Monika Hifinger1, Polina Putrik2, Sofia Ramiro3, András P Keszei4, Ihsane Hmamouchi5, Maxime Dougados6, Laure Gossec7, Annelies Boonen8. 1. Rheumatology, Maastricht University Medical Center, CAPHRI, Maastricht, Department of Internal Medicine, Maastricht University Medical Centre, monikahifinger@gmx.de. 2. Rheumatology, Maastricht University Medical Center, CAPHRI, Maastricht, Health Promotion, Maastricht University, CAPHRI, Maastricht. 3. Rheumatology, Leiden University Medical Center, Leiden, the Netherlands. 4. Medical Informatics, Uniklinik RWTH Aachen University, Aachen, Germany. 5. Mohammed V University, Faculty of Medicine, Laboratory of Clinical Research and Epidemiology, Rheumatology Department, El Ayachi Hospital, Rabat, Morocco. 6. Paris Descartes University, Rheumatology Department, Cochin Hospital, Assistance Publique-Hôpitaux de Paris, INSERM (U1153): Clinical Epidemiology and Biostatistics, PRES Sorbonne, Paris-Cité and. 7. Department of Rheumatology, Sorbonne Universités, UPMC Univ Paris 06, Institut Pierre Louis d' Epidémiologie et de Santé Publiquez, GRC-UPMC 08 (EEMOIS); AP-HP, Pitié Salpêtrière Hospital, Paris, France. 8. Rheumatology, Maastricht University Medical Center, CAPHRI, Maastricht, Department of Internal Medicine, Maastricht University Medical Centre.
Abstract
OBJECTIVES: To investigate the relationship between country of residence and fatigue in RA, and to explore which country characteristics are related to fatigue. METHODS: Data from the multinational COMORA study were analysed. Contribution of country of residence to level of fatigue [0-10 on visual analogue scale (VAS)] and presence of severe fatigue (VAS ⩾ 5) was explored in multivariable linear or logistic regression models including first socio-demographics and objective disease outcomes (M1), and then also subjective outcomes (M2). Next, country of residence was replaced by country characteristics: gross domestic product (GDP), human development index (HDI), latitude (as indicator of climate), language and income inequality index (gini-index). Model fit (R(2)) for linear models was compared. RESULTS: A total of 3920 patients from 17 countries were included, mean age 56 years (s.d. 13), 82% females. Mean fatigue across countries ranged from 1.86 (s.d. 2.46) to 4.99 (s.d. 2.64) and proportion of severe fatigue from 14% (Venezuela) to 65% (Egypt). Objective disease outcomes did not explain much of the variation in fatigue ([Formula: see text] = 0.12), while subjective outcomes had a strong negative impact and partly explained the variation in fatigue ([Formula: see text]= 0.27). Country of residence had a significant additional effect (increasing model fit to [Formula: see text] = 0.20 and [Formula: see text] = 0.36, respectively). Remarkably, higher GDP and better HDI were associated with higher fatigue, and explained a large part of the country effect. Logistic regression confirmed the limited contribution of objective outcomes and the relevant contribution of country of residence. CONCLUSION: Country of residence has an important influence on fatigue. Paradoxically, patients from wealthier countries had higher fatigue.
OBJECTIVES: To investigate the relationship between country of residence and fatigue in RA, and to explore which country characteristics are related to fatigue. METHODS: Data from the multinational COMORA study were analysed. Contribution of country of residence to level of fatigue [0-10 on visual analogue scale (VAS)] and presence of severe fatigue (VAS ⩾ 5) was explored in multivariable linear or logistic regression models including first socio-demographics and objective disease outcomes (M1), and then also subjective outcomes (M2). Next, country of residence was replaced by country characteristics: gross domestic product (GDP), human development index (HDI), latitude (as indicator of climate), language and income inequality index (gini-index). Model fit (R(2)) for linear models was compared. RESULTS: A total of 3920 patients from 17 countries were included, mean age 56 years (s.d. 13), 82% females. Mean fatigue across countries ranged from 1.86 (s.d. 2.46) to 4.99 (s.d. 2.64) and proportion of severe fatigue from 14% (Venezuela) to 65% (Egypt). Objective disease outcomes did not explain much of the variation in fatigue ([Formula: see text] = 0.12), while subjective outcomes had a strong negative impact and partly explained the variation in fatigue ([Formula: see text]= 0.27). Country of residence had a significant additional effect (increasing model fit to [Formula: see text] = 0.20 and [Formula: see text] = 0.36, respectively). Remarkably, higher GDP and better HDI were associated with higher fatigue, and explained a large part of the country effect. Logistic regression confirmed the limited contribution of objective outcomes and the relevant contribution of country of residence. CONCLUSION: Country of residence has an important influence on fatigue. Paradoxically, patients from wealthier countries had higher fatigue.
Authors: S Yilmaz-Oner; B Ilhan; M Can; F Alibaz-Oner; O Polat-Korkmaz; G Ozen; G Mumcu; H M Kremers; S Tuglular; H Direskeneli Journal: Z Rheumatol Date: 2017-12 Impact factor: 1.372
Authors: Vivek Nagaraja; Constance Mara; Puja P Khanna; Rajaie Namas; Amber Young; David A Fox; Timothy Laing; William J McCune; Carol Dodge; Debra Rizzo; Maha Almackenzie; Dinesh Khanna Journal: Qual Life Res Date: 2017-10-05 Impact factor: 4.147
Authors: Kristien Van der Elst; Ann Bremander; An De Groef; Ingrid Larsson; Elke G E Mathijssen; Johanna E Vriezekolk; Rene Westhovens; Yvonne J L van Eijk-Hustings Journal: BMJ Open Date: 2019-03-27 Impact factor: 2.692
Authors: Kristien Van der Elst; Elke G E Mathijssen; Ellen Landgren; Ann Bremander; An De Groef; Elisabet Lindqvist; Maria Nylander; Alma Peters; Frank Van den Hoogen; Yvonne van Eijk-Hustings; Gerard Verhoeven; Johanna E Vriezekolk; Rene Westhovens; Ingrid Larsson Journal: RMD Open Date: 2020-09