Late-Life Dementia Care: Identifying Distinct Care Patterns in the Last Years of Life
Bibliographic record
Abstract
OBJECTIVES: To identify common long-term care patterns in the 6 years before death among individuals aged ≥65 years with dementia in the Netherlands and to examine how sociodemographic and health factors are associated with these patterns. DESIGN: A longitudinal retrospective study was conducted using routinely recorded data for the entire Dutch population on health care use, cause of death, and sociodemographics. SETTING AND PARTICIPANTS: We identified 43,578 individuals who passed away in 2021 with dementia based on their use of dementia-related services and recorded cause of death. METHODS: Using ordered logit latent class analysis, we estimated the likelihood of individuals using long-term care (1: no formal LTC; 2: community-based home care services; 3: nursing home care) in each of the 6 years prior to death. Predictors included age, gender, migration background, partner status, polypharmacy, chronic illness groups, homeownership status, and household income quartiles. RESULTS: Three groups with distinct care patterns were identified: the Late Formal Care Group initially did not receive formal LTC but transitioned to community-based home care services and nursing homes 4 years before death, primarily relying on nursing home care. The Mixed Care Group used community-based home care services 6 years before death and shifted to nursing home care in their final years. A large share of the Early Nursing Home Group already used nursing home care 6 years before death, with nearly all individuals residing in nursing homes during their last 3 years. The Late Formal Care Group typically had higher income, greater homeownership rates, more often had a partner, and exhibited better health than the other groups, which accessed formal care earlier. CONCLUSIONS AND IMPLICATIONS: Variations in care patterns highlight that greater socioeconomic resources, stronger support, and better health relate to later formal care use. Understanding these patterns is vital for informed policy planning and resource allocation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".