Numerous studies have highlighted the significant impact of disasters on mental health, often leading to psychiatric disorders among affected individuals. Timely identification of disaster-related mental health problems is crucial to prevent long-term negative consequences and improve individual and community resilience. To address the limitations of prior research that has focused solely on isolated incidents, we analyzed the impact of a recurring Halloween event in Itaewon, South Korea, which culminated tragically in a crowd crush incident in 2022. We conducted sentiment analysis on big data from Korean Twitter to gauge the impact of this disaster on public sentiment. We collected tweets 2 weeks before and after the annual festival from 2020 to 2022, allowing for the consideration of variability across years and days before the disaster. Using a pre-trained RoBERTa neural network model fine-tuned with public sentiment datasets, we categorized tweets into seven pre-defined emotional categories: Anger, sadness, happiness, disgust, fear, surprise, and neutrality. These sentiments were then analyzed as daily time-series data. The overall tweet volume across all sentiment categories increased, particularly showing an increase in the number of tweets indicating \"Sadness\" in 2022 compared with that in previous years. Post-disaster, a substantial increase was noted in the proportion of tweets expressing \"Sadness\" and \"Fear.\" This trend was confirmed by Seasonal Autoregressive Integrated Moving Average with Exogenous Regressor models. Notably, there was an increase in the number of tweets expressing all sentiments, including \"Happy.\" However, significant changes in proportions were observed only in tweets categorized as expressing \"Sadness\" [0.046 (95% CI: 0.024-0.068, P < 0.0001)] and \"Fear\" [0.033 (95% CI: 0.014-0.051, P < 0.0001)]. Our study demonstrates the feasibility of using sentiment data from social media, combined with sentiment classification, to assess distinct public mental health features following disasters. This approach provides valuable insights into the emotional impact of each event.

UNASSIGNED: The use of large language models (LLMs) as writing assistance for medical professionals is a promising approach to reduce the time required for documentation, but there may be practical, ethical, and legal challenges in many jurisdictions complicating the use of the most powerful commercial LLM solutions.
UNASSIGNED: In this study, we assessed the feasibility of using nonproprietary LLMs of the GPT variety as writing assistance for medical professionals in an on-premise setting with restricted compute resources, generating German medical text.
UNASSIGNED: We trained four 7-billion-parameter models with 3 different architectures for our task and evaluated their performance using a powerful commercial LLM, namely Anthropic\'s Claude-v2, as a rater. Based on this, we selected the best-performing model and evaluated its practical usability with 2 independent human raters on real-world data.
UNASSIGNED: In the automated evaluation with Claude-v2, BLOOM-CLP-German, a model trained from scratch on the German text, achieved the best results. In the manual evaluation by human experts, 95 (93.1%) of the 102 reports generated by that model were evaluated as usable as is or with only minor changes by both human raters.
UNASSIGNED: The results show that even with restricted compute resources, it is possible to generate medical texts that are suitable for documentation in routine clinical practice. However, the target language should be considered in the model selection when processing non-English text.

The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required - from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.

Player movement is a fundamental component of evaluating performance in most team sports. Movement can be evaluated across multiple scales, referring to the function of anatomical structures through various planes of motion or an individual regulating their field position based on the movement of opposition players. Developments in commercially available tracking systems have afforded end users the ability to investigate the spatiotemporal features of movement in fine detail. These advancements, in conjunction with overlaid contextual information, have provided insights into the strategies adopted by players in relation to their movement. Understanding movement beyond its semantic value allows practitioners to make informed decisions surrounding performance evaluation and training design. This investigation proposes a framework to guide the analysis of player movement within team sports environments. The framework describes how operational standards for assessing movement can be designed in reference to theory and a set training philosophy. Such practice allows for the spatial and temporal complexities within team sports to be described and could potentially lead to better-applied outcomes through greater interdisciplinary collaboration and an improved holistic understanding of movement. To inform its development, this study evaluates the current research and identifies several open questions to guide future investigations.

Recombinant adeno-associated virus (rAAV) is the most widely used viral vector for in vivo human gene therapy. To ensure safety and efficacy of gene therapy products, a comprehensive analytical profile of the rAAVs is needed, which provides crucial information for therapeutic development and manufacturing. Besides information on rAAV quantities and possible contaminating DNA and protein species, assessing rAAV quality is of utmost importance. In vitro biopotency and methods to determine the full/empty ratio of rAAV capsids are commonly applied, but methods to assess the integrity of the viral genome are still rarely used. Here we describe an orthogonal approach to characterize rAAV quality. Two biologically different rAAV9s from different stages of the bioprocess, generated each with two different transfection reagents, were investigated. In vitro biopotency tests in all cases demonstrated that rAAV9s generated with transfection reagent FectoVIR® possessed a higher biological activity. Mass-based analytical methods, such as sedimentation velocity analytical ultracentrifugation (AUC) and mass photometry, showed a high share of full capsids (>80 %) at late process stages but did not detect any differences in the rAAV9s from the different transfection reagents. Multiplex dPCR and Nanopore long-read sequencing both demonstrated that, also in late-stage process samples, sample heterogeneity was relatively high with a rather small share of full-length transgenes of ∼10-40 %. Intriguingly, both methods detected a higher share of complete transgenes in rAAV9 generated with transfection reagent FectoVIR® instead of Polyethylenimine (PEI), and thereby explain the differences already observed in the biopotency assays. This study therefore emphasizes the necessity to utilize multiple, orthogonal methods to gain a better understanding of recombinantly manufactured AAVs.

Datasets on the amounts of different medicines used over time and location are a valuable resource, with the power to reveal insights into healthcare trends, cost efficiencies, and geographic disparities. In England, primary care prescription data has been openly accessible for analysis for some time through a web tool, providing significant benefits. Since 2020, the National Health Service in England has also released data on secondary care medicine usage, processed from stock control databases, which provides detailed information on medicine usage within hospitals. This is an important dataset, but until now has been available only in a raw form that requires considerable technical skills to be used for even the analysis of basic trends. I have built a web application that enables anyone to easily analyse trends in this data, which is available at hospitalmedicines.genomium.org.
Medicine prescription data is a valuable resource, with the power to reveal insights into healthcare trends, cost efficiencies, and geographic disparities. In England, primary care prescription data has been openly accessible for analysis for some time through a web tool, providing significant benefits. Since 2020, the National Health Service in England has also released data on secondary care prescriptions, i.e. prescribing within hospitals. This is an important dataset, but until now has been available only in a raw form that requires considerable technical skills to be used for even the analysis of basic trends. I have built a web application that enables anyone to easily analyse trends in this data, which is available at http://hospitalprescriptions.genomium.org.

Like most organizations, The Children\'s Hospital of Philadelphia (CHOP) generates a lot of data of varying types, from clinical to staffing and operations. However, these datasets are separated, preventing effective analytics. To improve workforce management and care delivery, CHOP\'s Nursing department collaborated with the Data & Analytics team to create a dashboard-based platform for analyzing workforce metrics and patient outcomes, aiding nursing leaders in decision-making. This project involved multiple phases with contributions from Nursing, Data & Analytics, CHOP\'s Data Trust Office, and Human Resources. Key performance indicators were identified, and a variety of data sources were aggregated to provide a comprehensive view of the nursing enterprise. The resulting platform offers automated, current, and reliable analytics on various aspects, including nursing demographics, education, survey results, staffing actuals by job group, and patient and family experience data. The platform\'s usability was assessed using a modified Health Information Technology Usability Evaluation Scale survey, with a 29% response rate primarily from senior directors and managers. The findings showed high usability and satisfaction, indicating the dashboard is a valuable decision-support tool. Lessons learned include the need for analytics education for nurses and mid-managers, the inclusion of critical nursing-specific metrics (and development of the data pipelines making them possible), and the integration of multidisciplinary team metrics for comprehensive nursing analytics.

UNASSIGNED: Predictive models incorporating relevant clinical and social features can provide meaningful insights into complex interrelated mechanisms of cardiovascular disease (CVD) risk and progression and the influence of environmental exposures on adverse outcomes. The purpose of this targeted review (2018-2019) was to examine the extent to which present-day advanced analytics, artificial intelligence, and machine learning models include relevant variables to address potential biases that inform care, treatment, resource allocation, and management of patients with CVD.
UNASSIGNED: PubMed literature was searched using the prespecified inclusion and exclusion criteria to identify and critically evaluate primary studies published in English that reported on predictive models for CVD, associated risks, progression, and outcomes in the general adult population in North America. Studies were then assessed for inclusion of relevant social variables in the model construction. Two independent reviewers screened articles for eligibility. Primary and secondary independent reviewers extracted information from each full-text article for analysis. Disagreements were resolved with a third reviewer and iterative screening rounds to establish consensus. Cohen\'s kappa was used to determine interrater reliability.
UNASSIGNED: The review yielded 533 unique records where 35 met the inclusion criteria. Studies used advanced statistical and machine learning methods to predict CVD risk (10, 29%), mortality (19, 54%), survival (7, 20%), complication (10, 29%), disease progression (6, 17%), functional outcomes (4, 11%), and disposition (2, 6%). Most studies incorporated age (34, 97%), sex (34, 97%), comorbid conditions (32, 91%), and behavioral risk factor (28, 80%) variables. Race or ethnicity (23, 66%) and social variables, such as education (3, 9%) were less frequently observed.
UNASSIGNED: Predictive models should adjust for race and social predictor variables, where relevant, to improve model accuracy and to inform more equitable interventions and decision making.

UNASSIGNED: Partially observed confounder data pose challenges to the statistical analysis of electronic health records (EHR) and systematic assessments of potentially underlying missingness mechanisms are lacking. We aimed to provide a principled approach to empirically characterize missing data processes and investigate performance of analytic methods.
UNASSIGNED: Three empirical sub-cohorts of diabetic SGLT2 or DPP4-inhibitor initiators with complete information on HbA1c, BMI and smoking as confounders of interest (COI) formed the basis of data simulation under a plasmode framework. A true null treatment effect, including the COI in the outcome generation model, and four missingness mechanisms for the COI were simulated: completely at random (MCAR), at random (MAR), and two not at random (MNAR) mechanisms, where missingness was dependent on an unmeasured confounder and on the value of the COI itself. We evaluated the ability of three groups of diagnostics to differentiate between mechanisms: 1)-differences in characteristics between patients with or without the observed COI (using averaged standardized mean differences [ASMD]), 2)-predictive ability of the missingness indicator based on observed covariates, and 3)-association of the missingness indicator with the outcome. We then compared analytic methods including \"complete case\", inverse probability weighting, single and multiple imputation in their ability to recover true treatment effects.
UNASSIGNED: The diagnostics successfully identified characteristic patterns of simulated missingness mechanisms. For MAR, but not MCAR, the patient characteristics showed substantial differences (median ASMD 0.20 vs 0.05) and consequently, discrimination of the prediction models for missingness was also higher (0.59 vs 0.50). For MNAR, but not MAR or MCAR, missingness was significantly associated with the outcome even in models adjusting for other observed covariates. Comparing analytic methods, multiple imputation using a random forest algorithm resulted in the lowest root-mean-squared-error.
UNASSIGNED: Principled diagnostics provided reliable insights into missingness mechanisms. When assumptions allow, multiple imputation with nonparametric models could help reduce bias.

UNASSIGNED: Influencer-based social media marketing campaigns are a popular strategy to engage customers in many non-research industries (e.g., retail), but have been increasingly used in public health campaigns to reach and engage specific populations. However, few studies have directly compared the performance of influencer-based marketing with other ad strategies (e.g., paid ads) in achieving these goals.
UNASSIGNED: From March to September 2023, we conducted an influencer-focused marketing campaign in which we identified and partnered with predominantly Black LGBTQ + influencers in the United States South to promote engagement in our ongoing research. We then used web analytics and interest form data to compare performance of influencer posts versus paid ads over the same time period.
UNASSIGNED: We contacted a total of 358 influencers, 20 of whom ultimately agreed to post (85% Black/African American) and made a total of 28 posts on our behalf. A significantly higher percentage of users who clicked through influencer posts were Black (40% vs. 15%), were not currently using pre-exposure prophylaxis (PrEP) (67% vs. 62%), had no history of PrEP use (78% vs. 72%), and reported higher medical mistrust (12% vs. 8%) compared to those who clicked through paid ads. The percentage of Black men who have sex with men who were at high HIV risk, who were not taking PrEP, had no history of PrEP, or were high in mistrust, were all 2-3 times higher among those who clicked through influencer posts relative to paid ads.
UNASSIGNED: Influencer-focused marketing may be a powerful tool to efficiently reach and engage high-priority and hard to reach populations.