Late-Life Dementia Care: Identifying Distinct Care Patterns in the Last Years of Life

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.