Pietro Giorgio Lovaglio

Health of the nation outcome scales evaluation in a community setting population

PurposeTo explore the internal structure of the Health of the Nation Outcome Scales (HoNOS-12), proposing a shorter one-dimensional version for routine use in community-oriented Mental Heath services.MethodsA validation study involving four Mental Health Departments, located in the Province of Milan (Italy). Eligible patients were outpatients and residential inpatients rated on three occasions during the year 2009, with a range of mental illnesses and diagnoses. Methodologically, we use both exploratory factor analysis (EFA) with holdout validation and Rasch approaches and parallel analysis.ResultsEFA, Rasch analysis and parallel analysis demonstrate a large violation of unidimensionality. Both EFA (training sample) and Rasch analyses yield convergent results, generating the same unidimensional abbreviated version of the HoNOS-12, resulting in a six-item scale (HoNOS-6) which demonstrates unidimensionality, good item fit, a solid factor structure (strong loadings and communalities) and acceptable model fit, evaluated using confirmatory factor analysis on a validation sample.ConclusionsThe HoNOS-12 does not measure a single, underlying construct of mental health status. Nevertheless, the instrument can be utilized in a reduced version (HoNOS-6), as a clinically acceptable outcome scale (measuring self-perceived clinical and social needs for community support, rather than global mental disorder) for routine use in a community setting population.

On the relationships among latent variables and residuals in PLS path modeling: The formative-reflective scheme

A new approach for the estimation and the validation of a structural equation model with a formative-reflective scheme is presented. The basis of the paper is a proposal for overcoming a potential deficiency of PLS path modeling. In the PLS approach the reflective scheme assumed for the endogenous latent variables (LVs) is inverted; moreover, the model errors are not explicitly taken into account for the estimation of the endogenous LVs. The proposed approach utilizes all the relevant information in the formative manifest variables (MVs) providing solutions which respect the causal structure of the model. The estimation procedure is based on the optimization of the redundancy criterion. The new approach, entitled redundancy analysis approach to path modeling (RA-PM) is compared with both traditional PLS Path Modeling and LISREL methodology, on the basis of real and simulated data.

Formative Indicators and Effects of a Causal Model for Household Human Capital with Application

Dagum and Slottje (2000) estimated household human capital (HC) as a latent variable (LV) and proposed its monetary estimation by means of an actuarial approach. This paper introduces an improved method for the estimation of household HC as an LV by means of formative and reflective indicators in agreement with the accepted economic definition of HC. The monetary value of HC is used in a recursive causal model to obtain short- and long-term multipliers that measure the direct and total effects of the variables that determine household HC. The new method is applied to estimate US household HC for year 2004.

Benchmarking Strategies for Measuring the Quality of Healthcare: Problems and Prospects

Over the last few years, increasing attention has been directed toward the problems inherent to measuring the quality of healthcare and implementing benchmarking strategies. Besides offering accreditation and certification processes, recent approaches measure the performance of healthcare institutions in order to evaluate their effectiveness, defined as the capacity to provide treatment that modifies and improves the patients state of health. This paper, dealing with hospital effectiveness, focuses on research methods for effectiveness analyses within a strategy comparing different healthcare institutions. The paper, after having introduced readers to the principle debates on benchmarking strategies, which depend on the perspective and type of indicators used, focuses on the methodological problems related to performing consistent benchmarking analyses. Particularly, statistical methods suitable for controlling case-mix, analyzing aggregate data, rare events, and continuous outcomes measured with error are examined. Specific challenges of benchmarking strategies, such as the risk of risk adjustment (case-mix fallacy, underreporting, risk of comparing noncomparable hospitals), selection bias, and possible strategies for the development of consistent benchmarking analyses, are discussed. Finally, to demonstrate the feasibility of the illustrated benchmarking strategies, an application focused on determining regional benchmarks for patient satisfaction (using 2009 Lombardy Region Patient Satisfaction Questionnaire) is proposed.

Structural Equation Models in a Redundancy Analysis Framework With Covariates

A recent method to specify and fit structural equation modeling in the Redundancy Analysis framework based on so-called Extended Redundancy Analysis (ERA) has been proposed in the literature. In this approach, the relationships between the observed exogenous variables and the observed endogenous variables are moderated by the presence of unobservable composites, estimated as linear combinations of exogenous variables. However, in the presence of direct effects linking exogenous and endogenous variables, or concomitant indicators, the composite scores are estimated by ignoring the presence of the specified direct effects. To fit structural equation models, we propose a new specification and estimation method, called Generalized Redundancy Analysis (GRA), allowing us to specify and fit a variety of relationships among composites, endogenous variables, and external covariates. The proposed methodology extends the ERA method, using a more suitable specification and estimation algorithm, by allowing for covariates that affect endogenous indicators indirectly through the composites and/or directly. To illustrate the advantages of GRA over ERA we propose a simulation study of small samples. Moreover, we propose an application aimed at estimating the impact of formal human capital on the initial earnings of graduates of an Italian university, utilizing a structural model consistent with well-established economic theory.

Does community care work? A model to evaluate the effectiveness of mental health services

The aim of this paper is to evaluate the effectiveness of community Mental Health Departments in Lombardy (Italy), and analyse the eventual differences in outcome produced by different packages of care. The survey was conducted in 2000 on 4,712 patients treated in ten Mental Health Departments. Patients were assessed at least twice in a year with HoNOS (Health of the Nation Outcome Scales). Data on treatment packages were drawn from the regional mental health information system, which includes all outpatient and day-care contacts, as well as general hospital and inpatient admissions provided by Mental Health Departments. Multilevel growth models were used for outcomes statistical analysis, expressed in terms of change of the total HoNOS score. On the whole, Mental Health Departments were effective in reducing HoNOS scores. The main predictor of improvement was treatment, while length of care, gender and diagnosis were weaker predictors. After severity adjustment, some packages of care proved more effective than others. Appropriate statistical methods, comprehensive treatment descriptions and routine outcome assessment tools are needed to evaluate the effectiveness of community mental health services in clinical settings.

Component Analysis for Structural Equation Models with Concomitant Indicators

A new approach to structural equation modelling based on so-called Extended Redundancy Analysis has been recently proposed in literature, enhanced with the added characteristic of generalizing Redundancy Analysis and Reduced-Rank Regression models for more than two blocks. However, in presence of direct effects linking exogenous and endogenous variables, the latent composite scores are estimated by ignoring the presence of the specified direct effects. In this paper, we extend Extended Redundancy Analysis, permitting us to specify and fit a variety of relationships among latent composites and endogenous variables. In particular, covariates are allowed to affect endogenous indicators indirectly through the latent composites and/or directly.

Multilevel dimensionality-reduction methods

When data sets are multilevel (group nesting or repeated measures), different sources of variations must be identified. In the framework of unsupervised analyses, multilevel simultaneous component analysis (MSCA) has recently been proposed as the most satisfactory option for analyzing multilevel data. MSCA estimates submodels for the different levels in data and thereby separates the “within”-subject and “between”-subject variations in the variables. Following the principles of MSCA and the strategy of decomposing the available data matrix into orthogonal blocks, and taking into account the between- and the within data structures, we generalize, in a multilevel perspective, multivariate models in which a matrix of response variables can be used to guide the projections (formed by responses predicted by explanatory variables or by a limited number of their combinations/composites) into choices of meaningful directions. To this end, the current paper proposes the multilevel version of the multivariate regression model and dimensionality-reduction methods (used to predict responses with fewer linear composites of explanatory variables). The principle findings of the study are that the minimization of the loss functions related to multivariate regression, principal-component regression, reduced-rank regression, and canonical-correlation regression are equivalent to the separate minimization of the sum of two separate loss functions corresponding to the between and within structures, under some constraints. The paper closes with a case study of an application focusing on the relationships between mental health severity and the intensity of care in the Lombardy region mental health system.

The balanced scorecard in health care: a multilevel latent variable approach

Purpose – The purpose of this paper is to propose a practical conceptualization of the balanced scorecard (BSC) to describe the mechanism producing creation of monetary value for hospitals in the territorial context of Lombardy region (Italy).Design/methodology/approach – The authors propose a model‐building strategy that assigns key indicators to key performance areas, and identifies causal relationships between key performance areas. Second, the authors utilize a suitable statistical approach to estimate causal relationships among involved latent variables, taking into account the hierarchical structure of data. Utilizing a suitable data decomposition, the causal model is applied separately to the within data (hospitals) and to the between data (local health agencies).Findings – In the measurement model a new latent construct (medical human capital) was found that resumes the amount of formal training and the performance of surgical staff in hospitals. The estimated causal models reflect the usual direc...

Efficacia relativa e di impatto di iniziative nell'ambito dei servizi alla persona di pubblica utilita'

The present paper addresses in detail the specification of a model that values the effectiveness of institutional plans within clinical or epidemiologist projects previewing the supply of a service to the person and jointly the effectiveness of agencies (Agents) that distribute the same service with different modalities, supplying the rationale for a rigorous procedure of objective evaluation. Finally the model proposed is applied to a public plan promoted from the Regione Lombardia in 2002, consisting in an economic contribution to family that decide to assist in house the not self-sufficient old, like alternative to the shelter in Residences.