Identification of multimorbidity patterns in older adults receiving long-term care in Canada, Italy, Finland and New Zealand: results from the ICARE4OLD project
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".