An end-of-life
end of life quality of care
measure ((QM)) for nursing
g
homes based on MDS and
Medicare claims data
Dana B. Mukamel,
k
Thomas Caprio, Richardd Ahn,
A
Nan Tracy Zheng, Helena Temkin-Greener
The University of California, Irvine
We gratefully acknowledge funding by NINR grant # 010727
 One
in three Americans dies in a
nursing
i h
home or iin a h
hospital
i l shortly
h l
after transfer from a nursing home
Nursing homes are responsible for the
quality
li off end-of-life
d f lif (EOL) care for
f an
estimated 500,000
,
people
p p annuallyy
 Current
research suggests poor EOL
care in nursing homes:
Pain
 Low rates of advance directives
 Inappropriate hospitalizations


Nursingg Home Compare
p does not include
QMs specific to EOL care
Studyy Objective:
j
 To
develop a Quality Measure (QM)
for assessment of EOL care that can
be calculated from administrative data
for all US nursing homes
The “hospitalization
hospitalization at the EOL
EOL” QM
reflects the expectation that, when
appropriate, nursing home residents are
b tt r off
better
ff avoiding
idi transfer
tr f r to
t the
th
hospital
p to die.
QM Definition:
Q
QM = Observed rate – Expected rate
Expected rate: Risk-adjusted, reflecting
practice styles of the average nursing home
QM > 0
QM = 0
QM < 0
poor quality
li
average quality
high quality
Data and Sample:
p



Minimum Data Set (MDS)
Hospital Claims
Medi are Enrollment files
Medicare
SStudy
d cohort:
h
N
Nursing
i h
home residents
id
iin the
h llast 90 ddays
prior to death excluding managed care



National data for 2003 - 2007
2,526,289 decedents
15,913 nursing homes
Approach:
pp

Focus on long-term care residents

Development and tests data sets

Identify initial set of risk factors based on
literat re review
literature
re ie and clinical judgment
j dgment

Estimate risk adjustment models for two types
of patient/information strata predicting death in
th hospital
the
h it l or nott

Construct QM
Imputation/Information
p
/
Types:
yp

Risk factors derived from last assessment prior
to death

Information content varies by assessment
Full information: Admission, Annual, Significant change in health
status
P i l Information:
Partial
I f
i Quarterly
Q
l assessments
To maximize
T
i i the
h use off information
i f
i we imputed
i
d information
i f
i
from prior assessments and estimated separate models
Descriptive Statistics
Mean
Standard Deviation
Hospitalization
p
rate
0.20
0.39
Male
0.31
0.47
Do Not Resuscitate
0.74
Do Not Hospitalize
0.08
Diabetes Mellitus
0 27
0.27
HIV
0.02 x 10-2
Tuberculosis
0.04 x 10-2
Cancer
0.18
R
Renal
l Failure
F il
0 11
0.11
Pneumonia
0.11
Septicemia
0.02
Internal Bleeding
0.02
Hip Fracture in Last 180 Days
0.03
Other Fracture in Last 180 Days
0.03
Feeding Tube
0.09
Dialysis
0.02
Radiation
0.04 x 10-1
Tracheostomy Care
0.01
Has Either Asthma, COPD or Both
0.24
Pressure or Stasis Ulcer
0.22
Age in Years
86.30
7.86
Cardiovascular Disease
1.47
1.12
Musculoskeletal Disease
0.08
0.28
Ne rological Disease
Neurological
0 32
0.32
0 55
0.55
Models predicting hospitalizations for LTC patients
Full Information Quarterly Imputed
Male
0.068***
0.089***
Age
-0.334***
-0.443***
Age Squared
0.440 x 10-2***
0.582 x 10-2***
Age Cubed
-0.198 x 10-4***
-0.259 x 10-4***
Do Not Resuscitate
-0.692***
-0.630***
Do Not Hospitalize
-0.626***
-0.809***
Diabetes
0.113***
0.194***
Cardiovascular Disease
0.063***
0.090***
Musculoskeletal Disease
0.062***
N/A
Neurological Disease
-0.049***
N/A
Asthma, COPD or Both
0.116***
0.119***
Pressure or Stasis Ulcer Stage 2 or Higher
-0.199***
-0.262***
Cancer
-0.078***
N/A
-0.034**
N/A
-0.118***
N/A
N/S
0.406**
Septicemia
-0.102***
N/A
Internal Bleeding
d
-0.094***
N/A
/
Hip Fracture in Last 180 Days
-0.074***
N/S
Other Fracture in Last 180 Days
0.053***
0.139***
Feeding Tube
0.154***
0.072***
Di l i
Dialysis
0 417***
0.417***
N/A
Radiation
-0.131**
N/A
-0.305***
N/A
0.61
0.63
748 611
748,611
718 994
718,994
Renal Failure
Pneumonia
Tuberculosis
Tracheostomy Care
C Statistic
N
**p < 0.01
***p < 0.001
Properties of QMs (2006)
All Nursing
Homes
Number of nursing homes nationally
15,913
Nursing Homes
with More than 17
Decedents
8,127
Mean
0.012
0.009
Standard deviation
0.159
0.152
2003
0.42 (0.000)
0.62 (0.000)
2004
0.47 (0.000)
0.68 (0.000)
2005
0.51 (0.000)
0.74 (0.000)
Correlation with the QM in:
Stabilityy of the QM
Q in Identifying
y g
High Quality Outliers: 2005 vs. 2006
Outliers defined as top decile in 2005:
All Nursing
Homes
N b off nursing
Number
i h
homes
15 036
15,036
% remaining high quality in 2006
25.6%
Outliers defined as top quartile in 2005:
All Nursing
Homes
Number of nursing homes
15,036
% remaining high quality in 2006
43.8%
Nursing Homes Nursing Homes
with More than with More than 30
17 Decedents
Decedents
6 892
6,892
2 225
2,225
32.5%
37.6%
Nursing Homes Nursing Homes
with More than with More than 30
17 Decedents
Decedents
6,892
2,225
49.5%
55.5%
Stabilityy of the QM
Q in Identifying
y g
Low Quality Outliers: 2005 vs. 2006
Outliers defined as bottom decile in 2005:
All Nursing Nursing Homes Nursing Homes
Homes with More than 17 with More than 30
Decedents
Decedents
Number of nursing homes
15,036
5,036
6,892
6,89
2,225
, 5
% remaining high quality in 2006
47.7%
63.9%
70.6%
Outliers defined as top quartile
il in 2005:
All Nursing Nursing Homes Nursing Homes
Homes with More than 17 with More than 30
D d
Decedents
D d
Decedents
Number of nursing homes
15,036
6,892
2,225
% remaining high quality in 2006
56.3%
66.7%
73.0%
Conclusions:

It is feasible to use the MDS data merged to
Medicare hospitalization claims to predict risk
riskadjusted hospitalizations prior to death

The risk-adjustment
risk adjustment model has prediction properties
(C statistic) comparable to other risk adjustment
ode s for
o this
s popu
population
a o
models

QMs are moderately correlated over time with about
50% of nursing homes staying within one decile
between consecutive years

The QMs
Th
QM are better
b tt att identifying
id tif i llow quality
lit nursing
i
homes and stability increases if reporting is
restricted to larger nursing homes