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Browsing by Author "Wilson, Ander, committee member"

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    Advances in statistical analysis and modeling of extreme values motivated by atmospheric models and data products
    (Colorado State University. Libraries, 2018) Fix, Miranda J., author; Cooley, Daniel, advisor; Hoeting, Jennifer, committee member; Wilson, Ander, committee member; Barnes, Elizabeth, committee member
    This dissertation presents applied and methodological advances in the statistical analysis and modeling of extreme values. We detail three studies motivated by the types of data found in the atmospheric sciences, such as deterministic model output and observational products. The first two investigations represent novel applications and extensions of extremes methodology to climate and atmospheric studies. The third investigation proposes a new model for areal extremes and develops methods for estimation and inference from the proposed model. We first detail a study which leverages two initial condition ensembles of a global climate model to compare future precipitation extremes under two climate change scenarios. We fit non-stationary generalized extreme value (GEV) models to annual maximum daily precipitation output and compare impacts under the RCP8.5 and RCP4.5 scenarios. A methodological contribution of this work is to demonstrate the potential of a "pattern scaling" approach for extremes, in which we produce predictive GEV distributions of annual precipitation maxima under RCP4.5 given only global mean temperatures for this scenario. We compare results from this less computationally intensive method to those obtained from our GEV model fitted directly to the RCP4.5 output and find that pattern scaling produces reasonable projections. The second study examines, for the first time, the capability of an atmospheric chemistry model to reproduce observed meteorological sensitivities of high and extreme surface ozone (O3). This work develops a novel framework in which we make three types of comparisons between simulated and observational data, comparing (1) tails of the O3 response variable, (2) distributions of meteorological predictor variables, and (3) sensitivities of high and extreme O3 to meteorological predictors. This last comparison is made using quantile regression and a recent tail dependence optimization approach. Across all three study locations, we find substantial differences between simulations and observational data in both meteorology and meteorological sensitivities of high and extreme O3. The final study is motivated by the prevalence of large gridded data products in the atmospheric sciences, and presents methodological advances in the (finite-dimensional) spatial setting. Existing models for spatial extremes, such as max-stable process models, tend to be geostatistical in nature as well as very computationally intensive. Instead, we propose a new model for extremes of areal data, with a common-scale extension, that is inspired by the simultaneous autoregressive (SAR) model in classical spatial statistics. The proposed model extends recent work on transformed-linear operations applied to regularly varying random vectors, and is unique among extremes models in being directly analogous to a classical linear model. We specify a sufficient condition on the spatial dependence parameter such that our extreme SAR model has desirable properties. We also describe the limiting angular measure, which is discrete, and corresponding tail pairwise dependence matrix (TPDM) for the model. After examining model properties, we then investigate two approaches to estimation and inference for the common-scale extreme SAR model. First, we consider a censored likelihood approach, implemented using Bayesian MCMC with a data augmentation step, but find that this approach is not robust to model misspecification. As an alternative, we develop a novel estimation method that minimizes the discrepancy between the TPDM for the fitted model and the estimated TPDM, and find that it is able to produce reasonable estimates of extremal dependence even in the case of model misspecification.
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    Assessing community-wide health impacts of natural disasters: studies of a severe flood in Beijing and tropical cyclones in the United States
    (Colorado State University. Libraries, 2018) Yan, Meilin, author; Anderson, G. Brooke, advisor; Peel, Jennifer L., advisor; Magzamen, Sheryl, committee member; Wilson, Ander, committee member
    Death and injury tolls occurring during natural disasters have traditionally been estimated using a disaster surveillance approach, where each death or injury is considered case-by-case to determine if it can be attributed to the disaster. This approach may not always capture the overall community-wide health effects associated with disaster exposure, especially in cases where much of the excess morbidity and mortality result from outcomes common outside of disaster periods (e.g., heart attacks, respiratory problems) rather than well-characterized disaster-related risks that are rarer outside of storm events (e.g., drowning, carbon monoxide poisoning, trauma). The goal of this dissertation is to examine the community-wide impacts of natural disasters on some common health outcomes. To achieve this goal, we assessed the community-wide health risks from exposure to two types of climate-related natural disasters, a severe flood and tropical cyclones, as compared with matched unexposed days in the same community. Our results can provide new evidence on how natural disasters affect human health, contributing to and complementing the large base of existing literature generated using a disaster surveillance approach. Mortality risk of a severe flood. On July 21–22, 2012, Beijing, China, suffered its heaviest rainfall in 60 years, which caused heavy flooding throughout Beijing. We conducted a matched analysis comparing mortality rates on the peak flood day and the four following days to similar unexposed days in previous years (2008–2011), controlling for potential confounders, to estimate the relative risks (RRs) of daily mortality among Beijing residents associated with this flood. Compared to the matched unexposed days, mortality rates were substantially higher during the flood period for all-cause, circulatory, and accidental mortality, with the highest risks observed on the peak flood day. No evidence of increased risk of respiratory mortality was observed in this study. We estimated a total of 79 excess deaths among Beijing residents on July 21–22, 2012; by contrast, only 34 deaths were reported among Beijing residents in a study estimating the flood's fatality toll using a traditional surveillance approach. Results were robust to study design and modeling choices. Our results indicate considerable impacts of this flood on public health, and that much of this impact may come from increased risk of non-accidental deaths. To our knowledge, this is the first study analyzing the community-wide changes in mortality rates during the 2012 flood in Beijing, and one of the first to do so for any major flood worldwide. This study offers critical evidence in assessing flood-related health impacts, as urban flooding is expected to become more frequent and severe in China. Health risk of tropical cyclones. To measure storm exposure, we separately considered five metrics—distance to storm track; cumulative rainfall; maximum sustained wind speed; flooding; and tornadoes. For mortality outcomes, we used community vital records for 78 large eastern United States (U.S.) communities, 1988–2005, to estimate the risks of storm exposure on four mortality outcomes. For emergency hospitalization outcomes, we used Medicare claims for 180 eastern US counties, 1999–2010, to estimate storm-related risks on emergency hospitalizations from cardiovascular and respiratory disease among Medicare beneficiaries. We compared the health outcome rates across the study population (all community residents for the mortality analysis; community Medicare beneficiaries for the hospitalization analysis) on storm-exposed days versus similar unexposed days within each community. For each combination of exposure metric and health outcome, we estimated storm-associated health risks for a window from two days before to seven days after the day of storm's closest approach. For the mortality analysis, 92 Atlantic Basin tropical cyclones were considered based on U.S. landfall or close approach, with 70 communities exposed to at least one storm; for the hospitalization analysis, 74 storms were considered for 175 exposed counties. Under the wind-based exposure metric, we found substantially elevated risk for all mortality outcomes considered compared with matched unexposed days, with risk typically highest on the day of the storm's closest approach. When excluding the ten most severe storm events based on wind exposures, however, we did not observe significantly increased risk for the remaining storm exposures on any mortality outcomes. Among Medicare beneficiaries, the cumulative risks of respiratory hospitalizations were increased under all storm exposure metrics considered, for all storm exposures and across all exposed counties; these risks remained significantly elevated even when the ten most severe storm exposures (based on wind exposure) were excluded. Our findings on community-wide health risks from tropical cyclones add important insights to results from disaster surveillance: first, the impacts of tropical cyclones on non-accidental mortality can, in some cases, be much greater than identified in case-by-case surveillance studies; second, there is strong evidence that risks of Medicare emergency hospital admissions due to non-injury morbidity are elevated during the storm exposure period; and third, intense wind exposure can characterize many of the tropical cyclone exposures with particularly high risk on non-accidental mortality, as well as respiratory hospitalizations in the elderly.
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    Associations between air pollution emitted from cookstoves and central hemodynamics, arterial stiffness, and blood lipids in laboratory and field settings
    (Colorado State University. Libraries, 2019) Walker, Ethan Sheppard, author; Peel, Jennifer, advisor; Clark, Maggie, advisor; Dinenno, Frank, committee member; Volckens, John, committee member; Wilson, Ander, committee member
    To view the abstract, please see the full text of the document.
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    Cookstove startup material characterization and quantification and acute cardiopulmonary effects from controlled exposure to cookstove air pollution
    (Colorado State University. Libraries, 2018) Fedak, Kristen M., author; Peel, Jennifer L., advisor; Volckens, John, advisor; Clark, Maggie, committee member; Nelson, Tracy, committee member; Wilson, Ander, committee member
    To view the abstract, please see the full text of the document.
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    Household air pollution among women using biomass stoves in Honduras: exposure characterization and associations with exhaled nitric oxide and markers of systemic inflammation
    (Colorado State University. Libraries, 2018) Benka-Coker, Megan Leigh, author; Clark, Maggie, advisor; Peel, Jennifer, committee member; Volckens, John, committee member; Wilson, Ander, committee member
    To view the abstract, please see the full text of the document.
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    Methodology in air pollution epidemiology for large-scale exposure prediction and environmental trials with non-compliance
    (Colorado State University. Libraries, 2023) Ryder, Nathan, author; Keller, Kayleigh, advisor; Wilson, Ander, committee member; Cooley, Daniel, committee member; Neophytou, Andreas, committee member
    Exposure to airborne pollutants, both long- and short-term, can lead to harmful respiratory, cardiovascular, and cardiometabolic outcomes. Multiple challenges arise in the study of relationships between ambient air pollution and health outcomes. For example, in large observational cohort studies, individual measurements are not feasible so researchers use small sets of pollutant concentration measurements to predict subject-level exposures. As a second example, inconsistent compliance of subjects to their assigned treatments can affect results from randomized controlled trials of environmental interventions. In this dissertation, we present methods to address these challenges. We develop a penalized regression model that can predict particulate matter exposures in space and time, including penalties to discourage overfitting and encourage smoothness in time. This model is more accurate than spatial-only and spatiotemporal universal kriging (UK) models when the exposures are missing in a regular (semi-daily) pattern. Our penalized regression model is also faster than both UK models, allowing the use of bootstrap methods to account for measurement error bias and monitor site selection in a two-stage health model. We introduce methods to estimate causal effects in a longitudinal setting by latent "at-the-time" principal strata. We implement an array of linear mixed models on data subsets, each with weights derived from principal scores. In addition, we estimate the same stratified causal effects with a Bayesian mixture model. The weighted linear mixed models outperform the Bayesian mixture model and an existing single-measure principal scores method in all simulation scenarios, and are the only method to produce a significant estimate for a causal effect of treatment assignment by strata when applied to a Honduran cookstove intervention study. Finally, we extend the "at-the-time" longitudinal principal stratification framework to a setting where continuous exposure measurements are the post-treatment variable by which the latent strata are defined. We categorize the continuous exposures to a binary variable in order to use our previous method of weighted linear mixed models. We also extend an existing Bayesian approach to the longitudinal setting, which does not require categorization of the exposures. The previous weighted linear mixed model and single-measure principal scores methods are negatively biased when applied to simulated samples, while the Bayesian approach produces the lowest RMSE and bias near zero. The Bayesian approach, when applied to the same Honduran cookstove intervention study as before, does not find a significant estimate for the causal effect of treatment assignment by strata.
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    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 member
    Exposure 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.