The Geography of Health Care Utilization and Outcomes

1. The Geography of Health Care Utilization and Outcomes Jonathan Skinner Department of Economics, Dartmouth College The Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth Medical School jon.skinner@dartmouth.edu November 9, 2010 Institute of Medicine, Washington DC

2. The Focus of This Talk: Costs and Quality in the Medicare Population Congressional Budget Office, June 2010 (revised Aug 2010), Extended baseline .

3. Two Policy Questions • What are the causes of geographic variation in health care? • What are the consequences of geographic variation in health care? Today’s objective: Understanding how patient cohorts can help us to answer each question

4. Unexplained regional variation in Medicare expenditures Source: Zuckerman, Berenson, Hadley, 2010

5. Where do we go from here? Source: Zuckerman, Berenson, Hadley, 2010

6. Choices, choices • Complete risk adjustment using Medicare claims data • Further risk adjustment using ecological (state or county) data • Better risk adjustment approaches (biomarkers, etc.) • Considering cohorts of patients with similar diseases

7. “Diagnosis creep” in high-intensity regions • Low-diagnosis • High-diagnosis • 19% lower “risk-adjusted” costs • 15% better “risk-adjusted” outcomes

8. Choices, choices • Complete risk adjustment using Medicare claims data • Further risk adjustment using ecological (state or county) data

9. Potential Pitfalls of Using Ecological Risk-Adjusters Cutler and Sheiner, AER May 1999

10. Chicago-area hospitals sacrifice revenue as they prepare for health care reform By Mike Colias, Crain’s Chicago Business, July 12, 2010 "It's not about building new facilities or mergers and acquisitions…The hospitals that will fare best are the ones that are dealing most seriously with getting ready for these reimbursement changes and partnering with the right doctors.”* But… Market-Level Explanations for Utilization are Valid: First, Chicago…. * Michael Nugent, Navigant Consulting

11. Then New York: Fighting for Market Share Soaring cancer-care costs strain budgets - Hospitals pour millions into new devices, talent as margins thin • By Judith Messina, Crain’s New York Business, August 22, 2010 • All told, the city's major hospitals have spent more than $2 billion on cancer research and treatment over the past five years, in a race to carve out a bigger piece of a fast-growing pie.

12. Choices, choices • Complete risk adjustment using Medicare claims data • Further risk adjustment using ecological (state or county) data • Better risk adjustment approaches (biomarkers, etc.) • Considering cohorts of patients with similar diseases

13. Possible “Low Variation” Cohorts • Hip fracture • AMI • Stroke • Colon/Lung Cancer • Risk-adjusted end-of-life cohorts (by condition and presence of multiple conditions)

14. Example: AMI Cohorts • One-year after AMI • Risk adjustment for type of AMI (location of the infarct), comorbidities (e.g., diabetes, COPD, cancer, dementia), zip code income, % poverty in income, price adjustment, age-sex-race. • Currently Part A through 2005 – could be Parts A,B, and D through 2008 or 2009.

15. Risk & Price-Adjusted AMI and End-of-Life Expenditures 2000-05 by Hospital One-Year AMI Spending N = 1985, At least 400 AMIs

16. Two Policy Questions • What are the causes of geographic variation in health care? • What are the consequences of geographic variation in health care?

17. Provider-Specific Measures of Quality & Spending Typically Look Like This Survival/Quality Spending

18. This is waste Survival/Quality Spending Low Cost

19. But this waste is even more important! Survival/Quality Best practice Spending

20. A Simple Graph: Spending vs. Survival/Quality of Life Survival/Quality You are here A Spending

21. A Tale of Two Hospitals: Every New Adoption is Medically Effective Survival/Quality Hospital X Hospital Y A Spending But hospital X gets better outcomes – at lower costs!

22. Causes a Negative Correlation Between Spending and Outcomes Survival/Quality Hospital X Hospital Y A Spending

23. Differences in Efficiency: A Concrete Example Some Firms Get Double the Output at the Same Cost! Deviation from Mean in Total Factor Productivity Syverson, C., “Market Structure and Productivity: A Concrete Example,” JPE 2004.

24. Quintile 5 (23.5-37.8%) Quintile 4 (19.2-23.5%) Quintile 3 (15.5-19.2%) Quintile 2 (11.5-15.5%) Quintile 1 (<11.5%) Not Populated New Approaches: Percent Men Age 80+ Receiving PSA Screening by HRR Source: Bynum, J., JAGS 58(4) April 2010

25. One measure of a “bad” outcome, 2006-07 Cohort: Nursing home patients with advanced dementia admitted to hospital

26. Yet more ways to measure outcomes • Changes over time in measures of care: • % Deaths in hospitals or hospice, 2003-07 (D. Goodman et al.) • Low or high quality prescription (Part D) • Y. Zhang et al. (2010), N. Morden et al. (2010) • Burdensome transitions for nursing home patients (J. Teno, et al, 2010) • Multiple hospitalizations for UTI, pneumonia in last 120 days • Transitions in the last 72 hours of life • Lack of continuity among nursing homes

27. Conclusions • Challenges in measuring utilization and outcomes – but necessary to reward efficiency • Serious limits to current risk-adjustment measures • Defining meaningful cohorts of patients – reasonable step forward • Looking ahead: new approaches to measuring outcomes