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

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.