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Cost-effectiveness and budget impact of pro-active and personalised relapse and recurrence prevention in depressed patients.

Projectomschrijving

Kosteneffectiviteit van gepersonaliseerde terugvalpreventie bij depressie

In dit project wordt een consortium gestart dat een onderzoeksvoorstel uitwerkt waarin de kosteneffectiviteit van gepersonaliseerde terugvalpreventie bij depressie wordt onderzocht. Bij dit onderzoek wordt op basis van data vanuit ggz-instellingen en een zorgverzekeraar voorspeld wie na succesvolle behandeling van een depressie met grote kans weer een nieuwe depressie ontwikkelt. Op basis van literatuuronderzoek wordt in kaart gebracht welke vormen van bewezen effectieve terugvalpreventie het beste kunnen worden aangeboden aan deze personen met een hoog risico op terugval. Met gezondheidseconomische simulatiemodellen wordt vervolgens in kaart gebracht wat de kosteneffectiviteit zou zijn van deze gepersonaliseerde terugvalpreventie bij depressie en wat de totale impact op benodigde zorgbudgets zou zijn. Daarbij kan bovendien de impact van verschillende gepersonaliseerde strategieën worden vergeleken.

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Samenvatting van de aanvraag

Depressive disorder has a high lifetime incidence and is associated with a high burden of disease and substantial societal costs. Approximately 70% of individuals who recover from a first depressive episode will have additional depressive episodes within their lifetime with the risk of each new episode increasing with the number of previous episodes. This indicates the potential for recurrent depression prevention to improve the cost-effectiveness of healthcare services targeting depression. There are various forms of evidence-based, non-pharmacological recurrent depression prevention interventions available that are effective in reducing the onset of recurrent depression. This project aims to address the question to whom exactly these evidence-based interventions need to be provided, in order to maximise the effectiveness and therefore cost-effectiveness of these services, and to determine the improvement in cost-effectiveness and the associated budget impact of wide scale implementation of targeting patients at high risk of developing a recurrent depressive episode. This requires a three-step approach. First, observational data from mental healthcare institutions and from a large health insurance company will be used to determine the risk profiles of patients at high risk of developing a recurrent depressive episode. Risk profiles will be determined using a machine learning approach, where predictors available at the end of regular depression treatment will be used, such that at the end of regular treatment it could be decided to augment treatment with additional therapy aimed at preventing future recurrences. Second, with the help of methodological experts from VU Medical Centre, available literature on non-pharmacological interventions targeting prevention of recurrent depression will be mapped, with the goal to determining differences in effectiveness of recurrent depression prevention for different subgroups of patients, such that available interventions can be matched as optimally as possible to patients with high risk profiles determined in the previous step. Third, the long term effectiveness, cost-effectiveness and budget impact will be determined of wide scale implementation of targeted recurrent depression prevention, using a model-based health economic evaluation approach. Throughout each of these steps, the patient representative in the project team will augment the approach to incorporate the additional considerations relevant from the patient perspective. The current application requests budget for setting up a consortium combining the relevant stakeholders and expertise mentioned above, writing the project idea and writing the full project proposal.

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Kenmerken

Projectnummer:
10390092012218
Looptijd: 100%
Looptijd: 100 %
2021
2022
Onderdeel van programma:
Gerelateerde subsidieronde:
Projectleider en penvoerder:
dr. J. Lokkerbol
Verantwoordelijke organisatie:
Vrije Universiteit Amsterdam