EQ-5D as a predictor of mortality and hospitalization in elderlypeople

Giulia Cavrini • Serena Broccoli • Aurora Puccini •

Marco Zoli

Accepted: 19 May 2011 / Published online: 9 June 2011

� Springer Science+Business Media B.V. 2011

Abstract

Purpose The objective of this study was to assess whe-

ther the EQ-5D independently predicts all-cause mortality

and first hospitalization in a generic elderly population and

to evaluate the relationship between the EQ-5D ‘‘Class of

problems,’’ the EQ-5D index, the EQ-Visual Analogue

Scale (EQ-VAS), and the number of hospitalizations

occurring over a 2-year period.

Methods The data were collected in a longitudinal study

(over 5 years) conducted on 9,644 elderly people in

northern Italy with 5,256 completed questionnaires

returned. Multivariate Cox’s proportional hazards models

were used to assess the effect of the EQ-5D questionnaire

on 12 and 24 month mortality and first hospitalization risk,

controlling for sociodemographic and medical covariates.

Multivariate Zero-Inflated Poisson models were performed

to test the association between the EQ-5D ‘‘Class of

problems,’’ the EQ-5D index and EQ-VAS and the number

of hospitalizations over 2 years.

Results The EQ-5D ‘‘Class of problems,’’ EQ-5D index,

and EQ-VAS were consistently associated with mortality

and first hospitalization rates. The EQ-VAS was associated

with the number of hospitalizations over 2 years, though

the EQ-5D ‘‘Class of problems’’ and EQ-5D index were

not.

Conclusions The EQ-5D questionnaire is a useful pre-

dictor of mortality and first hospitalization in a generic

elderly population in Italy. This suggests that it would be a

good instrument for evaluating the effect of social/medical

actions in a population-based study.

Keywords Aged � EQ-5D � Hospitalization � Mortality �Quality of life

Abbreviations

CI Confidence interval

HRQOL Health-related quality of life

IRR Incidence rate ratio

PASE Physical activity scale for the elderly

VAS Visual analogue scale

ZIP Zero-inflated poisson

Introduction

One of the most important outcomes of epidemiological

research is the identification of the risk factors of mortality

and hospitalization. Most studies are focused on clinical

risk factors, and less attention has been given to the role of

subjective measures in health. Being able to demonstrate

that these measures can independently predict mortality

and hospitalization would draw greater attention to their

clinical value. An important self-perceived measure of

health is health-related quality of life (HRQOL). HRQOL

can be measured using two types of instrument: disease-

specific questionnaires or generic questionnaires. Over the

past 20 years, the majority of studies on HRQOL’s pre-

dictive proprieties of mortality have been concerned with

G. Cavrini (&) � S. Broccoli

Department of Statistics ‘‘Paolo Fortunati’’, University

of Bologna, Via delle Belle Arti, 41, 40126 Bologna, Italy

e-mail: giulia.cavrini@unibo.it

M. Zoli

Department of Internal Medicine, Cardioangiology, Hepatology,

University of Bologna, Bologna, Italy

A. Puccini

Drug Policy Service, Emilia-Romagna Regional Health

Authority, Bologna, Italy

123

Qual Life Res (2012) 21:269–280

DOI 10.1007/s11136-011-9937-0

specific instruments. It has been demonstrated that HRQOL

is an independent prognostic factor for survival in specific

ill populations if measured using specific disease instru-

ments [1–16].

The association between disease-specific HRQOL and

hospitalization has only been recently investigated and not

to any great degree. Some studies have shown that specific

questionnaires predict hospitalization for specific causes,

e.g., the Seattle Angina Questionnaire scores are associated

with hospitalization for acute coronary syndrome [10], the

Seattle Obstructive Lung Disease Questionnaire scores are

associated with hospitalization for chronic obstructive

pulmonary disease [13], the ITG-Asthma Short Form bat-

tery questionnaire is associated with future asthma-related

hospitalization [17], and the Kansas City Cardiomyopathy

Questionnaire scores are associated with hospitalization for

cardiovascular events [15], but to our knowledge, the

association between all-cause hospitalization and specific

HRQOL has not yet been studied.

Regarding generic instruments, the SF-36 [18] ques-

tionnaire is the most studied measure of HRQOL as a

predictor of mortality. The SF-36 scores (with some dif-

ferences between the Physical Component Score and the

Mental Component Score) have been associated with

mortality rates in patients undergoing coronary artery

bypass graft surgery [19, 20], in patients on maintenance

hemodialysis [21], in people with chronic obstructive

pulmonary disease [12, 22], in veterans with self-reported

physician diagnosed arthritis [23], in patients affected by

heart failure [24], in elderly populations [25], in patients

with life-threatening ventricular arrhythmia [26], and in

cancer populations [27].

Furthermore, the SF-36 scores are associated with hos-

pitalization in patients on maintenance hemodialysis [21],

in patients with osteoarthritis and rheumatoid arthritis [28],

in subjects with chronic obstructive pulmonary disease [12,

22], and in veterans with self-reported physician-diagnosed

arthritis [23].

Other studies also show that less popular generic

instruments are good predictors of mortality or hospital-

ization [4, 29, 30].

The EQ-5D is the questionnaire used in this study. It is a

brief, standardized, generic measure of HRQOL that pro-

vides a profile of patient function and a global health state

rating [31–33].

Although the EQ-5D questionnaire is frequently used to

measure HRQOL and a number of studies confirm the

association between HRQOL measured by other instru-

ments and mortality and hospitalization, only a few studies

on specific populations have been conducted to assess the

relationship between the EQ-5D outcome and mortality or

hospitalization. These demonstrate that a low perception of

quality of life is associated with an increased risk of all-

cause mortality in patients with Type 2 Diabetes [34] and

coronary artery disease [35], as well as being a brief and

prognostically useful predictor of mortality and hospital-

ization in HIV-infected adults under care [36], and in ter-

minal cancer patients [37].

However, the effect of HRQOL measured by EQ-5D on

mortality and hospital care utilization outside of clinical pop-

ulations has not been adequately examined. The good predic-

tive ability of EQ-5D for the previously mentioned sufferers of

specific illnesses and of other similar instruments on several

groups of subjects encourages the hypothesis that the same

propriety could be valid in a generic elderly population.

In accordance with this idea, the main objective of this

study is to test if HRQOL measured by EQ-5D is a pre-

dictor of short-term (12 months) and medium-term

(24 months) survival and short-term (12 months) and

medium-term (24 months) hospitalization for any cause

among elderly people, even after adjusting for traditional

clinical risk variables. The second objective is to evaluate

the relationship between HRQOL and the number of hos-

pitalizations occurring over 2 years. An instrument that

predicts events such as hospitalization or even mortality

could help decision makers decide which subjects need

more preventive action.

To evaluate our hypothesis, we used data collected

within a longitudinal study conducted in northern Italy on a

population of 9,644 community-dwelling elderly people

recruited from a population-based survey.

Materials and methods

Study design and population

Subjects were noninstitutionalized elderly people enrolled

as part of the Pianoro Study [38], an observational com-

munity-based cohort study, organized by the Internal

Medicine, Cardioangiology and Hepatology Department of

the Faculty of Medicine and Surgery of the University of

Bologna, in collaboration with the Health Authority of

Bologna and both the Statistics Department and the Phys-

ical Science Department of the University of Bologna. The

aim of the Pianoro Study was to evaluate the state of health

of a cohort of elderly residents in three municipalities in

northern Italy. This cohort was followed for 5 years.

The three municipalities selected (Pianoro, Sasso Mar-

coni and Zola Predosa) were substantially homogeneous

for gender and age structure. Moreover, they were repre-

sentative of the different geographical characteristics of the

Health District of Bologna and, in general, of northern Italy

(hill, mountain, and plain, respectively). The population of

these municipalities could be considered representative of

the northern Italian population.

270 Qual Life Res (2012) 21:269–280

123

Considering the Registry of personal data held by the

Health Authority, the number of subjects aged 65 years or

more (i.e., born before December 31, 1938) in the three

municipalities was 9,644 people, excluding residents in

Residential Facilities. They were involved in the first phase

of the study (the whole population). Every resident

received a postal questionnaire in November 2003, which

included an informed written consent. A letter of invitation

introducing the survey was mailed to each subject with the

questionnaire. The study protocol was approved by Health

Authority’s Ethical Committee. In this paper, we analyze

the 5,256 completed questionnaires (response rate 54.5%).

The age structure and gender distributions of the respon-

dents (2,385 men and 2,871 women) were quite similar to

those of the subjects aged over 65 years.

All of the 9,644 elderly people were monitored for

2 years until October 2005 with a deterministic record

linkage to the death registry and the hospitalization data-

base of the Health Authority of Bologna using name, date

of birth, and gender. The precise date of death or hospi-

talization was used to calculate the event time. Life status

and hospitalization events in 12 and 24 months (two nested

periods) were recorded.

Assessment instruments and definitions

All the information was collected on an 8-page postal

questionnaire, requiring approximately 15 min to be filled

in. The questionnaire was designed to obtain detailed

information about sociodemographic, lifestyle and health

status characteristics and physical activity. Specifically,

information was collected on age, gender, educational

level, marital status (married, widowed and a category

including single, separated, and divorced), living alone

(Yes/No), and working activity (Yes/No). Level of edu-

cation, a proxy measure for social class, was categorized as

follows: no school; primary school; secondary school; and

higher education. We subsequently grouped them into two

classes: no University and University as when ‘‘Educa-

tional level’’ was coded into four classes, none of the levels

lower than University level was significantly different from

the reference (No school). By dichotomizing the variable

into ‘‘no University’’ versus ‘‘University,’’ we were able to

highlight the importance of a higher educational level as a

protective factor.

Other variables collected were anthropometric measures

(height and weight), smoking history, and alcohol intake.

Body mass index (BMI) was calculated as weight (Kg)/

height (m)2 and was expressed using the World Health

Organization categorization: less than 18.5 (underweight),

from 18.5 to 24.9 (normal weight), from 25 to 29.9

(overweight), and 30 or greater (obese). To assess smoking

habits, the following variables were considered: smoking

status (never smoked; ex-smoker; and current smoker),

number of cigarettes smoked per day, and number of years

of smoking. Alcohol consumption was expressed as num-

ber of units per day, where a glass of wine, a pint of beer,

or a small glass of spirits were all considered an alcoholic

unit (nearly 8 g. of alcohol).

To detect possible impairment in socio-biological

functions, we used the self-administered version of the

ADL scale (Activities of Daily Life), originally proposed

by Katz et al. [39], and the IADL scale (Instrumental

Activities of Daily Life) [40]. With respect to these two

scales, the individual situation was defined as problematic

when the subject reported problems in performing at least

one of the considered activities.

Personal medical history was investigated using a check

list. The participants were asked whether they had been

diagnosed with a chronic disease (i.e., hypertension,

hypercholesterolemia, diabetes, cardiovascular disease,

stroke, and arthritis). The presence of chronic diseases was

defined on the basis of a clinical diagnosis and/or on

treatment with specific drugs. A family history of myo-

cardial infarction was defined as having a first degree rel-

ative who was affected by a myocardial infarction before

60 years of age.

Finally, we recorded all previous diagnoses of ischemic

events, the presence of musculoskeletal pain, the number of

falls and fractures in the previous 3 years, and the drugs

currently in use. As far as ischemic events were concerned,

only clinically reported events were requested, together

with the date of the most recent occurrence of each event:

myocardial infarction, angina pectoris, or stroke.

Physical activity was assessed by the PASE (Physical

Activity Scale for the Elderly) questionnaire, which is

specifically designed to quantify physical activity in the

elderly during the previous week, and has been extensively

validated [41, 42]. It consists of two sections. The first

section concerns general and working activities, consider-

ing heavy and light home activities separately, home

repairs, nonsedentary working activities, agricultural work,

gardening, and caring for others. The second section con-

cerns the quantification of leisure-time activities (number

of minutes per day and number of days per week), with

particular reference to walking, jogging, cycling, swim-

ming, exercising both at home and in the gym, dancing,

and performing some sports. The questionnaire does not

estimate energy expenditure, but produces a score with

which subjects can be compared. The total PASE score was

computed by associating the time spent in each leisure-time

activity (hours per week), or the participation in a general

or working activity, with an empirically derived weight,

and then a summing up of all weights.

To assess the quality of life, we adopted the EuroQoL-5

Dimension (EQ-5D) questionnaire, developed by the

Qual Life Res (2012) 21:269–280 271

123

EuroQoL Group [31]. The questionnaire has been widely

validated by previous epidemiological studies [32, 33] and,

due to its simplicity, is particularly suitable for use with

elderly people. The EQ-5D is the most widely used generic

preference-based measure of health-related quality of life,

which produces utility scores anchored at 0 for death and 1

for perfect health. The utility scores represent preferences

for particular health states. The descriptive system has five

dimensions (mobility, self-care, usual activity, pain/discom-

fort, and anxiety/depression) and three levels (no problems,

some problems, extreme problems), which create 243

unique health states [43, 44] or can be converted into a single

summary index (EQ-5D index) [45–47] by applying scores

from a set of general population preference weights. This

study uses the UK TTO value set [43–47].

The EQ-5D questionnaire also includes a Visual Analog

Scale (VAS), by which respondents can report their per-

ceived health status with a grade ranging from 0 (the worst

possible health status) to 100 (the best possible health

status). As the EQ-5D summary index in Italy was not

carried out with local preference weights, we used the UK

index and we also focused our analyses on the VAS scale.

Moreover, we obtained a third measure of HRQOL by

building the variable ‘‘Classes of Problems’’ as a combina-

tion of the answers to the five dimensions: 0 = no problem in

any dimension, 1 = a moderate problem in only one

dimension, 2 = at least two moderate problems in any

dimension, and 3 = at least one severe problem. The use of a

combination of the five questions enables us to obtain a

single synthetic measure of HRQOL instead of five different

measures for five different facets of the same construct. The

arbitrary choice of the classes is justified by the use of the

same classes in other similar studies [34, 48] and by the good

results obtained by the model. In particular, in Cavrini [48],

we demonstrated that these classes have a specific corre-

spondence with a precise range of the EQ-5D index:

‘‘no problem in any dimension’’ (EQ-5D index = 1); ‘‘a

moderate problem in only one dimension’’ (0.80 B EQ-5D

index 1 B 0.99); ‘‘at least two moderate problems in any

dimension’’ (0.5 B EQ-5D index B 0.79); and ‘‘at least

one extreme problem’’ (EQ-5D index B 0,49).

Statistical analyses

The association between ‘‘Classes of Problems,’’ sociode-

mographic characteristics, and medical variables was

assessed by a Pearson Chi-square test. The endpoint for the

analysis of the overall survival was any death occurrence;

the endpoint for the analysis of the hospitalization survival

was first hospitalization for any reason.

Survival time was calculated as the time from November

1, 2003 (conventional time of HRQOL assessment) to the

endpoints of the study, censoring after 12 (short time) or

24 months (medium time) for overall survival models (two

different models) and censoring after 12 or 24 months or

corresponding to the death date for the hospitalization

survival models (two different models).

In order to test if there has been a selection bias in terms

of mortality and hospitalization probability, a comparison

between respondent and nonrespondent survival function

was performed using the Kaplan–Meier method.

Multivariate Cox’s proportional hazard models were

applied to evaluate the effect of HRQOL (measured as

classes of problems or EQ-5D index or EQ-VAS) on short

and medium term overall survival and hospitalization with

adjustments for sex, age, education, smoking habit, living

alone, physical activity, body mass index, elementary

and instrumental autonomy, cardiovascular multimorbidity,

and the presence of diabetes, hypercholesterolemia, or

hypertension.

Adjusted hazard ratios and 95% confidence intervals

(CIs) were calculated for each independent variable in the

models. The Kaplan–Meier method was used to compute

survival rates, and a log-rank test was applied to test the

differences in survival between subjects with different‘‘

Classes of Problems’’. The proportional hazards assump-

tion underlying all models was assessed using a test based

on Schoenfeld residuals. The Cox–Snell residuals were

used to test the goodness of fit for Cox models.

An adjusted multivariate zero-inflated Poisson (ZIP)

analysis [49] was performed to measure the association

between the HRQOL (measured as ‘‘Classes of Problems’’

or EQ-5D index or EQ-VAS) and the number of hospital-

izations over a period of 2 years. In order to check if and to

what extent the factors related to hospitalization could

explain the number of admissions in the analysis period,

the same covariates of the Cox’s models were used. The

rationale behind the selection of the variables is consistent

with the results from the Cox’s models. Variables that were

found to be significant in events survival analyses have

been used in the logit model, while in the Poisson’s model,

all the available variables were taken into account. This

method has been implemented taking into account the fact

that the logit model estimates the probability of not being

affected by any event, while the Poisson’s model describes

the effective number of events.

The ZIP regression model was chosen to deal with the

over dispersion of the data due to the excess number of

zero counts caused by individuals not hospitalized. The

basic idea is that the presence or absence of hospitalization

and the positive counts of the frequency of hospital

admissions are generated by separate processes. The pres-

ence/absence model is specified by a logit model, the

second part is truncated at a zero count model that is only

focused on hospitalized individuals. The results were

presented in terms of incidence rate ratio (IRR).

272 Qual Life Res (2012) 21:269–280

123

Significance level was defined at 5%, and all statistical

tests were two-sided. The statistical analyses were per-

formed by using STATA/SE 10 software [50].

Results

Table 1a and b summarize the sociodemographic and

medical characteristics of the 5,256 elderly included in the

study. The mean (SD) age was 74.5 (7.1). At baseline, 892

subjects (17%) reported not having problems in any

dimension, 1,044 subjects (19.9%) reported a problem in

one dimension, 1,912 subjects (36.3%) reported problems

in two or more dimensions, 698 subjects (13.3%) reported

at least one extreme problem in any dimension, and 710

subjects (13.5%) did not complete the EQ-5D question-

naire. Compared with healthy subjects, those who reported

more moderate problems or at least one extreme problem

were significantly more likely to be women, older, less

educated, more likely to be living alone, not to do any

physical activity, to be underweight, and to present limi-

tations in elementary autonomy. With respect to medical

characteristics, they were more likely to be affected by

cardiovascular multimorbidity, diabetes, and hypertension.

Of the respondents, 171 deaths and 1,069 first hospi-

talization events occurred over a 12-month study period

and 372 deaths and 1,737 first hospitalization events

occurred over a 24-month study period. The same analysis

was conducted on nonrespondents in order to assess whe-

ther there were differences in survival and hospitalization

time. Of the nonrespondents, 264 deaths occurred over a

12-month study period and 490 at the 24-month study

period. Eight hundred and eighty-four first hospitalization

events occurred over a 12-month study period and 1,585 at

the 24-month study period. There is no evidence of sig-

nificant differences between the two groups in hospital-

ization time, and there is a negligible difference in survival

time.

The Kaplan–Meier survival curve showed that subjects

who report fewer problems on the EQ-5D descriptive

system have better overall survival rates and hospitaliza-

tion survival rates (Fig. 1).

One thousand and seven hundred and thirty-seven sub-

jects (33.0%) were hospitalized at least once during the

2 years analyzed in this study. Considering this subgroup

alone, the mean number of hospitalizations is 2.0 (SD

1.80).

Multivariate Cox regression model

Table 2 illustrates the association between HRQOL

(measured by ‘‘Classes of Problems,’’ EQ-5D index, and

EQ-VAS score) assessed at the beginning of the study

and short- and medium-term mortality and short- and

medium-term hospitalization measured by multivariate

Cox regression models.

The assumption of proportional hazard, tested by

Schoenfeld residuals, was not rejected. The Cox–Snell

residuals were used to test the goodness of fit for Cox

models. The fit was good for 24 months for all the three

measure of HRQOL; there were negligible problems with

the fit at 12 months, probably due to the low number of

events.

Classes of problems

The hazard ratio estimates indicate that subjects who have

at least one extreme problem in any dimension are at three

times the risk of death in the following 12 months and at

twice the risk of death in the following 24 months com-

pared to those who have ‘‘No problem’’. Compared to

answering ‘‘No problem,’’ answering ‘‘At least two mod-

erate problems in any dimension’’ is associated with about

a 40% higher risk of first hospitalization (44% in

12 months and 37% in 24 months with overlapping CIs),

and answering ‘‘Extreme in any dimension’’ is associate

with about a 55% higher risk of first hospitalization (57%

in 12 months and 55% in 24 months with overlapping CIs)

(Fig. 2).

EQ-5D index

HRQOL measured by the EQ-5D index was also signifi-

cantly associated with mortality and hospitalization at 12

and 24 months. The estimated hazard ratio refers to a

variation of a unit of the index: for an increase in the index

value, the risk of the occurrence of the events decreased by

69 and 58%, respectively, for mortality at 12 and

24 months and it decreased by 42% for hospitalization at

12 and 24 months.

EQ-VAS score

The EQ-VAS score was significantly associated with

mortality and hospitalization at 12 and 24 months. The

estimated hazard ratio refers to a variation in a unit of the

score: for an increase in the index value, the risk of the

occurrence of the events decreased by 2 and 1%, respec-

tively, for mortality at 12 and 24 months. The same results

are obtained for hospitalization at 12 and 24 months,

respectively.

Multivariate ZIP regression model

Table 3 shows the association between HRQOL (measured

by ‘‘Classes of Problems,’’ EQ-5D index, and EQ-VAS

Qual Life Res (2012) 21:269–280 273

123

Table 1 Baseline (a) sociodemographic characteristics, (b) medical characteristics of elderly completing the EQ-5D enrolled in the Pianoro

study, Bologna, Italy, 2003–2008

Subjects

characteristics

% of all

cases

(n = 4,546)

Classes of problems

% No

problem:

n = 892)

% A moderate problem in

only one dimension:

(n = 1,044)

% 2 or more moderate

problems in any dimensions:

(n = 1,912)

% At least 1 severe

problem:

(n = 698)

P valuea

(a)

Sex

Male 44.7 64.7 52.7 37.7 26.6 \0.001

Female 55.3 35.3 47.3 62.3 73.4

Classes of age

65–69 30.1 41.3 34.3 28.4 14.5 \0.001

70–74 25.0 27.9 28.1 25.2 16.2

75–79 20.9 19.4 21.0 21.7 20.2

80? 24.0 11.4 16.7 24.7 49.1

Body mass index

Underweight 2.0 1.2 1.7 1.6 4.4 \0.001

Normal

weight

37.6 36.7 39.9 36.3 40.7

Overweight 41.8 44.8 43.7 42.7 36.0

Obesity 13.2 13.1 11.1 14.5 12.5

Unknown 5.4 4.2 3.6 4.9 6.5

Education

No university 96.9 94.2 95.4 98.1 99.6 \0.001

University 3.1 5.8 4.6 1.9 0.4

Live alone

No 78.1 83.9 80.6 75.4 73.1 \0.001

Yes 20.2 14.7 18.2 23.8 24.6

Unknown 1.7 1.5 1.3 0.8 2.3

Smoking

Nonsmoker 56.2 53.3 54.5 57.2 66.8 \0.001

Ex-smoker 31.3 35.7 33.3 31.5 22.2

Smoker 9.5 10.1 11.1 8.5 7.5

Unknown 3.0 1.0 1.1 2.9 3.6

Classes PASE

Inactive 33.5 26.0 22.4 31.7 64.8 \0.001

Moderate

activity

15.2 13.2 12.7 18.5 12.5

Lifestyle

active

25.6 26.6 31.0 26.9 12.8

Intensive

activity

25.6 34.2 33.8 22.9 10.0

(b)

Multimorbidity

None 74.9 84.6 82.8 71.7 59.3 \0.001

1 16.6 9.9 12.5 19.0 24.8

2 or more 8.5 5.5 4.8 9.3 15.9

Diabetes

No 88.8 92.3 91.1 87.3 84.8 \0.001

Yes 11.2 7.7 8.9 12.7 15.2

274 Qual Life Res (2012) 21:269–280

123

score) assessed at the beginning of the study and the

number of hospitalizations over 2 years measured by

multivariate ZIP regression models. The Vuong test sug-

gests that the ZIP models are a significant improvement on

a standard Poisson model (P \ 0.001).

Classes of problems

The HRQOL measured as ‘‘Classes of problems’’ predicted

the presence of hospitalization (logit model) but not the

number of admissions.

EQ-5D index

The EQ-5D index also predicted the presence of hospital-

ization (logit model) but not the number of admissions.

EQ-VAS score

The EQ-VAS and the number of hospitalizations were

associated. An EQ-VAS increase of 10 points corresponded

to an IRR decrease of about 10%.

Discussion

A great deal of research has highlighted the importance of

HRQoL and self-rated health in predicting health outcome

[6, 7, 13, 14, 16, 19, 20, 22–29, 34, 36, 51, 52]. The study

of self-ratings of health as predictors of mortality in pop-

ulation-based longitudinal research has flourished in the

past 15 years. Most of them have resulted in a significant

association, which persists when numerous health status

indicators and other relevant covariates are included.

Idler [51] reviewed 27 community studies from 1991 to

1996. In 22 of them, the target population was elderly and

the sample was less than 5,000. The other five studies

considered a general population, and the size was less than

7,500. In comparison with these, our study would be con-

sidered very large.

The subjects of our study were the elderly residents in

three municipalities of the Health District of Bologna

(northern Italy). Using the Registry of personal data held

by the Health Authority, the number of subjects aged

65 years or more was 9,644 people, excluding residents in

Residential Facilities. In this paper, we analyze the 5,256

returned questionnaires. The response rate of 54.5% was

reasonable if we consider similar studies with postal

interviews.

In order to investigate the aims of this research, all of the

9,644 elderly people were monitored for 2 years with a

deterministic record linkage to the death registry and the

hospitalization database of the Health Authority of Bolo-

gna. It is important to highlight that record linkage with the

administrative database enables access to information on

mortality or hospitalization eliminating the need for sub-

sequent follow-up with respondents.

In studies such as this, there is a wide variety of follow-

up periods. Idler [51] revised twenty-seven papers in which

the follow-up periods ranged from 2 to 7 years in twenty of

the studies, from 9 to 13 years in six studies, and 28 years

in one study. Mapes [14], Kao [53], and Kanwal [54]

Table 1 continued

Subjects

characteristics

% of all

cases

(n = 4,546)

Classes of problems

% No

problem:

n = 892)

% A moderate problem in

only one dimension:

(n = 1,044)

% 2 or more moderate

problems in any dimensions:

(n = 1,912)

% At least 1 severe

problem:

(n = 698)

P valuea

Hypertension

No 71.9 78.4 74.9 68.5 68.5 \0.001

Yes 28.1 21.6 25.1 31.5 31.5

Cholesterol

No 80.0 86.5 80.3 76.4 80.9 \0.001

Yes 20.0 13.5 19.7 23.6 19.1

ADL

Independent 68.9 91.3 83.1 65.9 28.8 \0.001

Dependent 27.9 8.4 16.5 33.6 58.6

Unknown 3.2 0.3 0.5 0.5 12.6

IADL

Independent 58.4 81.2 74.2 53.4 16.9 \0.001

Dependent 37.9 18.6 25.4 45.9 65.9

Unknown 3.7 0.2 0.4 0.7 17.2

a Pearson v2 test

Qual Life Res (2012) 21:269–280 275

123

observed a 2-year follow-up period. In previous studies of

healthy elderly people, there is no consistency in deter-

mining the long-term cutoff, which varies from 12 months

[30] to 36 months [25]. In addition, McEwen [55] found no

correlation between EQ-5D scores and time to death.

In accordance with other studies and considering the

time period(s) of the Pianoro study required for the con-

clusion of specific activities for the population under

investigation, the periods of 12 and 24 months were con-

sidered representative of the initial stage at which external

interventions aimed at improving the perceived of quality

of life is, respectively, limited or absent.

At the time, the records were accessed and there were a

number of problems for a period longer than 2 years,

particularly for hospitalization. The database was incom-

plete and we preferred to use the same period throughout

the analysis. Therefore, we chose a follow-up period of

2 years.

Although many studies have investigated the effect of

HRQOL on mortality and hospitalization, the predictive

ability of the EQ-5D questionnaire has not yet been ade-

quately evaluated in an elderly population. To our knowl-

edge, this study is the first to assess the relationship

between HRQOL measured by the EQ-5D questionnaire

and mortality and first hospitalization for any reason in a

general elderly population.

Our study has demonstrated that EQ-5D can predict the

risk of all-cause mortality and the risk of hospitalization in

elderly people. It is important to highlight that the pre-

dictive proprieties of the EQ-5D were constant even when

using different indexes such as ‘‘Classes of Problems,’’ EQ-

5D index, and EQ-VAS. We have shown a consistent

p<0.0001

0.50

0.75

1.00

0 200 400 600 800

Hospitalization 24 months

p<0.0001

0.50

0.75

1.00

0 200 400 600 800

Mortality 24 months

X No Problems ----- A moderate problem in only one dimension

..... Two or more moderate problems in any dimensions

Times (days) since events

Sur

viva

l pro

babi

lity

______ At least one severe problem

Fig. 1 Survival curves for

elderly, by classes of problems

for short- and medium-term

survival and hospitalization,

Pianoro study, Bologna, Italy,

2003–2008. Survival curves

were calculated without

adjustment for covariates

Table 2 Association based on multivariate proportional hazard models of HRQOL measure by EQ-5D with short- and medium-term survival

and hospitalization for elderly in the Pianoro study, Bologna, Italy, 2003–2008

All causes mortality Hospitalization

12 Months 24 Months 12 Months 24 Months

HR (SE)a P value HR (SE)a P value HR (SE)a P value HR (SE)a P value

Level of problem

No problem Ref Ref Ref Ref

A moderate problem in only one dimension 1.08 (0.55) 0.876 0.85 (0.23) 0.538 1.06 (0.13) 0.612 1.04 (0.10) 0.684

Two or more moderate problems in any dimensions 1.56 (0.68) 0.309 1.01 (0.24) 0.966 1.44 (0.16) 0.001 1.37 (0.12) \0.001

At least one severe problem 3.24 (1.57) 0.015 1.96 (0.54) 0.014 1.57 (0.23) 0.002 1.55 (0.18) \0.001

EQ-VAS 0.98 (0.006) 0.004 0.99 (0.004) 0.022 0.98 (0.002) 0.000 0.99 (0.001) \0.001

EQ-5D index 0.31 (0.11) 0.001 0.42 (0.09) \0.001 0.58 (0.08) \0.001 0.58 (0.06) \0.001

a Adjusted for sex, classes of age, classes of BMI, education, live alone, multimorbidity, diabetes, hypertension, cholesterol, smoking, and classes of PASE, ADL,

and IADL

276 Qual Life Res (2012) 21:269–280

123

relationship between all the different indexes and mortality

after adjusting for demographic, socioeconomic, and

clinical risk factors for death. The ‘‘Classes of Problems’’

and the EQ-5D index were predictive for events, but only

the EQ-VAS was also able to predict the number of

hospitalizations.

In the majority of studies, it should be noted, however,

that the association between HRQOL and death was much

stronger than the association between HRQOL and hospi-

talization. In our study, however, the associations are quite

similar, most likely due to the short time period analyzed.

The main implication of this study in terms of economic

evaluations is that patients with lower EQ-5D index, VAS,

and classes are likely to have an increased future risk of

mortality and hospitalization.

Our results show that EQ-5D might be used as a health

planning instrument, which could focus on investigating a

wide range of specific programs, such as encouraging a

healthy lifestyle with fitness courses or diets.

There are also some limitations. Firstly, the mortality

rate is low, but it is acceptable when compared with other

similar studies [25, 43–45]. Secondly, information col-

lected was based mainly on the self-reporting of subjects;

thus, some bias might exist. Finally, the limitation of this

study was that 45.5% of community-dwelling older people

did not answer the questionnaire and that 13.5% of subjects

did not fill in or completes the EQ-5D questionnaire.

Finally, the use of ‘‘Classes of problems’’ might be

subject to bias due to choices made by the authors, which

might be considered a limitation on the applicability of the

24 months

12 months

24 months

12 months

24 months

12 months

In one dimension

In two or more dimensions

Extreme in any dimension

Leve

l of P

robl

ems

.5 1 1.5 20

HR (95% CI)

Hospitalization events

0 2 4 6 8

HR (95% CI)

MortalityFig. 2 Hazard ratios for classes

of problems expressed relative

to the response category ‘‘No

problem’’

Table 3 Association based on two multivariate zero-inflated Poisson regression models of HRQOL measure by EQ-5D and EQ-VAS with

number of hospitalizations in 24 months for elderly in the Pianoro study, Bologna, Italy, 2003–2008

Number of admission Logit model

IRR (SE)a P value IRR (SE) P value

Classes of problems

No problem Ref Ref

A moderate problem in only one dimension 0.88 (0.08) 0.165 -0.15 (0.13) 0.291

Two or more moderate problems in any dimensions 0.93 (0.07) 0.322 -0.58 (0.12) \0.001

At least one severe problem 0.84 (0.08) 0.076 -0.97 (0.17) \0.001

EQ-VAS 0.99 (0,001) 0.004 0.016 (0.002) \0.001

EQ-5D index 1.09 (0.103) 0.350 1.174 (0.177) \0.001

a Adjusted for sex, classes of age, classes of BMI, education, live alone, multimorbidity, diabetes, hypertension, cholesterol, smoke, and classes

of PASE, ADL, and IADL

Qual Life Res (2012) 21:269–280 277

123

results. Despite this, we believe that after comparing the

models of the two standard indices (EQ-VAS and EQ-

index) with the model of ‘‘Classes of problems,’’ the bias is

negligible and the questionnaire is easy for physicians and

other health professionals to interpret.

In conclusion, we found that HRQOL measured by EQ-

5D is a ‘‘useful’’ predictor of mortality and first hospital-

ization in a generic elderly population, in part because of

its brevity and its low respondent (and user) burden. This is

of particular interest in large population surveys, where

space is often at a premium. Other generic instruments may

be equally ‘‘useful’’ predictors of mortality and hospital-

ization, but are often not as concise and easy to use.

Moreover, this instrument could be relevant for decision

makers in the Italian health care system, particularly when

the focus is on the health of elderly people.

Acknowledgment The study was supported by grants from:

(1) Fondazione Cassa di Risparmio in Bologna. (2) Regione Emilia

Romagna, Piani per la Salute.

Appendix: Pianoro study group

Dipartimento di Medicina Interna, Cardioangiologia,

Epatologia—University of Bologna

Steering Committee: Marco Zoli (Chief), Giampaolo

Bianchi, Donatella Magalotti, Antonio Muscari

Medical Staff: Annalisa Berzigotti, Nicola Castaldini,

Grazia Maria Costa, Susanna Dapporto, Gianmarco

Drago, Claudia Giannoni, Cosimo Ludovico, Fausta

Montesi, Francesco Nicolino, Stefano Ramilli, Dario

Sbano, Paola Zappoli

Technical Staff: Raffaela Chianese, Franca Ferri, Giorgia

Passerini

Dipartimento di Istologia, Embriologia e Biologia

Applicata—University of Bologna

Physical Training Staff: Pasqualino Maietta, Claudio

Tentoni, Erika Nerozzi, Carlo Ravaioli, Paola Masi,

Irene Negrini

Dipartimento di Scienze Statistiche—University of

Bologna

Statistical Staff: Giulia Cavrini, Andrea Mattivi, Serena

Broccoli

Azienda USL Bologna

Epidemiological staff: Marilina Colombo, Paolo

Pandolfi, Barbara Pacelli

Laboratory staff: Amedeo Ligabue, Loretta Dini,

Annalisa Zacchini

General Practitioners: Alberto Melli, John Sonnino,

Antonio Ajuti, Stefano Bacci, Luca Bruni, Gemma

Chiarini, Giampaolo Corradini, Fulvia Nucci, Elisabetta

Ongaro, Elisabetta Scandellari, Serena Selva

Municipality of Pianoro

Simonetta Saliera (Mayor), Gianalberto Cavazza, Anto-

nella Grazia, Maria Pia Mezzini, Daniela Mignogna,

Emanuela Torchi

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