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 November 9, 2010 Institute of Medicine, Washington DC

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

Two Policy Questions 1.What are the causes of geographic variation in health care? 2.What are the consequences of geographic variation in health care? Today’s objective: Understanding how patient cohorts can help us to answer each question

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

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

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

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

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

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

But… Market-Level Explanations for Utilization are Valid: First, Chicago…. 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.”* * Michael Nugent, Navigant Consulting

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.

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

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

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.

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

Two Policy Questions 1.What are the causes of geographic variation in health care? 2.What are the consequences of geographic variation in health care?

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

This is waste Spending Survival/Quality Low Cost

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

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

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

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

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

Quintile 5 ( %) Quintile 4 ( %) Quintile 3 ( %) Quintile 2 ( %) 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

One measure of a “bad” outcome, Feeding Tube Use (%) Brigham- Women’s (MA) 4.7% Lawnwood Regional (FL) 37.5% UCLA0.0% Cedars Sinai14.6% US~6.3% Cohort: Nursing home patients with advanced dementia admitted to hospital

Yet more ways to measure outcomes Changes over time in measures of care: ▫% Deaths in hospitals or hospice, (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

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