Quality, Spending, Utilization, and Outcomes Among Dual-Eligible Medicare-Medicaid Beneficiaries in Integrated Care Programs: A Systematic Review.

Importance: Most dual-eligible Medicare-Medicaid beneficiaries are enrolled in bifurcated insurance programs that pay for different components of care. Therefore, policymakers are prioritizing expansion of integrated care plans (ICPs) that manage both Medicare and Medicaid benefits and spending. Objective: To review evidence of the association between ICPs and health care spending, quality, utilization, and patient outcomes among dual-eligible beneficiaries. Evidence Review: A search was conducted of PubMed/MEDLINE (January 1, 2010, through November 1, 2023) and Google Scholar (January 1, 2010, through October 1, 2023) and augmented with reports from US federal and state government websites. Three categories of ICPs were evaluated: Programs of All-Inclusive Care for the Elderly (PACE), Medicare-Medicaid Plans (MMPs), and Fully Integrated Dual Eligible Special Needs Plans (FIDE-SNPs) and related models aligning Medicare and Medicaid coverage. The review included studies that evaluated beneficiaries dually eligible for and enrolled in full Medicaid; compared an ICP to a nonintegrated arrangement; and evaluated utilization, spending, care coordination, patient experience, or health for 100 or more beneficiaries. Findings: In all, 26 ICP evaluations met the inclusion criteria and were included in the analysis: 5 of PACE, 13 of MMPs, and 8 of FIDE-SNPs and other aligned models. Evidence generally showed associated reductions in long-term nursing home stays in PACE (3 of 4 studies) and FIDE-SNPs and related aligned models (3 of 5 studies) but was mixed in evaluations of MMPs. Four of 9 studies of MMPs and 2 of 3 studies of FIDE-SNPs found higher outpatient use, although other studies showed no difference. Evidence on Medicaid spending was limited, whereas 8 of 10 studies of MMPs showed an association between these plans and higher Medicare spending. Evidence was mixed or inconclusive regarding care coordination and hospitalizations, and it was insufficient to evaluate patient satisfaction, health, and outcomes in beneficiary subgroups (eg, those with serious mental illness). Furthermore, studies had limited ability to control for bias from unmeasured differences between enrollees of ICPs compared with nonintegrated models. Conclusions and Relevance: This systematic review found variability and gaps in evidence regarding ICPs and spending, quality, utilization, and outcomes. Studies found some ICPs were associated with reductions in long-term nursing home admissions, and several identified increases in outpatient care. However, MMPs were primarily associated with higher Medicare spending. Evidence for other outcomes was limited or inconclusive. Research addressing these evidence gaps is needed to guide ongoing efforts to integrate coverage and care for dual-eligible beneficiaries.

Effects of Medicaid managed care on early detection of cancer: Evidence from mandatory Medicaid managed care program in Pennsylvania.

OBJECTIVE: To examine changes in late- versus early-stage diagnosis of cancer associated with the introduction of mandatory Medicaid managed care (MMC) in Pennsylvania. DATA SOURCES AND STUDY SETTING: We analyzed data from the Pennsylvania cancer registry (2010-2018) for adult Medicaid beneficiaries aged 21-64 newly diagnosed with a solid tumor. To ascertain Medicaid and managed care status around diagnosis, we linked the cancer registry to statewide hospital-based facility records collected by an independent state agency (Pennsylvania Health Care Cost Containment Council). STUDY DESIGN: We leveraged a natural experiment arising from county-level variation in mandatory MMC in Pennsylvania. Using a stacked difference-in-differences design, we compared changes in the probability of late-stage cancer diagnosis among those residing in counties that newly transitioned to mandatory managed care to contemporaneous changes among those in counties with mature MMC programs. DATA COLLECTION/EXTRACTION METHODS: N/A. PRINCIPAL FINDINGS: Mandatory MMC was associated with a reduced probability of late-stage cancer diagnosis (-3.9 percentage points; 95% CI: -7.2, -0.5; p = 0.02), particularly for screening-amenable cancers (-5.5 percentage points; 95% CI: -10.4, -0.6; p = 0.03). We found no significant changes in late-stage diagnosis among non-screening amenable cancers. CONCLUSIONS: In Pennsylvania, the implementation of mandatory MMC for adult Medicaid beneficiaries was associated with earlier stage of diagnosis among newly diagnosed cancer patients with Medicaid, especially those diagnosed with screening-amenable cancers. Considering that over half of the sample was diagnosed with late-stage cancer even after the transition to mandatory MMC, Medicaid programs and managed care organizations should continue to carefully monitor receipt of cancer screening and design strategies to reduce barriers to guideline-concordant screening or diagnostic procedures.

Patterns of Infliximab Biosimilar Uptake for Medicare, Medicaid, and Private Insurance from 2016 to 2022.

OBJECTIVE: Biosimilars have the potential to reduce spending on biologic drugs, yet uptake has been slower than anticipated. We investigated how successive introductions of infliximab biosimilars influenced their adoption by major US insurance providers. METHODS: Data came from the Rheumatology Informatics System for Effectiveness, a national registry with electronic health records from more than 1,100 US rheumatologists. All infliximab administrations (bio-originator or biosimilar) to patients aged ≥18 years from April 2016 to September 2022 were included. We used an interrupted time series to model the effect of each infliximab biosimilar release (infliximab-dyyb, November 2016; infliximab-adba, July 2017; and infliximab-axxq, July 2020) on uptake across Medicare, Medicaid, and private insurers. RESULTS: With the first and second biosimilar releases, biosimilar uptake rose slowly, with average annual increases of ≤5% from 2016 to June 2020 (Medicare 3.2%, Medicaid 5.2%, and private insurance 1.8%). With the third biosimilar release in July 2020, the average annual increase reached 13% for Medicaid and 16.4% for private insurance but remained low for Medicare (5.6%). By September 2022, uptake was higher for Medicaid (43.8%) and private insurance (38.5%) than for Medicare (24%). CONCLUSION: Our results have two key findings for policy makers. First, our results suggest that one or two biosimilars may not generate enough competition to speed adoption rates for biosimilars. Second, Medicare, which covers most patients receiving biologics nationally, had slow adoption rates even after the third biosimilar was introduced. Policy levers to speed adoption among Medicare beneficiaries are needed.

Racial and Ethnic Differences in Health Care Experiences for Veterans Receiving VA Community Care from 2016 to 2021.

BACKGROUND: Prior research documented racial and ethnic disparities in health care experiences within the Veterans Health Administration (VA). Little is known about such differences in VA-funded community care programs, through which a growing number of Veterans receive health care. Community care is available to Veterans when care is not available through the VA, nearby, or in a timely manner. OBJECTIVE: To examine differences in Veterans' experiences with VA-funded community care by race and ethnicity and assess changes in these experiences from 2016 to 2021. DESIGN: Observational analyses of Veterans' ratings of community care experiences by self-reported race and ethnicity. We used linear and logistic regressions to estimate racial and ethnic differences in community care experiences, sequentially adjusting for demographic, health, insurance, and socioeconomic factors. PARTICIPANTS: Respondents to the 2016-2021 VA Survey of Healthcare Experiences of Patients-Community Care Survey. MEASURES: Care ratings in nine domains. KEY RESULTS: The sample of 231,869 respondents included 24,306 Black Veterans (mean [SD] age 56.5 [12.9] years, 77.5% male) and 16,490 Hispanic Veterans (mean [SD] age 54.6 [15.9] years, 85.3% male). In adjusted analyses pooled across study years, Black and Hispanic Veterans reported significantly lower ratings than their White and non-Hispanic counterparts in five of nine domains (overall rating of community providers, scheduling a recent appointment, provider communication, non-appointment access, and billing), with adjusted differences ranging from - 0.04 to - 0.13 standard deviations (SDs) of domain scores. Black and Hispanic Veterans reported higher ratings with eligibility determination and scheduling initial appointments than their White and non-Hispanic counterparts, and Black Veterans reported higher ratings of care coordination, with adjusted differences of 0.05 to 0.21 SDs. Care ratings improved from 2016 to 2021, but differences between racial and ethnic groups persisted. CONCLUSIONS: This study identified small but persistent racial and ethnic differences in Veterans' experiences with VA-funded community care, with Black and Hispanic Veterans reporting lower ratings in five domains and, respectively, higher ratings in three and two domains. Interventions to improve Black and Hispanic Veterans' patient experience could advance equity in VA community care.

Glycosyl transferase GT2 genes mediate the biosynthesis of an unusual (1,3;1,4)-ß-glucan exopolysaccharide in the bacterium Sarcina ventriculi.

Linear, unbranched (1,3;1,4)-ß-glucans (mixed-linkage glucans or MLGs) are commonly found in the cell walls of grasses, but have also been detected in basal land plants, algae, fungi and bacteria. Here we show that two family GT2 glycosyltransferases from the Gram-positive bacterium Sarcina ventriculi are capable of synthesizing MLGs. Immunotransmission electron microscopy demonstrates that MLG is secreted as an exopolysaccharide, where it may play a role in organizing individual cells into packets that are characteristic of Sarcina species. Heterologous expression of these two genes shows that they are capable of producing MLGs in planta, including an MLG that is chemically identical to the MLG secreted from S. ventriculi cells but which has regularly spaced (1,3)-ß-linkages in a structure not reported previously for MLGs. The tandemly arranged, paralogous pair of genes are designated SvBmlgs1 and SvBmlgs2. The data indicate that MLG synthases have evolved different enzymic mechanisms for the incorporation of (1,3)-ß- and (1,4)-ß-glucosyl residues into a single polysaccharide chain. Amino acid variants associated with the evolutionary switch from (1,4)-ß-glucan (cellulose) to MLG synthesis have been identified in the active site regions of the enzymes. The presence of MLG synthesis in bacteria could prove valuable for large-scale production of MLG for medical, food and beverage applications.

Differences in healthcare utilization between enrollees of fully integrated dual eligible special needs plans versus non-fully integrated plans.

BACKGROUND: Policymakers advocate care integration models to enhance Medicare and Medicaid service coordination for dually eligible individuals. One rapidly expanding model is the fully integrated dual eligible (FIDE) plan, a sub-type of the dual eligible special needs plan (D-SNP) in which a parent insurer manages Medicare and Medicaid spending for dually eligible individuals. We examined healthcare utilization differences among dually eligible individuals aged 65 years or older enrolled in D-SNPs by plan type (FIDE vs non-FIDE). METHODS: Using 2018 Medicare Advantage encounters and Medicaid claims of FIDE and non-FIDE enrollees in six states (AZ, CA, FL, NY, TN, WI), we compared healthcare utilization between plan types, adjusting for enrollee characteristics and county indicators. We applied propensity score weighting to address differences between FIDE and non-FIDE plan enrollees. RESULTS: In our main analysis, which included all dually eligible individuals in our sample, we observed no significant difference in healthcare utilization between FIDE and non-FIDE plan enrollees. However, we identified some differences in healthcare utilization between FIDE and non-FIDE plan enrollees in subgroup analyses. For example, among home and community-based service (HCBS) users, FIDE plan enrollees had 6.0 fewer hospitalizations per 1000 person-months (95% CI: -7.9, -4.0) and were 7.0 percentage points more likely to be discharged to home (95% CI: 2.6, 11.5) after hospitalization, compared to non-FIDE plan enrollees. CONCLUSION: While we found no differences in healthcare utilization between FIDE and non-FIDE plan enrollees when considering all dually eligible individuals in our sample, some differences emerged when focusing on subgroups. For example, HCBS users with FIDE plans had fewer hospitalizations and were more likely to be discharged to their home following hospitalization, compared to HCBS users with non-FIDE plans. These findings suggest that FIDE plans may improve care coordination for specific subsets of dually eligible individuals.

DLSIA: Deep Learning for Scientific Image Analysis.

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

Do Medicaid Expenditures Increase After Adults Exit Permanent Supportive Housing?

The effects of homelessness and permanent supportive housing (PSH) on health care utilization have been well documented. Prior research on the association between PSH entry and Medicaid expenditures have indicated that such housing support could result in savings to Medicaid programs; however, whether changes occur in health care use and expenditures after individuals exit PSH is unknown. If efficiency gains from PSH persist after the individual leaves PSH, the savings to payers such as Medicaid may continue even after the costs to provide housing for a PSH recipient have ended. We used linked Medicaid and housing data from Pennsylvania to examine changes in the level and composition of Medicaid expenditures for 580 adult enrollees during the 12 months before and after exit from PSH adjusting for relevant covariates. In adjusted analyses, we estimated that monthly spending declined by $200.32 (95% CI: $323.50, $75.15) in the first quarter post-exit and by $267.63 (95% CI: $406.10, $127.10) in the third quarter. Our findings suggest that PSH may have sustained budgetary benefits to state Medicaid agencies even for beneficiaries exiting the program. However, more research is needed to understand if these reductions in expenditures last beyond 12 months and do not reflect under-use of care that may be important for managing health over the long-term.

Medicaid Eligibility Loss Among Dual-Eligible Beneficiaries Before and During COVID-19 Public Health Emergency.

Importance: Medicaid coverage loss can substantially compromise access to and affordability of health care for dual-eligible beneficiaries. The extent to which this population lost Medicaid coverage before and during the COVID-19 public health emergency (PHE) and the characteristics of beneficiaries more at risk for coverage loss are currently not well known. Objective: To assess the loss of Medicaid coverage among dual-eligible beneficiaries before and during the first year of the PHE, and to examine beneficiary-level and plan-level factors associated with heightened likelihood of losing Medicaid. Design, Setting, and Participants: This repeated cross-sectional study used national Medicare data to estimate annual rates of Medicaid loss among dual-eligible beneficiaries before (2015 to 2019) and during the PHE (2020). Individuals who were dual eligible for Medicare and Medicaid at the beginning of a given year and who continuously received low-income subsidies for Medicare Part D prescription drug coverage were included in the sample. Multivariable regression models were used to examine beneficiary-level and plan-level factors associated with Medicaid loss. Data analyses were conducted between March 2023 and October 2023. Exposure: Onset of PHE. Main Outcomes and Measures: Loss of Medicaid for at least 1 month within a year. Results: Sample included 56 172 736 dual-eligible beneficiary-years between 2015 and 2020. In 2020, most dual-eligible beneficiaries were aged over 65 years (5 984 420 [61.1%]), female (5 868 866 [59.9%]), non-Hispanic White (4 928 035 [50.3%]), full-benefit eligible (6 837 815 [69.8%]), and enrolled in traditional Medicare (5 343 537 [54.6%]). The adjusted proportion of dual-eligible beneficiaries losing Medicaid for at least 1 month increased from 6.6% in 2015 to 7.3% in 2019 and then dropped to 2.3% in 2020. Between 2015 and 2019, dual-eligible beneficiaries who were older (ages 55-64 years: -1.4%; 95% CI, -1.8% to -1.0%; ages 65-74 years: -2.0%; 95% CI, -2.5% to -1.5%; ages 75 and older: -4.5%; 95% CI, -5.0% to -4.0%), disabled (-0.8%; 95% CI, -1.1% to -0.6%), and in integrated care programs were less likely to lose Medicaid. In 2020, the disparities within each of these demographic groups narrowed significantly. Notably, while Black (0.6%; 95% CI, 0.2% to 0.9%) and Hispanic (0.7%; 95% CI, 0.3% to 1.2%) dual-eligible beneficiaries were more likely to lose Medicaid than their non-Hispanic White counterparts between 2015 and 2019, such gap was eliminated for Black beneficiaries and narrowed for Hispanic beneficiaries in 2020. Conclusions and Relevance: During the PHE, Medicaid coverage loss declined significantly among dual-eligible beneficiaries, and disparities were mitigated across subgroups. As the PHE unwinds, it is crucial for policymakers to implement strategies to minimize Medicaid coverage disruptions and racial and ethnic disparities, especially given that loss of Medicaid was slightly increasing over time before the PHE.

Association of Medicare eligibility with access to and affordability of care among older cancer survivors.

PURPOSE: Older cancer survivors have substantial needs for ongoing care, but they may encounter difficulties accessing care due to cost concerns. We examined whether near-universal insurance coverage through Medicare-a key source of health insurance coverage in this population-is associated with improvements in care access and affordability among older cancer survivors around age 65. METHODS: In a nationally representative sample of cancer survivors (aged 50-80) from 2006-2018 National Health Interview Survey, we employed a quasi-experimental, regression discontinuity design to estimate changes in insurance coverage, delayed/skipped care due to cost, and worries about or problems paying medical bills at age 65. RESULTS: Medicare coverage sharply increased from 8.3% at age 64 to 98.2% at age 65, ensuring near-universal insurance coverage (99.5%). Medicare eligibility at age 65 was associated with reductions in delayed/skipped care due to cost (discontinuity, - 5.7 percentage points or pp; 95% CI, - 8.1, - 3.3; P < .001), worries about paying for medical bills (- 7.7 pp; 95% CI, - 12.0, - 3.2; P = .001), and problems paying medical bills (- 3.2 pp; 95% CI, - 6.1, - 0.2; P = .036). However, a sizable proportion reported any access or affordability problems (29.7%) between ages 66 and 80. CONCLUSIONS: Near-universal Medicare coverage at age 65 was associated with a reduction-but not elimination-of access and affordability problems among cancer survivors. IMPLICATIONS FOR CANCER SURVIVORS: These findings reaffirm the role of Medicare in improving access and affordability for older cancer survivor and highlight opportunities for reforms to further alleviate financial burden of care in this population.

Revisiting Race, Ethnicity, and Disparities in Rheumatology Educational Materials: An Update Since 2020.

OBJECTIVE: In 2020, one study by Strait and colleagues raised awareness that the clinical images in rheumatology educational materials underrepresent people with skin of color (P-SOC). Since then, publishers of rheumatology educational materials have focused on addressing this shortcoming. This study investigates the change in representation of P-SOC following the review of Strait et al. METHODS: We used the methods of the aforementioned study to collect images from commonly referenced rheumatology educational materials and categorized the skin tones within them as "light" or "dark." We calculated the proportional change in images depicting dark skin tones between 2020 and 2022 from the American College of Rheumatology (ACR) Image Library, the 10th edition of Kelley's Textbook of Rheumatology, and New England Journal of Medicine (NEJM) as well as between 2020 and 2024 from rheumatology articles within UpToDate. We compared results using one-sided Z-tests. RESULTS: Overall, the proportion of images depicting dark skin tones increased 40.6% (P < 0.0001). The 10th edition of Kelley's Textbook of Rheumatology most significantly increased inclusion of P-SOC (90.1%; P = 0.0039), with ACR Image Library, UpToDate, and NEJM also enhancing representation (41.9%, P < 0.0001; 31.0%, P = 0.0083; 28.2%, P = 0.3046, respectively). CONCLUSION: This study assesses the progress of rheumatology educational materials toward equitable representation of P-SOC. It demonstrates that awareness coupled with focused efforts from educational publishers can enhance the proportion of images depicting dark skin tones, thereby enriching the quality of foundational knowledge relayed to rheumatology providers with the goal of improving health experiences and outcomes for P-SOC with rheumatic diseases.

Latent Tuberculosis Screening Among New Users of a Biologic or Targeted Synthetic Disease-Modifying Antirheumatic Drug: Gaps in Screening Overall and Among Janus Kinase Inhibitors.

OBJECTIVE: We combined claims and electronic health record (EHR) data to provide contemporary and accurate estimates of latent tuberculosis (TB) screening among new users of a biologic or targeted synthetic disease-modifying antirheumatic drug (b/tsDMARD) and assess potential gaps in testing by drug type, patient characteristics, and practice. METHODS: Our denominator population was patients in the Rheumatology Informatics System for Effectiveness (RISE) registry and Medicare using a b/tsDMARD in 2018 without a claim or prescription in the year prior. TB screening was assessed in both Medicare and RISE 1 and 3 years before the medication start date. We calculated the proportion screened overall, by medication class, and by practice. We tested for demographic differences in screening using logistic regression. RESULTS: In the year before drug starts, 65.6% of patients had any TB screening; in a 3-year window, 72.9% had any TB screening. Rates of screening within 1 year by drug type were greater or equal to the overall screening rate for most drugs except for JAK inhibitors (JAKis) (46%) and interleukin-17 inhibitors (IL-17is) (11.5%). A lower proportion of Hispanic and Asian patients were screened compared with White patients. Practice screening rates ranged from 20.0% to 92.9% of patients within 1 year. CONCLUSION: We report higher screening rates than have previously been published because of combining claims and EHR data. However, important safety gaps remain, namely, reduced screening among new users of a JAKi or IL-17i and among Asian and Hispanic patients, as well as low-performing practices. Educational initiatives, team-based care delivery, task shifting, and technological interventions to address observed gaps in patient safety procedures are needed.

Early impacts of the Pennsylvania Rural Health Model on potentially avoidable utilization.

The Pennsylvania Rural Health Model (PARHM) is a novel alternative payment model for rural hospitals that aims to test whether hospital-based global budgets, coupled with delivery transformation plans, improve the quality of health care and health outcomes in rural communities. Eighteen hospitals joined PARHM in 3 cohorts between 2019 and 2021. This study assessed PARHM's impact on changes in potentially avoidable utilization (PAU)-a measure of admission rates policymakers explicitly targeted for improvement in PARHM. Using a difference-in-differences analysis and all-payer hospital discharge data for Pennsylvania hospitals from 2016 through 2022, we found no significant overall reduction in community-level PAU rates up to 4 years post-PARHM implementation, relative to changes in rural Pennsylvania communities whose hospitals did not join PARHM. However, heterogeneous treatment effects were observed across cohorts that joined PARHM in different years, and between critical access vs prospective payment system hospitals. These findings offer insight into how alternative payment models in rural health care settings may have heterogeneous impacts based on contextual factors and highlight the importance of accounting for these factors in proposed expansions of alternative payment models for rural health systems.

Economic insecurities and patient-reported outcomes in patients with systemic lupus erythematosus in the USA: a cross-sectional analysis of data from the California Lupus Epidemiology Study.

BACKGROUND: Social determinants of health are consistently associated with systemic lupus erythematosus (SLE) outcomes. However, social determinants of health are typically measured with conventional socioeconomic status factors such as income or education. We assessed the association of economic insecurities (ie, food, housing, health care, and financial insecurity) with patient-reported outcomes in a cohort of patients with SLE. METHODS: In this cross-sectional analysis, data were derived from the California Lupus Epidemiology Study based in the San Francisco Bay Area, CA, USA. Participants were recruited between Feb 25, 2015, and Jan 10, 2018, from rheumatology clinics. Inclusion criteria were Bay Area residency; oral fluency in English, Spanish, Cantonese, or Mandarin; 18 years or older; ability to provide informed consent; and a physician confirmed SLE diagnosis. Food, housing, health care, and financial economic insecurities were assessed by validated screening tools. Patient-reported outcomes were obtained using PROMIS, Quality of Life in Neurological Disorders (known as Neuro-QoL) Cognitive Function short form, Patient Health Questionnaire (PHQ)-8, and General Anxiety Disorder (GAD)-7 instruments. Poverty was defined as household income of 125% or less of the federal poverty limit. Lower education was defined as less than college-graduate education. The association of economic insecurities with patient-reported outcomes was assessed by multivariable linear regression models adjusting for demographics, SLE disease characteristics, and comorbidities. We tested for interactions of insecurities with poverty and education. FINDINGS: The final cohort included 252 participants. Mean age was 49·7 (SD 13·4) years, 228 (90%) of 252 were women and 24 (10%) were men. 80 (32%) individuals self-identified as Asian, 26 (10%) as Black, 101 (40%) as White, eight (3%) as mixed race, and 37 (15%) as other race; 59 (23%) self-identified as Hispanic. 135 (54%) individuals had at least one insecurity. Insecurities were highly prevalent, and more common in those with poverty and lower education. Adjusted multivariate analyses revealed that participants with any insecurity had significantly worse scores across all measured patient-reported outcomes. For physical function, no insecurity had an adjusted mean score of 48·9 (95% CI 47·5-50·3) and any insecurity had 45·7 (44·3-47·0; p=0·0017). For pain interference, no insecurity was 52·0 (50·5-53·5) and any insecurity was 54·4 (53·0-55·8; p=0·031). For fatigue, no insecurity was 50·5 (48·8-52·3) and any insecurity was 54·9 (53·3-56·5; p=0·0005). For sleep disturbance, no insecurity was 49·9 (48·3-51·6) and any insecurity was 52·9 (51·4-54·5; p=0·012). For cognitive function, no insecurity was 49·3 (47·7-50·9) and any insecurity was 45·6 (44·1-47·0; p=0·0011). For PHQ-8, no insecurity was 4·4 (3·6-5·1) and any insecurity was 6·1 (5·4-6·8; p=0·0013). For GAD-7, no insecurity was 3·3 (2·6-4·1) and any insecurity was 5·2 (4·5-5·9; p=0·0008). Individuals with more insecurities had worse patient-reported outcomes. There were no statistically significant interactions between insecurities and poverty or education. INTERPRETATION: Having any economic insecurity was associated with worse outcomes for people with SLE regardless of poverty or education. The findings of this study provide insight into the relationship between economic insecurities and SLE outcomes and underscore the need to assess whether interventions that directly address these insecurities can reduce health disparities in SLE. FUNDING: US Centers for Disease Control, Rheumatology Research Foundation, and National Institute of Arthritis and Musculoskeletal and Skin Diseases.

Deploying Machine Learning Based Segmentation for Scientific Imaging Analysis at Synchrotron Facilities.

Scientific user facilities present a unique set of challenges for image processing due to the large volume of data generated from experiments and simulations. Furthermore, developing and implementing algorithms for real-time processing and analysis while correcting for any artifacts or distortions in images remains a complex task, given the computational requirements of the processing algorithms. In a collaborative effort across multiple Department of Energy national laboratories, the "MLExchange" project is focused on addressing these challenges. MLExchange is a Machine Learning framework deploying interactive web interfaces to enhance and accelerate data analysis. The platform allows users to easily upload, visualize, label, and train networks. The resulting models can be deployed on real data while both results and models could be shared with the scientists. The MLExchange web-based application for image segmentation allows for training, testing, and evaluating multiple machine learning models on hand-labeled tomography data. This environment provides users with an intuitive interface for segmenting images using a variety of machine learning algorithms and deep-learning neural networks. Additionally, these tools have the potential to overcome limitations in traditional image segmentation techniques, particularly for complex and low-contrast images.