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You searched for +publisher:"University of Michigan" +contributor:("Zelner, Jonathan Leigh"). Showing records 1 – 2 of 2 total matches.

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University of Michigan

1. Segaloff, Hannah. Acute Respiratory Infection Among Hospitalized Individuals: Prediction and Prevention of Severe Influenza.

Degree: PhD, Epidemiological Science, 2019, University of Michigan

Influenza is a serious respiratory virus in terms of global morbidity and mortality. Patients hospitalized with influenza generally have comorbidities contributing to their disease severity and are most at risk for further severe influenza-related outcomes. Despite the importance of this group, there are few studies investigating interventions in populations of patients hospitalized due to influenza. Specifically, robust evaluation is needed of the two most used interventions against severe influenza, vaccination and neuraminidase inhibitors. The best available protection against influenza illness is vaccination, which is recommended annually in the United States and in many other countries worldwide. Treatment with neuraminidase inhibitors has been shown to prevent severe influenza outcomes and reduce symptomatic illness; antiviral treatment is recommended for all hospitalized patients with suspected or confirmed influenza in the United States. This dissertation examines two components of prevention of severe influenza: vaccine effectiveness against hospitalization, and the prevention of severity in individuals at high risk for severe influenza outcomes. Influenza vaccine effectiveness against hospitalization of Israeli children who are fully or partially vaccinated was determined through use of medical record data over three influenza seasons in chapter 2. Vaccination was found to be effective for fully, but not partially, vaccinated children over all three seasons. This result supports guidelines by the Advisory Committee on Immunization Practices in the United States and the Israeli Ministry of Health, which recommend two inoculations in the first season of vaccination for children under nine years of age. In chapter 3 we focused on the methodological validity of the test negative design in the inpatient setting. We tested specimens previously collected for vaccine effectiveness estimation against hospitalization in adults participating in the HAIVEN study for a variety of respiratory viruses. We calculated VE in three ways: using the traditional influenza-negative control group, using an influenza negative but other virus positive control group, and using a pan-negative control group, in order to evaluate whether inclusion of individuals without a true ARI in the influenza-negative control group biases VE estimates in the hospital. We did not find consistent differences in VE by control group, suggesting that this bias is not a persistent problem when estimating vaccine effectiveness against hospitalization. In the next two chapters, we focused on characterization of influenza severity and risk factors for severe influenza. In chapter 4 we studied adults hospitalized with influenza over two seasons. Using inverse probability weighted logistic and linear models, we found that rapid antiviral treatment was associated with reduced odds of lower pulmonary disease and that obese patients were treated more rapidly with antiviral medication than non-obese patients, making antiviral treatment timing a potential… Advisors/Committee Members: Martin, Emily Toth (committee member), Lauring, Adam (committee member), Marrs, Carl F (committee member), Monto, Arnold S (committee member), Zelner, Jonathan Leigh (committee member).

Subjects/Keywords: Influenza Severity; Public Health; Health Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Segaloff, H. (2019). Acute Respiratory Infection Among Hospitalized Individuals: Prediction and Prevention of Severe Influenza. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/150022

Chicago Manual of Style (16th Edition):

Segaloff, Hannah. “Acute Respiratory Infection Among Hospitalized Individuals: Prediction and Prevention of Severe Influenza.” 2019. Doctoral Dissertation, University of Michigan. Accessed November 15, 2019. http://hdl.handle.net/2027.42/150022.

MLA Handbook (7th Edition):

Segaloff, Hannah. “Acute Respiratory Infection Among Hospitalized Individuals: Prediction and Prevention of Severe Influenza.” 2019. Web. 15 Nov 2019.

Vancouver:

Segaloff H. Acute Respiratory Infection Among Hospitalized Individuals: Prediction and Prevention of Severe Influenza. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Nov 15]. Available from: http://hdl.handle.net/2027.42/150022.

Council of Science Editors:

Segaloff H. Acute Respiratory Infection Among Hospitalized Individuals: Prediction and Prevention of Severe Influenza. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/150022


University of Michigan

2. Havumaki, Joshua. Using Mathematical Models to Understand Causal Mechanisms Underlying Counterintuitive Epidemiological Data.

Degree: PhD, Epidemiological Science, 2019, University of Michigan

Analyzing epidemiological data (e.g., from observational studies or surveillance) can reveal results contrary to what might be expected given a priori knowledge about the study question. In these cases, a clear mechanistic understanding of why counterintuitive results are observed is critical to minimize bias in study designs and implement effective interventions targeting diseases. Mathematical modeling approaches provide a flexible way to connect mechanisms with real-world data. In this dissertation, we describe the use of mathematical models to explore 3 cases in which seemingly counterintuitive results have been observed. First, we examined the obesity paradox or the apparent protective effect of obesity on mortality among certain high-risk groups, e.g. diabetic ever-smokers. Second, we examined how to leverage spatial and contact heterogeneity to optimize tuberculosis screening interventions in a variety of settings including those with high incidence-levels where household-based interventions have unexpectedly limited population-level effects. Finally, we examined why norovirus outbreaks are explosive in nature, but result in relatively low attack rates (the percentage of individuals who become diseased) in school and daycare settings. In Aim 1, we developed a method to simulate epidemiological studies using compartmental models (CMs) derived from directed acyclic graphs (DAGs). We illustrated our approach using the obesity paradox as a case study. Specifically, we examined how altering underlying causal mechanisms (i.e. CM structure), can cause spurious associations in the data. We found that incorporating study design bias (e.g., including covariates in the causal mechanism and not adjusting for them), can lead to the obesity paradox. Overall, we showed how mathematical modeling of DAGs can be used to inform analyses, and explore underlying biases which may be helpful for designing sound observational studies and obtaining accurate measures of effect. In Aim 2, we explored how variation in community contact and endemic incidence levels can affect the impact of household or community-targeted screening interventions using an individually-based network model. Overall, we found that the community drives transmission in high incidence settings. In general, more protection was conferred by targeted interventions and in lower incidence settings within networks that had fewer numbers of contacts, or shorter distance between contacts. Ultimately, these results may help identify the settings in which household or community targeted screening interventions will be effective. In Aim 3, we explored mechanisms that underlie norovirus outbreak dynamics using a disease transmission model. We compared different scenarios, including a partially immune population, stochastic extinction, and individual exclusion, and calibrated our model to daycare and school outbreaks from surveillance data. We found that incorporating both a partially immune population and individual exclusion was sufficient to recreate explosive… Advisors/Committee Members: Eisenberg, Marisa Cristina (committee member), Berrocal, Veronica J (committee member), Eisenberg, Joseph Neil (committee member), Meza, Rafael (committee member), Zelner, Jonathan Leigh (committee member).

Subjects/Keywords: transmission modeling; norovirus; obesity paradox; tuberculosis; directed acyclic graphs; compartmental models; Public Health; Mathematics; Science (General); Statistics and Numeric Data; Health Sciences; Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Havumaki, J. (2019). Using Mathematical Models to Understand Causal Mechanisms Underlying Counterintuitive Epidemiological Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151666

Chicago Manual of Style (16th Edition):

Havumaki, Joshua. “Using Mathematical Models to Understand Causal Mechanisms Underlying Counterintuitive Epidemiological Data.” 2019. Doctoral Dissertation, University of Michigan. Accessed November 15, 2019. http://hdl.handle.net/2027.42/151666.

MLA Handbook (7th Edition):

Havumaki, Joshua. “Using Mathematical Models to Understand Causal Mechanisms Underlying Counterintuitive Epidemiological Data.” 2019. Web. 15 Nov 2019.

Vancouver:

Havumaki J. Using Mathematical Models to Understand Causal Mechanisms Underlying Counterintuitive Epidemiological Data. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Nov 15]. Available from: http://hdl.handle.net/2027.42/151666.

Council of Science Editors:

Havumaki J. Using Mathematical Models to Understand Causal Mechanisms Underlying Counterintuitive Epidemiological Data. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151666

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