Measuring Social Inclusion in Europe: a non-additive approach with the expert-preferences of public policy planners.

This paper introduces a normative, expert-informed, time-dependent index of Social Inclusion for European administrative regions in five countries, using longitudinal data from Eurostat. Our contribution is twofold: first, our indicator is based on a non-additive aggregation operator (the Choquet Integral), which allows us to model many preferences' structures and to overcome the limitations embedded in other approaches. Second, we elicit the parameters of the aggregation operator from an expert panel of Italian policymakers in Social Policy, and Economics scholars. Our results highlight that Mediterranean countries exhibit lower Inclusion levels than Northern/Central countries, and that this disparity has grown in the last decade. Our results complement and partially challenge existing evidence from data-driven aggregation methods.

Constructing a comprehensive disaster resilience index: The case of Italy.

Measuring disaster resilience is a key component of successful disaster risk management and climate change adaptation. Quantitative, indicator-based assessments are typically applied to evaluate resilience by combining various indicators of performance into a single composite index. Building upon extensive research on social vulnerability and coping/adaptive capacity, we first develop an original, comprehensive disaster resilience index (CDRI) at municipal level across Italy, to support the implementation of the Sendai Framework for Disaster Risk Reduction 2015-2030. As next, we perform extensive sensitivity and robustness analysis to assess how various methodological choices, especially the normalisation and aggregation methods applied, influence the ensuing rankings. The results show patterns of social vulnerability and resilience with sizeable variability across the northern and southern regions. We propose several statistical methods to allow decision makers to explore the territorial, social and economic disparities, and choose aggregation methods best suitable for the various policy purposes. These methods are based on linear and non-liner normalization approaches combining the OWA and LSP aggregators. Robust resilience rankings are determined by relative dominance across multiple methods. The dominance measures can be used as a decision-making benchmark for climate change adaptation and disaster risk management strategies and plans.

A Multi-Criteria Decision Analysis based methodology for quantitatively scoring the reliability and relevance of ecotoxicological data.

Ecotoxicological data are highly important for risk assessment processes and are used for deriving environmental quality criteria, which are enacted for assuring the good quality of waters, soils or sediments and achieving desirable environmental quality objectives. Therefore, it is of significant importance the evaluation of the reliability of available data for analysing their possible use in the aforementioned processes. The thorough analysis of currently available frameworks for the assessment of ecotoxicological data has led to the identification of significant flaws but at the same time various opportunities for improvement. In this context, a new methodology, based on Multi-Criteria Decision Analysis (MCDA) techniques, has been developed with the aim of analysing the reliability and relevance of ecotoxicological data (which are produced through laboratory biotests for individual effects), in a transparent quantitative way, through the use of expert knowledge, multiple criteria and fuzzy logic. The proposed methodology can be used for the production of weighted Species Sensitivity Weighted Distributions (SSWD), as a component of the ecological risk assessment of chemicals in aquatic systems. The MCDA aggregation methodology is described in detail and demonstrated through examples in the article and the hierarchically structured framework that is used for the evaluation and classification of ecotoxicological data is shortly discussed. The methodology is demonstrated for the aquatic compartment but it can be easily tailored to other environmental compartments (soil, air, sediments).

A risk-based methodology for ranking environmental chemical stressors at the regional scale.

A "Risk-based Tool for the Regional Ranking of Environmental Chemical Stressors" has been developed, aimed at supporting decision-makers in the identification of priority environmental contaminants, as well as priority areas, to be further assessed. The tool implements a methodology based on a quantitative Weight-of-Evidence approach, integrating three types of information, identified as "Lines-of-Evidence" (LoE), namely: LoE "Environmental Contamination" (including data on chemical contamination in environmental matrices in the region, thus providing information on potential population exposure), LoE "Intake" (including results from human biomonitoring studies, i.e. concentration of chemicals in human biological matrices, thus providing an integrated estimation of exposure) and LoE "Observed Effects" (including information on the incidence of adverse health outcomes associated with environmental exposure to chemicals). A Multi-Criteria Decision Analysis (MCDA) methodology based on fuzzy logic has been developed to support the integration of information related to these three LoEs for each chemical stressor. The tool allows one to rank chemical stressors at different spatial scales, such as at the regional level as well as within each sub-area (e.g., counties). Moreover, it supports the identification of priority sub-areas within the region, where environmental and health data suggest possible adverse health effects and thus more investigation efforts are needed. To evaluate the performance of this newly developed tool, a case-study in the Flemish region (north of Belgium) has been selected. In the case-study, data on soil contamination by metals and organic contaminants were integrated with data on exposure and effect biomarkers measured in adolescents within the framework of the human biomonitoring study performed by the Flemish Centre of Expertise on Environment and Health in the period 2002-2006. The case-study demonstrated the performance of the tool in integrating qualitative and quantitative data with expert judgement for the identification of priority contaminants and areas. The proposed approach proved to be flexible, allowing for the incorporation of individual decision-maker's preferences, and, at the same time, to be transparent since all assumptions and value attributions are traceable.

Interinstitutional variation of caesarean delivery rates according to indications in selected obstetric populations: a prospective multicenter study.

The aim of the study was to identify which groups of women contribute to interinstitutional variation of caesarean delivery (CD) rates and which are the reasons for this variation. In this regard, 15,726 deliveries from 11 regional centers were evaluated using the 10-group classification system. Standardized indications for CD in each group were used. Spearman's correlation coefficient was used to calculate (1) relationship between institutional CD rates and relative sizes/CD rates in each of the ten groups/centers; (2) correlation between institutional CD rates and indications for CD in each of the ten groups/centers. Overall CD rates correlated with both CD rates in spontaneous and induced labouring nulliparous women with a single cephalic pregnancy at term (P = 0.005). Variation of CD rates was also dependent on relative size and CD rates in multiparous women with previous CD, single cephalic pregnancy at term (P < 0.001). As for the indications, "cardiotocographic anomalies" and "failure to progress" in the group of nulliparous women in spontaneous labour and "one previous CD" in multiparous women previous CD correlated significantly with institutional CD rates (P = 0.021, P = 0.005, and P < 0.001, resp.). These results supported the conclusion that only selected indications in specific obstetric groups accounted for interinstitutional variation of CD rates.

The application of the Ten Group classification system (TGCS) in caesarean delivery case mix adjustment. A multicenter prospective study.

BACKGROUND: Caesarean delivery (CD) rates are commonly used as an indicator of quality in obstetric care and risk adjustment evaluation is recommended to assess inter-institutional variations. The aim of this study was to evaluate whether the Ten Group classification system (TGCS) can be used in case-mix adjustment. METHODS: Standardized data on 15,255 deliveries from 11 different regional centers were prospectively collected. Crude Risk Ratios of CDs were calculated for each center. Two multiple logistic regression models were herein considered by using: Model 1- maternal (age, Body Mass Index), obstetric variables (gestational age, fetal presentation, single or multiple, previous scar, parity, neonatal birth weight) and presence of risk factors; Model 2- TGCS either with or without maternal characteristics and presence of risk factors. Receiver Operating Characteristic (ROC) curves of the multivariate logistic regression analyses were used to assess the diagnostic accuracy of each model. The null hypothesis that Areas under ROC Curve (AUC) were not different from each other was verified with a Chi Square test and post hoc pairwise comparisons by using a Bonferroni correction. RESULTS: Crude evaluation of CD rates showed all centers had significantly higher Risk Ratios than the referent. Both multiple logistic regression models reduced these variations. However the two methods ranked institutions differently: model 1 and model 2 (adjusted for TGCS) identified respectively nine and eight centers with significantly higher CD rates than the referent with slightly different AUCs (0.8758 and 0.8929 respectively). In the adjusted model for TGCS and maternal characteristics/presence of risk factors, three centers had CD rates similar to the referent with the best AUC (0.9024). CONCLUSIONS: The TGCS might be considered as a reliable variable to adjust CD rates. The addition of maternal characteristics and risk factors to TGCS substantially increase the predictive discrimination of the risk adjusted model.

Development of a site-specific ecological risk assessment for contaminated sites: part I. A multi-criteria based system for the selection of ecotoxicological tests and ecological observations.

A two modules Decision Support System (DSS-ERAMANIA) was developed in order to support the site-specific Ecological Risk Assessment (ERA) for contaminated sites. Within the first module, the TRIAD and the Weight of Evidence approaches were used to develop a site-specific Ecological Risk Assessment framework including three tires of investigation. Selected ecological observations and ecotoxicological tests were compared according to Multi Criteria Decision Analysis (MCDA) methods and expert judgment, and the obtained ranking was used to identify a suitable set of tests, at each investigation tier, to be applied to the examined case study. A simplified application of the proposed methodology, implemented in the Module 1 of the DSS-ERA-MANIA, is described and discussed.

Decision support-oriented selection of remediation technologies to rehabilitate contaminated sites.

A methodology for selecting remediation technologies is presented as part of a decision support system for the rehabilitation of contaminated sites. It includes 2 steps: In the 1st step, a pool of suitable technologies is selected within a technologies database according to their applicability to site-specific conditions; in the 2nd step, the selected technologies: are ranked according to a multicriteria decision analysis (MCDA) approach. The MCDA was applied to allow for a transparent procedure and for the integration of expert analyses. The methodology was implemented in a previously developed georeferenced information system-based decision support system for the rehabilitation of contaminated sites and then applied to a case study (Porto Marghera, Venice, Italy). On the basis of the obtained results, the proposed methodology appeared suitable to select remediation technologies according to both technical features and requirements of available technologies, as well as site-specific environmental conditions of the site of concern, such as chemical contamination levels and remediation objectives.