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Data from: Mental health ecosystem of Gipuzkoa (2015) for Bayesian network modelling

Data files

Mar 29, 2022 version files 247.86 KB

Abstract

This dataset include data from Mental Health network of Gipuzkoa (Spain). It is included information on resources (inputs) and outcomes (outputs) of care, which are described in the manuscript: “Almeda, N., Garcia-Alonso, C. R., Gutierrez-Colosia, M. R., Salinas-Perez, J. A., Iruin-Sanz, A., & Salvador-Carulla, L. (2022). Modelling the balance of care: Impact of an evidence-informed policy on a mental health ecosystem. PLoS ONE, 17(1 January), 1–16. https://doi.org/10.1371/journal.pone.0261621”. This manuscript has been published in Plos One journal.

This research focused on developing a formal causal model based on Bayesian network prototypes which were designed by formalizing expert knowledge (by using Expertbased Cooperative Analysis) and resulting in Direct Acyclic Graphs. The best Bayesian networks and their corresponding regression models were used to estimate the statistical ranges or confidence intervals for the dependent variable (potential effect, consequence, or output) given the independent variable values. These ranges, adjusted to delimited statistical distributions (triangular, trapezoidal and gamma), were managed by a Monte Carlo simulation engine for intervention assessment. A computer-based Decision Support System (DSS) was used to assess the status of ecosystem performance: RTE, statistical stability and entropy.

Main results of the analyses pointed out that by combining causal reasoning and statistical methods, decision makers can obtain a deep view of both pre-implementing and post-implementing situations. Knowing the causal levers, it is possible to act directly to the causes in order to potentially produce de appropriate results considering the uncertainty: to provide a more balanced and integrated MH care provision in the community. In this particular case, an improvement in the outpatient workforce increases both ecosystem performance (RTE) and stability and slightly decreases entropy.