Discusion

Resumen

En esta investigación se estudia la validez de una versión castellana de la escala «Environmental Concern Scale» (ECS) llevada a cabo con 716 estudiantes. Las puntuaciones obtenidas por los sujetos proambientalistas y no proambientalistas resultan significativamente diferentes. Asímismo, se estudian las relaciones entre las actitudes hacia la preocupación por el medio ambiente, la energía nuclear y la ideología política, mostrándose una relación entre ellas de diferente intensidad. Mientras la energía nuclear está fuertemente vinculada a la ideología política, no sucede en tal medida entre ésta y las actitudes conservacionistas, ya que tanto los de derechas como los de izquierdas participan de la preocupación por el medio ambiente, aunque no en la misma medida.     

Effects of air pollution and weather on the initial COVID-19 outbreaks in United States,Italy, Spain,and China: A comparative study

Contrasting effects have been identified in association of weather (temperature and humidity) and pollutant gases with COVID-19 infection, which could be derived from the influence of lockdowns and season change. The influence of pollutant gases and climate during the initial phases of the pandemic, before the closures and the change of season in the northern hemisphere, is unknown. Here, we used a spatial-temporal Bayesian zero-inflated-Poisson model to test for short-term associations of weather and pollutant gases with the relative risk of COVID-19 disease in China (first outbreak) and the countries with more cases during the initial pandemic (the United States, Spain and Italy), considering also the effects of season and lockdown. We found contrasting association between pollutant gases and COVID-19 risk in the United States, Italy, and Spain, while in China it was negatively associated (except for SO₂). COVID-19 risk was positively associated with specific humidity in all countries, while temperature presented a negative effect. Our findings showed that short-term associations of air pollutants with COVID-19 infection vary strongly between countries, while generalized effects of temperature (negative) and humidity (positive) with COVID-19 was found. Our results show novel information about the influence of pollution and weather on the initial outbreaks, which contribute to unravel the mechanisms during the beginning of the pandemic.     

A novel quantification of information for longitudinal data analyzed by mixed‐effects modeling

Nonlinear mixed‐effects (NLME) modeling is one of the most powerful tools for analyzing longitudinal data especially under the sparse sampling design. The determinant of the Fisher information matrix is a commonly used global metric of the information that can be provided by the data under a given model. However, in clinical studies, it is also important to measure how much information the data provide for a certain parameter of interest under the assumed model, for example, the clearance in population pharmacokinetic models. This paper proposes a new, easy‐to‐interpret information metric, the “relative information” (RI), which is designed for specific parameters of a model and takes a value between 0% and 100%. We establish the relationship between interindividual variability for a specific parameter and the variance of the associated parameter estimator, demonstrating that, under a “perfect” experiment (eg, infinite samples or/and minimum experimental error), the RI and the variance of the model parameter estimator converge, respectively, to 100% and the ratio of the interindividual variability for that parameter and the number of subjects. Extensive simulation experiments and analyses of three real datasets show that our proposed RI metric can accurately characterize the information for parameters of interest for NLME models. The new information metric can be readily used to facilitate study designs and model diagnosis.     

Factors of regional poverty reduction in Colombia: Do institutional conditions matter?

Colombia has a unique history, which has been heavily conditioned by armed conflict lasting more than 50 years. This study examines the institutional conditions for success and failure in reducing poverty in Colombian departments by considering changes that took place between 2003 and 2014. Fuzzy‐set qualitative comparative analysis identifies the changes in regional conditions that reduce poverty over time. The pathways for poverty reduction are multidimensional, and many involve changes in institutional attributes such as government transparency, absence of violence, and electoral turnout. The framework developed in this paper can be used to monitor necessary and sufficient pathways in regional clusters.     

Educational Differences in Desired Family Size and Attitudes Toward Childbearing in Latina Women

Attitudes toward childbearing and desired family size influence women's reproductive choices. This may be especially true in the Latina culture, where motherhood and large families are valued highly. To determine whether these attitudes differ by educational status, Latina women in an OB/GYN clinic (N=351) completed a survey assessing attitudes toward childbearing and desired family size. Attitudes of women with and without a high school education were compared. Respondents without a high school education desired a mean of 3.1 children, while those with a high school education desired a mean of 2.7. Women without a high school education endorsed more attitudes favoring large families. Among women without a high school education, those who wished to have a son to carry on the family name tended to desire larger families. Results suggest that the desire for large families among Latina women with low education may result from traditional cultural attitudes.     

GIS without GPS: new opportunities in technology and survey research to link people and place

This paper presents innovative ways to relate survey data to GIS maps, thereby making the connection of people and place more accessible for the research community. Based on data from rural areas in the Brazilian Amazon, we describe a successful effort to sample households while linking farm-level data to property boundaries, these boundaries generated from subjects’ interpretations of satellite images on a computer screen. The sampling framework is based on legislation requiring farmers to report to a government agency in a four-week period, and the farmers’ input allows for a more efficient means of identifying property boundaries as compared to GPS. We show that the resulting sampling is statistically representative. We discuss the potential of this association of institutional design and low-cost methods of data collection to allow for more cost-effective generation of spatial data and of geospatial analysis.     

Percentiles of sums of heavy-tailed random variables: beyond the single-loss approximation

A perturbative approach is used to derive approximations of arbitrary order to estimate high percentiles of sums of positive independent random variables that exhibit heavy tails. Closed-form expressions for the successive approximations are obtained both when the number of terms in the sum is deterministic and when it is random. The zeroth order approximation is the percentile of the maximum term in the sum. Higher orders in the perturbative series involve the right-truncated moments of the individual random variables that appear in the sum. These censored moments are always finite. As a result, and in contrast to previous approximations proposed in the literature, the perturbative series has the same form regardless of whether these random variables have a finite mean or not. For high percentiles, and specially for heavier tails, the quality of the estimate improves as more terms are included in the series, up to a certain order. Beyond that order the convergence of the series deteriorates. Nevertheless, the approximations obtained by truncating the perturbative series at intermediate orders are remarkably accurate for a variety of distributions in a wide range of parameters.