A Bayesian Measure of Model Accuracy.

Ensuring that the proposed probabilistic model accurately represents the problem is a critical step in statistical modeling, as choosing a poorly fitting model can have significant repercussions on the decision-making process. The primary objective of statistical modeling often revolves around predicting new observations, highlighting the importance of assessing the model's accuracy. However, current methods for evaluating predictive ability typically involve model comparison, which may not guarantee a good model selection. This work presents an accuracy measure designed for evaluating a model's predictive capability. This measure, which is straightforward and easy to understand, includes a decision criterion for model rejection. The development of this proposal adopts a Bayesian perspective of inference, elucidating the underlying concepts and outlining the necessary procedures for application. To illustrate its utility, the proposed methodology was applied to real-world data, facilitating an assessment of its practicality in real-world scenarios.

Regression models for the full distribution to exceedance data.

The list of occurrences linked to significant climate change has grown in recent decades. These changes can be influenced by a set of covariates, such as temperature, location and period of the year. Analyzing the relation among elements and factors that influence the behavior of such events is extremely important for decision-making in order to minimize damages and losses. Exceedance analysis uses the tail of the distribution based on Extreme Value Theory (EVT). Extensions for these models have been proposed in literature, such as regression models for the tail parameters and a parametric or semi-parametric distribution for the part that comes before the tail (well known as bulk distribution). This work presents a new extension to exceedance model, in which the parameters for the bulk distribution capture the effect of covariates such as location and seasonality. We considered a Bayesian approach in the inference procedure. The estimation was done using MCMC -- Markov Chain Monte Carlo methods. Application results for modeling maximum and minimum temperature data showed an efficient estimation of extreme quantiles and a predictive advantage compared to models previously used in literature.

Intra-urban variability of long-term exposure to PM2.5 and NO2 in five cities in Colombia.

Rapidly urbanizing cities in Latin America experience high levels of air pollution which are known risk factors for population health. However, the estimates of long-term exposure to air pollution are scarce in the region. We developed intraurban land use regression (LUR) models to map long-term exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in the five largest cities in Colombia. We conducted air pollution measurement campaigns using gravimetric PM2.5 and passive NO2 sensors for 2 weeks during both the dry and rainy seasons in 2021 in the cities of Barranquilla, Bucaramanga, Bogotá, Cali, and Medellín, and combined these data with geospatial and meteorological variables. Annual models were developed using multivariable spatial regression models. The city annual PM2.5 mean concentrations measured ranged between 12.32 and 15.99 µg/m3 while NO2 concentrations ranged between 24.92 and 49.15 µg/m3. The PM2.5 annual models explained 82% of the variance (R2) in Medellín, 77% in Bucaramanga, 73% in Barranquilla, 70% in Cali, and 44% in Bogotá. The NO2 models explained 65% of the variance in Bucaramanga, 57% in Medellín, 44% in Cali, 40% in Bogotá, and 30% in Barranquilla. Most of the predictor variables included in the models were a combination of specific land use characteristics and roadway variables. Cross-validation suggests that PM2.5 outperformed NO2 models. The developed models can be used as exposure estimate in epidemiological studies, as input in hybrid models to improve personal exposure assessment, and for policy evaluation.

Cluster-Based Multinomial Logistic Regression Model for Health-Related Quality of Life Among People Living with HIV in Brazil.

Improvement in treatment options has increased the survival of people living with HIV (PLHIV). Thus, we evaluated the factors associated with better health-related quality of life (HRQoL) among PLHIV in Brazil. This was a cross-sectional study carried out among 349 PLHIV. Data were collected using an interview-based questionnaire, and HRQoL was assessed by the Brazilian version of the WHOQOL HIV BREF instrument. We used non-hierarchical cluster analysis (K-means) to compile the WHOQOL HIV BREF's overall and domain scores into a unique more multidimensional measure for HRQoL consisting of three clusters: poor, fair and good; associations with clusters of better HRQoL were assessed using multinomial logistic regression models. The mean and median overall HRQoL scores were 15.13 (SD = 3.39) and 16, respectively. The reliability and validity of the Brazilian version of the WHOQOL HIV BREF instrument was confirmed among PLHIV in a non-metropolitan, medium-sized municipality of Brazil, which reaffirmed the cross-cultural validity of this instrument. The factors male sex; heterosexual and asexual orientations; higher individual income; undetectable viral load; absence of any comorbidity and presence of an infectious or a chronic comorbidity, with mental illness as the reference; and never having consumed illegal substances were independently associated with good HRQoL. Thus, the compilation of the WHOQOL HIV BREF's overall and domain scores into a unique multidimensional measure for HRQoL, which this study proposed for the first time, may facilitate more robust interpretations and models of predictors. These differentials could simplify HRQoL as an indicator of health and wellbeing to be routinely used as a key outcome in the clinical management of patients and in the global monitoring of health system responses to HIV.

RESUMEN: La mejora en las opciones de tratamiento ha aumentado la supervivencia de las personas que viven con el VIH (PVVIH). Por lo tanto, evaluamos los factores asociados con una mejor calidad de vida relacionada con la salud (CVRS) entre las PVVIH en Brasil. Se trata de un estudio transversal realizado con 349 PVVIH. Los datos se recopilaron mediante un cuestionario basado en entrevistas y la CVRS se evaluó mediante la versión brasileña del instrumento WHOQOL VIH BREF. Usamos un análisis de conglomerados no jerárquico (K-medias) para compilar las puntuaciones generales y de dominios del WHOQOL HIV BREF en una medida única más multidimensional para la CVRS que consta de tres conglomerados: deficiente, regular y bueno; y las asociaciones con grupos de mejor CVRS se evaluaron mediante modelos de regresión logística multinomial. Las puntuaciones de la CVRS global media y mediana fueron 15,13 (DE = 3,39) y 16. La confiabilidad y validez del WHOQOL VIH BREF versión brasileña fue confirmada entre personas que viven con el VIH en un municipio no metropolitano de mediana población de Brasil, lo que reafirma la validez transcultural de este instrumento. Los factores sexo masculino; orientaciones heterosexuales y asexuales; mayores ingresos individuales; carga viral indetectable; ausencia de comorbilidad y presencia de comorbilidad infecciosa o crónica, teniendo como referencia la enfermedad mental; y nunca haber consumido sustancias ilegales se asociaron de forma independiente con una buena CVRS. Por lo tanto, la compilación de las puntuaciones generales y de dominio del WHOQOL HIV BREF en una medida multidimensional única para la CVRS, que este estudio propuso por primera vez, puede facilitar interpretaciones y modelos de predictores más robustos. Estos diferenciales podrían simplificar la HRQoL como un indicador de salud y bienestar para ser utilizado de forma rutinaria como un resultado clave en el manejo clínico de los pacientes y en el monitoreo global de las respuestas del sistema de salud al VIH.

Impact of heat stress on genetic evaluation of oocyte and embryo production in Gir dairy cattle.

Identifying and selecting genotypes tolerant to heat stress might improve reproductive traits in dairy cattle, including oocyte and embryo production. The temperature-humidity index (THI) was used, via random regression models, to investigate the impact of heat stress on genetic parameters and breeding values of oocyte and embryo production in Gir dairy cattle. We evaluated records of total oocytes (TO), viable oocytes (VO), cleaved embryos (CE), and viable embryos (VE) from dairy Gir donors. Twenty-four models were tested, considering age at ovum pick-up (AOPU) and THI means as a regressor in the genetic evaluation. We computed THI in eight periods, from 0 to 112 days before ovum pick-up, which were adjusted by different orders of Legendre polynomials (second, third, and fourth). The best-fit model according to Akaike's information criterion (AIC) and Model Posterior Probabilities (MPP) considered Legendre polynomials of third order and THI means of 112 days for TO, fourth order and 56 days for VO, second order and 28 days for CE, and second order and 42 days for VE, respectively. The heritability (h2) estimates across AOPU and THI scales ranged from 0.34 to 0.62 for TO, 0.31 to 0.58 for VO, 0.26 to 0.39 for CE, and 0.15 to 0.26 for VE, respectively. The fraction of the phenotypic variance explained by the permanent environment in different AOPU and THI scales ranged from 0.03 to 0.25 for TO, 0.05 to 0.26 for VO, 0.09 to 0.36 for CE, and 0.15 to 0.27 for VE, respectively. Spearman's rank correlation between the estimated breeding values in different AOPU and THI scale from the top 5% sires and females ranged from 0.18 to 0.90 for TO, 0.31 to 0.95 for VO, 0.14 to 0.85 for CE, and 0.47 to 0.94 for VE, respectively. The h2 estimates for all evaluated traits varied from moderate to high magnitude across AOPU and THI scales, indicating that genetic selection can result in rapid genetic progress for the evaluated traits. There was a reranking among the best animals in different AOPU and THI. It is possible to select dairy Gir cattle tolerant to heat stress to improve oocyte and embryo production.

A proposal to analyze the progression of non-dialytic chronic kidney disease by surrogate endpoints: introducing parametric survival models.

Introduction: Chronic kidney disease (CDK) progression studies increasingly use surrogate endpoints based on the estimated glomerular filtration rate. The clinical characteristics of these endpoints bring new challenges in comparing groups of patients, as traditional Cox models may lead to biased estimates mainly because they do not assume a hazard function. Objective: This study proposes the use of parametric survival analysis models with the three most commonly used endpoints in nephrology based on a case study. Estimated glomerular filtration rate (eGFR) decay > 5 mL/year, eGFR decline > 30%, and change in CKD stage were evaluated. Method: The case study is a 5-year retrospective cohort study that enrolled 778 patients in the predialysis stage. Exponential, Weibull, Gompertz, lognormal, and logistic models were compared, and proportional hazard and accelerated failure time (AFT) models were evaluated. Results: The endpoints had quite different hazard functions, demonstrating the importance of choosing appropriate models for each. AFT models were more suitable for the clinical interpretation of the effects of covariates on these endpoints. Conclusion: Surrogate endpoints have different hazard distributions over time, which is already recognized by nephrologists. More flexible analysis techniques that capture these relevant clinical characteristics in decision-making should be encouraged and disseminated in nephrology research.

Genomic-based genetic parameters for milkability traits derived from automatic milking systems in North American Holstein cattle.

The number of dairy farms adopting automatic milking systems (AMS) has considerably increased around the world aiming to reduce labor costs, improve cow welfare, increase overall performance, and generate a large amount of daily data, including production, behavior, health, and milk quality records. In this context, this study aimed to (1) estimate genomic-based variance components for milkability traits derived from AMS in North American Holstein cattle based on random regression models; and (2) derive and estimate genetic parameters for novel behavioral indicators based on AMS-derived data. A total of 1,752,713 daily records collected using 36 milking robot stations and 70,958 test-day records from 4,118 genotyped Holstein cows were used in this study. A total of 57,600 SNP remained after quality control. The daily-measured traits evaluated were milk yield (MY, kg), somatic cell score (SCS, score unit), milk electrical conductivity (EC, mS), milking efficiency (ME, kg/min), average milk flow rate (FR, kg/min), maximum milk flow rate (FRM, kg/min), milking time (MT, min), milking failures (MFAIL), and milking refusals (MREF). Variance components and genetic parameters for MY, SCS, ME, FR, FRM, MT, and EC were estimated using the AIREMLF90 software under a random regression model fitting a third-order Legendre orthogonal polynomial. A threshold Bayesian model using the THRGIBBS1F90 software was used for genetically evaluating MFAIL and MREF. The daily heritability estimates across days in milk (DIM) ranged from 0.07 to 0.28 for MY, 0.02 to 0.08 for SCS, 0.38 to 0.49 for EC, 0.45 to 0.56 for ME, 0.43 to 0.52 for FR, 0.47 to 0.58 for FRM, and 0.22 to 0.28 for MT. The estimates of heritability (± SD) for MFAIL and MREF were 0.02 ± 0.01 and 0.09 ± 0.01, respectively. Slight differences in the genetic correlations were observed across DIM for each trait. Strong and positive genetic correlations were observed among ME, FR, and FRM, with estimates ranging from 0.94 to 0.99. Also, moderate to high and negative genetic correlations (ranging from -0.48 to -0.86) were observed between MT and other traits such as SCS, ME, FR, and FRM. The genetic correlation (± SD) between MFAIL and MREF was 0.25 ± 0.02, indicating that both traits are influenced by different sets of genes. High and negative genetic correlations were observed between MFAIL and FR (-0.58 ± 0.02) and MFAIL and FRM (-0.56 ± 0.02), indicating that cows with more MFAIL are those with lower FR. The use of random regression models is a useful alternative for genetically evaluating AMS-derived traits measured throughout the lactation. All the milkability traits evaluated in this study are heritable and have demonstrated selective potential, suggesting that their use in dairy cattle breeding programs can improve dairy production efficiency in AMS.

Explaining the stickiness of supply chain relations in the Brazilian soybean trade.

The global trade of agricultural commodities has profound social-ecological impacts, from potentially increasing food availability and agricultural efficiency, to displacing local communities, and to incentivizing environmental destruction. Supply chain stickiness, understood as the stability in trading relationships between supply chain actors, moderates the impacts of agricultural commodity production and the possibilities for supply-chain interventions. However, what factors determine stickiness, that is, how and why farmers, traders, food processors, and consumer countries, develop and maintain trading relationships with specific producing regions, remains unclear. Here, we use data on the Brazilian soy supply chain, a mixed methods approach based on extensive actor-based fieldwork, and an explanatory regression model, to identify and explore the factors that influence stickiness between places of production and supply chain actors. We find four groups of factors to be important: economic incentives, institutional enablers and constraints, social and power dimensions, and biophysical and technological conditions. Among the factors we explore, surplus capacity in soy processing infrastructure, (i.e., crushing and storage facilities) is important in increasing stickiness, as is export-oriented production. Conversely, volatility in market demand expressed by farm-gate soy prices and lower land-tenure security are key factors reducing stickiness. Importantly, we uncover heterogeneity and context-specificity in the factors determining stickiness, suggesting tailored supply-chain interventions are beneficial. Understanding supply chain stickiness does not, in itself, provide silver-bullet solutions to stopping deforestation, but it is a crucial prerequisite to understanding the relationships between supply chain actors and producing regions, identifying entry points for supply chain sustainability interventions, assessing the effectiveness of such interventions, forecasting the restructuring of trade flows, and considering sourcing patterns of supply chain actors in territorial planning.

Relationship between body weight and scrotal circumference in growing Pelibuey sheep raised under tropical conditions / Relação entre o peso corporal e a circunferência escrotal em ovinos Pelibuey em crescimento criados em condições tropicais

The body weight (BW) of an animal is a vital economic trait that might help in decision-making in the handling of animals. The objective of the present study was to develop equations for the prediction of BW in Pelibuey sheep using scrotal circumference (SC). The BW (23.40 ± 6.96 kg) and SC (20.25 ± 6.19 cm) have been recorded in 405 male Pelibuey at the Southeastern Center for Ovine Integration, Mexico. Linear, logarithmic, quadratic, exponential, cubic, and power regression models were used for data analysis. Pearson correlation (R), Coefficient of determination (R2), Adjusted coefficient of determination (Adj.R2) Root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to select the best model. Power regression model showed the highest R (0.93), R2 (0.86), Adj.R2 (0.86) and lowest RMSE (0.02), AIC (-989.44) and BIC (-981.44). The current study suggests that SC might be used as the only predictor for BW of growing Pelibuey sheep raised under tropical conditions.

O peso corporal (PC) do animal é uma característica econômica importante, que pode auxiliar na tomada de decisões no manejo dos animais. O objetivo do presente estudo foi desenvolver equações para a predição do PC em ovinos Pelibuey por meio da circunferência escrotal (CE). O PC (23,40±6,96kg) e a CE (20,25±6,19cm) foram registrados em 405 ovinos machos da raça Pelibuey no Centro de Integração Ovina da Região Sudeste do México. Os modelos lineares, logarítmicos, quadráticos, exponenciais, cúbicos e de regressão de potência foram utilizados para a análise dos dados. A correlação de Pearson (R), o coeficiente de determinação (R2), o coeficiente de determinação ajustado (Adj.R2), o erro do quadrado médio (EQM), o critério de informação de Akaike (AIC) e o critério de informação bayesiano (BIC) foram usados para selecionar o melhor modelo. O modelo de regressão de potência apresentou maiores R (0,93), R2 (0,86), Adj.R2 (0,86) e menores EQM (0,02), AIC (-989,44) e BIC (-981,44). O estudo atual sugere que a CE pode ser usada como um único preditor para o PC de ovinos Pelibuey em crescimento criadas em condições tropicais.

Memoria de trabajo y planificación como predictores de las competencias matemáticas tempranas

Introducción: La relación entre funciones ejecutivas y habilidades matemáticas ha sido ampliamente estudiada. Sin embargo, no existe consenso respecto de la contribución específica de la memoria de trabajo y la planificación en el desarrollo de competencias matemáticas tempranas. El objetivo de este estudio fue determinar la capacidad predictiva de estos dos dominios ejecutivos sobre las competencias matemáticas de preescolares. Método: Se implementó un diseño no experimental ex post facto, con una muestra de 104 niños/as chilenos/as. La evaluación de sus funciones ejecutivas se realizó con la tarea "inversión de números" de la Batería IV Woodcock-Muñoz para evaluar la memoria de trabajo verbal, la subprueba "Torpo, el topo torpe" del Test de Evaluación Neuropsicológica Infantil (TENI) para evaluar la memoria de trabajo visoespacial y el Test de Laberintos de Porteus para evaluar la planificación. Con el fin de evaluar las habilidades matemáticas se utilizó el Test de Evaluación Matemática Temprana Utrecht (TEMT-U), versión chilena. Se realizaron análisis descriptivos, correlaciones y modelos de regresión múltiple. Resultados: La memoria de trabajo verbal seguida por la memoria de trabajo visoespacial y la planificación fueron los mejores predictores de las competencias matemáticas de los/as niños/as. Conclusiones: Estos resultados sugieren que estas funciones ejecutivas desempeñan un papel clave en el aprendizaje de las matemáticas y aportan información específica a las/os educadoras/es para que puedan planificar sus estrategias de enseñanza en función de las demandas cognitivas que requiere cada habilidad matemática, lo que puede ser una vía potencial para promover mejores logros de aprendizaje en esta importante disciplina.

Introduction: The relationship between executive functions and mathematical skills has been extensively studied. However, there is no consensus regarding the specific contribution of working memory and planning in the development of early mathematical skills. The aim of this study was to determine the predictive capacity of these two executive domains on preschoolers' mathematical skills. Method: A non-experimental ex post facto design was implemented with a sample of 104 Chilean children. The evaluation of their executive functions was performed with the "number inversion" task of the Woodcock-Muñoz IV Battery to assess verbal working memory, the "Clumsy Mole the Clumsy Mole" subtest of the TENI Child Neuropsychological Evaluation Test to assess visuospatial working memory, and the Porteus Maze Test to assess planning. To assess mathematical skills, the Test de Evaluación Matemática Temprana Utretch TEMT-U, Chilean version, was used. Descriptive analyses, correlations and multiple regression models were performed. Results: Verbal working memory followed by visuospatial working memory and planning were the best predictors of children's mathematical skills. Conclusions: These results suggest that these executive functions play a key role in mathematics learning and provide specific information to educators so that they can plan their teaching strategies according to the cognitive demands required by each mathematical skill, which may be a potential way to promote better learning achievements in this important discipline.

Residual Mechanical Properties and Durability of High-Strength Concrete with Polypropylene Fibers in High Temperatures.

Investigations into the fire resistance of high-strength concrete (HSC) is extremely important to optimize structural design in construction engineering. This work describes the influence of polypropylene fibers on the mechanical properties and durability of HSC at high temperatures (25, 100, 200, 400, 600 and 800 °C). HSC specimens with 2 kg/m3 composed of polypropylene fibers are tested in a temperature range of 25 to 800 °C, followed by microstructural analysis. In addition, a statistical analysis is designed to identify the effect of factors, namely temperature and polypropylene fibers, and their interactions on mechanical properties and water absorption, electrical resistivity, mass loss and ultrasonic velocity. Most of the properties are improved by the incorporation of fibers, obtaining highly predictable regression models. However, the polypropylene fibers reduce compressive strength but improve the residual mechanical properties up to 400 °C.

Anthropometric Equations to Determine Maximum Height in Adults ≥ 60 Years: A Systematic Review.

Although it is common to measure bone lengths for study, methodological errors in data measurement and processing often invalidate their clinical and scientific usefulness. This manuscript reviews the validity of several published equations used to determine the maximum height in older adults, since height is an anthropometric parameter widely employed in health sciences. A systematic review of original articles published in the English, Spanish, or Portuguese languages was performed in PubMed, ScienceDirect, EBSCO, Springer Link, and two institutional publisher integrators (UACJ and CONRICYT). The search terms were included in the metasearch engines in a combined way and text form using the Boolean connectors AND and OR {(Determination OR Estimation OR Equation) AND Height AND (Elderly OR "Older adults")}. Eleven manuscripts were selected from 1935 records identified through database searching after applying the following criteria: (1) original articles that designed and validated equations for the determination of height by anthropometric methods in adults 60 years of age and older and (2) manuscripts that presented robust evidence of validation of the proposed regression models. The validity of the reported linear regression models was assessed throughout a manuscript review process called multi-objective optimization that considered the collection of the models, the prediction errors, and the adjustment values (i.e., R2, standard error of estimation, and pure error). A total of 64 equations were designed and validated in 45,449 participants (57.1% women) from four continents: America (85.3%, with 46 equations), Asia (8.1%, with 10), Europe (4.6%, with 7), and Africa (2.0%, with 1); the Hispanic American ethnic group was the most numerous in participants and equations (69.0%, with 28). Due to various omissions and methodological errors, this study did not find any valid and reliable equations to assess the maximum height in older adults by anthropometric methods. It is proposed to adjust allometric mathematical models that can be interpreted in the light of ontogenetic processes.

Correlated geometric models of order k and its application to intensive care unit and leprosy data.

Geometric models are used to analyse the discrete time until the occurrence of an event of interest (success or consecutive successes). In two real data sets, named leprosy and intensive care unit (ICU), the events correspond, respectively, to abandoning the clinical treatment of leprosy, where abandonment corresponds to four consecutive patient absences from treatment, and the patient's discharge from the ICU. The distribution proposed in this article, called the correlated geometric distribution of order k (or correlated k-order geometric distribution), k≥1 , consists of including a correlation parameter in the geometric distribution of order k, thus considering the dependence between patient responses until the occurrence of the event. This model proves to be a better option for real data analysis where the effect of individual correlation is considered. The model is applied to real leprosy data to estimate the treatment abandonment probability. Bayesian methods are used to determine the parameter estimators of the models and to evaluate regression models. The covariates are related to the probability of the event by an appropriate link function chosen by Bayesian selection criteria. A diagnostic analysis evaluates the models fit by posterior randomized quantile residuals and influential observations by ψ -divergence measures. This methodology is illustrated by simulation studies and real ICU admission data analysis. Studies show a good fit of the proposed model. Real data analyses also find that the probabilities of the event of interest can be overestimated or underestimated when modeled without considering the effect of dependency on the model.

Household food insecurity and health in a high-migration area in rural Honduras.

Household food insecurity (HFI) is a significant problem in the developing world. Relationships between HFI and nutrition, physical growth, and development have been elucidated; less is known about the non-nutritional impacts among individuals living in rural areas in low-income countries. The aim of this study was to determine if HFI is a risk factor for suboptimal mental health and overall health in rural Honduras. In a population of 24,696 adults with 176 isolated villages in western Honduras, we collected data on household food insecurity and physical and mental health outcome measures. Using logistic regression with and without adjusting for village and household level unobservables invariant across individual respondents, we show that females (OR: 1.11, p <0.01)), indigenous people (OR: 2:00, p < 0.01), and those planning to migrate (OR: 1.24, p <0.01) have higher odds of experiencing food insecurity. The risks of food insecurity and poor health were mitigated among respondents living where they were born and having multi-generations of relatives living in the same village-a measure of the opportunity and availability of social networks. Living in a food insecure compared to a food secure household was associated with 77 percent higher odds of being depressed, 35 percent higher odds of low overall mental health, and 20 percent higher odds for low overall health.

Modelo estadístico para estimar el impacto histórico de la influenza sobre la mortalidad en Cuba / Statistical model to estimate the historical impact of influenza on mortality in Cuba

Introducción: La influenza tiene elevado impacto en la mortalidad humana y en Cuba la categoría influenza y neumonía ocupa el cuarto lugar entre sus causales generales. En los países templados, con marcada estacionalidad, esto se capta con modelos estadísticos, tarea que se dificulta en el trópico y pendiente en Cuba por la ausencia de igual definición estacional. Objetivo: Estimar el impacto histórico de la influenza tipo A y B y los subtipos A(H3N2) y A(H1N1) sobre la mortalidad mediante el ajuste de un modelo de regresión a las condiciones estacionales específicas de Cuba. Métodos: Se ejecutó un estudio longitudinal y retrospectivo. En un primer paso se ajustaron dos modelos de Poisson con la mortalidad influenza y neumonía total y las personas ≥ 65 años de edad como variables respuestas en los cinco meses de mayor positividad en influenza, desde la temporada 1987-1988 hasta la 2004-2005 y los positivos en tipo A y en tipo B como explicatorias. En otro par de modelos se estimó el impacto del A(H3N2) y el A(H1N1), considerando como respuesta los fallecidos atribuidos previamente al tipo A. Resultados: Se atribuyeron a la influenza 7803 fallecidos entre todas las edades y 6152 entre las personas ≥ 65 años de edad, con un 56,3 por ciento asociados al A(H3N2), el 17,6 por ciento al A(H1N1) y el 26,1 por ciento al tipo B. Conclusiones. Se logró estimar el impacto de la influenza sobre la mortalidad mediante el ajuste para Cuba de un modelo estadístico que permitió demostrar la asociación de la circulación de estos virus con la mortalidad en el país, lo que ratifica la necesidad de reforzar la vigilancia, el control y la vacunación contra esta infección viral. Se demuestra la posibilidad de ajustar estos modelos de regresión a otros virus respiratorios y a la actual pandemia por la COVID-19, en las condiciones estacionales de Cuba(AU)

Introduction: Influenza has a high impact on human mortality and in Cuba influenza and pneumonia rank fourth among its general causes. In temperate climate countries, with marked seasonality, this is captured by statistical models, a task that is difficult in the tropics and pending in Cuba due to the absence of the same seasonal definition. Objective: Estimate the historical impact of influenza type A and B and subtypes A(H3N2) and A(H1N1) on mortality, by adjusting a regression model to the specific seasonal conditions of Cuba. Methods: A longitudinal and retrospective study was performed. In a first step, two Poisson models were adjusted with influenza and total pneumonia mortality and people ≥ 65 years old as response variables in the five months with the highest positivity to influenza in the period 1987-1988 to 2004-2005, and the positive ones to type A and type B as explanatory variables. In another pair of models was estimated the impact of A(H3N2) and A(H1N1), considering as a response the deaths previously attributed to type A. Results: 7 803 deaths among all ages and 6 152 among 65-year-olds were attributed to influenza, with 56.3 percent associated to A(H3N2), 17.6 percent to A(H1N1) and 26.1 percent to type B. Conclusions: It was possible to estimate the impact of influenza on mortality by adjusting for Cuba a statistical model that demonstrated the association of the circulation of these viruses with the mortality in the country, which confirms the need to strengthen surveillance, control and vaccination against this viral infection. The possibility of adjusting in the seasonal conditions of Cuba these regression models to other respiratory viruses and the current pandemic by COVID-19 is demonstrated(AU)

Comparison between Highly Complex Location Models and GAMLSS.

This paper presents a discussion regarding regression models, especially those belonging to the location class. Our main motivation is that, with simple distributions having simple interpretations, in some cases, one gets better results than the ones obtained with overly complex distributions. For instance, with the reverse Gumbel (RG) distribution, it is possible to explain response variables by making use of the generalized additive models for location, scale, and shape (GAMLSS) framework, which allows the fitting of several parameters (characteristics) of the probabilistic distributions, like mean, mode, variance, and others. Three real data applications are used to compare several location models against the RG under the GAMLSS framework. The intention is to show that the use of a simple distribution (e.g., RG) based on a more sophisticated regression structure may be preferable than using a more complex location model.

Augmented-limited regression models with an application to the study of the risk perceived using continuous scales.

Studies of risk perceived using continuous scales of [0,100] were recently introduced in psychometrics, which can be transformed to the unit interval, but the presence of zeros or ones are commonly observed. Motivated by this, we introduce a full inferential set of tools that allows for augmented and limited data modeling. We considered parameter estimation, residual analysis, influence diagnostic and model selection for zero-and/or-one augmented beta rectangular (ZOABR) regression models and their particular nested models, which is based on a new parameterization of the beta rectangular distribution. Different from other alternatives, we performed maximum-likelihood estimation using a combination of the EM algorithm (for the continuous part) and Fisher scoring algorithm (for the discrete part). Also, we perform an additional step, by considering other link functions, besides the usual logistic link, for modeling the response mean. By considering randomized quantile residuals, (local) influence diagnostics and model selection tools, we identified that the ZOABR regression model is the best one. We also conducted extensive simulations studies, which indicate that all developed tools work properly. Finally, we discuss the use of this type of models to treat psychometric data. It is worthwhile to mention that applications of the developed methods go beyond to Psychometric data. Indeed, they can be useful when the response variable in bounded, including or not the respective limits.

Linear censored regression models with skew scale mixtures of normal distributions.

A special source of difficulty in the statistical analysis is the possibility that some subjects may not have a complete observation of the response variable. Such incomplete observation of the response variable is called censoring. Censorship can occur for a variety of reasons, including limitations of measurement equipment, design of the experiment, and non-occurrence of the event of interest until the end of the study. In the presence of censoring, the dependence of the response variable on the explanatory variables can be explored through regression analysis. In this paper, we propose to examine the censorship problem in context of the class of asymmetric, i.e., we have proposed a linear regression model with censored responses based on skew scale mixtures of normal distributions. We develop a Monte Carlo EM (MCEM) algorithm to perform maximum likelihood inference of the parameters in the proposed linear censored regression models with skew scale mixtures of normal distributions. The MCEM algorithm has been discussed with an emphasis on the skew-normal, skew Student-t-normal, skew-slash and skew-contaminated normal distributions. To examine the performance of the proposed method, we present some simulation studies and analyze a real dataset.

Normative data of neuropsychological tests of attention and executive functions in Ecuadorian adult population.

OBJECTIVE: The purpose of this study was to generate normative data for five tests of attention and executive functions (M-WCST, Stroop test, TMT, BTA, and SDMT), in a group of 322 Ecuadorian adults from Quito between the ages of 18 and 85. METHOD: Multiple regression analyzes taking into account age, education, and gender were used to generate the normative data. RESULTS: Age and education were significantly related to test performance such that scores decreased with age and improved as a function of education. An online calculator is provided to generate normative test scores. CONCLUSIONS: This is the first study that presents normative data for tests of executive functions and attention in an Ecuadorian adult population. This data will improve the clinical practice of neuropsychology and help to develop the field in the country.

The assessment of leading traits in the taxonomy of the Bacillus cereus group.

Bacillus cereus sensu lato strains (B. cereus group) are widely distributed in nature and have received interest for decades due to their importance in insect pest management, food production and their positive and negative repercussions in human health. Consideration of practical uses such as virulence, physiology, morphology, or ill-defined features have been applied to describe and classify species of the group. However, current comparative studies have exposed inconsistencies between evolutionary relatedness and biological significance among genomospecies of the B. cereus group. Here, the combined analyses of core-based phylogeny and all versus all Average Nucleotide Identity values based on 2116 strains were conducted to update the genomospecies circumscriptions within B. cereus group. These analyses suggested the existence of 57 genomospecies, 37 of which are novel, thus indicating that the taxonomic identities of more than 39% of the analyzed strains should be revised or updated. In addition, we found that whole-genome in silico analyses were suitable to differentiate genomospecies such as B. anthracis, B. cereus and B. thuringiensis. The prevalence of toxin and virulence factors coding genes in each of the genomospecies of the B. cereus group was also examined, using phylogeny-aware methods at wide-genome scale. Remarkably, Cry and emetic toxins, commonly assumed to be associated with B. thuringiensis and emetic B. paranthracis, respectively, did not show a positive correlation with those genomospecies. On the other hand, anthrax-like toxin and capsule-biosynthesis coding genes were positively correlated with B. anthracis genomospecies, despite not being present in all strains, and with presumably non-pathogenic genomospecies. Hence, despite these features have been so far considered relevant for industrial or medical classification of related species of the B. cereus group, they were inappropriate for their circumscription. In this study, genomospecies of the group were accurately affiliated and representative strains defined, generating a rational framework that will allow comparative analysis in epidemiological or ecological studies. Based on this classification the role of specific markers such as Type VII secretion system, cytolysin, bacillolysin, and siderophores such as petrobactin were pointed out for further analysis.