published observations. The model posits a few required recessive causal genes with incomplete penetrance explaining virtually all of the puzzling features of T1D, including its rising incidence and the specific low T1D incidence rates among first-degree relatives of patients. Since historic selection against any causal gene could prevent T1D, we postulate that the rising incidence is because of increasing population mixing of parents from some previously isolated populations that had selected against different causal genes.

Type 1 diabetes and measles, mumps and rubella childhood infections within the Italian Insulin-dependent Diabetes Registry

AIMS

Several studies confirmed the growing rate of Type 1 diabetes mellitus in childhood coinciding with increasing diagnosis of viral infections. A study investigating the incidence of Type 1 diabetes during 1996-1997 showed a higher notification of viral infections in the Pavia District. The aim was to confirm these results.

METHODS

This study evaluated the relationship between new cases of Type 1 diabetes and those of measles, mumps and rubella in 1996-2001, analysing data of newly-diagnosed Type 1 diabetes children, aged 0-14 years and enrolled into the RIDI (Italian Insulin-dependent Diabetes Registry) during the same years. Measles, rubella and mumps rates were calculated using as denominator the estimated 'population at risk', represented by the number of 0- to 14 year-old subjects who did not undergo the MMR (measles, mumps and rubella) vaccination. In order to investigate the association between Type 1 diabetes incidence and measles, rubella and mumps respectively, Spearman's rank correlation was used.

RESULTS

The analysis of the whole Registries data did not at first show any statistical significance between age-standardized Type 1 diabetes incidence density and estimated rates of measles, mumps and rubella notifications. Excluding data from Sardinia Registry, a significant association was observed between Type 1 diabetes incidence and mumps (P = 0.034) and rubella (P = 0.014), respectively, while there was no statistical significance between the incidence of measles cases and diabetes rates (P = 0.269).

CONCLUSIONS

According to our findings, mumps and rubella viral infections are associated with the onset of Type 1 diabetes. The statistical significance observed after exclusion of the Sardinian data suggests that other environmental factors may operate over populations with different genetic susceptibility.

Creating a national register of childhood type 1 diabetes using routinely collected hospital data

INTRODUCTION

There is no national register of childhood type 1 diabetes mellitus for England. Our aim was to assess the feasibility of using routine hospital admissions data as a surrogate for a childhood diabetes register across England, and to create a geographically referenced childhood diabetes dataset for use in epidemiologic studies and health service research.

METHODS

Hospital Episodes Statistics data for England from April 1992 to March 2006 referring to a type 1 diabetes diagnosis in 0-14 yr olds were cleaned to approximate an incident dataset. The cleaned data were validated against regional population-based register data, available for Yorkshire and the area of the former Oxford Regional Health Authority.

RESULTS

There were 32 665 unique cases of type 1 and type unknown diabetes over the study period. The hospital-derived data improved in quality over time (91% concordance with regional register data over the period 2000-2006 vs. 52% concordance over the period 1992-1999), and data quality was better for younger (0-9 yr) (86.5% concordance with regional register data) than older cases (10-14 yr). Overall incidence was 24.99 (95% confidence interval 24.71-25.26) per 100 000. Basic trends in age distribution, seasonality of onset, and incidence matched well with previously reported findings.

CONCLUSION

We were able to create a surrogate register of childhood diabetes based on national hospital admissions data, containing approximately 2300 cases/yr, and geo-coded to a high resolution. For younger cases (0-9 yr) and more recent years (from 2000) these data will be a useful resource for epidemiological studies exploring the determinants of childhood diabetes.

Clustering of cases of type 1 diabetes in high socioeconomic communes in Santiago de Chile: spatio-temporal and geographical analysis

The objective of this study was to describe spatial and space-time patterns of type 1 diabetes in children less than 15 years old, diagnosed between 2000 and 2005 with residence in the Metropolitan Region of Chile. Knox and Mantel tests were used to detect space-time interaction between cases. An ecological Bayesian model adjusted by socioeconomic factor and year was proposed to estimate the incidence by communes. Initially, there was no space-time interaction between cases, but there is evidence of clustering effect in urban areas of the region. The incidence rate for the overall study period was estimated by 6.18/100,000 (95% CI: 5.69-6.70), with a significant annual trend of 8.2% (P < 0.01). The geographical incidence could be explained by the human development index, as a socioeconomic factor. These results suggest that children living in communes with higher socioeconomic levels may be at higher risk of developing type 1 diabetes. Our findings support the hypothesis of an aetiological role of environmental factors in the onset of type 1 diabetes.

Infectious diseases and Type 1 diabetes in children

Type 1 diabetes (T1D) is an autoimmune disease triggered by environmental factors. Among those of infectious origin, viruses mostly associated to T1D are rubella virus, enteroviruses (Rotavirus, Coxackie B), Cytomegalovirus and mumps virus. The role of bacterial infections is still controversial, acting either as modulators or precipitating factors of an already started autoimmune process. Polymorphic genes of innate immunity, such as Toll-like receptors, nucleotide-binding oligomerization domain (NOD) 1 and NOD2 and mannose-binding lectin (MBL) genes, did not show a strict association with T1D onset, while protein tyrosine phosphatase (PTPN22), cytotoxic T-lymphocyte antigen (CTLA)4 and natural killer cells immunoglobulin-like receptor (KIR) genes appear to play an important role. However, the adaptive immune response genes (HLA) still provide the major contribution to T1D susceptibility. Here, we review the mechanism by which microorganisms might induce autoimmunity.

Space–time interaction as an indicator of local spread during the 2001 FMD outbreak in the UK

During the 2001 FMD outbreak in the UK, decisions on the level of implementation of control measures were supported by predictive models. Models were mainly used as macro-level tools to predict the behaviour of the disease in the whole country rather than at the local level. Here we explore the use of the magnitude and characteristics of the space-time interaction as an indicator of local spread and, indirectly, of the effectiveness of control measures aimed at reducing short-range transmission during the course of a major livestock disease epidemic. The spatiotemporal evolution patterns are described in the four main clusters that were observed during the outbreak by means of the hazard rate and space-time K-function (K(s,t)). For each local outbreak, the relative measure D(0)(s,t), derived from K(s,t), which represents the excess risk attributable to the space-time interaction was calculated for consecutive 20-day temporal windows to represent the dynamics of the space-time interaction. The dynamics of the spatiotemporal interaction were very different among the four local clusters, suggesting that the intensity of local spread, and therefore the effectiveness of control measures, markedly differed between local outbreaks. The large heterogeneity observed in the relative impact of being close in time and space to an infected premises suggests that the decision making in relation to control of the outbreak would have benefited from indicators of local spread which could be used to complement global predictive modelling results. Despite its limitations, our results suggest that the real-time analysis of the space-time interaction can be a valuable decision support tool during the course of a livestock disease epidemic.