ArticleAging clinical and experimental research2021
Multidimensional prognostic index (MPI) predicts successful application for disability social benefits in older people.
Article in Aging clinical and experimental research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The Assessment of Disability in Italy: The Laborious Procedure and Sharing of Objectives.International journal of environmental research and public health · 2022Article
- Multidimensional frailty and quality of life: data from the English Longitudinal Study of Ageing.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2022Article
- The challenge of the multifaceted prognosis in the older people and the Multidimensional Prognostic Index.European geriatric medicine · 2021Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimThe 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.
methodsFrom 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.
resultsMPI 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.
conclusionMPI is an excellent predictor of social benefits' release by local and national agencies.
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