Socioeconomic indicators and ethnicity as determinants of regional mortality rates in Slovakia

Regional differences in mortality might reflect socioeconomic and ethnic differences between regions. The present study examines the relationship between education, unemployment, income, Roma population and regional mortality in the Slovak Republic. Separately for males and females, data on standardised mortality in the Slovak population aged 20-64 years in the year 2002 were calculated for each of the 79 districts. Similarly the proportions of respondents with tertiary education, unemployed status, Roma ethnicity and income data were calculated per district. A linear regression model was used to analyse the data. Socioeconomic differences in regional mortality were found among males, but not among females. While education and unemployment rate significantly contributed to mortality differences between regions, income and the proportion of Roma population did not. The model explained 32.9% of the variance in standardised mortality rate among districts for males and 7.6% for females. Low education and high unemployment rate seems to be an indicator of regions with high mortality of male and therefore should be targeted by policy measures aimed at decreasing mortality in productive age

Does social support mediate or moderate socioeconomic differences in self-rated health among adolescents?

OBJECTIVE: Social support is assumed to be a protective social determinant of health. The aim of this cross-sectional study was to explore whether social support from the father, mother and friends mediates or moderates the association between socioeconomic position and self-rated health among adolescents. METHODS: The sample consisted of 1,863 secondary school students from the Kosice region in Slovakia (mean age 16.85; 53.3% females, response rate 98.9%). We assessed the mediation and moderation effects of social support from the mother, father and friends on the relation between socioeconomic position and self-rated health, performing binary logistic regression models. Socioeconomic position was measured by parents’ education, the family affluence scale and financial strain. RESULTS: Social support from the father mediated the association between family affluence and self-rated health among both males and females and the association between financial strain and self-rated health among males only. No moderating effect of social support on socioeconomic differences in self-rated health was found. CONCLUSION: Father involvement seems to have the potential to mediate socioeconomic differences in health during adolescence

How parents can affect excessive spending of time on screen-based activities

Background: The aim of this study is to explore the association between family-related factors and excessive time spent on screen-based activities among school-aged children. Methods: A cross-sectional survey using the methodology of the Health Behaviour in School-aged Children study was performed in 2013, with data collected from Slovak (n = 258) and Czech (n = 406) 11- and 15-year-old children. The effects of age, gender, availability of a TV or computer in the bedroom, parental rules on time spent watching TV or working on a computer, parental rules on the content of TV programmes and computer work and watching TV together with parents on excessive time spent with screen-based activities were explored using logistic regression models. Results: Two-thirds of respondents watch TV or play computer games at least two hours a day. Older children have a 1.80-times higher chance of excessive TV watching (CI: 1.30-2.51) and a 3.91-times higher chance of excessive computer use (CI: 2.82-5.43) in comparison with younger children. More than half of children have a TV (53%) and a computer (73%) available in their bedroom, which increases the chance of excessive TV watching by 1.59 times (CI: 1.17-2.16) and of computer use by 2.25 times (CI: 1.59-3.20). More than half of parents rarely or never apply rules on the length of TV watching (64%) or time spent on computer work (56%), and their children have a 1.76-times higher chance of excessive TV watching (CI: 1.26-2.46) and a 1.50-times greater chance of excessive computer use (CI: 1.07-2.08). A quarter of children reported that they are used to watching TV together with their parents every day, and these have a 1.84-times higher chance of excessive TV watching (1.25-2.70). Conclusions: Reducing time spent watching TV by applying parental rules or a parental role model might help prevent excessive time spent on screen-based activities

Regional mortality by socioeconomic factors in Slovakia:a comparison of 15 years of changes

Background: Like most Central European countries Slovakia has experienced a period of socioeconomic changes and at the same time a decline in the mortality rate. Therefore, the aim is to study socioeconomic factors that changed over time and simultaneously contributed to regional differences in mortality. Methods: The associations between selected socioeconomic indicators and the standardised mortality rate in the population aged 20-64 years in the districts of the Slovak Republic in the periods 1997-1998 and 2012-2013 were analysed using linear regression models. Results: A higher proportion of inhabitants in material need, and among males also lower income, significantly contributed to higher standardised mortality in both periods. The unemployment rate did not contribute to this prediction. Between the two periods no significant changes in regional mortality differences by the selected socioeconomic factors were found. Conclusions: Despite the fact that economic growth combined with investments of European structural funds contributed to the improvement of the socioeconomic situation in many districts of Slovakia, there are still districts which remain "poor" and which maintain regional mortality differences

Implementation of a new solution to the multivariate Behrens-Fisher problem

Krishnamoorthy and Yu (2004, Statistics and Probability Letters 66: 161–169) published a new approximate solution to the multivariate Behrens–Fisher problem. It is a modification of Nel and Van der Merwe’s (1986, Communications in Statistics, Theory and Methods 15: 3719–3735) test. The test is invariant and identical to Welch’s test for one-dimensional data. In this article, I describe an implementation of the test in Stata. The hotelmnm command allows you to perform the test easily and returns computed values for possible further computations

On multivariate Gaussian copulas

Gaussian copulas are handy tool in many applications. However, when dimension of data is large, there are too many parameters to estimate. Use of special variance structure can facilitate the task. In many cases, especially when different data types are used. Pearson correlation is not a suitable measure of dependence. We study the properties of Kendall and Spearman correlation coefficients-which have better properties and are invariant under monotone transformations-used at the place of Pearson coefficients. Spearman correlation coefficient appears to be more suitable for use in such complex applications. (C) 2009 Elsevier B.V. All rights reserved

Implementation of a new solution to the multivariate Behrens-Fisher problem

Krishnamoorthy and Yu (2004, Statistics and Probability Letters 66: 161–169) published a new approximate solution to the multivariate Behrens– Fisher problem. It is a modification of Nel and Van der Merwe’s (1986, Communications in Statistics, Theory and Methods 15: 3719–3735) test. The test is invariant and identical to Welch's test for one-dimensional data. In this article, I describe an implementation of the test in Stata. The hotelmnm command allows you to perform the test easily and returns computed values for possible further computations. Copyright 2009 by StataCorp LP.hotelmnm, multivariate Behrens–Fisher problem, Nel and Van der Merwe's test, Welch's test

Special variance structures in the growth curve model

Standard and extended growth curve model (multivariate linear model) with practically important variance structures are considered and a method for parameters estimation is proposed.Growth curve model Extended growth curve model Variance components Multivariate regression Variance structure