The journal of mental health policy and economics
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J Ment Health Policy Econ · Sep 1999
Measuring costs of guideline-driven mental health care: the Texas Medication Algorithm Project.
Algorithms describe clinical choices to treat a specific disorder. To many, algorithms serve as important tools helping practitioners make informed choices about how best to treat patients, achieving better outcomes more quickly and at a lower cost. Appearing as flow charts and decision trees, algorithms are developed during consensus conferences by leading experts who explore the latest scientific evidence to describe optimal treatment for each disorder. Despite a focus on "optimal" care, there has been little discussion in the literature concerning how costs should be defined and measured in the context of algorithm-based practices. AIMS OF THE STUDY: This paper describes the strategy to measure costs for the Texas Medication Algorithm project, or TMAP. Launched by the Texas Department of Mental Health and Mental Retardation and the University of Texas Southwestern Medical Center at Dallas, this multi-site study investigates outcomes and costs of medication algorithms for bipolar disorder, schizophrenia and depression. ⋯ To balance costs with outcomes, we turned to cost-effectiveness analyses as a framework to define and measure costs. Alternative strategies (cost-benefit, cost-utility, cost-of-illness) were inappropriate since algorithms are not intended to guide resource allocation across different diseases or between health- and non-health-related commodities. "Costs" are operationalized consistent with the framework presented by the United States Public Health Service Panel on Cost Effectiveness in Medicine. Patient specific costs are calculated by multiplying patient units of use by a unit cost, and summing over all service categories. Outpatient services are counted by procedures. Inpatient services are counted by days classified into diagnosis groups. Utilization information is derived from patient self-reports, medical charts and administrative file sources. Unit costs are computed by payer source. Finally, hierarchical modeling is used to describe how costs and effectiveness differ between algorithm-based and treatment-as-usual practices. DISCUSSION: Cost estimates of algorithm-based practices should (i) measure opportunity costs, (ii) employ structured data collection methods, (iii) profile patient use of both mental health and general medical providers and (iv) reflect costs by payer status in different economic environments. IMPLICATION FOR HEALTH CARE PROVISION AND USE: Algorithms may help guide clinicians, their patients and third party payers to rely on the latest scientific evidence to make treatment choices that balance costs with outcomes. IMPLICATION FOR HEALTH POLICIES: Planners should consider consumer wants and economic costs when developing and testing new clinical algorithms. IMPLICATIONS FOR FURTHER RESEARCH: Future studies may wish to consider similar methods to estimate costs in evaluating algorithm-based practices.
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J Ment Health Policy Econ · Jun 1999
The productivity of mental health care: an instrumental variable approach.
Like many other medical technologies and treatments, there is a lack of reliable evidence on treatment effectiveness of mental health care. Increasingly, data from non-experimental settings are being used to study the effect of treatment. However, as in a number of studies using non-experimental data, a simple regression of outcome on treatment shows a puzzling negative and significant impact of mental health care on the improvement of mental health status, even after including a large number of potential control variables. The central problem in interpreting evidence from real-world or non-experimental settings is, therefore, the potential "selection bias" problem in observational data set. In other words, the choice/quantity of mental health care may be correlated with other variables, particularly unobserved variables, that influence outcome and this may lead to a bias in the estimate of the effect of care in conventional models. AIMS OF THE STUDY: This paper addresses the issue of estimating treatment effects using an observational data set. The information in a mental health data set obtained from two waves of data in Puerto Rico is explored. The results using conventional models - in which the potential selection bias is not controlled - and that from instrumental variable (IV) models - which is what was proposed in this study to correct for the contaminated estimation from conventional models - are compared. ⋯ The results in this paper are supportive of the concerns on the credibility of evaluation results using observation data set when the endogeneity of the treatment variable is not controlled. Unobserved factors contribute to the downward bias in the conventional models. The IV approach is shown to be an appropriate method to reduce the selection bias for the group in most need for mental health care, which is also the group of most policy and treatment concern. IMPLICATIONS FOR HEALTH CARE PROVISION AND USE: The results of this work have implications for resource allocation in mental health care. Evidence is found that mental health care provided in Puerto Rico is productive, and is most helpful for persons in most need for mental health care. According to what estimated from the IV models, on the margin, receiving formal mental health care significantly increases the probability of obtaining a better mental health outcome by 19.2%, and one unit increase in formal treatment increased the probability of becoming healthier by 6.2% to 8.4%. Consistent with other mental health literature, an individual's baseline mental health status is found to be significantly related to the probability of improvement in mental health status: individuals with previous treatment history are less likely to improve. Among demographic factors included in the production function, being female, married, and high education were found to contribute to a higher probability of improvement. IMPLICATION FOR FURTHER RESEARCH: In order to provide accurate evidence of treatment effectiveness of medical technologies to support decision making, it is important that the selection bias be controlled as rigorously as possible when using information from a non-experimental setting. More data and a longer panel are also needed to provide more valid evidence. tion.
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J Ment Health Policy Econ · Mar 1999
Incorporating economic analysis in evidence-based guidelines for mental health: the profile approach.
Many western health systems are currently developing the role of clinical guidelines to promote effective and efficient health care. However, introducing economic data into guideline methodology designed to assess the effectiveness of interventions raises some methodological issues. These include providing valid and generalizable cost estimates, the weight placed upon cost "evidence" and presenting cost-effectiveness information in a way that is helpful to clinicians. AIM OF THE STUDY: To explore a framework for including economic concepts in the development of a series of primary care guidelines, two of which address mental health conditions. ⋯ A method has been applied in a series of primary care guidelines, which appears to enable clinicians to consider the issue of resource use alongside the various clinical attributes associated with treatment decisions. The basis of this work is the belief that guidance presenting physical measures describing effectiveness, adverse events, safety, compliance and quality of life, alongside resource consequences, is most likely to appropriately inform doctor-patient interactions. IMPLICATIONS FOR HEALTH CARE PROVISON AND USE: This research may provide a useful platform for other groups considering how to introduce cost-effectiveness concepts into guideline development groups. Whether guidelines change clinical behaviour remains a research question, and the subject of forthcoming trials. IMPLICATIONS FOR HEALTH POLICY FORMULATION: It is important that government agencies realize that guideline development is a health policy tool with prescribed methods to produce valid guidelines. Attempts to tamper with the methodology for cost-containment purposes or other political reasons are likely to discredit a useful mechanism for improving the scientific basis of health care provision. IMPLICATIONS FOR FURTHER RESEARCH: There are a number of limitations to completed work: for example it has a primary care focus and addresses fairly narrowly defined conditions. Work is ongoing to extend the scope to broader disease areas and to secondary care.
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J Ment Health Policy Econ · Dec 1998
The role of mental health service research in promoting effective treatment for adults with schizophrenia*
Significant gaps exist between scientific knowledge about the efficacy of treatments for mental disorders and the availability of efficacious treatments in routine practice. Mental health service research can help bridge this gap between basic clinical research and the usual care afforded adults with mental disorders. AIMS: To illustrate this potential, data on the efficacy of treatment for schizophrenia are reviewed. ⋯ Major deficiencies in treatment that were identified include inappropriate dosing with antipsychotic agents, underutilization of adjunctive antidepressant therapy, very low rates of prescription of psychosocial interventions and lack of continuity between inpatient and outpatient settings. DISCUSSION: These findings raise serious concerns about access to care and the appropriateness and quality of care that is offered. IMPLICATIONS: This knowledge about what treatments work for schizophrenia and the patterns of current care suggest the following major questions be addressed by mental health services research: What is the nature of care currently being offered adults with mental disorders? To what degree does this care measure up to scientifically derived quality of care and treatment standards? What is the effectiveness of new technologies under usual practice conditions? For which patients are they cost-effective and under what conditions? How should financial incentives be structured within systems of care to promote the most cost-effective use of new technologies? How should service systems themselves be organized to promote appropriate access and utilization? What educational, organizational and financing interventions promote adoption of effective interventions? Do we have valid methods for assessing quality of care? What strategies (interventions) are effective at improving the quality of care? In addition, we need to develop strategies that transfer mental health services research technologies into practice. These include: (i) development of outcome measures that meet scientific standards and that are practical for general application in service systems to facilitate "outcome management"; (ii) development of quality of care assessment methodologies that are practical and scientifically sound and (iii) cost-effectiveness methodologies. Mental health services research can facilitate the translation of knowledge developed from basic clinical research into more effective systems of care. The tools used by health services research to this end include examination of patterns of usual care in relation to scientifically established standards of efficacious care, interventions to improve the effectiveness of care and examination of the impacts of the organization and financing of services on outcomes and costs. In short, mental health service research holds high on its agenda the translation of basic and clinical research into practice. All of us must face the challenges posed by our rapidly changing mental health care system, changes driven not only by managed care and cost containment, but by exciting new developments in the treatment of mental disorders. We take on these challenges as researchers, clinicians, administrators, patients, families and taxpayers. Here I seek to provide a perspective on what we know about the treatment of adults with mental disorders and to discuss the implications of this knowledge for the work of mental health service research. Each of us has a particular window on this scene; mine is primarily that of a clinical mental health services researcher who studies schizophrenia. I will briefly summarize current knowledge about the efficacy of treatments for schizophrenia and the services research questions that this knowledge raises in its translation to clinical practice. The lessons from this examination readily generalize to the treatment of other adult mental disorders.
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Mental health benefits in private health insurance plans in the United States are typically less generous than benefits for physical health care services, driving reform efforts to achieve parity in coverage. While there is growing evidence about the effects such legislation would have on the utilization and cost of mental health services, less is known about the impact parity would have on reducing the risk of large out-of-pocket expenses that families would face in the event of mental illness. AIMS OF THE STUDY: We seek to understand the impact that mental health parity would have on the out-of-pocket burden that families would face in the event of mental illness. We focus in particular on variations in coverage across the privately insured population. ⋯ Parity would substantially increase generosity of mental health coverage for most of the privately insured population. The wide variation in the generosity of existing mental health benefits suggests that there are likely to be differential impacts from a parity mandate. Those with limited physical health coverage would still be at significant financial risk for catastrophic mental illness.