Abstract
Objective
To assess changes in juvenile idiopathic arthritis (JIA) treatments and outcomes in Canada, comparing 2005–2010 and 2017–2021 inception cohorts.
Methods
Patients enrolled within three months of diagnosis in the Research in Arthritis in Canadian Children Emphasizing Outcomes (ReACCh-Out) and the Canadian Alliance of Pediatric Rheumatology Investigators Registry (CAPRI) cohorts were included. Cumulative incidences of drug starts and outcome attainment within 70 weeks of diagnosis were compared with Kaplan–Meier survival analysis and multivariable Cox regression.
Results
The 2005–2010 and 2017–2021 cohorts included 1128 and 721 patients, respectively. JIA category distribution and baseline clinical juvenile idiopathic arthritis disease activity (cJADAS10) scores at enrolment were comparable. By 70 weeks, 6% of patients (95% CI 5, 7) in the 2005–2010 and 26% (23, 30) in the 2017–2021 cohort had started a biologic DMARD (bDMARD), and 43% (40, 47) and 60% (56, 64) had started a conventional DMARD (cDMARD), respectively. Outcome attainment was 64% (61, 67) and 83% (80, 86) for inactive disease (Wallace criteria), 69% (66, 72) and 84% (81, 87) for minimally active disease (cJADAS10 criteria), 57% (54, 61) and 63% (59, 68) for pain control (<1/10), and 52% (47, 56) and 54% (48, 60) for good health-related quality of life (≥9/10).
Conclusion
Although baseline disease characteristics were comparable in the 2005–2010 and 2017–2021 cohorts, cDMARD and bDMARD use increased with a concurrent increase in minimally active and inactive disease. Improvements in parent and patient-reported outcomes were smaller than improvements in disease activity.
Keywords: juvenile arthritis, treatment, outcomes, prognosis
Rheumatology key messages.
cDMARD and bDMARD use for juvenile idiopathic arthritis increased in Canada from 2005–2010 to 2017–2021.
A higher proportion of patients achieved inactive and minimally active disease in 2017–2021 versus 2005–2010.
Improvements in parent and patient-reported outcomes were smaller than improvements in disease activity.
Introduction
Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic disease of childhood and a significant cause of disability [1]. It is a heterogeneous condition comprising seven categories [2]. Treatments for JIA have changed substantially in the last decade with the increasing use of disease-modifying antirheumatic drugs (DMARDs) [3]. It is important to document these treatment changes and to determine concurrent changes in patient outcomes, to better counsel newly diagnosed families.
In 2015, the Research in Arthritis in Canadian Children Emphasizing Outcomes (ReACCh-Out) Investigators published results from a 2005–2010 JIA inception cohort showing that 45% of children with JIA in Canada attained inactive disease at least once within the first year following diagnosis [4]. Initial results from the Canadian Alliance of Pediatric Rheumatology Investigators (CAPRI) Registry, using data from 166 children recruited in 2017–2018, suggested that attainment of inactive disease had improved to 81% by 1 year after diagnosis [5] but a direct comparison to the ReACCh-Out estimate would be misleading due to the small sample size in the CAPRI Registry and differences in the way the data was captured and analysed. We are now able to report a comparison between 2005–2010 and 2017–2021 cohorts that accounts for the differences in methods between the cohorts and includes a much larger number of children from the CAPRI Registry. Our objective was to document changes in treatment in the intervening decade between the cohorts, and concurrent changes in disease outcomes.
Methods
Data from the ReACCh-Out study (2005–2010) were compared with data from the CAPRI Registry (2017–2021). Both studies were Canadian multicentre cohorts, with most centres from ReACCh-Out also participating in the CAPRI Registry. There were differences in eligibility criteria and the schedule of follow-up study visits between the cohorts that needed to be addressed. Patients were recruited within 12 months of JIA diagnosis in ReACCh-Out and within three months in the CAPRI Registry. Prospective data were collected in ReACCh-Out every six months for two years, then yearly, whilst data in the CAPRI Registry were collected at every clinic visit.
In order to improve comparability between the cohorts, only patients recruited within three months of diagnosis were included in this study and Kaplan–Meier estimates of the cumulative incidence of medication use and attainment of outcomes were compared within 70 weeks of diagnosis. This time was chosen because it equated to one year after diagnosis plus a four-month window for data capture in ReACCh-Out. By this time the effect of interval censoring due to fixed time visits in ReACCh-Out was minimum and survival curves had stabilized, rendering estimates at that time comparable to the CAPRI Registry, where interval censoring had minimal effect because data was captured at every clinic visit. In this way, the comparison included patients with similar disease duration at enrolment and similar follow-up time after diagnosis.
Clinically inactive disease (our main outcome) was defined using the Wallace criteria with the addition of ‘absence of enthesitis’ [6]. Inactive and minimally active disease were defined using the 2021 clinical Juvenile Arthritis Disease Activity Score 10 (cJADAS10) cut-offs (secondary outcomes) [7]. Specifically, inactive disease was a cJADAS10 score ≤1.1 for oligoarthritis and ≤2.5 for polyarthritis; minimally active disease was a score ≤4 for oligoarthritis and ≤5 for polyarthritis. Current medication use, including conventional DMARD (cDMARD) and biologic DMARD (bDMARD), intra-articular corticosteroids and systemic corticosteroids, was documented at every study visit and medication start time was the time between diagnosis and the first visit where the patient was receiving the medication. Due to its vast use by a majority of patients shortly after diagnosis, nonsteroidal anti-inflammatory drugs (NSAIDs) are not reported here. JAK inhibitors were only used in five subjects in the 2017–2021 cohort and were not analysed further.
ReACCh-Out and the CAPRI Registry involve human participants and were approved by local Research Ethics Boards at all the centres involved. Central approval provided by the University of British Columbia—Children’s & Women’s Health Centre of BC Research Ethics Board (H04-70641 H16-00309).
Parents or legal guardians provided informed written consent and patients provided assent as appropriate for their age.
Parent- and patient-reported outcomes
Pain intensity in the last week, parent global assessment [8], and Health-Related Quality of My Life (HR-QoML) scores [9] were measured using a 0–10 cm horizontal visual analogue scale in the 2005–2010 cohort, and using a 21-point horizontal numerical rating scale ranging from 0 to 10 in the 2017–2021 cohort. Values from the 2005–2010 cohort were rounded to the closest 0.5 interval (e.g. values of 0.8 cm or 0.9 cm were rounded to 1.0) to increase comparability between cohorts. A rating of pain intensity <1 was considered a good outcome (minimal pain). A good parent global assessment score was defined as <1 on a scale from 0, very well, to 10, very poor [8]. A good HR-QoML was defined as a rating of ≥9 on a scale from 0, the worst, to 10, the best [9]. In the 2005–2010 cohort, parents were instructed to complete the HR-QoML questionnaire on behalf of their younger children, while in the 2017–2021 cohort, parents were instructed not to complete the questionnaire for their children but to have their children complete the questionnaire if they were mature enough to understand the questions. To account for this difference, we compared values of HR-QoML only for children over six years of age at disease onset. In sensitivity analyses, we compared only children over eight years of age and examined an alternative cut-off for good HR-QoML (≥8).
Statistical analysis
Descriptive statistics and Kaplan–Meier estimates of cumulative incidence of events of interest within 490 days (70 weeks) after diagnosis were calculated for the whole cohort and each JIA category with STATA/SE version 15.0 (STATA Corp, College, TX, USA). Kaplan–Meier methods use all visits and all available information up to a chosen time to estimate cumulative incidence and do not require set times for visits or windows of observation. Comparisons between the two cohorts were assessed by the 95% confidence interval limits of the Kaplan–Meier estimates and by Cox proportional hazards models. Multivariable Cox models were used to assess the statistical significance of differences in treatments and outcomes between the 2005–2010 and 2017–2021 cohorts, after adjusting for differences in baseline characteristics (age, sex, disease duration, number of active joints and JIA category). Missing data was coded as such and the number of subjects with available data for each variable is reported in tables. Data imputation was not used.
Results
Overall, 1128 patients were included from the 2005–2010 cohort and 721 patients from the 2017–2021 cohort, at a median of 0 (25th, 75th centiles: 0, 6) and 2 (0, 8) weeks after diagnosis, respectively. The baseline characteristics at the time of enrolment in both cohorts were comparable, including the frequency of JIA categories and the median age at diagnosis, at 9.5 years (3.9, 13.2) and 9.4 years (4.1, 13.2), respectively (Table 1). At enrolment, the median cJADAS10 scores were also comparable, at 8.2 (4.4, 14.6) and 8.0 (4.5, 13).
Table 1.
Patient characteristics at time of enrolment
| Characteristic | 2005–2010 Cohort | 2017–2021 Cohort |
|---|---|---|
| Number of patients | 1128 | 721 |
| Female sex, n (%) | 716 (64.9) | 429 (61.3) |
| n = 1104 | n = 700 | |
| Age at enrolment (years) | 9.5 (3.9, 13.2) | 9.4 (4.1, 13.2) |
| n = 1093 | n = 691 | |
| Disease duration (weeks from onset to enrolment) | 22 (12, 24) | 25 (13, 52) |
| n = 1074 | n = 702 | |
| Weeks from diagnosis to enrolment | 0 (0, 6) | 2 (0, 8) |
| n = 1127 | n = 717 | |
| JIA Category, n (%) | n = 1128 | n = 721 |
| Oligoarthritis | 458 (40.6) | 321 (44.5) |
| Polyarthritis RF-negative | 228 (20.2) | 131 (18.2) |
| Enthesitis-related arthritis | 150 (13.3) | 116 (16.1) |
| Systemic | 67 (5.9) | 31 (4.3) |
| Psoriatic | 69 (6.1) | 46 (6.4) |
| Polyarthritis RF-positive | 44 (3.9) | 26 (3.6) |
| Undifferentiated | 112 (9.9) | 50 (6.9) |
| Active joint count (0 = best to 71 = worst) | 2 (1, 6) | 2 (1, 4) |
| n = 1109 | n = 721 | |
| Physician global assessment (0 = inactive to 10 = very active) | 3 (1.3, 5) | 3 (1.5, 5) |
| n = 1115 | n = 719 | |
| Parent global assessment (0 = very well to 10 = very poor) | 1.9 (0.4, 4.7) | 2 (0.5, 5) |
| n = 920 | n = 669 | |
| Function (CHAQ score, 0 = best to 3 = severe disability) | 0.37 (0.12, 1.0) | 0.38 (0, 0.88) |
| n = 872 | n = 668 | |
| Quality of life (HR-QoML scale, 0 = worst to 10 = best)a | 7.1 (4.7, 9) | 7.5 (5, 9) |
| n = 586 | n = 398 | |
| Pain intensity (0 = no pain to 10 = very severe pain) | 3 (0.8, 5.8) | 3 (1, 6) |
| n = 926 | n = 667 | |
| Disease activity (cJADAS10, 0 = inactive to 30 = very active) | 8.2 (4.4, 14.6) | 8.0 (4.5, 13) |
| n = 900 | n = 667 |
All numbers are median and 25th, 75th centiles unless otherwise specified. Numbers preceded by ‘n’ are the number of patients with valid values for that variable in each cohort.
Values for children >6 y at disease onset.
Among the 2005–2010 cohort, 6% of patients (95% CI 5, 7) had started a bDMARD by 70 weeks after diagnosis, compared with 26% (23, 30) in the 2017–2021 cohort (Table 2 and Fig. 1). The use of cDMARD also increased, from 43% (40, 47) to 60% (56, 64) within 70 weeks of diagnosis. Conversely, intra-articular corticosteroid use decreased from 43% (40, 46) to 36% (32, 40). Oral prednisone use remained stable, from 16% (14, 18) to15% (13, 18).
Table 2.
Cumulative incidence of use of medications and attainment of outcomes within 70 weeks of JIA diagnosis in two Canadian cohorts
| Medications and outcomes | 2005–2010 Cohort | 2017–2021 Cohort |
|---|---|---|
| All patients (N) | 1128 | 721 |
| Conventional DMARD | 43 (40, 47) | 60 (56, 64) |
| Biologic DMARD | 6 (5, 7) | 26 (23, 30) |
| Prednisone | 16 (14, 18) | 15 (13, 18) |
| Joint injection | 43 (40, 46) | 36 (32, 40) |
| Inactive (Wallace) | 64 (61, 67) | 83 (80, 86) |
| Inactivea (cJADAS10) | 50 (47, 53) | 69 (65, 73) |
| Minimally activeb (cJADAS10) | 69 (66, 72) | 84 (81, 87) |
| Pain <1c | 57 (54, 61) | 63 (59, 68) |
| Parent global <1 | 62 (59, 66) | 70 (66, 74) |
| HR-QoML ≥9d | 52 (47, 56) | 54 (48, 60) |
| Oligoarthritis (n) | 458 | 321 |
| Conventional DMARD | 21 (18, 26) | 46 (39, 52) |
| Biologic DMARD | 2 (1, 4) | 10 (6, 15) |
| Prednisone | 5 (3, 7) | 6 (4, 10) |
| Joint injection | 56 (51, 61) | 55 (49, 62) |
| Inactive (Wallace) | 78 (74, 82) | 90 (85, 93) |
| Inactivea (cJADAS10) | 56 (51, 61) | 73 (67, 79) |
| Minimally activeb (cJADAS10) | 75 (71, 79) | 89 (85, 93) |
| Pain <1 | 64 (59, 69) | 72 (66, 77) |
| Parent global <1 | 71 (66,75) | 74 (68, 80) |
| HR-QoML ≥9c | 61 (54, 69) | 65 (56, 75) |
| Polyarthritis RF negative (n) | 228 | 131 |
| Conventional DMARD | 78 (72, 84) | 84 (76, 90) |
| Biologic DMARD | 6 (4, 11) | 41 (32, 51) |
| Prednisone | 18 (13, 24) | 22 (16, 31) |
| Joint injection | 38 (32, 45) | 29 (21, 39) |
| Inactive (Wallace) | 53 (47, 60) | 85 (77, 91) |
| Inactivea (cJADAS10) | 46 (39, 53) | 80 (72, 87) |
| Minimally activeb (cJADAS10) | 64 (58, 71) | 87 (80, 93) |
| Pain <1 | 58 (52, 65) | 63 (53, 72) |
| Parent global <1 | 59 (52, 66) | 73 (64, 82) |
| HR-QoML ≥9c | 43 (35, 54) | 56 (42, 70) |
| Enthesitis related arthritis (n) | 150 | 116 |
| Conventional DMARD | 45 (36, 55) | 56 (45, 67) |
| Biologic DMARD | 5 (2, 10) | 43 (33, 54) |
| Prednisone | 16 (11, 24) | 13 (7, 22) |
| Joint injection | 29 (22, 37) | 18 (11, 28) |
| Inactive (Wallace) | 51 (43, 60) | 74 (64, 83) |
| Inactivea (cJADAS10) | 40 (32, 49) | 46 (35, 57) |
| Minimally activeb (cJADAS10) | 58 (49, 67) | 71 (60, 81) |
| Pain <1 | 43 (34, 52) | 43 (33, 56) |
| Parent global <1 | 48 (40, 58) | 57 (46, 68) |
| HR-QoML ≥9c | 49 (40, 59) | 38 (28, 50) |
| Systemic arthritis (n) | 67 | 31 |
| Conventional DMARD | 63 (51, 75) | 47 (29, 69) |
| Biologic DMARD | 18 (10, 30) | 66 (49, 83) |
| Prednisone | 77 (66, 87) | 83 (65, 95) |
| Joint injection | 8 (3, 18) | 0 (0, 0) |
| Inactive (Wallace) | 68 (56, 79) | 91 (76, 98) |
| Inactivea (cJADAS10) | 62 (50, 74) | 78 (59, 92) |
| Minimally activeb (cJADAS10) | 80 (69, 89) | 85 (67, 96) |
| Pain <1 | 68 (56, 80) | 74 (56, 89) |
| Parent global <1 | 73 (61, 84) | 84 (66, 95) |
| HR-QoML ≥9c | 66 (49, 82) | 60 (36, 85) |
| Psoriatic arthritis (n) | 69 | 46 |
| Conventional DMARD | 41 (29, 55) | 78 (62, 90) |
| Biologic DMARD | 5 (2, 15) | 22 (10, 44) |
| Prednisone | 14 (8, 26) | 7 (2, 21) |
| Joint injection | 41 (30, 55) | 21 (11, 39) |
| Inactive (Wallace) | 68 (56, 80) | 85 (70, 95) |
| Inactivea (cJADAS10) | 51 (39, 65) | 68 (51, 84) |
| Minimally activeb (cJADAS10) | 68 (55, 80) | 91 (76, 98) |
| Pain <1 | 46 (34, 61) | 62 (44, 81) |
| Parent global <1 | 55 (42, 69) | 66 (46, 84) |
| HR-QoML ≥9c | 49 (34, 67) | 49 (31, 71) |
| Polyarthritis RF positive (n) | 44 | 26 |
| Conventional DMARD | 90 (77, 98) | 90 (73, 98) |
| Biologic DMARD | 30 (18, 47) | 37 (20, 61) |
| Prednisone | 55 (39, 72) | 40 (23, 64) |
| Joint injection | 35 (22, 52) | 25 (11, 52) |
| Inactive (Wallace) | 31 (19, 48) | 54 (35, 75) |
| Inactivea (cJADAS10) | 41 (28, 58) | 46 (29, 68) |
| Minimally activeb (cJADAS10) | 46 (32, 63) | 67 (49, 85) |
| Pain <1 | 50 (36, 66) | 53 (34, 74) |
| Parent global <1 | 60 (46, 76) | 68 (49, 85) |
| HR-QoML ≥9c | 41 (26, 60) | 28 (13, 55) |
| Undifferentiated arthritis (n) | 112 | 50 |
| Conventional DMARD | 43 (33, 54) | 63 (49, 77) |
| Biologic DMARD | 5 (2, 12) | 25 (14, 42) |
| Prednisone | 16 (10, 25) | 13 (5, 28) |
| Joint injection | 47 (38, 58) | 26 (15, 42) |
| Inactive (Wallace) | 56 (46, 66) | 71 (56, 84) |
| Inactivea (cJADAS10) | 41 (32, 51) | 68 (53, 83) |
| Minimally activeb (cJADAS10) | 71 (61, 80) | 75 (61, 88) |
| Pain <1 | 51 (41, 61) | 62 (46, 79) |
| Parent global <1 | 56 (46, 67) | 60 (43, 77) |
| HR-QoML ≥9c | 43 (32, 57) | 69 (51, 85) |
Numbers are Kaplan–Meier estimates expressed as % (95% CI). NSAIDs are not included because >95% of patients received NSAIDS at or shortly after diagnosis. JAK inhibitors are not included because only five subjects used them, all in the 2017–2021 cohort.
cJADAS10 scores of ≤1.1 and ≤2.5, for oligo- and polyarthritis, respectively, including patients with systemic signs.
cJADAS10 scores ≤4 and ≤5, for oligo- and polyarthritis, respectively, including patients with systemic signs.
Pain intensity attributed to arthritis in the last week as reported by parents.
The HR-QoML calculations include only children that were older than 6 at disease onset. Supplementary Table S1, available at Rheumatology online reports results when alternative cut-offs for score and age are used.
Figure 1.
Cumulative incidence curves for treatments and outcomes in two Canadian JIA cohorts. Shown are Kaplan–Meier curves for the three years after diagnosis for patients diagnosed in 2005–2010 and patients diagnosed in 2017–2021. The serrated appearance of outcome curves for the 2005–2010 cohort is due to the use of fixed intervals for data collection every 6 months. This does not apply to treatment curves or to the outcome curves from the 2017–2021 cohort because that data was collected at every visit to the clinic. The numbers in brackets indicate the number of observed events during each time period
Using the Wallace criteria, the frequency of attainment of inactive disease within 70 weeks of diagnosis increased from 64% (61, 67) to 83% (80, 86), from the 2005–2010 cohort to the 2017–2021, respectively. Defining inactive disease using the cJADAS10 criteria yielded a similar increase, from 50% (47, 53) to 69% (65, 73). Attainment of minimally active disease was 69% (66, 72) and 84% (81, 87), respectively. Compared with disease activity, improvements in parent-reported outcomes were smaller and their 95% CI often overlapped between the cohorts (Table 2). Attainment of a Pain <1 was 57% (54, 61) and 63% (59, 68), respectively, and attainment of a parent global assessment <1 was 62% (59, 66) and 70% (66, 74). Attainment of a good HR-QoML (≥9) as reported by the patient changed little; it was 52% (47, 56) in the 2005–2010 cohort and 54% (48, 60) in the 2017–2021 cohort. Of note, changing the definition of a good HR-QoML from a score of ≥9 to ≥8 resulted in 65% (61, 69) of patients reporting a good HR-QoML within 70 weeks of diagnosis in the 2005–2010 cohort, and 71% (65, 76) in the 2017–2021 cohort. Comparing attainment of HR-QoML ≥9 only in patients over eight years of age at disease onset resulted in 51% (46, 55) and 50% (44, 56), respectively. Corresponding data for each JIA category are shown in online Supplementary Table S1 (available at Rheumatology online).
There were variations across JIA categories (Fig. 2 and Table 2). From the 2005–2010 to the 2017–2021 cohort, cDMARD use more than doubled in patients with oligoarthritis, while a non-significant decrease of the same was observed among patients with systemic JIA. The increase in bDMARD use was more marked in patients with systemic JIA, RF-negative polyarthritis, and enthesitis-related arthritis than in patients with other categories of JIA. The increase in attainment of inactive disease was most marked in patients with RF-negative polyarthritis (>30% improvement in absolute terms), while the 95% CI in attainment of good HR-QoML generally overlapped between cohorts for all JIA categories.
Figure 2.
Treatments and outcomes across JIA categories in two Canadian cohorts. Shown are Kaplan–Meier estimates of the cumulative incidence of events within 70 weeks after JIA diagnosis for patients diagnosed in 2005–2010 and for patients diagnosed in 2017–2021
Cox proportional hazards models adjusted for differences in baseline characteristics (age, sex, disease duration, number of active joints and JIA category) showed that changes in DMARD use and disease activity were highly statistically significant (P < 0.001), while changes in HR-QoML were not (P = 0.271, Table 3). Supplementary Table S2 (available at Rheumatology online) shows details of Cox models for clinical inactive disease.
Table 3.
Adjusted hazard ratios for treatments and outcomes in the 2017–2021 cohort relative to the 2005–2010 cohort
| Number of subjects included in models |
2017–2021 vs 2005–2010 cohort | |||
|---|---|---|---|---|
| 2005–2010 | 2017–2021 | HR (95% CI) | P-value | |
| Treatments: | ||||
| Conventional DMARD | 898 | 636 | 1.46 (1.26, 1.68) | <0.001 |
| Biologic DMARD | 998 | 645 | 6.10 (4.66, 7.97) | <0.001 |
| Prednisone | 943 | 640 | 0.96 (0.75, 1.24) | 0.783 |
| Joint injection | 965 | 608 | 0.76 (0.64, 0.89) | 0.001 |
| Outcomes: | ||||
| Inactive (Wallace) | 984 | 645 | 1.88 (1.68, 2.11) | <0.001 |
| Inactivea (cJADAS10) | 991 | 648 | 1.89 (1.67, 2.14) | <0.001 |
| Minimally activeb (cJADAS10) | 932 | 608 | 1.78 (1.58, 2.00) | <0.001 |
| Pain <1c | 899 | 608 | 1.24 (1.09, 1.41) | 0.001 |
| Parent global <1 | 899 | 581 | 1.53 (1.35, 1.74) | <0.001 |
| HR-QoML ≥9d | 556 | 360 | 1.11 (0.92, 1.32) | 0.271 |
Hazard ratios (HR) were calculated with Cox proportional hazards models that included the dependent variable and cohort, age, sex, JIA category, number of active joints and disease duration at baseline. Only subjects with valid values in all variables were included in a model. A HR >1 indicates greater likelihood of the treatment or outcome in the 2017–2021 cohort.
cJADAS10 scores of ≤1.1 and ≤2.5, for oligo- and polyarthritis, respectively, including patients with systemic signs.
cJADAS10 scores ≤4 and ≤5, for oligo- and polyarthritis, respectively, including patients with systemic signs.
Pain intensity attributed to arthritis in the last week as reported by parents.
The HR-QoML calculations include only children that were older than 6 at disease onset.
Discussion
In this study, we compared two Canadian cohorts with similar baseline characteristics recruited shortly after JIA diagnosis: a 2005–2010 cohort from the ReACCh-Out study and a 2017–2021 cohort from the CAPRI Registry. Within 70 weeks of diagnosis, our results show higher attainment of minimally active and inactive disease among patients in the 2017–2021 cohort compared with those in the earlier cohort. This was accompanied by an increased use of DMARDs. Overall, our findings suggest that increased DMARD use was associated with higher rates of minimally active and inactive disease, as defined by both the cJADAS10 and Wallace criteria. On the other hand, improvements in attainment of good parent and patient-reported outcomes were smaller or not significant.
Increased DMARD use and concurrent attainment of more favourable outcomes have been observed in patients with JIA in many countries. Over a 12-year timespan, a multicentre observational Dutch study reported a growing trend of initiating bDMARDs to newly diagnosed JIA patients with lower JADAS scores, leading to an increased use of DMARDs in the country [10]. Further, using data from a German registry, Minden et al. concluded that patients attain lower disease activity if bDMARDs are started less than two years after JIA diagnosis [11]. A shift towards using bDMARDs as first-line treatment or as monotherapy without concomitant methotrexate has been observed in some studies [10, 12]. Using a nationwide claims database, Hata et al. reported an increase in the use of bDMARDs and a simultaneous decrease in the use of methotrexate among JIA patients in Japan from 2012 to 2018 [12]. In our study, the increase in bDMARDs use was proportionally greater than the increase in cDMARD use (6% to 26% vs 43% to 60%, respectively).
In our study, systemic corticosteroid use remained stable across the two cohorts but other studies have reported an overall decrease in the use of corticosteroids over time [11, 13]. This difference may be related to the historically low use of corticosteroids for JIA in Canada relative to other countries such as the Netherlands, where a gradual shift was noted from corticosteroid use to earlier use of bDMARDs for the same indication [13].
We found some discrepancies between changes in disease activity and the outcomes reported by patients and parents. Improvements in attainment of a pain score <1 and a good parent global assessment were smaller than improvements in disease activity, and there was little improvement in HR-QoML reported by patients.
Several factors may contribute to the negligible improvement in attainment of optimal HR-QoML in the more recent cohort (from 52% to 54%, P = 0.271) despite substantial improvement in attainment of inactive disease (from 64% to 83%, P < 0.001). These may include increased DMARD side effects, increased frequency of hospital visits for administration of medications, differences in measurement of HR-QoML between cohorts, changes in patient expectations, or random variation. Oen et al. reported that increases in treatment may lead to improved HR-QoML because of decreases in disease activity, but to worsened HR-QoML because of treatment-related psychosocial impacts and side effects [14]. Further, Chédeville et al. reported that more intensive treatment regimens in JIA are associated with more side effects and a negative impact on HR-QoML [15]. Alternatively, the negligible improvement in HR-QoML could be due to secular changes in expectations of what is a good quality of life, or to changes in other aspects of care besides medications. It can also be due to differences in study design, as parents in ReACCh-Out were asked to complete the HR-QoML scale on behalf of their children irrespective of their age, whereas parents in the CAPRI Registry were instructed to have their children complete the scale if they were mature enough to understand the question. HR-QoML could improve with longer follow-up, as changes in HR-QoML may lag behind changes in disease activity [16], but our survival curves suggest this is not the case for up to 3 years after diagnosis. If our findings are confirmed in other studies, the question then becomes whether this negligible improvement in HR-QoML in the short term is an acceptable trade-off in exchange for the expected better HR-QoML in the long term, achieved by the prevention of joint damage and disability. This would be an important question to explore with patients and their families.
The main strengths of our study are the comparability of patient baseline characteristics and similar study design and outcome measures for both the ReACCh-Out study and the CAPRI Registry. The main limitation is that, firstly, while similar, the study designs were not identical and analyses had to be adjusted in multiple ways to enhance comparability. Secondly, the restricted availability of financial coverage for bDMARDs in 2005–2010 compared with 2017–2021, rather than the willingness of physicians and families to use these agents, may explain differences in usage between the cohorts. Thirdly, the absence of normative values for the HR-QoML scale is another limitation, and hence, a reason to conduct our sensitivity analyses with alternative score cut-offs. Finally, as is true for most observational studies, causal attributions of the changes in outcomes to the changes in treatment cannot be confirmed in our study.
In conclusion, our comparison of 2005–2010 and 2017–2021 JIA inception cohorts in Canada revealed that JIA treatment now includes more extensive use of cDMARDs and bDMARDs, although disease characteristics at first presentation to rheumatology care remained stable. Overall, increased DMARD use was accompanied by a concurrent substantial increase in the attainment of minimally active and inactive disease. However, improvements in parent and patient-reported outcomes were smaller or not significant.
Supplementary Material
Acknowledgements
All ReACCh-Out and CAPRI Registry Investigators can be found in the authorship list.
Contributor Information
Kelly Nguyen, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
Julie Barsalou, Department of Pediatrics, Université de Montréal, Montréal, QC, Canada.
Daniah Basodan, Department of Pediatrics, University of Alberta, Edmonton, AB, Canada.
Michelle Batthish, Department of Pediatrics, McMaster University, Hamilton, ON, Canada.
Susanne M Benseler, Department of Pediatrics, University of Calgary, Calgary, AB, Canada.
Roberta A Berard, Department of Pediatrics, Western University, London, ON, Canada.
Nicholas Blanchette, Health Sciences North, Sudbury, ON, Canada.
Gilles Boire, Department of Pediatrics, Université de Sherbrooke, Sherbrooke, QC, Canada.
Roxana Bolaria, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
Alessandra Bruns, Department of Pediatrics, Université de Sherbrooke, Sherbrooke, QC, Canada.
David A Cabral, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
Bonnie Cameron, Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
Sarah Campillo, Department of Pediatrics, McGill University, Montreal, QC, Canada.
Tania Cellucci, Department of Pediatrics, McMaster University, Hamilton, ON, Canada.
Mercedes Chan, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
Gaëlle Chédeville, Department of Pediatrics, McGill University, Montreal, QC, Canada.
Anne-Laure Chetaille, Department of Pediatrics, CHU de Québec-Université Laval, Québec, Canada.
Amieleena Chhabra, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
Julie Couture, Department of Pediatrics, Université de Montréal, Montréal, QC, Canada.
Paul Dancey, Department of Pediatrics, Memorial University, St. John’s, NL, Canada.
Jean-Jacques De Bruycker, Department of Pediatrics, Université de Montréal, Montréal, QC, Canada.
Erkan Demirkaya, Department of Pediatrics, Western University, London, ON, Canada.
Muhammed Dhalla, Department of Pediatrics, University of Calgary, Calgary, AB, Canada.
Ciarán M Duffy, Department of Pediatrics, University of Ottawa, Ottawa, ON, Canada.
Brian M Feldman, Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
Debbie E Feldman, Department of Pediatrics, Université de Montréal, Montréal, QC, Canada.
Tommy Gerschman, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
Elie Haddad, Department of Pediatrics, Université de Montréal, Montréal, QC, Canada.
Liane Heale, Department of Pediatrics, McMaster University, Hamilton, ON, Canada.
Julie Herrington, Department of Pediatrics, McMaster University, Hamilton, ON, Canada.
Kristin Houghton, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
Adam M Huber, Department of Pediatrics, Dalhousie University, Halifax, NS, Canada.
Andrea Human, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
Nicole Johnson, Department of Pediatrics, University of Calgary, Calgary, AB, Canada.
Roman Jurencak, Department of Pediatrics, University of Ottawa, Ottawa, ON, Canada.
Bianca Lang, Department of Pediatrics, Dalhousie University, Halifax, NS, Canada.
Maggie Larché, Department of Pediatrics, McMaster University, Hamilton, ON, Canada.
Ronald M Laxer, Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
Claire M LeBlanc, Department of Pediatrics, McGill University, Montreal, QC, Canada.
Jennifer J Y Lee, Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
Deborah M Levy, Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
Lillian Lim, Department of Pediatrics, University of Alberta, Edmonton, AB, Canada.
Lily S H Lim, Department of Pediatrics, University of Manitoba, Winnipeg, MB, Canada.
Nadia Luca, Department of Pediatrics, University of Ottawa, Ottawa, ON, Canada.
Tara McGrath, Department of Pediatrics, University of Alberta, Edmonton, AB, Canada.
Tamara McMillan, Department of Pediatrics, University of Manitoba, Winnipeg, MB, Canada.
Paivi M Miettunen, Department of Pediatrics, University of Calgary, Calgary, AB, Canada.
Kimberly A Morishita, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
Hon Yan Ng, Department of Pediatrics, University of Saskatchewan, Saskatoon, SK, Canada.
Kiem Oen, Department of Pediatrics, University of Manitoba, Winnipeg, MB, Canada.
Jonathan Park, Department of Pediatrics, Western University, London, ON, Canada.
Ross E Petty, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
Jean-Philippe Proulx-Gauthier, Department of Pediatrics, CHU de Québec-Université Laval, Québec, Canada.
Suzanne Ramsey, Department of Pediatrics, Dalhousie University, Halifax, NS, Canada.
Johannes Roth, Department of Pediatrics, University of Ottawa, Ottawa, ON, Canada.
Alan M Rosenberg, Department of Pediatrics, University of Saskatchewan, Saskatoon, SK, Canada.
Evelyn Rozenblyum, Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
Dax G Rumsey, Department of Pediatrics, University of Alberta, Edmonton, AB, Canada.
Heinrike Schmeling, Department of Pediatrics, University of Calgary, Calgary, AB, Canada.
Rayfel Schneider, Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
Rosie Scuccimarri, Department of Pediatrics, McGill University, Montreal, QC, Canada.
Natalie J Shiff, Department of Pediatrics, University of Saskatchewan, Saskatoon, SK, Canada.
Earl Silverman, Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
Gordon Soon, Health Sciences North, Sudbury, ON, Canada; North York General Hospital, North York, Toronto, ON, Canada.
Lynn Spiegel, Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
Elizabeth Stringer, Department of Pediatrics, Dalhousie University, Halifax, NS, Canada.
Herman Tam, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
Shirley M Tse, Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
Lori B Tucker, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
Stuart Turvey, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
Marinka Twilt, Department of Pediatrics, University of Calgary, Calgary, AB, Canada.
Karen Watanabe Duffy, Department of Pediatrics, University of Ottawa, Ottawa, ON, Canada.
Rae S M Yeung, Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
Jaime Guzman, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
Supplementary material
Supplementary material is available at Rheumatology online.
Data availability
The data underlying this article will be shared on reasonable request to the corresponding author.
Contribution statement
All authors contributed to the acquisition of the data and drafting of the manuscript, and approved the final version. K.N. and J.G. analysed the data and they take responsibility for the integrity of the data analysis.
Funding
ReACCh-Out was supported by a New Emerging Team research grant from the Canadian Institutes of Health Research (QNT 69301) and the Fast Foundation; The CAPRI Registry was supported by grants from The Arthritis Society Canada [CAPRI-15-001 SOG 18-0352] and the Canadian Institutes of Health Research (PJT-175235); Dr Guzman’s work was supported by a Clinical Investigator Award from the BC Children’s Hospital Research Institute, Vancouver, Canada.
Disclosure statement: Dr. Natalie J. Shiff is currently an employee of J&J Innovative Medicine and owns stock in Johnson and Johnson, AbbVie, Gilead, and Iovance. Dr. Shiff participated in data collection for the ReACCh-Out study and in the design of the CAPRI Registry before becoming a J&J Innovative Medicine employee.
Patient and public involvement: Patients and their parents or legal guardians contributed to data acquisition as study participants. Patients and/or the public were not involved in the design, conduct, reporting, or dissemination plans of this research.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data underlying this article will be shared on reasonable request to the corresponding author.