Dementia is a progressive condition that impairs cognitive processes such as memory, decision making, and the ability to manage daily activities. Recent estimates suggest that more than half of all dementia cases could be preventable by addressing their risk factors, including disease comorbidities such as diabetes and vision loss. Yet, we lack a comprehensive molecular map of dementia comorbidities. In this work, we analyzed Austrian nationwide hospital claims data, comprising 13 million hospital stays from 2015 to 2019, to systematically assess dementia-related risk across disease comorbidity patterns, covering both their molecular relationships and their epidemiological overrepresentation. We identified disease trajectories occurring before and at the time of dementia diagnosis, revealing both sex-specific and shared comorbidity patterns. Overall, we identified 51 potential risk factors, with a prominent contribution from endocrine and metabolic disorders. While Parkinson's disease emerged as a strong molecularly related driver of dementia, we also identified emerging and previously under chracterized risk factors, including vitamin D deficiency. This integrative framework provides a comprehensive view of dementia associated disease networks and identifies novel, potentially modifiable risk factors. These results offer new opportunities for targeted prevention strategies and advance our understanding of the complex interplay between comorbidities and dementia development.

International migrants face well-documented barriers to healthcare access, yet the extent to which these barriers shape patterns of disease co-occurrence remains poorly understood. Drawing on a nationwide dataset of approximately 13 million hospital admissions from around 4 million individuals in Austria (2015-2019), we constructed and compared comorbidity networks between Austrian nationals and non-Austrian migrants, matched 1:1 by age, sex, and time of first hospital admission (272,779 per group). Following matching, metabolic and cardiovascular diagnoses, including type 2 diabetes and myocardial infarction, were more common among non-Austrians, while depression was more common among Austrians. Comorbidity network analysis showed that among all disease pairs that differed significantly between groups, 70% showed stronger co-occurrence in Austrian patients and 30% in non-Austrian patients. Distinct sex-specific patterns appeared: Austrian males showed stronger associations between alcohol use disorder and mental health diagnoses, whereas non-Austrian males more frequently presented with acute somatic conditions. Among non-Austrian women, a pronounced cluster of recurrent depression, somatoform disorders, and dorsalgia was observed. We interpret the disproportionately fewer comorbidity links observed in non-Austrians not as evidence of lower disease burden, but as a likely reflection of structural access barriers, including language differences, cultural factors, and crisis-oriented admission patterns, that prevent comprehensive diagnostic assessment, though a contribution from the healthy migrant effect cannot be excluded. These findings stress the need for culturally aware care strategies and earlier identification of high-risk multimorbidity profiles in migrant populations.

BACKGROUND Fractures in older adults arise from complex interactions between bone fragility, falls, and comorbid conditions. This study aimed to map the comorbidity environment of osteoporosis (M80/M81) by sex and age, and assess whether osteoporosis is systematically coded at the time of fracture-related hospitalization across fracture types. METHODS We conducted a nationwide retrospective analysis of Austrian inpatient data collected for insurance claims between 2003 and 2014, including 1,705,684 hospitalized patients aged 50-79 years. Comorbidity network analysis was used to characterize sex- and age-specific diagnoses co-occurring with osteoporosis (ICD-10: osteoporosis with pathological fracture M80 and osteoporosis without pathological fracture M81). In paralell, fracture-centered networks were constructed for major fracture types (S22, S32, S42, S52, S62, S72) to assess whether osteoporosis was systematically coded during fracture related hospitalizations. Sex-specific odds ratios (ORs) were calculated to compare comorbidity associations between males and females across three age groups (50-59, 60-69, 70-79 years). RESULTS Across all age groups, females exhibited substantially broader M81-associated comorbidity networks than males, with the number of linked diagnoses increasing from 142 to 245 to 330 in females and from 76 to 137 to 164 in males between ages 50-59, 60-69, and 70-79 years, respectively. Fracture-centered analyses showed that across age groups, fractures were embedded within multimorbidity networks rather than forming isolated diagnostic links with osteoporosis. Hip fractures (S72) showed the strongest co-occurrence with osteoporosis diagnoses, particularly in females aged ≥60 years, whereas upper limb fractures - forearm (S52), wrist and hand (S62), and humerus (S42) - showed no osteoporosis diagnoses among significant co-diagnoses in any age group despite these fractures being considered early fragility fractures. In patients aged 50-69 years, male fracture networks showed stronger co-occurrence with trauma-related and alcohol-related diagnoses, while in older age groups networks in both sexes increasingly included neurodegenerative, cardiovascular, respiratory, and renal diseases. In sex-stratified analyses, the co-occurrence between osteoporosis without pathological fracture (M81) and osteoporosis with pathological fracture (M80) was consistently stronger in males than in females across all ages (e.g. OR 63.74 vs 18.53 at 50-59 years). CONCLUSIONS Osteoporosis is not systematically coded across all fracture types in routine hospital data. Upper limb and thoracic fractures were rarely recorded alongside osteoporosis diagnoses despite being recognized as potential fragility fractures. These patterns suggest under-recognition of osteoporosis at the time of fracture, particularly in younger patients and in males.

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. T. Gisinger: None. K. Fenz: None. P. Klimek: None. E. Dervic: None. A. Kautzky-Willer: None.

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% decline in edges during the 2022 chip shortage, rapid increases in the centrality of AI supply-chain bottleneck firms such as NVIDIA, and geographic realignment of interfirm relations amid geopolitical turbulence. This generalizable framework overcomes barriers to transparency and provides essential, up-to-date maps for assessing resilience and informing policy across strategically relevant sectors.

Political discourse attributes the pressure on European welfare systems to foreign nationals. Yet projections of service demand rarely disaggregate service demand by citizenship status. We develop a structural demographic model and project healthcare, education, and housing demand in Austria through 2050, disaggregated by citizenship status and regions across migration scenarios. We find that migration, ageing, and fertility shape each sector differently. In healthcare, the ageing of Austrian nationals contributes 4.7 times more to demand growth than immigration, with the most acute pressures in rural, low-migration regions. In housing, migration accounts for the entire net growth in demand, concentrated in metropolitan hubs. In education, aggregate demand contracts regardless of migration assumptions, whereas future needs are driven more by the births of foreigners in Austria than by new arrivals. Foreign nationals consume services in proportion to their demographic weight, with deviations explained by age structure rather than over-utilisation. These results show that the drivers of service demand are sector-specific: migration restrictions could ease housing pressure, but would not address ageing-driven healthcare demand and may accelerate contraction in the education system.

Comorbidity networks, which capture disease-disease co-occurrence usually based on electronic health records, reveal structured patterns in how diseases cluster and progress across individuals. However, how these networks evolve across different age groups and how this evolution relates to properties like disease prevalence and mortality remains understudied. To address these issues, we used publicly available comorbidity networks extracted from a comprehensive dataset of 45 million Austrian hospital stays from 1997 to 2014, covering 8.9 million patients. These networks grow and become denser with age. We identified groups of diseases that exhibit similar patterns of structural centrality throughout the lifespan, revealing three dominant age-related components with peaks in early childhood, midlife, and late life. To uncover the drivers of this structural change, we examined the relationship between prevalence and degree. This allowed us to identify conditions that were disproportionately connected to other diseases. Using betweenness centrality in combination with mortality data, we further identified high-mortality bridging diseases. Several diseases show high connectivity relative to their prevalence, such as iron deficiency anemia (D50) in children, nicotine dependence (F17), and lipoprotein metabolism disorders (E78) in adults. We also highlight structurally central diseases with high mortality that emerge at different life stages, including cancers (C group), liver cirrhosis (K74), subarachnoid hemorrhage (I60), and chronic kidney disease (N18). These findings underscore the importance of targeting age-specific, network-central conditions with high mortality for prevention and integrated care.

Legal systems shape not only the recognition of migrants and refugees but also the pace and stability of their integration. Refugees often shift between multiple legal classifications, a process we refer to as the"legal journey". This journey is frequently prolonged and uncertain. Using a network-based approach, we analyze legal transitions for over 350,000 migrants in Austria (2022 to 2024). Refugees face highly unequal pathways to stability, ranging from two months for Ukrainians to nine months for Syrians and 20 months for Afghans. Women, especially from these regions, are more likely to gain protection; Afghan men wait up to 30 months on average. We also find that those who cross the border without going through official border controls face higher exit rates and lower chances of securing stable status. We show that legal integration is not a uniform process, but one structured by institutional design, procedural entry points, and unequal timelines.

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.

The global surge in displacement, with nearly 110 million people uprooted due to violence, underscores the pressing need to comprehend the challenges faced by refugees. Population growth, environmental crises, and political instability contribute to this crisis, projecting an escalating trend in the decades ahead. While hosting countries strive to address concerns related to labour markets, state provisions, and cultural integration, understanding the well-being of refugees upon entry needs to be more adequately explored. This study focuses on refugee stability and integration, employing Austria as a case study. Stability is assessed through residential movement, where more frequent moves indicate instability. Utilising comprehensive administrative data spanning November 2022 to November 2023, we examine residence movements as a proxy for stability. Our findings reveal a stark contrast in the stability of refugees compared to other migrant groups. Analysing movement profiles, we establish that refugees exhibit significantly higher rates of residential mobility than their counterparts, especially among male refugees. This imbalance persists even when comparing refugees to migrants from top refugee-sending countries without official refugee status. This study contributes valuable insights into the intricate dynamics of refugee stability, shedding light on the enduring challenges faced by this population. By examining movement patterns as a key indicator, we provide a nuanced understanding of the residential experiences of refugees, that can inform targeted policies and interventions for enhanced refugee well-being and integration.

The deployment of diverse data-generating technologies in livestock farming holds the promise of early disease detection and improved animal well-being. In this paper, we combine routinely collected dairy farm and herd data with weather and high frequency sensor data from 6 farms to predict new lameness events in various future periods, spanning from the following day to 3 weeks. A Random Forest classifier, using input features selected by the Boruta Algorithm, was used for the prediction task; effects of individual features were further assessed using partial dependence plots. We achieve precision scores of up to 93% when predicting lameness for the next 3 weeks and when using information from the last 3 weeks, combined with a balanced accuracy of 79%. Removing sensor data results have tendency to reduce the precision for predictions, especially when using information from the last one,2 or 3 weeks. Moving to a larger data set (without sensor data) of 44 farms keeps the similar balanced accuracy but reduces precision by more than 30%, revealing a substantial a trade-off in model quality between false positives (false lameness alerts) and false negatives (missed lameness events). Sensor data holds promise to further improve the precision of these models, but can be partially compensated by high resolution data from other systems, such as automated milking systems.

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.

An increasing number of countries are investigating options to stop the spread of the emerging zoonotic infection Salmonella (S.) Dublin, which mainly spreads among bovines and with cattle manure. Detailed surveillance and cattle movement data from an 11-year period in Denmark provided an opportunity to gain new knowledge for mitigation options through a combined social network and simulation modeling approach. The analysis revealed similar network trends for non-infected and infected cattle farms despite stringent cattle movement restrictions imposed on infected farms in the national control program. The strongest predictive factor for farms becoming infected was their cattle movement activities in the previous month, with twice the effect of local transmission. The simulation model indicated an endemic S. Dublin occurrence, with peaks in outbreak probabilities and sizes around observed cattle movement activities. Therefore, pre- and post-movement measures within a 1-mo time-window may help reduce S. Dublin spread.

Abstract Migration’s impact spans various social dimensions, including demography, sustainability, politics, economy, and gender disparities. Yet, the decision-making process behind migrants choosing their destination remains elusive. Existing models primarily rely on population size and travel distance to explain the spatial patterns of migration flows, overlooking significant population heterogeneities. Paradoxically, migrants often travel long distances and to smaller destinations if their diaspora is present in those locations. To address this gap, we propose the diaspora model of migration, incorporating intensity (the number of people moving to a country), and assortativity (the destination within the country). Our model considers only the existing diaspora sizes in the destination country, influencing the probability of migrants selecting a specific residence. Despite its simplicity, our model accurately reproduces the observed stable flow and distribution of migration in Austria (postal code level) and US metropolitan areas, yielding precise estimates of migrant inflow at various geographic scales. Given the increase in international migrations, this study enlightens our understanding of migration flow heterogeneities, helping design more inclusive, integrated cities.

We aim to comprehensively identify typical life-spanning trajectories and critical events that impact patients’ hospital utilization and mortality. We use a unique dataset containing 44 million records of almost all inpatient stays from 2003 to 2014 in Austria to investigate disease trajectories. We develop a new, multilayer disease network approach to quantitatively analyze how cooccurrences of two or more diagnoses form and evolve over the life course of patients. Nodes represent diagnoses in age groups of ten years; each age group makes up a layer of the comorbidity multilayer network. Inter-layer links encode a significant correlation between diagnoses (p < 0.001, relative risk > 1.5), while intra-layers links encode correlations between diagnoses across different age groups. We use an unsupervised clustering algorithm for detecting typical disease trajectories as overlapping clusters in the multilayer comorbidity network. We identify critical events in a patient’s career as points where initially overlapping trajectories start to diverge towards different states. We identified 1260 distinct disease trajectories (618 for females, 642 for males) that on average contain 9 (IQR 2–6) different diagnoses that cover over up to 70 years (mean 23 years). We found 70 pairs of diverging trajectories that share some diagnoses at younger ages but develop into markedly different groups of diagnoses at older ages. The disease trajectory framework can help us to identify critical events as specific combinations of risk factors that put patients at high risk for different diagnoses decades later. Our findings enable a data-driven integration of personalized life-course perspectives into clinical decision-making.