Hepatitis C Among Persons Who Inject Drugs in New York State: Progress Towards Elimination

Background: Hepatitis C virus (HCV) infection is one of the leading causes of liver disease in the United States (US) and worldwide. In the United States today, HCV is transmitted primarily through injection drug use (IDU) and therefore disproportionately impacts persons who inject drugs (PWID), a population with a range of complex social and health needs. HCV is not vaccine-preventable but can be cured in over 90% of cases with an 8–12-week course of daily direct-acting antiviral (DAA) medication. Since approval of curative medications in 2014, various jurisdictions have developed plans to eliminate HCV. In 2021, the New York State (NYS) Department of Health released a plan that outlined priorities, metrics, and goals for eliminating HCV as a public health problem from the state by 2030, including a special focus on PWID. However, the baseline burden of HCV in this population, including the number of individuals with HCV, is not yet known. Understanding the burden of HCV among PWID and the demographic and risk factors associated with HCV will inform public health resource allocation, including prevention, testing, and treatment services. It may also inform data-driven interventions and investments in HCV services for PWID, to reduce disease burden in the population. Methods: This dissertation aimed to assess the baseline burden of HCV among PWID in NYS, by estimating the number of PWID in the state (Aim 1), identifying demographic and behavioral factors associated with HCV infection (Aim 2), and modeling progress towards HCV elimination in this population given current HCV transmission and treatment dynamics (Aim 3). Aim 1 used a multiplier method anchored on nonfatal overdose emergency department visits and hospitalizations, adjusted by substance use disorder treatment admission data and data from a biobehavioral study of PWID (described in Aim 2), to estimate the number of PWID in NYS from 2019-2021. Aim 2 describes the results of a biobehavioral study that elicited a community-based sample of PWID recruited from syringe service programs (SSPs) and the surrounding community in NYS to assess HCV testing, diagnosis, and treatment history, and prevalence of HCV infection (current or past infection). This study utilized multilevel modeling to identify key demographic and risk behaviors that are associated with HCV status while controlling for clustering by SSP recruitment site. Informed by the results from Aim 1 and Aim 2, in Aim 3, I developed compartmental models of HCV transmission among PWID in NYS to assess progress towards HCV elimination targets in this population. Initial state values, and incidence, diagnosis, treatment, and reinfection rates were informed by studies of the NYS PWID population to best represent the local HCV epidemic. Results from this model were used to identify gaps and opportunities where interventions may improve achievement of elimination targets. Results: In Aim 1, I estimated that from 2019-2021, approximately 99,206-107,705 PWID resided in NYS, representing 0.65-0.70% of the NYS 18+ population. In Aim 2, I found that 29.0% of PWID had current HCV infection, and an additional 29.0% had a previous infection, indicating that approximately 30,000 PWID in NYS were currently living with HCV infection. Risk factors for current infection included younger age, frequent injection, skin wounds and abscesses, and experiencing homelessness in the past year. Results from the models in Aim 3 indicate that prevalence of current HCV infection among PWID in NYS may decline by 2030, and treatment/clearance among the diagnosed may increase, which represents important progress towards HCV elimination targets. However, the percent of all current HCV cases that are diagnosed may decline by 2030, indicating that improving diagnosis in the PWID population is crucial for HCV elimination. Conclusion: This dissertation comprehensively describes the current state of HCV among PWID in NYS and highlights areas where the state should enhance its effort to progress towards HCV elimination goals. These findings represent the baseline HCV burden among PWID in NYS, estimating that 29.0% of PWID in NYS, or approximately 30,000 individuals, have current HCV infection. Current HCV infection was associated with several key demographic and risk factors, including homelessness, higher injection frequency, and recent skin wounds, The findings of this dissertation will inform targeted efforts to get these PWID diagnosed, linked to care, and treated for their HCV infection. Given current incidence, diagnosis, treatment, and reinfection rates, the prevalence of current HCV infection may decrease, treatment may increase, and the percent of current HCV infections that are diagnosed may decrease by 2030. These findings will inform resource allocation and elimination initiatives to improve HCV outcomes in the NYS PWID population, particularly in improving diagnosis. Importantly, each of these three projects can be replicated with newer data to monitor changes in the PWID population size, HCV burden, and progress towards HCV elimination targets as HCV testing, diagnosis, and treatment improve among PWID in NYS over time. Continued monitoring of the burden of HCV among PWID will be crucial as we continue to progress towards HCV elimination in NYS

In-State Retention of non-U.S. International Medical Graduates in New York State: An Analysis of Facilitators and Barriers

International medical graduates are physicians who received their medical school education outside the United States. International medical graduates, comprised of both U.S. citizens and citizens of foreign countries, have bridged physician workforce needs for decades. Most international medical graduates tend to practice in ten states (including New York), in larger cities and metropolitan areas, and in primary care specialties. Physician workforce analyses project significant workforce shortages in the coming decades (HRSA, 2023; Robert Graham Center, n.d.). International medical graduates contribute greatly to the U.S. healthcare system, caring for vulnerable populations, and serving in underserved areas (Malayala et al., 2021). International medical graduates, comprising nearly a quarter of all physicians, face significant challenges both during their residency training, and while transitioning to post-residency practice. International medical graduates who are not U.S. citizens (non-U.S. international medical graduates) face challenges including navigating the US culture, adapting to the US graduate medical education collective and the US healthcare system, communication skills, racial discrimination, emotional distress, finances, and planning and advisement for post residency work (Zepeda et al., 2022). There is limited research about challenges faced by non-U.S. international medical graduates, and there is very little to no research that discuss challenges faced by non-U.S. international medical graduates specific to New York. The aims of the study were to describe the barriers and facilitators that inform whether non-U.S. international medical graduates will continue to stay in New York post-residency; to describe the motivations for some non-U.S. international medical graduates in New York to permanently leave their residency program after being offered a residency training position or during any point of their residency training in New York; and to propose evidence-based policy and program recommendations to support non-U.S. international medical graduates in New York during their residency and improve retention of non-U.S. international medical graduates in New York post-residency. The quantitative component of the study included an analysis of 21,136 responses from New York Resident Exit Surveys between 2014-2023. Visa sponsorship emerged as the strongest predictor of job acceptance among non-U.S. international medical graduates, particularly for J-1 physicians. Cost of living and proximity to family also significantly influenced retention. The qualitative component included 12 key informant interviews with non-U.S. international medical graduates, four J-1 visa waiver program staff, and one residency program director. Qualitative findings highlighted non-U.S. international medical graduates’ strong commitment to career development, the critical role of program culture and mentorship, and the impact of workload, bias, and other systemic barriers which impact well-being and retention in residency. The perspective of J-1 visa waiver program staff highlighted the current policy and regulatory landscape, inequitable waiver utilization, administrative challenges, and inadequate monitoring of long-term outcomes. As healthcare needs of the population increase and evolve, it is more crucial than ever to ensure there is a sufficient physician workforce to meet the emergent health needs of New Yorkers. Understanding facilitators and mitigating the effects of barriers impacting the in-state retention of non-U.S. international medical graduates in New York can improve retention of non-U.S. international medical graduates in their residency and post-residency in New York

Population Genomics And Evolution Of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a Gram-negative bacterium ubiquitous in diverse environments and an opportunistic pathogen in humans. It is a leading cause of chronic lung infections in cystic fibrosis patients and of hospital-acquired pneumonia and sepsis. With a large flexible genome and abundant mobile genetic elements, P. aeruginosa exhibits high levels of antimicrobial resistance (AMR), making it a significant public health threat. Evolutionary processes such as homologous recombination, and horizontal gene transfer generate extensive genomic diversity in this species and facilitate the spread of resistance genes. In this dissertation, I investigate the genetic and evolutionary factors shaping P. aeruginosa population structure and evolutionary dynamics. Using hundreds of publicly available genomes, I analyze patterns of homologous recombination and accessory gene content (Chapter 2), genome-wide associations between genetic variants and infection source (Chapter 3), and the distribution of mobile genetic elements and anti-phage defense systems (Chapter 4). Chapter 1 reveals two major phylogenetic lineages with distinct recombination patterns and unique accessory genome networks. Chapter 2 identifies genetic loci (SNPs, unitigs and accessory genes) differentiating isolates from cystic fibrosis lungs versus bloodstream infections, reflecting niche-specific adaptation. Chapter 3 shows that mobile genetic elements (plasmids, prophages, and insertion sequences) frequently co-occur with AMR genes and anti-phage defense systems in the same genomes, facilitating their spread. Together, these findings provide novel insights into the evolutionary dynamics that facilitate the emergence of epidemic strain clones, and antibiotic resistance, in P. aeruginosa, which can guide efforts in tracking outbreaks, infection control, and public health preparedness

Using eye tracking to explore how auditory-visual chunking modulates visual search strategies in expert musicians in the presence of auditory interference

Experts show performance advantages during visual search due to their extensive experience with domain-specific stimuli. Experts form memory representations for meaningful visual patterns, called chunks, that group together multiple domain-specific features into larger patterns. The ability of experts to form chunks could allow them to more precisely encode a search template, which could facilitate visual search performance. In the domain of music reading, expert musicians might form chunks that are multimodal (i.e., using visual and auditory modalities). The current studies addressed the possibility that chunks are multimodal by extending a previous cross-modal visual search task to manipulate the presence of auditory interference either during encoding or retrieval, comparing expert and non-musicians’ eye movements. In addition, two exploratory studies compared fixation locations of experts and non-musicians during encoding, as well as correlations between self-report music awareness and memory and eye movement measures. Results showed that, compared to non-musicians, experts had higher accuracy, and this was magnified in the presence of interference during encoding, indicating experts\u27 performance advantages. In addition, compared to non-musicians, experts were able to modulate their search performance in the presence of auditory interference during retrieval only. This was evidenced by faster first fixations on the target to the trial end. Finally, compared to non-musicians, experts show greater effects of relevancy, as indicated by their dwell-based pattern of results across Studies 1 and 2. Fixation patterns also reveal that experts fixate on more relevant regions of a bar of music, in order to encode it into a meaningful pattern or chunk. Not surprisingly, experts, compared to non-musicians had significant correlations between eye movement patterns and self-report measures on musical awareness and memory, suggesting that experts who rate themselves at having better musical memory and awareness of musical structure perform better on a music-related visual search task. Together, these findings suggest that experts are using both auditory and visual information to precisely encode a bar of music into a chunk, allowing them to efficiently search for the target within a search array. The presence of auditory interference impacts non-musicians more, compared to experts, suggesting that expertise modulates auditory distraction

ACADEMIC OUTCOMES OF ASSOCIATE DEGREE TRANSFER STUDENTS FROM SELECTED TWO-YEAR CAMPUSES TO A PUBLIC URBAN RESEARCH UNIVERSITY IN THE NORTHEAST U.S: AN EMPIRICAL ANALYSIS USING ADMINISTRATIVE UNIT RECORD DATA

This study examined academic outcomes for associate degree transfer students at an urban, public research university in the Northeast United States. In particular, this study examined differences in rates of retention, rates of graduation, and cumulative upper-division grade point average (GPA) at graduation between associate degree transfers and direct-entry undergraduates at the same institution, taking into account the two-year college attended. This approach provides a lens into campus effects, considering the profiles of associate degree transfers from identified two-year colleges to search for similarities and differences in academic outcomes for transfers by two-year college attended. The study relied on student-level information in academic administrative unit records for all students from 2012 to 2018.  The data made available for the study are routinely collected data and used for institutional purposes as well as state and federal reporting.  Selected restricted populations for analysis comprised associate degree transfers commencing studies in 2014 and direct-entry undergraduates commencing studies in 2012 who had completed the equivalent of two years of full-time study by 2014.  Information on attendance and academic performance to 2018 was available for analysis.  Coverage provided cell sizes sufficient to support separate analyses of the outcomes of associate degree transfers for each of two urban public two-year colleges. Detailed analyses, consisting of descriptive statistics, chi-square analyses, multivariate regression, and propensity score matching, generated results to respond to and inform two research questions: Research Question 1: Do rates of retention, bachelor’s completion, and cumulative upper-division GPA at graduation differ between first-time public two-year college transfers with associate degrees and otherwise similar direct-entry undergraduates at a public, urban research university in the Northeast, by the two-year college attended? Research Question 2: Do rates of retention, bachelor’s completion, and cumulative upper-division GPA at graduation differ between first-time public two-year college transfers with associate degrees and otherwise similar direct-entry undergraduates, by selected student attributes, and career/field choices and the two-year college attended? While broadly aligned with prior research, the study yielded several detailed results and nuanced findings of interest. First, relatively large differences in retention rates and graduation rates between associate degree transfers and direct-entry undergraduates entering their third year of study tend to narrow when account is taken of student attributes (racial/ethnic background, gender, Pell grant status) and academic variables (lower division grade point average, STEM field).   However, this overall tendency differs in extent when academic performance is examined separately by the two-year college attended. Second, student attributes and academic variables (e.g. Pell grant recipient) were as important as associate degree transfer status in accounting for variation in retention rates and six-year graduation rates. Third, differences in academic performance (by cumulative upper-division GPA) tended to be modest, with somewhat larger, more meaningful differences for sub-groups. Fourth, results of propensity score matching offer some evidence that, at the participating public urban research university, few direct-entry undergraduates (retained to the third year) are “otherwise similar” but for transfer status to associate degree transfers.  While the results owe in part to the study design and data limitations, they provide some support for consideration of policies and practices targeted at associate degree transfers. Fifth, associate degree transfers were much less likely than direct-entry undergraduates (commencing the equivalent of a third year of study) to pursue STEM as a field

Bayesian Modelling on Periodically and Multiple Periodically Correlated Time Series Data

Time series with multiple periodically correlated (MPC) components present a complex challenge, with relatively limited prior research. Most existing models are designed for simpler periodically correlated (PC) components and often struggle with over-parameterization, optimization issues, and capturing complex PC patterns within a time series. Frequency separation techniques can help preserve the correlation structure of individual PC components, while Bayesian methods can integrate new and prior information to refine beliefs about these components. This study proposes a two-stage approach that combines frequency separation and Bayesian techniques to forecast PC and MPC time series data. This method aims to demonstrate improved effectiveness in modeling MPC components compared to traditional approaches. Among the various forecasting methods available, the seasonal autoregressive integrated moving average (SARIMA) model and exponential smoothing technique are regarded as traditional approaches. However, their basic forms are primarily designed for modeling single PC components and often face difficulties in handling MPC patterns. Many studies have attempted to adapt these classical statistical models to better accommodate MPC components. Notable examples include the double seasonal ARIMA model, an extension of the exponential smoothing technique based on the Holt-Winters method, and the hidden Markov model with multiple periods. Despite these developments, existing methods frequently encounter over-parameterization and optimization issues and are often inadequate in effectively capturing complex seasonal patterns in time series data. Because theoretical and practical limits must be established in terms of both frequency separations and Bayesian modelling, along with a practical application that is comprehensive, this purpose was operationalized through three objectives. The first objective is to investigate the presence and characteristics of MPC—including daily, weekly, annual patterns, and their respective harmonics—in Turkey’s industrial electricity consumption time series using the Variable Bandpass Periodic Block Bootstrap (VBPBB) method. A core focus is to integrate the Kolmogorov-Zurbenko (KZ) filter framework (including its extension, the Kolmogorov-Zurbenko Fourier Transform (KZFT) filter) into the VBPBB workflow. Compare the performance of VBPBB with the Generalized Seasonal Block Bootstrap (GSBB) in terms of confidence interval (CI) size and accuracy. Validate the statistical significance of the identified MPC components by examining their 95% CIs, and quantify the contribution of each significant component to total electricity consumption variability using the coefficient of determination; Demonstrate the practical utility of the VBPBB method in resolving MPC data and preserving the correlation structure of individual periodic components—addressing a key limitation of traditional periodic block bootstrap (PBB) methods that only focus on single frequencies. The second objective was to propose a two-stage VBPBB-Bayesian integrated method that combines VBPBB and Bayesian techniques with Markov Chain Monte Carlo (MCMC) via the Metropolis–Hastings algorithm to address the challenges of forecasting time series with MPC components. Specifically, it aims to: 1) Use VBPBB to isolate significant single periodic correlated (PC) components (and their harmonics) from MPC time series by filtering frequencies, preserving the correlation structure of each target component while attenuating noise and unrelated signals; 2) Employ Bayesian modeling to estimate the amplitude (the key parameter) of each separated PC component, avoiding over-parameterization associated with modeling individual data points; 3) Validate the method’s effectiveness through simulations (with single and double PC components) and real data application (U.S. monthly milk production); The third objective was to validate the effectiveness and robustness of the proposed VBPBB-Bayesian model in forecasting MPC time series. Specifically, it aims to test the model’s performance across diverse simulation scenarios (single/double PC components, varying frequency levels (low/medium/high), different noise variances (10/100/1000), and harmonic frequencies); 4) Compare the VBPBB-Bayesian model with a standard Bayesian model (without VBPBB) to demonstrate its superiority in accurate amplitude estimation; 5) Verify the model’s suitability for handling complex MPC patterns by showcasing consistent performance across varied data conditions. The potential applications of VBPBB and Bayesian analytic methods extend across the entire spectrum of health sciences and human services, as well as span the full range of scientific disciplines and technological fields—their scope is nearly boundless. In light of this, the present Dissertation validated the efficacy of these methods by systematically and comprehensively defining and illustrating both their theoretical constraints and practical limitations

The Evolving Landscape of Cybercrime: The Impact of The Data Economy and State-Sponsored Threats

This thesis explores the evolving landscape of cybercrime, examining how the rise of the data economy, coupled with state-sponsored cyber operations has influenced threat trends. It explores into the broader cyber threat landscape and how incident response strategies implemented by the public sector, private sector, and regulatory bodies have adapted over time. By analyzing historical data of cyber incidents, regulatory changes, and technological advancements; this research aims to identify patterns in cybercrime escalation and assess how varying responses affect the resiliency of critical infrastructure. Utilizing a mixed-method approach consisting of a literature review and data-driven cyber threat intelligence, this research seeks to contribute a comprehensive insight on future risks and regulatory challenges that are associated with cybercrime in an increasingly interconnected world. Key questions include: how has the reliance on data influenced the sophistication and frequency of cybercrime? What role do state-sponsored actors play in shaping incident response? And what indicators can be used to evaluate the effectiveness of existing cyber mitigation strategies and prediction of future cybercrime trends

Cognitive Development Following Prenatal Stress: The Role of Maternal Sensitivity

Maternal stressful life events experienced during pregnancy may influence infant postnatal developmental and cognitive outcomes. However, less is known about how factors like infant sex and age of cognitive assessment may influence their associations. The current study investigated the relations between prenatal stress, infant birth outcomes, infant cognitive development, and the potential moderating role of maternal sensitivity. Longitudinal data from 322 low income, Mexican American mother infant dyads were examined during prenatal, 12-month, and 24-month time periods. Mothers self-reported prenatal stress during pregnancy, and infants’ cognitive development was assessed at both 12 months and 24 months. Birth outcomes (birth weight and gestational age) were obtained from hospital birth records. Results indicated that more maternal sensitivity was associated with higher cognitive development scores. Further, associations between prenatal stress and cognitive development depended on maternal sensitivity, such that prenatal stress was only associated with higher cognitive scores at 24 months at low levels of maternal sensitivity. While birth outcomes were not found to mediate these relations, females with poorer birth outcomes did have significantly poorer cognitive development. Results suggest that maternal sensitivity may support early infant cognitive development and underscore the importance of examining potential enduring and latent effects across the infancy and early childhood period

The Effects of Vitamin K on Neuronal Ferroptosis

Dementia is a crippling illness affecting millions worldwide causing significant public health burdens with long-term care. The lack of treatment options results in many years of suffering for affected individuals and their loved ones who often become primary caretakers. Decades of study have focused on treating dementia with little-to-no success, so an emphasis is needed on attempting to prevent disease progression altogether. This research explored vitamin K as a potential strategy for prevention of dementia onset or progression through the mechanism of suppressing ferroptosis. Ferroptosis is a type of cell death suggested to contribute to the hallmark traits of Alzheimer’s disease (the most common form of dementia), such as beta amyloid plaque buildup and tau aggregation. Targeting ferroptosis pathways that ultimately cause neurodegeneration has the potential to significantly lower the global burden of this disease. Understanding the mechanism of action of vitamin K in neuronal ferroptosis may lead to new non-toxic, easily implemented prevention strategies for dementia

The First Amendment on Trial: Hate Speech, Free Speech and College Campuses

This thesis examines the tension between the First Amendment’s guarantee of free speech and the harmful impact of hate speech on college campuses. As incidents of targeted, discriminatory rhetoric rise in academic settings, the need to reevaluate what constitutes protected speech has become increasingly urgent. Through legal analysis, including case law such as Brandenburg v. Ohio and Schenck v. United States, this paper argues that the Supreme Court must distinguish between constitutionally protected speech and harmful hate speech. The thesis uses contemporary case studies from college campuses across the country. It includes firsthand accounts from the University at Albany to demonstrate how hate speech, particularly racially and religiously motivated rhetoric, has compromised students’ emotional, psychological, and physical safety. It critiques the inaction of the Supreme Court and compares American policy to Canada’s precedent in R. v. Keegstra, suggesting that legal definitions and limitations on hate speech are both necessary and possible. The paper concludes by calling for the Supreme Court to modernize its interpretation of the First Amendment to ensure that free speech no longer enables discrimination and harassment in academic spaces