An episode refers to the observation time from initiation of use of a study regimen through the end of follow-up. National Medicare/Medicaid data do not include Tennessee. TennCare is the Medicaid program in Tennessee; PAAD/PACE indicates New Jersey's Pharmaceutical Assistance to the Aged and Disabled and Pennsylvania's Pharmaceutical Assistance Contract for the Elderly; and KPNC, Kaiser Permanente Northern California. SABER indicates Safety Assessment of Biologic Therapy; TNF, tumor necrosis factor. Eligible patients had a diagnosis of autoimmune disease, used study medications, and had 365 days of available baseline data.
For each disease, there was no significant difference between tumor necrosis factor (TNF)-α antagonist and comparator groups. SABER indicates Safety Assessment of Biologic Therapy.
Each comparison required a separate propensity score matching iteration. TNF indicates tumor necrosis factor.
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Grijalva CG, Chen L, Delzell E, et al. Initiation of Tumor Necrosis Factor-α Antagonists and the Risk of Hospitalization for Infection in Patients With Autoimmune Diseases. JAMA. 2011;306(21):2331–2339. doi:10.1001/jama.2011.1692
Context Although tumor necrosis factor (TNF)-α antagonists are increasingly used in place of nonbiologic comparator medications, their safety profile remains incomplete.
Objectives To determine whether initiation of TNF-α antagonists compared with nonbiologic comparators is associated with an increased risk of serious infections requiring hospitalization.
Design, Setting, and Patients Within a US multi-institutional collaboration, we assembled retrospective cohorts (1998-2007) of patients with rheumatoid arthritis (RA), inflammatory bowel disease (IBD), and psoriasis, psoriatic arthritis, or ankylosing spondylitis (psoriasis and spondyloarthropathies) combining data from Kaiser Permanente Northern California, New Jersey and Pennsylvania Pharmaceutical Assistance programs, Tennessee Medicaid, and national Medicaid/Medicare. TNF-α antagonists and nonbiologic regimens were compared in disease-specific propensity score (PS)–matched cohorts using Cox regression models with nonbiologics as the reference. Baseline glucocorticoid use was evaluated as a separate covariate.
Main Outcome Measure Infections requiring hospitalization (serious infections) during the first 12 months after initiation of TNF-α antagonists or nonbiologic regimens.
Results Study cohorts included 10 484 RA, 2323 IBD, and 3215 psoriasis and spondyloarthropathies matched pairs using TNF-α antagonists and comparator medications. Overall, we identified 1172 serious infections, most of which (53%) were pneumonia and skin and soft tissue infections. Among patients with RA, serious infection hospitalization rates were 8.16 (TNF-α antagonists) and 7.78 (comparator regimens) per 100 person-years (adjusted hazard ratio [aHR], 1.05 [95% CI, 0.91-1.21]). Among patients with IBD, rates were 10.91 (TNF-α antagonists) and 9.60 (comparator) per 100 person-years (aHR, 1.10 [95% CI, 0.83-1.46]). Among patients with psoriasis and spondyloarthropathies, rates were 5.41 (TNF-α antagonists) and 5.37 (comparator) per 100 person-years (aHR, 1.05 [95% CI, 0.76-1.45]). Among patients with RA, infliximab was associated with a significant increase in serious infections compared with etanercept (aHR, 1.26 [95% CI, 1.07-1.47]) and adalimumab (aHR, 1.23 [95% CI, 1.02-1.48]). Baseline glucocorticoid use was associated with a dose-dependent increase in infections.
Conclusion Among patients with autoimmune diseases, compared with treatment with nonbiologic regimens, initiation of TNF-α antagonists was not associated with an increased risk of hospitalizations for serious infections.
Although the introduction of tumor necrosis factor (TNF)-α antagonists revolutionized the treatment of autoimmune diseases, concerns about the safety of these biologic drugs remain.1,2 Several studies reported serious infections in users of TNF-α antagonists.1-3 However, whether the risk of serious infections with TNF-α antagonists is greater than that with comparator nonbiologic medications is unclear.
Available information from randomized clinical trials of TNF-α antagonists is limited because of insufficient power to assess safety outcomes conclusively.4 Moreover, the selected populations participating in the trials warrant caution in extrapolating results to the broader population of patients who receive these agents.4 Furthermore, many efficacy trials of biologics were placebo controlled, which limits inference about alternative treatment options for autoimmune diseases.1,2,4-7
Although observational studies have tried to fill this knowledge gap, several published reports had important limitations.2,3,8-13 Some studies aggregated TNF-α antagonists into a single category, precluding the assessment of individual medications. Similarly, serious infections were aggregated, but the effect of TNF-α antagonists on specific infections, such as pneumonia, remains unknown. Furthermore, methodological differences may have contributed to dissimilar and sometimes conflicting results. Larger studies, addressing specific methodological concerns,14,15 are needed to quantify the risk of serious infections in users of specific TNF-α antagonists.
As part of a large US federally funded multi-institutional initiative, the Safety Assessment of Biologic Therapy (SABER) project,16 we evaluated whether initiation of TNF-α antagonists was associated with an increased risk of serious infections among patients with autoimmune diseases, and whether risk varied by specific TNF-α antagonist.
This retrospective cohort study combined data from 4 large US automated databases.16 Exposure to TNF-α antagonists and other medications was determined using pharmacy and procedures data, and serious infections resulting in hospitalization were identified using discharge diagnoses and validated definitions. The incidence of serious infections between disease-specific propensity score (PS)–matched exposure groups was compared using Cox proportional hazard regression models. Planned sensitivity and subgroup analyses evaluated the robustness of the main findings and key study assumptions.
Study databases included the following: national US Medicaid and Medicare databases, excluding Tennessee (Medicaid Analytic eXtract, 2000-2005; Medicare, 2000-2006; and Medicare Part D, 2006); Tennessee Medicaid (TennCare, 1998-2005); New Jersey's Pharmaceutical Assistance to the Aged and Disabled and Pennsylvania's Pharmaceutical Assistance Contract for the Elderly (PAAD/PACE, 1998-2006); and Kaiser Permanente Northern California (KPNC, 1998-2007). We used these data and a common programming algorithm to assemble retrospective cohorts of patients with autoimmune diseases who were initiating selected medications.
For each database, we identified patients with study-defined autoimmune diseases, using the earliest International Classification of Diseases, Ninth Revision (ICD-9) –coded health care encounter, who subsequently filled a prescription or received an infusion for a TNF-α antagonist or comparator medication (see “Exposures” for medication details).16-18 We required a baseline period of 365 days with continuous enrollment in the respective database preceding the first infusion or prescription fill to ascertain other selection criteria and study covariates. Using baseline information, patients were categorized into 3 mutually exclusive groups: rheumatoid arthritis (RA); inflammatory bowel disease (IBD); and psoriasis, psoriatic arthritis, or ankylosing spondylitis(herein, psoriasis and spondyloarthropathies group). Patients with diagnoses for more than 1 autoimmune disease were excluded (Figure 1).
Among potential cohort members, we identified new users of study medications,19 defined by a filled prescription for a study medication after 365 baseline days without prescriptions filled for the specific study medication or others in the same group. This “first” filling date (t0) marked the beginning of follow-up. Vital information for each cohort member was assessed and follow-up continued from t0 through the earliest of the following dates: death, loss of enrollment, study outcome (see “Outcomes”), switching to another regimen or the discontinuation of the current regimen (30 days without medication), study end, or 365th day of follow-up. We restricted the follow-up to 365 days because we were most interested in the period shortly after initiation of therapy,12,13,20 to limit the effect of time-varying covariates such as glucocorticoid use, and because long-term users likely differ from patients who discontinue therapy within the first year of treatment.
A detailed description of the design of the SABER study has been reported elsewhere.16 Institutional review boards of all participating institutions approved the study and waived patient consent requirements.
We used pharmacy and procedures data to determine medication exposure. Study medications were classified into 2 groups: TNF-α antagonists (including infliximab, adalimumab, and etanercept [not approved for treatment of IBD and thus excluded for that group]) and comparator medications. For RA, the comparator regimens were initiation of leflunomide, sulfasalazine, or hydroxychloroquine after any use of methotrexate in the previous year (“nonbiologic regimens”). For IBD, the comparator group was initiation of azathioprine or mercaptopurine. For psoriasis and spondyloarthropathies, the comparator group was initiation of methotrexate, hydroxychloroquine, sulfasalazine, or leflunomide.
Exposure encompassed all person-time covered by prescription fills and up to 30 person-days without subsequent medication available. This 30-day grace period was allowed because some residual effects of study medications could extend beyond the last day of use and to account for imperfect adherence. Both the TNF-α antagonist and nonbiologic comparator regimens allowed concurrent use of methotrexate. Analyses of IBD allowed for continuation of or simultaneous initiation of azathioprine or mercaptopurine in the TNF-α antagonist group.
The primary study outcome was serious infections, defined as those that required hospitalization.8,10,21 These infections were identified using definitions based on principal discharge diagnoses and included infections of the respiratory tract, skin and soft tissue, genitourinary tract, gastrointestinal tract, central nervous system, and septicemia/sepsis. Pneumonia, the most common infection, was also assessed separately. Considering medical chart reviews as the reference, our definitions for serious infection requiring hospitalization have consistently shown positive predictive values of 80% or higher.22-25 Opportunistic infections and tuberculosis were not considered study outcomes.26
Baseline covariates included demographics: age, sex, race (gathered routinely by the databases used), residence (urban/rural), nursing home/community dwelling, area income, calendar year; generic markers of comorbidity and health care utilization: number of hospitalizations, outpatient and emergency department visits, number of different medication classes filled; surrogate markers of disease severity: extra-articular disease manifestations, number of intra-articular and orthopedic procedures, number of laboratory tests ordered for inflammatory markers, use of selected medications8,10,27-29; and other known risk factors for infections: previous hospitalizations for infections, chronic obstructive pulmonary disease (COPD), diabetes, and antibiotic use.10,16
Baseline use of oral glucocorticoids was categorized according to the average daily dose of prednisone equivalents in the 6 months preceding t0: 0, >0 to <5 (low dose), 5-10 (medium dose), and >10 mg (high dose).30,31 This variable was not included in the matching strategy and its association with serious infections was ascertained separately in the final outcome models.
The effects of potential confounders were controlled for using a propensity score matching strategy. For each database and disease group, logistic regression models estimated the predicted probabilities of exposure (to the reference regimen) for each episode of use. Thus, a propensity score value summarized covariate information for each episode, allowing confounding control through propensity score matching and ensuring shared data were not individually identifiable.16,32 Episodes of use were propensity score matched using a greedy matching algorithm. Although subsequent analyses were restricted to matched episodes, crude hospitalization rates for infections estimated before and after propensity score matching were similar across exposures and diseases (data not shown).
Cox proportional hazard regression models assessed the association between exposure groups and study outcomes, with stratification by database to allow the baseline hazard to vary. Because we compared propensity score–matched cohorts and because patients could contribute more than 1 episode of new use (with an updated set of covariates), we accounted for these additional correlations using the Huber-White “sandwich” variance estimator and calculated robust standard errors for all estimates.33 The proportional hazard assumption was verified for the main study exposures,34 and the final disease-specific outcome models for the propensity score–matched cohort analyses included only the exposure groups and the indicator for baseline glucocorticoid use.
All statistical tests were 2-sided, and P <.05 was considered to indicate statistical significance. Using available information, we estimated that for RA, our comparison of TNF-α antagonists vs nonbiologic regimens would have a power of 80% to detect a hazard ratio of at least 1.15 at a significance level of .05. Similarly, the detectable hazard ratios were 1.30 or higher for IBD and 1.36 or higher for psoriasis and spondyloarthropathies.35,36 All analyses were performed using SAS version 9.2.
Planned subgroup analyses included estimates by database, presence of baseline COPD, diabetes mellitus, hospitalization for a serious infection, and glucocorticoid use. Each subgroup analysis required a separate propensity score matching iteration. Sensitivity analyses evaluated the effects of restricting the TNF-α antagonists group to those who had used methotrexate during baseline among RA patients; restricting follow-up to 6 months; and evaluating pneumonia hospitalizations as a separate outcome.
We identified 407 319 patients with autoimmune diseases who had filled prescriptions for study medications and had complete baseline data preceding that fill date. A total of 170 788 (42%) patients with other autoimmune diseases, more than 1 study disease, or initiating nonstudy regimens were excluded. We identified 35 235 patients initiating study regimens for RA, 7332 for IBD, and 12 905 for psoriasis and spondyloarthropathies (Figure 1).
After application of selection criteria and propensity score matching, the final RA cohorts included 10 484 matched episodes of TNF-α antagonists and comparator medications. The respective IBD and psoriasis and spondyloarthropathies cohorts included 2323 and 3215 matched pairs. Overall, 20% of patients were aged 65 years or older. Patients with RA initiating TNF-α antagonists and comparator medications had similar baseline characteristics (Table 1). For patients with RA, the mean age was 58 years, 87% were women, 61% were white, and 24% resided in rural areas. Of patients initiating TNF-α antagonists, 70% had used methotrexate during baseline. Similarly, after propensity score matching, there were no substantial differences in the distribution of covariates between exposure groups for patients with IBD and psoriasis and spondyloarthropathies (eTables 1 and 2). Overall, follow-up was censored due to death in 225 patients, including 148 of 20 968 patients (0.7%) with RA, 38 (0.8%) with IBD, and 39 (0.6%) with psoriasis and spondyloarthropathies.
Overall, we identified 1172 serious infections, most of which (53%) were pneumonia and skin and soft tissue infections (eTable 3). The case fatality ratio during hospitalizations for serious infections was 3.6% (30/823) for RA, 2.1% (4/194) for IBD, and 7.1% (11/155) for psoriasis and spondyloarthropathies.
Rheumatoid Arthritis. The hospitalization rate for serious infections in the TNF-α antagonist group did not differ significantly from the rate for the nonbiologic agent group (adjusted hazard ratio [aHR], 1.05 [95% CI, 0.91-1.21]) (Figure 2). However, baseline glucocorticoid use was significantly associated with increased hospitalization risk compared with no baseline use (aHR, 1.32, 1.78, and 2.95 for low, medium and high doses of glucocorticoids, respectively) (Table 2).
Within TNF-α antagonists, the rate of serious infections among those taking infliximab was higher than that for nonbiologic regimens (aHR, 1.25 [95% CI, 1.07-1.48]), whereas the rates for either etanercept or adalimumab were not. Infection rates were significantly higher for infliximab compared with etanercept (aHR, 1.26 [95% CI, 1.07-1.47]) and adalimumab (1.23 [95% CI, 1.02-1.48]). Rates did not differ significantly between adalimumab and etanercept (Figure 3). Concurrent use of methotrexate at t0 and 180 days of follow-up was 41% and 41% for adalimumab vs 44% and 41% for etanercept. The respective proportions were 42% and 42% vs 52% and 50% for adalimumab vs infliximab, and 49% and 48% vs 41% and 36% for infliximab vs etanercept.
Inflammatory Bowel Disease. The rate of serious infections among those initiating TNF-α antagonist regimens was not significantly higher than for those taking azathioprine or mercaptopurine (aHR, 1.10 [95% CI, 0.83-1.46]). Among patients with IBD, there was no significant increase in the risk of serious infections associated with baseline use of glucocorticoids (Table 2).
Psoriasis and Spondyloarthropathies. Among patients with psoriasis, psoriatic arthritis, or ankylosing spondylitis, the rate of serious infections for those initiating regimens of TNF-α antagonists was not significantly higher than for those taking comparator medications (aHR, 1.05 [95% CI, 0.76-1.45]). Baseline use of glucocorticoids was associated with a significantly increased risk of serious infections, compared with no baseline use (Table 2).
Subgroup analyses indicated that the presence of selected baseline characteristics (hospitalization for serious bacterial infections, diabetes mellitus,2 COPD, and glucocorticoid use) increased the absolute risk of serious infections in both those initiating TNF-α antagonists and in patients initiating comparator therapies to a similar degree. For example, patients with COPD had a 2- to 3-fold greater absolute risk of infection compared with patients without COPD. However, within each subgroup, the relative risk of infection for TNF-α antagonists vs the comparator groups was similar, consistent with the main study findings. Similarly, estimates stratified by database generally yielded consistent results (eTables 4 and 5), although for RA and psoriasis and spondyloarthropathies, crude infection rates were lower at KPNC compared with other databases.
A sensitivity analysis that compared initiation of TNF-α antagonists after baseline use of methotrexate (a subset of the main TNF-α antagonists group) with the nonbiologic regimens among patients with RA yielded results similar to those from the main analyses (aHR, 1.01 [95% CI, 0.87-1.18]). Similarly, when follow-up was truncated at 6 months, the aHR was 1.11 (95% CI, 0.94-1.31). Finally, restricting the study outcomes to pneumonia hospitalizations yielded results consistent with the main findings for TNF-α antagonists compared with comparator regimens (aHR, 1.11 [95% CI, 0.94-1.31] for RA, 1.12 [95% CI, 0.81-1.54] for IBD, and 1.13 [95% CI, 0.79-1.62]) for psoriasis and spondyloarthropathies.
In a large US multi-institution research initiative, we observed that initiation of TNF-α antagonists (as a group) was not associated with a significant increase in the risk of serious infections requiring hospitalization compared with initiation of comparator nonbiologic medications. These findings were consistent across study diseases: RA, IBD, and psoriasis, psoriatic arthritis, or ankylosing spondylitis. Nevertheless, among patients with RA, initiation of infliximab-based regimens was significantly associated with an increased risk of serious infections compared with other TNF-α antagonist regimens and nonbiologic comparator medications. We also observed a dose-dependent increase in the risk of serious infections associated with baseline use of glucocorticoids among patients with RA or psoriasis and spondyloarthropathies.
A number of meta-analyses have summarized results from randomized clinical trials examining whether TNF-α antagonists increase the risk of infections, mainly in patients with RA. These studies indicated that TNF-α antagonists increase the risk of infections (serious or nonserious) by 1.2- to 2.0-fold compared with placebo or other regimens, with the vast majority being placebo-controlled.4,5A meta-analysis of randomized trials of TNF-α antagonists among patients with IBD reported no difference in the frequency of serious infections between the anti-TNF and the placebo-controlled groups.6 A recent systematic review of randomized trials that evaluated whether use of TNF-α antagonists increased the risk of serious infections among patients with ankylosing spondylitis (mostly small and short-duration trials) reported no significant increase in the risk of serious infections compared with placebo.7 Thus, available clinical trial data are not consistent, and caution is warranted when interpreting data that combine studies with different comparators and selection criteria. Furthermore, underserved, vulnerable patients are typically excluded from clinical trials, and data on the safety of biologics for these populations are scarce.
Several observational studies examined the association of TNF-α antagonists and the risk of serious infections (mostly in RA), but results again are inconsistent. For our analyses, we considered some of the methodological challenges that could explain differences in these studies.14,15 Some observational studies that used registry or administrative data observed an increased risk of serious infections associated with initiation or prevalent use of TNF-α antagonists compared with prevalent use of comparator drugs.8,12,13,37,38 Prevalent users have “survived” their initiation of therapy and their risk of serious outcomes may be lower than that for new users. Hence, to ensure comparability of exposure groups, we applied a new-user design.19 Other observational studies that used a new-user design failed to identify significant increases in the risk of hospitalizations for serious infections among initiators of TNF-α antagonists compared with initiators of methotrexate.9-11 Similarly, a recent study in US veterans suggested that initiation of TNF-α antagonists was not associated with an increase in the risk of serious infections compared with initiation of other medications, including methotrexate, used to treat moderate disease.39
We reduced exposure misclassification by using pharmacy and procedure data to classify each day of follow-up during new episodes of medication use. Pharmacy records are an excellent source of exposure data because they are not subject to recall bias.40 We reduced outcome misclassification by using validated algorithms.22-26 These considerations are important for comparing our findings with those of other studies. For example, a registry study that reported an increased incidence of serious infections among users of TNF-α antagonists in RA used an inpatient database to identify a comparison group but information on nonbiologic medication exposure for that group was unclear.41 Another registry-based study combined mild and severe infections but gave no specific estimate for serious infections requiring hospitalization.42
Because disease severity is an important predictor of treatment with TNF-α antagonists and could independently affect the risk of infections,43 it must be controlled during assessments of medication effects. Although direct measurements of disease severity are available from disease registries,37,38,41,42 studies that relied on administrative data, as ours did, use available covariates as surrogates for disease severity. Nevertheless, these surrogates appear to be closely correlated with objective measurements of disease severity.8-10,14 Our study focused on initiation of study medications as a proxy for disease activity and should minimize concerns about confounding by identifying an extensive list of relevant covariates and balancing their distribution between exposure groups using a propensity score matching strategy.
Among patients with RA and psoriasis and spondyloarthropathies, baseline glucocorticoid use was associated with an increased risk of serious infections in a dose-response manner irrespective of other medication regimens. Although glucocorticoid use (especially high doses) could also be a surrogate for disease severity, these associations persisted after adjustment for measured covariates, several of which are likely correlated with disease severity. Furthermore, this dose-dependent association has been consistently found in previous observational studies that addressed a similar research question.8-10,44-46
Unlike most prior studies, the SABER study was large enough to provide estimates for individual TNF-α antagonist use among patients with RA. We observed that initiation of an infliximab-based regimen was associated with a significant increase in the risk of serious infections compared with other TNF-α antagonist regimens. This is consistent with recent studies conducted in US veterans,39 in the German disease registry,42 and in 2 different large US health care insurance databases.20,47 However, this differential effect was not observed in the British registry data.37 Differences in pharmacological properties, mechanisms of action, and administration modes including use of a loading dose among TNF-α antagonists have been postulated to explain these observations, but a definitive explanation for this finding is lacking.2,20,42,47-49 This observation may have important implications for the interpretation of studies that report on TNF-α antagonists as a group because the prevalence of infliximab use could influence the observed associations. Although our estimates evaluated the association between serious infections and infliximab-based regimens, as they are used in clinical practice, methotrexate was commonly used concurrently with infliximab to reduce the production of anti-infliximab antibodies. Disentangling the effects of individual drugs when used concurrently is difficult. Nevertheless, we noted that concurrent methotrexate use was similar for the other TNF-α antagonists.
Our findings must be interpreted in the light of several limitations. First, although pharmacy files provide excellent information on medications dispensed, the actual use of most medications is unknown. Second, we relied on coded information from administrative claims and other data not directly collected for clinical care to identify study outcomes. Misclassification of outcomes would make it more difficult to demonstrate true associations.8,22 However, we minimized outcome misclassification by using previously validated definitions. Third, despite the enormous effort to aggregate data from 4 major US data sources, our study had insufficient numbers to evaluate the role of specific TNF-α antagonists on serious infections for IBD and psoriasis and spondyloarthropathies. Furthermore, our power to detect small risk increases in these groups was limited. Fourth, we were not able to study deaths due to infections as an outcome because information on cause of death was not available from all sources. Additionally, we included hospitalizations for selected infections in our outcome definitions excluding opportunistic infections, which sometimes do not require hospitalization and which will be examined separately. Finally, the availability of clinical covariates for statistical adjustment was limited in our databases and we relied on surrogate measurements. However, although residual confounding could not be ruled out, our results were consistent in several subgroup and sensitivity analyses.
In conclusion, in this large retrospective cohort study of predominantly low-income and vulnerable US patients with autoimmune diseases, we observed higher absolute rates of infection compared with previously published cohort studies and randomized controlled trials. We found no increased risk of hospitalizations for serious infections among patients initiating a TNF-α antagonist (as a group) compared with those taking comparator nonbiologic therapies. However, our results also suggest that, among patients with RA, infliximab-based regimens were associated with an increase in the risk of serious infections compared with other TNF-α antagonist–based regimens. For RA and psoriasis and spondyloarthropathies, this large study also demonstrated that glucocorticoid use was associated with a strong dose-dependent increase in the risk of serious infections requiring hospitalization.
Corresponding Author: Carlos G. Grijalva, MD, MPH, Division of Pharmacoepidemiology, Department of Preventive Medicine, Vanderbilt University School of Medicine, 1500 21st Ave S, Ste 2600, The Village at Vanderbilt, Nashville, TN 37212 (firstname.lastname@example.org)
Published Online: November 6, 2011. doi:10.1001/jama.2011.1692
Author Contributions: Dr Chen had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Grijalva, Chen, Delzell, Beukelman, Winthrop, Griffin, Liu, Patkar, Solomon, Lewis, Saag, Curtis.
Acquisition of data: Winthrop, Griffin, Herrinton, Liu, Solomon, Curtis.
Analysis and interpretation of data: Grijalva, Chen, Delzell, Baddley, Beukelman, Winthrop, Griffin, Herrinton, Liu, Ouellet-Hellstrom, Patkar, Solomon, Xie, Saag, Curtis.
Drafting of the manuscript: Grijalva, Curtis.
Critical revision of the manuscript for important intellectual content: Grijalva, Chen, Delzell, Baddley, Beukelman, Winthrop, Griffin, Herrinton, Liu, Ouellet-Hellstrom, Patkar, Solomon, Lewis, Xie, Saag, Curtis.
Statistical analysis: Chen, Baddley, Beukelman, Winthrop, Herrinton, Liu, Ouellet-Hellstrom, Xie, Curtis.
Obtained funding: Griffin, Herrinton, Liu, Patkar, Solomon, Lewis, Saag, Curtis.
Administrative, technical, or material support: Winthrop, Herrinton, Patkar, Solomon, Curtis.
Study supervision: Grijalva, Herrinton, Patkar, Saag, Curtis.
Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Delzell reported receiving research support from Amgen. Dr Baddley reported consulting for Abbott and Merck. Dr Herrinton reported receiving research support from Genentech, Centocor, and Procter & Gamble. Dr Solomon reported receiving research support from Amgen, Abbott, Eli Lilly, and Bristol-Myers Squibb. Dr Lewis reported consulting for Genentech, Abbott, CORRONA (Consortium of Rheumatology Researchers of North America Inc), Millennium Pharmaceuticals, Elan, AstraZeneca, Procter & Gamble, GlaxoSmithKline, Alios Therapeutics, Roche, and Amgen; he also reported receiving research support from Centocor, Takeda, and Shire, and consultant honoraria from Amgen and Pfizer. Dr Winthrop reported receiving consultant fees from Genentech and Abbott, research support from Pfizer, and speakers honoraria from Amgen and Pfizer. Dr Saag reported receiving research support from Amgen, Genentech, and Pfizer. Dr Curtis reported receiving consultant fees and research grants from Roche/Genentech, UCB biopharma, Centocor, CORRONA, Amgen, Pfizer, Bristol-Myers Squibb, Crescendo, and Abbott. Other authors reported no conflicts.
Funding/Support: This work was supported by the Food and Drug Administration (FDA), US Department of Health and Human Services (DHHS), and the Agency for Healthcare Research and Quality (AHRQ) grant U18 HS17919. Dr Curtis receives support from the National Institutes of Health (NIH; AR053351) and AHRQ (R01HS018517). Dr Beukelman was supported by NIH grant 5KL2 RR025776-03 via the University of Alabama at Birmingham Center for Clinical and Translational Science. Drs Grijalva and Griffin receive support from the National Institute of Arthritis and Musculoskeletal and Skin Diseases, grant 5P60AR56116.
Role of the Sponsors: Members of the funding organizations (Dr Ouellet-Hellstrom [from FDA] and Dr Parivash Nourjah [from AHRQ]) participated in the design and conduct of the study. The sponsors had the opportunity to review and comment on the final version of the report.
The SABER Collaboration:AHRQ: Parivash Nourjah; Brigham and Women's Hospital: Robert Glynn, Mary Kowal, Joyce Lii, Jeremy Rassen, Sebastian Schneeweiss, Daniel Solomon; Fallon Medical Center and University of Massachusetts: Leslie Harrold; FDA: David Graham, Carolyn McCloskey, Rita Ouellet-Hellstrom, Kristin Phucas; Kaiser Permanente Northern California: Lisa Herrinton, Liyan Liu; Kaiser Permanente Colorado: Marcia Raebel; Universityof Alabama at Birmingham: Lang Chen, Jeffrey Curtis, Elizabeth Delzell, Nivedita Patkar, Kenneth Saag, Fenglong Xie; University of Pennsylvania: Kevin Haynes, James Lewis; Vanderbilt University: Marie Griffin, Carlos Grijalva, and Ed Mitchel.
Disclaimer: The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the FDA or AHRQ.
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