United States Social Security Administration

Most Social Security Disability Insurance (SSDI) beneficiaries face a mandatory two-year waiting period for Medicare eligibility. Because of concerns that lack of health coverage during the waiting period could have life-threatening consequences for some beneficiaries, selected subpopulations are exempt from the waiting period. In 2025, legislation was introduced in Congress to expand the scope of these exemptions. However, there is limited empirical evidence on the twenty-four-month mortality rate of the SSDI beneficiary population, how it compares with that of the general population, how it varies by age and primary impairment, and how it has changed over time. Using 2000-19 Social Security Administration data, we provide a descriptive analysis of mortality rates during the Medicare waiting period. In 2019, the twenty-four-month mortality rate of SSDI beneficiaries was 5.96percent, whereas the adjusted rate in the general population was 1.25percent. The mortality rate was highest among beneficiaries with neoplasms, at 39.3percent. The twenty-four-month mortality rate of SSDI beneficiaries has risen since 2011, reflecting changes in the demographic and impairment characteristics of new beneficiaries. These findings demonstrate the SSDI population's vulnerability and can inform policy conversations about expanding the scope of exemptions from the Medicare waiting period.

We examine the earnings assimilation of unauthorized Hispanic immigrants using an innovative strategy to estimate which individuals may lack legal work authorization. Our strategy relies on restricted-use data linking respondents of the Current Population Survey to Social Security records. A successful match with Social Security records, along with survey information, is used as evidence that an immigrant is authorized. Using a successful match with Social Security records allows us to rule out as being unauthorized half of all individuals otherwise classified as unauthorized. Our results indicate that unauthorized immigrant men have significantly lower earnings than authorized immigrant men over the first 15 years since arrival. However, despite facing additional obstacles, immigrants without work authorization make the same amount of progress in catching up with the earnings of native Whites. Unauthorized immigrant women have similar earnings as women with work authorization for much of their careers. Overall, we find a pattern of persistent inequality whereby both authorized and unauthorized Hispanic immigrants make modest progress catching up with natives’ earnings. We test alternative explanations for the observed pattern in earnings trajectories and find that the occupation and industry in which immigrants are employed, along with the number of hours worked, appear to explain a large part of the disparity relative to native Whites.

The global expansion of artificial intelligence (AI) and its increasing availability in educational settings present both unprecedented opportunities and major pedagogical challenges. The performance and learning outcomes enabled by AI raise fundamental questions about its meaningful, ethically responsible, and pedagogically justified integration into student learning processes. This article explores the possibilities and limitations of effectively incorporating AI into the educational environment from didactic, psychological, and ethical perspectives. It emphasizes how the use of AI tools can transform the dynamics of teaching and learning, redefining the roles of teacher and student in an increasingly technologized classroom.The theoretical foundations are rooted in personalized and adaptive learning theories, constructivist approaches to knowledge acquisition, and cognitive psychology focused on information processing and metacognition. The article also draws from current research on human-machine interaction and self-regulated learning, examining how AI-driven platforms can support differentiated instruction and formative assessment. Furthermore, it discusses potential risks such as overreliance on algorithms, loss of critical thinking, and ethical dilemmas arising from data privacy, authorship, and transparency of automated decisions.From a practical standpoint, the analysis reveals that while AI has the potential to strengthen inclusivity, enhance learner motivation, and optimize instructional efficiency, its educational effectiveness depends on the teacher’s pedagogical autonomy and digital competence. The authors propose a balanced approach that integrates AI as a supportive – not substitutive – partner in education, preserving the humanistic dimension of learning and the social- emotional interaction between teacher and student.The article concludes with theoretically and empirically informed recommendations for school practice, addressing the conditions necessary for responsible AI implementation. These include teacher training, curriculum adaptation, ethical policies for AI use, and strategies for maintaining equal access to technology. The paper is aimed at the professional community of educators, psychologists, and policymakers seeking evidence-based guidelines for making artificial intelligence a truly pedagogically meaningful element of contemporary education.

Named Entity Recognition (NER) is a critical component for real-time public opinion monitoring and threat perception in big data systems. Compared with English, Chinese suffers from more grammatical ambiguities, such as fuzzy word boundaries and polysemous words, which can make contextual information insufficient to support Chinese NER. To address this issue, recent works have suggested that retrieving background knowledge as assistance is a potential solution, yet how to obtain and leverage related background knowledge for the NER task remains a challenge. In this paper, we propose a neural-based approach that utilizes auxiliary knowledge from search engines for Chinese NER in big data systems. Specifically, we retrieve external related texts via the search engine with a query generated from the input sentence. Then, a multi-channel semantic fusion model is adopted to aggregate the original sentences with the retrieved external related texts. Experiments have demonstrated the superiority of our model across four NER datasets, including formal and social media language contexts, which further proves the effectiveness of our approach. By improving the accuracy and robustness of Chinese NER, this work provides a practical solution for safeguarding big data content security.

The use of estimators instead of stochastic gradients for updates has been shown to improve algorithm convergence rates of, but their impact on generalization remains under-explored. In this paper, we investigate how estimators influence generalization. Our focus is on two widely studied problems: stochastic optimization (SO) and stochastic compositional optimization (SCO), both under convex and non-convex settings. For SO problems, we first analyze the generalization error of the STORM algorithm as a foundational step. We then extend our analysis to SCO problems by introducing an algorithmic framework that encompasses several popular algorithmic approaches. Through this framework, we conduct a generalization analysis, uncovering new insights into the impact of estimators on generalization. Subsequently, we provide a detailed analysis of three specific algorithms within this framework: SCGD, SCSC, and COVER, to explore the effects of different estimator strategies. Furthermore, in the context of SCO, we propose a novel definition of stability and a new decomposition of excess risk in the non-convex setting. Our analysis indicates two key findings: (1) In SCO problems, eliminating the estimator for the gradient of the inner function does not impact generalization performance while significantly reducing computational and storage overhead. (2) Faster convergence rates are consistently associated with better generalization performance.

As digital ecosystems expand across organizational and geopolitical boundaries, the need for robust, multi-layered Zero Trust Architectures (ZTA) has become critical for safeguarding sensitive data within federated enterprise networks and high-risk operational environments.Traditional perimeter-based security models are increasingly obsolete, offering limited protection against insider threats, lateral movement, and advanced persistent threats (APTs).This paper proposes an integrated Zero Trust framework that operates across data, application, identity, and network layers to enforce context-aware access controls, continuous validation, and leastprivilege principles in heterogeneous and federated infrastructures.The study begins by outlining the limitations of conventional security paradigms in dynamic, multi-domain environments such as multinational enterprises, military command systems, and supply chain ecosystems.It then transitions into an in-depth exploration of Zero Trust principles specifically, identity federation, microsegmentation, policy-based access enforcement, and behavioral analytics and how they can be orchestrated across on-premises, hybrid cloud, and edge computing layers.A multi-layered ZTA blueprint is presented, combining software-defined perimeters (SDP), secure service edge (SSE) technologies, decentralized identity management, and federated trust brokers.The framework emphasizes interoperability between sovereign IT domains while maintaining compliance with data protection regulations such as GDPR, CCPA, and NIST SP 800-207.Particular focus is placed on securing mission-critical systems in high-risk sectors such as defense, healthcare, and critical infrastructure, where resilience and integrity are non-negotiable.By integrating Zero Trust with continuous risk scoring, AI-driven anomaly detection, and policy orchestration across domains, this architecture enables a shift from reactive security to adaptive, proactive defense.Ultimately, it provides a strategic foundation for operationalizing data-centric protection in globally distributed, threat-prone environments.

Over the past thousand years, one can observe a preponderance of evidence demonstrating the emergence and application of safety principles progressing from a crude beginning to the modern era in all human accomplishments. For more than a thousand years, we have seen evidence of the application of safety principles, albeit primitive compared to those of today, for a reasonable approach to accomplish a task. The limited knowledge available in the past, along with a comparative lack of resources, did not deter these early investigators from adapting their thought processes to create solutions to the problems of their day. Notwithstanding their limited knowledge and lack of resources, these early investigators were able to apply their thought processes to reach a goal. Collectively, their practices, experimental results, and findings provided the foundation for the evolution of the disciplines of microbiology, epidemiology, public health, and safety, and eventually biosafety. Many contributions were made in decontamination methodologies and technologies and the use of protective clothing, engineering controls, vaccine development, food preservation, infection control, aseptic practices, and containment. It is wonderful to learn what germs have taught us! This paper provides an overview of historical safety and biosafety events and how they have both influenced and contributed to the development of modern principles and practices.

ObjectivesTo estimate the impact of occupational injury and illness on opioid-related mortality while accounting for confounding by preinjury opioid use.MethodsWe employed a retrospective cohort study design using Washington State workers’ compensation data for 1994–2000 injuries linked to US Social Security Administration earnings and mortality data and National Death Index (NDI) cause of death data from 1994 to 2018. We categorised injuries as lost-time versus medical-only, where the former involved more than 3 days off work or permanent disability. We determined death status and cause of death from NDI records. We modelled separate Fine and Gray subdistribution hazard ratios (sHRs) and 95% CIs for injured men and women for opioid-related and all drug-related mortality through 2018. We used quantitative bias analysis to account for unmeasured confounding by preinjury opioid use.ResultsThe hazard of opioid-related mortality was elevated for workers with lost-time relative to medical-only injuries: sHR for men: 1.53, 95% CI 1.41 to 1.66; for women: 1.31, 95% CI 1.16 to 1.48. Accounting for preinjury opioid use, effect sizes were reduced but remained elevated: sHR for men was 1.43, 95% simulation interval (SI) 1.20 to 1.69; for women: 1.27, 95% SI 1.10 to 1.45.ConclusionsOccupational injuries and illnesses severe enough to require more than 3 days off work are associated with an increase in the hazard of opioid-related mortality. The estimated increase is reduced when we account for preinjury opioid use, but it remains substantial. Reducing work-related injuries and postinjury opioid prescribing and improving employment and income security may decrease opioid-related mortality.