Assessment of Embedding Schemes in a Hybrid Machine Learning/Classical Potentials (ML/MM) Approach.

Machine learning (ML) methods have reached high accuracy levels for the prediction of in vacuo molecular properties. However, the simulation of large systems solely through ML methods (such as those based on neural network potentials) is still a challenge. In this context, one of the most promising frameworks for integrating ML schemes in the simulation of complex molecular systems are the so-called ML/MM methods. These multiscale approaches combine ML methods with classical force fields (MM), in the same spirit as the successful hybrid quantum mechanics-molecular mechanics methods (QM/MM). The key issue for such ML/MM methods is an adequate description of the coupling between the region of the system described by ML and the region described at the MM level. In the context of QM/MM schemes, the main ingredient of the interaction is electrostatic, and the state of the art is the so-called electrostatic-embedding. In this study, we analyze the quality of simpler mechanical embedding-based approaches, specifically focusing on their application within a ML/MM framework utilizing atomic partial charges derived in vacuo. Taking as reference electrostatic embedding calculations performed at a QM(DFT)/MM level, we explore different atomic charges schemes, as well as a polarization correction computed using atomic polarizabilites. Our benchmark data set comprises a set of about 80k small organic structures from the ANI-1x and ANI-2x databases, solvated in water. The results suggest that the minimal basis iterative stockholder (MBIS) atomic charges yield the best agreement with the reference coupling energy. Remarkable enhancements are achieved by including a simple polarization correction.

PresRecST: a novel herbal prescription recommendation algorithm for real-world patients with integration of syndrome differentiation and treatment planning.

OBJECTIVES: Herbal prescription recommendation (HPR) is a hot topic and challenging issue in field of clinical decision support of traditional Chinese medicine (TCM). However, almost all previous HPR methods have not adhered to the clinical principles of syndrome differentiation and treatment planning of TCM, which has resulted in suboptimal performance and difficulties in application to real-world clinical scenarios. MATERIALS AND METHODS: We emphasize the synergy among diagnosis and treatment procedure in real-world TCM clinical settings to propose the PresRecST model, which effectively combines the key components of symptom collection, syndrome differentiation, treatment method determination, and herb recommendation. This model integrates a self-curated TCM knowledge graph to learn the high-quality representations of TCM biomedical entities and performs 3 stages of clinical predictions to meet the principle of systematic sequential procedure of TCM decision making. RESULTS: To address the limitations of previous datasets, we constructed the TCM-Lung dataset, which is suitable for the simultaneous training of the syndrome differentiation, treatment method determination, and herb recommendation. Overall experimental results on 2 datasets demonstrate that the proposed PresRecST outperforms the state-of-the-art algorithm by significant improvements (eg, improvements of P@5 by 4.70%, P@10 by 5.37%, P@20 by 3.08% compared with the best baseline). DISCUSSION: The workflow of PresRecST effectively integrates the embedding vectors of the knowledge graph for progressive recommendation tasks, and it closely aligns with the actual diagnostic and treatment procedures followed by TCM doctors. A series of ablation experiments and case study show the availability and interpretability of PresRecST, indicating the proposed PresRecST can be beneficial for assisting the diagnosis and treatment in real-world TCM clinical settings. CONCLUSION: Our technology can be applied in a progressive recommendation scenario, providing recommendations for related items in a progressive manner, which can assist in providing more reliable diagnoses and herbal therapies for TCM clinical task.

Using Standardised International Oral Health-Related Datasets in 6 Countries.

INTRODUCTION: Oral diseases affect a significant proportion of the world's population, yet international comparisons involving oral health outcomes have often been limited due to differences in the way country-level primary data are collected. In response to this, the World Dental Federation (FDI) Oral Health Observatory project was launched with the goal of collecting and producing standardised international data on oral health across countries. The aim of this descriptive cross-sectional study was to examine associations between self-reported general health and a range of factors (sociodemographics, oral health-related behaviours, oral impacts, clinical variables) using these standardised international datasets. METHODS: Dentists within FDI member National Dental Associations who chose to take part in the project were selected using a multistage sampling method. The number of dentists in each cluster was set according to the proportion of the national population living in the area, and 50 patients per dentist were systematically approached to take part. Patients and dentists completed 2 separate questionnaires on a mobile app. Ordinal logistic regression (conducted in December 2022) was used to analyse the linked patient and dentist data from 6 countries: China (n = 2242); Colombia (n = 1029); India (n = 999); Italy (n = 711); Japan (n = 1271); and Lebanon (n = 798). Self-reported general health was the dependent variable, with age, sex, education, self-reported oral health-related behaviours, self-reported oral impacts, and clinical variables acting as the independent variables. RESULTS: The results demonstrated a different pattern of associations in the different countries. Better self-reported general health was associated with degree-level education in all 6 countries and with reporting no oral impact and no sensitive teeth in 4 countries. Several country-specific patterns were also found, including the importance of tooth brushing in Colombia, periodontal health in Italy, and differing associations with sugary drinks consumption in India and Japan. CONCLUSIONS: These descriptive findings provide a basis for further research and, importantly, for advocacy in identifying patient oral health care needs according to both person-reported and clinical aspects. This can facilitate optimisation of service provision and potentially influence policy and investments.

Supplemental Nutrition Assistance Program and Adherence to Antihypertensive Medications.

Importance: Nonadherence to antihypertensive medications is associated with uncontrolled blood pressure, higher mortality rates, and increased health care costs, and food insecurity is one of the modifiable medication nonadherence risk factors. The Supplemental Nutrition Assistance Program (SNAP), a social intervention program for addressing food insecurity, may help improve adherence to antihypertensive medications. Objective: To evaluate whether receipt of SNAP benefits can modify the consequences of food insecurity on nonadherence to antihypertensive medications. Design, Setting, and Participants: A retrospective cohort study design was used to assemble a cohort of antihypertensive medication users from the linked Medical Expenditure Panel Survey (MEPS)-National Health Interview Survey (NHIS) dataset for 2016 to 2017. The MEPS is a national longitudinal survey on verified self-reported prescribed medication use and health care access measures, and the NHIS is an annual cross-sectional survey of US households that collects comprehensive health information, health behavior, and sociodemographic data, including receipt of SNAP benefits. Receipt of SNAP benefits in the past 12 months and food insecurity status in the past 30 days were assessed through standard questionnaires during the study period. Data analysis was performed from March to December 2021. Exposure: Status of SNAP benefit receipt. Main Outcomes and Measures: The main outcome, nonadherence to antihypertensive medication refill adherence (MRA), was defined using the MEPS data as the total days' supply divided by 365 days for each antihypertensive medication class. Patients were considered nonadherent if their overall MRA was less than 80%. Food insecurity status in the 30 days prior to the survey was modeled as the effect modifier. Inverse probability of treatment (IPT) weighting was used to control for measured confounding effects of baseline covariates. A probit model was used, weighted by the product of the computed IPT weights and MEPS weights, to estimate the population average treatment effects (PATEs) of SNAP benefit receipt on nonadherence. A stratified analysis approach was used to assess for potential effect modification by food insecurity status. Results: This analysis involved 6692 antihypertensive medication users, of whom 1203 (12.8%) reported receiving SNAP benefits and 1338 (14.8%) were considered as food insecure. The mean (SD) age was 63.0 (13.3) years; 3632 (51.3%) of the participants were women and 3060 (45.7%) were men. Although SNAP was not associated with nonadherence to antihypertensive medications in the overall population, it was associated with a 13.6-percentage point reduction in nonadherence (PATE, -13.6 [95% CI, -25.0 to -2.3]) among the food-insecure subgroup but not among their food-secure counterparts. Conclusions and Relevance: This analysis of a national observational dataset suggests that patients with hypertension who receive SNAP benefits may be less likely to become nonadherent to antihypertensive medication, especially if they are experiencing food insecurity. Further examination of the role of SNAP as a potential intervention for preventing nonadherence to antihypertensive medications through prospectively designed interventional studies or natural experiment study designs is needed.

Modelling dataset bias in machine-learned theories of economic decision-making.

Normative and descriptive models have long vied to explain and predict human risky choices, such as those between goods or gambles. A recent study reported the discovery of a new, more accurate model of human decision-making by training neural networks on a new online large-scale dataset, choices13k. Here we systematically analyse the relationships between several models and datasets using machine-learning methods and find evidence for dataset bias. Because participants' choices in stochastically dominated gambles were consistently skewed towards equipreference in the choices13k dataset, we hypothesized that this reflected increased decision noise. Indeed, a probabilistic generative model adding structured decision noise to a neural network trained on data from a laboratory study transferred best, that is, outperformed all models apart from those trained on choices13k. We conclude that a careful combination of theory and data analysis is still required to understand the complex interactions of machine-learning models and data of human risky choices.

Remote collaboration fuses fewer breakthrough ideas.

Theories of innovation emphasize the role of social networks and teams as facilitators of breakthrough discoveries1-4. Around the world, scientists and inventors are more plentiful and interconnected today than ever before4. However, although there are more people making discoveries, and more ideas that can be reconfigured in new ways, research suggests that new ideas are getting harder to find5,6-contradicting recombinant growth theory7,8. Here we shed light on this apparent puzzle. Analysing 20 million research articles and 4 million patent applications from across the globe over the past half-century, we begin by documenting the rise of remote collaboration across cities, underlining the growing interconnectedness of scientists and inventors globally. We further show that across all fields, periods and team sizes, researchers in these remote teams are consistently less likely to make breakthrough discoveries relative to their on-site counterparts. Creating a dataset that allows us to explore the division of labour in knowledge production within teams and across space, we find that among distributed team members, collaboration centres on late-stage, technical tasks involving more codified knowledge. Yet they are less likely to join forces in conceptual tasks-such as conceiving new ideas and designing research-when knowledge is tacit9. We conclude that despite striking improvements in digital technology in recent years, remote teams are less likely to integrate the knowledge of their members to produce new, disruptive ideas.

A deep learning approach to estimate x-ray scatter in digital breast tomosynthesis: From phantom models to clinical applications.

BACKGROUND: Digital breast tomosynthesis (DBT) has gained popularity as breast imaging modality due to its pseudo-3D reconstruction and improved accuracy compared to digital mammography. However, DBT faces challenges in image quality and quantitative accuracy due to scatter radiation. Recent advancements in deep learning (DL) have shown promise in using fast convolutional neural networks for scatter correction, achieving comparable results to Monte Carlo (MC) simulations. PURPOSE: To predict the scatter radiation signal in DBT projections within clinically-acceptable times and using only clinically-available data, such as compressed breast thickness and acquisition angle. METHODS: MC simulations to obtain scatter estimates were generated from two types of digital breast phantoms. One set consisted of 600 realistically-shaped homogeneous breast phantoms for initial DL training. The other set was composed of 80 anthropomorphic phantoms, containing realistic internal tissue texture, aimed at fine tuning the DL model for clinical applications. The MC simulations generated scatter and primary maps per projection angle for a wide-angle DBT system. Both datasets were used to train (using 7680 projections from homogeneous phantoms), validate (using 960 and 192 projections from the homogeneous and anthropomorphic phantoms, respectively), and test (using 960 and 48 projections from the homogeneous and anthropomorphic phantoms, respectively) the DL model. The DL output was compared to the corresponding MC ground truth using both quantitative and qualitative metrics, such as mean relative and mean absolute relative differences (MRD and MARD), and to previously-published scatter-to-primary (SPR) ratios for similar breast phantoms. The scatter corrected DBT reconstructions were evaluated by analyzing the obtained linear attenuation values and by visual assessment of corrected projections in a clinical dataset. The time required for training and prediction per projection, as well as the time it takes to produce scatter-corrected projection images, were also tracked. RESULTS: The quantitative comparison between DL scatter predictions and MC simulations showed a median MRD of 0.05% (interquartile range (IQR), -0.04% to 0.13%) and a median MARD of 1.32% (IQR, 0.98% to 1.85%) for homogeneous phantom projections and a median MRD of -0.21% (IQR, -0.35% to -0.07%) and a median MARD of 1.43% (IQR, 1.32% to 1.66%) for the anthropomorphic phantoms. The SPRs for different breast thicknesses and at different projection angles were within ± 15% of the previously-published ranges. The visual assessment showed good prediction capabilities of the DL model with a close match between MC and DL scatter estimates, as well as between DL-based scatter corrected and anti-scatter grid corrected cases. The scatter correction improved the accuracy of the reconstructed linear attenuation of adipose tissue, reducing the error from -16% and -11% to -2.3% and 4.4% for an anthropomorphic digital phantom and clinical case with similar breast thickness, respectively. The DL model training took 40 min and prediction of a single projection took less than 0.01 s. Generating scatter corrected images took 0.03 s per projection for clinical exams and 0.16 s for one entire projection set. CONCLUSIONS: This DL-based method for estimating the scatter signal in DBT projections is fast and accurate, paving the way for future quantitative applications.

Persistent mortality and heart failure burden of anterior ST-segment elevation myocardial infarction following primary percutaneous coronary intervention: real-world evidence from the US Medicare Data Set.

OBJECTIVES: We sought to compare the temporal trends in the incidence of death and rehospitalisation for congestive heart failure (CHF) following anterior ST-elevation myocardial infarction (STEMI) in a Medicare cohort of beneficiaries treated with primary percutaneous coronary intervention (PCI) in 2005 (n=1479) with those treated in 2016 through quarter (Q) 2 of 2017 (n=22 432). DESIGN: This retrospective analysis examined outcomes using both descriptive and regression analysis to control for differences in patient clinical characteristics over time. PRIMARY OUTCOME MEASURES: The primary outcomes are 1 year and 2 year rates of mortality and re-hospitalisation for CHF. RESULTS: The 1 year mortality rate was numerically higher in the 2016 cohort at 10.3% (95% CI 9.9 to 10.7) versus 8.9% (CI 7.4 to 10.3; p=0.068). The 2 year mortality rate was significantly higher in the 2016 cohort at 14.5% (CI 13.9 to 15.1) versus 11.4% (CI 9.2 to 13.6; p<0.01). The 1 year rehospitalisation for CHF was lower in the 2016 cohort at 10.6% (CI 10.0 to 11.2) versus 16.7% (CI 14.0 to 19.4; p<0.001), but the 2 year rate was not significantly different at 19.3% (CI 17.7 to 20.9) versus 20.7% (CI 16.4 to 24.9; p=0.55). After adjustment for covariates with two models, the 1 year mortality increased by 2.3% (CI 0.8 to 3.7; p<0.01) and 4.1% (CI 2.6 to 5.6; p<0.001) in the 2016 cohort. The 2 year adjusted mortality also increased by 4.2% (CI 2.0 to 6.4; p<0.001) and 6.5% (CI 4.2 to 8.7; p<0.001) in the 2016 cohort. The risk adjusted trends for rehospitalisation for CHF were similar to the unadjusted findings. CONCLUSIONS: Despite prior improvements in STEMI outcomes in the reperfusion era related to the broad adoption of timely PCI, there is a persistent high mortality and CHF burden in Medicare beneficiaries with anterior STEMI. New strategies that address reperfusion injury and enhance myocardial salvage are needed.

Trends in diabetes incidence in Austria 2013-2017.

This study aims to quantify whether age and sex groups in Austrian regions are equally affected by the rise of type 2 diabetes. Population-wide medical claims data was obtained for citizens in Austria aged above 50 year, who received antihyperglycemic treatments or underwent HbA1c monitoring between 2012 and 2017. Diabetes incidence was measured using an epidemiological diabetes progression model accounting for patients who discontinued antihyperglycemic therapy; the erratic group. Out of 746,184 patients, 268,680 (140,960 females) discontinued their treatment and/or monitoring for at least one year. Without adjusting for such erratic patients, incidence rates increase from 2013 to 2017 (females: from 0·5% to 1·1%, males: 0·5% to 1·2%), whereas they decrease in all groups after adjustments (females: - 0·3% to - 0·5%, males: - 0·4% to - 0·5%). Higher mortality was observed in the erratic group compared to patients on continued antihyperglycemic therapy (mean difference 12% and 14% for females and males, respectively). In summary, incidence strongly depends on age, sex and place of residency. One out of three patients with diabetes in Austria discontinued antihyperglycemic treatment or glycemic monitoring for at least one year. This newly identified subgroup raises concern regarding adherence and continuous monitoring of diabetes care and demands further evaluation.

The impact of commercial health datasets on medical research and health-care algorithms.

As the health-care industry emerges into a new era of digital health driven by cloud data storage, distributed computing, and machine learning, health-care data have become a premium commodity with value for private and public entities. Current frameworks of health data collection and distribution, whether from industry, academia, or government institutions, are imperfect and do not allow researchers to leverage the full potential of downstream analytical efforts. In this Health Policy paper, we review the current landscape of commercial health data vendors, with special emphasis on the sources of their data, challenges associated with data reproducibility and generalisability, and ethical considerations for data vending. We argue for sustainable approaches to curating open-source health data to enable global populations to be included in the biomedical research community. However, to fully implement these approaches, key stakeholders should come together to make health-care datasets increasingly accessible, inclusive, and representative, while balancing the privacy and rights of individuals whose data are being collected.

Optimal nitrogen rate strategy for sustainable rice production in China.

Avoiding excessive agricultural nitrogen (N) use without compromising yields has long been a priority for both research and government policy in China1,2. Although numerous rice-related strategies have been proposed3-5, few studies have assessed their impacts on national food self-sufficiency and environmental sustainability and fewer still have considered economic risks faced by millions of smallholders. Here we established an optimal N rate strategy based on maximizing either economic (ON) or ecological (EON) performance using new subregion-specific models. Using an extensive on-farm dataset, we then assessed the risk of yield losses among smallholder farmers and the challenges of implementing the optimal N rate strategy. We find that meeting national rice production targets in 2030 is possible while concurrently reducing nationwide N consumption by 10% (6-16%) and 27% (22-32%), mitigating reactive N (Nr) losses by 7% (3-13%) and 24% (19-28%) and increasing N-use efficiency by 30% (3-57%) and 36% (8-64%) for ON and EON, respectively. This study identifies and targets subregions with disproportionate environmental impacts and proposes N rate strategies to limit national Nr pollution below proposed environmental thresholds, without compromising soil N stocks or economic benefits for smallholders. Thereafter, the preferable N strategy is allocated to each region based on the trade-off between economic risk and environmental benefit. To facilitate the adoption of the annually revised subregional N rate strategy, several recommendations were provided, including a monitoring network, fertilization quotas and smallholder subsidies.

Papers and patents are becoming less disruptive over time.

Theories of scientific and technological change view discovery and invention as endogenous processes1,2, wherein previous accumulated knowledge enables future progress by allowing researchers to, in Newton's words, 'stand on the shoulders of giants'3-7. Recent decades have witnessed exponential growth in the volume of new scientific and technological knowledge, thereby creating conditions that should be ripe for major advances8,9. Yet contrary to this view, studies suggest that progress is slowing in several major fields10,11. Here, we analyse these claims at scale across six decades, using data on 45 million papers and 3.9 million patents from six large-scale datasets, together with a new quantitative metric-the CD index12-that characterizes how papers and patents change networks of citations in science and technology. We find that papers and patents are increasingly less likely to break with the past in ways that push science and technology in new directions. This pattern holds universally across fields and is robust across multiple different citation- and text-based metrics1,13-17. Subsequently, we link this decline in disruptiveness to a narrowing in the use of previous knowledge, allowing us to reconcile the patterns we observe with the 'shoulders of giants' view. We find that the observed declines are unlikely to be driven by changes in the quality of published science, citation practices or field-specific factors. Overall, our results suggest that slowing rates of disruption may reflect a fundamental shift in the nature of science and technology.

[Diagnostic Prevalence of Dental Findings in Children, Adolescents and Young Adults with Mental Disorders Compared to Healthy People - an Analysis and Estimation Based on Claims Data from 2019]. / Zahnmedizinische Diagnoseprävalenzen bei Kindern, Jugendlichen und jungen Erwachsenen mit psychischen Störungen im Vergleich zu Gesunden-Analyse und Abschätzung kassenärztlicher Abrechnungsdaten (2019).

AIM: To analyse oral health-related diagnostic prevalences in children, adolescents (KiJu), young adults with mental/behavioural disorders (PEVS) and those of the general population in Germany based on claims data. METHOD: Anonymously provided ZI (Zentralinstitut)-data sets of GKV-insured persons (0-44 years) were i. e. stratified according to gender, age groups, ICD-K00-K14 diagnosis. RESULT: No reliable oral or dental health-related information was generated from the requested data set. Regardless of the F diagnosis, according to the data set, 1.8% of all 11,854,384 KiJu-GKV-insured persons and 0.2% of 18-44-year-olds (23,348,399 GKV-insured persons) had a diagnosis related to the dental hard tissue (ICD-10 K02/K03.2). Based on available literature on the prevalence of caries in KiJu with PEVS, a mean unweighted prevalence of 51% can be assumed. According to the available literature on the prevalence of caries in KiJu with PEVS a mean unweighted prevalence of 51% can be calculated. Following this and the diagnosis prevalence of PEVS in 0- to 17-year-olds from 2017, an estimated 957.952 children with PEVS should also have a caries. CONCLUSION: The nationwide data on the prevalence of oral health-related diagnoses made by physicians in the general population and in people of the same age with PEVS are very low. Apparently, dental diagnoses are only given very rarely by general practitioners and paediatricians, among others. The existing literature as well as the clinical experience of the authors indicate that this claim data is not suitable to realistically represent the dental and oral health of the examined group of people. Thus, for networking, the improvement of health care research and the care, e. g. of subgroups of the society, is an interprofessional dental/medical, data protection-compliant central database to be driven forward.

Quantitative and qualitative workload assessment in steep terrain forest operations: fostering a safer work environment through yarder automation.

Many forestry roles have changed from being manual tasks with a high physical workload to being a machine operator task with a high mental workload. Automation can support a decrease in mental fatigue by removing tasks that are repetitive and monotonous for the operators. Cable yarding presents an ideal opportunity for early adoption of automation technology; specifically the carriage movement along a defined corridor. A Valentini V-850 cable yarder was used in an Italian harvesting setting, in order to gauge the ergonomic benefit of carriage control automation. The study showed that automating yarder carriage movements improved the ergonomic situation of the workers directly involved in the related primary tasks. However, the caveat is that improving one work task may negatively affect the other work tasks, and therefore introducing automation to a worksite must be done after considering all impacts on the whole system. Practitioner summary: Automation decreased the winch operator's mental workload while improving overall productivity. At the same time, the mental and physiological workload of the operator tasked with bucking were slightly increased. Ideally, winch automation should be coupled with bucking mechanisation to balance the intervention and boost both operator well-being and productivity.

Social capital I: measurement and associations with economic mobility.

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 health1-8. 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 papers9, 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 date10,11. 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 inequality12-14. To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at https://www.socialcapital.org .

Social capital II: determinants of economic connectedness.

Low levels of social interaction across class lines have generated widespread concern1-4 and are associated with worse outcomes, such as lower rates of upward income mobility4-7. Here we analyse the determinants of cross-class interaction using data from Facebook, building on the analysis in our companion paper7. 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 .

The challenge of unprecedented floods and droughts in risk management.

Risk management has reduced vulnerability to floods and droughts globally1,2, yet their impacts are still increasing3. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change3.

How real can we get in generating real world evidence? Exploring the opportunities of routinely collected administrative data for evaluation of medical devices.

Real-world data are considered a potentially valuable source of evidence for assessing medical technologies in clinical practice, but their widespread use is hampered by numerous challenges. Using the case of coronary stents in Italy, we investigate the potential of administrative databases for estimating costs and health outcomes associated with the use of medical devices in real world conditions. An administrative dataset was created ad hoc by merging hospital records from patients admitted between 2013 and 2019 for stent implantations with ambulatory records, pharmaceutical use data and vital statistics. Health outcomes were multifold: all-cause and cardiac mortality and myocardial infarction, within 30 days, 1, 2, 5 years. Costs were estimated from the National Health System perspective. We used multivariable Cox models and propensity score (PS) methods (PS matching; stratification on PS; inverse probability of treatment weighting using PS; PS adjustment). 257,907 coronary stents were implanted in 113,912 patients. For all health outcomes and follow-up times, and across all methods, patients receiving drug-eluting stents (DES) presented lower risk. For all-cause mortality, the DES patient advantage over bare-metal stent (BMS) patients declined over time but remained significant even at 5 years. For myocardial infarction, results remained quite stable. The DES group presented lower cumulative total costs (ranging from 3264 to 2363 Euros less depending on methods). Our results confirm the consolidated evidence of the benefits of DES compared to BMS. The consistency of results across methods suggests internal validity of the study, while highlighting strengths and limitations of each depending on research context. Administrative data yield great potential to perform comparative effectiveness and cost-effectiveness analysis of medical devices provided certain conditions are met.

Beyond Dunbar circles: a continuous description of social relationships and resource allocation.

We discuss the structure of human relationship patterns in terms of a new formalism that allows to study resource allocation problems where the cost of the resource may take continuous values. This is in contrast with the main focus of previous studies where relationships were classified in a few, discrete layers (known as Dunbar's circles) with the cost being the same within each layer. We show that with our continuum approach we can identify a parameter [Formula: see text] that is the equivalent of the ratio of relationships between adjacent circles in the discrete case, with a value [Formula: see text]. We confirm this prediction using three different datasets coming from phone records, face-to-face contacts, and interactions in Facebook. As the sample size increases, the distributions of estimated parameters smooth around the predicted value of [Formula: see text]. The existence of a characteristic value of the parameter at the population level indicates that the model is capturing a seemingly universal feature on how humans manage relationships. Our analyses also confirm earlier results showing the existence of social signatures arising from having to allocate finite resources into different relationships, and that the structure of online personal networks mirrors those in the off-line world.

Evolution of Incidence, Management, and Outcomes Over Time in Sports-Related Sudden Cardiac Arrest.

BACKGROUND: Major efforts have been made to reduce the burden of sports-related sudden cardiac arrest (SrSCA). The extent to which the incidence, management, and outcomes changed over time has not been investigated. OBJECTIVES: The purpose of this study was to assess temporal trends in SrSCA incidence, management, and survival. METHODS: Using data from the French National Institute of Health and Medical Research, we evaluated the evolution of incidence, prehospital management, and survival at hospital discharge of SrSCA among subjects aged 18 to 75 years, over 6 successive 2-year periods between 2005 and 2018. RESULTS: Among the 377 SrSCA, 20 occurred in young competitive athletes (5.3%), whereas 94.7% occurred in middle-aged recreational sports participants. Comparing the last 2-year to the first 2-year period, SrSCA incidence remained stable (6.24 vs 7.00 per million inhabitants/y; P = 0.51), with no significant differences in patients' mean age (46.6 ± 13.8 years vs 51.0 ± 16.4 years; P = 0.42), sex (men 94.7% vs 95.2%; P = 0.99), and history of heart disease (12.5% vs 15.9%; P = 0.85). However, frequency of bystander cardiopulmonary resuscitation and public automated external defibrillator use increased significantly (34.9% vs 94.7%; P < 0.001 and 1.6% vs 28.8%; P = 0.006, respectively). Survival to hospital discharge improved steadily, reaching 66.7% in the last study period compared with 23.8% in the first (P < 0.001). CONCLUSIONS: Incidence of SrSCA remained relatively stable over time, suggesting a need for improvement in screening strategies. However, major improvements in on-field resuscitation led to a 3-fold increase in survival, underlining the value of public education in basic life support that should serve as an example for SCA in general.