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This article contains a short update on the work of the sigma Accessibility Special Interest Group. We announce the release of resources to assist mathematics tutors and coordinators with the support of mature students and those with dyslexia, dyscalculia and dyspraxia. We provide a brief background to the development of these resources and describe their pilot in two institutions, one in England and the other in Ireland. We close with a description of the next stages of work for the special interest group and a call for additional people to get involved.

Background: The comparative real-world outcomes of older patients with atrial fibrillation (AF) treated with anticoagulation compared with left atrial appendage occlusion (LAAO) may be different from those in clinical trials because of differences in anticoagulation strategies and patient demographics, including a greater proportion of women. We sought to compare real-world outcomes between older patients with AF treated with anticoagulation and those treated with LAAO by sex. Methods: Using Medicare claims data from 2015 to 2019, we identified LAAO-eligible beneficiaries and divided them into sex subgroups. Patients receiving LAAO were matched 1:1 to those receiving anticoagulation alone through propensity score matching. The risks of mortality, stroke or systemic embolism, and bleeding were compared between matched groups with adjustment for potential confounding characteristics in Cox proportional hazards models. Results: Among women, 4085 LAAO recipients were matched 1:1 to those receiving anticoagulation; among men, 5378 LAAO recipients were similarly matched. LAAO was associated with a significant reduction in the risk of mortality for women and men (hazard ratio [HR], 0.509 [95% CI, 0.447–0.580]; and HR, 0.541 [95% CI, 0.487–0.601], respectively; P P P Conclusions: In a real-world population of older Medicare beneficiaries with AF, compared with anticoagulation, LAAO was associated with a reduction in the risk of death, stroke, and long-term bleeding among women and men. These findings should be incorporated into shared decision-making with patients considering strategies for reduction in AF-related stroke.

Community detection in networks is the process by which unusually well-connected sub-networks are identified–a central component of many applied network analyses. The paradigm of modularity quality function optimization stipulates a partition of the network’s vertexes that maximizes the difference between the fraction of edges within communities and the corresponding expected fraction if edges were randomly allocated among all vertex pairs while conserving the degree distribution. The modularity quality function incorporates exclusively the network’s topology and has been extensively studied whereas the integration of constraints or external information on community composition has largely remained unexplored. We define a greedy, recursive-backtracking search procedure to identify the constitution of high-quality network communities that satisfy the global constraint that each community be comprised of at least one vertex among a set of so-called special vertexes and apply our methodology to identifying health care communities (HCCs) within a network of hospitals such that each HCC consists of at least one hospital wherein at least a minimum number of cardiac defibrillator surgeries were performed. This restriction permits meaningful comparisons in cardiac care among the resulting health care communities by standardizing the distribution of cardiac care across the hospital network.

In 2019, the Department of Mathematics and Statistics at Maynooth University commenced a project which sought to address, through the provision of mathematics learning supports, the issues of poor engagement and retention of computer science students studying mathematics. In this paper, we present preliminary engagement and performance data along with interviews conducted with eight students. We discuss how the quantitative data seemed to indicate that computer science students were engaging at similar levels to their peers, but several factors, including the quality of this engagement and their mathematical backgrounds may explain their poor exam performance. It also emerged that, while students were largely negative about their experiences in large lectures and their awareness of the relevance of mathematics to computer science, they were generally positive about smaller teaching situations such as tutorials, mathematics support drop-in and opportunities to work with their peers.

Each summer, the Department of Mathematics and Statistics at Maynooth University delivers a three-week bridging course for mature student applicants. This course serves a dual purpose. First, it acts as a refresher of fundamental mathematical skills, and second, for some, the summative assessments form part of the screening process to determine if their mathematics is of a sufficient level to make the move to higher education. This paper describes the results of student feedback on this course obtained through an online survey of past participants. Respondents indicate that not only is this course effective for building mathematical fluency and confidence, but that they also benefit from gaining a familiarity of the campus as well as making friends with their peers.

In this paper we report on the main themes which emerged from analysis of a survey of students registered with the Access, Disability and Mature Student offices at Maynooth University. The survey focussed on the students’ experiences of mathematics and the mathematics academic supports available to them. The majority of student feedback was positive, for example their engagement with the Mathematics Support Centre, improved understanding of mathematics and the influence of tutors and peers. However, some issues emerged in relation to impact on learning, such as inflexible departmental structures with fixed deadlines and a lack of student awareness of the supports available. In addition to discussing the main results, we outline how the findings will guide the future provision of such supports.

In clinical trials, sacubitril/valsartan has demonstrated significant survival benefits compared to angiotensin converting enzyme inhibitors or angiotensin receptor blockers (ACEi/ARB). Whether older patients with heart failure with reduced ejection fraction (HFrEF) benefit as much, due to higher rates of comorbidities, frailty and drug discontinuation, is unknown.Using a cohort of Medicare beneficiaries hospitalized with HFrEF between 2016 and 2018, we determined all-cause mortality and HF-readmission rates among patients not given ACEi/ARB or sacubitril/valsartan at hospital discharge, by age. We then used risk reductions from the SOLVD, PARADIGM-HF and PIONEER-HF trials to estimate the benefits of ACEi/ARB and sacubitril/valsartan. We then incorporated age-specific estimates of drug discontinuation from Medicare. A Markov decision process model was used to simulate 5-year survival and estimate number needed to treat, comparing discharge on ACEi/ARB vs sacubitril/valsartan by age. After accounting for drug discontinuation rates, which were surprisingly slightly higher among those discharged on ACEi/ARB (2.3%/month vs 1.9%/month), there was a small but significant survival advantage to discharge on sacubitril/valsartan over 5 years (+0.81 months [95% CI 0.80, 0.81]). The benefit of sacubitril/valsartan over ACEi/ARB did not decrease with increasing age - the number needed to treat among 66 to 74-year-old patients was 84 and among 85+ year-old patients was 67.Even after accounting for "real world" rates of drug discontinuation, discharge on sacubitril/valsartan after conferred a small, but significant, survival advantage which does not appear to wane with increasing age.

Background Pancreatectomy is a necessary component of curative intent therapy for pancreatic cancer, and patients living in non-metropolitan areas may face barriers to accessing timely surgical care. We evaluated the intersecting associations of rurality, socioeconomic status (SES), and race on treatment and outcomes of Medicare beneficiaries with pancreatic cancer. Methods We conducted a retrospective cohort study, using fee-for-service Medicare claims of beneficiaries with incident pancreatic cancer (2016-2018). We categorized beneficiary place of residence as metropolitan, micropolitan, or small town/rural. Measures of SES were Medicare-Medicaid dual eligibility and the Area Deprivation Index (ADI). Primary study outcomes were receipt of pancreatectomy and one-year mortality. Exposure-outcome associations were assessed with competing risks and logistic regression. Results We identified 45,915 beneficiaries with pancreatic cancer, including 78.4%, 10.9%, and 10.7% residing in metropolitan, micropolitan, and rural areas, respectively. In analyses adjusted for age, sex, comorbidity, and metastasis, residents of micropolitan and rural areas were less likely to undergo pancreatectomy (adjusted subdistribution hazard ratio=0.88 for rural, 95%CI 0.81-0.95) and had higher one-year mortality (aOR=1.25 for rural, 95%CI 1.17-1.33), compared with metropolitan residents. Adjustment for measures of SES attenuated the association of non-metropolitan residence with mortality, and there was no significant association of rurality with pancreatectomy after adjustment. Black beneficiaries had lower likelihood of pancreatectomy than White, non-Hispanic beneficiaries (aSHR=0.80, 95%CI 0.72-0.89, adjusted for SES). One-year mortality in metropolitan areas was higher for Black beneficiaries (aOR=1.15 95%CI 1.05-1.26). Conclusions Rurality, socioeconomic deprivation, and race have complex interrelationships and are associated with disparities in pancreatic cancer treatment and outcomes.

Tuberculosis (TB) is the archetypical chronic infection, with patients having months of symptoms before diagnosis. In the two years after successful therapy, survivors of TB have a three-fold increased risk of death.Guinea pigs were infected withGuinea pigs with TB demonstrated DNA hypermethylation and showed system-level similarity to humans with TB (In both guinea pigs and humans, TB perturbs epigenetic processes, promoting premature cellular aging and inflammation, a plausible means to explain the long-term detrimental health outcomes after TB.

IMPORTANCE: Children with medical complexity (CMC) have substantial health care needs and frequently experience poor health care quality. Understanding the population prevalence and associated health care needs can inform clinical and public health initiatives. OBJECTIVE: To estimate the prevalence of CMC using open-source pediatric algorithms, evaluate performance of these algorithms in predicting health care utilization and in-hospital mortality, and identify associations between medical complexity as defined by these algorithms and clinical outcomes. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used all-payer claims data from Colorado, Massachusetts, and New Hampshire from 2012 through 2017. Children and adolescents younger than 18 years residing in these states were included if they had 12 months or longer of enrollment in a participating health care plan. Analyses were conducted from March 12, 2021, to January 7, 2022. EXPOSURES: The pediatric Complex Chronic Condition Classification System, Pediatric Medical Complexity Algorithm, and Children With Disabilities Algorithm were applied to 3 years of data to identify children with complex and disabling conditions, first in their original form and then using more conservative criteria that required multiple health care claims or involvement of 3 or more body systems. MAIN OUTCOMES AND MEASURES: Primary outcomes, examined over 2 years, included in-hospital mortality and a composite measure of health care services, including specialized therapies, specialized medical equipment, and inpatient care. Outcomes were modeled using logistic regression. Model performance was evaluated using C statistics, sensitivity, and specificity. RESULTS: Of 1 936 957 children, 48.4% were female, 87.8% resided in urban core areas, and 45.1% had government-sponsored insurance as their only primary payer. Depending on the algorithm and coding criteria applied, 0.67% to 11.44% were identified as CMC. All 3 algorithms had adequate discriminative ability, sensitivity, and specificity to predict in-hospital mortality and composite health care services (C statistic = 0.76 [95% CI, 0.73-0.80] to 0.81 [95% CI, 0.78-0.84] for mortality and 0.77 [95% CI, 0.76-0.77] to 0.80 [95% CI, 0.79-0.80] for composite health care services). Across algorithms, CMC had significantly greater odds of mortality (adjusted odds ratio [aOR], 9.97; 95% CI, 7.70-12.89; to aOR, 69.35; 95% CI, 52.52-91.57) and composite health care services (aOR, 4.59; 95% CI, 4.44-4.73; to aOR, 18.87; 95% CI, 17.87-19.93) than children not identified as CMC. CONCLUSIONS AND RELEVANCE: In this study, open-source algorithms identified different cohorts of CMC in terms of prevalence and magnitude of risk, but all predicted increased health care utilization and in-hospital mortality. These results can inform research, programs, and policies for CMC.

Background Clinical and real-world effectiveness data for the COVID-19 vaccines have shown that they are the best defense in preventing severe illness and death throughout the pandemic. However, in the US, some groups remain more hesitant than others about receiving COVID-19 vaccines. One important group is long-term care workers (LTCWs), especially because they risk infecting the vulnerable and clinically complex populations they serve. There is a lack of research about how best to increase vaccine confidence, especially in frontline LTCWs and healthcare staff. Our aims are to: (1) compare the impact of two interventions delivered online to enhanced usual practice on LTCW COVID-19 vaccine confidence and other pre-specified secondary outcomes, (2) determine if LTCWs’ characteristics and other factors mediate and moderate the interventions’ effect on study outcomes, and (3) explore the implementation characteristics, contexts, and processes needed to sustain a wider use of the interventions. Methods We will conduct a three-arm randomized controlled effectiveness-implementation hybrid (type 2) trial, with randomization at the participant level. Arm 1 is a dialogue-based webinar intervention facilitated by a LTCW and a medical expert and guided by an evidence-based COVID-19 vaccine decision tool. Arm 2 is a curated social media web application intervention featuring interactive, dynamic content about COVID-19 and relevant vaccines. Arm 3 is enhanced usual practice, which directs participants to online public health information about COVID-19 vaccines. Participants will be recruited via online posts and advertisements, email invitations, and in-person visits to care settings. Trial data will be collected at four time points using online surveys. The primary outcome is COVID-19 vaccine confidence. Secondary outcomes include vaccine uptake, vaccine and booster intent for those unvaccinated, likelihood of recommending vaccination (both initial series and booster), feeling informed about the vaccines, identification of vaccine information and misinformation, and trust in COVID-19 vaccine information provided by different people and organizations. Exploration of intervention implementation will involve interviews with study participants and other stakeholders, an in-depth process evaluation, and testing during a subsequent sustainability phase. Discussion Study findings will contribute new knowledge about how to increase COVID-19 vaccine confidence and effective informational modalities for LTCWs. Trial registration NCT05168800 at ClinicalTrials.gov, registered December 23, 2021.

Social network analysis has created a productive framework for the analysis of the histories of patient-physician interactions and physician collaboration. Notable is the construction of networks based on the data of “referral paths” – sequences of patient-specific temporally linked physician visits – in this case, culled from a large set of Medicare claims data in the United States. Network constructions depend on a range of choices regarding the underlying data. In this paper we introduce the use of a five-factor experiment that produces 80 distinct projections of the bipartite patient-physician mixing matrix to a unipartite physician network derived from the referral path data, which is further analyzed at the level of the 2,219 hospitals in the final analytic sample. We summarize the networks of physicians within a given hospital using a range of directed and undirected network features (quantities that summarize structural properties of the network such as its size, density, and reciprocity). The different projections and their underlying factors are evaluated in terms of the heterogeneity of the network features across the hospitals. We also evaluate the projections relative to their ability to improve the predictive accuracy of a model estimating a hospital’s adoption of implantable cardiac defibrillators, a novel cardiac intervention. Because it optimizes the knowledge learned about the overall and interactive effects of the factors, we anticipate that the factorial design setting for network analysis may be useful more generally as a methodological advance in network analysis.

Background In response to the COVID-19 pandemic, cancer centers rapidly adopted telehealth to deliver care remotely. Telehealth will likely remain a model of care for years to come and may not only affect the way oncologists deliver care to their own patients but also the physicians with whom they share patients. Objective This study aimed to examine oncologist characteristics associated with telehealth use and compare patient-sharing networks before and after the COVID-19 pandemic in a rural catchment area with a particular focus on the ties between physicians at the comprehensive cancer center and regional facilities. Methods In this retrospective observational study, we obtained deidentified electronic health record data for individuals diagnosed with breast, colorectal, or lung cancer at Dartmouth Health in New Hampshire from 2018-2020. Hierarchical logistic regression was used to identify physician factors associated with telehealth encounters post COVID-19. Patient-sharing networks for each cancer type before and post COVID-19 were characterized with global network measures. Exponential-family random graph models were performed to estimate homophily terms for the likelihood of ties existing between physicians colocated at the hub comprehensive cancer center. Results Of the 12,559 encounters between patients and oncologists post COVID-19, 1228 (9.8%) were via telehealth. Patient encounters with breast oncologists who practiced at the hub hospital were over twice as likely to occur via telehealth compared to encounters with oncologists who practiced in regional facilities (odds ratio 2.2, 95% CI 1.17-4.15; P=.01). Patient encounters with oncologists who practiced in multiple locations were less likely to occur via telehealth, and this association was statistically significant for lung cancer care (odds ratio 0.26, 95% CI 0.09-0.76; P=.01). We observed an increase in ties between oncologists at the hub hospital and oncologists at regional facilities in the lung cancer network post COVID-19 compared to before COVID-19 (93/318, 29.3%, vs 79/370, 21.6%, respectively), which was also reflected in the lower homophily coefficients post COVID-19 compared to before COVID-19 for physicians being colocated at the hub hospital (estimate: 1.92, 95% CI 1.46-2.51, vs 2.45, 95% CI 1.98-3.02). There were no significant differences observed in breast cancer or colorectal cancer networks. Conclusions Telehealth use and associated changes to patient-sharing patterns associated with telehealth varied by cancer type, suggesting disparate approaches for integrating telehealth across clinical groups within this health system. The limited changes to the patient-sharing patterns between oncologists at the hub hospital and regional facilities suggest that telehealth was less likely to create new referral patterns between these types of facilities and rather replace care that would otherwise have been delivered in person. However, this study was limited to the 2 years immediately following the initial outbreak of COVID-19, and longer-term follow-up may uncover delayed effects that were not observed in this study period.

ImportancePhysician headcounts provide useful information about the cancer care delivery workforce; however, efforts to track the oncology workforce would benefit from new measures that capture how essential a physician is for meeting the multidisciplinary cancer care needs of the region. Physicians are considered linchpins when fewer of their peers are connected to other physicians of the same specialty as the focal physician. Because they are locally unique for their specialty, these physicians’ networks may be particularly vulnerable to their removal from the network (eg, through relocation or retirement).ObjectiveTo examine a novel network-based physician linchpin score within nationwide cancer patient-sharing networks and explore variation in network vulnerability across hospital referral regions (HRRs).Design, Setting, and ParticipantsThis cross-sectional study analyzed fee-for-service Medicare claims and included Medicare beneficiaries with an incident diagnosis of breast, colorectal, or lung cancer from 2016 to 2018 and their treating physicians. Data were analyzed from March 2022 to October 2022.ExposuresPhysician characteristics assessed were specialty, rurality, and Census region. HRR variables assessed include sociodemographic and socioeconomic characteristics and use of cancer services.Main Outcomes and MeasuresOncologist linchpin score, which examined the extent to which a physician’s peers were connected to other physicians of the same specialty as the focal physician. Network vulnerability, which distinguished HRRs with more linchpin oncologists than expected based on oncologist density. χ2 and Fisher exact tests were used to examine relationships between oncologist characteristics and linchpin score. Spearman rank correlation coefficient (ρ) was used to measure the strength and direction of relationships between HRR network vulnerability, oncologist density, population sociodemographic and socioeconomic characteristics, and cancer service use.ResultsThe study cohort comprised 308 714 patients with breast, colorectal, or lung cancer. The study cohort of 308 714 patients included 161 206 (52.2%) patients with breast cancer, 76 604 (24.8%) patients with colorectal cancer, and 70 904 (23.0%) patients with lung cancer. In our sample, 272 425 patients (88%) were White, and 238 603 patients (77%) lived in metropolitan areas. The cancer patient-sharing network included 7221 medical oncologists and 3573 radiation oncologists. HRRs with more vulnerable networks for medical oncology had a higher percentage of beneficiaries eligible for Medicaid (ρ, 0.19; 95% CI, 0.08 to 0.29). HRRs with more vulnerable networks for radiation oncology had a higher percentage of beneficiaries living in poverty (ρ, 0.17; 95% CI, 0.06 to 0.27), and a higher percentage of beneficiaries eligible for Medicaid (ρ, 0.21; 95% CI, 0.09 to 0.31), and lower rates of cohort patients receiving radiation therapy (ρ, –0.18; 95% CI, –0.28 to –0.06; P = .003). The was no association between network vulnerability for medical oncology and percent of cohort patients receiving chemotherapy (ρ, –0.03; 95% CI, –0.15 to 0.08).Conclusions and RelevanceThis study found that patient-sharing network vulnerability was associated with poverty and lower rates of radiation therapy. Health policy strategies for addressing network vulnerability may improve access to interdisciplinary care and reduce treatment disparities.

Concerns about avoidance or delays in seeking emergency care during the COVID-19 pandemic are widespread, but national data on emergency department (ED) visits and subsequent rates of hospitalization and outcomes are lacking. Using data on all traditional Medicare beneficiaries in the US from October 1, 2018, to September 30, 2020, we examined trends in ED visits and rates of hospitalization and thirty-day mortality conditional on an ED visit for non-COVID-19 conditions during several stages of the pandemic and for areas that were considered COVID-19 hot spots versus those that were not. We found reductions in ED visits that were largest by the first week of April 2020 (52 percent relative decrease), with volume recovering somewhat by mid-June (25 percent relative decrease). These reductions were of similar magnitude in counties that were and were not designated as COVID-19 hot spots. There was an early increase in hospitalizations and in the relative risk for thirty-day mortality, starting with the first surge of the pandemic, peaking at just over a 2-percentage-point increase. These results suggest that patients were presenting with more serious illness, perhaps related to delays in seeking care.

In this paper, we consider the changes to mathematics learning support (MLS) at Maynooth University due to the COVID-19 pandemic, including the provision of novel online study groups aimed at increasing student engagement and interaction. We briefly outline the local, national and international impact of COVID-19 on MLS and then focus on the results of a student survey. Respondents who regularly used online MLS were broadly positive about their experiences. They cited, in particular, the influence of tutors and the scheduled study groups, which provided structure and motivation as well as the opportunity to work with others and ask questions in less intimidating small groups. However, some respondents highlighted factors that impacted negatively on their engagement. These included low attendance or interaction from peers, timetabling issues or busy schedules, lack of awareness of the details of the services and increased feelings of discomfort and anxiety in an online environment. We consider how this student feedback may influence our future online and in-person supports.

Background Transcarotid artery revascularization (TCAR) was approved by the Food and Drug Administration in 2015 for patients with carotid artery stenosis. However, no randomized trial to evaluate TCAR has been performed to date, and previous reports have important limitations. Accordingly, we measured stroke or death after TCAR compared with carotid endarterectomy (CEA) and transfemoral carotid artery stenting (TF‐CAS). Methods and Results We used the Vascular Quality Initiative registry to study patients who underwent TCAR, CEA, or TF‐CAS from September 2016 to June 2021. Our primary outcomes were perioperative and 1‐year stroke or death. We used logistic regression for risk adjustment for perioperative outcomes and Cox regression for risk adjustment for 1‐year outcomes. We used a 2‐stage residual inclusion instrumental variable (IV) method to adjust for selection bias and other unmeasured confounding. Our instrument was a center's preference to perform TCAR versus CEA or TF‐CAS. We performed a subgroup analysis stratified by presenting neurologic symptoms. We studied 21 234 patients who underwent TCAR, 82 737 who underwent CEA, and 14 595 who underwent TF‐CAS across 662 centers. The perioperative rate of stroke or death was 2.0% for TCAR, 1.7% for CEA, and 3.7% for TF‐CAS ( P P Conclusions Perioperative stroke or death was greater following TCAR when compared with CEA. However, at 1 year there was no statistically significant difference in stroke or death between the 2 procedures. TCAR performed favorably compared with TF‐CAS at both time points. Although CEA remains the gold standard procedure for patients with carotid stenosis, TCAR appears to be a safe alternative to CEA and TF‐CAS when used selectively and may be useful when treating symptomatic patients.

BACKGROUND: The demographics of heart failure are changing. The rate of growth of the “older” heart failure population, specifically those ≥ 75, has outpaced that of any other age group. These older patients were underrepresented in the early beta-blocker trials. There are several reasons, including a decreased potential for mortality benefit and increased risk of side effects, why the risk/benefit tradeoff may be different in this population. OBJECTIVE: We aimed to determine the association between receipt of a beta-blocker after heart failure discharge and early mortality and readmission rates among patients with heart failure and reduced ejection fraction (HFrEF), specifically patients aged 75+. DESIGN AND PARTICIPANTS: We used 100% Medicare Parts A and B and a random 40% sample of Part D to create a cohort of beneficiaries with ≥ 1 hospitalization for HFrEF between 2008 and 2016 to run an instrumental variable analysis. MAIN MEASURE: The primary measure was 90-day, all-cause mortality; the secondary measure was 90-day, all-cause readmission. KEY RESULTS: Using the two-stage least squared methodology, among all HFrEF patients, receipt of a beta-blocker within 30-day of discharge was associated with a − 4.35% (95% CI − 6.27 to − 2.42%, p < 0.001) decrease in 90-day mortality and a − 4.66% (95% CI − 7.40 to − 1.91%, p = 0.001) decrease in 90-day readmission rates. Even among patients ≥ 75 years old, receipt of a beta-blocker at discharge was also associated with a significant decrease in 90-day mortality, − 4.78% (95% CI − 7.19 to − 2.40%, p < 0.001) and 90-day readmissions, − 4.67% (95% CI − 7.89 to − 1.45%, p < 0.001). CONCLUSION: Patients aged ≥ 75 years who receive a beta-blocker after HFrEF hospitalization have significantly lower 90-day mortality and readmission rates. The magnitude of benefit does not appear to wane with age. Absent a strong contraindication, all patients with HFrEF should attempt beta-blocker therapy at/after hospital discharge, regardless of age. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11606-021-06901-7

Background COVID-19 severity is amplified among individuals with obesity, which may have influenced mainstream media coverage of the disease by both improving understanding of the condition and increasing weight-related stigma. Objective We aimed to measure obesity-related conversations on Facebook and Instagram around key dates during the first year of the COVID-19 pandemic. Methods Public Facebook and Instagram posts were extracted for 29-day windows in 2020 around January 28 (the first US COVID-19 case), March 11 (when COVID-19 was declared a global pandemic), May 19 (when obesity and COVID-19 were linked in mainstream media), and October 2 (when former US president Trump contracted COVID-19 and obesity was mentioned most frequently in the mainstream media). Trends in daily posts and corresponding interactions were evaluated using interrupted time series. The 10 most frequent obesity-related topics on each platform were also examined. Results On Facebook, there was a temporary increase in 2020 in obesity-related posts and interactions on May 19 (posts +405, 95% CI 166 to 645; interactions +294,930, 95% CI 125,986 to 463,874) and October 2 (posts +639, 95% CI 359 to 883; interactions +182,814, 95% CI 160,524 to 205,105). On Instagram, there were temporary increases in 2020 only in interactions on May 19 (+226,017, 95% CI 107,323 to 344,708) and October 2 (+156,974, 95% CI 89,757 to 224,192). Similar trends were not observed in controls. Five of the most frequent topics overlapped (COVID-19, bariatric surgery, weight loss stories, pediatric obesity, and sleep); additional topics specific to each platform included diet fads, food groups, and clickbait. Conclusions Social media conversations surged in response to obesity-related public health news. Conversations contained both clinical and commercial content of possibly dubious accuracy. Our findings support the idea that major public health announcements may coincide with the spread of health-related content (truthful or otherwise) on social media.

Hospitalizations account for the largest share of health care spending. New payment models increasingly encourage health care providers to reduce hospital admissions. Although emergency department ...

Beta-blockers (BB) and renin angiotensin system inhibitors (RASi) are foundational for the treatment of heart failure with reduced ejection fraction (HFrEF). However, given the increased risk of side effects in older patients, uncertainly remains as to whether, on net, older patients benefit as much as the younger patients studied in trials.Using the GWTG-HF registry linked with Medicare data, overlap propensity weighted Cox proportional hazard models were used to examine the association between BB and RASi use at hospital discharge 30-day and 1-year outcomes among patients with HFrEF.Among the 48,711 patients (age ≥65 years) hospitalized with HFrEF, there were significant associations between BB and/or RASi use at discharge and lower rates of 30-day and 1-year mortality, including those over age 85 (30-day HR=0.56 [95% CI 0.45, 0.70]; 1-year HR=0.69 [95% CI 0.61, 0.78]). In addition, the magnitude of benefit associated with BB and/or RASi use after discharge did not decrease with increasing age. Even among the oldest patients, those over age 85, with hypotension, renal insufficiency or frailty, BB and/or RASi at discharge was still associated with lower 1-year mortality or readmission.Among older patients hospitalized with HFrEF, BB and/or RASi use at discharge is associated with lower rates of 30-day and 1-year mortality across all age groups and the magnitude of this benefit does not appear to decrease with increasing age. These data suggest that, absent a clinical contraindication, BB and RASi should be considered in all patients hospitalized with HFrEF before or at hospital discharge, regardless of age.

BACKGROUND COVID-19 severity is amplified among individuals with obesity, which may have influenced mainstream media coverage of the disease by both improving understanding of the condition and increasing weight-related stigma. OBJECTIVE We aimed to measure obesity-related conversations on Facebook and Instagram around key dates during the first year of the COVID-19 pandemic. METHODS Public Facebook and Instagram posts were extracted for 29-day windows in 2020 around January 28 (the first US COVID-19 case), March 11 (when COVID-19 was declared a global pandemic), May 19 (when obesity and COVID-19 were linked in mainstream media), and October 2 (when former US president Trump contracted COVID-19 and obesity was mentioned most frequently in the mainstream media). Trends in daily posts and corresponding interactions were evaluated using interrupted time series. The 10 most frequent obesity-related topics on each platform were also examined. RESULTS On Facebook, there was a temporary increase in 2020 in obesity-related posts and interactions on May 19 (posts +405, 95% CI 166 to 645; interactions +294,930, 95% CI 125,986 to 463,874) and October 2 (posts +639, 95% CI 359 to 883; interactions +182,814, 95% CI 160,524 to 205,105). On Instagram, there were temporary increases in 2020 only in interactions on May 19 (+226,017, 95% CI 107,323 to 344,708) and October 2 (+156,974, 95% CI 89,757 to 224,192). Similar trends were not observed in controls. Five of the most frequent topics overlapped (COVID-19, bariatric surgery, weight loss stories, pediatric obesity, and sleep); additional topics specific to each platform included diet fads, food groups, and clickbait. CONCLUSIONS Social media conversations surged in response to obesity-related public health news. Conversations contained both clinical and commercial content of possibly dubious accuracy. Our findings support the idea that major public health announcements may coincide with the spread of health-related content (truthful or otherwise) on social media.

The target of inference in microbiome analyses is usually relative abundance (RA) because RA in a sample (e.g., stool) can be considered as an approximation of RA in an entire ecosystem (e.g., gut). However, inference on RA suffers from the fact that RA are calculated by dividing absolute abundances (AA) over the common denominator (CD), the summation of all AA (i.e., library size). Because of that, perturbation in one taxon will result in a change in the CD and thus cause false changes in RA of all other taxa, and those false changes could lead to false positive/negative findings. We propose a novel analysis approach (IFAA) to make robust inference on AA of an ecosystem that can circumvent the issues induced by the CD problem and compositional structure of RA. IFAA can also address the confounding effect of library size and handle zero-inflated data structures. IFAA identifies microbial taxa associated with the covariates in Phase one and estimates the association parameters by employing an independent reference taxon in Phase two. Two real data applications are presented and extensive simulations show that IFAA outperforms other established existing approaches by a big margin in the presence of confounding effect of library size., Corresponding email: zhigang.li@ufl.edu

Due to Covid-19, mathematics support at Maynooth University transitioned from in-person to online over a very short period in March 2020. This paper provides a brief description of the technologies used by six tutors to facilitate this move. We outline why the tutors picked these technologies and how they used them. We also consider the issues tutors wanted to resolve and reflect on the outcomes of their experiences.

First introduced in early 2000s, the accountable care organization (ACO) is designed to lower health care costs while improving quality of care and has become one of the most important coordinated care technologies in the United States. In this research, we use the Medicare fee-for-service claims data from 2009-2014 to estimate the heterogeneous effects of Medicare ACO programs on hospital admissions across hospital referral regions (HRRs) and provider groups. To conduct our analysis, a model for a difference-in-difference (DID) study is embellished in multiple ways to account for intricacies and complexity with the data not able to be accounted for using existing models. Of particular note, we propose a Gaussian mixture model to account for the inability to observe the practice group affiliation of physicians if the organization they worked for did not become an ACO, which is needed to ensure appropriate partitioning of variation across the different units. The results suggest that the ACO programs reduced the rate of readmission to hospital, that the ACO program may have reduced heterogeneity in readmission rates, and that the effect of joining an ACO varied considerably across medical groups.

Despite the importance of emotional closeness (EC) in families, few researchers have accurately measured the construct in a systemic way. Additionally, existing measures rely on ratings from one informant, typically the mother, to provide information on closeness within the entire family system. We examined EC in 140 individuals (37 families) using the Emotional Tone Index for Families (ETIF), a novel, multi-informant measure that obtains bidirectional information about EC within every family relationship. The parent identified as most familiar with the family also completed two widely used single-informant measures: The McMaster Family Assessment Device and the Family Adaptability and Cohesion Evaluation Scales, version IV. The ETIF exhibited good test-retest reliability, high internal consistency, and concurrent validity with the single-informant measures. Though the primary respondent scores correlated highly with overall family closeness, results revealed only a modest association between closeness ratings within each dyad and parents rated higher levels of closeness toward their children than children rated closeness toward parents. These findings suggest that ratings from multiple informants provide valuable information about discrepancies in perceived closeness between family members and other complex family dynamics that cannot be captured by single-informant measures. Limitations, future directions, and implications for practice are discussed.A pesar de la importancia de la cercanía emocional en las familias, pocos investigadores han medido con precisión el constructo de una manera sistémica. Además, las herramientas de medición existentes dependen de las valoraciones de un informante, normalmente la madre, para proporcionar información sobre la cercanía dentro de todo el sistema familiar. Analizamos la cercanía emocional en 140 personas (37 familias) usando el Índice de Tono Emocional para las Familias (ITEF), una herramienta de medición novedosa que obtiene información bidireccional sobre la cercanía emocional dentro de cada relación familiar por parte de varios informantes. El progenitor identificado como el más familiarizado con la familia también completó dos evaluaciones de un solo informante ampliamente utilizadas: el Dispositivo de McMaster de Evaluación Familiar (McMaster Family Assessment Device) y las Escalas de Evaluación de la Cohesión y la Adaptabilidad Familiar (Family Adapatability and Cohesion Evaluation Scales), versión IV. El ITEF demostró buena fiabilidad de prueba-reprueba, una alta coherencia interna y validez simultánea con las evaluaciones de un solo informante. Aunque los puntajes del encuestado principal se correlacionaron en gran medida con la cercanía familiar general, los resultados revelaron solo una asociación modesta entre las valoraciones de cercanía dentro de cada díada, y los padres indicaron niveles más altos de cercanía hacia sus hijos de lo que los hijos lo hicieron hacia sus padres. Estos resultados sugieren que las valoraciones de varios informantes proporcionan información valiosa acerca de las discrepancias en la cercanía percibida entre los miembros de la familia y otra dinámica familiar compleja que no puede captarse mediante las mediciones de un solo informante. Se explican las limitaciones, las futuras direcciones y las implicancias para la práctica.尽管情感亲密度在家庭中很重要，但很少有研究人员能以系统的方式准确地量化这一概念。此外，现有的测量方法依赖于单个信息提供者的评分，通常是母亲，来提供整个家庭系统内有关亲密程度的信息。本研究使用家庭情感基调指数(ETIF)对140个个体(37个家庭)的EC进行了研究。这是一种全新的、由多个个体提供信息的测量方法，可以在每个家庭关系中获得关于EC的双向信息。被确认为最熟悉家庭的父母也完成了两个广泛使用的单一信息测量即麦克马斯特家庭评估工具和家庭适应性和凝聚力评估量表的第四版。ETIF表现出良好的测试-重测可靠性，高度的内部一致性，以及与单一信息测量的同步有效性。 虽然主要受访者的得分与整体家庭亲密程度高度相关，但结果显示，每对夫妇之间的亲密度评分之间只有适度的关联，父母对孩子的亲密度评分高于孩子对父母的亲密度评分。父母对子女的亲密程度评分高于子女对父母的亲密程度评分。这些研究结果表明，来自多个信息提供者的评级提供了宝贵的信息，说明家庭成员之间感知的亲密度差异和其他复杂的家庭动态，这些信息不能被单一信息提供者的测量所捕获。本文讨论了本研究的局限性、未来的方向和对实践的意义。.

Purpose We examined self‐reported financial toxicity and out‐of‐pocket expenses among adult women with breast cancer. Methods Patients spoke English, Spanish, or Mandarin Chinese, were aged 18+ years, had stage I–IIIA breast cancer, and were eligible for breast‐conserving and mastectomy surgery. Participants completed surveys about out‐of‐pocket costs and financial toxicity at 1 week, 12 weeks, and 1 year postsurgery. Results Three hundred ninety‐five of 448 eligible patients (88.2%) from the parent trial completed surveys. Excluding those reporting zero costs, crude mean ± SD out‐of‐pocket costs were $1,512 ± $2,074 at 1 week, $2,609 ± $6,369 at 12 weeks, and $3,308 ± $5,000 at 1 year postsurgery. Controlling for surgery, cancer stage, and demographics with surgeon and clinic as random effects, higher out‐of‐pocket costs were associated with higher financial toxicity 1 week and 12 weeks postsurgery (p < .001). Lower socioeconomic status (SES) was associated with lower out‐of‐pocket costs at each time point (p = .002–.013). One week postsurgery, participants with lower SES reported financial toxicity scores 1.02 points higher than participants with higher SES (95% confidence interval [CI], 0.08–1.95). Black and non‐White/non‐Black participants reported financial toxicity scores 1.91 (95% CI, 0.46–3.37) and 2.55 (95% CI, 1.11–3.99) points higher than White participants. Older (65+ years) participants reported financial toxicity scores 2.58 points lower than younger (, The term “financial toxicity” describes both the financial hardship and the psychological distress associated with health care costs. This article examines financial toxicity of women diagnosed with breast cancer at three time points up to 1 year post‐surgery, assessing demographic and clinical variables associated with higher financial toxicity and higher out‐of‐pocket expenses.

Health systems continue to grow in size. Financial integration-the ownership of hospitals or physician practices-often has anticompetitive effects that contribute to the higher prices for health care seen in the US. To determine whether the potential harms of financial integration are counterbalanced by improvements in quality, we surveyed nationally representative samples of hospitals (n = 739) and physician practices (n = 2,189), stratified according to whether they were independent or were owned by complex systems, simple systems, or medical groups. The surveys included nine scales measuring the level of adoption of diverse, quality-focused care delivery and payment reforms. Scores varied widely across hospitals and practices, but little of this variation was explained by ownership status. Quality scores favored financially integrated systems for four of nine hospital measures and one of nine practice measures, but in no case favored complex systems. Greater financial integration was generally not associated with better quality.

In this paper we consider survey results which focus on student use of online resources, where all respondents were attendees of a Mathematics Support Centre. We see that while most respondents are engaging with online resources, some are not using resources in an appropriate fashion. We discuss how these preliminary findings might inform discussions on the merits, or otherwise, of institutions providing training for students on their use of online materials. We also identify several areas of potential further research.

In this paper, we focus on survey results of students’ use of a Mathematics Support Centre website. We discuss potential modifications to the advertisement of our online services based on student responses. We also consider the purpose of an MSC website and who should be responsible, in an institution, for the provision of topic specific online resources for students.

BackgroundMany machine learning heuristics integrate well with Electronic Medical Record (EMR) systems yet often fail to surpass traditional statistical models for biomedical applications.ObjectiveWe sought to compare predictive performances of 12 machine learning and traditional statistical techniques to predict the occurrence of Hospital Acquired Pressure Injuries (HAPI).MethodsEMR information was collected from 57,227 hospitalizations acquired from Dartmouth Hitchcock Medical Center (April 2011 to December 2016). Twelve classification algorithms, chosen based upon classic regression and recent machine learning techniques, were trained to predict HAPI incidence and performance was assessed using the Area Under the Receiver Operating Characteristic Curve (AUC).ResultsLogistic regression achieved a performance (AUC = 0.91 ± 0.034) comparable to the other machine learning approaches. We report discordance between machine learning derived predictors compared to the traditional statistical model. We visually assessed important patient-specific factors through Shapley Additive Explanations.ConclusionsMachine learning models will continue to inform clinical decision-making processes but should be compared to traditional modeling approaches to ensure proper utilization. Disagreements between important predictors found by traditional and machine learning modeling approaches can potentially confuse clinicians and need to be reconciled. These developments represent important steps forward in developing real-time predictive models that can be integrated into EMR systems to reduce unnecessary harm.

ImportanceApproximately 1 in 6 youth in the US have a mental health condition, and suicide is a leading cause of death among this population. Recent national statistics describing acute care hospitalizations for mental health conditions are lacking.ObjectivesTo describe national trends in pediatric mental health hospitalizations between 2009 and 2019, to compare utilization among mental health and non–mental health hospitalizations, and to characterize variation in utilization across hospitals.Design, Setting, and ParticipantsRetrospective analysis of the 2009, 2012, 2016, and 2019 Kids’ Inpatient Database, a nationally representative database of US acute care hospital discharges. Analysis included 4 767 840 weighted hospitalizations among children 3 to 17 years of age.ExposuresHospitalizations with primary mental health diagnoses were identified using the Child and Adolescent Mental Health Disorders Classification System, which classified mental health diagnoses into 30 mutually exclusive disorder types.Main Outcomes and MeasuresMeasures included number and proportion of hospitalizations with a primary mental health diagnosis and with attempted suicide, suicidal ideation, or self-injury; number and proportion of hospital days and interfacility transfers attributable to mental health hospitalizations; mean lengths of stay (days) and transfer rates among mental health and non–mental health hospitalizations; and variation in these measures across hospitals.ResultsOf 201 932 pediatric mental health hospitalizations in 2019, 123 342 (61.1% [95% CI, 60.3%-61.9%]) were in females, 100 038 (49.5% [95% CI, 48.3%-50.7%]) were in adolescents aged 15 to 17 years, and 103 456 (51.3% [95% CI, 48.6%-53.9%]) were covered by Medicaid. Between 2009 and 2019, the number of pediatric mental health hospitalizations increased by 25.8%, and these hospitalizations accounted for a significantly higher proportion of pediatric hospitalizations (11.5% [95% CI, 10.2%-12.8%] vs 19.8% [95% CI, 17.7%-21.9%]), hospital days (22.2% [95% CI, 19.1%-25.3%] vs 28.7% [95% CI, 24.4%-33.0%]), and interfacility transfers (36.9% [95% CI, 33.2%-40.5%] vs 49.3% [95% CI, 45.9%-52.7%]). The percentage of mental health hospitalizations with attempted suicide, suicidal ideation, or self-injury diagnoses increased significantly from 30.7% (95% CI, 28.6%-32.8%) in 2009 to 64.2% (95% CI, 62.3%-66.2%) in 2019. Length of stay and interfacility transfer rates varied significantly across hospitals. Across all years, mental health hospitalizations had significantly longer mean lengths of stay and higher transfer rates compared with non–mental health hospitalizations.Conclusions and RelevanceBetween 2009 and 2019, the number and proportion of pediatric acute care hospitalizations due to mental health diagnoses increased significantly. The majority of mental health hospitalizations in 2019 included a diagnosis of attempted suicide, suicidal ideation, or self-injury, underscoring the increasing importance of this concern.

Background Malaria is a major cause of mortality and morbidity in Uganda. Despite Uganda’s efforts to distribute bed nets, only half of households have achieved the World Health Organization (WHO) Universal Coverage Criteria (one bed net for every two household members). The role of peer influence on bed net ownership remains underexplored. Data on the complete social network of households were collected in a rural parish in southwestern Uganda to estimate the association between household bed net ownership and peer household bed net ownership. Methods Data on household sociodemographics, bed net ownership, and social networks were collected from all households across one parish in southwestern Uganda. Bed nets were categorized as either purchased or free. Purchased and free bed net ownership ratios were calculated based on the WHO Universal Coverage Criteria. Using network name generators and complete census of parish residents, the complete social network of households in the parish was generated. Linear regression models that account for network autocorrelation were fitted to estimate the association between households’ bed net ownership ratios and bed net ownership ratios of network peer households, adjusting for sociodemographics and network centrality. Results One thousand seven hundred forty-seven respondents were interviewed, accounting for 716 households. The median number of peer households to which a household was directly connected was 7. Eighty-six percent of households owned at least one bed net, and 41% of households met the WHO Universal Coverage Criterion. The median bed net ownership ratios were 0.67 for all bed nets, 0.33 for free bed nets, and 0.20 for purchased bed nets. In adjusted multivariable models, purchased bed net ownership ratio was associated with average household wealth among peer households (b = 0.06, 95% CI 0.03, 0.10), but not associated with average purchased bed net ownership ratio of peer households. Free bed net ownership ratio was associated with the number of children under 5 (b = 0.08, 95% CI 0.05, 0.10) and average free bed net ownership ratios of peer households (b = 0.66, 95% CI 0.46, 0.85). Conclusions Household bed net ownership was associated with bed net ownership of peer households for free bed nets, but not for purchased bed nets. The findings suggest that public health interventions may consider leveraging social networks as tools for dissemination, particularly for bed nets that are provided free of charge.

Protecting medical privacy can create obstacles in the analysis and distribution of healthcare graphs and statistical inferences accompanying them. We pose a graph simulation model which generates networks using degree and property augmentation (GRANDPA) and provide a flexible R package that allows users to create graphs that preserve vertex attribute relationships and approximating retaining topological properties observed in the original graph (e.g., community structure). We support our proposed algorithm using a case study based on Zachary's karate network and a patient-sharing graph generated from Medicare claims data in 2019. In both cases, we find that community structure is preserved, and normalized root mean square error between cumulative distributions of the degrees is low (0.0508 and 0.0514 respectively)., Comment: 13 pages, 5 figures

Network centrality measures assign importance to influential or key nodes in a network based on the topological structure of the underlying adjacency matrix. In this work, we define the importance of a node in a network as being dependent on whether it is the only one of its kind among its neighbors’ ties. We introduce linchpin score, a measure of local uniqueness used to identify important nodes by assessing both network structure and a node attribute. We explore linchpin score by attribute type and examine relationships between linchpin score and other established network centrality measures (degree, betweenness, closeness, and eigenvector centrality). To assess the utility of this measure in a real-world application, we measured the linchpin score of physicians in patient-sharing networks to identify and characterize important physicians based on being locally unique for their specialty. We hypothesized that linchpin score would identify indispensable physicians who would not be easily replaced by another physician of their specialty type if they were to be removed from the network. We explored differences in rural and urban physicians by linchpin score compared with other network centrality measures in patient-sharing networks representing the 306 hospital referral regions in the United States. We show that linchpin score is uniquely able to make the distinction that rural specialists, but not rural general practitioners, are indispensable for rural patient care. Linchpin score reveals a novel aspect of network importance that can provide important insight into the vulnerability of health care provider networks. More broadly, applications of linchpin score may be relevant for the analysis of social networks where interdisciplinary collaboration is important.

Introduction: Given the increasing age and comorbidity burden of HFrEF patients, there are questions regarding whether the net risk/benefit of guideline directed medical therapy (GDMT) varies across the age spectrum. The aim of this study was to use Get With The Guidelines-Heart Failure data linked with Medicare claims to determine whether the association between selected GDMT and outcomes in HFrEF varies across the age spectrum. Methods: We identified 48,711 eligible patients from 330 different centers admitted with HFrEF between 2008 and 2016 using GWTG-HF and Medicare. We created two cohorts: those prescribed evidence-based beta blockers (BB) and/or renin-angiotensin system inhibitors (RASI) and those prescribed neither. We used overlap weighting to match the patient populations within age strata (65-74, 75-84 and 85+). We then used Cox proportional hazard models to determine the association between use at discharge and 30-day and 1-year mortality and readmissions. Results: The average HFrEF patient in this dataset was 79 (95% CI 72, 85) years old and 41% were female. Eighty percent were white. Ninety-seven percent (=47,299) received BB and/or RASI; 3% (n-1,412) received neither. The hazard ratio (HR) for BB and/or RASI vs. no BB or RASI for 1-year mortality or readmission among those aged 65-74 was HR=0.82 (95% CI 0.70-0.95); for those aged 75-84 HR=0.86 (95% CI 0.78-0.95) and for those aged 85+, the HR=0.78 (95% CI 0.71, 0.86). The p-value comparing HR across the age strata was 0.41. Similar results were observed for 30-day outcomes (Table 1) . Conclusions: The association between use of BB and/or RASI at hospital discharge and lower 30-day and 1-year mortality or readmission does not appear to vary across the age spectrum. Absent a contraindication, BB and/or RASI should be prescribed for all patients at discharge regardless of age.

Non-alcoholic steatohepatitis (NASH) is a liver disease characterized by excessive lipid accumulation and disease progression is typically assessed through inspection of a Trichrome stain for Fibrosis staging. As the public health burden of NASH worsens due to evolving lifestyle habits, pathology laboratory resources will become increasingly strained due to rising demand for specialized stains. Virtual staining processes, computational methods which can synthesize the application of chemical staining reagents, can potentially provide resource savings by obviating the need to acquire specialized stains. Virtual staining technologies are assessed by comparing virtual and real tissue stains for their realism and ability to stage. However, these assessment methods are rife with statistical mistreatment of observed phenomena that are difficult to account for. Bridge category ratings represent a phenomenon where a pathologist may assign two adjacent stages simultaneously, which may bias and/or reduce the power of research findings. Such stage assignments were frequently reported in a large-scale assessment of Virtual Trichrome technologies yet were unaccounted for since no statistical adjustment procedures existed. In this work, we provide an updated assessment of Virtual Trichrome technologies using Bridge Category Models, which account for these bridge ratings. We report that two of four pathologists tended to assign lower Fibrosis stages to virtually stained tissue while the other two pathologists assigned similar stages. These research findings differ when bridge ratings are not accounted for. While promising, these results indicate further room for algorithmic finetuning of Virtual Trichrome technologies.

Background Reddit is a popular social media platform that has faced scrutiny for inflammatory language against those with obesity, yet there has been no comprehensive analysis of its obesity-related content. Objective We aimed to quantify the presence of 4 types of obesity-related content on Reddit (misinformation, facts, stigma, and positivity) and identify psycholinguistic features that may be enriched within each one. Methods All sentences (N=764,179) containing “obese” or “obesity” from top-level comments (n=689,447) made on non–age-restricted subreddits (ie, smaller communities within Reddit) between 2011 and 2019 that contained one of a series of keywords were evaluated. Four types of common natural language processing features were extracted: bigram term frequency–inverse document frequency, word embeddings derived from Bidirectional Encoder Representations from Transformers, sentiment from the Valence Aware Dictionary for Sentiment Reasoning, and psycholinguistic features from the Linguistic Inquiry and Word Count Program. These features were used to train an Extreme Gradient Boosting machine learning classifier to label each sentence as 1 of the 4 content categories or other. Two-part hurdle models for semicontinuous data (which use logistic regression to assess the odds of a 0 result and linear regression for continuous data) were used to evaluate whether select psycholinguistic features presented differently in misinformation (compared with facts) or stigma (compared with positivity). Results After removing ambiguous sentences, 0.47% (3610/764,179) of the sentences were labeled as misinformation, 1.88% (14,366/764,179) were labeled as stigma, 1.94% (14,799/764,179) were labeled as positivity, and 8.93% (68,276/764,179) were labeled as facts. Each category had markers that distinguished it from other categories within the data as well as an external corpus. For example, misinformation had a higher average percent of negations (β=3.71, 95% CI 3.53-3.90; P6 letters (β=−1.47, 95% CI −1.85 to −1.10; P Conclusions There are distinct psycholinguistic properties between types of obesity-related content on Reddit that can be leveraged to rapidly identify deleterious content with minimal human intervention and provide insights into how the Reddit population perceives patients with obesity. Future work should assess whether these properties are shared across languages and other social media platforms.

The public health burden of non-alcoholic steatohepatitis (NASH), a liver condition characterized by excessive lipid accumulation and subsequent tissue inflammation and fibrosis, has burgeoned with the spread of western lifestyle habits. Progression of fibrosis into cirrhosis is assessed using histological staging scales (e.g., NASH Clinical Research Network (NASH CRN)). These scales are used to monitor disease progression as well as to evaluate the effectiveness of therapies. However, clinical drug trials for NASH are typically underpowered due to lower than expected inter-/intra-rater reliability, which impacts measurements at screening, baseline, and endpoint. Bridge ratings represent a phenomenon where pathologists assign two adjacent stages simultaneously during assessment and may further complicate these analyses when ad hoc procedures are applied. Statistical techniques, dubbed Bridge Category Models, have been developed to account for bridge ratings, but not for the scenario where multiple pathologists assess biopsies across time points. Here, we develop hierarchical Bayesian extensions for these statistical methods to account for repeat observations and use these methods to assess the impact of bridge ratings on the inter-/intra-rater reliability of the NASH CRN staging scale. We also report on how pathologists may differ in their assignment of bridge ratings to highlight different staging practices. Our findings suggest that Bridge Category Models can capture additional fibrosis staging heterogeneity with greater precision, which translates to potentially higher reliability estimates in contrast to the information lost through ad hoc approaches.

Black and Hispanic people are more likely to contract COVID-19, require hospitalization, and die than White people due to differences in exposures, comorbidity risk, and healthcare access.To examine the association of race and ethnicity with treatment decisions and intensity for patients hospitalized for COVID-19.Retrospective cohort analysis of manually abstracted electronic medical records.7,997 patients (62% non-Hispanic White, 16% non-Black Hispanic, and 23% Black) hospitalized for COVID-19 at 135 community hospitals between March and June 2020 MAIN MEASURES: Advance care planning (ACP), do not resuscitate (DNR) orders, intensive care unit (ICU) admission, mechanical ventilation (MV), and in-hospital mortality. Among decedents, we classified the mode of death based on treatment intensity and code status as treatment limitation (no MV/DNR), treatment withdrawal (MV/DNR), maximal life support (MV/no DNR), or other (no MV/no DNR).Adjusted in-hospital mortality was similar between White (8%) and Black patients (9%, OR=1.1, 95% CI=0.9-1.4, p=0.254), and lower among Hispanic patients (6%, OR=0.7, 95% CI=0.6-1.0, p=0.032). Black and Hispanic patients were significantly more likely to be treated in the ICU (White 23%, Hispanic 27%, Black 28%) and to receive mechanical ventilation (White 12%, Hispanic 17%, Black 16%). The groups had similar rates of ACP (White 12%, Hispanic 12%, Black 11%), but Black and Hispanic patients were less likely to have a DNR order (White 13%, Hispanic 8%, Black 7%). Among decedents, there were significant differences in mode of death by race/ethnicity (treatment limitation: White 39%, Hispanic 17% (p=0.001), Black 18% (p0.0001); treatment withdrawal: White 26%, Hispanic 43% (p=0.002), Black 28% (p=0.542); and maximal life support: White 21%, Hispanic 26% (p=0.308), Black 36% (p0.0001)).Hospitalized Black and Hispanic COVID-19 patients received greater treatment intensity than White patients. This may have simultaneously mitigated disparities in in-hospital mortality while increasing burdensome treatment near death.

Concerns about avoidance or delays in seeking emergency care during the COVID-19 pandemic are widespread, but national data on emergency department (ED) visits and subsequent rates of hospitalization and outcomes are lacking. Using data on all traditional Medicare beneficiaries in the US from October 1, 2018, to September 30, 2020, we examined trends in ED visits and rates of hospitalization and thirty-day mortality conditional on an ED visit for non-COVID-19 conditions during several stages of the pandemic and for areas that were considered COVID-19 hot spots versus those that were not. We found reductions in ED visits that were largest by the first week of April 2020 (52 percent relative decrease), with volume recovering somewhat by mid-June (25 percent relative decrease). These reductions were of similar magnitude in counties that were and were not designated as COVID-19 hot spots. There was an early increase in hospitalizations and in the relative risk for thirty-day mortality, starting with the first surge of the pandemic, peaking at just over a 2-percentage-point increase. These results suggest that patients were presenting with more serious illness, perhaps related to delays in seeking care.

Background Rates of recommending percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) vary across clinicians. Whether clinicians agree on preferred treatment options for multivessel coronary artery disease patients has not been well studied. Methods and results We distributed a survey to 104 clinicians from the Northern New England Cardiovascular Study Group through email and at a regional meeting with 88 (84.6%) responses. The survey described three clinical vignettes of multivessel coronary artery disease patients. For each patient vignette participants selected appropriate treatment options and whether they would use a patient decision aid. The likelihood of choosing PCI only or PCI/CABG over CABG only was modeled using a multinomial regression. Across all vignettes, participants selected CABG only as an appropriate treatment option 24.2% of the time, PCI only 25.4% of the time, and both CABG or PCI as appropriate treatment options 50.4% of the time. Surgeons were less likely to choose PCI over CABG (RR 0.14, 95% CI 0.03, 0.59) or both treatments over CABG only (RR 0.10, 95% CI 0.03, 0.34) relative to cardiologists. Overall, 65% of participants responded they would use a patient decision aid with each vignette. Conclusions There is a lack of consensus on the appropriate treatment options across cardiologists and surgeons for patients with multivessel coronary artery disease. Treatment choice is influenced by both patient characteristics and clinician specialty.