Does Health Insurance Affect Health?
Evidence of Medicare’s Impact
on Cancer Outcomes
Srikanth Kadiyala, Ph.D.
RAND
Erin Strumpf, Ph.D.
McGill University
McGill Institute for Health and Social Policy
March 27, 2013
Preliminary
results: please do not cite
.
Draft available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2138454
Health Insurance and Health
• Does health insurance affect health
outcomes?
– Lower price paid for medical care
– Increased access to care
– Higher quantity (quality?) of care consumed
– If marginal health benefits from increased
care are positive, then we expect an effect on
health
Measuring the Impacts
• Empirically, identifying effects of health
insurance on health is complicated
– Many contexts have little to no variation in
health insurance status (national HI systems)
– In the U.S., insurance status is endogenous
• Are the risk-averse and healthy more likely to be
insured?
• Or are the sick more likely?
– If the benefits from marginal health care
are small, detection of any health benefit is
difficult
How do we study this question?
– Experimental designs are rare
• RAND HIE (1970s), Oregon HIE (ongoing)
– Descriptive studies lacking identification are
plentiful
– Better evidence from quasi-experimental studies*
• Find that insurance reduces mortality and improves
health status
• Most focus on acute conditions: heart attack, stroke,
car accidents, pregnancies
– Suggests effects result from better treatment conditional on
diagnosis
• Some examine impact of insurance on differences by
race, education, or previous insurance status
* Currie and Gruber 1996, Decker and Rappaport 2002, Decker 2005, Doyle 2005, Polsky et al 2006, Card
et al 2009, McWilliams et al 2009
Our Research Question
• Does Medicare affect cancer health
outcomes?
– Approximately 12% of the U.S. population is
uninsured at ages 55-64 (~4 million)*
– At age 65 nearly everyone becomes eligible for
Medicare
• What is the effect of Medicare coverage on
cancer detection?
– Cancer is the 2nd leading cause of death in the U.S.
– Medicare accounts for 45% of all spending on
cancer treatment**
– Cancer care is ~10% of Medicare spending
*Kaiser Family Foundation, Health Insurance Coverage for Older Adults, May 2009
Birnbaum and Patchias 2008 estimate 5% of the age 65+ population is ineligible for Medicare
**Cancer Action Network, ACS, Cancer and Medicare Chartbook 2009
Mechanisms: Detection vs.
Treatment
• Cancer is not necessarily a symptomatic
condition
– Does any effect of health insurance on health
work via disease detection?
– The fact that it’s not acute or symptomatic might
make it more possible to identify differential
effects for the uninsured
• Medicare reduces the price of both screening
tests and physician visits
– Screening rates and physician visits increase at
age 65*
*Lichtenberg 2002, McWilliams et al 2003, Ward et al 2007 , Card et al 2008
Cancer Detection Data
• U.S. Surveillance Epidemiology and End
Results (SEER) database, 2000-2006
– Cancer detection from 25% of the U.S.
population (12 states)
• CA, CT, GA, HI, IA, KY, LA, MI, NJ, NM, UT, WA
– Detailed information on staging, size of the
tumor and other measures of cancer severity
• Behavioral Risk Factor Surveillance
System (BRFSS), 2000-2006
– Provides covariate data at year*age level
Methodology
• Regression Discontinuity Design
– Uses the discontinuity in insurance status at
age 65 and compares cancer detection rates
on either side of this age threshold
• Assumes:
– Smoothness in other determinants of cancer
detection across the cutoff
• Education, marital status, employment, etc
– In the absence of treatment (insurance),
smoothness in the outcomes (detection)
• Cancer risk or unobserved true incidence is smooth
Analysis
• Graphical Evidence
• Estimate the magnitude of the discontinuity
using regression
– Cancer detection = α + β1(Mcare) + β2(Mcare*a-65)
+ β3(1-Mcare*a-65) + … + ε
– Adjust for age, age2 and age3; sex, race, education,
income, marital status, employment at year*year-ofage level from BRFSS
• Examine cancers with screening tests
separately from those that do not
– Breast, colorectal, prostate, cervical (BCPC)
• Assess heterogeneous impacts by preMedicare insurance status
100
50
500
-50
0
1000
1500
2000
# of Cases Per 100k
150
2500
200
All Cancers Detection
50
55
60
All Cancer Detection
SEER 2000-2006, 12 states; ages 50-80
65
Age
70
75
Age to Age Change in Detection
80
Impact of Medicare on
All Cancer Detection
OLS
OLS
Poisson
Medicare Cutoff
101.5***
108.4***
92.9***
Age Trend Above
75.8***
75.8***
150.2
Age Trend Below
87.4***
85.86***
179.0
6.4%
6.8%
5.9%
Covariates Controls
Included
No
Yes
Yes
N
77
77
77
% Increase relative to
Detection rate at age 64
N=77, * p<=.05, ** p<=.01, *** p<=.001; Data: U.S. SEER 2000-2006 (12 states); Controls: sex, race, income
quartiles, education (4), marital status (5), employment. Cutoff coefficient is robust to adding age2 and age3.
Poisson model includes age2 and age3.
0
500
1000
1500
2000
Breast, Colorectal, Prostate, Cervical Detection vs. Other Cancers
50
55
60
BCPC Detection
SEER 2000-2006, 12 states; ages 50-80
65
Age
70
75
Non-BCPC Detection
80
-50
0
50
100
150
BCPC vs Non-BCPC Age to Age Change in Detection
50
55
60
65
Age
BCPC
SEER 2000-2006, 12 states; ages 50-80
70
Non-BCPC
75
80
Effect of Medicare on
BCPC Detection
OLS
OLS
Poisson
Medicare Cutoff
65.7***
70.3***
63.2***
Age Trend Above
21.4***
22.8***
192.3
Age Trend Below
40.2***
41.9***
206.6
9.0%
9.6%
8.7%
Covariates Controls
Included
No
Yes
Yes
N
77
77
77
% Increase relative to
detection rate at 64
* p<=.05, ** p<=.01, *** p<=.001; Data: U.S. SEER 2000-2006 (12 states); Controls: sex, race, income
quartiles, education (4), marital status (5), employment. Cutoff coefficient is robust to adding age2 and age3.
Poisson model includes age2 and age3.
Effect of Medicare on
Non-BCPC Cancer Detection
OLS
OLS
Poisson
Medicare Cutoff
35.8***
38.0***
29.7**
Age Trend Above
54.4***
53.0***
-31.8
Age Trend Below
47.2***
44.0***
-17.8
4.2%
4.5%
3.5%
Covariates Controls
Included
No
Yes
Yes
N
77
77
77
% Increase relative to
detection rate at age 64
* p<=.05, ** p<=.01, *** p<=.001; Data: U.S. SEER 2000-2006 (12 states); Controls: sex, race, income
quartiles, education (4), marital status (5), employment. OLS cutoff coefficient is robust to adding age2 and
age3. Poisson model includes age2 and age3.
Stage at Detection for BCP Cancers
• 80% of newly detected breast cancer cases
are at local or regional stages
• 65% of newly detected CRC cases are at
regional or distant stages
• 81% of newly detected prostate cancer cases
are at the local stage
• About 45% of newly detected cases for these
three cancers are at “treatable” stages*
where we expect diagnosis to lead to
significant health benefits
– Conservative estimate based on concerns about
over-diagnosis and treatment
* Local and regional for breast; in-situ, local and regional for colorectal; regional for prostate
Initial Conclusions
• Medicare increases the cancer detection rate by a
substantial amount
• Medicare’s effects on cancer detection are larger
for cancers with recommended screening tests
– Breast cancer detection rate: 6% increase
– Colorectal and prostate: 9% increases
– No impact on cervical cancer detection
• Impacts for non-screening cancers as well
• An important share of newly detected cases are
treatable and health improvements are likely
Effects by Pre-Medicare Insurance
Status
• We expect differential detection effects for
those uninsured pre-Medicare
– Large change in insurance status
– But quality of insurance may change for those
previously insured (ie, screening mandates)
• SEER does not include insurance status
Insured vs. Uninsured Analysis
• State Insurance Rates
– Variation in insurance rates at age 64 by
state, 2000-06
• 77% in Louisiana, 92% Michigan
– Correlate RD estimates of the increase in
insurance coverage from age 64 to 65 with
RD estimates of the change in cancer
detection
– Control for state and time fixed effects
Correlation of RD Estimates
Detection Linked to Changes in
Insurance Status
• State-level variation in health insurance
discontinuities due to Medicare can
explain 37-71% of the state-level variation
in BCPC detection due to Medicare
• We did not identify a similar relationship
with respect to Non-BCPC detection
• Suggests that the extensive margin is
important, but it’s possible that quality of
insurance coverage also plays a role
Conclusions
• Health insurance plays a role in improved
health in the context of chronic, latent
disease
• Medicare increases the cancer detection
rate by 6.4%, about 100 cancers per
100,000 individuals
• Larger detection effects for screening
cancers, but also effects for non-screening
• The increase in insurance rates at age 65
can account for a significant share of the
increase in screening cancer detection