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9 results on '"Coviello E"'

1. LE SCELTE DI RISK MANAGEMENT NELLA GESTIONE DELLE CATASTROFI NATURALI

Author

Antonio Coviello e Giovanni Di Trapani

Subjects
calamità naturali, innovazione, risk management, assicurazioni
Abstract

Quello dei rischi catastrofali è un tema chiave del dibattito di politica economica del Paese, che mostra emblematicamente come occorre passare dalla "gestione" al "governo" dei rischi. E quindi dal trattamento delle technicalities operative connesse al risk management al cuore stesso della politica economica. La politica infatti per l'economista pubblico, soprattutto italiano, deve fare i conti con la carenza delle risorse finanziarie e strumentali, con le distorsioni indotte dall'intervento pubblico, con l'inefficienza la burocrazia e spesso la corruzione degli apparati. Questo è ancora più vero nel caso delle calamità naturali, rispetto a cui la domanda di politiche pubbliche appare oggi in crescita drammatica. L'analisi delle tendenze in atto mostra quanto siano vulnerabili rispetto a questi accadimenti l'intera società e la sua economia, e quanto questa vulnerabilità sia crescente. D'altro canto la domanda di protezione è anch'essa aumentata significativamente. Per questi motivi è apparso essenziale rivisitare l'intero approccio al problema e sviluppare strategie efficienti per mitigare e gestire finanziariamente i rischi derivanti dalle catastrofi. E' sembrato ineludibile adottare una logica di sistema, di "sistema-Paese". Qui entra in gioco il ruolo del settore privato, e in particolare dell'industria del risparmio assicurativo. Proprio l'OCSE, tra le buone pratiche di mitigazione e finanziamento dei rischi catastrofali , invita gli Stati a considerare il ricorso all'infrastruttura assicurativa esistente in ogni paese, come uno strumento fondamentale da rendere operativo in funzione della copertura dei rischi e dell'intervento di sostegno.

Published
2016

4. Italian cancer figures, report 2011: Survival of cancer patients in Italy

Author

Fusco, M, Buzzoni, C, Coviello, E, Rashid, I, Bianconi, F, Cuccaro, F, Castaing, M, De Angelis, R, Giacomin, A, Guzzinati, S, Mosso, Ml, Pisani, P, Quaglia, A, Randi, G, Ramazzotti, V, Russo, A, Senatore, G, Stracci, F, Traina, A, Vercelli, M, Zarcone, M, Ferretti, S, Mazzoleni, G, Bellú, F, Tschugguel, B, De Valiere, E, Facchinelli, G, Falk, M, Dal Cappello, T, Vercellino, Pc, Andreone, S, Busato, A, Marzola, L, Migliari, E, Carletti, N, Nenci, I, Crocetti, E, Caldarella, A, Corbinelli, A, Giusti, F, Intrieri, T, Manneschi, G, Nemcova, L, Romeo, G, Sacchettini, C, Zappa, M, Paci, E, Serraino, D, Angelin, T, Bidoli, E, Dal Maso, L, de Dottori, M, De Paoli, A, De Santis, E, Forgiarini, O, Lise, M, Zucchetto, A, Zanier, L, Orengo, Ma, Casella, C, Marani, E, Puppo, A, Celesia, Mv, Cogno, R, Manenti, S, Garrone, E, Pannozzo, F, Busco, S, Cercato, Mc, Battisti, W, Sperduti, I, Macci, L, Bugliarello, E, Bernazza, E, Tamburo, L, Rossi, M, Curatella, S, De Francesco, C, Tamburrino, S, Bisanti, L, Autelitano, M, Ghilardi, S, Leone, R, Filipazzi, L, Bonini, A, Giubelli, C, Federico, M, Artioli, Me, Valla, K, Braghiroli, B, Cirilli, C, Luminari, S, Pirani, M, Ferrari, L, Bellatalla, C, Panico, M, Perrotta, C, Vassante, B, Vitale, Mf, Michiara, M, Bozzani, F, Sgargi, P, Tumino, R, La Rosa MG, Cascone, G, Frasca, G, Giurdanella, Mc, Martorana, C, Morana, G, Nicita, C, Rollo, Pc, Ruggeri, Mg, Sigona, A, Spata, E, Vacirca, S, Mangone, L, Di Felice, E, Pezzarossi, A, Caroli, S, Pellegri, C, Vicentini, M, Storchi, C, Cavuto, S, Costa, J, Falcini, F, Colamartini, A, Bucchi, L, Balducci, C, Ravegnani, M, Vitali, B, Cordaro, C, Caprara, L, Giuliani, O, Giorgetti, S, Salvatore, S, Palumbo, M, Vattiato, R, Ravaioli, A, Foca, F, Rinaldi, E, Mancini, S, Tonelli, C, Amadori, M, Cremone, L, Iannelli, A, Zevola, A, Budroni, M, Cesaraccio, R, Pirino, D, Carboni, D, Fiori, G, Soddu, M, Mameli, G, Mura, F, Contrino, Ml, Madeddu, A, Tisano, F, Sciacca, S, Muni, A, Mizzi, M, Russo, M, Sacco, G, Aletta, P, Colanino Ziino, A, Tessandori, R, Fanetti, Ac, Maspero, S, Annulli, Ml, Moroni, E, Sanoja Gonzalez ME, Zanetti, R, Rosso, S, Patriarca, S, Prandi, R, Sobrato, I, Gilardi, F, Busso, P, Piffer, S, Gentilini, Ma, Battisti, L, Rizzello, R, Cappelletti, M, Moser, M, La Rosa, F, D'Alò, D, Scheibel, M, Costarelli, D, Spano, F, Rossini, S, Santucci, C, Petrinelli, Am, Solimene, C, Brunori, V, Crosignani, P, Tagliabue, G, Contiero, P, Preto, L, Tittarelli, A, Maghini, A, Codazzi, T, Frassoldi, E, Gada, D, Costa, E, di Grazia, L, Zambon, P, Baracco, M, Bovo, E, Dal Cin, A, Fiore, Ar, Greco, A, Monetti, D, Rosano, A, Stocco, C, Tognazzo, S, Donato, Francesco, Limina, Rosa Maria, Adorni, A, Andreis, P, Zani, G, Piovani, F, Salvi, O, Puleio, M, Vitarelli, S, Antonini, S, Candela, G, Pappalardo, G, Scuderi, T, Lottero, B, Ribaudo, M, Ricci, P, Guarda, L, Gatti, L, Bozzeda, A, Dall'Acqua, M, Pironi, V, Sutera Sardo, A, Mazzei, A, Sirianni, N, Lavecchia, Am, Mancuso, P, Usala, M, Pala, F, Sini, Gm, Pintori, N, Canu, L, Demurtas, G, Doa, N, Ponz de Leon, M, Domati, F, Rossi, G, Goldoni, Ca, Rossi, F, De Gaetani, C, Benatti, P, Roncucci, L, Di Gregorio, C, Pedroni, M, Pezzi, A, Maffei, S, Mariani, F, Borsi, E, Carruba, G, Cusimano, R, Amodio, R, Dolcemascolo, C, Staiti, R, Pastore, G, Magnani, C, Terracini, B, Cena, T, Alessi, D, Baussano, I, Merletti, F, Maule, M, Macerata, V, Cocchioni, M, Pascucci, C, Gennaro, V, Lazzarotto, A, Benfatto, L, Mazzucco, G, and Montanaro, F.

Subjects
Survival, Italy, cancer patients
Published
2011

5. [Pitfalls on estimation of the cumulative risk of developing cancer].

Author

Coviello E

Subjects
Aged, Female, Humans, Incidence, Italy epidemiology, Male, Middle Aged, Risk Assessment, Risk Factors, Neoplasms epidemiology
Abstract

The cumulative risk (CR) of developing cancer is a measure of the spread of cancer in a population used because it is intuitive to understand. However, there are various methods to calculate it leading to results with different interpretations. With real data we calculated the CR of developing any tumour in males and females to age 84 using three approaches. With the former, which uses only the cancer incidence, a CR to age 84 is estimated equal to 51.7% in males and 36% in females. With the second, which takes into account the competing risk of dying for other causes, the CR to age 84 is estimated equal to 44% in males and 32.9% in females. Finally, after adjusting for multiple primaries in the same person, the CR to age 84 boils down to 37.7% in males and 29.2% in females. Only methods taking into account the competing risk of dying for other causes and adjusting for multiple primaries in the same person are appropriate to estimate the real risk of developing any tumour in the course of life. For estimating the real risk of developing a specific form of cancer we must take into account the competing risk of dying while the adjustment for the occurrence of multiple primaries should be considered only for some long-survival sites. KEYWORDS: cumulative risk; cancer incidence; competing risks.

Published
2020
Full Text
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6. [Risk profiling in cancer surveillance in contaminated sites: an example from the ISS-AIRTUM collaborative study].

Author

Catelan D, Buzzoni C, Coviello E, Crocetti E, Pasetto R, Pirastu R, and Biggeri A

Subjects
Asbestos adverse effects, Bayes Theorem, Carcinogens, Environmental Pollution adverse effects, Humans, Incidence, Italy epidemiology, Neoplasms etiology, Population Surveillance, Public Health, Registries, Risk Assessment, Risk Factors, Environmental Exposure adverse effects, Environmental Pollutants adverse effects, Neoplasms epidemiology
Abstract

Epidemiological surveillance on high risk environmental areas or areas covered by cancer registration yields long inventories of relative risks. Summaries of the results' tables must be produced to identify priorities and tailor public health actions. The aim is, therefore, to draw conclusions from each area's disease profile, or from the area signature of each disease.With this inmind, we used data on cancer incidence from 17 Cancer Registries that participated in the ISS-AIRTUM (National Institute of Health-Italian Network of Cancer Registries) study, and we produced conditional and marginal rankings of areas/diseases using a multivariate hierarchical Bayesian model. In this context, it is important to obtain an uncertainty evaluation by calculating the credibility intervals of ranks. The areas marginal ranking shows a large overlapping of credibility intervals, such that it is not possible to speak of a limited number of ISS-AIRTUM areas as being particularly affected. Every ISS-AIRTUMarea, therefore,must be considered individually and ordering themby ranking of cancer incidence wouldn't be appropriate. Instead,marginal ranking of diseases highlights the impact of asbestos exposure in all the analyzed areas.

Published
2014

8. [The hazard function].

Author

Coviello E, Miccinesi G, Puliti D, and Paci E

Subjects
Adult, Aged, Breast pathology, Breast Neoplasms diagnostic imaging, Breast Neoplasms pathology, Cohort Studies, Female, Follow-Up Studies, Humans, Kaplan-Meier Estimate, Mammography, Middle Aged, Neoplasm Staging, Time Factors, Breast Neoplasms mortality, Proportional Hazards Models, Survival Analysis
Abstract

In cohort studies the event occurrence is usually described by the incidence rate and the survivor function. In comparison with these estimators the plot of the hazard function has the advantage to show the variations of the occurrence of the event along the period of observation, which often are important to be highlighted. Furthermore, when comparing individuals with different characteristics, the hazard function is a valuable support to check the assumption and to interpret the results of a Cox regression model. This paper illustrates the method for estimating the hazard function and an example is given from a real case by using the survival data of the breast cancers collected in the IMPACT study, aimed to detect the efficacy of the mammographic screening program. The relationship between the usual estimators and the hazard function is shown and its role in the survival regression modelling is emphasized. In the example the estimate of the hazard function allows to point out that the mortality rate of breast cancer in the first year after the diagnosis is lower than later and that the difference between the hazards of the invited cases and those of the not invited cases is approximately constant along the whole l0 years follow up, two important remarks both demonstrating the usefulness of the application of this function in the analysis of cohort studies.

Published
2007

9. [Statistical approaches to test for spatial heterogeneity of relative risks of cause-specific mortality in Alto Vicentino Municipalities (years 1991-2000)].

Author

Blengio G, Coviello E, Michieletto F, Falcone S, Fedrizzi F, and Saugo M

Subjects
Cardiovascular Diseases epidemiology, Catchment Area, Health, Diabetes Mellitus epidemiology, Environmental Monitoring statistics & numerical data, Epidemiological Monitoring, Female, Humans, Italy epidemiology, Male, Mental Disorders epidemiology, Neoplasms epidemiology, Risk Factors, Cardiovascular Diseases mortality, Diabetes Mellitus mortality, Disease classification, Mental Disorders mortality, Models, Statistical, Neoplasms mortality
Abstract

Objective: To review the statistical approaches to test for spatial heterogeneity of relative risks. Three different statistical tests (Gail, Martuzzi-Hills and Potthoff-Whittinghill) are reviewed and applied--as motivating example--to the analysis of cause-specific mortality records (years: 1991-2000) of the Municipalities belonging to the Local Health Unit Alto Vicentino., Methods: Spatial heterogeneity was found in 17 (Martuzzi-Hills) and 18 (Gail, Potthoff-Whittinghill) among 70 selected causes of death. Cohens Kappa test was chosen to assess the agreement among the tests (k = 0.596; p < or = 0.001)., Conclusion: Spatial heterogeneity must be interpreted with great caution, taking into account the available evidence about risk factors for specific causes of death and carefully evaluating available exposure data.

Published
2004

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