This dataset includes data from Mental Health supporting accommodation services in England. It includes information on resources (inputs) and outcomes (outputs) of care, which are described in the manuscript published in Plos One: “Almeda, N., García-Alonso, C. R., Killaspy, H., Gutiérrez-Colosía, M. R., & Salvador-Carulla, L. (2022). The critical factor: The role of quality in the performance of supported accommodation services for complex mental illness in England. Plos One, 17(3), e0265319. https://doi.org/10.1371/journal.pone.0265319"

The research associated with the present data focused on developing an analytical process for assessing the performance of the Mental health (MH) supporting accommodation services from 14 different regions of England considering the effect of the quality-of-care indicators in the performance. For doing so every service was classified in Residential Care (move on and non-move on oriented), Supported Housing or Floating Outreach. Then, information about the quality-of-care was collected from each domain of the instrument QuIRC-SA. Finally, a decision support system that integrated data envelopment analysis, Monte Carlo simulation and artificial intelligence was used.

The main results of the analyses pointed out that the incorporation of quality domains as variables (outputs) in DEA had a neutral-positive or positive global impact on the performance of MH-supported accommodation services.

Data for supported accommodation services from 14 nationally representative local authorities in England were collected for the QuEST study, which was funded by the National Institute of Health Research (2012-2017) (http://www.ucl.ac.uk/quest). Face-to-face interviews were conducted with service managers, key staff, and service users to assess the quality and characteristics of the services and those using them. The Quality Indicator for Rehabilitative Care–Supported Accommodation (QuIRC-SA) was completed with service managers. This standardized tool assesses service quality in seven domains: living environment, therapeutic environment, treatments and interventions, self-management and autonomy, social interface, human rights, and recovery-based practice. Data on the service’s annual budget, weekly cost per user, and service resources were also provided by the service managers to complement standard service costs for estimation of the cost-effectiveness of services.

A psychiatrist with national-level expertise in policy pertaining to people with severe MH problems, two national leaders in MH-supported accommodation service provision and policy, and three researchers who collected data from 87 supported accommodation services across England during the QuEST study designed the scenarios by choosing the variables and their interpretation in each scenario. The variables considered as relevant inputs were service budget (£ per place), places (number), and full-time equivalent staff (professionals per service user), and the relevant outputs were the average length of stay (years), occupied beds (%), the number of service users who moved to more independent accommodations (users per place), and the seven QuIRC-SA quality of care variables considering the service size (value of the domain×available places/100). By using the last mathematical transformation, original quality indicators considered the size of the service. All the considered transformations of original data render the selected services comparable by eliminating the potential “size” effect on performance assessments. Eight different scenarios, or input/output variable combinations, were designed to assess the RTE of residential care and supported housing services. For floating outreach services, only seven scenarios were identified because this type of care does not include the “living environment” QuIRC-SA service quality domain. Scenario 1 can be considered the “reference” scenario because it does not include any quality domains.

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, and entropy indicators.