TY - JOUR
T1 - Causal modelling for supporting planning and management of mental health services and systems
T2 - A systematic review
AU - Almeda, Nerea
AU - García-Alonso, Carlos R.
AU - Salinas-Pérez, José A.
AU - Gutiérrez-Colosía, Mencía R.
AU - Salvador-Carulla, Luis
N1 - Funding Information:
Funding: This research was funded by the Institute of Health Carlos III, REFINEMENT Spain project PI15/01986.
Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2019/2/1
Y1 - 2019/2/1
N2 - 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.
AB - 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.
KW - Bayesian networks
KW - Causal model
KW - Management
KW - Mental health care
KW - Mental health services
KW - Mental health systems
KW - Planning
KW - Policy-making
KW - Structural equation modelling
KW - Systematic review
UR - http://www.scopus.com/inward/record.url?scp=85060656610&partnerID=8YFLogxK
U2 - 10.3390/ijerph16030332
DO - 10.3390/ijerph16030332
M3 - Article
C2 - 30691052
AN - SCOPUS:85060656610
SN - 1661-7827
VL - 16
SP - 1
EP - 20
JO - International Journal of Environmental Research and Public Health
JF - International Journal of Environmental Research and Public Health
IS - 3
M1 - 332
ER -
