Economic context analysis in mental health care. Usability of health financing and cost of illness studies for international comparisons

This paper discusses an integrated approach to mental health studies on Financing of Illness (FoI) and health accounting, Cost of Illness (CoI) and Burden of Disease (BoD). In order to expand the mental health policies, the following are suggested: (a) an international consensus on the standard scope, methods to collect and to analyse mental health data, as well as to report comparative information; (b) mathematical models are also to be validated and tested in an integrated approach, (c) a better knowledge transfer between clinicians and knowledge engineers, and between researchers and policy makers to translate economic analysis into practice and health plannin

Geographical Hot Spot Analysis of ATAPS for Policy Planning

Studies on equity of mental health referrals have used qualitative approaches or service utilisation data, however little information is available on spatial equity. The Access to Allied Psychological Services (ATAPS) program enables patients to be referred to mental health professionals for lowcost mental health care to facilitate equality of care

The critical factor: The role of quality in the performance of supported accommodation services for complex mental illness in England

Rehabilitation services have a key role in ensuring integrated and comprehensive mental health (MH) care in the community for people suffering from long-term and severe mental disorders. MH-supported accommodation services aim to promote service users’ autonomy and independence. Given the complexity associated with MH-supported accommodation services in England, a comparative evaluation of critical performance indicators, including service provision and quality of care, seems to be necessary in designing evidence-informed policies. This study aims to explore the influence of service quality indicators on the performance of MH-supported accommodation services in England. The analysed sample includes supported accommodation services from 14 nationally representative local authorities in England from the QuEST study grouped by three main types of care: residential care homes (divided into two subgroups: move-on and non-move-on oriented), supported housing and floating outreach. EDeS-MH (efficient decision support-mental health) was used to assess the performance indicators for the selected services by combining a Monte Carlo simulation engine, data envelopment analysis and a fuzzy inference engine for integrating expert knowledge. Depending on the type of care, six/seven quality domains were sequentially included after a baseline scenario (only technical) was analysed. Relative technical efficiency scores for the baseline scenarios revealed high performance in all the selected supported accommodation services, but the statistical variability was high. Quality domains significantly improved performance in every type of care. The inclusion of quality indicators has a positive impact on the global performance of each type of care. Remaining at the corresponding services more than expected for two years has a negative impact on performance. These findings can be considered from a planning perspective to facilitate the design of pathways of care with more realistic expectations about gaining autonomy in two years

Integrating clinicians, knowledge and data: expert-based cooperative analysis in healthcare decision support

<p>Abstract</p> <p>Background</p> <p>Decision support in health systems is a highly difficult task, due to the inherent complexity of the process and structures involved.</p> <p>Method</p> <p>This paper introduces a new hybrid methodology <it>Expert-based Cooperative Analysis </it>(EbCA), which incorporates explicit prior expert knowledge in data analysis methods, and elicits implicit or tacit expert knowledge (IK) to improve decision support in healthcare systems. EbCA has been applied to two different case studies, showing its usability and versatility: 1) Bench-marking of small mental health areas based on technical efficiency estimated by <it>EbCA-Data Envelopment Analysis (EbCA-DEA)</it>, and 2) Case-mix of schizophrenia based on functional dependency using <it>Clustering Based on Rules (ClBR)</it>. In both cases comparisons towards classical procedures using qualitative explicit prior knowledge were made. Bayesian predictive validity measures were used for comparison with expert panels results. Overall agreement was tested by Intraclass Correlation Coefficient in case "1" and kappa in both cases.</p> <p>Results</p> <p>EbCA is a new methodology composed by 6 steps:. 1) Data collection and data preparation; 2) acquisition of "Prior Expert Knowledge" (PEK) and design of the "Prior Knowledge Base" (PKB); 3) PKB-guided analysis; 4) support-interpretation tools to evaluate results and detect inconsistencies (here <it>Implicit Knowledg </it>-IK- might be elicited); 5) incorporation of elicited IK in PKB and repeat till a satisfactory solution; 6) post-processing results for decision support. EbCA has been useful for incorporating PEK in two different analysis methods (DEA and Clustering), applied respectively to assess technical efficiency of small mental health areas and for case-mix of schizophrenia based on functional dependency. Differences in results obtained with classical approaches were mainly related to the IK which could be elicited by using EbCA and had major implications for the decision making in both cases.</p> <p>Discussion</p> <p>This paper presents EbCA and shows the convenience of completing classical data analysis with PEK as a mean to extract relevant knowledge in complex health domains. One of the major benefits of EbCA is iterative elicitation of IK.. Both explicit and tacit or implicit expert knowledge are critical to guide the scientific analysis of very complex decisional problems as those found in health system research.</p

Methodology for the development of a taxonomy and toolkit to evaluate health-related habits and lifestyle (eVITAL)

<p>Abstract</p> <p>Background</p> <p>Chronic diseases cause an ever-increasing percentage of morbidity and mortality, but many have modifiable risk factors. Many behaviors that predispose or protect an individual to chronic disease are interrelated, and therefore are best approached using an integrated model of health and the longevity paradigm, using years lived without disability as the endpoint.</p> <p>Findings</p> <p>This study used a 4-phase mixed qualitative design to create a taxonomy and related online toolkit for the evaluation of health-related habits. Core members of a working group conducted a literature review and created a framing document that defined relevant constructs. This document was revised, first by a working group and then by a series of multidisciplinary expert groups. The working group and expert panels also designed a systematic evaluation of health behaviors and risks, which was computerized and evaluated for feasibility. A demonstration study of the toolkit was performed in 11 healthy volunteers.</p> <p>Discussion</p> <p>In this protocol, we used forms of the community intelligence approach, including frame analysis, feasibility, and demonstration, to develop a clinical taxonomy and an online toolkit with standardized procedures for screening and evaluation of multiple domains of health, with a focus on longevity and the goal of integrating the toolkit into routine clinical practice.</p> <p>Trial Registration</p> <p>IMSERSO registry 200700012672</p

International comparability of reference unit costs of education services: when harmonizing methodology is not enough (PECUNIA project)

Background: Health problems can lead to costs in the education sector. However, these costs are rarely incorporated in health economic evaluations due to the lack of reference unit costs (RUCs), cost per unit of service, of education services and of validated methods to obtain them. In this study, a standardized unit cost calculation tool developed in the PECUNIA project, the PECUNIA RUC Template for services, was applied to calculate the RUCs of selected education services in five European countries. Methods: The RUCs of special education services and of educational therapy were calculated using the information collected via an exploratory gray literature search and contact with service providers. Results: The RUCs of special education services ranged from €55 to €189 per school day. The RUCs of educational therapy ranged from €6 to €25 per contact and from €5 to €35 per day. Variation was observed in the type of input data and measurement unit, among other. Discussion: The tool helped reduce variability in the RUCs related to costing methodology and gain insights into other aspects that contribute to the variability (e.g. data availability). Further research and efforts to generate high quality input data are required to reduce the variability of the RUCs