Studies of terrorism have explored a number of factors thought to drive the phenomenon. Authors often tie socioeconomic development to reducing terrorism. Among structural explanations of terrorism, however, authors generally neglect the effect of gender inequality, though studies show that gender inequality increases the risk of international and civil conflict. Therefore I explore the impact of gender inequality in important socioeconomic issues on terrorism for 143 countries from 1998-2009. I argue that socioeconomic gender inequality reflects poor state capacity, resulting in grievances that contribute to domestic non-suicide and suicide terrorism. I study gender inequality in the areas of education, labor participation, and life expectancy. Results indicate that education and life expectancy inequality increase the risk of terrorism, while labor inequality is unrelated. While the time frame and data used in this study limit generalizability, results indicate that states should provide socioeconomic gender parity to reduce the risk of domestic terrorism.

O enfrentamento do processo do adoecer é mais adequado quando os pacientes possuem esperança. É a esperança na recuperação da saúde que leva o paciente a enfrentar todas as adversidades decorrentes do adoecimento e do tratamento. A avaliação da esperança proporciona a implementação de intervenções que estimulam esperança em pacientes em cuidados paliativos e seus familiares. Diante da inexistência de um instrumento validado no Brasil para medir esse construto, optou-se pela realização da adaptação e validação do Herth Hope Index. Era também nossa intenção comparar o sentimento de esperança entre pessoas sadias, doentes com câncer e de doentes com outra doença crônica com características diferentes, como o diabetes. O estudo teve como objetivos fazer a adaptação cultural e a validação do instrumento Herth Hope Index, comparar os escores de esperança entre pacientes oncológicos, diabéticos e acompanhantes, analisar as relações entre o nível de esperança da amostra com as variáveis sócio-demográficas e analisar as relações entre o nível de esperança nos dois grupos de pacientes com variáveis clínicas de interesse. A adaptação cultural e validação do Hert Hope Index foram realizadas conforme o método preconizado pela literatura. A amostra foi composta por 131 indivíduos, divididos em 3 grupos. Os resultados obtidos demonstram que em relação às propriedades psicométricas, o instrumento apresentou um valor de alpha de Cronbach de 0.834 para a escala total. O teste-reteste conferiu a reprodutibilidade do instrumento. A validade de construto foi confirmada por meio da validade convergente que demonstrou correlação estatisticamente significativa entre a Escala de Esperança de Herth (denominação da versão brasileira) e a Escala de Auto-Estima de Rosenberg, e da validade divergente que também evidenciou correlação significante entre a Escala de Esperança de Herth e o Inventário de Depressão de Beck. A análise fatorial pelo método dos componentes principais não confirmou os três fatores da escala original confirmou apenas que existem três fatores, mas com composição diferente dos itens em relação à escala original. O nível de esperança nos três grupos foi elevado e não houve diferença estatística entre eles. A comparação entre o nível de esperança e as variáveis sócio-demográficas na amostra estudada não apresentou diferença estatisticamente significativa. O nível de esperança não foi relacionado com a dor e tipo de tumor nos pacientes oncológicos e nem com o tratamento ou coexistência de hipertensão arterial nos diabéticos. As propriedades psicométricas do instrumento foram demonstradas e, portanto ele pode ser utilizado para mensurar a esperança na população brasileira. Considera-se importante que a Escala de Esperança de Herth continue a ser testada quanto à sua confiabilidade e validade em diferentes contextos sócio-culturais da realidade brasileira / Facing the process of being sick is more properly done when patients have hope.It\'s the hope of recovering that makes the patient able to face all the difficulties caused by the disease and its treatment. The evaluation of hope makes possible to implement actions which stimulate hope in patients under palliative care and the people who take care of them, specially their relatives. Once in Brazil there is no validated instrument for measure this, the option was to adapt and validate the Herth Hope Index. Our intention was also to compare the feeling of hope of healthy persons, cancer patients and patients with other kinds of chronic disease, with different features, such as diabetes.This study aimed the cultural adaptation and validation of the Herth Hope Index, to compare the hope scores of oncology and diabetic patients and their family, to analyze the relation between their level of hope and social-demographic factors, and to analyze the relation between the level of hope of these two groups of patients and the relevant clinical variables.Cultural adaptation and validation of the Herth Hope Index were done according to the methods already described in literature. There were 131 patients, divided into 3 groups. The result shows that, concerning to psychometric properties, this instrument has presented alpha coefficient of 0.834 for total scale. The test-retest awarded the reliability of the instrument. The construct validity was confirmed by means of the convergent validity that significant correlation between Herth Hope Index (Brazilian version) demonstrated significant correlation with Rosenberg’s Self Steem Scale, and the divergent validity that also significant correlation between Herth Hope Index and the Beck Depression Inventory. The factorial analyses, by the main components method, has not confirmed the three factors of the original scale. It has confirmed only that there are three factors, but there is a different composition among the items of the original scale. The level of hope in these three groups was elevated and there was no statistical difference among them. Comparison between the level of hope and the social-demographical variables hasn\'t shown any statistically significant differences. The level of hope hasn\'t been related to pain and kind of tumor in oncology patients neither to treatment or coexistence of arterial hypertension in diabetic patients either. The psychometrics properties of this tool were demonstrated, it can be used in the evaluation of hope of the Brazilian people. It\'s important that the Herth Hope Index keeps been tested, specially regarding it\'s reliability and validity in different socio-cultural aspects of the Brazilian context

Data quality issues at advanced old age, such as incompleteness of registration of vital events and age misreporting, compromise estimates of the death rates and remaining life expectancy at those ages. Following up on Horiuchi and Coale (Population Studies 36: 317-326, 1982), Mitra (Population Studies 38: 313-319, 1984, Population Studies 39: 511-512, 1985), and Coale (Population Studies 39: 507-509, 1985), we examine the conventional approaches to constructing life tables from data deficient at advanced ages and the two adjustment methods by the mentioned authors. Contrary to earlier reports by Horiuchi, Coale, and Mitra, we show that the two methods are consistent and useful in drastically reducing the estimation errors in life expectancy as compared to the conventional approaches, i.e., the classical open age interval model and extrapolation of the death rates. Our results suggest complementing the classical estimates of life expectancy by adjustments using Horiuchi- Coale, Mitra, or other appropriate methods and avoiding the extrapolation method as a tool for estimating the life expectancy.

Modelos preditivos estimam o risco de eventos ou agravos relacionados à saúde e podem ser utilizados como ferramenta auxiliar em tomadas de decisão por gestores e profissionais de saúde. Algoritmos de machine learning (ML), por sua vez, apresentam potencial para identificar relações complexas e não-lineares presentes nos dados, com consequências positivas na performance preditiva desses modelos. A presente pesquisa objetivou aplicar técnicas supervisionadas de ML e comparar sua performance em problemas de classificação e de regressão para predizer respostas de interesse para a saúde pública e a medicina. Os resultados e discussão estão organizados em três artigos científicos. O primeiro apresenta um tutorial para o uso de ML em pesquisas de saúde, utilizando como exemplo a predição do risco de óbito em até 5 anos (frequência do desfecho 15%; n=395) para idosos do estudo \"Saúde, Bem-estar e Envelhecimento\" (n=2.677), segundo variáveis relacionadas ao seu perfil demográfico, socioeconômico e de saúde. Na etapa de aprendizado, cinco algoritmos foram aplicados: regressão logística com e sem penalização, redes neurais, gradient boosted trees e random forest, cujos hiperparâmetros foram otimizados por validação cruzada (VC) 10-fold. Todos os modelos apresentaram área abaixo da curva (AUC) ROC (Receiver Operating Characteristic) maior que 0,70. Para aqueles com maior AUC ROC (redes neurais e regressão logística com e sem penalização) medidas de qualidade da probabilidade predita foram avaliadas e evidenciaram baixa calibração. O segundo artigo objetivou predizer o risco de tempo de vida ajustado pela qualidade de vida de até 30 dias (frequência do desfecho 44,7%; n=347) em pacientes com câncer admitidos em Unidade de Terapia Intensiva (UTI) (n=777), mediante características obtidas na admissão do paciente à UTI. Seis algoritmos (regressão logística com e sem penalização, redes neurais, árvore simples, gradient boosted trees e random forest) foram utilizados em conjunto com VC aninhada para estimar hiperparâmetros e avaliar performance preditiva. Todos os algoritmos, exceto a árvore simples, apresentaram discriminação (AUC ROC > 0,80) e calibração satisfatórias. Para o terceiro artigo, características socioeconômicas e demográficas foram utilizadas para predizer a expectativa de vida ao nascer de municípios brasileiros com mais de 10.000 habitantes (n=3.052). Para o ajuste do modelo preditivo, empregou-se VC aninhada e o algoritmo Super Learner (SL), e para a avaliação de performance, o erro quadrático médio (EQM). O SL apresentou desempenho satisfatório (EQM=0,17) e seu vetor de valores preditos foi utilizado para a identificação de overachievers (municípios com expectativa de vida superior à predita) e underachievers (município com expectativa de vida inferior à predita), para os quais características de saúde foram comparadas, revelando melhor desempenho em indicadores de atenção primária para os overachievers e em indicadores de atenção secundária para os underachievers. Técnicas para a construção e avaliação de modelos preditivos estão em constante evolução e há poucas justificativas teóricas para se preferir um algoritmo em lugar de outro. Na presente tese, não foram observadas diferenças substanciais no desempenho preditivo dos algoritmos aplicados aos problemas de classificação e de regressão analisados. Espera-se que a maior disponibilidade de dados estimule a utilização de algoritmos de ML mais flexíveis em pesquisas de saúde futuras. / Predictive models estimate the risk of health-related events or injuries and can be used as an auxiliary tool in decision-making by public health officials and health care professionals. Machine learning (ML) algorithms have the potential to identify complex and non-linear relationships, with positive implications in the predictive performance of these models. The present research aimed to apply various ML supervised techniques and compare their performance in classification and regression problems to predict outcomes of interest to public health and medicine. Results and discussion are organized into three articles. The first, presents a tutorial for the use of ML in health research, using as an example the prediction of death up to 5 years (outcome frequency=15%; n=395) in elderly participants of the study \"Saúde, Bemestar e Envelhecimento\" (n=2,677), using variables related to demographic, socioeconomic and health characteristics. In the learning step, five algorithms were applied: logistic regression with and without regularization, neural networks, gradient boosted trees and random forest, whose hyperparameters were optimized by 10-fold cross-validation (CV). The area under receiver operating characteristic (AUROC) curve was greater than 0.70 for all models. For those with higher AUROC (neural networks and logistic regression with and without regularization), the quality of the predicted probability was evaluated and it showed low calibration. The second article aimed to predict the risk of quality-adjusted life up to 30 days (outcome frequency=44.7%; n=347) in oncologic patients admitted to the Intensive Care Unit (ICU) (n=777), using patients\' characteristics obtained at ICU admission. Six algorithms (logistic regression with and without regularization, neural networks, basic decision trees, gradient boosted trees and random forest) were used with nested CV to estimate hyperparameters values and to evaluate predictive performance. All algorithms, with exception of basic decision trees, presented acceptable discrimination (AUROC > 0.80) and calibration. For the third article, socioeconomic and demographic characteristics were used to predict the life expectancy at birth of Brazilian municipalities with more than 10,000 inhabitants (n=3,052). Nested CV and the Super Learner (SL) algorithm were used to adjust the predictive model, and for evaluating performance, the mean squared error (MSE). The SL showed good performance (MSE=0.17) and its vector of predicted values was used for the identification of underachievers and overachievers (i.e. municipalities showing worse and better outcome than predicted, respectively). Health characteristics were analyzed revealing that overachievers performed better on primary health care indicators, while underachievers fared better on secondary health care indicators. Techniques for constructing and evaluating predictive models are constantly evolving and there is scarce theoretical justification for preferring one algorithm over another. In this thesis no substantial differences were observed in the predictive performance of the algorithms applied to the classification and regression problems analyzed herein. It is expected that increase in data availability will encourage the use of more flexible ML algorithms in future health research.

Modelos preditivos estimam o risco de eventos ou agravos relacionados à saúde e podem ser utilizados como ferramenta auxiliar em tomadas de decisão por gestores e profissionais de saúde. Algoritmos de machine learning (ML), por sua vez, apresentam potencial para identificar relações complexas e não-lineares presentes nos dados, com consequências positivas na performance preditiva desses modelos. A presente pesquisa objetivou aplicar técnicas supervisionadas de ML e comparar sua performance em problemas de classificação e de regressão para predizer respostas de interesse para a saúde pública e a medicina. Os resultados e discussão estão organizados em três artigos científicos. O primeiro apresenta um tutorial para o uso de ML em pesquisas de saúde, utilizando como exemplo a predição do risco de óbito em até 5 anos (frequência do desfecho 15%; n=395) para idosos do estudo \"Saúde, Bem-estar e Envelhecimento\" (n=2.677), segundo variáveis relacionadas ao seu perfil demográfico, socioeconômico e de saúde. Na etapa de aprendizado, cinco algoritmos foram aplicados: regressão logística com e sem penalização, redes neurais, gradient boosted trees e random forest, cujos hiperparâmetros foram otimizados por validação cruzada (VC) 10-fold. Todos os modelos apresentaram área abaixo da curva (AUC) ROC (Receiver Operating Characteristic) maior que 0,70. Para aqueles com maior AUC ROC (redes neurais e regressão logística com e sem penalização) medidas de qualidade da probabilidade predita foram avaliadas e evidenciaram baixa calibração. O segundo artigo objetivou predizer o risco de tempo de vida ajustado pela qualidade de vida de até 30 dias (frequência do desfecho 44,7%; n=347) em pacientes com câncer admitidos em Unidade de Terapia Intensiva (UTI) (n=777), mediante características obtidas na admissão do paciente à UTI. Seis algoritmos (regressão logística com e sem penalização, redes neurais, árvore simples, gradient boosted trees e random forest) foram utilizados em conjunto com VC aninhada para estimar hiperparâmetros e avaliar performance preditiva. Todos os algoritmos, exceto a árvore simples, apresentaram discriminação (AUC ROC > 0,80) e calibração satisfatórias. Para o terceiro artigo, características socioeconômicas e demográficas foram utilizadas para predizer a expectativa de vida ao nascer de municípios brasileiros com mais de 10.000 habitantes (n=3.052). Para o ajuste do modelo preditivo, empregou-se VC aninhada e o algoritmo Super Learner (SL), e para a avaliação de performance, o erro quadrático médio (EQM). O SL apresentou desempenho satisfatório (EQM=0,17) e seu vetor de valores preditos foi utilizado para a identificação de overachievers (municípios com expectativa de vida superior à predita) e underachievers (município com expectativa de vida inferior à predita), para os quais características de saúde foram comparadas, revelando melhor desempenho em indicadores de atenção primária para os overachievers e em indicadores de atenção secundária para os underachievers. Técnicas para a construção e avaliação de modelos preditivos estão em constante evolução e há poucas justificativas teóricas para se preferir um algoritmo em lugar de outro. Na presente tese, não foram observadas diferenças substanciais no desempenho preditivo dos algoritmos aplicados aos problemas de classificação e de regressão analisados. Espera-se que a maior disponibilidade de dados estimule a utilização de algoritmos de ML mais flexíveis em pesquisas de saúde futuras. / Predictive models estimate the risk of health-related events or injuries and can be used as an auxiliary tool in decision-making by public health officials and health care professionals. Machine learning (ML) algorithms have the potential to identify complex and non-linear relationships, with positive implications in the predictive performance of these models. The present research aimed to apply various ML supervised techniques and compare their performance in classification and regression problems to predict outcomes of interest to public health and medicine. Results and discussion are organized into three articles. The first, presents a tutorial for the use of ML in health research, using as an example the prediction of death up to 5 years (outcome frequency=15%; n=395) in elderly participants of the study \"Saúde, Bemestar e Envelhecimento\" (n=2,677), using variables related to demographic, socioeconomic and health characteristics. In the learning step, five algorithms were applied: logistic regression with and without regularization, neural networks, gradient boosted trees and random forest, whose hyperparameters were optimized by 10-fold cross-validation (CV). The area under receiver operating characteristic (AUROC) curve was greater than 0.70 for all models. For those with higher AUROC (neural networks and logistic regression with and without regularization), the quality of the predicted probability was evaluated and it showed low calibration. The second article aimed to predict the risk of quality-adjusted life up to 30 days (outcome frequency=44.7%; n=347) in oncologic patients admitted to the Intensive Care Unit (ICU) (n=777), using patients\' characteristics obtained at ICU admission. Six algorithms (logistic regression with and without regularization, neural networks, basic decision trees, gradient boosted trees and random forest) were used with nested CV to estimate hyperparameters values and to evaluate predictive performance. All algorithms, with exception of basic decision trees, presented acceptable discrimination (AUROC > 0.80) and calibration. For the third article, socioeconomic and demographic characteristics were used to predict the life expectancy at birth of Brazilian municipalities with more than 10,000 inhabitants (n=3,052). Nested CV and the Super Learner (SL) algorithm were used to adjust the predictive model, and for evaluating performance, the mean squared error (MSE). The SL showed good performance (MSE=0.17) and its vector of predicted values was used for the identification of underachievers and overachievers (i.e. municipalities showing worse and better outcome than predicted, respectively). Health characteristics were analyzed revealing that overachievers performed better on primary health care indicators, while underachievers fared better on secondary health care indicators. Techniques for constructing and evaluating predictive models are constantly evolving and there is scarce theoretical justification for preferring one algorithm over another. In this thesis no substantial differences were observed in the predictive performance of the algorithms applied to the classification and regression problems analyzed herein. It is expected that increase in data availability will encourage the use of more flexible ML algorithms in future health research.

Using the Health and Retirement Study Panel core files from 1996 to 2014, I analyze how Medicare Part D impacted access to prescription drug coverage by various demographic factors such as race, gender, and income. In Chapter 1, I find the highest take-up rates for those who were white, female, and with higher incomes. However, increases in coverage were high across the board, such that Medicare Part D also improved drug insurance coverage for those who were black, male, and with lower income. Thus, although Medicare Part D did increase prescription drug insurance coverage for seniors across the board, I also find potential for improvement in enrollment for difficult-to-reach groups. Next, Chapter 2 examines the impact of Medicare Part D on mortality. Although I do not find an impact on the life expectancy of respondents as a whole, I do find a significant positive effect for black respondents, indicating that Medicare Part D may have mattered more for disadvantaged groups. The largest impact is for black men, who have an additional 9 percentage point chance of living to age 73 for an additional 8 years of coverage (significant at the 5% level). When looking only at cardiovascular mortality, which is more likely to be influenced by drug coverage, I find improvements in life expectancy for the total population, with stronger effects for minorities and men. Overall, my findings suggest that Medicare Part D did move the needle on its goal: to improve the health of those who, without government intervention, had the most difficulty paying for prescription drugs. Chapter 3 looks at the impact of Medicare Part D prescription drug coverage on cost-related medication adherence, food insecurity, and finances among seniors. It would be reasonable to assume that Medicare Part D, which led to near-universal drug coverage among senior citizens, could allow seniors to shift money previously spent on drug expenditures to other areas. The strongest effect of Medicare Part D is on cost-related medication nonadherence, leading to a 21% decrease for an additional 8 years of Medicare Part D coverage. The impact is even stronger for the black male population (30%). I fail to reject the null hypothesis that Medicare Part D did not reduce food insecurity or household debt. Overall, Medicare Part D appears to have improved the financial stability of seniors.

Thesis (D.P.A.)--University of La Verne, 2003. / Includes bibliographical references (p. 216-227).

Thesis (D.P.A.)--University of La Verne, 2003. / Includes bibliographical references (leaves 216-227).

A high degree of physical functioning is necessary for independently performing the numerous routine and valued tasks of daily life. Poor functioning not only hinders independent living, it can lower the quality of life, impede full social participation, and elevate the risk of death. However, not all adults are at equal risk of poor functioning: women experience worse functioning and live a greater number of years functionally impaired compared with men. Studies of this gap have focused on inequities in adult circumstances, such as socioeconomic status, but have generally fallen short of fully accounting for it. Recasting this research within a life-course, epidemiological framework points to the potential role of early-life circumstances. Early-life circumstances may impart a biological imprint, and they may also launch long-term trajectories of social circumstances, that could differentially shape functioning for men and women. Thus, this dissertation examines the life course origins of the gender gap in functioning and active life expectancy among older U.S. adults using two nationally-representative datasets: the National Survey of Midlife Development in the United States and the Health and Retirement Study. In sum, the findings reveal that: (a) a host of early-life circumstances, such as parents’ education levels, leave an indelible stamp on functional ability and active life expectancy for women and men, irrespective of adult circumstances, (b) while some early-life adversities, such as extreme poverty, were marginally more consequential for women’s than men’s functioning, they appear to be primarily more consequential for precipitating metabolic conditions such as diabetes and obesity rather than directly impacting functioning, (c) explanations of the gap must incorporate endogenous biological differences between men and women; explanations that focus exclusively on socially-structured inequities are insufficient, and (d) exposures to socioeconomic resources accumulate across the life course to shape functioning differently for men than women; particularly between white men, who enjoy better functioning with higher educational attainment irrespective of early-life socioeconomic exposures, and white women whose functioning gains plateau if they experienced early-life socioeconomic adversities. Overall, the results underscore the importance of a life course perspective in explicating gender disparities in functioning, longevity, and active life expectancy. / text

This dissertation is a historically comparative study of adult mortality between Taiwan and the United States throughout the 20th century. The 20th century was characterized by the largest rise in life expectancy at birth and the most rapid decrease in mortality in recorded human history. This dissertation aims not only to examine and compare the trends and levels of life expectancy in Taiwan and the United States over an extended period of time, but also to evaluate the extent to which smoking behavior and obesity play an important role in the recent levels of adult mortality in the United States. I used logistic models of mortality to examine and compare the trends and levels of life expectancy in Taiwan from 1906 to 2008 and in the United States from 1933 to 2007. Second, I re-estimated life expectancy by introducing smoking-attributable mortality to further compare the levels of life expectancy between the two countries. Third, I estimated event history models to investigate whether and how smoking behavior and obesity are related to mortality in the United States in the 1990 to 2006 and the 2000 to 2006 periods. At the end of the 20th century, the level of life expectancy at birth for females in the U.S. was higher than in Taiwan, but they were close. In this century, however, the level of life expectancy at birth in Taiwan has increased to a higher level than in the U.S. The levels of male life expectancy at birth for the two countries are similar in this century, but there were significant differences in the 20th century. The great improvements in juvenile, background and senescent mortality rates in Taiwan may be used to explain this correspondence of life expectancy between the two countries today. Besides, higher smoking-attributed mortality can also serve as another possible reason for the stagnant levels of life expectancy in the U.S. Finally, smoking-related and obesity-related mortality have become progressively more important as predictors of adult mortality in the U.S. in past decades.