Matching Items (24)
Description
Background: This study examines how pro-vaccine flu messages, guided by the Extended Parallel Process Model (EPPM), affect parents’ intentions to vaccinate their children.
Methods: Parents of children six months to five years old (N = 975) were randomly exposed to one of four high-threat/high-efficacy messages (narrative, statistical, combined, control) and completed a follow-up survey. Differences between message conditions were assessed with one-way ANOVAs, and binary logistic regressions were used to show how constructs predicted intentions.
Results: There were no significant differences in the ANOVA results at p = .05 for EPPM variables or risk EPPM variables. There was a significant difference between message conditions for perceived manipulation (p = 0.026), authority, (p = 0.024), character (p = 0.037), attention (p < .000), and emotion (p < .000). The EPPM model and perceptions of message model (positively), and the risk EPPM model and fear control model (negatively), predicted intentions to vaccinate. Significant predictor variables in each model at p < .05 were severity (aOR = 1.83), response efficacy (aOR = 4.33), risk susceptibility (aOR = 0.53), risk fear (aOR = 0.74), issue derogation (aOR = 0.63), perceived manipulation (aOR = 0.64), character (aOR = 2.00), and personal relevance (aOR = 1.88). In a multivariate model of the significant predictors, only response efficacy significantly predicted intentions to vaccinate (aOR = 3.43). Compared to the control, none of the experimental messages significantly predicted intentions to vaccinate. The narrative and combined conditions significantly predicted intentions to search online (aOR = 2.37), and the combined condition significantly predicted intentions to talk to family/friends (aOR = 2.66).
Conclusions: The EPPM may not be effective in context of a two-way threat. Additional constructs that may be useful in the EPPM model are perceptions of the message and fear control variables. One-shot flu vaccine messages will be unlikely to directly influence vaccination rates; however they may increase information-seeking behavior. The impact of seeking more information on vaccination uptake requires further research. Flu vaccine messages should be presented in combined form. Future studies should focus on strategies to increase perceptions of the effectiveness of the flu vaccine.
Methods: Parents of children six months to five years old (N = 975) were randomly exposed to one of four high-threat/high-efficacy messages (narrative, statistical, combined, control) and completed a follow-up survey. Differences between message conditions were assessed with one-way ANOVAs, and binary logistic regressions were used to show how constructs predicted intentions.
Results: There were no significant differences in the ANOVA results at p = .05 for EPPM variables or risk EPPM variables. There was a significant difference between message conditions for perceived manipulation (p = 0.026), authority, (p = 0.024), character (p = 0.037), attention (p < .000), and emotion (p < .000). The EPPM model and perceptions of message model (positively), and the risk EPPM model and fear control model (negatively), predicted intentions to vaccinate. Significant predictor variables in each model at p < .05 were severity (aOR = 1.83), response efficacy (aOR = 4.33), risk susceptibility (aOR = 0.53), risk fear (aOR = 0.74), issue derogation (aOR = 0.63), perceived manipulation (aOR = 0.64), character (aOR = 2.00), and personal relevance (aOR = 1.88). In a multivariate model of the significant predictors, only response efficacy significantly predicted intentions to vaccinate (aOR = 3.43). Compared to the control, none of the experimental messages significantly predicted intentions to vaccinate. The narrative and combined conditions significantly predicted intentions to search online (aOR = 2.37), and the combined condition significantly predicted intentions to talk to family/friends (aOR = 2.66).
Conclusions: The EPPM may not be effective in context of a two-way threat. Additional constructs that may be useful in the EPPM model are perceptions of the message and fear control variables. One-shot flu vaccine messages will be unlikely to directly influence vaccination rates; however they may increase information-seeking behavior. The impact of seeking more information on vaccination uptake requires further research. Flu vaccine messages should be presented in combined form. Future studies should focus on strategies to increase perceptions of the effectiveness of the flu vaccine.
ContributorsHall, Sarah (Author) / Jehn, Megan (Thesis advisor) / Mongeau, Paul (Committee member) / Hruschka, Daniel (Committee member) / Margolis, Eric (Committee member) / Arizona State University (Publisher)
Created2015
Description
Vaccinations are important for preventing influenza infection. Maximizing vaccination uptake rates (80-90%) is crucial in generating herd immunity and preventing infection incidence. Vaccination of healthcare professionals (HCP) against influenza is vital to infection control in healthcare settings, given their consistent exposure to high-risk patients like: those with compromised immune systems, children, and the elderly (Johnson & Talbot, 2011). Though vaccination is vital in disease prevention, influenza vaccination uptake among HCP is low overall (50% on average) (Pearson et al., 2006). Mandatory vaccination policies result in HCP influenza vaccination uptake rates substantially higher than opt-in influenza vaccination campaigns (90% vs. 60%). Therefore, influenza vaccination should be mandatory for HCP in order to best prevent influenza infection in healthcare settings. Many HCP cite individual objections to influenza vaccination rooted in personal doubts and ethical concerns, not best available scientific evidence. Nevertheless, HCP ethical responsibility to their patients and work environments to prevent and lower influenza infection incidence overrules such individual objections. Additionally, mandatory HCP influenza vaccination policies respect HCP autonomy via including medical and religious exemption clauses. While vaccination as a prevention method for influenza is logically sound, individuals’ actions are not always rooted in logic. Therefore, I analyze HCP perceptions and actions toward influenza vaccination in an effort to better explain low HCP uptake rates of the influenza vaccine and individual objections to influenza vaccination. Such analysis can aid in gaining HCP trust when implementing mandatory HCP influenza vaccination policies. In summary, mandatory HCP influenza vaccination policies are ethically justified, effective, scientifically-supported method of maximizing HCP influenza vaccine uptake and minimizing the spread of the influenza virus within healthcare settlings.
ContributorsGur-Arie, Rachel (Author) / Maienschein, Jane (Thesis advisor) / Hurlbut, Ben (Thesis advisor) / Ellison, Karin (Committee member) / Arizona State University (Publisher)
Created2016
Description
Bayesian phylogeography is a framework that has enabled researchers to model the spatiotemporal diffusion of pathogens. In general, the framework assumes that discrete geographic sampling traits follow a continuous-time Markov chain process along the branches of an unknown phylogeny that is informed through nucleotide sequence data. Recently, this framework has been extended to model the transition rate matrix between discrete states as a generalized linear model (GLM) of predictors of interest to the pathogen. In this dissertation, I focus on these GLMs and describe their capabilities, limitations, and introduce a pipeline that may enable more researchers to utilize this framework.
I first demonstrate how a GLM can be employed and how the support for the predictors can be measured using influenza A/H5N1 in Egypt as an example. Secondly, I compare the GLM framework to two alternative frameworks of Bayesian phylogeography: one that uses an advanced computational technique and one that does not. For this assessment, I model the diffusion of influenza A/H3N2 in the United States during the 2014-15 flu season with five methods encapsulated by the three frameworks. I summarize metrics of the phylogenies created by each and demonstrate their reproducibility by performing analyses on several random sequence samples under a variety of population growth scenarios. Next, I demonstrate how discretization of the location trait for a given sequence set can influence phylogenies and support for predictors. That is, I perform several GLM analyses on a set of sequences and change how the sequences are pooled, then show how aggregating predictors at four levels of spatial resolution will alter posterior support. Finally, I provide a solution for researchers that wish to use the GLM framework but may be deterred by the tedious file-manipulation requirements that must be completed to do so. My pipeline, which is publicly available, should alleviate concerns pertaining to the difficulty and time-consuming nature of creating the files necessary to perform GLM analyses. This dissertation expands the knowledge of Bayesian phylogeographic GLMs and will facilitate the use of this framework, which may ultimately reveal the variables that drive the spread of pathogens.
I first demonstrate how a GLM can be employed and how the support for the predictors can be measured using influenza A/H5N1 in Egypt as an example. Secondly, I compare the GLM framework to two alternative frameworks of Bayesian phylogeography: one that uses an advanced computational technique and one that does not. For this assessment, I model the diffusion of influenza A/H3N2 in the United States during the 2014-15 flu season with five methods encapsulated by the three frameworks. I summarize metrics of the phylogenies created by each and demonstrate their reproducibility by performing analyses on several random sequence samples under a variety of population growth scenarios. Next, I demonstrate how discretization of the location trait for a given sequence set can influence phylogenies and support for predictors. That is, I perform several GLM analyses on a set of sequences and change how the sequences are pooled, then show how aggregating predictors at four levels of spatial resolution will alter posterior support. Finally, I provide a solution for researchers that wish to use the GLM framework but may be deterred by the tedious file-manipulation requirements that must be completed to do so. My pipeline, which is publicly available, should alleviate concerns pertaining to the difficulty and time-consuming nature of creating the files necessary to perform GLM analyses. This dissertation expands the knowledge of Bayesian phylogeographic GLMs and will facilitate the use of this framework, which may ultimately reveal the variables that drive the spread of pathogens.
ContributorsMagee, Daniel (Author) / Scotch, Matthew L (Thesis advisor) / Gonzalez, Graciela H (Committee member) / Taylor, Jesse E (Committee member) / Arizona State University (Publisher)
Created2017
Description
Influenza remains a constant concern for public health agencies across the nation and worldwide. Current methods of surveillance suffice but they fall short of their true potential. Incorporation of evolutionary data and analysis through studies such as phylogeography could reveal geographic sources of variation. Identification and targeting of such sources for public health initiatives could yield increased effectiveness of influenza treatments. As it stands there is a lack of evolutionary data available for such use, particularly in the southwest. Our study focused on the sequencing and phylogeography of southwestern Influenza A samples from the Mayo Clinic. We fully sequenced two neuraminidase genes and combined them with archived sequence data from the Influenza Research Database. Using RAxML we identified the clade containing our sequences and performed a phylogeographic analysis using ZooPhy. The resultant data were analyzed using programs such as SPREAD and Tracer. Our results show that the southwest sequences emerged from California and the ancestral root of the clade came from New York. Our Bayesian maximum clade credibility (MCC) tree data and SPREAD analysis implicates California as a source of influenza variation in the United States. This study demonstrates that phylogeography is a viable tool to incorporate evolutionary data into existing forms of influenza surveillance.
ContributorsTurnock, Adam Ryan (Author) / Scotch, Matthew (Thesis director) / Halden, Rolf (Committee member) / Pycke, Benny (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2013-05
ContributorsPerkins, Caitlin (Author) / Jacobs, Bertram (Thesis director) / Gile, Gillian (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2023-05
ContributorsPerkins, Caitlin (Author) / Jacobs, Bertram (Thesis director) / Gile, Gillian (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2023-05
Description
In completing this thesis project, I attempted to hypothesize the trigger in my own personal diagnosis of type 1 diabetes through literature research as well as further research on viruses and their contribution to autoimmune disorders. I had previously hypothesized that, based on my own family life, type 1 diabetes could possibly be a non-heritable disease despite its consistent inheritance pattern discovered by researchers; however, the research presented in this thesis project rejects this idea and supports the theory that I may have been previously susceptible to this disorder and would have developed type 1 diabetes naturally. There were multiple viruses discovered during the literature research conducted that could possibly have been triggers in the acceleration of my disease. The major link between enteroviruses and autoimmune disorders was discovered, as well as influenza A and SARS-COV-2 and this is explained further in this project.
ContributorsPerkins, Caitlin (Author) / Jacobs, Bertram (Thesis director) / Gile, Gillian (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2023-05
Description
Background: Healthy People 2020, a government organization that sets health goals for the United States, has established the benchmark objective of 70% influenza vaccination coverage. National trends show immunization rates are a dismal 41.7% for the adult population. Persons
experiencing homelessness are a vulnerable population in which access to preventative health care services is lacking. Prevention of acute illness, whenever possible, is crucial to maintaining the health of this population. The purpose of this project is to increase influenza vaccinations through staff education at a homeless clinic.
Methods: Eighty-eight volunteer staff, at a student led homeless clinic, received education on the influenza vaccinations. The education occurred at the first orientation meeting of the fall semester in 2016 and consisted of; the importance of immunizations, goals of Healthy People 2020, and an emphasis on addressing patient objections. The effectiveness of the program
compared the percentage of patients immunized from August - December 2016 to 2015.
Results: Post intervention, 44% of the clinic patients were immunized against influenza,
compared to 18% (pre-intervention). This finding resulted in a statistically significant increase in
vaccinations (Z= -5.513, p= < .001, Wilcoxon signed rank test). Eighty-eight volunteers were
present at the influenza vaccination educational intervention and 82 returned their surveys
(response rate 93%). The average score of the posttest was 96% (range 70-100%).
Conclusions: These findings support staff education on influenza vaccinations as a strategy for
increasing vaccination in the homeless population. Such interventions provide promise to
increase influenza vaccinations, however, they fall short of meeting the goals of Healthy People
2020. Identifying innovative interventions is critical to meet the goals of Healthy People 2020.
experiencing homelessness are a vulnerable population in which access to preventative health care services is lacking. Prevention of acute illness, whenever possible, is crucial to maintaining the health of this population. The purpose of this project is to increase influenza vaccinations through staff education at a homeless clinic.
Methods: Eighty-eight volunteer staff, at a student led homeless clinic, received education on the influenza vaccinations. The education occurred at the first orientation meeting of the fall semester in 2016 and consisted of; the importance of immunizations, goals of Healthy People 2020, and an emphasis on addressing patient objections. The effectiveness of the program
compared the percentage of patients immunized from August - December 2016 to 2015.
Results: Post intervention, 44% of the clinic patients were immunized against influenza,
compared to 18% (pre-intervention). This finding resulted in a statistically significant increase in
vaccinations (Z= -5.513, p= < .001, Wilcoxon signed rank test). Eighty-eight volunteers were
present at the influenza vaccination educational intervention and 82 returned their surveys
(response rate 93%). The average score of the posttest was 96% (range 70-100%).
Conclusions: These findings support staff education on influenza vaccinations as a strategy for
increasing vaccination in the homeless population. Such interventions provide promise to
increase influenza vaccinations, however, they fall short of meeting the goals of Healthy People
2020. Identifying innovative interventions is critical to meet the goals of Healthy People 2020.
ContributorsVossoughi, Tiffany (Author) / Harrell, Liz (Thesis advisor)
Created2017-04-17
Description
This study tested the preliminary effectiveness of a health belief and text messaging intervention for parents of five- to eight-year-old children to determine whether health beliefs and influenza vaccine receipt differ when compared to a text messaging control group. Children are almost four times more likely to be infected with influenza than adults (Belshe Piedra, & Block, 2009), shed the greatest quantities of influenza virus, and have been recognized as vectors for spread of disease (Neuzil, Mellen, Wright, Mitchel, Jr., & Griffin, 2002b). The influenza immunization rate for school-age children is less than 56% (Centers for Disease Control and Prevention [CDC], 2014). Reasons for the low vaccination rate include parents’ misperceptions of influenza disease and vaccinations (Bhat-Schelbert et al., 2012; Taylor et al., 2002). There are few theory-based interventions for increasing influenza vaccination rates of school-age children; however, promising results have been found when using the constructs of the health belief model (HBM) (Chen et al., 2011; Coe, Gatewood, Moczygemba, Goode, & Beckner, 2012). Mobile technology using Short Message Service (SMS) text messaging may increase vaccination rates to a greater extent than traditional vaccine reminders (Daley et al., 2002; Grajalva, 2006). Prior to starting this study, only one randomized controlled trial testing text messaging to increase children’s influenza vaccination rates was found (Stockwell et al., 2012). In this study, text messaging was effective in promoting behavioral changes leading to a 4% increase in influenza vaccination (27.1% vs. 22.8%, RR = 1.19, p < .001). This study was a randomized controlled trial using a two-group pre- and posttest experimental design. This study found that a theory-based intervention (SayNo2Flu) guided by the HBM and combined with the use of mobile technology (SMS text messaging) did change parents’ influenza vaccination perceptions. It had an overall increase of 38.1% in Influenza vaccination rates in the intervention group (OR: 4.46, 95% CL, 1.705-11.706, p < .001). These results offer some insight into the use of theory-based preventative interventions for parents of young school-age children.
ContributorsWiseman, Patricia (Author) / Reifsnider, Elizabeth G. (Thesis advisor) / Cesarotti, Evelyn (Committee member) / Black, Andy (Committee member) / Kim, Sunny (Committee member) / Arizona State University (Publisher)
Created2015
Description
The 2009-10 influenza and the 2014-15 Ebola pandemics brought once again urgency to an old question: What are the limits on prediction and what can be proposed that is useful in the face of an epidemic outbreak?
This thesis looks first at the impact that limited access to vaccine stockpiles may have on a single influenza outbreak. The purpose is to highlight the challenges faced by populations embedded in inadequate health systems and to identify and assess ways of ameliorating the impact of resource limitations on public health policy.
Age-specific per capita constraint rates play an important role on the dynamics of communicable diseases and, influenza is, of course, no exception. Yet the challenges associated with estimating age-specific contact rates have not been decisively met. And so, this thesis attempts to connect contact theory with age-specific contact data in the context of influenza outbreaks in practical ways. In mathematical epidemiology, proportionate mixing is used as the preferred theoretical mixing structure and so, the frame of discussion of this dissertation follows this specific theoretical framework. The questions that drive this dissertation, in the context of influenza dynamics, proportionate mixing, and control, are:
I. What is the role of age-aggregation on the dynamics of a single outbreak? Or simply speaking, does the number and length of the age-classes used to model a population make a significant difference on quantitative predictions?
II. What would the age-specific optimal influenza vaccination policies be? Or, what are the age-specific vaccination policies needed to control an outbreak in the presence of limited or unlimited vaccine stockpiles?
Intertwined with the above questions are issues of resilience and uncertainty including, whether or not data collected on mixing (by social scientists) can be used effectively to address both questions in the context of influenza and proportionate mixing. The objective is to provide answers to these questions by assessing the role of aggregation (number and length of age classes) and model robustness (does the aggregation scheme selected makes a difference on influenza dynamics and control) via comparisons between purely data-driven model and proportionate mixing models.
This thesis looks first at the impact that limited access to vaccine stockpiles may have on a single influenza outbreak. The purpose is to highlight the challenges faced by populations embedded in inadequate health systems and to identify and assess ways of ameliorating the impact of resource limitations on public health policy.
Age-specific per capita constraint rates play an important role on the dynamics of communicable diseases and, influenza is, of course, no exception. Yet the challenges associated with estimating age-specific contact rates have not been decisively met. And so, this thesis attempts to connect contact theory with age-specific contact data in the context of influenza outbreaks in practical ways. In mathematical epidemiology, proportionate mixing is used as the preferred theoretical mixing structure and so, the frame of discussion of this dissertation follows this specific theoretical framework. The questions that drive this dissertation, in the context of influenza dynamics, proportionate mixing, and control, are:
I. What is the role of age-aggregation on the dynamics of a single outbreak? Or simply speaking, does the number and length of the age-classes used to model a population make a significant difference on quantitative predictions?
II. What would the age-specific optimal influenza vaccination policies be? Or, what are the age-specific vaccination policies needed to control an outbreak in the presence of limited or unlimited vaccine stockpiles?
Intertwined with the above questions are issues of resilience and uncertainty including, whether or not data collected on mixing (by social scientists) can be used effectively to address both questions in the context of influenza and proportionate mixing. The objective is to provide answers to these questions by assessing the role of aggregation (number and length of age classes) and model robustness (does the aggregation scheme selected makes a difference on influenza dynamics and control) via comparisons between purely data-driven model and proportionate mixing models.
ContributorsMorales, Romarie (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Mubayi, Anuj (Thesis advisor) / Towers, Sherry (Committee member) / Arizona State University (Publisher)
Created2016