the international classification of functioning, disability, and health … · 2010-05-26 · the...

Journal of Clinical Epidemiology 62 (2009) 899e911

The International Classification of Functioning, Disability, and Healthcould be used to measure functioning

Alarcos Ciezaa,b, Roger Hilfikerb, Somnath Chatterjic, Nenad Kostanjsekd, Bedirhan T. Ustund,Gerold Stuckia,b,e,*

aICF Research Branch of the WHO Collaborating Center for the Family of International Classifications at the German Institute of Medical Documentation

and Information (DIMDI), IHRS, Ludwig-Maximilian University, Munich, GermanybSwiss Paraplegic Research, Nottwil, Switzerland

cDepartment of Measurement and Health Information Systems, Multi-Country Studies, World Health Organization, SwitzerlanddClassifications, Terminology and Standards, World Health Organization, Switzerland

eDepartment of Physical Medicine and Rehabilitation, University Hospital Munich, Ludwig-Maximilian-University, Munich, Germany

Accepted 23 January 2009

Abstract

Objective: To explore whether it is possible to construct clinical measures of functioning by integrating information obtained across thecategories of the International Classification of Functioning, Disability, and Health (ICF) using the ICF Core Set of osteoarthritis (OA) asa case in point.

Study Design and Setting: Psychometric study using data from 437 patients with OA from Germany, Italy, Hungary, Serbia, and Sin-gapore. The analyses were performed with the ICF categories of the comprehensive ICF Core Set for OA addressing functioning and usingthe Rasch model for ordered response options.

Results: A clinical measure with 74 country-specific and seven common ICF categories was created with the pooled data of all coun-tries but Hungary. The overall fit statistic according to the c2 was c2

df54055451:73, P 5 0.054, and the Z-fit statistic was Zmean 5�0.041(Zstandard deviation [SD] 5 1.01) for items and Zmean 5�0.15 (ZSD 5 1.19) for persons. The Person Separation Index rb was 0.92.

Conclusion: For the first time, a cross-cultural clinical measure of functioning was constructed which integrates ICF categories. Theresults of this investigation are promising and can contribute to the acceptance and usefulness of the ICF in clinical practice. � 2009Elsevier Inc. All rights reserved.

Keywords: Health status indicators; Outcome assessment; Rasch measurement; Psychometrics; Classification; Disability

1. Introduction

Functioning and disability are at the center of health-care provision. Ultimately, any health-care intervention isintended to restore impaired body structures and functions,to overcome activity limitations and participation restric-tions, and to prevent new symptoms and disability fromdeveloping [1]. Accordingly, information on functioningand disability is essential for everyday clinical routine,for example, for diagnosis, assignment to interventions,intervention management, and evaluation of treatmentoutcomes [2].

* Corresponding author. Department of Physical Medicine and Reha-

bilitation, University Hospital Ludwig-Maximilian University, Munich,

Marchioninistr. 15, 81377 Munich, Germany. Tel.: þ49-89-7095-4050;

fax: þ49-89-7095-8836.

E-mail address: [email protected] (G. Stucki).

0895-4356/09/$ e see front matter � 2009 Elsevier Inc. All rights reserved.

doi: 10.1016/j.jclinepi.2009.01.019

Clinicians have relied on classifications for the diagnosisof health conditions for many decades [3], but a universallyaccepted classification of functioning and disability hasonly recently become availabledthe International Classifi-cation of Functioning, Disability, and Health (ICF) [4]. TheICF allows clinicians to comprehensively describe andcategorize functioning and disability of their patients ina systematic and standardized way that can be understoodby all health professionals involved in the care of patients[5]. The ICF is increasingly being used in different sectorsincluding health, social affairs, labor, and education [2].

The ICF as a classification represents an exhaustive cat-alog of 1,424 ICF categories that refer to body functions,body structures, activities and participation, and environ-mental factors.

The description of functioning using the ICF involves therating of ICF categories with the ICF qualifier. The ICF qual-ifier is a rating scale with five response options ranging from

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900 A. Cieza et al. / Journal of Clinical Epidemiology 62 (2009) 899e911

zero to four. Qualifier ratings across a number of ICF cate-gories result in a categorical profile. ICF Core Sets representa selection of ICF categories relevant to people with a deter-mined health condition [6]. A categorical profile across ICFcategories of an ICF Core Set, as illustrated in Fig. 1, thusprovides a useful guide for the planning, follow-up, andreporting of health-care interventions, for example, in multi-disciplinary rehabilitation [7]. Because the ICF qualifier isa rating scale, clinicians consider whatever sources of infor-mation are available to perform the rating in each of the ICFcategories included in the corresponding ICF Core Set.

Although categorical profiles across ICF categories aremeaningful and useful, clinicians often rely on summaryscores that integrate different aspects of functioning andwhich are usually constructed by adding up the responsesto different items. Summary scores allow clinicians to esti-mate the overall level of functioning of patients, to monitordisease and rehabilitation management, and to followpatients along the continuum of care and over the life span.

One unanswered question is whether it is possible todevelop clinical measures that provide summary scoresacross a number of ICF categories relevant to patients witha specific condition, such as osteoarthritis (OA). The termclinical measure is used because these measures would beapplied in practice using the ratings of clinicians in eachof the ICF categories contained in a determined ICF CoreSet. These scores would complement the informationgained from categorical profiles and would provide clini-cians with an intuitive overall understanding of a patient’soverall level of functioning. If they could be applied indifferent regions or cultures this would allow cross-culturalcomparisons.

The objective of our article is thus to explore whether it is,in principle, possible to construct clinical measures of func-tioning by integrating information obtained across the ICFcategories using the ICF Core Set of OA as a case in point.

2. Materials and methods

2.1. Study design

Psychometric study using data from a convenience sampleof patients with OA included in an ongoing multicenter

Assessment

Body functions, Body structures, Activity and Parti

b280 Sensation of painb710 Mobility of joint functionsb730 Muscle power functionss750 Structure of lower extremitys730 Structure of upper extremityd440 Fine hand used445 Hand and arm used450 Walkingd540 Dressing

Fig. 1. Part of a categorical profile using the International Classification of Funct

qualifier (0 5 no impairment/restriction; 1 5 mild impairment/restriction; 2 5

4 5 complete impairment/restriction).

international cross-sectional study performed in cooperationwith World Health Organization (WHO), with the aim of col-lecting ICF-based data using different condition-specific ICFCore Sets [8,9]. The convenience sample of OA patients forthis study was derived from 10 study centers in Germany,two in Italy, three in Hungary, one in Serbia, and one inSingapore. Even though data from other countries were avail-able, they were not considered for this study because the sam-ple sizes were too small to perform country-specific analyses.

The study protocol and the informed consent forms wereapproved by the responsible Ethics Committees in eachinvolved center and country. Inclusion criteria for patientswere diagnosis of OA according to the American Collegeof Rheumatology criteria, at least 18-years-of-age, suffi-cient knowledge of the official language of the correspond-ing country, comprehension of the purpose of the study, andsigned informed consent.

2.2. Measures

Sociodemographic data included gender, year of birth,and current work status (Table 1). Disease characteristicsincluded disease duration, type, and location of the OAand noneOA-related comorbidities which were assessedby the Self-Administered Comorbidity Questionnaire [10].

The Comprehensive ICF Core Set for OA representsa selection of 55 ICF categories from the whole ICF [11].The structure of the Comprehensive ICF Core Set for OA isidentical to the structure of the ICF classification, that is, itcontains four different lists of ICF categoriesda list of 13body functions, a list of six body structures, a list of 19 activ-ities and participation categories, and a list of 17 environ-mental factors. For this study, only the data referring to thedimension functioning (38 ICF categories), namely bodyfunctions and structures and activity and participation, wereconsidered.

The level of impairment, limitation, or restriction foreach category was rated with the ICF qualifier (0 5 noimpairment/restriction; 1 5 mild impairment/restriction;2 5 moderate impairment/restriction; 3 5 severe impair-ment/restriction; and 4 5 complete impairment/restriction).Response options of ‘‘8dnot specified’’ and ‘‘9dnot appli-cable’’ are also provided.

ICF Qualifier

cipation 0 1 2 3 4

ioning, Disability, and Health (ICF) Core Set for osteoarthritis and the ICF

moderate impairment/restriction; 3 5 severe impairment/restriction; and

Table 1

Demographic and disease characteristics of 437 patients with OA included in this study

Patient characteristics D H I SRB SGP

N 75 87 76 77 122

Sociodemographic data

Male patients; n (%) 40 (53) 25 (28) 20 (26) 42 (55) 26 (21)

Age: yr; mean (SD) 59 (11) 63 (11) 69 (12) 63 (9) 65 (11)

Current work status

Paid employment; % 45 13 7 17 12

Self-employed; % 4 2 1 3 2

Unemployed (due to OA); % 3 2 1 d 3

Unemployed (due to another reason) 9 d d 8 3

Pensioned due to OA 21 23 3 4 89

Keeping house/homemaker; % 8 2 22 12 71

Retired; % 29 76 68 61 13

Student; % d d d d 2

Disease characteristics

Duration of disease: yr; mean (SD) 9.6 (12.3) 13.1 (14.5) 10.8 (8.7) 8.7 (7.0) 6.1 (4.7)

Type of OA

Primary; n (%) 53 (71) 62 (71) 73 (96) 74 (96) 122 (100)

Secondary; n (%) 12 (16) 25 (28) 3 (4) 3 (4) dLocation of OA

Localized; n (%) 53 (71) 71 (81) 62 (82) 59 (77) 120 (98)

Generalized; n (%) 10 (13) 13 (15) 13 (17) 17 (22) 2 (2)

Knee 40 (53) 35 (40) 51 (67) 60 (78) 119 (98)

Hip 32 (43) 66 (75) 28 (37) 36 (47) 3 (3)

Hands 9 (12) 2 (2) 2 (3) 9 (12) 1 (1)

Shoulder 6 (8) d d d d

Spine 8 (11) d 1 (1) 4 (5) dNumber of comorbiditiesadmean (SD); median 2 (0); 2 4 (2.0); 5 3 (1.5); 3 3 (0.9); 3 3 (0.8); 3

Abbreviations: OA, osteoarthritis; SD, standard deviation; D, Germany; H, Hungary; I, Italy; SRB, Serbia; SGP, Singapore.a Only not OA-related comorbidities resulting in impairment in functioning are included.

901A. Cieza et al. / Journal of Clinical Epidemiology 62 (2009) 899e911

2.3. Data collection procedures

Patient recruitment and data collection were performedby health professionals at each study center. The health pro-fessionals were trained in either a structured one-day work-shop or using a training video provided by researchers ofthe WHO ICF Collaborating Center at the Ludwig-Maximi-lian University in Munich.

2.4. Analysis

Descriptive statistics were used to characterize the studypopulation. Either means or medians are reported, depend-ing on the distribution of the variables according to the Kol-mogoroveSmirnov test [12] with a ! 0.05.

The Rasch model for ordered response options [13,14]was used to examine whether ICF categories ranging acrossall ICF components referring to functioning can be inte-grated in a psychometrically, cross-culturally valid clinicalmeasure of functioning.

The Rasch model implies that the parameters of person’sability and item’s difficulty (ID) are both placed along oneand the same single dimension, which indicates the latenttrait to be measured. In this study, the latent trait to be mea-sured is functioning in OA. The units of this dimension asdefined by the model are logits (the natural log odds of suc-cess vs. failure), which make up an equidistant scale.

For analyses using the Rasch model, the responseoptions ‘‘8dnot specified’’ and ‘‘9dnot applicable’’ weredeleted from the database and considered as missing values.However, the estimation processes within the Rasch frame-work readily deal with missing values [15].

In this study, country-specific analyses were performedbefore pooling data from different countries because con-clusions can only be drawn regarding the pooled data ifthe data of the specific countries fit the model [16]. Thus,the data of countries not fitting the Rasch model were notconsidered in the pooled analyses.

The following properties were studied first for eachcountry separately and then for the pooled datadunidimen-sionality, reliability, item fit, functioning of the responseoptions of the ICF qualifier, the targeting between theICF categories and the person’s abilities, and differentialitem functioning (DIF) with respect to different factors.

The unidimensionality of the ICF categories waschecked by the Z-fit statistic [17] and the itemepersoninteraction chi square (c2) [18]. A significant c2 probabilityof !0.05 indicates misfit of the measure to the model.

Reliability was studied with the Person Separation Index rß,which ranges between zero and one, where the value of oneindicates perfect reproducibility of person placements [19,20].

The test of fit of the individual ICF categories was alsoconducted based on c2 statistics. A Bonferroni-corrected


significance level was applied. Additionally, the standard-ized fit residual values ‘‘z’’ are considered. They shouldrange between �2.5 and þ2.5 to indicate model fit [21].These values provide information on the direction of thedeviation of the data from the model.

The functioning of the response options was studiedbased on threshold estimates for each ICF category. Con-secutive thresholds within an ICF category should haveincreasing values [19,22]. When threshold values are disor-dered, the response options are collapsed, taking into con-sideration frequency distributions and their probabilitycurves, to obtain thresholds that display the intendedincreasing order.

Misfit of items and disordered category thresholds ques-tion the validity of all further conclusions. Therefore, in thepresence of misfitting items, the data were purified in twostepsdby collapsing response options and stepwise dele-tion of ICF categories with the smallest c2 probabilities.The model is recalibrated, and unidimensionality and itemfit are checked again until itemeperson interaction chisquare (c2) does not provide a significant result.

The targeting of the ICF categories for each of the coun-tries and the pooled data were studied by examining therespective distribution of persons’ abilities and items’ diffi-culties of the ICF categories along the latent trait contin-uum. A comparison of the mean person location with themean item location, which is by definition set to zero, indi-cates domain targeting. The smaller the difference, the bet-ter the targeting.

Within the framework of the Rasch model, a scaleshould work in the same way, irrespective of the group be-ing assessed. In the case of functioning, the probability ofpersons (positioned at the same level of functioning) havinga problem in a determined item (or ICF category) should bethe same irrespective of, for example, the country of origin.Items that violate this criterion exhibit DIF. DIF wasstudied in the pooled data using an analysis of variance(ANOVA) of the personeitem deviation residuals, withgender, OA location (knee and hip), and type of patient(inpatient/outpatient/patient treated in a day clinic) andclass intervals (group along the dimension functioning)serving as factors. Only the OA location knee and hip wereconsidered for the DIF analyses, because the sample sizesof the other OA locations were too small. The country oforigin was also used as a factor in the pooled data. Twotypes of DIF can be identifiedduniform and nonuniformDIF. With the former, there is a constant difference betweengroups (ANOVA main effect), and with the latter, the differ-ence varies across the underlying dimension functioning(ANOVA interaction effect). In the pooled data, the ICFcategories with DIF were rendered unique to the countriesthat display DIF, that is, ICF categories were split into asmany items as countries that display DIF. Thus, the ICFcategories were allowed to vary across countries. The exactprocedure has been explained elsewhere in detail [23,24].Those items without DIF for country served as links in

the calibration. Unidimensionality, reliability, item fit, func-tion of the response options of the ICF qualifier, and target-ing were again calculated with the clinical measurecontaining the pooled data and split items. Items still dis-playing misfit to the model were removed. Rasch analysesare conducted using RUMM2020 Software [25].

3. Results

After deleting the response options ‘‘8dnot specified’’and ‘‘9dnot applicable’’ from the database and taking asreference the total number of data that should have beenavailable (number of ICF categories� number of patients)in each country, 2.8% of the data was missing in Germany,5.8% in Hungary, 1.8% in Italy, 0.8% in Serbia, and 1.3%in Singapore. No special pattern could be seen in the distri-bution of the missing data.

3.1. Country-specific analyses

Table 2 presents the overall fit statistics according to the c2

and the Z-fit statistic and the reliability according to the Per-son Separation Index rb after accounting for the disorderedresponse options and after removing misfitting ICF cate-gories in all countries. These results indicate that, for allcountries but Hungary, most of the ICF categories includedin the dimension functioning of the ICF Core Set for OAreliably measured a single dimension in the respective coun-tries. Table 2 also shows the ICF categories out of the original38 that were removed from the analyses because they did notfit the Rasch model in each countrydtwo ICF categories inthe German and Singaporean data, four in the Serbian, sixItalian, and 23 in the Hungarian.

Table 3 presents the ICF categories fitting the Raschmodel, that is, presenting no significant probability associ-ated with the c2, together with their level of difficulty andtheir rank order according to their difficulty. In the contextof this article, easy ICF categories with a low rank ordercorrespond to those that are a problem for most of thepatients and hence allow for a differentiation of personswith no to minor limitations. Difficult ICF categories arethose that differentiate persons with moderate to severe lim-itations in functioning. Table 3 also presents the ICF cate-gories that presented disordered response options and thecorresponding collapse strategy. For example, the collapsestrategy 01233 indicates that response option 4 (completeproblem) of the qualifier scale was collapsed to responseoption 3 (severe problem) and that the corresponding ICFcategory will only have four response options (0 5 noimpairment/restriction; 1 5 mild impairment/restriction;2 5 moderate impairment/restriction; and 3 5 severe tocomplete impairment/restriction).

Figure 2 shows the distribution of persons and the ICFcategories along the measurement continuum of the dimen-sion functioning for the different countries, with ICF cate-gories represented by their respective threshold parameters

Table 2

Overall fit statistics according to the c2 and the Z-fit statistic and the reliability according to the Person Separation Index rb after accounting for the disordered response options and after removing misfitting

ICF categories in all countriesGermany Italy Hungary Serbia Singapore

c2 df P

Item Person

rb c2 df P

Item Person

rb c2 df P

Item Person

rb c2 df P

Item Person

rb c2 df P

Item Person

rb

Zmean

(ZSD)

Zmean

(ZSD)

Zmean

(ZSD)

Zmean

(ZSD)

Zmean

(ZSD)

Zmean

(ZSD)

Zmean

(ZSD)

Zmean

(ZSD)

Zmean

(ZSD)

Zmean

(ZSD)

Model after

collapsing

response

options and

deleting misfitting

ICF categories

90.44 72 0.07 0.12

(1.04)

�0.11

(1.56)

0.95 83.32 64 0.05 0.20

(1.00)

0.02

(1.08)

0.93 40.50 30 0.10 �0.31

(0.71)

�0.25

(1.00)

0.84 82.61 68.00 0.11 0.06

(0.91)

�0.14

(1.26)

0.92 87.46 72 0.10 0.03

(0.88)

�0.14

(0.96)

0.91

Misfitting ICF categories, and those removed from the analyses

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�

b710 Mobility

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functions

�

b715 Stability

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�

b720 Mobility

of bone functions

�

b760 Control

of voluntary

movement

functions

�

b780 Sensations

related to muscles

and movement

functions

�

s740 Structure

of pelvic region

�

s750 Structure

of lower extremity

�

s770 Additional

musculoskeletal

structures related to

movement

� �

d410 Changing

basic body position

� �

d415 Maintaining

body position

�

d430 Lifting and

carrying objects

� �

d450 Walking �d455 Moving

around

� �

d470 Using

transportation

� �

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of goods

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�

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as obtained after the data purification procedures. The re-sponse options span 12.6 logits in the German sample,19.0 in the Italian, 21.7 in the Serbian, 11.5 in the Hungar-ian, and 17.9 in the Singaporean. In general, the mean per-son location, which was �1.01 in the German sample,�0.89 in the Italian, �2.04 in the Serbian, �2.15 in theHungarian, and �1.72 in the Singaporean, reveals that,compared with the mean item location 0.0, the density ofICF categories is low with respect to the density of patientsat the lower level of the continuum in all countries.

The 23 ICF categories from Hungary misfitting the Raschmodel were analyzed separately. Three ICF categories,namely d850 Remunerative employment, d660 Assistingothers, and d770 Intimate relationships did not fit the Raschmodel. After collapsing the response options and omittingthe three named misfitting ICF categories, the overall fit statis-tics were c2

df540585:15, P 5 0.000042, and the Z-fit statisticsfor items and persons, respectively, were Zmean 5 0.15,Zstandard deviation (SD) 5 0.69 and Zmean 5�0.06, ZSD 5 1.01,indicating that a second dimension was measured in Hungary.The Person Separation Index rb was 0.75. Because the datafrom Hungary seem to address two dimensions and not one,as in the other four countries, the data from Hungary werenot considered for the pooled analyses.

3.2. Analyses of pooled data

The analyses of the pooled data containing all countriesbut Hungary, and the 38 ICF categories of the dimensionfunctioning, showed that 17 ICF categories presented disor-dered thresholds in the response options. These 17 ICF cate-gories were accordingly collapsed. Thirty of the 38 ICFcategories showed uniform DIF by country. Only one of the38 also showed nonuniform DIF by country. The eight ICFcategories that did not display DIF were b130 Energy anddrive functions, b134 Sleep functions, b720 Mobility of bonefunctions, b730 Muscle power functions, d640 Doing house-work, d770 Intimate relationships, d910 Community life, andd920 Recreation and leisure. The measure was adjusted forDIF by creating 86 country-specific ICF categories and usingthe eight ICF categories not showing DIF as calibratingitems. This produced a total of 94 items (ICF categories).The response options of 20 of the 94 ICF categories haddisordered thresholds and were collapsed. Twelve country-specific ICF categories and one of the calibrating ICF cate-gories (d920 Recreation and leisure) did not fit the modeland were removed from the analyses. Consequently, a clinicalmeasure with 74 country-specific and seven common ICFcategories was created. The overall fit statistic accordingto the c2 statistic was c2

df54055451:73, P 5 0.054, and theZ-fit statistic was Zmean 5�0.041 (ZSD 5 1.01) for itemsand Zmean 5�0.15 (ZSD 5 1.19) for persons. The PersonSeparation Index rb was 0.92. These results indicate thatthe 81 ICF categories included in the cross-cultural clinicalmeasure addressing functioning in OA reliably measured

Table 3

ICF categories fitting the Rasch model together with their level of difficulty, their rank order according to their difficulty, and the applied collapse strategy

Germany Italy Hungary Serbia Singapore

ICF category ID R CS ID R CS ID R CS ID R CS ID R CS

b130 Energy and drive

functions

�0.32 14 01222 2.02 29 �1.31 4 01123 0.65 23 �0.75 15 01222

b134 Sleep functions 0.04 22 �0.43 15 01122 �0.72 6 01111 �0.68 14 01123 2.00 31 01123

b152 Emotional functions 0.36 30 01223 0.33 18 01123 �0.26 8 01112 0.68 24 1.51 27

b280 Sensation of pain �0.50 9 01123 �1.79 5 �0.63 15 0.94 24 01111

b710 Mobility of joint

functions

�0.91 1 �2.14 4 01222 �1.56 8 �3.64 1

b715 Stability of joint

functions

�0.05 17 �1.78 2 �1.17 13 01223 �2.31 3

b720 Mobility of bone

functions

�0.38 13 �2.31 3 01111 0.59 21 1.74 30 01123

b730 Muscle power functions �0.63 6 1.37 25 0.30 11 0.38 19 0.32 21

b735 Muscle tone functions 1.45 35 1.66 27 1.43 12 01123 3.21 33 01112 0.60 22

b740 Muscle endurance

functions

�0.05 18 1.24 23 1.51 13 01123 1.98 28 01223 �0.85 12

b760 Control of voluntary

movement functions

0.99 33 01123 2.61 31 01123 1.64 26 01111 �0.15 18 01233

b770 Gait pattern functions 0.13 25 �1.11 7 �3.07 1 01123 �2.03 5 �1.87 5

b780 Sensations related to

muscles and movement

functions

0.27 29 �0.49 12 �0.08 17 �0.05 19 01222

s720 Structure of shoulder

region

�0.30 15 01111 1.68 28 01122 0.06 10 01123 1.67 27 01123 3.54 36 01111

s730 Structure of upper

extremity

0.18 26 01122 3.40 32 01123 �0.87 5 01111 2.95 31 01122 2.82 35 01111

s740 Structure of pelvic

region

�0.64 5 01122 0.43 19 01111 �1.64 7 1.44 26 01111

s750 Structure of lower

extremity

0.10 24 01123 0.53 20 01123 �2.58 2 01123 �1.57 10

s770 Additional

musculoskeletal

structures related

to movement

0.52 32 0.32 18 2.12 33 01111

s799 Structures related to

movement, unspecified

0.24 27 01233 2.03 30 01111 3.29 14 01112 1.41 25 01223 2.32 34 01111

d410 Changing

basic body position

�0.23 16 �0.87 9 �2.31 3

d415 Maintaining a body

position

0.10 23 �0.69 10 �1.96 6 �1.69 8

d430 Lifting and carrying

objects

�0.67 4 �2.09 4 01123 �2.03 4

d440 Fine hand use 0.02 21 01111 1.38 26 01122 3.48 15 01112 0.64 22 01122 1.59 29 01111

d445 Hand and arm use �0.48 10 01111 1.36 24 01122 0.00 9 01111 2.03 29 01223 1.26 25 01111

d450 Walking �0.03 19 �3.09 2 �1.23 11 �1.79 7

d455 Moving around �4.06 1 01122 �5.24 1 �2.75 2

d470 Using transportation �0.39 12 01233 �1.04 8 �1.80 6

d475 Driving �0.87 2 01122 �1.38 10 01111 2.00 32 01111

d510 Washing oneself 2.30 36 0.68 22 �0.59 7 01123 2.63 30 01223 �0.41 17 01111

d530 Toileting 1.39 34 �0.46 14 01111 2.97 32 01223 �0.75 14

d540 Dressing 0.45 31 01233 �0.68 11 �1.45 3 01123 0.48 20 1.58 28

d620 Acquisition of goods

and services

0.27 28 �1.20 6 01222 �0.49 16 �1.33 11

d640 Doing housework �0.53 8 �0.49 13 �1.20 12 �0.65 16

d660 Assisting others �0.81 3 01122 0.17 20 01111

d770 Intimate relationships �0.40 11 01111 0.55 21 01111 3.51 34 01112 0.91 23 01111

d910 Community life �0.01 20 �0.18 17 01222 �1.49 9 01222 �0.81 13

d920 Recreation and leisure �0.60 7 �0.25 16 �1.67 9

Abbreviations: ICF, International Classification of Functioning, Disability, and Health; D, Germany; I, Italy; H, Hungary; SRB, Serbia; SGP, Singapore;

ID, estimate of the item difficulty; R, rank order according to item-difficulty estimation; CS, collapsing strategy applied in case of disordered thresholds.



a single dimension. The distribution of persons and the ICFcategories along the measurement continuum is also pre-sented in Fig. 2. The response options span over 9.4 logits.The mean person location was�1.38, which, compared withthe mean item location 0.0, reveals again that the density ofICF categories is low with respect to the density of patientsat the lower level of the continuum.

Table 4 shows the ICF categories fitting the Raschmodel and remaining in the cross-cultural clinical measure,together with their level of difficulty, their rank order ac-cording to their level of difficulty, and the collapse strategyfor the response options of the ICF qualifier after adjustingfor DIF at the country level. To show which ICF categoriesare split by country, the letter of the country is written nextto the ICF category. For example, b152 Emotional func-tionsdI is an additional item (or ICF category) in the clin-ical measure that contains data exclusively from the Italiansample. B152 Emotional functionsdD, SRB, & SGP con-tains data from the remaining countries of Germany, Ser-bia, and Singapore.

Fig. 2. Personeitem threshold distribution of the Ger

The first column of Table 5 shows the raw scores that canbe obtained for a single OA patient by adding the ratings ofthe common and country-specific ICF categories. The secondcolumn contains the same values transformed to a more intu-itive interval scale ranging from zero to 100. The third to thesixth columns contain the corresponding country-specificlogits values. Thus, if a person obtains a raw score of 25,his/her score on the interval scale from 0 to 100 wouldbe 30 and the logit value approximately �2. A comparisonamong persons with OA within the same country and amongcountries is in this way possible. This specially applies for thevalues in the middle range of the scale, where the larger num-ber of patients is and an examination of the differences in log-its shows no clinical relevance according to the boundariesdefined by Lai and Eton [26].

4. Discussion

We found that, in principle, it is possible to constructcross-cultural clinical measures of functioning by

man, Italian, Serbian, and Singaporean samples.

Table 4

ICF categories fitting the Rasch model and remaining in the cross-

cultural clinical measure together with their level of difficulty, their

rank order according to their level of difficulty and the applied collapse

strategy

ID R CS

b130 Energy and drive functionsdD, I,

SRB, & SGP

0.94 61

b134 Sleep functionsdD, I, SRB, & SGP 0.36 53 01233

b152 Emotional functionsdD, SRB, & SGP 0.42 55

b152 Emotional functionsdI �0.32 35

b280 Sensation of paindD �0.63 29 01233

b280 Sensation of paindI �2.11 5 01233

b280 Sensation of paindSRB 0.53 56 01233

b280 Sensation of paindSGP 2.40 75 01233

b710 Mobility of joint functionsdD �1.15 19

b710 Mobility of joint functionsdI �1.05 22

b710 Mobility of joint functionsdSRB �1.17 18

b710 Mobility of joint functionsdSGP �3.43 2

b715 Stability of joint functionsdD & SRB �0.25 36

b715 Stability of joint functionsdSGP �2.05 6

b720 Mobility of bone functionsdD, I, SRB, &

SGP

0.26 50 01233

b730 Muscle power functionsdD, I, SRB, & SGP �0.55 30

b735 Muscle tone functionsdD & I 1.40 67

b735 Muscle tone functionsdSRB 3.69 80 01222

b735 Muscle tone functionsdSGP 0.77 59

b740 Muscle endurance functionsdD, I, & SGP 0.03 42 01233

b740 Muscle endurance functionsdSRB 2.44 76 01233

b760 Control of voluntary movement functionsdD

& SGP

0.28 51 01222

b760 Control of voluntary movement functionsdI 0.60 57 01222

b770 Gait pattern functionsdD 0.01 41

b770 Gait pattern functionsdI �1.45 14

b770 Gait pattern functionsdSGP �1.65 10

b780 Sensations related to muscles and movement

functionsdD

0.13 46


functionsdI & SRB

�0.84 25


functionsdSGP

0.17 48 01222

d410 Changing basic body positiondD & I �0.64 28

d410 Changing basic body positiondSRB �1.68 9

d415 Maintaining a body positiondI, SRB, & SGP �1.26 17

d415 Maintaining a body positiondD �0.04 40

d430 Lifting and carrying objectsdD �0.86 24

d430 Lifting and carrying objectsdSRB & SGP �1.70 8

d440 Fine hand usedD, I, & SRB 2.29 73 01222

d440 Fine hand usedSGP 1.82 71 01111

d445 Hand and arm usedD �0.12 37 01222

d445 Hand and arm usedI 1.05 64 01222

d445 Hand and arm usedSRB 0.64 58 01111

d445 Hand and arm usedSGP 1.81 70 01222

d450 WalkingdD & SRB �0.42 33

d450 WalkingdI �3.37 3

d450 WalkingdSGP �1.58 13

d455 Moving arounddI 0.22 49

d455 Moving arounddSRB �4.90 1 01233

d455 Moving arounddSGP 0.03 43

d470 Using transportationdD & SRB 2.04 72

d470 Using transportationdI 1.09 65

d470 Using transportationdSGP �1.41 16 01233

d475 DrivingdSRB �0.79 26 01111

d510 Washing oneselfdD & I 2.34 74

d510 Washing oneselfdSRB & SGP �0.36 34 01111

(Continued)

Table 4

Continued

ID R CS

d530 ToiletingdD & I 0.98 62

d530 ToiletingdSRB 1.52 68 01222

d530 ToiletingdSGP �0.52 31

d540 DressingdD �1.07 21 01222

d540 DressingdI �1.00 23

d540 DressingdSRB 0.98 63

d540 DressingdSGP 1.78 69 01111

d620 Acquisition of goods and servicesdD, I,

& SRB

�0.11 39

d620 Acquisition of goods and servicesdSGP �1.08 20

d640 Doing houseworkdD, I, SRB, & SGP �0.72 27

d660 Assisting othersdD �2.50 4 01111

d660 Assisting othersdI, SRB, & SGP �0.51 32 01

d770 Intimate relationshipsdD, I, SRB, & SGP 0.06 44 01111

d910 Community lifedD, I, SRB, & SGP �1.64 11 01111

s720 Structure of shoulder regiondD, I, & SRB 0.79 60 01222

s730 Structure of upper extremitydD & SRB 1.38 66 01233

s730 Structure of upper extremitydI 2.94 77 01233

s740 Structure of pelvic regiondI 0.10 45 01111

s740 Structure of pelvic regiondSRB �1.73 7 01222

s750 Structure of lower extremitydD �0.12 38 01233

s750 Structure of lower extremitydI 0.16 47 01233

s750 Structure of lower extremitydSRB & SGP �1.63 12 01233

s770 Additional musculoskeletal structures related

to movementdD

�1.43 15


to movementdSRB

0.32 52


to movementdSGP

3.05 78

s799 Structures related to movement,

unspecifieddD

0.36 54 01222

s799 Structures related to movement,

unspecifieddSRB & SGP

3.18 79 01222

s799 Structures related to movement, unspecifieddI 4.47 81 01222

Abbreviations: ICF, International Classification of Functioning, Dis-

ability, and Health; ID, estimate of the item difficulty; R, rank order ac-

cording to item-difficulty estimation; CS, collapsing strategy applied in

case of disordered thresholds; D, Germany; I, Italy; SRB, Serbia; SGP,

Singapore.


integrating information obtained from rating the ICF cate-gories of an ICF Core Set. This study also provides insightsrelevant for their development.

On the one hand, the clinical measure developed withinthis study contains ICF categories throughout all ICF com-ponents of functioning. That means that it integrates ICFcategories not only with respect to commonly used activityand participation domains (e.g. walking and maintainingor changing body position), which are the main conceptsaddressed in the context of current OA-specific patient-reported outcomes [27], but also ICF categories from thecomponents body functions and structures, and the lesscommonly used activity and participation domains, suchas community life.

On the other hand, the clinical measure does not containsome ICF categories that represent relevant areas of function-ing of patients with OA. Two good examples of these ICFcategories are d850 Remunerative employment and d920Recreational activities. Neither d850 Remunerative

Table 5

Raw scores that can be obtained for a single OA patient by adding the

ratings of the common and country-specific ICF categories, the same

values transformed to a more intuitive interval scale ranging from zero

to 100 and the corresponding country-specific logits values

Raw-score Score 0e100 ID I SRB SGP

0 0.0 �6.87 �11.55 �16.22 �10.62

1 5.1 �6.02 �8.49 �11.13 �8.24

2 8.7 �5.43 �6.97 �8.38 �7.01

3 11.2 �5.01 �6.21 �6.89 �6.37

4 13.1 �4.69 �5.66 �6.21 �5.91

5 14.7 �4.42 �5.23 �5.71 �5.53

6 16.1 �4.19 �4.89 �5.32 �5.22

7 17.3 �3.99 �4.59 �5 �4.94

8 18.5 �3.8 �4.34 �4.72 �4.69

9 19.4 �3.64 �4.11 �4.48 �4.46

10 20.4 �3.48 �3.9 �4.26 �4.25

11 21.3 �3.33 �3.72 �4.07 �4.06

12 22.1 �3.2 �3.55 �3.89 �3.88

13 22.9 �3.07 �3.39 �3.73 �3.71

14 23.6 �2.95 �3.24 �3.57 �3.55

15 24.3 �2.83 �3.1 �3.43 �3.39

16 25.0 �2.72 �2.96 �3.29 �3.25

17 25.6 �2.62 �2.84 �3.16 �3.11

18 26.2 �2.51 �2.71 �3.03 �2.97

19 26.8 �2.41 �2.6 �2.91 �2.84

20 27.4 �2.32 �2.49 �2.79 �2.71

21 27.9 �2.23 �2.38 �2.67 �2.59

22 28.5 �2.13 �2.28 �2.56 �2.46

23 29.0 �2.05 �2.18 �2.45 �2.35

24 29.5 �1.96 �2.08 �2.35 �2.23

25 30.0 �1.88 �1.99 �2.25 �2.11

26 30.5 �1.79 �1.89 �2.15 �2

27 31.0 �1.71 �1.8 �2.05 �1.89

28 31.5 �1.63 �1.72 �1.95 �1.78

29 32.0 �1.55 �1.63 �1.86 �1.67

30 32.4 �1.48 �1.55 �1.76 �1.57

31 32.9 �1.4 �1.46 �1.67 �1.46

32 33.3 �1.33 �1.38 �1.58 �1.36

33 33.8 �1.25 �1.3 �1.49 �1.26

34 34.2 �1.18 �1.22 �1.41 �1.16

35 34.6 �1.11 �1.15 �1.32 �1.06

36 35.1 �1.04 �1.07 �1.24 �0.96

37 35.5 �0.97 �0.99 �1.16 �0.87

38 35.9 �0.9 �0.92 �1.08 �0.77

39 36.3 �0.83 �0.85 �1 �0.68

40 36.7 �0.76 �0.77 �0.92 �0.59

41 37.2 �0.69 �0.7 �0.84 �0.5

42 37.5 �0.63 �0.63 �0.76 �0.4

43 37.9 �0.56 �0.56 �0.69 �0.32

44 38.3 �0.5 �0.49 �0.61 �0.23

45 38.7 �0.43 �0.41 �0.54 �0.14

46 39.1 �0.36 �0.34 �0.46 �0.05

47 39.5 �0.3 �0.27 �0.39 0.04

48 39.9 �0.24 �0.2 �0.31 0.12

49 40.3 �0.17 �0.13 �0.24 0.21

50 40.6 �0.11 �0.06 �0.17 0.29

51 41.1 �0.04 0.01 �0.1 0.38

52 41.4 0.02 0.08 �0.02 0.46

53 41.8 0.08 0.15 0.05 0.55

54 42.2 0.15 0.22 0.12 0.63

55 42.6 0.21 0.29 0.2 0.72

56 42.9 0.27 0.36 0.27 0.8

57 43.4 0.34 0.44 0.34 0.89

58 43.7 0.4 0.51 0.41 0.97

(Continued)

Table 5

Continued

Raw-score Score 0e100 ID I SRB SGP

59 44.1 0.47 0.58 0.49 1.05

60 44.5 0.53 0.66 0.56 1.14

61 44.9 0.59 0.73 0.64 1.23

62 45.3 0.66 0.81 0.71 1.31

63 45.7 0.73 0.88 0.79 1.4

64 46.1 0.79 0.96 0.86 1.49

65 46.5 0.86 1.04 0.94 1.58

66 46.8 0.92 1.12 1.02 1.68

67 47.3 0.99 1.2 1.1 1.78

68 47.7 1.06 1.28 1.18 1.87

69 48.1 1.13 1.36 1.26 1.98

70 48.5 1.2 1.45 1.35 2.09

71 48.9 1.27 1.54 1.44 2.2

72 49.4 1.34 1.63 1.52 2.31

73 49.8 1.42 1.72 1.61 2.44

74 50.3 1.49 1.81 1.71 2.57

75 50.8 1.57 1.91 1.8 2.71

76 51.2 1.65 2 1.9 2.86

77 51.7 1.73 2.11 2 3.02

78 52.2 1.81 2.21 2.11 3.2

79 52.7 1.89 2.32 2.22 3.39

80 53.2 1.97 2.43 2.33 3.61

81 53.7 2.06 2.55 2.45 3.85

82 54.2 2.15 2.68 2.58 4.11

83 54.8 2.24 2.81 2.71 4.4

84 55.4 2.34 2.95 2.85 4.7

85 56.0 2.44 3.09 3 5.04

86 56.6 2.54 3.25 3.16 5.47

87 57.2 2.64 3.42 3.34 6.23

88 57.8 2.75 3.61 3.53 7.48

89 58.6 2.87 3.82 3.74 8.52

90 59.3 2.99 4.06 3.98 9.39

91 60.1 3.12 4.33 4.26

92 60.9 3.25 4.66 4.59

93 61.8 3.4 5.07 4.99

94 62.7 3.55 5.58 5.5

95 63.7 3.72 6.21 6.16

96 64.8 3.91 6.91 6.88

97 66.1 4.12 7.64 7.52

98 67.5 4.35 8.39 8.13

99 69.1 4.62 9.24 8.76

100 71.1 4.96 10.21 9.53

101 73.7 5.38 10.46

102 77.1 5.95

103 82.2 6.8

104 89.5 8.02

105 100.0 9.76

Abbreviations: OA, osteoarthritis; ICF, International Classification of

Functioning, Disability, and Health; D, Germany; I, Italy; SRB, Serbia;

SGP, Singapore.

The formula Y 5 mþ (s� location) was used to transform the logits

scale along with the difficulty of the ICF categories and the ‘‘persons’ abil-

ities’’ are placed and with the arbitrary mean of zero to a more meaningful

scale ranging from zero to 100. s 5 (desired range)/(current range) and

m 5 (lowest desired value)� (current lowest value� s) [25].


employment nor d920 Recreational activities would be con-sidered to create a score on the level of functioning ofpatients with OA, because they showed positive fit values, in-dicating that they do not differentiate properly regarding thelevel of functioning of patients with OA. However, misfit


does not automatically imply a lack of validity. It is wellknown that work disability and difficulty with social activi-ties and roles are among the most severe and important com-plaints, as well as among the most relevant outcomes andhealth indicators associated with OA [28e30]. Therefore,both of them should definitely be taken into account for cre-ating a categorical profile, even though they will not be con-sidered for the creation of a score on the level of functioning.This example makes clear that clinical measures complementcategorical profiles based on ICF Core Sets but do not replacethem. A categorical profile is used in clinical practice as, forexample, a starting point for the rehabilitative managementof patients [7]. However, if clinicians also wish to obtaina score for the level of their patient’s functioning and com-pare it with the level of functioning of other patients, onlyICF categories contained in the clinical measure need beconsidered.

It is also important to mention that, when using the clin-ical measure in practice, the consequence of the splittingstrategy would be that country-specific items like ICF cat-egory b152 Emotional functionsdItaly would only containdata proceeding from Italy. Patients proceeding from othercountries would have missing data in this ICF category.Similarly, patients proceeding from Italy would have miss-ing data in the ICF category b152 Emotional functionsdGermany (D), Serbia (SRB), & Singapore (SGP). In thislast ICF category, only patients from Germany, Serbia,and Singapore would have values about the level of impair-ment in this ICF category.

In clinical every-day practice, a clinician who uses theICF Core Set for OA to describe the level of functioningand disability of her/his OA patients would first rate thelevel of impairment or limitation of a determined patientin each of the ICF categories included in the ICF CoreSet using all available clinical information. The result ofit is a profile describing all the problems of the patientin all relevant areas of functioning and disability. She orhe would use this profile as basis for planning her/his inter-ventions. However, based on this profile, she or he wouldnot have the information regarding what is the overall levelof functioning and disability of this concrete patient withregard to her/his other patients or with regard to otherOA patients in her/his country or in other countries. Toobtain this information, a clinician living in, for example,Italy would sum the values of all ICF categories containingan I from Italy in Table 4 to obtain a single score. Thecomparison of this score to the score of other patient orgroup of patients would provide the clinician with an intu-itive value of the overall level of functioning and disabilityof this concrete patient. A clinician from Germany wouldproceed in the same way. However, she or he would onlysum the value of ICF categories marked with D from Ger-many. The score of the patient in Italy could be comparedwith the score of the patient in Germany because all theICF categories have been calibrated in the same dimensionbased on the Rasch analyses presented in this article.

If two persons with OA in Italy have the scores 25 and 50in the interval scale ranging from zero to 100, one could saythat the person with a score of 25 has twice the level of func-tioning of the person with 50 (where zero means no problemin functioning). The same can be said of two persons in dif-ferent countries, namely that if a person from Italy has a scoreof 25, she or he has twice the level of functioning of a personwith 50 from any other of the three countries.

The purpose of this article was to study whether it is pos-sible, in principle, to construct cross-cultural clinical mea-sures of functioning by integrating information obtainedfrom rating the ICF categories of an ICF Core Set. The resultsregarding unidimensionality, reliability, and item fit of thefinal model with the pooled data confirm that, in principle,this is possible. However, it is important to emphasize thatit was not our intent to construct a definitive cross-culturalinstrument to assess functioning and disability in personswith OA. We are aware that a number of issues need to beaddressed for such construction.

Because the ICF is a classification developed for interna-tional application, the cross-cultural variability found in alarge number of ICF categories is a key aspect to be thoroughlyinvestigated. In our study, different factors might contributeto the cross-cultural variability. First, health professionalsfrom different backgrounds collected the data in the differentcountries. For example, the data from Italy were collectedexclusively by psychologists and the data from Singaporeexclusively by doctors. In the other countries, different healthprofessions were always involved in the data collection. Oneprofession, however, was always dominant. An ICF categorymay be evaluated in different ways by different healthprofessionals. In future studies, DIF for the health professionshould be studied. Secondly, the case record forms in whichthe data were documented, were in English. The differentlevels of knowledge of the English language among thehealth professionals collecting the data in the differentcountries may lead to ICF categories being interpreted indifferent ways.

There are also further factors that, although not specific ofour study, might also contribute to cross-country variability.One of these refers to the titles and definitions of the ICFcategories that are often very broad, allowing considerablemargin for different interpretations. Investigations in whichhealth professionals from different countries documentwhich aspects of a determined ICF category are being consid-ered when rating it, may contribute to explain some aspectsrelated to the cross-cultural variability.

Cross-cultural variability can also be caused by actualcultural differences in the ways people work, bathe, go tothe toilet, and get dressed. This result was also suggestedby the investigators of the PRO-ESOR project [24] in whichDIF was found in a large number of items of the FunctionalIndependence Measure [31] when studying its cross-culturalvalidity in three different European countries. Even thoughthe number of health-status measures in which the cross-cultural validity has been studied is still limited, it seems that


DIF is found in most of them [23,32e35]. Thus, it seemsthat cross-cultural variability is not an ICF-specific issueand that much can be learned from the suggestions of otherinvestigators.

To reduce cross-cultural differences and response biaswhen using the ICF during clinical assessment, codingand interview guidelines and application manuals shouldbe used, such as the Procedural Manual and Guide for theStandardized Application of the ICF [36] currently beingdeveloped by the American Psychological Association incollaboration with WHO.

If cross-culturally valid clinical measures based on theICF Core Sets are developed in the future, results referringto the measurement of different dimensions within specificcountries need also be further investigated. We found thattwo dimensions were measured in Hungary. The factors withwhich DIF analyses were performed in the country-specificsamples do not help clarify whether there is a concrete factorresponsible for the two dimensions (data not shown). In Hun-gary, only the ICF categories s740 Structure of the pelvic re-gion and s750 Structure of the lower extremity, presented DIFfor location of OA (hip/knee). No ICF categories presentedDIF for gender, type of patient (in, out, or day-clinic patient),or level of pain.

The decision to use a unidimensional model in this studywas based on two considerations: First, according to the con-ceptual model of functioning and disability on which the ICFis based, functioning is an umbrella term for body functionsand structures, activities and participation. Second, at somelevel of precision, no construct is unidimensional, whereas atsome level of precision, any construct is [18]. Multidimen-sional models, such as the partial credit multidimensionalrandom coefficients multinomial logit [37] models, may beused in the future to analyze ICF-based data to investigatewhether a multidimensional model would be more appropri-ate for the creation of clinical measures of functioning. It isconceivable that multidimensional analysis of other popula-tions will reveal subdimensions of functioning in OA.

Last but not least, the sample size, and how representativeit is, are important issues that have to be considered whendeveloping valid cross-cultural clinical measures of func-tioning. One has to consider that we performed the coun-try-specific analyses with sample sizes ranging from 75 inGermany to 122 in Singapore and including 38 ICF cate-gories. The pooled analyses were performed with the dataof 437 OA patients and 74 ICF categories. The proportionof patients in relation to ICF categories is low, possibly lead-ing to unstable item calibrations [19]. To be 99% confidentthat the ‘‘true’’ item difficulty is within one logit of its re-ported estimate, the estimate needs to have a standard error!0.385 logits [26]. Only three of the estimates of the levelof difficulty of the ICF categories included in the clinicalmeasure (3.7%) and 10 (2.9%) of the estimates of the pa-tients’ abilities present standard errors O0.385. However,based on the small sample sizes, not even the standard errorof the estimates of the ICF categories and the patients can

be considered a proof on the robustness of the results. There-fore, the results of this investigation have to be interpretedwith caution until comprehensive development of a valid,cross-cultural clinical measure for OA is undertaken, includ-ing the consideration of all of the discussed challenges.

5. Conclusion

For the first time, we studied whether it is possible to con-struct a cross-cultural clinical measure of functioning that in-tegrates ICF categories referring not only to activity andparticipation domains but also to body functions and struc-tures. If the results of this investigation are supported and val-idated in the future, and clinical measures are developed foruse in clinical routine, clinicians and their teams will be ableto calculate a score for the overall level of functioning of theirpatients from an ICF-based categorical profile fundamentalfor patient management in clinical practice. They will be ableto compare their patients’ scores to the scores of patients inother centers and countries. The results of this investigationare promising and can contribute to the acceptance and use-fulness of the ICF in clinical practice.

Acknowledgments

The authors thank Alicia Garza, Michaela Kirschneck,Andrea Glassel, and Heinrich Gall for their support conduct-ing this study, and the following collaboration partners fromthe ICF Research Branch, in the centers of which the datawere collected. Germany: Tanja Bossmann (Physioklinikim Aitrachtal), Dr. med. Karin Kucharski Botchen (MedicalPark St. Hubertus), Dr. med. FRCS Marcus-Stefan Gnad(Sachensklinik Bad Lausick), Prof. Dr. med. Thomas Henze(Klinik am Regenbogen), Dr. med. Birgit Leibbrand (Salze-talklinik), Dr. med. Franz-Josef Ludwig (Rehazentrum BadEilsen), Dr. med. Susanne Rennert-Pisternick (Rehabilita-tionsklinik Bad Steben Klinik Franken), Dr. Rudolf Siegert(Klinikum Bremen-Ost GmbH), Dr. med. Stephan Vick(Moorbad Bad Doberan), and Dr. med. Mathias Wiezoreck(Praxis fur Physiklische Medizin und Rehabilitation). Italy:Dr. Maurizio Maini (Instituto Scientifico di Montescano,Foundation S. MaugeridIRCCS) and Prof. Fausto Salaffi(Cattedra Reumatologia-Universita Politecnica delle Mar-che). Hungary: Prof. Lajos Kullmann (National Institutefor Medical Rehabilitation), Dr. Markus Ilona (NationalInstitute of Rheumatology), and Dr. Judit Ortutay (NationalInstitute of Rheumatology and Physiotherapy). Serbia:Dr. Nemeth Emese (Dr. Gero Istvan Egeszsegugyi Hospital).Singapore: Dr. Shu Chuen Li (University of Singapore). Thisstudy was partially supported by a grant from the EuropeanLeague Against Rheumatism (EULAR).

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