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

Nerea Almeda, Carlos R. García-Alonso, José A. Salinas-Pérez, Mencía R. Gutiérrez-Colosía and Luis Salvador-Carulla
Additional contact information
Nerea Almeda: Universidad Loyola Andalucía, Department of Psychology, C/Energía Solar 1, 41014 Seville, Spain
Carlos R. García-Alonso: Universidad Loyola Andalucía, Department of Quantitative Methods, C/Energía Solar 1, 41014 Seville, Spain
José A. Salinas-Pérez: Universidad Loyola Andalucía, Department of Quantitative Methods, C/Energía Solar 1, 41014 Seville, Spain
Mencía R. Gutiérrez-Colosía: Universidad Loyola Andalucía, Department of Psychology, C/Energía Solar 1, 41014 Seville, Spain
Luis Salvador-Carulla: Centre for Mental Health Research, Research School of Population Health, Australian National University, 63 Eggleston Rd, Acton, ACT 2601, Australia

IJERPH, 2019, vol. 16, issue 3, 1-20

Abstract: 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.

Keywords: mental health systems; mental health services; mental health care, management; policy-making; planning; causal model; Bayesian networks; structural equation modelling; systematic review (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/16/3/332/pdf (application/pdf)
https://www.mdpi.com/1660-4601/16/3/332/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:16:y:2019:i:3:p:332-:d:200792

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().