Modelling Global Burden of Disease Measures in Selected European Countries Using Robust Dynamic Spatial Panel Data Models

Acta Universitatis Lodziensis. Folia Oeconomica

Agnieszka Orwat-Acedańska¹ 

[1] University of Economics in Katowice, Faculty of Informatics and Communication Department of Demography and Economic Statistics;

关键词: dynamic spatial panel data models (dspd);    m-estimation;    fixed effects;    short panels;    disease burden measures;    socio-economic factors;   

DOI  :  10.18778/0208-6018.347.07

来源: DOAJ

【 摘 要 】

The aim of the paper is to study relationships between selected socio‑economic factors and health of European citizens. The health level is measured by selected global burden of disease measures – DALYs (Disability Adjusted Life Years) and its two components: YLL (Years of Life Lost) and YLD (Years Lived with Disability). We identify which factors significantly affect these indicators of health. The empirical study uses a panel data comprising 16 countries mostly from the old‑EU in the period 2003–2013. Fixed‑effects dynamic spatial panel data (DSPD) models are used to account for autocorrelations of the dependent variables across time and space. The models are estimated with a novel, modified quasi maximum likelihood Yang method based on M‑estimators. The approach is robust on the distribution of the initial observations. The empirical analysis covers specification, estimation, and verification of the models. The results show that changes in YLD are significantly related to alcohol consumption, healthcare spending, social spending, GDP growth rate and years of education. Exactly the same set of factors is associated with variation in DALYs. Sensitivity of the YLL component to the socio‑economic factors is considerably weaker.

【 授权许可】

Unknown