Healthcare Service Utilisation of People Living With Non-Alzheimer’s Dementia: A Systematic Review
Bibliographic record
Abstract
IntroductionThe global rise in dementia presents significant challenges for healthcare systems. While Alzheimer's disease (AD) dominates dementia care, people with non-Alzheimer's dementias (non-AD), such as dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), vascular dementia (VD), and Parkinson's disease dementia (PDD), often have distinct and unmet healthcare needs.AimThis systematic review aimed to summarise evidence on healthcare utilisation (HCU) patterns and factors affecting care among people living with non-AD dementias.MethodsFollowing a PROSPERO-registered protocol (CRD42024568391), comprehensive searches of Embase, Ovid MEDLINE, Global Health, PsycINFO, and PubMed were conducted in February and June 2024. Peer-reviewed English-language studies reporting on HCU or its determinants in DLB, FTD, VD, or PDD were included. Reviews, case reports, grey literature, and studies without subtype-specific data were excluded. Quality was assessed using the Newcastle-Ottawa Scale.ResultsThirty-one studies (16 cohort; 10 cross-sectional, 4 case-description, and 1 chart review) were included. HCU varied by dementia subtype and was influenced by sociodemographic, cognitive, and clinical factors. Compared with AD, non-AD dementias had higher healthcare use and costs. PDD showed the highest inpatient, outpatient, and skilled nursing care use, driven by severe cognitive decline. DLB was linked to unplanned hospital admissions and frequent ambulance use, often due to falls and pneumonia. FTD resulted in extended hospital stays related to behavioural symptoms, while VD incurred high costs due to chronic comorbidities and long-term care needs.ConclusionPeople with non-AD dementias have greater and distinct healthcare needs. Future research should develop standardised measures and tailored interventions to address their complex socioeconomic and clinical requirements.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".