Ingeborg Waernbaum

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

OBJECTIVE During the past few decades, a rapidly increasing incidence of childhood type 1 diabetes (T1D) has been reported from many parts of the world. The change over time has been partly explained by changes in lifestyle causing rapid early growth and weight development. The current study models and analyzes the time trend by age, sex, and birth cohort in an exceptionally large study group. RESEARCH DESIGN AND METHODS The present analysis involved 14,721 incident cases of T1D with an onset of 0–14.9 years that were recorded in the nationwide Swedish Childhood Diabetes Registry from 1978 to 2007. Data were analyzed using generalized additive models. RESULTS Age- and sex-specific incidence rates varied from 21.6 (95% CI 19.4–23.9) during 1978–1980 to 43.9 (95% CI 40.7–47.3) during 2005–2007. Cumulative incidence by birth cohort shifted to a younger age at onset during the first 22 years, but from the birth year 2000 a statistically significant reversed trend (P < 0.01) was seen. CONCLUSIONS 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.

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

Aims/hypothesisDiabetes 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.MethodsIn a retrospective population-based study we evaluated the medical records of all 302 adult patients who were admitted to the Umeå 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 Umeå University Hospital, and data on the background population were collected from the Swedish population registry.ResultsThe 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/interpretationThese 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.

Model misspecification and robustness in causal inference : comparing matching with doubly robust estimation

In this paper, we compare the robustness properties of a matching estimator with a doubly robust estimator. We describe the robustness properties of matching and subclassification estimators by showing how misspecification of the propensity score model can result in the consistent estimation of an average causal effect. The propensity scores are covariate scores, which are a class of functions that removes bias due to all observed covariates. When matching on a parametric model (e.g., a propensity or a prognostic score), the matching estimator is robust to model misspecifications if the misspecified model belongs to the class of covariate scores. The implication is that there are multiple possibilities for the matching estimator in contrast to the doubly robust estimator in which the researcher has two chances to make reliable inference. In simulations, we compare the finite sample properties of the matching estimator with a simple inverse probability weighting estimator and a doubly robust estimator. For the misspecifications in our study, the mean square error of the matching estimator is smaller than the mean square error of both the simple inverse probability weighting estimator and the doubly robust estimators.

Effects of correlated covariates on the asymptotic efficiency of matching and inverse probability weighting estimators for causal inference

In observational studies, the overall aim when fitting a model for the propensity score is to reduce bias for an estimator of the causal effect. To make the assumption of an unconfounded treatment plausible researchers might include many, possibly correlated, covariates in the propensity score model. In this paper, we study how the asymptotic efficiency of matching and inverse probability weighting estimators for average causal effects change when the covariates are correlated. We investigate the case with multivariate normal covariates, a logistic model for the propensity score and linear models for the potential outcomes and show results under different model assumptions. We show that the correlation can both increase and decrease the large sample variances of the estimators, and that the correlation affects the asymptotic efficiency of the estimators differently, both with regard to direction and magnitude. Moreover, the strength of the confounding towards the outcome and the treatment plays an important role.

Estimating a marginal causal odds ratio in a case‐control design: analyzing the effect of low birth weight on the risk of type 1 diabetes mellitus

Estimation of marginal causal effects from case-control data has two complications: (i) confounding due to the fact that the exposure under study is not randomized, and (ii) bias from the case-control sampling scheme. In this paper, we study estimators of the marginal causal odds ratio, addressing these issues for matched and unmatched case-control designs when utilizing the knowledge of the known prevalence of being a case. The estimators are implemented in simulations where their finite sample properties are studied and approximations of their variances are derived with the delta method. Also, we illustrate the methods by analyzing the effect of low birth weight on the risk of type 1 diabetes mellitus using data from the Swedish Childhood Diabetes Register, a nationwide population-based incidence register.

Impact of Parental Socioeconomic Status on Excess Mortality in a Population-Based Cohort of Subjects With Childhood-Onset Type 1 Diabetes

OBJECTIVE The aim of this study was to analyze the possible impact of parental and individual socioeconomic status (SES) on all-cause mortality in a population-based cohort of patients with childhood-onset type 1 diabetes. RESEARCH DESIGN AND METHODS Subjects recorded in the Swedish Childhood Diabetes Registry (SCDR) from 1 January 1978 to 31 December 2008 were included (n = 14,647). The SCDR was linked to the Swedish Cause of Death Registry (CDR) and the Longitudinal Integration Database for Health Insurance and Labour Market Studies (LISA). RESULTS At a mean follow-up of 23.9 years (maximum 46.5 years), 238 deaths occurred in a total of 349,762 person-years at risk. In crude analyses, low maternal education predicted mortality for male patients only (P = 0.046), whereas parental income support predicted mortality in both sexes (P < 0.001 for both). In Cox models stratified by age-at-death group and adjusted for age at onset and sex, parental income support predicted mortality among young adults (≥18 years of age) but not for children. Including the adult patient’s own SES in a Cox model showed that individual income support to the patient predicted mortality occurring at ≥24 years of age when adjusting for age at onset, sex, and parental SES. CONCLUSIONS Exposure to low SES, mirrored by the need for income support, increases mortality risk in patients with childhood-onset type 1 diabetes who died after the age of 18 years.

Data-driven algorithms for dimension reduction in causal inference

In observational studies, the causal effect of a treatment may be confounded with variables that are related to both the treatment and the outcome of interest. In order to identify a causal effect, such studies often rely on the unconfoundedness assumption, i.e., that all confounding variables are observed. The choice of covariates to control for, which is primarily based on subject matter knowledge, may result in a large covariate vector in the attempt to ensure that unconfoundedness holds. However, including redundant covariates can affect bias and efficiency of nonparametric causal effect estimators, e.g., due to the curse of dimensionality. Data-driven algorithms for the selection of sufficient covariate subsets are investigated. Under the assumption of unconfoundedness the algorithms search for minimal subsets of the covariate vector. Based, e.g., on the framework of sufficient dimension reduction or kernel smoothing, the algorithms perform a backward elimination procedure assessing the significance of each covariate. Their performance is evaluated in simulations and an application using data from the Swedish Childhood Diabetes Register is also presented.

Incidence of fractures among children and adolescents in rural and urban communities - analysis based on 9,965 fracture events

BackgroundPrevious work has explored the significance of residence on injuries. A number of articles reported higher rates of injury in rural as compared to urban settings. This study aimed to evaluate the importance of residency on the occurrence of fractures among children and adolescents within a region in northern Sweden.MethodsIn a population based study with data from an injury surveillance registry at a regional hospital, we have investigated the importance of sex, age and place of residency for the incidence of fractures among children and adolescents 0-19 years of age using a Poisson logistic regression analysis. Data was collected between 1998 and 2011.ResultsThe dataset included 9,965 cases. Children and adolescents growing up in the most rural communities appeared to sustain fewer fractures than their peers in an urban municipality, risk ratio 0.81 (0.76-0.86). Further comparisons of fracture rates in the urban and rural municipalities revealed that differences were most pronounced for sports related fractures and activities in school in the second decade of life.ConclusionResults indicate that fracture incidence among children and adolescents is affected by place of residency. Differences were associated with activity at injury and therefore we have discussed the possibility that this effect was due to the influence of place on activity patterns.The results suggest it is of interest to explore how geographic and demographic variables affect the injury pattern further.

Decreasing Cumulative Incidence of End-Stage Renal Disease in Young Patients With Type 1 Diabetes in Sweden: A 38-Year Prospective Nationwide Study

OBJECTIVE Diabetic nephropathy is a serious complication of type 1 diabetes. Recent studies indicate that end-stage renal disease (ESRD) incidence has decreased or that the onset of ESRD has been postponed; therefore, we wanted to analyze the incidence and time trends of ESRD in Sweden. RESEARCH DESIGN AND METHODS In this study, patients with duration of type 1 diabetes >14 years and age at onset of diabetes 0–34 years were included. Three national diabetes registers were used: the Swedish Childhood Diabetes Register, the Diabetes Incidence Study in Sweden, and the National Diabetes Register. The Swedish Renal Registry, a national register on renal replacement therapy, was used to identify patients who developed ESRD. RESULTS We found that the cumulative incidence of ESRD in Sweden was low after up to 38 years of diabetes duration (5.6%). The incidence of ESRD was lower in patients with type 1 diabetes onset in 1991–2001 compared with onset in 1977–1984 and 1985–1990, independent of diabetes duration. CONCLUSIONS The risk of developing ESRD in Sweden in this population is still low and also seems to decrease with time.

Estimating marginal causal effects in a secondary analysis of case-control data

When an initial case-control study is performed, data can be used in a secondary analysis to evaluate the effect of the case-defining event on later outcomes. In this paper, we study the example in which the role of the event is changed from a response variable to a treatment of interest. If the aim is to estimate marginal effects, such as average effects in the population, the sampling scheme needs to be adjusted for. We study estimators of the average effect of the treatment in a secondary analysis of matched and unmatched case-control data where the probability of being a case is known. For a general class of estimators, we show the components of the bias resulting from ignoring the sampling scheme and demonstrate a design-weighted matching estimator of the average causal effect. In simulations, the finite sample properties of the design-weighted matching estimator are studied. Using a Swedish diabetes incidence register with a matched case-control design, we study the effect of childhood onset diabetes on the use of antidepressant medication as an adult. Copyright