Examining the structure of spatial health effects in Germany using Hierarchical Bayes Models

Ziebarth, Nicolas R.

doi:10.1016/j.regsciurbeco.2014.06.005

Cited by 38 publications

(27 citation statements)

References 95 publications

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“…The DICs displayed in Table 4 indicate that out of the three distributions the gamma distribution (M 3) is the best suited distribution, as it has the lowest DIC both for males and females. As one can observe there are significant changes associated with the different states, which is in line with the literature on regional health differences (Eibich and Ziebarth, 2014 [-0.190; 0.195] -0.309[-0.413;-0.197 …”

Section: A3 Model Selectionsupporting

confidence: 88%

“…The SF-12 is a 12-item subset of Quality Metric's SF-36v2 TM , which is used widely in the recent literature (e.g. Marcus, 2013;LaMontagne et al, 2014;Eibich and Ziebarth, 2014) and provides measures of self-rated health in eight domains. The SF-12 comprises 12 items that aim to capture "practical, reliable and valid information about functional health and well-being from the patient's point of view" (OPTUM, 2015).…”

Section: The Sf12mentioning

confidence: 99%

“…This allows us to control for some environmental factors like the general prosperity of the area of residence and associated aspects like health care infrastructure. We use a hierarchical regional effect which accounts for differences between former West and East Germany and subsequently differentiates between individual federal states of residence, as in Eibich and Ziebarth (2014).…”

Section: The Explanatory Variablesmentioning

confidence: 99%

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Reconsidering the Income-Illness Relationship Using Distributional Regression An Application to Germany

et al. 2017

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. 2 University of Pennsylvania, USA. Terms of use: Documents in AbstractIn this paper we reconsider the relationship between income on health, taking a distributional perspective rather than one centered on conditional expectation. Using StructuredAdditive Distributional Regression, we find that the association between income on health is larger than generally estimated because aspects of the conditional health distribution that go beyond the expectation imply worse outcomes for those with lower incomes. Looking at German data from the Socio Economic Panel, we find that the risk of very bad health is roughly halved when doubling the net equivalent income from 15,000 Euro to 30,000 Euro, which is more than tenfold of the magnitude of change found when considering expected health measures. This paper therefore argues that when studying health outcomes, a distributional perspective that considers stochastic variation among observationally equivalent individuals is warranted.

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Section: A3 Model Selectionsupporting

confidence: 88%

Section: The Sf12mentioning

confidence: 99%

Section: The Explanatory Variablesmentioning

confidence: 99%

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Reconsidering the Income-Illness Relationship Using Distributional Regression An Application to Germany

et al. 2017

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“…Recently, other studies used the German setting to analyse regional differences in health care, too. These are Eibich & Ziebarth () and Sundmacher & Busse () on variations in health, Augurzky et al (), Kopetsch & Schmitz () and Eibich & Ziebarth () on utilization of health services. All these papers find significant regional variations which cannot be fully explained.…”

Section: Introductionmentioning

confidence: 99%

Determinants of Regional Variation in Health Expenditures in Germany

Göpffarth

Kopetsch²,

Schmitz

2015

Health Economics

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Health care expenditure in Germany shows clear regional differences. Such geographic variations are often seen as an indicator for inefficiency. With its homogeneous health care system, low co-payments and uniform prices, Germany is a particularly suited example to analyse regional variations. We use data for the year 2011 on expenditure, utilization of health services and state of health in Germany's statutory health insurance system. This data, which originate from a variety of administrative sources and cover about 90% of the population, are enriched with a wealth of socio-economic variables, data on pollutants, prices and individual preferences. State of health and demography explains 55% of the differences as measured by the standard deviation while all control variables account for a total of 72% of the differences at county level. With other measures of variation, we can account for an even greater proportion. A higher proportion of variation than usually supposed can thus be explained. Whilst this study cannot quantify inefficiencies, our results contradict the thesis that regional variations reflect inefficiency. Copyright © 2015 John Wiley & Sons, Ltd.

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“…There are a number of reasons why the relationship between commuting and absence may vary with such spatial characteristics (Eibich and Ziebarth, 2014). First, employees living in rural regions may be healthier, have a higher quality of life compared to individuals living in urban regions (Ziersch et al, 2009;Zeng et al, 2015) and may commute longer distances.…”

mentioning

confidence: 99%