Mark V. Pauly

Estimating hospital costs. A multiple-output analysis.

This study explores a new approach to estimating the cost of inpatient and outpatient services provided by hospitals. Data from a nationwide survey of non-federal, short-term, U.S. hospitals are used to make cost estimates based on a multiple-output cost function. The results provide information on the structure of hospital costs, and include estimates of the marginal and average incremental cost of outpatient care. Because of the innovative specification of the cost function, the study is of interest for its methodology as well as empirical results.

Avoiding bias in the conduct and reporting of cost-effectiveness research sponsored by pharmaceutical companies.

Because of the growing focus on containing health care costs, pharmaceutical companies are trying to demonstrate the cost effectiveness of their products relative to alternatives. In Europe and Aus...

Neglecting Disaster: Why Don't People Insure Against Large Losses?

This paper provides a theoretical explanation for the common observation that people often fail to purchase insurance against low-probability high-loss events even when it is offered at favorable premiums. We hypothesize that individuals maximize expected utility but face an explicit or implicit cost to discovering the true probability of rare events. This cost constitutes a threshold that may inhibit purchase but may be offset in several ways by suppliers of insurers and state regulators.

Economic analysis of health care technology: A report on principles

Preamble Although economic outcomes research is an evolving field in health services research, there are correct and incorrect ways to conduct and report on economic outcomes studies. Research practices that help to minimize real or perceived bias will increase the quality and usefulness of such studies for those who sponsor, publish, and use them. Because of public concerns about the potential for bias in the design, analysis, and reporting of economic analyses of health care technology, we formed a task force to develop principles to enhance the credibility of these studies. The Task Force on Principles for Economic Analysis of Health Care Technology included participants from academia, the pharmaceutical industry, the public sector, and private research organizations. As health care resources become increasingly constrained, the information used to make resource allocation decisions must be as reliable, valid, and free of bias as possible. Getting it right at the level at which economic results are produced will help to protect consumers and will advance the health of the public. Bias stems from two broad categories: lack of appropriate independence for researchers and lack of consensus about methods. We focused heavily on the first of these categories for two major reasons. First, few have yet considered the unique issues of researcher independence in economic outcomes research [1, 2]. Second, other investigators have begun to consider and define proper methods for economic outcomes work (an area of considerable controversy) [3-5]. We also looked closely at the requirements for the reporting of economic analyses, which are intended to ensure methodologic transparency and accountability. The Need for Voluntary Guidelines Widespread use of economic analysis as part of the development of pharmaceutical, biotechnologic, and medical devices is relatively new. To date, many economic analyses of health care have focused on pharmaceutical agents, and many such studies have been funded by pharmaceutical companies. Results of these economic outcomes studies are used by medical technology firms to support the pricing and marketing of new interventions and to influence national health care systems and third-party payers in their development of coverage and payment decisions. Managed care organizations, hospitals, and government-subsidized health care programs rely on economic analysis of medical technology to help make formulary purchasing and utilization decisions. Physicians may use the results of these analyses to help guide treatment and prescription decisions. Health care economic outcomes projects are sponsored and conducted by pharmaceutical, biotechnologic, and medical device companies; government agencies; nonprofit foundations; academic investigators; and private research and consulting firms. The standards and methods used to evaluate the safety and efficacy of pharmaceutical products in randomized, controlled trials have evolved over 50 years of collaboration among researchers in private, public, and academic settings. Compared with those established to ensure the safety and efficacy of clinical trials, the principles and methods for the conduct of economic studies of health care technology are far less developed. Problems of conduct, reporting, and bias exist in all types of research [6-14]. In response, codes of conduct, such as those developed by the American Federation for Clinical Research and the Institute of Medicine, have been developed for many scientific disciplines [15-24]. These are good models on which to base principles of conduct for economic outcomes analysis. Although many published principles apply to economic studies, others should be modified and new ones should be developed to guide the conduct and reporting of economic outcomes analyses. Economic outcomes research requires unique guidelines for the following reasons: 1) As a field, it continues to evolve and is often misunderstood by end users; 2) peer review of it requires special expertise that often exceeds the capabilities of reviewers and scientific journals; 3) it often uses secondary data and requires that many assumptions be made [for example, attribution of a dollar cost to a unit of resource use]; 4) it offers unique methodologic choices, such as which types of costs to include (direct, indirect, intangible, induced), which perspective to apply (that of society, payer, provider, patient), which design to adopt (cost-identification, costbenefit, cost-effectiveness, costutility), from where to obtain costs [indemnity database, managed care or capitated database, hospital cost systems, Medicare, Medicaid], and whether to collect resource consumption data prospectively or retrospectively through various modeling techniques; and 5) economic studies play an increasingly important role in health care decision making because of increasing financial constraints throughout the health care industry. The financial and medical implications of decision making done on the basis of these studies, coupled with the lack of widely accepted guidelines about the conduct and reporting of economic analyses, undermine the credibility of this research. A major issue is that the primary source of funding for this research is often the primary financial beneficiary of positive study results. Unfortunately, even valid studies done under the best of circumstances may be suspect [25-29]. This has led at least one major journal to conclude that these analyses should be viewed much like editorials or review articles are viewed in terms of potential for conflicts of interest [30]. We developed the guidelines reported here after extensive consultation with experts from the public, private, and academic sectors. We recommend that researchers and sponsors adhere to these guidelines, and we suggest they state publicly within their manuscripts that they have done so. End users, including journal editors and readers, consumers, and social decision makers, may then feel more secure in accepting the results of the research, while recognizing that intensive critique of the research will always be necessary. Operations of the Task Force The Task Force on Principles for Economic Analysis of Health Care Technology was initiated and organized by faculty from the Leonard Davis Institute (LDI) Center for Health Policy of the University of Pennsylvania. However, because not all LDI faculty were involved, this paper does not represent an official LDI position statement. The Task Force was funded by a coalition of pharmaceutical companies (Appendix A). Funding was also requested (but not obtained) from various government and private foundations. All funding was provided in the form of unrestricted research grants or gifts to the University of Pennsylvania. Guidelines derive their credibility in part from the composition of the panel that creates them and the process by which they are developed. Candidates for participation in the Task Force from the private sector and the academic research community were identified by the frequency with which they were cited in the health economics literature, which was obtained using a MEDLINE search of literature related to economic analyses of medical technology published between 1983 and 1992. Approximately 15 members attended each meeting. Minutes were distributed after each meeting and approved by all members present. Members from the sponsoring pharmaceutical companies and the academic organizers rotated so that they numbered three and one, respectively, at the table for each formal Task Force meeting. Several professional and governmental organizations, including the Institute of Medicine, the Agency for Health Care Policy and Research, the Food and Drug Administration (FDA), the Health Care Financing Administration, the Centers for Disease Control and Prevention, and a managed care organization, were asked to suggest persons who might participate. In addition, other organizations with a stake in economic analyses were each asked to suggest a person who, because of his or her professional background, had extensive knowledge of or experience with economic analyses. An academic pharmacist, an academic researcher, a private researcher, an ethicist and patient advocate, and an attorney specializing in medical ethics rounded out the Task Force (Appendix B). Two members withdrew from participation. A small audience consisting of industry sponsors, academic organizers, staff, and a few other interested parties were invited to each meeting and allowed to comment. Four formal meetings of the Task Force were held in Philadelphia during 1993 and 1994, and the most substantial work was done by various subcommittees between these meetings. The three main subcommittees were titled Ethical Conduct, Responsibility and Control (the findings of these two subcommittees were later merged into one report), and Reporting Requirements for Economic Evaluations (Appendix C). The Task Force was assisted by a professional facilitator. Although Task Force members sought consensus wherever possible on the key issues, consensus was not forced and recommendations were issued only when substantial agreement existed among the members. Task Force members were not asked to formally represent any organization. Formal endorsement of the final document was not sought. Copies of this report will be distributed to all organizations that were asked to suggest a participant and to other parties who responded to announcements placed in The New England Journal of Medicine, Scrip, and the Pink Sheet. The Task Force findings will also be reported at academic conferences, medical profession meetings, and appropriate trade conventions. Findings and Recommendations Valid approaches to economic analyses can be defined; acceptable methods can be differentiated from unacceptable ones. Bias in economic research stems from two major sources: lack of appropriate independence for researc

Access to Kidney Transplantation: Has the United States Eliminated Income and Racial Differences?

We analyzed the effect of patient and dialysis unit characteristics on access to kidney transplantation using several different approaches, including an analysis of individual patient data from a systematic random sample of 2900 new dialysis patients from each year 1981 to 1985 (14721 patients total). Additional analyses focused on the composition of transplant waiting lists and aggregate data from a 1984 census of 1133 dialysis and transplant units. White, male, young, nondiabetic, high-income patients treated in smaller units are more likely to receive a cadaver transplant under Medicare than are other kidney patients. Profit status of the dialysis unit was not found to be correlated to access to transplantation, although size of the unit may be correlated to access. Future analysis should focus on whether patient access has been inappropriately compromised. Possible factors unexplored in this analysis include differential patient preferences and medical suitability, as well as differential medical access.

A Randomized Controlled Trial of Financial Incentives for Smoking Cessation

Background: Although 435,000 Americans die each year of tobacco-related illness, only ∼3% of smokers quit each year. Financial incentives have been shown to be effective in modifying behavior within highly structured settings, such as drug treatment programs, but this has not been shown in treating chronic disease in less structured settings. The objective of this study was to determine whether modest financial incentives increase the rate of smoking cessation program enrollment, completion, and quit rates in a outpatient clinical setting. Methods: 179 smokers at the Philadelphia Veterans Affairs Medical Center who reported smoking at least 10 cigarettes per day were randomized into incentive and nonincentive groups. Both groups were offered a free five-class smoking cessation program at the Philadelphia Veterans Affairs Medical Center. The incentive group was also offered

First-cycle blood counts and subsequent neutropenia, dose reduction, or delay in early-stage breast cancer therapy.

20 for each class attended and

Alternative valuations of work loss and productivity.

100 if they quit smoking 30 days post program completion. Self-reported smoking cessation was confirmed with urine cotinine tests. Results: The incentive group had higher rates of program enrollment (43.3% versus 20.2%; P < 0.001) and completion (25.8% versus 12.2%; P = 0.02). Quit rates at 75 days were 16.3% in the incentive group versus 4.6% in the control group (P = 0.01). At 6 months, quit rates in the incentive group were not significantly higher (6.5%) than in the control group (4.6%; P > 0.20). Conclusion: Modest financial incentives are associated with significantly higher rates of smoking cessation program enrollment and completion and short-term quit rates. Future studies should consider including an incentive for longer-term cessation. (Cancer Epidemiol Biomarkers Prev 2006;15(1):12–8)

CONTRACTUAL ARRANGEMENTS BETWEEN HMOS AND PRIMARY CARE PHYSICIANS : THREE-TIERED HMOS AND RISK POOLS

PURPOSE If patients could be ranked according to their projected need for supportive care therapy, then more efficient and less costly treatment algorithms might be developed. This work reports on the construction of a model of neutropenia, dose reduction, or delay that rank-orders patients according to their need for costly supportive care such as granulocyte growth factors. PATIENTS AND METHODS A case series and consecutive sample of patients treated for breast cancer were studied. Patients had received standard-dose adjuvant chemotherapy for early-stage nonmetastatic breast cancer and were treated by four medical oncologists. Using 95 patients and validated with 80 additional patients, development models were constructed to predict one or more of the following events: neutropenia (absolute neutrophil count [ANC] < or = 250/microL), dose reduction > or = 15% of that scheduled, or treatment delay > or = 7 days. Two approaches to modeling were attempted. The pretreatment approach used only pretreatment predictors such as chemotherapy regimen and radiation history; the conditional approach included, in addition, blood count information obtained in the first cycle of treatment. RESULTS The pretreatment model was unsuccessful at predicting neutropenia, dose reduction, or delay (c-statistic = 0.63). Conditional models were good predictors of subsequent events after cycle 1 (c-statistic = 0.87 and 0.78 for development and validation samples, respectively). The depth of the first-cycle ANC was an excellent predictor of events in subsequent cycles (P = .0001 to .004). Chemotherapy plus radiation also increased the risk of subsequent events (P = .0011 to .0901). Decline in hemoglobin (HGB) level during the first cycle of therapy was a significant predictor of events in the development study (P = .0074 and .0015), and although the trend was similar in the validation study, HGB decline failed to reach statistical significance. CONCLUSION It is possible to rank patients according to their need of supportive care based on blood counts observed in the first cycle of therapy. Such rankings may aid in the choice of appropriate supportive care for patients with early-stage breast cancer.

P4P4P: An Agenda For Research On Pay-For-Performance For Patients

In this article, we examine the indirect costs (ie, work loss and productivity costs) of employee illness from the employer’s perspective. We provide a conceptual framework to help employers consider alternative views with regard to assessing indirect costs and valuing the health care they purchase. First, we discuss the matter of perspective and how an employer should view and assess indirect costs. We briefly review current models of measuring indirect costs, and we critique these models. Then we introduce a simple, conceptual framework based on the ideas of health capital and labor productivity, and we lay out the effects of health investment on indirect costs while considering what employees desire and employers can provide. Finally, we offer an agenda for further research.