arXiv subjects

Katharina Ledebur

Publications and source records attributed to Katharina Ledebur.

10 recordsLinked to original sources

arXiv2026

Sequence models reveal diagnosis accumulation pathways beyond comorbidity burden in population-scale hospital data

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.

physics.soc-ph↗

arXiv2025

Cultural evolution of human beauty standards

Beauty standards shape self-perception and health through social comparison and objectification, while exposure to idealized imagery exacerbates body-image concerns. Media and fashion are central arbiters of these ideals, yet long-term, quantitative, intersectional studies on how representation has changed remain scarce. We assembled a dataset of 793199 records spanning 25 years of advertising, magazine covers, runway shows, and editorials to quantify changes in anthropometric and demographic representation. We find a paradox in the evolution of beauty ideals: while representational diversity has increased, the median model physique remains stable. This is driven by selective plus-size inclusion at the upper tail, while the typical physique continues to diverge from the US population. Intersectionally, non-white models are 4.5 times more likely to be plus-size, indicating that progress in size inclusivity falls disproportionately on multiple underrepresented identities. Stratifying the industry via a data-driven prestige hierarchy, we find that thinness is overrepresented at the top tier. Finally, comparing two regulatory interventions we observe that numeric thresholds are more effective at reducing underweight appearances. Our results quantify the cultural evolution in media and fashion, revealing that inclusion has increased; however, gains are uneven and intersectionally concentrated on size and ethnicity, whereas the prevailing thin ideal remains largely unchanged.

physics.soc-ph↗

arXiv2025

Generalizing Multimorbidity Models Across Countries: A Comparative Study of Austria and Denmark

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.

physics.soc-ph↗

arXiv2025

Projecting Multimorbidity and Mortality under Demographic Change and Preventive Interventions

As populations age, the rise of multimorbidity poses a significant healthcare challenge. However, our ability to quantitatively forecast the progression of multimorbidity remains limited. Leveraging a nationwide dataset comprising approximately 45 million hospital stays spanning 17 years in Austria, we develop a new compartmental model for chronic disease trajectories across 132 distinct multimorbidity patterns (compartments). Each compartment represents a distinct 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 empirically observed co-occurrence patterns 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 disease (I10--I15) leads to a 0.57 (SD 0.06)% reduction in all-cause mortality over a 15-year period, and a 0.57 (SD 0.07)% reduction in mortality for malignant neoplasms (C00--C97). We also evaluate long-term impacts of SARS-CoV-2 sequelae, projecting earlier and more frequent hospitalizations across a range of diagnoses. Our fully data-driven modelling approach identifies leverage points for proactive preparation by physicians and policymakers to reduce the overall disease burden in the population, emphasizing patient-centered healthcare planning in aging societies.

physics.soc-ph↗

arXiv2025

Characterization Of Diseases In Temporal Comorbidity Networks

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.

physics.soc-ph↗

arXiv2023

Supply Chain Due Diligence Risk Assessment for the EU: A Network Approach to estimate expected effectiveness of the planned EU directive

Globalization has had undesirable effects on the labor standards embedded in the products we consume. This paper proposes an ex-ante evaluation of supply chain due diligence regulations, such as the EU Corporate Sustainable Due Diligence Directive (CSDDD). We construct a full-scale network model derived from structural business statistics of 30 million EU firms to quantify the likelihood of links to firms potentially involved in human rights abuses in the European supply chain. The 900 million supply links of these firms are modeled in a way that is consistent with multiregional input-output data, EU import data, and stylized facts of firm-level production networks. We find that this network exhibits a small world effect with three degrees of separation, meaning that most firms are no more than three steps away from each other in the network. Consequently we find that about 8.5% of EU companies are at risk of having child or forced labor in the first tier of their supply chains, about 82.4% are likely to have such offenders at the second tier and more than 99.1% have such offenders at the third tier. We also profile companies by country, sector, and size for the likelihood of having human rights violations or child and forced labor violations at a given tier in their supply chain, revealing considerable heterogeneity across EU companies. Our results show that supply chain due diligence regulations that focus on monitoring individual buyer-supplier links, as currently proposed in the CSDDD, are likely to be ineffective due to a high degree of redundancy and the fact that individual company value chains cannot be properly isolated from the global supply network. Rather, to maximize cost-effectiveness without compromising due diligence coverage, we suggest that regulations should focus on monitoring individual suppliers.

cs.SI↗

arXiv2023

A parameter-free population-dynamical approach to health workforce supply forecasting of EU countries

Many countries faced challenges in their health workforce supply like impending retirement waves, negative population growth, or a suboptimal distribution of resources across medical sectors even before the pandemic struck. Current quantitative models are often of limited usability as they either require extensive individual-level data to be properly calibrated or (in the absence of such data) become too simplistic to capture key demographic changes or disruptive epidemiological shocks like the SARS-CoV-2 pandemic. We propose a novel population-dynamical and stock-flow-consistent approach to health workforce supply forecasting that is complex enough to address dynamically changing behaviors while requiring only publicly available timeseries data for complete calibration. We demonstrate the usefulness of this model by applying it to 21 European countries to forecast the supply of generalist and specialist physicians until 2040, as well as how Covid-related mortality and increased healthcare utilization might impact this supply. Compared to staffing levels required to keep the physician density constant at 2019 levels, we find that in many countries there is indeed a significant trend toward decreasing density for generalist physicians at the expense of increasing densities for specialists. The trends for specialists are exacerbated in many Southern and Eastern European countries by expectations of negative population growth. Compared to the expected demographic changes in the population and the health workforce, we expect a limited impact of Covid on these trends even under conservative modelling assumptions. It is of the utmost importance to devise tools for decision makers to influence the allocation and supply of physicians across fields and sectors to combat these imbalances.

stat.AP↗

arXiv2022

Small coverage effect in epidemic network models shows that masks can become more effective with less people wearing them

The effectiveness of non-pharmaceutical interventions to curb the spread of SARS-CoV-2 is determined by numerous contextual factors, including adherence. Conventional wisdom holds that the effectiveness of protective behaviour such as wearing masks always increases with the number of people adopting it. Here we show in a simulation study that this is not true in general. We employ a parsimonious network model based on the well-established empirical facts that (i) adherence to such interventions wanes over time and (ii) individuals tend to align their adoption strategies with their close social ties (homophily). When combining these assumptions, a broad dynamical regime emerges where the individual-level infection risk reduction for those adopting protective behaviour increases as the adherence to protective behavior decreases. For instance, for a protective coverage of 10% we find the infection risk for adopting individuals can be reduced by close to 30% compared to situations where the coverage is 60%. Using estimates for the effectiveness of surgical masks, we find that reductions in relative risk of masking versus non-masking individuals range between 5% and 15%, i.e., vary by a factor of three. This small coverage effect originates from system-dynamical network properties that conspire to increase the chance that an outbreak will be over before the pathogen is able to invade small but tightly connected groups of individuals that protect themselves. Our results contradict the popular belief that masking becomes ineffectual as more people drop their masks and might have far-reaching implications for the protection of vulnerable population groups under resurgent infection waves.

physics.soc-ph↗

arXiv2021

Meteorological factors and non-pharmaceutical interventions explain local differences in the spread of SARS-CoV-2 in Austria

The drivers behind regional differences of SARS-CoV-2 spread on finer spatio-temporal scales are yet to be fully understood. Here we develop a data-driven modelling approach based on an age-structured compartmental model that compares 116 Austrian regions to a suitably chosen control set of regions to explain variations in local transmission rates through a combination of meteorological factors, non-pharmaceutical interventions and mobility. We find that more than 60% of the observed regional variations can be explained by these factors. Decreasing temperature and humidity, increasing cloudiness, precipitation and the absence of mitigation measures for public events are the strongest drivers for increased virus transmission, leading in combination to a doubling of the transmission rates compared to regions with more favourable weather. We conjecture that regions with little mitigation measures for large events that experience shifts toward unfavourable weather conditions are particularly predisposed as nucleation points for the next seasonal SARS-CoV-2 waves.

physics.soc-ph↗

arXiv2021

Agent-based simulations for protecting nursing homes with prevention and vaccination strategies

Due to its high lethality amongst the elderly, the safety of nursing homes has been of central importance during the COVID-19 pandemic. With test procedures becoming available at scale, such as antigen or RT-LAMP tests, and increasing availability of vaccinations, nursing homes might be able to safely relax prohibitory measures while controlling the spread of infections (meaning an average of one or less secondary infections per index case). Here, we develop a detailed agent-based epidemiological model for the spread of SARS-CoV-2 in nursing homes to identify optimal prevention strategies. The model is microscopically calibrated to high-resolution data from nursing homes in Austria, including detailed social contact networks and information on past outbreaks. We find that the effectiveness of mitigation testing depends critically on the timespan between test and test result, the detection threshold of the viral load for the test to give a positive result, and the screening frequencies of residents and employees. Under realistic conditions and in absence of an effective vaccine, we find that preventive screening of employees only might be sufficient to control outbreaks in nursing homes, provided that turnover times and detection thresholds of the tests are low enough. If vaccines that are moderately effective against infection and transmission are available, control is achieved if 80% or more of the inhabitants are vaccinated, even if no preventive testing is in place and residents are allowed to have visitors. Since these results strongly depend on vaccine efficacy against infection, retention of testing infrastructures, regular voluntary screening and sequencing of virus genomes is advised to enable early identification of new variants of concern.

physics.soc-ph↗