Identification of multimorbidity patterns in older adults receiving long-term care in Canada, Italy, Finland and New Zealand: results from the ICARE4OLD project

Abstract

Background: Older adults receiving home care (HC) services and living in long-term care homes (LTC) experience high levels of multimorbidity. In this project - called iCARE4OLD, we aimed first to identify and compare subgroups of care dependent individuals sharing the same patterns of chronic diseases. For these subgroups, we will identify care paths and try to make integration of services and continuity of care possible. 
 Settings and participants: We studied 102,000 individuals 60+ years receiving HC services or living in LTC homes in Canada, Italy, Finland and New Zealand.
 Methods: This is a cross-sectional study including the baseline interRAI HC and LTCF assessments of older people in the period of 2014 until 2018. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement number 965341 and from the New Frontiers Research Fund, grant number NFRFG-2020-00500.
 Latent Class Analysis (LCA) was used to classify individuals according to their underlying diseases patterns starting from a list of 19 conditions. 
 Results: Mean age of the sample was 80 years (65% females). After assessing several fit parameters, a 5-class solution was chosen as the best model for both HC and LTC. The following 5 disease patterns were identified in all countries: (1) Alzheimer/dementia; (2) psychiatric diseases; (3) cardio-pulmonary diseases; (4) stroke/hemiplegia; (5) other dementias. The distribution of sociodemographic, clinical and functional characteristics varied across the different multimorbidity patterns, with the cardio-pulmonary disease and the stroke/hemiplegia patterns showing the highest complexity and impairment.
 Results: Our results show that, by using a common assessment tool, it is possible to identify homogeneous morbidity patterns in older patients receiving long-term care. These may be useful to compare health status in care-dependent individuals across different settings and countries, as well as to predict health trajectories and care needs. 
 Discussion: By applying this methodology to whole populations, care paths can be drawn for home care and residential care clients in a more evidence-based way. The goals would be to use these algorithms to design more integrated care plans for older persons, so that clients and their families are better served, and policy makers can finance the right services and offer targeted care.