Loading…

Flash Player 9 (or above) is needed to view presentations.
We have detected that you do not have it on your computer. To install it, go here.

Like this presentation? Why not share!

Gaynor.slides.10.22.10

on

  • 233 views

 

Statistics

Views

Total Views
233
Views on SlideShare
232
Embed Views
1

Actions

Likes
0
Downloads
0
Comments
0

1 Embed 1

http://ldi.upenn.edu 1

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Gaynor.slides.10.22.10 Gaynor.slides.10.22.10 Presentation Transcript

    • Death by Market Power: Reform, Competition and Patient Outcomes  in the British National Health Service Martin Gaynor Carnegie Mellon University, University of Bristol, RAND, & NBER Rodrigo Moreno‐Serra Imperial College London  Carol Propper Imperial College London & University of Bristol Leonard Davis Institute of Health Economics University 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. Theory a. 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 mixed a. Regulated prices i. Medicare – competition improves quality (e.g., Kessler & McClellan,  2000) b. Market determined prices i. 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 effects a. ‘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   HHI   s n 2 i 1 i 3. Post‐policy a hospital in a less concentrated market  faces greater exposure to the policy.
    • 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 year iii. 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
    • Data, cont’d. 2. Measures of quality and performance a. 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 sources a. NHS staffing data b. Small area characteristics (wages, mortality)
    • Measures of Concentration 1. HHIs for hospitals based on patient flows HHI   s n 2 i 1 i 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
    • 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 Foot 28 Day AMI Mortality Rate and HHI
    • The Paper While Standing on One Foot 28 Day AMI Mortality Rate and HHI
    • Still on One Foot  28 Day All Causes Mortality 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 % % change change (2003- (2003- 2003 2007 07) 2003 2007 07) Number of elective 33,985 38,274 12.6% 41,398 45,132 9.0% admissions Average distance 11.4 11.7 2.4% 10.0 10.1 1.1% travelled by patients Share of patients 0.37 0.39 5.4% 0.45 0.43 -4.4% bypassing nearest hospital Number of 33 33 32 32 hospitals
    • Hospitals More Exposed to Policy are  Attracting More Patients Market concentration: HHI (2003) Low (bottom quartile) High (top quartile) % % change change (2003- (2003- 2003 2007 07) 2003 2007 07) Number of elective 21,757 26,924 23.8% 55,253 61,049 10.5% admissions Average distance 8.1 8.3 2.3% 15.5 15.5 0.5% travelled by patients Share of patients 0.45 0.46 2.2% 0.47 0.47 0.0% bypassing nearest hospital 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 =  + I(t=2007) +I(t=2007)*HHIit +  HHIit + Xit i +it
    • 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) 30 day 28 day AMI 28 day AMI mortality 28 day all mortality Attendance mortality rate causes rate Patients s rate (on or after mortality (in- MRSA waiting spending (in- discharge, rate hospital, bacterae 3 less than hospital, ages 35- (in- excluding mia months 4 hours in ages 55+) 74) hospital) AMI) rate or more 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 (1) (2) (3) (4) (5) (6) Expenditure and Length-of-stay and admissions productivity Non- Mean Elective elective Operating length-of- Total admissions admissions Operating expenditure stay admissions (share of (share of expenditure (£1,000) (days) (number) total) total) (£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 33
    • 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) Qualified clinical Index of Multiple Charlson 30 day AMI 28 day Doctors staff Area Deprivation index 28 day AMI mortality rate all-cause AMI (share of (share of standardized (average for (average for mortality rate (on or after mortality Total admissions clinical clinical mortality Case patients’ areas of admissions at (in-hospital, discharge, rate Covariate admissions (ages 55+) staff) staff) rate mix residence) the hospital) ages 55+) ages 35-74) (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 28 day all-cause Mean mortality rate mortality rate length-of-stay Robustness test (in-hospital, ages 55+) (in-hospital) (days) 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) Observations 250 309 309 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) Observations 250 323 323 4. Controlling for the Charlson index 0.246*** 0.067** 0.239*** (0.084) (0.027) (0.060) Observations 250 323 323 5. Controlling for the Index of Multiple Deprivation 0.278*** 0.067** 0.263*** (0.085) (0.029) (0.061) Observations 250 323 323 6. Controlling for surpluses/deficits 0.242** 0.076** 0.229*** (0.093) (0.030) (0.066) Observations 236 302 302 7. All hospitals (weighted by number of admissions) 0.138** 0.069*** 0.261*** (0.069) (0.024) (0.061) Observations 299 323 323 8. Using levels of the dependent variable and HHI 0.170** 0.069*** 0.197*** (implied elasticity) Observations 250 323 323 9. Controlling for income (male wage in area) 0.247*** 0.061** 0.258*** (0.086) (0.029) (0.062) Observations 248 319 319 10. Controlling for the share of urgent ambulance calls 0.238** responded within eight minutes (0.100) Observations 233
    • Magnitude of Effects 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 41
    • No change in patient type except for  IMD for good hospitals Change 2007-2005 (1) (2) (3) (4) (5) (6) Mean Number of distance Mean Elective MSOAs travelled IMD Charlson Number of admissions (electives) (electives) ranking index 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.  42
    • Competitive hospitals in 2005 not attracting observably  sicker/different patients but are attracting more patients Change 2007-2005 (1) (2) (3) (4) (5) (6) Mean Number of distance Mean Elective MSOAs travelled IMD Charlson Number of admissions (electives) (electives) ranking index 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.    43
    • Predicted HHIs based on predicted patient flows based on  MNL model of hospital choice  nkj  2 K ˆ J  njk  ˆ HˆIj  HˆIk, H   H HˆIk   H  n k  ˆj  1 j  n  1 ˆk n nk J nk nk nj ˆij , ˆ  nk ˆij  nk, ˆ  1 nkj njk ˆij ˆ ˆ  i1 i1 j1 i1 i1 44