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The 15-D Measure of Health Related Quality of Life: Reliability, Validity and Sensitivity of its Health State Descriptive System

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The Centre for Health Program Evaluation (CHPE) is a research and teaching organisation established in 1990 to: • undertake academic and applied research into health programs, health systems and current policy issues; • develop appropriate evaluation methodologies; and • promote the teaching of health economics and health program evaluation, in order to increase the supply of trained specialists and to improve the level of understanding in the health community.
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WORKING PAPER 41
The 15-D Measure of Health Related Quality of
Life: Reliability, Validity and Sensitivity of its
Health State Descriptive System
Harri Sintonen
Professor,
Department of Health Policy and Management, University of Kuopio, Finalnad,
and
Visiting Fellow, National Centre for Health Program Evaluation
October, 1994
ISSN 1038-9547
ISBN 1 875677 36 4
CENTRE PROFILE
The Centre for Health Program Evaluation (CHPE) is a research and teaching organisation
established in 1990 to:
undertake academic and applied research into health programs, health systems and
current policy issues;
develop appropriate evaluation methodologies; and
promote the teaching of health economics and health program evaluation, in order to
increase the supply of trained specialists and to improve the level of understanding in the
health community.
The Centre comprises two independent research units, the Health Economics Unit (HEU) which is
part of the Faculty of Business and Economics at Monash University, and the Program Evaluation
Unit (PEU) which is part of the Department of General Practice and Public Health at The University
of Melbourne. The two units undertake their own individual work programs as well as collaborative
research and teaching activities.
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PO Box 477
West Heidelberg Vic 3081, Australia
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ACKNOWLEDGMENTS
The Health Economics Unit of the CHPE receives core funding from the National Health and
Medical Research Council and Monash University.
The Program Evaluation Unit of the CHPE is supported by The University of Melbourne.
Both units obtain supplementary funding through national competitive grants and contract
research.
The research described in this paper is made possible through the support of these bodies.
AUTHOR ACKNOWLEDGMENTS
The financial support of the Academy of Finland for this study is gratefully acknowledged. My
special thanks are to Markku Pekurinen for his invaluable contribution to the development of 15D
in many ways. I am also indebted to Mats Brommels, Ritva Kauppinen, Timo Leino, Jouko
Lönnqvist, Markku Partinen, Jukka Rautonen, Pekka Rissanen, Hannele Tikanoja and all others,
who have shared either their data and/or experiences or helped otherwise in developing and
testing the 15D. My special thanks are to The National Centre for Health Program Evaluation for
an excellent working environment while preparing this paper and to Prosessor Jeff Richardson for
valuable comments.
ABSTRACT
The 15D is a generic, 15-dimensional, standardised, self-administered measure of HRQOL, that
can be used both as a profile and as a single index score measure. This paper examines the
acceptability, reliability, validity and sensitivity of two versions (15D.1 and 15D.2) of its health state
descriptive system as a profile measure compared with the Nottingham Health Profile (NHP), SF-
20 and EuroQol by using several data sets and methods. The response and completion rates
show that the acceptability is comparable to NHP, SF-20 and EuroQol. Reliability in terms of
repeatability is high, even higher than for NHP. There is substantial evidence of content and
construct validity (cross-sectional and longitudinal) and depression-related criterion validity. On
roughly comparable dimensions the discriminatory power of 15D.1 appears to be superior to NHP,
at least equivalent to SF-20, that of 15D.2 superior to EuroQol and 15D.1, and the responsiveness
of 15D.1 to change seems to be similar to NHP and SF-20. The remaining 9-10 dimensions of
15D provide a large extra reserve in these respects. It is concluded that as a profile measure 15D
performs equally well as NHP and SF-20, in some respects even better. The properties as a single
index score measure will be explored separately.
The 15D-Measure of Health-Related Quality of
Life: Reliability, Validity and Sensitivity of its
Health State Descriptive System
1 Introduction
The interest in the measurement of health-related quality of life (HRQOL) has increased
considerably in recent years. Disease-specific and generic (non-disease specific) measures are
increasingly applied in different research contexts. Existing measures are being developed further
and new ones created in a search for 'better' instruments. However, at least as far as generic
measures are concerned (the class focused on here) none of the measures and approaches
developed so far can claim to have established a position as the measure, either as a
standardised system of describing health states or as a method of valuing them.
This is no wonder since the research area is very difficult. The problems start from the very lack of
general agreement on the notion of HRQOL. Yet there is a broad acceptance that HRQOL is a
multidimensional concept that encompasses the physical, emotional and social components
associated with illness or treatment (Revicki 1989). There is also increasing recognition that
HRQOL is a subjective matter and therefore how these components are affected by illness and
treatment should be assessed by the individuals themselves (eg Slevin et al. 1988). How many
and what dimensions should be included to represent each component, that is, how to
operationalise the components as measurable dimensions, is far less agreed.
There are also other areas of disagreement. One school argues that the measurements in
different dimensions should be kept separate and presented as a profile. In this way it shows
where problems with HRQOL exist and possible changes in HRQOL take place. The other school
(mainly economists concerned with resource allocation), although not disputing the usefulness of
the profile approach, maintains that the measurements should (also) be aggregated into a single
index number to obtain an overall picture of the level of HRQOL and its changes (Bullinger 1993).
However, within the latter school there is further disagreement on how the measurements should
be valued or weighted in the aggregation.
There are thus two major methodological problems in constructing an HRQOL measure, that
produces a single index score (Culyer 1976). The first is the creation of a standardised health state
descriptive system, that is, the choice of the dimensions (attributes) in which health is to be
measured and the division of each dimension into discrete levels by which more or less of the
attribute can be identified. The second is the valuation of the different combinations (profiles) of
the levels, one from each dimension, that is, of the health states thus defined.
The purpose of this paper is to describe, how the health state descriptive system of the 15D, a 15-
dimensional measure of HRQOL, was created, and to evaluate its properties as a profile measure
in terms of several criteria such as reliability, validity and sensitivity. The valuation component of
15D will be described and evaluated elsewhere. The design principles of 15D are discussed in
section 2, the conceptual basis and operationalisation in section 3. The data and methods used in
evaluating the properties of the 15D health state descriptive system theoretically and empirically
against a set of criteria are described in section 4. The results are presented in section 5 and
discussed in section 6.
2 Design Principles of 15D
The basic objective has been to develop a generic, multi-dimensional, standardised, self-
administered measure of HRQOL, that could be used primarily as a single index score measure,
but also as a profile measure at least in the following areas:
First, to evaluate the effectiveness and efficiency (cost-effectiveness/utility) of different health care
programs and technologies within disease categories in clinical trials or in average practice and
across disease categories, and to facilitate thus resource allocation decisions both at the level of
clinical and health care policy. Second, in population studies to describe and quantify the HRQOL
of population groups and whole populations cross-sectionally and changes in the HRQOL over
time (eg to assess the need for and effect of resource reallocation between regions). Third, to
assist and improve clinical practice and individual clinical decisions by pinpointing problems that
need attention and to assess clinical outcomes. Finally, to describe the patient mix of various
health care units (such as hospitals, health centres) and to standardise it when analysing and
comparing their productivity.
In the literature several requirements have been set for a useful generic measure (Chen et al.
1975; Gilson et al. 1975; Kaplan et al. 1976; Torrance et al. 1982; Boyle & Torrance 1984; Kirsher
& Guyatt 1985; Guyatt et al. 1989). These can be condensed into the following:
Feasibility and general applicability
The measure should minimise measurement burden to respondents (be brief and acceptable) and
users (inexpensive to administer, easy to compute and analyse). The information needed for the
measure should be possessed by the respondents without prior use of clinical, laboratory or other
health services.
Reliability
The repeatability of measurements with a minimum of random error.
Validity
The degree of confidence that can be placed in the inferences drawn from the scores of a
measure.
Sensitivity
The ability of the measure to distinguish between individuals and groups in different health states
(discrimination) and to detect changes in individuals or groups over time (responsiveness to
change in health status).
The 15D was developed to meet these criteria as far as possible. Since some of the criteria are
conflicting no measure can satisfy them all completely. The purpose was to find a reasonable
balance between them. The health state descriptive system of 15D is evaluated against these
criteria both theoretically and empirically by using several methods and data sets.
3 Conceptual Basis and Operationalisation of 15D
Conceptually 15D is based on the results of a thorough review of how health is conceptualised in
official Finnish health policy documents. Two familiar major aspects emerged: the quantity or
length of life and the quality of life, ie what people's life is like regarding health and ability to
perform. By subscribing to the WHO definition of health as a state of complete physical, mental
and social well-being, the quality aspect was seen to be composed of three main components:
biological organismic functioning (functioning of the psycho-physical system), experiential
component (perceived health/illness) and social functioning in terms of ability to perform in usual
roles and tasks. Two types of social functioning were of a special importance: ability to work and
social participation (Sintonen 1981a).
12D
In the first 12-dimensional health state descriptive system the functioning of the psycho-physical
system was operationalised in terms of 9 basic physiological functions: moving, seeing, hearing,
breathing, eating, sleeping, eliminating, communicating (speaking) and mental functioning. Social
functioning comprised two dimensions: ability to work and social participation. The experiential
component was measured by perceived health. It was thought to capture mental health problems
and felt symptoms. Each dimension was divided into 4-5 levels (Sintonen 1981a, 1981b).
15D.1
The feedback from medical profession led to a revision in 1986. Some commentators, especially
psychiatrists felt that the measure was too much oriented towards physical health, neglecting, or at
least dealing inadequately with the mental side. There was also some discontent with how the
levels of some dimensions were worded. For these reasons, three dimensions, depression,
distress and pain, were added and levels rephrased. Thus the first 15D health state descriptive
system (self-administered 15D questionnaire), to be called 15D.1, was established (Sintonen &
Pekurinen 1989, 1993).
Since over 20 projects have been launched where this version was/is being used. In addition,
1815 patients (aged 12-92) filled in a 15D.1 questionnaire upon arrival to primary health care
centres around the country. The questionnaire included two further questions. The first was: "We
have measured your health regarding the following 15 attributes (a numbered list). Considering
how health in your opinion should be measured, does this list contain attributes that are not
important and could therefore be omitted? Please circle the number of the attributes that could be
omitted". The second was: "Considering how health in your opinion should be measured, are there
any important attributes that are not in the list. If there are, could you please briefly describe what
they are?" About 300 patients provided some answer. A similar survey was carried out among
1100 primary health care patients upon arrival to health centres and outpatient clinics in Helsinki.
This time about 200 patients replied to these questions.
Very few indicated attributes to be omitted. Those who did, usually listed attributes in which they
themselves had no problems. They thus did not understand the question in general terms as the
idea was. No clear candidate(s) for omission emerged.
Most of the respondents wanted to add something. The answers were interpreted and grouped by
a public health nurse. Four major categories were found: 1) descriptions of a clinical condition or
measurement (such as rheumatism, blood pressure, cholesterol level), 2) unpleasant physical
symptoms (nausea, itching, dizziness, constipation etc), 3) descriptions relating to lack of vitality
(lack of energy, tiredness, exhaustion, burn-out etc) and 4) mental problems (stress, nervousness,
tenseness etc). There were also numerous comments relating to sexual problems.
15D.2
Based on these results, feedback on the descriptive system from many users of 15D.1, and factor
analyses of empirical data in various patient groups, the descriptive system was revised in 1993.
Some changes were made in the dimensions, number of levels and wording. Correlations and
factor analyses consistently showed that ability to work (Q9) and social participation (Q10) were
closely associated so they were merged into usual activities. A similar association was found
between depression (Q13) and distress (Q14). Therefore, to make a clearer distinction between
them, the contents of the latter were changed from feeling distressed and fearful to feeling
anxious, stressed and nervous. Factor analyses also indicated that perceived health was a
'summary' of the other dimensions so it was replaced by a dimension of vitality (dealing with
energy, tiredness and exhaustion). Also a dimension relating to sexual activity was added.
The reason for changing all 4 level scales into 5 level scales was to increase sensitivity and to
make it easier for the patients to rate themselves on the scales. Level descriptions were reworded
to increase clarity, completeness of content (eg bowel function was added to elimination, pain was
broadened to encompass all unpleasant physical symptoms) and sensitivity, especially at the
upper ('better') end of the scale. The levels of sleeping and breathing were reformulated on the
advice of specialists in sleep problems and respiratory diseases, respectively. The revised
questionnaire 15D.2 is in Appendix 1.
4 Evaluation of the 15D Descriptive System
Data and methods
Feasibility and general applicability is judged theoretically by considering the type of information
needed to respond, how applicable the level descriptions of the dimensions are in different social
and cultural settings, and measurement burden. Empirically the evaluation takes place regarding
fill-up time and response and completion rates.
The reliability of the measurement scores is concerned with the degree to which they can be
repeated (McDowell and Newell 1987). It is estimated by taking repeated measurements (test-
retest) and determining the agreement between them. Usually this is expressed as a correlation
coefficient, but Bland and Altman (1986) have shown that it is "a totally inappropiate method": it
measures the strength of a relation between two variables, not the agreement between them.
Therefore a two-step method suggested by them is used here. First, the mean and standard
deviation of the differences between the test and retest scores are calculated to find out, whether
the mean difference deviates significantly from zero. If it does, the data cannot be used to assess
repeatability. If it does not, in the second step a repeatability coefficient is calculated, defined as
the percentage of differences (cases) falling within two standard deviations from the mean
difference with 95% being an acceptable standard.
The repeatability of 15D.1 and Nottingham Health Profile (NHP) (Hunt et al. 1981) was examined
among patients waiting for bypass operation (n=123, aged 31-66) (Brommels 1990). After the first
measurement (test), those to be operated more urgently were chosen (n=57) and the rest (n=66)
remained on the waiting list. A new measurement (retest) was taken in both groups on average in
three months time. Those remaining on the list were on a conservative treatment to keep their
health state stable so they are suitable for a test-retest comparison. As in all analyses in this
paper, the levels of each 15D dimension were scaled separately onto a 0-1 scale (1=best level,
0=dead) based on their average relative desirability elicited from a sample of Finnish population
(n=243 for 15D.1 and n=213 for 15D.2) by using a continuous 0-100 ratio scale (Sintonen 1981b).
The NHP scores on a 0-100 scale (0=best, 100=worst) were derived by using Finnish population
weights (Koivukangas et al. 1992).
To test the reliability of responses by proxies 22 patients (average age 68, range 35-85) in the
Tampere Hospice for terminal cancer treatment rated themselves on 15D.1 and their personal
nurse rated the patient independently. The nurse had known the patient on average for 39 days.
Validity indicates the extent to which accurate inferences can be made based on a measure.
Validation is a process of hypothesis testing, by which the degree of confidence that can be placed
in the inferences to be drawn from scores on scales is determined (Streiner & Norman 1989). In
this testing several aspects (types of validity) can be explored.
Content validity refers to how adequately the content of the measure reflects its aims. The
question is: do all the items appear relevant to the concept being measured and are all aspects
covered (McDovell & Newell 1987). A measure that includes a more representative sample of
dimensions lends itself to more accurate inferences. The higher the content validity, the broader
are the inferences that can be validly drawn about the person or group under a variety of
conditions and circumstances (Streiner & Norman 1989).
There are no absolute standards for judging content validity. However, at least three aspects are
pertinent to good content validity. Firstly, the measure should be composed of a comprehensive
set of clearly defined, one-concept dimensions, each of which should make on independent and
distinguishable contribution to variation in health status. Secondly, the dimensions should be
relevant and socially important and capable of being affected by health care/policy (Torrance et al.
1982, Boyle and Torrance 1984). Thirdly, Kaplan et al. (1976) argue that a value component,
reflecting the relative importance or goodness of the health states, is a critical element of content
validity.
Content validity is evaluated here by looking, whether the process of selecting the dimensions can
be expected to produce a comprehensive set of independent dimensions that adequately cover
the conceptual basis of RHQOL, and whether it allows for different views about their social
relevance and importance.
McDowell and Newell (1987) suggest that factor analysis can be used to establish content validity
empirically. However, we strongly agree with Kaplan et al. (1976) that factor analysis can be used
(and was used here) for data reduction, that is, to find the largest number of independent (non-
correlating) dimensions, not to create the construct of HRQOL. The final choice of dimensions has
to be made on the basis of social relevance and importance, not on what the correlational
structure of the dimensions, which is besides different in different patient groups, happens to be.
Criterion validity of a measure is examined by correlating it with another measure, ideally a 'gold
standard'. If the measure correlates with the criterion measure when given at the same time, the
measure shows concurrent validity. If the measure is able to predict a future criterion value, it
shows predictive validity (Streiner & Norman 1989).
Since there is no gold standard, criterion validity of HRQOL measures can not be proven.
However, to the extent that the Hamilton Depression Rating Scale (HDRS, Hamilton 1967) can be
regarded as a gold standard for measuring depression, we can examine the depression-related
concurrent validity of the mental health dimensions of 15D.1 and SF-20. The data (subsequently
the depression data) are based on a six-week RCT of 209 patients in Finland. The patients
enrolled were over 18 years of age and met the DMS-III-R criteria for depressive disorder with the
minimum score of 16 on the HDRS. The simultaneous measurements with HDRS, 15D.1 and SF-
20 took place at weeks 0, 2 and 6 (Lönnqvist et al. 1994b).
The concurrent validity was studied by correlating all simultaneous measurements of the
depression and distress dimensions of 15D.1, mental health dimension of SF-20 and HDRS. A
logit model was used to examine, how well the scores on all dimensions of 15D.1 and SF-20 are
able to predict, whether the simultaneous HDRS score was 16 or >16.
Construct validation involves gathering external empirical evidence, convergent or discriminant,
so that meaningful inferences can be made with the measure. To show convergent validity the
measure should correlate highly with other variables and other measures of the same construct, to
which it should correlate on theoretical grounds. Discriminant validity implies that the measure
should not correlate with dissimilar, unrelated variables or measures (Streiner & Norman 1989).
To exhibit convergent evidence, extreme groups comparison with t-test is used to test the following
hypotheses: 1) the elderly (65+ years) tend to have a lower mean score on each dimension than
young people (17-35 years), 2) people reporting an illness or impairment tend to have a lower
mean score on each dimension than people without an illness or impairment. The data consist of
the combined random population samples used in eliciting valuations for 15D. The final sample
sizes were 719 for 15D.1 and 1288 for 15D.2. These data sets are subsequently referred to as
15D.1 and 15D.2 valuation data, respectively.
Furthermore, t-tests are used to test the following hypotheses: 1) at baseline the depression
patients show significantly lower scores than the general adult population at least in depression,
distress, perceived health, sleeping, mental function, working and social participation, 2) at
baseline the patients waiting for a bypass operation report significantly lower scores than the adult
population at least in mobility, breathing, pain, sleeping, working, social participation, depression
and distress, and 3) bypass patients report significantly lower scores than depression patients in
mobility, breathing, and pain, whereas the opposite applies to depression, distress, sleeping and
mental function. These hypotheses are based on well-known attributes of these patient groups.
Multitrait-multimethod matrix (Campbell & Fiske 1959) is used to look at convergent and
discriminant validity simultaneously. It is expected that the Pearson correlations of 15D with
comparable 15D and NHP, SF-20 and EuroQol dimensions are higher (convergent validity) than
with non-comparable ones (discriminant validity). When comparing 15D.1 and NHP we use the
bypass follow-up data (all measurements). The comparison of 15D.1 and SF-20 is based on the
depression data (all measurements), and that of 15D.2 and EuroQol on a random population
sample of 500 (aged 17-91, response rate 72%, subsequently called the 500 data).
Sensitivity of a measure entails two aspects. First, the ability to distinguish between individuals
and groups in different health states cross-sectionally (discriminatory power) and second, to detect
changes in individuals or groups over time (responsiveness to change in health status) (Kirshner &
Guyatt 1985).
Patrick and Erickson (1993) mention three criteria for evaluating discriminatory power. First, the
ability to detect health problems, especially in a relatively healthy population. Second, the ability to
detect improving health among quite healthy people, and to avoid the 'ceiling' effect of having no
better health state to go to. Third, the ability to detect worsening health among people who are
already quite ill, that is, to avoid the 'floor' effect of having no worse health state to go to.
Sensitivity is evaluated theoretically by considering factors that should contribute to sensitivity.
The discriminatory power is examined empirically by looking at the percentages of respondents in
various patient and population groups that score the 'ceiling' for different dimensions and the
measure as a whole. The corresponding percentages at the 'floor' indicate the range of health
states used. The skewness coefficient reflects the skewness of frequency distributions of scores
and thus sensitivity over the range. A coefficient value of 0 indicates a normal distribution. The
more negative or positive the coefficient is, the more skewed the distribution is to the left or right,
respectively.
Responsiveness to change was explored by comparing the percentages at the 'ceiling' and 'floor'
and skewness coefficients in two patient groups at baseline and after treatment. In addition two
measures of responsiveness were calculated for different dimensions: the effect size and
standardised mean response (SMR). Effect size is defined as (change in the mean score from
baseline to follow-up)/(standard deviation at baseline) (Kazis et al. 1989). Cohen (1977) regards an
effect size of .20-.49 as small, .50-.79 as moderate and >.80 as large. The SMR is (mean
response)/(standard deviation of responses), which equals the paired t-statistic without sample
size factor (Liang et al. 1990).
Sensitivity of 15D.1 vs. NHP was analysed by using the bypass follow-up data. 'Ceiling' and 'floor'
effects and skewness were defined for the whole sample at baseline and at one year follow-up,
effect size and SMR for those operated. The comparison of 15D.1 and SF-20 is based on the
depression data (baseline vs. 6 weeks). Discriminatory power of 15D.2 versus EuroQol in a
relatively healthy population was evaluated by using the 500 data. A corresponding comparison
between 15D.1 and 15D.2 was carried out by using the 15D.1 and 15D.2 valuation data,
respectively. The samples were made comparable and compatible with the age and gender
structure of the whole adult population (Statistics Finland 1993) by appropriate weighting.
5 Results
Feasibility and general applicability
Theoretical considerations
Answering the questionnaire does not require any special information - the information needed is
possessed by the respondents without prior use of clinical, laboratory or other health services. The
level descriptions have been phrased to minimise the effect of people's varying external social and
environmental circumstances and socially differentiated sex roles on the rating. Therefore the
measure is well suited for populations in different cultural and social settings.
The questionnaire is brief and designed to be self-administered. It should not impose any major
burden upon respondents. If wanted or required, there should not be any major problems with
interviewer-administration. If the person is for physical or mental reasons unable to reply in either
way (can not read or see, confused etc), the questionnaire can be filled up for him/her by someone
who knows the person well.
Empirical evidence: response and completion rates
It usually takes 5-10 minutes to fill in the 15D questionnaire. An extensive experience shows that
15D is well received and accepted. This is also reflected in response and completion rates. In
postal patient or population surveys in Finland the range of response rates has been 65-80%
depending on whether reminders have been mailed and what else the questionnaire includes
(Pekurinen et al. 1991; Apajasalo et al. 1994; Rissanen, unpublished data).
The response rate is also very good in long-term follow-ups. For example, breast cancer patients
on two drugs have been followed-up in an RCT until disease progression. They have filled in a
15D.1 questionnaire at two months intervals. During the first six years of follow-up 13.9% of
measurements were missing (study in progress).
In the one-year follow-up of bypass patients, the completion rate by dimensions was 99-100% for
15D.1 and 93-97% for NHP among those, who did not drop out. In the depression data, the
corresponding rates were 96-97% for 15D.1 and 97-98% for SF-20. In a population sample of
2000, the rates for 15D.1 were 97-99% (Rissanen, unpublished data). In the 500 data, the
completion rates were 96-99% for 15D.2 (except sexual activity 90%) and 97-98% for EuroQol.
Regarding response and completion rates, the acceptability is thus comparable to NHP, SF-20 and
EuroQol. The main reasons for non-response or non-completion are probably not measurement
burden or unacceptability of the questions. The lower completion rate for the dimension of sexual
activity may indicate that this dimension is slightly less acceptable than the others.
However, a small number of missing responses on any dimension is not a problem, since they can
be predicted with a great accuracy. For example, in the 500 data, the level rated by the respondent
on any dimension was correctly predicted on average in 80% of cases (range 67-98%) with a
regression model with the level rated as the dependent variable and the levels rated on the other
dimensions, age and gender as explanatory variables. It appeared that a more parsimonious
model would do almost equally well so a missing response for a few dimensions can be predicted
quite accurately.
Reliability
Among the patients waiting for a bypass operation the mean difference between the test and retest
scores at three months varied from -0.05 to 0.03 on the 15D.1 dimensions and from -3.2 to 6.5 on
the NHP dimensions. None of the differences were significantly different from zero (not even at a
10% level). By dimensions, the percentage of cases lying within two standard deviations from the
mean difference was 92-100% for 15D.1 and 89-95 for NHP. Thus the repeatibility of 15D
compares equally, even favourably with that of the NHP, being quite high for both measures in this
data set.
The agreement between the responses of cancer patients and their personal nurses varied
depending on the dimension. For the dimension "working", the agreement was 100%. On the other
hand, the nurse ratings were significantly (p < 0.05) better for seeing, eating, elimination and
mental functioning and worse for depression and distress. For many dimensions the mean
differences showed great variation (sd > average distance between two levels). Partly these
results reflect the small sample size. Overall the agreement was not very good, tending to be
lowest, as one could expect, for the subjective dimensions. This is in line with earlier findings (eg
Epstein et al. 1989, Slevin et al. 1988) emphasising the fact that HRQOL is a subjective matter
and should be assessed subjectively by the individuals themselves if possible. Perhaps a close
family member might be able to give more reliable responses.
Content validity
Theoretical considerations
The 15D covers the physical, psychological and social aspects of health as defined by the WHO
and widely regarded as a conceptual basis for RHQOL measures. When operationalising them into
15 measurable dimensions and their levels (15D.2), health politicians, physicians, patients, other
researchers and empirical research data have had a fair say. The dimensions of 15D.2 are also in
concordance with those suggested by Fallowfield (1990) based on a broad analysis of literary,
philosophical and scientific sources. In most cultures unimpaired basic physiological functions and
other dimensions of 15D are characteristic and relevant for an individual to be regarded as
healthy. Moreover, the dimensions are such that they can be affected at least to some extent by
health care/policy. Kaplan et al. (1976) argue that not accounting for symptoms is a major
sacrifice of content validity - in 15D.2 they are accounted for. Moreover, 15D includes a value
component (to be discussed elsewhere), reflecting the relative importance or goodness of the
health states as experienced by the general public.
Empirical evidence
In extensive patient surveys, no clear dimensions to be omitted emerged. The most frequently
suggested missing attributes were added. The high completion rates indicate that the dimensions
appear relevant so the respondents do not object to or omit them. The 15D.2 includes the same
dimensions as the other well-known measures of a similar type do - and much more (see, eg
Lovatt 1992; Rosser 1993) being thus more comprehensive in sampling items for the construct of
HRQOL. In the light of theoretical and empirical evidence and relative to many other measures the
content validity of 15D is assuring.
Criterion validity
The correlations of the depression and distress scores of 15D.1 with the HDRS score were -.62
and -.59, respectively. When the scores were summed, the correlation was -.64. The correlation
between the mental health score of SF-20 and the HDRS score was -.73. In 77% of
measurements, the scores on all 15D.1 dimensions were able to predict correctly, whether the
simultaneous HDRS score was 16 or >16. For SF-20 this was 81%. These figures provide
substantial evidence for the depression-related criterion validity of 15D.1 also longitudinally.
Construct validity
The information of three multitrait-multimethod matrices has been condensed into Table 1. For
example, in the column entitled NHP the correlations of 15D dimensions with comparable NHP
dimensions are in bold print, and the range of correlations with non-comparable ones, either on
15D or NHP, are in parentheses. Table 1 shows that the correlations of 15D with comparable NHP,
SF-20 and EuroQol dimensions are consistently higher than the correlations with non-comparable
scales measuring dissimilar attributes. This is a pattern that scales with convergent and
discriminant validity are expected to exhibit. In general, the correlations of 15D dimensions with
comparable SF-20 and EuroQol dimensions are higher than with comparable NHP dimensions.
Correlations of a similar magnitude and pattern between comparable 15D.1 and NHP dimensions
were observed by Rissanen et al. (1994) among hip and knee replacement patients. These
findings provide solid convergent and discriminant evidence for the construct validity of 15D.
Table 1
Multitrait-multimethod matrix of correlations of 15D dimensions (.1=15D.1 and .2=15D.2) with
comparable NHP, SF-20 and EuroQol dimensions (the range of correlations with the
non-comparable ones in parentheses in absolute values)
15D dimensions 15D.1 vs NHP 15D.1 vs SF-20 15D.2 vs EUROQOL
Mobility.1 [Mobility.2] PM/N: -.49
(.22-.43)
PF/S: .53
(.14-.41)
[MO/E: -.77]
(.17-71)
Sleeping.1 S/N: -.68
(.32-.44)
Pain.1
[Discomfort/Symptoms.2]
P/N: -.51
(.30-.43)
P/S: .73
(.15-.43)
[PD/E: -.65]
(.31-.46)
Depression.1 [Depression.2] EM/N: -.60
(.29-.44ϖ)
MH/S: .77
(.15-.66ϖϖ)
[MD/E: -.67]
(.13-.36ϖϖϖ)
Distress.1 [Distress.2] EM/N: -.53
(.24-.40ϖ)
MH/S: .70
(.16-.58ϖϖ)
[MD/E: -.58]
(.13-.43ϖϖϖ)
Working.1 RF/S: .69
(.31-.68)
Social participation.1 SF/S: .74
(.25-.68)
Perceived health.1 HP/S: .67
(.35-.66)
[Usual activities.2] [UA/E: .76]
(.24-.65)
Notes
ϖ Correlation between depression.1 and distress.1 = .70.
ϖϖ Correlation between depression.1 and distress.1 = .76.
ϖϖϖ Correlation between depression.2 and distress.2 = .63.
NHP PM/N = physical mobility, S/N = sleep, P/N = pain, EM/N = emotional reactions.
SF-20 PF/S = physical functioning, RF/S = role functioning, MH/S = mental health, HP/S = health perceptions,
P/S=pain, SF/S=social functioning.
EUROQOL MO/E=mobility, UA/E=usual activities, PD/E=pain or discomfort, MD/E=mood.
The extreme group comparisons showed that apart from speech (communicating), depression and
distress, the elderly (65+ years) had a lower (p<.01) mean score on each dimension of 15D.1 than
young people (17-35 years). On 15D.2 the elderly (65+ years) had a lower (p<.001) mean score on
each dimension except depression (p=.11). People reporting an illness or impairment had a lower
(p<.001) mean score on each dimension of 15D.1 except speech (p=.013) and depression
(p=.008) than people without an illness or impairment. On 15D.2 the difference was significant
(p<.001) on each dimension. Thus the results to a great extent support our hypotheses and
provide thus substantial convergent evidence of construct validity.
Still further evidence can be derived from the fact that all the expected differences between the
general population and depression and bypass patients as well as between these patient groups
were confirmed. This allows distinctive profiles be created for different patient groups compared
with the population. As an example, the profiles for these groups are depicted in Figure 1.
Figure 1
The profiles of adult population, depression and bypass patients on 15D.1
Sensitivity
Theoretical considerations
Patrick and Erickson (1993) suggest that to increase the discriminatory power and responsiveness
to change (for better) among quite healthy and to avoid the 'ceiling' effect the measure should
include dimensions like emotional well-being, positive affect, vitality and health perceptions. The
15D.1 includes emotional well-being (depression, distress) and perceived health, and the 15D.2
depression, distress, vitality and symptoms/discomfort. These dimensions should help to
discriminate between, and measure change in relatively well populations. The lower levels of the
15D dimensions like mobility, breathing, eating, elimination and mental function should guarantee
the ability to detect worsening health among people who are already quite ill and thus to avoid the
'floor' effect even in a very frail elderly population.
Theoretically, 15D.1 defines 10 billion and 15D.2 over 30.5 billion mutually exclusive health states
(plus unconscious and dead). There is thus a great potential for discriminatory power and
responsiveness to change (an enormous number of states to be in and to go to). Although most of
them may never occur in practice, still regarding the number of health states defined, both
versions are more sensitive than most comparable instruments producing a single index score.
For example Rosser/Kind defines 28 states (Rosser & Kind 1978), EuroQol 243 states (Sintonen
1993), GWB 1548 states (Kaplan & Anderson 1988) and McMaster 960 states (Torrance et al.
1982).
Empirical evidence: discriminatory power by dimensions
Table 2 shows the discriminatory power of 15D.1 vs. NHP and SF-20, and of 15D.2 vs. EuroQol by
comparable dimensions. Apart from pain and possibly mobility, 15D.1 and NHP show similar
percentages of patients at the 'ceiling' and 'floor' both at baseline and end. The pain dimension of
NHP does not seem to detect mild pain. The differences in percentages for mobility may reflect
different contents of the dimensions: physical mobility/N reflects also self-care and body
movements, not just ability to walk as mobility.1. The frequency distributions of the NHP responses
were much more skewed than those of the 15D.1 indicating thus less sensitivity over the range.
Moreover, at the one-year follow-up, the skewness of the NHP responses had increased
considerably. These findings obviously reflect a lack of discriminatory power for the NHP.
Table 2
The discriminatory power in terms of `ceiling' and `floor' effects and skewness coefficient, and responsiveness to change in
terms of effect size and standardised mean response of comparable 15D (.1=15D.1 and .2=15D.2), NHP (/N), SF-20 (/S)
and EuroQol (/E) dimensions
At ceiling
%
At floor
%
SkewnessData and dimensions
Baseline End Baseline End Baseline End
Effect
size
Standard
mean
response
Bypass
Mobility.1
25.2
59.3
0.9
0.3
-1.0
1.1
1.1
Physical mobility/N
20.0
44.8
-
0.7
1.7
0.8
0.9
Sleeping.1
23.0
38.9
0.9
-0.5
-0.5
0.5
0.5
Sleep/N
21.0
40.7
3.7
0.7
1.4
0.5
0.6
Pain.1
3.3
20.9
-
-0.7
-0.2
0.8
0.7
Pain/N
23.3
47.7
0.9
1.0
1.4
0.7
0.8
Depression.1
22.1
41.1
0.9
-1.0
-0.6
0.4
0.4
Distress.1
34.4
44.6
0.9
-0.4
-0.8
0.4
0.4
Emotional reactions/N
25.4
37.0
2.8
1.1
1.6
0.4
0.4
Depression
Mobility.1
74.9
80.6
-
-1.9
-1.8
0.1
0.1
Physical functioning/S
25.6
44.0
1.8
-0.5
-1.2
0.4
0.5
Working.1
8.7
24.5
7.5
0.3
-0.2
0.5
0.5
Role functioning/S
15.8
41.3
46.1
1.2
0.1
0.5
0.5
Depression.1
1.9
10.0
2.5
0.5
-0.5
1.0
0.8
Mental health/S
0.5
-
-
0.6
-0.2
1.4
1.1
Perceived health.1
2.9
11.3
0.6
0.2
-0.3
0.7
0.7
Health perceptions/S
2.9
0.6
-
1.1
0.8
1.0
0.7
Pain.1
21.8
31.9
0.6
-0.4
-0.4
0.3
0.3
Pain/S
14.4
28.1
-
0.4
-0.3
0.5
0.5
Social participation.1
21.8
31.3
3.8
0.1
-0.3
0.3
0.3
Social functioning/S
12.6
25.6
-
0.7
-0.3
0.7
0.6
Population
Mobility.2
82.3
-
Mobility/E
78.8
0.2
Usual activities.2
63.4
1.0
Usual activities/E
79.1
1.7
Depression.2
61.1
1.2
Distress.2
62.6
0.8
Mood/E
85.1
1.1
Discomfort/symptoms.2
40.4
0.4
Pain or discomfort/E
58.2
1.8
The differences in percentages at the 'ceiling' and 'floor' are a little more marked between 15D.1
and SF-20 (Table 2). The differences in percentages may reflect more differences in the contents
of the dimensions than in discriminatory power. The difference in contents is most marked for
mobility: mobility.1 measures ability to walk, not also body movements and self-care as physical
functioning/S, which are at least partly picked up by some of the remaining 15D dimensions. Role
functioning/S obviously suffers from a lack of sensitivity. Apart from physical functioning/S and
pain/S the distributions of the SF-20 responses at baseline were more skewed than those of the
15D.1. At the end of the follow-up, the skewness was comparable to 15D.1. Apart from mobility,
the much higher percentages at the 'ceiling' for EuroQol suggest that it has less discriminatory
power than comparable 15D.2 dimensions.
Table 3 shows the discriminatory power of 15D.1 vs. 15D.2. The percentages at the floor are quite
similar, but those at the ceiling are usually clearly lower for 15D.2. With a few exceptions, also the
frequency distributions of 15D.2 responses are less skewed. This suggests that we have
succeeded with the 15D.2 in the aim of increasing the sensitivity of some scales, especially at
their upper ('better') end.
It thus appears that on roughly comparable dimensions, 15D.1 is superior to NHP and about
equivalent to SF-20 in discriminatory power. The 15D.2 shows much greater discrimination than
EuroQol. Moreover, it must be borne in mind that the remaining 9-10 dimensions of 15D provide
an extra sensitivity reserve both at the ceiling and floor (see the discriminatory power of the
measure as a whole below). The 15D.2 seems to possess greater discrimination than 15D.1.
Discriminatory power of the measure as a whole
Among the patients waiting for bypass operation, NHP located 6.5% of the patients at the 'ceiling',
that is 'healthy', 15D none ('healthy'= at the best level of each dimension). None was at the 'floor'
on either measure. At one-year follow-up (including those operated and those still on the list), the
corresponding proportions were 13.9% and 7.6%. Thus the discriminatory power of 15D.1 seems
better than that of the NHP.
Among the depression patients, neither 15D nor SF-20 detected a single 'healthy' or 'floor' patient
at baseline or at two weeks of treatment. After the trial (week 6) 15D detected one 'healthy' patient
(1/159), SF-20 none. The discriminatory power of these measures is similar in this patient group.
Table 3
The discriminatory power of comparable 15D.1 and 15D.2 dimensions
in two representative population samples
At ceiling
%
At floor
%
SkewnessDimensions
15D.1 15D.2 15D.1 15D.2 15D.1 15D.2
Mobility 85.5 80.7 0.7 0.2 -2.9 -2.3
Vision 88.4 78.5 0.3 0.3 -3.0 -2.7
Hearing 91.5 82.6 0.2 0.1 -3.5 -2.6
Breathing 74.6 69.6 1.4 0.7 -1.8 -1.5
Sleeping 47.9 48.1 0.5 0.3 -0.6 -1.1
Eating 96.9 96.7 0.2 0.1 -6.6 -6.7
Speech 95.9 89.4 0.2 0.1 -5.3 -3.4
Elimination 84.4 73.3 0.4 0.2 -2.4 -1.7
Mental function 72.5 73.2 0.5 0.2 -1.4 -1.5
Depression 38.3 53.6 1.0 0.6 -1.0 -1.4
Distress 59.4 56.2 1.0 0.5 -1.3 -1.0
Pain.1/Discomfort and symptoms.2 27.0 40.2 2.1 0.4 -0.6 -0.9
Working.1/Usual activities.2 68.0 71.2 2.2 0.9 -1.9 -1.9
Social participation.1/Usual
activities.2
81.6 71.2 1.6 0.9 -2.5 -1.9
Vitality 46.4 0.6 -1.3
Sexual activity 73.9 2.4 -1.9
In a population sample of 500, EuroQol classified 51.6% as 'healthy', 15D.2 only 20.7% (none
were at the 'floor' on either method). Thus the discriminatory power of 15D.2 is much better than
that of EuroQol in the general public.
Responsiveness to change
Table 2 shows that in the light of the effect size and SMR, 15D.1 is roughly comparable to NHP
and SF-20 in the responsiveness to change.
6 Discussion
The goal has been to develop a generic, multi-dimensional, standardised, self-administered
measure of HRQOL, that would meet the criteria of acceptability and general applicability,
reliability, validity and sensitivity, and could be used primarily as a single index score measure, but
also as a profile measure for several purposes. The development work has resulted in two 15-
dimensional versions 15D.1 and 15D.2. This paper has focussed on their properties as a profile
measure compared with the Nottingham Health Profile and SF-20, both explicitly designed as
profile measures, and with EuroQol, which is primarily a single index score measure.
The empirical evidence suggests that both versions are very well received and accepted. Both
versions take 5-10 minutes to complete. In postal patient or population surveys the response rates
have varied from 65 to 80%. However, besides the 15D questionnaire, these studies have
included a lot of other questions as well or have not mailed reminders. With a 15D questionnaire
alone and two reminders a response rate of well over 80% can be expected at least in Finland.
Unfortunately, such a survey has not yet been carried out. Also the response rate in long-term
follow-up has proved to be high. Of course, in other countries, the response rates may be different.
The completion rates by dimensions have been very high for both versions. For 15D.1 the rates
have varied from 96 to 100%, being equivalent to SF-20 and slightly better than for NHP. A similar
range was found for 15D.2 with a slight exception, as one might expect, of sexual activity. Yet the
inclusion of that dimension is both theoretically and empirically well grounded. Moreover, missing
values on any dimension can be accurately predicted with the scores on other dimensions by
using a regression model.
The test-retest repeatability of 15D.1 was quite high, even higher than that of NHP. This result was
obtained among patients waiting for a bypass operation with the interval between the
measurements being about three months. The patients were on a conservative treatment to keep
their status stable. Yet three months is a so long period that changes in the patients' health status
may have occurred on some dimensions thus possibly detracting from reliability. With a
customary 1-2 week interval, the repeatability might have been even better. Unfortunately,
empirical evidence on the repeatability of 15D.2 is not yet available, but producing it soon is solidly
on agenda. Meanwhile, given the process by which 15D.2 was developed from 15D.1 there is no
reason to expect that the reliability of 15D.2 would be inferior to that of 15D.1, rather on the
contrary.
The results also provide substantial evidence of several types of validity. As to content validity,
15D is more comprehensive in sampling dimensions for the construct of HRQOL than most other
well-known measures of a similar type. Some of the dimensions are such that at least serious
problems on them are rare (eg eating, seeing). This means that they correlate poorly with other
dimensions and contribute little to total variation and would therefore be omitted, if the choice of
dimensions is based purely, eg on factor analysis. Therefore someone might question their
inclusion. Yet these dimensions may be socially highly relevant and important and when problems
on them occur they may have a profound effect on HRQOL. In this way they provide extra
sensitivity for the measure. Operationalising the core of the measure in terms of basic
physiological functions seems to add to the content and face validity in a clinical sense for
physicians, since signs and symptoms in these functions are routinely examined by them.
The multitrait-multimethod matrix provided clear convergent and discriminant evidence of
construct validity. The convergent validity correlations of 15D were higher with comparable SF-20
dimensions than NHP dimensions suggesting that 15D may be more closely related to the former.
In general, these validity coefficients were quite high being between .49-and .77. In their review of
health measures McDowell and Newell (1987) found that the validity coefficients fell typically
between .20 and .60. The extreme group comparisons and the ability of 15D.1 to discriminate in a
predictable way between patient groups and patient groups from general population provided
further evidence of cross-sectional clinical construct validity. The latter feature allows distinctive
profiles be created for different patient groups compared with the general population. Such profiles
are available for several patient groups as well as standards for various population groups for both
versions of 15D.
Theoretical and empirical evidence also suggest that 15D has a good discriminatory power and
responsiveness to change. In discriminatory power on roughly comparable dimensions 15D.1
appears to be superior to NHP and at least equivalent to SF-20, and 15D.2 superior to EuroQol
and 15D.1. In fairness to EuroQol, it was never designed to be a stand-alone measure, let alone
profile measure, but a simple linkage tool between more comprehensive measures (EuroQol
Group 1990). In that capacity it can be quite useful, since it takes less than 1 minute to complete
and is far less demanding than any other measures considered here. On comparable dimensions
the responsiveness of 15D.1 to change seems similar to NHP and SF-20, providing thus also
evidence for longitudinal construct validity. In addition it has to be borne in mind that in
comparison to these three measures, the remaining 9-10 dimensions of 15D add the
comprehensiveness of content and provide a huge extra reserve of discriminatory power and
responsiveness to change.
It has been emphasised that reliability and discriminatory power may be conflicting features
(Streiner & Norman 1989) as may be discriminatory power and responsiveness to change
(Kirshner & Guyatt 1985). It has been particularly encouraging to learn that the 15D seems to meet
these properties to a high degree simultaneously. These features are necessary in most areas,
where 15D is thought to be used, especially in health program and technology evaluations.
Even if 15D is thought to be primarily a single index score measure (its properties in this capacity
will be explored elsewhere), 15D.1 seems to perform as a profile measure equally well as purpose-
built profile measures NHP and SF-20, in some respects even better. For obvious reasons there
are more empirical results on the older 15D.1 version than 15D.2. However, bearing in mind the
multi-stage process and the experiences of numerous users over several years that were used
when developing 15D.2 from 15D.1 there are all reasons to assume that 15D.2 is even better in
reliability and validity; in sensitivity it has already been shown to be better. Data are being
collected to substantiate this assumption. Anyway the author will use the new version (15D.2) in
future studies and advises the other to do the same.
15D has already proven to be a useful tool in measuring effectiveness or utility of medical
interventions (eg Rissanen et al. 1994; Sintonen et al. 1994), in assisting and improving clinical
practice and individual clinical decisions (Markku Partinen, personal communication) and
population surveys. It is hoped that this paper will encourage its wider use in various areas.
1
1
When writing this, the 15D.2 questionnaire is available in English, Finnish and Norwegian. Translations
into German, Japanese and Swedish are in progress.
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APPENDIX 1
Quality of Life Questionnaire (New 15D)
Please read through all the alternative responses to each question before placing a cross (x)
against the alternative which best describes your present status. Continue through all 15 questions
in this manner, giving only one answer to each.
Question 1 Mobility
1 ( ) I am able to walk normally (without difficulty) indoors, outdoors and on stairs.
2 ( ) I am able to walk without difficulty indoors, but outdoors and/or on stairs I have
slight difficulties.
3 ( ) I am able to walk without help indoors (with or without an appliance), but
outdoors and/or on stairs only with considerable difficulty or with help from
others.
4 ( ) I am able to walk indoors only with help from others.
5 ( ) I am completely bed-ridden and unable to move about.
Question 2 Vision
1 ( ) I see normally, ie I can read newspapers and TV text without difficulty (with or
without glasses).
2 ( ) I can read papers and/or TV text with slight difficulty (with or without glasses).
3 ( ) I can read papers and/or TV text with considerable difficulty (with or without
glasses).
4 ( ) I cannot read papers or TV text either with glasses or without, but I can see
enough to walk about without guidance.
5 ( ) I cannot see enough to walk about without a guide, ie I am almost or completely
blind.
Question 3 Hearing
1 ( ) I can hear normally, ie normal speech (with or without a hearing aid).
2 ( ) I hear normal speech with a little difficulty.
3 ( ) I hear normal speech with considerable difficulty; in conversation I need voices
to be louder than normal.
4 ( ) I hear even loud voices poorly; I am almost deaf.
5 ( ) I am completely deaf.
Question 4 Breathing
1 ( ) I am able to breathe normally, ie with no shortness of breath or other breathing
difficulty.
2 ( ) I have shortness of breath during heavy work or sports, or when walking briskly
on flat ground or slightly uphill.
3 ( ) I have shortness of breath when walking on flat ground at the same speed as
others my age.
4 ( ) I get shortness of breath even after light activity, eg washing or dressing myself.
5 ( ) I have breathing difficulties almost all the time, even when resting.
Question 5 Sleeping
1 ( ) I am able to sleep normally, ie I have no problems with sleeping.
2 ( ) I have slight problems with sleeping, eg difficulty in falling asleep, or sometimes
waking at night.
3 ( ) I have moderate problems with sleeping, eg disturbed sleep, or feeling I have
not slept enough.
4 ( ) I have great problems with sleeping, eg having to use sleeping pills often or
routinely, or usually waking at night and/or too early in the morning.
5 ( ) I suffer severe sleeplessness, eg sleep is almost impossible even with full use
of sleeping pills, or staying awake most of the night.
Question 6 Eating
1 ( ) I am able to eat normally, ie with no help from others.
2 ( ) I am able to eat by myself with minor difficulty (eg slowly, clumsily, shakily, or
with special appliances).
3 ( ) I need some help from another person in eating.
4 ( ) I am unable to eat by myself at all, so I must be fed by another person.
5 ( ) I am unable to eat at all, so I am fed either by tube or intravenously.
Question 7 Speech
1 ( ) I am able to speak normally, ie clearly, audibly and fluently.
2 ( ) I have slight speech difficulties, eg occasional fumbling for words, mumbling, or
changes of pitch.
3 ( ) I can make myself understood, but my speech is eg disjointed, faltering,
stuttering or stammering.
4 ( ) Most people have great difficulty understanding my speech.
5 ( ) I can only make myself understood by gestures.
Question 8 Elimination
1 ( ) My bladder and bowel work normally and without problems.
2 ( ) I have slight problems with my bladder and/or bowel function, eg difficulties with
urination, or loose or hard bowels.
3 ( ) I have marked problems with my bladder and/or bowel function, eg occasional
`accidents', or severe constipation or diarrhoea.
4 ( ) I have serious problems with my bladder and/or bowel function, eg routine
`accidents', or need of catheterization or enemas.
5 ( ) I have no control over my bladder and/or bowel function.
Question 9 Usual activities
1 ( ) I am able to perform my usual activities (eg employment, studying, housework,
free-time activities) without difficulty.
2 ( ) I am able to perform my usual activities slightly less effectively or with minor
difficulty.
3 ( ) I am able to perform my usual activities much less effectively, with considerable
difficulty, or not completely.
4 ( ) I can only manage a small proportion of my previously usual activities.
5 ( ) I am unable to manage any of my previously usual activities.
Question 10 Mental function
1 ( ) I am able to think clearly and logically, and my memory functions well
2 ( ) I have slight difficulties in thinking clearly and logically, or my memory
sometimes fails me.
3 ( ) I have marked difficulties in thinking clearly and logically, or my memory is
somewhat impaired.
4 ( ) I have great difficulties in thinking clearly and logically, or my memory is
seriously impaired.
5 ( ) I am permanently confused and disoriented in place and time.
Question 11 Discomfort and symptoms
1 ( ) I have no physical discomfort or symptoms, eg pain, ache, nausea, itching etc.
2 ( ) I have mild physical discomfort or symptoms, eg pain, ache, nausea, itching etc.
3 ( ) I have marked physical discomfort or symptoms, eg pain, ache, nausea, itching
etc.
4 ( ) I have severe physical discomfort or symptoms, eg pain, ache, nausea, itching
etc.
5 ( ) I have unbearable physical discomfort or symptoms, eg pain, ache, nausea,
itching etc.
Question 12 Depression
1 ( ) I do not feel at all sad, melancholic or depressed.
2 ( ) I feel slightly sad, melancholic or depressed.
3 ( ) I feel moderately sad, melancholic or depressed.
4 ( ) I feel very sad, melancholic or depressed.
5 ( ) I feel extremely sad, melancholic or depressed.
Question 13 Distress
1 ( ) I do not feel at all anxious, stressed or nervous.
2 ( ) I feel slightly anxious, stressed or nervous.
3 ( ) I feel moderately anxious, stressed or nervous.
4 ( ) I feel very anxious, stressed or nervous.
5 ( ) I feel extremely anxious, stressed or nervous.
Question 14 Vitality
1 ( ) I feel healthy and energetic.
2 ( ) I feel slightly weary, tired or feeble.
3 ( ) I feel moderately weary, tired or feeble.
4 ( ) I feel very weary, tired or feeble, almost exhausted.
5 ( ) I feel extremely weary, tired or feeble, totally exhausted.
Question 15 Sexual activity
1 ( ) My state of health has no adverse effect on my sexual activity.
2 ( ) My state of health has a slight effect on my sexual activity.
3 ( ) My state of health has a considerable effect on my sexual activity.
4 ( ) My state of health makes sexual activity almost impossible.
5 ( ) My state of health makes sexual activity impossible.
... Generic and disease-specific instruments have different assets. Generic HRQoL instruments, such as the 15D [1,2], provide a wider perspective of an individual's quality of life and can be used for calculation of quality-adjusted life years (QALYs) [3][4][5][6][7]. Disease-specific instruments, such as the Canadian Cardiovascular Society (CCS) [8,9] grading used to assess symptom severity in coronary artery disease (CAD), are usually more sensitive to changes in the specific symptoms of a disease. ...
... Based on the dimension scores, a single index score ranging from 0 (being dead) to 1 (being in full health) can be calculated. Missing values in 3 or less dimensions were imputed as described previously [1] and the index score was calculated from the health state descriptive system by utilizing the Finnish utility weights [2]. ...
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Background Generic health-related quality of life (HRQoL) and disease-specific instruments measure HRQoL from different aspects, although generic instruments often contain dimensions that reflect common symptoms. We evaluated how the change in 15D HRQoL and Canadian Cardiovascular Society (CCS) grading of angina severity correlate among coronary artery disease patients during 12-month follow-up. Methods Altogether 1 271 patients scheduled for coronary angiography between June 2015 and February 2017 returned the 15D HRQoL and CCS questionnaires before angiography and after one-year follow-up as a part of routine clinical practice. Spearman correlations between one-year changes in the CCS and the 15D and its dimensions were evaluated. Changes in 15D were classified into 5 categories based on the reported minimal important difference (MID) for the instrument. Results Change in the CCS grade correlated moderately with the MID-based change in the 15D (r = 0.33, 95% confidence interval 0.27–0.39). Correlations between these instruments were similar in different age groups, between sexes and treatment modalities. Of the individual 15D dimensions, changes in breathing (r = 0.40) and vitality (r = 0.30) had the strongest correlations with CCS change. Conclusion The symptom-based evaluation of the change in the CCS grade may not catch the full benefit or harm of the treatment and vice versa, a generic instrument, such as 15D, likely does not fully capture change in disease-specific symptoms. Thus, generic and disease-specific instruments are complementary and should be used in conjunction.
... After administering the medicine, the SpO 2 levels were recorded till it reached ≥90% for every 5 minutes and for the patient whose SpO 2 level reduced to 65% or below, they were referred to higher center and was considered drop out of the study. The quality of life was assessed before and after the treatment using 15D-HRQoL [5]. After administering the ultra-high dilutions of homoeopathic medicine, the SpO 2 level will be monitored continuously every 5 minutes until the oxygen saturation of the patient becomes normal. ...
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Background: Oxygen is one of the most important elements that support the life. So, the demand and delivery of oxygen should be well balanced in order to maintain homeostasis in our body. The maintenance of this homeostasis and oxygen delivery revolves around respiratory and cardiovascular system of our body. Thus, any dysfunction or abnormal function to these systems can leads to an imbalance in homeostasis causing hypoxemia and their complication including multi-organ dysfunction. Objective: To obtain an effective homoeopathic management for hypoxemia in patients under palliative care by using ultra-high dilutions of various homoeopathic medicines and by thus improving the Quality of Life of the patient. Methods: The methodology comprises of selection of patients from palliative care unit of Sarada Krishna Homoeopathic Medical College, Kulasekharam, whose SpO2 level falls between 95%-80% by the help of a pulse-oximeter and after the selection of patient appropriate ultra-high dilutions of homoeopathic medicine will be administered. Results: From this study, it was observed that Homoeopathic treatment with oxygen therapy can improve Hypoxemia as well as Quality of Life (using 15D-HRQoL) in hypoxemic patients under palliative care treatment. Conclusion: With Homoeopathic intervention, we can alleviate the sufferings of Hypoxemic patients in palliative care treatment.
... The findings on generic health-related quality of life instruments were similar: 29% in HUS registries versus 35-40% in the Swedish registries [17]. However, the choice of measure was not the same: 15D-a generic measure developed in Finland [39,40]-was most used in HUS registries, whereas other countries most often used EQ-5D and SF-36/RAND-36 [41][42][43][44][45]. The choice of PROMs should be harmonized with international development to enable broader comparability and research collaboration. ...
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Background The choice of what patient outcomes are included in clinical quality registries is crucial for comparable and relevant data collection. Ideally, a uniform outcome framework could be used to classify the outcomes included in registries, steer the development of outcome measurement, and ultimately enable better patient care through benchmarking and registry research. The aim of this study was to compare clinical quality registry outcomes against the COMET taxonomy to assess its suitability in the registry context. Methods We conducted an organizational case study that included outcomes from 63 somatic clinical quality registries in use at HUS Helsinki University Hospital, Finland. Outcomes were extracted and classified according to the COMET taxonomy and the suitability of the taxonomy was assessed. Results HUS clinical quality registries showed great variation in outcome domains and in number of measures. Physiological outcomes were present in 98%, resource use in all, and functioning domains in 62% of the registries. Patient-reported outcome measures were found in 48% of the registries. Conclusions The COMET taxonomy was found to be mostly suitable for classifying the choice of outcomes in clinical quality registries, but improvements are suggested. HUS Helsinki University Hospital clinical quality registries exist at different maturity levels, showing room for improvement in life impact outcomes and in outcome prioritization. This article offers an example of classifying the choice of outcomes included in clinical quality registries and a comparison point for other registry evaluators.
... Brown (2015) posits that the maintenance of healthy habits has been linked to longevity of man and in essence the quality of life. Quality of life has also been used to measure the innovations and rate the health care services made available to the citizenry of a nation (Sintonen, 1994). Rodriguez-Artalejo et al. (2005) posits that maintaining a good quality of life level have a negative correlation with the rate of fatality and with sicknesses. ...
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Based on the last human development index report released in 2019 which has a range of 0.377 to 0.954, Nigeria had a score of 0.534 which is low and below the minimum acceptable score of 0.55. This raises a concern for the standard of living level in Nigeria. In addition to this, there has been rancor over the allocation of Value Added Tax among states in Nigeria. Hence, this study is aimed at examining the current effect of Value Added Tax among states in Nigeria. This study is hinged on the endogenous growth theory. The Ex-post facto research design was used with secondary data collected in respect to all the 36 states in Nigeria which was gathered and used to carry out this study. The results of this study reveal that VAT allocation and internal source of finance have very low positive effect on the level of literacy of Nigerians. The result further shows that VAT allocation has a very low positive effect on the quality of life in Nigeria. The conclusion points out that VAT allocation have no significant effect on human development among states in Nigeria. Hence, it is recommended that state government focus on developing economic policies that promotes the literacy level and the quality of life of their citizens.
... For this work, we chose to use the 15D instrument, which has more dimensions than EQ-5D-3L 14 . The 15D is developed in Finland 15,16 and is used as a generic and comprehensive instrument for measuring participants' health-related quality of life in 15 dimensional measures (mobility, vision, hearing, breathing, sleeping, eating, speech, excretion, usual activity, mental function, discomfort/symptoms, depression, distress, vitality and sexual www.nature.com/scientificreports/ activity 17 ). ...
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The prevalence of unruptured intracranial aneurysms (UIAs) is around 2–3% in the general population. We hypothesized that the prevalence of small UIAs is higher among 50 to 60-year-old female smokers, since the incidence of aneurysmal subarachnoid hemorrhage (aSAH) is exceptionally high in 60 to 70-year-old female smokers. Ethics approval for this pilot study of 50 women was obtained from the hospital ethics committee. In order to minimize recruitment bias, preliminary invitation letters were sent to 50 to 60-year-old women who were known to be active smokers. Those interested in participating were further informed about the study rationale and protocol. Following written consent, participants filled a detailed questionnaire and underwent computed tomography angiography (CTA) analysis. All abnormalities were recorded. Of the 158 preliminary invitation letters, 70 potential participants initially replied. Of these, 50 returned questionnaires and written consents, 43 of which underwent CTA analysis. Most (39; 91%) were postmenopausal, and 9 (21%) were hypertensive. Two reported a family history (≥ 1 first-degree members) of intracranial aneurysms. UIAs (maximum sizes of 2, 2, 3, 3 and 7 mm) were found in five (12%) female smokers. One woman was operated on, and the remaining four were treated with non-invasive preventive actions (smoking cessation and follow-ups). Small UIAs, which may be best suited for non-invasive preventive actions, may be relatively common in 50 to 60-year-old female smokers. Whether this kind of targeted screening leads to improved health in female smokers requires further investigation.
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Submission ID: 1893175153 File name: IJPHRD_Faiza_Yuniati_2020_nocover.pdf (357.8K) Word count: 3370 Character count: 17595
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Background The aim of this study was to create a Finnish scar assessment scale by translating and evaluating the psychometric properties of the Patient Scar Assessment Scale (PSAS), a part of the Patient and Observer Scar Assessment Scale (POSAS), with burn patients to enable its use in burn care. Methods The translation process followed international guidelines with forward and backward translations and cognitive debriefing with patients. Psychometric validation was performed with adult patients with burns who had been treated at the Helsinki Burn Centre between 2006 and 2017 with skin grafting following the excision of deep second- or third-degree burns. To ensure reproducibility, the PSAS was sent to the study participants twice. The correlation between the PSAS and health-related quality of life (HRQL) was also tested. Results In total, 192 patients, of whom 71% were male, participated in this study. The mean (SD) age of the participants was 57 (17) years. The internal consistency of the PSAS was good, Cronbach's α 0.89 (95% CI: 0.86 to 0.91). The reproducibility was also good concerning all items and the total score, ICC from 0.77 to 0.89. As expected, the total PSAS score correlated negatively with HRQL. Conclusion The PSAS was successfully translated and culturally adapted into Finnish and the newly translated version has good validity and reproducibility for assessing mature burn scars.
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Introduction Multimorbidity, defined as the co-occurrence of two or more long-term medical conditions, is an increasing public health concern worldwide causing enormous burden to individuals, healthcare systems and societies. The most effective way of decreasing the burden caused by multimorbidity is to find tools for its successful prevention but gaps in research evidence limit capacities to develop prevention strategies. The aim of the MOLTO study (Multimorbidity - identifying the most burdensome patterns, risk factors and potentials to reduce future burden) is to provide novel evidence required for cost-effective prevention of multimorbidity by defining the multimorbidity patterns causing the greatest burden at the population level, by examining their risk and protective factors and by estimating the potentials to reduce the future burden. Methods and analysis The MOLTO study is based on the data from the Finnish population-based cross-sectional (FINRISK 2002–2012, FinHealth 2017 the Migrant Health and Well-being Study 2010–2012) and longitudinal (Health 2000/2011) health examination surveys with individual-level link to administrative health registers, allowing register-based follow-up for the study participants. Both cross-sectional and longitudinal study designs will be used. Multimorbidity patterns will be defined using latent class analysis. The burden caused by multimorbidity as well as risk and protective factors for multimorbidity will be analysed by survival analysis methods such as Cox proportional hazards and Poisson regression models. Ethics and dissemination The survey data have been collected following the legislation at the time of the survey. The ethics committee of the Hospital District of Helsinki and Uusimaa has approved the data collection and register linkages for each survey. The results will be published as peer-reviewed scientific publications.
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Question The aim was to systematically collate and synthesise existing, publicly available patient-reported outcome measure (PROM) information suitable for quality of life (QOL)/well-being measurement in mental health economic evaluations, with specific focus on their applicability in multisectoral, multinational, multiperson economic evaluations and to develop an electronic PROM compendium with meta-data. Study selection and analysis A systematic literature search for non-disease-pecific PROMs and their versions suitable for the measurement of QOL/well-being or recovery was conducted from 2008 to February 2020. Six criteria were applied to judge their suitability in multisectoral, multinational, multiperson economic evaluations: (i) availability of separate adult and child/adolescent versions, (ii) availability of a proxy-completion option, (iii) assessing outcomes beyond health, (iv) availability of translations (≥2 language versions), (v) availability of a preference-based valuation, (vi) availability of value sets in more than one country. Findings The final ProgrammE in Costing, resource use measurement and outcome valuation for Use in multisectoral National and International health economic evaluAtions (PECUNIA) PROM-MH Compendium includes 204 unique scales, out of which 88 are individual instruments, while the remaining 116 scales belong to 46 PROM families with more than one distinctive version. Out of the total 134 individual PROMs/PROM families, 72% have at least two language versions, 8% measure broader well-being beyond health-related QOL, 11% have preference-based valuation, with multiple country sets available for 60% of these. None of the identified PROMs met all six proposed criteria. Conclusions The PECUNIA PROM-MH Compendium provides a unique overview of the relevant PROMs and their linked meta-data, and should be a helpful tool when choosing a suitable instrument for future mental health economic evaluations.
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work. Abstract At present, there is scarce evidence about the burden associated with isolation of
Book
It was over five years ago that the Centre for Medicines Research organized a workshop entitled "Quality of Life: Assessment and Application". This workshop brought together a unique group of participants, some of whom had been involved in studies on quality of life for well over a decade, whilst others were meeting the subject for the first time. This blend of experienced researchers and enthusiastic newcomers was a great stimulus to the discus­ sions which followed individual presentations as well as that resulting from the study groups. In the ensuing publication, a balance was sought between a consideration of the complex principles underlying the assessment of quality of life and the application of such assessments to specific clinical conditions which necessitated this approach. The organization in 1991 of a second workshop entitled "Quality of Life Assessment: Key Issues in the 1990s" resulted in a further consideration of the quality of life philosophy, concepts and key instruments together with an update on assessing quality of life in a number of major disease areas. Of particular importance was an examination of various viewpoints concerned with ethical questions and their implications, and quality oflife from industry, regulatory and health care purchasers' perspectives. As a result of this second workshop, the editors of the original book referred to above decided to produce a second edition with a number of updates and additional chapters.
Chapter
In assessing quality of life, the question arises which measurement strategy is most appropriate in a given clinical trials situation. Choices may pertain to the inclusion of generic versus disease-specific instruments, to patient self-ratings versus expert ratings or to the administration interviews versus questionnaires1,10. Of special importance for the type of data gathered is the decision between the use of indices versus profiles for the assessment of quality of life. This decision requires a reflection on the conceptual, methodological and practical issues involved in making such a choice.
Chapter
How best to organize primary health care services within a publicly operated health system has remained an unresolved question. Despite continuing emphasis in northern Europe upon the primary care as the backbone of an equitable and efficient health care system, there are divergent views as to how primary care should be organized, delivered and remunerated, in particular with regard to general practitioners (GPs).
Article
The rise of medical technology assessment and QALY ideology have intensified the need and demand for a generic (disease-independent), sensitive, valid, reliable and easy-to-use measure of health-related quality of life (HRQOL). However, none of the measures and approaches suggested and developed over the years can claim to have established a position as the measure, either as a way of classifying and describing states of HRQOL or for valuing them. In addition, most of them have problems in meeting more than one of the above criteria.
Chapter
Health-care professionals from many specialties and disciplines are becoming increasingly aware that one of the major goals of medical care and technology is to improve patients’ quality of life. Enhancing quality of life is as important as other goals of health and medical care, such as preventing disease, effecting a cure, alleviating symptoms or pain, averting complications, providing humane care, and prolonging life.
Article
This timely and topical book, aimed at all those in the caring professions as well as the general public, sets out to show that the aim of prolonging life is not necessarily always the best goal in terms of enabling the individual to continue with a life that he or she considers worth living. Definitions of what constitutes quality of life draw on literary and philosophical sources as well as those from medical sciences, and the author discusses in detail some of the different measures that have been developed for specific disease states, including cancer, heart disease, arthritis, AIDS [acquired immune deficiency syndrome] and the universal problems of ageing and dying. The author argues strongly for a reassessment of policies which may lead to a concentration on cost-effective treatments at the expense of those which, ignoring the human factor, may appear less economically worth while. Based on extensive research, this important book asks all who are involved in caring for others to take stock and re-examine their priorities. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Article
The concept of health involves two dimensions: The level of function at a point in time and the probability of transition to other levels at future times. By applying measured social values to the distribution of the population among a set of levels, a Function Status Index aptly summarizes the Level-of-Well-Being of a population at a point in time. By incorporating empirically determined transition probabilities into a simple stochastic model, a Quality-Adjusted Life Expectancy can be computed that approximates a comprehensive social indicator for health. The indicators possess the statistical properties required for time series and interpopulation comparisons, for studying outcomes and quality of medical care, and for health system optimization in planning and policy analysis.