ECON 440 Lecture Notes - Lecture 23: Neurosurgery, Caesarean Section, Coronary Artery Bypass Surgery
13 Jun 2018
School
Department
Course
Professor
Health care utilization
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Health care spending
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Health outcomes
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We observe variation across geographic regions and across socioeconomic groups in:
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Do they represent failures of efficiency? Equity?
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What does research in health economics tell us about what causes these differences?
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Should government policy aim to reduce or eliminate these variations?
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Outline
Miami: $15,260
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Minneapolis: $8,300
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Medicare spending per enrollee, 2013
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Nunavut: $13,150
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Newfoundland and Labrador: $7,130
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Quebec: $5,530
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Total (public and private) spending per capita, 2013
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Health care spending varies greatly across geographic regions
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Start with the Observation that:
Which of these explanations is (are) most important?
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Is higher spending a "good" thing?
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Could we reduce spending in all regions to that of the lowest region and provide care more
efficiently (less money, same outcomes)?
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What are we getting for the extra money?
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The normative implications
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Growing body of research trying to understand
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What's going on? Should we care?
Use Medicare utilization and spending data
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306 discrete medical markets or health systems
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Each contains at least one hospital that performs major cardiovascular procedures and
neurosurgery
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Almost all people who live there receive care within that region
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Developed the Hospital Referral Region (HRR)
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Researchers at Dartmouth (Wennberg, Fisher, et al) have been very influential
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Per Capita Medicare Reimbursement
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Dartmouth Atlas Project
Lecture 23 - Geographic Variation and Disparities
Monday, April 9, 2018
2:06 PM
ECON 440 Page 1
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So variation in spending and utilization across HRRs is established
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Age-sex-race adjusted spending on hospital and physician services in the last 6 months of life
(nationally standardized prices) by HRR
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End-of-Life expenditure index
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The outcome is always the same: risk of death = 100%
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Predictive of total spending, but not correlated with severity (case mix) or patient preferences across
HRRs
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But, how do we separate differences in patients' (severity) or in prices from differences in practice patterns
(utilization)?
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Age and sex at minimum
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EOL index: risk of death is the same (100%)
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Comparing spending and outcomes across regions vs. across hospitals
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Health behaviors or health status?
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Appropriate comparisons require adjusting for underlying health (or risk) differences across populations that
are outside the control of the units being compared
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Also reduces providers' willingness to participate
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Without appropriate risk adjustment, potential for inaccurate comparisons generates incentives to engage in
selection and to game outcome measures
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Risk Adjustment (again!)
Mostly driven by differences in the amount and types of care used: local capacity, not need
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Less than half of the variation in spending across regions is explained by differences in prices of services or
population differences
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More care of proven benefit
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More preference-sensitive care (multiple valid treatment options with different risks and benefits)
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High-spending HRRs don't get:
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Weak or absent scientific evidence for providing service
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Use of the service is strongly associated with the local supply of providers or hospital beds
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More visits, tests, minor procedures; greater use of hospital and specialists
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But they do get more "supply-sensitive care"
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Spending Variation is Driven by Local Capacity
Patient Satisfaction
ECON 440 Page 2
Document Summary
We observe variation across geographic regions and across socioeconomic groups in: Health care spending varies greatly across geographic regions. Total (public and private) spending per capita, 2013. Researchers at dartmouth (wennberg, fisher, et al) have been very influential. Each contains at least one hospital that performs major cardiovascular procedures and neurosurgery. Almost all people who live there receive care within that region. So variation in spending and utilization across hrrs is established. Age-sex-race adjusted spending on hospital and physician services in the last 6 months of life (nationally standardized prices) by hrr. The outcome is always the same: risk of death = 100% Predictive of total spending, but not correlated with severity (case mix) or patient preferences across. Appropriate comparisons require adjusting for underlying health (or risk) differences across populations that are outside the control of the units being compared. Eol index: risk of death is the same (100%) Comparing spending and outcomes across regions vs. across hospitals.
