Renáta Németh

A comparative health survey of the inhabitants of Roma settlements in Hungary.

OBJECTIVES We compared the health of people living in Roma settlements with that of the general population in Hungary. METHODS We performed comparative health interview surveys in 2003 to 2004 in representative samples of the Hungarian population and inhabitants of Roma settlements. RESULTS In persons older than 44 years, 10% more of those living in Roma settlements reported their health as bad or very bad than did those in the lowest income quartile of the general population. Of those who used any health services, 35% of the Roma inhabitants and 4.4% of the general population experienced some discrimination. In Roma settlements, the proportion of persons who thought that they could do much for their own health was 13% to 15% lower, and heavy smoking and unhealthy diet were 1.5 to 3 times more prevalent, than in the lowest income quartile of the general population. CONCLUSIONS People living in Roma settlements experience severe social exclusion, which profoundly affects their health. Besides tackling the socioeconomic roots of the poor health of Roma people, specific public health interventions, including health education and health promotion programs, are needed.

Does socioeconomic status fully mediate the effect of ethnicity on the health of Roma people in Hungary

Background: Several models have been proposed to explain the association between ethnicity and health. It was investigated whether the association between Roma ethnicity and health is fully mediated by socioeconomic status in Hungary. Methods: Comparative health interview surveys were performed in 2003–04 on representative samples of the Hungarian population and inhabitants of Roma settlements. Logistic regression models were applied to study whether the relationship between Roma ethnicity and health is fully mediated by socioeconomic status, and whether Roma ethnicity modifies the association between socioeconomic status and health. Results: The health status of people living in Roma settlements was poorer than that of the general population (odds ratio of severe functional limitation after adjustment for age and gender 1.8 (95% confidence interval 1.4 to 2.3)). The difference in self-reported health and in functionality was fully explained by the socioeconomic status. The less healthy behaviours of people living in Roma settlements was also related very strongly to their socioeconomic status, but remained significantly different from the general population when differences in the socioeconomic status were taken into account, (eg odds ratio of daily smoking 1.6 (95% confidence interval 1.3 to 2.0) after adjustment for age, gender, education, income and employment). Conclusion: Socioeconomic status is a strong determinant of health of people living in Roma settlements in Hungary. It fully explains their worse health status but only partially determines their less healthy behaviours. Efforts to improve the health of Roma people should include a focus on socioeconomic status, but it is important to note that cultural differences must be taken into account in developing public health interventions.

Parameterization and estimation of path models for categorical data

The paper discusses statistical models for categorical data based on directed acyclic graphs (DAGs) assuming that only effects associated with the arrows of the graph exist. Graphical models based on DAGs are similar, but allow the existence of effects not directly associated with any of the arrows. Graphical models based on DAGs are marginal models and are best parameterized by using hierarchical marginal log-linear parameters. Path models are defined here by assuming that all hierarchical marginal log-linear parameters not associated with an arrow are zero, providing a parameterization with straightforward interpretation. The paper gives a brief review of log-linear, graphical and marginal models, presents a method for the maximum likelihood estimation of path models and illustrates the use of path models, with special emphasis on the interpretation of estimated parameter values, to real data.

On the Application of Discrete Marginal Graphical Models

Graphical models are defined by general and possibly complex conditional independence assumptions and are well suited to model direct and indirect associations and effects that are of central importance in many problems of sociology. Such relevance is apparent in research on social mobility. This article provides a unified view of many of the graphical models discussed in a largely scattered literature. The marginal modeling framework proposed here relies on parameters that capture aspects of associations among the variables that are relevant for the graph and, depending on the substantive problem at hand, may lead to deeper insight than other approaches. In this context, model search, which uses a sequence of nested models, means the restriction of increasing subsets of parameters. As a special case, general path models for categorical data are introduced. These models are applied to the social status attainment process, generating substantive results and gaining new insights into the difference between liberal and conservative welfare systems. To help others use these models, all details of the analyses are posted on the Web site for this article at http://nemethr.web.elte.hu/discrete-graphical-models/. Researchers can thus easily modify the analyses to their own data and models.

Discrete Graphical Models in Social Mobility Research - A Comparative Analysis of American, Czechoslovakian and Hungarian Mobility before the Collapse of State Socialism

Variants of path models have been widely used for the analysis of the social status attainment process. The methods presented here differ from earlier approaches in several ways. Social status is considered a categorical variable and path models are developed starting from graphical models, using the marginal log-linear approach. Overall model fit may be tested by standard techniques. Under these models, the status attainment process is completely characterized by a set of parameters that measure the strengths of the relevant effects. This is in sharp contrast with estimating and interpreting ad hoc parameters, without paying attention to overall model fit and to other effects influencing the process. The method is applied to the social status attainment process in the USA, Hungary and Czechoslovakia at the end of the last century, and shows that policies in the latter socialist countries to prevent status inheritance had little success.

Mapping the Nottingham Health Profile onto the preference-based EuroQol-5D instrument for patients with diabetes

OBJECTIVE The aim of this study was to derive a function that can map the Nottingham Health Profile (NHP) questionnaire onto a utility measure, the EuroQol five-dimensional (EQ-5D) questionnaire index, for diabetic patients. METHODS A cross-sectional study was performed on diabetic patients in Hungary with different complications in which quality of life was measured by using both the NHP questionnaire and the EQ-5D questionnaire. Ordinary stepwise-backward least-squares regression was used to develop a mapping function. Adjusted R2, Akaikes information criterion, and root mean square error were used to assess the performance of the model. The robustness of the models was tested using 10-fold cross-validation and bootstrapping. RESULTS The best-fitting models were those that contained all the NHP statements as predictors and a stepwise reduced version that contained only 19 statements. The latter model, however, showed considerable variability in the selection of predictors. The adjusted R2 of the former model was 0.68, the root mean square error was 174, and the Akaikes information criterion was -559.9. CONCLUSIONS The expected value of the EQ-5D questionnaire can be reasonably predicted on the basis of results of the NHP in patients with diabetes mellitus. The mapping function of the NHP onto the EQ-5D questionnaire is capable of estimating the expected EQ-5D questionnaire utility values in a group of patients with diabetes. The functions applicability for individual-level predictions, however, is limited. Further research is needed to find out whether mapping functions developed in Central-Eastern European countries are transferable to Western European countries.

Rejoinder : On the Application of Discrete Marginal Graphical Models

We thank Antonio Forcina for his comments and we certainly agree with him that cutting edge research, including his current paper (Forcina, 2012) has more to say on the topic than presented here. Our aim was to show a relatively simple part of the results in this field, that we hoped could be used directly and is intuitive enough to have the potential of becoming part of the standard tools of social scientists.

Health & Morbidity in the New Member States

The analysis aims to describe processes of demographic and epidemiological changes, as well as health status self-assessment in selected Central and Eastern European countries. Countries selected for analysis represent groups characterized by similar tendencies and specific health and demographic characteristics. Estonia represents the Baltic states, Bulgaria – Balkan countries, and Slovakia represents countries of Central Europe. Poland is a specific country, with demographic and epidemiological characteristics similar to Slovakia, but much larger, with a high share of rural population. In Hungary, demographic processes related to the second demographic transition began much earlier and are still dynamic, and as such constitute a reference for other CEE countries. Analysis is presented in the context of medical service utilization, impact on frequency and structure of services use. Special attention is given to those demographic and epidemiological changes that have direct impact on the frequency of medical service utilization and, as such, determine the increase of healthcare costs. The ageing process and health status improvement are the main hypothetical determinants of healthcare cost increases, and thus they are presented in more detail. Additionally, changes in health behaviour – mainly in the utilization of medical services – are discussed in the context of institutional changes in the healthcare sector.