Journal of Mental Health Policy and Economics最新文献

Background: The costs of substance abuse in the USA are enormous and varied. Seldom are they comprehensively assessed. A new study jointly published by the National Institute on Drug Abuse (NIDA) and the National Institute on Alcoholism and Alcohol Abuse (NIAAA) has done just this. Aims: Researchers for the economic cost of alcohol and drug abuse in the United States, 1992 used systematic cost-of-illness measurement methods to evaluate the burden drug abuse and dependency place on the US economy. This burden includes widespread disability, morbidity, premature death, and diversion of economic resources to drug-related activities. Conceptualizing, identifying, and measuring this burden was a major undertaking; the report describes the methods in detail. Method: Costs are measured as the value of resources used (direct costs) or lost during a one year period. As adopted here, the human capital approach estimates an individual’s value to society in terms of his or her production potential. The value of future lost earnings is discounted to present time. Finally, the study adopts a societal point of view that is consistent with the recommendations of the Panel on Cost-Effectiveness in Health and Medicine that was convened by the U.S. Public Health Service in 1993. Therefore, this study considers all health and non-health outcomes and costs created by drug abuse and dependency for the entire population. Results: For drug abuse, the annual cost in 1992 is estimated at $98 billion. By 1995, this estimate rose to $110 billion after adjusting for inflation and population change. For 1988, a previous and similar study estimated a cost of $58 billion. The distribution of costs is of particular concern.

Background: 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. Methods: 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. Copyright © 1999 John Wiley & Sons, Ltd.

Collaboration between MCOs and researchers holds promise for benefiting consumers by working on quality-of-care-related research. There are at least three areas of collaboration that might benefit both researchers and MCOs: (1) the developing and validating of management and fiscal indicators, (2) developing and validating clinical indicators and (3) studying access to treatment for vulnerable populations. These three areas offer benefits to the MCO and unusual research opportunities for investigators. Barriers for both MCOs and researchers must be overcome before this work can be carried out, not the least of which is who will pay for the work to be done.

Background: The rise of managed behavioral health care in the United States was accompanied by reductions in costs, which has shifted the policy debate from concerns about rising costs to questions of universal access, mental health benefits at parity with medical benefits and quality of care. To meet these new challenges, managed care organizations, the purchasers of health care and academic services researchers must work together in new ways. Aims of the Study: This paper discusses collaborative efforts between a for-profit managed care firm, academia and purchasers of health care coverage to study parity for mental health and substance abuse and how this effort has become part of a research strategy to inform policy. Historical, strategic and methodological issues are presented. Methods: Case Study. Results: Although the benefits from cooperative research are substantial, there are severe hurdles. Managed care organizations often have data that could answer pressing policy questions, yet these data are rarely used by researchers because it is difficult to obtain access and because analyzing the data requires computing facilities and skills that are not common in health services research. In turn, managed care organizations can learn how to design and implement more informative data systems that eventually lead to more cost-effective care, but there often are more immediately pressing business considerations and sometimes resistance to outside scrutiny. Important features that made this cooperation successful include strong support from the senior management in the company, including complete access to their extensive databases, and established funding for a managed care research center by the National Institute of Mental Health. Conclusion: This paper illustrates the potential of collaborative research. New research challenges, such as the linkages between quality and cost-effectiveness in actual practice settings, can only be met successfully if we build alliances among payors, managed care companies and academic researchers. Copyright © 1999 John Wiley & Sons, Ltd.

The most valuable research integrates from thre levels of investigation: clinical efficacy, ‘real life’ effectiveness (including cost-effectiveness) and policy research. Successful applications of systematic reviews have largely been limited to clinical efficacy questions. The contribution of systematic reviews/meta-analyses to effectiveness and economic questions in mental health has been very minor and their contribution to inform policy is negligible. The latter is unlikely to change due to the different type of information that policy makers need.

From an economic viewpoint, the amount of primary research conducted on a topic at any given point in time depends on grantmaker and researcher incentives. The potential addresses of research findings often set these incentives. Following this logic, there is an economic explanation provided for the availability of primary data in effcacy studies. This also explains the lack of data in other important fields of health care. This article evaluates why there are few studies on effectiveness and cost-effectiveness then discusses how research incentives might be changed to overcome this problem. As a result of cost containment efforts in some countries, this process has already been initialized.