Fredric D. Wolinsky
The University of Iowa
June 29
J
29, 2010
Academy Health Annual Research Meeting
Collaborators
Suzanne Bentler
Maksym Obrizan
Paula Weigel
Mike Jones
Jason Hockenberry
Brian Kaskie
Robert Ohsfeldt
Gary Rosenthal
Robert Wallace
Support
Over the years, my work on this issue has been
generously supported by several NIH Grants:
 K04-AG-00328
 R01-AG-06618
R01 AG 06618
 R37 AG-09692
 R01 AG
AG-022913
022913
 R03 AG-027741
 R21 AG
AG-030333
030333
 R21 AG-031307
Prior Hospitalizations and the
S bseq ent Use of Health Ser
Subsequent
Services
ices
 For at least four decades
decades, it has been shown that having
been hospitalized in period “A” (pre-baseline) predicts the
use of health services in period “B” (post-baseline)
 At least five explanations of why have been offered:
 The propensity (or capacity) to institutionalize
 Learned (or patterned) behavior
 Reflects poorly or unmeasured health needs
 Frailty (circling the drain, terminal drop)
 Lack of support, access, continuity of care, etc.
Re-Hospitalization as an Offshoot of
Prior Hospitalizations
 Re-hospitalization is a serious and widespread problem
 In
I 1984 A
Anderson
d
and
d St
Steinberg
i b
reported
t d th
thatt 22
22.5%
5% off
all hospitalized Medicare beneficiaries were re-admitted
within 60 days of their index hospital episode
 Last year, Jencks et al. showed that 60-day re-
hospitalization rates among Medicare beneficiaries had
risen to 28.2%, reflecting reduced lengths of stay in index
hospitalizations and the rise of ambulatory surgery
hospitalizations,
Number (and Percent) of Medicare Re-hospitalizations and Deaths
(adapted from Jencks et al., 2009 NEJM).
Interval after
Discharge
Patients at Risk at Beginning
of Period
Cumulative Rehospitalizations
by End of Period
Cumulative Deaths without
Re-hospitalization by End of
Period
0-30 days
2,961,460 (100.0)
579,903 (19.6)
103,741 (3.5)
31-60 days
2,277,816 (76.9)
834,369 (28.2)
134,697 (4.5)
61-90 days
1,992,394 (67.3)
1,006,762 (34.0)
151,901 (5.1)
91-180 days
1 802 797 (60
1,802,797
(60.9)
9)
1 325 645 (44
1,325,645
(44.8)
8)
177 234 (6.0)
177,234
(6 0)
181-365 days
1,458,581 (49.3)
1,661,396 (56.1)
200,852 (6.8)
>365 days
1,099,212 (37.1)
Predictors of 30-Day Re-Hospitalization (adapted from Jencks et al., 2009 NEJM).
Variable
Hospital's ratio of observed to expected hospitalizations
National re-hospitalization rate for DRG
Number of re-hospitalizations since October 1, 2003
0
1
2
≥3
Length of stay
sta
>2 times that expected for DRG
0.5-2 times that expected for DRG
<0.5 times that expected for DRG
Race
Black
Other
Disability
End-stage renal disease
R
Receipt
i t off Supplemental
S
l
t lS
Security
it IIncome
Male sex
Age
<55 yr
55-64
55
64 yr
65-69 yr
70-74 yr
75-79 yr
80-84 yr
85-89 yr
>89 yr
Hazard Ratio (95%
Confidence Interval)
1.097 (1.096-1.098)
1.268 (1.267-1.270)
1.00
1.378 (1.374-1.383)
1.752 (1.746-1.759)
2.504 (2.495-2.513)
1.266 (1.261-1.272)
1.00
0.875 (0.872-0.877)
1.057 (1.053-1.061)
1.00
1.130 (1.119-1.141)
1.417 (1.409-1.425)
1 117 (1
1.117
(1.113-1.122)
113 1 122)
1.056 (1.053-1.059)
1.00
0.983 (0.978-0.988)
(0.978 0.988)
0.999 (0.989-1.009)
1.023 (1.012-1.035)
1.071 (1.059-1.084)
1.101 (1.089-1.113)
1.123 (1.111-1.136)
1.118 (1.105-1.131)
An Alternative Approach:
Prior Hospitalizations as Health Shocks
 Conceptually consistent with the human capital and
health capital perspectives of Becker and Grossman
Grossman,
such that health shocks can be defined as stochastic
changes
g in health stocks
 It is generally assumed that health shocks are
unanticipated drops in the value of one’s health stock
 Prior hospitalizations are good indicators of health
shocks because they reflect “sea changes”
 Health shocks capture the dynamic nature of health
stocks, especially when treated as time dependent
covariates
Overview of Our Approach
 AHEAD is a large, nationally representative sample
whose participants were > 70 years old at their baseline
interviews in 1993-1994
 Medicare claims for calendar years 1993
1993-2007
2007 were
linked for post-baseline health shock surveillance
 Baseline interviews p
provided an array
y of covariates
 Propensity score methods adjusted for selection bias
Data
 Data weighted to adjust for multi
multi-stage
stage cluster and overover
sampling of African Americans, Hispanics, and Floridians
 Baseline response rate of 80
80.4%
4% (N=7
(N=7,447)
447)
 Analyses restricted to 5,511 AHEAD subjects who were:
 self-respondents
self respondents at baseline
 linkable to their Medicare claims
 not enrolled in managed Medicare at baseline
 Participants were censored at the time of either of two
competing
p
g risks—death or enrollment in managed
g care
Selection Bias
 To adjust for potential selection bias due to the three
exclusions, we used propensity score methods to reexclusions
weight the data
 Multiple logistic regression modeled inclusion in the
analytic sample using baseline interview data
 The fit of this model was good
g
 C statistic = .72
 Hosmer-Lemeshow statistic = .15
Selection Bias…
 Within each propensity score (predicted probability)
decile we determined the average participation rate (P)
 The inverse (1/P) was used to re-weight the data
 This gives greater influence to participants in the analytic
sample most like those not included
 The propensity score weights were adjusted so that the
final weighted N was equal to the actual number of
participants in the analytic sample (i.e., 5,511)
Measurement and Modeling
 Surveillance started at the baseline interview date
 Hip fractures
fractures, strokes
strokes, and heart attacks were identified




using appropriate ICD9-CM diagnostic codes
These outcomes had to occur at least one day after the
baseline interview
Multivariable proportional hazards regression with
competing risks were used
Model building
g and evaluation used standard p
procedures
Covariates were selected separately for each outcome
Baseline Covariates
 Demographics
 Socioeconomics
 Geographic Factors
 Health
H lth B
Behaviors
h i
 Disease History
 Functional
F
ti
l Status
St t
 Cognitive Status
Health Shocks
 Time-dependent covariates were switched “on” when the
person was admitted to a hospital for a primary ICD9-CM
diagnosis other than the model outcome
 It stayed “on”
on for n days after discharge, and was then
switched “off”
 It could be switched back “on” at the onset of another
pre-censoring hospital admission for something other
than the outcome in the model
 Sensitivity analyses determined which of several values
of n was most predictive of the target outcome
Health Shocks…
 Health shocks were experienced by about 80%
 Hip fractures were experienced by 8
8.9%
9% (N=491)
 Strokes were experienced by:
 9.9%
9 9% (N=545) using high sensitivity algorithm
 6.8% (N=374) using high specificity algorithm
 Heart attacks were experienced by 8
8.8%
8% (N=483)
 There were 40-45K person-years of surveillance (mean
of 7
7-8
8 years per-person)
per person)
Descriptive Results
 mean age was 77
 mean income was
 38% were men
$25,417
$25
417
 25% had arthritis
 9% had angina
 13% had cancer
 12% had diabetes
 46% had hypertension
 10% were African
American
 4% were Hispanic
 41% were widowed
 25% had only been to
grade school
The Bottom Line on Health Shocks
Adjusted Hazard Ratios (AHRs) for Time-Dependent Prior Hospitalization Marker by Selected Catchment Periods
for Hip
p Fracture,, Stroke and Heart Attack
Hip
Fracture
(n=5,511)
Stroke
Sensitivity
(n=5,511)
7 days
2.03
14 days
Catchment
Periods
Stroke
Specificity
(n=5,511)
5.36
First-Ever
Stroke
Sensitivity
(n 5 039)
(n=5,039)
5.80
2.90
First-Ever
Stroke
Specificity
(n 5 039)
(n=5,039)
3.37
2.51
4.65
4.98
3.10
30 days
1.91
3.53
3.60
60 days
1.93
3.04
90 days
1.97
6 months
Heart
Attack
(n=5,511)
First-Ever
Heart Attack
(n=5,137)
4.66
5.38
3.41
3.90
4.20
2.82
2.94
2.86
2.91
3.23
2.73
2.95
2.81
2.76
2.98
3.13
2.78
2.98
2.58
2.39
1.84
2.75
2.89
2.49
2.66
1.99
1.93
1 year
1.79
2.37
2.50
2.09
2.19
1.82
1.74
1.5 years
1.68
2.16
2.18
1.89
1.87
1.79
1.74
2 years
1.56
2.01
2.05
1.78
1.79
1.65
1.60
Sources: J Gerontol: Med Sci, 2009;64A:249-255 (hip fracture)
BMC Geriatrics, 2009;9(17):1-11 (stroke)
J Gerontol: Med Sci, 2010;65A769-777 ((heart attack))
Discussion and Implications
 Health shocks significantly and substantially improved the fit of




all of three risk models
The health shocks did not diminish the effects of the known
risk factors, but represent new (previously unmeasured) risks
Health shocks are NOT the index visits for re-hospitalizations
Peak health shock effects occur in the first two weeks,
suggesting
ti clear
l
value
l iin post-discharge
t di h
monitoring
it i and
d
intervention during transition periods
Future work should examine whether some health shocks are
more important for specific health outcomes than others
and as always,
always
may the Lord’s Blessings be upon you,
as they have been upon me