E ti ti the
Estimating
th Comparative
C
ti
Efficiency of Inpatient Care Settings
for Pre
Pre--term Birth
Glen P.
P Mays,
Mays PhD
Department of Health Policy and Management
Fay W. Boozman College of Public Health
University of Arkansas for Medical Sciences
Acknowledgements
Supported by Award Number 1UL1RR029884 from the
NIH National Center for Research Resources to the
UAMS Center for Clinical and Translational Research
Hospital discharge and vital statistics data provided by
the Arkansas Department of Health
Research assistance provided by Health Gauss, MS
Motivation
Hospitals vary in capacity to care for acutely premature
and low birth weight (LBW) neonates
Prior studies suggest improved outcomes for LBW
delivered at NICU-equipped hospitals
Acutely ill neonates delivered at non-NICU
non NICU hospitals
may face higher risks of complication and delays in
treatment, thereby requiring longer lengths of stay and
higher resource use
Perinatal Care System in Arkansas
Four facilities in Arkansas and surrounding states
provide advanced NICU care: regional academic
perinatal centers with 24-hour neonatologist and
maternal-fetal medicine coverage
Expanding numbers of community hospitals have NICUs
with neonatologist coverage (Level III facilities) –
currently 6 facilities
Remaining 42 hospitals provide basic neonatal care
Research Question and Challenges
Are there differences in resource use for LBW infants
delivered at NICU v. non-NICU facilities?
Selection Problem: complex and high-risk cases are
more likely to be referred to higher-level facilities for
delivery
Censoring Problem: facilities may differ in rates of inin
hospital mortality due to case mix and quality
differences, thereby distorting measures of resource use
Methods
Dependent variable: infant total length of stay (days) at
delivery and transfer hospitals during delivery episode
Explanatory variable: Type of delivery hospital
(Level 4, 3, or 0-2), regardless of subsequent transfer
Censoring variable: Infant death prior to discharge
Control variables: maternal risks
risks, demographics
demographics,
insurance status, prenatal care
Data: Hospital discharge records on all LBW infants
(500g-2499g) born in Arkansas during 2001-04
Comparison of three analytic models
1. Single-equation model (Negative Binomial)
E(LOSit)
=Xitβ+Hosplev4itδ+Hosplev3itλ+υt+εit
2. Single-equation model with mortality control (Negative Binomial)
E(LOSit)
=X
Xitβ
β+Hosplev4
Hosplev4itδ
δ+Hosplev3
Hosplev3itλ
λ+Mort
Mortitη
η+υ
υt+εεit
3. Instrumental variables model with selection and censoring correction
(Multinomial logit, Logit, Negative Binomial)
Pr(Hosplevit=4)
=Φ(Xitβ4+Zitθ4+υt+ρ4μi)
Pr(Hosplevit<3)
Pr(Hosplevit=3)
=Φ(Xitβ3+Zitθ3+υt+ρ3μi)
Pr(Hosplevit<3)
Pr(Mortit=1)
=Φ(Xitβm+Hosplev4itδm+Hosplev3itλm+υt+ρmμi)
E(LOSit)
=Xitβ+Hosplev4itδ+Hosplev3itλ+Mortitη+υt+ ρLμi+εit
Instrumental variables model
Identify variables that are predictive of hospital type but
unrelated to mortality & LOS
These variables “mimic” randomization
Plausible candidates: differential distance from patient
residence to nearest hospital of each level
Results apply
patients whose hospital
“choice” is
pp y only
y to p
p
influenced in part by differential distance
Instrumental variables model
Use discrete factor approximations to account for
unobserved heterogeneity that simultaneously influences
hospital selection, mortality, and length of stay
Offers improved precision over bivariate probit
Pr(Hosplevit=4)
=Φ(Xitβ4+Zitθ4+υt+ρ4μi)
Pr(Hosplevit<3)
Pr(Hosplevit=3)
=Φ(Xitβ3+Zitθ3+υt+ρ3μi)
Pr(Hosplevit<3)
Pr(Mortit=1)
1)
=Φ(X
Φ(Xitβm+Hosplev4itδm+Hosplev3itλm+υt+ρmμi)
E(LOSit)
=Xitβ+Hosplev4itδ+Hosplev3itλ+Mortitη+υt+ ρLμi+εit
Mroz and Guilkey 1999
Data
Hospital discharge data records for all births to Arkansas
residents during CY 2001-2004
Linked with birth records and death records
Limited to births <2500 grams
Limited to singleton births
12 258 births
Total of 12,258
15.3%
<1500 grams
18 8%
18.8%
1500
1500-1999
1999 grams
62.8%
2000-2499 grams
Control variables used
Birth weight
Gestational age
Maternal race & ethnicity
Maternal age
Maternal education
Insurance source
Prenatal care adequacy
Smoking and other maternal risks
Characteristics by
y Hospital
p
Type
yp
Hospital Type (Mean)
Variable
Birth weight (g)
Gestational age (weeks)
Preterm (%)
Prenatal visits ((#))
Maternal risks (#)
Congenital anomalies (%)
In-hospital mortality (%)
Hospital LOS (days)
Regional
NICU
n=2205
Community
NICU
n=3606
Non-NICU
n=6448
1689
32.72
88.71
7.70
0.69
1963 **
34.61 **
73.38 **
9.81 **
0.41
2132 **
35.78 **
65.42 **
9.87 **
0.44
4 99
4.99
3 00
3.00
2 57 **
2.57
4.44
21 00
21.00
2.92 **
14 99 **
14.99
2.35 **
8 10 **
8.10
Multivariate Results: LOS Analysis
Model
IRR
95% CI
1 Si
1.
Single
l equation
i
Regional NICU†
1.25
1.23, 1.27
Community NICU†
1.32
1.30, 1.34
2. Single equation controlling for mortality
g
NICU †
1.13
1.12,, 1.15
Regional
Community NICU†
1.24
1.22, 1.26
In-hospital mortality
0.74
0.53, 0.95
3 IV M
3.
Model
d l controlling
t lli ffor h
hospital
it l selection
l ti & mortality
t lit
Regional NICU †
0.93
0.88, 0.98
Communityy NICU†
1.17
1.14,, 1.20
In-hospital mortality
0.23
0.11, 0.36
IRR = Adjusted incidence rate ratio, from negative binomial model
†Reference = Community hospitals without NICU
Multivariate Results: In
In--hospital Mortality
Model
1. Single equation
g
NICU†
Regional
Community NICU†
2 IV model
2.
Regional NICU†
Community NICU†
† Reference
OR
95% CI
0.50
0.62
0.36,, 0.71
0.44, 0.88
0.63
0 63
0.63
0.42, 0.89
0 41 0
0.41,
0.90
90
= Community hospital without NICU
Discussion
Substantial
S b t ti l selection
l ti bias
bi and
d mortality
t lit
censoring exists when estimating LOS
differences via conventional methods
When correcting for these problems, LOS
appears lower
lo er at regional perinatal NICUs than
at other facilities, especially for very LBW infants
th d allow
ll
t
l mortality
t lit
IV methods
us tto di
disentangle
differences from LOS differences when
comparing alternative types of hospitals
Discussion
Community NICU hospitals appear to be the costliest
settings for VLBW pre-term deliveries, due to a
combination of lower adjusted in-hospital mortality and
higher adjusted LOS.
Higher LOS appears attributable in part to neonatal
t
transports
t
The adjusted LOS differences imply that inpatient cost
savings are possible by shifting patterns of delivery
• 12-15% reduction for each VLBW delivery shifted
from non
non-NICU
NICU to regional NICU settings
• 18-23% reduction for each delivery shifted from
community NICU to regional NICU settings.
Discussion
Longer
term outcomes and outpatient care/costs
Longer-term
need to be examined
Arkansas currently exploring mechanisms to shift
patterns of delivery through statewide guidelines,
telemedicine consultations
ACOs may provide a future mechanism for shifting
perinatal care to the most effective and efficient settings