1. Healthcare Spending and Utilization in Public and Private Medicare
Vilsa Curto, Liran Einav,
Amy Finkelstein, Jonathan Levin,
and Jay Bhattacharya
The Health Sector and The Economy
BFI, University of Chicago
September 30, 2016

2. Motivation
Two very different forms of health insurance provision for the elderly:
Public: “Traditional Medicare” (TM) involves FFS reimbursement of provides
Private: “Medicare Advantage” (MA) involves public payments to regulated private (mostly PPO/HMO) plans who then enroll individuals
About 30 percent of enrollees are in MA but all the work on Medicare healthcare spending is on TM
Big question: how (and why) does health care spending and medical practice pattern vary across these two systems
Differences in overall levels of spending and components
Differences in scope and nature of geographic variation

3. Approach Prior work on topic limited by data
Medicare enrollees “disappear” from the claims data when they go into MA
Some evidence of lower utilization (Landon et al., 2012; Duggan et al., 2015)
Some indirect estimates (e.g. Curto et al., 2015 and others) of savings
This paper: newly available data from Health Care Cost Institute (HCCI) allows us to see inside the “MA black box”
HCCI aggregates claims for Aetna, Humana, UnitedHealthcare (~45% of MA)

4. Overview of Findings Level differences in Spending
Healthcare spending is 27% lower ($237 / enrollee month) for observably similar people in MA relative to TM
Holds across subsets of enrollees and broad categories of spending
Primarily reflects lower utilization (“prices” are comparable)
Geographic variation in spending
MA displays somewhat larger geographic variation in spending
Smaller geographic variation in prices
Areas with higher TM spending have bigger MA “savings”
Treatment vs selection
Attempting to adjust for unobservables using mortality differences
Raw data has 30% difference; Difference is 27% after adjusting for rich observables
Based on mortality adjustment, between 25 and 50 percent of difference may be attributed to a combination of “upcoding” and selection on unobservables
Some suggestive evidence on “mechanism” – e.g. MA has lower rate of discharge to post acute care, more outpatient surgeries, larger declines in specialist care than primary care

5. Outline of Talk Background: Institutions and Data
Levels of Spending and Utilization in MA and TM
Selection vs. Treatment

6. Outline of Talk Background: Institutions and Data
Levels of Spending and Utilization in MA and TM
Selection vs. Treatment

7. Medicare Advantage
Allows Medicare beneficiaries to opt out of traditional, fee-for-service Medicare, and enroll in private insurance plans, mostly HMOs & PPOs
Plans must provide at least the insurance benefits of standard Medicare (parts A and B), and typically provide more generous financial coverage
CMS pays plans a capitated amount for each enrollee – plans submit bids that are discounts off a benchmark capitation rate, set county-by-county
What we think we already know
MA doesn’t appear to save money for taxpayers
MA enrolls somewhat healthier seniors
MA might(?) have lower medical claims cost

8. Expansion of Medicare Advantage
Data for talk
(2010)

9. Why would medical spending be different?
Medical spending: payments to medical providers for services (claims) – includes insurer and out-of-pocket payments
Treatment – how insurers reduce spending
Limit “low value” services (gatekeeping, physician incentives)
Substitute to cheaper mix of services (also lower quality?)
Negotiate lower prices (but perhaps lack CMS bargaining power)
Selection
On geography (e.g. MA enrollment high in urban areas): we can adjust for this
On observed health conditions: we can adjust for this
On unobserved health and/or preferences for care: more difficult

10. Data Construction Data for the project “Combining” Data Sources
CMS. Beneficiary file with enrollment, demographic, health and mortality information on all TM and MA enrollees
CMS. Claims for all TM enrollees
HCCI. Claims for enrollees in “most” Aetna, Humana, UnitedHealthcare (“HCCI Insurers”) plans (2010)
“Combining” Data Sources
DUAs don’t allow us to combine / match individual data across CMS / HCCI
But (as explained shortly) we can adjust CMS data for HCCI characteristics and compare means
Sample Selection
Using CMS beneficiary file, can identify enrollees in all HCCI Insurer plans
HCCI is “missing” 25% of AHU enrollees. Most missing enrollees/plans are in specific areas (e.g. CA, NV). We restrict to states within 10% of CMS count

11. MA share of Medicare in “Complete Data” States

12. Baseline sample

13. Comparing overall expenditure

14. Regional Variation
Circle sizes proportional to medicare enrollment in state.
Slightly larger coeff. of variation across states in MA (0.13) vs. TM (0.11).

15. Reweighting strategy
Reweight TM sample to “look like” HCCI sample in the spirit of CMS approach (county and risk score)
Consider an outcome 𝑦 𝑖 associated with enrollee 𝑖
Average HCCI outcome 𝑦 𝐻𝐶𝐶𝐼 ; we want a comparable number for TM
Define discrete bins 𝑏=1,…,𝐵 (partition of possible Xs).
Assign each Medicare beneficiary to his/her bin
Let 𝑁 𝑏 be the number of AHU enrollees in b. Let N= 𝑏 𝑁 𝑏
Let 𝑤 𝑏 = 𝑁 𝑏 /𝑁 be the share of AHU enrollees in bin b
Define average TM outcome for each bin: 𝑦 𝑏,𝑇𝑀
Note: If no TM enrollee in bin b, interpolate between “closest” populated TM risk score bins within county – sometimes necessary at high risk scores
Reweighted TM outcome: 𝑦 𝑇𝑀 = 𝑏 𝑤 𝑏 𝑦 𝑏,𝑇𝑀

16. Reweighting: Spending results

17. Outline of Talk Background: Institutions and Data
Levels of Spending and Utilization in MA and TM
Selection vs. Treatment

18. Spending by types of enrollees

19. Distribution of Spending

20. MA “Savings” vs. TM Spending
Note: circle sizes proportional to medicare enrollment in state.

21. Categories of spending

22. Geographic variation

23. Utilization and Spending per encounter

24. Price differences for Major DRGs

25. Regional variation in inpatient prices
Circle sizes proportional to medicare enrollment in state.
Slightly smaller coeff. of variation in prices across states in MA (0.063) vs. TM (0.077).

26. Investigating Potential Mechanisms for Savings
Reducing “low-value” relative to “high-value” utilization
Diagnostic tests and Imaging
Preventive care
Substituting less expensive care for more expensive care
Use of post-acute care facilities
Outpatient vs. inpatient surgery
Primary vs. specialist care

27. “Low value” and “High value” care

28. Post-acute care

29. Substitution to less expensive types of care

30. Outline of Talk Background: Institutions and Data
Levels of Spending and Utilization in MA and TM
Selection vs. Treatment

31. Standard Treatment vs. Selection
Individual Level Notation
Individual treatment 𝑊 𝑖 =0 𝑇𝑀 or 𝑊 𝑖 =1 𝑀𝐴
Individual cost 𝐶 𝑖 = 𝐶 𝑖 𝑇𝑀 if 𝑊 𝑖 =0, or 𝐶 𝑖 = 𝐶 𝑖 𝑀𝐴 if 𝑊 𝑖 =1
Individual treatment effect Δ 𝑖 = 𝐶 𝑖 𝑇𝑀 − 𝐶 𝑖 𝑀𝐴
Decomposing Selection and Treatment
Treatment (on treated) effect: T=𝐸 𝐶 𝑖 𝑇𝑀 − 𝐶 𝑖 𝑀𝐴 | 𝑊 𝑖 =1
Selection effect: S=𝐸 𝐶 𝑖 𝑇𝑀 | 𝑊 𝑖 =0 −𝐸 𝐶 𝑖 𝑇𝑀 | 𝑊 𝑖 =1
Observed cost difference: 𝐸 𝐶 𝑖 𝑇𝑀 | 𝑊 𝑖 =0 −𝐸 𝐶 𝑖 𝑀𝐴 | 𝑊 𝑖 =1
Decomposition: Observed Cost Difference=𝑆+𝑇

32. Accounting for Selection: On observables
Individual observables 𝑋 𝑖
Assumption: 𝐶 𝑖 𝑇𝑀 ⊥ 𝑊 𝑖 | 𝑋 𝑖
Non-parametric reweighting strategy to account for observables
Currently re-weighting TM on county and risk score
Results don’t change when we use a host of other observables and re0weight on county and propensity score

33. Selection on Unobservables
Model of selection bias
Selection into MA: 𝑊 𝑖 =𝟏 𝑢 𝑋,𝑟,𝜂 ≤0
(Log) cost equation: 𝐸 𝑐 𝑇𝑀 |𝑋,𝑟,𝜃 =𝑋 𝛼 𝑥 + 𝛼 𝑟 𝑟+𝜃
Cost under selection: 𝐸 𝑐 𝑇𝑀 |𝑋,𝑟,𝑊 =𝑋 𝛼 𝑥 + 𝛼 𝑟 𝑟+𝐸 𝜃|𝑋,𝑟,𝑊
Problem if 𝐸 𝜃|𝑋,𝑟,𝑊=1 ≠𝐸 𝜃|𝑋,𝑟,𝑊=0
Use model of mortality to estimate 𝐄 𝜽|𝑿,𝒓,𝑾 for 𝑾=𝟎,𝟏
No “missing data” on mortality!
Two key assumptions:
No MA treatment effect on (short run) mortality
Same 𝜃 enters cost and mortality equations

34. Selection on Unobservables
Recall: 𝐸 𝑐 𝑇𝑀 |𝑋,𝑟,𝑊 =𝑋 𝛼 𝑥 + 𝛼 𝑟 𝑟+𝐸 𝜃|𝑋,𝑟,𝑊
Practical Approach:
Mortality:
𝐸 𝑚|𝑋,𝑟,𝜃 = exp 𝑋 𝛽 𝑥 + 𝛽 𝑟 𝑟+𝜃 1+exp 𝑋 𝛽 𝑥 + 𝛽 𝑟 𝑟+𝜃
Derive log-odds ratio:
𝑚 𝑋,𝑟,𝜃 = ln 𝐸 𝑚|𝑋,𝑟,𝜃 1−𝐸 𝑚|𝑋,𝑟,𝜃 =𝑋 𝛽 𝑥 + 𝛽 𝑟 𝑟+𝜃
Log-Odd under selection:
E 𝑚 |𝑋,𝑟,𝑊 =𝑋 𝛽 𝑥 + 𝛽 𝑟 𝑟+E 𝜃|𝑋,𝑟,𝑊
We can now estimate 𝐸 𝜃|𝑋,𝑟,𝑊 for 𝑊=0,1

35. Selection on unobservables
Results: About 23% of observed log cost difference due to selection, remaining 77% of the difference due to treatment Alternatively: Can apply similar idea in the context of the propensity score and find about half of spending difference due to selection:

36. Conclusion
Rare opportunity for “side by side” comparison of public and private health insurance systems operating on similar scale, for same population, in same markets with same providers
Main findings:
Healthcare spending for enrollees in MA is 27% less than for enrollees of same risk score and county in TM
25-50 percent of this difference may reflect selection and “upcoding”
Spending differences driven by utilization; prices are very similar
Some channels for saving are different use of post-acute care and substitution to less expensive care (primary care for specialist, outpatient surgery for inpatient)
Geographic variation in spending slighter higher in MA than TM
Questions for further study
Implications for consumers and insurers
Impact of competition

37. BACKUP SLIDES

38. Mortality vs. Spending in TM and MA
Note: circle sizes proportional to enrollment in state for that insurance regime.

39. Utilization Differences

40. Spending per use

41. DRG prices

42. State-level prices

43. Diagnostic and imaging

44. Preventive care

45. Discharge patterns

46. Hospital discharges (in shares)

47. Surgeries and specialists

48. Propensity score distributions