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Table 2 Summary of included studies

From: Decision modelling of non-pharmacological interventions for individuals with dementia: a systematic review of methodologies

  Model (reference) Intervention and setting Main outcome(s) Modelling approach/ framework, time horizon and cycle Data sources
Disease onset, progression and mortality data Intervention effectiveness Utilities/outcomes measure Costs
Primary prevention Zhang, Kivipelto [26] Hypothetical intervention reducing risk of AD onset in Sweden QALYs Purpose-built Markov model with 3 health states. 20-year time horizon, 1 year cycles CAIDE population based study on risk factors of 1409 individuals [44] Hypothetical intervention Swedish studies (EQ5D) [41, 57] Swedish National Board of Health and Welfare
Tsiachristas & Smith [32] Preventative treatment with B-vitamin supplement for people aged 60 and over with elevated levels of tHcy in the UK Life-Years; QALYs Stochastic probabilistic decision tree; lifetime horizon. Disease progression not modelled. Disease onset based on prevalence data; mortality from life tables. Effectiveness of intervention based on a systematic review in lieu of randomised controlled trials [37] General population EQ5D survey [36] Taken from a UK study [58]
Secondary prevention McMahon [28] Functional neuroimaging vs. standard work-up of patients for AD diagnosis at specialised AD clinics in the US QALYs Markov model based on a previously published study [40]; 6-week cycles, 18-month time horizon. Progression within AD and AD mortality from CERAD study [40]. Non-AD mortality from CDC. Screening effectiveness from US-based study [59] Utility weights obtained from the Neumann et al. [40] Primary data from hospital databases; existing literature
Silverman, Gambhir [31] PET vs. standard diagnostic methods in clinical diagnosis of AD in the US Number of accurate diagnoses Purpose-built decision-tree, unspecified time horizon Adapted from a wide range of published data 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. MMSE score change Monte Carlo model. Lifetime horizon, 1 year cycles 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 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 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]