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  • The figure for NCHOD AMI and 1sd increase in HHI is 13% increase in death rate.
    For 1sd decrease in HHI, Cooper et al. estimate 0.3% reduction in AMI deaths in 2006, plus 0.3% per year afterwards; or 1% for the entire period 2006-08. Our results imply a 10% reduction for the period 2003-07. Mean AMI death rate = 13.2%.

  • 1sd decrease in HHI: Depending on estimate of LOS (c. £250-£1,000) and evaluated at mean admission per hospital of 72,500 saving = £2.5-£10m per hospital.
  • HHI falls by118 2003-2007. Avg. hosp. mortality would fall 0.2% from this change in HHI. Avg. age of death for the people who die in-hospital is 77. 77 yr. old male in Britain has additional life expectancy of 9.5 years and female 11 yrs.
  • The table reports coefficients from separate OLS regressions where the dependent variable is the change between 2007-05 in the levels of the variable displayed in the corresponding column, and the regressor is the level of the variable (measured in 2005) displayed in the respective row. No age/gender controls for cols (2) and (3).

  • Transcript

    • 1. 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
    • 2. INTRODUCTION
    • 3. 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
    • 4. 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.).
    • 5. 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
    • 6. 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.
    • 7. 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
    • 8. 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.
    • 9. 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.
    • 10. 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 
    • 11. 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
    • 12. 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)
    • 13. 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 
    • 14. WHAT THE RAW DATA SHOW
    • 15. 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?
    • 16. The Paper While Standing on One Foot 28 Day AMI Mortality Rate and HHI
    • 17. The Paper While Standing on One Foot 28 Day AMI Mortality Rate and HHI
    • 18. Still on One Foot 28 Day All Causes Mortality and HHI
    • 19. Still on One Foot 28 Day All Causes Mortality and HHI
    • 20. Decrease in Concentration
    • 21. Levels and Changes in Concentration by Location
    • 22. 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)
    • 23. 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
    • 24. 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
    • 25. REGRESSION ANALYSIS
    • 26. 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
    • 27. 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?
    • 28. 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.
    • 29. 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
    • 30. Regression Results 1. Mortality, Waiting Times 2. Quantity, Expenditure 3. Robustness Checks 30
    • 31. 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
    • 32. 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
    • 33. 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
    • 34. 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
    • 35. 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.
    • 36. 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.
    • 37. Additional slides
    • 38. Hospitals Used by GPs and Distances Travelled by Patients
    • 39. 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
    • 40. 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) 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
    • 41. 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
    • 42. 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.
    • 43. 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. 44 Predicted HHIs based on predicted patient flows based on MNL model of hospital choice 2 1 1 1 1 1 1 1 ˆ ˆ ˆ ˆ ˆ, ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ, 1 , k k k K J kj jk j k k k jj k n n nn J j ij k ij k kj jk ij i i j i i n n HHI HHI HHI n n n n n n n                                  