Comparison of Caregiver and General Population Preferences for Dependency‑Related Health States

Vol.:(0123456789)
Applied Heal h Economics and Heal h Policy (2025) 23:105–117
h ps://doi.o g/10.1007/s40258-024-00908-x
ORIGINAL RESEARCH ARTICLE
Compa ison o Ca egi e andGene al Popula ion P e e ences
o Dependency‑Rela ed Heal h S a es
E aRod íguez‑Míguez1,2 · An onioSampayo3
Accep ed: 8 Augus 2024 / Published online: 10 Sep embe 2024
© The Au ho (s) 2024
Abs ac
Objec i e We assess whe he he p e e ences ega ding dependency- ela ed heal h s a es as s a ed by in o mal ca egi e s
a e aligned wi h hose exp essed by he gene al popula ion.
Me hods The p e e ences o a sample o 139 Spanish in o mal ca egi e s o dependen pa ien s a e compa ed wi h hose
ob ained ia a sample o 312 pe sons, also om he Spanish gene al popula ion. We assess 24 dependency s a es ex ac ed
om he DEP-6D using he ime ade-o me hod. Desc ip i e s a is ics and eg ession me hods a e used o explo e di e -
ences be ween he wo samples.
Resul s Mean di e ence es s es ablish ha , o all bu one o he 24 s a es, he e a e no signi ican di e ences be ween he samples.
The es ima ed mean alues anged om − 0.64 o 0.60 o he ca egi e sample and om − 0.60 o 0.65 o he gene al popula ion
sample, wi h a co ela ion o 0.96. On a e age, he classi ica ion o s a es as be e o wo se han dead ma ched in bo h samples
(excep o one s a e). Reg ession models also show ha sample ype does no ha e a signi ican a e age impac . A e we in oduce
in e ac ion e ec s, only he mos se e e le el o wo dimensions, cogni i e p oblems and housewo k, esul in signi ican di e -
ences—wi h he ca egi e sample epo ing highe alues o he o me , and lowe alues o he la e .
Conclusion Ca egi e s and he gene al popula ion exhibi qui e simila p e e ences conce ning dependency- ela ed heal h
s a es. This sugges s ha he esul s o cos -u ili y analyses, and he esou ce alloca ion decisions based on hem, would
likewise no be signi ican ly a ec ed by he p e e ences used o gene a e he weigh ing algo i hm.
Key Poin s o Decision Make s
The ca egi e s’ p e e ences o dependency- ela ed
heal h s a es a e e y simila , on a e age, o he p e e -
ences o he gene al popula ion.
The analysis by dimension shows ha he cogni i e
p oblems gene a e mo e disu ili y o he gene al popu-
la ion han o ca e s, and he opposi e is ue o he
housewo k dimension.
Cos -u ili y analysis and public decision making would
ha dly be a ec ed by he p e e ences used o gene a e
he dependency s a es alues, and he use o p e e ences
om gene al popula ion is ecommended.
1 In oduc ion
The apid ageing o he popula ion will b ing new chal-
lenges, one o which will be o delay as much as possible
he loss o heal h- ela ed quali y o li e (QoL) o olde
people. A equen consequence o his de e io a ion in
heal h associa ed wi h ageing is he loss o he abili y o
ca y ou basic ac i i ies o daily li ing, esul ing in he
need o help om o he s. This si ua ion o dependency,
no necessa ily associa ed wi h ageing, ansla es in o
an eno mous cos o ca e, which is added o he heal h
expendi u e gene a ed by hese si ua ions. In ac , i is
common o unding decisions o change when hese cos s
a e included [1, 2].
* E a Rod íguez-Míguez
[email p o ec ed]
An onio Sampayo
a .sampay[email p o ec ed]
1 ECOBAS, Uni e sidade de Vigo, GRIEE, 36310Vigo,
Spain
2 Ins i u o de In es igación Sani a ia Galicia Su , Hospi al
Ál a o Cunquei o, 36213Vigo, Spain
3 ECOBAS, Uni e sidade de San iago de Compos ela,
15782San iagodeCompos ela, Spain
106 E.Rod íguez-Míguez, A.Sampayo
The e a e mul iple s a egies—bo h pha macologic [3,
4] and non-pha macologic [5, 6]— o p omo e heal h o
olde adul s, which con ibu e bo h o p e en o delay
si ua ions o dependency, and o dec ease he deg ee o
dependency when i occu s (p e en ing s okes, delaying
bo h he onse and he p og ession o Alzheime ’s disease
and o he demen ias, ehabili a ion, echnical aids, educa-
ion p og ammes o p e en alls, e c.). The bene i s and
cos s o hese s a egies and o he s expec ed o eme ge in
he u u e need o be quan i ied o make in o med deci-
sions. Se e al ins umen s a e a ailable o assess he QoL
associa ed wi h a si ua ion o dependency—dependency-
ela ed QoL— ha make i possible o es ima e he e ec-
i eness o such p og ammes, some o which a e widely
used, such as he Ka z scale [7], he Ba hel Index [8] o
he Law on and B ody scale [9]. Howe e , hese ins u-
men s a e no p e e ence-based and, in consequence, do
no allow o measu e he a ia ion o QoL using scales
ecommended o economic e alua ion, such as quali y-
adjus ed li e yea s (QALYs). To o e come hese limi a-
ions, gene ic p e e ence-based ins umen s can be used,
wi h he EQ-5D being he mos equen ly men ioned in
guidelines om na ional heal h echnology assessmen
agencies [10]. Howe e , he main limi a ion o hese
ins umen s is ha gene ic measu es, as opposed o spe-
ci ic measu es o dependency, may no be sensi i e enough
o iden i y ele an changes in dependency s a us [11, 12].
Fo example, egaining he abili y o ea on one's own in
a Pa kinson's su e e (ei he h ough a emo - educing
d ug o an elec onic de ice ha compensa es o emo s)
is a majo change a he indi idual and amily le el, bu
would a ely lead o a change in any o he gene ic p e -
e ence-based measu es,in pa ien swi hmul iplecomo -
bidi ies. The weak co ela ion be ween he Ba hel index
and he EQ-5D ound in he li e a u e suppo s his e i-
dence [13–15]. In he con ex o dependency, he e is also
a g owing in e es in he Adul Social Ca e Ou comes
Toolki (ASCOT) [16, 17]. The ASCOT is ano he p e -
e ence-based ins umen ha can be applied o economic
e alua ion. Howe e , his ool does no measu e changes in
he se e i y o dependency bu a he changes in he social
ca e– ela ed QoL.
The DEP-6D, is a speci ic ins umen designed o meas-
u e changes in dependency- ela ed QoL, which in eg a es
he disc imina o y capaci y o ins umen s designed spe-
ci ically o dependency wi h he desi able p ope ies o
use in economic e alua ion [18]. As he au ho s poin ou
“ he p oposed ins umen p o ides complemen a y u ili ies
o gene ic measu es wi h a wide ocus and should be used
alongside hese o p o ide a mo e accu a e measu emen ”.
This ins umen p o ides classical QALYs ancho ed on a ull
heal h-dead scale as commonly used in cos -u ili y analy-
sis and i has been used in di e en con ex s, such as he
es ima ion o disabili y-adjus ed li e expec ancy [19], o in
measu ing he impac o se e i y o dependency on ca egi e
bu den [20]. Using he COSMIN g id in a sys ema ic e iew
o gene ic and speci ic ins umen s used o es ima e QALYs,
Tou é e al. [21] ound ha 63.2% o he quali y c i e ia
assessed we e a ed as e y good in he DEP-6D, compa ed
o 56% o he mean o all ins umen s assessed.
The DEP-6D amoun s o a sys em o classi ying depend-
ency s a es along six dimensions o h ee o ou le els each,
and i inco po a es a p e e ence-based sco ing algo i hm ha
assigns weigh s o each o he dimension le els. The weigh s
a e based on p e e ences o Spanish ci izens ob ained by
way o he ime ade-o (TTO) me hod. The use o gene al
popula ion p e e ences o es ima e hese weigh s is a s a egy
o en ecommended in he economic e alua ion o heal h
p og ammes [22, 23]. Howe e , i would be desi able o ana-
lyse whe he he esul s would be simila i based on he p e -
e ences o o he in ol ed g oups. The DEP-6D desc ibes, in
gene al, mo e se e e p oblems han hose usually desc ibed
by means o gene ic p e e ence-based measu es, wi h he
minimum u ili y sco e p edic ed by he DEP-6D algo i hm
being −0.84 ( alued on he1-0 ullheal h–deadQALY
scale), which is conside ably lowe han he minimum alue
p edic ed by he Spanish algo i hms o he mos widely used
gene ic ins umen s, such as he EQ-5D-3L [24], he EQ-
5D-5L [25] and he SF-6D [26], whose minimum alues
a e − 0.65, − 0.25 and − 0.36, espec i ely. The au ho s
also ind ha mo e han hal (14 om 24) o he di ec ly
a ed dependency s a es had a nega i e alue— ha is, hey
we e conside ed o be wo se han dead. Gi en he low alues
ob ained, one wonde s whe he g ea e knowledge based on
pe sonal expe ience migh lead o a di e en assessmen o
hese s a es, p oducing an algo i hm wi h di e en weigh s.
Following Cubi-Molla e al. [27], di e en pe spec i es
can be adop ed o inco po a e expe ience in he assessmen
o heal h s a es. One o hem uses pa ien s o assess hei
own s a e o heal h. Howe e , i has s ong limi a ions i we
wan o assess dependency si ua ions ha a e e y se e e
o linked o cogni i e p oblems. While i is ue ha o he
s udies ha e su eyed people wi h mild o mode a e cog-
ni i e p oblems, he asks o be pe o med we e aimed a
posi ioning hemsel es in one o he le els o he dimen-
sions assessed ( o example in he EQ-5D mul i-a ibu e
classi ica ion sys em). When u ili y weigh s need o be es i-
ma ed, as in ou s udy, only pa ien s wi h a mild cogni i e
impai men can pa icipa e [28]. I should be no ed ha he
me hodology commonly used o ob ain u ili y weigh s, o
example he TTO me hod, equi es e y demanding asks
om a cogni i e poin o iew, which makes i impossible
o many o he heal h condi ions desc ibed by he DEP-6D
o be assessed by pa ien s expe iencing such a heal h con-
di ion. Ano he pe spec i e ha can also be conside ed o
e alua e heal h s a es is ha p o ided by dependen people
107
P e e ences o Ca egi e s and he Public o Dependency S a es
e alua ing hypo he ical si ua ions. In his case, dependen
pe sons wi hou cogni i e p oblems would be able o assess
he 24 heal h s a es p e iously assessed by he gene al popu-
la ion. Howe e , his op ion has also been ejec ed in ou
s udy o se e al easons. On he one hand, he e may be an
asymme ic bias be ween assessmen s o heal h s a es ha
a e mo e simila o hose o he pa ien being in e iewed, in
which adap a ion may play a ole, and hose heal h s a es ha
a e mo e di e en [29, 30]. On he o he hand, he elici a ion
o expe ience-based alues would be comp omised in he
case o mo e se e e hypo he ical s a es.
Ano he g oup o in e es widely used in he li e a u e
o inco po a e expe ience-based alues in o he analysis is
ca egi e s o dependen pe sons ( ica ious expe ience) [31,
32]. This is he g oup we use in ou s udy. The answe s
o in o mal ca egi e s can be a good p oxy because hey
a e amilia wi h he limi a ions, needs, and eelings o he
indi iduals o whom hey ca e and as he Na ional Ins i-
u e o Heal h and Clinical Excellence (NICE) poin s ou ,
when “i is no possible o ge measu emen s di ec ly om
pa ien s, hese should come om he pe son who ac s as
hei ca e ” [22]. In addi ion, using he ca egi e ’s pe spec-
i e allows o he inco po a ion o expe ience in a wide
ange o dependency si ua ions, bo h in e ms o cu en and
pas expe ience—i is common o he same ca egi e o
ha e expe ience in di e en dependency si ua ions ha he
ca ed- o pe son has been h ough. Gi en ha ou aim is
o analyse whe he he p e e ences on dependency- ela ed
heal h s a es exp essed by in o mal ca egi e s coincide wi h
hose exp essed by he gene al popula ion, as desc ibed in
Rod íguez-Míguez e al., he assessmen o he same 24
hypo he ical heal h s a es also e alua ed by he gene al
popula ion seems o us o be he mos app op ia e s a egy
[18]. Fu he mo e, he e is e idence ha when i comes o
hypo he ical heal h s a es, pa ien and ca egi e p e e ences
do no di e signi ican ly [31].
The e is, a p io i, no clea expec a ion. On he one hand,
ca e p o ision may induce a p ocess o adap a ion ha leads
o highe e alua ions o hese s a es; his phenomenon is
equen ly epo ed in he li e a u e compa ing he p e e -
ences o pa ien s wi h hose o he gene al popula ion [33].
Al hough his “disabili y pa adox” e e s o pa ien s’ p e e -
ences, a simila e ec migh a ise when ca egi e s’ p e e -
ences a e analysed. On he o he hand, close con ac wi h
dependen pe sons may p o ide mo e in o ma ion abou he
limi a ions and losses o well-being ha such si ua ions p o-
duce, which in u n could exace ba e he nega i e pe cep ion
o hese s a es [28]. Finally, i may be ha he wo g oups
exp ess simila p e e ences, ein o cing he ecommenda-
ion ( om s udies o economic e alua ion) o use gene al
popula ion samples.
2 Me hod
2.1 The DEP‑6D Ins umen
The DEP-6D is an ins umen used o cha ac e ise he le el
o daily dependence on o he s [18]. I comp ises a ques-
ionnai e and a weigh ing algo i hm ha assigns a sco e o
each o he esponses p o ided. The DEP-6D classi ica ion
sys em is based on six a ibu es o dimensions—ea ing,
incon inence, pe sonal ca e, mobili y, housewo k, and cog-
ni ion p oblems—each ha ing ei he h ee o ou le els (see
Table1). Co ela ion analysis and ocus g oups we e used
o selec dimensions and le els, and he TTO me hod was
used o es ima e a p e e ence-based sco ing algo i hm. A
majo ad an age o his dependency assessmen ool is ha
i enables he es ima ion o QALYs, which unde pin cos -
u ili y analysis.
2.2 Samples andQues ionnai e
This s udy uses wo c oss-sec ional samples om wo
g oups: he gene al popula ion and ca egi e s. Mic oda a
om he gene al popula ion sample we e p o ided by Rod-
íguez-Míguez e al. [18]. This sample consis s o 312 ci i-
zens d awn om he gene al popula ion o Galicia (a egion
in Spain) ec ui ed a home in 2011 by means o s a i ied
andom sampling. Ca egi e s’ p e e ences we e ob ained in
2016 om a con enience sample o 139 p ima y in o mal
ca egi e s o ch onically ill dependen adul s who need help
wi h ac i i ies o daily li ing. The ca egi e s we e con ac ed
in p ima y ca e cen es in Pon e ed a (Galicia) and in e -
iewed by heal h and social wo ke s. In bo h samples, he
in e iewe s we e ained o conduc he in e iew as ol-
lows; i s , he con en o he ques ionnai e was explained
in de ail and hen a mock in e iew was conduc ed wi h one
o he esea che s.
Face- o- ace in e iews we e conduc ed in he pa ic-
ipan 's home in bo h samples. In o de o ep oduce as
closely as possible, he in e iew wi h he gene al popu-
la ion, a pen-and-pape in e iew was also conduc ed
wi h he sample o ca egi e s. Only in e iews ha , due
o in e iewe e o , did no ollow he p e-es ablished
TTO sequence (explained below) we e elimina ed—in
hese si ua ions he TTO alues canno be ob ained. The
ques ionnai e begins wi h he p esen a ion o he dimen-
sions and le els o he DEP-6D. Since he ques ionnai e
is designed so ha in many cases i can be sel -comple ed,
no addi ional explana ion is p o ided, unless eques ed
by he pa icipan . Nex , pa icipan s we e asked o alue
six DEP-6D dependency s a es. To inc ease compa abil-
i y, he ques ionnai e and expe imen aldesignused o he
108 E.Rod íguez-Míguez, A.Sampayo
ca egi e sample we e iden ical o he one used p e iously
o he gene al popula ion sample. I included a o al o 24
dependency s a es, di ided in o ou blocks o size six (see
Table4 in he Resul s sec ion o ollow); he s a es and
blocks we e ob ained in he seminal wo k using an op imal
design ha cap u es only main e ec s, which means ha
in e ac ion e ec s canno be es ed. The blocks and he
s a es we e andomly assigned o he pa icipan s. Assess-
men o he dependency s a es was ca ied ou using he
TTO me hod. A e a ing he six s a es in sequence, each
pa icipan was shown he six s a es oge he (on sepa-
a e ca ds) and asked o ank hem om mos o leas p e-
e ed. Pa icipan s’ socioeconomic cha ac e is ics we e
also collec ed.
2.3 U ili y Valua ion Me hod
The adi ional TTO me hod was used o assess he depend-
ency s a es. Gi en ha he assessmen o heal h s a es
may a y depending on he aming o he TTO ques ions
[34–37], o a oid con ounding e ec s when compa ing he
p e e ence o ca egi e s o he gene al popula ion, we used
he same amewo k as in he o iginal s udy. Pa icipan s
we e asked o place hemsel es in a hypo he ical si ua ion
in which a doc o in o med hem ha hey su e om a
se ious illness which, i no ea ed u gen ly, will degen-
e a e and apidly lead o dea h. The doc o also in o med
hem ha he e was a ea men ha , al hough i would no
cu e he disease, would allow hem o li e 10 mo e yea s
in a ce ain s a e o dependency (a his momen , one o
he six s a es o be e alua ed is shown). Pa icipan s we e
asked o choose be ween being ea ed o no . I hey choose
he ea men , his means ha he s a e is ega ded as be e
han dead (BTD); i he ea men is ejec ed, hen he s a e
is conside ed wo se han dead (WTD). Depending on he
answe gi en o his ini ial ques ion, he ollowing line o
inqui y is adop ed.
• I hey accep ea men , hen an i e a i e up–down p o-
cedu e was applied o ind he numbe o yea s,
Y∗
BTD
,
in ull hea h (FH) a which pa icipan s we e indi e en
be ween 10 yea s in he s a e being e alua ed and
Y∗
BTD
in
FH. The s a ing alue o
YBTD
was se equal o 5 yea s.
Thus, pa icipan s mus choose be ween wo op ions: A,
10 yea s in he e alua ed s a e; o B,
YBTD =5
yea s in
FH. Depending on he answe p o ided,
YBTD
is adjus ed
up o down un il he indi e ence poin —o he in e -
al con aining i —(
Y∗
BTD
) is bounded as desc ibed nex
(indi e ence is allowed in all choices, in which case he
p ocess ends).
– I pa icipan chooses A →
YBTD =8
:
1) i A →
YBTD =9
;
2) i B →
YBTD =7
→ i B →
YBTD =6.
Table 1 Le els and dimensions o DEP-6D
Le els o dimensions Dimensions
1. Does no need assis ance o ea o d ink
2. Needs pa ial aid o ea o d ink (cu ing, se ing, e c.)
3. Needs o be gi en ood and d ink
Feeding
1. Does no ha e incon inence o does no need help
2. Has u ina y (o aecal) incon inence and needs help o hygiene
3. Has bo h u ina y and aecal incon inence and needs help o hygiene
Incon inence
1. Does no need help o pe sonal ca e: ba hing, g ooming, d essing, e c.
2. Needs help only o ba he bu no o he es o pe sonal ca e
3. Needs help o mos pe sonal ca e ac i i ies
4. Is incapable o ca ying ou pe sonal ca e; needs someone else o pe o m his ac i i y
Pe sonal ca e
1. Mo es independen ly
2. Does no need help o mo e wi hin he home bu does ou o home
3. Needs help o mo e bo h in and ou o home
4. Is incapable o changing posi ion; bed- idden o chai - idden
Mobili y
1. Does no need help o ca y ou daily housewo k (making ood, cleaning c ocke y, e c.)
2. Needs daily help o daily housewo k
3. Is incapable o ca ying ou mos daily housewo k
Housewo k
1. Does no need help due o cogni i e/men al p oblems o has no hese p oblems
2. Needs assis ance o manage money, medica ion o o ake some basic e e yday decisions;
gene ally collabo a i e a i ude wi h he ca egi e
3. Incapable o aking basic decisions; canno li e alone. Does no esis help
4. Incapable o aking basic decisions; canno li e alone. Does no collabo a e and usually o e s esis ance o help
Cogni ion p oblems
109
P e e ences o Ca egi e s and he Public o Dependency S a es
– I pa icipan chooses B →
YBTD =2
:
1) i A →
YBTD =4
→ i B →
YBTD =3
;
2) i B →
YBTD =1
.
• I hey e use ea men , he nex choice was be ween:
A,
YWTD
yea s in ull heal h ollowed by
10 −YWTD
yea s
in he s a e being e alua ed; o B, dying. As be o e, he
alue o
YWTD
as ini ially se o 5 yea s and hen mo ed
up o down un il he con e gence p ocess e mina ed.
– I pa icipan chooses A →
YWTD
=
2
:
1) i A →
YWTD
=
1
;
2) i B →
YWTD
=
4
→ i B →
YWTD
=
3.
– I pa icipan chooses B →
YBTD
=
8
:
1) i A →
YWTD
=
7
→ i B →
YWTD
=
6
;
2) i B →
YBTD
=
9.
.
U ili y o s a e S, deno ed
U(S)
, was ob ained by using
he QALY model assump ions o ch onic heal h s a es
– ha is, he alue o exis ing Y yea s in s a e S is equal
o
U(S)×Y
. By con en ion, he u ili y o FH is 1 and he
u ili y o dea h is 0; hence
U(S)
is ob ained as ollows. I a
s a e is ega ded as BTD hen, unde he p e ious assump-
ions,
U(S)=Y∗
BTD∕10
. Fo s a es ha a e ega ded as
WTD,
U(
S
)=−
Y
∗
WTD∕(
10
−
Y
∗
WTD)
. Howe e , because
he nega i e u ili ies calcula ed in his way do no ha e a
lowe bound (and so esul in dis ibu ions ha a e skewed
s ongly o he le ), we bound nega i e alues a −1 ia
applying he ans o ma ion sugges ed by Pa ick e al.
[38]:
U(S)=−Y∗
WTD∕10
. Owing o ou i e a i e up–down
p ocedu e, usually a ange o alues (and no an indi e -
ence alue) is ob ained o bo h
Y∗
BTD
and
Y∗
WTD
. The ollow-
ing example illus a es he p ocedu e used o ob ain
U(S)
.
Suppose a pa icipan , who conside s a s a e be e han
dead, has chosen op ion A when
YBTD =5
, op ion B when
YBTD =8
, and op ion A when
YBTD =7
. In ha e en ,
YBTD
lies in he in e al (0.7,0.8). Visual aids (iden ical o bo h
samples) we e used in all ques ions o help subjec s unde -
s and he ques ions posed; as an example, Fig.1 shows he
isual aid p esen ed immedia ely a e he s a e has been
ound o be conside ed BTD by he pa icipan .
2.4 S a is ical Analysis
Se e al analyses we e conduc ed o de e mine whe he he
p e e ences o ca egi e s a e di e en om hose o he gen-
e al popula ion. Fi s , we checked o whe he he le el o
inconsis ency—bo h in e nal and ex e nal—and in a iance
o he esponses is simila in bo h samples. In e nal consis -
ency is analysed by iden i ying non-compliance wi h he
dominance es , which is iola ed when a esponden places
mo e alue on a s a e ha is logically wo se in he sense ha
none o i s dimensions p esen s a be e si ua ion. Ex e nal
consis ency is measu ed as he ex en o co ela ion be ween
he anking o s a es as de i ed om he TTO exe cises and
he di ec anking p o ided by pa icipan s. Wi h ega d o
in a iance, we iden i y hose pa icipan s who assign he
same alue o all s a es, a pa icula case o which in ol es
indi iduals who simply do no ade o (
U(S)=1
o all
s a es).
Second, o each o he 24 s a es assessed we de e mined
he mean u ili y by sample (gene al popula ion s ca egi e s)
and checked o signi ican di e ences be ween hose wo
samples. The e is no consensus on how bes o p oceed wi h
inconsis en pa icipan s [39, 40]; he e we ollowed o he
au ho s in op ing o exclude pa icipan s wi h mo e han one
inconsis ency. In his way, we seek o dis inguish be ween
sys ema ic depa u es om dominance—as exhibi ed by he
excluded pa icipan s—and andom e o [18, 41]. We epo
he s a es’ mean alues o he ull sample and also o he
educed sample (he eina e e e ed o as he “consis en
pa icipan s”). Only he sample o consis en pa icipan s is
used o all o he aspec s o he s a is ical analyses.
Thi d, andom-e ec s in e al-da a eg ession models
we e used o ob ain he weigh ing algo i hms o each
o he samples, and also o look o signi ican be ween-
sample di e ences in p e e ences conce ning depend-
ency- ela ed heal h s a es. These eg ession models allow
us o conside bo h unce ain y abou he exac alue
o indi e ence and he p esence o le -censo ed alue
[25]. On he one hand, in mos o he a ings we do no
ob ain a poin o indi e ence, bu an in e al in which he
unknown alue lies. On he o he hand, by cons uc ion,
he alues ob ained had a minimum TTO alue bounded
a −1, bu esponden s could hypo he ically con inue ad-
ing beyond he le bound a −1. The e o e, we elax his
lowe bound assump ion and conside esponses a he
lowe bound (−1) o be censo ed. The model also con ains
a andom e ec o accoun o he dependence o epea ed
obse a ions wi hin esponden s. The dependen a ia-
ble o all models was
U(S)
. In model 1 and model 2 we
es ima e sepa a ely he weigh ing algo i hms o each o
he samples (ca egi e s and gene al popula ion), and he
independen a iables a e he se e i y le els o he dimen-
sions. To acili a e compa isons, he ela i e impo ance
o each dimension is ob ained by di iding i s ange by he
sum o he anges o all a ibu es. Model 3 uses he ull
da a se ; in his model, he independen a iables a e he
se e i y le els o dimensions and a dummy a iable se o
1 o ca egi e esponden s o o 0 o esponden s om
he gene al popula ion. Model 4 ep oduces model 3 bu

110 E.Rod íguez-Míguez, A.Sampayo
con ols o he mos ele an sociodemog aphic a iables
(gende , age, and educa ional le el). Finally, he inde-
penden a iables in model 5 a e he in e ac ions be ween
se e i y le els and sample ype; hus, we explo e whe he
(and, i so, how) he sample’s e ec s di e among he
dimensions.
W i en in o med consen was ob ained om each
pa icipan , and he Commi ee on E hics o Clinical
Resea ch in Galicia app o ed he s udy.
3 Resul s
In he sample o ca egi e s, all ques ions ha e co ec ly
ollowed he es ablished i ine a y o ob ain TTO alues—
in he gene al popula ion sample only 2 obse a ions had
been elimina ed. Table2 p esen s a desc ip i e analysis o
he sample. As expec ed, he sample o ca egi e s di e s
ma kedly om ha o he gene al popula ion. In pa icu-
la , he o me consis s mos ly o women and has a highe
mean age han he gene al popula ion. The ca egi e sam-
ple is also cha ac e ized by a lowe educa ional le el and
a lowe equency o people wo king, wi h a qua e o
he sample being e i ed people. The dis ibu ion o am-
ily income (excluding missing da a) is simila ac oss he
samples.
Table3 epo s he le el o consis ency and in a iance by
sample. The wo samples yield simila esul s, wi h no signi i-
can di e ences being obse ed. The e is a high pe cen age o
pa icipan s who mee all he consis ency es s, and i we include
hose who commi no mo e han one inconsis ency hen his pe -
cen age ises o 94% in bo h samples. The co ela ion be ween
he di ec anking and he esul ing TTO me hod is only mode -
a e ye is simila in bo h samples. The numbe o pa icipan s
who always p o ide he same answe o all s a es (in a ian
u ili y) is highe in he gene al popula ion han in ca egi e s,
al hough he di e ence is no s a is ically signi ican (p = 0.228).
Table4 shows he mean alues o he 24 s a es ha we e
di ec ly e alua ed. The labelling o he s a es e e s o he
le els in which he s a e is ound in each o he six dimensions
ha make up he DEP-6D (e.g., a dependen in s a e 122222
does no need assis ance o ea o d ink bu has a le el-2 se e -
i y in all he o he dimensions). Figu e2 plo s he mean al-
ues, by sample, o de ed acco ding o he sum o he (o dinal)
Fig. 1 Example o isual aid
T ansla ion: Now suppose ha you a e in hes a e you ha e chosen (see ca d) and
you will li ein ha si ua ion o he nex 10 yea s. On anew isi o he doc o ,
you a e in o med ha he eisanew ea men ha wouldallowyou o egainyou
heal h, bu you will li e ewe yea s. The e o e, hesi ua ionyou ace is he
ollowing:
WITHOUT TREATMENT: 10 YEARS WITH THOSE LIMITATIONS
WITH TREATMENT: 5 YEARS IN GOOD HEALTH
Would you unde go he ea men ?
111
P e e ences o Ca egi e s and he Public o Dependency S a es
alues o he le els ha make up each s a e. Acco ding o he
mean di e ence es s whose esul s a e epo ed in Table4,
he e a e no signi ican di e ences be ween he samples (ca -
egi e s s gene al popula ion)—excep o he s a e 112132
(p = 0.008 in he ull samples and p = 0.013 in he samples
o consis en pa icipan s). Pea son’s coe icien o co ela ion
be ween he mean alues o he wo samples is 0.96. Mean
alues o he consis en samples anged om − 0.69 o 0.64
o he ca egi e ’s sample and om − 0.62 o 0.66 o he
gene al popula ion sample. On a e age, he classi ica ion o
s a es as be e o wo se han dead coincides in bo h o hose
samples (excep o s a e 212223).
Table5 (models 1 and 2) displays he impac o dimen-
sion le els on he u ili y weigh s (weigh ing algo i hm) by
sample. Fo he es ima ion, he wo mos se e e le els o he
dimension “pe sonal ca e” (le el 3 and le el 4 in Table1)
ha e been combined in o a single le el. The eason is ha ,
in he gene al popula ion sample, le el 4 o pe sonal ca e
had less o an e ec on QoL han le el 3 o pe sonal ca e
(al hough he wo pa ame e s a e no signi ican ly di e -
en , p = 0.568); hence, we ollowed he app oach o Rod-
íguez-Míguez e al. [18] in g ouping hem oge he . We also
g ouped hem in he ca egi e sample o acili a e compa i-
sons be ween samples; in his case, he pa ame e s e lec ed
he hypo hesized e ec s—le el 3 o pe sonal ca e had less o
an e ec on QoL han le el 4 o pe sonal ca e—(p = 0.058).
The emaining pa ame e s ha e he expec ed sign and di ec-
ion: as he si ua ion in a gi en dimension wo sens, i s u ili y
declines. Compa ing models 1 and 2 e eals ha he la g-
es di e ences a e obse ed in he mos se e e le el o he
“housewo k” dimension (ca egi e s epo g ea e disu il-
i y han he gene al popula ion) and in he se e es le el o
“cogni i e p oblems” ( o which ca egi e s epo less disu-
ili y). Table6 shows he ela i e impo ance o he 6 dimen-
sions, as well as hei con idence in e als, o each o he
samples. The ela i e impo ance is qui e simila be ween
samples o all dimensions excep o he housewo k and
cogni i e p oblems dimensions, bo h wi h a di e ence o
a ound 9 pe cen age poin s. Models 1 and 2 allow he es i-
ma ion o he u ili y sco e associa ed wi h any dependency
si ua ion desc ibed by he DEP-6D by adding o he cons an
he es ima ed alue o each o i s le els.
We can see om model 3 ha , a he agg ega e le el, he
a e age sco e in he sample o ca egi e s is 0.063 poin s
less han he gene al popula ion sample—al hough his di -
e ence is no signi ican (p = 0.36). Model 4 es ablishes
ha hese esul s do no change subs an ially when we con-
ol o sociodemog aphic a iables. And in model 5, whe e
we allowed o di e ences by dimension, again he e a e no
signi ican di e ences be ween samples excep o he mos
se e e le el o cogni i e p oblems (− 0.607 o he gene al
popula ion and − 0.444 o ca egi e s; p = 0.032) and he
mos se e e le el o housewo k (− 0.104 o he gene al
popula ion and − 0.240 o ca egi e s; p = 0.031). These
esul s do no change subs an ially when we con ol o soci-
odemog aphic a iables ( esul s no displayed).
Table 2 Cha ac e is ics o he wo samples (%)
CD close dependen
Ca egi e s (n
= 139)
Gene al popu-
la ion (n = 312)
Female 91.4 47.4
Age (mean) 55.0 41.5
Educa ion
P ima y s udies o less 59.7 37.5
Seconda y 30.2 39.4
Uni e si y 10.1 23.1
Li es wi h pa ne 33.8 39.9
Labo s a us
Employed 20.1 59.6
Pensione / e i ed 25.9 10.9
Unemployed 41.0 15.7
Domes ic asks 10.1 8.3
S uden /o he s 2.9 5.5
Home income (€ mon hly)
< 500 1.4 5.9
500–999 15.8 13.2
1000–1499 29.5 30.5
1500–1999 25.9 25.7
2000–2999 19.4 16.9
≥ 3000 7.9 7.7
# Household membe s (mean) 3.1 3.5
CD
No CD 0.0 53.2
CD (no li ing oge he ) 16.55 40.1
CD (li ing oge he ) 83.45 6.7
Table 3 Consis ency and in a iance by sample
TTO ime ade-o ,
U(S)
u ili y o s a e
Ca egi e s
(n = 139)
Gene al
popula ion (n
= 312)
Mee all dominance es s 80.6% 79.2%
Fail only one dominance es 13.7% 14.7%
Indi idual Spea man’s ho be ween
TTO and di ec anking (mean)
0.65 0.66
U(S) = 1 o all s a es (no ade-o ) 0% 0%
Same u ili y o all s a es 3.6% 6.4%
112 E.Rod íguez-Míguez, A.Sampayo
Table 4 Desc ip i e s a is ics o obse ed TTO alues by sample
SD s anda d de ia ion, TTO ime ade-o
a The i s numbe o he s a e e e s o he le el o he i s dimension, and so on
S a eaAll pa icipan s Consis en pa icipan s
Ca egi e s Gene al popula ion p alue Ca egi e s Gene al popula ion p alue
nMean SD nMean SD nMean SD nMean SD
Block 1 122222 34 0.09 0.65 82 0.17 0.54 0.465 32 0.12 0.66 77 0.18 0.53 0.607
133334 34 − 0.37 0.56 82 − 0.45 0.47 0.403 32 0.39 0.56 77 − 0.49 0.45 0.335
211121 34 0.60 0.49 82 0.58 0.47 0.888 32 0.64 0.45 77 0.60 0.46 0.696
214232 34 0.00 0.64 82 0.06 0.59 0.605 32 0.02 0.65 77 0.04 0.60 0.871
313331 34 0.11 0.71 82 0.17 0.60 0.614 32 0.15 0.71 77 0.17 0.61 0.873
323433 34 − 0.42 0.54 82 − 0.49 0.51 0.480 32 − 0.41 0.55 77 − 0.53 0.48 0.252
Block 2 111221 36 0.60 0.48 78 0.65 0.42 0.518 35 0.62 0.46 77 0.66 0.42 0.664
112132 36 0.02 0.68 78 0.35 0.57 0.008 35 0.04 0.68 77 0.35 0.57 0.013
112211 36 0.51 0.58 78 0.60 0.45 0.355 35 0.50 0.58 77 0.60 0.45 0.322
223234 36 − 0.44 0.52 78 − 0.47 0.54 0.822 35 − 0.43 0.52 77 − 0.48 0.53 0.610
234333 36 − 0.54 0.43 78 − 0.42 0.51 0.235 35 − 0.57 0.40 77 − 0.44 0.50 0.185
333122 36 − 0.37 0.55 78 − 0.23 0.62 0.259 35 − 0.37 0.55 77 − 0.24 0.61 0.304
Block 3 111112 33 0.38 0.49 75 0.40 0.57 0.905 30 0.44 0.46 66 0.50 0.49 0.594
113233 33 − 0.27 0.60 75 − 0.12 0.66 0.263 30 − 0.31 0.60 66 − 0.12 0.66 0.202
213322 33 − 0.26 0.60 75 − 0.07 0.64 0.144 30 − 0.30 0.59 66 − 0.07 0.64 0.098
222131 33 0.08 0.58 75 0.24 0.60 0.191 30 0.10 0.58 66 0.26 0.60 0.235
234431 33 − 0.57 0.49 75 − 0.37 0.62 0.102 30 − 0.60 0.48 66 − 0.37 0.62 0.075
334234 33 − 0.64 0.40 75 − 0.54 0.51 0.293 30 − 0.69 0.35 66 − 0.55 0.51 0.179
Block 4 123121 36 0.29 0.57 77 0.30 0.57 0.984 34 0.31 0.59 73 0.32 0.58 0.917
212223 36 0.02 0.66 77 − 0.16 0.67 0.198 34 0.00 0.67 73 − 0.15 0.68 0.301
233432 36 − 0.46 0.53 77 − 0.45 0.58 0.946 34 − 0.52 0.48 73 − 0.48 0.57 0.743
314434 36 − 0.55 0.45 77 − 0.60 0.51 0.563 34 − 0.58 0.42 73 − 0.62 0.50 0.686
324332 36 − 0.45 0.49 77 − 0.32 0.61 0.234 34 − 0.46 0.50 73 − 0.32 0.61 0.244
333231 36 − 0.21 0.65 77 − 0.19 0.64 0.841 34 − 0.26 0.64 73 − 0.21 0.65 0.709
Fig. 2 Values o DEP-6D s a es
by sample
113
P e e ences o Ca egi e s and he Public o Dependency S a es
Table 5 DEP-6D models: Compa ing ca egi e s and he gene al popula ion (GP)
Inconsis en obse a ions ha e been excluded in all models. Boo s ap s anda d e o s in all models
Coe . coe icien , SE s anda d e o
MODEL 1 (ca e s) MODEL 2 (GP) MODEL 3 ( ull da a) MODEL 4 ( ull da a) MODEL 5 ( ull da a)
Coe . SE pCoe . SE pCoe . SE pCoe . SE pCoe . SE p
Cons 0.839 0.068 <0.001 0.776 0.047 <0.001 0.875 0.097 <0.001 0.837 0.152 <0.001 Cons 0.794 0.040 <0.001
Feeding_2 − 0.095 0.031 0.002 − 0.159 0.031 <0.001 − 0.139 0.023 <0.001 − 0.139 0.026 <0.001 Feeding _2 × GP − 0.158 0.031 <0.001
Feeding _3 − 0.182 0.038 <0.001 − 0.220 0.037 <0.001 − 0.207 0.028 <0.001 − 0.207 0.029 <0.001 Feeding _2 × ca e − 0.095 0.036 0.008
Incon inence_2 − 0.113 0.046 0.014 − 0.134 0.031 <0.001 − 0.129 0.023 <0.001 − 0.129 0.027 <0.001 Feeding _3 × GP − 0.219 0.039 <0.001
Incon inence_3 − 0.300 0.040 <0.001 − 0.286 0.034 <0.001 − 0.293 0.026 <0.001 − 0.293 0.027 <0.001 Feeding _3 × ca e − 0.179 0.046 <0.001
Pe sonal_2 − 0.230 0.067 <0.001 − 0.122 0.044 0.006 − 0.153 0.031 <0.001 − 0.153 0.034 <0.001 Incon inence_2 × GP − 0.133 0.029 <0.001
Pe sonal_3+4 − 0.327 0.067 <0.001 − 0.249 0.057 <0.001 − 0.271 0.042 <0.001 − 0.271 0.043 <0.001 Incon inence_2 × ca e − 0.113 0.041 0.006
Mobili y_2 − 0.071 0.049 0.147 − 0.099 0.037 0.008 − 0.090 0.031 0.003 − 0.090 0.023 <0.001 Incon inence_3 × GP − 0.285 0.030 <0.001
Mobili y_3 − 0.163 0.058 0.005 − 0.144 0.043 0.001 − 0.150 0.035 <0.001 − 0.150 0.030 <0.001 Incon inence_3 × ca e − 0.303 0.036 <0.001
Mobili y_4 − 0.289 0.068 <0.001 − 0.363 0.048 <0.001 − 0.337 0.042 <0.001 − 0.336 0.036 <0.001 Pe sonal_2 × GP − 0.126 0.039 <0.001
Housewo k_2 − 0.169 0.059 0.004 − 0.067 0.043 0.117 − 0.096 0.030 <0.001 − 0.096 0.030 0.001 Pe sonal_2 × ca e − 0.221 0.054 <0.001
Housewo k_3 − 0.260 0.061 <0.001 − 0.096 0.045 0.035 − 0.146 0.030 <0.001 − 0.145 0.033 <0.001 Pe sonal_3+4 × GP − 0.250 0.057 <0.001
Cogni ion_2 − 0.278 0.040 <0.001 − 0.249 0.028 <0.001 − 0.257 0.020 <0.001 − 0.257 0.027 <0.001 Pe sonal_3+4 × ca e − 0.322 0.065 <0.001
Cogni ion_3 − 0.349 0.046 <0.001 − 0.437 0.035 <0.001 − 0.410 0.026 <0.001 − 0.410 0.027 <0.001 Mobili y_2 × GP − 0.101 0.031 0.001
Cogni ion_4 − 0.442 0.046 <0.001 − 0.609 0.038 <0.001 − 0.554 0.033 <0.001 − 0.554 0.034 <0.001 Mobili y_2 × ca e − 0.063 0.042 0.133
Ca e − 0.063 0.069 0.360 –0.074 0.061 0.228 Mobili y_3 × GP − 0.147 0.038 <0.001
Age 0.000 0.002 0.856 Mobili y_3 × ca e − 0.156 0.043 <0.001
Female 0.043 0.049 0.377 Mobili y_4 × GP − 0.364 0.050 <0.001
Seconda y –0.015 0.068 0.829 Mobili y_4 × ca e − 0.280 0.069 <0.001
Uni e si y 0.030 0.068 0.663 Housewo k_2 × GP − 0.077 0.033 0.021
Housewo k_2 × ca e − 0.144 0.044 0.001
Housewo k_3 × GP − 0.104 0.036 0.004
Housewo k_3 × ca e − 0.240 0.063 <0.001
Cogni ion_2 × GP − 0.250 0.023 <0.001
Cogni ion_2 × ca e − 0.275 0.036 <0.001
Cogni ion_3 × GP − 0.435 0.038 <0.001
Cogni ion_3 × ca e − 0.352 0.055 <0.001
Cogni ion_4 × GP − 0.607 0.038 <0.001
Cogni ion_4 × ca e − 0.444 0.057 <0.001
# Pa icipan s 131 293 424 424 424
# Obse a ions 786 1758 2544 2544 2544
# Le -cens. Obs. 112 316 428 428 428
Wald χ21095.23 906.39 1797.28 2239.40 3786.02
Rho 0.640 0.037 0.509 0.030 0.547 0.024 0.546 0.032 0.550 0.021