A hierarchical Bayesian birth cohort analysis from incomplete registry data: evaluating the trends in the age of onset of insulin-dependent diabetes mellitus (T1DM)

Incidence of type 1 diabetes in Sweden among individuals aged 0-34 years, 1983-2007: an analysis of time trends

OBJECTIVE

To clarify whether the increase in childhood type 1 diabetes is mirrored by a decrease in older age-groups, resulting in younger age at diagnosis.

RESEARCH DESIGN AND METHODS

We used data from two prospective research registers, the Swedish Childhood Diabetes Register, which included case subjects aged 0-14.9 years at diagnosis, and the Diabetes in Sweden Study, which included case subjects aged 15-34.9 years at diagnosis, covering birth cohorts between 1948 and 2007. The total database included 20,249 individuals with diabetes diagnosed between 1983 and 2007. Incidence rates over time were analyzed using Poisson regression models.

RESULTS

The overall yearly incidence rose to a peak of 42.3 per 100,000 person-years in male subjects aged 10-14 years and to a peak of 37.1 per 100,000 person-years in female subjects aged 5-9 years and decreased thereafter. There was a significant increase by calendar year in both sexes in the three age-groups <15 years; however, there were significant decreases in the older age-groups (25- to 29-years and 30- to 34-years age-groups). Poisson regression analyses showed that a cohort effect seemed to dominate over a time-period effect.

CONCLUSIONS

Twenty-five years of prospective nationwide incidence registration demonstrates a clear shift to younger age at onset rather than a uniform increase in incidence rates across all age-groups. The dominance of cohort effects over period effects suggests that exposures affecting young children may be responsible for the increasing incidence in the younger age-groups.

Evaluating geographic variation in type 1 and type 2 diabetes mellitus incidence in youth in four US regions

We evaluated geographic variation in type 1 and type 2 diabetes mellitus (T1DM, T2DM) in four regions of the United States. Data on 807 incident T1DM cases diabetes and 313 T2DM cases occurring in 2002-03 in South Carolina (SC) and Colorado (CO), 5 counties in Washington (WA), and an 8 county region around Cincinnati, Ohio (OH) among youth aged 10-19 years were obtained from the SEARCH for Diabetes in Youth Study. Geographic patterns were evaluated in a Bayesian framework. Incidence rates differed between the study regions, even within race/ethnic groups. Significant small-area variation within study region was observed for T1DM and T2DM. Evidence for joint spatial correlation between T1DM and T2DM was present at the county level for SC (r(SC)=0.31) and CO non-Hispanic Whites (r(CO)=0.40) and CO Hispanics (r(CO)=0.72). At the tract level, no evidence for meaningful joint spatial correlation was observed (r(SC)=-0.02; r(CO)=-0.02; r(OH)=0.03; and r(WA=)0.09). Our study provides evidence for the presence of both regional and small area, localized variation in type 1 and type 2 incidence among youth aged 10-19 years in the United States.

Incidence trends for childhood type 1 diabetes in Europe during 1989-2003 and predicted new cases 2005-20: a multicentre prospective registration study

BACKGROUND

The incidence of type 1 diabetes in children younger than 15 years is increasing. Prediction of future incidence of this disease will enable adequate fund allocation for delivery of care to be planned. We aimed to establish 15-year incidence trends for childhood type 1 diabetes in European centres, and thereby predict the future burden of childhood diabetes in Europe.

METHODS

20 population-based EURODIAB registers in 17 countries registered 29 311 new cases of type 1 diabetes, diagnosed in children before their 15th birthday during a 15-year period, 1989-2003. Age-specific log linear rates of increase were estimated in five geographical regions, and used in conjunction with published incidence rates and population projections to predict numbers of new cases throughout Europe in 2005, 2010, 2015, and 2020.

FINDINGS

Ascertainment was better than 90% in most registers. All but two registers showed significant yearly increases in incidence, ranging from 0.6% to 9.3%. The overall annual increase was 3.9% (95% CI 3.6-4.2), and the increases in the age groups 0-4 years, 5-9 years, and 10-14 years were 5.4% (4.8-6.1), 4.3% (3.8-4.8), and 2.9% (2.5-3.3), respectively. The number of new cases in Europe in 2005 is estimated as 15 000, divided between the 0-4 year, 5-9 year, and 10-14 year age-groups in the ratio 24%, 35%, and 41%, respectively. In 2020, the predicted number of new cases is 24 400, with a doubling in numbers in children younger than 5 years and a more even distribution across age-groups than at present (29%, 37%, and 34%, respectively). Prevalence under age 15 years is predicted to rise from 94 000 in 2005, to 160 000 in 2020.

INTERPRETATION

If present trends continue, doubling of new cases of type 1 diabetes in European children younger than 5 years is predicted between 2005 and 2020, and prevalent cases younger than 15 years will rise by 70%. Adequate health-care resources to meet these children's needs should be made available.

FUNDING

European Community Concerted Action Program.

Bayesian statistics in medicine: a 25 year review

This review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.