Browsing by Author "Magzamen, Sheryl, advisor"
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Item Embargo Analyzing early cancer etiology in golden retrievers using Golden Retriever Lifespan Study (GRLS) data(Colorado State University. Libraries, 2023) Hodo, Kiara, author; Magzamen, Sheryl, advisor; LaRue, Susan, committee member; Gutilla, Margaret, committee memberBackground: Although cancer is a burden in both humans and dogs, humans medicine is characterized by established health care organizations, interdisciplinary networks, and databases from which data and research can be complied and shared. No such organization exists in veterinary medicine. Individual registries provide useful data and information on cancer in dogs, but no mechanism exists to summarize data to detect cancer trends, breed-specific measurements of occurrence, and treatment responses. Therefore, there are vast knowledge gaps related to cancers in dogs, especially among early cases. The Golden Retriever Lifetime Study (GRLS) data collected by Morris Animal Foundation is a unique opportunity to evaluate cancer prevalence in a large number of golden retrievers with known pedigree. Data were evaluated for each state and compared to human cancer prevalence provided by the CDC. Differences in cancer prevalence between young and old dogs was evaluated, along with their resident state, sex status, and cancer type. Golden retrievers were recruited from 2012-2015 to participate in the GRLS cohort study and were confirmed to be free of life limiting conditions by a veterinarian. Owners had to have at least a 3-generation pedigree of their dog to be enrolled. Information regarding the dog's health and condition were recorded annually via owner and veterinarian questionnaire, as well as sample collections, and added to the GRLS study data. The GRLS data was refined and cleaned in SAS and R studio evaluate state of diagnosis, age at diagnosis, and sex at diagnosis. The highest prevalence of cancer among GRLS participants was in Louisiana (38.5%) with Arizona as the second highest (17.5%). A cluster of higher prevalence regions were observed in the upper east coast, similarly to the CDC's human data. Although the prevalence was highest in Louisiana and Arizona, neither were found to be statistically significant based on the difference of proportion calculations. A statistically significant difference was found in average age at diagnosis between male neutered and intact cancer dogs, but not when comparing female spayed and intact cancer bearing dogs or when comparing all 4 sex statuses. The average age at diagnosis based on tumor types (mammary, hemangiosarcoma, histiocytoma, lymphoma) was significantly different, most likely due to higher numbers of hemangiosarcoma cases in older dogs and histiocytoma cases observed in younger dogs. Older, male neutered dogs were more susceptible to hemangiosarcoma development (85.5% of cases were old), and younger dogs that had been spayed or neutered were more susceptible to histiocytomas (100% of cases were young). Discussion: One of the interesting findings of this analysis was that there was a statistically significant difference in average age at diagnosis between intact and neutered male dogs, but not between intact and spayed females. Small sample size of cancer dogs could have impacted the power of statistical test results and been a contributor to statistical insignificance seen throughout the analysis. Dogs moving multiple times throughout the duration of the study can affect interpretations and implications from prevalence by state findings. Prevalence was also calculated using only the total GRLS study population the resided in respective states as the denominator, effecting generalizability of the analysis findings.Item Open Access Culicoides species and livestock overlay analysis: a habitat suitability framework for Culicoides insignis, stellifer, and venustus and potential Bluetongue virus presence using environmental and meteorological variables to enhance trap detection(Colorado State University. Libraries, 2022) Kessinger, Peter James, author; Magzamen, Sheryl, advisor; Bosco-Lauth, Angela, advisor; Schaeffer, Joshua, committee member; Mayo, Christie, committee memberCulicoides spp. midges are blood feeding insects capable of transmitting a variety of pathogens. Of particular concern are Bluetongue virus and Epizootic Hemorrhagic Disease virus. Bluetongue virus is extremely dangerous for ruminants, infecting mainly sheep and cattle, and is a growing concern for areas like the United States. There is little known about the range and habitat preference for Culicoides midges, especially in the United States. Our study focuses on predicting habitat suitability for three species of concern: Culicoides insignis, Culicoides. stellifer, and Culicoides. venustus. Each of these species are linked to the spread or potential spread of Bluetongue virus. We obtained data from the Southeastern Cooperative Wildlife Disease Study that included the presence and absence data from midge traps for each of the species of interest from 2008-2020. We combined these data with meteorological data and environmental data to generate a habitat suitability model. The maps were then used to predict the probability of midge species presence in that area and create an overlay analysis for each species of midge and livestock of interest: goats, sheep, and cattle. For the statistical analysis, we used both generalized linear models with binomial regression and random forest models to predict potential midge habitat suitability. We then used the AUC scores to determine model fit using both training and test datasets. Our results indicated that environmental and meteorological variables of significance vary between the species of interest. Most variables were significant for the species of interest, with the most common exception being wind direction. The generalized linear models performed better than the random forest model overall, with C. insignis, C. stellifer, and C. venustus having AUC scores of 0.86, 0.70, and 0.71, for generalized linear models respectively. Overall, prediction models were successful in visualizing and predicting midge presence on the provided environmental and meteorological variables. However, further sampling should be conducted, and variables reassessed for suitability.Item Open Access Evaluating statistical methods to predict indoor black carbon in an urban birth cohort(Colorado State University. Libraries, 2022) WeMott, Sherry D., author; Magzamen, Sheryl, advisor; Koslovsky, Matthew, committee member; Rojas-Rueda, David, committee memberThough individuals in the United States spend a majority of their time indoors, epidemiologic studies often use ambient air pollution data for exposure assessment. We used several modeling approaches to predict indoor black carbon (BC) from outdoor BC and housing characteristics to support future efforts to estimate personal air pollution exposure given time spent indoors. Households from the Healthy Start cohort in Denver, CO were recruited to host two paired indoor/outdoor low-cost air samplers for one-week sampling periods during spring 2018, summer 2018, and winter 2019. Participants also completed questionnaires about housing characteristics like building type, flooring, and use of heating and cooling systems. Sampled filters were analyzed for BC using transmissometry. Ridge, Lasso and multiple regression techniques were used to build the best predictive model of indoor BC given the available set of covariates. Leave-one-out cross-validation was used to assess the predictive accuracy of each model. A total of 27 households participated in the study, and BC data were available for 39 filters. We had limited comparable data on seasonality as winter data were excluded from the analysis due to high variability and low confidence in outdoor measurements. Shortened runtimes and other performance issues suggest insufficient weatherproofing of our monitors for low temperatures. Of the three modeling approaches, Ridge LSE showed the best predictive performance (MPSE 0.50). The final inference model included the following covariates: outdoor PM2.5, outdoor BC, hard floors, and pets in the home (adj. R2=0.27). These factors accounted for approximately 27% of the variability in indoor BC concentrations measured in Denver, CO homes. In the absence of personal monitoring, household characteristics and time-activity patterns may be used to calibrate ambient air pollution concentrations to the indoor environment for improved estimation of personal exposure.Item Open Access Geospatial analyses of childhood malaria following repeated village-wide Ivermectin administrations: secondary analyses for the RIMDAMAL pilot study(Colorado State University. Libraries, 2017) Barnett, Chelcie A., author; Magzamen, Sheryl, advisor; Foy, Brian, committee member; Hahn, Micah, committee memberMalaria has long been a major public health concern, with historic roots dating back thousands of years. This febrile disease is caused by a parasite that is transmitted among vertebrates by mosquitoes. Over the past century, global eradication programs have focused on minimizing populations of the insect vectors, and administering treatments to people infected, especially young children and pregnant women, as they are the most vulnerable to suffering severe complications. Overall, these programs have decreased the geographic distribution and global disease burden; however, malaria remains a major problem in regions where these efforts have been unsuccessful. In 2015, there were an estimated 214 million cases throughout the world, resulting in approximately 438,000 deaths; however, over 3 billion people are living at risk of becoming infected with malaria. Widespread use of the few available effective insecticides and anti-malarial drugs has conferred resistance in both parasitic and mosquito species, decreasing the effectiveness of current interventions. As anti-malarial resistance and insecticide resistance spread, the need for novel malaria interventions becomes more urgent. One novel approach to combatting malaria was pilot-tested by researchers in the Department of Microbiology, Immunology and Pathology at Colorado State University. The Repeated Ivermectin Mass Drug Administration to control Malaria, or the RIMDAMAL study, evaluated the safety and effectiveness of repeated village-wide administrations of an anti-parasitic drug to prevent malaria in children 5 years old. The RIMDAMAL study was a randomized trial carried out in Burkina Faso, a small tropical country in West Africa. Ivermectin (IVM) is a common anti-parasitic used around the world to prevent and treat parasitic diseases. Recent evidence has demonstrated that IVM is toxic to malaria-transmitting mosquitoes, and can inhibit the propagation of some life stages of malaria parasites. Initial analyses of the RIMDAMAL data found significantly fewer childhood malaria cases in intervention villages that received repeated IVM administrations, compared to control villages. This study is a geospatial analysis of the RIMDAMAL data to provide further insight as to how this intervention could be implemented. There were two study aims for this research: 1) identify significant clustering of high and low childhood malaria incidence within each study village; and 2) identify significant clustering of high and low childhood malaria incidence throughout the entire study region. In total, eight villages were enrolled in the study, four of which served as controls, while the other four received the intervention. Residents of each village live in concessions, or compounds of extended family. Geospatial coordinates were collected for each concession within a study village, along with data on the participants within each concession. Using this data, incidence density of malaria among children 5 years old or younger was calculated at the concession level. Concessions were mapped, and spatial clustering of incidence density values was evaluated using the Getis-Ord Gi* (G-I-star) spatial autocorrelation statistic. To evaluate within village clustering, each of the eight study villages were analyzed individually, and between village clustering was evaluated by analyzing the entire study region. Within each village, several "hot spots," or statistically significant clusters of high malaria incidence density values were recognized during analyses with max clustering, at the 95% confidence level. Statistically significant clusters of low incidence density were identified in one study village during the analysis with max clustering. The proportion of concessions identified as significant clusters varied by village, ranging from 12% to 91.3%. There seems to be no trend in clustering patterns seen within each village; some villages had randomly distributed hot or cold spots, while others appeared more clustered. The spatial clustering patterns in the whole study region are more telling. Max clustering occurs in a bimodal pattern with two peaks; at 2,100 meters and 10,000 meters. The clustering patterns that occur indicate regions of similar malaria incidence. The proximity and locations of these villages may imply the RIMDAMAL protocol has regional impacts. Additional research is needed to evaluate how to most effectively implement this intervention to protect against malaria.Item Open Access Longitudinal analysis and characterization of Escherichia coli O157:H7 shedding in dairy cattle in northern Colorado(Colorado State University. Libraries, 2016) Burket, Victoria L., author; Magzamen, Sheryl, advisor; Reynolds, Stephen, committee member; McConnel, Craig, committee memberEscherichia coli O157:H7 (STEC) is an enterohemorrhagic Gram-negative bacteria that is a common source of foodborne illness around the world. Annually, O157 is responsible for approximately 100,000 cases, 3,000 hospitalizations, and 90 deaths in the United States, and has been diagnostically confirmed on every continent except for Antarctica. Dairy cattle serve as asymptomatic carriers of the O157 bacteria, maintaining a continuous cycle of reinfection through their environment, and have been implicated as a potential source of contamination of the food chain. Gathering data on prevalence and shedding cycles of O157 in dairy cattle can provide insight into the scope of the problem and potential mitigation strategies. The primary objective of this study was to investigate the association between shedding status on a randomly selected day and- shedding on subsequent consecutive days (n=4), daily proportions and patterns of shedding, and how shed status on one day affects shed status on the next day. Two local Northern Colorado dairies were selected for study. Fecal samples were taken from 25 cows from Dairy A and 49 cows from Dairy B and tested for presence of the O157 pathogen. Based on those results, twenty cows from each dairy were randomly chosen for the study, with 10 “shedders” (i.e. cows that tested positive for O157 on Day 1) and 10 “non-shedders” (i.e. cows that tested negative for O157 on Day 1) selected from each dairy for a total of forty study subjects. The cows were then resampled once daily for an additional four days, testing for rfb, stx1, stx2, and eae genes as well as collecting overall health information. Health information variables were dichotomized based on scoring systems and logistic regression, generalized linear models, and generalized linear mixed models were used for analysis of research questions. Our study had three main aims and five research questions of interest. Our first aim was to analyze overall shedding events, split into two research questions. First we wanted to know if shedding status on Day 1 was associated with shedding on any subsequent day. We used a logistic regression model with any subsequent shedding as the outcome and Day 1 shedding status, dairy, parity, temperature, days in milk, body condition score, hygiene score, and fecal score as the covariates. Next, we wanted to know what risk factors were associated with cumulative days of shedding. For this question we used a generalized mixed model with a poisson regression. The count of total shedding days was used as the outcome variable and Day 1 shedding status, dairy, parity, temperature, days in milk, body condition score, hygiene score, and fecal score were the covariates. Additionally, we aimed to analyze day-to-day shedding patterns within the cattle cohort so see if shedding status on one day was associated with shedding status on the next day. First we used a generalized linear mixed model to compare paired days, specifying Day 1 vs Day 2, Day 2 vs Day 3, Day 3 vs Day 4, and Day 4 vs Day 5. The outcome variable was daily shedding status and the primary risk factor was shedding status on the stated previous day, with additional variables including Day 1 shedding status, dairy, parity, temperature, days in milk, body condition score, hygiene score, and fecal score were the covariates. We then used a generalized linear model with a logit link to assess the overall association between day-to-day shedding patterns averaged over the five-day study period, with the outcome variable as daily shedding status. The primary risk factor was shedding status on the previous day, with additional variables including Day 1 shedding status, dairy, parity, temperature, days in milk, body condition score, hygiene score, and fecal score were the covariates. Our last research question aimed to assess the associations between our risk factors of interest and daily shedding status, as well as daily shedding patterns. We used a generalized linear model with a logit link to model risk factor associations, with the outcome variable being daily shedding status and the risk factor variables including Day 1 shedding status, dairy, parity, temperature, days in milk, body condition score, hygiene score, and fecal score. We then used proportion testing to assess the differences in proportions of gene and shedding prevalence between Day 1 Shedders and Day 1 Non-Shedders. Initial shedders had a higher proportion of daily shedding than non-shedders during every sample day, 60% vs 35% on Day 2, 60% vs 45% on Day 3, 50% vs 30% on Day 4, and 45% vs 35% on Day 5, however none of these were statistically significant. Shedders similarly also had a higher overall prevalence of targeted O157 genes than Non-Shedders; 20% vs 10% for Stx1, 35% vs 30% for Stx2, and 30% vs 20% for eae. There were no significant differences in gene prevalence between cows from Dairy A and cows from Dairy B for Stx1 or eae, but there was for Stx2; 15% for both groups for Stx1, 25% vs 40% for Stx2, and 25% for both groups for eae. Cows in the Shedder cohort were twice as likely to shed O157 on any subsequent sampling day than non-shedders based on logistic regression analysis (OR 2.0, 95% CI: 1.1,3.8). Day 1 shedding status (p <0.0001), fecal score >3 vs 3 (p 0.02), and temperature (p 0.04) were significantly associated with an increase in cumulative days of shedding. Day 1 shedding status was also a significant predictor of daily shedding status (OR: 1.7, 95% CI: 1.1,2.5). Interestingly, shedding status on one day was not significantly associated with shedding status on the next day, whether looking at specific days (Day 1 vs Day 2- OR: 1.6, 95% CI: 0.4,2.5; Day 2 vs Day 3- OR: 1.5, 95% CI: 0.2,1.8; Day 3 vs Day 4- OR:1.8, 95% CI: 0.6,4.0; Day 4 vs Day 5- OR: 1.6, 95% CI: 0.3,2.2) or averaged over the 5 day study period (OR: 1.5, 95% CI: 0.9,2.3). Overall, we found inconsistent and transient shedding patterns among all of our cohorts, which is similar to findings in past literature. Day 1 shedding status was the only variable consistently found to be associated with any subsequent shedding. Although Day 1 Shedders had a higher daily proportion of shedding throughout the entire study period than Day 1 Non-Shedders, these results were not statistically significant. Past literature has said that shedding cycles likely last between two and six days, but we found that shedding status on one day was not associated with shedding status on the next day, whether looking at pair of days or averaged over the five-day period. The inconsistency in our results calls in to question whether shedding patterns are truly transient acts or whether the sampling methods used potentially misclassify Shedders as Non-Shedders.Item Open Access Respiratory morbidity in susceptible populations: the role of joint exposure to multiple environmental chemicals and pollutants(Colorado State University. Libraries, 2019) Benka-Coker, Wande, author; Magzamen, Sheryl, advisor; Peel, Jennifer, committee member; Wilson, Ander, committee member; Anderson, Brooke, committee memberExposure to ambient pollution from environmental chemicals and pollutants has been associated with a range of adverse respiratory outcomes; susceptible populations are disproportionately affected. Children with asthma are particularly at risk for adverse respiratory effects of environmental agents. The recent increase in US and worldwide pediatric asthma prevalence has encouraged new lines of inquiry focusing on environmental factors, rather than genetic factors, as the main etiologic agent in asthma-related morbidity; the complex relationship between individuals and their environment requires improved characterization and quantification.Item Open Access Studying the impact of air pollution and pesticide mixtures on respiratory health in Fresno and Tulare counties of central California(Colorado State University. Libraries, 2022) Hughes, Matthew Lawrence, author; Magzamen, Sheryl, advisor; Anderson, Brooke, committee member; Schaeffer, Joshua, committee member; Bosco-Lauth, Angela, committee memberResidents of California's Central Valley are exposed to some of the worst air quality in the United States, as well as high levels of pesticides owing to the region's large agricultural economy. There is ample evidence that exposure to air pollution is associated with adverse respiratory health outcomes, and some evidence from occupational and community-based studies that exposure to pesticides has negative impacts on respiratory health as well. Epidemiologic research on air pollution and pesticides often considers these exposures one at a time in relation to health outcomes, but humans are exposed to pollutants simultaneously in mixtures. In this study we used multiple linear regression models to look at linear relationships of three criteria air pollutants and biomarkers of organophosphates (dialkyl-phosphates or DAPs) with urinary leukotriene E4 (LTE4), a biomarker of respiratory inflammation, in participants in four Central California communities (n=80). We then used Bayesian Kernel Machine Regression models to study these criteria air pollutants and DAPs as a mixture and determine if this mixture had a relationship with respiratory health in this population. We also studied these relationships at two different times of the year (January and June) to study whether and how this relationship between an air pollution-pesticide mixture and the respiratory health outcome changed seasonally. Our multiple linear regression models revealed that dimethyl-phosphates had a statistically significant association with respiratory health in January, which suggests that LTE4 can be used as a biomarker for respiratory inflammation in populations with low asthma prevalence. The results of our BKMR analysis were not statistically significant but did suggest interactions between the exposures in our air pollution-pesticide mixture. Despite a small sample size, this study adds to the limited research on environmental mixtures, and the effects of pesticide exposure on respiratory health.Item Open Access Using the dog as a model to investigate environmental and genetic risk factors for mature, antigen-driven lymphoproliferative disorders(Colorado State University. Libraries, 2017) Labadie, Julia, author; Magzamen, Sheryl, advisor; Avery, Anne, advisor; Anderson, Brooke, committee member; Feigelson, Heather, committee member; Morley, Paul, committee member; Page, Rodney, committee memberTo view the abstract, please see the full text of the document.