Macroeconomics of public sector deficits : the case of Zimbabwe

Zimbabwe has the uncommon combination of a high public deficit, a balanced current account, low inflation, and low levels of investment and growth. Despite a surplus in the current account, the nonfinancial public sector has run deficits exceeding 10 percent of GDP since 1981. Inflation is low but interest rates are rising because of partial financial liberalization and rising domestic public debt stocks. Heavy public spending crowded out private consumption and investment in the 1980s. The private saving rate is a staggering 20 percent of GDP, which finances all of Zimbabwe's investment. The fiscal adjustment begun in 1987 helped stabilize the public debt and improved recovery of investment. But more fiscal adjustment is needed to improve macroeconomic and financial stability and growth prospects. Public deficits must be reduced to ensure a sustainable path for public debt. High deficits are crowding out both private consumption and private investment. The public sector must be adjusted and foreign trade must be reformed to improve capital formation - a prerequisite for improving growth prospects in Zimbabwe.Environmental Economics&Policies,Economic Theory&Research,Banks&Banking Reform,Public Sector Economics&Finance,Economic Stabilization

Salud Callejera: Mobilizing Cuidado at the Margins of Neoliberalism; Reimagining Care for People Experiencing Homelessness in Buenos Aires

On any given night, thousands of individuals sleep on the streets of the Ciudad Autónoma de Buenos Aires. Without secure housing, people in situación de calle (experiencing homelessness) suffer elevated rates of physical trauma, transmissible and chronic diseases, and symptoms of depression. Nevertheless, two-thirds of this population do not receive annual health consultations, with the majority solely accessing the emergency department when their conditions severely worsen. This study finds that municipal services and, to a lesser extent, the public health system render individuals responsible for housing insecurity by adopting a neoliberal subjectivity of homo economicus, medicalizing poverty as a symptom of psychosocial illness potentially curable through economic and social rehabilitation. Those who do not conform with such pathologization or other employment-based demands confront heightened criminalization and exclusion from care services. As an alternative response, this project investigates the actions of civil society networks, which employ a contrary notion of homo politicus, reimagining care as a collective right and site of political mobilization. This thesis draws upon interviews with people experiencing or at risk of homelessness, members of civil society organizations, public health providers, and municipal social workers, as well as observations from street-outreach

Technology Management for Accelerated Recovery during COVID-19: A Data-Driven Machine Learning Approach

Objective- The research looks forward to extracting strategies for accelerated recovery during the ongoing Covid-19 pandemic. Design - Research design considers quantitative methodology and evaluates significant factors from 170 countries to deploy supervised and unsupervised Machine Learning techniques to generate non-trivial predictions. Findings - Findings presented by the research reflect on data-driven observation applicable at the macro level and provide healthcare-oriented insights for governing authorities. Policy Implications - Research provides interpretability of Machine Learning models regarding several aspects of the pandemic that can be leveraged for optimizing treatment protocols. Originality - Research makes use of curated near-time data to identify significant correlations keeping emerging economies at the center stage. Considering the current state of clinical trial research reflects on parallel non-clinical strategies to co-exist with the Coronavirus

Application of Artificial Intelligence and Blockchain in healthcare management - donor organ transplant system

Purpose: Research ventures to expand the reach of organ transplant mechanisms to improve the abysmally low organ transplant rate in the country. The research deploys state of the art technologies to promote deceased organ donation using the donor organ transplant system. Research methodology: The exploratory study focuses on addressing the limitation of resources using a Socio-material view.  The research utilizes qualitative content analysis to reflect on the knowledge drawn from the artifacts. Results: The presented study leverages the capabilities of Artificial Intelligence and Blockchain technologies to benefit from the convergence. In line with the concept of 'Texture of Practices,' research provides recommendations to augment the organ transplant system in terms of procurement, coordination, and transplantation. Limitations: Drawing the knowledge from the case studies, research strives to understand the reality and interaction of actors in a healthcare context. Considering the complex nature of the organ transplant process, the study is limited to the Indian scenario and cannot be generalized. Contribution: Research identifies the requirement of a unified digital interface and encourages the integration of emergency health services to facilitate operational processes during organ transplants. Keywords: Healthcare, Organ transplant, A.I., Blockchain, Texture of practice

Presentación de las dos instrucciones de la Sagrada Congregación para la doctrina de la fe acerca de la teología de la liberación

Ningun

Mobile telephone-delivered contingency management interventions promoting behaviour change in individuals with substance use disorders: a meta-analysis

BACKGROUND/AIMS: Contingency management (CM) interventions have gained considerable interest due to their success in the treatment of addiction. However, their implementation can be resource-intensive for clinical staff. Mobile telephone-based systems might offer a low-cost alternative. This approach could facilitate remote monitoring of behaviour and delivery of the reinforcer and minimize issues of staffing and resources. This systematic review and meta-analysis assessed the evidence for the effectiveness of mobile telephone-delivered CM interventions to promote abstinence (from drugs, alcohol and tobacco), medication adherence and treatment engagement among individuals with substance use disorders. DESIGN: A systematic search of databases (PsychINFO, CINAHL, MEDLINE PubMed, CENTRAL, Embase) for randomized controlled trials and within-subject design studies (1995-2019). The review was conducted in accordance with the PRISMA statement. The protocol was registered on PROSPERO. SETTING: All included studies originated in the United states. PARTICIPANTS: Seven studies were found, including 222 participants; two targeted alcohol abstinence among frequent drinkers and four targeted smoking cessation (in homeless veterans and those with post-traumatic stress disorder). One targeted medication adherence. MEASURES: The efficacy of CM to increase alcohol and nicotine abstinence was compared with control using several outcomes; percentage of negative samples (PNS), quit rate (QR) and longest duration abstinent (LDA) at the end of the intervention. FINDINGS: The random-effects meta-analyses produced pooled effect sizes of; PNS [d = 0.94, 95% confidence interval (CI) = 0.63-1.25], LDA (d = 1.08, 95% CI = 0.69-1.46) and QR (d = 0.46, 95% CI = 0.27-0.66), demonstrating better outcomes across the CM conditions. Most of the studies were rated as of moderate quality. 'Fail-safe N' computations for PNS indicated that 50 studies would be needed to produce a non-significant overall effect size. None could be calculated for QR and LDA due to insufficient number of studies. CONCLUSION: Mobile telephone-delivered contingency management performs significantly better than control conditions in reducing tobacco and alcohol use among adults not in treatment for substance use disorders

Diagnostic and Therapeutic Potential of Extracellular Vesicles in B-Cell Malignancies

Extracellular vesicles (EV), comprising microvesicles and exosomes, are particles released by every cell of an organism, found in all biological fluids, and commonly involved in cell-to-cell communication through the transfer of cargo materials such as miRNA, proteins, and immune-related ligands (e.g., FasL and PD-L1). An important characteristic of EV is that their composition, abundance, and roles are tightly related to the parental cells. This translates into a higher release of characteristic pro-tumor EV by cancer cells that leads to harming signals toward healthy microenvironment cells. In line with this, the key role of tumor-derived EV in cancer progression was demonstrated in multiple studies and is considered a hot topic in the field of oncology. Given their characteristics, tumor-derived EV carry important information concerning the state of tumor cells. This can be used to follow the outset, development, and progression of the neoplasia and to evaluate the design of appropriate therapeutic strategies. In keeping with this, the present brief review will focus on B-cell malignancies and how EV can be used as potential biomarkers to follow disease progression and stage. Furthermore, we will explore several proposed strategies aimed at using biologically engineered EV for treatment (e.g., drug delivery mechanisms) as well as for impairing the biogenesis, release, and internalization of cancer-derived EV, with the final objective to disrupt tumor–microenvironment communication.Fil: Gargiulo, Ernesto. Luxembourg Institute of Health; LuxemburgoFil: Morande, Pablo Elías. Luxembourg Institute of Health; Luxemburgo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; ArgentinaFil: Largeot, Anne. Luxembourg Institute of Health; LuxemburgoFil: Moussay, Etienne. Luxembourg Institute of Health; LuxemburgoFil: Paggetti, Jérôme. Luxembourg Institute of Health; Luxemburg

Reinforcing Positive Cognitive States with Machine Learning: An Experimental Modeling for Preventive Healthcare

Societal evolution has resulted in a complex lifestyle where we give most attention to our physical health leaving psychological health less prioritized. Considering the complex relationship between stress and psychological well-being, this study bases itself on the cognitive states experienced by us. The presented research offers insight into how state-of-the-art technologies can be used to support positive cognitive states. It makes use of the brain-computer interface (BCI) that drives the data collection using electroencephalography (EEG). The study leverages data science to devise machine learning (ML) model to predict the corresponding stress levels of an individual. A feedback loop using “Self Quantification” and “Nudging” offer real-time insights about an individual. Such a mechanism can also support the psychological conditioning of an individual where it does not only offer spatial flexibility and cognitive assistance but also results in enhanced self-efficacy. Being part of quantified self-movement, such an experimental approach could showcase personalized indicators to reflect a positive cognitive state. Although ML modeling in such a data-driven approach might experience reduced diagnostic sensitivity and suffer from observer variability, it can complement psychosomatic treatments for preventive healthcare