Revisiting Health Inequalities in Germany

Background: Our aim is a wide-ranging analysis of the determinants of health ine-qualities, which scrutinizes the propositions of the main theoretical approaches (ma-terialist or neo-materialist approach, cultural and behavioural approaches, psycho-social explanations, the life-course perspective and the newer capability approach) within one model thereby offering insights into their relative explanatory power. Methods: Using Fields’s (2004) regression techniques we decompose total variance into its factors and thereby generate insights about the contribution of specific vari-ables (and approaches) to explain health inequalities in Germany. Moreover, we stratify our sample by age and compare the contribution of each of the factors (con-stituting the different approaches) in four age groups. Data: The data is taken from the 2006 wave of the German Socioeconomic Panel (GSOEP). The GSOEP is a representative longitudinal study of private households and their members above the age of 16, which was started in 1984 and originally consisted of 12 000 individuals. We use the physical health scores derived from the 2006 GSOEP data wave as the dependent variable in our analysis. The scores are derived using an algorithm presented by Anderson et al., which is based on the 2004 GSOEP data wave as the norm sample. Furthermore, we use a comprehensive set of covariates capturing information on demographics, socio-economic background, life-style, social capital, self-assessed stress levels, feelings of national belonging, insurance status and regional levels of pollution, crime, noise and provision of health care to test the relative weight of the theoretical explanations. Results: Overall, we find that understanding the mechanisms of health inequalities crucially depends on taking a holistic perspective on individual’s health. Socio-eco-nomic factors, working conditions and lifestyle independently, interacted and com-pounded explain variation in health in specific age-groups in our analysis. Studies which take a reductionist approach and do not allow for the possibility that health inequalities are generated by a complex co-action of many factors may forego in-sightful findings. Online-Version published by Universitätsverlag der TU Berlin (www.univerlag.tu-berlin.de)

The wider determinants of inequalities in health: a decomposition analysis

<p>Abstract</p> <p>Background</p> <p>The common starting point of many studies scrutinizing the factors underlying health inequalities is that material, cultural-behavioural, and psycho-social factors affect the distribution of health systematically through income, education, occupation, wealth or similar indicators of socioeconomic structure. However, little is known regarding if and to what extent these factors can assert systematic influence on the distribution of health of a population independent of the effects channelled through income, education, or wealth.</p> <p>Methods</p> <p>Using representative data from the German Socioeconomic Panel, we apply Fields' regression based decomposition techniques to decompose variations in health into its sources. Controlling for income, education, occupation, and wealth, we assess the relative importance of the explanatory factors over and above their effect on the variation in health channelled through the commonly applied measures of socioeconomic status.</p> <p>Results</p> <p>The analysis suggests that three main factors persistently contribute to variance in health: the capability score, cultural-behavioural variables and to a lower extent, the materialist approach. Of the three, the capability score illustrates the explanatory power of interaction and compound effects as it captures the individual's socioeconomic, social, and psychological resources in relation to his/her exposure to life challenges.</p> <p>Conclusion</p> <p>Models that take a reductionist perspective and do not allow for the possibility that health inequalities are generated by factors over and above their effect on the variation in health channelled through one of the socioeconomic measures are underspecified and may fail to capture the determinants of health inequalities.</p

Determinants of hospital costs and performance variation : Methods, models and variables for the EuroDRG project

Empirical studies of variation in hospital costs fall into two camps: those based on analysis of the costs of individual patients and those – the vast majority – that analyse costs reported at the hospital level. In this review, we consider how patient-level and hospital-level data are related and outline approaches to analyzing them. The second part of the review considers general specification choices and methods of efficiency analysis. Moreover, we specify a model to be used in the empirical analyses of the EuroDRG project

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Scheller-KreinsenBlumelandBusse

CHRONIC DISEASE MANAGEMENT Policy makers across Europe increasingly recognise that chronic disease management (CDM), the ongoing management of conditions over a period of years or decades, is one of the most important challenges that European health systems face. Chronic conditions and diseases are the leading cause of mortality and morbidity in Europe and research suggests that complex conditions, such as diabetes and depression, will impose an even larger health burden on societies across Europe in the future. The World Health Organization &apos;Global Burden of Disease&apos; study estimated that, as of 2002, chronic or noncommunicable conditions accounted for 87% of deaths in high income countries. By comparison, 7% of deaths were attributed to communicable conditions and nutritional deficiencies, and 6% to injuries. Worldwide, the proportion of deaths due to non-communicable or chronic diseases is projected to rise from 59% in 2002 to 69% in 2030. 1 CDM embraces not only the &apos;classical&apos; conditions such as cardiovascular disease, diabetes and asthma, but also many types of cancer and HIV/AIDS (as survival rates and times have visibly improved), mental disorders (for example, depression, schizophrenia and dementia) as well as certain disabilities (for example, visual impairment). CDM is a complex response over an extended period with coordinated input from a wide range of health professionals, as well as access to drugs and equipment and patient empowerment going beyond medical care into the social care setting. This is in contrast with most health care today, which is structured round acute, episodic models of care. It has been shown that the economic implications of chronic diseases and conditions are severe from both the macro-and microeconomic perspectives. Chronic diseases impact on wages, workforce participation, labour productivity and hours worked. Often, chronic conditions contribute to early retirement, high job turnover and disability. Overall, diseaserelated impairment of households&apos; consumption and educational performance affects the gross domestic product (GDP) negatively. In addition, expenditure on chronic care is rising across Europe and consumes increasing portions of public and private budgets. Suhrcke and Urban 2 demonstrated that the cost of chronic diseases and their risk factors, as measured by cost-of-illness studies, is sizeable, ranging up to 6.77% of a country&apos;s GDP. Policy makers across Europe have developed heterogeneous CDM strategies, such as disease management programmes (DMPs) or prevention and early detection interventions. However, research suggests that many of these current approaches to CDM face substantial structural problems and hence have failed to fulfil hopes and promises. 3 This article briefly outlines the principal CDM strategies and summarises existing evidence on their effectiveness. We also highlight common obstacles impeding successful CDM and outline a series of steps that policy makers need to take to improve the conditions for effectively managing chronic diseases in Europe.* CDM strategies Disease prevention and early detection interventions aim to reduce the burden of chronic disease through activities that avoid impairment to health or reduce the likelihood of chronic conditions developing. Prevention includes primary