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In this study, we assessed the psychometric properties of the Spanish Patient-Reported Outcomes Measurement Information System (PROMIS) Depression in an adult population-representative sample from Spain (n = 1,503). We tested unidimensionality and local independence item response theory (IRT) assumptions with confirmatory factor and bifactor models under the exploratory structural equations modeling framework. We evaluated item monotonicity assumption with Mokken scaling analysis. We calibrated the items with an IRT-graded response model and assessed score reliability and test information, and evidence of validity with regard to scores on external measures. To examine differential item functioning by age, sex, education, and country (United States vs. Spain, N = 2,271), we used ordinal logistic regression. Results support compliance with IRT assumptions. We found few signs of differential item functioning: Only one item showed country differential functioning between the United States (n = 768) and Spain, with minimal impact on the overall score. Information values were equivalent to reliabilities over 0.90 from -1 (low depression) to +4 SD (high depression) around the population score mean. Evidence of validity in relation to concurrent measures was supported by the expected correlation pattern with external variables of depression, but higher than expected correlations with anxiety were found. Results indicate that the Spanish version of PROMIS Depression is adequate for assessing and monitoring depression levels in the general population and that PROMIS Depression is especially suitable for cross-national comparisons. (PsycINFO Database Record (c) 2019 APA, all rights reserved). [ABSTRACT FROM AUTHOR]

This study aims to characterize at landscape level the spatio-temporal dynamics of a massive oak decline that is occurring in dehesas ecosystems. We are looking at possibilities of matching with Phytophthora disease behavior, a harmful disease detected in the studied area, in order to interpret its implications within the context of the disease management. Spatial locations of affected trees from 2001, 2009 and 2016 identified through photointerpretation were analyzed with the inhomogeneous Ripley's K-function to assess their spatial pattern. Multivariate Adaptive Regression Splines (MARS), a non-parametric data mining method, was used to investigate the influence of a range of landscape descriptors of different nature on the proneness to oak decline, using the location of affected trees in comparison with that of healthy spots (points randomly extracted from areas covered by healthy trees). Affected trees showed a strong clustering pattern that decreased over time. The reported spatial patterns align with the hypothesis of Phytophthora cinnamomi Rands. being the main cause of oak decline in Mediterranean forests. Location of affected trees detected in different years was found to be spatially related, suggesting the implication of a contagion process. MARS models from 2001, 2009 and 2016 reported Area Under the Curve (AUC) values of 0.707, 0.671 and 0.651, respectively. Slope was the most influential landscape descriptor across the three years, with distance to afforestations being the second for 2001 and 2009. Landscape descriptors linked to human factors and soil water content seem to influence oak decline caused by Phytophthora cinnamomi at landscape level. Afforestations carried out as part of the afforestation subsidy program promoted by the European Commission in 1992 could have acted as an initial source of Phytophthora cinnamomi infection. These findings together with the consideration of the spatial and temporal scale of the spreading are essential when planning the management of oak decline in open woodlands. • Exponential increase oak decline since 2001 compatible with Phytophthora dynamics. • Afforestations could have acted as the initial source of tree decaying. • Human influence and soil water content seem to favor occurrence of the disease. • Disease evolves to different stages of maturity where primary factors loss importance. • Temporal and spatial scale are of key importance when studying the decaying process. [ABSTRACT FROM AUTHOR]