The use of Poisson regression in the sociological study of suicide

This paper explains how Poisson regression can be used in studies in which the dependent variable describes the number of occurrences of some rare event such as suicide. After pointing out why ordinary linear regression is inappropriate for treating dependent variables of this sort, we go on to present the basic Poisson regression model and show how it fits in the broad class of generalized linear models. Then we turn to discussing a major problem of Poisson regression known as overdispersion and suggest possible solutions, including the correction of standard errors and negative binomial regression. The paper ends with a detailed empirical example, drawn from our own research on suicide.

Variables affecting the success of older applicants for employment in a public organization: An age -cued cognitive model of administrative decision-making

The dissertation takes a multivariate approach to answer the question of how applicant age, after controlling for other variables, affects employment success in a public organization. In addition to applicant age, there are five other categories of variables examined: organization/applicant variables describing the relationship of the applicant to the organization; organization/position variables describing the target position as it relates to the organization; episodic variables such as applicant age relative to the ages of competing applicants; economic variables relating to the salary needs of older applicants; and cognitive variables that may affect the decision maker's evaluation of the applicant. ^ An exploratory phase of research employs archival data from approximately 500 decisions made in the past three years to hire or promote applicants for positions in one public health administration organization. A logit regression model is employed to examine the probability that the variables modify the effect of applicant age on employment success. A confirmatory phase of the dissertation is a controlled experiment in which hiring decision makers from the same public organization perform a simulated hiring decision exercise to evaluate hypothetical applicants of similar qualifications but of different ages. The responses of the decision makers to a series of bipolar adjective scales add support to the cognitive component of the theoretical model of the hiring decision. A final section contains information gathered from interviews with key informants. ^ Applicant age has tended to have a curvilinear relationship with employment success. For some positions, the mean age of the applicants most likely to succeed varies with the values of the five groups of moderating variables. The research contributes not only to the practice of public personnel administration, but is useful in examining larger public policy issues associated with an aging workforce. ^

Nonparametric Evaluation of Quantitative Traits in Population-Based Association Studies when the Genetic Model is Unknown

Statistical association between a single nucleotide polymorphism (SNP) genotype and a quantitative trait in genome-wide association studies is usually assessed using a linear regression model, or, in the case of non-normally distributed trait values, using the Kruskal-Wallis test. While linear regression models assume an additive mode of inheritance via equi-distant genotype scores, Kruskal-Wallis test merely tests global differences in trait values associated with the three genotype groups. Both approaches thus exhibit suboptimal power when the underlying inheritance mode is dominant or recessive. Furthermore, these tests do not perform well in the common situations when only a few trait values are available in a rare genotype category (disbalance), or when the values associated with the three genotype categories exhibit unequal variance (variance heterogeneity). We propose a maximum test based on Marcus-type multiple contrast test for relative effect sizes. This test allows model-specific testing of either dominant, additive or recessive mode of inheritance, and it is robust against variance heterogeneity. We show how to obtain mode-specific simultaneous confidence intervals for the relative effect sizes to aid in interpreting the biological relevance of the results. Further, we discuss the use of a related all-pairwise comparisons contrast test with range preserving confidence intervals as an alternative to Kruskal-Wallis heterogeneity test. We applied the proposed maximum test to the Bogalusa Heart Study dataset, and gained a remarkable increase in the power to detect association, particularly for rare genotypes. Our simulation study also demonstrated that the proposed non-parametric tests control family-wise error rate in the presence of non-normality and variance heterogeneity contrary to the standard parametric approaches. We provide a publicly available R library nparcomp that can be used to estimate simultaneous confidence intervals or compatible multiplicity-adjusted p-values associated with the proposed maximum test.

Explaining dinophysis cf. acuminata abundance in Antifer (Normandy, France) using dynamic linear regression

Classical regression analysis can be used to model time series. However, the assumption that model parameters are constant over time is not necessarily adapted to the data. In phytoplankton ecology, the relevance of time-varying parameter values has been shown using a dynamic linear regression model (DLRM). DLRMs, belonging to the class of Bayesian dynamic models, assume the existence of a non-observable time series of model parameters, which are estimated on-line, i.e. after each observation. The aim of this paper was to show how DLRM results could be used to explain variation of a time series of phytoplankton abundance. We applied DLRM to daily concentrations of Dinophysis cf. acuminata, determined in Antifer harbour (French coast of the English Channel), along with physical and chemical covariates (e.g. wind velocity, nutrient concentrations). A single model was built using 1989 and 1990 data, and then applied separately to each year. Equivalent static regression models were investigated for the purpose of comparison. Results showed that most of the Dinophysis cf. acuminata concentration variability was explained by the configuration of the sampling site, the wind regime and tide residual flow. Moreover, the relationships of these factors with the concentration of the microalga varied with time, a fact that could not be detected with static regression. Application of dynamic models to phytoplankton time series, especially in a monitoring context, is discussed.

A demand-driven analysis of tourist accommodation price: A quantile regression of room bookings

Tourist accommodation expenditure is a widely investigated topic as it represents a major contribution to the total tourist expenditure. The identification of the determinant factors is commonly based on supply-driven applications while little research has been made on important travel characteristics. This paper proposes a demand-driven analysis of tourist accommodation price by focusing on data generated from room bookings. The investigation focuses on modeling the relationship between key travel characteristics and the price paid to book the accommodation. To accommodate the distributional characteristics of the expenditure variable, the analysis is based on the estimation of a quantile regression model. The findings support the econometric approach used and enable the elaboration of relevant managerial implications.

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.

Obtaining environmental favourability functions from logistic regression

Logistic regression is a statistical tool widely used for predicting species’ potential distributions starting from presence/absence data and a set of independent variables. However, logistic regression equations compute probability values based not only on the values of the predictor variables but also on the relative proportion of presences and absences in the dataset, which does not adequately describe the environmental favourability for or against species presence. A few strategies have been used to circumvent this, but they usually imply an alteration of the original data or the discarding of potentially valuable information. We propose a way to obtain from logistic regression an environmental favourability function whose results are not affected by an uneven proportion of presences and absences. We tested the method on the distribution of virtual species in an imaginary territory. The favourability models yielded similar values regardless of the variation in the presence/absence ratio. We also illustrate with the example of the Pyrenean desman’s (Galemys pyrenaicus) distribution in Spain. The favourability model yielded more realistic potential distribution maps than the logistic regression model. Favourability values can be regarded as the degree of membership of the fuzzy set of sites whose environmental conditions are favourable to the species, which enables applying the rules of fuzzy logic to distribution modelling. They also allow for direct comparisons between models for species with different presence/absence ratios in the study area. This makes themmore useful to estimate the conservation value of areas, to design ecological corridors, or to select appropriate areas for species reintroductions.

Uma caracterização dos imigrantes no distrito de Évora

Uma vez que existe uma grande necessidade de se obter um maior conhecimento das comunidades imigrantes residentes no Distrito, este trabalho tem como principal objectivo caracterizar a população de imigrantes comunitários residentes no distrito de Évora. Em particular, procuramos analisar os casamentos realizados não só entre os não nacionais e portugueses, bem como entre os nãos nacionais entre si. Consideramos os 354 imigrantes que constituem os imigrantes comunitários inscritos legalmente nos Serviços Estrangeiros e Fronteiras de Évora entre 2006 a 2009. Por outro lado, analisamos, para o mesmo período, os casamentos dos imigrantes que casaram com os portugueses num total de 165 indivíduos. Começamos por levar a efeito uma investigação básica de tipo descritivo para uma caracterização dos imigrantes relativamente a diferentes variáveis e investigamos algumas associações entre elas via tabelas de contingência. Refira-se que a maioria dos imigrantes é do sexo feminino, sendo a nacionalidade mais representativa a brasileira. Com base no modelo de regressão de Cox identificamos factores de risco e diferentes perfis associados à rotura do casamento. Mostramos que há evidência estatística para considerar que um menor nível de escolaridade, o pertencer a um país da União Europeia e o ter entrado à procura de trabalho são níveis de factores que aumentam o risco de rotura do casamento. Analisam-se, ainda, de uma forma crítica, a abordagem paramétrica, procurando modelar os dados através dos modelos Exponencial, Weibull, Lognormal e Log-logístico. ABSTRACT: There is a need to obtain a greater understanding of immigrant communities in the district; this work has as main objective to characterize the population of immigrant community residing in the district of Évora. ln particular, we analyze not only marriages between Portuguese and non-nationals, as well as among non-nationals among themselves. We consider the 354 immigrants who are legally registered immigrants in the Community Service of Foreigners and Frontiers of Évora, from 2006 to 2009. Moreover, we analyze, for the same period, the marriages of immigrants who intermarried with the Portuguese for a total of 165 individuals. We start by carrying out a basic descriptive research in order to characterize the immigrants in relation to several variables and investigated some associations between them by contingency tables. It should be noted that most immigrants are women, and the more representative the Brazilian nationality. Based on the Cox regression model, were possible to identify risk factors and identify profiles of high and low risk associated with the rupture of the marriage. We show that there is statistical evidence to conclude that a less educated levels, belonging to a country inside the European Union and have gone looking for work are factors that increase the risk of rupture of the marriage. The parametric approach is analyzed also, in a critical way, seeking to model the data using exponential, Weibull, Lognormal and Log-logistic.

Temporomandibular Dysfunction Post-craniotomy: Evaluation Between Pre- And Post-operative Status.

To identify risk factors associated with post-operative temporomandibular joint dysfunction after craniotomy. The study sample included 24 patients, mean age of 37.3 ± 10 years; eligible for surgery for refractory epilepsy, evaluated according to RDC/TMD before and after surgery. The primary predictor was the time after the surgery. The primary outcome variable was maximal mouth opening. Other outcome variables were: disc displacement, bruxism, TMJ sound, TMJ pain, and pain associated to mandibular movements. Data analyses were performed using bivariate and multiple regression methods. The maximal mouth opening was significantly reduced after surgery in all patients (p = 0.03). In the multiple regression model, time of evaluation and pre-operative bruxism were significantly (p < .05) associated with an increased risk for TMD post-surgery. A significant correlation between surgery follow-up time and maximal opening mouth was found. Pre-operative bruxism was associated with increased risk for temporomandibular joint dysfunction after craniotomy.

[diet Quality And Associated Factors Among The Elderly: A Population-based Study In Campinas, São Paulo State, Brazil].

The aim of this study was to assess the quality of diet among the elderly and associations with socio-demographic variables, health-related behaviors, and diseases. A population-based cross-sectional study was conducted in a representative sample of 1,509 elderly participants in a health survey in Campinas, São Paulo State, Brazil. Food quality was assessed using the Revised Diet Quality Index (DQI-R). Mean index scores were estimated and a multiple regression model was employed for the adjusted analyses. The highest diet quality scores were associated with age 80 years or older, Evangelical religion, diabetes mellitus, and physical activity, while the lowest scores were associated with home environments shared with three or more people, smoking, and consumption of soft drinks and alcoholic beverages. The findings emphasize a general need for diet quality improvements in the elderly, specifically in subgroups with unhealthy behaviors, who should be targeted with comprehensive strategies.

Factors Associated With Reporting Of Abuse Against Children And Adolescents By Nurses Within Primary Health Care.

to analyze the factors associated with the underreporting on the part of nurses within Primary Health Care of abuse against children and adolescents. cross-sectional study with 616 nurses. A questionnaire addressed socio-demographic data, profession, instrumentation and knowledge on the topic, identification and reporting of abuse cases. Bivariate and multivariate logistic regression was used. female nurses, aged between 21 and 32 years old, not married, with five or more years since graduation, with graduate studies, and working for five or more years in PHC predominated. The final regression model showed that factors such as working for five or more years, having a reporting form within the PHC unit, and believing that reporting within Primary Health Care is an advantage, facilitate reporting. the study's results may, in addition to sensitizing nurses, support management professionals in establishing strategies intended to produce compliance with reporting as a legal device that ensures the rights of children and adolescents.

Determination Of Detergent And Dispensant Additives In Gasoline By Ring-oven And Near Infrared Hypespectral Imaging.

A method using the ring-oven technique for pre-concentration in filter paper discs and near infrared hyperspectral imaging is proposed to identify four detergent and dispersant additives, and to determine their concentration in gasoline. Different approaches were used to select the best image data processing in order to gather the relevant spectral information. This was attained by selecting the pixels of the region of interest (ROI), using a pre-calculated threshold value of the PCA scores arranged as histograms, to select the spectra set; summing up the selected spectra to achieve representativeness; and compensating for the superimposed filter paper spectral information, also supported by scores histograms for each individual sample. The best classification model was achieved using linear discriminant analysis and genetic algorithm (LDA/GA), whose correct classification rate in the external validation set was 92%. Previous classification of the type of additive present in the gasoline is necessary to define the PLS model required for its quantitative determination. Considering that two of the additives studied present high spectral similarity, a PLS regression model was constructed to predict their content in gasoline, while two additional models were used for the remaining additives. The results for the external validation of these regression models showed a mean percentage error of prediction varying from 5 to 15%.

Factors associated with the prevalence of hypertension and control practices among elderly residents of São Paulo city, Brazil

The aim of this study was to analyze the prevalence of hypertension and control practices among the elderly. The survey analyzed data from 872 elderly people in São Paulo, Brazil, through a cluster sampling, stratified according to education and income. A Poisson multiple regression model checked for the existence of factors associated with hypertension. The prevalence of self-reported hypertension among the elderly was 46.9%. Variables associated with hypertension were self-rated health, alcohol consumption, gender, and hospitalization in the last year, regardless of age. The three most common measures taken to control hypertension, but only rarely, are oral medication, routine salt-free diet and physical activity. Lifestyle and socioeconomic status did not affect the practice of control, but knowledge about the importance of physical activity was higher among those older people with higher education and greater income. The research suggests that health policies that focus on primary care to encourage lifestyle changes among the elderly are necessary.

Wives of pathological gamblers: personality traits, depressive symptoms and social adjustment

Objective: Wives of pathological gamblers tend to endure long marriages despite financial and emotional burden. Difficulties in social adjustment, personality psychopathology, and comorbidity with psychiatric disorders are pointed as reasons for remaining on such overwhelming relationships. The goal was to examine the social adjustment, personality and negative emotionality of wives of pathological gamblers. Method: The sample consisted of 25 wives of pathological gamblers, mean age 40.6, SD = 9.1 from a Gambling Outpatient Unit and at GAM-ANON, and 25 wives of non-gamblers, mean age 40.8, SD = 9.1, who answered advertisements placed at the Universidade de São Paulo hospital and medical school complex. They were selected in order to approximately match demographic characteristics of the wives of pathological gamblers. Subjects were assessed by the Social Adjustment Scale, Temperament and Character Inventory, Beck Depression Inventory and State-Trait Anxiety Inventory. Results: Three variables remained in the final Multiple Logistic Regression model, wives of pathological gamblers presented greater dissatisfaction with their marital bond, and higher scores on Reward Dependence and Persistence temperament factors. Both, Wives of pathological gamblers and wives of non-gamblers presented well-structured character factors excluding personality disorders. Conclusion: This personality profile may explain wives of pathological gamblers emotional resilience and their marriage longevity. Co-dependence and other labels previously used to describe them may work as a double edged sword, legitimating wives of pathological gamblers problems, while stigmatizing them as inapt and needy.

Mortalidade cardiovascular por sexo e faixa etária em São Paulo, Brasil: 1996 a 1998 e 2003 a 2005

FUNDAMENTO: O conhecimento da evolução da mortalidade cardiovascular é importante para levantar hipóteses sobre a sua ocorrência e subsidiar medidas de prevenção e controle. OBJETIVOS: Comparar a mortalidade pelo conjunto das doenças cardiovasculares e seus principais subgrupos: doença isquêmica do coração e cerebrovasculares (DIC e DCBV), no município de São Paulo, por sexo e idade, de 1996 a 1998 e 2003 a 2005. MÉTODOS: Foram usados dados de óbitos do Programa de Aprimoramento das Informações de Mortalidade para o Município (PROAIM) e estimativas populacionais da Fundação Sistema Estadual de Análise de Dados (SEADE) do Estado de São Paulo. A magnitude na mortalidade e as mudanças entre os triênios foram medidas pela descrição de coeficientes e variação percentual relativa. O modelo de regressão de Poisson foi usado também para estimar a mudança na mortalidade entre os períodos. RESULTADOS: Observou-se redução importante da mortalidade cardiovascular. Os coeficientes aumentam com a idade em ambos os sexos. Também são mais elevados na população masculina, na faixa a partir dos 70 anos. Os coeficientes de mortalidade por DIC são maiores que aqueles por DCBV, tanto nos homens como nas mulheres de 50 anos ou mais. O declínio pelo conjunto das doenças cardiovasculares foi maior em mulheres de 20 a 29 anos (-30%) e em homens de 30 a 39 anos (-26%). CONCLUSÃO: A força da intensidade da mortalidade cardiovascular diminuiu entre 1996 e 1998, a 2003 e 2005. Ainda assim há diferenças entre os grupos. Essa redução pode significar, em parte, um maior acesso aos métodos diagnósticos e terapêuticos.