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Category Archives: I11

The Effect of Competition on Strategic Discharge at Long-Term Acute-Care Hospitals

By Michael Karamardian

Because Medicare’s prospective payment system for long-term acute-care hospitals (LTCHs) makes a large lump-sum form of payment once patients reach a minimum length-ofstay threshold, LTCHs have a unique opportunity to maximize profits by strategically discharging patients as soon as the payment is received. This analysis explores how the level of competition between LTCHs in geographic markets affects the probability of a patient being strategically discharged. The results show that patients at LTCHs in more competitive markets have a lower probability of being strategically discharged than at those in less competitive markets, suggesting increased competition could help save Medicare funding.

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Advisors: Kent Kimbrough and James Roberts | JEL Codes: D22, I11, I18

Medicare’s Prospective Payment System: Do Differences in the Reimbursement Rate Affect Quantity of Care Delivered and Hospital Billing Practices?

By Russell Hollis

When the government changes Medicare policy, payment structures often accommodate the change through lowering reimbursement rates. Changes in reimbursements raise the question of what effect changes have on patient care. Using data sets from the Center for Medicare and Medicaid Services, I examine the diagnosis of major replacement or reattachment of the lower extremity and how the length of stay for patients responds to changing reimbursement rates. I extend my investigation of price incentives to monitor fraudulent coding by hospitals. In a sample of over 470,000 patients in 2,696 hospitals for fiscal year 2012, I find that a 1% increase in reimbursement leads to a .007% increase in length of stay for DRG 470 (without complications) patients and a .057% percent increase for DRG 469 (with complications) patients. I then find that a 10% decrease in reimbursement for DRG 470 or one percent increase for DRG 469 leads to a .0011 increase in fraction of DRG 469 patients in a particular hospital. Lastly, I comment on these results, which point to the evidence of price incentives in quantity of care an the possibility of “upcoding”1.

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Advisor: Allan Collard-Wexler | JEL Codes: H50, H51, I11, I13, I18 | Tagged: Diagnosis Related Group, Length of Stay, Medicare, Reimbursement, “Upcoding”

Incentives and Characteristics that Explain Generic Prescribing Practices

By Rahul Nayak

This study uses the National Ambulatory Medical Care Survey (2006-2010) and Health Tracking Physician Survey (2008) to study the incentives and characteristics that explain physician generic prescribing habits. The findings can be characterized into four main categories: (1) financial/economic, (2) informational, (3) patient- dependent and (4) drug idiosyncratic effects. Physicians in practices owned by HMOs or practices that had at least one managed care contract are significantly more likely to prescribe generic medicines. Furthermore, physicians who have drug industry influence are less likely to prescribe generic medicines. This study also finds consistent evidence that generic prescribing is reduced for patients with pri- vate insurance compared to self-pay patients. Drug-specific characteristics play an important role for whether a drug is prescribed as a generic or brand-name – in- cluding not only market characteristics, such as monopoly duration length, public familiarity with the generic and the quality of the generic, but also non-clinical drug characteristics, such as the length of the generic name compared the length of the brand-name. In particular, the public’s familiarity with the generic has a large effect on the generic prescribing rate for a given drug. There are few differences between the generic prescribing habits of primary care physicians and specialists after controlling for the drugs prescribed.

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Advisor: Frank Sloan | JEL Codes: D82, D83, I11, I13, I18 | Tagged: Drug Market Charac- teristics, Efficient Prescribing, Electronic Prescribing, Generic Prescribing, HTPS, Industry Influence, NAMCS, Patient Preferences, Physician Incentives, Principle- Agent Problem

Price Discrimination and Bargaining Power in the Global Vaccine Market

By Linda Li

Since the 1980’s, the market structure of vaccines has become increasingly oligopolistic, and in some cases monopolistic. Alongside these supply trends, we see the emergence and growth of group procurement schemes on the demand side of the market. National government and international organization procure vaccines on behalf of end users. Two such organizations include the UNICEF Supply Division and the PAHO EPI Revolving Fund, for which participation is based on income or geography. Consistent with one of the main goals of group procurement, these groups obtain price discounts on vaccines relative to the private sector. This paper seeks to disentangle two possible explanations for this observed price dispersion using vaccine price data over the years 2002-2012 from UNICEF, PAHO, and the U.S. The two explanations are that of price discrimination and bargaining power. Using proxy variables in a fixed effects model, I find that price discrimination does have a significant impact on price discount. I also find support for a bargaining power effect, however with less certainty, and the existence of supply constraints. These findings have important policy implications for national governments, as well as procurement groups.

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Advisor: David Ridley | JEL Codes: I11, I18, L22 | Tagged: Bargaining Power, Group Procurement, Price Discrimination, Vaaccines

Debunking the Cost-Shifting Myth: An Analysis of Dnamic Price Discrimination in California Hospitals

By Omar Nazzal

Cost-shifting, a dynamic form of price discrimination, is a phenomenon in which hospitals shift the burden of decreases in government-sponsored healthcare reimbursement rates to private health insurers. In this paper, I construct a data set spanning 2007 – 2011 that matches financial metrics of California hospitals to hospital- and market-specific characteristics with theoretical implications in price discrimination. The subsequent analysis is split into three stages. In the first and second stages, I use a fixed-effects OLS model to derive a point estimate of the inverse correlation between private revenue and government revenue that is consistent with recent empirical work in cost-shifting, a body of literature almost entirely reliant upon fixed-effects and difference-in-difference OLS. These types of models are encumbered by the inherent causality loop connecting public and private payment sources. I address this endogeneity problem in the third stage by specifying a fixed-effects 2SLS model based on an instrument for government revenue constructed with data from the California Department of Health Care Services and the U.S. Census. This instrument performed well in canonical tests for relevance and validity. I find that an increase in government payments causes an increase in private payments, and that the relationship is statistically-significant at all reasonable levels. In addition, I comment on properties of the data set that suggest that the original inverse correlation was due to inadequate measurements of market power. I conclude with policy implications and suggestions for future research.

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Advisor: Frank Sloan | JEL Codes: I11, I13, I18, L11, L80 | Tagged: Health Insurance, Market Structure, Medicaid, Medicare, Price Discrimination

Integrating Medicare and Medicaid Healthcare Delivery and Reimbursement Policies for Dual Eligible Beneficiaries: A Cost-Efficiency Analysis of Managed Care

By Kan Zhang

The extreme underpricing of Chinese Initial Public Offerings in the early days of the Chinese equity markets was reduced by several reforms instituted by the Chinese government from around 2000 to 2002. These reforms reduced 1-day returns on IPOs from 295% to 72%. The reforms reduced IPO underpricing by decreasing the inequality between IPO supply and demand. These reforms, while announced between 2000 and 2002, likely took until around 2004 to take full effect. In addition to inequality between supply and demand, other factors such as information asymmetry and government/quality signaling contributed to underpricing both before and after the reforms.

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Advisor: Frank Sloan | JEL Codes: D61, I0, I11, I12, I18 | Tagged: Dual Eligibles, Managed Care, Medicare

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