Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation.

There are now many options for doubly robust estimation; however, there is a concerning trend in the applied literature to believe that the combination of a propensity score and an adjusted outcome model automatically results in a doubly robust estimator and/or to misuse more complex established doubly robust estimators. A simple alternative, canonical link generalized linear models (GLM) fit via inverse probability of treatment (propensity score) weighted maximum likelihood estimation followed by standardization (the g $$ g $$ -formula) for the average causal effect, is a doubly robust estimation method. Our aim is for the reader not just to be able to use this method, which we refer to as IPTW GLM, for doubly robust estimation, but to fully understand why it has the doubly robust property. For this reason, we define clearly, and in multiple ways, all concepts needed to understand the method and why it is doubly robust. In addition, we want to make very clear that the mere combination of propensity score weighting and an adjusted outcome model does not generally result in a doubly robust estimator. Finally, we hope to dispel the misconception that one can adjust for residual confounding remaining after propensity score weighting by adjusting in the outcome model for what remains 'unbalanced' even when using doubly robust estimators. We provide R code for our simulations and real open-source data examples that can be followed step-by-step to use and hopefully understand the IPTW GLM method. We also compare to a much better-known but still simple doubly robust estimator.

Causal inference in survival analysis under deterministic missingness of confounders in register data.

Long-term register data offer unique opportunities to explore causal effects of treatments on time-to-event outcomes, in well-characterized populations with minimum loss of follow-up. However, the structure of the data may pose methodological challenges. Motivated by the Swedish Renal Registry and estimation of survival differences for renal replacement therapies, we focus on the particular case when an important confounder is not recorded in the early period of the register, so that the entry date to the register deterministically predicts confounder missingness. In addition, an evolving composition of the treatment arms populations, and suspected improved survival outcomes in later periods lead to informative administrative censoring, unless the entry date is appropriately accounted for. We investigate different consequences of these issues on causal effect estimation following multiple imputation of the missing covariate data. We analyse the performance of different combinations of imputation models and estimation methods for the population average survival. We further evaluate the sensitivity of our results to the nature of censoring and misspecification of fitted models. We find that an imputation model including the cumulative baseline hazard, event indicator, covariates and interactions between the cumulative baseline hazard and covariates, followed by regression standardization, leads to the best estimation results overall, in simulations. Standardization has two advantages over inverse probability of treatment weighting here: it can directly account for the informative censoring by including the entry date as a covariate in the outcome model, and allows for straightforward variance computation using readily available software.

Model misspecification and bias for inverse probability weighting estimators of average causal effects.

Commonly used semiparametric estimators of causal effects specify parametric models for the propensity score (PS) and the conditional outcome. An example is an augmented inverse probability weighting (IPW) estimator, frequently referred to as a doubly robust estimator, because it is consistent if at least one of the two models is correctly specified. However, in many observational studies, the role of the parametric models is often not to provide a representation of the data-generating process but rather to facilitate the adjustment for confounding, making the assumption of at least one true model unlikely to hold. In this paper, we propose a crude analytical approach to study the large-sample bias of estimators when the models are assumed to be approximations of the data-generating process, namely, when all models are misspecified. We apply our approach to three prototypical estimators of the average causal effect, two IPW estimators, using a misspecified PS model, and an augmented IPW (AIPW) estimator, using misspecified models for the outcome regression (OR) and the PS. For the two IPW estimators, we show that normalization, in addition to having a smaller variance, also offers some protection against bias due to model misspecification. To analyze the question of when the use of two misspecified models is better than one we derive necessary and sufficient conditions for when the AIPW estimator has a smaller bias than a simple IPW estimator and when it has a smaller bias than an IPW estimator with normalized weights. If the misspecification of the outcome model is moderate, the comparisons of the biases of the IPW and AIPW estimators show that the AIPW estimator has a smaller bias than the IPW estimators. However, all biases include a scaling with the PS-model error and we suggest caution in modeling the PS whenever such a model is involved. For numerical and finite sample illustrations, we include three simulation studies and corresponding approximations of the large-sample biases. In a dataset from the National Health and Nutrition Examination Survey, we estimate the effect of smoking on blood lead levels.

The incidence of childhood-onset type 1 diabetes, time trends and association with the population composition in Sweden: a 40 year follow-up.

AIMS/HYPOTHESIS: During the 1980s and 1990s, the incidence of childhood-onset type 1 diabetes more than doubled in Sweden, followed by a plateau. In the present 40 year follow-up, we investigated if the incidence remained stable and whether this could be explained by increased migration from countries reporting lower incidences. METHODS: We used 23,143 incident cases of childhood-onset type 1 diabetes reported between 1978 and 2019 to the nationwide, population-based Swedish Childhood Diabetes Registry and population data from Statistics Sweden. Generalised additive models and ANOVA were applied to analyse the effects of onset age, sex, time trends and parental country of birth and interaction effects between these factors. RESULTS: The flattening of the incidence increase seems to remain over the period 2005-2019. When comparing the incidence of type 1 diabetes for all children in Sweden with that for children with both parents born in Sweden, the trends were parallel but at a higher level for the latter. A comparison of the incidence trends between individuals with Swedish backgrounds (high diabetes trait) and Asian backgrounds (low diabetes trait) showed that the Asian subpopulation had a stable increase in incidence over time. CONCLUSIONS/INTERPRETATION: In Sweden, the increase in incidence of childhood-onset type 1 diabetes in the late 20th century has been approaching a more stable albeit high level over the last two decades. Increased immigration from countries with lower incidences of childhood-onset type 1 diabetes does not provide a complete explanation for the observed levelling off.

Trial emulation and survival analysis for disease incidence registers: A case study on the causal effect of pre-emptive kidney transplantation.

When drawing causal inference from observed data, failure time outcomes present additional challenges of censoring often combined with other missing data patterns. In this article, we follow incident cases of end-stage renal disease to examine the effect on all-cause mortality of starting treatment with transplant, so-called pre-emptive kidney transplantation, vs starting with dialysis possibly followed by delayed transplantation. The question is relatively simple: which start-off treatment is expected to bring the best survival for a target population? To address it, we emulate a target trial drawing on the long term Swedish Renal Registry, where a growing common set of baseline covariates was measured nationwide. Several lessons are learned which pertain to long term disease registers more generally. With characteristics of cases and versions of treatment evolving over time, informative censoring is already introduced in unadjusted Kaplan-Meier curves. This leads to misrepresented survival chances in observed treatment groups. The resulting biased treatment association may be aggravated upon implementing IPW for treatment. Aware of additional challenges, we further recall how similar studies to date have selected patients into treatment groups based on events occurring post treatment initiation. Our study reveals the dramatic impact of resulting immortal time bias combined with other typical features of long-term incident disease registers, including missing covariates during the early phases of the register. We discuss feasible ways of accommodating these features when targeting relevant estimands, and demonstrate how more than one causal question can be answered relying on the no unmeasured baseline confounders assumption.

Quality of Conduct and Reporting of Propensity Score Methods in Studies Investigating the Effectiveness of Antimicrobial Therapy.

Background: Propensity score methods are becoming increasingly popular in infectious disease medicine to correct for confounding in observational studies. However, applying and reporting propensity score techniques correctly requires substantial knowledge of these methods. The quality of conduct and reporting of propensity score methods in studies investigating the effectiveness of antimicrobial therapy is yet undetermined. Methods: A systematic review was performed to provide an overview of studies (2005-2020) on the effectiveness of antimicrobial therapy that used propensity score methods. A quality assessment tool and a standardized quality score were developed to evaluate a subset of studies in which antibacterial therapy was investigated in detail. The scale of this standardized score ranges between 0 (lowest quality) and 100 (excellent). Results: A total of 437 studies were included. The absolute number of studies that investigated the effectiveness of antimicrobial therapy and that used propensity score methods increased 15-fold between the periods 2005-2009 and 2015-2019. Propensity score matching was the most frequently applied technique (65%), followed by propensity score-adjusted multivariable regression (25%). A subset of 108 studies was evaluated in detail. The median standardized quality score per year ranged between 53 and 61 (overall range: 33-88) and remained constant over the years. Conclusions: The quality of conduct and reporting of propensity score methods in research on the effectiveness of antimicrobial therapy needs substantial improvement. The quality assessment instrument that was developed in this study may serve to help investigators improve the conduct and reporting of propensity score methods.

Socioeconomic variables and fracture risk in children and adolescents: a population-based study from northern Sweden.

OBJECTIVES: Previous studies have investigated the association between socioeconomic characteristics and fractures among children, producing different results. In a population-based study, we previously found an increased risk of fractures among children living in an urban municipality compared with rural municipalities. This study aimed to evaluate the importance of socioeconomic variables for the incidence of fractures among 0-17 year olds. SETTING, DESIGN AND OUTCOME MEASURE: We present a longitudinal, observational study of a population 0-17 years of age. Data from an injury database were linked with additional socioeconomic data for the population at risk. These were 55 758 individuals residing within the primary catchment area of a regional hospital in northern Sweden. Using the number of fractures as the outcome, we fitted a generalised linear mixed model for a Poisson response with socioeconomic variables at the family level as independent variables while controlling for age, sex and place of residence. RESULTS: We found a significant association between higher levels of family income and the risk of fracture, rate ratio 1.40 (1.28-1.52) p<0.001 when comparing the highest income quintile to the lowest as well as the number of siblings and the risk of fracture. Children with one or two siblings had a rate ratio of 1.28 (1.19-1.38) p<0.001 when compared with children with no siblings. Parents' educational level and having a single parent showed no significant association with fractures. The previously observed association between municipalities and fracture risk was less pronounced when taking family-level socioeconomic variables into account. CONCLUSION: Our results indicate that children from families with higher income and with siblings are at greater risk of sustaining fractures.

Formulating causal questions and principled statistical answers.

Although review papers on causal inference methods are now available, there is a lack of introductory overviews on what they 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 www.ofcaus.org, where SAS and Stata code for analysis is also provided.

Perinatal risk factors for type 1 diabetes revisited: a population-based register study.

AIMS/HYPOTHESIS: Single-centre studies and meta-analyses have found diverging results as to which early life factors affect the risk of type 1 diabetes during childhood. We wanted to use a large, nationwide, prospective database to further clarify and analyse the associations between perinatal factors and the subsequent risk for childhood-onset type 1 diabetes using a case-control design. METHODS: The Swedish Childhood Diabetes Register was linked to the Swedish Medical Birth Register and National Patient Register, and 14,949 cases with type 1 diabetes onset at ages 0-14 years were compared with 55,712 matched controls born from the start of the Medical Birth Register in 1973 to 2013. After excluding confounders (i.e. children multiple births, those whose mother had maternal diabetes and those with a non-Nordic mother), we used conditional logistic regression analyses to determine risk factors for childhood-onset type 1 diabetes. We used WHO ICD codes for child and maternal diagnoses. RESULTS: In multivariate analysis, there were small but statistically significant associations between higher birthweight z score (OR 1.08, 95% CI 1.06, 1.10), delivery by Caesarean section (OR 1.08, 95% CI 1.02, 1.15), premature rupture of membranes (OR 1.08, 95% CI 1.01, 1.16) and maternal urinary tract infection during pregnancy (OR 1.39, 95% CI 1.04, 1.86) and the subsequent risk of childhood-onset type 1 diabetes. Birth before 32 weeks of gestation was associated with a lower risk of childhood-onset type 1 diabetes compared with full-term infants (OR 0.54, 95% CI 0.38, 0.76), whereas birth between 32 and 36 weeks' gestation was associated with a higher risk (OR 1.24, 95% CI 1.14, 1.35). In subgroup analyses (birth years 1992-2013), maternal obesity was independently associated with subsequent type 1 diabetes in the children (OR 1.27, 95% CI 1.15, 1.41) and rendered the association with Caesarean section non-significant. In contrast to previous studies, we found no association of childhood-onset type 1 diabetes with maternal-child blood-group incompatibility, maternal pre-eclampsia, perinatal infections or treatment of the newborn with phototherapy for neonatal jaundice. The proportion of children with neonatal jaundice was significantly higher in the 1973-1982 birth cohort compared with later cohorts. CONCLUSIONS/INTERPRETATION: Perinatal factors make small but statistically significant contributions to the overall risk of childhood-onset type 1 diabetes. Some of these risk factors, such as maternal obesity, may be amendable with improved antenatal care. Better perinatal practices may have affected some previously noted risk factors over time.

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.

Marginal structural model to evaluate the joint effect of socioeconomic exposures on the risk of developing end-stage renal disease in patients with type 1 diabetes: a longitudinal study based on data from the Swedish Childhood Diabetes Study Group.

PURPOSE: Diabetic nephropathy is a severe complication of type 1 diabetes (T1D) that may lead to renal failure and end-stage renal disease (ESRD) demanding dialysis and transplantation. The etiology of diabetic nephropathy is multifactorial and both genes and environmental and life style-related factors are involved. In this study, we investigate the effect of the socioeconomic exposures, unemployment and receiving income support, on the development of ESRD in T1D patients, using a marginal structural model (MSM) in comparison with standard logistic regression models. METHODS: The study is based on the Swedish Childhood Diabetes Register which in 1977 started to register patients developing T1D before 15 years of age. In the analyses, we include patients born between 1965 and 1979, developing diabetes between 1977 and 1994, and followed until 2013 (n = 4034). A MSM was fitted to adjust for both baseline and time-varying confounders. RESULTS: The main results of the analysis indicate that being unemployed for more than 1 year and receiving income support are risk factors for the development of ESRD. Multiple exposures over time to these risk factors increase the risk associated with the disease. CONCLUSIONS: Using a MSM is an advanced method well suited to investigate the effect of exposures on the risk of complications of a chronic disease with longitudinal data. The results show that socioeconomic disadvantage increases the risk of developing ESRD in patients with T1D.

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 © 2017 John Wiley & Sons, Ltd.

Low mean temperature rather than few sunshine hours are associated with an increased incidence of type 1 diabetes in children.

The well-known north-south gradient and the seasonal variability in incidence of childhood type 1 diabetes indicate climatological factors to have an effect on the onset. Both sunshine hours and a low temperature may be responsible. In the present study we tried to disentangle these effects that tend to be strongly connected. Exposure data were sunshine hours and mean temperature respectively obtained from eleven meteorological stations in Sweden which were linked to incidence data from geographically matched areas. Incident cases during 1983-2008 were retrieved from the population based Swedish childhood diabetes register. We used generalized additive models to analyze the incidence as a function of mean temperature and hours of sun adjusted for the time trend, age and sex. In our data set the correlation between sun hours and temperature was weak (r = 0.36) implying that it was possible to estimate the effect of these variables in a regression model. We fit a general additive model with a smoothing term for the time trend. In the model with sun hours we found no significant effect on T1 incidence (p = 0.17) whereas the model with temperature as predictor was significant (p = 0.05) when adjusting for the time trend, sex and age. Adding sun hours in the model where mean temperature was already present did not change the effect of temperature. There is an association with incidence of type 1 diabetes in children and low mean temperature independent of a possible effect of sunshine hours after adjustment for age, sex and time trend. The findings may mirror the cold effect on insulin resistance and accords with the hypothesis that overload of an already ongoing beta cell destruction may accelerate disease onset.

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.

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

BACKGROUND: Previous 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. METHODS: In 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. RESULTS: The 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. CONCLUSION: Results 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.

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.

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.

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.

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 > or =13 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 1 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.

Signs of nephropathy may occur early in young adults with diabetes despite modern diabetes management: results from the nationwide population-based Diabetes Incidence Study in Sweden (DISS).

OBJECTIVE: To estimate the occurrence of early-onset renal involvement in a nationwide population-based cohort of young adults with diabetes in Sweden and relate the findings to glycemic control, type of diabetes, sex, smoking, and blood pressure. RESEARCH DESIGN AND METHODS: The Diabetes Incidence Study in Sweden aims to register all incident cases of diabetes in the age-group 15-34 years. In 1987-1988, 806 patients were reported and invited to participate in a follow-up study focusing on microvascular complications. Of them, 469 subjects participated. The assessment was based on questionnaires (n = 469), blood samples (n = 424), urine samples (n = 251) and, when appropriate, medical records (n = 186). RESULTS: During the follow-up time, median 9 years (range 6-12), 31 of 469 patients (6.6%) with incipient or overt diabetic nephropathy (i.e., micro- or macroalbuminuria) were found, 24 of 426 (5.6%) in type 1 and 7 of 43 (16%) in type 2 diabetic subjects (P = 0.016). Additionally, 24 of 31 patients (77%) had microalbuminuria and 7 (23%) had macroalbuminuria, which mainly occurred in patients with type 2 diabetes. In a Cox regression analysis, high mean HbA(1c) during the follow-up period and high blood pressure at follow-up increased the risk of developing signs of nephropathy (P = 0.020 and P = 0.003, respectively). Compared with patients with type 1 diabetes, those with type 2 diabetes tended to have an increased risk of renal involvement (P = 0.054) when adjusting for sex, tobacco use, glycemic control, and blood pressure. CONCLUSIONS: Despite modern treatment and self-monitoring of blood glucose, young adult patients with diabetes may still develop renal involvement during the first 10 years of diabetes duration. Inadequate HbA(1c), high blood pressure, and type 2 diabetes appear to be risk markers for early occurrence of diabetic nephropathy.