63,078 research outputs found

    Internalizing Symptoms as Predictors of School Absenteeism Severity at Multiple Levels: Ensemble and Classification and Regression Tree Analysis

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    School attendance problems are highly prevalent worldwide, leading researchers to investigate many different risk factors for this population. Of considerable controversy is how internalizing behavior problems might help to distinguish different types of youth with school attendance problems. In addition, efforts are ongoing to identify the point at which children and adolescents move from appropriate school attendance to problematic school absenteeism. The present study utilized ensemble and classification and regression tree analysis to identify potential internalizing behavior risk factors among youth at different levels of school absenteeism severity (i.e., 1+%, 3+%, 5+%, 10+%). Higher levels of absenteeism were also examined on an exploratory basis. Participants included 160 youth aged 6–19 years (M = 13.7; SD = 2.9) and their families from an outpatient therapy clinic (39.4%) and community (60.6%) setting, the latter from a family court and truancy diversion program cohort. One particular item relating to lack of enjoyment was most predictive of absenteeism severity at different levels, though not among the highest levels. Other internalizing items were also predictive of various levels of absenteeism severity, but only in a negatively endorsed fashion. Internalizing symptoms of worry and fatigue tended to be endorsed higher across less severe and more severe absenteeism severity levels. A general expectation that predictors would tend to be more homogeneous at higher than lower levels of absenteeism severity was not generally supported. The results help confirm the difficulty of conceptualizing this population based on forms of behavior but may support the need for early warning sign screening for youth at risk for school attendance problems

    Novel Methods for Multivariate Ordinal Data applied to Genetic Diplotypes, Genomic Pathways, Risk Profiles, and Pattern Similarity

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    Introduction: Conventional statistical methods for multivariate data (e.g., discriminant/regression) are based on the (generalized) linear model, i.e., the data are interpreted as points in a Euclidian space of independent dimensions. The dimensionality of the data is then reduced by assuming the components to be related by a specific function of known type (linear, exponential, etc.), which allows the distance of each point from a hyperspace to be determined. While mathematically elegant, these approaches may have shortcomings when applied to real world applications where the relative importance, the functional relationship, and the correlation among the variables tend to be unknown. Still, in many applications, each variable can be assumed to have at least an “orientation”, i.e., it can reasonably assumed that, if all other conditions are held constant, an increase in this variable is either “good” or “bad”. The direction of this orientation can be known or unknown. In genetics, for instance, having more “abnormal” alleles may increase the risk (or magnitude) of a disease phenotype. In genomics, the expression of several related genes may indicate disease activity. When screening for security risks, more indicators for atypical behavior may constitute raise more concern, in face or voice recognition, more indicators being similar may increase the likelihood of a person being identified. Methods: In 1998, we developed a nonparametric method for analyzing multivariate ordinal data to assess the overall risk of HIV infection based on different types of behavior or the overall protective effect of barrier methods against HIV infection. By using u-statistics, rather than the marginal likelihood, we were able to increase the computational efficiency of this approach by several orders of magnitude. Results: We applied this approach to assessing immunogenicity of a vaccination strategy in cancer patients. While discussing the pitfalls of the conventional methods for linking quantitative traits to haplotypes, we realized that this approach could be easily modified into to a statistically valid alternative to a previously proposed approaches. We have now begun to use the same methodology to correlate activity of anti-inflammatory drugs along genomic pathways with disease severity of psoriasis based on several clinical and histological characteristics. Conclusion: Multivariate ordinal data are frequently observed to assess semiquantitative characteristics, such as risk profiles (genetic, genomic, or security) or similarity of pattern (faces, voices, behaviors). The conventional methods require empirical validation, because the functions and weights chosen cannot be justified on theoretical grounds. The proposed statistical method for analyzing profiles of ordinal variables, is intrinsically valid. Since no additional assumptions need to be made, the often time-consuming empirical validation can be skipped.ranking; nonparametric; robust; scoring; multivariate

    Multi-criteria analysis: a manual

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    Doctor of Philosophy

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    dissertationFamily health history (FHH) is an independent risk factor for predicting an individual's chance of developing selected chronic diseases. Though various FHH tools have been developed, many research questions remain to be addressed. Before FHH can be used as an effective risk assessment tool in public health screenings or population-based research, it is important to understand the quality of collected data and evaluate risk prediction models. No literature has been identified whereby risks are predicted by applying machine learning solely on FHH. This dissertation addressed several questions. First, using mixed methods, we defined 50 requirements for documenting FHH for a population-based study. Second, we examined the accuracy of self- and proxy-reported FHH data in the Health Family Tree database, by comparing the disease and risk factor rates generated from this database with rates recorded in a cancer registry and standard public health surveys. The rates generated from the Health Family Tree were statistically lower than those from public sources (exceptions: stroke rates were the same, exercise rates were higher). Third, we validated the Health Family Tree risk predictive algorithm. The very high risk (≥2) predicted the risk of all concerned diseases for adult population (20 ~ 99 years of age), and the predictability remained when using disease rates from public sources as the reference in the relative risk model. The referent population used to establish the expected rate of disease impacted risk classification: the lower expected disease rates generated by the Health Family Tree, in comparison to the rates from public iv sources, caused more persons to be classified at high risk. Finally, we constructed and evaluated new predictive models using three machine learning classifiers (logistic regression, Bayesian networks, and support vector machine). A limited set of information about first-degree relatives was used to predict future disease. In summary, combining FHH with valid risk algorithms provide a low cost tool for identifying persons at risk for common diseases. These findings may be especially useful when developing strategies to screen populations for common diseases and identifying those at highest risk for public health interventions or population-based research

    Congenital Chagas Disease in the United States: Cost Savings Through Maternal Screening

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    Chagas disease, caused by Trypanosoma cruzi, is transmitted by insect vectors through transfusions, transplants, insect feces in food, and from mother to child during gestation. Congenital infection could perpetuate Chagas disease indefinitely, even in countries without vector transmission. An estimated 30% of infected persons will develop lifelong, potentially fatal, cardiac or digestive complications. Treatment of infants with benznidazole is highly efficacious in eliminating infection. This work evaluates the costs of maternal screening and infant testing and treatment of Chagas disease in the United States. We constructed a decision-analytic model to find the lower cost option, comparing costs of testing and treatment, as needed, for mothers and infants with the lifetime societal costs without testing and the consequent morbidity and mortality due to lack of treatment or late treatment. We found that maternal screening, infant testing, and treatment of Chagas disease in the United States are cost saving for all rates of congenital transmission greater than 0.001% and all levels of maternal prevalence above 0.06% compared with no screening program. Newly approved diagnostics make universal screening cost saving with maternal prevalence as low as 0.008%. The present value of lifetime societal savings due to screening and treatment is about $634 million saved for every birth year cohort. The benefits of universal screening for T. cruzi as part of routine prenatal testing far outweigh the program costs for all U.S. births

    Links Between Social Support, Thwarted Belongingness, and Suicide Ideation among Lesbian, Gay, and Bisexual College Students

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    Emerging adults with a lesbian, gay, or bisexual (LGB) identity are at greater risk for engaging in suicide-related behaviors. This disparity highlights a need to elucidate specific risk and protective factors associated with suicide-related behaviors among LGB youth, which could be utilized as targets for suicide prevention efforts in this population. Informed by the interpersonal-psychological theory of suicide, the present study hypothesized that social support would be indirectly associated with decreased suicide ideation via lower thwarted belongingness. A sample of 50 emerging adults (62.0% male, 70.0% Hispanic) who identified as gay, lesbian, bisexual, questioning, or “other” orientation, with a mean age of 20.84 years (SD = 3.30 years), completed self-report assessments. Results indicated that support from both family and the LGB community were associated with lower thwarted belongingness over and above the effects of age, sex, and depressive symptoms. Indirect effects models also indicated that both family and LGB community support were associated with suicide ideation via thwarted belongingness. The results of the present study suggest that family and LGB community support may represent specific targets for reducing thwarted belongingness that could be leveraged in suicide prevention efforts for LGB emerging adults

    Integrating Pharmacotherapy and Psychotherapy for Paediatric Bipolar Disorder: Translating Science to Service

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    Objective: For comprehensive management of paediatric bipolar disorder (PBD), it is imperative to combine psychopharmacotherapy with specific psychotherapy. This article proposes a model that incorporates (1) an overview of psychopathology, (2) a review of outcomes in psychopharmacotherapy trials, and (3) a summary of evidence-based forms of psychotherapy to complement pharmacotherapy. Results: The psychopathology of PBD is unique compared to that of adult bipolar disorder with prominent irritability, rapid cycling, high rates of co-morbid attention deficit hyperactivity disorder, mixed episodes and chronicity. Combination therapy with a second generation antipsychotic and a mood stabilizer is proving to be more effective than monotherapy with a mood stabilizer. Empirical findings for the support of family-focused, cognitive behavioral therapies with individual family or multifamily psychoeducation groups suggest that these psychosocial treatments are valuable complementary tools for clinicians who treat youths diagnosed with PBD. Conclusion: As pharmacotherapy and psychotherapy are most beneficial when applied together, the clinician’s understanding of the science behind these forms of treatment is likely to be of great value in effectively providing services to youths diagnosed with PBD
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