Investing in Rural Healthcare: Impact of Private Equity Acquisition on Financial and Utilization Outcomes of Rural Hospitals
by Amanda He
Abstract
Private equity investment in the healthcare sector has risen considerably in recent decades, yet the impact of private equity ownership in rural hospital markets is largely unknown. Existing research points to a correlation between private equity acquisition and increased hospital incomes and charges. Rural hospitals, however, are structurally and operationally different from their urban counterparts, with lower occupancy rates and higher susceptibility to financial distress. This paper seeks to (1) characterize the types of rural hospitals acquired by private equity firms and (2) examine the changes in rural hospital financial, utilization, and survivability outcomes following private equity ownership. Using a 15-year panel of Medicare data, I estimate the impact of 352 private equity deal-hospitals across nine financial and utilization outcomes. Additionally, I estimate the impact of private equity on hospital closures. I find that private equity acquisition improves profitability for both urban and rural hospitals, but the magnitude is smaller for rural hospitals. My results suggest that private equity-owned hospitals increase profits by reducing operating expenses. Among rural hospitals, private equity ownership is associated with fewer discharges and lower occupancy rates, which may be a concern for long-term viability. I find a statistically significant negative correlation between private equity acquisition of rural hospitals and an increased likelihood of closure. PE-acquired hospitals have a negative spillover effect on other hospitals within the same hospital referral region, leading to a higher probability of closing.
Professor Ryan McDevitt, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
Professor Grace Kim, Faculty Advisor
JEL classification: G23, G33, G34, I10, I11
Impact of Language Access Laws on LEP Infant Mortality Rates
by Andrew Ryan Griffin
Abstract
Starting with Executive Order 13166 in 2000, the United States federal government began to address the language disparity issues in health care. Around the same time, several states have begun to pass language access (LA) legislation mandating translation and interpretation services at hospitals for limited English proficient (LEP) individuals. This study uses these multiple discontinuities to evaluate the effect of language access laws on infant mortality rates, adequacy of care, Apgar scores, and the number of prenatal visits from the years 1995 to 2004 for limited English proficient families. I find ambiguous results of language access laws positively impacting infant mortality rates or Apgar scores, but I find clear positive impacts on the adequacy of care and the number of prenatal visits. These findings suggest that language access laws have a clear effect on reducing barriers for limited English proficient mothers, and improving the care mothers receive. Furthermore, there is limited evidence that it improves infant health or outcomes, but the increase of prenatal visits and adequacy of care likely indirectly leads to improving infant mortality rates and Apgar scores. More research is needed into discovering how those mechanisms work and the costs of language services.
Professor Michelle Conolly, Faculty Advisor
JEL Codes: I10, I18, I19
Analysis of Brain Diagnoses and the Incidence of Chronic Traumatic Encephalopathy (CTE)
by Arjun Lakhanpal
Abstract
Chronic traumatic encephalopathy (CTE) has become a significant area of scientific inquiry in relation to various sports with contact exposure, specifically boxing and professional football, resulting from many individuals who participated in these sports being diagnosed with CTE neuropathology after death. This paper contributes to the CTE literature by analyzing the various predictors of the progression of neurodegenerative disorders, including CTE, that are associated with a history of head impact exposure. In addition, it analyzes how manner of death shifts depending on an individual’s clinical brain diagnosis, which is a decision based upon the clinical record and case review of a patient.
Through data from the NIH NeuroBioBank, the VA-BU-CLF Brain Bank, and data self-collected from living individuals with symptoms associated with CTE, this paper explores an analysis of various brain diagnoses through a large control population and small subset of athletes and veterans. Logistic regression models are established to analyze explanatory variables of clinical brain diagnosis, manner of death, and CTE presence and severity.
These logistic regression models confirm previous research surrounding the potential racial influence present in Black populations with schizophrenia related diagnoses and illustrate the degree to which neurodegenerative disorders, specifically Parkinson’s Disease, are influenced by increased age. Specific to CTE, the analysis conducted through the sample population illustrates the influence of an extra year of football played at the professional level, while counteracting existing literature regarding the association between position and CTE.
Professor Jason Luck,Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: I10, Z20, J15
Myocardial Infarction, Health Behavior, and the Grossman Model
by Emma Mehlhop
Abstract
This paper contributes an empirical test of Michael Grossman’s model of the demand for health and a novel application of the model to myocardial infarction (MI) incidence. Using data from the University of Michigan’s Health and Retirement Study (HRS), I test Grossman’s assumptions regarding the effects of hourly wage, sex, educational attainment, and age on health demand along with the effects of new variables describing health behaviors, whether or not a respondent is insured, and whether or not they are allowed sufficient paid sick leave. I use logistic regression to estimate health demand schedules using five different health demand indicators: exercise, doctor visits, drinking, smoking, and high BMI. I apply the Cox Proportional Hazard model to examine two equations for the marginal product of health investment both in terms of propensity to prevent death and to prevent MI, one of the leading causes of mortality in the United States. This study considers the effects of the aforementioned health demand indicators, among other factors, on the marginal product of health investment for the prevention of death compared to the prevention of MI. Additionally, there is significant evidence of a negative effect of health insurance on likelihood of exercising regularly, implying some effect of moral hazard on the health demand schedule.
Professor Charles Becker,Faculty Advisor
Professor M. Kate Bundorf, Faculty Advisor
Professor Grace Kim, Faculty Advisor
Professor Frank Sloan, Faculty Advisor
JEL Codes: I1, I10, I12
The Effects of Leveraged Buyouts on Health Outcomes
by Robert Williams
Abstract
Private equity firms first began acquiring hospitals in the United States during the early 1990s, yet the effects of private equity ownership on patient outcomes and treatment costs are still not clear. Some argue that although private equity firms are adept at improving operating efficiencies and introducing managerial expertise, these cost-cutting measures may come at the expense of patient outcomes.
Because acute myocardial infarctions (AMIs) serve as proxies for patient outcomes and treatment costs, I collect information on 30-day mortality rates and Medicare reimbursements for treatments of AMIs at US Medicare-certified short-term acute care general hospitals from 2014 to 2019. This paper uses fixed effects models to analyze the impact of leveraged buyouts, relative to strategic acquisitions, on patient outcomes. After integrating both hospital and time fixed effects, I find that private equity ownership does not lead to significant changes in Medicare reimbursements or mortality rates for AMI treatments.
Professor Ryan McDevitt, Faculty Advisor
Professor Grace Kim, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: I0, I110, G340
Global Warming and Obesity: The Effect of Ambient Temperature on BMI
By Aakash Jain
Previous research has shown that ambient temperature affects human metabolism and behavior. Inspired by these findings, this study examines the effect of lagged annual temperatures in the United States on average reported BMI. The results indicate that higher temperatures in the future will lead to increases in average BMI. A conservative estimate suggests that a 1 °C increase in temperature sustained for 10 years would result in a 0.15 unit increase in average BMI and an additional $15.5 billion in annual health care expenditure.
Advisor: Billy Pizer, Michelle Connolly | JEL Codes: Q5, Q54, I1, I10
The Cost-Effectiveness of Shared Medical Appointments for Type II Diabetes at Duke Family Medicine
By Lauren Nahouraii
With increasing healthcare expenditures above the rate of inflation, new health care delivery models are needed. Since care for chronic health conditions accounts for a majority of spending, more cost-effective ways to manage these conditions are especially necessary and could be the most effective in decreasing health care costs. Shared medical appointments (SMAs) are a promising solution because they increase patient education through group appointments while simultaneously increasing productivity by allowing a provider to see patients in a group but bill for them individually. In this study, 38 patient volunteers participated in an SMA as part of a pilot program at Duke Family Medicine (DFM). As part of this program, patients were randomly assigned to groups that offered varying versions of an SMA curriculum over the course of 3 years. Data collected included HbA1c scores, number and type of medications, type of insurance and payments, number and type of visit (including hospital admissions, emergency room visits, primary care and specialty visits), laboratory tests completed, and home address. Data was collected during, after, and for the six months prior to starting the SMAs. Data points from six months prior to the SMAs serve as a control. HbA1c served as the measure of health outcome while the rest of the data was used in estimating the total healthcare costs of control and treatment periods. Any changes in HbA1c were converted into changes in quality adjusted life years (QALYs) for the cost-effectiveness calculations. The estimated total costs and changes in QALYs were used to calculate the average cost-effectiveness of both the control and treatment periods. Given the small sample size, the SMAs appeared to be more cost-effective for patients that attended a majority of the SMA sessions. The cost-effectiveness comparison for all patients was inconclusive. This study’s calculations should be repeated once more patients complete SMAs in order to increase the power of the tests and provide conclusive results for all patients.
Advisor: Tracy Falba, Ralph Snyderman | JEL Codes: I10, I12, I13, I18
The Relationship between and Geographic Distribution of Breast Cancer Statistics: Diagnosis, Survival, and Mortality in Selected Areas in the United States, 1973-2004
By Timothy Rooney
Using breast cancer registry data from the United States and regression models controlling for race, marital status, and county-level variation, this research analyzes the connections between these statistics and the geographic variation of each of them. In doing so, it determines that stage of diagnosis has a significant impact on survival likelihood and the likelihood of death due to breast cancer. It also determines that survival reduces mortality likelihood. Additionally, it determines that stage of diagnosis, survival, and mortality all vary geographically, postulating that the reason for this variation is due to lifestyle variation and uneven medical talent distribution.
Advisor: Charles Becker, Michelle Connolly | JEL Codes: I1, I10, I19 | Tagged: Cancer, Diagnosis, Health, Mortality, Survival
Trauma Center Efficacy: Certification Status and its Effect on Traffic Fatalities at Varying Radii
By Robert Van Dusen
The goal of the paper is to better inform policy makers on the optimal placement of trauma center facilities. Below, I examine the effect of Californian trauma centers vs. standard emergency departments on traffic fatalities for 2002 to 2008. Hospital addresses are geocoded and compared to the geographic coordinates of fatal car accidents provided through USDOT in order to create a dependent fatality density variable for every hospital at different radii. Demographic controls for different radii are constructed using ArcGIS
to serve as a model for traffic fatalities.
Advisor: Frank Sloan, Kent Kimbrough | JEL Codes: I1, I10, I18 | Tagged: Healthcare,
Trauma, Trauma Center
