INSERLAB: una aplicación telemática para determinar la inserción laboral de los egresados andaluces

El presente artículo recoge el trabajo de un grupo de profesores de la Universidad de Jaén y Oviedo para desarrollar una aplicación telemática que permita identificar la inserción laboral de los egresados universitarios. Dicho trabajo tiene su origen en un proyecto de investigación obtenido en la convocatoria del 2004 sobre Grupos de Estudios y Análisis en temas de calidad sobre la Enseñanza Universitaria en Andalucía procedente de la UCUA (Unidad para la Calidad de las Universidades Andaluzas)

The ALHAMBRA survey: Estimation of the clustering signal encoded in the cosmic variance

[Aims]: The relative cosmic variance (σv) is a fundamental source of uncertainty in pencil-beam surveys and, as a particular case of count-in-cell statistics, can be used to estimate the bias between galaxies and their underlying dark-matter distribution. Our goal is to test the significance of the clustering information encoded in the σv measured in the ALHAMBRA survey. [Methods]: We measure the cosmic variance of several galaxy populations selected with B-band luminosity at 0.35 ≤ z< 1.05 as the intrinsic dispersion in the number density distribution derived from the 48 ALHAMBRA subfields. We compare the observational σv with the cosmic variance of the dark matter expected from the theory, σv,dm. This provides an estimation of the galaxy bias b. [Results]: The galaxy bias from the cosmic variance is in excellent agreement with the bias estimated by two-point correlation function analysis in ALHAMBRA. This holds for different redshift bins, for red and blue subsamples, and for several B-band luminosity selections. We find that b increases with the B-band luminosity and the redshift, as expected from previous work. Moreover, red galaxies have a larger bias than blue galaxies, with a relative bias of brel = 1.4 ± 0.2. [Conclusions]: Our results demonstrate that the cosmic variance measured in ALHAMBRA is due to the clustering of galaxies and can be used to characterise the σv affecting pencil-beam surveys. In addition, it can also be used to estimate the galaxy bias b from a method independent of correlation functions.This work has been mainly funded by the FITE (Fondos de Inversiones de Teruel) and the projects AYA2012-30789, AYA2006-14056, and CSD2007-00060. We also acknowledge support from the Spanish Ministry for Economy and Competitiveness and FEDER funds through grants AYA2010-15081, AYA2010-15169, AYA2010-22111-C03-01, AYA2010-22111-C03-02, AYA2011-29517-C03-01, AYA2012-39620, AYA2013-40611-P, AYA2013-42227-P, AYA2013-43188-P, AYA2013-48623-C2-1, AYA2013-48623-C2-2, ESP2013-48274, AYA2014-58861-C3-1, Aragon Government Research Group E103, Generalitat Valenciana projects Prometeo 2009/064 and PROMETEOII/2014/060, Junta de Andalucia grants TIC114, JA2828, P10-FQM-6444, and Generalitat de Catalunya project SGR-1398. A.J.C. and C.H.-M. are Ramon y Cajal fellows of the Spanish government. A. M. acknowledges the financial support of the Brazilian funding agency FAPESP (Post-doc fellowship - process number 2014/11806-9). M.P. acknowledges financial support from JAE-Doc program of the Spanish National Research Council (CSIC), co-funded by the European Social Fund.Peer Reviewe

Consenso colombiano de atención, diagnóstico y manejo de la infección por SARS-COV-2/COVID-19 en establecimientos de atención de la salud Recomendaciones basadas en consenso de expertos e informadas en la evidencia

The “Asociación Colombiana de Infectología” (ACIN) and the “Instituto de Evaluación de Nuevas Tecnologías de la Salud” (IETS) created a task force to develop recommendations for Covid 19 health care diagnosis, management and treatment informed, and based, on evidence. Theses reccomendations are addressed to the health personnel on the Colombian context of health services. © 2020 Asociacion Colombiana de Infectologia. All rights reserved

Future and potential spending on health 2015-40: Development assistance for health, and government, prepaid private, and out-of-pocket health spending in 184 countries

Background: The amount of resources, particularly prepaid resources, available for health can affect access to health care and health outcomes. Although health spending tends to increase with economic development, tremendous variation exists among health financing systems. Estimates of future spending can be beneficial for policy makers and planners, and can identify financing gaps. In this study, we estimate future gross domestic product (GDP), all-sector government spending, and health spending disaggregated by source, and we compare expected future spending to potential future spending. Methods: We extracted GDP, government spending in 184 countries from 1980-2015, and health spend data from 1995-2014. We used a series of ensemble models to estimate future GDP, all-sector government spending, development assistance for health, and government, out-of-pocket, and prepaid private health spending through 2040. We used frontier analyses to identify patterns exhibited by the countries that dedicate the most funding to health, and used these frontiers to estimate potential health spending for each low-income or middle-income country. All estimates are inflation and purchasing power adjusted. Findings: We estimated that global spending on health will increase from US$9.21 trillion in 2014 to$24.24 trillion (uncertainty interval [UI] 20.47-29.72) in 2040. We expect per capita health spending to increase fastest in upper-middle-income countries, at 5.3% (UI 4.1-6.8) per year. This growth is driven by continued growth in GDP, government spending, and government health spending. Lower-middle income countries are expected to grow at 4.2% (3.8-4.9). High-income countries are expected to grow at 2.1% (UI 1.8-2.4) and low-income countries are expected to grow at 1.8% (1.0-2.8). Despite this growth, health spending per capita in low-income countries is expected to remain low, at $154 (UI 133-181) per capita in 2030 and$195 (157-258) per capita in 2040. Increases in national health spending to reach the level of the countries who spend the most on health, relative to their level of economic development, would mean $321 (157-258) per capita was available for health in 2040 in low-income countries. Interpretation: Health spending is associated with economic development but past trends and relationships suggest that spending will remain variable, and low in some low-resource settings. Policy change could lead to increased health spending, although for the poorest countries external support might remain essential

Characteristics of Early-Onset vs Late-Onset Colorectal Cancer: A Review.

The incidence of early-onset colorectal cancer (younger than 50 years) is rising globally, the reasons for which are unclear. It appears to represent a unique disease process with different clinical, pathological, and molecular characteristics compared with late-onset colorectal cancer. Data on oncological outcomes are limited, and sensitivity to conventional neoadjuvant and adjuvant therapy regimens appear to be unknown. The purpose of this review is to summarize the available literature on early-onset colorectal cancer. Within the next decade, it is estimated that 1 in 10 colon cancers and 1 in 4 rectal cancers will be diagnosed in adults younger than 50 years. Potential risk factors include a Westernized diet, obesity, antibiotic usage, and alterations in the gut microbiome. Although genetic predisposition plays a role, most cases are sporadic. The full spectrum of germline and somatic sequence variations implicated remains unknown. Younger patients typically present with descending colonic or rectal cancer, advanced disease stage, and unfavorable histopathological features. Despite being more likely to receive neoadjuvant and adjuvant therapy, patients with early-onset disease demonstrate comparable oncological outcomes with their older counterparts. The clinicopathological features, underlying molecular profiles, and drivers of early-onset colorectal cancer differ from those of late-onset disease. Standardized, age-specific preventive, screening, diagnostic, and therapeutic strategies are required to optimize outcomes

Tracking development assistance for health and for COVID-19 : a review of development assistance, government, out-of-pocket, and other private spending on health for 204 countries and territories, 1990-2050

Background The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020. Methods We estimated domestic health spending and development assistance for health to generate total health-sector spending estimates for 204 countries and territories. We leveraged data from the WHO Global Health Expenditure Database to produce estimates of domestic health spending. To generate estimates for development assistance for health, we relied on project-level disbursement data from the major international development agencies' online databases and annual financial statements and reports for information on income sources. To adjust our estimates for 2020 to include disbursements related to COVID-19, we extracted project data on commitments and disbursements from a broader set of databases (because not all of the data sources used to estimate the historical series extend to 2020), including the UN Office of Humanitarian Assistance Financial Tracking Service and the International Aid Transparency Initiative. We reported all the historic and future spending estimates in inflation-adjusted 2020 US$, 2020 US$ per capita, purchasing-power parity-adjusted US$per capita, and as a proportion of gross domestic product. We used various models to generate future health spending to 2050. Findings In 2019, health spending globally reached$8. 8 trillion (95% uncertainty interval [UI] 8.7-8.8) or $1132 (1119-1143) per person. Spending on health varied within and across income groups and geographical regions. Of this total,$40.4 billion (0.5%, 95% UI 0.5-0.5) was development assistance for health provided to low-income and middle-income countries, which made up 24.6% (UI 24.0-25.1) of total spending in low-income countries. We estimate that $54.8 billion in development assistance for health was disbursed in 2020. Of this,$13.7 billion was targeted toward the COVID-19 health response. $12.3 billion was newly committed and$1.4 billion was repurposed from existing health projects. $3.1 billion (22.4$2.4 billion (17.9%) was for supply chain and logistics. Only $714.4 million (7.7$1519 (1448-1591) per person in 2050, although spending across countries is expected to remain varied. Interpretation Global health spending is expected to continue to grow, but remain unequally distributed between countries. We estimate that development organisations substantially increased the amount of development assistance for health provided in 2020. Continued efforts are needed to raise sufficient resources to mitigate the pandemic for the most vulnerable, and to help curtail the pandemic for all. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Peer reviewe

Search for leptophobic Z ' bosons decaying into four-lepton final states in proton-proton collisions at root s=8 TeV

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Search for black holes and other new phenomena in high-multiplicity final states in proton-proton collisions at root s=13 TeV

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