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Donald H. Taylor, Jr. Duke University

Do Selection or Treatment Effects Explain Differences in Medicare EOL Costs Among Hospice and Normal Care Users?. Donald H. Taylor, Jr. Duke University Funded by The Robert Wood Johnson Foundation’s Changes in Health Care Financing and Organization (HCFO) Initiative. Question.

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Donald H. Taylor, Jr. Duke University

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  1. Do Selection or Treatment Effects Explain Differences in Medicare EOL Costs Among Hospice and Normal Care Users? Donald H. Taylor, Jr. Duke University Funded by The Robert Wood Johnson Foundation’s Changes in Health Care Financing and Organization (HCFO) Initiative

  2. Question • Does hospice save Medicare money? • Are observed differences in cost due to treatment effects (hospice) or selection effects?

  3. Background/Context • Hospice improves patient QOL • Large cost to Medicare near end-of-life • Hospice/cost effect is mixed in past work • Medicare hospice use expanding

  4. Data • Screening sample for NLTCS • 10,319 decedents (died 1993-2000) • Medicare claims 2 yrs. prior to death • Total Medicare-financed payments

  5. Analytic Approach • Match decedents on pred prob of hospice • Compare cost--hospice users v. matches • Marginal & cumulative costs • Varied follow-up periods • Predicted weekly costs in LYOL • Regressed cost on time, t2, t3, t4

  6. Results • 13% died in hospice (1,309/10,319) • 143 began, but did not die in hospice • Median LOS=15 days; mean=50 • cancer dx longer

  7. Hospice Save Medicare Money? • Marginal acctg—Yes. Hospice saves Medicare ~$1,800 (50%) in the last 2 wks • Cumulative acctg--Yes. Hospice saves Medicare ~$1,600 (15%) during last 60 days

  8. Policy/Comment/Questions • Matching increased cost savings ~10% • Strong effect—savings in last 2 wks show cumulative savings in last 2 months • Answer is sensitive to follow up period • Earlier use of hospice will improve QOL and potentially save money

  9. Matching • Logit of hospice use • Predictors of use: younger, white, later death, higher Medicare costs in year prior, dx cancer, urban • Pred prob ranged from 0.03-0.49 • Matches were close; largest difference between hospice user and match was 0.01

  10. Limitations/Extensions • Time to death is assumed exogenous to choice of hospice/no hospice • Partial solution: predict time of hospice entry and costs prospectively from date of study entry • Predict daily prob of hospice entry • Calculate costs between each day and time of death • Calculate expected cost after hospice entry by integrating over all days (for hospice users and non-users)

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