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

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

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

STRengthening analytical thinking for observational studies: The STRATOS initiative

The validity and practical utility of observational medical research depends critically on good study design, excellent data quality, appropriate statistical methods and accurate interpretation of results. Statistical methodology has seen substantial development in recent times. Unfortunately, many of these methodological developments are ignored in practice. Consequently, design and analysis of observational studies often exhibit serious weaknesses. The lack of guidance on vital practical issues discourages many applied researchers from using more sophisticated and possibly more appropriate methods when analyzing observational studies. Furthermore, many analyses are conducted by researchers with a relatively weak statistical background and limited experience in using statistical methodology and software. Consequently, even 'standard' analyses reported in the medical literature are often flawed, casting doubt on their results and conclusions. An efficient way to help researchers to keep up with recent methodological developments is to develop guidance documents that are spread to the research community at large. These observations led to the initiation of the strengthening analytical thinking for observational studies (STRATOS) initiative, a large collaboration of experts in many different areas of biostatistical research. The objective of STRATOS is to provide accessible and accurate guidance in the design and analysis of observational studies. The guidance is intended for applied statisticians and other data analysts with varying levels of statistical education, experience and interests.In this article, we introduce the STRATOS initiative and its main aims, present the need for guidance documents and outline the planned approach and progress so far. We encourage other biostatisticians to become involved

Diabetes duration and health-related quality of life in individuals with onset of diabetes in the age group 15-34 years - a Swedish population-based study using EQ-5D

Background: Diabetes with onset in younger ages affects both length of life and health status due to debilitating and life-threatening long-term complications. In addition, episodes and fear of hypoglycaemia and of long-term consequences may have a substantial impact on health status. This study aims to describe and analyse health-related quality of life (HRQoL) in individuals with onset of diabetes at the age of 15-34 years and with a disease duration of 1, 8, 15 and 24 years compared with control individuals matched for age, sex and county of residence. Methods: Cross-sectional study of 839 individuals with diabetes and 1564 control individuals. Data on socioeconomic status and HRQoL using EQ-5D were collected by a postal questionnaire. Insulin treatment was self-reported by 94% of the patients, the majority most likely being type 1. Results: Individuals with diabetes reported lower HRQoL, with a significantly lower mean EQ VAS score in all cohorts of disease duration compared with control individuals for both men and women, and with a significantly lower EQ-5D(index) for women, but not for men, 15 years (0.76, p = 0.022) and 24 years (0.77, p = 0.016) after diagnosis compared with corresponding control individuals. Newly diagnosed individuals with diabetes reported significantly more problems compared with the control individuals in the dimension usual activities (women: 13.2% vs. 4.0%, p = 0.048; men: 11.4% vs. 4.1%, p = 0.033). In the other dimensions, differences between individuals with diabetes and control individuals were found 15 and 24 years after diagnosis: for women in the dimensions mobility, self-care, usual activities and pain/discomfort and for men in the dimension mobility. Multivariable regression analysis showed that diabetes duration, being a woman, having a lower education and not being married or cohabiting had a negative impact on HRQoL. Conclusions: Our study confirms the negative impact of diabetes on HRQoL and that the difference to control individuals increased by disease duration for women with diabetes. The small difference one year after diagnosis could imply a good management of diabetes care and a relatively quick adaptation. Our results also indicate that gender differences still exist in Sweden, despite modern diabetes treatment and management in Sweden