Adolescent suicide attempts and adult adjustment

Background: Adolescent suicide attempts are disproportionally prevalent and frequently of low severity, raising questions regarding their long-term prognostic implications. In this study, we examined whether adolescent attempts were asso- ciated with impairments related to suicidality, psychopathology, and psychosocial functioning in adulthood (objective 1) and whether these impairments were better accounted for by concurrent adolescent confounders (objective 2). Method: Eight hundred and sixteen adolescents were assessed using interviews and question- naires at four time points from adolescence to adulthood. We examined whether lifetime suicide attempts in adolescence (by T2, mean age 17) predicted adult out- comes (by T4, mean age 30) using linear and logistic regressions in unadjusted models (objective 1) and adjusting for sociodemographic background, adolescent psychopathology, and family risk factors (objective 2). Results: In unadjusted analyses, adolescent suicide attempts predicted poorer adjustment on all outcomes, except those related to social role status. After adjustment, adolescent attempts remained predictive of axis I and II psychopathology (anxiety disorder, antisocial and borderline personality disorder symptoms), global and social adjustment, risky sex, and psychiatric treatment utilization. However, adolescent attempts no longer predicted most adult outcomes, notably suicide attempts and major depressive disorder. Secondary analyses indicated that associations did not differ by sex and attempt characteristics (intent, lethality, recurrence). Conclusions: Adolescent suicide attempters are at high risk of protracted and wide-ranging im- pairments, regardless of the characteristics of their attempt. Although attempts specifically predict (and possibly influence) several outcomes, results suggest that most impairments reflect the confounding contributions of other individual and family problems or vulnerabilites in adolescent attempters.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.