ArticleNeurology and therapy2022
A Simulation Model to Evaluate the Potential Impact of Disease-Modifying Treatments on Burden of Illness in Alzheimer's Disease.
Article in Neurology and therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Estimating the Future Health and Social Care Costs of Alzheimer's Disease Dementia in the UK: Impact of Disease Modifying Therapy Efficacy, Uptake, and Care Model - A Scenario Modelling Study.International journal of geriatric psychiatry · 2026Article
- Navigating the future of Alzheimer's care in Ireland - a service model for disease-modifying therapies in small and medium-sized healthcare systems.BMC health services research · 2024Article
- U.S. dementia care spending by state: 2010-2019.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Predicting the Societal Value of Lecanemab in Early Alzheimer's Disease in Japan: A Patient-Level Simulation.Neurology and therapy · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAlzheimer's disease (AD) is a progressive neurodegenerative disease that places a substantial burden on patients, caregivers, and society. The advent of disease-modifying treatments (DMTs) would represent a major advancement in the management of AD, particularly in early AD. It is important to understand the potential value of these therapies to individuals and society.
methodsA modeling framework was developed to estimate the potential clinical and economic burden of AD in the USA by simulating the impact, relative to that of usual care, of a DMT with hypothesized availability beginning from 2022. The model assessed AD epidemiology, disease progression, and burden of illness from 2020 to 2050. Model outcomes included the total number of Americans with mild cognitive impairment (MCI) due to AD and mild, moderate, or severe AD dementia in community or residential care settings and their associated care costs, including direct medical and non-medical costs for healthcare resource use and indirect costs for caregiving.
resultsA hypothetical DMT was compared to the usual care under different effect scenarios based on delay in onset of AD (1, 3, and 5 years) and DMT uptake (25%, 50%, and 100%). A delay in the onset of AD by 5 years would reduce the prevalence of AD in 2050 by 6%, 12%, and 25%, resulting in savings of $0.783, $1.566, and $3.132 trillion from 2022 to 2050 for the 25%, 50%, and 100% uptake scenarios, respectively.
conclusionThis analysis demonstrated that DMTs that provide even small delays in the onset of AD can lead to an increase in disease-free years and sizable savings in the cost of care, providing significant benefits to patients, caregivers, and society.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.