Medical Research and Health Care Financing: Academic Medical Centers Following the 1997 Medicare Cuts

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Medical Research and Health Care Financing
Academic Medical Centers Following the 1997
Medicare Cuts

• Pierre Azoulay
• pa2009_at_columbia.edu
• Columbia UniversityGraduate School of Business
• Wharton School, University of Pennsylvania
• Leonard Davis Institute of Health
  EconomicsFebruary 6th, 2004

Abigail Tay at436_at_columbia.edu Columbia
UniversityDepartment of Economics
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Research Agenda

• Academic Medical Centers (AMCs) play a crucial
  role in the American system of biomedical
  innovation
• Research within AMCs comes from 4 sources
• Public sources mostly NIH, about 65
• Foundations ignored in this paper, about 10
• Industry mostly clinical trials, about 15
• Institutional Funds X-subsidies from
  patient-care activities, about 10
• Cross-subsidies have been a traditional source of
  seed-research funds, especially for
  physician-scientists
• What have been the effects of changes in health
  care financing on the level and composition of
  research in AMCs?

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Two views of X-subsidies

• Old Boys Network
• Substitute with other sources of funding
• Essential Lubricant
• Complement other sources of funding

Financial Slack
X-Subsidies
Research
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Evolution of Extramural NIH Funds, by Degree of
Investigator, 1970-2002
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Research Strategy

• Pure time series analysis will be contaminated by
  secular trends such as the massive expansion of
  the NIH budget during the 1990s
• Cross-sectional comparisons across disease areas
  or research institutions will suffer from omitted
  variable bias (scientific opportunities, etc.)
• We focus on the impact of a discrete shock to
  hospital financesCuts in the Medicare Indirect
  Medical Education (IME) subsidy following the
  Balanced Budget Act of 1997
• Compare grant awards, before and after 1997,
  between hospitals that faced a potential large
  decrease in the level of Medicare reimbursements
  with those that faced merely a modest decrease

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Preview of Results

• Elasticity of NIH grant awards with respect to
  health care reimbursements About .15
• Endowments cushion the effect of the reform
• Only very weak evidence that effect is driven by
  substitution of residents by full-time faculty
• Results consistent with the view that hospital
  X-subsidies complement external sources of
  funding
• Effect shows up too soon
• One would have expected a 1-2 year lag
• Suggests that the reform was anticipated
• No response of industry-funded research activity
• But more affected hospitals see a rebalancing of
  their research portfolio towardsclinical trials
  away from NIH-funded research
• Important differences in the magnitude of the
  response across types of investigators
• MDs MD/PhDs are more affected than PhDs
• Human-subjects research is more affected than
  lab-based research
• Young investigators are more affected than
  experienced faculty members(the result is not
  consistent across measures of experience)

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A Primer on the Medicare PPS System

• Since 1984, Medicare reimburses inpatient care
  prospectively, based on the following formula

Reimbursed Std. Amount DRG weight
(1 Teaching Adjustment Medicaid Adjustment)
IME
DSH
IME a (1 Residents/Beds).405 1
alt1997 1.89 a1998 1.72 agt1999 1.60
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Data

• NIH Consolidated Grant Applicant File
• Clinical trial grant data from FastTrack Systems,
  Inc.
• American Hospital Association Survey
• AAMC Faculty Roster
• HCFA/CMS Cost Reports and IMPACT Files
• Area Resource File
• HMO penetration variable

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Data Issues JHU and Affiliated Hospitals
Johns Hopkins Hospital
Franklin Square Hospital
Howard County General Hospital
Johns Hopkins Bayview Med. Center
Good Samaritan Hospital
Greater Baltimore Med. Center
Sinai Hospital of Baltimore
Johns Hopkins School of Medicine
Basic Science Departments(Anatomy, Microbiology)
Clinical Departments (Medicine, Surgery)
Johns Hopkins Bayview Med. Center
Good Samaritan Hospital
Greater Baltimore Med. Center
Sinai Hospital of Baltimore
Kennedy Krieger Children's Hospital
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2
3
4
5
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Descriptive Statistics163 Hospitals/Hospital
Aggregates
  Obs. Mean Std. Dev. Min. Max.
NIH Grant Awards, Total 1,301 20,221,863 30,200,380 0 186,525,440
NIH Grant Awards, MDs only 1,301 11,275,818 17,139,616 0 105,685,576
NIH Grant Awards, PhDs only 1,301 5,906,290 8,673,354 0 53,164,632
NIH Grant Awards, MD/PhDs only 1,301 2,990,594 5,713,233 0 43,639,076
NIH Grant Awards, clinical only 1,301 10,058,137 15,589,789 0 100,212,728
NIH Grant Awards, nonclinical only 1,301 10,114,564 15,157,144 0 94,685,632
NIH Grant Awards, Career Age lt 5 1,301 2,238,714 5,008,235 0 64,818,416
NIH Grant Awards, Career Age gt 5 1,301 14,237,951 22,118,375 0 156,667,904
NIH Grant Awards, Compet. Funds 1,301 5,297,328 8,167,470 0 55,544,696
NIH Grant Awards, Noncompet. Funds 1,301 13,935,713 21,261,604 0 135,425,456
Industry Grant Awards 1,301 1,580,419 1,591,144 0 10,156,377
Counterfactual Medicare Payments 1,301 73,951,984 56,429,525 2,223,885 362,655,768
Hospital Employment 1,301 4,626 3,384 135 28,643
Hospital Employment in the HSA 1,301 36,550 31,228 1,831 104,031
Population in HSA 1,301 2,502,211 2,461,715 166,977 12,527,938
Fraction of the pop. 65 in HSA 1,301 12.30 2.00 7.04 21.00
Per-capita income in HSA 1,301 27,223 6,725 15,567 53,340
HMO penetration in HSA 1,301 26.60 12.70 1.00 77.80
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Distribution of Average Yearly NIH
Awards,1994-2001
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Distribution of Average Yearly Industry Awards,
1994-2001
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Evolution of Industry Expenditures on Clinical
Trials by Type of Site, 1991-2000
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Parameterizing the Reform

• Regressing research outputs on actual Medicare
  reimbursements is problematic, because hospitals
  can change behavior in response to the reform
• We create a measure of Counterfactual Medicare
  Payments
• Before the Act, Counterfactual Actual
• After the Act, CMP corresponds to the payments
  that would have accrued to the hospital based on
  the new formula if the underlying determinants of
  reimbursement levels (patient mix, residents,
  beds, Medicare discharges) had remained at
  their average pre-reform level
• The CMP variable is defined entirely as of the
  before period nothing that the hospital does
  after the passage of the act (e.g., close beds,
  DRG upcoding...) will affect it

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Mean Counterfactual Medicare Payments(Balanced
PPS Sample, 1994 Real 106)
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Impact of BBA Reform 1
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Impact of BBA Reform 2
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Impact of BBA Reform 3
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Regression Analyses

• b2 lt 0 or gt 0 ?

• Regression weighted by average grant amounts in
  the pre-period (unweighted residuals exhibit
  extreme form of heteroskedasticity)
• Standard errors clustered by medical schools
• Equations estimated jointly by SUR to account for
  contemporaneous correlations of the residuals

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Scatterplot of Unweighted Residuals Against
Chosen Weights
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Table 3 After Dummy Summarizes the Passage of
the Reform
NIH Grants Industry Grants Industry Grants/ Total Grants to MDs MDs Only PhDs Only MD/PhDs Only Clinical Research Non-clinical Research
Ln(CMP) -0.077 -0.295 -0.164 0.068 -0.592 0.348 0.049 -0.142
Ln(CMP) 0.219 0.386 0.327 0.277 0.394 0.643 0.369 0.317
Ln(CMP)After 0.147 0.076 -0.109 0.224 0.125 0.322 0.247 0.097
Ln(CMP)After 0.035 0.062 0.056 0.045 0.063 0.103 0.059 0.051
Ln(Employees) 0.220 0.090 -0.050 0.194 0.284 0.098 0.210 0.167
Ln(Employees) 0.100 0.176 0.172 0.127 0.180 0.293 0.169 0.145
Observations 1,301 1,301 1,158 1,301 1,301 1,301 1,301 1,301
R2 0.89 0.79 0.82 0.92 0.84 0.84 0.88 0.87
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Table 4 Robustness Checks
Basic Specification State-specific Time Trends Hospital Employment/ Year Interactions Total Inpatient Revenue Control
Ln(CMP) -0.077 -0.065 -0.051 -0.115
Ln(CMP) 0.219 0.111 0.089 0.094
Ln(CMP)After 0.147 0.210 0.093 0.151
Ln(CMP)After 0.035 0.080 0.049 0.042
Ln(Employees) 0.220 0.212 0.157 0.203
Ln(Employees) 0.100 0.103 0.080 0.079
Ln(Total Inpatient Revenue) 0.094
Ln(Total Inpatient Revenue) 1.059
R2 0.89 0.90 0.89 0.89
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Table 5 Year-specific Slopes for the Impact of
the Reform
NIH Grants Industry Grants MDs Only PhDs Only MD/PhDs Only Clinical Research Non-clinical Research
Ln(CMP)1995 0.007 0.125 0.018 -0.195 0.216 0.048 -0.090
Ln(CMP)1995 0.067 0.118 0.085 0.120 0.196 0.113 0.097
Ln(CMP)1996 0.030 0.131 0.077 -0.237 0.327 0.078 -0.075
Ln(CMP)1996 0.067 0.119 0.085 0.121 0.197 0.113 0.097
Ln(CMP)1997 0.046 0.103 0.097 -0.174 0.466 0.035 -0.054
Ln(CMP)1997 0.068 0.120 0.086 0.122 0.199 0.115 0.099
Ln(CMP)1998 0.114 0.118 0.261 -0.094 0.523 0.222 -0.004
Ln(CMP)1998 0.068 0.121 0.087 0.123 0.200 0.116 0.099
Ln(CMP)1999 0.138 0.250 0.289 -0.022 0.764 0.278 0.049
Ln(CMP)1999 0.068 0.120 0.086 0.123 0.200 0.115 0.099
Ln(CMP)2000 0.153 0.202 0.301 -0.043 0.676 0.288 0.056
Ln(CMP)2000 0.069 0.122 0.087 0.124 0.202 0.116 0.100
Ln(CMP)2001 0.280 0.093 0.245 0.047 0.349 0.371 0.069
Ln(CMP)2001 0.070 0.123 0.088 0.125 0.203 0.117 0.101
Ln(Employees) 0.229 0.084 0.180 0.303 0.035 0.222 0.173
Ln(Employees) 0.101 0.178 0.128 0.181 0.294 0.170 0.146
R2 0.89 0.79 0.92 0.84 0.84 0.88 0.87
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Are X-subsidies Driving the Effect? 1
Endowment Below Median Endowment Above Median
Ln(CMP) -0.120 -0.061
Ln(CMP) 0.197 0.103
Ln(CMP)After 0.182 0.096
Ln(CMP)After 0.072 0.031
Ln(Employees) 0.420 0.122
Ln(Employees) 0.266 0.056
Observations 656 645
R2 0.83 0.99
Note Endowment Measure is the sum of investment
income and contributions, bequests and gifts
during the 4 years before the reform.
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Are X-subsidies Driving the Effect? 2
Dep. Variable Log of number of FTE Residents
All Hospitals Hospitals Above the BBA Cap in the Pre-Reform Period Hospitals Below the BBA Cap inthe Pre-Reform Period
Ln(CMP) 0.250 0.158 0.381
Ln(CMP) 0.134 0.136 0.237
Ln(CMP)After -0.005 -0.038 0.022
Ln(CMP)After 0.018 0.023 0.022
Ln(Beds) 0.559 0.574 0.427
Ln(Beds) 0.158 0.189 0.117
Observations 1301 640 661
R2 0.98 0.99 0.98
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Concluding Thoughts

• Our results do not suggest that cutting the IME
  subsidy was a bad idea rather, we highlight
  unintended consequences of the reform
• Health economists have examined how insurance
  type, for profit/not-for-profit care, etc.
  influence current health outcomes
• Meta-analysis of this literature health care
  financing does not seem to explain much once
  selection issues are dealt with adequately
• Our results suggest that financing may affect
  future health outcomes through its effect on the
  pace of medical progress
• Congress, NIH, academics often focus on the
  efficiency of the horizontal allocation of public
  research funds across diseases (Lichtenberg,
  2001)
• But the imbalances along the vertical chain of
  biomedical innovation may ultimately be of
  greater importance

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Youngsters vs. Old-timersContradictory Results
First Grantees Repeat Grantees Career Age lt 5 years Career Age gt 5 years No R01 Yet At least One R01 lt 500K cum. funding gt 500k cum. funding
Ln(CMP) -0.968 -0.086 -0.843 0.865 -0.179 -0.057 -0.285 -0.339
Ln(CMP) 0.858 0.239 0.777 0.398 0.394 0.325 0.404 0.367
Ln(CMP)After 0.016 0.158 0.446 0.034 0.188 0.206 0.182 0.413
Ln(CMP)After 0.138 0.038 0.125 0.064 0.063 0.052 0.065 0.059
Ln(Employees) 0.062 0.226 -0.488 0.274 0.102 0.307 0.149 0.422
Ln(Employees) 0.392 0.109 0.355 0.182 0.180 0.149 0.184 0.168
R2 0.66 0.88 0.80 0.90 0.77 0.87 0.83 0.99
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Competing vs. Noncompeting FundsCounterintuitive
Results
Competing Funds Noncompeting Funds
Ln(CMP) -0.137 0.012
Ln(CMP) 0.436 0.259
Ln(CMP)After 0.088 0.125
Ln(CMP)After 0.070 0.042
Ln(Employees) 0.066 0.290
Ln(Employees) 0.199 0.118
R2 0.83 0.92