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Primary prevention
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Zhang, Kivipelto [26]
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Hypothetical intervention reducing risk of AD onset in Sweden
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QALYs
|
Purpose-built Markov model with 3 health states. 20-year time horizon, 1 year cycles
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CAIDE population based study on risk factors of 1409 individuals [44]
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Hypothetical intervention
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Swedish studies (EQ5D) [41, 57]
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Swedish National Board of Health and Welfare
|
|
Tsiachristas & Smith [32]
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Preventative treatment with B-vitamin supplement for people aged 60 and over with elevated levels of tHcy in the UK
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Life-Years; QALYs
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Stochastic probabilistic decision tree; lifetime horizon.
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Disease progression not modelled. Disease onset based on prevalence data; mortality from life tables.
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Effectiveness of intervention based on a systematic review in lieu of randomised controlled trials [37]
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General population EQ5D survey [36]
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Taken from a UK study [58]
|
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Secondary prevention
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McMahon [28]
|
Functional neuroimaging vs. standard work-up of patients for AD diagnosis at specialised AD clinics in the US
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QALYs
|
Markov model based on a previously published study [40]; 6-week cycles, 18-month time horizon.
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Progression within AD and AD mortality from CERAD study [40]. Non-AD mortality from CDC.
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Screening effectiveness from US-based study [59]
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Utility weights obtained from the Neumann et al. [40]
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Primary data from hospital databases; existing literature
|
|
Silverman, Gambhir [31]
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PET vs. standard diagnostic methods in clinical diagnosis of AD in the US
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Number of accurate diagnoses
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Purpose-built decision-tree, unspecified time horizon
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Adapted from a wide range of published data
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Results of PET screening reported in the study
|
Not used - CEA
|
Defined by Medicare reimbursement rates
|
|
Weimer and Sager [30]
|
Early detection and treatment of AD patients in a US (Wisconsin) setting. Two treatments considered.
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MMSE score change
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Monte Carlo model. Lifetime horizon, 1 year cycles
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Adapted from a range of published data and estimates. Data from CVD risk study on 5000 people was used to estimate hazard ratio for death.
|
A range of published data and estimates
|
Adapted from [40]
|
A range of published data and estimates
|
|
Dixon, Ferdinand [35]
|
One-off screen of 75 year olds in England and Wales
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Number of additional diagnoses
|
Static decision model with lifetime time horizon
|
Not provided
|
Results of screening based on MMSE (assumed 89% sensitivity, 95.5% specificity)
|
Not used – CBA
|
A range of published data and estimates
|
|
Saito, Nakamoto [27]
|
Community based dementia screening in a US setting
|
Dementia diagnosis through MMSE
|
Purpose-built Markov model with 6-state 10-year time horizon, 1 year cycles
|
Adapted from [46, 48] which investigated 61 and 1145 patients, respectively
|
Results of screening program reported in study
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Not used - CEA
|
Adapted from a Canadian study [60]
|
|
Tertiary prevention
|
McDonnell, Redekop [33]
|
A hypothetical intervention which slows cognitive decline in AD patients in the Netherlands
|
MMSE score change, care setting, mortality
|
Two regression-based simulation models – one modelling MMSE score, another- care setting and mortality. 10-year time horizon, 6 month cycles
|
Calculated from a Dutch study [38] with 7528 participants.
|
Hypothetical intervention
|
Not used – CEA
|
From Dutch national data, agencies/ ministries
|
|
Martikainen, Valtonen [29]
|
Cognitive-behavioural family intervention to delay admission to nursing home in Finland
|
Time to nursing home admission
|
Markov model. Adapted from [40]. Model has 4 states, 5-year time horizon, 1 year cycles
|
Adapted from the original US-based model (with minor adjustments) – based on longitudinal study with 1145 patients [40]
|
Based on a US study of 206 subjects [61]
|
From the original US-based model
|
From national datasets; some resource utilisation based on expert panel
|
|
Mirsaeedi-Farahani, Halpern [34]
|
Deep-brain stimulation therapy for slowing memory loss in AD patients compared to standard treatment
|
QALYs
|
Purpose-built Markov model with 5 states, 5-year horizon, 1 year cycles
|
Adapted from Neumann et al. [46] and Spackman et al. [47]
|
Actual success rate of deep brain stimulation is unknown, so was varied from 0 to 100%
|
A range of published data
|
Costs obtained from [62]
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