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Abstract
Background Persistently active rheumatoid arthritis (pactiveRA) may be due to the interplay between biological and non-biological factors. The role of socioeconomic factors remains unclear.
Objectives To explore which biological and non-biological factors associate with pactiveRA.
Methods Adults with early RA in the National Early Inflammatory Arthritis Audit, recruited from May 2018 to October 2022, were included if having pactiveRA or persistently low RA (plowRA). The pactiveRA was defined as three consecutive Disease Activity Score-28 joints (DAS28) of >3.2 at baseline, 3 and 12 months. The plowRA was defined as DAS28 ≤3.2 at 3 and 12 months. Stepwise forward logistic regression was used to explore associations with pactiveRA (outcome). Age and gender were included a priori, with socioeconomic factors and comorbidities as exposure variables.
Results 682 patients with pactiveRA and 1026 plowRA were included. Compared with plowRA, patients with pactiveRA were younger (58, IQR: 49–67) versus (62, IQR: 52–72), and included more women (69% vs 59%). The pactiveRA was associated with worse scores in patient-reported outcomes at baseline, and anxiety and depression screens. Overall, there was clear social patterning in pactiveRA, with age-by-gender interaction. Logistic regression indicated age, gender, social deprivation and previous or current smoking, were independently associated with pactiveRA, after controlling for disease severity markers (seropositivity). Depression, lung disease, gastric ulcers and baseline corticosteroid use, were also associated with pactiveRA (p<0.05 for all).
Conclusion Socioeconomic factors and deprivation were associated with pactiveRA, independent of clinical and disease characteristics. Identifying ‘adverse’ socioeconomic drivers of pactiveRA can help tailor interventions according to individual need.
- arthritis, rheumatoid
- arthritis
- outcome assessment, health care
Data availability statement
Data are available upon reasonable request. Data used in this study were collected for the National Early Inflammatory Arthritis Audit and are available on request to the data controllers (the Healthcare Quality Improvement Partnership). Data are available upon reasonable request by any qualified researchers who engage in rigorous, independent scientific research, and will be provided following review and approval of a research proposal and Statistical Analysis Plan and execution of a Data Sharing Agreement. All data relevant to the study are included in the article. All figures and tables included in this article are original.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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WHAT IS ALREADY KNOWN ON THIS TOPIC
Poorer socioeconomic outcomes associate with worse clinical outcomes in rheumatoid arthritis.
The relative importance, role and interaction of biological and non-biological factors, particularly in the setting of persistently active disease, remain unknown.
WHAT THIS STUDY ADDS
Socioeconomic factors (age, gender), and living in socially deprived areas, are associated with persistently active rheumatoid arthritis, independent of clinical and disease characteristics.
Depression, known to be a comorbidity that is socially patterned, was significantly associated with persistently active rheumatoid arthritis.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
Addressing comorbidities, including depression, at an early stage, can minimise the risk of persistently active disease states.
Adopting management strategies to target ‘adverse’ socioeconomic drivers of persistently active disease can help risk-stratify patients and tailor interventions according to individual needs.
Introduction
Rheumatoid arthritis (RA) is an autoimmune disease that is characterised by chronic joint inflammation and associated synovial hyperplasia and extra-articular systemic manifestations. It has a prevalence of approximately 1% worldwide.1 Without optimal treatment, RA can lead to severe joint deformity and disability, impacting on clinical outcomes including overall quality of life.2
Despite significant therapeutic advances in RA treatments, a significant proportion of patients will continue to have ongoing, active disease.3 The identification of prognostic factors associated with unfavourable outcomes therefore remains crucial, to guide personalised treatment that can help reduce disease activity and minimise treatment resistance. Insights are needed into individual characteristics that may adversely affect the chances of achieving remission or low disease activity states according to treat-to-target recommendations.4
Successful management of RA requires careful considerations and assessment of the patient encompassing the various biological, psychological and socioeconomic (bio-psycho-social) aspects, their condition and their wider environment. Biological factors contributing to poorer outcomes in RA include a greater comorbidity burden, functional disability at diagnosis, greater disease activity at diagnosis and greater body mass index.5 6 Greater mental health burden, including the presence of depression and anxiety, as well as concomitant fibromyalgia, are also associated with difficult-to-treat RA.7 A recent systematic review of 30 studies conducted by our group demonstrated an association between low socioeconomic status and poor disease outcomes in people with RA.8 However, while the individual associations between biological, psychological and social factors and disease outcomes in RA have been studied, few data exist on the interaction and relative importance of each of these at both the individual and societal level. The need to consider biological, psychological and social factors when treating people with RA is emphasised in the recent European Alliance of Associations for Rheumatology guidance for the implementation of self-management strategies in patients with inflammatory arthritis.9
This study aimed to explore the clinical and wider factors associated with disease activity states in the first year of diagnosis in patients with RA, using a UK-wide cohort of people diagnosed with RA, with a focus on drivers of persistently active disease. Rheumatology practice in the UK follows treat-to-target strategies, with emphasis on early treatment intervention to control disease.
Methods
Data source
The National Audit for Early Inflammatory Arthritis (NEIAA) is commissioned by the Health Quality Improvement Partnership, on behalf of the National Health Services (NHS) England and the Welsh Government. The main aim of the audit is to promote quality improvement activity linked to early inflammatory arthritis (EIA) management by assessing care according to the six metrics defined by the National Institute for Health and Care Excellence guidelines Quality Standard (QS) 33.10
NEIAA started in May 2018 and collects data on adults referred to secondary care rheumatology services in England and Wales with suspected inflammatory arthritis. Patients with a confirmed EIA diagnosis are eligible for further follow-up at 3 and 12 months post diagnosis. The full methodology of data collection has been described in previous publications by British Society for Rheumatology and Adas et al.11 12
Study population
In this study, we included patients in England with a confirmed diagnosis of RA, enrolled in NEIAA from May 2018 until October 2022. The cohort has been described in depth previously by Yates et al.13 Data from Wales were excluded from this study due to limitations in data availability. In this cohort, patients with EIA are defined as individuals prior to, or at the time of, commencing conventional synthetic disease-modifying anti-rheumatic drug (csDMARD) therapy.
Inclusion and exclusion criteria
The study population was defined based on the following inclusion criteria: patients with a clinician-confirmed diagnosis of RA and with disease activity recorded at predefined time points: baseline (ie, first visit), 3 and 12 months. Patients included in the study should have started with a baseline Disease Activity Score-28 joints (DAS28) of >3.2 at the first recorded visit, with subsequent raised DAS28 >3.2 (for those in the persistently active group) and DAS28 ≤3.2 at the 3 and 12 months, if in the persistently low disease activity group.
Patients from Wales were excluded, due to limited data availability. Patients were also excluded from the study if they did not have disease activity recorded at the aforementioned time points.
RA disease activity state definitions
Patients were defined as having persistently active RA (pactiveRA) based on three consecutive DAS28 of >3.2 at baseline, at each of the 3-month and 12-month follow-up points. Persistently low RA (plowRA) was defined as patients with a DAS28 ≤3.2 at the 3-month and 12-month time points. DAS28-C-reactive protein (CRP) was used throughout as the measure of disease activity.
Data collection
As per a pre-set and validated proforma, clinicians and healthcare professionals working in rheumatology clinics collect clinical and socioeconomic data at the time of diagnosis (baseline), and at 3 and 12 months of follow-up. Additionally, patients were invited to complete a range of patient-reported outcome measures (PROMs) at these time points.
Independent variables
Socioeconomic variables
Socioeconomic factors collected at baseline included: age, gender, ethnicity, social deprivation level and being in paid work at baseline. Data on smoking (ever and never smoking) were also collected and included as a proxy for socioeconomic status.8 As the NEIAA does not collect information on individual income, social deprivation was measured using the index of multiple deprivation (IMD), which is an area-level indicator of social deprivation linked to a postal code; scores were grouped into 10 deciles based on the IMD rank according to the English IMD 2015 guidance.14 A lower rank indicates a more deprived area, with Decile 1 representing the most deprived 10% of areas, while Decile 10 represents the least deprived 10%. IMD is a composite score, comprising income, employment, education, health, crime, barriers to housing and services and living environment.
Comorbidities were recorded as per those included in the rheumatic disease comorbidity index (RDCI), a validated tool that gives a weighted measure reflecting comorbidity burden, which can be estimated using data on cardiovascular disease, hypertension, diabetes mellitus, chronic lung disease, peptic ulcer disease depression and cancer.15
Clinical variables
Clinical measurements recorded included: seropositivity status for rheumatoid factor (RF) and anti-citrullinated C-peptide antibody (CCP) at baseline; measures of disease activity at baseline, 3 and 12 months, including tender joint count (0–28 joints), swollen joint count (0–28 joints), patient-reported global assessment score (0–100 scale, from best to worst), CRP (mg/l) and/or erythrocyte sedimentation rate (mm/hour). These were used to calculate a DAS28 which is a validated tool to measure disease activity in RA.16 17 DAS28-CRP was used as the valid measure of disease activity in this study. Mental health was assessed with the four-item Patient Health Questionnaire (PHQ-4), which is the combined form of the two-item PHQ-2 depression scale and two-item Generalised Anxiety Disorder scale (GAD-2).18 19 The purpose of PHQ-4 is to facilitate a brief and accurate measurement of the core symptoms and signs of depression and anxiety, through a combination of PHQ-2 and GAD-2, both of which independently are good screening tools. PHQ-4 gives an overall measure of symptom burden, and if elevated, indicates a need to further establish the presence or absence of a clinical mental health disorder that may require treatment. Physical function was assessed using the Health Assessment Questionnaire V.2 (HAQ-II).20 The 10 items included in this scale are summed to provide a total score ranging from 0 to 3, where higher scores indicate worse function. Disease impact was assessed using the Musculoskeletal Health Questionnaire (MSK-HQ). This 15-item questionnaire evaluates how musculoskeletal symptoms affect day-to-day life across a range of domains including pain, fatigue, emotional well-being and work and social activities. Scores range from 0 to 56, with higher scores indicating better MSK health.21 The csDMARDs use was collected at baseline and at 3 months, both monotherapy and in combination. The drugs recorded were methotrexate (MTX), sulfasalazine (SSZ) and hydroxychloroquine (HCQ). Glucocorticoid use was only collected at baseline.
Statistical analysis
Patient characteristics were tabulated according to their DAS28 to pactiveRA and plowRA. For continuous measures, data were described as medians with IQR. For categorical measures, absolute number and percentages were applied.
We conducted a complete case analysis, with weighting for inverse probability of response. We used this approach as it accounts for bias introduced by systematic missingness in longitudinal data.22 We considered alternative approaches such as multiple imputation to be less appropriate given the magnitude of missing data across variables. None of the baseline variables (>95% complete) were significantly associated with the data being entered at 12 months.
Logistic regression models were used to explore associations between pactiveRA and: (1) socioeconomic factors (including age, gender, IMD, smoking); (2) clinical variables (comorbidities, seropositivity for RF or CCP); (3) treatment strategies within 3 months (any MTX, SSZ, HCQ monotherapies or csDMARDs combination therapies); (4) baseline glucocorticoid. The main analyses included separate models for each variable of interest, adjusted for age and gender and were weighted using inverse probability of response weights.23 To account for the nesting of patients within NHS Trusts, cluster robust SEs were used. For sensitivity analyses, unadjusted and unweighted models were conducted.
Margins plots, to explore socioeconomic factors, were generated in order to investigate the association between (1) IMD and pactiveRA, by gender and stratified by age and (2) age and pactiveRA, by gender and stratified by ethnicity (white, black, Asian, mixed and other). As fewer patients were from black backgrounds, Asian, mixed and other in NEIAA,12 these patients were grouped into a single ethnic minority group in our analysis.
Data were presented as OR for achieving the outcome, relative to the reference group (persistently low RA), with 95% CIs. No correction for multiple hypothesis testing was made. All statistical analyses were performed using Stata V.17.0 (StataCorp, College Station, Texas, USA).
Results
Baseline characteristics
A total of 15 626 patients with a confirmed diagnosis of RA were identified. 86% of these individuals had DAS recorded at baseline. 21% of the 15 626 patients had DAS recorded at all three time points (n=3296; figure 1). Ultimately, 1708 people with the predefined diagnosis of RA were included in this study, with 1026 (60.1%) defined as having plowRA, and 682 (39.9%) having pactiveRA. Overall, participants had a median age of 60 years (52–71), with 63.1% women (table 1). People with plowRA were overall older (median age 62 years) than those with pactiveRA (median age 58 years). The pactiveRA group also had a greater proportion of women (69.1%, compared with 59.2% in the plowRA group). The majority of included patients were white (87.3%) and (13%) were from ethnic minority backgrounds, and 44.0% of patients overall were in paid work.
Flowchart of participant selection. Patients with rheumatoid arthritis (RA), selected from those with confirmed early inflammatory arthritis (EIA), identified in the National Early Inflammatory Arthritis Audit (NEIAA) were included for this study if identified as having persistently active RA (pactiveRA) or persistently low RA (plowRA), and having Disease Activity Score-28 joints (DAS28) recorded at three time points (baseline visit, 3 months and 12 months). The pactiveRA was based on three consecutive DAS28 of >3.2 at baseline, at each of the 3-month and 12-month follow-up points. The plowRA was defined as patients with a DAS28 ≤3.2 at the 3-month and 12-month time points, (having started with a baseline DAS28 of >3.2).
Baseline characteristics of patients with pactiveRA and plowRA
People with plowRA were less likely to have ever smoked (51.7%) compared with people with pactiveRA (57.5%). A greater proportion of people with pactiveRA had a higher RDCI (score of 2 (29.0%)), compared with the plowRA group (24.3%), although this was non-significant. Thus, a higher comorbidity burden, as measured by the RDCI, was reported in patients with pactiveRA versus plowRA. Significantly greater rates of depression, lung disease, and gastrointestinal (GI) ulcers were seen in people with pactiveRA (p<0.001).
With regards to disease activity, people with pactiveRA had significantly greater CRP, tender joint count and patient global health scores at baseline. When reviewing PROMs, people with pactiveRA had significantly greater PHQ and GAD scores (denoting depression and anxiety). Only 21 (2%) of the included patients were on no DMARD strategy for 3 months. MTX and HCQ used in mono or combination strategies were more common in patients with plowRA (79% and 34%) compared with pactiveRA (72% and 28%). More patients from the pactiveRA group were on the SSZ strategy (12% vs 8%). The csDMARD mono or combination strategies were more common in patients with RF/CCP seropositive (online supplemental table 1).
Supplemental material
Predictors of pactiveRA
In the adjusted logistic regression analyses (table 2), female gender, ever-smoking, greater social deprivation, greater symptom duration and greater RDCI score were associated with greater odds of pactiveRA compared with plowRA. With regards to individual comorbidities, depression, lung disease and a history of GI ulcers were associated with pactiveRA, as were worse MSK-HQ and HAQ scores. With regards to treatment strategies, the use of methotrexate and hydroxychloroquine strategies (either mono therapy or combination therapy) for 3 months was less likely to be associated with pactiveRA. Sulfasalazine use was associated with increased odds of pactiveRA; however, this is likely due to the small sample size of patients included in this particular analysis. Unadjusted variables are presented in online supplemental table 2.
Weighted age-adjusted and gender-adjusted analysis: pactiveRA association with socioeconomic and clinical variables compared with plowRA
Additional analyses were undertaken to explore the joint contribution of socioeconomic factors, by including interaction terms between the variables in the IMD and ethnicity age-gender-adjusted models. IMD was significantly associated with an increased likelihood of pactiveRA across all ages, with a significantly increased likelihood in women compared with men in higher age groups (figure 2A). Increasing age above 40 years was overall associated with a decreased likelihood of pactiveRA. Above the age of 60 years, there was a greater separation in genders of the likelihood of pactiveRA, with increasing IMD being associated with more active disease in women compared with men. Below the age of 40, men were more likely to have pactiveRA than women, whereas, above the age of 40, women were more likely to be affected. No significant difference was seen in this association between ethnicities (figure 2B).
Margins plots exploring the association between gender, age, persistently active rheumatoid arthritis (pactiveRA) and index of multiple deprivation (IMD) and ethnicity. (A) Association between the IMD and pactiveRA, by age and gender. (B) Association between age and pactiveRA, by ethnicity and gender.
Discussion
This study provides evidence on how socio-economic factors in persistently active disease in RA, independent of clinical factors, in a large national cohort of patients with early RA, impact on disease progress. Specifically, younger age, female gender, greater social deprivation (as perceived by the IMD score) and smoking were associated with pactiveRA. Additionally, greater comorbidity burden (as denoted by RDCI), and specifically, depression, lung disease and GI ulcers, were associated with pactiveRA. Seropositivity was less likely to be associated with pactiveRA. This is likely due to this group of patients being more likely to receive more aggressive therapy for their RA.
IMD was associated with an increased likelihood of pactiveRA across all ages, but with a significant increase in women, compared with men, at increasing ages. This is an observation that has been noted in older cohorts, such as the Early Rheumatoid Arthritis Study (ERAS, 1986–2001) and Early Rheumatoid Arthritis Network (ERAN, 2002–2012), as well as previous work within the NEIAA.13 24 25 Studies in the ERAS and ERAN UK cohorts demonstrated that socioeconomic factors (including female gender and worse deprivation), independent of disease measures, were associated with poorer functional outcomes at 5 and 10 years, while a study within the NEIAA, using 1 year data (2018–2019) demonstrated an association between gender and socioeconomic position, and higher disease activity. It is interesting to note that, although there have been 20–30 years between the data collected in ERAS and ERAN, and NEIAA, the association between socioeconomic factors and persistently active disease remains strong. This has been repeatedly demonstrated across RA cohorts, not just in the UK but also elsewhere, such as large population studies in the USA.26
There is clearly an interaction between IMD, gender and age, which is more pronounced at the extremes of age in women, leading to a more persistently active disease state. Older age groups see greater separation between men and women, which may reflect a true difference in disease activity states between the two genders in older age or different accrual of certain characteristics, for example, more comorbidity such as pain syndromes or more psychosocial factors, adding to disease burden. Of note, at the other side of the age spectrum, younger men were also found to have an increased likelihood of pactiveRA. The reasons for this observation are again subject to speculation and could range from non-adherence to differences in health behaviour. Parallel work in this area is currently being undertaken by our group.
Adverse socioeconomic factors, such as social deprivation, coupled with factors such as age and gender, are evidently important drivers of pactiveRA. There are multiple possible causes for this situation, although exploration into these potential factors goes beyond the scope of this work. A recent study in the British Socity for Rheumatology Biologics Register- Rheumatoid Arthritis (BSRBR-RA) database found social deprivation to be associated with a reduced response to tumour necrosis factor (TNF) inhibitors, contributing to persistently active disease.27 A further explanation may be that other factors, such as obesity, may act as mediating factors in the association between social deprivation and pactiveRA, as demonstrated in data from the Rheumatoid Arthritis Medication Study.28 Access to a healthy diet and an active lifestyle, closely associated with deprivation, may go some way to explain this association, and reiterates the importance of self-management strategies in RA, supported by the multidisciplinary team (MDT).9 People of an older age may also have decreased levels of physical activity, for example, due to an increased comorbidity burden, isolation or lack of outdoor space. Our work therefore highlights the need to take a holistic approach to the care of people with RA, addressing both biological and non-biological factors that may drive persistently active disease. The NEIAA has previously identified this need for a holistic approach to management and improved access to key members of the MDT (eg, psychologist, occupational therapist), to provide an overview of a person’s condition and social contexts which may be modifiable in agreement with the patient and/or carer, for example, impacts of work. However, there remains a large discordance between recommendation and clinical practice.29 Social prescribing, for example, through improved access to recreational facilities and reduced levels of isolation is one such example of an intervention that is increasingly used to try and reduce these negative impacts.
Our study demonstrated an association between female gender and an increased likelihood of pactiveRA. The female preponderance for developing RA has long been known, with women having more severe disease and significantly lower remission rates compared with men, despite similar treatment.30–33 A possible biological explanation for the increased severity in women is oestrogen levels. Perimenopausal women have been found to be less likely to achieve remission compared with premenopausal women with RA, with the use of exogenous sex hormones, such as the combined contraceptive pill or hormone replacement therapy, being associated with increased likelihood of remission.34 A decrease in oestrogen levels in menopause and the postpartum period are associated with increased severity of RA symptoms, although attempts to investigate this as a potential treatment target have been inconclusive.35 Our findings are supportive of this association. The observed associations between female gender and worse disease activity in RA are likely to be driven by both biological and non-biological factors. This represents an area of unmet need to better understand the role of psychological and social determinants of disease activity in driving disease activity in women with RA. A Swedish study undertaken almost 20 years ago noted that the association between poorer socioeconomic status and worse RA outcomes was more pronounced in women, but the cause of this remains unknown.36 As discussed above, the reasons for this are subject to speculation and could include an accrual of both psychological and social burden over time, negatively impacting disease outcomes, directly and indirectly, for example, due to mental health comorbidities and increased isolation.
People with pactiveRA in our cohort had greater RDCI scores compared with plowRA. In particular, pactiveRA was associated with depression (also reflected in the higher PHQ-9 and GAD scores), lung and GI ulcer disease. Greater use of anti-inflammatory drugs in the context of active disease and/or other lifestyle factors, for example, smoking (also found to be significantly associated with pactiveRA), may explain the latter, although this is speculative. Greater comorbidity burden and worse disease activity in RA is not surprising and supported by other literature. For example, recent work in difficult-to-treat RA,6 and supported by data from the UK,37 have identified increased comorbidity burden in these patients.6 37 38 Comorbidities, especially depression, tend to be socially patterned and likely closely interact with socioeconomic status in people with poorer RA outcomes. An increased incidence of peptic ulcer disease has been noted in people with RA for decades, the most likely cause being non-steroidal anti-inflammatories (NSAIDs) or corticosteroid use.39 Younger age and impaired physical function are also associated with dyspepsia and gastro-oesophageal reflux disease.40 Of note, no significant difference was found in baseline corticosteroid use in our cohort; NSAID use was not investigated. More recent studies have described a declining incidence of GI ulcers, attributed to more effective use of gastroprotection in patients taking NSAIDs and/or corticosteroids.41 The observed association between GI ulcers and pactiveRA in our cohort could be due to various other mechanisms, all speculative due to the nature of the data. These factors include, for example, greater use of NSAIDs for pain relief, or psychological stress, which may play a role in the development of GI ulcers in RA and subsequent active disease.
Depression, a frequent comorbidity in RA,37 38 was also significantly associated with pactiveRA, with greater PHQ and GAD scores seen in the pactiveRA group compared with persistently low RA. Depression has a strong association with socioeconomic status; disparities in both psychological and physical health, in people with poorer socioeconomic status, have long been observed in RA.42–45 A significantly greater prevalence of depression has been identified in people with difficult-to-treat RA,38 and data from large epidemiological studies show depression and anxiety to decrease the likelihood of RA remission.46 Clinical RA remission reduces symptoms of depression and anxiety.47 Despite the growing evidence of biological contributors to a depressive state, as described above, non-biological drivers can play a significant role, for example, social isolation and disability arising due to RA, inadequate living environments and absenteeism from work.48 This highlights the need to both study and manage pactiveRA through a holistic approach, addressing both biological and non-biological drivers of disease.49 Depression can also impact on factors such as poorer medication adherence, learnt helplessness or presenteeism at work (which in turn may lead to forced or unenforced absences, cycling into the depressive state)48 50 and can influence the pactiveRA condition. Our study, supported by the existing literature, highlights the importance of addressing mental health as part of routine management of patients diagnosed with RA and ideally using a multidisciplinary team approach.41 42
Strengths and limitations
Our study is strengthened by a large sample size of people diagnosed with RA, with data collected from a diverse population via a national audit. Data comprise a wide range of factors including disease activity measures at three time points, clinical factors and socioeconomic factors including work and IMD. Study limitations include the large amount of missing data with regard to PROMs. PROMs are self-completed and missing data may be associated with factors such as health literacy and other social disparities of health. Given this study focused on those with higher disease activity, this may have contributed to poor data completion within this cohort. This may also have been the case for work data, which may be less likely to be completed by those with poorer socioeconomic background and indeed include those not in work at all, which may also lead these individuals to not complete these questions. It is important to note that data collection was part of a national audit programme, with data entry dependent on routine care rather than a research study; therefore the overall expected completion rate would be expected to be low. However, looking at patterns of data entry around the country, this is not dependent on disease severity. Nonetheless, due to the requirement for disease activity to be recorded at three defined time points for inclusion in this study (in order to define persistently low and active disease), our final sample size was relatively small compared with all patients with confirmed RA within the NEIAA. Patients were included at baseline, regardless of their treatment. The nature of the available data did not allow the identification of any treatment changes and thus any associations with changes in treatment, were not explored as part of this study. The numbers of patients in each group (persistently low disease activity and persistently active disease activity) on each DMARD are presented in table 1. Although approximately 35% of the study population commenced biological therapy during the study period, the time point at which they started was not defined, and this information was therefore not included within the models.
Conclusion
In this study, conducted in a recent UK national cohort of people which included people with RA, we have demonstrated socioeconomic factors (age, gender) and living in socially deprived areas to be associated with pactiveRA, independent of clinical and disease characteristics. Across the comorbidity spectrum, depression was among the comorbidities that were significantly associated with pactiveRA. Identifying ‘adverse’ socioeconomic factors that could drive persistently active disease following diagnosis can help risk-stratify patients and tailor interventions according to individual characteristics and needs.
Data availability statement
Data are available upon reasonable request. Data used in this study were collected for the National Early Inflammatory Arthritis Audit and are available on request to the data controllers (the Healthcare Quality Improvement Partnership). Data are available upon reasonable request by any qualified researchers who engage in rigorous, independent scientific research, and will be provided following review and approval of a research proposal and Statistical Analysis Plan and execution of a Data Sharing Agreement. All data relevant to the study are included in the article. All figures and tables included in this article are original.
Ethics statements
Patient consent for publication
Ethics approval
This study has been approved by the Health Research Authority in the UK, reference: 22/NS/0012. Participants gave informed consent to participate in the study before taking part.
Acknowledgments
The National Early Inflammatory Arthritis Audit is commissioned by the Healthcare Quality Improvement Partnership, funded by National Health Service (NHS) England and NHS Improvement and the Welsh government and carried out by the British Society for Rheumatology, King’s College London, King’s College Hospital and Net Solving. We used data provided by patients and staff within the NHS.
References
Supplementary materials
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Footnotes
X @ElenaNikiUK
Contributors EN: responsible for the overall conceptualisation, design and methodology of this work, as well as the data, drafts and final manuscript and guarantor. MA: analysis, preparing manuscript, editing manuscript, approval of final manuscript. MD: preparing manuscript, editing manuscript, approval of final manuscript. SN: analysis, editing manuscript, approval of final manuscript. HL: editing manuscript, approval of final manuscript. MHB: editing manuscript, approval of final manuscript. AC: conceptualisation, editing manuscript, approval of final manuscript. JG: analysis, editing manuscript, approval of final manuscript. All authors take responsibility for the final manuscript.
Funding The design, analysis and interpretation of this study represents part of a project funded as part of a FOREUM ECR 2020 grant awarded to EN.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.