Effects of aging on urinary tract epithelial homeostasis and immunity.

A global increase in older individuals creates an increasing demand to understand numerous healthcare challenges related to aging. This population is subject to changes in tissue physiology and the immune response network. Older individuals are particularly susceptible to infectious diseases, with one of the most common being urinary tract infections (UTIs). Postmenopausal and older women have the highest risk of recurrent UTIs (rUTIs); however, why rUTIs become more frequent after menopause and during old age is incompletely understood. This increased susceptibility and severity among older individuals may involve functional changes to the immune system with age. Aging also has substantial effects on the epithelium and the immune system that led to impaired protection against pathogens, yet heightened and prolonged inflammation. How the immune system and its responses to infection changes within the bladder mucosa during aging has largely remained poorly understood. In this review, we highlight our understanding of bladder innate and adaptive immunity and the impact of aging and hormones and hormone therapy on bladder epithelial homeostasis and immunity. In particular, we elaborate on how the cellular and molecular immune landscape within the bladder can be altered during aging as aged mice develop bladder tertiary lymphoid tissues (bTLT), which are absent in young mice leading to profound age-associated change to the immune landscape in bladders that might drive the significant increase in UTI susceptibility. Knowledge of host factors that prevent or promote infection can lead to targeted treatment and prevention regimens. This review also identifies unique host factors to consider in the older, female host for improving rUTI treatment and prevention by dissecting the age-associated alteration of the bladder mucosal immune system.

Changes in Cardiovascular Spending, Care Utilization, and Clinical Outcomes Associated With Participation in Bundled Payments for Care Improvement - Advanced.

BACKGROUND: Bundled Payments for Care Improvement - Advanced (BPCI-A) is a Medicare initiative that aims to incentivize reductions in spending for episodes of care that start with a hospitalization and end 90 days after discharge. Cardiovascular disease, an important driver of Medicare spending, is one of the areas of focus BPCI-A. It is unknown whether BPCI-A is associated with spending reductions or quality improvements for the 3 cardiovascular medical events or 5 cardiovascular procedures in the model. METHODS: In this retrospective cohort study, we conducted difference-in-differences analyses using Medicare claims for patients discharged between January 1, 2017, and September 30, 2019, to assess differences between BPCI-A hospitals and matched nonparticipating control hospitals. Our primary outcomes were the differential changes in spending, before versus after implementation of BPCI-A, for cardiac medical and procedural conditions at BPCI-A hospitals compared with controls. Secondary outcomes included changes in patient complexity, care utilization, healthy days at home, readmissions, and mortality. RESULTS: Baseline spending for cardiac medical episodes at BPCI-A hospitals was $25 606. The differential change in spending for cardiac medical episodes at BPCI-A versus control hospitals was $16 (95% CI, -$228 to $261; P=0.90). Baseline spending for cardiac procedural episodes at BPCI-A hospitals was $37 961. The differential change in spending for cardiac procedural episodes was $171 (95% CI, -$429 to $772; P=0.58). There were minimal differential changes in physicians' care patterns such as the complexity of treated patients or in their care utilization. At BPCI-A versus control hospitals, there were no significant differential changes in rates of 90-day readmissions (differential change, 0.27% [95% CI, -0.25% to 0.80%] for medical episodes; differential change, 0.31% [95% CI, -0.98% to 1.60%] for procedural episodes) or mortality (differential change, -0.14% [95% CI, -0.50% to 0.23%] for medical episodes; differential change, -0.36% [95% CI, -1.25% to 0.54%] for procedural episodes). CONCLUSIONS: Participation in BPCI-A was not associated with spending reductions, changes in care utilization, or quality improvements for the cardiovascular medical events or procedures offered in the model.

Year 1 of the Bundled Payments for Care Improvement-Advanced Model.

BACKGROUND: The Center for Medicare and Medicaid Innovation launched the Medicare Bundled Payments for Care Improvement-Advanced (BPCI-A) program for hospitals in October 2018. Information is needed about the effects of the program on health care utilization and Medicare payments. METHODS: We conducted a modified segmented regression analysis using Medicare claims and including patients with discharge dates from January 2017 through September 2019 to assess differences between BPCI-A participants and two control groups: hospitals that never joined the BPCI-A program (nonjoining hospitals) and hospitals that joined the BPCI-A program in January 2020, after the conclusion of the intervention period (late-joining hospitals). The primary outcomes were the differences in changes in quarterly trends in 90-day per-episode Medicare payments and the percentage of patients with readmission within 90 days after discharge. Secondary outcomes were mortality, volume, and case mix. RESULTS: A total of 826 BPCI-A participant hospitals were compared with 2016 nonjoining hospitals and 334 late-joining hospitals. Among BPCI-A hospitals, the mean baseline 90-day per-episode Medicare payment was $27,315; the change in the quarterly trends in the intervention period as compared with baseline was -$78 per quarter. Among nonjoining hospitals, the mean baseline 90-day per-episode Medicare payment was $25,994; the change in quarterly trends as compared with baseline was -$26 per quarter (difference between nonjoining hospitals and BPCI-A hospitals, $52 [95% confidence interval {CI}, 34 to 70] per quarter; P<0.001; 0.2% of the baseline payment). Among late-joining hospitals, the mean baseline 90-day per-episode Medicare payment was $26,807; the change in the quarterly trends as compared with baseline was $4 per quarter (difference between late-joining hospitals and BPCI-A hospitals, $82 [95% CI, 41 to 122] per quarter; P<0.001; 0.3% of the baseline payment). There were no meaningful differences in the changes with regard to readmission, mortality, volume, or case mix. CONCLUSIONS: The BPCI-A program was associated with small reductions in Medicare payments among participating hospitals as compared with control hospitals. (Funded by the National Heart, Lung, and Blood Institute.).

Unlocking the unfolded structure of ubiquitin: Combining time-resolved x-ray solution scattering and molecular dynamics to generate unfolded ensembles.

The unfolding dynamics of ubiquitin were studied using a combination of x-ray solution scattering (XSS) and molecular dynamics (MD) simulations. The kinetic analysis of the XSS ubiquitin signals showed that the protein unfolds through a two-state process, independent of the presence of destabilizing salts. In order to characterize the ensemble of unfolded states in atomic detail, the experimental XSS results were used as a constraint in the MD simulations through the incorporation of x-ray scattering derived potential to drive the folded ubiquitin structure toward sampling unfolded states consistent with the XSS signals. We detail how biased MD simulations provide insight into unfolded states that are otherwise difficult to resolve and underscore how experimental XSS data can be combined with MD to efficiently sample structures away from the native state. Our results indicate that ubiquitin samples unfolded in states with a high degree of loss in secondary structure yet without a collapse to a molten globule or fully solvated extended chain. Finally, we propose how using biased-MD can significantly decrease the computational time and resources required to sample experimentally relevant nonequilibrium states.

La26Ge19M5O5 (M = Ag, Cu): Rare-Earth Metal Suboxide Superconductors with [La18O5] Cluster Units.

Herein we report two reduced rare-earth metal-based superconductors, La26Ge19M5O5 (M = Ag, Cu), that feature an unprecedented [La18O5] cluster composed of five oxygen-centered [La6O] octahedra condensed through shared faces and capped with [Ge4] butterfly rings. The structure, determined by single-crystal X-ray diffraction, crystallizes in a tetragonal space group (P4/nmm), with a = 15.508(2) Å and c = 11.238(2) Å. Resistivity and magnetic susceptibility measurements show onsets of superconductivity at Tc = 5.4 and 6.4 K for the Ag and Cu compounds, respectively. Applying high pressures, up to 1.3 GPa, results in increased superconducting transition temperatures (Tc = 6.8 K for Ag and 7.2 K for Cu compounds), with no sign of saturation.

Care Disruption During COVID-19: a National Survey of Hospital Leaders.

BACKGROUND: The COVID-19 pandemic caused massive disruption in usual care delivery patterns in hospitals across the USA, and highlighted long-standing inequities in health care delivery and outcomes. Its effect on hospital operations, and whether the magnitude of the effect differed for hospitals serving historically marginalized populations, is unknown. OBJECTIVE: To investigate the perspectives of hospital leaders on the effects of COVID-19 on their facilities' operations and patient outcomes. METHODS: A survey was administered via print and electronic means to hospital leaders at 588 randomly sampled acute-care hospitals participating in Medicare's Inpatient Prospective Payment System, fielded from November 2020 to June 2021. Summary statistics were tabulated, and responses were adjusted for sampling strategy and non-response. RESULTS: There were 203 responses to the survey (41.6%), with 20.7% of respondents representing safety-net hospitals and 19.7% representing high-minority hospitals. Over three-quarters of hospitals reported COVID testing shortages, about two-thirds reported staffing shortages, and 78.8% repurposed hospital spaces to intensive care units, with a slightly higher proportion of high-minority hospitals reporting these effects. About half of respondents felt that non-COVID inpatients received worsened quality or outcomes during peak COVID surges, and almost two-thirds reported worsened quality or outcomes for outpatient non-COVID patients as well, with few differences by hospital safety-net or minority status. Over 80% of hospitals participated in alternative payment models prior to COVID, and a third of these reported decreasing these efforts due to the pandemic, with no differences between safety-net and high-minority hospitals. CONCLUSIONS: COVID-19 significantly disrupted the operations of hospitals across the USA, with hospitals serving patients in poverty and racial and ethnic minorities reporting relatively similar care disruption as non-safety-net and lower-minority hospitals.

Long-term outcomes of patients with conjunctival extranodal marginal zone lymphoma.

Comprehensive information on clinical features and long-term outcomes of primary conjunctival extranodal marginal zone lymphoma (PCEMZL) is scarce. We present a large single-institution retrospective study of 72 patients. The median age was 64 years, and 63.9% were female. Stage I was present in 87.5%. Radiation therapy (RT) alone was the most common treatment (70.8%). Complete response (CR) was 87.5%, and 100% in RT-treated patients. With a median follow-up of 6.7 years, relapse/progression and death occurred in 19.4% each, with one relapse within the RT field. The 10-year progression-free survival (PFS) and overall survival (OS) were 68.4% (95% CI 52.8%-79.8%) and 89.4% (95% CI 77.4%-95.2%), respectively. The 10-year rate for time to progression from diagnosis was 22.5% (95% CI 11.6%-35.7%). The 10-year PFS and OS of MALT-IPI 0 versus 1-2 were 83.3% versus 51.3%, (p = .022) and 97.6% versus 76.6%, (p = .0052), respectively. The following characteristics were associated with shorter survival: age > 60 years (PFS: HR = 2.93, 95% CI 1.08-7.95; p = .035, OS: HR = 9.07, 95% CI 1.17-70.26; p = .035) and MALT-IPI 1-2 (PFS: HR = 2.67, 95% CI 1.12-6.31; p = .027, OS: HR = 6.64, 95% CI 1.45-30.37; p = .015). CR following frontline therapy was associated with longer PFS (HR = 0.13, 95% CI 0.04-0.45; p = .001), but not OS. Using the Fine and Gray regression model with death without relapse/progression as a competing risk, RT and CR after frontline therapy were associated with lower risk of relapse (SHR = 0.34, 95% CI 0.12-0.96 p = .041 and SHR = 0.11, 95% CI 0.03-0.36; p < .001, respectively). Patients with PCEMZL treated with frontline RT exhibit excellent long-term survival, and the MALT-IPI score appropriately identifies patients at risk for treatment failure.

The American Cancer Society challenge goal to reduce US cancer mortality by 50% between 1990 and 2015: Results and reflections.

In 1996, the Board of Directors of the American Cancer Society (ACS) challenged the United States to reduce what looked to be possible peak cancer mortality in 1990 by 50% by the year 2015. This analysis examines the trends in cancer mortality across this 25-year challenge period from 1990 to 2015. In 2015, cancer death rates were 26% lower than in 1990 (32% lower among men and 22% lower among women). The 50% reduction goal was more fully met for the cancer sites for which there was enactment of effective approaches for prevention, early detection, and/or treatment. Among men, mortality rates dropped for lung cancer by 45%, for colorectal cancer by 47%, and for prostate cancer by 53%. Among women, mortality rates dropped for lung cancer by 8%, for colorectal cancer by 44%, and for breast cancer by 39%. Declines in the death rates of all other cancer sites were substantially smaller (13% among men and 17% among women). The major factors that accounted for these favorable trends were progress in tobacco control and improvements in early detection and treatment. As we embark on new national cancer goals, this recent past experience should teach us that curing the cancer problem will require 2 sets of actions: making new discoveries in cancer therapeutics and more completely applying those discoveries in cancer prevention we have already made. CA Cancer J Clin 2016;66:359-369. © 2016 American Cancer Society.

Multichannel laser diode to polymer waveguide array coupling with a double-aspheric lens.

An optical system for multichannel coupling of laser arrays to polymer waveguide array probes with a single biconvex lens is developed. The developed cylindrical module with 13 mm and 20 mm in diameter and length, respectively, enables coupling of eight individual optical channels using an aspheric lens. Specific coupling with crosstalk below -13d B for each channel and quasi-uniform coupling over all channels is achieved for a waveguide array with 100 µm lateral facet pitch at the incoupling site. The polymer waveguide technology allows for tapering of the lateral waveguide pitch to 25 µm toward the tip of the flexible waveguide array. SU-8 and PMMA are used as the waveguide core and cladding, respectively. The optical coupling module is designed as a prototype for preclinical evaluation of optical neural stimulators.

Applications of machine learning in animal and veterinary public health surveillance.

Machine learning (ML) is an approach to artificial intelligence characterised by the use of algorithms that improve their own performance at a given task (e.g. classification or prediction) based on data and without being explicitly and fully instructed on how to achieve this. Surveillance systems for animal and zoonotic diseases depend upon effective completion of a broad range of tasks, some of them amenable to ML algorithms. As in other fields, the use of ML in animal and veterinary public health surveillance has greatly expanded in recent years. Machine learning algorithms are being used to accomplish tasks that have become attainable only with the advent of large data sets, new methods for their analysis and increased computing capacity. Examples include the identification of an underlying structure in large volumes of data from an ongoing stream of abattoir condemnation records, the use of deep learning to identify lesions in digital images obtained during slaughtering, and the mining of free text in electronic health records from veterinary practices for the purpose of sentinel surveillance. However, ML is also being applied to tasks that previously relied on traditional statistical data analysis. Statistical models have been used extensively to infer relationships between predictors and disease to inform risk-based surveillance, and increasingly, ML algorithms are being used for prediction and forecasting of animal diseases in support of more targeted and efficient surveillance. While ML and inferential statistics can accomplish similar tasks, they have different strengths, making one or the other more or less appropriate in a given context.

L'apprentissage automatique (AA) est une approche de l'intelligence artificielle caractérisée par l'utilisation d'algorithmes qui améliorent leurs propres performances sur une tâche donnée (par exemple, la classification ou la prédiction) sur la base de données et sans avoir reçu d'instructions spécifiques ou complètes concernant la marche à suivre. Les systèmes de surveillance des maladies animales et des zoonoses sont tributaires de la mise en oeuvre efficace d'un large éventail de tâches, parmi lesquelles certaines sont susceptibles de fonctionner avec des algorithmes d'AA. Comme dans d'autres domaines, l'utilisation de l'AA s'est beaucoup développée ces dernières années dans le secteur de la surveillance de la santé animale et de la santé publique vétérinaire. Les algorithmes d'AA sont utilisés pour accomplir des tâches qui ne sont devenues possibles que grâce à l'arrivée de grandes séries de données, de nouvelles méthodes d'analyse et de capacités informatiques accrues. Parmi les exemples, on peut citer la capacité à déceler une structure sous-jacente dans de grands volumes de données provenant d'un flux continu de registres de saisies d'abattoirs, l'utilisation de l'apprentissage profond pour identifier les lésions révélées par les images numériques obtenues pendant l'abattage et l'extraction de texte libre à partir des registres sanitaires électroniques des cabinets vétérinaires à des fins de surveillance sentinelle. L'AA est cependant également appliqué dans des tâches qui s'appuyaient précédemment sur une analyse classique de données statistiques. Les modèles statistiques ont été largement utilisés pour déduire des relations entre prédicteurs et maladie afin d'étayer la surveillance fondée sur le risque ; les algorithmes d'AA sont de plus en plus utilisés pour prédire et pronostiquer des maladies animales en vue d'une surveillance plus ciblée et efficace. S'il est vrai que l'AA et la statistique inférentielle peuvent accomplir des tâches similaires, chaque approche présente ses propres atouts et pourra se révéler plus ou moins pertinente selon le contexte spécifique.

El aprendizaje automático es una vertiente de la inteligencia artificial que se caracteriza por el uso de algoritmos capaces de mejorarse a sí mismos en la ejecución de una determinada tarea (p.ej., procesos de clasificación o predicción) con empleo de datos y sin necesidad de recibir instrucciones explícitas y completas sobre la manera de lograrlo. Los sistemas de vigilancia de enfermedades animales y zoonóticas dependen de la ejecución eficaz de numerosas y muy diversas tareas, algunas de las cuales se prestan al uso de algoritmos de aprendizaje automático. Al igual que en otros campos, la aplicación del aprendizaje automático en sanidad animal y salud pública veterinaria se ha extendido sobremanera en los últimos años. Ahora se utilizan algoritmos de aprendizaje automático para realizar tareas que solo han empezado a ser factibles con el advenimiento de ingentes conjuntos de datos, nuevos métodos para analizarlos y una mayor capacidad de tratamiento informático. Entre otros ejemplos, cabe citar la determinación de la estructura subyacente de grandes volúmenes de datos procedentes de un flujo continuo de registros de los descartes de matadero; la utilización del aprendizaje profundo para detectar lesiones en imágenes digitales obtenidas durante las operaciones de sacrificio, o el análisis del texto libre de registros sanitarios electrónicos de procedimientos veterinarios con fines de vigilancia centinela. Con todo, el aprendizaje automático se está aplicando también a tareas que anteriormente reposaban en el análisis estadístico clásico de los datos. Los modelos estadísticos han sido extensamente utilizados para inferir relaciones entre una enfermedad y uno u otro predictor y alimentar a partir de ahí la vigilancia basada en el riesgo. Por otro lado, cada vez más se vienen empleando algoritmos de aprendizaje automático para predecir y anticipar enfermedades animales y conferir así más eficacia y especificidad a las actividades de vigilancia. Aunque el aprendizaje automático y la estadística inferencial realizan tareas parecidas, sus puntos fuertes son distintos, con lo cual, en función del contexto de que se trate, será preferible recurrir a uno u otro método.

Two-Year Evaluation of Mandatory Bundled Payments for Joint Replacement.

BACKGROUND: In 2016, Medicare implemented Comprehensive Care for Joint Replacement (CJR), a national mandatory bundled-payment model for hip or knee replacement in randomly selected metropolitan statistical areas. Hospitals in such areas receive bonuses or pay penalties based on Medicare spending per hip- or knee-replacement episode (defined as the hospitalization plus 90 days after discharge). METHODS: We conducted difference-in-differences analyses using Medicare claims from 2015 through 2017, encompassing the first 2 years of bundled payments in the CJR program. We evaluated hip- or knee-replacement episodes in 75 metropolitan statistical areas randomly assigned to mandatory participation in the CJR program (bundled-payment metropolitan statistical areas, hereafter referred to as "treatment" areas) as compared with those in 121 control areas, before and after implementation of the CJR model. The primary outcomes were institutional spending per hip- or knee-replacement episode (i.e., Medicare payments to institutions, primarily to hospitals and post-acute care facilities), rates of postsurgical complications, and the percentage of "high-risk" patients (i.e., patients for whom there was an elevated risk of spending - a measure of patient selection). Analyses were adjusted for the hospital and characteristics of the patients and procedures. RESULTS: From 2015 through 2017, there were 280,161 hip- or knee-replacement procedures in 803 hospitals in treatment areas and 377,278 procedures in 962 hospitals in control areas. After the initiation of the CJR model, there were greater decreases in institutional spending per joint-replacement episode in treatment areas than in control areas (differential change [i.e., the between-group difference in the change from the period before the CJR model], -$812, or a -3.1% differential decrease relative to the treatment-group baseline; P<0.001). The differential reduction was driven largely by a 5.9% relative decrease in the percentage of episodes in which patients were discharged to post-acute care facilities. The CJR program did not have a significant differential effect on the composite rate of complications (P=0.67) or on the percentage of joint-replacement procedures performed in high-risk patients (P=0.81). CONCLUSIONS: In the first 2 years of the CJR program, there was a modest reduction in spending per hip- or knee-replacement episode, without an increase in rates of complications. (Funded by the Commonwealth Fund and the National Institute on Aging of the National Institutes of Health.).

Changes in Employment Status and Access to Care During COVID-19 Pandemic Among Low-Income Adults in 4 Southern States.

BACKGROUND: While the impact of the COVID-19 recession on the economy is clear, there is limited evidence on how the COVID-19 pandemic-related job losses among low-income people may have affected their access to health care. OBJECTIVE: To determine the association of job loss during the pandemic with insurance coverage and access to and affordability of health care among low-income adults. DESIGN: Using a random digit dialing telephone survey from October 2020 to December 2020 of low-income adults in 4 states-Arkansas, Kentucky, Louisiana, and Texas-we conducted a series of multivariable logistic regression analyses, adjusting for demographics, chronic conditions, and state of residence. PARTICIPANTS: US citizens aged 19-64 with a family income less than 138% of the federal poverty line who became newly unemployed during pandemic, remained employed during pandemic, or were chronically unemployed before and during the pandemic. MAIN MEASURES: Rates of insurance, type of insurance coverage, measures of access to/affordability of care, and food/housing security KEY RESULTS: Of 1,794 respondents, 14.5% were newly unemployed, 49.6% were chronically unemployed, and 35.7% were employed. The newly unemployed were slightly younger and more likely Black or Latino. The newly unemployed were more likely to report uninsurance compared to the employed (+16.4 percentage points, 95% CI 6.0-26.9), and the chronically unemployed (+26.4 percentage points, 95% CI 16.2-36.6), mostly driven by Texas' populations. The newly unemployed also reported lower rates of access to care and higher rates of financial barriers to care. They were also more likely to report food and housing insecurity compared to others. CONCLUSIONS: In a survey of 4 Southern States during pandemic, the newly unemployed had higher rates of uninsurance and worse access to care-largely due to financial barriers-and reported more housing and food insecurity than other groups. Our study highlights the vulnerability of low-income populations who experienced a job loss, especially in Texas, which did not expand Medicaid.

Predictors of Success in the Bundled Payments for Care Improvement Program.

BACKGROUND: Hospitals participating in Medicare's Bundled Payments for Care Improvement (BPCI) program were incented to reduce Medicare payments for episodes of care. OBJECTIVE: To identify factors that influenced whether or not hospitals were able to save in the BPCI program, how the cost of different services changed to produce those savings, and if "savers" had lower or decreased quality of care. DESIGN: Retrospective cohort study. PARTICIPANTS: BPCI-participating hospitals. MAIN MEASURES: We designated hospitals that met the program goal of decreasing costs by at least 2% from baseline in average Medicare payments per 90-day episode as "savers." We used regression models to determine condition-level, patient-level, hospital-level, and market-level characteristics associated with savings. KEY RESULTS: In total, 421 hospitals participated in BPCI, resulting in 2974 hospital-condition combinations. Major joint replacement of the lower extremity had the highest proportion of savers (77.6%, average change in payments -$2235) and complex non-cervical spinal fusion had the lowest (22.2%, average change +$8106). Medical conditions had a higher proportion of savers than surgical conditions (11% more likely to save, P=0.001). Conditions that were mostly urgent/emergent had a higher proportion of savers than conditions that were mostly elective (6% more likely to save, P=0.007). Having higher than median costs at baseline was associated with saving (OR: 3.02, P<0.001). Hospitals with more complex patients were less likely to save (OR: 0.77, P=0.003). Savings occurred across both inpatient and post-acute care, and there were no decrements in clinical care associated with being a saver. CONCLUSIONS: Certain conditions may be more amenable than others to saving under bundled payments, and hospitals with high costs at baseline may perform well under programs which use hospitals' own baseline costs to set targets. Findings may have implications for the BPCI-Advanced program and for policymakers seeking to use payment models to drive improvements in care.

Preimplantation genetic testing for aneuploidy in patients with low embryo numbers: benefit or harm?

PURPOSE: We sought to explore the utility of preimplantation genetic testing for aneuploidy (PGT-A) in a poor prognosis group of women with few embryos available for transfer. METHODS: This was a retrospective matched cohort study examining records for first or second-cycle IVF patients with 1 to 3 blastocysts. The study group comprised 130 patients who underwent PGT-A on all embryos. The control group included 130 patients matched by age, BMI, and blastocyst number and quality who did not undergo PGT-A during the same time period. RESULTS: The live birth rate (LBR) per embryo transfer (ET) were similar in the PGT-A and control groups, and the spontaneous abortion (SAB) rate was the same (23%). However, we found a significantly higher LBR per oocyte retrieval in the control group vs the PGT-A group (43% vs 20%, respectively) likely due to the many no-euploid cycles in the PGT-A group. In a subgroup analysis for age, the similar LBR per ET persisted in women < 38. However, in older women, there was a trend to a higher LBR per ET in the PGT-A group (43%) vs the control group (22%) but a higher LBR per oocyte retrieval in the control group (31%) vs the PGT-A group (13%). CONCLUSIONS: Overall, we observed a significant increase in LBR per oocyte retrieval in women in the control group compared to women undergoing PGT-A, and no difference in SAB rate. Our data suggests that PGT-A has no benefit in a subpopulation of women with few embryos and may cause harm.

Changes in Health and Quality of Life in US Skilled Nursing Facilities by COVID-19 Exposure Status in 2020.

Importance: During the COVID-19 pandemic, the US federal government required that skilled nursing facilities (SNFs) close to visitors and eliminate communal activities. Although these policies were intended to protect residents, they may have had unintended negative effects. Objective: To assess health outcomes among SNFs with and without known COVID-19 cases. Design, Setting, and Participants: This retrospective observational study used US Medicare claims and Minimum Data Set 3.0 for January through November in each year beginning in 2018 and ending in 2020 including 15 477 US SNFs with 2 985 864 resident-years. Exposures: January through November of calendar years 2018, 2019, and 2020. COVID-19 diagnoses were used to assign SNFs into 2 mutually exclusive groups with varying membership by month in 2020: active COVID-19 (≥1 COVID-19 diagnosis in the current or past month) or no-known COVID-19 (no observed diagnosis by that month). Main Outcomes and Measures: Monthly rates of mortality, hospitalization, emergency department (ED) visits, and monthly changes in activities of daily living (ADLs), body weight, and depressive symptoms. Each SNF in 2018 and 2019 served as its own control for 2020. Results: In 2018-2019, mean monthly mortality was 2.2%, hospitalization 3.0%, and ED visit rate 2.9% overall. In 2020, among active COVID-19 SNFs compared with their own 2018-2019 baseline, mortality increased by 1.60% (95% CI, 1.58% to 1.62%), hospitalizations decreased by 0.10% (95% CI, -0.12% to -0.09%), and ED visit rates decreased by 0.57% (95% CI, -0.59% to -0.55%). Among no-known COVID-19 SNFs, mortality decreased by 0.15% (95% CI, -0.16% to -0.13%), hospitalizations by 0.83% (95% CI, -0.85% to -0.81%), and ED visits by 0.79% (95% CI, -0.81% to -0.77%). All changes were statistically significant. In 2018-2019, across all SNFs, residents required assistance with an additional 0.89 ADLs between January and November, and lost 1.9 lb; 27.1% had worsened depressive symptoms. In 2020, residents in active COVID-19 SNFs required assistance with an additional 0.36 ADLs (95% CI, 0.34 to 0.38), lost 3.1 lb (95% CI, -3.2 to -3.0 lb) more weight, and were 4.4% (95% CI, 4.1% to 4.7%) more likely to have worsened depressive symptoms, all statistically significant changes. In 2020, residents in no-known COVID-19 SNFs had no significant change in ADLs (-0.06 [95% CI, -0.12 to 0.01]), but lost 1.8 lb (95% CI, -2.1 to -1.5 lb) more weight and were 3.2% more likely (95% CI, 2.3% to 4.1%) to have worsened depressive symptoms, both statistically significant changes. Conclusions and Relevance: Among skilled nursing facilities in the US during the first year of the COVID-19 pandemic and prior to the availability of COVID-19 vaccination, mortality and functional decline significantly increased at facilities with active COVID-19 cases compared with the prepandemic period, while a modest statistically significant decrease in mortality was observed at facilities that had never had a known COVID-19 case. Weight loss and depressive symptoms significantly increased in skilled nursing facilities in the first year of the pandemic, regardless of COVID-19 status.

Evaluation of Medicare's Bundled Payments Initiative for Medical Conditions.

BACKGROUND: The Center for Medicare and Medicaid Innovation (CMMI) launched the Bundled Payments for Care Improvement (BPCI) initiative in 2013. A subsequent study showed that the initiative was associated with reductions in Medicare payments for total joint replacement, but little is known about the effect of BPCI on medical conditions. METHODS: We used Medicare claims from 2013 through 2015 to identify admissions for the five most commonly selected medical conditions in BPCI: congestive heart failure (CHF), pneumonia, chronic obstructive pulmonary disease (COPD), sepsis, and acute myocardial infarction (AMI). We used difference-in-differences analyses to assess changes in standardized Medicare payments per episode of care (defined as the hospitalization plus 90 days after discharge) for these conditions at BPCI hospitals and matched control hospitals. RESULTS: A total of 125 hospitals participated in BPCI for CHF, 105 hospitals for pneumonia, 101 hospitals for COPD, 88 hospitals for sepsis, and 73 hospitals for AMI. At baseline, the average Medicare payment per episode of care across the five conditions at BPCI hospitals was $24,280, which decreased to $23,993 during the intervention period (difference, -$286; P=0.41). Control hospitals had an average payment for all episodes of $23,901, which decreased to $23,503 during the intervention period (difference, -$398; P=0.08; difference in differences, $112; P=0.79). Changes from baseline to the intervention period in clinical complexity, length of stay, emergency department use or readmission within 30 or 90 days after hospital discharge, or death within 30 or 90 days after admission did not differ significantly between the intervention and control hospitals. CONCLUSIONS: Hospital participation in five common medical bundles under BPCI was not associated with significant changes in Medicare payments, clinical complexity, length of stay, emergency department use, hospital readmission, or mortality. (Funded by the Commonwealth Fund.).

Aryl Coenzyme A Ligases, a Subfamily of the Adenylate-Forming Enzyme Superfamily.

Aryl coenzyme A (CoA) ligases belong to class I of the adenylate-forming enzyme superfamily (ANL superfamily). They catalyze the formation of thioester bonds between aromatic compounds and CoA and occur in nearly all forms of life. These ligases are involved in various metabolic pathways degrading benzene, toluene, ethylbenzene, and xylene (BTEX) or polycyclic aromatic hydrocarbons (PAHs). They are often necessary to produce the central intermediate benzoyl-CoA that occurs in various anaerobic pathways. The substrate specificity is very diverse between enzymes within the same class, while the dependency on Mg2+, ATP, and CoA as well as oxygen insensitivity are characteristics shared by the whole enzyme class. Some organisms employ the same aryl-CoA ligase when growing aerobically and anaerobically, while others induce different enzymes depending on the environmental conditions. Aryl-CoA ligases can be divided into two major groups, benzoate:CoA ligase-like enzymes and phenylacetate:CoA ligase-like enzymes. They are widely distributed between the phylogenetic clades of the ANL superfamily and show closer relationships within the subfamilies than to other aryl-CoA ligases. This, together with residual CoA ligase activity in various other enzymes of the ANL superfamily, leads to the conclusion that CoA ligases might be the ancestral proteins from which all other ANL superfamily enzymes developed.

Effect of a Hospital-wide Measure on the Readmissions Reduction Program.

BACKGROUND: The Hospital Readmissions Reduction Program penalizes hospitals that have high 30-day readmission rates across specific conditions. There is support for changing to a hospital-wide readmission measure to broaden hospital eligibility and provide incentives for improvement across more conditions. METHODS: We used Medicare claims from 2011 through 2013 to evaluate the number of hospitals that were eligible for penalties, in that they met a volume threshold of 25 admissions over a 3-year period for a specific condition or 25 admissions over a 1-year period for the cohorts included in the hospital-wide measure. We estimated the expected effects that changing from the condition-specific readmission measures to a hospital-wide measure would have on average penalties for safety-net hospitals (i.e., hospitals that treat a large proportion of low-income patients) and other hospitals. RESULTS: Our sample included 6,807,899 admissions for the hospital-wide measure and 4,392,658 admissions for the condition-specific measures. Of 3443 hospitals, 688 were considered to be safety-net hospitals. Changing to the hospital-wide measure would result in 76 more hospitals being eligible to receive penalties. The hospital-wide measure would increase penalties (mean [±SE] Medicare payment reductions across all hospitals) from 0.42±0.01% to 0.89±0.01% of Medicare base diagnosis-related-group payments. It would also increase the disparity in penalties between safety-net hospitals and other hospitals from -0.03±0.02 to 0.41±0.06 percentage points. CONCLUSIONS: A transition to a hospital-wide readmission measure would only modestly increase the number of hospitals eligible for penalties and would substantially increase the penalties for safety-net hospitals.

Learning and the "Early Joiner" Effect for Medical Conditions in Medicare's Bundled Payments for Care Improvement Program: Retrospective Cohort Study.

BACKGROUND: Studies of medical conditions in the Bundled Payments for Care Improvement (BPCI) initiative did not show reductions in Medicare payments for the majority of conditions, but this could mask heterogeneity. OBJECTIVE: To determine whether earlier enrollment and/or longer participation in BPCI were associated with performance. DESIGN: We divided BPCI hospitals into wave 1 (joined 10/1/13, 1/1/14, or 4/1/14), wave 2 (joined 7/1/14, 10/1/14, 1/1/15, or 4/1/15), and wave 3 (joined 7/1/15, 10/1/15, or 1/1/16) and compared changes in Medicare payments for acute myocardial infarction, heart failure, pneumonia, sepsis, and chronic obstructive pulmonary disease between BPCI and matched controls in 6-month increments. SUBJECTS: US hospitals. MEASURES: Medicare payments. RESULTS: There were 120 hospital-condition pairs in wave 1, 264 in wave 2, and 300 in wave 3. Wave 1 hospitals had similar savings to controls early in the program (0-6 mo difference in differences -$10, P=0.976; 6-12 mo +$295, P=0.441; 12-18 mo -$540, P=0.218; 18-24 mo -$485, P=0.259) but had greater savings than controls at 24-30 months (difference in differences -$663, P=0.035). Wave 2 (0-6 mo +$193, P=0.524; 6-12 mo -$183, P=0.489; 12-18 mo -$162, P=0.618) and wave 3 hospitals (0-6 mo +$79, P=0.753; 6-12 mo -$32, P=0.876) did not achieve significant savings at any time interval. There were no differential changes in patient outcomes over time. CONCLUSIONS: Hospitals that joined BPCI earliest began to achieve savings at roughly 2 years of participation. These findings have implications for this and other alternative payment models.

Competing barnacle species with a time dependent reproduction rate.

A simple competition model with time varying periodic coefficients, in which two species use different reproduction strategies, is explored in this paper. The two species considered comprise a native species which reproduces once a year over a short time period and an invasive species which is capable of reproducing throughout the entire year. A monotonicity property of the model is instrumental for its analysis. The model reveals that the time difference between the peak of reproduction for the two species is a critical factor in determining the outcome of competition between these species. The impact of climate change and an anthropogenic disturbance, comprising the creation of additional substrate, is also investigated using a modified model. The results of this paper describe how climate change will favour the invasive species by reducing the time period between the reproductive peaks of the two species and how the addition of new substrates is likely to endanger a small population of either of the two species, depending on the timing of the introduction of the substrates.