The prevalence of respiratory symptoms indicative of asthma in children from Latin America has been largely ignored. As part of the International Study of Asthma and Allergies in Childhood (ISAAC), 17 centers in 9 different Latin American countries participated in the study, and data from 52,549 written questionnaires (WQ) in children aged 13-14 years and from 36,264 WQ in 6-7 year olds are described here. In children aged 13-14 years, the prevalence of asthma ever ranged from 5.5-28%, and the prevalence of wheezing in the last 12 months from 6.6-27%. In children aged 6-7 years, the prevalence of asthma ever ranged from 4.1-26.9%, and the prevalence of wheezing in the last 12 months ranged from 8.6-32.1%. The lower prevalence in centers with higher levels of atmospheric pollution suggests that chronic inhalation of polluted air in children does not contribute to asthma. Furthermore, the high figures for asthma in a region with a high level of gastrointestinal parasite infestation, and a high burden of acute respiratory infections occurring early in life, suggest that these factors, considered as protective in other regions, do not have the same effect in this region. The present study indicates that the prevalence of asthma and related symptoms in Latin America is as high and variable as described in industrialized or developed regions of the world. / Revisión por pares

<p>This thesis proposes a simplification of the model for dependent Bernoulli variables presented in Cox and Snell (1989). The simplified model, referred to as the simplified Cox model, is developed for identically distributed and dependent Bernoulli variables.</p><p>Properties of the model are presented, including expressions for the loglikelihood function and the Fisher information. The special case of a bivariate symmetric model is studied in detail. For this particular model, it is found that the number of design points in a locally D-optimal design is determined by the log-odds ratio between the variables. Under mutual independence, both a general expression for the restrictions of the parameters and an analytical expression for locally D-optimal designs are derived.</p><p>Focusing on the bivariate case, score tests and likelihood ratio tests are derived to test for independence. Numerical illustrations of these test statistics are presented in three examples. In connection to testing for independence, an E-optimal design for maximizing the local asymptotic power of the score test is proposed.</p><p>The simplified Cox model is applied to a dental data. Based on the estimates of the model, optimal designs are derived. The analysis shows that these optimal designs yield considerably more precise parameter estimates compared to the original design. The original design is also compared against the E-optimal design with respect to the power of the score test. For most alternative hypotheses the E-optimal design provides a larger power compared to the original design.</p>

This thesis proposes a simplification of the model for dependent Bernoulli variables presented in Cox and Snell (1989). The simplified model, referred to as the simplified Cox model, is developed for identically distributed and dependent Bernoulli variables. Properties of the model are presented, including expressions for the loglikelihood function and the Fisher information. The special case of a bivariate symmetric model is studied in detail. For this particular model, it is found that the number of design points in a locally D-optimal design is determined by the log-odds ratio between the variables. Under mutual independence, both a general expression for the restrictions of the parameters and an analytical expression for locally D-optimal designs are derived. Focusing on the bivariate case, score tests and likelihood ratio tests are derived to test for independence. Numerical illustrations of these test statistics are presented in three examples. In connection to testing for independence, an E-optimal design for maximizing the local asymptotic power of the score test is proposed. The simplified Cox model is applied to a dental data. Based on the estimates of the model, optimal designs are derived. The analysis shows that these optimal designs yield considerably more precise parameter estimates compared to the original design. The original design is also compared against the E-optimal design with respect to the power of the score test. For most alternative hypotheses the E-optimal design provides a larger power compared to the original design.

In this study, we consider high frequency transaction data of NYSE, and apply statistical methods to characterize each trade into two classes, influential and ordinary liquidity trades. First, a median based approach is used to establish a high R-square price-volume model for high frequency data. Next, transactions are classified into four states based on the trade price, trade volume, quotes, and quoted depth. Volume weighted transition probability of the four states are investigated and shown to be distinct for informed trades and ordinary liquidity trades. Furthermore, four market reaction factors are introduced and studied. Logistic regression models of the influential trades are established based on the four factors and odds ratios are used to select the cutoff points.

Historically, students from ethnically diverse backgrounds in grades K-12 have been over-represented in special education, yet little research on disproportionate representation has been conducted with preschool-aged children. This study examined if 72,525 preschool-aged children with disabilities from ethnically diverse backgrounds were disproportionately represented in special education within and across five southern states. Data were gathered from the 2006 December 1st Child Count reported by each State Department of Education to the U.S. Department of Education. Chosen states offered state-funded pre-kindergarten programs, which should have provided equal opportunities for inclusion across states. Analyses compared children with disabilities for disproportionate representation across state of residence, across special education eligibilities, across educational placements, and amount of inclusion provided. Data were analyzed for child and placement characteristics. Due to data suppression by individual states, analyses were conducted using children from Black and White backgrounds, and children from Hispanic backgrounds were used when reported by individual states. Child characteristics considered included the child’s: (a) type of disability eligibility category, (b) age, and (c) ethnicity. Placement characteristics included: (a) type of educational placement, (b) state in which child resided, and (c) amount of inclusion received. Indices of disproportionate representation were calculated using: (a) composition index, (b) risk index, (c) odds ratio, and (d) relative risk ratio. A 3 x 5 ANOVA was used to calculate placement differences between states. Factorial analysis was used to calculate determinants of placement status for preschool-aged children with disabilities. Results revealed disproportionate representation does occur at the preschool level, although between state variability was great, and patterns differed from the K-12 literature. Children from American Indian backgrounds were over-represented due to high proportions in states of Alabama and North Carolina, while children from Asian and Hispanic backgrounds were under-represented. Children from Black and White backgrounds were represented in special education at expected rates. The most common eligibility categories were speech/language impairments and developmental delay. Placement results revealed over-representation for White preschoolers and males, although type of state-funded pre-k program was a non-significant factor. Inclusion analyses favored Whites and males. Child demographic factors explained the majority of variability in inclusion status.

This paper develops a new approach to the analysis of longitudinal paired comparison data, where comparisons of the same objects by the same judges are made on more than one occasion. As an alternative to other recent approaches to such data, which are based on Kalman filter- ing, our approach treats the problem as one of multivariate multinomial data, allowing dependence terms between comparisons over time to be incorporated. The resulting model can be fitted as a Poisson log-linear model and has parallels with the quadratic binary exponential distribution of Cox. An example from the British Household Panel Survey illustrates the approach. (author´s abstract) / Series: Research Report Series / Department of Statistics and Mathematics

Orientador: Luzia Helena Queiroz / Banca: Ricardo Augusto Dias / Banca: Adolorata Aparecida Bianco Carvalho / Resumo: Milhões de pessoas são mordidas por cães todos os anos no Brasil e no mundo. Por meio de uma análise de casos e de um estudo caso-controle, objetivou-se caracterizar as agressões caninas e estabelecer fatores de risco para a ocorrência de agressões caninas a pessoas no Município de Araçatuba, SP. Foi realizado o teste qui-quadrado para variáveis categóricas e o teste t para variáveis numéricas, seguidos da análise estatística de Regressão Logística Binária, estabelecendo-se então o "Odds ratio" (OR) para determinadas variáveis. A maioria dos cães (71%) foi recebida como presente, sendo a busca por companhia o principal motivo de aquisição. Entre as vítimas, houve predomínio do sexo masculino em crianças e do sexo feminino em idosos. O cão agressor ter escapado (18,7%) foi a principal situação envolvida nas agressões. Consideraram-se fatores de risco o número de crianças do domicílio (OR = 1,70; IC 95% 1,03-2,82), o sexo do cão (machos, OR = 3,08; IC 95% 1,41-6,73), o estado reprodutivo (não esterilizados, OR = 4,28; IC 95% 1,05-17,45), o recebimento do animal como presente (OR = 3,99; IC 95% 1,85-8,64), a aquisição para proteção do domicílio (OR = 9,23; IC 95% 2,25-37,81) e a número de situações resultantes em agressividade (OR = 1,35; IC 95% 1,16-1,57). O número de adultos no domicílio (OR=0,65; IC 95% 0,47-0,91) foi associado negativamente à ocorrência de mordeduras. Foi possível equacionara influência conjunta de tais variáveis sobre a probabilidade de ocorrência de agressão. Estes resultados permitem que se estabeleçam programas preventivos e de posse responsável para agressões visando esclarecer sobre formas mais seguras de interação com cães, sobre os riscos e como evitá-los / Abstract: Millions of people are bitten by dogs each year in Brazil and worldwide. Through an analysis of cases and a case-control study, the aim of this paper was to characterize the epidemiology of canine aggression from occurring and to establish risk factors for the occurrence of canine aggression to people in the city of Araçatuba, SP. We performed the chi-square test for categorical variables and t test for numerical variables, followed by Binary Logistic Regression, settling then the odds ratio (OR) for certain variables. Most dogs (71%) were received as gifts, search for company being the main reason for the acquisition. Among the victims, children were predominantly male and elderly were predominantly females. The dogs had escaped (18.7%) was the main situation involving attacks. Were considered risk factors the number of children in the household (OR = 1.70, 95% CI 1.03-2.82), sex of the dog (males, OR = 3.08, 95% 1.41-6.73), reproductive status (intact, OR = 4.28, 95% CI 1.05-17.45), receipt of the animal as a present (OR = 3.99, 95% CI 1 0.85-8, 64), acquisition for protection of the home (OR = 9.23, 95% CI 2.25- 37.81) and the counting of situations resulting in aggressiveness (OR = 1.35, 95% 1.16-1.57). The number of adults in the household (OR = 0.65, 95% CI 0.47-0.91) was negatively associated with the occurrence of bites. It was possible to consider the combined influence of these variables on the likelihood of aggression. These results allow us to establish dog bites preventive and responsible dog ownership programs that aim to clarify about the safest ways to interaction with dogs, the risks and how to avoid them / Mestre

Background: Alzheimer’s disease (AD) is a type of Dementia and a neurodegenerative disease that is characterized by the gradual degrading of both memory and cognitive functions. According to the World Health Organization (WHO), the prevalence of AD is increasing globally. Currently, AD is the sixth leading cause of mortality in the United States. As the ageing population increases in the United States, it is possible that AD will move up the ladder in the top cause of mortality. Although the prevalence of AD in most urban parts of developed nations such as the United States is widely known, little is known about the prevalence and early diagnosis of the disease among the rural populations. According to a study by the Centers for Disease Control and Prevention (CDC), on deaths from AD between 1999 and 2014, most mortality are concentrated in the rural counties of the Appalachian region of the United States, where the mortality rate has increased by an alarming 75%. Our study focuses on the Northeast Tennessee region, which is a prominent part of the Appalachian region. We examine the prevalence of Alzheimer’s disease in the Northeast Tennessee region compared to other parts of the state of Tennessee. We sought to understand whether there is a likely association between the disease and the rural counties in the Northeast Tennessee region. Methods: We performed a cross-sectional study that computes and compares between the Prevalence Odds Ratio (POR) of the 2013 to 2015 Centers for Medicare and Medicaid Services Public Use Files data on rural versus urban counties in the Northeast Tennessee region followed by the Northeast Tennessee counties versus other counties in Tennessee. In addition, we collected primary data from 44 experts and professionals working in AD-related fields within the Northeast Tennessee region using an online survey that captures the perceived observation of the experts and professionals about the increasing prevalence of AD over the last five years. Results: Findings show that the rural counties within the Northeast Tennessee region had 18.3% (POR: 1.183, C.I: 1.113-1.258), 4.7% (POR: 1.047, C.I: 0.982-1.117), and 19% (POR: 1.190, C.I: 1.121-1.264) increased odds of prevalence of AD compared to the urban counties within the region in 2013, 2014, and 2015, respectively. Similarly, the Northeast Tennessee region as a whole, had increased odds of 22.7% (POR: 1.227, C.I: 1.203-1.250), 22.5% (POR: 1.225, C.I: 1.202-1.249), and 21.2% (POR: 1.212, C.I: 1.189-1.235) of AD compared to all other counties in Tennessee during the same periods. Conclusions: Statistical analysis and findings from experts and professionals working with patients with AD in the Northeast Tennessee region show that there are more cases of AD in the Northeast Tennessee region compared to the last five years. We suggest early screening strategies for possible decrease in the morbidity and mortality rates in Northeast Tennessee region.

Youth Tobacco Surveys have been conducted in several states in the U.S. in recent years, in order to design policies with the goal of reducing tobacco use among young people. Some primary analysis of those surveys has been done, but few analyses include modeling, and the study of independence has been addressed, mainly, in the bivariate context. In this work contemporary methods, which are of relative recent appearance in categorical data analysis, will be examined, including logistic and log-linear modeling as well as graphical displays and correspondence analysis. These methods will be applied to data from the 2000 Tennessee Youth Tobacco Survey. The objective is to demonstrate that methods of multivariate categorical data analysis can provide fresh insight about the behavior of adolescents with respect to tobacco use. The ultimate purpose of this work is to recommend methodology that goes beyond that which is currently published.

Point estimation of several association parameters in categorical data are presented. Typically, a constant is added to the frequency counts before the association measure is computed. We will study the accuracy of these adjusted point estimators based on frequentist and Bayesian methods respectively. In particular, amended estimators for the ratio of independent Poisson rates, relative risk, odds ratio, and the ratio of marginal binomial proportions will be examined in terms of bias and mean squared error.