Program Characteristics and Enrollees’Outcomes in the Program of All-InclusiveCare for the Elderly (PACE)
DANA B. MUKAMEL , DERICK R. PETERSON,HELENA TEMKIN-GREENER, RACHEL DELAVAN,DIANE GROSS , STEPHEN J . KUNITZ, andT. FRANKLIN WILLIAMS
University of California at Irvine; University of Rochester
The Program of All-Inclusive Care for the Elderly (PACE) is a unique pro-gram providing a full spectrum of health care services, from primary to acuteto long-term care for frail elderly individuals certified to require nursing homecare. The objective of this article is to identify program characteristics asso-ciated with better risk-adjusted health outcomes: mortality, functional status,and self-assessed health. The article examines statistical analyses of informa-tion combining DataPACE (individual-level clinical data), a survey of directcare staff about team performance, and interviews with management in twenty-three PACE programs. Several program characteristics were associated withbetter functional outcomes. Fewer were associated with long-term self-assessedhealth, and only one with mortality. These findings offer strategies that maylead to better care.
Keywords: PACE, long-term care, quality, risk-adjusted health outcomes.
One of the critical challenges facing the
health care system is delivering care to the growing pop-ulation of elderly, particularly those who are disabled and need
long-term care. The spectrum of services available to them is oftenfragmented and difficult for both patients and physicians to navigateand coordinate (Binstock, Cluff, and Von Mering 1996; Stone 2000). The
Address correspondence to: Dana B. Mukamel, University of California, Irvine,Center for Health Policy Research, 111 Academy, Suite 220, Irvine, CA 92697(email: [email protected]).
The Milbank Quarterly, Vol. 85, No. 3, 2007 (pp. 499–531)c© 2007 Milbank Memorial Fund. Published by Blackwell Publishing.
499
500 Dana B. Mukamel et al.
Program of All-Inclusive Care for the Elderly (PACE), a unique modelof care, was developed to address the needs of this population (Vladeck1996).
PACE is a managed care program that covers the spectrum of healthcare needs, from primary to acute to long-term care (Bodenheimer 1999;Chatterji et al. 2003; Eng et al. 1997; Gross et al. 2004). It is designed forpersons fifty-five years or older and whose disability level makes themeligible for nursing home care. The average PACE enrollee is eightyyears old, with 7.9 diagnoses and limitations in 3.6 activities of dailyliving (ADLs) and 7.2 instrumental activities of daily living (IADLs).Seventy-three percent are women. The program receives capitated fund-ing from Medicare and Medicaid, and because it is exempt from theregular benefits rules, PACE providers can tailor their services to theneeds of each enrollee. This financial structure allows PACE programsto offer a seamless service environment and to avoid the fragmentationof the usual system of care.
The objective of PACE is to enable its enrollees to live independentlyin the community. To this end, PACE provides a comprehensive set ofservices, including a day center, home care, and meals at home. Care isprovided by interdisciplinary teams (Temkin-Greener et al. 2004), whichinclude all individuals who interact with enrollees, professionals andparaprofessionals. The teams meet regularly to evaluate each enrollee’sneeds and design a care plan.
Currently, thirty-five PACE and five pre-PACE programs are serv-ing approximately fifteen thousand persons in twenty-four states, andtwenty-one rural programs are being developed as well. A number ofstudies have suggested that PACE may be an important and effectivemodel of care for an old and frail population (Chatterji et al. 2003; Enget al. 1997).
In this article we examine the associations between the programs’ char-acteristics and the patients’ risk-adjusted health outcomes. This informa-tion can inform quality improvement efforts by PACE as well as othermodels of care for frail, elderly individuals requiring long-term care.
We investigated the associations between PACE program character-istics and risk-adjusted mortality, risk-adjusted decline in functionalstatus at three months after enrollment, risk-adjusted decline in func-tional status at twelve months after enrollment, and risk-adjusted self-assessed health (SAH) at both three and twelve months after enrollment.
Program of All-Inclusive Care for the Elderly (PACE) 501
We looked at both short-term (three months) and long-term (twelvemonths) outcomes because they may be influenced by different care pro-cesses. We theorized that the outcomes at three months would reflect theprograms’ ability to address their enrollees’ care issues that were not metbefore enrollment, when they were in the regular care system, and thatlong-term outcomes (twelve months) would reflect the programs’ abilityto maintain enrollees in the best possible health for extended periods.
Methods
Sample and Data Sources
Our study investigated 3,042 newly enrolled persons in twenty-threePACE programs across the United States between January 1, 1997, andJune 29, 2001. We combined individual data on health outcomes andrisk factors at enrollment with the characteristics of each PACE program.Individual enrollee data were obtained from DataPACE, which includesa consistent set of variables collected by these PACE programs usingthe same guidelines and definitions (Mukamel, Temkin-Greener, andClark 1998). This information includes the enrollees’ demographics,socioeconomics, health status and disability, medical history, utilizationof health services, and date of death. It was obtained from several sources,including self-reports (e.g., self-assessed health), clinical assessments bythe programs’ nurses (e.g., ability to perform activities of daily living[ADLs], medical conditions), and encounter data (e.g., utilization of daycenter, hospital and nursing home stays).
Most program-level variables were collected from programs during asite visit, and interviews were conducted with the chief administrativeofficer, chief financial officer, and medical director. We calculated thevariable measuring team performance from a survey of all team members(all staff providing direct patient care), with a 65 percent response rate.We tested and validated the survey instrument (Temkin-Greener et al.2004) and have described its properties in detail elsewhere (Mukamelet al. 2006; Temkin-Greener et al. 2004).
Variables
Dependent Variables. We estimated five models with five dependentvariables. The dependent variable for the mortality outcome was defined
502 Dana B. Mukamel et al.
as time until death within one year after enrollment. The short- and long-term functional outcomes were defined as a change in functional statusat three and twelve months after enrollment, respectively. The changewas measured as the number of ADL limitations (ranging from 0 to7) at three and twelve months after enrollment minus the number ofADL limitations at enrollment, yielding fifteen possible values, rang-ing from −7 to 7. We also defined self-assessed health outcomes forthe short term, at three months of enrollment, and for the long term,at twelve months of enrollment. But because self-assessed health valueswere missing nonrandomly, as later discussed further, we did not definethe outcome variable as the change in self-assessed health levels. Rather,we defined the outcome variable as the level of self-assessed health afterenrollment, with values ranging from 1 (excellent) to 4 (poor), omit-ting observations with missing values after enrollment and adjusting forbaseline self-assessed health via indicator variables for each level, treating“missingness” as a distinct level.
Independent Variables. The independent variables were bothindividual-level risk factors and program-level variables. Table 1 lists allthe program variables, their definitions, and the hypotheses regardingtheir impact on enrollees’ health outcomes. Table 2 lists all the variables,including the dependent variables and the individual- and program-levelindependent variables and their descriptive statistics. Next we providemore details of some of the dependent variables.
The ADL limitations recorded in DataPACE are bathing, dressing,grooming, toileting, transfer, walking, and feeding. The instrumentalactivities of daily living (IADLs) are meal preparation, shopping, house-work, laundry, heavy chores, management of money, management ofmedications, and transportation. Cognitive status is measured by thenumber of errors in responding to the Short Portable Mental StatusQuestionnaire (Pfeiffer 1975), which can range from 0 to 10. The mea-surement of self-assessed health was based on enrollees’ responses towhether they were in excellent, good, fair, or poor health. When modeledas the outcome, we discarded observations missing self-assessed healthat three or twelve months, since it is unclear how we should interpret“missingness” relative to the “nonmissing” states. When included as apredictor, however, we treated missing self-assessed health as a distinctlevel of self-assessed health at enrollment rather than imputing values ordiscarding these observations. For the mortality analysis, we were ableto simplify the functional form of self-assessed health to a dichotomy
Program of All-Inclusive Care for the Elderly (PACE) 503
TA
BL
E1
Pro
gram
Cha
ract
eris
tics
Incl
uded
inth
eA
naly
ses
Pro
gram
Cha
ract
eris
tics
Def
init
ion
Hyp
othe
sis
I.F
inan
cial
fact
ors
1.P
erce
ntag
eof
part
icip
ants
’tim
eun
der
full
capi
tati
on
Per
cent
age
ofen
roll
men
tdu
rati
onof
the
PAC
Een
roll
eeun
der
both
Med
icar
ean
dM
edic
aid
capi
tati
on.T
ypic
ally
PAC
Epr
ogra
ms
star
tw
ith
Med
icai
dca
pita
tion
and
fee-
for-
serv
ice
Med
icar
epa
ymen
t.T
hepr
ogra
ms
mov
ein
tofu
llca
pita
tion
bybo
thpa
yers
wit
hin
aye
aror
two.
Indi
vidu
als
who
enro
lldu
ring
the
earl
ype
riod
ofth
epr
ogra
mre
ceiv
eso
me
(or
all)
ofth
eir
care
whi
leth
epr
ogra
mis
capi
tate
don
lyby
Med
icai
d.
The
prog
ram
’sse
rvic
esm
aych
ange
asth
efi
nanc
ial
ince
ntiv
esch
ange
whe
nth
epr
ogra
ms
mov
efr
omM
edic
are
fee-
for-
serv
ice
todu
alca
pita
tion
.
2.P
rofi
tabi
lity
ofth
epr
ogra
mD
icho
tom
ous
vari
able
indi
cati
ngw
heth
erth
epr
ogra
mw
asop
erat
ing
inth
ere
d.F
inan
cial
lyst
rain
edpr
ogra
ms
may
not
beab
leto
prov
ide
alls
ervi
ces
need
edby
thei
ren
roll
ees,
whi
chin
turn
may
lead
tow
orse
outc
omes
.3.
Free
stan
ding
site
Dic
hoto
mou
sva
riab
lein
dica
ting
whe
ther
orno
tth
ePA
CE
site
was
part
ofa
larg
eror
gani
zati
on(s
uch
asan
inte
grat
edde
live
rysy
stem
).
Free
stan
ding
site
sm
ayha
vem
ore
free
dom
tofo
llow
thei
row
npo
lici
esbu
tm
ayal
soha
vele
ssfi
nanc
ialb
acki
ngan
dsm
alle
rre
serv
es.
4.C
omm
unit
y-ba
sed
site
Dic
hoto
mou
sva
riab
lein
dica
ting
whe
ther
the
PAC
Esi
tew
aspa
rtof
aco
mm
unit
y-ba
sed
netw
ork.
Aco
mm
unit
y-ba
sed
netw
ork
mig
htpr
ovid
ead
diti
onal
supp
ort
that
may
not
beav
aila
ble
tofr
eest
andi
ngsi
tes
but
may
diff
erfr
oma
hosp
ital
-bas
edne
twor
k.
(Con
tinu
ed)
504 Dana B. Mukamel et al.
TAB
LE1—
Con
tinu
ed
Pro
gram
Cha
ract
eris
tics
Def
init
ion
Hyp
othe
sis
5.C
ount
yM
edic
are
paym
ent
leve
lSu
mof
part
Aan
dpa
rtB
ofth
eco
unty
’sad
just
edav
erag
epe
rca
pita
cost
s(A
AP
CC
).A
mea
sure
ofth
ege
nero
sity
ofth
ear
ea’s
Med
icar
epa
ymen
tsy
stem
,whi
chm
ayal
sobe
rela
ted
tolo
calp
ract
ice
styl
es.
II.P
erso
nnel
1.N
umbe
rof
disc
ipli
nes
atca
rem
eeti
ngs
The
num
ber
ofdi
stin
ctpr
ofes
sion
alan
dno
npro
fess
iona
ldis
cipl
ines
pres
ent
atw
eekl
yte
amm
eeti
ngs.
Ala
rger
num
ber
ofdi
scip
line
spr
esen
tat
the
care
-pla
nnin
gm
eeti
ngm
ayen
able
the
team
toad
dres
sm
ore
ofth
een
roll
ees’
dive
rse
need
s.2.
Eth
nic
over
lap
betw
een
enro
llee
san
dno
npro
fess
iona
lst
aff
Inde
x=
−1/ 2
∑
i=et
hni
city
Pen
rol
i(P
enro
li
−P
sta
ff
i),
whe
rePi
isth
epe
rcen
tage
ofen
roll
ees
orst
aff
ina
give
net
hnic
ity
grou
p.T
his
mea
sure
rang
esbe
twee
n0
and
0.5,
wit
hth
ehi
gher
num
bers
indi
cati
ngm
ore
over
lap.
Ifai
des
have
the
sam
eet
hnic
back
grou
ndas
thei
rpa
tien
ts,t
hey
may
com
mun
icat
em
ore
effe
ctiv
ely
and
prov
ide
bett
erca
re.
3.E
thni
cov
erla
pbe
twee
nen
roll
ees
and
prof
essi
onal
staf
f
Inde
x=
−1/ 2
∑
i=et
hni
city
Pen
rol
i(P
enro
li
−P
sta
ff
i),
whe
rePi
isth
epe
rcen
tage
ofen
roll
ees
orst
aff
ina
give
net
hnic
ity
grou
p.T
his
mea
sure
rang
esbe
twee
n0
and
0.5,
wit
hth
ehi
gher
num
bers
indi
cati
ngm
ore
over
lap.
Ifpr
ofes
sion
alst
affh
ave
the
sam
eet
hnic
back
grou
ndas
thei
rpa
tien
ts,t
hey
may
com
mun
icat
em
ore
effe
ctiv
ely
and
prov
ide
bett
erca
re.
4.A
med
ical
dire
ctor
trai
ned
inge
riat
rics
Dic
hoto
mou
sva
riab
lese
tto
1if
the
med
ical
dire
ctor
isa
geri
atri
cian
,0ot
herw
ise.
Am
edic
aldi
rect
orw
ith
spec
iali
zed
trai
ning
may
offe
rbe
tter
care
.
Program of All-Inclusive Care for the Elderly (PACE) 505
5.A
med
ical
dire
ctor
wit
hon
lyad
min
istr
ativ
ere
spon
sibi
liti
es
Dic
hoto
mou
sva
riab
lese
tto
1if
the
med
ical
dire
ctor
does
not
prov
ide
dire
ctpa
tien
tca
re,
0ot
herw
ise.
Med
ical
dire
ctor
sw
itho
utdi
rect
pati
ent
care
resp
onsi
bili
ties
may
befa
rthe
rre
mov
edfr
omen
roll
ees
and
less
awar
eof
thei
rne
eds
and
ofef
fect
ive
prac
tice
s.6.
Med
ical
dire
ctor
’sFT
EV
aria
ble
indi
cati
ngpr
opor
tion
ofti
me
that
the
med
ical
dire
ctor
has
been
empl
oyed
byth
ePA
CE
prog
ram
.Thi
sva
riab
lera
nges
from
0to
1.
The
mor
eti
me
that
med
ical
dire
ctor
ssp
end
inPA
CE
,the
bett
erth
eca
reth
eym
aybe
able
topr
ovid
e.
7.A
dmin
istr
ator
wit
hcl
inic
altr
aini
ngD
icho
tom
ous
vari
able
set
to1
ifth
ead
min
istr
ator
has
clin
ical
trai
ning
,0ot
herw
ise.
Cli
nica
lly
trai
ned
adm
inis
trat
ors
may
beab
leto
com
mun
icat
ebe
tter
wit
hst
aff,
lead
ing
tobe
tter
care
.8.
Per
sonn
elbe
long
toun
ion
Dic
hoto
mou
sva
riab
lese
tto
1if
any
ofth
est
aff
belo
ngto
aun
ion,
0ot
herw
ise.
Man
agem
ent
may
have
less
cont
rolo
ver
wor
kor
gani
zati
onan
dpr
acti
ces,
and
wag
esm
aybe
high
er.I
frev
enue
sar
efi
xed,
prog
ram
sfa
cing
high
erw
ages
wil
lhav
efe
wer
reso
urce
sfo
rse
rvic
es.
III.
Pra
ctic
eva
riab
les
1.To
taln
umbe
rof
FTE
spe
r10
0en
roll
ees
Num
ber
offu
ll-t
ime
equi
vale
nts
empl
oyed
byth
epr
ogra
mpe
r10
0en
roll
ees,
sum
med
over
all
empl
oyee
cate
gori
es.
Mor
est
affp
eren
roll
eesh
ould
lead
tobe
tter
care
and
outc
omes
.
2.R
atio
ofpr
ofes
sion
alFT
Es
tono
npro
fess
iona
lFT
Es
Am
ore
med
ical
lyor
ient
edst
affm
ayem
phas
ize
diff
eren
tou
tcom
es,f
orex
ampl
e,su
rviv
alov
erfu
ncti
onal
stat
us.
(Con
tinu
ed)
506 Dana B. Mukamel et al.
TAB
LE1—
Con
tinu
ed
Pro
gram
Cha
ract
eris
tics
Def
init
ion
Hyp
othe
sis
3.Sp
ecia
lty
conc
entr
atio
nD
efin
edas
∑ iS
2 i,w
here
S iis
the
FTE
’ssh
are
of
spec
ialt
yia
mon
gal
lFT
Es.
Ran
ges
are
from
0to
1,w
ith
high
ernu
mbe
rsin
dica
ting
grea
ter
conc
entr
atio
n.
Pro
gram
sw
ith
mor
edi
vers
esp
ecia
ltie
sm
aybe
able
toof
fer
mor
edi
vers
ese
rvic
es.
4.Se
rvic
eco
ncen
trat
ion
Def
ined
as∑ i
S2 i,w
here
S iis
the
shar
eof
serv
ice
i
amon
gal
lser
vice
spr
ovid
edby
the
prog
ram
.R
ange
sar
efr
om0
to1,
wit
hhi
gher
num
bers
indi
cati
nggr
eate
rco
ncen
trat
ion.
Mor
edi
vers
ese
rvic
esm
ayim
prov
eth
epr
ogra
m’s
abil
ity
tom
eet
enro
llee
s’di
vers
ene
eds.
5.Se
para
tede
men
tia
prog
ram
Dic
hoto
mou
sva
riab
leis
set
to1
ifth
epr
ogra
mha
sa
spec
iali
zed
prog
ram
for
enro
llee
sw
ith
dem
enti
a,0
othe
rwis
e.
Asp
ecia
lize
dde
men
tia
prog
ram
may
lead
tobe
tter
outc
omes
for
alle
nrol
lees
.
6.P
rope
nsit
yto
hosp
ital
ize
Aco
ntin
uous
vari
able
mea
suri
ngth
epr
ogra
m’s
risk
-adj
uste
dpr
open
sity
toho
spit
aliz
een
roll
ees
base
don
fixe
dpr
ogra
mef
fect
sin
are
gres
sion
mod
elpr
edic
ting
indi
vidu
alho
spit
aliz
atio
nan
dco
ntro
llin
gfo
rin
divi
dual
risk
fact
ors
(Muk
amel
etal
.200
6).
Hos
pita
liza
tion
sar
eex
pens
ive.
Ifre
venu
esar
efi
xed
(cap
itat
ed),
prog
ram
sth
atre
lym
ore
onho
spit
alca
rew
illh
ave
few
erre
sour
ces
avai
labl
efo
rot
her
serv
ices
.
7.Te
am’s
effe
ctiv
enes
sSc
ores
rang
efr
om1
to5,
wit
hhi
gher
num
bers
indi
cati
nggr
eate
ref
fect
iven
ess.
Cal
cula
ted
from
PAC
Ete
amm
embe
rs’r
espo
nse
toa
surv
ey(T
emki
n-G
reen
eret
al.2
004)
.
Enr
olle
esca
red
for
bym
ore
effe
ctiv
ete
ams
shou
ldha
vebe
tter
heal
thou
tcom
es.
Program of All-Inclusive Care for the Elderly (PACE) 507
IV.C
ase
mix
1.M
edia
nA
DL
Med
ian
num
ber
ofA
DL
lim
itat
ions
for
exis
ting
enro
llee
s.A
prog
ram
wit
hsi
cker
enro
llee
sm
ayha
vew
orse
outc
omes
whe
nit
sre
venu
esar
efi
xed
(cap
itat
ed).
2.V
aria
tion
inA
DLs
Var
ianc
ein
num
ber
ofA
DL
lim
itat
ions
for
exis
ting
enro
llee
s.A
mor
ehe
tero
gene
ous
pati
ent
popu
lati
onm
aybe
mor
edi
ffic
ult
and
cost
lyto
care
for,
poss
ibly
lead
ing
tow
orse
outc
omes
.3.
Per
cent
age
ofpa
rtic
ipan
tsw
ith
mid
dle-
loss
AD
Ls
Mid
dle-
loss
AD
Ls(M
orri
s,Fr
ies,
and
Mor
ris
1999
)fo
rex
isti
ngen
roll
ees
incl
ude
dres
sing
,to
ilet
ing,
tran
sfer
ring
,and
wal
king
.
Apr
ogra
mw
ith
sick
eren
roll
ees
may
have
wor
seou
tcom
esw
hen
reve
nues
are
fixe
d(c
apit
ated
).
4.P
erce
ntag
eof
part
icip
ants
wit
hla
te-l
oss
AD
Ls
Late
-los
sA
DLs
for
exis
ting
enro
llee
s(M
orri
s,Fr
ies,
and
Mor
ris
1999
)inc
lude
eati
ng.
Apr
ogra
mw
ith
sick
eren
roll
ees
may
have
wor
seou
tcom
esw
hen
reve
nues
are
fixe
d(c
apit
ated
).
5.N
umbe
rof
diag
nose
sA
vera
genu
mbe
rof
diag
nose
sre
cord
edfo
ral
lex
isti
ngen
roll
ees.
Apr
ogra
mw
ith
sick
eren
roll
ees
may
have
wor
seou
tcom
esw
hen
reve
nues
are
fixe
d(c
apit
ated
).6.
Con
cent
rati
onin
diag
nose
sD
efin
edas
∑ iS
2 i,w
here
S iis
the
shar
eof
diag
nosi
sia
mon
gal
ldia
gnos
es.R
ange
sar
efr
om0
to1,
wit
hhi
gher
num
bers
indi
cati
nggr
eate
rco
ncen
trat
ion.
Am
ore
hete
roge
neou
spa
tien
tpo
pula
tion
may
bem
ore
diff
icul
tan
dco
stly
toca
refo
r,po
ssib
lyle
adin
gto
wor
seou
tcom
es.
7.P
erce
ntag
eof
part
icip
ants
wit
hde
men
tia
Bas
edon
exis
ting
enro
llee
s.A
prog
ram
wit
hsi
cker
enro
llee
sm
ayha
vew
orse
outc
omes
whe
nre
venu
esar
efi
xed
(cap
itat
ed).
(Con
tinu
ed)
508 Dana B. Mukamel et al.
TAB
LE1—
Con
tinu
ed
Pro
gram
Cha
ract
eris
tics
Def
init
ion
Hyp
othe
sis
8.P
erce
ntag
eof
part
icip
ants
livi
ngal
one
Ahi
gher
perc
enta
geof
enro
llee
sli
ving
alon
em
ayre
quir
eth
epr
ogra
mto
prov
ide
mor
eho
me
care
and
may
incr
ease
the
burd
enon
its
reso
urce
s.V.
Pro
gram
size
1.E
nrol
lees
(in
100s
)PA
CE
prog
ram
sm
ayex
hibi
tsc
ale
effi
cien
cies
.2.
Num
ber
ofth
esi
te’s
day
cent
ers
PAC
Epr
ogra
ms
may
exhi
bit
scal
eef
fici
enci
es.
VI.
Pro
gram
age
1.A
tti
me
ofen
roll
men
tT
hepr
ogra
m’s
age
(fro
mda
teof
Med
icar
e/M
edic
aid
capi
tati
on)a
tth
eti
me
the
enro
llee
isad
mit
ted.
Ifth
ere
isa
lear
ning
curv
e,ol
der
prog
ram
sm
ayha
vebe
tter
outc
omes
than
new
erpr
ogra
ms
do.
VII
.Con
trac
tst
affi
ngD
icho
tom
ous
vari
able
s=
1if
any
cont
ract
aide
sar
eem
ploy
ed,0
othe
rwis
e.P
rogr
ams
may
impr
ove
outc
omes
byha
ving
mor
efl
exib
ilit
ybu
tm
ayha
vew
orse
outc
omes
ifco
ntra
ctai
des
are
not
fam
ilia
rw
ith
PAC
Ean
ddo
not
iden
tify
wit
hth
ePA
CE
team
.
Program of All-Inclusive Care for the Elderly (PACE) 509
TABLE 2Descriptive Statistics for All Variables Included in Initial and Subsequent
Analyses
Standard Deviation(Reported Only forNondichotomous
Mean Variables)
OutcomesMortality rate within 1 year of enrollment 11.9%Percentage of enrollees with ADLs decline
within 3 months of enrollment17.9%
Percentage of enrollees with no change inADLs within 3 months of enrollment
61.0%
Percentage of enrollees with ADLs declinewithin 12 months of enrollment
25.4%
Percentage of enrollees with no change inADLs within 12 months of enrollment
43.3%
Percentage of enrollees with decline inself-assessed health within 3 months ofenrollmenta
28.6%
Percentage of enrollees with no change inself-assessed health within 3 months ofenrollmenta
52.5%
Percentage of enrollees with decline inself-assessed health within 12 months ofenrollmenta
30.9%
Percentage of enrollees with no change inself-assessed health within 12 months ofenrollmenta
47.8%
Individual risk factors at enrollmentAge 77.2 9.6Percentage females 73.0%Percentage whites 42.5%Percentage blacks 35.2%Percentage Hispanics 17.7%Percentage Asians 3.4%Number of errors on the MSQ (1–10) 4.3 3.2Sum of ADLs 3.6 2.5Percentage with walking ADL 44.2%Percentage with grooming ADL 67.9%Percentage with toileting ADL 42.6%Percentage with bathing ADL 76.2%Percentage with feeding ADL 26.6%Percentage with transfer ADL 39.3%
(Continued)
510 Dana B. Mukamel et al.
TABLE 2—Continued
Standard Deviation(Reported Only forNondichotomous
Mean Variables)
Percentage with dressing ADL 63.4%Sum of IADLs 7.2 1.4Percentage with bladder incontinence 56.5%Percentage with bowel incontinence 24.9%Percentage diagnosed with cancer 9.5%Percentage diagnosed with renal failure 8.5%Percentage using oxygen daily 5.3%Percentage diagnosed with cerebrovascular
disease28.0%
Number of diagnoses 8.3 1.7Percentage living alone 30.3%Percentage living with relative 38.9%Percentage living with spouse 11.7%Percentage living with others 12.4%Percentage living in a nursing home 6.7%Self-assessed health = excellent 6.2%Self-assessed health = good 39.3%Self-assessed health = fair 30.4%Self-assessed health = poor 10.8%Self-assessed health = missing 13.2%Program’s characteristicsI. Financial factors
1. Percentage of enrollees’ time under fullcapitation
79.8% 39.2%
2. Programs losing money 17.2%3. Freestanding site 32.4%4. Community-based site 14.3%5. County-level Medicare payment(AAPCC)
407 110
II. Personnel1. Number of disciplines at care meetings 9.0 2.22. Ethnic overlap between
enrollees and nonprofessional staff 0.040 0.057enrollees and professional staff 0.028 0.045
3. Percentage of medical directors withgeriatric training
65.0%
(Continued)
Program of All-Inclusive Care for the Elderly (PACE) 511
TABLE 2—Continued
Standard Deviation(Reported Only forNondichotomous
Mean Variables)
4. Percentage of medical directors withonly administrative responsibilities
23.2%
5. Medical director’s FTE 0.698 0.332III. Practice variables
1. Total number of FTEs per 100 enrollees 34.8 22.22. Ratio of professional FTEs to
nonprofessional FTEs0.056 0.024
3. Specialty concentration (0 = low, 1 =high)
0.388 0.161
4. Service concentration (0 = low, 1 =high)
0.284 0.048
5. Percentage of programs with a separatedementia program
56%
6. Propensity to hospitalize 0.178 0.4797. Team’s effectiveness (1 = low, 5 =
high)4.21 0.21
8. Number of personnel belonging tounion
14.5%
IV. Case mix1. Median ADL (1–14) 6.21 1.122. Variation in ADLs 5.59 1.353. Percentage of participants with middle
ADLs66.6% 13.8%
4. Percentage of participants with lateADLs
42.5% 13.1%
5. Average number of diagnoses 8.3 1.76. Concentration in diagnoses (0 = low,
1 = high)0.041 0.007
7. Percentage of participants withdementia
54.7% 12.4%
8. Percentage of participants living alone 25.1% 12.9%V. Program size
1. Enrollees (in 100s) 2.83 1.44VI. Program age
1. At time of enrollment 5.95 2.86
Notes: Variables that were not significant at the 0.1 level in the initial models are shown in thistable, but not in tables 3 and 5.aCalculated excluding those with missing values at enrollment.
512 Dana B. Mukamel et al.
between “excellent” and “nonexcellent” (including missing) because thecoefficients for all four of the nonexcellent categories were not signifi-cantly different from one another.
We constructed several variables measuring program practice styles(e.g., hospitalizations) and the average disability burden for each program(e.g., percentage of enrollees with late ADLs) from the data on serviceuse reported in DataPACE. To avoid confounding, these variables werebased on data for existing enrollees, a sample different from the sampleused in the analysis.
We calculated team effectiveness from a survey of all team members(average response rate of 65 percent) regarding their perception of theirteam’s functioning. Team members refer to all staff providing directcare, including physicians, nurses, social workers, and paraprofessionalssuch as aides. They were asked to rate their agreement on a five-pointLikert scale with statements like “My team leader does not make her/hisexpectations clear to team members” and “Written plans and scheduleswithin our team are very effective” (for more details, see Temkin-Greeneret al. 2004). The team effectiveness variable was calculated as the averageof all the responses by the team members in each program.
Statistical Analyses
A series of multivariate regression models predicting each of the fiveoutcomes were fit. For all models, the unit of analysis was the enrollee.Mortality was modeled using the semiparametric Cox proportional haz-ards model. All other outcomes were modeled using linear models. Ouranalyses had four steps, each applied separately to each outcome.
First Set of Analyses. The first set of analyses was designed to quan-tify the effect of the PACE program on each outcome while controllingfor the effect of a parsimonious set of individual risk factors. We fitmodels containing all individual risk factors hypothesized to influencethe outcome (listed in table 2) as well as twenty-two program indicatorvariables. The final models included all twenty-two program indicatorvariables, along with those individual risk factors that were significantlyassociated with the outcomes at the 0.1 level (the significant individualrisk factors are shown in table 3). For each model we calculated the per-centage of the total explained variation that could be attributed to theprogram’s effects (first row in table 4, part A).
Program of All-Inclusive Care for the Elderly (PACE) 513
TA
BL
E3
Coe
ffic
ient
sfo
rIn
divi
dual
Ris
kFa
ctor
sIn
clud
edin
Out
com
eR
egre
ssio
nM
odel
s
Mor
tali
ty(T
ime
toD
eath
Cha
nge
inC
hang
ein
Self
-Ass
esse
dSe
lf-A
sses
sed
wit
hin
12M
onth
sfr
omA
DLs
wit
hin
3A
DLs
wit
hin
12H
ealt
hat
3H
ealt
hat
12E
nrol
lmen
t:R
elat
ive
Mon
ths
from
Mon
ths
from
Mon
ths
afte
rM
onth
saf
ter
Haz
ard)
a,b
Enr
ollm
enta,
cE
nrol
lmen
ta,c
Enr
ollm
enta,
cE
nrol
lmen
ta,c
Age
1.02
4∗∗1.
197∗∗
1.32
4∗∗Fe
mal
e0.
089∗
Non
-His
pani
c2.
117∗∗
MSQ
1.05
3∗∗0.
032∗∗
0.06
8∗∗−0
.019
∗∗Su
mof
AD
Ls−0
.243
∗∗−0
.444
∗∗Tr
ansf
erA
DL
1.95
2∗∗D
ress
ing
AD
L1.
511∗∗
Sum
ofIA
DLs
0.10
4∗∗0.
123∗∗
Bla
dder
inco
ntin
ence
0.15
4∗0.
339∗∗
Bow
elin
cont
inen
ce0.
172∗
0.45
5∗∗C
ance
rw
itho
utch
emot
hera
py1.
558∗∗
Can
cer
wit
hch
emot
hera
py8.
584∗∗
Ren
alfa
ilur
e1.
982∗∗
Dai
lyox
ygen
2.61
8∗∗C
ereb
rova
scul
ardi
seas
e0.
188∗
Num
ber
ofdi
seas
es0.
062∗
0.05
5∗∗0.
040∗∗
(Con
tinu
ed)
514 Dana B. Mukamel et al.
TAB
LE3—
Con
tinu
ed
Mor
tali
ty(T
ime
toD
eath
Cha
nge
inC
hang
ein
Self
-Ass
esse
dSe
lf-A
sses
sed
wit
hin
12M
onth
sfr
omA
DLs
wit
hin
3A
DLs
wit
hin
12H
ealt
hat
3H
ealt
hat
12E
nrol
lmen
t:R
elat
ive
Mon
ths
from
Mon
ths
from
Mon
ths
afte
rM
onth
saf
ter
Haz
ard)
a,b
Enr
ollm
enta,
cE
nrol
lmen
ta,c
Enr
ollm
enta,
cE
nrol
lmen
ta,c
Livi
ngsi
tuat
ion
rela
tive
toli
ving
alon
e:W
ith
rela
tive
0.22
9∗W
ith
spou
se0.
234∗
Wit
hot
hers
0.45
9∗∗In
anu
rsin
gho
me
1.44
9∗∗Se
lf-a
sses
sed
heal
that
base
line
rela
tive
toex
cell
ent:
Goo
d0.
422∗∗
0.14
6∗Fa
ir0.
723∗∗
0.46
2∗∗P
oor
1.12
6∗∗0.
721∗∗
Mis
sing
0.68
5∗∗0.
290∗∗
Self
-ass
esse
dhe
alth
at1.
907∗∗
base
line
not
exce
llen
t:
Not
es:a
Var
iabl
esth
atw
ere
list
edin
tabl
e2
and
not
incl
uded
here
wer
eno
tsi
gnif
ican
tly
asso
ciat
edw
ith
any
ofth
eou
tcom
es.
bC
oeff
icie
ntva
lues
belo
w1
indi
cate
that
the
prog
ram
char
acte
rist
icis
asso
ciat
edw
ith
low
erm
orta
lity
.c N
egat
ive
coef
fici
ent
valu
esin
dica
tebe
tter
func
tion
alan
dse
lf-a
sses
sed
heal
thou
tcom
es.
∗ p<
0.05
,∗∗p<
0.01
.
Program of All-Inclusive Care for the Elderly (PACE) 515
Second Set of Analyses. The second phase was aimed at identifyinggroups of specific program characteristics that partly explained the pro-gram’s overall effect. Retaining all the previously selected individual riskfactors, we fit models in which the saturated set of twenty-two programindicator variables were replaced by predefined groups of up to eightspecific program characteristics (e.g., five financial variables), notingthat these models could be viewed as proper nested submodels of thefull model, which included all twenty-two program indicators. We thenperformed likelihood ratio tests and F-tests to obtain p-values for eachgroup of program characteristics (table 4, part B).
Third Set of Analyses. In the third stage, we identified which specificprogram characteristics within each group significantly contributed tothe group’s overall significance, if the group as a whole had been foundto be significant in our second set of analyses. Table 5 displays thecoefficient estimates and associated p-values for the reduced subset ofsignificant predictors, adjusted for all previously selected individual riskfactors as well as the other significant program characteristics within thesame group.
Fourth Set of Analyses. In the fourth stage, we simultaneously in-cluded all the significant program characteristics from all groups in asingle model along with all previously selected individual risk factors.The second row of table 4, part A, gives the percentage of program varia-tion (based on all twenty-two program indicators) that all these selectedprogram characteristics jointly explain.
Limitation of These Analyses. Because we had more than twenty-twoprogram-level variables that were hypothesized to affect outcomes andonly twenty-three programs, we were unable to examine the contributionof each program characteristic in the presence of all others. Accordingly,we aggregated program variables into seven groups and reported on theassociation between them and the outcomes based on models includingonly variables within the same group. It is important to keep this in mindwhen interpreting the results. To the degree that variables in differentgroups are correlated, their estimated effect could be due partly to corre-lated variables in other groups. Therefore, we emphasize that the resultspresented in the next section are not meant to imply causal relationshipsbetween a program’s characteristics and its enrollees’ health outcome,but only statistical correlations, which may be confounded by missingvariables. They should be viewed as generating hypotheses and not as
516 Dana B. Mukamel et al.
TA
BL
E4
Ass
ocia
tion
betw
een
Pro
gram
Cha
ract
eris
tics
and
Ris
k-A
djus
ted
Hea
lth
Out
com
es
A.C
ontr
ibut
ion
ofP
rogr
amC
hara
cter
isti
csto
Exp
lain
edV
aria
tion
inH
ealt
hO
utco
mes
Mor
tali
ty(T
ime
toC
hang
ein
Cha
nge
inD
eath
wit
hin
AD
Lsw
ithi
nA
DLs
wit
hin
Self
-Ass
esse
dSe
lf-A
sses
sed
12M
onth
s3
Mon
ths
12M
onth
sH
ealt
hat
3H
ealt
hat
12fr
omfr
omfr
omM
onth
saf
ter
Mon
ths
afte
rE
nrol
lmen
t)E
nrol
lmen
tE
nrol
lmen
tE
nrol
lmen
tE
nrol
lmen
t
Per
cent
age
ofto
tale
xpla
ined
vari
atio
nat
trib
utab
leto
prog
ram
effe
ctsa
18%
18%
16%
9%23
%P
erce
ntag
eof
prog
ram
effe
cts
expl
aine
dby
spec
ific
prog
ram
char
acte
rist
ics
incl
uded
inan
alys
es24
%87
%90
%15
%49
%
(Con
tinu
ed)
Program of All-Inclusive Care for the Elderly (PACE) 517
TAB
LE4-
Con
tinu
ed
B.O
vera
llp-
Val
ues
Indi
cati
ngSt
atis
tica
lSig
nifi
canc
eof
Eac
hSe
tof
1th
roug
h8
Pro
gram
Cha
ract
eris
tics
inE
xpla
inin
gE
ach
Out
com
eb
Mor
tali
ty(T
ime
toC
hang
ein
Cha
nge
inD
eath
wit
hin
AD
Lsw
ithi
nA
DLs
wit
hin
Self
-Ass
esse
dSe
lf-A
sses
sed
12M
onth
s3
Mon
ths
12M
onth
sH
ealt
hat
3H
ealt
hat
12fr
omfr
omfr
omM
onth
saf
ter
Mon
ths
afte
rE
nrol
lmen
t)E
nrol
lmen
tE
nrol
lmen
tE
nrol
lmen
tE
nrol
lmen
t
Fin
anci
alfa
ctor
s(5
)0.
220
0.00
4<
0.00
10.
209
0.80
7P
erso
nnel
(8)
0.17
40.
008
0.00
60.
663
0.27
8C
ontr
act
staf
fing
(1)
0.87
40.
822
0.18
20.
659
0.63
6P
ract
ice
vari
able
s(7
)0.
002
0.00
3<
0.00
10.
377
0.00
1C
ase
mix
(8)
0.10
7<
0.00
1<
0.00
10.
372
0.02
4P
rogr
amsi
ze(2
)0.
194
0.00
9<
0.00
10.
088
0.12
2P
rogr
amag
e(1
)0.
140
<0.
001
<0.
001
0.03
20.
010
Not
es:
a The
tota
lex
plai
ned
vari
atio
nis
com
pose
dof
vari
atio
ndu
eto
indi
vidu
alen
roll
eech
arac
teri
stic
san
dpr
ogra
mef
fect
.T
his
tabl
eex
amin
esth
epr
ogra
m’s
cont
ribu
tion
toth
eex
plai
ned
vari
atio
n.b
Shad
edce
lls
wit
hp<
0.05
indi
cate
sign
ific
ant
asso
ciat
ion.
Num
bers
inpa
rent
hese
sin
dica
teth
enu
mbe
rof
char
acte
rist
ics
incl
uded
inea
chca
tego
ry.F
orex
ampl
e,fi
vefi
nanc
ialv
aria
bles
wer
ein
clud
edin
the
anal
ysis
offi
nanc
ialf
acto
rs.
518 Dana B. Mukamel et al.
TAB
LE5
Pro
gram
Cha
ract
eris
tics
Infl
uenc
ing
Ris
k-A
djus
ted
Hea
lth
Out
com
es
Mor
tali
ty(T
ime
toC
hang
ein
Cha
nge
inSe
lf-A
sses
sed
Self
-Ass
esse
dD
eath
wit
hin
12M
onth
sA
DLs
wit
hin
AD
Lsw
ithi
nH
ealt
hat
Hea
lth
atfr
omE
nrol
lmen
t:3
Mon
ths
from
12M
onth
sfr
om3
Mon
ths
afte
r12
Mon
ths
afte
rR
elat
ive
Haz
ard)
aE
nrol
lmen
tbE
nrol
lmen
tbE
nrol
lmen
tbE
nrol
lmen
tb
I.F
inan
cial
fact
ors
NSc
SS
NS
NS
1.P
erce
ntag
eof
part
icip
ant
−0.2
12∗∗
−0.5
33∗∗
tim
eun
der
full
capi
tati
on2.
Free
stan
ding
site
−0.2
11∗∗
−0.3
72∗∗
II.P
erso
nnel
NS
SS
NS
NS
1.N
umbe
rof
disc
ipli
nes
atca
rem
eeti
ngs
0.04
3∗−0
.451
∗2.
Eth
nic
over
lap
betw
een
part
icip
ants
and
nonp
rofe
ssio
nals
taff
−0.0
23∗∗
−0.0
17∗
3.M
edic
aldi
rect
orw
ith
geri
atri
ctr
aini
ng−0
.275
∗∗−0
.319
∗∗4.
Med
ical
dire
ctor
wit
hon
lyad
min
istr
ativ
ere
spon
sibi
liti
es0.
207∗∗
Program of All-Inclusive Care for the Elderly (PACE) 519
5.P
erce
ntFT
Eof
med
ical
dire
ctor
−0.0
04∗∗
III.
Pra
ctic
eva
riab
les
SS
SN
SS
1.To
talF
TE
spe
r10
0en
roll
ees
−0.5
19∗∗
2.R
atio
ofpr
ofes
sion
alFT
Es
tono
npro
fess
iona
lFT
Es
0.98
9∗∗1.
009∗∗
3.Sp
ecia
lty
conc
entr
atio
n0.
006∗
0.00
5∗∗4.
Serv
ice
conc
entr
atio
n0.
956∗∗
0.01
5∗0.
029∗∗
1.01
3∗∗5.
Sepa
rate
dem
enti
apr
ogra
m0.
194∗∗
6.P
rope
nsit
yto
hosp
ital
ize
0.21
0∗∗0.
233∗
7.Te
amef
fect
iven
ess
−0.0
11∗∗
IV.C
ase
mix
NS
SS
NS
S1.
Med
ian
AD
L−0
.257
∗∗2.
Var
iati
onin
AD
Ls0.
495∗
3.P
erce
ntag
eof
part
icip
ants
wit
hm
iddl
eA
DLs
1.74
6∗∗4.
103∗∗
4.P
erce
ntag
eof
part
icip
ants
wit
hla
teA
DLs
1.23
1∗∗
(Con
tinu
ed)
520 Dana B. Mukamel et al.
TAB
LE5—
Con
tinu
ed
Mor
tali
ty(T
ime
toC
hang
ein
Cha
nge
inSe
lf-A
sses
sed
Self
-Ass
esse
dD
eath
wit
hin
12M
onth
sA
DLs
wit
hin
AD
Lsw
ithi
nH
ealt
hat
Hea
lth
atfr
omE
nrol
lmen
t:3
Mon
ths
from
12M
onth
sfr
om3
Mon
ths
afte
r12
Mon
ths
afte
rR
elat
ive
Haz
ard)
aE
nrol
lmen
tbE
nrol
lmen
tbE
nrol
lmen
tbE
nrol
lmen
tb
5.A
vera
genu
mbe
rof
diag
nose
s−0
.032
∗∗6.
Con
cent
rati
onin
diag
nose
s0.
002∗
7.P
erce
ntag
eof
part
icip
ants
wit
hde
men
tia
−0.7
26−3
.028
∗∗8.
Per
cent
age
ofpa
rtic
ipan
tsli
ving
alon
e0.
854∗
0.41
2∗∗V.
Pro
gram
size
NS
SS
NS
NS
1.P
arti
cipa
nts
(in
100s
)−0
.079
∗∗−0
.124
∗∗V
I.P
rogr
amag
eN
SS
SS
S1.
At
tim
eof
enro
llm
ent
−0.0
42∗∗
−0.0
59∗∗
−0.0
13∗
−0.0
16∗∗
Not
es:a C
oeff
icie
ntva
lues
belo
w1
indi
cate
that
the
prog
ram
char
acte
rist
icis
asso
ciat
edw
ith
low
erm
orta
lity
.bN
egat
ive
coef
fici
ent
valu
esin
dica
tebe
tter
func
tion
alan
dse
lf-a
sses
sed
heal
thou
tcom
es.
c San
dN
Sde
note
sign
ific
ant
orno
tsi
gnif
ican
tas
soci
atio
nof
the
who
leca
tego
ryat
the
0.05
leve
l,as
show
nin
tabl
e4.
∗ p<
0.05
,∗∗ p
<0.
01.V
aria
bles
that
wer
eli
sted
inta
ble
2an
dno
tin
clud
edhe
rew
ere
not
sign
ific
antl
yas
soci
ated
wit
han
yof
the
outc
omes
.
Program of All-Inclusive Care for the Elderly (PACE) 521
showing definitive proof of the effect of the program’s characteristics onhealth outcomes.
Because we present a large number of analyses, some correlations maybe found to be significant solely by chance. We tested hypotheses regard-ing thirty-two program characteristics in relation to five outcomes (160comparisons). At the 5 percent level of significance, we expected eightvariables to show a significant association due to chance, but we foundforty-one significant correlations, a much larger number of significantrelationships.
Results
Because the main focus of this article is on the association betweenprograms’ characteristics and their enrollees’ health outcomes, we reportthe associations between individual risk factors and outcomes in table 3,without discussing them.
Organizational Characteristics Associatedwith Enrollees’ Health Outcomes
Table 4 examines the variation in enrollees’ health outcomes and char-acteristics related to the programs that contributed to this variation.
Table 4, part A (first row), shows the percentage of variation in en-rollees’ outcomes that is explained by the individual programs, aftercontrolling for individual risk factors. It ranges from a low of 9.3 per-cent for self-assessed health (SAH) at three months to a high of 23.4percent for SAH at twelve months.
Table 4, part A (second row), shows the percentage of variation dueto program effects explained by specific program characteristics. Theyare discussed in greater detail later. These program characteristics bestexplain the program-related variation in functional outcomes: they ex-plain 87 percent of the program-related variation at three months and90 percent at twelve months. The programs’ characteristics also explaina substantial percentage (49 percent) of the program-related variationin long-term SAH, but they explain less of the variation in short-termSAH (15 percent) and in mortality (24 percent).
Table 4, part B, shows the significance of the association be-tween each group of program characteristics and each outcome. Both
522 Dana B. Mukamel et al.
short- and long-term functional status outcomes were significantly as-sociated with all but one characteristic, suggesting that PACE programsmay have a strong and pervasive focus on improving these outcomes,as many aspects of the programs are associated with these outcomes.The SAH at twelve months was associated with three of the groups ofprogram characteristics. The SAH at three months and mortality wereassociated with only one group of program characteristics.
Program Characteristics Associated with BetterFunctional Outcomes
Most of the program characteristics that we examined were significantlyassociated with functional outcomes. The following associations wereparticularly noteworthy (the numbers in parentheses refer to the relevantrow in table 5). We offer next a short discussion of each association,speculating about the reasons for the significant associations that wefound.
Those persons enrolled in programs whose medical director was atrained geriatrician had better functional outcomes. Furthermore, med-ical directors who spent at least some time in direct patient care wereassociated with better short-term functional outcomes, and having themedical director spend more time in PACE (i.e., a higher FTE) was as-sociated with better outcomes at twelve months. These findings suggestthat the medical director may play an important role in a program likePACE, even though many of the program’s services are not medical (i.e.,home and personal care) (II, 3, 4, and 5).
Programs with more effective teams were associated with better func-tional outcomes at twelve months. This finding is consistent with thePACE philosophy that a cohesive and effective team can offer better care.This finding suggests that for a patient population like PACE’s enrollees,who require complex services, the coordination of these services and thedevelopment and implementation of care plans by a comprehensive teamthat cover all disciplines may be important (III, 7).
A staff composed of more aides than professionals and with moreethnic similarity between aides and enrollees was associated with bet-ter functional outcomes. The reason might be the fact that aides tendto spend a substantial amount of time with the individuals for whomthey care and thus have an opportunity to play an important social and
Program of All-Inclusive Care for the Elderly (PACE) 523
motivational role, such as encouraging enrollees to attempt more taskson their own. Such an interaction is likely to be more successful whenthe aides and the patients have a common cultural background, whichallows them to better understand each other and may lead to greaterempathy (II, 2, and III, 2).
The functional outcomes for those enrolled in programs with lowerhospitalization rates were, on average, better. This association mightreflect the constraints of the fixed, capitated revenues of PACE programs.A greater use of hospital care, which is the most expensive service thatPACE provides, limits the resources available for other services, includingthose that may be more important to maintaining functional status (III,6).
Enrollees receiving care during the period when the programs werecapitated for both Medicare and Medicaid had better outcomes. Thereason might be that when the programs receive capitated payment forboth Medicaid and Medicare, they have more flexibility in using financialresources where they are needed, for both acute and long-term care,without having to conform to the rigid rules of the usual system of care.PACE programs typically start with capitated Medicaid payments andfee-for-service Medicare payments. Medicare capitation usually beginsonly a year or two later. After this initial period, the program continueswith capitated payment from both payers (I, 1).
Better outcomes also were associated with larger and older programs.Because in PACE there is a high correlation between program size andage, with the oldest programs also being the largest, we also examinedprogram size and age in the same model. In this analysis only programage was significant. This finding might reflect the programs’ learningcurve, both for admitting enrollees who are better suited to their servicesand for learning how to serve their population better (V and VI).
The analysis suggests that a mix of enrollees skewed toward late-lossADLs, like eating (Morris, Fries, and Morris 1999), and middle-lossADLs, like dressing, toileting, transferring, and walking (Morris, Fries,and Morris 1999) is associated with worse outcomes. This may resultfrom the constraints of fixed revenue. A larger percentage of individualsrequiring ADL help limits the amount of help available for each en-rollee, which may result in worse outcomes. This is consistent with thefinding that a higher percentage of individuals living alone, and there-fore requiring more home care services, also is associated with worse
524 Dana B. Mukamel et al.
outcomes at three months, indicating that a balanced population maybe an advantage (IV, 1 and 8).
Program Characteristics Associated with BetterSelf-Assessed Health (SAH) Outcomes
Fewer program characteristics were associated with SAH outcomes.Higher staffing levels were associated with better SAH at twelve months.This finding differs from the other outcomes, to which the distributionof professionals versus nonprofessionals, but not the total staffing level,was important. This might reflect the impact of nonspecific care thatcan be provided by any staff member. Perhaps the greater number ofstaff allows them to spend more time with enrollees, and the increasedinteraction time may influence enrollees’ well-being, leading to better-perceived health (III, 1).
As with functional outcomes, a staff that is more diverse and pro-vides more diverse services was associated with better long-term SAHoutcomes (III, 3 and 4).
A higher percentage of enrollees living alone was associated withworse outcomes at twelve months. This again might be a reflection ofthe resources per enrollee, which may be less generous in programs thatneed to provide more services at home (IV, 8).
The programs’ maturity was associated with better SAH at both threeand twelve months, perhaps reflecting improvement as the programslearned how to better care for their enrollees (VI, 1).
Program Characteristics Associatedwith Survival
Unlike functional and SAH outcomes, only two factors were associatedwith mortality. Lower mortality was associated with having more pro-fessionals than nonprofessionals, in contrast to the finding for functionalstatus. The mix of staff skewed toward professionals may indicate a moremedically oriented program, which is consistent with a stronger empha-sis on survival (III, 2).
Lower mortality also was associated with a higher concentration ofservices, that is, with programs providing the same type of services tomost or all enrollees (III, 4).
Program of All-Inclusive Care for the Elderly (PACE) 525
Program Characteristics Not Associatedwith Any Outcomes
Several program characteristics were not associated with any of the out-comes we studied. Contracting for personnel, particularly nurses andnurses’ aides, is an alternative to employing salaried personnel. PACEprograms may use contract staff because they face labor shortages orbecause this provides more flexible scheduling. Our analysis (table 4)did not identify a relationship between outsourcing labor and enrollees’outcomes.
Although an ethnic overlap between the nonprofessional staff andthe enrollees was associated with better functional outcomes, the ethnicoverlap between enrollees and the professional staff was not associatedwith any of the outcomes. This perhaps indicates that the interactionbetween enrollees and professionals is dominated by professional stan-dards of conduct and structured around “professional” content and thusvaries less with cultural similarity. The reason may also be that nonpro-fessionals seem to be more important to functional outcomes and thatprofessionals are more important to mortality outcomes.
Discussion
PACE programs share many features such as the patient population,the delivery system, and the financial incentives of a managed caremodel. They also exhibit wide variations in risk-adjusted patient out-comes (Mukamel et al. 2004). In this study we examined these variationsin PACE enrollees’ health outcomes to understand whether and whichspecific program features are associated with better outcomes.
The first important observation is that a program’s characteristics doseem to matter, as they are associated with the enrollees’ outcomes andhealth status. The second observation is that the significance of theseassociations varies. Many program characteristics are associated with theprevention of functional decline. Fewer, however, are associated withinfluencing SAH and mortality. These findings might reflect the PACEprograms’ mission, which is to allow persons to remain in the communityliving independently for as long as possible and with the highest possiblequality of life. This mission has probably led the PACE programs to
526 Dana B. Mukamel et al.
focus their resources and services on preserving and improving functionalstatus and has made them less likely to follow a medical model thatemphasizes survival above all else. In fact, PACE programs encouragetheir enrollees to discuss advance directives and to make their preferencesknown (Temkin-Greener and Mukamel 2002; Temkin-Greener, Gross,and Mukamel 2005). Thus the focus of PACE on independent livingcould explain our finding of a much less pervasive program effect onmortality.
Another possibility is that the program characteristics we examinedare not those that are important to mortality outcomes. For example, wedid not have information about advance directives or program policieswith respect to end-of-life care, which may be associated with mortalityoutcomes.
The pervasiveness of program characteristics associated with func-tional outcomes suggests that PACE programs may be able to choosedifferent routes to improving care. For example, our findings suggestthat having a full-time medical director may be associated with betteroutcomes. Smaller programs with fewer patients may have difficultyjustifying employing a full-time medical director. Such programs may,however, require that their medical directors not only perform manage-ment duties but also provide direct patient care. Our findings show thatthis in itself might lead to better functional outcomes and might alsobe a way of increasing the medical director’s time commitment to theprogram, even in small programs. Alternatively, programs could chooseto focus on strengthening the ethnic similarity of enrollees and nonpro-fessional staff, or if this were difficult, perhaps to educate their staffabout their enrollees’ culture. Depending on their external and internalenvironments, different programs are likely to find some strategies eas-ier to implement than others. Our findings offer a menu of alternativeactions, all of which may be associated with improvement.
Self-assessed health outcomes were associated with programs’ charac-teristics mostly in the longer run, at twelve months. Furthermore, unlikefunctional status, many of the variables that we examined did not showa statistical relationship to these outcomes. Perhaps this is a reflection ofthe more complex nature of self-assessed health, which might be viewedas a measure of the person’s gestalt and thus is influenced by many aspectsof his or her health, both physical and mental. Accordingly, it might bemore difficult for programs to influence self-assessed health. The char-acteristic that seems to have the greatest effect on self-assessed health is
Program of All-Inclusive Care for the Elderly (PACE) 527
the total number of staff per one hundred enrollees. Unlike other qual-ity improvement initiatives, hiring more staff is usually a difficult wayto improve care because it requires a greater long-term commitment ofresources.
Another interesting finding is that the programs’ maturity was as-sociated with better functional and self-assessed health outcomes. Thissuggests that programs might have to learn what type of enrollee theycan best care for and to adjust their admission decisions in order to obtainenrollees who are better matched to their program’s strengths. Alterna-tively, programs might have to learn how to care better for those whomthey admit, thus leading to better outcomes. This raises the questionof whether the lessons that programs learn as they mature can be trans-ferred to newer programs, thereby shortening or even eliminating theirlearning curve.
The one consistent finding across all the outcomes we examined wasthe lack of significant association with contract labor. The reason may bethat contract labor may have both positive and negative effects on out-comes, thereby canceling each other out. Programs may employ contractpersonnel, most likely nurses and aides, if they have difficulty hiringpermanent staff or if this is a more financially advantageous approach.The use of contract staff may offer programs more flexibility and allowthem to avoid short-staffing situations, which, in turn, may be associ-ated with better outcomes. In contrast, contract staff is less likely to bepart of the “PACE culture,” may not be as effective team members, andmay be more subject to turnover. Turnover may result in less continuityof care for enrollees and more difficulties in creating and maintainingpersonal relationships.
Policy Implications for Programs Servingthe Frail Elderly
Although PACE is a unique program in that it provides comprehensiveservices addressing all the needs of a frail and older population, the lessonslearned from the PACE experience might offer useful insights to otherprograms serving similar populations.
The integration of acute and long-term care has long been envisionedas one of the key steps in improving care for those with comorbid chronicconditions and complex care needs. PACE was built as such a model,
528 Dana B. Mukamel et al.
integrating Medicare’s acute and Medicaid’s long-term care benefits andfunding streams, so that the programs could tailor their services to eachenrollee’s specific needs. The expectation has been that such a model ofcare would minimize its reliance on institutional care, thereby prevent-ing or delaying the enrollee’s placement in a nursing home, and wouldwork to optimize the functional independence of frail, community-basedelderly enrollees.
To foster such best practices, the PACE model of care relies on severalunique features. For example, PACE uses a staff model of medical care inwhich physicians are employed by each program to provide primary care.PACE physicians share the program’s values and philosophy of care (Enget al. 1997), and they spend a considerable amount of time in PACE-related patient activities, like being a member of an interdisciplinaryteam. The PACE model of care also relies on interdisciplinary teamworkfor both planning and delivering care (Temkin-Greener et al. 2004).Most care is provided in the day centers, which the enrollees attendseveral days per week for therapy, personal and medical services, meals,and supportive care.
Our findings show that many of these PACE-specific features areassociated with better risk-adjusted patient outcomes. For example, pro-grams with better-performing teams are associated with better functionaloutcomes. The association we found between the time that the programs’medical directors devote to medical practice and better functional out-comes also appears to support the rationale for the staff model of carefor this population. Similarly, the availability of diverse services, mostof which are provided at the day centers, seems to be associated withbetter risk-adjusted outcomes. An evaluation of PACE, sponsored by theCenters for Medicare and Medicaid Services (CMS) has shown substantialreductions in the use of institutional care for PACE enrollees, comparedwith that for a control group, and has attributed this to a broad scope ofcare coordination among diverse disciplines at the day center (Chatterjiet al. 2003).
Although these PACE characteristics may indeed be examples of pro-gram features well suited to best practices for the very frail elderly, thesesame features have been criticized as being responsible for PACE’s slowerthan expected growth (Eleazer and Fretwell 1999; Gross et al. 2004;Kane 1999). A number of newer programs, which have been modeledon PACE, began to experiment with less restrictive models that do not
Program of All-Inclusive Care for the Elderly (PACE) 529
require staff physicians, center-based care, or formal interdisciplinaryteamwork (Muskie School of Public Service 1997). The Wisconsin Part-nership Program (WPP), the Minnesota Senior Health Options (MSHO),the Massachusetts Senior Care Options (MSCO), and similar initiativesin other states represent efforts to liberalize the PACE model so as toappeal to a broader market. A recent study comparing PACE and WPPfound PACE to be significantly more effective in controlling hospitaland emergency room utilization for its enrollees (Kane et al. 2006). Ef-forts to relax the PACE model’s programmatic features may exact a pricein terms of worse process of care measures and poorer outcomes.
More recently, as a result of the Medicare Modernization Act of2003, Congress created a new type of Medicare Advantage coordi-nated care plan focused on individuals with special needs (SNP) serv-ing the institutionalized, the dually eligible, and/or those with se-vere or disabling chronic conditions. By 2006, there were 276 SNPsserving more than 600,000 beneficiaries, most of them dually eligi-ble (see http://www.cms.hhs.gov/SpecialNeedsPlans/Downloads/06SNPEnrollment by Type11-9-06.pdf; accessed June 14, 2007). Unlike
PACE, SNPs are not required to implement a specific model of caredelivery. According to a recent study underwritten by the Medicare Pay-ment Advisory Commission (Mathematica Policy Research, Inc. 2006),most SNPs have not made major changes to their organization or in-frastructure, such as adding new departments, staff, or data systems.Even though SNPs are committed to better coordination of Medicareand Medicaid services, many do not have SNP-specific care coordinationand management programs. It still is too early to derive any lessons fromthe SNP experience. But lessons from PACE suggest that fundamentalchanges in practice patterns are key to changing utilization and pro-moting good patient outcomes for the frail elderly. The PACE modelfor integrating care is expensive to develop and to operate and so maybe difficult to promote broadly. But the trade-off that comes with lessrestrictive models may come at a price of poorer outcomes.
Although our analyses are limited by the small number of PACE pro-grams, the findings did generate hypotheses regarding quality improve-ment. Programs that undertake such activities should be aware that theirown culture and environment may be different and that therefore ourfindings may not be fully generalizable. Efforts to improve care shouldbe tailored to each program and monitored and adjusted as they progress.
530 Dana B. Mukamel et al.
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Acknowledgments: The authors gratefully acknowledge funding from the Na-tional Institutes on Aging (grant no. R01AG1755) and thank the participatingPACE programs and the National PACE Association for their participation inthe study and their support.
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