Environmental and Occupational Health Sciences Institute
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Increasing HPV vaccination rates: a quality improvement project
No full textPurpose of the project: Human papillomavirus (HPV) is the most common sexually transmitted infection (STI) that can be easily managed through vaccination. The potential reasons for low vaccine rates among patients and parents include inadequate information and knowledge about the vaccine and a lack of provider recommendations. HPV infections significantly impact patients, families, and public health. The project goal is to increase HPV vaccination rates among adolescents 11-18 years old in a Federally Qualified Health Center through an educational campaign.
Methods: The FADE model was the framework of choice. The project included eight staff members who attended two educational sessions regarding HPV and HPV vaccines, and how to address parental/patient concerns about the vaccine. Posters and educational materials were made available for parents/patients throughout the site. The number of HPV vaccines administered or refused were the primary outcomes measured.
Results: A chart review revealed an increase in HPV vaccine uptake. A Chi-square test was used, showing a p-value of p<0.05 for initiation and a p-value of p-0.001 for series completion. There was no significant change in vaccine refusal. Lack of documentation of vaccine offering or refusal significantly decreased by 26%.
Implications: Administering the HPV vaccine is a cost-effective preventive measure that can significantly impact patient outcomes and healthcare costs. Promoting the HPV vaccine at every encounter and bundling it with other required vaccines can increase vaccine acceptance. Healthcare personnel’s basic knowledge about HPV and its consequences can lead to more effective parent/patient counseling and patient-provider communication.D.N.P.Includes bibliographical reference
Improving pain reassessment and documentation in the emergency room
No full textPurpose: The Quality Improvement initiative targeted increased pain reassessment and documentation frequency among adult ER patients aged 18 and above, facilitated by ED RNs. During the TJS/CMS survey spanning 2020-2022, this site demonstrated insufficient pain assessment and documentation compliance.
Methodology: The study spanned four weeks and incorporated a multifaceted approach to intervention. The interventions included comprehensive in-service training for ED staff, a review and revision of the pain management policy documentation, implementation of hard stops within the medical record system to prompt pain assessment, the introduction of timed intervals for pain reassessment, and the incorporation of visual aid reminder tools to reinforce assessment practices. Data abstraction encompassed pain assessment and documentation rates, particularly focusing on the frequency and accuracy of pain reassessment among the specified patient population. The pre-implementation data served as a baseline, while post-implementation data allowed for an evaluation of the effectiveness of the interventions in improving pain assessment compliance.
Results: The study findings indicated a notable increase in the frequency of pain assessments among patients following the implementation of the pain assessment education program. Statistical analyses, including chi-square tests and two-sample t-tests, were conducted to examine outcome differences between the pre- and post-intervention groups. Pain assessments were conducted more frequently at post-med in the post-sample group, accounting for 55.7% of the time, compared to 26.7% in the pre-sample group. Furthermore, there was compelling evidence suggesting that the pain education program led to a greater reduction in patients' pain levels.
Implications for Practice: The project findings recommended a comprehensive approach to enhance RN compliance with pain assessment and documentation in the Emergency Department (ED). Strategies involved implementing monthly pain champion announcements, integrating the organization's pain policy and TJC/CMS orientation for new RNs, adopting more frequent chest pain reassessments, conducting regular pain audits involving charge nurses and Emergency Management personnel, and considering a future upgrade to the EPIC MAR with color-coded prompts. These measures aimed to cultivate a culture of vigilance toward pain assessment protocols, potentially improving patient care quality and aligning with regulatory standards in the Emergency Department.D.N.P.Includes bibliographical reference
Frustrated total internal reflection
No full textTheories of both friction and adhesion can be used to analyze biological phenomena, like the feet of a gecko, but will fall short in painting a full picture of the contact dynamics. This is difficult to accomplish without knowing how much of a surface is actually in contact, and how the body comes into contact. Our goal is to use Frustrated Total Internal Reflection (FTIR) analyze contact area. When light from a denser medium is incident on a less dense medium at an angle greater than the critical angle, it is totally internally reflected. The transmitted light’s intensity reaches 0, but does not disappear, it becomes an evanescent wave. Unlike a standard ray of light, an evanescent wave tapers off exponentially as it gets further away from the initial surface. When a 3rd dense medium is introduced in a close proximity (within the nanoscale) to the evanescent wave, light passes into it, subtracting from reflected light’s intensity (FTIR). By observing the absence of light from the reflected ray, the contact area can be measured precisely.Presented at the annual Celebration of Undergraduate Research and Creative Activity while the author was an undergraduate student at Rutgers University-Camden
Magic of music: a celebration of Latinos in theatre
No full textMany little girls around the world dream of being princesses and wishing upon stars. I was no different, so it’s no surprise to anyone when I say that animated Disney movies were the biggest part of my childhood. So, what better way to end my undergraduate experience than to make my childhood dream come true for one night? I came up with the idea of a bilingual cabaret while listening to “I See the Light” from Tangled, and while listening, I wondered how it would sound if it was mashed up with the Spanish version. I decided that I wanted to hear it, and my very dear friend Andrew Merkle was kind enough to sing it with me for a choir concert, where I sang most of my parts in Spanish and he sang his in English with some overlap in between. In the process of creating this arrangement, I realized that I was bringing together two of the most important parts of my life: my girlhood and my heritage, and I thought it would be the perfect concept for my capstone.Presented at the annual Celebration of Undergraduate Research and Creative Activity while the author was an undergraduate student at Rutgers University-Camden
Quantitative estimates for multidimensional polynomial ergodic averages
No full textWe prove uniform jump, variation, oscillation, and ℓ2-valued maximal inequalities forthe polynomial ergodic averages and truncated singular operators of Cotlar type modeled over multidimensional subsets of integers or primes. This is a contribution to the Rosenblatt-Wierdl conjecture with averages taken over primes. In the averages case, these results combine to prove corresponding multiparameter oscillation estimates. This provides a fuller quantitative description of the pointwise convergence of the mentioned averages and is a generalization of the polynomial Dunford-Zygmund ergodic theorem attributed to Bourgain.
To prove these results, we use the Calder´on transference principle to reduce the problem to the integer shift system and then exploit the Hardy–Littlewood circle method to analyze the appropriate Fourier multipliers. The main tools used to handle the estimates for the multiplier operators are: an appropriate generalization of Weyl’s inequality, the Ionescu-Wainger multiplier theorem, the Rademacher–Menshov inequality, multiplier approximations using Gauss sums, and the Magyar–Stein–Wainger sampling principle. Throughout, we also use the Marcinkiewicz-Zygmund inequality to extend scalar inequalities to their vector-valued analogues.Ph.D.Includes bibliographical reference
Diversity in the Atacama: population genetics and assessment of genetic structure of desert arthropods
No full textIn recent decades, the biodiversity of arthropods has declined globally. Decreased population sizes, while of ecological concern on their own, frequently include knock on genomic effects, such as the loss of genomic diversity, increased genomic distances between populations, and in some cases, increased rates of inbreeding. These effects can compromise conservation efforts by reducing organismal fitness and the ability of populations to respond to environmental change through adaptation. A reduction in population sizes and diversity of arthropods can fundamentally affect food webs, the regulation of plant communities, and organic matter decomposition. Despite the essential role arthropods play in ecosystems, their demographic and evolutionary responses to climate change remain uncertain. Of particular concern are arthropods from arid lands, which might be more vulnerable to higher temperatures and reduced precipitation. This project aims to: (1) develop genomic libraries for common arthropod species collected from the Atacama Desert, Chile; (2) quantify arthropod genomic variation across space; and (3) test for patterns of population divergence based on spatial and climatic distances. We hypothesize that desert arthropod populations will be more genetically diverse in regions with higher precipitation, which support larger arthropod populations. From each sampling site, we will select twenty individuals for DNA extraction and sequencing. Genomic libraries will then be submitted for double-digest restriction-site associated DNA sequencing. We will identify genetic polymorphisms and calculate observed heterozygosity, inbreeding coefficients, and nucleotide diversity for each sampling site. We will determine population genetic structure among sampling locations, allowing us to infer the number of distinct populations, accounting for the effect of spatial distance in contributing to genetic differences between samples. Finally, we will test for correlations between measurements of genomic diversity and climatic variables. We expect that precipitation will be correlated with metrics of genetic diversity and that populations will be more genetically divergent when climatic differences are greater between geographic locations. It is important to understand the links between climate and genomic diversity for understanding the ecology of desert species and for assessing conservation needs.Presented at the annual Celebration of Undergraduate Research and Creative Activity while the author was an undergraduate student at Rutgers University-Camde
Essays on machining learning and big data in finance
No full textRecent easy access to large amounts of data in finance has necessitated a growing number of techniques and methodologies that are capable of analyzing such large data sets. Statistical approaches and computational procedures are constantly being developed, updated, and applied in new ways to the flood of data that is finding its way into financial decision making. In particular, machine learning and big data aggregation methods have become extremely prominent in financial and econometric analysis. As the number of potential techniques that can be used to analyze these large data sets increases, the amount of situations where a particular model may exhibit greater predictive power or show superior performance versus another model grows as well. More data and more processes make it more difficult to find the parse through said data or find meaningful relations within a sea of noise, should they even exist. Even if we suppose there is a 'best' modeling technique, this idea is largely dependent on the situation in which one is conducting analysis. Differing methods of analysis will see superior performance only when they are applied in the ideal situation. Unsurprisingly, it is often not possible to assess which situation one is in until it has concluded, making hindsight one of the ultimate arbiters in the process of both analysis and data selection.
In this dissertation, I examine the usage of big data methodologies and machine learning algorithms in finance through multiple avenues. In my first essay, I seek to apply modern machine learning and big data aggregation techniques to the estimation of integrated volatility from high-frequency financial stock data. In my second essay, I investigate whether machine learning and big data aggregation techniques are currently being adopted by the traditional banking sector, with this adoption being facilitated through partnerships with fintech firms. Chapter one presents a brief introduction to these topics.
The usage of big data aggregation on high frequency stock data has led to large number of non-parametric volatility estimation techniques. Given this large amount of methods to estimate volatility, considering which estimation technique to apply becomes a non-trivial task. The second chapter of this dissertation synthesizes the work done in forecast combination with estimation methods for integrated volatility in high frequency financial data, in addition to using machine learning methods to estimate volatility. Given that integrated volatility is a latent variable, the utility and accuracy of estimates obtained through combination and machine learning is assessed by applying a trading strategy motivated by the volatility feedback effect, which highlights the negative relation between volatility and returns. I further motivate the use of machine learning with Monte-Carlo simulated data. By extending this to existing stock price data, I find that using by combining existing estimators through traditional forecast combination as well as newer machine learning techniques may both provide useful estimates of integrated volatility compared to existing methods.
In the third chapter, I investigate how the use of modern machine learning and data aggregation techniques have affected the banking sector. Fintech firms tend to lend to under-served demographics using cutting edge algorithms and novel metrics. Given this, I hypothesize that banks that enter partnerships with these fintech firms would likely begin to use these tools and emulate their lending behavior. In particular, I investigate whether banks which enter partnerships with fintech firms see an increase in the probability of lending to the under-served demographics of consumers that previously had been overlooked by the traditional banking lending methods. I examine the lending activities of traditional banks by analyzing credit offers, credit originations, and credit limits extended to consumers with low credit ratings or no credit ratings on file, and see to what degree these activities change following partnerships with fintech firms.Ph.D.Includes bibliographical reference
The effects of child maltreatment and exposure to intimate partner violence on child behavioral outcomes: an examination of the severity, frequency, and co-occurrence
No full textChild maltreatment (CM) and exposure to intimate partner violence (IPV) are both recognized as serious social problems that jeopardize child behavioral health. Even though several studies have investigated the effects of CM and IPV exposure on children, the existing evidence is still inconclusive regarding the complicated nature and consequences of these severe issues. For instance, there is a limited understanding of how the severity, frequency, and co-occurrence of CM and IPV exposure affect children’s behavioral health. Furthermore, existing evidence primarily focuses on broad-band internalizing and externalizing behavioral problems; fewer studies have looked at specific types of behavioral problems. Therefore, this dissertation is divided into three separate papers that specifically address the effects of severity, frequency, and co-occurrence of CM and IPV exposure on children’s anxious/depressive symptoms and aggressive behavior. This dissertation targets children in toddlerhood and early childhood (i.e., ages 1–6 years), when these young children are extremely vulnerable to both CM and IPV exposure. The specific aims for the overall study include: (1) exploring how the severity patterns of CM and exposure to physical IPV (PIPV) relate to children’s anxious/depressive symptoms and aggressive behavior (Paper 1); (2) examining how the frequency of CM and PIPV exposure contributes to children’s co-development of anxious/depressive symptoms and aggressive behavior over time (Paper 2); and (3) investigating whether the co-occurrence of CM and PIPV exposure is associated with worse anxious/depressive symptoms and aggressive behavior than either CM or IPV exposure only (Paper 3).
Secondary data analysis was conducted using the second National Survey of Child and Adolescent Well-Being (NSCAW II), a national longitudinal study that examines the well-being of children and families who were investigated by Child Protective Services for alleged child abuse and/or neglect. Using latent class analysis, Paper 1 identifies four classes of children with distinct severity patterns of CM and PIPV exposure at baseline. Among these four classes of children, one class had low to moderate probability of experiencing violence, and the other three classes had high probabilities of experiencing at least one type of violence with various severity levels. When comparing class differences in anxious/depressive symptoms and aggressive behavior, children in the latter three classes indicated more anxious/depressive symptoms and aggressive behavior than those in the first class. Paper 2 employs latent growth curve modeling to examine how the frequency of CM and PIPV exposure affect children’s co-development of anxious/depressive symptoms and aggressive behavior over time. Results suggest that higher initial levels of anxious/depressive symptoms were correlated to higher initial levels of aggressive behavior, and the rates of change of both outcomes were positively correlated. CM was associated with worse anxious/depressive symptoms and aggressive behavior at all three time points, and IPV exposure was associated with worse outcomes at two time points. Paper 3 investigates the effects of the co-occurrence of CM and PIPV exposure on anxious/depressive symptoms and aggressive behavior. Regression results indicate that the co-occurrence was associated with worse anxious/depressive symptoms and aggressive behavior when compared to CM only and/or PIPV exposure only. Further specification analyses show that children who experienced co-occurring neglect and PIPV exposure reported worse anxious/depressive symptoms and aggressive behavior than children who were only exposed to PIPV.
Overall, the findings of this dissertation inform both research and practice. Future research should continue investigating how nuanced characteristics of CM and IPV exposure may independently affect the behavioral health of young children. Studies should also explore age differences in the relationship between CM and IPV exposure and child behavioral outcomes. A longitudinal research design can advance future research by allowing the exploration of mechanisms and moderators that may explain some children’s resilience against CM and IPV exposure. Child welfare practice protocol should integrate ongoing and effective IPV training so that child welfare workers are more prepared to serve children and families that experience co-occurring CM and IPV. Clinicians should develop a thorough diagram about clients’ experiences of violence at an early age utilizing a comprehensive assessment. Trauma-informed practice would be an important tool for clinicians to better understand clients’ current problems considering the impacts of early trauma.Ph.D.Includes bibliographical reference
Symbolic computation to study explicit Gröbner bases and lattice path enumeration
No full textExperimental mathematics involves using computation and algorithms to study mathematical objects, typically with computer-assisted proving. This dissertation demonstrates experimental methods in researching various problems.
The first project expands upon Haglund, Rhoades, and Shimonozo’s work on finding the reduced Gröbner basis of the ideal of elementary symmetric polynomials in n variables of degree d for d = n − k + 1,...,n. Using symbolic computation and experimentation, we construct the reduced Gröbner basis for the ideal generated by the elementary symmetric polynomials in n variables of arbitrary degrees.
The remaining projects focus on Dyck, Motzkin, and similar paths. Using Zeilberger’s automated procedures to find the weight enumerator for specific families of restricted Dyck paths, we extend these findings to infinite families through grammatical proofs. We then generalize the procedures to find the weight enumerator for restricted Motzkin paths. The next project explains how to automatically generate the weight enumerator of generalized Dyck paths, i.e. paths in the xy−plane from (0, 0) to (n, 0) with an arbitrary set of atomic steps that never go below the x−axis. Expanding on this, we compute the generating functions for the sum of the areas under such paths as well as the sum of a given power of the areas.Ph.D.Includes bibliographical reference