Association of Dietary Intake of Polyphenols with an Adequate Nutritional Profile in Postpartum Women from Argentina.

HJ-Biplot analysis is a multivariate graphic representation that collects data covariation structure between variables and individuals to represent them in a low-dimensional space with the highest quality in the same reference system. Consequently, it is a promising technique for evaluating dietary exposure to polyphenols and accurately characterizing female nutrition. Herein, we hypothesized that polyphenol intake defines specific clusters with dietary impacts, which can be assessed using HJ-Biplot, based on a cross-sectional study in Argentina. The study included 275 healthy postpartum women who provided information about their food frequency intake and other conditions, which were then used to evaluate polyphenolic intake using the Phenol-Explorer database. Outcomes were established using HJ-Biplot for clustering and ANOVA to compare their impact on diet quality indicators. Two HJ-Biplot models were run (for intakes >20 mg/d and 5∼20 mg/d, respectively) to identify three clusters per model with excellent statistical fitness to explain the data. Thus, specific polyphenolic clusters with potentially bioactive and safe compounds were defined despite significant interindividual variability. In fact, women with the lowest polyphenolic intake exhibited worse dietary quality, body fat, and physical activity. As a result, HJ-Biplot proved to be an effective technique for clustering women based on their dietary intake of these compounds. Furthermore, cluster membership improved the intake of antioxidants, water, fiber, and healthy fats. Additionally, women with formal jobs and a higher educational level showed a better diet. Dietary polyphenols are critical during postpartum because they exert beneficial effects on women and breastfed infants.

Clúster espacial de mortalidad por cáncer de mama en Ecuador / Space cluster of mortality for breast cancer in Ecuador

En la actualidad, los análisis de distribución espacial mediante el uso de técnicas de clusters para enfermedades crónicas como el cáncer de mama, son relevantes para la identificación de patrones espaciales de la mortalidad por cáncer según áreas geográficas. Identificar clústeres espaciales de la mortalidad por cáncer de mama en mujeres a nivel de las provincias del Ecuador, entre 2004 al 2018. Estudio observacional, de tipo descriptivo, ecológico multigrupal que compara a nivel espacio ­ temporal las tasas de mortalidad por cáncer de mama en mujeres según las provincias del Ecuador, utilizando el índice de Móran para el análisis de autocorrelación y el algoritmo de k-medias para el análisis de agrupamiento en períodos quinquenales mediante el programa informático ArcGIS versión 10.5. Resultados. En el Ecuador, el 86,5% de las muertes por cáncer de mama en mujeres se registraron en el área urbana, dichas muertes tienen un patrón no aleatorio según el índice de Morán, distinto al área rural que tiene un patrón aleatorio; se identificó diferencia en el agrupamiento de la mortalidad por cáncer de mama en las provincias urbanas y rurales, donde se obtuvo para el área urbana, clústeres con altas, media-altas, media-baja y bajas tasas de mortalidad, mientras que en lo rural se obtuvieron solo clústeres con altas, medias y bajas tasas de mortalidad. La distribución espacial y el análisis de agrupamiento identificó clústeres de la mortalidad por cáncer de mama en el Ecuador, evidenciando entre lo urbano y rural diferencias en los clústeres obtenidos, siendo esta información de utilidad para la implementación de estrategias de control del cáncer en el país.

Currently spatial distribution analyzes through the use of cluster techniques for chronic diseases such as breast cancer are revealing for the identification of spatial patterns of cancer mortality according to geographic areas. Objective. Identify spatial clusters of breast cancer mortality in women at the level of the provinces of Ecuador, between 2004 to 2018. We used an observational, descriptive, ecological multigroup study that compares at a Spatio-temporal level the rates of breast cancer mortality in women according to the provinces of Ecuador, using the Moran index for the autocorrelation analysis and the k-, means algorithm for cluster analysis in five-year periods using the ArcGIS version 10.5 software. Results. In Ecuador, 86.5% of breast cancer deaths in women were recorded in the urban area, these deaths have a non-random pattern according to the Morán Index different from the rural area that has a random pattern; difference was identified in the grouping of breast cancer mortality in urban and rural provinces, where it was obtained for urban areas, clusters with high, medium. high, medium-low and low mortality rates. While in rural areas only clusters with high, medium and low mortality rates were obtained. Conclusions. The spatial distribution and cluster analysis identified clusters of breast cancer mortality in Ecuador; evidencing between urban and rural differences in the clusters obtained, this information is useful for the development of cancer control strategies in the country.

Análisis clúster para big data: una aplicación con variables demográficas en provincias del Ecuador / Cluster analysis for big data: an application with demographic variables in provinces of Ecuador

Los métodos de clasificación permiten explorar y analizar grandes conjuntos de datos visualmente, lo cual es de gran utilidad para tomar decisiones rápidas. El objetivo fue comparar dos métodos de análisis de clúster para big data en variables demográficas de las provincias del Ecuador. Se hizo uso de un estudio observacional de tipo comparativo mediante la representación simultanea del HJ-Biplot y el método Two Step (clúster bietápico), a través del software MultBiplot y SPSS. Los datos corresponden a variables demográficas de interés sociosanitarias tasa de mortalidad general, tasa de mortalidad infantil, tasa de natalidad, densidad poblacional, porcentaje urbano y esperanza de vida, medidas en las provincias del Ecuador. Se utilizaron datos provenientes del Instituto de Estadísticas y Censos INEC. Se analizó la asociación entre variables y se identificaron clústeres de las provincias del Ecuador según estas variables demográficas. Según la representación simultánea del HJ-Biplot se identificaron 3 clústeres, el clúster 1 son provincias con mayor densidad poblacional y tasas de mortalidad general, pero valores bajos de tasas de natalidad, el clúster 2 agrupa provincias con mayor esperanza de vida y tasas de mortalidad infantil pero bajos valores de tasa de natalidad y el clúster 3 están las provincias con valores altos de tasas de natalidad y valores bajos de densidad poblacional, esperanza de vida, tasas de mortalidad general y mortalidad infantil, distintos resultados se obtuvieron con el método Two Step. Se pudo concluir que estos métodos son de utilidad para explorar las similitudes entre las provincias según variables demográficas.

The classification methods allow to explore and analyze big data sets visually, which is very useful for making quick decisions. This work aimed to compare of two methods of cluster analysis for big data in demographic variables of the provinces of Ecuador. An observational study of comparative type was carried out through the simultaneous representation of the HJ/Biplot and the Two Step method (two-stage cluster), through the MultBiplot and SPSS software. The data correspond to demographic variables of socio-health interest, general mortality rate, infant mortality rate, birth rate, population density, urban percentage and life expectancy, measured in the provinces of Ecuador. Data from Statistics and Census Institute were used. The association between variables was analyzed and clusters of the provinces of Ecuador were identified according to these demographic variables. According to the simultaneous representation of the HJBiplot, 3 clusters were identified, cluster 1 are provinces with higher population density and general mortality rates, but low birth rates values, cluster 2 are provinces with higher life expectancy and mortality rates infantile but low birth rate values and cluster 3 are the provinces with high birth rates values and low population density, life expectancy, general mortality and infant mortality rates, different results were obtained with the Two Step method. It was concluded that these methods are useful for exploring the similarities between provinces according to demographic variables.

Seven methods to determine the dimensionality of tests: application to the General Self-Efficacy Scale in twenty-six countries / Siete métodos para evaluar la dimensionalidad de los test: aplicación a la General Self-Efficacy Scale en veintiséis países

BACKGROUND: One of the most important concepts within Cognitive Social Theory as framed by Bandura is the perceived self-efficacy; this concept became widespread in 1981 when Mathias Jerusalem and Ralf Schwarzer, using 10 items, established a one-dimensional and universal construct of this scale. The main purpose of this study is to show that the General Self-Efficacy Scale (GSE) is not a one-dimensional and universal construct, as is currently assumed. METHOD: The data from 19,719 people from 26 countries were analyzed. In order to identify and understand invariance we applied seven multivariate statistical techniques. RESULTS: The findings suggest the existence of a multidimensional structure and differential item functioning by country. Insofar as there is differential item functioning by country and it is not possible to universalize it, and there are several items on the scale that statistically constitute additional factors. The results confirm that the self-efficacy construct is neither universal nor unidimensional. CONCLUSIONS: A psychometric instrument must be valued and used with great care; the one in question is being used in a generalized way

ANTECEDENTES: uno de los conceptos más importantes en la Teoría Social Cognitiva desarrollada por Bandura es la auto-eficacia percibida. Este concepto ha sido generalizado en 1981 por Mathias Jerusalem and Ralf Schwarzer con una escala de 10 ítems, quienes establecieron que esta escala es un constructo unidimensional y universal. El objetivo principal de este trabajo es demostrar que la Escala General de Autoeficacia (GSE) no es un constructo unidimensional ni universal, como actualmente se asume. MÉTODO: los datos analizados corresponden a 19.719 personas de 26 países. Con el fin de identificar y entender la invariancia hemos utilizado siete técnicas estadísticas multivariantes. RESULTADOS: los hallazgos sugieren la existencia de una estructura multidimensional y un funcionamiento diferencial por país. En la medida que haya funcionamiento diferencial por país, no es posible universalizar el constructo. También existen varios ítems de la escala que constituyen factores adicionales. El resultado confirma que el constructo auto-eficacia no es universal ni unidimensional. CONCLUSIONES: un instrumento psicométrico debe ser evaluado y usado con extremo cuidado, la escala GSE analizada está siendo utilizada de manera general

Estimating the effect of emotional intelligence in wellbeing among priests / Estimación del efecto de la inteligencia emocional sobre el bienestar en sacerdotes

Background/Objective: Emotional intelligence is a variable which has been the subject of significantly increased research in recent years. Relationships have been shown with both physical and psychological problems. The aim of this study is to analyse the direct and indirect effect that emotional intelligence exerts over the manifestation of physical and psychological clinical symptomatology and to severe disorders such as burnout syndrome in a professional group in which social function is fundamental. Method: So that, 881 Latin-American catholic priests (M age = 45.89; SD age = 11.58) were evaluated using the Trait Meta-Mood Scale-24, the Maslach Burnout Inventory-22 and the General Health Questionnaire-28. Results: Using path analysis, emotional intelligence, especially Attention and Emotional Clarity, shows a high effect on psychological and somatic issues. Conclusions: Thus, both specific disorders and general wellbeing are related to emotional intelligence. Providing effective emotional intelligence training sessions seems to be able to reduce possible physical and emotional disorders (AU)

Antecedentes/Objetivo: La inteligencia emocional es una variable que ha sido objeto de un incremento notable de investigación a lo largo de los últimos años. La inteligencia emocional ha mostrado numerosas relaciones con múltiples variables psicológicas, destacando su conexión con diversos trastornos físicos y psicológicos. El objetivo del presente estudio es analizar los efectos directos e indirectos que la inteligencia emocional ejerce sobre la manifestación de sintomatología somática y psicológica, así como sobre el desarrollo de trastornos como el síndrome de burnout en un grupo de profesionales cuya labor social resulta esencial. Método: Se aplicaron las escalas Trait Meta-Mood Scale-24, Maslach Burnout Inventory-22 y General Health Questionnaire-28 a un total de 881 sacerdotes católicos latinoamericanos (M edad = 45,89; DT edad = 11,58). Resultados: Mediante path analysis se muestra el efecto que la inteligencia emocional, especialmente la atención y claridad emocional, ejerce sobre las alteraciones somáticas y psicológicas. Conclusiones: Tanto los trastornos específicos como el bienestar general presentan una relación clara con la inteligencia emocional. Proporcionar programas formativos efectivos que desarrollen la inteligencia emocional podría reducir la aparición de trastornos físicos y emocionales (AU)

Estimating the effect of emotional intelligence in wellbeing among priests.

Background/Objective: Emotional intelligence is a variable which has been the subject of significantly increased research in recent years. Relationships have been shown with both physical and psychological problems. The aim of this study is to analyse the direct and indirect effect that emotional intelligence exerts over the manifestation of physical and psychological clinical symptomatology and to severe disorders such as burnout syndrome in a professional group in which social function is fundamental. Method: So that, 881 Latin-American catholic priests (M age = 45.89; SD age = 11.58) were evaluated using the Trait Meta-Mood Scale-24, the Maslach Burnout Inventory-22 and the General Health Questionnaire-28. Results: Using path analysis, emotional intelligence, especially Attention and Emotional Clarity, shows a high effect on psychological and somatic issues. Conclusions: Thus, both specific disorders and general well-being are related to emotional intelligence. Providing effective emotional intelligence training sessions seems to be able to reduce possible physical and emotional disorders.

Antecedentes/Objetivo: La inteligencia emocional es una variable que ha sido objeto de un incremento notable de investigación a lo largo de los últimos años. La inteligencia emocional ha mostrado numerosas relaciones con múltiples variables psicológicas, destacando su conexión con diversos trastornos físicos y psicológicos. El objetivo del presente estudio es analizar los efectos directos e indirectos que la inteligencia emocional ejerce sobre la manifestación de sintomatología somática y psicológica, así como sobre el desarrollo de trastornos como el síndrome de burnout en un grupo de profesionales cuya labor social resulta esencial. Método: Se aplicaron las escalas Trait Meta-Mood Scale-24, Maslach Burnout Inventory-22 y General Health Questionnaire-28 a un total de 881 sacerdotes católicos latinoamericanos (M edad = 45,89; DT edad = 11,58). Resultados: Mediante path analysis se muestra el efecto que la inteligencia emocional, especialmente la atención y claridad emocional, ejerce sobre las alteraciones somáticas y psicológicas. Conclusiones: Tanto los trastornos específicos como el bienestar general presentan una relación clara con la inteligencia emocional. Proporcionar programas formativos efectivos que desarrollen la inteligencia emocional podría reducir la aparición de trastornos físicos y emocionales.

Introducing Meta-Partition, a Useful Methodology to Explore Factors That Influence Ecological Effect Sizes.

The study of the heterogeneity of effect sizes is a key aspect of ecological meta-analyses. Here we propose a meta-analytic methodology to study the influence of moderators in effect sizes by splitting heterogeneity: meta-partition. To introduce this methodology, we performed a meta-partition of published data about the traits that influence species sensitivity to habitat loss, that have been previously analyzed through meta-regression. Thus, here we aim to introduce meta-partition and to make an initial comparison with meta-regression. Meta-partition algorithm consists of three steps. Step 1 is to study the heterogeneity of effect sizes under the assumption of fixed effect model. If heterogeneity is found, we perform step 2, that is, to partition the heterogeneity by the moderator that minimizes heterogeneity within a subset while maximizing heterogeneity between subsets. Then, if effect sizes of the subset are still heterogeneous, we repeat step 1 and 2 until we reach final subsets. Finally, step 3 is to integrate effect sizes of final subsets, with fixed effect model if there is homogeneity, and with random effects model if there is heterogeneity. Results show that meta-partition is valuable to assess the importance of moderators in explaining heterogeneity of effect sizes, as well as to assess the directions of these relations and to detect possible interactions between moderators. With meta-partition we have been able to evaluate the importance of moderators in a more objective way than with meta-regression, and to visualize the complex relations that may exist between them. As ecological issues are often influenced by several factors interacting in complex ways, ranking the importance of possible moderators and detecting possible interactions would make meta-partition a useful exploration tool for ecological meta-analyses.

Clustering of lifestyle characteristics and their association with cardio-metabolic health: the Lifestyles and Endothelial Dysfunction (EVIDENT) study.

Little is known about the clustering patterns of lifestyle behaviours in adult populations. We explored clusters in multiple lifestyle behaviours including physical activity (PA), smoking, alcohol use and eating habits in a sample of adult population. A cross-sectional and multi-centre study was performed with six participating groups distributed throughout Spain. Participants (n 1327) were part of the Lifestyles and Endothelial Dysfunction (EVIDENT) study and were aged between 20 and 80 years. The lifestyle and cardiovascular risk (CVR) factors were analysed using a clustering method based on the HJ-biplot coordinates to understand the variables underlying these groupings. The following three clusters were identified. Cluster 1: unhealthy, 677 subjects (51%), with a slight majority of men (58.7%), who were more sedentary and smokers with higher consumption of whole-fat dairy products, bigger waist circumference as well as higher TAG levels, systolic blood pressure (SBP) and CVR. Cluster 2: healthy/PA, 265 subjects (20%), including 24.0% of males with high PA. Cluster 3: healthy/diet, including 29% of the participants, with a higher consumption of olive oil, fish, fruits, nuts, vegetables and lower alcohol consumption. Using the unhealthy cluster as a reference, and after adjusting for age and sex, the multiple regression analysis showed that belonging to the healthy/PA cluster was associated with a lower waist circumference, body fat percentage, SBP and CVR. In summary, the three clusters were identified according to lifestyles. The 'unhealthy' cluster had the least favourable clinical parameters, the 'healthy/PA' cluster had good HDL-cholesterol levels and low SBP and the 'healthy/diet' cluster had lower LDL-cholesterol levels and clinical blood pressure.