The Importance of Unmeasured
Casemix Differences when Evaluating
the Outcomes of Premature Infants
Delivered at High Volume Neonatal
Intensive Care Units
SA Lorch, M Baiocchi, C Fager, DS Small
Department of Pediatrics
Pediatrics, The Children’s Hospital of
Philadelphia
Division of Statistics
Statistics, The Wharton School
Background
• Neonatal intensive care units (NICUs)
developed
p in 1970s to manage
g
premature infants.
• Because of the high costs
costs, a
“regionalized” system was proposed to
organize the system.
Studies on Regionalization
• St
Studies
di off NICU
NICUs suggestt th
thatt
outcomes improved at high-level
NICUs.
• Recently,
y though,
g many
y states report
p
increased de-regionalization of
neonatal care in the 1990s.
• Few studies of the impact of this
change.
Limitations in Current Literature
• Studies only examine mortality, not
other morbidities
• No data on state differences in this
association
• Studies do not account for unmeasured
difference in casemix between NICUs.
Traditional Casemix Adjustment
• Regression
R
i
Analysis
A l i
• “Adjust away” differences between hospitals
Figure 1: The Impact of Regionalization on Neonatal Outcome
Hospital Factors
(quality)
Structure
Degree of
Regionalization
Access to Regional
Perinatal Center
Neonatal
Outcomes in
Region
- Mortality
- Complications
- Failure-toRescue
- Readmission
Patient Factors
(Severity, SES)
• Examples of unmeasured factors include
– Clinical data,
data such as fetal heart tracing results
– Severity of maternal comorbid conditions
Unmeasured Confounding
• 2 methods
th d
– Randomized Trial
– Instrumental Variables Approach
• Instrument has 2 characteristics:
– Influences whether a patient receives the
treatment of interest
– Does not directly influence the outcome of
patient, except by treatment of interest
• May require some additional adjustment, for
measured factors such as race or SES
Instrumental Variables
Conceptual Framework
Figure 1: The Impact of Regionalization on Neonatal Outcome
Hospital Factors
Known Pt Factors
Structure
Degree of
Regionalization
Access to Regional
Perinatal Center
Neonatal
Outcomes in
Region
- Mortality
- Complications
Failure-toto
- Failure
Rescue
- Readmission
Instrument
A gold standard instrument is a random number used
to determine treatment in a clinical trial.
Objective
T determine
To
d t
i the
th association
i ti between
b t
delivery at a high-level NICU and
neonatal outcomes before and after
adjusting for unmeasured differences
in casemix
Patient Population
•
•
•
•
•
All deliveries
23-36
23
36 weeks gestational age
1995-2005
Pennsylvania, California, and Missouri
“High-level
High-level NICU
NICU” =
– Level III NICU
– 50+ deliveries/year of infant with birth
weight < 1500 grams
Data Sources
• Data Sources
– Birth and Death Certificates
– Maternal and Neonatal Hospital Records
• D
Data
t li
linked
k d by
b name, date
d t off birth,
bi th
race, gender, insurance status,
hospital, and zip code
• Successful linkage of 98-99%
98 99% of all
records.
Outcome Measures
• Neonatal death
• Fetal death
• Complications of premature birth
– Bronchopulmonary Dysplasia (BPD)
– Necrotizing
g Enterocolitis (NEC)
(
)
– Sepsis
– Retinopathy of Prematurity (ROP)
– Abdominal Surgery
Confounding Variables
• Maternal
M t
l Characteristics
Ch
t i ti
– Age
– Chronic medical conditions
– Antepartum
p
complications
p
– Race
– Education
• Infant Characteristics
– Birth
Bi th W
Weight
i ht and
d Gestational
G t ti
l age
– Presence of Congenital Anomalies
Instrument
• Uses prior data that women tend to
deliver at closest hospital.
p
• IV = difference in distance between
high level NICU and nearest delivery
hospital for a patient’s zip code
• S
Smaller
ll number:
b
live
li closer
l
to
t high
hi h level
l
l
NICU, and should deliver at high level
NICU att hi
higher
h ffrequencies
i
Data Analysis
• Unadjusted
U dj t d results
lt
• Measured casemix control:
– Propensity score analysis
• Unmeasured casemix control:
– Instrumental variables approach,
matching patients who live close and far
from a high-level NICU on maternal and
infant characteristics.
• Analyses performed for each state
individually.
Demographics
Pennsylvania
California
Missouri
HL
NICU
Other
NICU
HL
NICU
Other
NICU
HL
NICU
Other
NICU
Differential Travel
Ti
Time
(min)
( i )
6 97
6.97
21 81
21.81
3 45
3.45
14 52
14.52
14 99
14.99
39 50
39.50
Birth Weight
2,473
2,724
2,716
2,935
2,649
2,853
Gestational Age
34.66
35.72
34.85
35.42
34.71
35.32
White
0.64
0.78
0.59
0.60
0.78
0.75
Black
0 22
0.22
0 09
0.09
0 10
0.10
0 06
0.06
0 19
0.19
0 23
0.23
Asian
0.01
0.01
0.09
0.09
0.02
0.02
Other
0.03
0.03
0.20
0.23
0.01
0.01
0.81
0.87
0.88
0.92
0.86
0.91
Preterm Labor
0 49
0.49
0 40
0.40
0 34
0.34
0 22
0.22
0 37
0.37
0 27
0.27
PROM
0.21
0.15
0.12
0.09
0.16
0.11
PIH
0.12
0.08
0.08
0.05
0.10
0.08
Race
Singleton Birth
Maternal Complication
Strength
g of Instrument
Impact of Unmeasured Casemix
on Outcomes,
O t
PA 1995
1995-2005
2005
Results,
Unadjusted
RD
Neonatal
Death
Fetal
Death
1 09%
1.09%
0.76%
Results, adjusted for
measured factors
Results, adjusted for measured
and unmeasured factors
RR
RD
RR
RD
RR
1 88
1.88
-0.10%
( 0 21% 0
(-0.21%,
0.02%)
02%)
0.90
(0 78 1
(0.78,
1.02)
02)
-0.99%
( 1 36% -0.61%)
(-1.36%,
0 61%)
0.50
(0 32 0
(0.32,
0.69)
69)
2.73
-0.02%
(-0.1%, 0.06%)
0.94
(0.71, 1.17)
-0.05%
(-0.29%, 0.19%)
0.92
(0.55, 1.29)
Positive Risk Difference (RD) = higher mortality at highlevel NICU
Negative Risk Difference = lower mortality at high-level
NICU
Impact of Unmeasured Casemix
on Outcomes,
O t
PA 1995
1995-2005
2005
Results,
Unadjusted
RD
BPD
NEC
Fungal
Sepsis
Bacterial
Sepsis
ROP
ROP
g y
Surgery
Laparotomy
RR
Results, adjusted for
measured factors
Results, adjusted for measured
and unmeasured factors
RD
RR
RD
RR
1.86% 3.83
0.49%
(0.37%, 0.6%)
1.69
(1.52, 1.85)
0.05%
(-0.31%, 0.41%)
1.03
(0.83, 1.22)
0.63% 2.98
0.09%
(0%, 0.17%)
1.25
(1.00, 1.49)
0.16%
(-0.09%, 0.41%)
1.25
(0.86, 1.65)
0.77% 2.97
0.43%
(0.32%, 0.54%)
2.07
(1.80, 2.33)
0.50%
(0.24%, 0.77%)
1.77
(1.36, 2.17)
2.48% 1.94
0.19%
(-0.03%, 0.41%)
1.07
(0.99, 1.15)
0.96%
(0.40%, 1.51%)
1.28
(1.12, 1.44)
0.77% 3.28
0 5%
0.15%
(0.06%, 0.24%)
1.44
(1.19, 1.70)
-0.10%
0 0%
(-0.35%, 0.16%)
08
0.87
(0.54, 1.21)
0.12% 3.79
0.03%
((-0.01%, 0.06%))
1.58
((0.87, 2.29))
-0.10%
((-0.21%, 0.02%))
0.38
((-0.33, 1.10))
0.18% 1.97
0.02%
(-0.03%, 0.07%)
1.18
(0.77, 1.59)
-0.05%
(-0.21%, 0.11%)
0.82
(0.26, 1.38)
Unadjusted and Adjusted
Outcomes
Conclusions
• Unmeasured differences in casemix
contribute to lack of difference in
outcomes between hospitals.
• Different results depending on the
outcome studied.
• Large differences between states.
Implications for Policy
• Ignoring unmeasured casemix differences
may provide support for a deregionalized
system of perinatal care.
• Delivery
y location remains important to
optimize perinatal outcomes.
• The instrumental variables approach may
improve the assessment of outcomes while
awaiting future EHR methods to control for
differences in casemix.
Thanks
• Departments of Health
– California
– Pennsylvania
– Missouri
• AHRQ R01 HS015696