Introduction


The Question: Is HMO market share associated
with adoption of cardiac-care technologies, and,
in turn with treatments and outcomes for heart
attack patients
Approach:
Use hospital-level hazard models to study
relationships between HMO market share and
adoption
 Use patient-level models to study relationship
between availability and treatments and outcomes

Cardiac Care Technologies

We focus on three cardiac technologies







Diagnostic: Cardiac catheterization
Therapeutic: PTCA
Therapeutic: CABG
All involve the adoption of equipment and staff
Catheterization and CABG first developed in the
1960s; PTCA in the 1970s
Catheterization equipment is used to do PTCA
PTCA and CABG are usually adopted together
Hospital-Level Data


We focus on 2,873 hospitals in MSAs in operation in
1985
We use Medicare Claims data from 1985-2000 to
identify hospitals that adopt these technologies and the
year of adoption


Hospitals with 10 claims for a given service in a calendar year
are defined as having the technology in that year
Based on patterns in the data, we study 3 adoption
states: none, catheterization only, and all techologies
Hospital-Level Data

We classify hospitals according to the average 19901999 HMO market share in their MSA



Low: <10%
Medium: 10-30%
High: >30%
Hospital-Level Adoption Modeling

Discrete-time hazard models
Competing risks for probability of moving from
none to cath only or none to all
 Standard hazard model for probability of moving
from cath only to all


Controls include a range of potential
confounders, including urbanization,
demographics, hospital characteristics
Hazard Model Results
None to Cath Only
Medium HMO
0.174
(0.119)
[1.190]
0.170
(0.120)
[1.186]
0.175
(0.120)
[1.191]
High HMO
0.616 **
(0.213)
[1.851]
0.611 **
(0.215)
[1.843]
0.611 **
(0.215)
[1.842]
0.711 **
(0.127)
0.713 **
(0.127)
CICU in 1982
---
MSA AMI mortality
---
---
0.703
(1.381)
yes
yes
yes
State dummies
Standard errors in parentheses. Relative Hazards in brackets. Models include additional covariates. * denotes p<0.05; ** denotes p<0.01
Hazard Model Results
Cath Only to All
Medium HMO
-0.202
(0.134)
[0.817]
-0.209
(0.134)
[0.812]
-0.223
(0.136)
[0.800]
High HMO
-0.431 #
(0.231)
[0.650]
-0.426 #
(0.231)
[0.653]
-0.440 #
(0.231)
[0.644]
0.501 *
(0.225)
0.505 *
(0.227)
CICU in 1982
---
MSA AMI mortality
---
---
-0.815
(1.330)
yes
yes
yes
State dummies
Standard errors in parentheses. Relative Hazards in brackets. Models include additional covariates. * denotes p<0.05; ** denotes p<0.01
Technology availability, treatments,
and outcomes



HMO activity affects the probability a heart
attack patient will be treated in a hospital with
the technology
Whether or not the hospital of treatment has
the technology affects the probability of actually
receiving a treatment
Receiving treatments affects mortality rates
Medicare AMI Data




Claims data on a 20% sample of FFS Medicare
patients in MSAs with a new AMI between 1996
and 2000
N=148,170
Measure technology status of index hospital,
treatment receipt within 90 days of initial
admission, and 1 year mortality
Data also contain detailed data on comorbidities
and other characteristics
Statistics


Estimate individual-level models
Control for a range of characteristics

sex; race; age; admission in the prior 2 years for IHD, CHF,
VA, or any other cause; conditions at admission: cancer,
diabetes, dementia, heart failure, hypertension, stroke,
peripheral vascular disease, chronic obstructive pulmonary
disease, respiratory failure, renal failure, or hip fracture; area
per capita income, total area population and population
density; % population graduated high school/college; % of
the work force white collar; squared terms for area
characteristics; year
Index Hospital Capabilities
Multinomial Logit
(results relative to all technologies)
Probability that index hospital has
Nothing
Cath only
Medium HMO
-0.004
(0.068)
0.220 **
(0.050)
High HMO
-0.586 **
(0.089)
0.430 **
(0.071)
% of cases
12.5
29.4
Models are multinomial logit regressions and include additional covariates and state dummies as well as interactions between HMO
variables and year. * denotes p<0.05; ** denotes p<0.01
Treatments Received
Multinomial Logit
(relative to medical management)
Cath
Index Facility Cath Only
Index Facility All
N
Percentage of all cases
PTCA
CABG
-0.474 **
0.059
-0.174 **
0.058
-0.109 #
0.057
0.475 **
0.040
0.979 **
0.040
0.603 **
0.041
148,170
0.149
148,170
0.225
148,170
0.154
Models are multinomial logistic regressions of the probability of receiving cath, PTCA, CABG, or medical management within 90
days of initial hospitalization. Models include additional covariates, state dummies, and interactions between tech variables and year.
# denotes p<0.10, * denotes p<0.05; ** denotes p<0.01
Treatments and 1-year Mortality
Logistic Regression
Patient got Catheterization
-0.222 **
(0.007)
Patient got PTCA
-0.292 **
(0.007)
Patient got CABG
-0.295 **
(0.007)
N
DV mean
148,170
0.339
From logistic regression of the probability of 1 year mortality. Models include additional covariates, state dummies, and interactions between
tech variables and year. # denotes p<0.10, * denotes p<0.05; ** denotes p<0.01
HMO Market Share and 1-Year
Mortality
Logistic Regression
Coefficient
(SE)
P-value
medium HMO
0.013
(0.009)
0.128
high HMO
0.017
(0.013)
0.188
N
DV mean
148,170
0.339
From logistic regression of the probability of 1 year mortality. Models include additional covariates, state dummies, and interactions between
HMO variables and year. # denotes p<0.10, * denotes p<0.05; ** denotes p<0.01
Conclusions


Managed care activity affected the adoption
of cardiac technologies
This could well be associated with worse
outcomes for AMI patients

impacts on other patients, and other outcomes,
are unknown
Means of Hospital Level Variables
Low HMO prevalence
Medium HMO prevalence
High HMO prevalence
Teaching hospital
Medical school affiliation
Total beds, hundreds
MSA per capita income, $ thousands
MSA percentage of population - urban
MSA percentage of population - urban squared
MSA percentage of population - 65 and older
MSA percentage of population - high school
MSA percentage of population - college
MSA population, millions
MSA population, millions squared
MSA population density, millions/mile
MSA population density squared
MSA hospitals per thousand population
MSA generalists per thousand population
MSA specialists per thousand population
MSA cardiologists per thousand population
Mean Standard Deviation
0.148
0.355
0.653
0.476
0.199
0.400
0.289
0.453
0.265
0.442
2.63
2.03
19.4
3.49
0.842
0.129
0.725
0.202
0.121
0.030
0.766
0.058
0.217
0.057
2.01
2.35
9.56
20.2
0.968
1.49
3.15
12.10
0.023
0.008
0.082
0.033
0.722
0.371
0.065
0.036
N
2,873
Kaplan-Meier Adoption Probabilities for PTCA and
CABG, 1985-2000
0.45
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
Cumulative Adoption Probability
0.40
PTCA
CABG
Mortality
patient got catheterization
catheterization*1997
catheterization*1998
catheterization*1999
catheterization*2000
patient got PTCA
PTCA*1997
PTCA*1998
PTCA*1999
PTCA*2000
patient got CABG
CABG*1997
CABG*1998
CABG*1999
CABG*2000
coefficient
-0.218
-0.002
-0.004
0.008
0.002
-0.287
0.000
-0.003
-0.003
0.010
-0.292
0.005
-0.001
-0.002
0.014
SE
0.007 **
0.011
0.011
0.011
0.011
0.007 **
0.010
0.010
0.010
0.010
0.007 **
0.011
0.011
0.011
0.011
Models are OLS regressions of the probability of 1 year all-cause mortality. Models include additional covariates. * denotes p<0.05;
** denotes p<0.01
Predicted PTCA adoption probability in
low, medium, and high HMO markets
Cumulative Probability of PTCA adoption
1.000
0.900
Cumulative Adoption Probability
0.800
0.700
0.600
low HMO
0.500
medium HMO
high HMO
0.400
0.300
0.200
0.100
0.000
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Year
Predicted CABG adoption probability in
low, medium, and high HMO markets
Cumulative Probability of CABG adoption
1.000
0.900
Cumulative Adoption Probability
0.800
0.700
0.600
low HMO
0.500
medium HMO
high HMO
0.400
0.300
0.200
0.100
0.000
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Year