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Low back pain in 17 countries, a Rasch analysis of the ICF core set for low back pain

CHENG ROE M; BAUTZ HOLTER E; CIEZA A
INT J REHABIL RES , 2013, vol. 36, n° 1, p. 38-47
Doc n°: 161550
Localisation : Documentation IRR

D.O.I. : http://dx.doi.org/DOI:10.1097/MRR.0b013e328357883c
Descripteurs : CE51 - LOMBALGIE, JQ - CIF

Previous studies indicate that a worldwide measurement tool may be developed
based on the International Classification of Functioning Disability and Health
(ICF) Core Sets for chronic conditions. The aim of the present study was to
explore the possibility of constructing a cross-cultural measurement of
functioning for patients with low back pain (LBP) on the basis of the
Comprehensive ICF Core Set for LBP and to evaluate the properties of the ICF Core
Set. The Comprehensive ICF Core Set for LBP was scored by health professionals
for 972 patients with LBP from 17 countries. Qualifier levels of the categories,
invariance across age, sex and countries, construct validity and the ordering of
the categories in the components of body function, body structure, activities and
participation were explored by Rasch analysis. The item-trait chi2-statistics
showed that the 53 categories in the ICF Core Set for LBP did not fit the Rasch
model (P<0.001). The main challenge was the invariance in the responses according
to country. Analysis of the four countries with the largest sample sizes
indicated that the data from Germany fit the Rasch model, and the data from
Norway, Serbia and Kuwait in terms of the components of body functions and
activities and participation also fit the model. The component of body functions
and activity and participation had a negative mean location, -2.19 (SD 1.19) and
-2.98 (SD 1.07), respectively. The negative location indicates that the ICF Core
Set reflects patients with a lower level of function than the present patient
sample. The present results indicate that it may be possible to construct a
clinical measure of function on the basis of the Comprehensive ICF Core Set for
LBP by calculating country-specific scores before pooling the data.

Langue : ANGLAIS

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