Crea sito

National Projects

AIFA-ANZIANI (Inappropriate Prescribing In The Elderly Patient) Project

Members of the group collaborating to the project:

• Giovanni Corrao (Scientific responsible)
• Antonella Zambon
• Arianna Ghirardi

Project description:

The co-occurrence of multiple chronic illnesses is common in elderly people. Because of the complexity of the geriatric patient, physicians should carefully assess the benefit/risk ratio of any drug prescription. Indeed, although the potential benefits of pharmacotherapy are unquestionable, the negative outcomes of medications in older people are a relevant issue. The definition of “Inappropriate Prescribing” (IP) in the geriatric population is still debated. Nowadays, all available IP criteria (e.g. Beers) are based on experts’ consensus, and they often lack a formal validation based on ‘hard’ end-points (e.g. hospitalization). Furthermore, IP criteria are not specifically tailored to cardiovascular diseases, which constitute the most frequent conditions affecting the elderly population.
This project aims to: 1) define a series of IP indicators among elderly patients who suffer from cardiovascular diseases and other chronic comorbidities; 2) evaluate the relationship between the IP and ‘hard’ end-points (one-year acute cardiovascular events, all-cause hospitalization and mortality) using population-based healthcare databases (Health Information Systems – HIS) and Nursing Home (NH) databases.

Principal Investigator: Alessandro Mugelli, Università degli Studi di Firenze

Activities of the group in the project:

Activities of the group in the project:

Other than providing data-management support, the unit contributes to the project with the following statistical activities:

• Provide expertise to conduct several conventional statistical elaborations required in the study, both in the planning phase (i.e. sample size determination) and the analysis phase (e.g. implementation of Cox regression models).
• Develop longitudinal models based on either generalized estimating equations or mixed-models to assess the association between IP exposure and changes in functional and/or cognitive status observed during the course of the study.
• Apply several advanced statistical methods to adjust the obtained estimates provided for the impact of potential confounding by indication, severity and/or unmeasured confounders (e.g. Propensity Score analysis, Monte Carlo Sensitivity Analysis).

Pages: 1 2 3

Powered by AlterVista