Excess Mortality Rate During Adulthood Among Danish Adoptees

BACKGROUND AND OBJECTIVE: Adoption studies have been used to disentangle the influence of genes from shared familial environment on various traits and disease risks. However, both the factors leading to adoption and living as an adoptee may bias the studies with regard to the relative influence of genes and environment compared to the general population. The aim was to investigate whether the cohort of domestic adoptees used for these studies in Denmark is similar to the general population with respect to all-cause mortality and cause-specific mortality rates. METHODS: 13,111 adoptees born in Denmark in 1917, or later, and adopted in 1924 to 1947 were compared to all Danes from the same birth cohorts using standardized mortality ratios (SMR). The 12,729 adoptees alive in 1970 were similarly compared to all Danes using SMR as well as cause-specific SMR. RESULTS: The excess in all-cause mortality before age 65 years in adoptees was estimated to be 1.30 (95% CI 1.26-1.35). Significant excess mortality before age 65 years was also observed for infections, vascular deaths, cancer, alcohol-related deaths and suicide. Analyses including deaths after age 65 generally showed slightly less excess in mortality, but the excess was significant for all-cause mortality, cancer, alcohol-related deaths and suicides. CONCLUSION: Adoptees have an increased all-cause mortality compared to the general population. All major specific causes of death contributed, and the highest excess is seen for alcohol-related deaths

DAKOTA, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis:version 4.0 reference manual

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a reference manual for the commands specification for the DAKOTA software, providing input overviews, option descriptions, and example specifications

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis:version 4.0 developers manual.

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a developers manual for the DAKOTA software and describes the DAKOTA class hierarchies and their interrelationships. It derives directly from annotation of the actual source code and provides detailed class documentation, including all member functions and attributes

Genetic Influences on Incidence and Case-Fatality of Infectious Disease

BACKGROUND: Family, twin and adoption studies suggest that genetic susceptibility contributes to familial aggregation of infectious diseases or to death from infections. We estimated genetic and shared environmental influences separately on the risk of acquiring an infection (incidence) and on dying from it (case fatality). METHODS: Genetic influences were estimated by the association between rates of hospitalization for infections and between case-fatality rates of adoptees and their biological full- and half- siblings. Familial environmental influences were investigated in adoptees and their adoptive siblings. Among 14,425 non-familial adoptions, granted in Denmark during the period 1924-47, we selected 1,603 adoptees, who had been hospitalized for infections and/or died with infection between 1977 and 1993. Their siblings were considered predisposed to infection, and compared with non-predisposed siblings of randomly selected 1,348 adoptees alive in 1993 and not hospitalized for infections in the observation period. The risk ratios presented were based on a Cox regression model. RESULTS: Among 9971 identified siblings, 2829 had been hospitalised for infections. The risk of infectious disease was increased among predisposed compared with non-predisposed in both biological (1.18; 95% confidence limits 1.03-1.36) and adoptive siblings (1.23; 0.98-1.53). The risk of a fatal outcome of the infections was strongly increased (9.36; 2.94-29.8) in biological full siblings, but such associations were not observed for the biological half siblings or for the adoptive siblings. CONCLUSION: Risk of getting infections appears to be weakly influenced by both genetically determined susceptibility to infection and by family environment, whereas there appears to be a strong non-additive genetic influence on risk of fatal outcome

DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis.

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the DAKOTA software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of DAKOTA-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of DAKOTA's iterative analysis capabilities

DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, user's manual.

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the DAKOTA software and provides capability overviews and procedures for software execution, as well as a variety of example studies

DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, developers manual.

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a developers manual for the DAKOTA software and describes the DAKOTA class hierarchies and their interrelationships. It derives directly from annotation of the actual source code and provides detailed class documentation, including all member functions and attributes

DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, user's reference manual.

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a reference manual for the commands specification for the DAKOTA software, providing input overviews, option descriptions, and example specifications

The role of integrated home-based care in patient adherence to antiretroviral therapy O papel da assistência domiciliar integrada na adesão do paciente à terapia anti-retroviral

Non-adherence is one of the primary obstacles to successful antiretroviral therapy in HIV+ patients worldwide. In Brazil, the Domiciliary Therapeutic Assistance is a multidisciplinary and integrated home-based assistance program provided for HIV+ patients confined in their homes due to physical deficiency. This study investigated ADT's ability to monitor and promote appropriate adherence to ARV therapy. Fifty-six individuals were recruited from three study groups: Group 1 - patients currently in the ADT program, Group 2 - 21 patients previously treated by the ADT program, and Group 3 - 20 patients who have always been treated using conventional ambulatory care. Using multivariable self-reporting to evaluate adherence, patients in the ADT program had significantly better adherence than patients in ambulatory care (F = 6.66, p = 0.003). This effect was independent of demographic and socioeconomic characteristics as well as medical history. Patients in the ADT program also showed a trend towards greater therapeutic success than ambulatory patients. These results suggest the incorporation of characteristics of ADT in conventional ambulatory care as a strategy to increase adherence to ARV therapy.<br>O sucesso da terapia antiretroviral depende da adesão ao tratamento. A Assistência Domiciliar Terapêutica é um programa de atendimento multidisciplinar a pacientes com HIV/AIDS e com dificuldades de se deslocar para atendimento ambulatorial. Este estudo compara a adesão de pacientes ao esquema ARV em um programa ADT com aqueles em tratamento ambulatorial convencional. Foram estudados: Grupo 1 - 15 pacientes no programa de ADT, Grupo 2 - 21 pacientes em tratamento ambulatorial convencional, Grupo 3 - 20 pacientes em tratamento ambulatorial convencional que nunca freqüentaram o programa ADT. Os pacientes inscritos no programa ADT apresentaram significativamente maior adesão ao tratamento do que pacientes ambulatoriais (F = 6.66, p= 0,003). Os resultados observados não foram influenciados pelas características demográficas, características socioeconômicas, ou histórico médico. Pacientes em programa de ADT também mostraram uma tendência a melhor resposta terapêutica do que os ambulatoriais. Este estudo sugere a utilização das características do ADT como estratégia para melhorar a adesão à terapia antiretroviral