How Well Do Travel Cost Models Measure the Effect of Hospital Mergers?

COMPETITION ECONOMICS llc

How Well Do Travel Cost Models Measure the Effect of Hospital Mergers?

Michael DoaneCompetition Economics LLC

Luke FroebOwen Graduate School of Business Administration

Vanderbilt University

Larry Van HornOwen Graduate School of Business Administration

Institute of Medicine and Public HealthVanderbilt University


Outline of talk

Policy Background

Hospital Merger simulation shows small effects when measured in minutes of travel time

Similarly, cardiac catheterization patients are unwilling to travel more than a few minutes to get to a much lower risk hospital

Discussion


Co-authors

Michael DoaneCompetition Economics LLC

Larry Van HornOwen Graduate School of Business Administration Institute of Medicine and Public HealthVanderbilt University


Number of Community Hospitals,

1988 – 2008

Source: Avalere Health analysis of American Hospital Association Annual Survey data, 2008, for community hospitals.(1) All nonfederal, short-term general, and specialty hospitals whose facilities and services are available

to the public.(2) Data on the number of urban and rural hospitals in 2004 and beyond were collected using coding different from

previous years to reflect new Centers for Medicare & Medicaid Services wage area designations.

(2)

All Hospitals

Urban Hospitals

Rural Hospitals


Number of Hospitals in Health Systems, 2000 – 2008

Source: Avalere Health analysis of American Hospital Association Annual Survey data, 2008, for community hospitals. (1) Hospitals that are part of a corporate body that may own and/or manage health provider facilities or

health-related subsidiaries as well as non-health-related facilities including freestanding and/or subsidiary corporations.


Consolidation incentives are strong

Not-for-profits continue to struggle financially

Merger is a way to cut overhead, improve delivery

Push to form Accountable Care Organizations

Easier for systems to hire physicians

Increased market power of merged hospitals


US antitrust agencies resisted consolidation, but lost most of their merger challenges

Year Case Winner Reason for Decision1994 Adventist Health System West Hospitals DOJ failed to identify a relevant geo market

1995 FTC v. Freeman Hosp Hospitals FTC failed to identify a relevant geo market

1995 U.S. v. Mercy Health System Hospitals FTC failed to identify a relevant geo market

1996 FTC v. Butterworth Health Corp Hospital Not-for-profit merger

1997 U.S. v. Long Island Jewish Med Hospital DOJ failed to identify a relevant geo market

1998 FTC v. Tenet Healthcare FTC FTC PI request granted but later reversed

1999 FTC v. Tenet Healthcare Hospitals FTC failed to identify a relevant geo market

1999 CA ex rel. Lockyer v. Sutter Hospitals Insufficient evidence on relevant market

2005 Evanston Northwestern Health FTC Merger substantially lessened competition


The “shipments” methodology played a big role in the government losses

Shipments methodology led to big geographic markets …

… based on inpatient flow data (“Elzinga-Hogarty Test”)

• Geographic extent of demand – “Little In From the Outside” (LIFO)

• Geographic extent of supply – “Little Out From the Inside” (LOFI)

• Market is the smallest area such that LOFI and LIFO are below, e.g., 25%


FTC’s hospital merger retrospectives

In 2002, Chairman Muris announces studies of consummated hospital mergers

20% price increase found for Evanston• Leads to merger challenge (2004), and relief• Trial discredits “shipments” test• Role of physician consolidation?

What about the other retrospectives?

Rise of “antitrust trolls?”


Post-Evanston: How should we evaluate hospital mergers?

Policy vacuum created by demise of “shipments tests” filled by “hospital merger simulation”

Measures competition between hospitals to get into favored tiers in payer networks

Value of system to network is estimated by travel-cost models of patient demand

Methodology results in smaller markets (4 vs. 10 hospitals)


How can mergers create market power?

Big idea: payers create competition among hospitals by threatening to “steer” patients to hospitals with lower prices

• Mergers can eliminate this competition.

Bargaining theory: If the merger can make payers more eager to reach agreement (by making the alternative worse) then merger can create market power.


FTC uses model to estimate increased bargaining power of merged hospital

3

WHY use hospital merger simulation?

• Judges want to know how much prices will increase

• Judges want to know what patients’ second (and third) choices are for hospitals.– Choice model provides more than just an input

for the WTP/predicted price effects analysis• Allows us to construct diversion ratios

– The essence of unilateral effects analysis

4

WHY use hospital merger simulation (cont)

• Access to incredibly rich data set– Test core antitrust assumption: Prices increase as

market concentration increases.

• Merger simulation models entirely consistent with how these markets actually work– Leverage v. leverage

• Able to isolate and control for many different variables


Post-Evanston FTC Enforcement

2006 Inova/PWHS• FTC challenged, merged share >73% in Northern Virginia

2010 UHS/PSI• Divestitures in a few isolated markets: Las Vegas, Delaware, and

Puerto Rico

13


How Does Hospital Merger Simulation Work?

Four-Step Process:1. Estimate Hospital Choice Model using “travel time” as a proxy for the

“price” that patients “pay” 2. Hospitals’ bargaining power is measured by patient’s willingness to

pay (WTP) for inclusion into network, measured in minutes of travel time.

3. Estimate relationship between hospital WTP and negotiated prices (Price Model)

4. Use Price Model to simulate price effect of proposed merger given predicted change in WTP.


Example: Hypothetical Merger

Hypothetical merger of Univ. of PA Health System (3 hospitals) and Thomas Jefferson Hospital in Philadelphia

Data Source: Pennsylvania “all-payer” data set• Calendar Year 2008• Commercial inpatients only• Treatment for primary Major Diagnoses Category (“MDC”)• 371,570 inpatients


Inpatient Draw Area:University of PA Health System


Inpatient Draw Area:Thomas Jefferson University Hospital


Inpatient Draw Overlap


Step 1: Estimate Hospital Demand

Measure patients’ demand for hospitals as a function of • Travel time (minutes)• Hospital quality (nursing intensity, teaching status, etc.)• Inpatient demographics (age, income, etc.)

Assumes patients choose hospital that gives them the highest utility


Hospital Choices Vary by Zip Code

Univ of PA

Thomas Jefferson

Hospital A Hospital B

Zip Code 1 Zip Code 3

Zip Code 2 Zip Code 4


Appendix A: Estimated Hospital Choice Model

(see next slide)


Variable Coef. Std. Err. Variable Coef. Std. Err. Variable Coef. Std. Err.fp -0.749*** 0.045 cap*child -0.112*** 0.017 time*urinary 0.006*** 0.001fp*male 0.112*** 0.017 cap*age -0.007*** 0.000 time*genital 0.018*** 0.001fp*white -0.025 0.020 cap*income 7.370E-06*** 0.000 time*psych -0.003* 0.001fp*elderly -0.062** 0.029 cap*lstay -0.005*** 0.001 time*liver 0.009*** 0.001fp*child -0.443*** 0.041 cap*ndx 0.016*** 0.002 time*endor 0.029*** 0.001fp*age 0.002*** 0.001 cap*npx 0.016*** 0.002 time*infection 0.018*** 0.001fp*income 0*** 0.000 h_transplant -0.096*** 0.005 time*integ 0.001 0.001fp*lstay 0.004*** 0.001 h_nerv 0.228*** 0.007 time*myelop 0.076*** 0.001fp*ndx 0.038*** 0.004 h_nerv*nerv 0.249*** 0.028 time*injury 0.014*** 0.002fp*npx -0.099*** 0.005 h_cardio 0.309*** 0.006 time*ent 0.018*** 0.002nurse -222.169*** 9.569 h_cardio*cardio 0.104*** 0.014 time*image -0.012*** 0.002nurse*male 28.194*** 3.399 h_labor 0.461*** 0.007 time*male 0.002*** 0.000nurse*white 81.947*** 4.445 h_labor*labor 1.365*** 0.020 time*white 0.012*** 0.001nurse*elderly -26.476*** 5.946 h_mri -0.453*** 0.006 time*elderly -0.001 0.001nurse*child 116.046*** 8.112 h_mri_image 0.665*** 0.062 time*child 0.008*** 0.001nurse*age -0.055 0.168 h_psych 0.278*** 0.005 time*age -4.182E-04*** 0.000nurse*income -0.001*** 0.000 h_psych*psych 2.259*** 0.042 time*income -7.930E-07*** 0.000nurse*lstay -0.517 0.336 time -0.090*** 0.002 time*lstay 2.802E-04*** 0.000nurse*ndx 10.306*** 0.742 time*cardio -0.001 0.001 time*ndx 0.002*** 0.000nurse*npx 11.815*** 1.053 time*labor -0.005*** 0.001 time*npx 0.009*** 0.000cap 0.143*** 0.021 time*resp -0.004*** 0.001 time*fp 0.011*** 0.001cap*male -0.081*** 0.007 time*digest 0.001 0.001 time*nurse 4.694*** 0.125cap*white -0.093*** 0.009 time*muscl 0.036*** 0.001 time*cap -0.002*** 0.000cap*elderly 0.088*** 0.013 time*nerv 0.036*** 0.001

Number of choices: 2 to 18; average: 18,Wald chi2(71) = 174,177.04 ; Prob > chi2 = 0.000Log likelihood = -583,785.21*** significant at 1%; significant at 5%; significant at 10%.


What do the demand estimates mean?

If inpatient passes one hospital to get to another, then value of extra quality > extra travel time

Analogy to environmental economics:• If tourist passes one lake to get to cleaner one, then value of pollution

reduction > extra travel time• Compute value of pollution reduction from increased travel time @ $25/hour

(i.e., opportunity cost of leisure)


Step 2: Compute patients’ willingness to pay (WTP) for each hospital

Question: what is the value of each hospital to a network?

Answer: take the hospital out of the network, and see how much patients are hurt.

• This measures the bargaining power of an individual hospital


Expected travel time for patient with three hospitals to choose from

(1)Choice

(2)Travel Time (min)

(3)Pr (hospital chosen)

(4) = (2) × (3)Expected Travel Time

Hospital 1 10 0.6 6

Hospital 2 20 0.3 6

Hospital 3 30 0.1 3

Total 1.0 10

With hospital 1 in the network, this patient faces an expected travel time of 10 minutes


Expected travel time for a prospective inpatient when Hospital 1 is removed

(1)Choice

(2)Travel Time (min)

(3)Pr (hospital chosen)

(4) = (2) × (3)Expected Travel Time

Hospital 1 10 0.6 6

Hospital 2 20 0.75 15

Hospital 3 30 0.25 7.5

Total 1.0 22.5

With hospital 1 is removed from the network, this patient faces an expected travel time of 22.5 minutes

Value of hospital 1 to patient is 12.5 minutes


Change in WTP: [522,291/(345,963+109,304)] -1 = 14.7%

Thomas Jefferson

U Penn

Combined

0 100000200000300000400000500000600000

WTP

Value of combined U Penn and TJ to network (measured in min. of travel time)


What explains Change in WTP: Look at Patients 2nd and 3rd Choices for Hospitals?

Diversion Ratio for U of PAHospital Prob(Chosen)

Thomas Jefferson 0.26

Hahneman 0.10

Main Line – Lankenau 0.10

Temple Univ 0.06

Main Line – Bryn Mawr 0.06

Abington 0.05

Delaware County Memorial 0.04

Holy Redeemer 0.03

Mercy Fitzgeral 0.03

Albert Einstein 0.03

Diversion Ratio for Thomas JeffersonHospital Prob(Chosen)

Hospital of U of PA 0.16

PA Hospital 0.15

PA Presbyterian 0.07

Main Line – Lankenau 0.07

Hahneman 0.06

Temple Univ 0.05

Abington 0.04

Holy Redeemer 0.04

Delaware County Memorial 0.03

Albert Einstein 0.03


Step 3: Estimate relationship between hospital WTP and “Prices”

Regression analysis: used to quantify relationship between negotiated per-diem prices and hospital WTP

Price

Price = α + β WTP + ε

WTP

Negotiated prices are generally not public but are available to the FTC via 3rd-party CIDs


Step 4: Estimate merger price effect

Change in WTP (%) 14.7%

x

Elasticity of Price with respect to WTP 0.6

=

Merger Price Effect (%) 8.8%


Shipments Test implies much smaller merger effect at 85% threshold

Elzinga-Hogarty Threshold

Pre-Merger HHI Post-Merger HHI Change in HHI

75% (7.5 mile radius) 1454 2326 872

85% (20 mile radius) 939 1387 448


Question: Can we accurately measure hospital demand w/travel-cost approach methodology?

Travel costs are small relative to actual costs

Insurance indemnifies patients

Can patients measure quality?

Do patients know about hospital quality?

Health care is an experience good

Decision biases surrounding of low probability of negative outcomes

Do physicians make the decision?


External validity check: How would environmental economist predict price effect of merger?

67,024 minute change in WTP ÷33,175 patients (=2 minutes/patient)X$25/hour =$0.84/patient of increased value

Average hospital admission charge is about $10,000

Price effect < 0.01%


External validity check: how much would you pay to reduce risk of heart attack (by taking Lipitor)?

Lipitor is a blockbuster drug that reduces risk of a heart attack

• Absolute risk reduction of 1% (Placebo 3% vs. Lipitor 2%)• Huge Demand (> 12 $billion in sales)


External validity check (cont.): how much would you pay to reduce risk of heart attack (by travelling further)?

Heart catheterization mortality rates vary across hospitals from 0% to 9%

Model says that an absolute risk reduction of 1% is worth only 3 minutes of travel time!

• Same risk reduction as Lipitor• Estimated from hospital choice model on 1,477 commercial inpatients in

Philadelphia area in 2008


Heart Catheterization hospital-choice model

Number of Inpatients 1477 1477Number of Hospitals in Choice Set 2 to 10 34Likelihood Function (no predictors) -2094.0 -2940.9Likelihood Function (at convergence) -2038.7 -2847.4

Parameter EstimatesTime -0.025** -0.123***Income*Time -4.41E-07*** -1.04E-07For-Profit Status -0.441*** -0.838***Nursing Intensity -323.014*** -318.638***Capital Intensity 0.13 0.139Transplant Service Offered -0.149 0.046MRI Service Offered -0.710*** -0.927***Teaching Status 0.520*** 0.987***Computed Tomography Service Offered 0.290** 0.569***Cardiac Beds -0.012*** -0.020***Mortality Index -0.131*** -0.088***

Marginal Effect of Time(evaluated at median income) -0.05 -0.13

Implied WTP for of 1% Change in Mortality (valued in minutes) 2.6 0.68

*** significant at 1%, ** significant at 5%

COMPETITION ECONOMICS llc 04/22/2023

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How Well Do Travel Cost Models Measure the Effect of Hospital Mergers?

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