The Latent Dimensions of Poor Self-Rated Health: How Chronic Diseases,Functional and Emotional Dimensions Interact Influencing Self-Rated Health in Italian Elderly

Self-Rated Health (SRH) is becoming one of the most popular indicator of population health. Nevertheless, a limited understanding still remains about the elements to which individuals refer when evaluating their health and how those elements act and interact in the evaluation process. In this study we use a structural equation model with latent variables to identify direct and indirect influences of various health dimensions (chronic morbidity, functional abilities and emotional health) and socio-demographic covariates (age, gender and education) on poor SRH. The sample consists of 25,183 Italian elderly aged 65 years and over, interviewed in the 2005 National Health Interview Survey. The results have pointed out the higher direct effect of psychological and emotional health on SRH, while the higher total effect is caused by chronic morbidity, which influences SRH both directly and altering functional and emotional health. Growing older, being a woman and having a low education negatively impacts on SRH. However, this is almost completely the result of the indirect effect exerted by the covariates, while their direct effect is not significant (gender), negative (age) or very modest (education).     

Temporal blindspots in Occupy Philadelphia

ABSTRACT

This paper analyzes the disjunctive temporalities of Occupy Philadelphia’s political constituencies. Drawing on both an ethnographic participant observation study of the Occupy Philadelphia movement and Philadelphia’s neoanarchist political communities, and on recent social scientific theorization of events, the paper argues that contradictory ideas about temporal timescales, momentum, duration, sequences, and rhythms of tactical and strategic action problematized interaction and coordination among movement participants. These points of coordinative disjuncture can be traced back to differences in participants’ ideas about prefigurative politics and strategic temporalities. Limning the temporal expectations and experiences of social movement participants, this paper contributes to the examination of both linkages and disjunctures between eventful temporalities experienced in moments of protest and in social movements with diverse timescales.     

Demographics,Public Spending and Social Programs

The demographic trends of the population of Italy are characterized by a very low fertility rate (in the 1990s the total fertility rate was 1.1–1.3, i.e. one of the lowest in the world) and very rapid aging (Italy was the first country in the world in which people aged 60+ outnumbered people aged less than 20). These trends require continuous and deep changes in the organization of society and in public services to avoid an increasing imbalance between demand and supply. They also require new social policies based on equity, efficiency and rationality.     

Modified quasi-profile likelihoods from estimating functions

We discuss higher-order adjustments for a quasi-profile likelihood for a scalar parameter of interest, in order to alleviate some of the problems inherent to the presence of nuisance parameters, such as bias and inconsistency. Indeed, quasi-profile score functions for the parameter of interest have bias of order O(1)$\mathrm{O}(1)$, and such bias can lead to poor inference on the parameter of interest. The higher-order adjustments are obtained so that the adjusted quasi-profile score estimating function is unbiased and its variance is the negative expected derivative matrix of the adjusted profile estimating equation. The modified quasi-profile likelihood is then obtained as the integral of the adjusted profile estimating function. We discuss two methods for the computation of the modified quasi-profile likelihoods: a bootstrap simulation method and a first-order asymptotic expression, which can be simplified under an orthogonality assumption. Examples in the context of generalized linear models and of robust inference are provided, showing that the use of a modified quasi-profile likelihood ratio statistic may lead to coverage probabilities more accurate than those pertaining to first-order Wald-type confidence intervals.     

Vitality and malaise in demography: outlook and strategies for the future

"When dealing with age structures...the question could be posed...whether in the age structure such pronounced 'distortions' already exist as to make a major contribution to plotting the course of demography and then, as a more or less inevitable consequence, to shaping the social and economic context. In other words, whether conditions of 'demographic malaise' exist linked, on the one hand, to an extraordinarily young age structure and a consequent 'excess' population growth...and, on the other, to an extraordinarily old age structure and a subsequent 'excess' population decline.... This study proposes to focus particularly on this second instance, which pertains to contemporary western populations or sub-populations, [using the example of Italy]."     

Drivers of Youth Labour Market Integration Across European Regions

Territorial disparities and youth labour markets have been often considered as separated themes, due to challenges in data availability. Comparative regional or sub-regional research on youth labour market integration (YLMI) have been therefore scarce. In this article, we address this gap by presenting a composite measure of YLMI that covers a wide range of indicators and sheds light on the EU territorial divide of young peoples’ opportunities at regional level. In order to build the YLMI index, we use benefit-of-the-doubt-weighting, a seminal methodology on composite indicators (CI) that combines sequence with conditional weights based on the range of each sub-indicator. To proof the usefulness of YLMI, we analyze the evolution of regional YLMI in the EU before and after the economic crisis; and the trends of homogenization or differentiation across EU territories. Furthermore, we investigate to what extent employment conditions, skills supply and technological resources explain cross-regional variations in YLMI.

     

Extending conditional likelihood in models for stratified binary data

The conditional likelihood is widely used in logistic regression models with stratified binary data. In particular, it leads to accurate inference for the parameters of interest, which are common to all strata, eliminating stratum-specific nuisance parameters. The modified profile likelihood is an accurate approximation to the conditional likelihood, but has the advantage of being available for general parametric models. Here, we propose the modified profile likelihood as an ideal extension of the conditional likelihood in generalized linear models for binary data, with generic link function. An important feature is that for the implementation we only need standard outputs of routines for generalized linear models. The accuracy of the method is supported by theoretical properties and is confirmed by simulation results.This research was supported by MIUR COFIN 2001-2003.     

A computer algebra package for approximate conditional inference

This paper presents a set of REDUCE procedures that make a number of existing higher-order asymptotic results available for both theoretical and practical research. Attention has been restricted to the context of exact and approximate inference for a parameter of interest conditionally either on an ancillary statistic or on a statistic partially sufficient for the nuisance parameter. In particular, the procedures apply to regression-scale models and multiparameter exponential families. Most of them support algebraic computation as well as numerical calculation for a given data set. Examples illustrate the code.     

Restricted likelihood inference for generalized linear mixed models

We aim to promote the use of the modified profile likelihood function for estimating the variance parameters of a GLMM in analogy to the REML criterion for linear mixed models. Our approach is based on both quasi-Monte Carlo integration and numerical quadrature, obtaining in either case simulation-free inferential results. We will illustrate our idea by applying it to regression models with binary responses or count data and independent clusters, covering also the case of two-part models. Two real data examples and three simulation studies support the use of the proposed solution as a natural extension of REML for GLMMs. An R package implementing the methodology is available online.