Evaluation of Policy Effectiveness by Mathematical Modeling for the Opioid Crisis with Spatial Study and Trend Analysis

The current opioid epidemic in the US presents a great problem which calls for policy supervision and regulation. In this work, the opioid cases of five states were used for trend analysis and modeling for the estimation of potential policy effects. An evaluation model was established to analyze the severity of the opioid abuse based on the entropy weight method (EWM) and rank sum ratio (RSR). Four indexes were defined to estimate the spatial distribution of development and spread of the opioid crisis. Thirteen counties with the most severe opioid abuse in five states were determined using the EWM-RSR model and those indexes. Additionally, a forecast of the development of opioid abuse was given based on an autoregressive (AR) model. The RSR values of the thirteen counties would increase to the range between 0.951 and 1.226. Furthermore, the least absolute shrinkage and selection operator (LASSO) method was adopted. The previous indexes were modified, incorporating the comprehensive socioeconomic effects. The optimal penalty term was found to facilitate the stability and reliability of the model. By using the comprehensive model, it was found that three factors—VC112, VC114, VC115—related to disabled people have a great influence on the development of opioid abuse. The simulated policies were performed in the model to decrease the values of the indicators by 10%–50%. The corresponding RSR values can decline to the range between 0.564 and 0.606. Adopting policies that benefit the disabled population should inhibit the trend of opioid abuse.

Do Textbooks Matter for Reading Comprehension? A Study in Flemish Primary Education

This study assessed whether textbooks affect academic performance and engagement in reading comprehension in primary education in Flanders (Belgium). The data of the Progress in International Reading Literacy Study 2016 and a reassessment of this study in 2018 were used to describe students’ learning progress in reading comprehension and evolution in engagement between the fourth and sixth grade. The sample consisted of 3051 students in 98 schools. The averages of students’ learning progress and engagement were compared for five textbooks by using multilevel autoregression model and multilevel change score models. Contrasts between textbooks in average learning progress and engagement were also estimated. To control for differences between student populations that are educated with the different textbooks, we controlled for student’s socioeconomic status, language and initial academic performance in fourth grade at the student- and school-level. The main hypotheses were that textbooks affect learning progress and reading engagement. This was based on the literature and prior (mainly) cross-sectional research which describe textbooks as playing an important role in the curriculum that is taught to students on a daily basis. The results of both models showed that textbooks do not affect student’s average learning progress in reading comprehension and evolution in engagement between the fourth grade and sixth grade in Flanders. Hence, the hypotheses were rejected.

Changes in Landscape Greenness and Climatic Factors over 25 Years (1989-2013) in the USA

Monitoring and quantifying changes in vegetation cover over large areas using remote sensing can be achieved using the Normalized Difference Vegetation Index (NDVI), an indicator of greenness. However, distinguishing gradual shifts in NDVI (e.g., climate related-changes) versus direct and rapid changes (e.g., fire, land development) is challenging as changes can be confounded by time-dependent patterns, and variation associated with climatic factors. In the present study, we leveraged a method that we previously developed for a pilot study to address these confounding factors by evaluating NDVI change using autoregression techniques that compare results from univariate (NDVI vs. time) and multivariate analyses (NDVI vs. time and climatic factors) for 7,660,636 1 km × 1 km pixels comprising the 48 contiguous states of the USA, over a 25-year period (1989-2013). NDVI changed significantly for 48% of the nation over the 25-year period in the univariate analyses where most significant trends (85%) indicated an increase in greenness over time. By including climatic factors in the multivariate analyses of NDVI over time, the detection of significant NDVI trends increased to 53% (an increase of 5%). Comparisons of univariate and multivariate analyses for each pixel showed that less than 4% of the pixels had a significant NDVI trend attributable to gradual climatic changes while the remainder of pixels with a significant NDVI trend indicated that changes were due to direct factors. While most NDVI changes were attributable to direct factors like wildfires, drought or flooding of agriculture, and tree mortality associated with insect infestation, these conditions may be indirectly influenced by changes in climatic factors.