Causal Effect of Managed Care
on Health Care Quality:
Evidence from Cancer Screening
Guideline Discontinuities
Srikanth Kadiyala*
Grant Miller**
Harvard University
Dr. Sandy MacColl [one of the founders of GHC]
wrote that he and his colleagues sought a “system
of family care…directed towards a goal of good
care, health maintenance and preventive services”
Crowley,To serve the greatest number:
A History of the GHC of Puget Sound
Funding: *Sloan Foundation, **NIH
Managed Care
• Held Great Promise for Quality
Improvements
– Lower Cost
– Appropriate Use of Medical Care
• Conventional View is that it has Failed
• We Contend Jury is Still Out
Previous Research
• Randomized Control Trial
– Rand HI experiment (late 1970s)
• Cross-Sectional Studies
– Selection problem since assignment to
insurance type is NOT random
– Control for observables
– Findings Equivocal
Cancer Screenings Recommendations
New Empirical Strategy
Discontinuity design using age-specific
preventive service guidelines
– Within plan comparisons of preventive service
use across guideline thresholds difference out
selection effects
– Guidelines are “bright lines”- Nodiscrete
increase in cancer risk at these ages
• U.S Preventive Task Force (USPSTF) and
American Cancer Society (ACS)
– Colorectal Cancer
• USPSTF & ACS – Both recommend screening for individuals age 40+
• No recommendation on screening technology
– Breast Cancer
• ACS-Recommended mammography for women ages 40+ since early 1980s
• USPSTF-Recently switched to 40+, previously 50+
• Thus we look for changes over both the 40 and 50 year thresholds
– Prostate Cancer
• USPSTF-Does not recommend PSA
• ACS-Physicians should offer PSA
• Screening is Recommended for these diseases
ONLY for asymptomatic people above a certain
age
• IOM/ Quality Chasm report: Cancer
Screenings UNDERUSED
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Natural Experiment
Framework
Managed Care
Preguideline
Regression Discontinuity
Preguideline
Post
Guideline
FFS
Managed Care
FFS
Post
Guideline
Post
Guideline
Post
Guideline
49
49
49
A
49
B
E
B
F
50
50
C
50
D
G
50
D
H
H
Difference-In-Difference-In-Difference =
[ (D-B)-(C-A) ] – [(H-F)-(G-E)]
•Diff.-In-Diff.=[(D-B)] – [(H-F)]
-This assumes that [(G-E)-(C-A)] is zero, which is a
plausible assumption
Data
Colorectal Cancer:
Any Screening in Last Year
by Plan and Age
• National Health Interview Survey(NHIS):
• National Sample of Individuals
• Breast Cancer (N=6807,Years 1998-2000)
• Colorectal Cancer (N=3426,Year 2000)
• Prostate Cancer(N=1543,Year 2000)
• Insurance Plan Types
• Group/Staff Models, IPA, POS, PPO, Fee-ForService(FFS)
• Rich Set of Covariates
– Income, Education, Race, Region, Marital Status
• Also MarketScan Data 1997-2001(these
results not reported)
Breast Cancer:
Mammogram Use in Last Year
by Plan and Age
50
45
40
35
30
25
20
15
10
5
0
F
40
35
30
25
All
Group/Staff
IPA
FFS
20
15
10
5
0
45-47
48-49
50-51
52-54
NHIS Data-Year 2000
Breast Cancer:
Mammogram Use in Last Year
by Plan and Age
60
50
All
Group/Staff
IPA
FFS
40
All
Group/Staff
IPA
FFS
30
20
10
0
35-37
38-39
40-41
NHIS Data: 1998-2000
42-44
45-47
48-49
50-51
52-54
NHIS Data: 1998-2000
2
Regression Discontinuity Estimate using Colorectal
Cancer: Means by Plan and Age Group
Prostate Cancer
PSA Test Use in Last Year
by Plan and Age
45
Plan
40
35
30
All
Group/Staff
IPA
FFS
25
20
15
10
5
0
45-47
48-49
50-51
52-54
NHIS DATA: Year 2000
Regression Estimates of Screening Use
Plan
Colorectal
Mam.(35-44)
Mam.(45-54) PSA
GHMO
-.092(.027)
-.086(.042)
.05(.05)
-.079(.07)
IHMO
-.008(.021)
-.055(.021)
.069(.026)
-.054(.036)
PPO
-.021(.023)
-.06 (.024)
.034(.030)
-.01(.044)
POS
.001(.032)
-.039(.027)
.017(.034)
-.038(.047)
GHMO*AGRP .24(.075)
.124(.065)
-.012(.07)
.07(.11)
IHMO*AGRP
.022(.029)
.087(.026)
-.014(.03)
-.007(.048)
PPO*AGRP
.035(.033)
.103(.03)
.001(.036)
.075(.065)
POS*AGRP
-.032(.04)
.078(.037)
.025(.045)
-.011(.07)
N
3426
6807
5629
1543
Standard Errors in parantheses. Bold indicates point estimate is significant at the 5%
level. Italics means significant at the 10% level.
Regression models adjust for age,sex,race, education, income,marital status, region
and time where appropriate.
Interpretation of Results
• Change in Use across Age thresholds
generally larger in Managed Care Plans
– Large statistically significant differences for
Colorectal and Breast Cancer screenings
– No Statistically Significant differences for
Prostate Cancer Screening
– Strongest Results for the Group/Staff Managed
Care Models
GHMO
(N=171)
Ages45-49 Ages50-54 Diff.-In-Diff .
Relative to FFS
2.3%
36%
[+33.7%] - [12.9%]
= +20.8%
(.017)
(.07)
IHMO
(N=1369)
12%
(.014)
21%
(.018)
[+9%] – [+12.9%]
= - 3.9%
PPO
(N=745)
11%
(.018)
22%
(.022)
[+11%] – [+12.9]
= -1.9%
FFS
(N=619)
8.8%
(.017)
21.7%
(.027)
+12.9%
POS
(N=520)
13.6%
(.029)
17%
(.027)
[+3.4%] – [+12.9]
= +9.5%
Results from Cross-Section
Regressions
Plan
Colorectal
Mam (35-44)
GHMO
.019 (.04)
-.02 (.036)
IHMO
.003 (.02)
-.009 (.02)
PPO
-.005 (.02)
-.006 (.02)
POS
-.012 (.027)
.003 (.02)
N
3426
6807
Standard Errors in parantheses. Bold indicates point estimate is significant at the 5% level.
Regression models adjust for age,sex,race, education, income,marital status, region and time
where appropriate.
Supply or Demand
• Survey data indicates individuals don’t know the
right age cutoffs
• We know whether people were offered screening
services in the 2000 NHIS data
– Using the same framework as above we find large
statistically significant changes in Offer rates across the
relevant age thresholds
• This indicates that supply side responses drive
changes in use over the age thresholds.
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Future Work
• How does Managed Care do it?
– Plan Characteristics
• Health Effects
• Other treatments with Age Thresholds
– Ex. Cholesterol Screening
4