Evidence map›Paper›PMID 39044513›Full record

ArticleThe journal of prevention of Alzheimer's disease2024

Estimated Investment Need to Increase England's Capacity to Diagnose Eligibility for an Alzheimer's Treatment to G7 Average Capacity Levels.

S Mattke, Z Shi, M Hanson, S Mitchell, C Lynch, K MacLean Kalonji, L Lanman

Abstract read
In one paragraph

Article in The journal of prevention of Alzheimer's disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

S MattkeSoeren Mattke, M.D., D.Sc., Director, Center for Improving Chronic Illness Care, Research Professor of Economics, USC Dornsife, 635 Downey Way, #505N, Los Angeles, CA 90089, Mobile: +1 202 468 5797, mattke@usc.edu.
Z Shi
M Hanson
S Mitchell
C Lynch
K MacLean Kalonji
L Lanman

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs disease-modifying Alzheimer's (AD) treatments are becoming available, concerns have been raised that even high-income countries lack the diagnostic capacity to accurately identify eligible patients in a timely manner.

objectivesWe analyze how much NHS England would have to invest in capacity for AD specialists, biomarker testing with PET scans or CSF testing and MRI scans to reach G7 average levels and estimate the effect on wait times in the diagnostic process.

designDesk research and expert interviews for cost and capacity data. Markov model to estimate wait times.

settingNHS England. MEASUREMENTS: AD specialists, and PET and MRI scanners per capita in G7 countries and wait times in England under different investment scenarios.

resultsEngland has the lowest number of PET and MRI scanners and the second-lowest of AD specialists per capita among the G7 countries. An investment of GBP 14 billion over ten years would be needed to reach G7 average levels, of which 31%, 22%, 10%, 37% would be devoted to capacity for memory assessment services, PET scanning, CSF analysis, and MRI scanning, respectively. This investment would reduce estimated average wait times by around 87% between 2023 and 2032.

conclusionsThe NHS England has large gaps in diagnostic capacity for AD. Without substantial investments, AD patients in England would experience substantial wait times and avoidable disease progression.

Indexed as

Alzheimer DiseasePositron-Emission TomographyEnglandHumansInvestmentsMagnetic Resonance ImagingMarkov ChainsState MedicineWaiting ListsAlzheimer’s diseasecapacitydementiaDisease-modifying treatmentEnglandhealth systeminvestmentNHSpreparedness

Identifiers

PMID39044513
PMCPMC11266384

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.