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Predictors of filing claims and receiving compensation in malignant mesothelioma patients

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  • Marinaccio, Alessandro
  • Gariazzo, Claudio
  • Di Marzio, Davide
  • Iavicoli, Sergio

Abstract

Although the predominant occupation origin of mesothelioma is well known, determinant factors involved in filing compensation are scarcely investigated. A linkage between incident mesothelioma cases collected by Italian mesothelioma register (ReNaM) and compensation claims and assignment by Italian national insurance Institute (INAIL) has been conducted for cases diagnosed in the period 2010–2015 and occupational exposure to asbestos. Logistic regression models and decision tree models have been used to identify demographic, diagnostic and anamnestic factors significant for filing and receiving compensation. We have included in the analyses 5019 mesothelioma cases, and among them, 3321 (66.2 %) were found in INAIL archives as mesothelioma cases who fil claims for compensation. The modalities of asbestos exposure, sector of working activities and job type are crucial factors. Furthermore, gender, age at diagnosis, area of residence have been found to be significant predictors of probability to fil claims. Relative risks to fil claims were obtained for the above determinants and conditions to maximize the probability to obtain compensation identified. Our findings demonstrate that there is a need to enforce policies for improving awareness of the occupational origin for mesothelioma cases. Stakeholders, occupational health and safety institutions can play an important role for improving the sensitization regarding the rights of compensation benefits, ensuring the equity and the effectiveness of insurance, welfare and public health systems.

Suggested Citation

  • Marinaccio, Alessandro & Gariazzo, Claudio & Di Marzio, Davide & Iavicoli, Sergio, 2020. "Predictors of filing claims and receiving compensation in malignant mesothelioma patients," Health Policy, Elsevier, vol. 124(9), pages 1032-1040.
    • Handle: RePEc:eee:hepoli:v:124:y:2020:i:9:p:1032-1040
    DOI: 10.1016/j.healthpol.2020.06.005
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    1. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
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    1. Alessandra Binazzi & Davide Di Marzio & Marina Verardo & Enrica Migliore & Lucia Benfatto & Davide Malacarne & Carolina Mensi & Dario Consonni & Silvia Eccher & Guido Mazzoleni & Vera Comiati & Corrad, 2021. "Asbestos Exposure and Malignant Mesothelioma in Construction Workers—Epidemiological Remarks by the Italian National Mesothelioma Registry (ReNaM)," IJERPH, MDPI, vol. 19(1), pages 1-12, December.
    2. Olivia Pérol & Nadège Lepage & Hugo Noelle & Pierre Lebailly & Benoit de Labrusse & Bénédicte Clin & Mathilde Boulanger & Delphine Praud & Françoise Fournié & Géraud Galvaing & Frédéric Dutheil & Brig, 2023. "A Multicenter Study to Assess a Systematic Screening of Occupational Exposures in Lung Cancer Patients," IJERPH, MDPI, vol. 20(6), pages 1-15, March.

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