Development and Validation of a Prediction Model for 6-Month Societal Costs in Older Community Care-Recipients in Multiple Countries; the IBenC Study

• Published in: Health Services Insights
• Main Authors: van Lier, Lisanne I, Bosmans, Judith E, van der Roest, Henriëtte G, Heymans, Martijn W, Garms-Homolová, Vjenka, Declercq, Anja, V Jónsson, Pálmi, van Hout, Hein PJ
• Format: Article in Journal/Newspaper
• Language: English
• Published: SAGE Publications 2020
• Subjects: Iceland
• Online Access: https://doi.org/10.1177/1178632920980462; https://journals.sagepub.com/doi/pdf/10.1177/1178632920980462; https://journals.sagepub.com/doi/full-xml/10.1177/1178632920980462

This study aims to develop and validate a prediction model of societal costs during a period of 6-months in older community care-recipients across multiple European countries. Participants were older community care-recipients from 5 European countries. The outcome measure was mean 6-months total societal costs of resource utilisation (healthcare and informal care). Potential predictors included sociodemographic characteristics, functional limitations, clinical conditions, and diseases/disorders. The model was developed by performing Linear Mixed Models with a random intercept for the effect of country and validated by an internal-external validation procedure. Living alone, caregiver distress, (I)ADL impairment, required level of care support, health instability, presence of pain, behavioural problems, urinary incontinence and multimorbidity significantly predicted societal costs during 6 months. The model explained 32% of the variation within societal costs and showed good calibration in Iceland, Finland and Germany. Minor model adaptations improved model performance in The Netherland and Italy. The results can provide a valuable orientation for policymakers to better understand cost development among older community care-recipients. Despite substantial differences of countries’ care systems, a validated cross-national set of key predictors could be identified.