Evaluation of long-term health care services through a latent Markov model with covariates

We focus on the evaluation of the long-term health care services provided to elderly patients by nursing homes of four different health districts in the Umbria region (Italy). To this end, we analyze data coming from a longitudinal survey aimed at assessing several aspects of patient health conditions and develop an extended version of the latent Markov model with covariates, which allows us to deal with dropout and intermittent missing data patterns that are common in longitudinal studies. Maximum likelihood estimates are obtained by a two-step approach that allows for fast estimation of model parameters and prevents some drawbacks of the standard maximum likelihood method encountered in the presence of many response variables and covariates. In the application to the observed data, we show how to obtain indicators of the effectiveness of the health care services delivered by each health district, by means of a resampling procedure.     

Which Aspects of Elderly Living Conditions are Important to Predict Mortality? The Complex Role of Family Ties at Home and in Institutions

Social Indicators Research - Besides health and socio-economic status, the social relationships maintained during elderly play an important role in shaping the living conditions at older ages. In...     

Intergenerational Programs Review: Study Design and Characteristics of Intervention,Outcomes, and Effectiveness

There is a growing interest in intergenerational connectivity through intergenerational programs. In this work, a review of intergenerational programs was performed, with focus on the program design and objectives as well as in their outcomes.

We used a systematic review method in which we screened 3,796 articles. After analyzing titles, abstracts, and full paper analysis, 16 articles were retained. Each study was reviewed, and data were extracted related to target population, study design, characteristics of intervention, outcomes, and effectiveness.

Intergenerational programs included educational programs and art, Information technology development, cultural heritage, health education, and therapeutic activities. Most of the programs collected both quantitative and/or qualitative data. Seven studies collected data in the beginning and at the end of the program. Significant diversity in sample size and intervention length was found. Measurement of outcomes was performed in both young and/or elderly group of participants. Programs impact evaluation varied between studies, including validated scales, interviews, observation, focus groups, and conversation analysis, narratives, videotaped sessions, and field notes.

Our study highlighted the diversity in the design of studies and in the program’s effectiveness evaluation. More randomized design studies are required to support researchers and practitioners in the development of future intergenerational programs.     

Wildfire forecasting: between criminal act and unintentional events

Forecasting represents the new credo in the reorganization of risks prevention. What does the introduction of such technology mean in terms of fire-fighting practice and policy? By applying Practice-Based Studies, forecasting practice emerges as a form of practical knowledge resulting from the alignment of the forecasting tool with foresters’ former competences, expertise, practices and tools. The acknowledgement of practical and scientific knowledge linked to forecasting allows the identification of the different organizational cultures linked to fire-fighting. For foresters wildfire is mainly a criminal act and forecasting a policing activity. This use of the artefact silences alternative approaches to wildfire-fighting such as the prevention of unintentional acts. While forecasting technology may reproduce forms of blindness in the future, anticipation becomes then an interesting research objet, embedded in dominant professional cultures and forms of knowledge.     

Bias for Vocalic Over Consonantal Information in 6‐Month‐Olds

Human languages rely on the ability to learn and produce an indefinite number of words by combining consonants and vowels in a lawful manner. The categorization of speech representations into consonants and vowels is evidenced by the tendency of adult speakers, attested in many languages, to use consonants and vowels for different tasks. Consonants are favored in lexical tasks, while vowels are favored to learn structural regularities. Recent results suggest that this specialization is already observable at 12 months of age in Italian participants. Here, we investigated the representations of younger infants. In a series of anticipatory looking experiments, we showed that Italian 6‐month‐olds rely more on vowels than on consonants when learning the predictions made by individual words (Experiment 1) and are better at generalizing a structure when it is implemented over vowels than when it is implemented over consonants (Experiments 2 and 3). Until 6 months of age, infants thus show a general vocalic bias, which contrasts with the specialization previously observed at 12 months. These results suggest the format of speech representations changes during the second semester of life.     

Robust analysis of bibliometric data

This work stems from the idea of describing the scientific productivity of Italian statisticians. There are several problems that must be addressed in achieving this goal: What data should be used? Have the data been cleaned? What techniques can be used? We propose the use of multiple sources and multiple metrics to get a complete information base. We check the correctness of the data using multivariate outlier identification techniques. We appropriately transform the data. We apply robust clustering to verify the existence of homogeneous groups. We suggest the use of forward search to establish a ranking among scholars. The proposed methodology, which, in this case, allowed us to group scholars into four homogeneous groups and sort them according to multidimensional data, can be applied to other similar applications in bibliometrics.     

Influence Measures in Quantile Regression Models

In this article, we use the asymmetric Laplace distribution to define a new method to determine the influence of a certain observation in the fit of quantile regression models. Our measure is based on the likelihood displacement function and we propose two types of measures in order to determine influential observations in a set of conditional quantiles conjointly or in each conditional quantile of interest. We verify the validity of our average measure in a simulated data set as well in an illustrative example with data about air pollution.     

Generalized bartlett correction

This paper provides a general method of modifying a statistic of interest in such a way that the distribution of the modified statistic can be approximated by an arbitrary reference distribution to an order of accuracy of O(n ^-1/2) or even O(n ^-1). The reference distribution is usually the asymptotic distribution of the original statistic. We prove that the multiplication of the statistic by a suitable stochastic correction improves the asymptotic approximation to its distribution. This paper extends the results of the closely related paper by Cordeiro and Ferrari (1991) to cope with several other statistical tests. The resulting expression for the adjustment factor requires knowledge of the Edgeworth-type expansion to order O(n^-1) for the distribution of the unmodified statistic. In practice its functional form involves some derivatives of the reference distribution. Certain difference between the cumulants of appropriate order in n of the unmodified statistic and those of its first-order approximation, and the unmodified statistic itself. Some applications are discussed.     

A Review of Some Modern Approaches to the Problem of Trend Extraction

This article presents a review of some modern approaches to trend extraction for one-dimensional time series, which is one of the major tasks of time series analysis. The trend of a time series is usually defined as a smooth additive component which contains information about the time series global change, and we discuss this and other definitions of the trend. We do not aim to review all the novel approaches, but rather to observe the problem from different viewpoints and from different areas of expertise. The article contributes to understanding the concept of a trend and the problem of its extraction. We present an overview of advantages and disadvantages of the approaches under consideration, which are: the model-based approach (MBA), nonparametric linear filtering, singular spectrum analysis (SSA), and wavelets. The MBA assumes the specification of a stochastic time series model, which is usually either an autoregressive integrated moving average (ARIMA) model or a state space model. The nonparametric filtering methods do not require specification of model and are popular because of their simplicity in application. We discuss the Henderson, LOESS, and Hodrick–Prescott filters and their versions derived by exploiting the Reproducing Kernel Hilbert Space methodology. In addition to these prominent approaches, we consider SSA and wavelet methods. SSA is widespread in the geosciences; its algorithm is similar to that of principal components analysis, but SSA is applied to time series. Wavelet methods are the de facto standard for denoising in signal procession, and recent works revealed their potential in trend analysis.