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Artificial Intelligence (AI) has become a disruptive technology, promising to grant a significant economic and strategic advantage to nations that harness its power. China, with its recent push towards AI adoption, is challenging the U.S.'s position as the global leader in this field. Given AI's massive potential, as well as the fierce geopolitical tensions between China and the U.S., several recent policies have been put in place to discourage AI scientists from migrating to, or collaborating with, the other nation. Nevertheless, the extent of talent migration and cross-border collaboration are not fully understood. Here, we analyze a dataset of over 350,000 AI scientists and 5,000,000 AI papers. We find that since 2000, China and the U.S. have led the field in terms of impact, novelty, productivity, and workforce. Most AI scientists who move to China come from the U.S., and most who move to the U.S. come from China, highlighting a notable bidirectional talent migration. Moreover, the vast majority of those moving in either direction have Asian ancestry. Upon moving, those scientists continue to collaborate frequently with those in the origin country. Although the number of collaborations between the two countries has increased since the dawn of the millennium, such collaborations continue to be relatively rare. A matching experiment reveals that the two countries have always been more impactful when collaborating than when each works without the other. These findings suggest that instead of suppressing cross-border migration and collaboration between the two nations, the science could benefit from promoting such activities., Competing Interests: Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Natural disasters trigger complex chains of events within human societies 1 . Immediate deaths and damage are directly observed after a disaster and are widely studied, but delayed downstream outcomes, indirectly caused by the disaster, are difficult to trace back to the initial event 1,2 . Tropical cyclones (TCs)-that is, hurricanes and tropical storms-are widespread globally and have lasting economic impacts 3-5 , but their full health impact remains unknown. Here we conduct a large-scale evaluation of long-term effects of TCs on human mortality in the contiguous United States (CONUS) for all TCs between 1930 and 2015. We observe a robust increase in excess mortality that persists for 15 years after each geophysical event. We estimate that the average TC generates 7,000-11,000 excess deaths, exceeding the average of 24 immediate deaths reported in government statistics 6,7 . Tracking the effects of 501 historical storms, we compute that the TC climate of CONUS imposes an undocumented mortality burden that explains a substantial fraction of the higher mortality rates along the Atlantic coast and is equal to roughly 3.2-5.1% of all deaths. These findings suggest that the TC climate, previously thought to be unimportant for broader public health outcomes, is a meaningful underlying driver for the distribution of mortality risk in CONUS, especially among infants (less than 1 year of age), people 1-44 years of age, and the Black population. Understanding why TCs induce this excess mortality is likely to yield substantial health benefits., (© 2024. The Author(s).)

Twenty genetic therapies have been approved by the US Food and Drug Administration to date, a number that now includes the first CRISPR genome-editing therapy for sickle cell disease-CASGEVY (exagamglogene autotemcel, Vertex Pharmaceuticals). This extraordinary milestone is widely celebrated owing to the promise for future genome-editing treatments of previously intractable genetic disorders and cancers. At the same time, such genetic therapies are the most expensive drugs on the market, with list prices exceeding US$4 million per patient. Although all approved cell and gene therapies trace their origins to academic or government research institutions, reliance on for-profit pharmaceutical companies for subsequent development and commercialization results in prices that prioritize recouping investments, paying for candidate product failures and meeting investor and shareholder expectations. To increase affordability and access, sustainable discovery-to-market alternatives are needed that address system-wide deficiencies. Here we present recommendations of a multidisciplinary task force assembled to chart such a path. We describe a pricing structure that, once implemented, could reduce per-patient cost tenfold and propose a business model that distributes responsibilities while leveraging diverse funding sources. We also outline how academic licensing provisions, manufacturing innovation and supportive regulations can reduce cost and enable broader patient treatment., (© 2024. Springer Nature Limited.)

Schools in the United States remain heavily segregated by race and income. Previous work demonstrates districts can promote group diversity within their schools with policies like redrawing attendance zones. Yet, the promise of such policies in many areas is limited by the fact that most school segregation occurs between school districts, and not between schools in the same district. I adapt Markov Chain Monte Carlo algorithms from legislative redistricting to redraw school district boundaries that decrease segregation while maintaining desirable criteria like distance to school and using only existing school facilities. Focusing on New Jersey, where the segregation of Black and Hispanic students from White and Asian students is among the worst in the country, I demonstrate that redrawing school districts could reduce more than 40% of existing segregation in the median New Jersey county, compared to less than 5% for redrawing attendance zones alone. Finally, I show how my proposed methodology can be applied to as few as two districts to reduce segregation in proposed consolidations, when small districts are merged into a larger district., (© 2024. The Author(s).)

During the start of the COVID-19 pandemic in 2020, lockdowns and movement restrictions were thought to negatively impact population mental health, since depression and anxiety symptoms were frequently reported. This study investigates the effect of COVID-19 mitigation measures on mental health across the United States, at county and state levels using difference-in-differences analysis. It examines the effect on mental health facility usage and the prevalence of mental illnesses, drawing on large-scale medical claims data for mental health patients joined with publicly available state- and county-specific COVID-19 cases and lockdown information. For consistency, the main focus is on two types of social distancing policies, stay-at-home and school closure orders. Results show that lockdown has significantly and causally increased the usage of mental health facilities in regions with lockdowns in comparison to regions without such lockdowns. Particularly, resource usage increased by 18% in regions with a lockdown compared to 1% decline in regions without a lockdown. Also, female populations have been exposed to a larger lockdown effect on their mental health. Diagnosis of panic disorders and reaction to severe stress significantly increased by the lockdown. Mental health was more sensitive to lockdowns than to the presence of the pandemic itself. The effects of the lockdown increased over an extended time to the end of December 2020., (© 2024. The Author(s).)

The sea level along the US coastlines is projected to rise by 0.25-0.3 m by 2050, increasing the probability of more destructive flooding and inundation in major cities 1-3 . However, these impacts may be exacerbated by coastal subsidence-the sinking of coastal land areas 4 -a factor that is often underrepresented in coastal-management policies and long-term urban planning 2,5 . In this study, we combine high-resolution vertical land motion (that is, raising or lowering of land) and elevation datasets with projections of sea-level rise to quantify the potential inundated areas in 32 major US coastal cities. Here we show that, even when considering the current coastal-defence structures, further land area of between 1,006 and 1,389 km 2 is threatened by relative sea-level rise by 2050, posing a threat to a population of 55,000-273,000 people and 31,000-171,000 properties. Our analysis shows that not accounting for spatially variable land subsidence within the cities may lead to inaccurate projections of expected exposure. These potential consequences show the scale of the adaptation challenge, which is not appreciated in most US coastal cities., (© 2024. The Author(s).)

Social capital-the strength of an individual's social network and community-has been identified as a potential determinant of outcomes ranging from education to health 1-8 . However, efforts to understand what types of social capital matter for these outcomes have been hindered by a lack of social network data. Here, in the first of a pair of papers 9 , we use data on 21 billion friendships from Facebook to study social capital. We measure and analyse three types of social capital by ZIP (postal) code in the United States: (1) connectedness between different types of people, such as those with low versus high socioeconomic status (SES); (2) social cohesion, such as the extent of cliques in friendship networks; and (3) civic engagement, such as rates of volunteering. These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analysing their associations with economic mobility across areas. The share of high-SES friends among individuals with low SES-which we term economic connectedness-is among the strongest predictors of upward income mobility identified to date 10,11 . Other social capital measures are not strongly associated with economic mobility. If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality 12-14 . To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at https://www.socialcapital.org ., (© 2022. The Author(s).)

Low levels of social interaction across class lines have generated widespread concern 1-4 and are associated with worse outcomes, such as lower rates of upward income mobility 4-7 . Here we analyse the determinants of cross-class interaction using data from Facebook, building on the analysis in our companion paper 7 . We show that about half of the social disconnection across socioeconomic lines-measured as the difference in the share of high-socioeconomic status (SES) friends between people with low and high SES-is explained by differences in exposure to people with high SES in groups such as schools and religious organizations. The other half is explained by friending bias-the tendency for people with low SES to befriend people with high SES at lower rates even conditional on exposure. Friending bias is shaped by the structure of the groups in which people interact. For example, friending bias is higher in larger and more diverse groups and lower in religious organizations than in schools and workplaces. Distinguishing exposure from friending bias is helpful for identifying interventions to increase cross-SES friendships (economic connectedness). Using fluctuations in the share of students with high SES across high school cohorts, we show that increases in high-SES exposure lead low-SES people to form more friendships with high-SES people in schools that exhibit low levels of friending bias. Thus, socioeconomic integration can increase economic connectedness in communities in which friending bias is low. By contrast, when friending bias is high, increasing cross-SES interactions among existing members may be necessary to increase economic connectedness. To support such efforts, we release privacy-protected statistics on economic connectedness, exposure and friending bias for each ZIP (postal) code, high school and college in the United States at https://www.socialcapital.org ., (© 2022. The Author(s).)

We sought to assess if COVID-19 infection recovery is associated with increased rates of newly diagnosed erectile dysfunction. Using IBM MarketScan, a commercial claims database, men with prior COVID-19 infection were identified using ICD-10 diagnosis codes. Using this cohort along with an age-matched cohort of men without prior COVID-19 infection, we assessed the incidence of newly diagnosed erectile dysfunction. Covariates were assessed using a multivariable model to determine association of prior COVID-19 infection with newly diagnosed erectile dysfunction. 42,406 men experienced a COVID-19 infection between January 2020 and January 2021 of which 601 (1.42%) developed new onset erectile dysfunction within 6.5 months follow up. On multivariable analysis while controlling for diabetes, cardiovascular disease, smoking, obesity, hypogonadism, thromboembolism, and malignancy, prior COVID-19 infection was associated with increased risk of new onset erectile dysfunction (HR 1.27; 95% CI 1.1-1.5; P = 0.002). Prior to the widespread implementation of the COVID-19 vaccine, the incidence of newly diagnosed erectile dysfunction is higher in men with prior COVID-19 infection compared to age-matched controls. Prior COVID-19 infection was associated with a 27% increased likelihood of developing new-onset erectile dysfunction when compared to those without prior infection., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

The move from reading to writing the human genome offers new opportunities to improve human health. The United States National Institutes of Health (NIH) Somatic Cell Genome Editing (SCGE) Consortium aims to accelerate the development of safer and more-effective methods to edit the genomes of disease-relevant somatic cells in patients, even in tissues that are difficult to reach. Here we discuss the consortium's plans to develop and benchmark approaches to induce and measure genome modifications, and to define downstream functional consequences of genome editing within human cells. Central to this effort is a rigorous and innovative approach that requires validation of the technology through third-party testing in small and large animals. New genome editors, delivery technologies and methods for tracking edited cells in vivo, as well as newly developed animal models and human biological systems, will be assembled-along with validated datasets-into an SCGE Toolkit, which will be disseminated widely to the biomedical research community. We visualize this toolkit-and the knowledge generated by its applications-as a means to accelerate the clinical development of new therapies for a wide range of conditions.

The social cost of methane (SC-CH 4 ) measures the economic loss of welfare caused by emitting one tonne of methane into the atmosphere. This valuation may in turn be used in cost-benefit analyses or to inform climate policies 1-3 . However, current SC-CH 4 estimates have not included key scientific findings and observational constraints. Here we estimate the SC-CH 4 by incorporating the recent upward revision of 25 per cent to calculations of the radiative forcing of methane 4 , combined with calibrated reduced-form global climate models and an ensemble of integrated assessment models (IAMs). Our multi-model mean estimate for the SC-CH 4 is US$933 per tonne of CH 4 (5-95 per cent range, US$471-1,570 per tonne of CH 4 ) under a high-emissions scenario (Representative Concentration Pathway (RCP) 8.5), a 22 per cent decrease compared to estimates based on the climate uncertainty framework used by the US federal government 5 . Our ninety-fifth percentile estimate is 51 per cent lower than the corresponding figure from the US framework. Under a low-emissions scenario (RCP 2.6), our multi-model mean decreases to US$710 per tonne of CH 4 . Tightened equilibrium climate sensitivity estimates paired with the effect of previously neglected relationships between uncertain parameters of the climate model lower these estimates. We also show that our SC-CH 4 estimates are sensitive to model combinations; for example, within one IAM, different methane cycle sub-models can induce variations of approximately 20 per cent in the estimated SC-CH 4 . But switching IAMs can more than double the estimated SC-CH 4 . Extending our results to account for societal concerns about equity produces SC-CH 4 estimates that differ by more than an order of magnitude between low- and high-income regions. Our central equity-weighted estimate for the USA increases to US$8,290 per tonne of CH 4 whereas our estimate for sub-Saharan Africa decreases to US$134 per tonne of CH 4 .

We study biological aging of elderly U.S. Americans born 1904-1966. We use thirteen waves of the Health and Retirement Study and construct a frailty index as the number of health deficits present in a person measured relative to the number of potential deficits. We find that, on average, Americans develop 5% more health deficits per year, that men age slightly faster than women, and that, at any age above 50, Caucasians display significantly fewer health deficits than African Americans. We also document a steady time trend of health improvements. For each year of later birth, health deficits decline on average by about 1%. This health trend is about the same across regions and for men and women, but significantly lower for African Americans compared to Caucasians. In non-linear regressions, we find that regional differences in aging follow a particular regularity, akin to the compensation effect of mortality. Health deficits converge for men and women and across American regions and suggest a life span of the American population of about 97 years.

There is debate over whether Asian American students face additional barriers, relative to white students, when applying to selective colleges. Here we present the results from analyzing 685,709 applications submitted over five application cycles to 11 highly selective colleges (the "Ivy-11"). We estimate that Asian American applicants had 28% lower odds of ultimately attending an Ivy-11 school than white applicants with similar academic and extracurricular qualifications. The gap was particularly pronounced for students of South Asian descent (49% lower odds). Given the high yield rates and competitive financial aid policies of the schools we consider, the disparity in attendance rates is likely driven, at least in part, by admissions decisions. In particular, we offer evidence that this pattern stems from two factors. First, many selective colleges give preference to the children of alumni in admissions. We find that white applicants were substantially more likely to have such legacy status than Asian applicants. Second, we identify geographic disparities potentially reflective of admissions policies that disadvantage students from certain regions of the United States. We hope these results inform discussions on equity in higher education., (© 2024. The Author(s).)

Gene therapy is a new class of medical treatment that alters part of a patient's genome through the replacement, deletion, or insertion of genetic material. While still in its infancy, gene therapy has demonstrated immense potential to treat and even cure previously intractable diseases. Nevertheless, existing gene therapy prices are high, raising concerns about its affordability for U.S. payers and its availability to patients. We assess the potential financial impact of novel gene therapies by developing and implementing an original simulation model which entails the following steps: identifying the 109 late-stage gene therapy clinical trials underway before January 2020, estimating the prevalence and incidence of their corresponding diseases, applying a model of the increase in quality-adjusted life years for each therapy, and simulating the launch prices and expected spending of all available gene therapies annually. The results of our simulation suggest that annual spending on gene therapies will be approximately $20.4 billion, under conservative assumptions. We decompose the estimated spending by treated age group as a proxy for insurance type, finding that approximately one-half of annual spending will on the use of gene therapies to treat non-Medicare-insured adults and children. We conduct multiple sensitivity analyses regarding our assumptions and model parameters. We conclude by considering the tradeoffs of different payment methods and policies that intend to ensure patient access to the expected benefits of gene therapy., (© 2023. The Author(s).)

Steady improvements in ambient air quality in the USA over the past several decades, in part a result of public policy 1,2 , have led to public health benefits 1-4 . However, recent trends in ambient concentrations of particulate matter with diameters less than 2.5 μm (PM 2.5 ), a pollutant regulated under the Clean Air Act 1 , have stagnated or begun to reverse throughout much of the USA 5 . Here we use a combination of ground- and satellite-based air pollution data from 2000 to 2022 to quantify the contribution of wildfire smoke to these PM 2.5 trends. We find that since at least 2016, wildfire smoke has influenced trends in average annual PM 2.5 concentrations in nearly three-quarters of states in the contiguous USA, eroding about 25% of previous multi-decadal progress in reducing PM 2.5 concentrations on average in those states, equivalent to 4 years of air quality progress, and more than 50% in many western states. Smoke influence on trends in the number of days with extreme PM 2.5 concentrations is detectable by 2011, but the influence can be detected primarily in western and mid-western states. Wildfire-driven increases in ambient PM 2.5 concentrations are unregulated under current air pollution law 6 and, in the absence of further interventions, we show that the contribution of wildfire to regional and national air quality trends is likely to grow as the climate continues to warm., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

This study compared the ability of guideline-proposed office blood pressure (OBP) screening thresholds [European Society of Hypertension (ESH) guidelines: 130/85 mmHg for individuals with an OBP < 140/90 mmHg; American College of Cardiology/American Heart Association (ACC/AHA) guidelines: 120/75 mmHg for individuals with an OBP < 130/80 mmHg] and novel screening scores to identify normotensive individuals at high risk of having masked hypertension (MH) in an office setting. We cross-sectionally evaluated untreated participants with an OBP < 140/90 mmHg (n = 22,266) and an OBP < 130/80 mmHg (n = 10,005) who underwent home blood pressure monitoring (HBPM) (derivation cohort) from 686 Brazilian sites. MH was defined according to criteria suggested by the ESH (OBP < 140/90 mmHg; HBPM ≥ 135/85 mmHg), Brazilian Society of Cardiology (BSC) (OBP < 140/90 mmHg; HBPM ≥ 130/80 mmHg) and ACC/AHA (OBP < 130/80 mmHg; HBPM ≥ 130/80 mmHg). Scores were generated from multivariable logistic regression coefficients between MH and clinical variables (OBP, age, sex, and BMI). Considering the ESH, BSC, and ACC/AHA criteria, 17.2%, 38.5%, and 21.2% of the participants had MH, respectively. Guideline-proposed OBP screening thresholds yielded area under curve (AUC) values of 0.640 (for ESH criteria), 0.641 (for BSC criteria), and 0.619 (for ACC/AHA criteria) for predicting MH, while scores presented as continuous variables or quartiles yielded AUC values of 0.700 and 0.688 (for ESH criteria), 0.720 and 0.709 (for BSC criteria), and 0.671 and 0.661 (for ACC/AHA criteria), respectively. Further analyses performed with alternative untreated participants (validation cohort; n = 2807 with an OBP < 140/90 mmHg; n = 1269 with an OBP < 130/80 mmHg) yielded similar AUC values. In conclusion, the accuracy of guideline-proposed OBP screening thresholds in identifying individuals at high risk of having MH in an office setting is limited and is inferior to that yielded by scores derived from simple clinical variables., (© 2022. The Author(s), under exclusive licence to The Japanese Society of Hypertension.)

Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. IMPACT: Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research., (© 2022. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.)

Faculty hiring and retention determine the composition of the US academic workforce and directly shape educational outcomes 1 , careers 2 , the development and spread of ideas 3 and research priorities 4,5 . However, hiring and retention are dynamic, reflecting societal and academic priorities, generational turnover and efforts to diversify the professoriate along gender 6-8 , racial 9 and socioeconomic 10 lines. A comprehensive study of the structure and dynamics of the US professoriate would elucidate the effects of these efforts and the processes that shape scholarship more broadly. Here we analyse the academic employment and doctoral education of tenure-track faculty at all PhD-granting US universities over the decade 2011-2020, quantifying stark inequalities in faculty production, prestige, retention and gender. Our analyses show universal inequalities in which a small minority of universities supply a large majority of faculty across fields, exacerbated by patterns of attrition and reflecting steep hierarchies of prestige. We identify markedly higher attrition rates among faculty trained outside the United States or employed by their doctoral university. Our results indicate that gains in women's representation over this decade result from demographic turnover and earlier changes made to hiring, and are unlikely to lead to long-term gender parity in most fields. These analyses quantify the dynamics of US faculty hiring and retention, and will support efforts to improve the organization, composition and scholarship of the US academic workforce., (© 2022. The Author(s).)

The American Rescue Plan provides a pathway for states to expand postpartum Medicaid coverage for low-income mothers through 12 months after delivery. Data suggests that extension of post-partum Medicaid coverage should improve access to outpatient health care services, increase healthcare utilization, improve chronic disease management for at-risk mothers, and reduce disparities in care for racial/ethnic groups over-represented in Medicaid. Opportunities to provide increased preventive care for perinatal mood disorders and smoking cessation also exist. Further, this policy may reduce the burden of late maternal mortality. While improved access to contraceptive service postpartum provides a potential mechanism by which birth outcomes may improve, the effect of this policy on NICU admission, low birth weight (LBW) infants, and preterm birth is unknown. We discuss possible birth, infant and maternal health outcomes which may result from this expansion, drawing on data from the 2010 Medicaid Expansion via the Affordable Care Act., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Global warming is expected to enhance drought extremes in the United States throughout the twenty-first century. Projecting these changes can be complex in regions with large variability in atmospheric and soil moisture on small spatial scales. Vapor Pressure Deficit (VPD) is a valuable measure of evaporative demand as moisture moves from the surface into the atmosphere and a dynamic measure of drought. Here, VPD is used to identify short-term drought with the Standardized VPD Drought Index (SVDI); and used to characterize future extreme droughts using grid dependent stationary and non-stationary generalized extreme value (GEV) models, and a random sampling technique is developed to quantify multimodel uncertainties. The GEV analysis was performed with projections using the Weather Research and Forecasting model, downscaled from three Global Climate Models based on the Representative Concentration Pathway 8.5 for present, mid-century and late-century. Results show the VPD based index (SVDI) accurately identifies the timing and magnitude short-term droughts, and extreme VPD is increasing across the United States and by the end of the twenty-first century. The number of days VPD is above 9 kPa increases by 10 days along California's coastline, 30-40 days in the northwest and Midwest, and 100 days in California's Central Valley., (© 2022. The Author(s).)

Objective: Our objective was to assess differences in pregnancy outcomes during the COVID-19 pandemic compared to the previous year., Study Design: In a cross-sectional study of delivery hospitalizations in the Premier Healthcare Database Special COVID-19 Release, we assessed differences in selected maternal and pregnancy outcomes occurring April-December in 2019 and 2020 in the United States., Result: Among 663,620 deliveries occurring in 2019 and 614,093 deliveries occurring in 2020, we observed an increase in in-hospital maternal death from 2019 to 2020, which was no longer statistically significant after excluding deliveries with a COVID-19 diagnosis. Intensive care unit admission and preterm birth decreased from 2019 to 2020. There was no difference in the prevalence of most other outcomes examined., Conclusion: The full impact of the COVID-19 pandemic on maternal and pregnancy outcomes remains to be understood. Most outcomes investigated experienced minimal change from 2019 to 2020., (© 2022. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

The acute clinical manifestations of COVID-19 have been well characterized 1,2 , but the post-acute sequelae of this disease have not been comprehensively described. Here we use the national healthcare databases of the US Department of Veterans Affairs to systematically and comprehensively identify 6-month incident sequelae-including diagnoses, medication use and laboratory abnormalities-in patients with COVID-19 who survived for at least 30 days after diagnosis. We show that beyond the first 30 days of illness, people with COVID-19 exhibit a higher risk of death and use of health resources. Our high-dimensional approach identifies incident sequelae in the respiratory system, as well as several other sequelae that include nervous system and neurocognitive disorders, mental health disorders, metabolic disorders, cardiovascular disorders, gastrointestinal disorders, malaise, fatigue, musculoskeletal pain and anaemia. We show increased incident use of several therapeutic agents-including pain medications (opioids and non-opioids) as well as antidepressant, anxiolytic, antihypertensive and oral hypoglycaemic agents-as well as evidence of laboratory abnormalities in several organ systems. Our analysis of an array of prespecified outcomes reveals a risk gradient that increases according to the severity of the acute COVID-19 infection (that is, whether patients were not hospitalized, hospitalized or admitted to intensive care). Our findings show that a substantial burden of health loss that spans pulmonary and several extrapulmonary organ systems is experienced by patients who survive after the acute phase of COVID-19. These results will help to inform health system planning and the development of multidisciplinary care strategies to reduce chronic health loss among individuals with COVID-19.

Objective: Inequity between genders with regards to leadership achievement, compensation, scholarly productivity, and grant funding exist among physicians. This study explores whether similar inequities exist among board certified neonatologists within the USA., Study Design: A voluntary anonymous survey was distributed to 3575 members of the American Academy of Pediatrics Section on Neonatal-Perinatal Medicine with 560 respondents (15.7% response rate). The survey contained questions assessing clinician characteristics, work environment, compensation, professional productivity, and social factors. Statistical analysis was done using JMP Pro 15.0.0 by SAS., Results: Female neonatologists, compared to male peers, were less likely to hold leadership positions (OR 0.36, p = 0.005), received lower compensation by an average of $34,916 or 12.47% (p < 0.001), and had 6.71 fewer primary authored publications (p = 0.025) after adjusting for several confounding factors., Conclusion: Gender remains a significant independent factor influencing leadership attainment, compensation, and academic productivity in this cohort of neonatologists.

Very low birth weight (VLBW; <1500 g birth weight) infants are substantially more likely to be born to black than to non-black mothers, predisposing them to potentially preventable morbidities that increase the risk for costly lifelong health problems. Mothers' own milk (MOM) may be considered the ultimate "personalized medicine" since milk composition and bioactive components vary among mothers and multiple milk constituents provide specific protection based on shared exposures between mother and infant. MOM feedings reduce the risks and associated costs of prematurity-associated morbidities, with the greatest reduction afforded by MOM through to NICU discharge. Although black and non-black mothers have similar lactation goals and initiation rates, black VLBW infants are half as likely to receive MOM at NICU discharge in the United States. Black mothers are significantly more likely to be low-income, single heads of household and have more children in the home, increasing the burden of MOM provision. Although rarely considered, the out-of-pocket and opportunity costs associated with providing MOM for VLBW infants are especially onerous for black mothers. When MOM is not available, the NICU assumes the costs of inferior substitutes for MOM, contributing further to disparate outcomes. Novel strategies to mitigate these disparities are urgently needed. IMPACT: Mother's own milk exemplifies personalized medicine through its unique biologic activity. Hospital factors and social determinants of health are associated with mother's own milk feedings for very low-birth-weight infants in the neonatal intensive care unit. Notably, out-of-pocket and opportunity costs associated with providing mother's own milk are borne by mothers. Conceptualizing mother's own milk feedings as an integral part of NICU care requires consideration of who bears the costs of MOM provision-the mother or the NICU?

Medical folk wisdom (MFW) refers to widely held, but factually inaccurate, beliefs about disease, immunity, pregnancy, and other medically-relevant topics. Examples include the idea that fasting when feverish ("starving a fever") can increase the pace of recovery, or that showering after sex can prevent pregnancy. The pervasiveness of MFW, and whether or not it-like other forms of medically-relevant misinformation-shapes Americans' health behaviors and policy preferences is an important and under-studied question. We begin this research by proposing and validating a novel measure of MFW; including a short-form scale suitable for administration in public opinion surveys. We find that nearly all Americans-irrespective of socio-economic status, political orientation, and educational background-endorse at least some aspects of MFW. Concerningly, and consistent with the idea that folk wisdom challenges scientific expertise, we additionally find that those highest in MFW tend to place less value on medical expertise and the role experts play in shaping health policy. However, this skepticism does not appear to translate to peoples' health actions, as MFW appears to have an inconsistent effect on public participation in healthy behaviors.

Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful 1 . Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives 2 . Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening.

Human achievements are often preceded by repeated attempts that fail, but little is known about the mechanisms that govern the dynamics of failure. Here, building on previous research relating to innovation 1-7 , human dynamics 8-11 and learning 12-17 , we develop a simple one-parameter model that mimics how successful future attempts build on past efforts. Solving this model analytically suggests that a phase transition separates the dynamics of failure into regions of progression or stagnation and predicts that, near the critical threshold, agents who share similar characteristics and learning strategies may experience fundamentally different outcomes following failures. Above the critical point, agents exploit incremental refinements to systematically advance towards success, whereas below it, they explore disjoint opportunities without a pattern of improvement. The model makes several empirically testable predictions, demonstrating that those who eventually succeed and those who do not may initially appear similar, but can be characterized by fundamentally distinct failure dynamics in terms of the efficiency and quality associated with each subsequent attempt. We collected large-scale data from three disparate domains and traced repeated attempts by investigators to obtain National Institutes of Health (NIH) grants to fund their research, innovators to successfully exit their startup ventures, and terrorist organizations to claim casualties in violent attacks. We find broadly consistent empirical support across all three domains, which systematically verifies each prediction of our model. Together, our findings unveil detectable yet previously unknown early signals that enable us to identify failure dynamics that will lead to ultimate success or failure. Given the ubiquitous nature of failure and the paucity of quantitative approaches to understand it, these results represent an initial step towards the deeper understanding of the complex dynamics underlying failure.

A case of admitted scientific fraud has shed new light on the system that ensures the integrity of the scientific literature. Certain lapses from generally accepted standards of research may be more frequent than is commonly believed.