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

Predictive properties of novel anthropometric and biochemical indexes for prediction of cardiovascular risk

Objective: Our aim was to examine the correlation between CVDs and various anthropometric and biochemical indices in the Iranian population. Methods: 9704 healthy individuals without CVD aged 35–65 were enrolled in our study. The anthropometric indices including Body Adiposity Index (BAI), Abdominal Volume Index (AVI), Body Roundness Index (BRI), Waist to Hip Ratio (WHR), Weight-adjusted Waist Index (WWI), Conicity Index (C-Index), A Body Shape Index (ABSI), Waist to Height Ratio (WHtR), Body Surface Area (BSA), Body Mass Index (BMI), Lipid Accumulation Product (LAP) and Visceral Adiposity Index (VAI) were calculated. The biochemical indices including Cardiac Risk Ratio (CRR), Atherogenic Index of Plasma (AIP), Triglycerides-Glucose Index (TyG), Cardiac Risk Index (CRI), Atherogenic Coefficient (AC), and high-sensitivity C-Reactive Protein (hs-CRP) were investigated. The association of the above indices with CVD was analyzed using logistic regression (LR) and the decision tree (DT) models. Results: The LR showed age, hs-CRP, AIP, AVI, LAP, and TyG had significant associations with CVDs in men (p-value = 48, AIP > = 0.94, TyG > = 9.71, and AVI > = 14.24 had CVDs. Also, 97% of women with age > = 54, TyG > = 8.33, and hs-CRP > = 36.69 had CVDs. Conclusion: Age, TyG, AIP, AVI, hs-CRP and LAP were the best predictors of CVD in men. Moreover, age, TyG, hs-CRP and BAI were the best indicators of CVD in women.</p