Explanatory Variables of Dropout in Colombian Public Education: Evolution Limited to Coronavirus Disease

<p style="text-align: justify;">Student dropout, defined as the temporary or definitive suspension of the exercise of the right to education, is attributable to multiple variables classified into individual, academic, institutional, and socioeconomic determinants which may be exacerbated in the context of the Coronavirus disease (COVID-19) pandemic. Consequently, this work aims to synthesize, from the available evidence, the behaviour and influence of the explanatory variables of school dropout in infant school, primary school and, high school in Colombia for the period 2014-2019 compared to the period 2020-2021 under the COVID-19 pandemic conditions. The research methodology consisted of a systematic review of 125 indexed articles for 2014-2019 and 32 reports related to dropout in Colombian Basic education for the 2020-2021 period. The systematic review of the 157 articles revealed that dropout was studied and explained in both time periods, mainly from the academic determinant whose most cited explanatory variables were: ‘teachers’, ‘curriculum’ and ‘methodologies used’. Moreover, it could be perceived that in the period 2014-2019, the socioeconomic variable was the second dropout determinant, considering ‘family income” as the most important indicator, while in 2020-2021 the “infrastructure” and the ‘political environment’ remained as the most dominant. Lastly, in 2020-2021, the variable ‘teachers’ was highly cited showing that their practice made students maintain their interest despite the physical distance.</p>

Improved prediction of new COVID-19 cases using a simple vector autoregressive model: Evidence from seven New York State counties

With the rapid spread of COVID-19, there is an urgent need for a framework to accurately predict COVID-19 transmission. Recent epidemiological studies have found that a prominent feature of COVID-19 is its ability to be transmitted before symptoms occur, which is generally not the case for seasonal influenza and SARS. Several COVID-19 predictive epidemiological models have been proposed; however, they share a common drawback-they are unable to capture the unique asymptomatic nature of COVID-19 transmission. Here, we propose vector autoregression (VAR) as an epidemiological county-level prediction model that captures this unique aspect of COVID-19 transmission by introducing newly infected cases in other counties as lagged explanatory variables. Using the number of new COVID-19 cases in seven New York State counties, we predicted new COVID-19 cases in the counties over the next 4 weeks. We then compared our prediction results with those of 11 other state-of-the-art prediction models proposed by leading research institutes and academic groups. The results showed that VAR prediction is superior to other epidemiological prediction models in terms of the root mean square error of prediction. Thus, we strongly recommend the simple VAR model as a framework to accurately predict COVID-19 transmission.

Determinants of Inorganic Fertilizer Use Intensity on Cereal Crop among Smallholder Farmers: The Case of Toke Kutaye District, West Shewa Zone, Oromia National Regional State, Ethiopia

The study was aimed to analysis determinants of inorganic fertilizer use intensity on cereal crops among small holders in Toke Kutaye District, West Shewa Zone, Ethiopia. Correctional data were collected from 156 respondents using two stage random sampling methods. Data analyses were carried out using descriptive statistics and Double hurdle model. Result of the first hurdle reveals that out of twelve explanatory variables Sex ,Education, Off/non-farm income, Land size and Improved seed were determine positively whereas Age and Distance from nearest market determine small holders use of inorganic fertilizer negatively. The result of second stage of double hurdle model indicate that, out of twelve explanatory variables Sex, family size and Land size were positively affect extent (intensity) of inorganic fertilizer use whereas Age and Distance of household from nearest market determine use intensity negatively. Therefore, these results implied that there is a room to increase inorganic fertilizer use intensity on cereal crop productions. Hence, Farmers capacity to purchase this input beginning from lower income farmers to model farmers should be acknowledged; and should be designed the means to address those who have no ability to use inorganic fertilizer in their own farm through diverse development interventions.

Starting and Subsequent Earnings of Young Workers in Different Sized Firms

Based upon the highly innovative Project TALENT Longitudinal Database, this study tracks the starting earnings and subsequent early earnings growth of young males who began their work careers at either a smaller (<100 employees) or larger private firm more than a generation ago. Prior evidence based upon less rich databases found that earnings were systematically higher in larger firms but did not have access to many other variables that could affect projected earnings which are available in the TALENT database. Earnings regressions are estimated here including not only usual explanatory variables of years of schooling and labor market experience, but also adding other variables pertaining to prior job experience, military service, IQ, socioeconomic background and some other factors. The findings indicate that while starting earnings of those in this database were indeed higher in larger firms, the gap evaporated fairly quickly with projected earnings of those in smaller firms featuring a small earnings premium. The results here suggest guidance based upon the body of prior evidence may have been less reliable than thought, and that evidence itself may not provide as useful a baseline as desired for subsequent research addressing whether this pattern continues for recent cohorts.

COVID-19 risk perceptions and precautions among the elderly: A study of CALD adults in South Australia

Background: Coping with COVID-19 is a challenge for culturally and linguistically diverse (CALD) older adults. In Australia, little attention has been given to understanding associations between cultural contexts, health promotion, and socio-emotional and mental health challenges of older CALD adults during the COVID-19 pandemic. Therefore, we have collected data from older CALD adults to examine their COVID-19 risk perceptions and its association with their health precautions, behavioural dimensions and emergency preparation. Methods: A cross-sectional survey was conducted in South Australia. The CALD population aged 60 years and above were approached through 11 South Australian multicultural NGOs. Results: We provide the details of 155 older CALD South Australians’ demographics, risk perceptions, health precautions (problem-and-emotion-focused), behavioural dimensions and emergency preparation. The explanatory variables included demographic characteristics (age, gender, education and ethnicity); and risk perception (cognitive [likelihood of being affected] and affective dimension [fear and general concerns], and psychometric paradigm [severity, controllability, and personal impact]. The outcome measure variables were health precautions (problem-focused and emotion-focused), behavioral adaptions and emergency preparation. Conclusions: This dataset may help the researchers who investigate multicultural health or aged care in the pandemic and or who may have interest to link with other datasets and secondary use of this primary dataset in order to develop culturally tailored pandemic-related response plan. The data set is available from Harvard Dataverse.

Association of red blood cell and platelet transfusions with persistent inflammation, immunosuppression, and catabolism syndrome in critically ill patients

The objective of this single-center retrospective cohort study was to investigate the relationship between blood transfusion and persistent inflammation, immunosuppression, and catabolism syndrome (PIICS). The study was conducted at the Critical Care Center at Toho University Omori Medical Center, Japan. We included 391 patients in the PIICS group (hospitalization for > 15 days, C-reactive protein > 3.0 mg/dL or albumin < 3.0 mg/dL or lymph < 800/μL on day 14) and 762 patients in the non-PIICS group (hospitalization for > 15 days and not meeting the PIICS criteria). We performed univariate and multivariate logistic regression analyses using PIICS as the objective variable and red blood cell (RBC) or fresh frozen plasma or platelet (PLT) transfusion and other confounding factors as explanatory variables. In addition, we conducted a sensitivity analysis using propensity score matching analysis. The multivariate and propensity score analyses showed that RBC and PLT transfusions were significantly associated with PIICS. This is the first study to report an association between RBC and PLT transfusions and PIICS. Our findings have contributed to better understanding the risk factors of PIICS and suggest that physicians should consider the risk of PIICS occurrence when administering blood transfusions in intensive care unit (ICU) patients.

Model averaging with the hybrid model: An asymptotic study and demonstration

In this paper, we present a new model averaging technique that can be applied in medical research. The dataset is first partitioned by the values of its categorical explanatory variables. Then for each partition, a model average is determined by minimising some form of squared errors, which could be the leave-one-out cross-validation errors. From our asymptotic optimality study and the results of simulations, we demonstrate under several high-level assumptions and modelling conditions that this model averaging procedure may outperform jackknife model averaging, which is a well-established technique. We also present an example where a cross-validation procedure does not work (that is, a zero-valued cross-validation error is obtained) when determining the weights for model averaging.

Pre-Emptively Managing Overtourism by Promoting Rural Tourism in Low-Density Areas: Lessons from Madeira

Overtourism refers not only to situations in which carrying capacity levels have been exceeded, but also to those in which tourists and residents share negative feelings of discomfort and other emotions, loss of quality of life and unpleasant experiences in their activities of daily life. The growing number of places struggling with the problem of overtourism suggests that brand new approaches are required to minimize the effects of excessive tourism. However, the impacts of overtourism are place-specific and a one-size-fits-all approach is inappropriate. Many destinations still have a considerable margin to manoeuvre but are nonetheless heading towards increasingly unsustainable levels of tourists per square kilometer. Such regions have time to take some pre-emptive measures based on principles of sustainable development using greener and energy-saving technologies. Over the past few decades, degrowth has arisen as an unorthodox approach based on principles of fairness and social and environmental justice. In certain areas, such as island economies, the economic dynamics remain largely dependent on the tourism sector, which forces the local actors to think and act differently. In this study, we analyze the strategies employed by Madeira to counter the negative effects of oversaturation in a pre-emptive way. The findings of this case study, based on the data at the county level, are enhanced by a panel data analysis of a number of relevant explanatory variables explaining the dispersion of tourists to the rural hinterland. The results suggest that the development of the rural hinterland has proven capable of exerting a progressively positive influence well beyond the borders of the rural hinterland by accommodating a growing share of the increasing numbers of tourists welcomed in the region in the 2002–2019 period, at the expense of the main capital city. This study confirms the importance and potential of the development of the rural hinterland to tackle overtourism in the main tourism areas. In terms of recommendations, it is suggested that local operators and policy-makers must develop efforts to research new ways to adopt energy-saving projects and develop tourisms products that incorporate eco-friendly behaviors.

Reviewing taxonomic bias in a megadiverse country: primary biodiversity data, cultural salience, and scientific interest of South African animals

Taxonomic bias, resulting in some taxa receiving more attention than others, has been shown to persist throughout history. Such bias in primary biodiversity data needs to be addressed because the data are vital to environmental management. This study reviews taxonomic bias in South African primary biodiversity data obtained from the Global Biodiversity Information Facility (GBIF). The focus was specifically on animal classes, and regression analysis was used to assess the influence of scientific interest and cultural salience on taxonomic bias. A higher resolution analysis of the two explanatory variables’ influence on taxonomic bias is conducted using a generalised linear model on a subset of herpetofaunal families from the focal classes. Furthermore, the potential effects of cultural salience and scientific interest on a taxon’s extinction risk are investigated. The findings show that taxonomic bias in South Africa’s primary biodiversity data has similarities with global scale taxonomic bias. Among animal classes, there is strong bias towards birds while classes such as Polychaeta and Maxillopoda are under-represented. Cultural salience has a stronger influence on taxonomic bias than scientific interest. It is, however, unclear how these explanatory variables may influence the extinction risk of taxa. We recommend that taxonomic bias can be reduced if primary biodiversity data collection has a range of targets that guide (but do not limit) accumulation of species occurrence records per habitat. Within this range, a lower target of species occurrence records accommodates species that are difficult to detect. The upper target means occurrence records for any species are less urgent but nonetheless useful and thus data collection efforts can focus on species with fewer occurrence records.

Explaining Aggregated Recovery Rates

This study on explaining aggregated recovery rates (ARR) is based on the largest existing loss and recovery database for commercial loans provided by Global Credit Data, which includes defaults from 5 continents and over 120 countries. The dependence of monthly ARR from bank loans on various macroeconomic factors is examined and sources of their variability are stated. For the first time, an influence of stochastically estimated monthly growth of GDP USA and Europe is quantified. To extract monthly signals of GDP USA and Europe, dynamic factor models for panel data of different frequency information are employed. Then, the behavior of the ARR is investigated using several regression models with unshifted and shifted explanatory variables in time to improve their forecasting power by taking into account the economic situation after the default. An application of a Markov switching model shows that the distribution of the ARR differs between crisis and prosperity times. The best fit among the compared models is reached by the Markov switching model. Moreover, a significant influence of the estimated monthly growth of GDP in Europe is observed for both crises and prosperity times.