Abstract
Objective Patients with rheumatoid arthritis (RA) have an increased risk of developing herpes zoster (HZ) compared to the general population. We aimed to measure healthcare utilization (HCU) and related costs of HZ among patients with RA, from the public payer’s perspective.
Methods Adult patients with RA diagnosed with HZ between 2008 and 2020 were matched by sex, age, and date of HZ infection to (1) patients with RA without HZ, (2) the non-RA population with HZ, and (3) the non-RA population without HZ. Unadjusted gamma distribution models and generalized estimating equations were used to compare HCU costs and the number of clinical events (CEs), including hospital admissions and emergency department and physician visits, in patients with RA with HZ to each matched cohort.
Results We identified 15,573 patients with RA diagnosed with HZ and a similar number for each of the 3 matched cohorts. From year 1 to year 10, mean total cost ranged from CAD $13,507 to CAD $17,120 for the RA with HZ cohort compared to CAD $12,651 to CAD $14,534 in the RA without HZ cohort. Physician billing and inpatient hospital costs were the largest drivers of increased costs for all cohorts. Compared to patients with RA with HZ, each matched cohort experienced a significantly lower mean number of total CEs, with the highest difference in total CEs 1 year following an HZ infection.
Conclusion HCU and related costs were higher in patients with RA with HZ compared to patients with RA without HZ and non-RA populations with and without HZ. Treatment strategies that minimize the risk of HZ and encourage patients to keep up to date with vaccinations should be considered.
Herpes zoster (HZ), commonly known as shingles, is a viral infection caused by the reactivation of the varicella zoster virus.1 HZ infections are common among seniors and are especially concerning among patients who are elderly, female, and immunosuppressed. HZ complications, such as postherpetic neuralgia and chronic pain, are also more common in the elderly and the immunocompromised.2 An increase in the crude and age- and sex-adjusted incidence rate of HZ over time has been reported and may be related to an aging population, an increasing immunocompromised population, and/or a larger number of immunosuppressant medications available for treatments.3
Rheumatoid arthritis (RA) populations present a unique challenge because the incidence of RA rises with age, female individuals are more likely to have RA, and the conventional synthetic disease-modifying antirheumatic drugs (DMARDs) and biologic DMARDs (bDMARDs) used to treat RA are immunosuppressants.4,5
Patients with RA have an approximately 2-fold increased risk of developing HZ compared to the general population.1,2,6 This elevated risk is attributed to the RA disease itself and the related therapies (eg, steroids, bDMARDs, Janus kinase inhibitors [JAKi]) used to manage RA.1,7
Beginning in 2016, Ontario seniors 65-70 years of age were eligible to receive publicly funded live-attenuated zoster vaccine (LZV). The Ontario publicly funded shingles immunization program transitioned from LZV to the recombinant zoster vaccine (RZV) in 2020. This program was found to reduce disease burden and the use of healthcare services8; however, the Ontario shingles immunization program does not offer publicly funded RZV to immunocompromised patients under the age of 65. In a study published in 2020, only 18.4% of patients attending a rheumatology clinic in Ontario reported having received an HZ vaccine, and cost was found to be the most common reason for not being vaccinated.9
Although it has been shown that, compared to the general population, there are additional healthcare utilization (HCU) costs for patients with immunocompromising conditions who develop HZ, few studies have looked at these costs specifically in patients with RA.10,11 Quantifying the benefit in avoiding serious HZ infections among patients with RA would emphasize the importance of choosing appropriate treatment strategies and preventive measures such as vaccinations. Therefore, we aimed to measure HCU and related costs of HZ infections among patients with RA from the healthcare payer perspective.
METHODS
We conducted a matched population-based retrospective cohort study of all adult (≥ 18 years) patients with RA with newly incident HZ between January 1, 2008, and March 31, 2020, in Ontario, Canada.
Study population. Patients with RA were identified from the Ontario Rheumatoid Arthritis Database (ORAD), a population-based registry assembled using a validated algorithm that has a 78% positive predictive value, 78% sensitivity, and ~100% specificity12,13 at identifying an RA diagnosis. Patients are included in ORAD if they are admitted to hospital with an RA diagnosis or have at least 3 RA physician service claims over 2 years, with ≥ 1 of these claims originating from a musculoskeletal specialist. ORAD also includes physician billing diagnosis codes identified in the Ontario Health Insurance Plan (OHIP) database, with 1 diagnosis code representing the main reason for the visit.12
HZ was defined by the presence of an International Classification of Diseases, 9th revision (ICD-9) code 53, ICD-10 code B02, or OHIP code 53 in the Canadian Institute for Health Information (CIHI) Discharge Abstract Database (DAD), National Ambulatory Care Reporting System (NACRS), Same Day Surgery (SDS) Database, or OHIP Database.3,14,15 Only the first episode of HZ was considered as a study outcome in patients with multiple episodes during the study period.
Data sources. We used the Ontario administrative healthcare databases stored at ICES. ICES is an independent, not-for-profit research institute whose legal status under Ontario’s health information privacy law allows them to collect and analyze health-related data without requiring patient consent. Records of publicly funded health care, for all residents with OHIP coverage, are captured in the ICES databases.
For this study, the following ICES databases were used: DAD, SDS, NACRS for emergency department (ED) visits, OHIP for physician billings (diagnostic and fee codes), and the Registered Persons Database, a population registry of vital statistics. The following ICES-derived or -acquired databases were also used: ORAD, Chronic Obstructive Pulmonary Disease (COPD), Ontario Asthma Dataset (ASTHMA), Ontario Hypertension Dataset (HYPER), Ontario Congestive Heart Failure (CHF) database, Ontario Mental Health Reporting System, Ontario Diabetes Dataset, and the Ontario Cancer Registry. These datasets were linked using encrypted, unique health insurance numbers and were analyzed at ICES. A full list of the databases used with their descriptions and administrative codes are included in Supplementary Tables S1 and S2 (available from the authors upon request).
Main exposure. Three matched (control) cohorts were identified: (1) Ontario patients with RA not diagnosed with HZ (RA without HZ), (2) Ontario individuals without RA diagnosed with HZ (non-RA with HZ), and (3) Ontario individuals without RA not diagnosed with HZ (non-RA without HZ). Control cohorts were matched based on sex, ± 3 years of birth, and ± 60 days of HZ infection (index date). Control groups without HZ were assigned a random index date based on the distribution of index dates from the case cohort. We excluded patients with missing or invalid OHIP coverage (ie, nonresidents), those < 18 years of age, and those with < 1 year of follow-up (based on death date and OHIP eligibility). Each person was followed for up to 10 years from his or her index date, until death or the end of follow-up (March 31, 2021), whichever occurred first.
Outcomes. Primary outcomes were total healthcare cost per patient per year in 2022 Canadian dollars (CAD) from the public payer’s perspective.16 In Ontario, most medical services are paid for by a single publicly funded insurer, the Ontario Ministry of Health. Funded services include all physician visits, hospital care, ED visits, and prescription medications among patients > 64 years of age or those receiving social assistance. Costs not included are drug costs for those < 65 years of age, except those on social assistance or with medication expenses > 4% of their net income. Other uninsured healthcare costs are chiropractors, elective surgeries, and nonevidence-informed treatments or procedures. Costs were aggregated and reported yearly up to 10 years after HZ diagnosis. Results were reported as total annual costs and split by costing categories (hospitalizations, ED visits, hospital outpatient clinic visits, same-day surgery, dialysis clinic visits, Ontario Drug Benefit [ODB] program, rehabilitation services, complex continuing care, homecare services, laboratory services, physician billings, nonphysician billings, long-term care, admission to designated mental health beds, chemotherapy drugs, and other OHIP services). Individual events were costed for fee-for-service physician, nonphysician, and laboratory billings from the OHIP database. Physician services in capitation models were costed by applying payments weighted by age and complexity criteria. Medication costs were measured at the prescription level (list prices) from the ODB program for all prescriptions after age 65, and for prescriptions > 4% of a patient’s after-tax income below age 65 or recipients of social assistance. Inpatient hospitalizations, ED visits, and same-day surgeries were costed using resource intensity weighting, with oncology, dialysis, and hospital outpatient clinic visits derived using an analogous ambulatory care weighting system.17 Complex continuing care, inpatient mental health, and rehabilitation costs18,19 were calculated based on length of hospital stay and case mix,20 whereas long-term care and homecare services were costed using average unit costs of service per hour or day.21 All unit costs and weighting values were obtained from the Ontario Ministry of Health and Long-Term Care and the CIHI; these datasets were linked using unique encoded identifiers and analyzed at ICES.
Secondary outcomes were yearly health events up to 10 years of follow-up, including the number of hospitalization admissions, ED visits, same-day surgeries, physician visits, and total clinical events (CEs).
We also looked at yearly chronic and acute medical conditions, the Charlson Comorbidity Index (CCI), and death as secondary outcomes. Chronic medical conditions included COPD, asthma, diabetes mellitus (DM), multiple sclerosis, hypertension (HTN), Crohn disease and colitis, cystic fibrosis, HIV, dementia, Parkinson disease, chronic kidney disease (CKD), CHF, depression, epilepsy, myasthenia gravis (MG), ischemic heart disease (IHD), and cancer. Acute medical conditions included joint replacements, acute myocardial infarction (MI), atrial fibrillation, stroke/transient ischemic attack (TIA), and trauma events.
We identified chronic and acute medical conditions using validated case ascertainment algorithms for the following medical conditions: COPD,22 asthma,23 DM24,25, MS24,25, HTN,26 Crohn disease and colitis,27 dementia,28 Parkinson29-31, CKD29-31, CHF,29-31 depression,32 epilepsy,33 MG,34 IHD,35 cancer,36 stroke/TIA,35 AF,37 and acute MI.38 Where a validated algorithm did not exist, an algorithm similar to that used to derive the validated cohorts was used (ie, ≥ 1 inpatient or 2 outpatient diagnoses within a 2-year period; see Supplementary Table S3, available from the authors upon request).
Covariates. For all cohorts, preexisting comorbidities and patient demographics were determined using a 2-year lookback period from the index date. Sociodemographic variables included age, sex, residency status (rural vs urban),39 and neighborhood income quintile.
The overall number of main comorbidities and individual main comorbidities included cardiovascular disease (CVD), HTN, DM, lung disease, cancer, and depression.
Statistical analysis. Baseline characteristics were reported as means (SDs) for continuous variables and as numbers and percentages for categorical variables.
For primary outcomes (healthcare costs), an unadjusted gamma distribution model was used to compare pairwise mean healthcare costs in the RA with HZ cohort to each of the 3 matched control cohorts. Gamma distribution models are commonly used for nonnegative, continuous, and right-skewed data, such as healthcare costs.40-43 Healthcare costs were adjusted to account for the 2022 inflation rate in Canada.
For secondary outcomes, unadjusted generalized estimating equations (GEEs) with negative binomial distribution were used to compare health events with count data. GEE is one of the methods that accounts for correlated observations in clustered/longitudinal data.44 It is a population-level approach based on a quasi-likelihood function and provides population-averaged estimates of the parameters.45
We determined the mean number of visits (hospitalizations, ED visits, and outpatient physician visits) at each year of follow-up and compared this number in the RA with HZ cohort to each of the 3 matched control cohorts.
For chronic clinical conditions, we calculated the incidence proportion per 1000 patients at each follow-up year and compared this proportion in the RA with HZ cohort to each of the 3 matched control cohorts.
A P value of < 0.05 was considered statistically significant. In accordance with ICES data privacy policies, cell sizes ≤ 5 individuals were not reported.
RESULTS
Baseline characteristics. We identified 15,573 patients with RA diagnosed with HZ between January 1, 2008, and March 31, 2020 (see Supplementary Table S4, available from the authors upon request, for the number of patients with RA with HZ at each year). The 3 matched control cohorts contained the same number of individuals (Supplementary Figure S1A,B). All cohorts were followed up to 10 years (see Supplementary Table S5 for the number of individuals in each cohort at each year of follow-up).
Mean age was 65.5 years for the case cohort (RA with HZ) and for all 3 matched control cohorts. A similar proportion of individuals lived in rural areas (range 12-13.6%) in all 4 cohorts (Table 1). Compared to the other 3 cohorts, the non-RA without HZ cohort had a lower mean number of prior comorbidities and a lower proportion of prior CVD, lung disease, cancer, and depression (Table 1).
Baseline characteristics for ORAD RA with and without HZ cohorts and RPDB non-RA with and without HZ cohorts (2-year lookback period).
Healthcare costs. The RA with HZ cohort had significantly (P < 0.05) higher total healthcare costs at each year of the 10-year follow-up period compared to the 3 matched control cohorts (Figure 1A).
(A) Total healthcare cost and (B) healthcare costs for physician visits, inpatient hospitalization, ED visits, Ontario drug benefits, and same day surgery per year for RA with HZ compared to each of 3 matched control cohorts. ED: emergency department; FFS: fee-for-service; HZ: herpes zoster; NACRS: National Ambulatory Care Reporting System; OHIP: Ontario Health Insurance Program; RA: rheumatoid arthritis.
The mean total cost ranged from CAD $13,507 at year 1 to CAD $17,120 at year 10 for the RA with HZ cohort compared to CAD $12,651 to CAD $14,534 in the RA without HZ, CAD $7592 to CAD $9487 in the non-RA with HZ, and CAD $6964 to CAD $9028 in the non-RA without HZ (Figure 1A).
Physician billing costs and inpatient hospital costs were the largest cost drivers for all cohorts. The former was significantly (P < 0.05) higher in the RA with HZ cohort compared to each of 3 matched cohorts (Figure 1B). There was no significant difference between patients with RA with and without HZ cohorts for inpatient hospitalization costs. However, inpatient hospitalization costs were significantly (P < 0.05) higher in the RA with HZ compared to the non-RA cohorts at all follow-up years (Figure 1B). Of interest, we did not find a large difference for inpatient hospitalization costs between the non-RA cohorts with and without HZ during follow-up.
ED visits, ODB program costs, and same-day surgery costs were significantly (P < 0.05) higher in patients with RA with HZ cohort compared to the 3 matched control cohorts.
The other attributable healthcare costs are shown in the Supplementary Figure S2 (available from the authors upon request).
HCU. Overall, HCU was higher in the RA cohorts compared to the non-RA cohorts. Compared to RA with HZ, each of 3 matched cohorts experienced significantly (P < 0.05) less mean number of the total CEs (Figure 2A). The difference in total CEs was highest 1 year after HZ infection, with a mean total of 28.6 for the RA with HZ cohort compared to 23.4 in the RA without HZ, 18.1 in the non-RA with HZ, and 12.9 in the non-RA without HZ cohorts. However, there was a slight decrease in total CEs through the 10 years of follow-up across all cohorts.
Healthcare utilization per year for RA with HZ compared to each of 3 matched control cohorts by (A) total clinical events and (B) physician visits, inpatient hospitalization admission, same day surgeries, and emergency department visits. HZ: herpes zoster; RA: rheumatoid arthritis.
Physician visits were the main driver for clinical visits in most of the 10 years of follow-up (Figure 2B). The mean number of visits to a physician or ED, same-day surgeries, and inpatient hospitalizations were also higher in the RA cohorts compared to the non-RA cohorts.
Chronic clinical condition. In terms of CCI, there was no significant difference between RA with and without HZ over most of the 10 years of follow-up. The mean CCI was higher in RA vs non-RA cohorts (Figure 3A).
(A) CCI and (B) chronic medical conditions (IHD, hypertension, DM, COPD, cancer, and asthma) per year for RA with HZ compared to each of the 3 matched control cohorts. CCI: Charlson Comorbidity Index; COPD: chronic obstructive pulmonary disease; DM: diabetes mellitus; HZ: herpes zoster; IHD: ischemic heart disease; RA: rheumatoid arthritis.
Incidence of IHD, asthma, COPD, and cancer per 1000 patients was higher in the RA cohorts compared to the non-RA cohorts over the follow-up years. However, the difference between RA with and without HZ was not consistent for these medical conditions (Figure 3B). In contrast, there was no significant difference in incidence of HTN and DM between the RA with HZ cohort and each of the 3 matched control cohorts, except at year 3 of follow-up (Figure 3B).
Death and acute medical conditions. Overall, death events were higher in RA vs non-RA cohorts. The difference in the number of deaths between RA with and without HZ cohorts was not stable. Death events were higher from year 2 to 6 and lower from year 7 to 10 in the RA without HZ cohort compared to the RA with HZ cohort (Table 2). In terms of acute medical conditions, overall, there was no significant difference in incidence of atrial fibrillation, MI, stroke/TIA, joint replacement, and trauma events between the RA with and without HZ cohorts. However, the incidence of these acute conditions was relatively higher in RA cohorts compared to non-RA cohorts. For example, the incidence of MI was significantly higher in the RA with HZ cohort (9.60, 9.54, and 9.58 per 1000 patients) compared to the non-RA with HZ (7.24, 6.98, and 6.76 per 1000 patients) and the non-RA without HZ (6.78, 6.85, and 6.11 per 1000 patients) in years 1, 2, and 3 of follow-up, respectively (Table 2). Similar results were observed for stroke/TIA over 10 years of follow-up. As expected, the incidence of joint replacement was significantly higher in the RA with HZ compared to the non-RA with or without HZ cohorts (Table 2).
Death events and acute medical conditions per year for RA with HZ compared to each of the 3 matched control cohorts.
DISCUSSION
In this study, patients with RA with HZ had the highest total healthcare cost compared to patients with RA without HZ and non-RA cohorts with and without HZ. This higher cost was not only observed the year following an HZ diagnosis but remained higher over the 10 years of follow-up. The effect of having both RA and HZ on healthcare costs was almost double that of individuals without RA and without HZ. Our findings are in line with a study that showed that following an HZ diagnosis, all-cause HCU and medical costs are higher in patients with RA with HZ compared to patients with RA without HZ.46 We also showed that most of the healthcare costs were attributed to inpatient hospitalizations, physician visits, and the ODB program, as reported by other studies.46,47
In our study, there was no significant difference in mean inpatient hospitalization costs between RA with and without HZ over 10 years of follow-up. Using data from the National Inpatient Sample database, Potera et al showed similar results for mean hospital charges (USD) in patients with RA with HZ ($35,124) compared to patients with RA without HZ ($35,899), although their study was not stratified by year.47 Similar patterns of higher healthcare costs in other immunocompromising diseases with HZ compared to those without HZ have also been observed.10,48
We found that HCU was higher in patients with RA with HZ compared to the 3 matched cohorts, particularly 1 year after the HZ diagnosis. This is in line with findings from other studies.46-48 For example, Singer et al showed a higher unadjusted incidence rate of HCU among patients with RA with HZ compared to those without HZ during different timepoints of observation, including 1 year after diagnosis of HZ.46 Johnson et al showed that immunocompromised patients with HZ had significantly higher HCU (inpatient visits, ED visits, outpatient visits, and other outpatient services) compared to immunocompetent patients without HZ.48 Moreover, Potera showed that the proportion of hospitalizations was 0.1% in patients with RA with HZ compared to 0.04% in patients with RA without HZ.47 Similar findings have been shown in other immunocompromising diseases.10,11,48-50 For example, Li et al showed that immunocompromised patients with HZ had greater use of inpatient, ED, and outpatient services and pain medications compared to matched controls without HZ.10 Ghaswalla et al showed that HCU is higher in patients with COPD with HZ vs without HZ.49 The higher HCU in HZ cohorts found in our study may be partly attributable to the risk of multiple HZ infections in the HZ cohorts.
We found total CEs decreased over 10 years in all cohorts. This might be due to obvious increase in CEs around the time of HZ diagnosis. Another consideration may be that the HCU behavior of patients changed over time. One reason for decrease in HCU behavior may be due to the coronavirus disease 2019 (COVID-19) pandemic and the public health interventions on social distancing starting in 2019 and 2020 (during the study period), likely discouraging physician, ED, and hospital visits.
As expected, we found that incidence of chronic clinical conditions, particularly IHD, asthma, and COPD, was higher in the RA cohorts compared to the non-RA cohorts. This finding is consistent with the literature.51-53 However, the difference was not significant between the RA with and without HZ cohorts, suggesting HZ may not have a large effect on these particular chronic conditions. In contrast to our findings, a previous study has shown increased long-term risk of major cardiovascular diseases among participants from 3 large US cohorts with an HZ diagnosis.54
The reasons for the lack of a significant finding in the incidence of HTN and DM between patients with RA with HZ and other cohorts in our study are not entirely clear. Past studies show inconsistent results in this regard. Some case-control studies have shown that DM is more frequent in patients with RA with HZ compared to patients with RA without HZ,6,55 whereas other case-control studies have shown that DM was slightly less frequent among patients with HZ.56,57 On the other hand, some studies have shown an opposite cause-effect relationship, in which DM, HTN, and hyperlipidemia were significantly associated with an increased risk of developing HZ infection.58,59
We found similar patterns for death events and acute medical conditions (eg, acute MI), with higher rates in the RA cohorts and nonsignificant differences between the RA with and without HZ cohorts. This is also in line with other studies.60,61
In our dataset, despite seeing a slight decrease in the number of hospitalizations for the treatment of HZ, we saw a significant increase in hospitalization costs over time. We believe this may be related to more complicated cases being admitted for treatment, whereas uncomplicated cases are being treated as outpatients, as we also saw an increase in the number of cases with HZ seen per year by general practitioners.3
Our study has some limitations. The nature of this study was mostly descriptive. Therefore, our findings regarding significant differences between the patients with RA with HZ cohort and the other 3 cohorts are subject to bias. Although we matched the cohorts for 3 main variables (age, sex, and date of HZ infection), we did not adjust for all potential confounders. For example, administrative databases do not allow for the assessment of variables such as disease activity and medication use (ie, immunosuppressants), which may be important to consider. Misclassification of some patients may also have affected the association between RA diagnosis and study outcomes. We assume that the effect of misclassification would be small as we used the ORAD to identify patients with RA, which has been created using a validated algorithm.13
For the secondary outcomes in our longitudinal data, we used GEE models to provide an estimate at the population-level. However, one of the most common limitations of the GEE model is the challenge of model selection due to the lack of absolute goodness-of-fit tests to aid comparisons among several plausible models. This can affect the accuracy and reliability of the results.44,62
In this study, the HCU-related cost was measured from the healthcare payers’ perspective. This approach typically emphasizes immediate cost containment and direct healthcare expenses, which can be seen as a limitation when compared to the societal perspective. The societal perspective, by adopting a more comprehensive approach, aims to enhance overall population well-being.63,64
Regarding medication costs for patients aged < 65 years, in Ontario, ODB includes data for patients who are < 65 years and are eligible for special government-funded programs, such as the Home Care Program and Trillium Drug Program, and social assistance through Ontario Works and Ontario Disability Support Program. Other medication costs are not captured if they are not covered by these programs.
Our study’s inclusion of a large number of patients within RA and non-RA cohorts over an extended accrual period, with 10 years of follow-up from the index date, strengthens the representativeness of our study sample for the Ontario population.
In conclusion, using population-based health administrative databases in Ontario, we found that HCU and related costs were higher in patients with RA with HZ compared to RA without HZ and non-RA cohorts with and without HZ. Thus, rheumatologists should consider treatment strategies that minimize the risk of HZ and ensure patients’ vaccinations are up to date. Considering that this study is a cost-identification study, we acknowledge the need for more studies assessing cost-benefit and cost-utility of HZ vaccines in patients with RA.
ACKNOWLEDGMENT
This study made use of deidentified data from the ICES Data Repository, which is managed by the Institute for Clinical Evaluative Sciences with support from its funders and partners: Canada’s Strategy for Patient-Oriented Research (SPOR), the Ontario SPOR Support Unit, the Canadian Institutes of Health Research, and the Government of Ontario. Parts of this material are based on data and/ or information compiled and provided by Canadian Institute for Health Information and the Ontario Ministry of Health. We thank IQVIA Solutions Canada Inc. for use of their Drug Information File. The analyses, conclusions, opinions, and statements expressed herein are solely those of the authors and do not reflect those of the funding or data sources; no endorsement is intended or should be inferred. Parts of this material are based on data and information provided by Cancer Care Ontario (CCO). The opinions, results, view, and conclusions reported in this paper are those of the authors and do not necessarily reflect those of CCO. No endorsement by CCO is intended or should be inferred. GSK was provided with the opportunity to review and comment on this manuscript to ensure against inadvertent disclosure of confidential or scientifically/factually inaccurate background information; however, the opinions, results, and conclusions reported are those of the authors. We would like to thank all OBRI investigators: Drs. Ahluwalia, V., Ahmad, Z., Akhavan, P., Albert, L., Alderdice, C., Ali, T., Aubrey, M., Aydin, S., Bajaj, S., Bell, M., Bensen, W., Bhavsar, S., Bobba, R., Bombardier, C., Bookman, A., Brophy, J., Cabral, A., Carette, S., Carmona, R., Chow, A., Choy, G., Ciaschini, P., Cividino, A., Cohen, D., Dhillon, R., Dixit, S., Faraawi, R., Haaland, D., Hanna, B., Haroon, N., Hochman, J., Jaroszynska, A., Johnson, S., Joshi, R., Kagal, A., Karasik, A., Karsh, J., Keystone, E., Khalidi, N., Kuriya, B., Lake, S., Larche, M., Lau, A., LeRiche, N., Leung, Fe., Leung, Fr., Mahendira, D., Matsos, M., McDonald-Blumer, H., McKeown, E., Midzic, I., Milman, N., Mittoo, S., Mody, A., Montgomery, A., Mulgund, M., Ng, E., Papneja, T., Pavlova, V., Perlin, L., Pope, J, Purvis, J., Rai, R., Rawn, S., Rohekar, G., Rohekar, S., Ruban, T., Samadi, N., Sandhu, S., Shaikh, S., Shickh, A., Shupak, R., Smith, D., Soucy, E., Stein, J., Thompson, A., Thorne, C., Wilkinson, S.
Footnotes
CONTRIBUTIONS
MM, MT, AC, and CB contributed to the conception or design of the work. The ICES Data & Analytic Service team conducted the data analysis. MM drafted the manuscript. JEP contributed to writing, reviewing & editing, and visualization. All authors critically revised the work and approved the final version of the manuscript.
FUNDING
This work was supported by an Investigator Sponsored Study grant from GlaxoSmithKline Biologicals SA.
COMPETING INTERESTS
CB has received grant/research support from OBRI—which was funded by peer-reviewed grants from Canadian Institute for Health Research, Ontario Ministry of Health and Long-Term Care, and Canadian Arthritis Network—and unrestricted grants from AbbVie, Amgen, Aurora, BMS, Celgene, Hospira, Janssen, Lilly, Medexus, Merck, Novartis, Pfizer, Roche, Sanofi, and UCB. The remaining authors declare no conflicts of interest relevant to this article.
ETHICS AND PATIENT CONSENT
Ethics approval for this study obtained from University Health Network: UHN REB# is 22-5883. As the data used in this study are stored at ICES, patient consent was not required per ICES’ legal status under Ontario’s health information privacy laws.
- Accepted for publication February 11, 2025.
- Copyright © 2025 by the Journal of Rheumatology
