‘Baby Ban’ and ‘We are not the same’: Creative non-fiction dialogue exploring pregnancy and motherhood for a funded Paralympic and unfunded Olympic athlete

Elite athlete mothers are an under researched population. This is remiss given their increasing prevalence, sporting successes, and importance as role models that challenge dominant and traditional views of pregnancy and women in sport. Indeed, sporting organisations are gradually adopting policies to support elite athlete mothers. That said, elite sport is part of a complex social and economic environment that is likely to shapes support available to elite athlete mothers. Accordingly, the purpose of this study was to explore the shared and different experiences of elite athlete mothers in and out of UK national funding programmes. Longitudinal semi-structured individual interviews with two elite athlete mothers, one Paralympian receiving funding and one Olympian not in receipt of funding, were conducted. Bengtsson’s 4-stage process was adopted to analyse data through the bioecological model. Data were merged and reorganised into two creative non-fiction dialogues; ‘Baby Ban’ and ‘We are not the same’. These reveal that national funding bodies’ pregnancy and motherhood guidelines, and funding policy were found to conflict, causing confusion and unsupportive experiences. Inconsistencies appeared in the exosystem guidelines and policy, and resulted in differences in communication, pressure, and support that prompted more positive interpersonal experiences for funded athletes than unfunded. Effective communication appeared as crucial for understanding athletes' needs and to foster a supportive environment, irrespective of funding status. This study was the first to apply a creative non-fiction approach and a bioecological lens to elite athlete mothers’ postpartum experiences and explore the inequalities between those who are funded and unfunded

Racial Segregation, Income Inequality, and Mortality in US Metropolitan Areas

Evidence of the association between income inequality and mortality has been mixed. Studies indicate that growing income inequalities reflect inequalities between, rather than within, racial groups. Racial segregation may play a role. We examine the role of racial segregation on the relationship between income inequality and mortality in a cross-section of US metropolitan areas. Metropolitan areas were included if they had a population of at least 100,000 and were at least 10% black (N = 107). Deaths for the time period 1991–1999 were used to calculate age-adjusted all-cause mortality rates for each metropolitan statistical area (MSA) using direct age-adjustment techniques. Multivariate least squares regression was used to examine associations for the total sample and for blacks and whites separately. Income inequality was associated with lower mortality rates among whites and higher mortality rates among blacks. There was a significant interaction between income inequality and racial segregation. A significant graded inverse income inequality/mortality association was found for MSAs with higher versus lower levels of black–white racial segregation. Effects were stronger among whites than among blacks. A positive income inequality/mortality association was found in MSAs with higher versus lower levels of Hispanic–white segregation. Uncertainty regarding the income inequality/mortality association found in previous studies may be related to the omission of important variables such as racial segregation that modify associations differently between groups. Research is needed to further elucidate the risk and protective effects of racial segregation across groups

The stellar and sub-stellar IMF of simple and composite populations

The current knowledge on the stellar IMF is documented. It appears to become top-heavy when the star-formation rate density surpasses about 0.1Msun/(yr pc^3) on a pc scale and it may become increasingly bottom-heavy with increasing metallicity and in increasingly massive early-type galaxies. It declines quite steeply below about 0.07Msun with brown dwarfs (BDs) and very low mass stars having their own IMF. The most massive star of mass mmax formed in an embedded cluster with stellar mass Mecl correlates strongly with Mecl being a result of gravitation-driven but resource-limited growth and fragmentation induced starvation. There is no convincing evidence whatsoever that massive stars do form in isolation. Various methods of discretising a stellar population are introduced: optimal sampling leads to a mass distribution that perfectly represents the exact form of the desired IMF and the mmax-to-Mecl relation, while random sampling results in statistical variations of the shape of the IMF. The observed mmax-to-Mecl correlation and the small spread of IMF power-law indices together suggest that optimally sampling the IMF may be the more realistic description of star formation than random sampling from a universal IMF with a constant upper mass limit. Composite populations on galaxy scales, which are formed from many pc scale star formation events, need to be described by the integrated galactic IMF. This IGIMF varies systematically from top-light to top-heavy in dependence of galaxy type and star formation rate, with dramatic implications for theories of galaxy formation and evolution.Comment: 167 pages, 37 figures, 3 tables, published in Stellar Systems and Galactic Structure, Vol.5, Springer. This revised version is consistent with the published version and includes additional references and minor additions to the text as well as a recomputed Table 1. ISBN 978-90-481-8817-

Data-Driven Identification of Unusual Prescribing Behavior: Analysis and Use of an Interactive Data Tool Using 6 Months of Primary Care Data From 6500 Practices in England.

BACKGROUND: Approaches to addressing unwarranted variation in health care service delivery have traditionally relied on the prospective identification of activities and outcomes, based on a hypothesis, with subsequent reporting against defined measures. Practice-level prescribing data in England are made publicly available by the National Health Service (NHS) Business Services Authority for all general practices. There is an opportunity to adopt a more data-driven approach to capture variability and identify outliers by applying hypothesis-free, data-driven algorithms to national data sets. OBJECTIVE: This study aimed to develop and apply a hypothesis-free algorithm to identify unusual prescribing behavior in primary care data at multiple administrative levels in the NHS in England and to visualize these results using organization-specific interactive dashboards, thereby demonstrating proof of concept for prioritization approaches. METHODS: Here we report a new data-driven approach to quantify how "unusual" the prescribing rates of a particular chemical within an organization are as compared to peer organizations, over a period of 6 months (June-December 2021). This is followed by a ranking to identify which chemicals are the most notable outliers in each organization. These outlying chemicals are calculated for all practices, primary care networks, clinical commissioning groups, and sustainability and transformation partnerships in England. Our results are presented via organization-specific interactive dashboards, the iterative development of which has been informed by user feedback. RESULTS: We developed interactive dashboards for every practice (n=6476) in England, highlighting the unusual prescribing of 2369 chemicals (dashboards are also provided for 42 sustainability and transformation partnerships, 106 clinical commissioning groups, and 1257 primary care networks). User feedback and internal review of case studies demonstrate that our methodology identifies prescribing behavior that sometimes warrants further investigation or is a known issue. CONCLUSIONS: Data-driven approaches have the potential to overcome existing biases with regard to the planning and execution of audits, interventions, and policy making within NHS organizations, potentially revealing new targets for improved health care service delivery. We present our dashboards as a proof of concept for generating candidate lists to aid expert users in their interpretation of prescribing data and prioritize further investigations and qualitative research in terms of potential targets for improved performance

Staphylococcus aureus Host Cell Invasion and Virulence in Sepsis Is Facilitated by the Multiple Repeats within FnBPA

Entry of Staphylococcus aureus into the bloodstream can lead to metastatic abscess formation and infective endocarditis. Crucial to the development of both these conditions is the interaction of S. aureus with endothelial cells. In vivo and in vitro studies have shown that the staphylococcal invasin FnBPA triggers bacterial invasion of endothelial cells via a process that involves fibronectin (Fn) bridging to α5β1 integrins. The Fn-binding region of FnBPA usually contains 11 non-identical repeats (FnBRs) with differing affinities for Fn, which facilitate the binding of multiple Fn molecules and may promote integrin clustering. We thus hypothesized that multiple repeats are necessary to trigger the invasion of endothelial cells by S. aureus. To test this we constructed variants of fnbA containing various combinations of FnBRs. In vitro assays revealed that endothelial cell invasion can be facilitated by a single high-affinity, but not low-affinity FnBR. Studies using a nisin-inducible system that controlled surface expression of FnBPA revealed that variants encoding fewer FnBRs required higher levels of surface expression to mediate invasion. High expression levels of FnBPA bearing a single low affinity FnBR bound Fn but did not invade, suggesting that FnBPA affinity for Fn is crucial for triggering internalization. In addition, multiple FnBRs increased the speed of internalization, as did higher expression levels of FnBPA, without altering the uptake mechanism. The relevance of these findings to pathogenesis was demonstrated using a murine sepsis model, which showed that multiple FnBRs were required for virulence. In conclusion, multiple FnBRs within FnBPA facilitate efficient Fn adhesion, trigger rapid bacterial uptake and are required for pathogenesis

Cyclic voles and shrews and non-cyclic mice in a marginal grassland within European temperate forest

Cyclic population dynamics of small mammals are not restricted to the boreal and arctic zones of Eurasia and North America, but long-term data series from lower latitudes are still less common. We demonstrated here the presence of periodic oscillations in small mammal populations in eastern Poland using 22-year (1986–2007) trapping data from marginal meadow and river valley grasslands located in the extensive temperate woodland of Białowieża Primeval Forest. The two most common species inhabiting meadows and river valleys, root vole Microtus oeconomus and common shrew Sorex araneus, exhibited synchronous periodic changes, characterised by a 3-year time lag as indicated by an autocorrelation function. Moreover, the cycles of these two species were synchronous within both habitats. Population dynamics of the striped field mouse Apodemus agrarius was not cyclic. However, this species regularly reached maximum density 1 year before the synchronized peak of root voles and common shrews, which may suggest the existence of interspecific competition. Dynamics of all three species was dominated by direct density-dependent process, whereas delayed density dependent feedback was significant only in the root vole and common shrew. Climatic factors acting in winter and spring (affecting mainly survival and initial reproduction rates) were more important than those acting in summer and autumn and affected significantly only the common shrew. High temperatures in winter and spring had positive effects on autumn-to-autumn changes in abundance of this species, whereas deep snow in combination with high rainfall in spring negatively affected population increase rates in common shrew

Scientific Synergy between LSST and Euclid

Euclid and the Large Synoptic Survey Telescope (LSST) are poised to dramatically change the astronomy landscape early in the next decade. The combination of high-cadence, deep, wide-field optical photometry from LSST with high-resolution, wide-field optical photometry, and near-infrared photometry and spectroscopy from Euclid will be powerful for addressing a wide range of astrophysical questions. We explore Euclid/LSST synergy, ignoring the political issues associated with data access to focus on the scientific, technical, and financial benefits of coordination. We focus primarily on dark energy cosmology, but also discuss galaxy evolution, transient objects, solar system science, and galaxy cluster studies. We concentrate on synergies that require coordination in cadence or survey overlap, or would benefit from pixel-level co-processing that is beyond the scope of what is currently planned, rather than scientific programs that could be accomplished only at the catalog level without coordination in data processing or survey strategies. We provide two quantitative examples of scientific synergies: the decrease in photo-z errors (benefiting many science cases) when high-resolution Euclid data are used for LSST photo-z determination, and the resulting increase in weak-lensing signal-to-noise ratio from smaller photo-z errors. We briefly discuss other areas of coordination, including high-performance computing resources and calibration data. Finally, we address concerns about the loss of independence and potential cross-checks between the two missions and the potential consequences of not collaborating

Impact of COVID-19 on broad-spectrum antibiotic prescribing for common infections in primary care in England: a time-series analyses using OpenSAFELY and effects of predictors including deprivation.

BACKGROUND: The COVID-19 pandemic impacted the healthcare systems, adding extra pressure to reduce antimicrobial resistance. Therefore, we aimed to evaluate changes in antibiotic prescription patterns after COVID-19 started. METHODS: With the approval of NHS England, we used the OpenSAFELY platform to access the TPP SystmOne electronic health record (EHR) system in primary care and selected patients prescribed antibiotics from 2019 to 2021. To evaluate the impact of COVID-19 on broad-spectrum antibiotic prescribing, we evaluated prescribing rates and its predictors and used interrupted time series analysis by fitting binomial logistic regression models. FINDINGS: Over 32 million antibiotic prescriptions were extracted over the study period; 8.7% were broad-spectrum. The study showed increases in broad-spectrum antibiotic prescribing (odds ratio [OR] 1.37; 95% confidence interval [CI] 1.36-1.38) as an immediate impact of the pandemic, followed by a gradual recovery with a 1.1-1.2% decrease in odds of broad-spectrum prescription per month. The same pattern was found within subgroups defined by age, sex, region, ethnicity, and socioeconomic deprivation quintiles. More deprived patients were more likely to receive broad-spectrum antibiotics, which differences remained stable over time. The most significant increase in broad-spectrum prescribing was observed for lower respiratory tract infection (OR 2.33; 95% CI 2.1-2.50) and otitis media (OR 1.96; 95% CI 1.80-2.13). INTERPRETATION: An immediate reduction in antibiotic prescribing and an increase in the proportion of broad-spectrum antibiotic prescribing in primary care was observed. The trends recovered to pre-pandemic levels, but the consequence of the COVID-19 pandemic on AMR needs further investigation. FUNDING: This work was supported by Health Data Research UK and by National Institute for Health Research

The impact of COVID-19 on antibiotic prescribing in primary care in England: Evaluation and risk prediction of appropriateness of type and repeat prescribing.

BACKGROUND: This study aimed to predict risks of potentially inappropriate antibiotic type and repeat prescribing and assess changes during COVID-19. METHODS: With the approval of NHS England, we used OpenSAFELY platform to access the TPP SystmOne electronic health record (EHR) system and selected patients prescribed antibiotics from 2019 to 2021. Multinomial logistic regression models predicted patient's probability of receiving inappropriate antibiotic type or repeat antibiotic course for each common infection. RESULTS: The population included 9.1 million patients with 29.2 million antibiotic prescriptions. 29.1% of prescriptions were identified as repeat prescribing. Those with same day incident infection coded in the EHR had considerably lower rates of repeat prescribing (18.0%) and 8.6% had potentially inappropriate type. No major changes in the rates of repeat antibiotic prescribing during COVID-19 were found. In the 10 risk prediction models, good levels of calibration and moderate levels of discrimination were found. CONCLUSIONS: Our study found no evidence of changes in level of inappropriate or repeat antibiotic prescribing after the start of COVID-19. Repeat antibiotic prescribing was frequent and varied according to regional and patient characteristics. There is a need for treatment guidelines to be developed around antibiotic failure and clinicians provided with individualised patient information

Trends, variation, and clinical characteristics of recipients of antiviral drugs and neutralising monoclonal antibodies for covid-19 in community settings: retrospective, descriptive cohort study of 23.4 million people in OpenSAFELY

Objective: To ascertain patient eligibility status and describe coverage of antiviral drugs and neutralising monoclonal antibodies (nMAB) as treatment for covid-19 in community settings in England. Design: Retrospective, descriptive cohort study, approved by NHS England. Setting: Routine clinical data from 23.4 million people linked to data on covid-19 infection and treatment, within the OpenSAFELY-TPP database. Participants: Outpatients with covid-19 at high risk of severe outcomes. Interventions: Nirmatrelvir/ritonavir (paxlovid), sotrovimab, molnupiravir, casirivimab/imdevimab, or remdesivir, used in the community by covid-19 medicine delivery units. Results: 93 870 outpatients with covid-19 were identified between 11 December 2021 and 28 April 2022 to be at high risk of severe outcomes and therefore potentially eligible for antiviral or nMAB treatment (or both). Of these patients, 19 040 (20%) received treatment (sotrovimab, 9660 (51%); molnupiravir, 4620 (24%); paxlovid, 4680 (25%); casirivimab/imdevimab, 50 (<1%); and remdesivir, 30 (<1%)). The proportion of patients treated increased from 9% (190/2220) in the first week of treatment availability to 29% (460/1600) in the latest week. The proportion treated varied by high risk group, being lowest in those with liver disease (16%; 95% confidence interval 15% to 17%); by treatment type, with sotrovimab favoured over molnupiravir and paxlovid in all but three high risk groups (Down's syndrome (35%; 30% to 39%), rare neurological conditions (45%; 43% to 47%), and immune deficiencies (48%; 47% to 50%)); by age, ranging from ≥80 years (13%; 12% to 14%) to 50-59 years (23%; 22% to 23%); by ethnic group, ranging from black (11%; 10% to 12%) to white (21%; 21% to 21%); by NHS region, ranging from 13% (12% to 14%) in Yorkshire and the Humber to 25% (24% to 25%) in the East of England); and by deprivation level, ranging from 15% (14% to 15%) in the most deprived areas to 23% (23% to 24%) in the least deprived areas. Groups that also had lower coverage included unvaccinated patients (7%; 6% to 9%), those with dementia (6%; 5% to 7%), and care home residents (6%; 6% to 7%). Conclusions: Using the OpenSAFELY platform, we were able to identify patients with covid-19 at high risk of severe outcomes who were potentially eligible to receive treatment and assess the coverage of these new treatments among these patients. In the context of a rapid deployment of a new service, the NHS analytical code used to determine eligibility could have been over-inclusive and some of the eligibility criteria not fully captured in healthcare data. However targeted activity might be needed to resolve apparent lower treatment coverage observed among certain groups, in particular (at present): different NHS regions, ethnic groups, people aged ≥80 years, those living in socioeconomically deprived areas, and care home residents