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The Role of Public Programs for Early Cancer Detection and Access to Care Among Cancer Survivors
No full textMedicare and Medicaid play a critical role in providing health insurance coverage to the large population of patients who are diagnosed with cancer. Medicare is the largest payer of health services for cancer patients and survivors, most of whom are aged 65 or older. Medicaid is a growing source of coverage for younger adults with low income or disabilities, who historically have faced adverse cancer outcomes. Considering the rising incidence and burden of cancer, rigorous policy analysis to inform effective cancer prevention and control in Medicare and Medicaid is crucial.
This dissertation investigates elements of the Medicare and Medicaid programs in the context of cancer prevention and control. In Aim 1, I examine eligibility for Medicare at age 65 and its implications for older cancer survivors' access to and ability to afford care. Using a regression discontinuity design, I find that Medicare eligibility is associated with significant reductions in, but not elimination of, cost-related barriers to care. Aim 2 assesses the impact of Medicaid managed care on early cancer detection. I exploit the expansion of mandatory managed care in Pennsylvania Medicaid as a natural experiment and find that this expansion was associated with improvements in early detection. In Aim 3, I evaluate disparities in cancer screening associated with experiences of homelessness and housing insecurity, an increasing focus of Medicaid programs seeking to address nonmedical determinants of health. Using a novel linkage of Pennsylvania's administrative Medicaid and housing services records, I find that women adult Medicaid beneficiaries experiencing or at risk of homelessness are significantly less likely to receive guideline-recommended mammograms.
These results point to several avenues for reforming Medicare and Medicaid for effective cancer prevention and control. Aim 1 findings suggest that expanding eligibility and cost protection within Medicare can lessen the financial burden of care among older cancer survivors. Aim 2 illustrates the potential for managed care to address the high incidence of advanced-stage cancer, a key driver of adverse outcomes in Medicaid. Aim 3 demonstrates that optimal cancer prevention will require greater efforts among public programs to address vulnerability linked to housing insecurity and other health-related social needs
Contextualizing Fidelity: Assessing Adherence to Evidence-Based Core Components and Associated Factors in a VA National Quality Improvement Program
No full textBackground: High-risk patients, or complex care patients with high multimorbidity, contribute to the majority share of healthcare cost at Veterans Affairs (VA) Hospitals and are at an increased risk for poor health outcomes. At the VA, primary care teams are best geared to treat high-risk patients and prevent high hospital utilization and poor outcomes, but currently do not use practices that target this population. The RIVET QUERI program is a national quality improvement program aimed at increasing uptake of evidence-based practices designed for high-risk patients.
Aims: This study evaluates fidelity to a RIVET evidenced-based practice (EBP), the care assessment and care plan tool, as well as the number of unmet needs discovered through use of the tool. This study contextualized fidelity by describing the patterns of use of the tool amongst implementing sites, and the context in which these patterns take place.
Public Health Significance: EBP fidelity is a vital component of evaluating intervention and implementation studies and can also be used as an early process outcome. The data from this study provides study leaders with information about the setting in which high fidelity occurs. This information will be used to guide future implementation efforts of this tool.
Methods and Results: RIVET EBP data was collected through VA electronic health data and analyzed through descriptive statistics. Contextual factors were collected through a clinician survey administered at the start of implementation and analyzed through descriptive statistics.
Conclusion: Sites in the RIVET QUERI program demonstrated high EBP fidelity (80-90%), indicating some early success of implementation. Sites were organized into two different patterns of use- ‘complete use’ and ‘variable use’ sites. ‘Complete use’ sites, which were characterized by more comprehensive use of the tool, found more unmet needs in the patients that were assessed
Essays in Sustainable and Socially Responsible Operations
No full textThis dissertation is directed toward understanding sustainable and socially responsible investments made by supply chains. The first essay in my dissertation examines sustainable investment specifically firm competition using corporate social responsibility and green investments as competitive tools. The second essay in my dissertation examines different forms of corporate social responsibility made by firms, specifically general dollar investments and product donation.
In essay 1, I examine how firms can use sustainability in competition. Using a three-period model, we examine two competitive supply chains, each of which consists of a retailer and an exclusive manufacturer. One retailer invests in both direct (greening) and indirect (corporate social responsibility activities, abbrev. CSR) forms of sustainable investment, while the other invests strictly in CSR. A 3-period game is used to identify equilibria for CSR investment (Stage 1), wholesale prices (Stage 2) and retail prices (Stage 3). We find that under certain conditions, either of the retailers can obtain higher profits, depending on the cost of indirect investment and its effectiveness. In studying the impact of parameter changes on the equilibria, we attain counterintuitive results. For instance, when the green retailer has a smaller market share and thus lower profit, he may try to reverse his weakness by increasing CSR investment. Therefore, higher indirect investment cost may incentivize the green retailer to increase his CSR investment. We compare the competitive environment results with a monopoly setting and find that competition decreases overall CSR investment. We consider when manufacturers can commit to their pricing over an extended period. When given this first mover’s advantage manufacturers decrease their wholesale pricing to incentivize CSR investment by retailers. By differentiating between greening products and CSR and modeling their synergistic effect, we show the potential of sustainable investment leading to competitive advantage.
In essay 2, I analyze the options that firms have in making corporate social responsibility (CSR) investment. CSR is broadly classified into four types: environmental efforts, philanthropy, ethical labor practices and volunteering (Schooley, 2020). The focus of this analysis is on philanthropic CSR, specifically two types: monetary and donation based. Monetary CSR (m-CSR) focuses on donating a set amount of cash into the community, largely through a nonprofit organization. Donation-based CSR (d-CSR) is when a firm donates their product into a community that has a need for it. Using a two-period model, we examine a competitive context of two vertically integrated supply chains. Each of the supply chains can invest in CSR and may choose between m-CSR and d-CSR. We optimize to identify equilibrium CSR program choice and investment amount. We identify the conditions under which either of the CSR programs can dominate (both supply chains use the same program) along with conditions for asymmetry in program choice. This equilibrium largely depends on the effectiveness of the CSR programs and the polarization of consumers. We find that when CSR programs are equally effective, the profits of both supply chains are maximized by choosing d-CSR. In the asymmetric environment when d-CSR is at advantage it will always lead to higher profits, this is not necessarily the case when the effectiveness advantage is held by m-CSR
The Relational Academic Library in Theory and Practice
No full textExplores the nascent model of relational librarianship emerging from the social turn in communities, professions, the economy, higher education, and academic libraries. Traces its evolution from the information commons movement and novel liaison models of the early 2000s to the current focus on building, developing, strengthening and transforming relationships with colleagues, learners, researchers and administrators on a library-wide basis. Covers conceptual foundations, theoretical frameworks, salient practices and core values. Identifies issues, controversies and problems in implementing the relational model. Concludes with a values-based vision of relational library practice, a seven-point organisation development agenda for academic libraries, and a select bibliography
Multivariate Functional Brain Imaging Signatures of Cardiovascular Reactivity During Psychological Stress
No full textCardiovascular reactions to psychological stressors are associated with cardiovascular disease (CVD) risk. Human brain imaging studies have identified brain regions and systems implicated in generating and regulating stressor-evoked cardiovascular reactivity, yet the reliability and generalizability of these findings remain unclear. Predictive modeling using multivariate and machine learning approaches has the promise of developing signatures of brain activity that can reliably predict outcomes, yet few studies have applied these approaches toward identifying signatures of stressor-evoked cardiovascular reactivity. Thus, the aims of the present study were (1) to develop novel multivariate signatures of stressor-evoked brain activity that could reliably predict concurrent cardiovascular physiology during stress within individuals, and (2) to evaluate whether previously reported brain signatures of cardiovascular reactivity generalize to new individuals, stressor contexts, and measures of cardiovascular physiology. Participants were 242 midlife adults (118 men and 124 women; age 30 to 51 years; 71% white) without psychiatric, immune, or cardiovascular diagnoses. Participants completed two validated cognitive stressor tasks during functional magnetic resonance imaging (fMRI) and concurrent monitoring of systolic blood pressure (SBP) and heart rate (HR). Multivariate machine learning models combining dimensionality reduction, regularized regression, and cross-validation were used to predict within-participant changes in SBP and HR during stress. Separately, two previously published multivariate signatures were applied to maps of stressor-evoked brain activity to predict SBP and HR. Contrary to hypotheses and prior reports, multivariate patterns of stressor-evoked brain activity did not reliably predict changes in SBP and HR during stress. Notwithstanding their unreliable prediction of SBP and HR, brain activity patterns relating to SBP and HR were comprised of brain regions implicated in psychological stress and physiological control processes. In addition, two previously published multivariate brain signatures of stressor-evoked cardiovascular reactivity were found to modestly predict changes in SBP and HR during stress. These findings extend our understanding of the reliability and stability of fMRI-based signatures reflecting brain processes that may link stressful experiences to CVD risk
Social modulation of pain: A mediation model with shared reality and emotion
No full textPhysical pain is a ubiquitous human experience with complexities spanning from physical to psychological domains. Though pain has historically been thought of as a physical and sensory process, the modern definition of pain now includes cognitive, social and emotional factors (Craig, 2002; iasp-pain.org/terminology; Williams & Craig, 2016), indicating growing appreciation of the impact of broader psychological context on people’s pain experiences. Support for this distinction comes from literatures exploring the many constructs proposed to moderate or mediate pain experience through psychological channels. In the pain literature social contact, which can include interactions with others, the mere presence of another person, or even the perceived presence of another person, often predicts reduced perceptions of acute experimental pain. However, this association is not always present and, at times, social contact enhances perceived pain (Che, Cash, Chung, Fitzgerald, & Fitzgibbon, 2018a; Che, Cash, Ng, Fitzgerald, & Fitzgibbon, 2018b; Krahé, Springer, Weinman, & Fotopoulou, 2013). The mixed influence of social contact may suggest that, in the face of pain, not all social contact is created equal. A parallel literature investigating the influence of emotion on pain perception demonstrates that modulating positive and negative emotion can more consistently predict pain outcomes, such that increasing positive emotion reduces perceived pain, while increasing negative emotion increases perceived pain (e.g., Zelman, Howland, Nichols, & Cleeland, 1991). Therefore it was hypothesized that emotion modulation resulting from social contact may predict perceived pain. Shared reality, the experience of validating one’s perception of reality through social contact, has been theorized to modulate emotion (Echterhoff, Higgins & Levine, 2009). Therefore the current work investigated sharing reality as a key moderator of the relationship between social contact and perceived pain. It was predicted that social contact involving shared reality would reduce perceived pain during a cold pressor task by decreasing negative emotions and increasing positive emotions. Although hypotheses were not supported in the present study, it is hoped that this work inspires future research on the effect of shared reality on emotion and pain perception that will eventually elucidate interventions for pain reduction
Computational Studies of Multistep Homogeneous and Heterogeneous Electrochemical Processes
No full textCatalysis plays a crucial role in industrial chemical processes by increasing reaction rates and improving selectivity and efficiencies. Electrochemical processes can further drive chemical reactions via applied potentials that change the chemical potential of transferring electrons. Under especially complex reaction environments, reaction mechanisms can be challenging to understand, and both homogeneous and heterogeneous reaction pathways can be in play. This work outlines progress in understanding how to elucidate electrocatalytic processes with the end goal of computationally designing idealized catalysts. We first computationally studied homogeneous electrochemical CO2 reduction reaction mechanisms in acetonitrile involving transition metal compounds that lack “non-innocent'” ligands (e.g. bipyridine ligands) that are typically assumed to be necessary. We used Kohn-Sham density functional theory (DFT) with continuum solvation methods to analyze the reaction pathways. After having a baseline understanding of computational tools for studying homogeneous reaction mechanisms, we then investigated heterogeneous electrochemical ozone production (EOP) processes on nickel and antimony doped SnO2 electrode catalysts (NATO). EOP is intriguing as a potentially sustainable method for generating powerful chemical oxidants and disinfectants, but little is presently known about its fundamental catalytic reaction mechanisms that would be needed for improved engineering of EOP electrocatalysts. We used DFT to investigate the thermodynamic feasibility of ozone-producing pathways to better rationalize how and why dopants would influence EOP catalysis. In consort with experimental results, we indicate that EOP is very complex and occurs via several pathways, including non-catalytic corrosion. In summary, we showed that computational modeling can provide insights to better rationalize how and why NATO can catalyze EOP, why the mechanism would likely be different on NATO than other electrodes such as PbO2. We find that EOP adsorbates are significantly stabilized by explicit hydrogen bonding that arises from dissociated co-adsorbed water molecules. Since these interactions are essential to a computational catalysis model that is thermodynamically consistent with experimental observations, it shows the critical importance of developing new computational tools to interrogate electrochemical reaction mechanisms with greater computational efficiency and accuracy
Insights into Nanoscale Adhesion via in situ Transmission Electron Microscopy
No full textSurface adhesion governs the performance of material interfaces, from large-scale applications such as the energy loss in an automobile engine to small-scale applications such as the stability of nanoparticles. At the nanometer length scale, there are many aspects of adhesion that are not well understood due to the inherent challenges in experimental investigations at the nanoscale. An emerging technique for investigating nanoscale adhesion is performing nanoscale contact-and-separation tests inside of a transmission electron microscope, coupling force and displacement information with high-resolution measurements of material composition, structure, and morphology. In the present work, this technique was leveraged to advance the fundamental understanding of nanoscale adhesion.
Surface adhesion governs the performance of material interfaces, from large-scale applications such as the energy loss in an automobile engine to small-scale applications such as the stability of nanoparticles. At the nanometer length scale, there are many aspects of adhesion that are not well understood due to the inherent challenges in experimental investigations at the nanoscale. An emerging technique for investigating nanoscale adhesion is performing nanoscale contact-and-separation tests inside of a transmission electron microscope, coupling force and displacement information with high-resolution measurements of material composition, structure, and morphology. In the present work, this technique was leveraged to advance the fundamental understanding of nanoscale adhesion.
There are three primary contributions from this work. First, we addressed the open question of how nanoscale adhesion changes with applied load in hard, technologically relevant materials. We showed that nanoscale adhesion was governed primarily by strength-limited separation (also called “pop-off”), rather than by crack-like separation (aka “peel off”) as previously believed. Here adhesion experiments of nanoscale contacts demonstrated the need for a paradigm shift from traditional contact mechanics models in interpreting nanoscale adhesion data. Second, we utilized insight from previous nanoscale adhesion experiments to measure the adhesion of noble-metal nanoparticles to oxide support. In these technologically relevant material systems, interfacial adhesion (through the established relationship between adhesion and nanoparticle sintering) governs the efficiency and lifetime of nanoparticles in applications such as chemical synthesis, energy generation, and biosensors. We tested six different metal/oxide systems, and validated the accuracy of our technique by matching materials trends in adhesion from prior literature. Third, once the accuracy of our technique was established, we expanded the scope of the investigation to the adhesion between bimetallic nanoparticles and oxide supports, looking specifically at how adhesion depends on nanoparticle surface composition. The results showed adhesion varies non-monotonically with surface composition which is attributed to the modification of adhesive bonding via intra-particle charge transfer between dissimilar metals. Finally, current and future work is presented, where adhesion experiments were performed for additional systems beyond the scope of current techniques to further develop the understanding of how nanoparticles adhere to their supports
Towards Unveiling the Potential of the 12-Lead Electrocardiogram in Predicting Acute Coronary Syndrome via Machine Learning
No full textNearly 7 million Americans visit the emergency department annually with a chief complaint of chest pain. Approximately 10\% of those patients have an acute disruption in blood supply to the heart attributed to underlying atherosclerotic disease in the coronary arteries, a life-threatening condition referred to as acute coronary syndrome. The prompt identification of acute coronary syndrome is a key challenge in clinical practice. The 12-lead electrocardiogram is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accuracy. In this research, we utilize advanced signal processing techniques and machine learning methods to study the prognostic value of the electrocardiogram in early screening for acute coronary syndrome and related adverse outcomes. We investigate the use of statistical, deep learning and hybrid techniques to (1) detect and localize artery occlusions, (2) improve non-invasive risk stratification in chest pain patients, and (3) enhance the sensitivity and precision of occlusion myocardial infarction (a particularly deadly subcategory of acute coronary syndrome) identification. These projects jointly aim at developing an improved interpretable decision-support system for electrocardiograms to alert clinicians in real-time to acute coronary syndrome and bypass suboptimal time-consuming biomarker-driven tests. This would increase the available therapeutic window for initiating adequate therapy in distressed patients and reduce unnecessary prolonged surveillance in non-specific chest pain. Such advances would lower costs associated with admission and more involved tests, and produce better outcomes for patients
Good Vibes Only: An In-Depth Analysis on the Implementation of In-Wheel Suspension in Manual Wheelchair Users
No full textThis dissertation aimed to investigate the potential of using in-wheel suspension to reduce harmful whole-body vibration (WBV) and improve comfort and mobility for manual wheelchair users (MWUs). The LoopWheels Urban is designed with three C-shaped carbon fiber springs to absorb vibration and provide a smoother ride and increased comfort to the MWU. Root Mean Square (RMS) for vibration and Vibration Dose Value (VDV) for more transient shocks are used as measures for WBV. LoopWheels was found to reduce harmful vibrations and shocks experienced by MWUs with spinal cord injury (n= 26) across various indoor/outdoor surfaces and obstacles by 10% at the backrest and 7% at the footrest (all p < 0.05) compared to standard spoked and Spinergy CLX wheels in a lab setting. Neck/back pain, fatigue, and WBV exposure were further analyzed through a 12-week community-based intervention with the LoopWheels. Participants experienced a median reduction of 15% in neck pain, 8% decrease in median perceived fatigue, and reported one fewer median pain problems (all p < 0.05) after the trial period. Community sensor data shows MWUs propelled an average of two hours and were exposed to WBV levels below hazardous thresholds defined by ISO 2631. LoopWheels shows a 35% reduction in vibration and a 50% reduction in shock when compared to previous community-based studies with standard wheels. Users indicated a smoother, more comfortable ride experience, but felt the wheels were harder to push. Rolling resistance (RR) testing indicated that LoopWheels had 118% more RR on linoleum and 44% more on carpet than standard spoked wheels. LoopWheels exhibited significantly higher static deformation (max 0.27 inch) compared to standard and CLX wheels over all loading conditions (p < 0.05). Propulsion testing showed larger oscillatory amplitudes in the anterior-posterior and vertical direction that varied in phase from the standard and CLX wheels. Periodic behavior could be contributing to the sensation of reduced propulsion efficiency. While LoopWheels show promise in reducing vibration and improving health, challenges such as increased RR and dynamic behavior necessitate further exploration