The semiconductor industry is foundational to modern technology, yet its complex global multi-relational firm network remains poorly understood, posing challenges to scientists, firms, and policymakers. Traditional analysis relies on proprietary databases that are often expensive, incomplete, and slowly updated, limiting their ability to capture rapidly evolving dependencies. Here, we demonstrate that a novel, generalizable methodology combining Large Language Models (LLMs) with open web data can reconstruct this network and its structural dynamics at scale. We identify and classify supply-chain, partnership, and ownership links from 170 million semiconductor firm webpages, yielding a temporal network of over 1,300 linked firms. We validate link-extraction quality (Precision: 0.884; F1-score: 0.784), network overlap and complementarity with a proprietary database, and consistency with aggregate economic data. Our network reveals a temporary 9

Aging trajectories vary among individuals of similar age and disease burden. Comorbidity indices, e.g. the Elixhauser index, summarize conditions cross-sectionally, but discard the timing, sequence, and pace of morbidity accumulation. Here we ask whether longitudinal hospital diagnosis histories contain information beyond age, sex, and comorbidity burden, and where it is concentrated. Using 13 years of Austrian inpatient data covering 7.4 million patients, we trained a visit-level contrastive transformer to encode diagnosis sequences and inter-admission timing into patient-history embeddings. In a downstream cohort of 1.7 million individuals, embeddings improved prediction over the Elixhauser-based comorbidity model for 93 of 131 incident ICD-10 disease-block outcomes, with a modest median AUC gain of 0.006. Gains concentrated in mental, musculoskeletal, nervous system, and metabolic disorders. We then evaluated event-free survival, defined as remaining alive without accumulating a second unrecorded ICD-10 disease block. The embedding model achieved an AUC of 0.726 versus 0.722 for the comorbidity model. However, among patients with similar age, sex, and comorbidity-model risk, those assigned high residual risk had 132–183 fewer event-free days over five years and observed event rates comparable to low-residual-risk patients more than a decade older. Together, these findings link the embedding's signal to the breadth, recency, and pace of prior disease accumulation.

Supply-chain due-diligence laws require firms to identify and mitigate human-rights risks beyond direct suppliers, but the role of production-network topology in shaping risk exposure and regulatory burden remains unclear. We reconstruct an ensemble of synthetic firm-level EU production networks containing about 30 million firms and 890 million supplier links. Calibrated to official economic statistics and combined with human-rights risk databases, the model reproduces known exposure patterns such as textiles, basic metals and mining. For large, in-scope companies in the implemented Corporate Sustainability Due Diligence Directive, risk-flagged suppliers increase from 12 at Tier 1 to 770 at Tier 2 and 4,600 at Tier 3, while the share of suppliers serving more than one in-scope company rises from 14% to 90%. Furthermore, taking one high-risk import sector as a test case, we show that certifying suppliers directly covers about as many risky supplier links as the standard company-by-company approach, but requires 73% fewer certificates.

As populations age, the rise of multimorbidity poses a significant healthcare challenge. However, our ability to quantitatively forecast the progression of hospital-recorded multimorbidity remains limited. Leveraging a nationwide dataset comprising approximately 45 million inpatient hospital stays spanning 17 years in Austria, we develop a compartmental model for chronic disease trajectories across 132 distinct multimorbidity patterns (compartments). Each compartment represents a constellation of co-occurring chronic conditions, with transitions modeled as age- and sex-dependent probabilities. We use the compartmental disease trajectory model (CDTM) to simulate disease trajectories to 2030, estimating the frequency of all observed co-occurrence patterns in hospitals among more than 100 diagnosis groups. We demonstrate the model’s utility in identifying high-impact prevention targets. A 5% reduction in new cases of hypertensive diseases (I10–I15) leads to a 0.57 (SD 0.06)% reduction in all-cause mortality over the 2015–2030 projection period, and a 0.57 (SD 0.07)% reduction in mortality for malignant neoplasms (C00–C97). We also evaluate the potential long-term impacts of SARS-CoV-2 sequelae using stress-test scenarios, projecting earlier and more frequent hospitalizations across a range of diagnoses. Our data-driven modeling approach identifies leverage points for proactive preparation by physicians and policymakers within hospital morbidity dynamics, emphasizing patient-centered healthcare planning in aging societies.

COVID-19 and previous pandemics have shown how diseases can disrupt, threaten, and transform daily life. Since pathogens and societies are continuously evolving, every pandemic is different. However, certain fundamental principles of disease transmission appear to hold true across different outbreaks. These “mechanisms” are grounded in natural laws or the very structure of our biology and societies. This paper compiles ten fundamental mechanisms, curated by a multidisciplinary team with backgrounds spanning public health, medicine, epidemiology, political science, mathematics, physics, and psychology. These mechanisms, although perhaps underappreciated, substantially shape how pandemics unfold and are controlled. The better we succeed in understanding these mechanisms and establishing this knowledge in our societies, the better we will be able to prepare for future pandemics and respond appropriately when they occur.

The steel industry is a major contributor to CO2 emissions, accounting for 7 of global emissions. The European steel industry is seeking to reduce its emissions by increasing the use of electric arc furnaces (EAFs), which can produce steel from scrap, marking a major shift towards a circular steel economy. Here, we show by combining trade with business intelligence data that this shift requires a deep restructuring of the global and European scrap trade, as well as a substantial scaling of the underlying business ecosystem. We find that the scrap imports of European countries with major EAF installations have steadily decreased since 2007 while globally scrap trade started to increase recently. Our statistical modelling shows that every 1,000 tonnes of EAF capacity installed is associated with an increase in annual imports of 550 tonnes and a decrease in annual exports of 1,000 tonnes of scrap, suggesting increased competition for scrap metal as countries ramp up their EAF capacity. Furthermore, each scrap company enables an increase of around 79,000 tonnes of EAF-based steel production per year in the EU. Taking these relations as causal and extrapolating to the currently planned EAF capacity, we find that an additional 730 (SD 140) companies might be required, employing about 35,000 people (IQR 29,000-50,000) and generating an additional estimated turnover of USD 35 billion (IQR 27-48). Our results thus suggest that scrap metal is likely to become a strategic resource. They highlight the need for a massive restructuring of the industry's supply networks and identify the resulting growth opportunities for companies.

Treatment of cancer involves heterogeneous, complex care pathways. The relationship between these longitudinal trajectories, baseline mental health, and prognostic outcomes remains poorly understood. We introduce an interpretable time-analysis framework leveraging these temporal dynamics, analyzing care patterns spanning up to 37 years for >8,000 patients. Using Dynamic Time Warping (DTW) and Hierarchical Clustering on sequence data of healthcare encounters, we identified nine distinct, robust trajectory phenotypes. We evaluated their prognostic utility by incorporating them into generalized linear models alongside conventional clinical, demographic, and socioeconomic covariates. The trajectory clusters significantly enhanced mortality prediction and maintained independent predictive significance. Compared to a low-utilization reference group (mortality 31.5 Unexpectedly, the high-utilization complexity clusters were associated with significantly lower baseline anxiety scores, highlighting a divergent relationship between trajectory intensity, mortality risk, and initial psychological burden. These results demonstrate that incorporating temporal healthcare utilization data uncovers robust trajectory phenotypes capturing multidimensional prognostic information. This offers significant explanatory power beyond established static variables for refining risk stratification in precision oncology.

Heatwaves increase the risk of morbidity and strain emergency medical services. However, prehospital data from Central Europe remain limited. Overall, 936,461 emergency dispatch records were linked to spatially matched meteorological data collected from a 506-point grid across Vienna. Heatwaves were defined based on the daily minimum, mean, and maximum temperatures using the duration-and-threshold approach. During the 2018-2021 study period, group comparison analyses and generalized linear models with a negative binomial distribution were used to estimate incidence rate ratios (IRRs), adjusting for calendar effects. Subgroup analyses assessed heterogeneity by age, sex, diagnostic category, and timing during and after heat events. Daily minimum temperature of ≥ 20.5 °C for 2 consecutive days yielded the strongest association with increased dispatch activity (IRR = 1.104, 95% CI 1.077-1.131, p < 0.001). The effects intensified with increasing heatwave severity. Subsequent heatwave days exhibited decreased but statistically significant impacts. Female patients (IRR = 1.094) and those aged 0-18 and 76-85 years presented with a disproportionately greater increase in dispatches. Dispatches for heat-related illness, COPD, unconsciousness, and trauma were significantly higher. The first heatwave each year had stronger effects (IRR = 1.118) than the subsequent events. Minimum temperature-based definitions had the highest predictive value. Our results support the need to adapt local heat-health warning systems to account for cumulative exposure, early season risks, and diagnosis-specific vulnerabilities.

Introduction and Objective: Sex differences in macrovascular complications of type 2 diabetes (T2D) are known, but evidence on microvascular differences remain limited. Methods: We used an Austrian medical claims database of 8.9 million individuals (1997-2014). Comorbidities co-occurring with T2D were analyzed using sex- and age-specific contingency tables across 2-year intervals (2003-2014), with odds ratios estimated via the Cochran-Mantel-Haenszel method. Only comorbidities with sufficient case numbers were retained. Sex differences were quantified by the differences of logarithmic ORs between male and females in units of pooled standard errors assessed using a Bonferroni-corrected test. Results: In 40 to 49 year olds, females had more retinopathy (OR 9.3 vs. 5.5), chronic kidney failure (CKI: OR 9.8 vs. 6.5), depression (OR 3.1 vs. 1.9) and cardiovascular complications including chronic ischemic heart disease (CIHD: OR 10.8 vs. 5.6), heart failure (HF: OR 12.1 vs. 7.7) and acute myocardial infarction (MI: OR 9.4 vs. 3.9, all p-values <0.001) compared to males with T2D. In 50 to 59 year olds, females had higher rates of CKI (OR 9.2 vs. 6.2), depression (OR 2.8 vs. 1.9) and cardiovascular complications (CIHD: OR 6.8 vs. 4.4, HF: OR 8.7 vs. 5.3, MI: OR 5.7 vs. 2.7) compared to males. Retinopathy showed no sex differences, but cerebral infarction was more frequent in females (OR 4.4 vs. 3.2). In age group 60 to 69 year olds, females had higher rates in cardiovascular complications (CIHD: OR 4.8 vs. 3.7, HF: OR 6.4 vs. 4.3, MI: OR 3.8 vs. 2.5), retinopathy (OR: 2.5 vs. 3.0), CKI (OR 7.3 vs. 4.9) and depression (OR: 2.5 vs. 2.0). Lastly, in 70 to 79 year olds the least differences with a higher rate of cardiovascular complications (CIHD: OR 3.5 vs. 3.2, MI: OR 3.1 vs. 2.3) and CKI (OR 5.0 vs. 4.0, all p-values<0.001) in females compared to males was reported. Conclusion: More comorbidities were associated with T2D in females than in males, including cardiovascular disease, retinopathy, nephropathy, and neuropathy. These results support more personalized and effective management strategies. Disclosure T. Gisinger: None. K. Fenz: None. P. Klimek: None. E. Dervic: None. A. Kautzky-Willer: None.

Comorbidity networks have become a valuable tool to support data-driven biomedical research. Yet, studies often are severely hindered by the availability of the necessary comprehensive data, often due to the sensitivity of health care information. This study presents a population-wide comorbidity network dataset derived from 45 million hospital stays of 8.9 million patients over 17 years in Austria. We present co-occurrence networks of hospital diagnoses, stratified by age, sex, and observation period in a total of 96 different subgroups. For each of these groups we report a range of association measures (e.g., count data, and odds ratios) for all pairs of diagnoses. The dataset provides the possibility to researchers to create their own, tailor-made comorbidity networks from real patient data that can be used as a starting point in quantitative and machine learning methods. This data platform is intended to lead to deeper insights into a wide range of epidemiological, public health, and biomedical research questions.

While gendered psycho-socio-cultural factors are recognized as major determinants of cardiovascular health, their contribution to our understanding of their effect on hypertension (HTN) in each country is poorly understood. Therefore, we investigated the role of these factors in HTN prevalence, focusing on sex- and gender-specific differences across countries. Data from the Canadian Community Health Survey (2015–2016, N = 109,659, women: 56.6%) and the European Health Interview Survey (2013–2015, N = 316,333, women: 51.3%) were analyzed. Primary endpoint was defined as HTN prevalence within 1-year. Relationship and interaction between sex, gender, and country with HTN prevalence were assessed using multivariate models. Federated analysis was conducted using DataShield. Prevalence of HTN was higher in Canada compared to Europe (30.1% vs 22.4%, P < .001). Amongst European countries, living in the Central-East region was associated with a greater risk of developing HTN. Women in the southern and central-east regions had higher prevalence of HTN. There was a significant interaction between socioeconomic status and sex in country-stratified analysis. This was more evident in central-east and southern countries compared to northern, western nations and Canada, where women with lower socioeconomic status, income, and education had a greater risk of developing HTN. Similar trends were observed regardless of country in women who were divorced or widowed. While immigrants were at higher risk of HTN, those in northern and southern Europe were at lower risk compared to central-east region. Sex- and gender-related factors and country should be considered in the prevention and control of HTN.

Global food production and trade networks are highly dynamic, especially in response to shortages when countries adjust their supply strategies. In this study, we examine adjustments across 123 agri-food products from 192 countries resulting in 23616 individual scenarios of food shortage, and calibrate a multi-layer network model to understand the propagation of the shocks. We analyze shock mitigation actions, such as increasing imports, boosting production, or substituting food items. Our findings indicate that these lead to spillover effects potentially exacerbating food inequality: an Indian rice shock resulted in a 5.8 Development Index (HDI) and a 14.2 Considering multiple interacting shocks leads to super-additive losses of up to 12 network. This framework allows us to identify combinations of shocks that pose substantial systemic risks and reduce the resilience of the global food supply.

Blood glucose is lower in mountain dwellers living under low partial oxygen pressure. We show that obese mice maintained under hypoxia exhibit a delayed but distinct decrease in blood glucose with improved insulin sensitivity, which is independent of changes in body weight. This effect of hypoxia is mediated by erythropoiesis and is a direct result of the rising hematocrit, which could be due to erythrocytes acting as carriers of glucose units in the blood. Glucose lowering by the red cell mass is evidenced by a prompt decrease in glycemia in mice receiving a blood transfusion. Furthermore, life under hypoxia as well as treatment with erythropoietin reduce glycemia also in mice expressing the erythropoietin receptor exclusively in hematopoietic cells, which contrasts with previous assumptions attributing metabolic actions of erythropoietin to direct action on nonhematopoietic tissues. Our results provide a rationale for associations between hematocrit and blood glucose in humans under anti-anemic therapy, polycythemia, smoking, and high-altitude exposure.

Understanding the factors associated with persistent symptoms after SARS-CoV-2 infection is critical to improving long-term health outcomes. Using a wearable-derived behavioral and physiological dataset (n = 20,815), we identified individuals characterized by self-reported persistent fatigue and shortness of breath after SARS-CoV-2 infection. Compared with symptom-free COVID-19 positive (n = 150) and negative controls (n = 150), these individuals (n = 50) had higher resting heart rates (mean difference 2.37/1.49 bpm) and lower daily step counts (mean 3030/2909 steps fewer), even at least three weeks prior to SARS-CoV-2 infection. In addition, persistent fatigue and shortness of breath were associated with a significant reduction in mean quality of life (WHO-5, EQ-5D), even before infection. Here we show that persistent symptoms after SARS-CoV-2 infection may be associated with pre-existing lower fitness levels or health conditions. These findings additionally highlight the potential of wearable devices to track health dynamics and provide valuable insights into long-term outcomes of infectious diseases.

Chronic diseases frequently co-occur in patterns that are unlikely to arise by chance, a phenomenon known as multimorbidity. This growing challenge for patients and healthcare systems is amplified by demographic aging and the rising burden of chronic conditions. However, our understanding of how individuals transition from a disease-free-state to accumulating diseases as they age is limited. Recently, data-driven methods have been developed to characterize morbidity trajectories using electronic health records; however, their generalizability across healthcare settings remains largely unexplored. In this paper, we conduct a cross-country validation of a data-driven multimorbidity trajectory model using population-wide health data from Denmark and Austria. Despite considerable differences in healthcare organization, we observe a high degree of similarity in disease cluster structures. The Adjusted Rand Index (0.998) and the Normalized Mutual Information (0.88) both indicate strong alignment between the two clusterings. These findings suggest that multimorbidity trajectories are shaped by robust, shared biological and epidemiological mechanisms that transcend national healthcare contexts.

Wastewater-based epidemiology offers a comprehensive yet cost-effective way to monitor pathogen circulation. However, it is not entirely clear how wastewater signals can be reliably mapped to case numbers and therefore to incidence. Here, we aim to estimate the number of total SARS-CoV-2 infections including reported and unreported cases in Austria by analysing two different longitudinal wastewater datasets covering 113 wastewater treatment plants from October 2021 to October 2022. These plants cover most of the Austrian population, which had one of the highest per capita testing rates during the Coronavirus Disease 2019 (COVID-19) pandemic. We empirically find that the relationship between reported COVID-19 cases and viral load in wastewater is significantly influenced by the number of tests performed. Based on this observation, we developed a method for estimating total cases by scaling reported cases by test activity and accounting for periods when different viral variants were dominant. We find that the ratio of estimated total to reported cases increased substantially over time, from a value around 1.49 at the peak of the BA.2 wave to a value of 5.48 at the peak of the BA.5 wave, and validate these results in two datasets. Our results also suggest that there was less shedding per case in periods where BA.5 was dominant than in periods where BA.1 and BA.2 were dominant, which in turn showed less shedding than in periods dominated by the Delta variant. The results of this study provide critical insight into the potential of wastewater measurements to provide a more accurate assessment of the dynamics of infectious disease transmission.

Dynamic input-output models are standard tools for understanding inter-industry dependencies and how economies respond to shocks like disasters and pandemics. However, traditional approaches often assume fixed prices, limiting their ability to capture realistic economic behavior. Here, we introduce an adaptive extension to dynamic input-output recovery models where producers respond to shocks through simultaneous price and quantity adjustments. Our framework preserves the economic constraints of the Leontief input-output model while converging towards equilibrium configurations based on sector-specific behavioral parameters. When applied to input-output data, the model allows us to compute behavioral metrics indicating whether specific sectors predominantly favor price or quantity adjustments. Using the World Input-Output Database, we identify strong, consistent regional and sector-specific behavioral patterns. These findings provide insights into how different regions employ distinct strategies to manage shocks, thereby influencing economic resilience and recovery dynamics.

The question of the cost effectiveness of medical interventions is one of the central issues in health economics. This narrative review examines the cost effectiveness of vaccination against influenza, SARS-CoV‑2 and respiratory syncytial virus (RSV) considering current health economic analyses. The annual influenza vaccination and the booster vaccination against SARS-CoV‑2 in 2023 and 2024 are proving to be cost effective and in some cases even cost saving, especially in high-risk groups. The cost effectiveness of the RSV vaccination, which was approved in 2023, is less clear. It strongly depends on the age group and the willingness to pay for a quality-adjusted life year (QALY) gained. The analysis shows that the evaluation of vaccinations requires a considerable amount of data. In addition to direct protective effects, model calculations on vaccinations must also consider indirect effects, such as the reduction of transmission in the population with higher vaccination rates. Sensitivity analyses make it clear that factors such as vaccine costs, effectiveness and disease incidence can have a decisive influence on cost effectiveness. One of the biggest challenges in health economic analyses is the fragmentation of health data in many countries, which makes comprehensive and precise assessments difficult. Initiatives such as the European Health Data Space could help and support evidence-based decision making in health policy. Overall, the cost effectiveness of vaccinations remains dependent on numerous factors, with SARS-CoV‑2 and influenza vaccinations receiving a positive assessment in the scenarios analysed.

Background: Equal access to health ensures that all citizens, regardless of socio-economic status, can achieve optimal health, leading to a more productive, equitable, and resilient society. Yet, migrant populations were frequently observed to have lower access to health. The reasons for this are not entirely clear and may include language barriers, a lack of knowledge of the healthcare system, and selective migration (a "healthy migrant" effect). Objective: To examine differences in hospital utilization and readmission rates between Austrian and non-Austrian populations using nationwide hospital claims data, with the aim of disentangling the effects of potential barriers to healthcare access. Methods: Here, we use extensive medical claims data from Austria (13 million hospital stays of approximately 4 million individuals between 2015 and 2019) to compare the healthcare utilization patterns between Austrians and non-Austrians. We looked at the differences in primary diagnoses and hospital sections of initial hospital admission across different nationalities. We hypothesize that cohorts experiencing the "healthy migrant" effect show lower readmission rates after hospitalization compared to migrant populations that are in poorer health but show lower hospitalization rates due to barriers in access. Results: We indeed find that all nationalities showed lower hospitalization rates than Austrians, except for Germans, who exhibit a similar healthcare usage to Austrians. Although around 20% of the population has a migration background, non-Austrian citizens account for only 9.4% of the hospital patients and 9.79% of hospital nights. However, results for readmission rates are much more divergent. Nationalities like Hungary, Romania, and Turkey (females) show decreased readmission rates in line with the healthy migrant effect. Patients from Russia, Serbia, and Turkey (males) show increased readmissions, suggesting that their lower hospitalization rates are more likely due to access barriers. Conclusion: Considering the surge in international migration, our findings shed light on healthcare access, usage behaviours and gender differences across patients with different nationalities, offering new insights and perspectives.