Osteoporosis and fragility fractures are managed by clinicians across many medical specialties. The key competencies of clinicians delivering bone health care have not been systematically established. We aimed to develop a decision rule to define the threshold of adequate skills and attributes associated with clinical competency in bone health for a clinician serving as a referral source for bone health care. Using a modified-Delphi method, we invited clinicians with expertise in treating osteoporosis and representatives of patient advocacy groups focused on bone health to create a list of desirable characteristics of a clinician with bone health competency. Characteristics were defined as “attributes” with “levels” within each attribute. Participants prioritized levels by perceived importance. To identify the cut points for defining adequate competency, participants next ranked 20 hypothetical clinicians defined by various levels of attributes from highest to lowest likelihood of having adequate bone health competency. Lastly, we conducted a discrete choice experiment (DCE) to generate a weighted score for each attribute/level. The threshold for competency was a priori determined as the total weighted score at which ≥70% of participants agreed a clinician had adequate bone health competency. Thirteen participants generated lists of desirable characteristics, and 30 participants ranked hypothetical scenarios and participated in the DCE. The modified-Delphi exercise generated 108 characteristics, which were reduced to 8 categories with 20 levels with associated points. The maximum possible score was 25 points. A summed threshold score of >12 points classified a clinician as having adequate bone health competency. We developed a numeric additive decision rule to define clinicians across multiple specialties as having adequate competency in managing bone health/osteoporosis. Our data provide a rigorously defined criteria for a clinician with competency in bone health and can be used to quantitate the skills of clinicians participating in bone health research and clinical care.

OBJECTIVE:Autoimmune or inflammatory rheumatic diseases (AIRDs) increase the risk for poor COVID-19 outcomes. Although rurality is associated with higher post-COVID-19 mortality in the general population, whether rurality elevates this risk among people with AIRD is unknown. We assessed associations between rurality and post-COVID-19 all-cause mortality, up to two years post infection, among people with AIRD using a large nationally sampled US cohort. METHODS:This retrospective study used the National COVID Cohort Collaborative, a medical records repository containing COVID-19 patient data. We included adults with two or more AIRD diagnostic codes and a COVID-19 diagnosis documented between April 2020 and March 2023. Rural residency was categorized using patient residential zip codes. We adjusted for AIRD medications and glucocorticoid prescription, age, sex, race and ethnicity, tobacco or substance use, comorbid burden, and SARS-CoV-2 variant-dominant periods. Multivariable Cox proportional hazards with inverse probability treatment weighting assessed associations between rurality and two-year all-cause mortality. RESULTS:Among the 86,467 SARS-CoV-2-infected persons with AIRD, we observed a higher risk for two-year post-COVID-19 mortality in rural versus urban dwellers. Rural-residing persons with AIRD had higher two-year all-cause mortality risk (adjusted hazard ratio 1.24, 95% confidence interval 1.19-1.29). Glucocorticoid, immunosuppressive, and rituximab prescriptions were associated with a higher risk for two-year post-COVID-19 mortality, whereas risk with nonbiologic or biologic disease-modifying antirheumatic drugs was lower. CONCLUSION:Rural residence in people with AIRD was independently associated with higher two-year post-COVID-19 mortality in a large US cohort after adjusting for background risk factors. Policymakers and health care providers should consider these findings when designing interventions to improve outcomes in people with AIRD following SARS-CoV-2 infection, especially among high-risk rural residents.

Background: During the COVID-19 pandemic, telemedicine was widely adopted as an approach to care for people with rheumatic diseases. Data on whether telemedicine is as good as in-person care in terms of patient satisfaction and effectiveness of care in rheumatology is limited. Objectives: We aimed to determine whether telemedicine visits were noninferior to in-person visits for patient satisfaction and other measures of care effectiveness. Methods: We conducted a parallel group, randomized, single-blind, noninferiority trial in rheumatology clinics at two academic medical centers in the United States during August 2021 to December 2022. Eligible patients were age ≥ 18 years with at least one rheumatic disease, who had ≥ 2 clinic visits in the previous 18 months (telemedicine or in-person), one of which was an in-person visit. The primary outcome was post-visit satisfaction rate (9 or 10 on a 0 – 10 satisfaction scale, higher values represent higher satisfaction). We also collected secondary outcome data on preference for the next visit type, self-efficacy for managing medications, and medication adherence. Preference for the next visit type was defined as: same as the group allocation, different than the group allocation or no preference. Exploratory outcomes included healthcare utilization after the visit (e.g., emergency room visits or hospitalizations), time missed from work and expenses incurred related to attending the visit, and appropriate laboratory monitoring based on medication use. Primary outcome was determined by assessing if the satisfaction rate with telemedicine versus in-person visits was noninferior, using a noninferiority margin of 10%. We performed modified intent-to-treat (mITT), and per protocol (PP) analyses to account for those that crossed over. Results: Out of 652 randomized participants, 501, 35.7% Black, 84.0% women, mean age 55.3 years completed surveys. In the mITT analysis, we were unable to reject the noninferiority hypothesis and also found that telemedicine visits were inferior to in-person visits for the proportion of people that were highly satisfied, 76.7% in the telemedicine group vs. 90.1% in the in-person group, difference 13.4% (95% CI, 7.0% to 19.8%). By arm, 38 participants crossed over in the in-person group and 46 in the telemedicine group (p = 0.5). In the PP analysis, the satisfaction rate for telemedicine visits was also inferior to in-person visits, difference 16.0% (95% CI, 8.8% to 23.3%). The proportion of participants who indicated they preferred the same type of visit for their next visit compared to a different visit type or no preference was significantly greater for those who had in-person visits (mITT analysis, 55.6% vs 19.4% in-person vs telemedicine, p < 0.0001). In both the mITT and PP analyses, most participants preferred an in-person visit for their next visit. There were no clinically or statistically significant differences between groups in self-efficacy for managing medications or medication adherence in either the mITT or PP analyses. In PP analyses, significantly more individuals in the in-person group vs. the telemedicine group had appropriate laboratory monitoring for conventional synthetic DMARDs, and more had reported expenses related to meals and transportation. There were no significant differences in healthcare utilization between groups. Conclusion: Among a large group of geographically, racially and ethnically diverse established rheumatology patients, although a majority of patients were satisfied with telemedicine visits, high patient satisfaction with rheumatology clinic visits and appropriate laboratory monitoring was lower for telemedicine than for in-person visits. Most participants preferred an in-person visit as their next visit type. Future studies should focus on methods to improve quality of care delivered by telemedicine. REFERENCES: NIL. Acknowledgements: NIL. Disclosure of Interests: Lesley Jackson: None declared, Justin Leach: None declared, Jinoos Yazdany Astra Zeneca, Aurinia, Gilead, Pfizer, Kenneth Saag Abbvie, Amgen, Arthrosi, Atom Bioscience, Bayer, CSL Behring, Daiichi Sankyo, Gilead, Horizon, Inflazome, LG Pharma, Mallinkrodt, Radius, Roche/Genentech, SOBI, Takeda, Allena, Amgen, Arthrosi, Dyve, Horizon, LG Chem, Radius, Shanton, SOBI, Takeda, Ultragenex, Jeffrey R Curtis AbbVie, Amgen, Bristol-Myers Squibb, CorEvitas, Eli Lilly and Company, Janssen, Myriad, Novartis, Pfizer, Sanofi, UCB, AbbVie, Amgen, Bristol-Myers Squibb, CorEvitas, Eli Lilly and Company, Janssen, Myriad, Novartis, Pfizer, Sanofi, UCB, Diana Paez: None declared, Sarah Goglin: None declared, Mary Margaretten: None declared, David Chae: None declared, Gary Cutter Alexion, Antisense Therapeutics, Avotres, Biogen, Clene Nanomedicine, Clinical Trial Solutions LLC, Entelexo Biotherapeutics, Genentech, Genzyme, GW Pharmaceuticals, Hoya Corporation, Immunic, Immunosis Pty Ltd, Klein-Buendel Incorporated, Merck/Serono, Novartis, Perception Neurosciences, Protalix Biotherapeutics, Regeneron, Roche, SAB Biotherapeutics, Maria Danila RheumNow, UCB, Horizon, Pfizer.

Falls and osteoporosis are risk factors for fragility fractures. Bone mineral density (BMD) assessment is associated with better preventative osteoporosis care, but it is underutilized by those at high fracture risk. We created a novel electronic medical record (EMR) alert-driven protocol to screen patients in the Emergency Department (ED) for fracture risk and tested its feasibility and effectiveness in generating and completing referrals for outpatient BMD testing after discharge. The EMR alert was configured in 2 tertiary-care EDs and triggered by the term "fall" in the chief complaint, age (>= 65 years for women, >= 70 years for men), and high fall risk (Morse score >= 45). The alert electronically notified ED study staff of potentially eligible patients. Participants received osteoporosis screening education and had BMD testing ordered. From November 15, 2020 to December 4, 2021, there were 2,608 EMR alerts among 2,509 patients. We identified 558 patients at high-risk of fracture who were screened for BMD testing referral. Participants were excluded for: serious illness (N = 141), no documented health insurance to cover BMD testing (N = 97), prior BMD testing/recent osteoporosis care (N = 58), research assistant unavailable to enroll (N = 53), concomitant fracture (N = 43), bedridden status (N = 38), chief complaint of fall documented in error (N = 38), long-term care residence (N = 34), participation refusal (N = 32), or hospitalization (N = 3). Of the 16 participants who had BMD testing ordered, 7 scheduled and 5 completed BMD testing. EMR alerts can help identify subpopulations who may benefit from osteoporosis screening, but there are significant barriers to identifying eligible and willing patients for screening in the ED. In our study targeting an innovative venue for osteoporosis care delivery, only about 1% of patients at high-risk of fracture scheduled BMD testing after an ED visit. Adequate resources during and after an ED visit are needed to ensure that older adults participate in preventative osteoporosis care. Falls and osteoporosis are risk factors for fragility fractures. Bone mineral density (BMD) assessment is associated with better preventative osteoporosis care, but it is underutilized by those at high fracture risk. We created a novel electronic medical record (EMR) alert-driven protocol to screen patients in the Emergency Department (ED) for fracture risk and tested its feasibility and effectiveness in generating and completing referrals for outpatient BMD testing after discharge. The EMR alert was configured in 2 tertiary-care EDs and was triggered among older adults that presented with a fall or were considered high fall risk. Eligible participants received osteoporosis screening education and had BMD testing ordered. From November 15, 2020 to December 4, 2021, we identified 558 patients at high-risk of fracture who were screened for BMD testing referral. Of the 16 participants who had BMD testing ordered, 7 scheduled and 5 completed BMD testing. There are significant barriers to identifying eligible and willing patients for screening in the ED. In our study targeting an innovative venue for osteoporosis care delivery, only about 1% of patients at high-risk of fracture scheduled BMD testing after an ED visit. Adequate resources during and after an ED visit are needed to ensure that older adults participate in preventative osteoporosis care. Graphical Abstract

(1) Background: Some severe COVID-19 patients develop hyperinflammatory cytokine storm syndrome (CSS). We assessed the efficacy of anakinra added to standard of care (SoC) in hospitalized COVID-19 CSS patients. (2) Methods: In this single-center, randomized, double-blind, placebo-controlled trial (NCT04362111), we recruited adult hospitalized patients with SARS-CoV-2 infection, evidence of pneumonia, new/increasing oxygen requirement, ferritin ≥ 700 ng/mL, and at least three of the following indicators: D-dimer ≥ 500 ng/mL, platelet count < 130,000/mm3, WBC < 3500/mm3 or lymphocyte count < 1000/mm3, AST or ALT > 2X the upper limit of normal (ULN), LDH > 2X ULN, C-reactive protein > 100 mg/L. Patients were randomized (1:1) to SoC plus anakinra (100 mg subcutaneously every 6 h for 10 days) or placebo. All received dexamethasone. The primary outcome was survival and hospital discharge without need for intubation/mechanical ventilation. The data were analyzed according to the modified intention-to-treat approach. (3) Results: Between August 2020 and January 2021, 32 patients were recruited, of which 15 were assigned to the anakinra group, and 17 to the placebo group. Two patients receiving the placebo withdrew within 48 h and were excluded. The mean age was 63 years (SD 10.3), 20 (67%) patients were men, and 20 (67%) were White. At Day 10, one (7%) patient receiving anakinra and two (13%) patients receiving the placebo had died (p = 1.0). At hospital discharge, four (27%) patients receiving anakinra and four (27%) patients receiving the placebo had died. The IL-6 level at enrollment was predictive of death (p < 0.01); anakinra use was associated with decreases in CXCL9 levels. (4) Conclusions: Anakinra added to dexamethasone did not significantly impact the survival of COVID-19 pneumonia patients with CSS. Additional studies are needed to assess patient selection and the efficacy, timing, and duration of anakinra treatment for COVID-19 CSS.

BackgroundOsteoporosis and fragility fractures are managed by clinicians across a variety of specialties and there is no specific certification in osteoporosis diagnosis and management. These clinicians are an integral part of interventions aimed to improve bone health care (e.g., fracture liaison service [FLS]). Yet, the key skills and attributes of a clinician with competence in bone health management have not been established in a systematic fashion.ObjectivesWe conducted a Delphi exercise and a discrete choice experiment (DCE) to generate a decision rule aiming to define the minimal attributes of a clinician best poised to assess and treat people with osteoporosis and serve as a referral source for post-fracture management.MethodsIn part 1, we used a modification of the Delphi method with two rounds. Clinicians with experience in treating osteoporosis and representatives of patient advocacy groups were purposively sampled to participate. Participants asynchronously generated a list of desirable characteristics/skills of a “clinician with competence in bone health”. Characteristics were coded and organized into non-overlapping themes or “attributes” with sub-themes or “levels” within each attribute. Participants prioritized and ranked levels in order of perceived importance for inclusion in the definition for a bone health clinician. Levels within attributes associated with the highest median scores were included in the final list of criteria. In part 2, participants ranked 20 hypothetical clinicians defined by various levels of attributes from highest to lowest likelihood of being a bone health clinician to identify the minimal threshold for defining competence in managing bone health. Consistency amongst rankings was evaluated using intraclass correlation coefficients (ICC). In part 3, we conducted a DCE to generate a weighted importance score for each independent and mutually exclusive attribute and level such that the sum of weights across the highest level within each attribute would equal 100%. The threshold for competence was the total weighted score at which ≥70% of participants agreed a clinician had bone health competence.ResultsPart 1 included 13 participants, and 11 completed the DCE survey. Those who completed part 1 included 3 endocrinologists, 3 rheumatologists, 1 orthopedist, 3 general internists, and 3 representatives of patient advocacy groups. The Delphi exercise generated a list of N=108 characteristics, which were coded and grouped into common themes/attributes. Through an iterative process with 2 rounds of piloting, the attribute categories were reduced to 8 broad categories with a total of 20 levels. The participants’ rankings of the relative probability that each of the 20 hypothetical clinician cases represented a clinician with adequate competence in bone health is plotted in Figure 1. The ICC for agreement across participants was 0.90 (95% confidence interval [CI]: 0.83, 0.95). The maximum possible score in the final criteria was 25. A threshold score of ≥12 classified a clinician as having adequate competence in bone health. For example, a clinician that prescribes all osteoporosis drugs, performs osteoporosis workup/ treatment monitoring, and leads or participates in an FLS would receive 5, 3.5, and 3.5 points, respectively, that summed would reach the threshold.ConclusionWe developed a numeric additive decision rule to classify clinicians across multiple specialties with competence in evaluating and treating patients with osteoporosis. Our data provides the critical definition of a “clinician with competence in bone health” that may be useful for identifying and qualifying the skill of clinicians who may be included in interventional studies or clinical activities that aim improve bone health care.REFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsLesley Jackson: None declared, Sindhu Johnson: None declared, Ellen McNeeley: None declared, Kenneth Saag Grant/research support from: Amgen, Horizon, LG Chem, Radius, SOBI, Maria Danila Consultant of: UCB, Grant/research support from: Pfizer.

ObjectiveThe objective is to update recommendations for prevention and treatment of glucocorticoid‐induced osteoporosis (GIOP) for patients with rheumatic or nonrheumatic conditions receiving >3 months treatment with glucocorticoids (GCs) ≥2.5 mg daily.MethodsAn updated systematic literature review was performed for clinical questions on nonpharmacologic, pharmacologic treatments, discontinuation of medications, and sequential therapy. Grading of Recommendations Assessment, Development and Evaluation approach was used to rate the certainty of evidence. A Voting Panel achieved ≥70% consensus on the direction (for or against) and strength (strong or conditional) of recommendations.ResultsFor adults beginning or continuing >3 months of GC treatment, we strongly recommend as soon as possible after initiation of GCs, initial assessment of fracture risks with clinical fracture assessment, bone mineral density with vertebral fracture assessment or spinal x‐ray, and Fracture Risk Assessment Tool if ≥40 years old. For adults at medium, high, or very high fracture risk, we strongly recommend pharmacologic treatment. Choice of oral or intravenous bisphosphonates, denosumab, or parathyroid hormone analogs should be made by shared decision‐making. Anabolic agents are conditionally recommended as initial therapy for those with high and very high fracture risk. Recommendations are made for special populations, including children, people with organ transplants, people who may become pregnant, and people receiving very high‐dose GC treatment. New recommendations for both discontinuation of osteoporosis therapy and sequential therapies are included.ConclusionThis guideline provides direction for clinicians and patients making treatment decisions for management of GIOP. These recommendations should not be used to limit or deny access to therapies.

Background Patients with acute gout are frequently treated in the emergency department (ED). Appropriate outpatient follow-up for an acute flare after an ED visit is variable and has not been systematically examined. Objectives We aimed to determine the characteristics of patients with gout treated in EDs, the nature of their ED visits, and to determine the types and rates of outpatient follow-up after an ED visit for an acute gout flare. Methods This study was conducted at one academic medical center that serves patients at 3 EDs (2 urban, 1 suburban) and 1 urban urgent care center. Patients were identified as having a possible acute gout flare upon initial triage in the ED using a previously developed electronic medical record (EMR) gout flare alert. We validated the presence/ absence of an acute gout flare through manual EMR review using adjudicated expert consensus assessed with kappa coefficient as the gold standard. Among those patients identified with an acute gout flare, we abstracted their medical records to determine the presence/ absence of an outpatient visit for gout care within 6 months of the index ED visit. This was defined as having had a documented outpatient visit with any provider (e.g., primary care, rheumatology) with a mention of ‘gout’ in the free text portion of history of present illness or assessment and plan. We used descriptive statistics to characterize patients with gout and reported the proportion of patients with an acute gout flare that followed up in the outpatient setting after an ED visit. Results From September 1, 2021 to February 28, 2022, there were 458 patients identified by the gout flare alert as possibly having an acute gout flare. Of these, 33 patients were excluded from this analysis due to participation in an ongoing randomized clinical trial testing a behavioral intervention to improve gout care. The remaining 425 patients included 72 patients (16.9%) who were determined to have a true gout flare at 85 unique ED visits by manual EMR review by 2 assessors. The kappa coefficient for agreement between the consensus expert determinations of acute gout flare was 0.8. Of those with an acute gout flare, 53 (74%) were men and 49 (68%) were Black or African American. At ED discharge, a majority of patients (64%) were prescribed corticosteroids, while 63% were prescribed opioids. A majority (54%) of ED visits for acute gout occurred between 8am and 5pm, with another 27% occurring between 5pm and midnight. The proportion of patients with an acute gout flare who followed up with an outpatient clinician in our healthcare system was 46% (Table 1), with 29% of patients having an outpatient visit within 30 days of the index ED encounter. Only 26 patients (36%) had an outpatient visit addressing gout, and of these, 17 (24%) occurred within 3 months of the index ED visit. Conclusion More than half of patients received opioids at discharge among patients with gout treated in the ED, who were mostly Black. Follow-up was ~46% among patients that received gout care in an acute setting, yet only a third saw a clinician who addressed gout. This data will inform sample size calculation for interventional studies testing behavioral interventions focused on promoting improved outpatient follow-up for gout flares. REFERENCES: NIL. Acknowledgements: NIL. Disclosure of Interests Lesley Jackson: None declared, Faith Mugeta: None declared, Ellen McNeeley: None declared, Norma C. Techarukpong: None declared, Kiara Aaron: None declared, James Booth: None declared, Jeff Foster: None declared, Gary Cutter Consultant of: Alexion, Antisense Therapeutics, Biogen, Clinical Trial Solutions LLC, Entelexo Biotherapeutics, Inc., Genzyme, Genentech, GW Pharmaceuticals, Immunic, Immunosis Pty Ltd, Klein-Buendel Incorporated, Merck/Serono, Novartis, Perception Neurosciences, Protalix Biotherapeutics, Regeneron, Roche, SAB Biotherapeutics. Data and Safety Monitoring Boards: Applied Therapeutics, AI therapeutics, AMO Pharma, Astra-Zeneca, Avexis Pharmaceuticals, Biolinerx, Brainstorm Cell Therapeutics, Bristol Meyers Squibb/Celgene, CSL Behring, Galmed Pharmaceuticals, Green Valley Pharma, Horizon Pharmaceuticals, Immunic, Karuna Therapeutics, Mapi Pharmaceuticals LTD, Merck, Mitsubishi Tanabe Pharma Holdings, Opko Biologics,Prothena Biosciences, Novartis, Regeneron, Sanofi-Aventis, Reata Pharmaceuticals, Teva Pharmaceuticals, NHLBI (Protocol Review Committee), University of Texas Southwestern, University of Pennsylvania, Visioneering Technologies, Inc., Employee of: Dr. Cutter is employed by the University of Alabama at Birmingham and President of Pythagoras, Inc. a private consulting company located in Birmingham AL., John Osborne: None declared, Kenneth Saag Grant/research support from: Amgen, Horizon, LG Chem, Radius, SOBI, Maria Danila Consultant of: UCB, Grant/research support from: Pfizer.Table 1Healthcare utilization among participants following an emergency department visit for acute gout.Healthcare OutcomeTotal, N=72Hospitalized, N (%)13 (18)Received care at Emergency Department or Urgent Care, N (%)34 (47)Any Outpatient Follow-up Service (N=33), N(%)*Internal Medicine Subspecialties†24 (33)Rheumatology4 (6)Surgical Subspecialty20 (28)General Internal Medicine15 (21)Family Medicine4 (6)Neurology3 (4)Palliative Care and Geriatrics3 (4)Dermatology3 (4)* Categories are not mutually exclusive.† Internal medicine subspecialties included cardiology, endocrinology, nephrology, oncology, pulmonology, and rheumatology.

OBJECTIVE:Patients with acute gout are frequently treated in the emergency department (ED) and represent a typically underresourced and understudied population. A key limitation for gout research in the ED is the timely ability to identify acute gout patients. Our goal was to refine a multicriteria, electronic medical record alert for gout flares and to determine its diagnostic characteristics in the ED.METHODS:The gout flare alert used electronic medical record data from ED nursing notes and was triggered by the term 'gout' preceding past medical history in the chief complaint, the term 'gout' and a musculoskeletal problem in the chief complaint, or the term 'gout' in the problem list and a musculoskeletal chief complaint. We validated its diagnostic properties to assess presence/absence of gout through manual medical record review using adjudicated expert consensus as the gold standard.RESULTS:In January 2020, we analyzed 202 patient records from 2 university-based EDs; from these records, 57 patients were identified by our gout flare alert, and 145 were identified by other means as potentially having an acute gout flare. The gout flare alert's positive predictive value was 47% (95% confidence interval [95% CI] 34-60%), negative predictive value was 94% (95% CI 90-98%), sensitivity was 75% (95% CI 61-89%), and specificity was 82% (95% CI 76-88%). The diagnostic properties were similar at both institutions.CONCLUSION:Our multicomponent gout flare alert had reasonable sensitivity and specificity, albeit a modest positive predictive value. An electronic gout flare alert may help enable the conduct of gout research in the ED setting.

OBJECTIVES:Gout prevalence is reportedly ∼20% higher in US Black adults than Whites, but racial differences in emergency department (ED) visits and hospitalizations for gout are unknown. We evaluated the latest US national utilization datasets according to racial/ethnic groups. METHODS:Using 2019 US National Emergency Department Sample and National Inpatient Sample databases, we compared racial/ethnic differences in annual population rates of ED visits and hospitalizations for gout (primary discharge diagnosis) per 100 000 US adults (using 2019 age- and sex-specific US census data). We also examined rates of ED visits and hospitalizations for gout among all US ED visits/hospitalizations and mean costs for each gout encounter. RESULTS:Compared with White patients, the per capita age- and sex-adjusted rate ratio (RR) of gout primary ED visits for Black patients was 5.01 (95% CI 4.96, 5.06), for Asian patients 1.29 (1.26, 1.31) and for Hispanic patients 1.12 (1.10, 1.13). RRs for gout primary hospitalizations were 4.07 (95% CI 3.90, 4.24), 1.46 (1.34, 1.58) and 1.06 (0.99, 1.13), respectively. Corresponding RRs among total US hospitalizations were 3.17 (95% CI 2.86, 3.50), 3.23 (2.71, 3.85) and 1.43 (1.21, 1.68) and among total ED visits were 2.66 (95% CI, 2.50, 2.82), 3.28 (2.64, 4.08), and 1.14 (1.05, 1.24), respectively. RRs were largest among Black women. Costs for ED visits and hospitalizations experienced by race/ethnicity showed similar disparities. CONCLUSIONS:These first nationwide data found a substantial excess in both gout primary ED visits and hospitalizations experienced by all underserved racial/ethnic groups, particularly by Black women, revealing an urgent need for improved care to eliminate inequities in gout outcomes.

Objective The goal of this study was to ascertain COVID‐19 vaccine uptake, reasons for hesitancy, and self‐reported flare in a large rheumatology practice‐based network. Methods A tablet‐based survey was deployed by 108 rheumatology practices from December 2021 to December 2022. Patients were asked about COVID‐19 vaccine status and why they might not receive a vaccine or booster. We used descriptive statistics to explore the differences between vaccination status and vaccine and booster hesitancy, comparing patients with and without autoimmune and inflammatory rheumatic diseases (AIIRDs). We used multivariable logistic regression to examine the association between vaccine uptake and AIIRD status and self‐reported flare and AIIRD status. We reported adjusted odds ratios (aORs). Results Of the 61,158 patients, 89% reported at least one dose of vaccine; of the vaccinated, 68% reported at least one booster. Vaccinated patients were less likely to have AIIRDs (44% vs 56%). A greater proportion of patients with AIIRDs were vaccine hesitant (14% vs 10%) and booster hesitant (21% vs 16%) compared to patients without AIIRDs. Safety concerns (28%) and side effects (23%) were the main reasons for vaccine hesitancy, whereas a lack of recommendation from the physician was the primary factor for booster hesitancy (23%). Patients with AIIRD did not have increased odds of self‐reported flare or worsening disease compared to patients without with AIIRD (aOR 0.99, 95% confidence interval [CI] 0.94–1.05). Among the patients who were vaccine hesitant and booster hesitant, 12% and 39% later reported receiving a respective dose. Patients with AIIRD were 32% less likely to receive a vaccine (aOR 0.68, 95% CI 0.65–0.72) versus patients without AIIRD. Conclusion Some patients who are vaccine and booster hesitant eventually receive a vaccine dose, and future interventions tailored to patients with AIIRD may be fruitful. image

Background: “Storytelling” interventions influence knowledge, attitudes and behavior to promote chronic disease management. We aimed to describe the development of a video “storytelling” intervention to increase gout knowledge and promote adherence to medications and follow-up care after an acute gout flare visit in the emergency department. Methods: We developed a direct-to-patient storytelling intervention to mitigate modifiable barriers to gout care and promote outpatient follow-up and medication adherence. We invited adult patients with gout as storytellers. We utilized a modified Delphi process involving gout experts to identify key themes to guide development of an intervention. Using a conceptual model, we selected stories to ensure delivery of evidence-based concepts and to maintain authenticity. Results: Our video-based storytelling intervention consisted of segments addressing modifiable barriers to gout care. Four diverse gout patients were recruited as storytellers and interviewed with questions that covered gout diagnosis and care. Eleven international gout experts from diverse geographic locations generated and ranked items they considered important messages to promote outpatient gout care follow-up and treatment adherence. Filmed videos were truncated into segments and coded thematically. Distinct segments that captured desired messages were combined to form a cohesive narrative story based on gout patient experiences that conveyed evidence-based strategies to manage gout. Conclusions: Using the Health Belief Model, we developed a culturally appropriate narrative intervention containing “storytelling” that can be tested as an approach to improve gout outcomes. The methods we describe may be generalizable to other chronic conditions requiring outpatient follow-up and medication adherence to improve outcomes.

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Background: Little is known about satisfaction with different modes of telemedicine delivery. The objective of this study was to determine whether patient satisfaction with phone-only was noninferior to video visits.Methods: We conducted a parallel group, randomized (1:1), single-blind, noninferiority trial in multispecialty clinics at a tertiary academic medical center. Adults age & GE; 60 years or with Medicare/Medicaid insurance were eligible. Primary outcome was visit satisfaction rate (9 or 10 on a 0-10 satisfaction scale). Noninferiority was determined if satisfaction with phone-only (intervention) versus video visits (comparator) was no worse by a -15% prespecified noninferiority margin. We performed modified intent-to-treat (mITT) and per protocol analyses, after adjusting for age and insurance.Results: 200 participants, 43% Black, 68% women completed surveys. Visit satisfaction rates were high. In the mITT analysis, phone-only visits were noninferior by an adjusted difference of 3.2% (95% CI, -7.6% to 14%). In the per protocol analysis, phone-only were noninferior by an adjusted difference of -4.1% (95% CI, -14.8% to 6.6%). The proportion of participants who indicated they preferred the same type of telemedicine visit as their next clinic visit were similar (30.2% vs 27.9% video vs phone-only, p = 0.78) and a majority said their medical concerns were addressed and would recommend a telemedicine visit.Conclusions: Among a group of diverse, established older or underserved patients, the satisfaction rate for phone-only was noninferior to video visits. These findings could impact practice and policies governing telemedicine.

Purpose of review We summarize the recent literature published in the last 2 years on healthcare disparities observed in the delivery of rheumatology care by telemedicine. We highlight recent research dissecting the underpinnings of healthcare disparities and identify potentially modifiable contributing factors. Recent findings The COVID-19 pandemic has had major impacts on care delivery and has led to a pronounced increase in telemedicine use in rheumatology practice. Telemedicine services are disproportionately underutilized by racial/ethnic minority groups and among patients with lower socioeconomic status. Disparities in telemedicine access and use among vulnerable populations threatens to exacerbate existing outcome inequalities affecting people with rheumatic disease. Summary Telemedicine has the potential to expand rheumatology services by reaching traditionally underserved communities. However, some areas lack the infrastructure and technology to engage in telemedicine. Addressing health equity and the digital divide may help foster more inclusive telemedicine care.