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

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

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)

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

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

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

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

Expansion of Medicare Advantage Data for talk (2010)

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

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

MA share of Medicare in “Complete Data” States

Baseline sample

Comparing overall expenditure

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).

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

Reweighting: Spending results

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

Spending by types of enrollees

Distribution of Spending

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

Categories of spending

Geographic variation

Utilization and Spending per encounter

Price differences for Major DRGs

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).

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

“Low value” and “High value” care

Post-acute care

Substitution to less expensive types of care

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

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

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

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

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

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:

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

BACKUP SLIDES

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

Utilization Differences

Spending per use

DRG prices

State-level prices

Diagnostic and imaging

Preventive care

Discharge patterns

Hospital discharges (in shares)

Surgeries and specialists

Propensity score distributions