Jean M. Abraham

ENTRY AND COMPETITION IN LOCAL HOSPITAL MARKETS

We extend the entry model developed by Bresnahan and Reiss to make use of quantity information, and apply it to data on the U.S. hospital industry. The Bresnahan and Reiss model infers changes in the toughness of competition from entry threshold ratios. Entry threshold ratios, however, identify the product of changes in the toughness of competition and changes in fixed costs. By using quantity data, we are able to identify separately changes in the toughness of competition from changes in fixed costs. This model is generally applicable to industries where there are good data on market structure and quantity, but not on prices, as for example in the quinquennial U.S. Economic Census. In the hospital markets we examine, entry leads to a quick convergence to competitive conduct. Entry reduces variable profits and increases quantity. Most of the effects of entry come from having a second and a third firm enter the market.

Entry and Competition in Local Hospital Markets

There has been considerable consolidation in the hospital industry in recent years. Over 900 deals occurred from 1994-2000, and many local markets, even in large urban areas, have been reduced to monopolies, duopolies, or triopolies. This surge in consolidation has led to concern about competition in local markets for hospital services. We examine the effect of market structure on competition in local hospital markets -- specifically, does the hardness of competition increase with the number of firms? We extend the entry model developed by Bresnahan and Reiss to make use of quantity information, and apply it to data on the U.S. hospital industry. In the hospital markets we examine, entry leads to a quick convergence to competitive conduct. Entry reduces variable profits and increases quantity. Most of the effects of entry come from having a second and a third firm enter the market. The fourth entrant has little estimated effect. The use of quantity information allows us to infer that entry is consumer-surplus-increasing.

The effectiveness of a health promotion program after 3 years: Evidence from the university of Minnesota

Background:Health promotion programs for the workplace are often sold to employers with the promise that they will pay for themselves with lowered health care expenditures and reduced absenteeism. In a recent review of the literature, it was noted that analysts often caution not to expect a positive return on investment until the third year of operation. Objective:This study investigates whether a positive return on investment was generated in the third year for the health promotion program used by the University of Minnesota. It further investigates what it is about the third year that would explain such a phenomenon. Measures:The study uses health care expenditure data and absenteeism data from 2004 to 2008 to investigate the effect of the University’s lifestyle and disease management programs. It also investigates the effectiveness of participation in Minnesota’s 10,000 Steps walking program and Miavita self-help programs. Research Design:A differences-in-differences equations approach is used to address potential selection bias. Possible regression to the mean is dealt with by using only those who were eligible to participate as control observations. Propensity score weighting was used to balance the sample on observable characteristics and reduce bias due to omitted variables. Results:The study finds that a 1.76 return on investment occurs in the third year of operation that is generated solely by the effect of disease management program participation in reducing health care expenditures. However, neither of the explanations for a third-year effect we tested seemed to be able to explain this phenomenon.

The effectiveness of health promotion at the University of Minnesota: expenditures, absenteeism, and participation in specific programs.

Objectives: To determine the effectiveness of the University of Minnesotas worksite health promotion program in reducing health care expenditures during the first 2 years of the program; to investigate the programs effect on absenteeism; and to study the effect of specific disease- or lifestyle-management programs on both health care expenditures and absenteeism. Methods: Health care expenditures and absenteeism of program participants were compared with those who were eligible but did not participate. Differences-in-differences regression equations with random effects were used to account for selection. Results: Participation in the general disease management program over 2 years was associated with significant reductions in expenditures, as was participation in programs for certain specific diseases. No consistently significant absenteeism or lifestyle management effects were found. Conclusions: Although the program significantly reduced expenditures, it did not generate a positive return on investment.

The diffusion of Magnet hospital recognition

BACKGROUND Magnet recognition is promoted by many in the practice community as the gold standard of nursing care quality. The Magnet hospital population has exploded in recent years, with about 8% of U.S. general hospitals now recognized. PURPOSE The purpose of this study was to identify the characteristics that distinguish Magnet-recognized hospitals from other hospitals within the framework of diffusion theory. METHODOLOGY/APPROACH We conceptualize Magnet recognition as an organizational innovation and Magnet-recognized hospitals as adopters of the innovation. We hypothesize that adoption is associated with selected characteristics of hospitals and their markets. The study population consists of the 3,657 general hospitals in the United States in 2008 located in metropolitan or micropolitan areas. We used logistic regression analysis to estimate the association of Magnet recognition with organizational and market characteristics. FINDINGS Empirical results support hypotheses that adoption is positively associated with hospital complexity and specialization, as measured by teaching affiliation, and with hospital size, slack resources, and not-for-profit or public ownership (vs. for-profit). Adopters also are more likely to be located in markets that are experiencing population growth and are more likely to have competitor hospitals within the market that also have adopted Magnet status. A positive association of adoption with baccalaureate nursing school supply is contrary to the hypothesized relationship. PRACTICE IMPLICATIONS Because of its rapid recent growth, consideration of Magnet program recognition should be on the strategic planning agenda of hospitals and hospital systems. Hospital administrators, particularly in smaller, for-profit hospitals, may expect more of their larger not-for-profit competitors, particularly teaching hospitals, to adopt Magnet recognition, increasing competition for baccalaureate-prepared registered nurses in the labor market.

Seek and Ye Shall Find: Consumer Search for Objective Health Care Cost and Quality Information

Significant investments have been made in developing and disseminating health care provider cost and quality information on the Internet with the expectation that stronger consumer engagement will lead consumers to seek providers who deliver high-quality, low-cost care. However, prior research shows that the awareness and use of such information is low. This study investigates how the information search process may contribute to explaining this result. The analysis reveals that the Web sites most likely to be found by consumers are owned by private companies and provide information based on anecdotal patient experiences. Web sites less likely to be found have government or community-based ownership, are based on administrative data, and contain a mixture of quality, cost, and patient experience information. Searches for information on hospitals reveal more cost and quality information based on administrative data, whereas searches that focus on clinics or physicians are more likely to produce information based on patient narratives.

How do health insurance loading fees vary by group size?: implications for Healthcare reform.

The health insurance loading fee represents the portion of the premium above the expected amount of medical care expenditures paid by the insurance company. The size of the loading fees and how they vary by employer group size have important implications for health policy given the recent passage of the Patient Protection and Affordable Care Act. Despite their policy relevance, there is surprisingly little empirical evidence on the magnitude and the determinants of health insurance loading fees. This paper provides estimates of the loading fees by firm size using data from the confidential Medical Expenditure Panel Survey Household Component–Insurance Component Linked File. Overall, we find an inverse relationship between employer group size and loading fees. Firms of up to 100 employees face similar loading fees of approximately 34%. Loads decline with firm size and are estimated to be on average 15% for firms with more than 100 employees, but less than 10,000 employees, and 4% for firms with more than 10,000 workers.

Does having two earners in the household matter for understanding how well employer-based health insurance works?

Using the 1996 Medical Expenditure Panel Survey, the authors investigate differences between households with two earners and those with a single earner in households’ access to employer-based health insurance and the generosity of insurance options. They examine whether a household has an offer of coverage, whether a household holds coverage, and whether all household members are covered. They also explore whether two-earner households have more generous options as measured by the number and types of plans available, as well as contribution requirements. The authors find that having a second earner in the household dramatically improves both access to employer health insurance and the generosity of health plan choices, particularly for workers generally acknowledged to have little access, such as part-time workers and workers in small establishments.

Consumer Response to a National Marketplace for Individual Health Insurance

The objective of this analysis is to simulate the difference between national and state-specific individual insurance markets on take-up of individual health insurance. This simulation analysis was completed in three steps. First, we reviewed the literature to characterize the state-specific individual insurance markets with respect to state regulations and to identify the effect of those regulations on health insurance premiums. Second, we used empirical data to develop premium estimates for the simulation that reflect case-mix as well as state-specific differences in health care markets. Third, we used a revised version of the 2005 Medical Expenditure Panel Survey (MEPS) to complete a set of simulations to identify the impact of three different scenarios for national market development. (National market estimates are based on the simulation model with competition among all 50 states and moderate impact assumptions.) We find evidence of a significant opportunity to reduce the number of uninsured under a proposal to allow the purchase of health insurance across state lines. The best scenario to reduce the uninsured, numerically, is competition among all 50 states with one clear winner. The most pragmatic scenario, with a good impact, is one winner in each regional market.

The impact of center accreditation on hematopoietic cell transplantation (HCT)

There are two voluntary center-accrediting organizations in the USA, the Foundation for the Accreditation of Cellular Therapy (FACT) and core Clinical Trial Network (CTN) certification, that are thought to improve and ensure hematopoietic cell transplantation (HCT) center quality care and certify clinical excellence. We sought to observe whether there are differences in outcomes between HLA-matched and -mismatched HCT by CTN and FACT status. Using the 2008–2010 Center for International Blood & Marrow Transplant Research data we created three center categories: non-FACT centers (24 centers), FACT-only certified centers (106 centers) and FACT and core clinical trial network (FACT/CTN) certified centers (32 centers). We identified patient characteristics within these centers and the relationship between FACT certification and survival. Our cohort consisted of 12 993 transplants conducted in 162 centers. After adjusting for patient and center characteristics we found that FACT/CTN centers had consistently superior results relative to non-FACT and FACT-only centers (P<0.05) especially for more complex HCT. However, non-FACT centers were comparable to FACT-only centers for matched related and unrelated patients. Although FACT status is an important standard of quality control that begins to define improved OS, our results indicate that FACT status alone is not an indicator for superior outcomes.