The excess direct social cost of dementia-related neuropsychiatric symptoms: a beyond silos region-wide cohort study

Abstract

Objectives
The objective was to estimate the excess formal social costs or direct non-healthcare costs of dementia-related neuropsychiatric symptoms (NPS).
Methods
The presence of dementia, NPS, antipsychotic and/or antidepressant use, somatic and psychiatric comorbidities, and formal social benefits were studied in a region-wide cohort of all over-60-year-olds. A random forest-based algorithm identified NPS and two-part regression models and entropy balance were used.
Results
Of 215,859 individuals, 7,553 (3.50%) had dementia, 74,845 (34.7%) had some NPS and 20,787 (9.63%) received long-term care benefits. Notably, nearly two-thirds (63.9%) of people with dementia received benefits. The probability of having social costs varied markedly with age (OR: 12.28 [10.17 - 14.82] for >90-year-olds category), and the presence of dementia (OR: 7.36 [6.13 - 8.84]) or NPS (OR: 3.23 [2.69 - 3.88]). NPS (RC: 1.39 [1.31 - 1.49]]) and dementia (RC: 1.32 [1.24 - 1.41) were associated with higher average benefit costs. Low socioeconomic status was significantly associated with both a higher probability of receiving benefits (OR: 1.52 [1.38 - 1.68]) and higher costs of their provision (RC: 1.18 [1.15 - 1.21]).
Conclusions
The burden of caring for NPS is greater than that indicated by the literature as these symptoms multiply the social costs of dementia by more than three due to the greater use of residential care and formal coverage reaches more patients than that indicated by the literature. The greater presence of dementia and NPS in the lower SES population indicates an inequality in health attenuated by a greater use of social benefits.

Key words
dementia; Alzheimer’ Disease; disparities; real-world data; socioeconomic status; comorbidities; formal social costs; neuropsychiatric symptoms

Publication
In Value in Health

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Lore Zumeta Olaskoaga
Lore Zumeta Olaskoaga
Biostatistical researcher

My research interests include survival analysis, mixed models and GAMs.