Objective To explore the independent contribution of individual-level and country level socioeconomic status (SES) determinants to disease activity and physical function in patients with spondyloarthritis (SpA).
Methods Data from the cross-sectional, multinational (n=22 countries worldwide) COMOSPA (COMOrbidities in SpA) study were used. Contribution of individual SES factors (gender, education) and country of residence to Ankylosing Spondylitis Disease Activity Score (ASDAS) and Bath Ankylosing Spondylitis Functional Index (BASFI) was explored in multilevel regression models, adjusting for clinical and demographic confounders. Next, the additional effects of national macroeconomic indicators (gross domestic product [GDP], Human Development Index, healthcare expenditure and Gini index) were explored. The mediating role of uptake of biologic disease-modifying antirheumatic drugs between education or GDP and ASDAS was explored by testing indirect effects.
Results In total, 3370 patients with SpA were included: 65% were male, with a mean age of 43 (SD 14), ASDAS of 2.0 (SD 1.1) and BASFI score of 3.1 (SD 2.7). In adjusted models, patients with low education and female patients had an OR of 1.7 (95% CI 1.3 to 2.2) and an OR of 1.7 (95% CI 1.4 to 2.0), respectively, of having ASDAS ≥2.1. They also reported slightly worse function. Large country differences were observed independent of individual SES and clinical confounders. Patients from less SES developed countries have worse ASDAS, while patterns for BASFI were insignificant. Uptake of biologicals did not mediate the relationship between individual-level or country-level SES and disease activity.
Conclusions Individual-level and country-level health inequalities exist also among patients with SpA. Women and lower educated persons had worse disease activity and somewhat worse physical function. While patients in less socioeconomically developed countries had higher disease activity, they reported similar physical function.
- disease activity
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What is already known about this subject?
Socio-economic inequalities at individual and country level have been documented in many diseases.
In rheumatoid arthritis (RA), the lower accessto costly innovative biologic disease -modifying anti-rheumatic drugs (bDMARDs) was suggested to be a possible pathway linking lower socio-economic status (SES) to higher disease activity.
What does this study add?
Individual-level (female gender, low education) and country-level socio economic health inequalities exist among patients with spondyloarthritis.
In SpA, the uptake of bDMARDS does not mediate the relationship between lower SES and higher disease activity.
While patients in less socio economically developed countries have higher disease activity, they report similar physical function (adjusted for diseaseactivity).
How might this impact on clinical practice or future developments?
Objective and subjective disease outcomes may exhibit different patterns depending on the socio-economic context of the patient.
The identified socio-economic inequalities indisease activity in patients with SpA are a call for a consolidated action of all stake holders.
Health is known to be influenced by a number of factors, including a complex interplay of personal determinants as well as the socioeconomic and physical environment of the individual.1–3 In an era in which demand for healthcare is increasing, but where at the same time austerity measures are taken to control budgets worldwide, alarming gaps in health among persons from different socioeconomic background become even more apparent.2–4 Inequalities have been observed in different populations at the individual, national and international levels, and across different measures of health, such as occurrence of disease, mortality and health outcomes.1 5 6 In rheumatoid arthritis (RA), previous studies observed inequalities across country-level as well as individual-level socioeconomic factors.7–12 A recent international study concluded that lower educated and older patients (individual-level factors), as well as those living in less wealthy countries (country-level factors, measured by the gross domestic product [GDP]), had higher disease activity even after adjusting for relevant clinical confounders; however, individual-level and country-level factors did not reinforce each other.11 When further exploring possible causes of inequities, studies in RA confirmed that lower access to costly innovative biologic disease-modifying antirheumatic drugs (bDMARDs) could be a possible pathway linking lower socioeconomic status (SES) to higher disease activity.13
Despite reasonable documentation of socioeconomic inequalities and inequities in RA, it is not known whether differences in health outcomes across individual-level or country-level socioeconomic factors also exist in patients with spondyloarthritis (SpA). Furthermore, although in European countries eligibility criteria for initiating treatment with bDMARDs in SpA are more homogeneous across countries compared with RA,13 14 uptake of bDMARDs in SpA in specialised centres across the globe has been demonstrated to vary importantly.15 In this view, it is of interest to investigate outcomes in patients with SpA in relation to socioeconomic factors and to additionally explore the role of uptake of biologicals in (possible) differences in outcomes. COMOrbidities in SpA (COMOSPA) is an international cross-sectional study involving 22 countries from across the world and offers a unique opportunity to explore individual-level and country-level socioeconomic differences in disease outcomes, as well as the role of uptake of bDMARDs on this relationship.
The objective of this study was (1) to explore independent associations between individual-level and country-level socioeconomic determinants with disease activity and physical function in patients with SpA, and (2) if existent to investigate whether the relationship between country wealth and disease activity is mediated by uptake of medication, more specifically bDMARDs.
Study design and data collection
Data from a cross-sectional, multinational (22 countries), observational, multicentre COMOSPA study were used. Consecutive patients visiting rheumatologists from different participating centres were invited to enrol in the study if they were at least 18 years of age, had been diagnosed with axial or peripheral SpA according to their rheumatologist, and were able to understand and complete the questionnaires that were administered. Patients additionally fulfilling the Assessment of SpondyloArthritis international Society SpA classification criteria were included in this study.16 17 Data were collected between January 2013 and September 2014. All local ethics committees approved the COMOSPA study protocol and all patients gave their informed consent on participation.18
The Ankylosing Spondylitis Disease Activity Score (ASDAS—C reactive protein [CRP]-based) and Bath Ankylosing Spondylitis Functional Index (BASFI) were the study outcomes. The ASDAS is a widely used disease activity measure consists of the patient’s assessment on a 0–10 Numeric Rating Scale (NRS) of overall back pain, duration of morning stiffness, global assessment of disease activity, overall peripheral pain/swelling and CRP.19 20 ASDAS was explored as a continuous variable, as well as dichotomised into inactive disease or moderate disease activity (ASDAS <2.1) versus high or very high disease activity (ASDAS ≥2.1).21 BASFI is a self-reported measure of physical function and consists of 10 questions scored on a 0–10 NRS, of which the first 8 questions evaluate activities related to functional anatomical limitations due to the course of this inflammatory disease and the final 2 questions evaluate the patient’s ability to cope with everyday life. The average of the 10 questions represents the total score.22
Variables of interest and potential confounders
Data on socioeconomic factors (gender, level of educational achievement [primary, secondary or university]), age, disease duration (since diagnosis, in years), lifestyle characteristics (smoking status [past, current, never smoked]), weight (kg) and height (cm) were collected. Body mass index (BMI) was computed and patients were classified into underweight (BMI <18.5), normal weight (18.5 ≥ BMI <25), overweight (25 < BMI ≤30) and obese (BMI >30).23 24 Furthermore, data on current biological therapy (yes vs no) and physician-diagnosed past and current comorbidities were collected, namely presence of ischaemic cardiovascular disease (myocardial infarction, stroke), cancer (colon, skin, lung, breast and uterus for women, prostate for men, and lymphoma), gastrointestinal diseases (diverticulitis, ulcers), infections (hepatitis), lung disease (chronic obstructive pulmonary disease and asthma) and psychiatric disorders (depression). Based on this information, the Rheumatic Disease Comorbidity Index (RDCI) was computed.25 26 This includes respiratory diseases, myocardial infarction, hypertension, diabetes, depression, ulcers or other gastrointestinal manifestations, fractures, and cancer. As information on fractures was not collected, these were not included in the index, resulting in a possible score range between 0 and 8. Similarly, the presence of physician-diagnosed past or current extra-articular manifestations (EAM) was collected, comprising uveitis, psoriasis and/or inflammatory bowel disease, bamboo spine, and the presence of radiographic sacroiliitis as defined by the modified New York (mNY criteria); human leukocyte antigen (HLA)B27 positivity was also recorded.
Country-level factors explored were the country of residence, world region (Africa, Europe, Asia, North America and South America), GDP and healthcare expenditure (HCE) per capita, Human Development Index (HDI) (composite statistic of life expectancy, education and income per capita indicators,27 with four ranks available, namely very high [the best ranking possible], high, medium and low), and Gini index of income inequality (range from 0 [absolute equality] to 100 [absolute inequality]). Information on GDP and HCE per capita (in international dollars, that is, adjusted for purchasing power parity to ensure comparability between countries) was extracted from the online database of the International Monetary Fund for 2013.28 HDI was extracted from United Nations Development Programme (UNDP) and Gini from UNDP and World Bank, for the most recent year available.29 30 GDP, HCE and Gini were classified as low, medium and high based on tertile distribution, with the lowest tertile corresponding to low GDP, HCE and Gini, respectively (table 1 and online supplementary table S1).
Multilevel linear and logistic regression models were computed, as appropriate, with ASDAS (both continuous and dichotomised at ≥2.1; distinguishing high and very high disease activity from inactive disease and moderate disease activity) and BASFI (only linear regression) as dependent variables. Country of residence was included as a random effect. Model fit was assessed using likelihood ratio (LR) test versus linear regression (cut-off p value of 0.05), and Snijders and Bosker R-squared for the two-level linear regression models was computed.31 Age, gender and education were the independent variables of interest at the individual level. Models were adjusted for potential clinical confounders, namely axial involvement (vs peripheral only), presence of EAM, disease duration, BMI, comorbidities (RDCI), HLA-B27 positivity and current pharmaceutical therapy. The model with BASFI was additionally adjusted for ASDAS, bamboo spine and the presence of radiographic sacroiliitis. The factors that showed a significant association with the outcome (p<0.10) or that were potential confounders in the relationship between the main variables of interest and the outcome were retained in the final model. Models were built on complete cases.
Next, the following country-level variables were added to the model, in separate models—(1) GDP (low vs high); (2) HDI (medium vs [very] high); (3) HCE (low vs high); (4) Gini index; or (5) region (Asia, n=5 countries; Africa, n=2; Europe, n=10; North America, n=2; and South America, n=3)—to investigate the contribution of socioeconomic welfare to differences in ASDAS and BASFI. Cross-level interactions between country-level factors with the individual-level factors of education, age and gender were investigated. Where interactions were significant (a priori defined as p<0.10), stratified analyses were performed.
Finally, the potential mediating role of current uptake of bDMARDs in the relationship between individual SES (education being the proxy) and/or country SES GDP and ASDAS was explored by testing indirect effects. For the computation of the indirect effects, the product of coefficients approach was used and the percentile bootstrap CIs were calculated.32 The statistical software Stata V.13 was used.33
In total, 3370 patients with axial or peripheral SpA from 22 countries (35–286 subjects/country) were included (table 1 and online supplementary tables S1 and S2). The mean age was 43 years (SD 14) and 66% of the patients were male. Twelve per cent of the patients (n=421) had primary education as the highest level of educational achievement, 44% had a secondary education diploma and the remaining 43% were university graduates. Of all included patients, 12% (n=415) had only peripheral involvement. The mean ASDAS was 2.0 (SD 1.1), ranging from 1.4 (1.0) in Spain to 2.8 (1.1) in Morocco; 1387 (44%) of patients had ASDAS ≥2.1, ranging across countries from 23% (South Korea) to 72% (Germany). The mean BASFI score was 3.1 (2.7), and ranged from 1.7 (2.1) in South Korea to 4.8 (2.6) in Germany. On average, patients had an RDCI of 0.7 (1.1). Five per cent (China) to 78% (Belgium) of patients in each country were currently treated with bDMARDs.
Relationship between individual socioeconomic and clinical factors and ASDAS and BASFI
Independent of the outcome, lower education and female gender were always significantly associated with worse health status in the multivariable models. Patients with only primary education had higher ASDAS (β=0.3 [95% CI 0.2 to 0.4]), higher odds of having ASDAS ≥2.1 (OR 1.7 [95% CI 1.3 to 2.2] and a higher BASFI score (β=0.6 [95% CI 0.3 to 0.8]) compared with patients with university education. Women had higher ASDAS (β=0.2 [95% CI 0.1 to 0.3]), 1.7 higher odds (95% CI 1.4 to 2.0) of having ASDAS ≥2.1 and had a higher BASFI score (β=0.4 [95% CI 0.3 to 0.6]). The effect of age was not large (approximately −0.1 and +0.2 for ASDAS and BASFI scales, respectively, for a 10-year age difference). Smoking and obesity were also always associated with worse ASDAS and BASFI (table 2). Those patients who were currently treated with a bDMARD had a lower ASDAS (β=−0.2 [−0.3 to −0.1]) and a somewhat higher BASFI (β=0.3 [0.1 to 0.4]), but importantly treatment status did not confound the associations between SES and the outcome.
Differences in ASDAS and BASFI between countries and regions
Large country differences were observed independent of individual socioeconomic and clinical factors. When predicting an ASDAS for a male patient with axial involvement, 43 years old, with a secondary education, normal BMI, one comorbidity, non-smoker and ASDAS of 1.98 (average values in the sample), differences in ASDAS for such patient between the countries would range between 1.4 (1.2 to 1.6) (Spain) and 2.7 (2.5 to 3.0) (Morocco) (figure 1A). For BASFI, these differences would be between 1.7 (1.4 to 2.1) (China) and 3.8 (3.5 to 4.2) (Russia) (figure 1B). Among the five world regions, after controlling for between-country differences, patients in African countries still had substantially higher ASDAS compared with all other regions. When BASFI was the outcome, patients from Asia scored consistently lower than the other four regions (table 2 and online supplementary table S3). A significant interaction between gender and region has been observed when ASDAS was the outcome, and stratified results revealed that the largest differences (0.46–0.50 higher for women on ASDAS scale) were present in Africa and South America, while no differences or substantially smaller differences (0.20) were seen in Asia, North America and Europe (table 3).
Further exploration of the country differences in the regression analyses revealed that patients from countries with low GDP had slightly higher ASDAS (0.2 [−0.1 to 0.5]) and higher odds for an active disease (≥2.1) (OR 1.5 [0.9 to 2. 5]). The difference between patients living in countries with medium and high HDI versus very high HDI was slightly more pronounced and statistically significant for both continuous (0.4 [0.1 to 0.6]) and dichotomized ASDAS (OR 1.8 [1.1 to 3.0] to have high disease activity). Such patterns were, however, not observed for BASFI as none of the country macroeconomic indicators showed significant association with the outcome. Gini or HCE did not show a significant relationship with any of the outcomes (table 2 and online supplementary table S3). Two-level model with country random effect always showed better fit even in the presence of one of the macroeconomic indicators (LR test vs linear regression p<0.001). The highest R-squared (the largest proportion of variability explained) was observed in models with HDI and region for ASDAS (continuous) as outcome, and for the model with region when BASFI was the outcome (online supplementrary table S4). No significant interactions between any of the macroeconomic indicators and individual age, gender or education were observed.
Uptake of bDMARDs in the relationship between individual-level and country-level SES with ASDAS
Mediation analyses did not reveal a mediating role of uptake of bDMARDs in the relationship between SES and disease activity. Current uptake of bDMARDs had negligible (<3%) mediating role in the relationship between lower individual-level (education) or country-level (GDP) SES and higher ASDAS.
In this multinational study, individual socioeconomic factors were found to be independently associated with disease activity and functional disability in patients with SpA. Patients with low education had consistently worse scores on both disease outcomes, and women were more likely to have high disease activity and report worse physical function. Notably, the effect on both outcomes was statistically significant but of moderate magnitude (especially for BASFI differences being less than 1 point on a 0–10 scale), and expectedly with most pronounced differences in dichotomised ASDAS. Strikingly, substantial variations in both outcomes were observed among countries, where a patient with similar personal demographic and clinical characteristics (set at average values in the sample) would, for example, have a predicted ASDAS in a range of 1.4 (Spain) to 2.7 (Morocco). BASFI score for such patient would range between 1.7 in China and 3.8 in Russia.
Differences in outcomes by education and gender have been previously observed in RA and other musculoskeletal diseases, as well as in other groups of patients.7 8 34 In Comorbidities in RA (COMORA), a similar study as the current but including patients with RA from 17 countries, large differences between countries in the Disease Activity Score-28 have been observed, and disease activity was also consistently higher in lower income countries. Clearly, the relationship between socioeconomic determinants and disease outcomes in patients with SpA follows the same pattern; however, the absolute differences seem smaller, within the limits that we know exist in such a comparison.
When trying to understand the variation among countries in ASDAS and BASFI through country-level indicators of socioeconomic development, very different patterns were observed for both outcomes. While in low GDP societies (vs high GDP) and societies having medium or high level of human development (vs very high) disease activity was somewhat higher, and patients from low GDP country had on average 1.5 higher odds of having high disease activity (ASDAS ≥2.1), no difference on physical function (BASFI) according to GDP or HDI or any other country-level socioeconomic indicator considered was observed. This means that the country variation in BASFI (after adjusting for disease activity) cannot be explained by country-level macroeconomic indicators. On this line, it was insightful to observe that world region, likely more reflective of common sociocultural traits than macroeconomic indicators, was associated with variations in BASFI. Patients in Asian countries reported relatively less difficulties with physical function compared with other regions. The role of world region in the experience of health reinforces our earlier observation in RA that shared cultural factors are likely to play an additional role in explaining differences in health outcome patterns.35 Future research should also consider the cultural, contextual and personality factors in patient-reported disease measures,36 such as BASFI or subjective components of ASDAS.
Importantly, most of individual and country/region factors did not reinforce each other; that is, patients with lower SES in wealthier countries do not have worse outcomes compared with low SES patients in less wealthy countries. Notable exception was that the effect of gender on ASDAS was different across world regions, with the largest adverse effect in women observed in South America and Africa as compared with small or no difference in other three regions. While no firm conclusions can be drawn from this exploratory analysis, it is plausible to suggest that some country or regional characteristics—system or cultural factors or social support in place—can increase or mitigate the socioeconomic gaps. Content validity of the outcomes across regions (ie, differential item bias in the BASFI by gender across countries) or presence of unmeasured effect modifiers cannot be excluded.
Attempting to understand the potential causes of inequalities in ASDAS by countries’ wealth and development, differences in the uptake of innovative biologic drugs might offer an explanation.15 Of note, a similar study in RA showed that the uptake of bDMARDs played a (small) mediating role in the relationship between socioeconomic disadvantage and disease activity in patients with RA.11 However, our data did not confirm that the uptake of biologic treatments explained (part of) the relationship between low socioeconomic welfare and ASDAS. A potential explanation for a less pronounced mediating effect of tumour necrosis factor uptake in the relation between GDP and outcome might be the higher acceptance of the treatment guidelines in SpA and the availability of more and cheaper alternatives (eg, methotrexate) to treat active RA. Therefore, other reasons should be also considered, such as better access to information and secondary prevention including exercise and physical therapy. It is worth mentioning that this study did not collect any data on specific measures of access to drugs, such as overall price, clinical criteria that regulate initiation of biologicals, private insurance or out-of-pocket payments, and only explored the actual update of biologic treatment in the available sample of patients.
Of note, the association between HDI and ASDAS was stronger and more consistent compared with the relationship with GDP or HCE, allowing to speculate that the reasons for inequalities should not be searched in wealth but in the development of populations. However, it should be noted that even HDI fails to capture aspects such as sustainability, inequalities, poverty, empowerment or access and quality of healthcare, and thus is likely not sensitive enough to capture the whole complexity and diversity of a country’s socioeconomic and cultural environment.
To our knowledge, this is the first international study that explored the socioeconomic factors in relation to disease outcomes in SpA. It is a large-scale, multinational study that collected data from an impressive number of patients and allowed analyses of both individual-level and country-level factors. Notwithstanding, this study also has many limitations. First, studies like COMOSPA cannot guarantee a representative sample of patients with SpA in participating countries, and it is possible that optimally treated group in each country is over-represented as participating centres were mainly tertiary centres specialised on SpA. In principle rheumatologists were asked to include consecutive patients in order to (at least partially) overcome this potential known problem; however, we cannot exclude that sampling procedures account for part of the differences between countries. Furthermore, data on many relevant confounders such as SES factors (eg, income and poverty, health beliefs and coping, immediate living environment such as neighbourhood SES37 38) that can further explain the observed differences in disease activity and physical function were not collected, and their potential contribution to the outcomes remains to be explored in future studies. Cross-sectional design carries inherent biases for causality assessment and increases variability in outcome assessment.
An important methodological question remains, namely whether assessing the effect of GDP or HDI (these two indicators also overlap to a certain extent) in multilevel models with country being a cluster variable does not lead to overadjustment and thus underestimate the true effect we are interested in. We should also recognise that in the absence of official thresholds for GDP classification, the GDP categories used in the current study were based on statistical tertiles and thus inherently defined by the available sample of countries. Proxies to cultural aspects (such as expectations, experiences stress, support from family) as well as to access and quality of care should be likely looked for to capture the country-level patterns; however, we are not aware of existence of such country-level indicators up to date. Further, the number of countries (n=22), although impressive from a logistic and world representation perspective, can in statistical terms be a limiting factor in the power of the analysis to detect country-level predictors of the outcome. This means that our results would be an underestimation of the reality, with differences possibly being even larger both across countries and particularly across countries with different socioeconomic background. Furthermore, the outcome measures used in this study have not been proven sensitive to cultural and socioeconomic factors. Notwithstanding, our analyses suggest a large influence by such factors that cannot be ignored when interpreting data from trials and international comparisons. Last but not least, the single-assessment, cross-sectional design hinders any conclusions about the direction of associations. Longitudinal studies are imperative to explore these patterns over time.
In conclusion, large country-level differences in disease outcomes among patients with SpA were seen, and they could be only partly explained by GDP, HDI or region of residence. Individual-level socioeconomic inequalities were also pronounced, disfavouring women and low educated persons. At the same time, while patients in less developed countries had higher objectively measured disease activity, they overall reported to have a similar physical function (after adjusting for disease activity). Our findings point at socioeconomic inequalities in disease activity in patients with SpA and call for a consolidated action of all stakeholders to take those into account.
The COMOSPA study was conducted under the umbrella of the International Society for Spondyloarthritis Assessment (ASAS). We would like to thank all ASAS-COMPOSPA collaborators: Fadoua Allali MD, Morocco; Raquel Almodovar González MD, Spain; Elena Alonso Blanco-Morales MD, Spain; Alejandro Alvarellos MD, Argentina; Maria Aparicio Espinar MD, Spain; Pamir Atagunduz MD, Turkey; Pauline Bakker MD, The Netherlands; Juan C Barreira MD, Argentina; Leila Benbrahim MD, Morocco; Bahia Benchekroun MD, Morocco; Alberto Berman MD, Argentina; Juergen Braun MD, Germany; Alain Cantagrel MD, PhD, France; Roberto Caporali MD, Italy; Pedro Carvalho MD, Portugal; Gustavo Casado MD, Argentina; James Cheng-Chung Wei MD, PhD, Taiwan; Francisco Colombres MD, Argentina; Eugenio del Miguel Mendieta MD, PhD, Spain; Juan D Diaz-Garcia MD, Mexico; Michel De Bandt MD, PhD, France; Vanesa Duarte MD, Argentina; Cristina Fernandez Carballido MD, Spain; Mari Cruz Fernandez Espartero MD, Spain; Manuel Fernandez-Prada MD, Spain; Rene-Marc Flipo MD, PhD, France; Pilar Font Ugalde MD, PhD, Spain; Philippe Gaudin MD, PhD, France; Philippe Goupille MD, France; Dolors Grados Cánovas MD, Spain; Jordi Gratacós Masmitjá MD, PhD, Spain; Vittorio Grosso MD, Italy; Naomi Ichikawa MD, Japan; Hisashi Inoue MD, Japan; Yuko Kaneko MD, PhD, Japan; Taku Kawasaki MD, PhD, Japan; Shigeto Kobayashi MD, Japan; Manjari Lahiri MD, Singapore; Hernán Maldonado-Ficco MD, Argentina; Marhadour MD, France; Alejandro Martínez MD, Argentina; Kazuo Matsui MD, Japan; Ramón Mazzuchelli Esteban MD, Spain; Corinne Micelli MD, PhD, France; Chisun Min MD, Japan; Mitsuhiro Morita MD, PhD, Japan; Juan Mulero Mendoza MD, PhD, Spain; Jose Raul Noguera Pons MD, Spain; Masato Okada MD, Japan; Alberto Ortiz MD, Argentina; Jon Packham DM, FRCP, UK; Gisela Pendón MD, Argentina; Dora Pereira MD, Argentina; José A Pereira da Silva MD, Portugal; Fernando Pimentel-Santos MD, Portugal; Hanan Rkain, MD, Morocco; Oscar Rillo MD, Argentina; Carlos Rodriguez Lozano MD, Spain; Adeline Ruyssen-Witrand MD, PhD, France; Adrián Salas MD, Argentina; Carlos Salinas-Ramos MD, Mexico; Amelia Santosa MD, Singapore; Alain Saraux MD, PhD, France; Raj Sengupta FRCP, PGCME, UK; Stefan Siebert PhD, UK; Martin Soubrier MD, PhD, CHU, France; Caroline Spiegel, Germany; Carmen Stolwijk MD, The Netherlands; Kurisu Tada MD, Japan; Naoho Takizawa MD, Japan; Yoshinori Taniguchi MD, PhD, Japan; Atsuo Taniguchi MD, PhD, Japan; Chung Tei Chou MD, Taiwan; Lay-Keng Teoh, Singapore; Tetsuya Tomita MD, PhD, Japan; Wen-Chan Tsai MD, PhD, Taiwan; Shigeyoshi Tsuji MD, PhD, Japan; Olga Tsyplenkova, Germany; Astrid van Tubergen MD, PhD, The Netherlands; Kiana Vakil-Gilani BS, MPH, USA; Rafael Valle-Oñate MD, Colombia; Gaelle Varkas MD, Belgium; Virginia Villaverde MD, Spain; Ai Yap, Singapore; Pedro Zarco Montejo MD, PhD, Spain.
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Handling editor Josef S Smolen
Contributors AB, RBML and DvdH have conceived the idea. PP has performed the analyses and drafted the first version. APK and SN provided statistical advice. All authors have contributed to the analyses and interpretation, as well as read and approved the final version of the manuscript.
Funding The COMOSPA study was conducted with the financial support from AbbVie, Pfizer and UCB, which provided an unrestricted grant to ASAS to fund the study. The funders did not have any role in the design or conduct of the study. This ancillary study did not receive any funding, and the sponsors of COMOSPA did not have any interference with this current study.
Competing interests None declared.
Patient consent for publication Obtained.
Ethics approval All local ethics committees approved the COMOSPA study protocol and all patients gave their informed consent upon participation.
Provenance and peer review Not commissioned; externally peer reviewed.
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