Underuse, Overuse, Comparative Advantage and Expertise in Healthcare

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1 Underuse, Overuse, Comparative Advantage and Expertise in Healthcare Amitabh Chandra Harvard and NBER Douglas Staiger Dartmouth and NBER

2 Highest Performance Lowest Performance Source: Chandra, Staiger and Skinner (IOM, 2010)

3 Large variations in utilization and outcomes across hospitals Variations found in other countries and even within hospitals Variations in utilization not consistently related to patient outcomes overuse? But higher utilization is also associated with higher returns to treatment expertise (TFP)? Economists know about heterogeneity from comparative-advantage!

4 Underuse, Overuse, Expertise and Comparative Advantage, as Explanations Underuse: Marginal patient would benefit from more treatment in low-use hospitals Overuse: Marginal patient is harmed by treatment in high-use hospitals Expertise: Some hospitals have an absolute advantage (higher TFP) from treatment Comparative Advantage: Hospitals with greater relative benefit from treatment optimally treat more patients

5 Basic Setup Expertise in Medical Management Outcome if Treated Medically Outcome if Treated Intensively Observed Outcome Expected Benefit from Treatment Y ih 0 h 0 X ih 0 ih 0 Y ih 1 h 1 X ih 1 ih 1 Y ih Y ih 0 Y ih T ih h 0 X ih 0 Y ih T ih ih 0 Y ih h X ih ih, where h h 1 h 0, 1 0, and ih ih 1 ih 0 Expertise in Intensive Management Difference in productivities represents hospital s comparative advantage in providing the treatment. Hospitals may have a comparative advantage in providing the treatment because of being good at the treatment or being bad at caring for patients without the treatment.

6 Roy Model B = Benefit from treatment = Threshold that must be exceeded to receive treatment τ h α h is Comparative Advantage Benefit = Xβ + α h + e τ h =0 Higher reflects treatment optimal care: for similar Pr(Treatment=1) = Pr (Benefit > τ h ) Patients patients receive can treatment be due to = Pr (Xβ + α h + e > τ h ) if positive comparative benefit advantage or = Pr (Xβ + (α h - τ h ) > -e) lower threshold = Pr (I > -e), where I = Xβ + (α h - τ h ) But look at treatment effect on the treated (TT): E(Benefit Treatment=1) = Xβ + α h + E(e I > -e) = I + τ h + E(e I > -e) = g(i) + τ h Conditional on treatment Higher benefit, conditional propensity (I), differences on treatment propensity in TT due to threshold, not means underuse; Lower comparative advantage 6 benefit means overuse

7 Benefit for Patients Over the Treatment Threshold E(Benefit Benefit > Threshold) Benefit 0 Propensity to get Treatment Benefit of Treatment is increasing in the propensity to receive it 1 Harm Threshold is set at zero: perform treatment until there is no more benefit

8 Increasing the Treatment Threshold Benefit E(Benefit Benefit > Threshold) Positive Benefit for least appropriate Higher Benefit for all patients 0 Propensity to get Treatment 1 Harm

9 Distinguishing Underuse and Overuse E(Benefit Benefit > Threshold) High Treatment Threshold Benefit Low Treatment Threshold τ high >0 (underuse) 0 Propensity to get Treatment 1 τ low <0 (overuse) Harm

10 Comparative Advantage E(Benefit Benefit > Threshold) Benefit 0 Harm Low Comparative Advantage Propensity to get Treatment High Comparative Advantage 1 Greater comparative advantage in treating Intensively, means patient propensity to receive treatment is higher

11 Predictions 1. Patients with higher propensity should receive higher benefit (key for economic model) 2. Lower thresholds implies patients with same treatment propensity get less benefit in high-use hospitals 3. Overuse implies that low-propensity patients receive negative benefits, instead of zero benefits. 4. Hospitals with higher hurdles should treat fewer patients and have higher benefits to treatment (conditional on I) 5. Comparative advantage increases patient propensity to be treated, but patients with same propensity receive same benefit in all hospitals.

12 Empirical Work Cooperative Cardiovascular Project (CCP) Chart data on ~140,000 Medicare beneficiaries (over 65) who had heart-attacks (fresh AMIs) Examine reperfusion within 12 hours Excellent patient controls let us estimate treatment effect. Can replicate RCT effect of reperfusion of 0.20 impact on log-odds of survival Comparative Advantage, expertise, overuse & underuse were relevant concerns for reperfusion at this time

13 Patient Controls for Reperfusion within 12 hours 1. Age, Race, Sex 2. previous revascularization (1=y) 3. hx old mi (1=y) 4. hx chf (1=y) 5. history of dementia 6. hx diabetes (1=y) 7. hx hypertension (1=y) 8. hx leukemia (1=y) 9. hx ef <= 40 (1=y) 10. hx metastatic ca (1=y) 11. hx non-metastatic ca (1=y) 12. hx pvd (1=y) 13. hx copd (1=y) 14. hx angina (ref=no) 15. hx angina missing (ref=no) 16. hx terminal illness (1=y) 17. current smoker 18. atrial fibrillation on admission 19. cpr on presentation 20. indicator mi = anterior 21. indicator mi = inferior 22. indicator mi = other 23. heart block on admission 24. chf on presentation 25. hypotensive on admission 26. hypotensive missing 27. shock on presentation 28. peak ck missing 29. peak ck gt non-ambulatory (ref=independent) 31. ambulatory with assistance 32. ambulatory status missing 32. albumin low(ref>=3.0) 33. albumin missing(ref>=3.0) 34. bilirubin high(ref<1.2) 35. bilirubin missing(ref<1.2) 36. creat 1.5-<2.0(ref=<1.5) 37. creat >=2.0(ref=<1.5) 38. creat missing(ref=<1.5) 39. hematocrit low(ref=>30) 40. hematocrit missing(ref=>30) 41. ideal for CATH (ACC/AHA criteria)

14 14

15 Estimation Step 1 We want to Estimate: Pr(Reperfusion=1) = F(Xβ + θ h ), θ h =(α h - τ h ) Strategy: 1. Use Conditional Logit for hospital fixed-effects 2. But worry that Fixed Effects estimation is not smart given number of hospitals with small n 3. Estimate θ h & I= Xβ + θ h using random effects logit (xtmelogit) 4. Produces MLE of coefficients (β), SD of the hospital RE and posterior estimates of the hospital RE (θ h ). 5. Combine to form an estimate of index (I= Xβ + θ h ) for each patient 15

16 Estimation Step 2 Want to estimate outcomes: Y ih Y ih 0 Y ih T ih h 0 X ih 0 Y ih T ih ih 0 Strategy: Empirical analog: Pr Sur vi val ih 1 For estimation, simplify: FTreated ih ih X ih 0 0 h Pr Sur vi val ih 1 ih 0 1 Î ih 2 ˆ FTreated ih 0 Treated ih Î ih, where ih h gi ih h 1 Treated ˆ ih h 2 X ih 0 0 h Treatment on Treated increasing in propensity (λ 1 >0) if hospitals If hospitals vary vary in in hurdle CA, but but not not in in comparative their minimum advantage, threshold then then λ λ 2 =0 2 <0; the treatment effect is smaller in hospitals with a high propensity to treat

17 Hospital Effects are Mean 0, so this effect of Reperfusion at typical hospital Index Normed to Zero: Effect for average patient effect Roy Model of Labor at average hospital Economics at work! Since these specifications do not condition on the propensity, coefficient is biased in the positive direction; not a strong test of whether hospitals differ in their minimum treatment threshold. 17

18 Figure 2: Survival Benefit from Reperfusion, According to Hospital Effect in Treatment Propensity All Patients Low Propensity Patients Hospital effect from propensity equation Effect of reperfusion on logodds of 30-day survival Effect of reperfusion on logodds of 30-day survival Hospital effect from propensity equation

19 Figure 3: Survival Benefit from Reperfusion According to Patient s Treatment Propensity. High & Low Treatment Rate Hospitals. Hospitals in Lowest Tercile of Hospital Effects from Propensity Equation Hospitals in Highest Tercile of Hospital Effects from Propensity Equation Effect of reperfusion on logodds of 30-day survival Effect of reperfusion on logodds of 30-day survival Patient Treatment Propensity Index Patient Treatment Propensity Index

20 Summary Part I 1. Patients with higher propensity receive higher benefit. Providers triage based on benefit 2. Intensive hospitals have lower benefit not consistent with comparative advantage; expect same benefit consistent with differences in minimum hurdle 3. Low-propensity patients are harmed in more aggressive hospitals consistent with overuse

21 Jointly Estimate Treatment Propensity and Survival Equation With Hospital-level Random Coefficients Propensity Equation: (1) Pr(Treatment=1) = F(Xβ + θ h ), θ h =(α h - τ h ) Survival Equation: Also add hospital-level random intercept in survival equation. Pr(Survival=1) = F(reperf Captures λ o + TFP (reperf*i) at medicine λ 1 + reperf* τ h + Xβ) split I = Xβ + θ h into Xβ and θ h to get: (2) Pr(Survival=1) = F(reperf λ 0 + (reperf*xβ) λ 1 + reperf* μ h + Xβ + δ h ) where μ h = λ 1 α h + (1- λ 1 ) τ h Joint estimation using hierarchical logit recovers joint distribution of expertise Hospital-level Use and thresholdrandom Xβ estimate from prior coefficients (joint reperfusion normal) logit 21

22 Table 3: Survival Benefit from Reperfusion According to Patient s Treatment Propensity Capture Treated on Treated Variation across Hospitals

23 Some (but far from all) of the Most of the variation across variation in treatment rates across hospitals in the observed hospitals (theta) is associated treatment is the result of variation with variation in the treatment in the treatment threshold rather Hospitals with better survival rates threshold than when comparative not using the advantage. treatment tend to set too low a treatment threshold and overuse the treatment perhaps hospitals that are highly skilled at caring for patients without reperfusion overestimate the benefits of treatment and therefore 23 overuse reperfusion.

24 Summary 1. Large variation in hospital outcomes: SD=0.45 in logodds (some due to being worse at non-reperfusion) 2. Substantial variation in threshold: SD=0.33 in logodds 3. Positive correlation in hurdle & comparative advantage lower expertise hospitals also overuse

25 Closing Thoughts Powerful framework for analyzing variation in treatment and outcomes across populations. Propensity score helps identify heterogeneous treatment effects key to finding overuse What factors drive differences across hospitals in expertise & threshold? If treatment variation due to expertise, there is a welfare loss from uniform treatment guidelines

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