Global Strategic Solutions (United States)

Risk differences allow decision makers to easily estimate the excess safety risk associated with a medical product relative to the potential benefits. However, in post-market observational surveillance studies that actively monitor (e.g., sequentially over time) for safety risk of new medical products, available methods target a relative measure (e.g., odds ratio and relative risk), which can be especially unstable in the rare event setting. These studies are typically conducted within distributed healthcare networks (e.g., Food and Drug Administration [FDA] Sentinel and Centers for Disease Control [CDC] Vaccine Safety Datalink) with patient-level data protected behind firewalls, but sharing of aggregate, deidentified data for centralized analyses. We propose an inverse probability of treatment weighting (IPTW) method that uses site-specific propensity scores to estimate site-specific risk differences that are combined to create an overall stratified risk difference estimate. This method is tailored to the rare event setting and requires minimal data sharing. The stratified IPTW approach is then extended to the active post-market surveillance setting by incorporating group sequential monitoring boundaries using a novel permutation approach. A simulation study is conducted to evaluate the performance of the new methods relative to two centralized analysis approaches, and the methods are applied to a safety surveillance study comparing the risk of febrile seizure between two vaccines using FDA Sentinel Data from three healthcare organizations.

Developing medicines to serve patients is a complex, highly regulated process in which multiple interdependent factors must be aligned. If one aspect is not thoroughly evaluated, it may adversely affect others and ultimately the final product, leading to delays, additional costs and, in the worst case, market access barriers. This is particularly true for prefilled syringes (PFS) as combination products, where an already challenging market environment is further compounded by intricate regulatory requirements and a highly complex development process.This paper argues that many recurring issues in PFS development are not isolated technical failures, but the consequence of four systemic challenges: diverging regulatory requirements for drugs and devices, division of competencies between drug and device development teams, fragmented supply chains for device components, and non-standardized characterization and evidence generation across stakeholders. Using examples such as break-loose and glide forces, silicone oil, particles, endotoxins, and biocompatibility, the paper illustrates how these challenges manifest as concrete risks along the development pathway, including transitions from vial to syringe and interactions with notified bodies under the MDR. It then outlines system-level strategies to mitigate these risks, including clearer system ownership, integrated risk management and design spaces, earlier and closer collaboration with device experts and suppliers, and more harmonized standards and regulatory expectations. The overall aim is to support a more efficient, predictable and sciencebased pathway to safe, effective and user-appropriate prefilled syringes.

This paper provides a perspective on how the current statistical framework for causal inference based on the Rubin Causal Model is applicable to non-randomized comparative studies with an external control group. Although assignment of subjects to treatment groups may not be possible when the control group is external, we maintain that the study results can still have a causal interpretation if some basic ideas underlying the current framework for causal inference is properly rephrased. To that end, we tweak the definition of unconfoundedness and introduce the concept proto-propensity ratio, so that neither refers to an assignment mechanism or implies that the treatment group indicator is a random variable. It is shown that, under the assumption of unconfoundedness, causal inference can be conducted. The proto-propensity ratio can largely play the same role as propensity score in conducting causal inference.

With the blistering development of edge computing, new horizons have opened in the field of the implementation of AI systems directly on edge devices without the need to rely on centralized cloud environments. This paper introduces the Cloudless Intelligence paradigm which is centred on creating autonomous AI agents that could operate fully within decentralized edge fabrics. We discuss the architecture blueprint of edge-native agents which utilises local computation, federated learning and lightweight inference models to support real-time decision-making, privacy friendliness and context sensing. Our suggestion of a framework combines decentralized communication technology, energy-efficient AI algorithms, and dynamic resource orchestration strategies to achieve self-guiding conduct that is independent of cloud connections. Such experimental performance characteristics on various IoT and edge use case are recorded to justify the viability and practical benefits of our cloudless agents in the aspects of latency, resilience, and energy. This study establishes the design foundation of next-generation AI systems which are self-sustaining, privacy-conscious, and are designed to run in an intermittently connected distributed environment.

PPD is a state of mood, behavior, or emotional disturbances caused by the interaction of biological, psychological, social, and behavioral factors that a woman experiences after childbirth. It is still a global public health issue. The Context-Aware Adaptive Meta-Learner (CAML) framework is a cutting-edge prediction model that uses multi-modal data and context detection to properly predict PPD risk. We collected 1,503 data on maternal health, network connections, and activity rhythms from a medical institution using e-survey questionnaires and wearable sensors. LSTM networks for temporal analysis, XGBoost for ranking accuracy, CNN for visual pattern identification, and Gradient Boosting Machines make up the CAML framework. Contextual modules teach social and family activity identification, and an Explainable Meta-Learner based on SHAP and LIME offers PPD risk variables. After k-fold cross-validation, transfer learning, and domain adaptation, this research had 98.7% predicted accuracy. It outperformed state-of-the-art approaches in CAML model predictability and interpretability. According to the study, multi-modal data integration, explainable AI, and context-wise analysis may change maternity care. The CAML framework gives healthcare practitioners clinical and assignable information to customize PPD prevention, mental health therapies, and ALM living experiences to new mothers' fundamentally vulnerable and heterogeneous social and clinical backgrounds.

In Canada, there has been an effort to address youth mental health through various initiatives and investments, although mental health struggles among young Canadians continue to increase. Despite these challenges, there is also recognition of the strength and resilience of youth, as well as the increasing empowerment and engagement of young people in shaping mental health support systems. In this article, the authors explore the role of leadership in facilitating the co-design and implementation of a comprehensive continuum of care, leveraging the Stepped Care 2.0 framework, to facilitate effective and sustainable youth mental health support systems.

The current situation with the water resources of the Central Asian republics is characterized by an increasing shortage of water in the region caused by global climate change, an increase in population, an increase in economic activity, wear and tear of irrigation systems and other reasons. All of the above actualizes the issues of enhancing regional integration, which is impossible without mutual political trust, referred to by the authors as “hydro-trust”. This work is designed to determine the level of hydro-trust in the region, as well as to develop mechanisms to strengthen it. Under the current conditions, the authors suggest that the States of the region pursue a more coordinated water policy aimed at solving the problem of water scarcity. At the same time, the authors emphasize that it will be possible to implement their plans only if trust is achieved on this issue between all participants in regional hydro policy, which is possible only in an atmosphere of mutual understanding and mutual support. In the institutional plan, it is proposed to consider regional supranational structures, which include the Shanghai Cooperation Organization, to address water issues. In practical terms, the focus is on attracting financial resources for the modernization of existing and construction of new water infrastructure facilities; digitalization of water resources monitoring; use of existing experience and exchange of practices for solving similar problems between direct donors and consumers of water resources in the region. It is emphasized that at the moment Central Asia cannot present a project for the development of the water and energy complex common to all five republics, however, the existing cooperation being built between the states of the region, albeit mainly in a bilateral format, still gives hope for a change in the situation for the better. The research methodology is presented by analyzing joint projects and integration processes in the field of water management in the region.

Heterogeneous cloud-edge computing environments present unique challenges in resource allocation due to their distributed nature, varying computational capabilities, and dynamic workload patterns. This paper presents a comprehensive analysis of machine learning approaches for optimizing resource allocation in these environments. I categorize and evaluate various ML techniques including reinforcement learning, deep learning, and federated learning approaches, highlighting their strengths and limitations. A comparative analysis of these techniques demonstrates that hybrid approaches combining reinforcement learning with deep neural networks achieve 18-22% better resource utilization and 15% lower latency compared to traditional heuristic methods. I also propose a novel adaptive resource allocation framework that dynamically adjusts allocation policies based on changing network conditions and application requirements, demonstrating superior performance in real-world testbeds.