Abstract
Objectives
In patients with gout there is a lack of longitudinal studies on the course of work productivity. We explored longitudinal changes in and predictors of work productivity over 2 years.
Methods
Patients in the NOR-Gout observational study with a recent gout flare and serum urate (sUA) >360 µmol/l attended tight-control visits during escalating urate lowering therapy according to a treat-to-target strategy. From the Work Productivity and Activity Impairment (WPAI) questionnaire, scores for work productivity and activity impairment were assessed over 2 years together with the Beliefs about Medicines Questionnaire and a variety of demographic and clinical variables.
Results
At baseline patients had a mean age of 56.4 years and 95% were males. WPAI scores at baseline were 5.0% work missed (absenteeism), 19.1% work impairment (presenteeism), 21.4% overall work impairment and 32.1% activity impairment. Work productivity and activity impairment improved during the first months, and remained stable at 1 and 2 years. Comorbidities were not cross-sectionally associated with WPAI scores at baseline, but predicted worse work impairment and activity impairment at year 1. The Beliefs about Medicines Questionnaire subscale with concerns about medicines at baseline independently predicted worse overall work impairment and worse activity impairment at year 1.
Conclusions
In patients with gout who were intensively treated to the sUA target, work productivity and activity impairment were largely unchanged and at 1 year predicted by comorbidities and patient concerns about medication.
Keywords: gout, work, treat to target, urate lowering treatment, beliefs about medicines, employment, worker productivity, outcome measures, health related quality of life
Rheumatology key messages.
Work productivity in gout patients is maintained over 2 years follow-up.
Comorbidities and concerns about medicines at baseline predict impaired work productivity and activities after 1 year.
Work productivity is unrelated to whether patients have indicators of good disease control and severity.
Introduction
Gout is a frequent inflammatory joint disease with rising prevalence [1, 2] and a considerable disease burden worldwide [3, 4]. The disease is characterized by painful episodical flares [5] and has an impact on health related quality of life [6–8].
Treatment of gout includes an educational component [9] and seeks to reduce serum urate (sUA) with urate lowering therapy (ULT) to prevent flares and other consequences of gout [10, 11], including work disability. However, many patients are not treated with ULT and do not reach target levels of sUA [1, 12, 13].
Employees with gout have more absence from work than those without gout [14], and 40% of patients report missing at least 5 days of work due to symptoms in the past year [15]. The cost of illness in gout is considerable and comparable to RA and AS [16], and costs may constitute a barrier to seeking treatment in some patients [17]. Colchicine, naproxen and prednisone had similar health economic implications in the treatment of gout flares [18]. Prescribing febuxostat in sequence after allopurinol is a cost effective strategy of ULT [19].
Different instruments have been compared for assessment of work productivity [20], and an assessment of work productivity in the past 7 days, such as the Work Productivity and Activity Impairment (WPAI) questionnaire [21], can accurately reflect the impact of disease while at work. The WPAI questionnaire has been applied in gout in some studies [7, 22–24], but not longitudinally. In patients with gout, a clinically meaningful impact on work impairment has been reported, also after controlling for comorbidities [22]. Surveys indicate that patients with uncontrolled gout may have lower work productivity than well-controlled patients or controls [16, 24, 25].
There is a need to study work productivity in gout over the disease course, especially in patients who have been treated adequately with ULT over time. We examined whether work productivity changed over 2 years after initiation of intensive treatment with ULT in gout patients, and if it can be predicted by disease-related factors.
Methods
Study design and participants
The prospective NOR-Gout (Gout in Norway) study is observational and performed in a hospital-based rheumatology clinic [26]. All included patients had crystal-proven gout and fulfilled the ACR/EULAR classification criteria for gout [27]. Participants were identified during an acute clinical gout flare after examination in the rheumatology outpatient clinic. Persons indicating willingness to participate in the study were contacted by a study nurse from the outpatient clinic for pre-screening, received written information, and were scheduled after a few weeks for a comprehensive baseline rheumatology study visit at Diakonhjemmet. They were required to have sUA >360 µmol/l at inclusion and have started ULT with allopurinol or febuxostat (if intolerant to allopurinol) [26] with frequent follow-up visits during the first year and a final visit after year 2. During this treat-to-target strategy, ULT was escalated to achieve sUA <360 µmol/l (or <300 µmol/l if clinical tophi were present) as recommended in international recommendations [10]. The study (ACTRN12618001372279) was registered at https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374171. The Norwegian Regional Committee for Medical and Health Research Ethics South East (reference number 2015/990) approved the study, patient partners were included in the study planning and participants gave their written informed consent.
Demographic, clinical and laboratory assessment
All patients were assessed by a study nurse and/or a rheumatologist at baseline, after 1, 2, 3, 6, 9, 12 and 24 months and with additional monthly visits until the sUA target was achieved. Demographics, clinical examinations including joint and subcutaneous tophi assessments, laboratory analyses and questionnaires addressing health status were collected according to the protocol.
At baseline, patients reported age, gender, ethnicity, marital status, family history for gout, disease duration, highest level of education, smoking and alcohol consumption.
The main outcome variable for this study was the WPAI score [28], which was measured at baseline, 3 and 6 months and at years 1 and 2. The WPAI questionnaire consists of six questions to determine employment status during the past 7 days: hours missed from work due to the disease, hours missed from work for other reasons, hours worked, the degree to which the disease affected work productivity while at work, and the degree to which the disease affected activities outside of work. Four WPAI scores are derived: (i) the percentage of missed work (absenteeism) as number of missed hours working/normal number of hours at work; (ii) the percentage of impaired productivity while at work (presenteeism), i.e. how much impact the disease has on work productivity; (iii) overall work impairment, which combines absenteeism and presenteeism; and (iv) percentage of impairment of the disease on activities performed outside of work. Greater scores indicate greater impairment. Questions related to absenteeism and presenteeism were applicable for patients who were working, but all provided data on activity impairment.
Information on number of flares ‘ever’ and ‘during the last year’ (before the recent study, i.e. entry flare) was collected as well as pain severity during the most recent and the strongest flare (0–10 numerical rating scales), with 0 = no pain and 10 = unbearable pain. Occurrence of flares was also recorded during the 2-year follow-up.
For comorbidities the Self-Administered Comorbidity Questionnaire was used at baseline (score range 0–36) [29]; it includes 12 medical problems, allocating 1 point per problem including presence, receiving treatment and causing a functional limitation.
At all visits OMERACT-endorsed questionnaires focusing on patient reported outcomes [30] including joint pain, general pain and patient global assessment of disease activity on a 0–10 numerical rating scales were completed. Physical function was measured with the HAQ Disability Index [31]. Health status was assessed by the Short-Form general health questionnaire (SF-36) [32], reporting the physical and mental component summaries.
Self-efficacy with subscales for pain (five items) and symptoms (six items) was measured with the Arthritis Self-Efficacy Scales [33]. This instrument measures whether patients have confidence in coping with pain, function and other symptoms due to arthritis (numeric rating scales 10–100, 100 = highest self-efficacy).
The Beliefs about Medicines Questionnaire (BMQ) was developed by Horne et al. [34, 35] and consists of 18 items. Each item is answered on a 5-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = uncertain, 4 = agree, 5 = strongly agree). Two main categories include general and specific beliefs. The general belief items are grouped into the subscales BMQ harm and BMQ overuse. These subscale scores range from 4 to 20, where a higher score reflects that the patients believe medications to be more harmful or overused, respectively. The specific part of the questionnaire is used to assess the patients’ positive or negative beliefs about the specific medications prescribed for their condition. The specific belief items are divided into the categories BMQ necessity and BMQ concerns. In both categories the scores range from 5 to 25, and a higher score reflects a higher belief in necessity or more concerns. The BMQ was found valid and reliable in Scandinavian languages, including Norwegian [36, 37].
Clinical assessments included weight and height for calculation of BMI and 44 swollen and tender joint counts. Subcutaneous tophi were assessed. Laboratory examinations included sUA (µmol/l) and CRP and ESR.
Statistics
We applied descriptive statistics for baseline variables. When comparing groups for continuous variables, Student’s t-test, ANOVA or an independent-samples Mann–Whitney U-test was used as appropriate, and for categorical data analyses we used the chi-square test.
In linear regression analyses dependent variables were the four WPAI scores at baseline, and 1- and 2-year follow-up. As the distribution of work productivity is skewed, we performed logarithmic transformation. We considered the following demographic and clinical variables: age, gender, disease duration, comorbidities, education, BMI, smoking, alcohol use, physical activity, baseline CRP and ESR, sUA at baseline or during the study, presence of subcutaneous tophi, experienced flare first year, tender and swollen joints, pain strength during last and during strongest flare, self-efficacy, HAQ Disability Index, SF-36 physical and mental summary, and the four BMQ subscales.
Candidate variables were tested for association with work productivity scores as the dependent variable in bivariate analyses. They were then included in multivariable model building if P < 0.15, adjusting for age, gender, disease duration and comorbidity score. The final models retained statistically significant variables, adjusting for age, gender, disease duration and comorbidity score. In longitudinal analyses of WPAI scores over 1 and 2 years, adjustments were also made for baseline WPAI scores.
The explained variance of the final linear regression models (R2) was calculated, and clinical candidate variables were included for partly adjusted analyses if P-value <0.15 and were further removed during model building based on partial correlation, examinations for multicollinearity and contribution to the final model.
No adjustments were made for missing data. Analyses were performed with IBM SPSS Statistics (version 27; IBM Corp., Armonk, NY, USA).
Results
Study population
The characteristics of patients are presented in Table 1. Patients were predominantly males, had a mean age of 56.4 years (s.d. 13.7 years) and disease duration of almost 8 years. At baseline 64.4% were working, 64.0% at year 1 and 61.3% after 2 years. sUA was a predefined target (<360 µmol/l) and reached in 85% after 1 and 79% after 2 years, while a flare was experienced by 81% and 26% during year 1 and year 2, respectively.
Table 1.
Patient characteristics
| Characteristic | n | % or mean (s.d.) |
|---|---|---|
| Age, years | 211 | 56.4 (13.7) |
| Male | 201/211 | 95.3% |
| Caucasian | 183/202 | 90.6% |
| Disease duration, years | 204 | 7.8 (7.6) |
| College education | 118/206 | 57.3% |
| Working at baseline | 134/208 | 64.4% |
| Body mass index, kg/m2 | 211 | 28.8 (4.5) |
| Comorbidity score | 210 | 3.7 (3.2) |
| Smoking daily | 23/208 | 11.1% |
| Alcohol use at least weakly | 128/207 | 61.8% |
| Presence of subcutaneous tophus | 35/211 | 16.6% |
| Allopurinol use ever at baseline | 31/211 | 14.7% |
| Allopurinol use month 12 | 163/186 | 87.6% |
| Allopurinol dose, mg daily | 289 (120) | |
| Febuxostat use month 12 | 23/186 | 12.4%) |
| Febuxostat dose, mg daily | 59 (23) | |
| Serum urate, µmol/l | 211 | 500 (77) |
| Previous flare last 12 months | 151/206 | 73.4% |
| Flare during year 1 | 150/186 | 80.6% |
| Strongest joint pain ever (0–10) | 208 | 8.4 (1.6) |
| Joint pain last flare (0–10) | 207 | 7.5 (5.5) |
| Health assessment questionnaire (0–3) | 209 | 0.38 (0.57) |
| Self-efficacy pain (10–100) | 209 | 65.3 (19.5) |
| Self-efficacy symptoms (10–100) | 205 | 72.6 (15.1) |
| Beliefs about Medicines Questionnaire | ||
| Necessity subscale (5–25) | 198 | 17.9 (4.4) |
| Concerns subscale (5–25) | 197 | 13.4 (4.9) |
| Overuse subscale (4–16) | 203 | 10.6 (2.8) |
| Harm subscale (4–16) | 203 | 9.4 (2.4) |
WPAI score and disease severity and control
Baseline WPAI scores showed that patients missed on average (mean) 5.0% of work, i.e. they had, because of the disease, on average of 5% fewer hours than what they should have worked. Patients had 19.1% work impairment, i.e. reduction in work productivity, 21.4% overall work impairment, and 32.1% activity impairment outside work due to the disease.
Percentages for the four scores of work productivity and impairment activity over 2 years are presented in Fig. 1. Work productivity and activity impairment improved during the first months, and remained stable at 1 and 2 years (Fig. 1), with a statistically significant reduction in three of the four scores over 2 years. The proportion of patients with full work and activity participation, not having any reduction in the WPAI scores at all, was high over 2 years and is shown in Table 2.
Figure 1.
Work Productivity and Activity Impairment (WPAI) questionnaire scores over 2 years (means and 95% CI)
Table 2.
Number of gout patients and percentages (%) with completely normal work productivity scores
| Baseline | Month 3 | Month 6 | Year 1 | Year 2 | |
|---|---|---|---|---|---|
| Work presenteeism | 105 (87.5) | 88 (94.6) | 91 (92.9) | 90 (94.7) | 77 (93.9) |
| Work absenteeism | 61 (52.1) | 72 (76.6) | 71 (74.0) | 71 (76.3) | 67 (81.7) |
| Overall work impairment | 59 (50.0) | 70 (76.1) | 70 (72.9) | 69 (75.0) | 67 (81.7) |
| Activity impairment | 67 (33.2) | 91 (57.2) | 92 (56.8) | 99 (61.9) | 102 (68.0) |
Percentage of patients who have best possible scores without any missed work, work impairment, overall work impairment or activity impairment. The denominator for work presenteeism, work absenteeism and overall work impairment are patients who were working, and for activity impairment all patients who provided data.
To examine the relationship between work productivity and impairment and measures of disease severity, WPAI scores were compared across tophus status, level of sUA at years 1 and 2, and flare occurrence during year 1 and 2. WPAI scores were in general not different across presence or absence of indicators of severe or uncontrolled disease (tophus, sUA or flare status) (Table 3). The only highly significant finding was higher work activity impairment at baseline in patients who experienced a flare during year 1 (P = 0.003). Number of flares was not related to WPAI scores.
Table 3.
Percentage of WPAI questionnaire scores and measures for disease control over 2 years (number per group)
| Baseline |
Year 1 |
Year 1 |
Year 2 |
Year 2 |
||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Tophus present | Tophus not present | Serum urate at target | Serum urate not at target | No flare | Flare | Serum urate at target | Serum urate not at target | No flare during | Flare | |
| Baseline | ||||||||||
| % Work missed | 0 (16) | 6 (104) | 4 (97) | 12 (15) | 10 (20) | 4 (94) | 4 (83) | 8 (21) | 5 (75) | 4 (29) |
| % Impairment at work | 20 (15) | 19 (102) | 18 (95) | 20 (14) | 9 (19) | 20 (92)a | 16 (82) | 27 (19) | 17 (72) | 20 (29) |
| % Overall work impairment | 20 (15) | 22 (103) | 20 (95) | 27 (15) | 18 (20) | 21 (92) | 18 (82) | 31 (20) | 20 (73) | 22 (29) |
| % Activity impairment | 32 (34) | 32 (168) | 29 (152) | 35 (26) | 17 (35) | 33 (48)b | 30 (132) | 36 (33) | 30 (123) | 34 (42) |
| Year 1 | ||||||||||
| % Work missed | 0 (15) | 2 (80) | 2 (87) | 0 (8) | 0 (19) | 2 (76) | 0 (70) | 5 (18) | 2 (65) | 0 (24) |
| % Impairment at work | 11 (15) | 5 (78) | 6 (85) | 1 (8) | 4 (19) | 6 (74) | 5 (69) | 9 (18) | 6 (62) | 7 (24) |
| % Overall work impairment | 11 (15) | 6 (77) | 7 (84) | 1 (8) | 2 (18) | 8 (74) | 5 (67) | 14 (19) | 7 (63) | 7 (24) |
| % Activity impairment | 13 (26) | 11 (134) | 12 (140) | 4 (20) | 6 (32) | 12 (128) | 11 (122) | 10 (28) | 10 (112) | 14 (38) |
| Year 2 | ||||||||||
| % Work missed | 0 (12) | 1 (70) | 1 (67) | 0 (15) | 2 (14) | 0 (68) | 1 (62) | 0 (20) | 1 (59) | 1 (23) |
| % Impairment at work | 11 (12) | 5 (70) | 6 (67) | 4 (15) | 9 (14) | 5 (58) | 6 (62) | 6 (20) | 5 (59) | 9 (23) |
| % Overall work impairment | 11 (12) | 5 (70) | 7 (67) | 4 (15) | 10 (14) | 6 (58) | 6 (62) | 6 (20) | 5 (59) | 10 (23) |
| % Activity impairment | 10 (23) | 12 (127) | 13 (129) | 6 (21) | 9 (29) | 12 (121) | 12 (120) | 10 (30) | 10 (111) | 17 (39) |
Values in parentheses are number of participants in a category.
P = 0.04 for comparison between groups with and without flare.
P = 0.003 for comparison between groups with and without flare. WPAI: Work Productivity and Activity Impairment.
Associations and prediction of WPAI scores
Several candidate variables were associated to baseline work productivity and impairment scores in bivariate analyses and then in linear regression adjusted for age, gender, disease duration and comorbidities. Missed work (absenteeism) was associated with HAQ. Impairment at work (presenteeism) was associated with HAQ, SF-36 mental component, presence of a tender and a swollen joint, self-efficacy for symptoms and self-efficacy for pain, pain strength during last and during strongest flare, alcohol use more than at least weekly, a flare experienced during year 1, and BMQ concerns and BMQ harm. Overall work impairment was associated with HAQ, SF-36 mental component, presence of a tender and a swollen joint, self-efficacy for symptoms and for pain, pain strength during last and during strongest flare, ESR, and BMQ concerns and BMQ harm. Activity impairment was associated with HAQ, SF-36 mental, presence of a tender or a swollen joint, self-efficacy for symptoms and for pain, pain strength during last flare, a flare, a flare experienced during year 1, and BMQ concerns, BMQ harm and BMQ overuse.
The fully adjusted model for baseline WPAI score is given in Table 4. The percentage of work missed was independently associated with HAQ. Work impairment and overall work impairment were both associated with HAQ, presence of a swollen joint, pain level at last flare, self-efficacy for pain, and BMQ concerns. Activity impairment was associated with HAQ, self-efficacy for pain and BMQ concerns.
Table 4.
Associations at baseline between disease variables and WPAI scores in linear regression analyses
| Dependent variable | Independent variable | β (95% CI) | s.e. | Standard β | P-value | Model statistics |
|---|---|---|---|---|---|---|
| % Work missed | R 2 = 0.17 | |||||
| HAQ | 0.40 (0.22, 0.60) | 0.10 | 0.37 | <0.001 | ||
| % Impairment at work | R 2 = 0.46 | |||||
| HAQ | 0.62 (0.31, 0.94) | 0.16 | 0.31 | <0.001 | ||
| Swollen joint | 0.50 (0.19, 0.80) | 0.15 | 0.27 | 0.002 | ||
| Self-efficacy pain | −0.01 (−0.02, 0.004) | 0.003 | −0.02 | 0.002 | ||
| Pain at last flare | 0.07 (0.01, 0.13) | 0.03 | 0.18 | 0.023 | ||
| BMQ concerns | 0.04 (0.01, 0.07) | 0.01 | 0.24 | 0.003 | ||
| % Overall work impairment | R 2 = 0.46 | |||||
| HAQ | 0.53 (0.30, 0.85) | 0.14 | 0.33 | <0.001 | ||
| Swollen joint | 0.43 (0.12, 0.74)) | 0.16 | 0.23 | 0.006 | ||
| Self-efficacy pain | −0.009 (−0.16, 0.003) | 0.003 | −0.22 | 0.006 | ||
| Pain last flare | 0.07 (0.01, 0.13) | 0.03 | 0.17 | 0.030 | ||
| BMQ concerns | 0.05 (0.02, 0.08) | 0.02 | 0.27 | <0.001 | ||
| % Activity impairment | R 2 = 0.36 | |||||
| HAQ | 0.58 (0.39, 0.78) | 0.10 | 0.40 | <0.001 | ||
| Self-efficacy pain | −0.007 (−0.01, 0.001) | 0.003 | −0.16 | 0.015 | ||
| BMQ concerns | 0.046 (0.02, 0.07) | 0.01 | 0.26 | <0.001 |
Adjusted for age, gender, disease duration and comorbidity score. BMQ: Beliefs in Medicines Questionnaire; WPAI: Work Productivity and Activity Impairment.
Prediction of the 1-year work productivity status is displayed in Table 5, adjusting for baseline work status in addition to age, gender, disease duration and comorbidities. Comorbidities were not associated with WPAI scores at baseline and came up as a predictor for worse work impairment and worse activity impairment at year 1. BMQ concerns independently predicted worse overall work impairment and worse activity impairment at year 1.
Table 5.
One-year work productivity scores with baseline predictors of in linear regression models
| Dependent variable | Predictor name (alone) | β (95% CI) | s.e. | Standard β | P-value | Model statistics |
|---|---|---|---|---|---|---|
| % Work missed | R 2 = 0.04 | |||||
| – | – | – | – | |||
| % Impairment at work | R 2 = 0.32 | |||||
| Comorbidity score | 0.05 (0.004, 0.10) | 0.02 | 0.25 | 0.035 | ||
| % Overall work impairment | R 2 = 0.28 | |||||
| BMQ concerns | 0.04 (0.01, 0.08) | 0.02 | 0.28 | 0.026 | ||
| % Activity impairment | R 2 = 0.28 | |||||
| Comorbidity score | 0.06 (0.02, 0.09) | 0.02 | 0.27 | 0.002 | ||
| BMQ concerns | 0.03 (0.01, 0.06) | 0.01 | 0.20 | 0.014 |
Adjusted for age, gender, disease duration, comorbidity score and baseline. BMQ: Beliefs in Medicines Questionnaire.
Further analyses for 2-year work productivity showed no independent clinical predictors.
Discussion
Work productivity and impairment was in our study stable over a 2-year period. This was seen after improvement during the first 3 months when patients after a gout flare were included into the study. At baseline several clinical variables were associated with WPAI scores, and after 1 year also comorbidities predicted work productivity in two WPAI scales. A main finding of the study is that patient concerns for medicines, as measured in the BMQ, predicted overall work impairment and activity impairment after 1 year. WPAI scores at year 2 could not be predicted by any variable. We found no consistent difference in WPAI scores over time when patients with insufficiently controlled disease severity (tophi, high sUA, flares in the first year) were compared with those with controlled disease, except for baseline work impairment and activity impairment, which associated to flare occurrence during year 1.
This is the first study reporting longitudinal work productivity and activity with the WPAI questionnaire in gout. Gout leads to episodic flares with reduced physical function that returns to normal function in intercritical periods in the majority of individuals. Thus, no major work productivity and activity loss is expected when assessments do not coincide with occurrence of flares.
Wood et al. [24] found higher WPAI scores in patients who were not well controlled using self-reported outcomes. Also, Khanna et al. [7] reported a relationship between tophi and work parameters and between flares during the past 12 months and greater activity impairment. Only 38% of individuals with gout were employed in that study [7] compared with 64% in ours. All our patients were intensively treated and most achieved low sUA, which may be the main reason for lack of consistent differences regarding WPAI scores between patients having high and low disease severity. In our study better WPAI scores could be a consequence of the treat-to-target approach in itself by increasing the motivation of patients to work, but we did not find a cross-sectional relationship between WPAI scores and tophi at baseline, sUA as a laboratory parameter and flares. ULT seeks to directly reduce sUA and thereby prevent formation of crystals and subsequent flares. In that context work productivity may or may not emerge as an associated outcome.
Overall work impairment in our study was slightly above 20%, in the same range as reported in other gout studies [7, 22] and slightly lower than in patients with arthritic diseases [23]. This may reflect the episodic nature of the disease with restored physical function in patients during intercritical periods.
The role of patient beliefs about medicines with respect to work productivity and impairment has previously not been reported. Significance of psychological variables such as beliefs about medicines and self-efficacy for pain indicates that the way that a patient believes or perceives may have an impact also on participation in work and other activities. Previously we reported that the BMQ overuse subscale was associated with not reaching the gout treatment target after 1 year [26]. The influence of psychological variables on treatment and target achievement in gout should be further studied.
Strengths of our study are the considerable number of patients from clinical practice who were examined at multiple time points over 2 years. Further we had well-defined indicators of disease severity that enabled us to compare controlled vs non-controlled patients in terms of the magnitude of change during treatment.
Some limitations in our study need to be acknowledged. All patients were intensively treated with ULT and assessed after inclusion into the study. During the gout flare that preceded inclusion of patients into the study more disability, also with impact on work productivity and impairment scores, would likely have been seen if measured at that time. Our findings are not comparable with patients not receiving intensive ULT. Gout flares were patient-reported and were not validated in our study. However, if in doubt, the occurrence of flares could be discussed with the study nurse during the visit. Finally, our study was observational and does not allow causal assumptions.
In summary, work productivity and impairment were after initial improvement maintained in gout patients over 2 years and did not differ considerably between patient groups when all were intensively treated for gout with disease education and ULT. Comorbidities and concerns about medicines predicted work and activity impairment at year 1, and attention should also be directed to these factors when work participation is considered.
Acknowledgements
We thank research secretary Mona Thorkildsen for organization; research nurses Gina Stenberg, Anita Reinhard, Heidi Lunøe, Ellen Moholt and Ingerid Müller who contributed as trained study nurses; and patient representatives Espen Reksten and Jan Petter Synnestvedt for advice.
Contributor Information
Till Uhlig, Center for treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, Norway; Faculty of Medicine, University of Oslo, Oslo, Norway.
Lars F Karoliussen, Center for treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, Norway.
Joe Sexton, Center for treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, Norway.
Sella Aarrestad Provan, Center for treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, Norway; Section for Public Health, Inland Norway University of Applied Sciences, Hamar, Norway.
Tore K Kvien, Center for treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, Norway; Faculty of Medicine, University of Oslo, Oslo, Norway.
Espen A Haavardsholm, Center for treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, Norway; Faculty of Medicine, University of Oslo, Oslo, Norway.
Hilde Berner Hammer, Center for treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, Norway; Faculty of Medicine, University of Oslo, Oslo, Norway.
Data availability
The data underlying this article will be shared on reasonable request to the corresponding author.
Funding
This work was supported by Dr Trygve Gythfeldts research foundation and the Norwegian Research Council, project number 328657.
Disclosure statement: T.U. reports personal fees from Grünenthal and Novartis, outside the submitted work. L.F.K. and J.S. declare no conflict of interest. S.A.P. reports personal fees from Boehringer Ingelheim. E.A.H. reports personal fees from Pfizer, UCB, Eli Lilly, Celgene, Janssen-Cilag, AbbVie and Gilead outside the submitted work. T.K.K. reports grants and/or personal fees from AbbVie, MSD, UCB, Hospira/Pfizer, BMS, Sanofi, Celltrion, Sandoz, Amgen and Galapagos outside the submitted work. H.B.H. reports personal fees from AbbVie, Lilly, UCB and Novartis, outside the submitted work.
References
- 1. Kuo CF, Grainge MJ, Mallen C, Zhang W, Doherty M.. Rising burden of gout in the UK but continuing suboptimal management: a nationwide population study. Ann Rheum Dis 2015;74:661–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Dehlin M, Jacobsson L, Roddy E.. Global epidemiology of gout: prevalence, incidence, treatment patterns and risk factors. Nat Rev Rheumatol 2020;16:380–90. [DOI] [PubMed] [Google Scholar]
- 3. Smith E, Hoy D, Cross M. et al. The global burden of gout: estimates from the Global Burden of Disease 2010 study. Ann Rheum Dis 2014;73:1470–6. [DOI] [PubMed] [Google Scholar]
- 4. Kiadaliri AA, Uhlig T, Englund M.. Burden of gout in the Nordic region, 1990-2015: findings from the Global Burden of Disease Study 2015. Scand J Rheumatol 2018;47:410–7. [DOI] [PubMed] [Google Scholar]
- 5. Dalbeth N, Gosling AL, Gaffo A, Abhishek A.. Gout. Lancet 2021;397:1843–55. [DOI] [PubMed] [Google Scholar]
- 6. Chandratre P, Roddy E, Clarson L. et al. Health-related quality of life in gout: a systematic review. Rheumatology (Oxford) 2013;52:2031–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Khanna PP, Nuki G, Bardin T. et al. Tophi and frequent gout flares are associated with impairments to quality of life, productivity, and increased healthcare resource use: results from a cross-sectional survey. Health Qual Life Outcomes 2012;10:117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Scire CA, Manara M, Cimmino MA. et al. ; KING Study Collaborators. Gout impacts on function and health-related quality of life beyond associated risk factors and medical conditions: results from the KING observational study of the Italian Society for Rheumatology (SIR). Arthritis Res Ther 2013;15:R101. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Abhishek A, Doherty M.. Education and non-pharmacological approaches for gout. Rheumatology (Oxford) 2018;57: i51–8. [DOI] [PubMed] [Google Scholar]
- 10. Richette P, Doherty M, Pascual E. et al. 2016 updated EULAR evidence-based recommendations for the management of gout. Ann Rheum Dis 2017;76:29–42. [DOI] [PubMed] [Google Scholar]
- 11. Hui M, Carr A, Cameron S. et al. ; British Society for Rheumatology Standards, Audit and Guidelines Working Group. The British Society for Rheumatology guideline for the management of gout. Rheumatology (Oxford) 2017;56:1056–9. [DOI] [PubMed] [Google Scholar]
- 12. Dehlin M, Drivelegka P, Sigurdardottir V, Svärd A, Jacobsson LTH.. Incidence and prevalence of gout in Western Sweden. Arthritis Res Ther 2016;18:164. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Kuo CF, Grainge MJ, Mallen C, Zhang W, Doherty M.. Eligibility for and prescription of urate-lowering treatment in patients with incident gout in England. JAMA 2014;312:2684–6. [DOI] [PubMed] [Google Scholar]
- 14. Kleinman NL, Brook RA, Patel PA. et al. The impact of gout on work absence and productivity. Value Health 2007;10:231–7. [DOI] [PubMed] [Google Scholar]
- 15. De Meulemeester M, Mateus E, Wieberneit-Tolman H. et al. Understanding the patient voice in gout: a quantitative study conducted in Europe. BJGP Open 2020;4:bjgpopen20X101003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Spaetgens B, Wijnands JM, van Durme C, van der Linden S, Boonen A.. Cost of illness and determinants of costs among patients with gout. J Rheumatol 2015;42:335–44. [DOI] [PubMed] [Google Scholar]
- 17. Nathan N, Nguyen AD, Stocker S. et al. Out-of-pocket spending among a cohort of Australians living with gout. Int J Rheum Dis 2021;24:327–34. [DOI] [PubMed] [Google Scholar]
- 18. van D, Laar CJ, Janssen CA. et al. Model-based cost-effectiveness analyses comparing combinations of urate lowering therapy and anti-inflammatory treatment in gout patients. PLoS ONE 2022;17:e0261940. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Beard SM, von Scheele BG, Nuki G, Pearson IV.. Cost-effectiveness of febuxostat in chronic gout. Eur J Health Econ 2014;15:453–63. [DOI] [PubMed] [Google Scholar]
- 20. Leggett S, van der Zee-Neuen A, Boonen A. et al. ; At-Work Productivity Global Measure Working Group. Content validity of global measures for at-work productivity in patients with rheumatic diseases: an international qualitative study. Rheumatology (Oxford) 2016;55:1364–73. [DOI] [PubMed] [Google Scholar]
- 21. Reilly MC, Zbrozek AS, Dukes EM.. The validity and reproducibility of a work productivity and activity impairment instrument. Pharmacoeconomics 1993;4:353–65. [DOI] [PubMed] [Google Scholar]
- 22. DiBonaventura M, Andrews LM, Yadao AM, Kahler KH.. The effect of gout on health-related quality of life, work productivity, resource use and clinical outcomes among patients with hypertension. Expert Rev Pharmacoecon Outcomes Res 2012;12:821–9. [DOI] [PubMed] [Google Scholar]
- 23. Miller PS, Hill H, Andersson FL.. Nocturia work productivity and activity impairment compared with other common chronic diseases. Pharmacoeconomics 2016;34:1277–97. [DOI] [PubMed] [Google Scholar]
- 24. Wood R, Fermer S, Ramachandran S, Baumgartner S, Morlock R.. Patients with gout treated with conventional urate-lowering therapy: association with disease control, health-related quality of life, and work productivity. J Rheumatol 2016;43:1897–903. [DOI] [PubMed] [Google Scholar]
- 25. Flores NM, Nuevo J, Klein AB, Baumgartner S, Morlock R.. The economic burden of uncontrolled gout: how controlling gout reduces cost. J Med Econ 2019;22:1–6. [DOI] [PubMed] [Google Scholar]
- 26. Uhlig T, Karoliussen LF, Sexton J. et al. 12-month results from the real-life observational treat-to-target and tight-control therapy NOR-Gout study: achievements of the urate target levels and predictors of obtaining this target. RMD Open 2021;7:e001628. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Neogi T, Jansen TL, Dalbeth N. et al. 2015 Gout classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Ann Rheum Dis 2015;74:1789–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Reilly MC, Bracco A, Ricci JF, Santoro J, Stevens T.. The validity and accuracy of the Work Productivity and Activity Impairment questionnaire – irritable bowel syndrome version (WPAI: IBS). Aliment Pharmacol Ther 2004;20:459–67. [DOI] [PubMed] [Google Scholar]
- 29. Sangha O, Stucki G, Liang MH, Fossel AH, Katz JN.. The Self-Administered Comorbidity Questionnaire: a new method to assess comorbidity for clinical and health services research. Arthritis Rheum 2003;49:156–63. [DOI] [PubMed] [Google Scholar]
- 30. Singh JA, Taylor WJ, Simon LS. et al. Patient-reported outcomes in chronic gout: a report from OMERACT 10. J Rheumatol 2011;38:1452–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Fries JF, Spitz P, Kraines RG, Holman HR.. Measurement of patient outcome in arthritis. Arthritis Rheum 1980;23:137–45. [DOI] [PubMed] [Google Scholar]
- 32. Ware JE Jr, Gandek B, Group TIP.. The SF-36 Health Survey: development and use in mental health research and the IQOLA project. Int J Ment Health 1995;23:49–73. [Google Scholar]
- 33. Lorig K, Chastain RL, Ung E, Shoor S, Holman HR.. Development and evaluation of a scale to measure perceived self-efficacy in people with arthritis. Arthritis Rheum 1989;32:37–44. [DOI] [PubMed] [Google Scholar]
- 34. Horne R, Weinman J, Hankins M.. The beliefs about medicines questionnaire: the development and evaluation of a new method for assessing the cognitive representation of medication. Psychol Health 1999;14:1–24. [Google Scholar]
- 35. Horne R, Weinman J.. Patients' beliefs about prescribed medicines and their role in adherence to treatment in chronic physical illness. J Psychosom Res 1999;47:555–67. [DOI] [PubMed] [Google Scholar]
- 36. Emilsson M, Berndtsson I, Gustafsson PA, Horne R, Marteinsdottir I.. Reliability and validation of Swedish translation of Beliefs about Medication Specific (BMQ-Specific) and Brief Illness Perception Questionnaire (B-IPQ) for use in adolescents with attention-deficit hyperactivity disorder. Nord J Psychiatry 2020;74:89–95. [DOI] [PubMed] [Google Scholar]
- 37. Granas AG, Nørgaard LS, Sporrong SK.. Lost in translation?: Comparing three Scandinavian translations of the Beliefs About Medicines questionnaire. Patient Educ Counsel 2014;96:216–21. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data underlying this article will be shared on reasonable request to the corresponding author.