Causal Modelling for Supporting Planning and Management of Mental Health Services and Systems: A Systematic Review

• Main Authors: Nerea Almeda, Carlos R. García-Alonso, José A. Salinas-Pérez, Mencía R. Gutiérrez-Colosía, Luis Salvador-Carulla
• Format: Article
• Language: English
• Published: MDPI AG 2019-01-01
• Series: International Journal of Environmental Research and Public Health
• Subjects: mental health systems; mental health services; mental health care, management; policy-making; planning; causal model; Bayesian networks; structural equation modelling; systematic review
• Online Access: https://www.mdpi.com/1660-4601/16/3/332

Mental health services and systems (MHSS) are characterized by their complexity. Causal modelling is a tool for decision-making based on identifying critical variables and their causal relationships. In the last two decades, great efforts have been made to provide integrated and balanced mental health care, but there is no a clear systematization of causal links among MHSS variables. This study aims to review the empirical background of causal modelling applications (Bayesian networks and structural equation modelling) for MHSS management. The study followed the PRISMA guidelines (PROSPERO: CRD42018102518). The quality of the studies was assessed by using a new checklist based on MHSS structure, target population, resources, outcomes, and methodology. Seven out of 1847 studies fulfilled the inclusion criteria. After the review, the selected papers showed very different objectives and subjects of study. This finding seems to indicate that causal modelling has potential to be relevant for decision-making. The main findings provided information about the complexity of the analyzed systems, distinguishing whether they analyzed a single MHSS or a group of MHSSs. The discriminative power of the checklist for quality assessment was evaluated, with positive results. This review identified relevant strategies for policy-making. Causal modelling can be used for better understanding the MHSS behavior, identifying service performance factors, and improving evidence-informed policy-making.