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Publicly Available Published by De Gruyter May 18, 2018

The role of pain in chronic pain patients’ perception of health-related quality of life: a cross-sectional SQRP study of 40,000 patients

  • Peter Molander , Huan-Ji Dong , Björn Äng , Paul Enthoven and Björn Gerdle EMAIL logo

Abstract

Background and aims

Health-related quality of life (Hr-QoL) reflects the burden of a condition on an overarching level. Pain intensity, disability and other factors influence how patients with chronic pain perceive their condition, e.g. Hr-QoL. However, the relative importance of these factors is unclear and there is an ongoing debate as to what importance pain measures have in this group. We investigated the importance of current pain level and mood on aspects of Hr-QoL in patients with chronic pain and investigated whether such relationships are influenced by demographics.

Methods

Data was obtained from the Swedish Quality Registry for Pain Rehabilitation (SQRP), between 2008 and 2016 on patients ≥18 years old who suffered from chronic pain and were referred to participating specialist clinics. Dependent variables were general Hr-QoL [using two scales from European Quality of Life instrument: EQ5D Index and the European Quality of Life instrument health scale (EQ thermometer)] and specific Hr-QoL [from the Short Form Health Survey (SF36) the physical component summary (SF36-PCS) and the mental (psychological) component summary (SF36-MCS)]. Independent variables were sociodemographic variables, pain variables, psychological distress and pain attitudes. Principal component analysis (PCA) was used for multivariate correlation analyses of all investigated variables and Orthogonal Partial Least Square Regression (OPLS) for multivariate regressions on health aspects.

Results

There was 40,518 patients (72% women). Pain intensity and interference showed the strongest multivariate correlations with EQ5D Index, EQ thermometer and SF36-PCS. Psychological distress variables displayed the strongest multivariate correlations with SF36-MCS. Demographic properties did not significantly influence variations in the investigated Hr-QoL variables.

Conclusions

Pain, mood and pain attitudes were significantly correlated with Hr-QoL variables, but these variables cannot explain most of variations in Hr-QoL variables. The results pinpoint that broad assessments (including pain intensity aspects) are needed to capture the clinical presentation of patients with complex chronic pain conditions.

1 Introduction

In Europe, chronic pain of moderate to severe intensity affects about one out of five people [1]. There is an overwhelming amount of literature that describes the wide spread detrimental consequences of chronic pain. Hence, negative implications are reported for mood and mental health status, quality of life (QoL), social functioning and work [2], [3], [4]. Beyond the serious individual suffering, there are prominent societal aspects to consider. For example, in 2003 chronic pain in Sweden cost almost 90 billion the Swedish currency (krona) (SEK) annually [5].

In light of the growing concerns about the serious harms associated with opioids, some authors have pointed out the danger of reliance on pain intensity ratings when assessing the chronic pain, since that may result in opioid prescriptions [6]. Thus, multiple measures are needed to properly elucidate a patient’s situation [6] so factors other than pain intensity should be considered when assessing the burden, suffering, disability and QoL in chronic pain patients [7]. Patients themselves, when seeking a health professional for chronic pain, prioritize reduction of pain intensity levels [8]. Moreover, pain levels can act as markers for level of suffering and higher pain levels are likely to also reflect presence of comorbidities. Moore et al. [9] reported that pain intensity reductions predicted improvements in other outcomes. Cognitive behavioral therapy (CBT) or acceptance commitment therapy (ACT) are often incorporated in multimodal (multidisciplinary) rehabilitation programs (MMRPs) [10]. A long-standing truth relating both to MMRPs and CBT/ACT has been that the intervention should not aim at decreasing the pain itself, but rather on increasing function in the areas of life that are affected and where the patient would most value a change [11].

Health-related quality of life (Hr-QoL) instruments describe the burden of disease on an overarching level and is often markedly reduced in patients with chronic pain [12], [13], [14]. Hr-QoL is identified as one of several core outcome domains for clinical trials in chronic pain conditions according to several committees [15], [16]. Pain intensity is negatively correlated with Hr-QoL according to a systematic review [17]; the correlation coefficient (r) varied between −0.3 and −0.7. As mentioned above chronic pain is often associated with increased prevalence of psychological symptoms, e.g. anxiety and depressive symptoms. Lamé et al. reported that psychological (i.e. anxiety) aspects were more important for Hr-QoL than pain intensity aspects [18]; similar results have been reported by other authors [19], [20]. Even though psychological variables (e.g. depressive and anxiety symptoms) show significant associations with Hr-QoL aspects [19], [21], [22], [23], [24], several studies report that pain aspects have stronger associations [21], [23], [25]. Hence, the literature is not in consensus with respect to the relative importance of pain and anxiety/depressive symptoms in relation to the Hr-QoL situation. Knowledge about these relationships in chronic pain patients may be important for the contents both of assessments and treatments.

For around two decades, patients with complex chronic pain conditions at specialist level pain centres in Sweden have been included in the Swedish Quality Registry for Pain Rehabilitation (SQRP). This registry consists of validated self-report questionnaires including sociodemographic items [21]. This large cross-sectional study explored the relative importance of pain characteristics, psychological factors and pain attitudes for Hr-QoL aspects by examining all patients registered in the SQRP between 2008 and 2016 with respect to the following two research questions:

  1. What relative importance does pain level and mood have for aspects of chronic pain patient’s Hr-QoL?

  2. Are such relationships influenced by demographical properties such as gender, education, or country of birth?

2 Materials and methods

2.1 The Swedish Quality Registry for Pain Rehabilitation (SQRP)

The SQRP, recognized as a national registry by the Swedish Association of Local Authorities and Regions, provides the results of MMRP at a group level to the participating clinical departments. Based on SQRP data, health care providers and researchers can develop and improve assessments of patients with chronic pain and continue to improve MMRPs. In a review of the overall quality of approximately 60 nationwide registries, the Boston Consulting Group considered the SQRP as one of the top 10 national registries in Sweden [26]. The SQRP is mainly based on questionnaires and includes descriptive variables of the patients’ background, pain characteristics and other symptoms such as depression and anxiety, function, activity/participation and Hr-QoL. The patients complete the SQRP questionnaires on as many as three occasions: (1) at the assessment during the first visit to the clinical department (pre-MMRP or baseline); (2) immediately after the MMRP and (3) 12 months after the MMRP. Demographic data are only collected at baseline. The present cross-sectional study is based on patients who answered the SQRP questionnaires at baseline.

2.2 Subjects

This study included SQRP data from women and men ≥18 years old who had chronic (≥3 months) non-malignant pain. Patients with complex chronic pain conditions and in the need for a bio-psycho-social assessment referred to the different specialist clinics associated with SQRP between 2008 and 2016 were selected to participate in SQRP. They were characterized as complex since they, e.g. had psychological comorbidities with difficult relationships with pain aspects, their condition severely affected working life and participation in social activities, and/or did not respond to routine pharmacological/physiotherapeutic treatments delivered in a monodisciplinary fashion. The total number of patients referred to the specialist clinics are not known but the steering committee of SQRP has estimated the response rate among those selected with complex chronic pain conditions to >90%. Data used from the SQRP included background variables, pain characteristics, psychological strain, pain attitudes and Hr-QoL aspects. A somewhat more thorough description of the variables of the SQRP have been described in previous publications and are thus shortened here [10].

2.2.1 Independent variables

2.2.1.1 Background variables
  • Age (years).

  • Gender (male or female).

  • Education level had the possible answerers: university or college; upper secondary school; elementary school and other. This variable was dichotomized into university vs. the other alternatives and denoted as University.

  • The variable concerning place of birth had the following possible answers: country outside Europe; Europe except Nordic country; another Nordic country and Sweden. It was dichotomized into Europe vs. outside Europe and in the following denoted as Non-Europe. The choice of this dichotomization was based upon the circumstance that Sweden have received many refugees from countries outside Europe and several of these have experienced war and associated situations.

2.2.1.2 Pain characteristics

Pain characteristics were represented by seven variables:

  • Average pain intensity during the last week (a numeric rating scale 0–10 where 0=no pain and 10=worst possible pain); denoted as average pain intensity the last week (NRS-7days).

  • Pain severity [the MPI subscale concerning pain severity (MPI-Pain-severity); 0=no pain to 6=very intense pain] and pain-related interference in everyday life [the MPI subscale pain related interference in everyday life (MPI-Pain-Interfer); 0=no interference to 6=extreme interference] were chosen from the West Haven-Yale Multidimensional Pain Inventory (WHY-MPI), which is a 61-item self-report questionnaire that measures psychosocial, cognitive and behavioral effects of chronic pain [27], [28].

  • A Pain Region Index (PRI) was created by letting participants mark anatomical areas that were hurting, with 36 possible area of which 18 are located on the front of the body and 18 on the back.

  • A single question: when did you first experience the pain you are currently troubled by (days)? This variable was denoted as number of days with pain (pain-duration).

  • A single question: if persistent pain exists, for how long (days)? This variable was denoted as number of days with persistent pain (pain-duration-persist).

  • The patients also reported if the pain was recurrent (coded 0) or persistent (coded 1). This variable was denoted as presence of persistent pain in contrast to recurrent pain (pain-persistent).

2.2.1.3 Psychological strain

Four variables were chosen to represent mood aspects:

  • The affective distress scale was chosen from the MPI [the MPI subscale concerning distress (MPI-distress); 0=no distress to 6=very distressed] [27], [28].

  • The Hospital Anxiety and Depression Scale (HADS) measures symptoms of anxiety and depression (i.e. distress) using 14 items [29]. HADS has seven items in each of the depression and anxiety subscales [here denoted as Hospital Anxiety and Depression Scale depression subscale (HADS-D) and Hospital Anxiety and Depression Scale anxiety subscale (HADS-A), respectively], which were both chosen for this study. Possible scores per subscale are between 21 and 0, with the lower score indicating less distress. HADS is a frequently used questionnaire in both clinical practice and in research settings [29], [30].

  • The scale concerning mental health (SF36-MH) was chosen from the Short Form Health Survey (SF36; see below).

2.2.1.4 Pain attitudes

Four variables were chosen for this area:

  • The scale concerning perceived Life Control [the MPI subscale Perceived Life Control (MPI-LifeCon); 0=poor control to 6=good control] was chosen from the MPI [27], [28].

  • Two subscales – the activity engagement scale [the Chronic Pain Acceptance Questionnaire subscale activity engagement (CPAQ-AE)], based upon 11 items, score range: 0–66) and the pain willingness scale [the Chronic Pain Acceptance Questionnaire subscale pain willingness (CPAQ-PW), based upon nine items, score range: 0–54] – were used from the Chronic Pain Acceptance Questionnaire (CPAQ) (a 20-item scale) [31]. Items are rated on a scale from 0 (never true) to 6 (always true). CPAQ aims at measuring acceptance behaviors and attitudes towards pain. The questionnaire has shown satisfactory psychometric properties both in the English and Swedish versions [31], [32].

  • The Tampa Scale for Kinesiophobia (Tampa) measures fear of movement [33] and rates items on a four-point Likert scale from ‘‘strongly disagree” to ‘‘strongly agree” for 17 items. Possible scores range from 17 to 68 [34]. The questionnaire has been a reliable assessment tool in chronic pain populations [33], [34], [35].

2.2.1.5 Dependent variables: health-related quality of life (Hr-OoL)

SQRP contains generic instruments reflecting both general and more specific Hr-QoL aspects.

2.2.1.5.1 General Hr-QoL aspects

The European Quality of Life instrument (EQ) captures a patient’s perceived state of health [36], [37], [38] and is a generic measure of Hr-QoL [39]. It consists of two parts. The first one has five dimensions: mobility, self-care, usual activities, pain/discomfort and anxiety/depression. By combining these five dimensions, the instrument calculates an index [the European Quality of Life instrument index (EQ5D index)]. The second part tasks the participant with making an estimation of today’s health on a thermometer-like scale ranging from 0 to 100 where higher numbers indicate better health EQ thermometer. This study uses both parts of the instrument.

2.2.1.5.2 Specific Hr-QoL aspects

The Short Form Health Survey (SF36) was constructed to represent multi-dimensional health concepts, and aims at measuring a full range of health states [40]. It is a frequently used measure and has previously been used in a large number of studies as well as in routine care. The instrument has eight different dimensions, each ranging from 0 to 100, and these can be combined in to a physical component summary (SF36-PCS) and a mental (psychological) component summary (SF36-MCS). This study uses both these summaries.

2.2.2 Statistics

All statistics were performed using either IBM SPSS Statistics (version 23.0) or SIMCA-P+ (version 13.0; Umetrics Inc., Umeå, Sweden). Due to the large number of subjects, a probability of <0.001 (two-tailed) was accepted as the criteria for significance. SQRP uses predetermined rules when handling single missing items of a scale or a subscale, an overview is available in an additional file (Supplement A1). For the analysis of gender differences, either Student’s t-test or χ2-test were used. Effects sizes (Cohen’s d) for variables associated with significant gender differences in the multivariate context (see below) were calculated as the difference between the group means/SD of the total sample [41]; the absolute effect sizes were classified as large for ≥0.80, medium for 0.50–0.79, small for 0.20–0.49 and insignificant for <0.20 [42].

The present study is part of a practice-based evidence (PBE) research project concerning multimodal/multidisciplinary rehabilitation. PBE evaluates whether the evidence reported in randomized controlled trials and systematic reviews also holds for a consecutive non-selected flow of patients in real-world practice settings. In PBE studies, advanced multivariate data analyses (MVDA) are suitable [43]. With classical statistical methods (i.e. regression), there is a risk for downplaying the interrelationships among different factors thus not facilitating correct conclusions [44]. Classical methods also assume variable independence when interpreting results [45] and it can be risky to consider one variable at a time [46]. If multicollinearity (i.e. high correlations) occurs among the X-variables, the regression coefficients become unstable and their interpretability breaks down. SIMCA-P+, in contrast to traditional statistical packages such as SPSS, uses the Nonlinear Iterative Partial Least Squares algorithm (NIPALS algorithm) when compensating for missing data – for variables/scales, max 60% missing data and for subjects, max 50% missing data. In the context of the problems with handling missing data and the obvious risks for multicollinearity problems (e.g. several measures of mood aspects), we have refrained from using multiple linear regression (MLR) and logistic regression (LR) in the present study. Instead we used advanced MVDA, i.e. principal component analysis (PCA) for the multivariate correlation analyses to detect outliers and Orthogonal Partial Least Square Regressions (OPLS) for the multivariate regressions using SIMCA-P+. MVDA does not require normal distribution [47]. PCA was used to check for multivariate outliers; this was done since outliers markedly can bias regressions. R2 describes the goodness of fit – the fraction of sum of squares of all the variables explained by a principal component [48]. Q2 describes the goodness of prediction – the fraction of the total variation of the variables that can be predicted by a principal component using cross validation methods [48]. Outliers were identified using two methods: (1) score plots in combination with Hotelling’s T2 and (2) distance to model in X-space [6]. No extreme outliers were detected.

OPLS was used to explore the relative roles of pain aspects and mood for explaining the variations in EQ5D index, EQ thermometer, SF36-PCS and SF36-MCS [48]. Since measures of an interesting aspect seldom are perfect several measures can be used in OPLS (and PCA) to capture the latent factor, e.g. pain intensity or mood. A OPLS-discriminant analysis [Orthogonal Partial Least Square Regression – discriminant analysis (OPLS-DA)] was used to understand which variables that differed between men and women (i.e. this analysis handles and takes advantage of the fact that several of the independent variables are intercorrelated). The variable influence on projection (VIP) indicates the relative relevance of each X-variable. VIP≥1.0 was considered significant if the VIP value had 95% jack-knife uncertainty confidence interval non-equal to zero [6]. P(corr) was used to note the direction of the relationship (positive or negative). P(corr) depicts the loading of each variable scaled as a correlation coefficient and thus standardizing the range from −1 to +1 [47]. P(corr) is stable during iterative variable selection and comparable between models. An absolute p(corr)>0.4–0.5 is generally considered significant [47]. For each regression, we report the R2, Q2 and the result (i.e. p-value) of a cross-validated analysis of variance (CV-ANOVA).

3 Results

3.1 Background data

There were 40,518 patients registered in the cohort and all of these patients were referred to the participating SQRP clinics throughout Sweden. The majority (72%) of the chronic pain patients were women. More men than women were born outside Europe (Table 1). A larger proportion of women than men had a university education. Men were significantly older than women (Table 2) and 86.3% of the patients reported that their pain was persistent (in contrast to recurrent) (Men: 85.7% vs. Women: 86.5%; χ2=4.073, df=1, p-value=0.044, ns). Although there were some significant gender differences (four out of seven pain variables, two out of four psychological variables, three out of four pain attitudes and two out of four health variables), these gender differences were numerically small except for pain spreading (PRI) (Table 2).

Table 1:

Background data.

Total Men Women Statisticsa (p-value)
Born outside Europe (%) 14.1% 17.6% 12.8% <0.001b
University education (%) 23.9% 18.5% 26.0% <0.001b
  1. a χ 2-test. bDenotes significant gender difference.

Table 2:

Age, pain variables, psychological variables, pain attitude variables and Hr-QoL aspects; mean, standard deviation (SD), median and range; statistical evaluation (p-values) with respect to gender differences.

Total
Men
Women
Statisticsa

p-Value
Mean SD Median Range Mean SD Median Range Mean SD Median Range
Age 43.3 11.3 44.00 84 44.4 11.3 45.00 84 42.9 11.2 44.00 70 <0.001b
Pain variables
 NRS-7days 7.0 1.8 7.00 10 6.9 1.9 7.00 10 7.1 1.7 7.00 10 <0.001b
 MPI Pain severity 4.5 1.0 4.67 6 4.4 1.0 4.33 6 4.5 0.9 4.67 6 <0.001b
 PRI 13.9 8.9 13.00 36 10.5 7.5 9.00 36 15.2 9.0 14.00 36 <0.001b
 MPI-Pain-Interfer 4.4 1.1 4.5 6 4.4 1.1 4.5 6 4.4 1.1 4.5 6 0.110
 Pain-duration 3,071 3,143 1,931 23,256 2,932 3,136 1,766 2,1434 3,126 3,144 1,998 23,256 <0.001b
 Pain-duration-persist 2,421 2,686 1,351 20,079 2,355 2,687 1,307 19,355 2,447 2,685 1,371 20,079 0.009
Psychological variables
 HADS-A 9.2 5.0 9.0 21 9.2 5.0 9.0 21 9.3 5.0 9.0 21 0.360
 HADS-D 8.7 4.7 9.0 21 9.0 4.9 9.0 21 8.6 4.7 8.0 21 <0.001b
 MPI-distress 3.5 1.3 3.7 6 3.5 1.4 3.7 6 3.5 1.3 3.7 6 0.447
 SF36-MH 53.8 22.7 56.0 100 52.7 23.5 52.0 100 54.2 22.4 56.0 100 <0.001b
Pain attitudes
 MPI-LifeCon 2.7 1.2 2.8 6 2.7 1.2 2.8 6 2.6 1.2 2.8 6 0.366
 CPAQ-AE 25.7 12.3 26.0 66 24.0 12.6 24.0 66 26.3 12.1 27.0 66 <0.001b
 CPAQ-PW 21.7 8.9 22.0 54 20.1 8.9 20.0 54 22.2 8.8 22.0 54 <0.001b
 TAMPA 39.5 9.3 39.0 51 42.7 9.4 43.0 50 38.4 9.0 38.0 51 <0.001b
Health aspects
 EQ5D-index 0.23 0.31 0.14 1.59 0.21 0.32 0.10 1.59 0.24 0.31 0.16 1.59 <0.001b
 EQ-thermometer 40.4 20.3 40.0 100 40.4 21.1 38.0 100 40.3 20.0 40.0 100 0.731
 SF36-PCS 28.7 8.3 28.3 66 29.8 8.3 29.4 64 28.3 8.2 27.9 66 <0.001b
 SF36-MCS 35.4 13.3 34.2 71 35.0 13.5 33.6 70 35.5 13.2 34.4 70 0.001b
  1. aStudent’s t-test (independent groups). For explanation of variables, see list of abbreviations. bIndicates significance.

A OPLS-DA was performed to understand which variables differentiated men and women. The significant regression OPLS-DA model (R2=0.10, Q2=0.10, CV-ANOVA (p-value)<0.001, n=38,861) identified the following variables as significant (i.e. VIP>1.0): PRI (VIP=3.00, p(corr)=0.78, p=0.63); Tampa (VIP=2.44, p(corr)=−0.53, p=−0.50); CPAQ-PW (VIP=1.45, p(corr)=0.22, p=0.23) and CPAQ-AE (VIP=1.07, p(corr)=0.36, p=0.34). Hence, female gender was associated with higher PRI, higher CPAQ-PW and CPAQ-AE and lower Tampa compared to men. The effect sizes (i.e. Cohen’s d) were insignificant to medium for the significant variables – PRI=0.52; Tampa=0.46; CPAQ-PW=0.24 and CPAQ-AE=0.19. Although the regression was highly significant, the explained variation was low (i.e. 10%), a finding that supports the conclusion that gender differences generally were small.

3.2 Variables multivarietly important for general Hr-QoL aspects

The significant regressions of the EQ5D index (Table 3) and the EQ thermometer (Table 4) showed several similarities. Two pain aspects (MPI-Pain-severity and MPI-Pain-Interfer) showed the strongest correlations (i.e. the most important regressors) with the two EQ variables. In the regression of EQ5D index (Table 3), the third important variable was NRS-7days followed by two pain attitude variables (CPAQ-AE and MPI-LifeCon). Then followed the four variables reflecting mood aspects (SF36-MH, HADS-D, MPI-distress and HADS-A). In addition, the regression of EQ-thermometer showed that pain attitudes were more strongly correlated than the four mood variables (Table 4). Neither the other pain aspects (i.e. spreading of pain, duration of pain and pain-persistent) nor age, gender and background variables such as education level and country of birth contributed significantly in the two analyses (Tables 3 and 4).

Table 3:

OPLS regression of EQ5D-index.

Regressors VIP p(corr)
MPI-Pain-severity 1.68 −0.86
MPI-Pain-Interfer 1.55 −0.79
NRS-7days 1.45 −0.78
CPAQ-AE 1.38 0.70
MPI-LifeCon 1.30 0.66
Sf36-mh 1.26 0.60
HADS-D 1.24 −0.63
MPI-distress 1.22 −0.64
HADS-A 1.09 −0.55
Tampa 0.98 −0.52
CPAQ-PW 0.84 0.46
PRI 0.53 −0.31
Non-Europe 0.44 −0.24
University 0.23 0.16
Pain-persistent 0.19 −0.10
Gender 0.11 0.03
Age 0.06 0.01
Pain-duration-persist 0.06 −0.04
Pain-duration 0.05 −0.04
R 2 0.42
Q 2 0.42
CV-ANOVA (p-value) <0.001
n 37,167
  1. VIP (VIP>1.0 is significant) and p(corr) are reported for each regressor. The sign of p(corr) indicates the direction of the correlation with the dependent variable (+=positive correlation; −=negative correlation). The four bottom rows of each model report R2, Q2, p-value of the CV-ANOVA and number of patients included in the regression (n). For explanation of variables, see list of abbreviations. Variables in bold are significant regressors (VIP>1.0).

Table 4:

OPLS regression of EQ thermometer.

Regressors VIP p(corr)
MPI-Pain-Interfer 1.57 −0.81
MPI-Pain-severity 1.52 −0.81
CPAQ-AE 1.51 0.74
MPI-LifeCon 1.46 0.72
HADS-D 1.36 −0.68
NRS-7days 1.33 −0.72
Sf36-mh 1.30 0.63
MPI-distress 1.23 −0.66
HADS-A 1.01 −0.55
Tampa 0.89 −0.46
CPAQ-PW 0.79 0.43
PRI 0.68 −0.38
Non-Europe 0.28 −0.15
University 0.20 0.13
Pain-duration-persist 0.15 −0.11
Pain-persistent 0.14 −0.07
Pain-duration 0.13 −0.11
Age 0.01 −0.03
Gender 0.01 −0.04
R 2 0.32
Q 2 0.32
CV-ANOVA (p-value) <0.001
n 36,486
  1. VIP (VIP>1.0 is significant) and p(corr) are reported for each regressor. The sign of p(corr) indicates the direction of the correlation with the dependent variable (+=positive correlation; −=negative correlation). The four bottom rows of each model report R2, Q2, p-value of the CV-ANOVA, and number of patients included in the regression (n). For explanation of variables, see list of abbreviations. Variables in bold are significant regressors (VIP>1.0).

3.3 Variables multivarietly important for specific Hr-QoL aspects

3.3.1 SF36-PCS

Four pain variables and two pain attitude variables were significantly associated with SF36-PCS (Table 5); the three variables with strongest correlations were MPI-Pain-Interfer, MPI-Pain-severity and NRS-7days, which correlated negatively with SF36-PCS. CPAQ-AE showed a positive significant relationship with SF36-PCS, and fear-avoidance (Tampa) had a negative correlation.

Table 5:

OPLS regression of SF36-PCS.

Regressors VIP p(corr)
MPI-Pain-Interfer 2.13 −0.69
MPI-Pain-severity 1.99 −0.69
NRS-7days 1.74 −0.61
CPAQ-AE 1.65 0.54
PRI 1.29 −0.41
Tampa 1.05 −0.34
MPI-LifeCon 0.79 0.25
CPAQ-PW 0.60 0.22
Age 0.51 −0.16
Sf36-mh 0.36 −0.09
Gender 0.36 −0.11
HADS-D 0.35 −0.11
University 0.31 0.10
Pain-duration 0.31 −0.04
Pain-duration-persist 0.28 −0.01
HADS-A 0.21 0.09
Non-Europe 0.21 −0.07
Pain-persistent 0.15 −0.05
MPI-distress 0.05 −0.02
R 2 0.43
Q 2 0.43
CV-ANOVA (p-value) <0.001
n 35,596
  1. VIP (VIP>1.0 is significant) and p(corr) are reported for each regressor. The sign of p(corr) indicates the direction of the correlation with the dependent variable (+=positive correlation; −=negative correlation). The four bottom rows of each model report R2, Q2, p-value of the CV-ANOVA, and number of patients included in the regression (n). For explanation of variables, see list of abbreviations. Variables in bold are significant regressors (VIP>1.0).

3.3.2 SF36-MCS

In this analysis, SF36-MH was excluded since it is used to calculate the SF36-MCS. However, the three remaining mood variables displayed the strongest correlations with SF36-MCS followed by two pain attitude variables (MPI-LIfeCon and CPAQ-AE) (Table 6). In contrast to the previous regression analyses, only one significant pain variable emerged here – the pain interference variable (MPI-Pain-Interfer).

Table 6:

OPLS regression of SF36-MCS.

Regressors VIP p(corr)
HADS-D 1.87 −0.86
MPI-distress 1.86 −0.87
HADS-A 1.81 −0.86
MPI-LifeCon 1.48 0.73
CPAQ-AE 1.17 0.56
MPI-Pain-Interfer 1.15 −0.55
CPAQ-PW 0.99 0.48
Tampa 0.87 −0.43
MPI-Pain-severity 0.70 −0.33
NRS-7days 0.55 −0.24
Non-Europe 0.48 −0.24
PRI 0.37 −0.17
Age 0.16 0.10
University 0.10 0.03
Pain-persistent 0.08 −0.03
Gender 0.05 0.04
Pain-duration-persist 0.02 0.04
Pain-duration 0.01 0.03
R 2 0.61
Q 2 0.61
CV-ANOVA (p-value) <0.001
n 35,693
  1. VIP (VIP>1.0 is significant) and p(corr) are reported for each regressor. The sign of p(corr) indicates the direction of the correlation with the dependent variable (+=positive correlation; −=negative correlation). The four bottom rows of each model report R2, Q2, p-value of the CV-ANOVA, and number of patients included in the regression (n). For explanation of variables, see list of abbreviations. Variables in type are significant regressors (VIP>1.0). Note that SF36-MH was not included as regressor.

4 Discussion

In this large cross-sectional study of patients with chronic pain we found that two pain-related variables including one of the pain intensity variables showed the strongest associations with the general Hr-QoL aspects – the EQ5D index and the EQ thermometer – and these were followed by pain attitude variables and mood variables. Pain-related variables, including pain intensity, followed by pain attitude aspects and mood variables were most strongly correlated with the specific physically-orientated Hr-QoL variable (i.e. SF36-PCS). The mood variables had the strongest associations with the mentally-orientated SF36-MCS followed by two pain attitude aspects. Age, gender and sociodemographic variables did not significantly influence the four investigated Hr-QoL variables in these contexts.

4.1 The relative importance of pain level and mood for general health aspects

A mix of variables were important in the regressions of the two general Hr-QoL aspects – i.e. the EQ5D index and the EQ thermometer. This finding emphasizes the need for assessing the character and interference of chronic pain conditions in health care settings. Hence, in this large cohort of patients with chronic pain conditions, pain aspects, pain attitude factors and mood variables contributed to the variations in the EQ5D index and EQ thermometer. The explained variation (R2) was somewhat higher for the EQ5D index than for EQ thermometer (42% vs. 32%) (Tables 3 and 4). There is no obvious reason for this difference and both analyses were unable to explain most of variation (i.e. R2) in the two general Hr-QoL variables. However, one plausible explanation could perhaps be that the EQ5D index consist of one item asking about pain levels, but as the research was conducted at specialist level pain clinics all the participants suffered from non-trivial pain. Using MLR, Mun et al. [39] found that pain intensity and mood aspects correlated significantly with EQ5D index but with EQ thermometer when controlling for gender, education, household income and several co-morbidities in elderly subjects. Nonetheless, these analyses underpin the need to adopt a multifactorial view for understanding the perception of Hr-QoL in patients with chronic pain conditions. Such a complexity has also been reported and advocated based on reports from smaller cohorts of chronic pain patients [4], [6], [19], [49], [50], [51], [52], [53], [54] and in studies investigating life satisfaction (i.e. a part of Hr-QoL) [55], [56]. The general pattern for the significant variables in relation to the EQ thermometer have been reported in small studies from the SQRP [23]. These observations and suggestions together with our results are in general agreement with a multifactorial framework [57], [58]. In the treatment context, the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) group also suggests such a multifactorial view [54].

Based on the serious concerns about the opioid epidemic in US, some authors have pointed out the danger of reliance on pain intensity ratings [6]. That is, factors other than pain intensity are important for the burden, suffering, disability and QoL in chronic pain patients [7], [18], [35]. The regressions of the EQ5D index and EQ thermometer (Tables 3 and 4) indeed demonstrated that several variables were important for general Hr-QoL perception and indicated the need to use many aspects, including pain intensity.

On the other hand, the relative importance of certain pain-related variables for the perceived general health according to EQ was obvious (Tables 3 and 4). In fact, two pain-related variables, including one of the pain intensity variables (MPI-Pain-severity), showed the strongest correlations with both EQ5D index and EQ thermometer. In addition, a second variable reflecting pain intensity (NRS-7days) was also significant in the two regressions: in the regression of the EQ5D index, NRS-7days was the third most important variable (Table 3) and in the regression of the EQ thermometer, NRS-7days was still significant as the sixth most important variable among the significant variables (Table 4). In addition, other studies have reported that pain intensity is a significant and important regressor of Hr-QoL [51], [59]. However, the literature does not present a consensus; several smaller studies of chronic pain patients do not favor pain intensity as one of the most strongly correlated variables with QoL aspects [19], [49], [50]. This difference in the literature may be the result of smaller sample sizes (i.e. fewer participants), so there is a higher risk of chance findings.

The other two most important variables – MPI-Pain-Interfer and CPAQ – reflect directly pain-related consequences of having pain (hindrances in daily activities such as work, leisure and relations with family). The next important variables include the two pain attitude aspects (CPAQ-AE and MPI-LifeCon) and generally the four mood variables. CPAQ-AE concerns the ability to live your life as you wish and carry on with valued commitments. From a certain point of view, CPAQ-AE and MPI-Pain-Interfer may be two sides of the same coin; a significant negative relation was found in a cohort of headache patients [60]. Acceptance appears to be linked to less pain, distress and disability and better well-being and health [31], [61], [62], [63], [64]. The significant pain and mood variables correlated negatively with the general Hr-QoL aspects. Their influences were “counteracted” by the two above mentioned pain attitude variables, which had positive correlations with the EQ5D index and EQ thermometer (Tables 3 and 4). Among the mood variables, depressive symptoms (HADS-D) appeared to be somewhat more important than anxiety-related symptoms (HADS-A) in both analyses of the two EQ variables. Depressive and anxiety symptoms had strong correlations with and were important regressors of QoL in other studies as well [19], [49].

Several pain aspects – i.e. pain durations and PRI – did not significantly contribute to the explained variation in the two general QoL variables. Previous studies found that pain durations did not influence Hr-QoL [21], [59], a finding that may be due to ceiling effects. Duration may be more important in acute and subacute phases of the course of a pain condition. The need to assess the spatial distribution has been noted [65] since pain conditions can be regarded as a continuum of spreading from single pain site to multisite pain [66], [67], [68], [69], [70], [71], [72], [73], [74]. In the present study, it was obvious that the patients on average had a relatively large spread of pain on the body. This observation agrees with other studies; multisite chronic pain is more prevalent than chronic pain at one site [67], [68], [70], [75], [76]. Findings among occupationally-active home care personnel, in contrast to the present study, indicated that pain spreading was associated with disability and low Hr-Qol [77].

4.2 The relative importance of pain level and mood for specific Hr-QoL aspects

The two analyses of the QoL indices of SF36 showed different patterns with respect to the important variables, which agrees with other studies [78]. Hence, the choice of Hr-QoL variables determines the conclusions that can be drawn from these analyses. Three pain variables were most strongly associated with the SF36-PCS and three mood variables were most important for SF36-MCS. Although SF36-PCS had a pain profile and SF36-MCS a mood profile, the variations of the two indices were not entirely explained by these factors and pain attitudes (SF36-PCS: CPAQ-AE and Tampa; SF36-MCS: CPAQ-AE and MPI-LifeCon) also contributed to the pain profile. The overall pattern of the regressors of SF36-MCS has been reported in a small cohort from the SQRP [23]. The importance of CPAQ-AE for physical and psychological QoL has also been reported earlier [79], but together with pain intensity it only explained a minority of the variations in QoL.

4.3 The importance of sociodemographic factors for Hr-QoL

When analysing the relative importance of various variables for variations in general and specific Hr-QoL variables it was evident that gender, age, educational level and country of birth was of little importance. A possible weakness could be that we only investigated a limited number of sociodemographic variables. Most subjects included in SQRP were women, a finding that agrees with results of small cohorts from the SQRP [10], [21]. Although several of the variables under investigation showed significant gender differences (Table 2) (e.g. women reported higher pain intensities and men reported a worse situation for several psychological variables), the numerical differences were small except for spreading of pain on the body (medians for PRI: men=9 and women=14). When also the intercorrelation pattern between the variables were considered using a OPLS-DA, these results were confirmed. Gender was not a significant variable in the analyses of the Hr-QoL variables (Tables 36) even though three of four health aspects showed significant gender differences in the omnibus testing (Table 2). This finding of small gender differences is comparable with studies that included patients from pain clinics [18], [80].

Our results that gender, age and education level were not significant variables in these multivariate analyses agree with other studies using the SF36 [18]. In contrast, a Spanish study (n=1,025) from 88 pain clinics reported that age and pain duration but not education level were significantly associated with the EQ5D index and EQ thermometer [4]. A study from the Netherlands reported that age and education only had substantial association with physical functioning of QoL [18].

In contrast to our results of chronic pain patients, sociodemographic factors appear to impact the QoL in the general population to a greater extent [81], [82], [83]. One possible explanation for this difference could be that in a chronic pain cohort pain itself and its related consequences overshadow other variables.

4.4 Strengths and limitations

An obvious strength of the present study is the large number of patients with chronic pain conditions included in the analyses. As the patients were reported from almost every specialist centre in the country, it is likely that the findings here are quite close to the true population values of people with complex chronic pain who seek specialist care. Hence, our results are reasonably highly representative for patients with complex chronic pain conditions. Another strength was that we used MVDA techniques to handle the fact that several of the regressors were intercorrelated with obvious risk for multicollinearity. Instead of presupposing independence between the regressors, we used statistical methods that better could handle regressors that were intercorrelated. The use of MVDA, which includes taking the internal correlation structure of the data set into account, markedly reduce the problem of multiple testing. Although outside of our aims, it is not possible (as in MLR or LR) to isolate the effects for a certain variable upon the dependent Hr-QoL aspects regressed. Another limitation is that our results cannot be generalized to persons with chronic pain in the community since we investigated patients referred to specialist clinics. Another limitation is that SQRP did not include the variable catastrophizing, which in other studies has been reported as an important variable for QoL [18]. Other important variables not included in this study are physical activity [84] and work-related psychosocial factors [85].

4.5 Clinical relevance

Hr-QoL reflects the burden of chronic pain on an overarching level and is recognized as one of several core outcome domains of treatments for patients with chronic pain conditions [15], [16]. Our results showed that pain, mood and pain attitudes are significantly correlated with Hr-QoL variables, but these variables cannot explain most of variations in Hr-QoL variables. Hence, broad assessments are needed in patients with complex chronic pain conditions referred to specialist clinics to capture important aspects of the clinical presentation. Even though pain intensity aspects are important to consider assessments cannot be reduced to only this aspect. Our results can also be important to consider when designing and optimizing interventions for patients with chronic pain conditions.

5 Conclusion

Two pain-related variables (MPI-Pain-severity and MPI-Pain-Interfer) were the most important variables explaining variations in the general Hr-QoL aspects, the EQ5D index and EQ thermometer, followed by pain attitudes and mood variables. SF36-PCS had a pain profile and SF36-MCS a mood profile, but pain attitudes also contributed to the explained variations in the analyses. Age, gender and sociodemographic variables did not influence any of the four investigated Hr-QoL aspects. The results pinpoint that broad assessments (including pain intensity aspects) are needed to capture the clinical presentation of patients with complex chronic pain conditions.


Corresponding author: Björn Gerdle, Professor, MD, PhD, Pain and Rehabilitation Centre, Department of Medical and Health Sciences, Linköping University, SE-581 85 Linköping, Sweden, Tel.: +46763927191

  1. Authors’ statements

  2. Research funding: This study was supported by grants from the Swedish Research Council, County Council of Östergötland (forsknings-ALF), and AFA insurance. The sponsors of the study had no role in study design, data collection, data analysis, data interpretation, writing of the report, or the decision to submit for publication. The authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.

  3. Conflict of interest: The authors declare no financial conflicts of interest and no competing interests.

  4. Informed consent: All participants received written information about the study and gave their written consent.

  5. Ethical approval: The study was conducted in accordance with the Helsinki Declaration and Good Clinical Practice and approved by the Ethical Review Board in Linköping (Dnr: 2015/108-31).

  6. Availability of data and material

  7. The datasets generated and/or analysed by this study are not publicly available as the Ethical Review Board has not approved the public availability of these data.

  8. Authors’ contributions

  9. All authors contributed to the conception of the study. BG extracted the data from SQRP and analysed the data. BG, PM, PE, BA and H-JD drafted the manuscript. All authors contributed to the writing and have approved the final version of the manuscript.

List of Abbreviations

CPAQ

Chronic Pain Acceptance Questionnaire

CPAQ-AE

The Chronic Pain Acceptance Questionnaire subscale activity engagement

CPAQ-PW

The Chronic Pain Acceptance Questionnaire subscale pain willingness

CV-ANOVA

cross-validated analysis of variance

EQ

The European Quality of Life instrument

EQ5D index

The European Quality of Life instrument index

EQ Thermometer

The European Quality of Life instrument health scale

HADS

Hospital Anxiety and Depression Scale

HADS-A

Hospital Anxiety and Depression Scale anxiety subscale

HADS-D

Hospital Anxiety and Depression Scale depression subscale

Hr-QoL

Health-related Quality of Life

IMMPACT

Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials

LR

Logistic regression

MLR

Multiple linear regression

MPI-distress

The MPI subscale concerning distress

MPI-Pain-severity

The MPI subscale concerning pain severity

MPI-LifeCon

The MPI subscale Perceived Life Control

MPI-Pain-Interfer

The MPI subscale pain related interference in everyday life

MVDA

Multivariate data analysis

NIPALS algorithm

Nonlinear Iterative Partial Least Squares algorithm

NRS-7days

Average pain intensity the last week

Non-Europe

Born outside Europe

OPLS

Orthogonal Partial Least Square Regression

OPLS-DA

Orthogonal Partial Least Square Regression – discriminant analysis

Pain-duration

Number of days with pain

Pain-duration-persist

number of days with persistent pain

Pain-persistent

presence of persistent pain in contrast to recurrent pain

PBE

Practice Based Evidence

PCA

Principal Component Analysis

PRI

Pain Region Index

SEK

the Swedish currency (krona)

SF36

The Short Form Health Survey

SF36-MCS

The Short Form Health Survey mental summary component

SF36-MH

The Short Form Health Survey subscale mental health

SF36-PCS

The Short Form Health Survey physical summary component

Tampa

The Tampa Scale for Kinesiophobia

University

University level education

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/sjpain-2018-0003).


Received: 2018-01-06
Revised: 2018-04-05
Accepted: 2018-04-24
Published Online: 2018-05-18
Published in Print: 2018-07-26

©2018 Scandinavian Association for the Study of Pain. Published by Walter de Gruyter GmbH, Berlin/Boston. All rights reserved.

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