Browsing by Author "Gilbert, Lauren R."

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Design and Implementation of a City-based Model for Simulating COVID-19 Spread
(2022-05-06) Vo, Hoang Duc; Eick, Christoph F.; Laszka, Aron; Gilbert, Lauren R.
Since 2019, COVID-19 has challenged health worldwide. To better understand the disease, scientists have developed agent-based and other models to simulate the spread of COVD-19. Simulations with agent-based models have become popular in recent years due to the development of new technologies and novel programming resources. Agent-based models create agents and allow agents to interact with each other based on a set of rules. Thanks to simulated results of agent-based models, scientists can better understand the spread of the disease and evaluate the effectiveness of particular measures against the spread of COVID-19. This thesis designed and implemented COVID19-CBABM, a city-based and agent-based model, to simulate the spread of COVID-19. The model simulates the spread of the pandemic disease inside a particular city based on given information about its population and its point-of-interests which are visited by people living in this city. COVID19-CBABM has two types of agents: Human agents and point-of-interest agents. Human agents mimic the movements and behaviors of citizens while point-of-interest agents model houses, schools, hospitals, offices, and other places in the city. COVID19-CBABM also reuses the SEIHRD framework to model how COVID-19 is transmitted among people. The model was initially developed to simulate the COVID-19 spread in New York City. We employed the Mesa framework to create an agent-based model in python 3.7. Mesa uses built-in components and customized classes to generate the agents in COVID19-CBABM. To evaluate the COVID19-CBABM model, we compared the simulated results with the actual data collected in New York City in August 2021. We also analyzed to which extent the developed model can be reused to simulate virus transmission in other cities, such as Houston City. One challenge of such a reuse is to adapt model parameters that were estimated based on New York City data, to match the specific characteristics of Houston. City
THE DEVELOPMENT OF THE PATIENT-REPORTED OUTCOME MEASURE: MEASURING OPIOID USE DISORDER-FINANCIAL TOXICITY (MOUD-FIT)
(2022-12) Anyanwu, Precious; Thornton, J. Douglas; Essien, Ekere James; Johnson, Michael L.; Gilbert, Lauren R.; Ogunsanya, Motolani E.
Introduction: Expanding access to medications for opioid use disorder (MOUD) is an effective strategy to combat the opioid epidemic. Unfortunately, many barriers exist for patients needing treatment for OUD, such as stigma, lack of readiness to quit, inability to access treatment, and financial barriers, including the inability to afford or maintain treatment costs. Popularized in oncology due to the extreme out-of-pocket cost to the patient, the term “financial toxicity” is described as the illness-related financial burden and the psychosocial effects associated with the cost of treatment, which can lead to diminished patient outcomes and a poorer quality of life. Currently, there is no patient-reported outcome measure (PROM) to describe the financial toxicity in patients with OUD.Methods: The content for the Measuring Opioid Use Disorder Financial Toxicity (MOUD-FiT) was created with three aims: aim 1) a scoping review, focus group interviews, and treatment expert interviews to generate items for inclusion, aim 2) consensus for item inclusion using cognitive debriefing interviews and the Lawshe method, and aim 3) item analysis to establish internal consistency by assessing the interitem (IIC) and item-total (ITC) correlations to identify redundancy. Within each pair of items with an IIC > 0.70, the item with the higher ITC was retained. ITCs were calculated to ensure that the retained items affected the instrument's score. Items with an ITC < 0.40 were eliminated. Results: In total, 65 individuals with OUD receiving treatment (28 participants participated in seven focus groups, 7 participants during the cognitive debriefing interviews and , and 30 participants for the Lawshe’s method and item analysis) participated in the PROM development. In step 1, the literature generated nine candidate items, seven focus groups generated 34 candidate items, and five treatment experts generated 56 candidate items. Members of the research team reduced the 96 items to 40 due to redundancy and items not addressing the construct of interest. In step 2, seven participants aided in modifying the instructions to the instrument through cognitive debriefing interviews, 30 participants completed the Lawshe method, and 22 items were retained. In step 3, 30 participants assessed the 22 items, six items were excluded because of high IIC, and one item was excluded because of low ITC. After excluding items, the Cronbach α coefficient for the 15-item MOUD-FiT PROM was 0.90, indicating excellent internal consistency.Conclusion: The development of the MOUD-FiT PROM is a significant step toward measuring the impact of financial distress on the lives of patients with OUD. The MOUD-FiT is a provisional instrument with established content validity and internal consistency. Additional research is needed to determine the correlation between financial toxicity and quality of life, treatment retention, and survival in OUD patients. Before its widespread adoption, other forms of validity and psychometric characteristics should be determined.
The Race to Achieve Herd Immunity: An Analysis of County-Level Racial/Ethnic Composition and COVID-19 Vaccination Rates across the United States
(2023-04-20) Wolski, Caroline; Anderson, Kathryn F.; Monserud, Maria A.; Gilbert, Lauren R.
The COVID-19 global pandemic has magnified the inequalities that marginalized groups in the United States face experiencing health outcomes and accessing healthcare. Unequal access to the COVID-19 vaccine is consequential to the individual and community and inhibits progress in achieving herd immunity. The current study examines county-level COVID-19 vaccination rates and explores social factors that may influence access to and uptake of COVID-19 preventive care. I conducted a quantitative secondary data analysis to explore the following research questions: (1) How are the COVID-19 vaccination rates related to the racial/ethnic composition of counties across the United States?, (2) How does the association between racial/ethnic composition across counties and vaccination rates change across different critical points in time of the pandemic?, and (3) How does the relationship between race/ethnicity and political affiliation influence vaccine uptake? When looking at the direct relationship between race/ethnicity and vaccine uptake, the results suggest that this relationship fluctuates across the pandemic, although counties with higher shares of racial/ethnic minorities demonstrate lower vaccine uptake compared to their White counterparts across all points in time. However, findings indicate that these relationships are contingent on political affiliation and the moderating effect changes over the course of the pandemic. During the initial stages of vaccine rollout, the relationships between racial/ethnic categories Black and Latino and vaccine uptake are negative in counties where Trump support is high. Conversely, in counties with greater shares of White individuals, vaccine uptake is greater where Trump support is high, and lower where Trump support is low. In the later stages of the pandemic, these relationships flipped, where the relationships between percent Black and vaccine uptake and percent Latino and vaccine uptake are positive where Trump support is high. Importantly, at time wave A3, counties with high percentages of White individuals demonstrate lower vaccine uptake where Trump support is high and greater vaccine uptake where Trump support is low. This analysis can guide policymakers and public health workers when implementing vaccination strategies by identifying the counties and groups that require additional resources. Additionally, understanding the social and political climate during critical points in time of COVID-19 vaccination rates can provide insight on what factors may influence changes in COVID-19 preventive care uptake.