Barbara Senesi1, Camilla Prete2, Giacomo Siri3, Alessandra Pinna2, Angela Giorgeschi2, Nicola Veronese2,4, Roberto Sulpasso5, Carlo Sabbà5, Alberto Pilotto2,5. 1. Geriatrics Unit, Center for Cognitive Disorders and Dementia (CDCD), Department of Geriatric Care, Orthogeriatrics and Rehabilitation, Galliera Hospital, Via Mura delle Cappuccine 14, 16128, Genova, Italy. barbara.senesi@galliera.it. 2. Geriatrics Unit, Center for Cognitive Disorders and Dementia (CDCD), Department of Geriatric Care, Orthogeriatrics and Rehabilitation, Galliera Hospital, Via Mura delle Cappuccine 14, 16128, Genova, Italy. 3. Scientific Coordination Office, Biostatistics, Galliera Hospital, Genova, Italy. 4. Primary Care Department, Azienda ULSS3 Serenissima, District 3, Venice, Italy. 5. Department of Interdisciplinary Medicine, University of Bari, Bari, Italy.
Abstract
AIM: The economic recognition of disability is of importance in daily practice, but the tools used in older people are still limited. Therefore, we aimed to investigate the effectiveness of the multidimensional prognostic index (MPI) to identify frail older subjects to be submitted to civil invalidity application for disability benefits including Attendance Allowance (AA) indemnity, Carer's Leave (Law 104) and/or Parking Card for people with disabilities. METHODS: From March 2018 to January 2019, 80 older people were included. The MPI was calculated from comprehensive geriatric assessment information including eight different domains. Civil benefits included attendance allowance (AA) indemnity by the Local Medico-Legal Committee (MLC-NHS) and by the National Institute of Social Security Committee (INPS), Carer's Leave (Law 104), and Parking Card for people with disabilities. RESULTS: MPI values were associated with an increased probability to obtain a 100% civil disability, AA indemnity, Carer's Leave and a parking card for people with disabilities. MPI score showed a very good accuracy in predicting the civil invalidity benefits with a area-under-curve (AUC) of 87.3 (95% CI 80.6-97.4) to predict the release of AA indemnity, 81.3 (95% CI 68.5-91.1) to predict Care's leave and 70.7 (95% CI 59.4-84.7) to predict the Parking Card release. Moreover, data showed that a cut-off score of MPI ≥ 0.75 could identify the 100% of older subjects who successfully obtained the indemnity release. CONCLUSION: MPI is an excellent predictor of social benefits' release by local and national agencies.
AIM: The economic recognition of disability is of importance in daily practice, but the tools used in older people are still limited. Therefore, we aimed to investigate the effectiveness of the multidimensional prognostic index (MPI) to identify frail older subjects to be submitted to civil invalidity application for disability benefits including Attendance Allowance (AA) indemnity, Carer's Leave (Law 104) and/or Parking Card for people with disabilities. METHODS: From March 2018 to January 2019, 80 older people were included. The MPI was calculated from comprehensive geriatric assessment information including eight different domains. Civil benefits included attendance allowance (AA) indemnity by the Local Medico-Legal Committee (MLC-NHS) and by the National Institute of Social Security Committee (INPS), Carer's Leave (Law 104), and Parking Card for people with disabilities. RESULTS: MPI values were associated with an increased probability to obtain a 100% civil disability, AA indemnity, Carer's Leave and a parking card for people with disabilities. MPI score showed a very good accuracy in predicting the civil invalidity benefits with a area-under-curve (AUC) of 87.3 (95% CI 80.6-97.4) to predict the release of AA indemnity, 81.3 (95% CI 68.5-91.1) to predict Care's leave and 70.7 (95% CI 59.4-84.7) to predict the Parking Card release. Moreover, data showed that a cut-off score of MPI ≥ 0.75 could identify the 100% of older subjects who successfully obtained the indemnity release. CONCLUSION: MPI is an excellent predictor of social benefits' release by local and national agencies.
Entities:
Keywords:
Disability; Disability benefits; Geriatric assessment; Multidimensional prognostic index
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