Search

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 ...

Accountable care organization (ACO) legislation was designed to improve patient outcomes by inducing greater coordination of care and adoption of best practices. Therefore, it is of interest to assess whether greater uniformity occurs among practices comprising an ACO post ACO formation. We develop a mixed-effect model with a difference-in-difference design to evaluate the effect of a patient receiving care from an ACO on patient outcomes and adapt this model to examine whether an ACO is associated with increased uniformity across its constituent practices. The task is complicated by the organizations within an ACO forming an additional layer in the multilevel model, due to medical practices and hospitals that form an ACOs being nested within the ACO, making the number of levels of the model variable and the dimension of the parameter space time-varying. We develop the model and a procedure for testing the hypothesis that ACO formation was associated with increased uniformity among its constituent practices. We apply our procedure to a cohort of medicare beneficiaries followed over 2009-2014. Although there is extensive heterogeneity of becoming an ACOs across practices, we find that the formation of an ACO appears to be associated with greater uniformity of patient outcomes among its constituent practices.

Background: The human microbiome can contribute to pathogeneses of many complex diseases by mediating disease-leading causal pathways. However, standard mediation analysis methods are not adequate to analyze the microbiome as a mediator due to the excessive number of zero-valued sequencing reads in the data and that the relative abundances have to sum to one. The two main challenges raised by the zero-inflated data structure are: (a) disentangling the mediation effect induced by the point mass at zero; and (b) identifying the observed zero-valued data points that are not zero (i.e., false zeros). Methods: We develop a novel marginal mediation analysis method under the potential-outcomes framework to address the issues. We also show that the marginal model can account for the compositional structure of microbiome data. Results: The mediation effect can be decomposed into two components that are inherent to the two-part nature of zero-inflated distributions. With probabilistic models to account for observing zeros, we also address the challenge with false zeros. A comprehensive simulation study and the application in a real microbiome study showcase our approach in comparison with existing approaches. Conclusions: When analyzing the zero-inflated microbiome composition as the mediators, MarZIC approach has better performance than standard causal mediation analysis approaches and existing competing approach.

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.

Objectives Musculoskeletal disorders (MSDs) account for the greatest burden of years lived with disability globally. To prevent disability, good-quality services need to be commissioned, appropriate for local need. We analysed data collected systematically from a new musculoskeletal service serving 70% of the population of Scotland to evaluate: age- and sex-specific occurrence; anatomical distribution; and impact and effect on work ability. Methods A new centralized telephone-based triage for people with musculoskeletal disorders was set up in Scotland in 2015. Available to most of the population aged >16 years (>3 million people), data were collected systematically into a database detailing: anatomical site, nature of onset, duration, impact/risk (modified STarT score), deprivation level and, for those in employment, sickness absence. Results Data were available from 219 314 new callers, 2015–18. Calls were more frequently from women (60%), increased with age until the eighth decade, and 66% reported symptoms that had been present for >6 weeks. Callers were more likely to be living in more deprived areas in each age band between 20 and 64 years and tended to have higher-impact symptoms. The majority (53%) of callers were in employment, and 19% of these were off sick because of their symptoms. Sickness absence was more common among those with highest impact/risk scores from deprived areas with more acute symptoms. Discussion Large-scale systematic data collection for MSDs emphasizes the size and impact of the burden among adults aged >16 years. A socio-economic gradient is evident in terms of prevalence and impact of MSDs, particularly for sickness absence.

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.