Spatial Quantile Regression In Analysis Of Healthy Life Years In The European Union Countries

Trzpiot, Grażyna; Orwat-Acedańska, Agnieszka

doi:10.1515/cer-2016-0044

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…This work builds on and extends the research carried out by (Orwat-Acedańska and Trzpiot, 2016b). After performing the model selection procedure that consists of specification, estimation and inference, the final estimates for different quantiles are clustered thus considerably facilitating the interpretation of the results.…”

Section: Introductionmentioning

confidence: 85%

The classification of spatial quantile regression models for healthy life years in European countries

Trzpiot

Orwat-Acedańska

2019

Argumenta Oeconomica

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EU countries are heterogeneous in terms of healthy life years (HLY). A quantile spatial autoregression model is employed to identify the factors affecting HLY. Quantile regression allows to measure the impact of covariates on different parts of the conditional distribution of a dependent variable. This is especially useful if the distribution is asymmetric or trimmed. Quantile spatial autoregression models add the spatial dimension to the standard quantile regressions. The aims of the study are as follows: identification of the factors affecting healthy life years of men and women in the EU countries, and clustering of the estimates obtained for the different quantile orders. We analyze to what extent spatial quantile regression can be useful in studying the behavior of HLY. We use exogenous factors belonging to three groups: socio-economic, healthcare and lifestyle. After initial estimation of the models for 19 quantile orders, the insignificant factors are removed and the remaining parameters are re-estimated. The final estimates are then clustered to facilitate the interpretation of the results. Instrumental variables method coupled with bootstrap techniques are employed for estimation and inference while the k-means algorithm is used for clustering. We find that the impact of the factors on HLY varies for different quantile orders. In general, the quantiles of low and medium orders are affected by the factors from all the three groups (socio-economic, healthcare and lifestyle). For the medium and high orders, the socioeconomic factors are no longer important. We argue that the spatial quantile autoregression models offer a new perspective for studying the factors affecting HLY in the EU countries.

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Section: Introductionmentioning

confidence: 85%

The classification of spatial quantile regression models for healthy life years in European countries

Trzpiot

Orwat-Acedańska

2019

Argumenta Oeconomica

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“…Following the methodology, the study is being split in three phases: phase 1) which is accounting for the spatial and econometric modelling of a narrow range of factors, phase 2) is including more factors into the analysis and more recent data, the last available is for 2016 and where values are missing due to the method in which factors are measured, data is being carried forward from previous period as in Trzpiot and Orwat-Acedanska (2017) and phase 3) which is aiming to use computer vision and deep learning methods in order to obtain data regarding urban green percentage, distribution but also access to it.…”

Section: Methodsmentioning

confidence: 99%

“…No significant relationship between GDP and HLY has been identified. One important fact to be observed was that the spatial AR parameters and their significance differ from one quantile to another (Trzpiot & Orwat-Acedanska, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

A Spatial Econometric Analysis of Health in Romania Augmented with Computer Vision

Jurchiş

2019

Proceedings of the International Conference on Applied Statistics

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The purpose of this study is to analyze the health status in Romania at regional NUTS3 level together with its influential socio-economic factors. Apart from statistical and classical econometrics which are being used in most studies, a spatial analysis has been conducted in order to determine possible similarities and dissimilarities among regions, accounting for the fact that events taking place in a specific area are interrelated with the events in the neighboring regions. The negative distribution of the dependent variable, life expectancy, involves the use of Quantile Spatial Autoregressive Model which also allows to observe the socio-economic and environmental factor influences in different parts of health status proxy distribution. The analysis has led to the conclusion that greater the gaps between rich and poor, or greater the difference between less versus better educated, the greater the differences in health status and life expectancy are. Hence a need for policies designed to reduce territorial health disparities has been identified across Romania’s counties. Moreover, Computer Vision and Deep Learning techniques have been used in order to showcase data collection for urban green spaces variables given that more than half of the globe population is living in urban areas and urban greenery has a high positive influence on health. Using Deep Learning on this particular matter together with the Quantile Spatial Autoregressive Model is an innovative approach that has the main aim of improving the classical econometric modelling.

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“…Since then quantile regression has been widely used, also in Bayesian spatial analysis by Reich et al [ 15 ]. Moreover, spatial quantile regression is widely used with other applications ranging from modelling of wildfire risk [ 16 ] to studying healthy life years expectancy [ 17 ] to economics [ 18 ]. In most works, however, the response variable is assumed to be continuously distributed and the asymmetric Laplace distribution (ALD) likelihood [ 19 ] is used to model the quantiles, irrespective of the data-generating distribution.…”

Section: Introductionmentioning

confidence: 99%

Joint quantile disease mapping with application to malaria and G6PD deficiency

Alahmadi,

van Niekerk,

Padellini

et al. 2024

R. Soc. open sci.

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Statistical analysis based on quantile methods is more comprehensive, flexible and less sensitive to outliers when compared to mean methods. Joint disease mapping is useful for inferring correlation between different diseases. Most studies investigate this link through multiple correlated mean regressions. We propose a joint quantile regression framework for multiple diseases where different quantile levels can be considered. We are motivated by the theorized link between the presence of malaria and the gene deficiency G6PD, where medical scientists have anecdotally discovered a possible link between high levels of G6PD and lower than expected levels of malaria initially pointing towards the occurrence of G6PD inhibiting the occurrence of malaria. Thus, the need for flexible joint quantile regression in a disease mapping framework arises. Our model can be used for linear and nonlinear effects of covariates by stochastic splines since we define it as a latent Gaussian model. We perform Bayesian inference using the R integrated nested Laplace approximation, suitable even for large datasets. Finally, we illustrate the model’s applicability by considering data from 21 countries, although better data are needed to prove a significant relationship. The proposed methodology offers a framework for future studies of interrelated disease phenomena.

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Spatial Quantile Regression In Analysis Of Healthy Life Years In The European Union Countries

Cited by 6 publications

References 10 publications

The classification of spatial quantile regression models for healthy life years in European countries

The classification of spatial quantile regression models for healthy life years in European countries

A Spatial Econometric Analysis of Health in Romania Augmented with Computer Vision

Joint quantile disease mapping with application to malaria and G6PD deficiency

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