Pain as an important predictor of psychosocial health in patients with rheumatoid arthritis.

Abstract

To examine the evolution of psychosocial aspects of health-related quality of life in rheumatoid arthritis (RA) patients, and to identify their predictors.
All patients within a Swiss RA cohort and a US RA cohort who completed a Short Form 36 (SF-36) scale at least twice within a 4-year period were included. The primary outcome was psychosocial health as measured by the mental component summary (MCS) score of the SF-36. The evolution of this outcome over time was analyzed using structural equation models, which distinguish between the stable, the variable, and the measurement error components of the outcome's variance.
A total of 15,282 patients (48,323 observations) were included. MCS scores were mostly stable over time (between 69% and 75% of the variance was not due to measurement error). The variable component of the SF-36 was mostly due to fluctuations at the moment of measurement and not to a global time trend of psychosocial health. Pain was the most important predictor of both the stable and variable components of psychosocial health, explaining ∼44% of the observed psychosocial health variance.
This large cohort study demonstrates that pain is the most important predictor of a patient's psychosocial health in RA patients. This suggests that physicians should place greater emphasis on pain management.

Full text

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Pain is the most important predictor of psychosocial health in patients with
Rheumatoid Arthritis

Delphine S. Courvoisier, PhD, MSc1, Thomas Agoritsas, MD1,2, Jérôme Glauser, MSc3, Kaleb Michaud,
PhD4,5, Fred Wolfe, MD4, Eva Cantoni, PhD6, Thomas V. Perneger, MD, PhD1, and Axel Finckh, MD7,
on the behalf of the physicians of the Swiss Clinical Quality Management Program for Rheumatoid
Arthritis and the National Data Bank for Rheumatic Diseases

Author Affiliations: 1 Division of Clinical Epidemiology, University Hospitals of Geneva and University
of Geneva, Switzerland; 2 Division of General Internal Medicine, University Hospitals of Geneva,
Switzerland; 3 Swiss Center for Affective Sciences, University of Geneva, Switzerland; 4 National Data
Bank for Rheumatic Diseases, Wichita, KS, USA; 5 University of Nebraska Medical Center, Omaha, NE,
USA; 6 Research Center for Statistics and Department of Economics, University of Geneva, Switzerland;
and 7Division of Rheumatology, University Hospitals of Geneva, Switzerland;
Corresponding Author: Delphine S. Courvoisier, Division of Clinical Epidemiology, 4, rue Perret-
Gentil, HUG, 1211 GE 14, Switzerland. Tel: +41 22 372 9029, Fax: +41 22 372 9135, e-mail:
delphine.courvoisier@hcuge.ch
Financial Support: None

Word count: 2627
Key words: rheumatoid arthritis, mental health, structural equation model, SF-36, quality of life,
HRQoL; cohort
Original Article Arthritis Care & Research
DOI 10.1002/acr.20652
© 2011 American College of Rheumatology
Received: Jun 20, 2011; Revised: Aug 25, 2011; Accepted: Sep 23, 2011

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Abstract:
Objective: To examine the evolution of psychosocial aspects of HRQoL in RA patients, and to identify
their predictors.
Methods: All patients within a Swiss RA cohort and a US RA cohort who completed a SF-36 scale at
least twice within a four year period were included. The primary outcome was psychosocial health as
measured by the mental component summary (MCS) score of the SF-36. The evolution of this outcome
over time was analyzed using structural equation models, which distinguish between the stable, the
variable and the measurement error components of the outcome’s variance.
Results: A total of 15,282 patients (48,323 observations) were included. MCS scores were mostly stable
over time (between 69 to 75% of the variance not due to measurement error). The variable component of
the SF-36 was mostly due to fluctuations at the moment of measurement and not to a global time trend of
psychosocial health. Pain was the most important predictor of both the stable and variable components of
psychosocial health, explaining around 44% of the observed psychosocial health variance.
Conclusion: This large cohort study demonstrates that pain is the most important predictor of patient’s
psychosocial health in RA patients. This suggests that physicians should place greater emphasis on pain
management.
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Significance and Innovations
• Psychosocial aspects of health-related quality of life (HRQoL) are mostly stable over a four year
period (around 75% of the true variance).
• Pain was the most important predictor of psychosocial aspects of HRQoL in treated RA patients,
explaining around 44% of the observed variance.
•
Disease activity and functional disability each explained only around 23% of the observed
psychosocial aspects of HRQoL.
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Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by progressive joint
destruction that has a major impact on Health Related Quality of life (HRQoL) (1). Indeed, RA patients
suffer from joint pain, extra-articular manifestations, and functional limitations that often lead to
permanent disability. Moreover, they are at greater risk of experiencing emotional disturbances (2), such
as poor subjective wellbeing, low self-esteem, and sleep problems; they are also more likely to suffer
from anxiety and depression (3, 4), resulting in an increased risk of suicide (5).
Although disease-modifying antirheumatic drugs (DMARDs) have been associated with a
significant reduction of disease progression and functional disability (6-12), the evolution and variability
of patients’ HRQoL have been less studied (13, 14). Most studies assessing treatment effects have
measured quality of life using disease-specific instruments centered on functional disability (7-12), such
as the Health Assessment Questionnaire (HAQ) (15-17). Other aspects of HRQoL, such as mental and
emotional health, vitality, and social functioning, all representing psychosocial health, are also important,
but have been less studied than physical or disease-related outcomes. The lack of focus on patients’
psychosocial health may result from the emphasis that current RA management guidelines place on
preventing permanent joint-damage and physical disability (18, 19).
The objectives of this study were (a) to examine the evolution of psychosocial health in RA
patients, (b) to determine its variability over time, and (c) identify patient- and disease-related predictors
of psychosocial health. As pain is a central complaint of RA patients, it may play an important role in
HRQoL, including its psychosocial aspects. The American College of Rheumatology has recently
appointed a Pain Management Task Force, which stated that insufficient efforts have been devoted to pain
management (20). Thus, disease-related predictors of psychosocial health include disease activity and
functional disability but also self-reported pain. Understanding the relative impact of various aspects of
patient’s experience on psychosocial aspects of HRQoL may help physicians to better understand
patients’ concerns and help patients not only to ‘do well’, but also to ‘feel well’.

METHODS
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Study population and measurements:
We used two longitudinal cohorts of RA patients: the Swiss Clinical Quality Management in
Rheumatic Diseases database (SCQM-RA) and the National Data Bank for Rheumatic Diseases (NDB),
which have been described in detail elsewhere (21-23). Patients in both cohorts are enrolled on a
continuous basis and assessed at regular intervals for disease activity, RA symptoms and quality of life.
The inclusion criteria for this analysis were a diagnosis of RA and at least two SF-36 assessments. We
included all patients enrolled until the end of March 2009 with a maximum follow-up of four years per
patient. Of the 22,995 patients of the combined cohorts, 15,282 fulfilled the inclusion criteria. In both
cohorts, patients are typically treated by rheumatologists, either in private practice or in hospital
outpatient clinics, with conventional (~50%) or biologic antirheumatic therapies (~50%), often in
association with low-dose glucocorticoids in 36-41% at baseline (24, 25). Mean age at baseline is
between 53 and 59 years and 69 to 78% are female (respectively SCQM and the NDB) (25). Socio-
economic status (28% - 26% of college graduates) and baseline levels of patient reported HRQoL (EQ-
5D, SF36-MCS, SF36-PCS) were also very comparable between the two cohorts (25).
The study’s primary outcome was an aspect of Health Related Quality of life, psychosocial health,
as measured by the mental component summary (MCS) score of the Medical Outcomes Study 36-item
Short Form version I (SF-36) (26). Both mental and physical component summary (MCS and PCS) were
computed using the US population norms, which, in a healthy population, yield a mean of 50 and a
standard deviation of 10. SF-36 has been shown to be a reliable and valid generic HRQoL measure for
RA patients (13-15). We chose to focus on the MCS in order to best capture the emotional, psychological
and social aspects of patients’ experiences. The physical and disease specific aspects are better assessed
by instruments such as the Rheumatoid Arthritis Disease Activity Index (RADAI) for RA disease activity,
and the Health Assessment Questionnaire – Disability Index (HAQ) for functional disability. Patients’
pain level was assessed using a 10-cm visual analog scale (VAS). Socio-demographic variables (sex, age,
education, and living arrangement, i.e., living with someone or alone) as well as disease duration were
also recorded.
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Analysis
We used a specific type of structural equation model, the latent state-trait (LST) model (27, 28), to
examine the longitudinal evolution and variability of psychosocial health. LST models offer the
advantage, over other longitudinal models such as the latent growth curve model, of allowing to analyze
data that show no time trend but only random fluctuations. LST models permit to distinguish various
components of psychosocial health as follows:
Psychosocial health = Stable part (‘trait’) + Variable part (‘state’) + Error of measurement
with Variable part = Global Time-trend + Fluctuations due to moment of measurement
With LST models, it is possible to decompose the variance of each observed variable into a proportion
due to the ‘true psychosocial health’, (i.e. the reliability of the measure) and a proportion due to error of
measurement. Similarly, the variance of the ‘true psychosocial health’ can be then separated into a stable
part that does not fluctuate over time (the ‘trait’) and a variable part (the ‘state’). Finally, the variable part
can be further broken down into a part due to the influence of the preceding measure (global time-trend)
and a part due to the situation at the moment of measurement (fluctuations).
Figure 1 shows the specific model applied to our data. The stable part of psychosocial health is
represented by one latent trait variable (psy-soc health). The variable part due to the moment of
measurement (fluctuations) is represented by a second set of latent variables (Year1 to Year4). Since there
are four MCS measures (one for each year), there are four latent occasion-specific variables measuring
the same construct (psychosocial health). To take into account the differences in formulation of the four
subscales, we included a latent variable representing the deviation of the second observed variable (mcs2)
from the trait (Psy-soc health dev) (29). With one score per year, the influence due to the moment of
measurement (e.g., decrease in psychosocial health) and measurement error cannot be distinguished. On
the contrary, when the same construct (psychosocial health) is measured at the same time with two or
more instruments, one may distinguish the random influences of measurement errors and the systematic
influences due to the time of measurement, as the yearly influences will impact all scores similarly,
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whereas measurement error will impact each score differently. Thus, SF-36 subscales were aggregated in
two test-halves scores for each occasion of measurement (mcs1 et mcs2 ) (30). In this parceling procedure,
each of these scores is comprised of four out of eight subscales of the SF-36 (mcs1: mental health, vitality,
global health, physical functioning; mcs2: role emotional, social functioning, bodily pain, role physical).
The four subscales were chosen in order to make each score equally informative (i.e., the sum of the MCS
weights are equal across scores) and computed according to the usual MCS weights (26) (for detailed
explanation, see Technical Appendix 1) To allow the assessment of the global time-trend of psychosocial
health, we added an autoregressive structure (i.e., each latent “year1” to “year4” variable was regressed
on the preceding year) (31, 32).
We imposed restrictions on the estimated parameters (e.g., constraining all the error variances of
the observed mcs variables to be equal) and used the sample size adjusted Bayesian information criterion
(aBIC) to determine the best-fitting model (see Technical Appendix 2). To examine the predictors of
stable psychosocial health, we regressed the latent trait variable on the socio-demographic variables,
disease duration and mean disease activity (RADAI), disability index (HAQ) and VAS pain scores. To
examine the predictors of the variable psychosocial health, we regressed the latent occasion-specific
variables on the year-specific RADAI, HAQ and VAS pain scores. All parameters were estimated with
Mplus 5.2 using the maximum likelihood estimator. Missing data were imputed by full information
maximum likelihood. A sensitivity analysis estimating the parameters separately for each country of
origin showed no evidence of effect modification (data not shown).

RESULTS
The combined database included 15,282 RA patients (11,223 from NDB, 4,059 from SCQM),
yielding a total of 48,323 observations (mean number of measures per patient: 3.2). Table 1 shows the
distribution of patient characteristics by country of origin.
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Based on the LST model, about 85% of total variability in MCS scores was attributable to true
differences in psychosocial health (table 2, column 2), and 15 % was attributable to measurement error.
The stable part of psychosocial health accounted for 69% up to 75% of true variance. This suggests that
psychosocial health as measured by the SF-36 does not evolve much over a period of 4 years in RA
patients (table 2, column 3).
The variable part of psychosocial health represented 25% to 31% of true variance over the four
years (table 2, column 4). Most of the variability was attributable to fluctuations due to the moment of
measurement (table 2, column 6), and not to a global time trend in psychosocial health. Indeed, the
longitudinal evolution of psychosocial health over the years predicted only 1 to 4% of the psychosocial
health assessment one year later (table 2, column 5). Expressed differently, this corresponded to
autoregression coefficients of 0.18 from the 1st to 2nd year, 0.31 from 2nd to 3rd, and 0.30 from 3rd to 4th.
These coefficients were not significantly different from 0.

Prediction of the stable part of psychosocial health
In univariate analyses, pain (measured by VAS) and disease activity (RADAI) were strongly
associated with the stable component of psychosocial health. In contrast functional disability (HAQ),
disease duration, age, education, and living alone (vs. not) showed more modest associations with
psychosocial health (Table 3). In multivariate analyses, pain and disease activity remained strongly
associated with psychosocial health, and the association with functional disability increased. All the other
associations between the stable trait of psychosocial health and demographic characteristics became
weak. Pain was the strongest predictor of the stable part of psychosocial health, both in univariate and in
multivariate analysis.

Prediction of the variable part of psychosocial health
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In univariate analyses, yearly measures of disease activity, function, and pain were modestly, but
significantly, associated with the variable part of psychosocial health (Table 4). In multivariate analyses,
only pain remained a significant predictor of the variable part of psychosocial health, whereas yearly
disease activity or function did not.

Overall, pain explained more than 60% (e.g., -0.822 for year 2) of the stable part of psychosocial
health and about 5% (e.g., -0.222 for year 2) of the variable part of psychosocial health (Figure 2).
Altogether, this represents 44% of observed psychosocial health. In contrast, disease activity and
functional disability respectively explained 35% and 36% of the stable part of psychosocial health, and
only a negligible fraction of the variable part. Altogether, this represents respectively 23 and 22% of
observed psychosocial health.

DISCUSSION
The ultimate goal of treating a chronic disease is to preserve the patients’ quality of life and not
primarily, at least according to patients, to prevent one additional erosion (33). In this study, we examined
the longitudinal evolution of patients’ psychosocial health, its variability and the predictors of change in
psychosocial health in a very large cohort of patients treated for RA. We found that psychosocial health,
as measured by the mental component of SF-36, is comparable to a healthy population. Moreover, it does
not change much over four years. Indeed, psychosocial health is mostly stable over time and the variable
component reflects essentially year-specific influences and not a consistent longitudinal trend (i.e.,
progressive deterioration). Longitudinal studies of RA patients have shown that the deterioration of
functional disability and the progression of structural joint damage have become very slow in recent
years, which may be explained by the widespread use of DMARDs (7, 10, 11, 24, 34, 35). This is
consistent with our finding of the stability of psychosocial health over a follow-up of 4 years. Since
treated RA patients have a mostly stable psychosocial health, analyses with very long follow-ups are
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required to determine whether psychosocial health either improves (15) or worsens (16) gradually over
longer periods of time, which was not demonstrable in this study.
Because psychosocial health does not change much over time, treatment strategies should focus on
factors that influence the stable part of psychosocial health. Our most interesting finding in this regard is
that self-perceived pain was the strongest predictor of the stable part of psychosocial health, more
important than disease activity and functional disability. Pain was also the only significant predictor of
the variable part of psychosocial health, although to a smaller extent, which suggests that antirheumatic
treatments have an insufficient effect on pain relief. Indeed, in this large cohort of patients, all treated for
their RA and followed by rheumatologists, contrary to psychosocial health, the reported level of pain was
considerable, with mean VAS above 6. These results match survey findings reporting that about two
thirds of RA patients have inadequate pain relief (36, 37), even when their disease is considered to be
“well-controlled” (37, 38). Furthermore, many patients report that their physician focuses on disease
control but is less concerned with pain relief (39). This is further corroborated in long term follow-up
studies, in which patient report of insufficient pain relief remains surprisingly stable, despite parallel
improvement in disease control and function (7, 10).
The reasons for high pain levels in RA patients are complex and intricate. On the one hand, some
patients may be ambivalent regarding the chronic use of analgesics and wary of their potential side effects
(39). On the other hand, rheumatologists’ main focus in RA patients has not been pain relief, but the
control of inflammation and the prevention of permanent damage or disability (18, 19). Most
rheumatologists would not consider themselves as ‘pain physicians’ in their professional identity (18, 20).
Other potential barriers to effective pain management include reluctance to prescribe opioids, inclination
for immunologic research over pain research, or inadequate financial compensation (18). Recently, a
newly appointed task force of the American College of Rheumatology has reviewed these issues,
acknowledging insufficient efforts devoted to pain management, and discussing new perspectives in
education and research (20). Yet whether better pain management will improve quality of life remains
uncertain and cannot be answered by our observational data. Nevertheless, our results are a reminder for
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clinicians to listen carefully to patients’ complaints and to not forget to treat arthritis pain, as this could
impact patients’ well being more than anything else.
As regards socio-demographic predictors, previous studies have suggested that the quality of
social support of RA patients may play an important role in their quality of life (40). We found that living
alone (vs. with someone) was weakly associated with psychosocial health, but the association was not
confirmed in multivariate analysis. However, we did not measure the quality of social support, which
seems to be a stronger determinant of psychosocial health than marital status or living arrangements (41-
43).
Strengths and limitations
The main limitation of our study is a follow-up limited to 4 years. As previously discussed, given
the stability of psychosocial health, a longer follow-up would be needed to capture minor time-trends.
Another limitation we did not measure the incidence of psychiatric co-morbidities, such as anxiety and
depression. However, although these specific diagnoses could be confounders of the relationship between
RA activity and psychosocial health, they can also stand in the causal pathway between them. For
example, chronic pain can cause depression which will impact on psychosocial health. Therefore, we
chose to focus on disease-related predictors of psychosocial health, such as disease activity, functional
disability, and self-reported pain. Finally, by restricting our sample to patients who answered the
questionnaires at least twice, we may have selected individuals who are more compliant and have a higher
quality of life.
The main strengths of our study are the large multi-centered cohorts that enhance the
generalizability of our results, and the use of structural equation modeling, that helped explore and
quantify all components of variability in psychosocial health measures. Moreover, we assessed
psychosocial health using the MCS of SF-36, an instrument that is well suited to capture the emotional,
psychological and social aspects of patients’ well-being, and has been shown to be a reliable and valid
generic HRQoL measure for RA patients (13-15).
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Conclusion
Pain level was the most important predictor of both the stable part and variable part of psychosocial
aspects of HRQoL in treated RA patients. This suggests an overall insufficient pain relief, independent of
the control of disease activity. Our results prompt greater focus and efforts on pain management in RA
patients.

Acknowledgements:
We are grateful to the SCQM and the NDB staff for data management and support and to
participating physicians and patients who made this study possible.

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