DOI: 10.5061/dryad.8w9ghx3mp
title: Data from: Mental health ecosystem of Gipuzkoa (2015) for Bayesian network modelling

description: 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.
description: The Mental Health Network of Gipuzkoa manages its community and
 rehabilitation MH services (774,700 residents). These services include
 outpatient and core health day care facilities closely related to both
 hospital services (inpatient) and the social care network for severe
 cases. The Mental Health Network of Gipuzkoa is divided into 13 catchment
 areas, all of which have a mental health centre, considered as a
 reference, with a distinct orientation to outpatient care (some of them
 also include health day care facilities).  Gipuzkoa has
 a population of 640,635 adults older than 17 years of age in 2015. It is
 one of the three historic territories of Basque Country autonomous
 community in Spain. The Department of Health in each historic territory
 has total governance capacity and centralizes healthcare management and
 provision. The MH ecosystem of Gipuzkoa is structured in 13 catchment
 areas, which are considered the decision-making units (DMUs) for policy
 assessment. Each catchment area of Gipuzkoa corresponds to a community MH
 centre. A single acute MH hospital unit provides care to all the DMUs. The
 13 catchment areas are: Alto Deba-Arrasate, Amara, Andoain, Azpeitia,
 Beasain, Eguia, Eibar, Irun, Ondarreta, Renteria, Tolosa, Zarautz and
 Zumarraga. This dataset content 49 variables, where
 combined generated 85 variables which were used to describe MH care
 provision and to assess ecosystem performance by using RTE scores.
 Seventy-five variables were identified as resources used by the DMUs to
 provide MH care, which were classified as inputs in Group C: quality of
 care. Ten variables were classified as outcomes (outputs) obtained by
 using the inputs, classified in Group B: service utilization. However,
 only a few of these variables are considered by experts for designing
 potential causal relationships between outpatient and residential MH
 care.
description: The readme file contains an explanation of each variable in the
 dataset, including its variable name, units and explanation. This dataset
 has been used for assessing relative technical efficiency, stability,
 entropy indicators and modelling.