Beta-binomial/Poisson regression models for repeated bivariate counts

We analyze data obtained from a study designed to evaluate training effects on the performance of certain motor activities of Parkinson`s disease patients. Maximum likelihood methods were used to fit beta-binomial/Poisson regression models tailored to evaluate the effects of training on the numbers of attempted and successful specified manual movements in 1 min periods, controlling for disease stage and use of the preferred hand. We extend models previously considered by other authors in univariate settings to account for the repeated measures nature of the data. The results suggest that the expected number of attempts and successes increase with training, except for patients with advanced stages of the disease using the non-preferred hand. Copyright (c) 2008 John Wiley & Sons, Ltd.

Problems with accessibility to health services by persons with disabilities in Sao Paulo, Brazil

Purpose. To describe the occurrence of self-reported problems of accessibility to health services used by persons with disabilities in terms of social and health services variables. Methods. We performed a cross-sectional household survey designed to assess problems with accessibility to health services faced by persons with disabilities. We interviewed 333 persons in Sao Paulo city, in 2007. Variables related to the presence of accessibility problems, disabilities, gender, age, family head income, ethnicity, use of health services and others were analysed using frequencies, percentages, chi(2)-test, ANOVA and Poisson regression models. Results. 15.92% of the interviewed persons reported problems with accessibility to health services. Persons having multiple (prevalence ratios; PR = 2.91) or mobility disability (PR = 6.46) had more problems with accessibility than persons with hearing disability. Persons younger than 78 years old had more problems with accessibility; those who needed help to go to the health service (PR = 3.01) also. Conclusions. Persons with multiple or mobility disability, younger than 78 years, and those who needed help of others to go to the health service were more likely to have problems with accessibility to health services. This information could be one of the first steps to the management and/or planning of appropriate health services for persons with disabilities.

Impact of socioeconomic and clinical factors on child oral health-related quality of life (COHRQoL)

Child oral health-related quality of life (COHRQoL) has been increasingly assessed; however, few studies appraised the influence of socioeconomic status on COHRQoL in developing countries. This study assessed the relationship of COHRQoL with socioeconomic backgrounds and clinical factors. This study followed a cross-sectional design, with a multistage random sample of 792 schoolchildren aged 12 years, representative of Santa Maria, a southern city in Brazil. Participants completed the Brazilian version of the Child Perceptions Questionnaire (CPQ(11-14)), their parents or guardians answered questions on socioeconomic status, and a dental examination provided information on the prevalence of caries, dental trauma and occlusion. The assessment of association used hierarchically adjusted Poisson regression models. Higher impacts on COHRQoL were observed for children presenting with untreated dental caries (RR 1.20; 95% CI 1.07-1.35) and maxillary overjet (RR 1.19; 95% CI 1.02-1.40). Socioeconomic factors also associated with COHRQoL; poorer scores were reported by children whose mothers have not completed primary education (RR 1.30; 95% CI 1.17-1.44) and those with lower household income (RR 1.13; 95% CI 1.02-1.26). Poor socioeconomic standings and poor dental status have a negative impact on COHRQoL; reducing health inequalities may demand dental programmes and policies targeting deprived population.

Sex and socioeconomic inequalities of lung cancer mortality in Barcelona, Spain and Sao Paulo, Brazil

The objective of this paper was to assess sex and socioeconomic inequalities in lung cancer mortality in two major cities of Europe and South America. Official information on mortality and population allowed the estimation of sex- and age-specific death rates for Barcelona, Spain and Sao Paulo, Brazil (1995-2003). Mortality trends and levels were independently assessed for each city and subsequently compared. Rate ratios assessed by Poisson regression analysis addressed hypotheses of association between the outcome and socioeconomic covariates (human development index, unemployment and schooling) at the inner-city area level. Barcelona had a higher mortality in men (76.9/100000 inhabitants) than Sao Paulo (38.2/100 000 inhabitants); although rates were decreasing for the former (-2%/year) and levelled-off for the [after. Mortality in women ranked similarly (9.1 for Barcelona, 11.5 for Sao Paulo); with an increasing trend for women aged 35-64 years (+ 7.7%/year in Barcelona and + 2.4%/year in Sao Paulo). The socioeconomic gradient of mortality in men was negative for Barcelona and positive for Sao Paulo; for women, the socioeconomic gradient was positive in both cities. Negative gradients indicate that deprived areas suffer a higher burden of disease; positive gradients suggest that prosmoking lifestyles may have been more prevalent in more affluent areas during the last decades. Sex and socioeconomic inequalities of lung cancer mortality reinforce the hypothesis that the epidemiologic profile of cancer can be improved by an expanded access to existing technology of healthcare and prevention. The continuous monitoring of inequalities in health may contribute to the concurrent promotion of well-being and social justice.

Self-rated oral health and associated factors in Brazilian elders

Objective: Self-rating provides a simple direct way of capturing perceptions of health. The objective of this study was to estimate the prevalence and associated factors of poor self-rated oral health among elders. Methods: National data from a cross-sectional population-based study with a multistage random sample of 4786 Brazilian older adults (aged 65-74) in 250 towns were analysed. Data collection included oral examinations (WHO 1997) and struct-ured interviews at elderly households. The outcome was measured by a single five-point-response-scale question dichotomized into `poor` (fair/poor/very poor) and `good` (good/very good) self-rated oral health. Data analyses used Poisson regression models stratified by sex. Results: The prevalence of poor self-rated oral health was 46.6% (95% CI: 45.2-48%) in the whole sample, 50.3% (48-52.5) in men and 44.2% (42.4-46) in women. Higher prevalence ratios (PR) were found in elders reporting unfavourable dental appearance (PR = 2.31; 95% CI: 2.02-2.65), poor chewing ability (PR = 1.64; CI: 1.48-1.8) and dental pain (PR = 1.44; CI: 1.04-1.23) in adjusted analysis. Poor self-perception was also associated with being men, black, unfavourable socioeconomic circumstances, unfavourable clinical oral health and with not using or needing a dental prosthesis. Conclusion: Assessment and understanding of self-rated oral health should take into account social factors, subjective and clinical oral symptoms.

Power series generalized nonlinear models

We introduce in this paper a new class of discrete generalized nonlinear models to extend the binomial, Poisson and negative binomial models to cope with count data. This class of models includes some important models such as log-nonlinear models, logit, probit and negative binomial nonlinear models, generalized Poisson and generalized negative binomial regression models, among other models, which enables the fitting of a wide range of models to count data. We derive an iterative process for fitting these models by maximum likelihood and discuss inference on the parameters. The usefulness of the new class of models is illustrated with an application to a real data set. (C) 2008 Elsevier B.V. All rights reserved.

Association between particulate matter from biomass burning and respiratory diseases in the southern region of the Brazilian Amazon

Objective. To investigate the short-term effects of exposure to particulate matter from biomass burning in the Amazon on the daily demand for outpatient care due to respiratory diseases in children and the elderly. Methods. Epidemiologic study with ecologic time series design. Daily consultation records were obtained from the 14 primary health care clinics in the municipality of Alta Floresta, state of Mato Grosso, in the southern region of the Brazilian Amazon, between January 2004 and December 2005. Information on the daily levels of fine particulate matter was made available by the Brazilian National Institute for Spatial Research. To control for confounding factors ( situations in which a non-causal association between exposure and disease is observed due to a third variable), variables related to time trends, seasonality, temperature, relative humidity, rainfall, and calendar effects ( such as occurrence of holidays and weekends) were included in the model. Poisson regression with generalized additive models was used. Results. A 10 mu g/m(3) increase in the level of exposure to particulate matter was associated with increases of 2.9% and 2.6% in outpatient consultations due to respiratory diseases in children on the 6th and 7th days following exposure. Significant associations were not observed for elderly individuals. Conclusions. The results suggest that the levels of particulate matter from biomass burning in the Amazon are associated with adverse effects on the respiratory health of children.

Factors associated with increased prevalence of human papillomavirus infection in a cohort of HIV-Infected Brazilian women

Objectives: Human papillomavirus (HPV) infection is a major risk factor for cervical disease. Using baseline data from the HIV-infected cohort of Evandro Chagas Clinical Research Institute at Fiocruz, Rio de Janeiro, Brazil, factors associated with an increased prevalence of HPV were assessed. Methods: Samples from 634 HIV-infected women were tested for the presence of HPV infection using hybrid capture 11 and polymerase chain reaction. Prevalence ratios (PR) were estimated using Poisson regression analysis with robust variance. Results: The overall prevalence of HPV infection was 48%, of which 94% were infected with a high-risk HPV. In multivariate analysis, factors independently associated with infection with high-risk HPV type were: younger age (<30 years of age; PR 1.5, 95% confidence interval (CI) 1.1-2.1), current or prior drug use (PR 1.3, 95% CI 1.0-1.6), self-reported history of HPV infection (PR 1.2, 95% CI 0.96-1.6), condom use in the last sexual intercourse (PR 1.3, 95% CI 1.1-1.7), and nadir CD4+ T-cell count <100 cells/mm(3) (PR 1.6, 95% CI 1.2-2.1). Conclusions: The estimated prevalence of high-risk HPV-infection among HIV-infected women from Rio de Janeiro, Brazil, was high. Close monitoring of HPV-related effects is warranted in all HIV-infected women, in particular those of younger age and advanced immunosuppression. (C) 2008 International Society for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Dietary calcium intake and overweight: An epidemiologic view

Objective: We evaluated the relation between overweight and calcium intake in adults living in the municipality of Sao Paulo, Brazil. Methods: This was a cross-sectional population-based Study on a sample of 1459 adults that was obtained by multistage cluster sampling. Dietary intake was measured by the 24-h recall method. Poisson`s and linear regression analyses were performed to evaluate the relation between overweight and quartiles of calcium intake adjusted for energy. Results: The prevalence of overweight was 43.1% and the average adjusted calcium intake was 448.6 mg. In the linear regression. analyses, the regression coefficient for adjusted calcium was significant and negative (P = 0.019, beta(1) = -0.0001). Although evaluated by quartiles, the prevalence ratio for overweight in the first quartile of calcium intake was 1.24 (95% confidence interval 1.00-1.54) and that in the second quartile was 1.24 (95% confidence interval 1.03-1.49). Conclusion: In the present study, calcium intake showed a significant negative association with body mass index. (C) 2008 Elsevier Inc. All rights reserved.

Using non-homogeneous Poisson models with multiple change-points to estimate the number of ozone exceedances in Mexico City

In this paper, we consider some non-homogeneous Poisson models to estimate the probability that an air quality standard is exceeded a given number of times in a time interval of interest. We assume that the number of exceedances occurs according to a non-homogeneous Poisson process (NHPP). This Poisson process has rate function lambda(t), t >= 0, which depends on some parameters that must be estimated. We take into account two cases of rate functions: the Weibull and the Goel-Okumoto. We consider models with and without change-points. When the presence of change-points is assumed, we may have the presence of either one, two or three change-points, depending of the data set. The parameters of the rate functions are estimated using a Gibbs sampling algorithm. Results are applied to ozone data provided by the Mexico City monitoring network. In a first instance, we assume that there are no change-points present. Depending on the adjustment of the model, we assume the presence of either one, two or three change-points. Copyright (C) 2009 John Wiley & Sons, Ltd.

Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients

In this paper, we present different ofrailtyo models to analyze longitudinal data in the presence of covariates. These models incorporate the extra-Poisson variability and the possible correlation among the repeated counting data for each individual. Assuming a CD4 counting data set in HIV-infected patients, we develop a hierarchical Bayesian analysis considering the different proposed models and using Markov Chain Monte Carlo methods. We also discuss some Bayesian discrimination aspects for the choice of the best model.

Heteroscedastic Nonlinear Regression Models

In this article, we present a generalization of the Bayesian methodology introduced by Cepeda and Gamerman (2001) for modeling variance heterogeneity in normal regression models where we have orthogonality between mean and variance parameters to the general case considering both linear and highly nonlinear regression models. Under the Bayesian paradigm, we use MCMC methods to simulate samples for the joint posterior distribution. We illustrate this algorithm considering a simulated data set and also considering a real data set related to school attendance rate for children in Colombia. Finally, we present some extensions of the proposed MCMC algorithm.

Regression Models with Heteroscedasticity using Bayesian Approach

In this paper, we compare the performance of two statistical approaches for the analysis of data obtained from the social research area. In the first approach, we use normal models with joint regression modelling for the mean and for the variance heterogeneity. In the second approach, we use hierarchical models. In the first case, individual and social variables are included in the regression modelling for the mean and for the variance, as explanatory variables, while in the second case, the variance at level 1 of the hierarchical model depends on the individuals (age of the individuals), and in the level 2 of the hierarchical model, the variance is assumed to change according to socioeconomic stratum. Applying these methodologies, we analyze a Colombian tallness data set to find differences that can be explained by socioeconomic conditions. We also present some theoretical and empirical results concerning the two models. From this comparative study, we conclude that it is better to jointly modelling the mean and variance heterogeneity in all cases. We also observe that the convergence of the Gibbs sampling chain used in the Markov Chain Monte Carlo method for the jointly modeling the mean and variance heterogeneity is quickly achieved.

Estimating the number of ozone peaks in Mexico City using a non-homogeneous Poisson model

In this paper, we consider the problem of estimating the number of times an air quality standard is exceeded in a given period of time. A non-homogeneous Poisson model is proposed to analyse this issue. The rate at which the Poisson events occur is given by a rate function lambda(t), t >= 0. This rate function also depends on some parameters that need to be estimated. Two forms of lambda(t), t >= 0 are considered. One of them is of the Weibull form and the other is of the exponentiated-Weibull form. The parameters estimation is made using a Bayesian formulation based on the Gibbs sampling algorithm. The assignation of the prior distributions for the parameters is made in two stages. In the first stage, non-informative prior distributions are considered. Using the information provided by the first stage, more informative prior distributions are used in the second one. The theoretical development is applied to data provided by the monitoring network of Mexico City. The rate function that best fit the data varies according to the region of the city and/or threshold that is considered. In some cases the best fit is the Weibull form and in other cases the best option is the exponentiated-Weibull. Copyright (C) 2007 John Wiley & Sons, Ltd.

Methods of Estimation in Multiple Linear Regression: Application to Clinical Data

Nesse artigo, tem-se o interesse em avaliar diferentes estratégias de estimação de parâmetros para um modelo de regressão linear múltipla. Para a estimação dos parâmetros do modelo foram utilizados dados de um ensaio clínico em que o interesse foi verificar se o ensaio mecânico da propriedade de força máxima (EM-FM) está associada com a massa femoral, com o diâmetro femoral e com o grupo experimental de ratas ovariectomizadas da raça Rattus norvegicus albinus, variedade Wistar. Para a estimação dos parâmetros do modelo serão comparadas três metodologias: a metodologia clássica, baseada no método dos mínimos quadrados; a metodologia Bayesiana, baseada no teorema de Bayes; e o método Bootstrap, baseado em processos de reamostragem.