Gaynor.slides.10.22.10

Death by Market Power:Reform, Competition and Patient Outcomes

in the British National Health Service

Martin GaynorCarnegie Mellon University, University of Bristol, RAND, & NBER

Rodrigo Moreno-SerraImperial College London

Carol PropperImperial College London & University of Bristol

Leonard Davis Institute of Health EconomicsUniversity of Pennsylvania

October 22, 2010

1

INTRODUCTION

Motivation

1. Health care reform is happening in most developed countries: U.S., U.K., Germany, Netherlands, Belgium, Israel, Australia,...

2. Market-oriented approaches to health care are an important reform model outside U.S.

a. Lack of strong research evidence w.r.t. quality.

b. Price not relevant in most systems outside the U.S., or for U.S. Medicare program (20% of U.S. spending).

3

Motivation, cont’d.

3. In U.S. consolidation in markets has led to questions about functioning of markets in health care.

a. How well are markets working?

b. Could further consolidation promoted by health reform be harmful?

4. Need to add to knowledge about conditions under which markets might work.

a. Quality, cost, prices (U.S.).

Competition and Quality in Health Care

1. Theorya. Regulated prices – competition increases quality (if price >

marginal cost; e.g., regulated airline models).i. Quality elasticity of firm’s demand increasing in the # of firms.ii. Competition increases management effectiveness, thereby increasing

quality.b. Market determined prices – anything can happen.

2. Evidence is mixeda. Regulated prices

i. Medicare – competition improves quality (e.g., Kessler & McClellan, 2000)

b. Market determined pricesi. U.S. private markets – not so clear (Volpp et al., 2003)ii. U.K. 90s reforms – competition reduced quality (Propper et al, 2008)

3. Little evidence from policies designed to introduce competition

Our Contribution

1. Most empirical evidence on the impact of competition on quality uses variation in market structure across existing hospital markets. a. We’d like to randomly assign hospitals to varying degrees

of competition.b. Clearly not the case.

2. We exploit a policy “experiment” (NHS) to assess the impact of competition.a. Examine a range of hospital outcomes (measures of

quality, quantity/access, spending).

3. Find that lower market concentration is associated with higher quality without a commensurate increase in expenditure.

The NHS Reforms

1. 1997- buyers and sellers operated under co-operation and negotiated annual budgets on price and volume.

a. Patients had little choice of hospital.

b. Quality not contracted on (except waiting times).

2. Policy change initiated in 2003, put in place in 2006

3. GoaI – to promote competition among hospitals

NHS Reforms, cont’d.

4. Key elements

a. ‘Choose and Book’ – patients must be offered choice of 5 hospitals.

b. Payment by Results (PbR) - movement from negotiated to fixed prices (HRGs - similar to U.S. DRGs).

i. PbR accounts for almost 70% of activity.

c. Reward /Penalties for Performance.i. Foundation Trust Status – Retain net income.

ii. Poor performance – management replacment, closure, merger.

Expected Effects of the Reform

1. Expected effectsa. ‘Choose and Book’ – increase elasticity of demand facing

hospitals.

b. PbR – change conduct .i. Hospitals paid for activity.

ii. Focus on quality as prices fixed.

2. Do hospitals have incentives to respond?a. Not for profit, annual budget constraint.

b. Poor financial & clinical performance heavily penalised.

c. PbR system is very highly geared.i. Levels of prices key.

What We Do

1. Exploit policy change in NHS 2006 to undertake difference in difference analyses .

2. Use time periods before/after reform and variation in market concentration.

a. Before/After: 2003/2007

b. Concentration: More/Less Concentrated

3. Post-policy a hospital in a less concentrated market faces greater exposure to the policy.

2

1 i

n

i sHHI

Data

1. HES (Hospital Episode Statistics) data from the NHS (http://www.hesonline.nhs.uk/Ease/servlet/ContentServer?siteID=1937)

a. Data on all admissions to NHS hospitals in England. i. Standard hospital discharge data set: diagnoses,

procedures, patient characteristics, location,etc.ii. ~13 million records per yeariii. We use data on ~160 short term general hospitals per

year

b. We use hospital level data for 2 years – 2003/04, 2007/08

c. Used to construct measures of concentration and some outcome measures

http://www.hesonline.nhs.uk/Ease/servlet/ContentServer?siteID=1937

Data, cont’d.

2. Measures of quality and performancea. Some calculated from HES data (e.g. in-hospital

deaths within 28 days of admission for various treatments, deaths in all locations after AMI admission, LOS)

b. Some derived from official data on hospital performance (e.g. waiting times data, CQC data)

3. Data from administrative sourcesa. NHS staffing data

b. Small area characteristics (wages, mortality)

Measures of Concentration

1. HHIs for hospitals based on patient flows

1. Built up from small area (~ 7000 persons) patient flows to hospitals

a. Calculate HHIs at MSOA level for 2003 and 2007

b. Use all non-emergency admissions

c. Allow market to be whole country

2. Aggregate to hospitals based on share of patient flows to hospital from each MSOA

2

1 i

n

i sHHI

WHAT THE RAW DATA SHOW

Raw Data

1. Did mortality rates go up (more) in more concentrated markets after the reform?

2. Did concentration change (before/after reform)?

3. Did demand change post-reform?

The Paper While Standing on One Foot28 Day AMI Mortality Rate and HHI

Still on One Foot 28 Day All Causes Mortality and HHI

Decrease in Concentration

Levels and Changes in Concentration by Location

Did Demand Change Post Reform?

1. Examine changes in patterns of patient care seeking 2003-07 by:

a. Quality of Hospitals (top vs. bottom quartiles of AMI mortality rate in 2003)

b. Exposure to competition (bottom vs top quartile

of HHI in 2003)

Better Hospitals are Attracting More Patients

AMI mortality rate (2003)

Bottom quartile Top quartile

2003 2007

%

change

(2003-

07) 2003 2007

%

change

(2003-

07)

Number of elective

admissions

33,985 38,274 12.6% 41,398 45,132 9.0%

Average distance

travelled by

patients

11.4 11.7 2.4% 10.0 10.1 1.1%

Share of patients

bypassing nearest

hospital

0.37 0.39 5.4% 0.45 0.43 -4.4%

Number of

hospitals

33 33 32 32

Hospitals More Exposed to Policy are Attracting More Patients

Market concentration: HHI (2003)

Low (bottom quartile) High (top quartile)

2003 2007

%

change

(2003-

07) 2003 2007

%

change

(2003-

07)

Number of elective

admissions

21,757 26,924 23.8% 55,253 61,049 10.5%

Average distance

travelled by patients

8.1 8.3 2.3% 15.5 15.5 0.5%

Share of patients

bypassing nearest

hospital

0.45 0.46 2.2% 0.47 0.47 0.0%

Number of hospitals 41 41 40 40

REGRESSION ANALYSIS

Econometric Strategy

1. Exploit policy change in NHS 2006 to undertake difference in difference analyses .

2. Use time periods before/after reform and variation in market concentration.

3. Identification from cross sectional and time series variation.

4. Policy effect = Market Concentration* Policy On (2007).a. Parameter δ in regression.

qit = a + bI(t=2007) +dI(t=2007)*HHIit + g HHIit + g2Xit+ mi +xit

Econometric Issues

1. There may remain concerns over endogeneity of concentration + patient heterogeneity.

2. Control for patient heterogeneity with observables.a. Patient age, sex, severity (Charlson index) .b. Local area health, income.c. Include hospital fixed effects.

3. Replace actual HHI with a measure of market structure based on factors unrelated to quality or unobserved patient heterogeneity.

4. Also concerns about whether DiD assumptions are met.a. Are there pre-existing differences (observable and

unobservable) between hospitals with different market structures?

Predicted HHI

1. Predicted HHIs from predicted patient flows from estimated MNL model of hospital choice.a. a la Kessler and McClellan (2000).b. Choice in MNL model depends on:

i. hospital characteristics (size, teaching status), ii. differential distance from patient’s MSOA centroid to

hospital,iii. patient characteristics (age, sex, level of co-morbidity).

c. Choice set – all hospitals within 100km extended to ensure that there is always a first and second choice within hospital type (size, teaching) with minimum of 50 admissions.

Tests of Difference in Difference Assumptions

1. If we find a relationship between mortality and market structure is it due to the policy or to pre-existing differences between hospitals with different market structures?

2. We examine:a. bivariate associations between the observed baseline conditions and the

subsequent four year change in the HHI;i. Admissions, AMI admissions, doctors, clinical staff, area mortality rate, case mix,

Index of Multiple Deprivation, Charlson Index.

b. bivariate associations between the initial levels of mortality and the subsequent changes in market structure.

i. In-hospital AMI mortality, 30 day AMI mortality, In-hospital all-cause mortality.

3. If the change in HHI is associated with pre-existing differences this may indicate that hospitals that differ in HHI growth may also differ in unobserved factors.

4. None of the associations signficantly different from zero.

29

Regression Results

1. Mortality, Waiting Times

2. Quantity, Expenditure

3. Robustness Checks

30

DiD Estimates of Market Structure on Outcomes and Waiting Times

(1) (2) (3) (4) (5) (6) (7)

28 day

AMI

mortality

rate

(in-

hospital,

ages 55+)

30 day

AMI

mortality

rate

(on or after

discharge,

ages 35-

74)

28 day all

causes

mortality

rate

(in-

hospital)

28 day

mortality

rate

(in-

hospital,

excluding

AMI)

MRSA

bacterae

mia

rate

Patients

waiting

3

months

or more

Attendance

s

spending

less than

4 hours in

A&E

DiD coefficient 0.246*** 0.313** 0.069** 0.066** -0.110 0.078 -0.005

(0.084) (0.116) (0.027) (0.028) (0.118) (0.167) (0.011)

Hospitals 133 133 162 162 161 162 150

Observations 250 250 323 323 318 323 299

Estimated Effect of the Policy

1. Hospitals in less concentrated markets had significantly lower mortality rates post-reform than those in more concentrated markets.

a. The policy “worked.”

2. 10% fall in HHI associated with a 2.46% reduction in in-hospital AMI mortality rate.

3. 1/3rd of a percentage point at mean AMI mortality rate (13.2%).4. Similar to #s from previous work.

a. Kessler and McClellan (2000) i. Change from top to bottom quartile of HHI leads to 3.37 percentage point

decrease in AMI death rate. Our equivalent #: 3.61.

b. Cooper et al. (2010)i. 1 s.d. change leads to 0.3 percentage point reduction in AMI death rate. Our

#: 0.33.

32

Policy Impacts on LOS, Admissions, and Expenditure

33

(1) (2) (3) (4) (5) (6)

Length-of-stay and admissions

Expenditure and

productivity

Mean

length-of-

stay

(days)

Total

admissions

(number)

Elective

admissions

(share of

total)

Non-

elective

admissions

(share of

total)

Operating

expenditure

(£1,000)

Operating

expenditure

(£1,000)

per admission

DiD coefficient 0.254*** -0.012 -0.005 -0.001 0.007 0.014

(0.059) (0.031) (0.017) (0.024) (0.072) (0.074)

Hospitals 162 162 162 162 162 162

Observations 323 324 324 324 319 319

Robustness Checks

1. Results may be driven by pre-existing differences between hospitals that are correlated with market structure.a. Placebo test using 2001 as before policy and 2003 as

after policy – insignificant.

2. Estimation using only pre-policy variation in market structure.a. HHI*2007 – significant.

3. Add further controls for patient heterogeneity; income shock from PbR; local area economic conditions (male wage, ambulance speeds).a. DiD estimates still significant, magnitudes almost

unchanged.

34

Did the Policy Matter?

1. Benefits from the observed change in market structure post reform.a. 3,354 life years saved = £227 million (=$350mil).

2. Cost of being in a concentrated market compared to being in a less concentrated one.a. An HHI of 2,000 less (=one s.d.) implies a saving of £3.7

billion (=$5.7 bil).

3. NHS budget is £100 billion (=$154 bil). a. Impact is 0.2% of NHS budget.b. Immediate impact is small, but we only value deaths

averted and longer term impact of reducing concentration considerably larger.

Conclusions

1. Robust evidence that under a regulated price regime, within two years a pro-competitive policy resulted in:a. an improvement in clinical outcomes, as measured in

death rates,

b. reduction in length of stay,

c. no increase in expenditures.

2. Conclude: policy appears to have saved lives and did not (measurably) increase costs.

3. Competition can be an important mechanism for enhancing the quality of care.

4. Monopoly kills.

Additional slides

Hospitals Used by GPs and Distances Travelled by Patients

Tests of DiD Assumptions

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

Covariate

Total

admissions

AMI

admissions

(ages 55+)

Doctors

(share of

clinical

staff)

Qualified

clinical

staff

(share of

clinical

staff)

Area

standardized

mortality

rate

Case

mix

Index of Multiple

Deprivation

(average for

patients’ areas of

residence)

Charlson

index

(average for

admissions at

the hospital)

28 day AMI

mortality rate

(in-hospital,

ages 55+)

30 day AMI

mortality rate

(on or after

discharge,

ages 35-74)

28 day

all-cause

mortality

rate

(in-hospital)

Coefficient -0.624 -0.051 5.329 -10.233 -1.695 -0.001 81.732 -1.080 -1.808 47.847

(0.642) (0.086) (8.792) (7.947) (1.936) (0.003) (72.197) (4.751) (6.623) (42.840)

P-value for Wald

test 0.129

Observations 162 151 161 161 162 162 162 162 130 130 162

Robustness tests(1) (2) (3)

28 day AMI

mortality rate

(in-hospital, ages 55+)

28 day all-cause

mortality rate

(in-hospital)

Mean

length-of-stay

(days) Robustness test

1. Baseline 0.246*** 0.069** 0.254***

(0.084) (0.027) (0.059)

Observations 250 323 323

2. Placebo DiD test for 2001-2003 -0.047 0.005 -0.036

(0.077) (0.027) (0.047)


3. Using time invariant pre-reform HHI level (2003) 0.216*** 0.066** 0.245***

as market structure measure (0.079) (0.028) (0.059)


4. Controlling for the Charlson index 0.246*** 0.067** 0.239***

(0.084) (0.027) (0.060)


5. Controlling for the Index of Multiple Deprivation 0.278*** 0.067** 0.263***

(0.085) (0.029) (0.061)


6. Controlling for surpluses/deficits 0.242** 0.076** 0.229***

(0.093) (0.030) (0.066)


7. All hospitals (weighted by number of admissions) 0.138** 0.069*** 0.261***

(0.069) (0.024) (0.061)


8. Using levels of the dependent variable and HHI 0.170** 0.069*** 0.197***

(implied elasticity)


9. Controlling for income (male wage in area) 0.247*** 0.061** 0.258***

(0.086) (0.029) (0.062)


10. Controlling for the share of urgent ambulance calls 0.238**

responded within eight minutes (0.100)

Observations 233

Magnitude of Effects

41

28 day

mortality rate

(all causes)

Panel A - Observed magnitudes

Average number of admissions (2003/04) 63,094

Average number of deaths (2003/04) 1,135.1

Average mortality rate (2003/04) (%) 1.799%

Average number of admissions (2007/08) 72,558

Average number of deaths (2007/08) 1,053.5

Average mortality rate (2007/08) (%) 1.452%

Average change in deaths (2003-07) (positive = deaths averted) 81.5

Average decrease in predicted HHI (2003/04-2007/08) -118

Panel B - Continuous HHI: magnitudes implied by estimated coefficient (summary stats refer to HHI 03)

Baseline coefficient (elasticity) (%) 0.069

Scenario 1: Average decrease in HHI (Policy impact)

Implied counterfactual percentage increase in the mortality rate 2007/08 per hospital (for elasticity calculated at mean HHI) 0.2%

Total lives saved for the whole sample of hospitals (N = 162) 327

Total number of years of life saved for the whole sample of hospitals 3,354

Total savings in £million (value of year of life = £60,000/p.a.) £201

Scenario 2: One standard deviation increase in HHI 03 (= increase of 1928 units, from 4,353 to 6,281 in the whole sample)

Implied counterfactual percentage increase in the mortality rate 2007/08 per hospital (for elasticity calculated at mean HHI) 3.1%

Total lives saved for the whole sample of hospitals (N = 162) 5,336

Total number of years of life saved for the whole sample of hospitals 54,771

Total savings in £million (value of year of life = £60,000/p.a.) £3,286

No change in patient type except for IMD for good hospitals

42

Change 2007-2005

(1) (2) (3) (4) (5) (6)

Elective

admissions

Number of

MSOAs

(electives)

Mean

distance

travelled

(electives)

Mean

IMD

ranking

Charlson

index

Number of

diagnoses

Mean waiting time 56.434* 0.694** 0.004* 13.836*** 0.00002 0.001

(elective admissions) (33.680) (0.295) (0.002) (3.061) (0.00013) (0.001)

Overall quality of services 1165.859 21.762** 0.078 207.347** -0.00030 0.024

(score) (897.260) (9.294) (0.076) (95.395) (0.00519) (0.031)

In-hospital mortality rate -942.177 14.816* -0.175* 14.927 -0.01229* -0.005

(all causes) (1255.291) (8.718) (0.103) (91.709) (0.00638) (0.050)

In-hospital mortality rate -91.980 -1.035 0.001 26.533 0.00086 0.002

(AMI) (227.052) (1.029) (0.011) (21.365) (0.00131) (0.005)

Teaching hospital status 1234.181 20.211 -0.098 335.378** -0.00041 -0.034

(2088.132) (17.763) (0.133) (143.230) (0.01348) (0.042)

Note: Index of Multiple Deprivation (over all patients), where patients in the most deprived locality in the year are attributed

the ranking of 1 and higher values are attributed to patients living in less deprived areas.

Competitive hospitals in 2005 not attracting observably sicker/different patients but are attracting more patients

43

Change 2007-2005

(1) (2) (3) (4) (5) (6)

Elective

admissions

Number of

MSOAs

(electives)

Mean

distance

travelled

(electives)

Mean

IMD

ranking

Charlson

index

Number of

diagnoses

Level of HHI -483.360 -2.620 0.032 41.200 0.002 -0.003

(505.402) (2.788) (0.025) (43.781) (0.002) (0.015)

Indicator for bottom 3156.728** 18.499* -0.084 -139.657 0.001 0.004

quartile of HHI (1513.533) (10.949) (0.096) (162.770) (0.008) (0.056)

Number of hospitals 162 162 162 162 162 162

Note: Index of Multiple Deprivation (over all patients), where patients in the most deprived locality in the year are attributed

the ranking of 1 and higher values are attributed to patients living in less deprived areas.

44

Predicted HHIs based on predicted patient flows based on MNL model of hospital choice

2

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Gaynor.slides.10.22.10

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Transcript of Gaynor.slides.10.22.10