Short-term therapies produce benefits more quickly than long-term psychodynamic psychotherapy but in the long run long-term psychodynamic psychotherapy is superior to short-term therapies. However, more research is needed to determine which patients should be given long-term psychotherapy for the treatment of mood or anxiety disorders.

A nationwide study of childhood Type 1 (insulin-dependent) diabetes mellitus was established in 1986 in Finland, the country with the highest incidence of this disease worldwide. The aim of the project called "Childhood Diabetes in Finland" is to evaluate the role of genetic, environmental and immunological factors and particularly the interaction between genetic and environmental factors in the development of Type 1 diabetes. From September 1986 to April 1989, 801 families with a newly-diagnosed child aged 14 years or younger at the time of diagnosis were invited to participate in this study. The vast majority of the families agreed to participate in the comprehensive investigations of the study. HLA genotypes and haplotypes were determined in 757 families (95%). Our study also incorporates a prospective family study among non-diabetic siblings aged 3-19 years, and two case-control studies among the young-onset cases of Type 1 diabetes. During 1987-1989, the overall incidence of Type 1 diabetes was about 35.2 per 100,000 per year. It was higher in boys (38.4) than in girls (32.2). There was no clear geographic variation in incidence among the 12 provinces of Finland. Of the 1,014 cases during these 3 years only six cases were diagnosed before their first birthday. The incidence was high already in the age group 1-4-years old: 33.2 in boys and 29.5 in girls. Of the 801 families 90 (11.2%) were multiple case families, of which 66 had a parent with Type 1 diabetes at the time of diagnosis of the proband.(ABSTRACT TRUNCATED AT 250 WORDS)

The population attributable fraction (PAF) is a useful measure for describing the expected change in an outcome if its risk factors are modified. Cohort studies allow researchers to assess the predictive value of the risk factor modification on the incidence of the outcome during a certain follow-up. Estimation of PAF for both mortality and morbidity in cohort studies with censored survival data has been developed in the recent years. So far, however, censoring due to death in the estimation of PAF for morbidity has been ignored, resulting in estimation of a quantity which is not relevant in practice as some people are likely to die during the follow-up. The risk factors related to the disease incidence may also be related to mortality, and modification of these risk factors is likely to delay the occurrence of both events. Thus, censoring due to death and the impact of risk factor modification must be considered when estimating PAF for disease incidence. We consider both and introduce two measures of disease burden: PAF for the incidence of disease during lifetime and PAF for the prevalence of disease in the population at a certain time. We demonstrate how consideration of censoring due to death changes the estimated PAF for disease incidence and its confidence interval. This underlines the importance of choosing a correct PAF measure depending on the outcome of interest and the risk factors of interest to obtain accurate and interpretable results.

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BackgroundTo assess the nonresponse rates in a questionnaire survey with respect to administrative register data, and to correct the bias statistically.MethodsThe Finnish Regional Health and Well-being Study (ATH) in 2010 was based on a national sample and several regional samples. Missing data analysis was based on socio-demographic register data covering the whole sample. Inverse probability weighting (IPW) and doubly robust (DR) methods were estimated using the logistic regression model, which was selected using the Bayesian information criteria. The crude, weighted and true self-reported turnout in the 2008 municipal election and prevalences of entitlements to specially reimbursed medication, and the crude and weighted body mass index (BMI) means were compared.ResultsThe IPW method appeared to remove a relatively large proportion of the bias compared to the crude prevalence estimates of the turnout and the entitlements to specially reimbursed medication. Several demographic factors were shown to be associated with missing data, but few interactions were found.ConclusionsOur results suggest that the IPW method can improve the accuracy of results of a population survey, and the model selection provides insight into the structure of missing data. However, health-related missing data mechanisms are beyond the scope of statistical methods, which mainly rely on socio-demographic information to correct the results.