Thirty Years of Prospective Nationwide Incidence of Childhood Type 1 Diabetes: The Accelerating Increase by Time Tends to Level Off in Sweden

Childhood T1D increased dramatically and shifted to a younger age at onset the first 22 years of the study period. We report a reversed trend, starting in 2000, indicating a change in nongenetic risk factors affecting specifically young children.</p

Cumulative Risk, Age at Onset, and Sex-Specific Differences for Developing End-Stage Renal Disease in Young Patients With Type 1 Diabetes: A Nationwide Population-Based Cohort Study

OBJECTIVE This study aimed to estimate the current cumulative risk of end-stage renal disease (ESRD) due to diabetic nephropathy in a large, nationwide, population-based prospective type 1 diabetes cohort and specifically study the effects of sex and age at onset. RESEARCH DESIGN AND METHODS In Sweden, all incident cases of type 1 diabetes aged 0-14 years and 15-34 years are recorded in validated research registers since 1977 and 1983, respectively. These registers were linked to the Swedish Renal Registry, which, since 1991, collects data on patients who receive active uremia treatment. Patients with years duration of type 1 diabetes were included (n = 11,681). RESULTS During a median time of follow-up of 20 years, 127 patients had developed ESRD due to diabetic nephropathy. The cumulative incidence at 30 years of type 1 diabetes duration was low, with a male predominance (4.1% [95% CI 3.1-5.3] vs. 2.5% [1.7-3.5]). In both male and female subjects, onset of type I diabetes before 10 years of age was associated with the lowest risk of developing ESRD. The highest risk of ESRD was found in male subjects diagnosed at age 20-34 years (hazard ratio 3.0 [95% CI 1.5-5.7]). In female subjects with onset at age 20-34 years, the risk was similar to patients diagnosed before age 10 years. CONCLUSIONS The cumulative incidence of ESRD is exceptionally low in young type 1 diabetic patients in Sweden. There is a striking difference in risk for male compared with female patients. The different patterns of risk by age at onset and sex suggest a role for puberty and sex hormones

Covariate selection for the nonparametric estimation of an average treatment effect

Observational studies in which the effect of a nonrandomized treatment on an outcome of interest is estimated are common in domains such as labour economics and epidemiology. Such studies often rely on an assumption of unconfounded treatment when controlling for a given set of observed pre-treatment covariates. The choice of covariates to control in order to guarantee unconfoundedness should primarily be based on subject matter theories, although the latter typically give only partial guidance. It is tempting to include many covariates in the controlling set to try to make the assumption of an unconfounded treatment realistic. Including unnecessary covariates is suboptimal when the effect of a binary treatment is estimated nonparametrically. For instance, when using a n1/2-consistent estimator, a loss of efficiency may result from using covariates that are irrelevant for the unconfoundedness assumption. Moreover, bias may dominate the variance when many covariates are used. Embracing the Neyman–Rubin model typically used in conjunction with nonparametric estimators of treatment effects, we characterize subsets from the original reservoir of covariates that are minimal in the sense that the treatment ceases to be unconfounded given any proper subset of these minimal sets. These subsets of covariates are shown to be identified under mild assumptions. These results lead us to propose data-driven algorithms for the selection of minimal sets of covariates.</p

Socioeconomic factors, rather than diabetes mellitus per se, contribute to an excessive use of antidepressants among young adults with childhood onset type 1 diabetes mellitus: a register-based study

AIMS/HYPOTHESIS: Mood disorders, including depression, are suggested to be prevalent in persons with type 1 diabetes and may negatively affect self-management and glycaemic control and increase the risk of diabetic complications. The aim of this study was to analyse the prevalence of antidepressant (AD) use in adults with childhood onset type 1 diabetes and to compare risk determinants for AD prescription among diabetic patients and a group of matched controls. METHODS: Young adults ≥18 years on 1 January 2006 with type 1 diabetes (n = 7,411) were retrieved from the population-based Swedish Childhood Diabetes Registry (SCDR) and compared with 30,043 age- and community-matched controls. Individual level data were collected from the Swedish National Drug Register (NDR), the Hospital Discharge Register (HDR) and the Labor Market Research database (LMR). RESULTS: ADs were prescribed to 9.5% and 6.8% of the type 1 diabetes and control subjects, respectively. Female sex, having received economic or other social support, or having a disability pension were the factors with the strongest association with AD prescription in both groups. Type 1 diabetes was associated with a 44% (OR 1.44, 95% CI 1.32, 1.58) higher risk of being prescribed ADs in crude analysis. When adjusting for potential confounders including sex, age and various socioeconomic risk factors, this risk increase was statistically non-significant (OR 1.11, 95% CI 0.99, 1.21). CONCLUSIONS/INTERPRETATION: The risk factor patterns for AD use are similar among type 1 diabetic patients and controls, and socioeconomic risk factors, rather than the diabetes per se, contribute to the increased risk of AD use in young adults with type 1 diabetes.</p

The risk of venous thromboembolism is markedly elevated in patients with diabetes

AIMS/HYPOTHESIS: Diabetes mellitus is associated with several changes in coagulation and fibrinolysis that may lead to a thrombogenic propensity. However, it is not known whether these perturbations actually cause increased risk of venous thromboembolism.METHODS: In a retrospective population-based study we evaluated the medical records of all 302 adult patients who were admitted to the Umea University Hospital with verified deep vein thrombosis or pulmonary embolism during the years 1997 to 1999. The patients were classified as diabetic (n=56) and non-diabetic (n=246) according to clinical information. The total number of diagnosed diabetic patients in different age groups in the catchment area was obtained from computerised registries in the primary health care centres and the Umea University Hospital, and data on the background population were collected from the Swedish population registry.RESULTS: The annual incidence rate of venous thromboembolism among diabetic patients in the population was 432 per 100,000 individuals (95% CI 375-496). In non-diabetic individuals it was 78 (95% CI 68-88). The age-adjusted incidence rate among the diabetic population was 274 (95% CI 262-286). The annual incidence rate of venous thromboembolism was elevated in type 1 and type 2 diabetic patients and the incidence rates were 704 (95% CI 314-1,566) and 412 (95% CI 312-544) respectively. The overall standardised morbidity ratio was 2.27 (95% CI 1.75-2.95), i.e. diabetic patients were more prone to venous thromboembolism after adjustment for age differences.CONCLUSIONS/INTERPRETATION: These results suggest that the age-adjusted risk for venous thromboembolism is more than two-fold higher among diabetic patients than in the non-diabetic background population.</p

Formulating causal questions and principled statistical answers

Although review papers on causal inference methods are now available, there is a lack of introductory overviews onwhatthey can render and on the guiding criteria for choosing one particular method. This tutorial gives an overview in situations where an exposure of interest is set at a chosen baseline ("point exposure") and the target outcome arises at a later time point. We first phrase relevant causal questions and make a case for being specific about the possible exposure levels involved and the populations for which the question is relevant. Using the potential outcomes framework, we describe principled definitions of causal effects and of estimation approaches classified according to whether they invoke the no unmeasured confounding assumption (including outcome regression and propensity score-based methods) or an instrumental variable with added assumptions. We mainly focus on continuous outcomes and causal average treatment effects. We discuss interpretation, challenges, and potential pitfalls and illustrate application using a "simulation learner," that mimics the effect of various breastfeeding interventions on a child's later development. This involves a typical simulation component with generated exposure, covariate, and outcome data inspired by a randomized intervention study. The simulation learner further generates various (linked) exposure types with a set of possible values per observation unit, from which observed as well as potential outcome data are generated. It thus provides true values of several causal effects. R code for data generation and analysis is available on , where SAS and Stata code for analysis is also provided.Clinical epidemiolog

Excess mortality in incident cases of diabetes mellitus aged 15 to 34 years at diagnosis: a population-based study (DISS) in Sweden

Aims/hypothesis: The objective of the study was to analyse the mortality, survival and cause of death patterns in incident cases of diabetes in the 15-34-year age group that were reported to the nationwide prospective Diabetes Incidence Study in Sweden (DISS). Methods: During the study period 1983-1999, 6,771 incident cases were reported. Identification of deaths was made by linking the records to the nationwide Cause of Death Register. Results: With an average follow-up of 8.5 years, resulting in 59,231 person-years, 159 deaths were identified. Diabetes was reported as the underlying cause of death in 51 patients (32%), and as a contributing cause of death in another 42 patients (26%). The standardised mortality ratio (SMR) was significantly elevated (RR=2.4; 95% CI: 2.0-2.8). The SMR was higher for patients classified by the reporting physician as having type 2 diabetes at diagnosis than for those classified as type 1 diabetic (2.9 and 1.8, respectively). Survival analysis showed significant differences in survival curves between males and females (p=0.0003) as well as between cases with different types of diabetes (p=0.005). This pattern was also reflected in the Cox regression model showing significantly increased hazard for males vs females (p=0.0002), and for type 2 vs type 1 (p=0.015) when controlling for age. Conclusions/interpretation: This study shows a two-fold excess mortality in patients with type 1 diabetes and a three-fold excess mortality in patients with type 2 diabetes. Thus, despite advances in treatment, diabetes still carries an increased mortality in young adults, even in a country with a good economic and educational patient status and easy access to health care