Model-Based Economic Evaluations of Interventions for Dementia: An Updated Systematic Review and Quality Assessment

There has been an increase in model-based economic evaluations of interventions for dementia. The most recent systematic review of economic evaluations for dementia highlighted weaknesses in studies, including lack of justification for model assumptions and data inputs.

This study aimed to update the last published systematic review of model-based economic evaluations of interventions for dementia, including Alzheimer's disease, with a focus on any methodological improvements and quality assessment of the studies.

Systematic searches in eight databases, including PubMed, Cochrane, Embase, CINAHL, PsycINFO, EconLit, international HTA database, and the Tufts Cost-Effectiveness Analysis Registry were undertaken from February 2018 until August 2022. The quality of the included studies was assessed using the Philips checklist and the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 checklist. The findings were summarized through narrative analysis.

This review included 23 studies, comprising cost-utility analyses (87%), cost-benefit analyses (9%) and cost-effectiveness analyses (4%). The studies covered various interventions, including pharmacological (n = 10, 43%), non-pharmacological (n = 4, 17%), prevention (n = 4, 17%), diagnostic (n = 4, 17%) and integrated (n = 1, 4%) [diagnostics-pharmacologic] strategies. Markov transition models were commonly employed (65%), followed by decision trees (13%) and discrete-event simulation (9%). Several interventions from all categories were reported as being cost effective. The quality of reporting was suboptimal for the Methods and Results sections in almost all studies, although the majority of studies adequately addressed the decision problem, scope, and model-type selection in their economic evaluations. Regarding the quality of methodology, only a minority of studies addressed competing theories or clearly explained the rationale for model structure. Furthermore, few studies systematically identified key parameters or assessed data quality, and uncertainty was mostly addressed partially.

This review informs future research and resource allocation by providing insights into model-based economic evaluations for dementia interventions and highlighting areas for improvement.