The combined incidence of taxes and public expenditures in the Philippines

Incidence studies of fiscal policy in developing countries typically examine either the distribution of tax burdens or the incidence of public expenditures. But the central issue for policymakers is the combined or net incidence of fiscal activities. One reason that combined incidence studies are so rare is that they require detailed data on both taxation and public spending. The authors show that the net incidence of fiscal policy in a country with average data - the Philippines - can be estimated using a variety of data sources and tools, using simplifying assumptions. For 20 years, the Philippine economy has experienced a series of balance of payments crises triggered by fiscal crises. It has had an unsatisfactory record of poverty alleviation. The authors examine net fiscal incidence to find out how poverty will be affected by the rise in taxes and the cut in spending. They found that: 1) the incidence pattern of taxes is basically neutral. Contrary to expectations, indirect taxes are only slightly regressive; and 2) it is the pattern of expenditures that drives the combined incidence, which is progressive.Public Sector Economics&Finance,Environmental Economics&Policies,Health Systems Development&Reform,Economic Theory&Research,Health Economics&Finance,Environmental Economics&Policies,Public Sector Economics&Finance,Economic Theory&Research,Health Economics&Finance,Banks&Banking Reform

Financial crises and the attainment of the SDGs: an adjusted multidimensional poverty approach

This paper analyses the impact of financial crises on the Sustainable Development Goal of eradicating poverty. To do so, we develop an adjusted Multidimensional Poverty Framework (MPF) that includes 15 indicators that span across key poverty aspects related to income, basic needs, health, education and the environment. We then use an econometric model that allows us to examine the impact of financial crises on these indicators in 150 countries over the period 1980–2015. Our analysis produces new estimates on the impact of financial crises on poverty’s multiple social, economic and environmental aspects and equally important captures dynamic linkages between these aspects. Thus, we offer a better understanding of the potential impact of current debt dynamics on Multidimensional Poverty and demonstrate the need to move beyond the boundaries of SDG1, if we are to meet the target of eradicating poverty. Our results indicate that the current financial distress experienced by many low-income countries may reverse the progress that has been made hitherto in reducing poverty. We find that financial crises are associated with an approximately 10% increase of extreme poor in low-income countries. The impact is even stronger in some other poverty aspects. For instance, crises are associated with an average decrease of government spending in education by 17.72% in low-income countries. The dynamic linkages between most of the Multidimensional Poverty indicators, warn of a negative domino effect on a number of SDGs related to poverty, if there is a financial crisis shock. To pre-empt such a domino effect, the specific SDG target 17.4 on attaining long-term debt sustainability through coordinated policies plays a key role and requires urgent attention by the international community

Climate and southern Africa's water-energy-food nexus

In southern Africa, the connections between climate and the water-energy-food nexus are strong. Physical and socioeconomic exposure to climate is high in many areas and in crucial economic sectors. Spatial interdependence is also high, driven for example, by the regional extent of many climate anomalies and river basins and aquifers that span national boundaries. There is now strong evidence of the effects of individual climate anomalies, but associations between national rainfall and Gross Domestic Product and crop production remain relatively weak. The majority of climate models project decreases in annual precipitation for southern Africa, typically by as much as 20% by the 2080s. Impact models suggest these changes would propagate into reduced water availability and crop yields. Recognition of spatial and sectoral interdependencies should inform policies, institutions and investments for enhancing water, energy and food security. Three key political and economic instruments could be strengthened for this purpose; the Southern African Development Community, the Southern African Power Pool, and trade of agricultural products amounting to significant transfers of embedded water

Stability of novel urinary biomarkers used for lupus nephritis

BackgroundThe Renal Activity Index for Lupus (RAIL) is a composite score of six urinary biomarkers (neutrophil gelatinase–associated lipocalin (NGAL), monocyte chemoattractant protein-1 (MCP-1), kidney injury molecule-1 (KIM-1), ceruloplasmin, adiponectin, and hemopexin) used to monitor lupus nephritis activity in children. We tested stability of RAIL biomarkers prior to meaningful clinical use.MethodsUrine samples were tested by ELISA under shipping conditions, freeze/thaw, ambient and longer-term storage. Statistical analysis was performed via Deming Regression, Bland-Altman and Spearman Correlation Coefficient.ResultsBiomarker concentration were comparable to freshly collected urine following storage at −80 °C for up to 3 months, and at 4 or 25 °C up to 48 h followed by −80 °C. Neither shipping on dry or wet ice exposure nor addition of two freeze-thaw cycles led to loss of signal, with excellent Spearman Correlation coefficients under all conditions.ConclusionsRAIL biomarkers are stable following short-term storage at clinically relevant conditions

Urine biomarker score captures response to induction therapy with lupus nephritis

BACKGROUND: The Renal Activity Index for Lupus (RAIL) consists of urine protein assessment of neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, monocyte chemotactic protein 1, adiponectin, hemopexin, and ceruloplasmin, which non-invasively identifies lupus nephritis (LN). We aimed to delineate RAIL scores with inactive versus active LN and changes over time with response to LN induction therapy. METHODS: There were 128 pediatric patients with systemic lupus erythematosus (SLE) and age-matched healthy controls recruited in a prospective case control study, with kidney biopsy confirmation of LN. Laboratory and clinical information was recorded and urine collected at diagnosis and end of induction and during maintenance therapy. Response to therapy was assessed by repeat kidney biopsy or laboratory parameters. Urine was assayed for RAIL biomarkers and the RAIL score calculated. RESULTS: Pediatric RAIL (pRAIL) scores from 128 children and young adults with SLE (with/without LN: 70/38) including 25 during LN induction therapy, differentiated clinically active LN from inactive LN or without LN, and controls (all p \u3c 0.0017). pRAIL scores significantly decreased with complete LN remission by 1.07 ± 1.7 (p = 0.03). CONCLUSIONS: The RAIL biomarkers differentiate LN patients based on activity of kidney disease, with decreases of ≥ 1 in pRAIL scores indicating complete response to induction therapy. Significantly lower RAIL scores in healthy controls and in SLE patients without known LN raise the possibility of subclinical kidney disease. A higher resolution version of the Graphical abstract is available as Supplementary information

Analysing an Imbalanced Stroke Prediction Dataset Using Machine Learning Techniques

A stroke is a medical condition characterized by the rupture of blood vessels within the brain which can lead to brain damage. Various symptoms may be exhibited when the brain's supply of blood and essential nutrients is disrupted. To forecast the possibility of brain stroke occurring at an early stage using Machine Learning (ML) and Deep Learning (DL) is the main objective of this study. Timely detection of the various warning signs of a stroke can significantly reduce its severity. This paper performed a comprehensive analysis of features to enhance stroke prediction effectiveness. A reliable dataset for stroke prediction is taken from the Kaggle website to gauge the effectiveness of the proposed algorithm. The dataset has a class imbalance problem which means the total number of negative samples is higher than the total number of positive samples. The results are reported based on a balanced dataset created using oversampling techniques. The proposed work used Smote and Adasyn to handle imbalanced problem for better evaluation metrics. Additionally, the hybrid Neural Network and Random Forest (NN-RF) utilizing the balanced dataset by Adasyn oversampling achieves the highest F1-score of 75% compared to the original unbalanced dataset and other benchmarking algorithms. The proposed algorithm with balanced data utilizing hybrid NN-RF achieves an accuracy of 84%. Advanced ML techniques coupled with thorough data analysis enhance stroke prediction. This study underscores the significance of data-driven methodologies, resulting in improved accuracy and comprehension of stroke risk factors. Applying these methodologies to medical fields can enhance patient care and public health outcomes. By integrating our discoveries, we can enhance the efficiency and effectiveness of the public health system

Development of a novel renal activity index of lupus nephritis in children & young adults

BACKGROUND: Noninvasive estimation of the degree of inflammation seen on kidney biopsy with lupus nephritis (LN) remains difficult. The objective of this study was to develop a Renal Activity Index for Lupus (RAIL) that, based solely on laboratory measures, accurately reflects histological LN activity. METHODS: We assayed traditional LN laboratory tests and 16 urine biomarkers (UBMs) in children (n=47) at the time of kidney biopsy. Histological LN activity was measured by the NIH Activity Index (NIH-AI) and the Tubulointerstitial Activity Index (TIAI). High LN-activity status (vs. moderate/low) was defined as NIH-AI scores \u3e 10 (vs.5 (vs.92% accuracy and LN-activityTIAI status with \u3e80% accuracy. RAIL accuracy was minimally influenced by concomitant LN damage. Accuracies between 71 and 85% were achieved without standardization of the UBMs. The strength of these UBMs to reflect LN-activity status was confirmed by principal component and linear discriminant analyses. CONCLUSION: The RAIL is a robust and highly accurate noninvasive measure of LN-activity. The measurement properties of the RAIL, which reflect the degree of inflammatory changes as seen on kidney biopsy, will require independent validation. This article is protected by copyright. All rights reserved