Skip to main content

    Esa Virtala

    The role of the serotonin transporter (SERT) in the pathophysiology of depression is unclear and only a few follow-up studies exist. Our aim was to measure changes in SERT availability during psychodynamic psychotherapy in patients with... more
    The role of the serotonin transporter (SERT) in the pathophysiology of depression is unclear and only a few follow-up studies exist. Our aim was to measure changes in SERT availability during psychodynamic psychotherapy in patients with major depression over a follow-up time of 12 or 18 months. The patients were studied with iodine-123 labelled 2β-carbomethoxy-3β-(4-iodophenyl) serial single-photon emission tomography imaging and clinical rating scales of symptoms. Changes in SERT availability had no correlation with the change of symptoms, but the change of SERT availability during psychotherapy in the midbrain was predicted by the baseline severity of the clinical symptoms measured by the Symptom Checklist Depression Scale and the Symptom Checklist Global Severity Index. With cut-off values applied, it was found that SERT availabilities increased in patients with high baseline symptoms, and decreased in patients with low baseline symptoms. Together with our earlier finding of decreased SERT in patients with depression, these results indicate a state-dependent and possibly a compensatory role of decreased SERT availability in depression.
    Knowledge is incomplete on whether long-term psychotherapy is more effective than short-term therapy in treating mood and anxiety disorder, when measured by improvements in psychosocial functioning and life quality. In the Helsinki... more
    Knowledge is incomplete on whether long-term psychotherapy is more effective than short-term therapy in treating mood and anxiety disorder, when measured by improvements in psychosocial functioning and life quality. In the Helsinki Psychotherapy Study, 326 outpatients with mood or anxiety disorder were randomized to solution-focused therapy (SFT), short-term psychodynamic psychotherapy (SPP), or long-term psychodynamic psychotherapy (LPP), and followed up for 5 years from the start of treatment. The outcome measures comprised 4 questionnaires on psychosocial functioning, assessing global social functioning (Social Adjustment Scale (SAS-SR), sense of coherence (Sense of Coherence Scale (SOC)), perceived competence (Self-Performance Survey), dispositional optimism (Life Orientation Test (LOT)), and 1 questionnaire assessing quality of life (Life Situation Survey (LSS)). Short-term therapies improved psychosocial functioning and quality of life more than LPP during the first year. The only exceptions were LOT and perceived competence, which did not differ between SPP and LPP. Later in the follow-up, SOC and perceived competence showed significantly more improvement in LPP than in the short-term therapy groups. No direct differences between SFT and SPP were noted. Short-term therapy has consistently more short-term effects on psychosocial functioning and quality of life than LPP, whereas LPP has some additional long-term benefits on psychosocial functioning.
    Only few randomized trials comparing sustained effects of short- and long-term psychotherapies in personality functioning are available. In this study we compared the effects of two short-term therapies and long-term psychodynamic... more
    Only few randomized trials comparing sustained effects of short- and long-term psychotherapies in personality functioning are available. In this study we compared the effects of two short-term therapies and long-term psychodynamic psychotherapy on patients' personality functioning during a 5-year follow-up. Altogether 326 patients of the Helsinki Psychotherapy Study, with anxiety or mood disorder, were randomly assigned to either short-term psychotherapy of about six months (solution-focused therapy (SFT, n=97) or short-term psychodynamic psychotherapy (SPP, n=101)), or to long-term psychodynamic psychotherapy (LPP, n=128), lasting on average three years. Outcomes in personality functioning (i.e., self-concept, defense style, interpersonal problems, and level of personality organization) were assessed five to seven times using, respectively, questionnaires (SASB, DSQ, IIP) and interview (LPO) during the 5-year follow-up from randomization. Personality functioning improved in all...
    Background To assess the nonresponse rates in a questionnaire survey with respect to administrative register data, and to correct the bias statistically. Methods The Finnish Regional Health and Well-being Study (ATH) in 2010 was based on... more
    Background
    To assess the nonresponse rates in a questionnaire survey with respect to administrative register data, and to correct the bias statistically.

    Methods
    The 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.

    Results
    The 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.

    Conclusions
    Our 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.
    Research Interests:
    The population attributable fraction (PAF) is a useful measure for quantifying the impact of exposure to certain risk factors on a particular outcome at the population level. Recently, new model-based methods for the estimation of PAF and... more
    The population attributable fraction (PAF) is a useful measure for quantifying the impact of exposure to certain risk factors on a particular outcome at the population level. Recently, new model-based methods for the estimation of PAF and its confidence interval for different types of outcomes in a cohort study design have been proposed. In this paper, we introduce SAS macros
    Virtually no comparisons of different psychotherapies with long follow-up times have been carried out until now. The Helsinki Psychotherapy Study is a randomized clinical trial, where patients were monitored for 12 months after the onset... more
    Virtually no comparisons of different psychotherapies with long follow-up times have been carried out until now. The Helsinki Psychotherapy Study is a randomized clinical trial, where patients were monitored for 12 months after the onset of study treatments, of which each lasted approximately 6 months. The patients' psychiatric status was measured at five pre-determined time points during the follow-up period. In general, the analyses of trials are complicated in cases where compliance with the given treatment is incomplete or the drop-out from the follow-up is non-ignorable. In the present study, the quality of the treatment deviated from the protocol for some patients and some patients took auxiliary treatments which had similar effects to the study treatment during the study treatment or follow-up period. This might have resulted in standard intention-to-treat analyses providing excessively conservative or liberal conclusions. Non-compliance may have been non-ignorable in some cases, so subject-specific latent factors may have influenced the outcome both directly and indirectly via compliance behaviour. The most and least healthy patients are the most likely to dropout from the follow-up a priori, so the missing data process is informative. The missing data can partly be augmented with surrogate information collected during interviews with patients who dropped out. A Bayesian hierarchical as-treated model, which uses random-effects-based selection models to account for non-ignorable missing data and non-compliance, was compared with different mixed effects models.
    We developed a test statistic based on an approach of Whittemore et al. (1987) to detect space-time clustering for non-infectious diseases. We extended the spatial test of Whittemore et al. by deriving conditional probabilities for... more
    We developed a test statistic based on an approach of Whittemore et al. (1987) to detect space-time clustering for non-infectious diseases. We extended the spatial test of Whittemore et al. by deriving conditional probabilities for Poisson distributed random variables. To combine spatial and time distances we defined a distance matrix D, where dij is the distance between the ith and jth cell in a three-dimensional space-time grid. Spatial and temporal components are controlled by a weight. By altering the weight, both marginal tests and the intermediate test can be reached. Allowing a continuum from a pure spatial to a pure temporal test, the best result will be gained by trying different weights, because the occurrence of a disease might show some temporal and some spatial tendency to cluster. We examined the behaviour of the test statistic by simulating different distributions for cases and the population. The test was applied to the incidence data of insulin-dependent diabetes mellitus in Finland. This test could be used in the analysis of data which are localized according to map co-ordinates, by addresses or postcodes. This information is important when using the Geographical Information System (GIS) technology to compute the pairwise distances needed for the proposed test.
    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... more
    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.
    Quality of object relations and self-concept reflect clinically relevant aspects of personality functioning, but their prediction as suitability factors for psychotherapies of different lengths has not been compared. This study compared... more
    Quality of object relations and self-concept reflect clinically relevant aspects of personality functioning, but their prediction as suitability factors for psychotherapies of different lengths has not been compared. This study compared their prediction on psychiatric symptoms and work ability in short- and long-term psychotherapy. Altogether 326 patients, 20-46 years of age, with mood and/or anxiety disorder, were randomized to short-term (solution-focused or short-term psychodynamic) psychotherapy and long-term psychodynamic psychotherapy. The Quality of Object Relations Scale (QORS) and the Structural Analysis of Social Behavior (SASB) self-concept questionnaire were measured at baseline, and their prediction on outcome during the 3-year follow-up was assessed by the Symptom Check List Global Severity Index and the Anxiety Scale, the Beck Depression Inventory and by the Work Ability Index, Social Adjustment Scale work subscale and the Perceived Psychological Functioning scale. Negative self-concept strongly and self-controlling characteristics modestly predicted better 3-year outcomes in long-term therapy, after faster early gains in short-term therapy. Patients with a more positive or self-emancipating self-concept, or more mature object relations, experienced more extensive benefits after long-term psychotherapy. The importance of length vs. long-term therapy technique on the differences found is not known. Patients with mild to moderate personality pathology, indicated by poor self-concept, seem to benefit more from long-term than short-term psychotherapy, in reducing risk of depression. Long-term therapy may also be indicated for patients with relatively good psychological functioning. More research is needed on the relative importance of these characteristics in comparison with other patient-related factors.
    Quantification of the impact of exposure to modifiable risk factors on a particular outcome at the population level is a fundamental public health issue. In cohort studies, the population attributable fraction (PAF) is used to assess the... more
    Quantification of the impact of exposure to modifiable risk factors on a particular outcome at the population level is a fundamental public health issue. In cohort studies, the population attributable fraction (PAF) is used to assess the proportion of the outcome that is attributable to exposure to certain risk factors in a given population during a certain time interval. This is done by combining information about the prevalence of the risk factor in the population with estimates of the strength of the association between the risk factor and the outcome. In case of mortality, the PAF demonstrates what proportion of mortality can be delayed during the given follow-up time. However, literature on carrying out model-based estimation of PAF and its variance in cohort studies while properly taking follow-up time into account is still scarce. In this article, the authors present formulas for estimation of PAF, its variance, and its confidence interval using the piecewise constant hazards model and apply a SAS macro created for the estimation of PAF (SAS Institute Inc., Cary, North Carolina) to estimate the mortality attributable to some common risk factors.
    INTRODUCTION: Common approaches in cost-effectiveness analyses do not adjust for confounders. In nonrandomized studies this can result in biased results. Parametric models such as regression models are commonly applied to adjust for... more
    INTRODUCTION:
    Common approaches in cost-effectiveness analyses do not adjust for confounders. In nonrandomized studies this can result in biased results. Parametric models such as regression models are commonly applied to adjust for confounding, but there are several issues which need to be accounted for. The distribution of costs is often skewed and there can be a considerable proportion of observations of zero costs, which cannot be well handled using simple linear models. Associations between costs and effectiveness cannot usually be explained using observed background information alone, which also requires special attention in parametric modeling. Furthermore, in longitudinal panel data, missing observations are a growing problem also with nonparametric methods when cumulative outcome measures are used.
    METHODS:
    We compare two methods, which can handle the aforementioned issues, in addition to the standard unadjusted bootstrap techniques for assessing cost-effectiveness in the Helsinki Psychotherapy Study based on five repeated measurements of the Global Severity Index (SCL-90-GSI) and direct costs during one year of follow-up in two groups defined by the Defence Style Questionnaire (DSQ) at baseline. The first method models cumulative costs and effectiveness using generalized linear models, multiple imputation and bootstrap techniques. The second method deals with repeated measurement data directly using a hierarchical two-part logistic and gamma regression model for costs, a hierarchical linear model for effectiveness, and Bayesian inference.
    RESULTS:
    The adjustment for confounders mitigated the differences of the DSQ groups. Our method, based on Bayesian inference, revealed the unexplained association of costs and effectiveness. Furthermore, the method also demonstrated strong heteroscedasticity in positive costs.
    CONCLUSIONS:
    Confounders should be accounted for in cost-effectiveness analyses, if the comparison groups are not randomized.
    Research Interests: