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Browsing by Author "Gutilla, Molly, committee member"

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    Colorado EnviroScreen as a predictor of mortality: an ecological analysis of 2019 county-level data
    (Colorado State University. Libraries, 2024) Pusker, Stephanie, author; Rojas-Rueda, David, advisor; Clark, Maggie, advisor; Gutilla, Molly, committee member
    Background In today's rapidly evolving landscape of environmental awareness in public health, Colorado stands at the forefront of innovation with its Environmental Justice (EJ) mapping and health screening tool, Colorado EnviroScreen (Colorado EnviroScreen, 2022). This tool, developed by Colorado Department of Public Health and Environment (CDPHE), empowers governmental agencies, research institutions, and the broader public to quantify and understand the interplay between environmental factors and community health by calculating an "EnviroScreen Score" (Colorado EnviroScreen, 2022). The higher the EnviroScreen score, the more likely the area will be affected by environmental health injustices at the census block group, census tract, and/or county levels (Colorado EnviroScreen, 2022). The purpose of this study is to bridge a gap in the current research landscape by exploring the association between aggregate county-level data derived from an EJ mapping tool and all-cause mortality rates. Specifically, we aim to investigate the relationship between the CO EnviroScreen score and the component scores – Demographics, Sensitive Populations, Climate Vulnerability, Environmental Effects, and Environmental Exposures – and all-cause mortality rates at the county level in Colorado in 2019. By conducting this ecological analysis, we seek to provide valuable insights into the potential impact of environmental justice factors on public health outcomes, thereby contributing to a more comprehensive understanding of the interaction between environmental conditions and mortality rates within communities. Methods An ecological study was conducted at the county-level spatial scale using a generalized linear model to assess the association between three EnviroScreen component score percentiles (Demographics, Environmental Exposures, and Climate Vulnerability) and age-standardized all-cause mortality at the county level. These three score percentiles were selected due to correlation with other scores, as well as the indicators included in some of the component scores being more comprehensive than others. County-level covariates included in the model were insufficient sleep, alcohol overindulgence, physical inactivity, and smoking. In addition to the full model, secondary models were created, including Demographics, Environmental Exposures, and Climate Vulnerability as independent predictors. Furthermore, the total EnviroScreen score percentile, which includes all component scores, was used in the analysis. Results In the fully adjusted model, a 10% increase in the EnviroScreen Environmental Exposures component score was associated with a 3% increase in all-cause mortality rate at the county level in Colorado in 2019 (95% CI: 1.00, 1.05). In the crude model, a 10% increase in EnviroScreen score was associated with a 5% increase in all-cause mortality rate at the county level in Colorado in 2019 (95% CI: 1.03, 1.07). Neither Demographics nor Climate Vulnerability component scores percentile were associated with an increase or decrease in all-cause mortality rates. Discussion This study suggests that there is a potential association between a higher EnviroScreen component score and an increase in age-standardized, all-cause mortality at the county level in Colorado. This is the first study to estimate the association between aggregate environmental and health-related scores from CO EnviroScreen with mortality. This study supports the notion of cumulative impacts as a tool to monitor possible health disparities and environmental injustice.
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    Investigating the association between public health system structure and system effectiveness
    (Colorado State University. Libraries, 2024) Orr, Jason, author; Golicic, Susan, advisor; Bradley, Thomas, committee member; Miller, Erika, committee member; Gutilla, Molly, committee member; Magzamen, Sheryl, committee member
    Public health systems in the United States face significant challenges due to their complexity and variability. This dissertation follows a three-paper format and examines these systems through a comprehensive analysis, using systems approaches, latent transition analysis (LTA), and ordinal regression to uncover patterns and inform improvements in public health governance and service delivery. The first essay (Chapter 2) explores the application of systems approaches to the design and improvement of public health systems. A scoping review was conducted, revealing a paucity of literature on the use of "hard" systems methodologies like systems analysis and engineering in public health. The findings highlight the potential for systems approaches to enhance the efficiency, effectiveness, and equity of public health services. However, the limited engagement by public health practitioners and the lack of depth in existing literature indicate significant gaps that need to be addressed to fully leverage systems science in public health governance and service delivery. Building on the literature review, the second essay (Chapter 3) introduces a novel typology of local health departments (LHDs) using LTA based on the National Association of County and City Health Officials (NACCHO) Profile study data. The LTA identified six distinct latent statuses of LHDs, characterized by variables such as governance centrality, colocation, and integration. This typology provides a robust framework for understanding the structural and operational diversity of LHDs, offering insights into how these factors influence public health outcomes. The final essay (Chapter 4) applies ordinal regression analyses to explore the relationship between the latent statuses of LHDs and various community health outcomes. Initial analyses using a cumulative logit model indicated a violation of the proportional odds assumption, necessitating a shift to a generalized logit model. This approach revealed significant predictors of latent statuses, such as poor physical health days, preventable hospital stays, and life expectancy. The findings underscore the complexity of public health systems and the need for careful selection of statistical models to accurately capture these dynamics. The study provides actionable insights for public health policy and strategic planning, highlighting areas for future research and potential interventions to optimize public health system design and operations. This dissertation underscores the importance of systems approaches in understanding and improving public health systems. By leveraging advanced statistical models and exploring the structural characteristics of LHDs, it contributes to a deeper understanding of the factors influencing public health governance and service delivery. The findings offer a foundation for future research and policy development aimed at enhancing the efficiency and effectiveness of public health systems to better serve communities.
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    Statistical models for COVID-19 infection fatality rates and diagnostic test data
    (Colorado State University. Libraries, 2023) Pugh, Sierra, author; Wilson, Ander, advisor; Fosdick, Bailey K., advisor; Keller, Kayleigh, committee member; Meyer, Mary, committee member; Gutilla, Molly, committee member
    The COVID-19 pandemic has had devastating impacts worldwide. Early in the pandemic, little was known about the emerging disease. To inform policy, it was essential to develop data science tools to inform public health policy and interventions. We developed methods to fill three gaps in the literature. A first key task for scientists at the start of the pandemic was to develop diagnostic tests to classify an individual's disease status as positive or negative and to estimate community prevalence. Researchers rapidly developed diagnostic tests, yet there was a lack of guidance on how to select a cutoff to classify positive and negative test results for COVID-19 antibody tests developed with limited numbers of controls with known disease status. We propose selecting a cutoff using extreme value theory and compared this method to existing methods through a data analysis and simulation study. Second, there lacked a cohesive method for estimating the infection fatality rate (IFR) of COVID-19 that fully accounted for uncertainty in the fatality data, seroprevalence study data, and antibody test characteristics. We developed a Bayesian model to jointly model these data to fully account for the many sources of uncertainty. A third challenge is providing information that can be used to compare seroprevalence and IFR across locations to best allocate resources and target public health interventions. It is particularly important to account for differences in age-distributions when comparing across locations as age is a well-established risk factor for COVID-19 mortality. There is a lack of methods for estimating the seroprevalence and IFR as continuous functions of age, while adequately accounting for uncertainty. We present a Bayesian hierarchical model that jointly estimates seroprevalence and IFR as continuous functions of age, sharing information across locations to improve identifiability. We use this model to estimate seroprevalence and IFR in 26 developing country locations.
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    The association between political environment and COVID-19 mortality in selected Colorado counties
    (Colorado State University. Libraries, 2023) DeBie, Kelly, author; Neophytou, Andreas, advisor; Peel, Jennifer, advisor; Gutilla, Molly, committee member; Rojas-Rueda, David, committee member
    The SARS-CoV-2 virus spread worldwide triggering a global Coronavirus (COVID-19) pandemic. COVID-19 remains a public health threat today and may continue to do so into the future dependent on the emergence of variants and our ability to mitigate harm through vaccines and other public health measures. The COVID-19 pandemic struck the United States during a time of great political tension and divide under the administration of President Donald Trump. State-level variation in mitigation measures may have been influenced by political views. COVID-19 mortality rates also varied by county. This paper seeks to investigate whether the county-level political environment was associated with differences in COVID-19 mortality in the state of Colorado. We examined the association between political environment and county-level age-adjusted COVID-19 mortality rates during 2020 and 2021. Political environment is measured using data from the 2016 and 2020 Presidential election vote distribution by county, obtained from the Colorado Secretary of State. Outcome data was obtained from the Colorado Department of Public Health and Environment (CDPHE), having already been age-adjusted using direct standardization based on the 2010 Census. Any counties with 3 of fewer deaths in a calendar year were excluded, leaving a total of 48 counties in 2020 and 56 in 2021. Rate ratios and 95% confidence intervals were estimated using Quasi-Poisson regression models, separately for 2020 and 2021 mortality data. The models were adjusted for population density, the percentage of county residents without health insurance, and the demographics percentile from the Colorado EnviroScreen Environmental Justice Tool. Models were further evaluated for the presence of effect modification by population density. There are a total of 64 counties in the state of Colorado. In the 2016 election, 42 counties voted for Donald Trump. In the 2020 election, that dropped to 40 counties. Age-adjusted mortality rates ranged from 14.3-458.0 per 100,000 over the two years of data. For 2021 mortality data, the estimated mean adjusted mortality rate was 78% higher among counties where aggregated individual votes were highest in percentage for Donald Trump in 2016 as compared to counties with highest vote percentage for Hilary Clinton. (RR = 1.78; 95% CI: 1.26-2.59). For 2020, the estimated mean adjusted mortality rate was found to be 24% higher among counties voting in highest percentage for Donald Trump in 2016 as compared to counties voting in highest percentage for Hilary Clinton, though this association was not statistically significant. (RR=1.24; 95% CI: 0.81-1.94). Similar results were observed for the 2020 election data (comparing county-level voting results for Trump vs. Biden). We did not observe evidence that the association was modified by population density. This study observed an association between county-level political environment and age-adjusted COVID-19 mortality rates, specifically finding an association that became statistically significant during the pandemic. These results build on a growing body of evidence studying the links between politics and COVID-19 outcomes. Strengths of this study include the use of publicly available datasets, state-wide analysis, multiple model options with similar results indicating robustness, and utilization of a novel environmental justice metric to adjust for multiple confounders simultaneously. As this was an ecological study, inference cannot be extended to individuals. Future research may want to further explore both the individual and community political exposures that may influence mortality. It may also be suggested to investigate election data as a continuous rather than binary variable to tease out the relationship in more detail. Studies such as this may be useful as the COVID-19 pandemic is still ongoing, and in preparation for any future pandemics.