Candace H. Feldman

Objective: We investigated prospective associations between mental health conditions (depression, anxiety, and posttraumatic stress disorder [PTSD]) and recurrent acute care use among patients with systemic lupus erythematosus (SLE). Methods: Within the All of Us Research Program (version 8), a US-wide cohort of adults who enrolled and consented for linkage to electronic health records, we identified those with SLE by International Classification of Diseases, 10th Revision (ICD-10) or Systematized Nomenclature of Medicine (SNOMED) codes before enrollment. We assessed baseline depression, anxiety, and/or PTSD using ICD-10 or SNOMED codes. Outcomes included emergency department (ED) visits and hospitalizations after enrollment. Incidence rates for acute care events among patients with vs without mental health diagnoses were calculated. Multivariable negative binomial regression models estimated incidence rate ratios (IRRs) with 95% CIs, adjusting for sociodemographic factors and comorbidities. Results: Of 2160 patients with SLE, 973 had depression, anxiety, and/or PTSD (mean age 49.4 [SD 13.5] years; 92.8% female); 1187 did not (mean age 50.4 [SD 14.9] years; 88.1% female). Those with ≥ 1 mental health condition had lower education levels and income and more smoking and comorbidities. Patients were followed up for a mean of 3.02 (SD 1.73) years. One or more mental health diagnosis was strongly associated with higher rates of ED visits (adjusted IRR [aIRR] 1.90, 95% CI 1.60-2.27) and hospitalizations (aIRR 1.33, 95% CI 1.08-1.65). This was true for ED visits and hospitalizations for SLE (aIRR 1.42, 95% CI 1.19-1.69) and for non-SLE conditions (aIRR 1.42, 95% CI 1.17-1.74). Conclusion: In this nationwide SLE cohort, those with vs without mental health diagnoses had increased acute care use for SLE and other causes. Further studies are needed to understand whether effective treatment for mental health diagnoses alters care patterns.

Objective: Our objective was to describe the social networks of Black individuals with rheumatic and musculoskeletal conditions and understand the clustering of health‐related behaviors to inform future community‐based, peer‐led interventions. Methods: We used an adapted Personal Network Survey for Clinical Research (PERSNET) to map the personal social networks of Black individuals with rheumatic conditions in Boston and Chicago. We used egocentric network analyses to quantify network composition and descriptive statistics and Welch's two‐sample t ‐tests to assess network characteristics and behavioral concordance between participants and their network members. Results: We mapped the social networks of 40 individuals with rheumatic conditions in Boston and Chicago. All participants self‐identified as Black, with six indicating Hispanic or Latino ethnicity. One participant self‐identified as male. Networks had a median size of 10, with 54% of all possible ties present and an average of 5 structurally distinct connections within each network. Boston networks had a larger proportion of kin (71%) compared to those in Chicago (40%). There was greater diversity in sex and education within Boston networks. Chicago networks displayed more balanced strong and weak ties and higher prevalence of health problems within networks. Smoking and drinking behaviors clustered within networks across both sites, which may reflect patterns of behavioral homophily or social influence. Conclusion: Differences in network density, constraint, and diversity highlight structural and relational patterns that may influence how health behaviors develop. They also reveal network configurations that position individuals to act as bridges, introducing new information and behaviors into communities where medical mistrust and systemic inequities undermine health messaging.

Objective: Ambulatory care–sensitive conditions (ACSCs) serve as indicators of access, quality, and performance of the health system. This study aimed to determine rheumatoid arthritis (RA)‐specific ACSCs for use in the evaluation of care system access and quality. Methods: A modified Delphi process was conducted on the preventability and importance of hospitalization for candidate medical conditions determined from a systematic literature review. Clinical panelists included health care providers with expertise in the care of people with RA. Patient panelists were people living with RA. Clinical panelists completed two rounds of anonymous voting, with an asynchronous discussion board hosted between rounds. Patient panelists completed the same process considering the remaining conditions for which consensus had not yet been reached. An interactive webinar preceded a final combined panel voting round. Consensus criteria were guided by the RAND/UCLA Appropriateness Method. Results: A total of 22 clinical and 8 patient panelists provided their expert opinion on 49 candidate conditions. At the conclusion of all voting rounds, 13 conditions were identified as proposed RA‐specific ACSCs: those specific to RA disease activity (RA disease flare, vasculitis), RA‐related associations and complications (anemia/pancytopenia, osteoporotic fracture, secondary osteoarthritis, cervical spine instability), and acute (upper respiratory infection, influenza/pneumonia, septic arthritis) and opportunistic infections (herpes zoster, tuberculosis reactivation, Pneumocystis jirovecii pneumonia, and other). Conclusion: Consensus was achieved for 13 proposed RA‐specific ACSCs that could be used to monitor care access and quality for persons living with RA, determine population‐level differences, and appropriately allocate resources. Next steps include operationalizing and testing performance of RA‐specific ACSCs.

Objective: Opioid use disorder (OUD) is associated with high morbidity and mortality. We aimed to describe the characteristics of patients with osteoarthritis (OA), systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), or spondyloarthritis (SpA) who have OUD and to evaluate their uptake of medications for OUD (MOUDs). Methods: Using the national All of Us Research Program (version 8), we identified individuals with at least two billing codes for SLE, RA, SpA, or OA. Patients with OUD were identified using at least one code for opioid dependence or abuse. We compared demographics, psychiatric disorders, prescription medications (opioids, glucocorticoids, and disease‐modifying antirheumatic drugs [DMARDs]), comorbidities, and acute care use between patients with and without OUD. New MOUD use was defined by a prescription for methadone, buprenorphine, or naltrexone following the first OUD code in patients without prior MOUD use documented in available All of Us data. Results: Of 37,282 patients with rheumatic conditions, 1,502 (4%) had OUD. Patients with versus without OUD were younger and were more likely to have Medicaid insurance, psychiatric and medical comorbidities, and higher acute care use. They were more likely to have received prior prescription opioids (75.7% vs 45.8%) and glucocorticoids but less likely to have used biologic, targeted synthetic, or conventional DMARDs. Of the patients with rheumatic diseases and OUD, 14.5% received MOUDs. Conclusion: Patients with rheumatic conditions and OUD exhibit distinct demographic and clinical characteristics including lower DMARD use and higher acute care use. MOUD uptake (14.5%) was significantly lower than national estimates (25%). Further research is needed to identify and address barriers to medication access in these patients.

Objective: Studying rare diseases requires assembling robust, correctly classified cohorts. We compared the performance of seven published International Classification of Diseases, Ninth Revision (ICD‐9) and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD‐10) code rule‐based algorithms in the electronic health record (EHR) to identify cases of idiopathic inflammatory myopathies (IIM). Methods: We identified patients seen at a multihospital academic medical center with one or more ICD‐9 or ICD‐10 codes for IIM (2000–2024). From this cohort, a random sample of 250 cases was selected for chart review for IIM based on the 2017 EULAR/American College of Rheumatology IIM classification criteria. Review was performed by two reviewers and adjudicated by a third. The performance of the algorithms was compared against (1) definite and (2) definite and probable cases of IIM. Positive predictive value (PPV), specificity, sensitivity, and negative predictive value were calculated. Results: Of 250 charts, 87 definite and 28 probable IIM cases were identified. The algorithm with the highest PPV (84%) relied on ICD 9/10 codes obtained during hospitalizations but excluded ≥80% of cases with a sensitivity of 14%. Use of two or more ICD codes ≤60 days apart obtained the second highest PPV (79%) and identified 82 cases. Use of two or more ICD codes between 30 and 365 days apart obtained the third highest PPV (78%). These top‐performing algorithms included definite and probable cases of IIM. Conclusion: Several rule‐based ICD code algorithms had PPVs ranging from 77% to 84% but had lower sensitivity (14%–63%). Limitations of this study include modifications to some of the original algorithms. Future algorithms may benefit from incorporating additional EHR data and natural language processing methods. image

Objective: Patients with social risk factors are more likely to have poorer health outcomes. We examined associations between social risk factors and acute care use (emergency department [ED] visits and hospitalizations) among patients with rheumatic conditions. Methods: We identified patients with ICD‐10 codes for rheumatic conditions on ≥2 encounters receiving rheumatology care at a multihospital system who completed social risk factor screening. We used negative binomial models, adjusted for sociodemographics and comorbidities, to estimate incidence rates and incidence rate ratios (IRRs) for number of ED visits and hospitalizations, as well as preventable ED visits, by social risk factor presence. Results: Among 9,590 patients, 1,614 (17%) reported ≥1 social risk factor. Adjusted models revealed higher rates of ED visits (IRR 1.74, 95% CI 1.53‐1.98) and hospitalizations (IRR 1.96, 95% CI 1.60‐2.43) among patients with versus without social risk factors. Food insecurity, housing insecurity, transportation challenges, difficulties with medication costs, utility bill needs, education needs, and caregiving needs were each individually associated with higher rates of ED visits and hospitalizations. Unemployment was associated with higher rates of ED visits only; digital access needs were associated with higher rates of hospitalizations only. We found no association between the presence versus absence of social risk factors and rates of preventable ED visits. Conclusion: Among patients with rheumatic conditions, social risk factor presence was associated with higher rates of acute care use, but not with preventable ED visits. Further studies are needed to determine whether addressing social risk factors can alter care use.

Objective: Immunocompromised individuals with rheumatic conditions have heightened risk of tuberculosis (TB) infection, reactivation, and severe disease. Patients with social risks are especially vulnerable. After an alert of increased TB rates among individuals experiencing homelessness in Massachusetts, we leveraged the existing social risk screening infrastructure to identify those at highest risk. Methods: We identified patients with rheumatic conditions receiving immunosuppressive or immunomodulatory medications and recent housing instability from the electronic health records (EHRs) of a multihospital medical system. We notified these patients’ rheumatologists and provided latent TB and TB symptom screening information. We tracked EHR documentation, orders, and laboratory results following this alert. Results: Thirty‐three patients with rheumatic conditions experienced housing instability. Of these, 23 (70%) patients cared for by 17 rheumatologists were prescribed ≥1 immunosuppressive or immunomodulatory medications. Following the alert, three (13%) patients had EHR documentation of symptom screening, one provider responded that their patient was not homeless, and one ordered an interferon‐gamma release assay (“T‐SPOT”). After the reminder, seven (30%) T‐SPOT tests were ordered, five (22%) patients had EHR documentation of symptom review at their subsequent rheumatology appointment, and four (18%) patients were messaged by their rheumatologists about their TB risk. Conclusion: The existing rheumatology‐based social risk factor screening infrastructure facilitated a rapid response to a relevant public health concern. Rheumatologists’ responses varied, possibly due to increased clinical stressors with limited support. Despite challenges to reaching patients with housing instability, many actions were taken. Efforts to prompt regular TB screening of patients with these vulnerabilities are underway.

Objective: We conducted formative research aimed at identifying solutions that address inequitable health outcomes in lupus due to adverse social determinants of health (SDoH). Methods: We conducted a search for keywords, which provided insights into potential solutions and initiatives underway. An advisory panel of lupus experts iteratively reviewed the list of literature‐scoped solutions in working sessions, filling knowledge gaps, which allowed for further defining and classifying solutions based on area of focus, feasibility, and impact. Seven‐in‐depth semistructured discussions and a modified Delphi survey approach were leveraged to align the advisory panel based on feasibility, impact, and costs of the proposed solutions. Results: Thirty‐three solutions were identified and classified into four key categories: financial safety net, patient education and shared decision‐making, physician education, and other solutions. High‐impact solutions that were prioritized included the following: “collecting granular information like patient‐reported outcomes to provide personalized care and accelerate development of new products,” “expanding Medicaid coverage via infrastructure,” and “supporting people living with lupus in applying and getting approval for disability.” Conclusion: Addressing health and health care disparities linked to negative SDoH is a key goal in the management of lupus, as disparities in outcomes can be stark. Increasing the visibility of potential solutions and aligning the community on top priorities can enable more efficient and effective contributions to health care equity and ultimately better health outcomes for people living with lupus.

Glucagon-like peptide-1 receptor agonists (GLP1RAs) lead to substantial weight loss and pain reduction in persons with knee osteoarthritis and obesity. To evaluate the cost-effectiveness of 2 GLP1RAs, semaglutide and tirzepatide, for patients with osteoarthritis and obesity. Osteoarthritis Policy Model, a validated microsimulation model of knee osteoarthritis, to estimate lifetime benefits and costs of weight loss strategies. Published data to derive treatment-related weight loss, pain reduction, and costs of GLP1RAs from the U.S. Office of Health Policy. Persons with knee osteoarthritis and obesity in the United States. The base-case cohort had a Western Ontario and McMaster Universities Osteoarthritis Index pain score of 71 (0 to 100, 100 worst) and a mean body mass index (BMI) of 40 kg/m². Lifetime. Health care, societal. Semaglutide, tirzepatide, laparoscopic sleeve gastrectomy (LSG), Roux-en-Y gastric bypass (RYGB), and diet and exercise. Quality-adjusted life-years (QALYs), cost, and incremental cost-effectiveness ratios (ICERs). Tirzepatide provided greater health benefits at lower costs than semaglutide and yielded a $57 400 per QALY ICER versus diet and exercise. For those eligible, RYGB provided greater health benefits at lower costs than the 2 GLP1RAs and had a $30 700 per QALY ICER versus LSG. Tirzepatide's ICER was most sensitive to changes in medication costs, treatment efficacy, and cohort baseline BMI. Tirzepatide had a 64% and semaglutide had a 34% probability of being cost-effective at a $100 000 per QALY threshold. Data from multiple sources. Both tirzepatide and semaglutide would be widely considered cost-effective when compared directly with usual care. Tirzepatide would offer the most favorable return on investment to decision makers whose cost-effectiveness thresholds exceed $57 400 per QALY. The Arthritis Foundation and National Institute of Arthritis and Musculoskeletal and Skin Diseases.

Social determinants of health (SDoH) contribute to disparities in SARS-CoV-2 infection, but their associations with long COVID are unknown. To determine associations between SDoH at the time of SARS-CoV-2 infection and risk for long COVID. Prospective observational cohort study. 33 states plus Washington, DC, and Puerto Rico. Adults (aged ≥18 years) enrolled in RECOVER-Adult (Researching COVID to Enhance Recovery) between October 2021 and November 2023 who were within 30 days of SARS-CoV-2 infection; completed baseline SDoH, comorbidity, and pregnancy questionnaires; and were followed prospectively. Social risk factors from SDoH baseline questionnaires, ZIP code poverty and household crowding measures, and a weighted score of 11 or higher on the Long COVID Research Index 6 months after infection. Among 3787 participants, 418 (11%) developed long COVID. After adjustment for demographic characteristics, pregnancy, disability, comorbidities, SARS-CoV-2 severity, and vaccinations, financial hardship (adjusted marginal risk ratio [ARR], 2.36 [95% CI, 1.97 to 2.91]), food insecurity (ARR, 2.36 [CI, 1.83 to 2.98]), less than a college education (ARR, 1.60 [CI, 1.30 to 1.97]), experiences of medical discrimination (ARR, 2.37 [CI, 1.94 to 2.83]), skipped medical care due to cost (ARR, 2.87 [CI, 2.22 to 3.70]), and lack of social support (ARR, 1.79 [CI, 1.50 to 2.17]) were associated with increased risk for long COVID. Living in ZIP codes with the highest (vs. lowest) household crowding was also associated with greater risk (ARR, 1.36 [CI, 1.05 to 1.71]). Selection bias may influence observed associations and generalizability. Participants with social risk factors at the time of SARS-CoV-2 infection had greater risk for subsequent long COVID than those without. Future studies should determine whether social risk factor interventions mitigate long-term effects of SARS-CoV-2 infection. National Institutes of Health.