Browsing by Author "Neophytou, Andreas, committee member"
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Item Open Access Association between exposure to cadmium and lead during gestation and adverse birth outcomes in the household air pollution intervention network (HAPIN) trial(Colorado State University. Libraries, 2024) Alhassan, Mohamed Adnan, author; Peel, Jennifer, advisor; Clark, Maggie, committee member; Keller, Kayleigh, committee member; Neophytou, Andreas, committee memberLow- and middle-income countries (LMICs) are particularly vulnerable to the adverse effects of metal exposure. These countries' rapid industrialization coupled with population growth, result in substantial environmental exposures, which many governments have limited capacity to formally regulate. Even when regulations exist, many governments have a limited capacity to enforce those regulations. Additionally, LMICs bear a disproportionate burden of adverse birth outcomes, including low birth weight and preterm birth, which carry long-term health implications such as increased risk of chronic diseases, developmental delays, and mortality. Several studies have examined the association between metals and adverse birth outcomes such as low birth weight and preterm births. Specifically, despite the low number of studies, cadmium has been consistently linked to lower birth weights, smaller sizes for gestational age, and reduced head circumference. However, the association between lead exposure and birth outcomes shows inconsistent results. This inconsistency in findings, along with the low number of studies overall, especially in LMICs, regarding lead has prompted further investigation in our current study. Here we utilized data from the Household Air Pollution Intervention Network (HAPIN) trial, a randomized controlled trial conducted in rural areas of Guatemala, Peru, Rwanda, and India. The HAPIN trial evaluated the impact of replacing biomass stoves with liquefied petroleum gas stoves on various health outcomes, including infant birth weight among 3200 participants. The participants in the current analysis included pregnant women with a live singleton birth with exposure and birth data (n=2396). Maternal exposure to cadmium and lead were evaluated by analyzing dried blood spots using inductively coupled mass spectrometry. Blood spots were collected at baseline (9 - <20 weeks gestational age) and 32-36 weeks gestational age; we also evaluated the average of these two measurements. Birth weight was measured using a digital infant scale, with low birth weight defined as <2500 grams, and gestational age at birth was determined using screening data and ultrasonography, with preterm birth defined as <37 weeks. We utilized linear regression for birth weight and gestational age, logistic regression for dichotomous low birth weight, and Cox proportional hazards model for preterm birth. The models accounted for infant sex, maternal age, nulliparity, body mass index, maternal hemoglobin, mother's dietary diversity, food insecurity, tobacco smoking in the household, and study arm. We assessed effect modification by study location, sex, and study arm by including an interaction term. In sensitivity analyses, we included study location, household assets, maternal education in the models; replaced values below the limits of detection (LOD) with LOD/√2, and evaluated metal concentrations standardized by potassium levels. We also excluded maternal hemoglobin from the main model. The mean birth weight was 3,020 (standard deviation [SD]=445.5) grams, and 10.3% of all births were classified as low birth weight. The mean gestational age was 39.5 weeks (SD=1.7 weeks), and 5.2% of the births were preterm. The median lead concentration across the time points was 1.4 μg/dL (IQR: 0.9 – 2.2 μg/dL), and the median cadmium concentration was 1.0 ng/mL (IQR: 0.7 – 1.4 ng/mL), values comparable to those found in other studies. Overall, the results did not indicate a consistent or strong association between lead or cadmium and adverse birth outcomes. Baseline cadmium levels showed a modest increase in the odds ratio for low birth weight (OR per IQR increase: 1.2, 95% CI: 0.97 to 1.47). Sensitivity analyses closely aligned with the main findings. All the results for effect modification did not indicate differences in the strata. The study found a suggestive, but inconsistent evidence between exposure to cadmium and low birth weight. This study has some limitations. There is potential for non-differential measurement error due to the hematocrit effect, which alters the estimated spot volume based on participants' hematocrit levels. A sensitivity analysis using potassium standardized metal concentrations partially addressed this, but individual hematocrit variability can still bias the observed association towards the null, with a moderate magnitude. The probability of the bias is moderate. The chromatographic effect, which can cause variations in concentration due to the interaction between blood and the analyte with the filter paper, was also partially addressed using internal standards, blanks, calibration samples, quality controls, and reference materials. This potential bias is of low probability and magnitude, biasing the observed association toward the null. Confounding bias was considered a concern due to incomplete adjustment for covariates like seasonal variation, which can affect metal exposure and birth outcomes. Sensitivity analyses supported the main model findings, suggesting a low probability and magnitude of confounding bias, which could bias the observed association towards or away from the null. Despite residual confounding concerns linked to socio-economic indicators like assets and diet diversity, the sensitivity analyses did not deviate from the main model findings, indicating a small probability and magnitude of the bias, which would bias the observed association in either direction. The study had several strengths including a large sample size compared to previous studies, especially those in LMICs and it was conducted in three distinct rural LMIC settings, which, to the best of our knowledge, had not been done before. This study's strength lies in its large sample size of 2,152 participants with complete data, enhancing its statistical robustness and addressing the common issue of small sample sizes and missing data in prior LMIC research. Additionally, its unique examination across three distinct rural LMIC settings provides valuable insights into the regional variations affecting the outcomes studied. Future steps include using whole blood samples instead of dried blood spots (DBS) and measuring exposure at multiple time points, particularly at birth via the umbilical cord, could yield more accurate concentrations. It is also recommended that subsequent studies employ better socio-economic indicators to reduce residual confounding effects. Expanding the geographical scope of the study to include a broader range of urban areas within the HAPIN countries would improve the generalizability of the findings. Additionally, future research should consider analyzing the effects of metal mixtures to better replicate real-world environmental conditions and interactions. The results are generally consistent with existing limited data indicating no evidence of an association between lead and adverse birth outcomes and a potential association between higher cadmium exposure during pregnancy with increased risk of low birth weight.Item Embargo Bayesian tree based methods for longitudinally assessed environmental mixtures(Colorado State University. Libraries, 2024) Im, Seongwon, author; Wilson, Ander, advisor; Keller, Kayleigh, committee member; Koslovsky, Matt, committee member; Neophytou, Andreas, committee memberIn various fields, there is interest in estimating the lagged association between an exposure and an outcome. This is particularly common in environmental health studies, where exposure to an environmental chemical is measured repeatedly during gestation for the assessment of its lagged effects on a birth outcome. The relationship between longitudinally assessed environmental mixtures and a health outcome is also of greater interest. For a single exposure, a distributed lag model (DLM) is a widely used method that provides an appropriate temporal structure for estimating the time-varying effects. For mixture exposures, a distributed lag mixture model is used to address the main effect of each exposure and lagged interactions among exposures. The main inferential goals include estimating the lag-specific effects and identifying a window of susceptibility, during which a fetus is particularly vulnerable. In this dissertation, we propose novel statistical methods for estimating exposure effects of longitudinally assessed environmental mixtures in various scenarios. First, we propose a method that can estimate a linear exposure-time-response function between mixture exposures and a count outcome that may be zero-inflated and overdispersed. To achieve this, we employ a Bayesian Pólya-Gamma data augmentation with a treed distributed lag mixture model framework. We apply the method to estimate the relationship between weekly average fine particulate matter (PM2.5) and temperature and pregnancy loss with live-birth identified conception time series design with administrative data from Colorado. Second, we propose a tree triplet structure to allow for heterogeneity in exposure effects in an environmental mixture exposure setting. Our method accommodates modifier and exposure selection, which allows for personalized and subgroup-specific effect estimation and windows of susceptibility identification. We apply the method to Colorado administrative birth data to examine the heterogeneous relationship between PM2.5 and temperature and birth weight. Finally, we introduce an R package dlmtree that integrates tree structured DLM methods into convenient software. We provide an overview of the embedded tree structured DLMs and use simulated data to demonstrate a model fitting process, statistical inference, and visualization.Item Open Access Bayesian treed distributed lag models(Colorado State University. Libraries, 2021) Mork, Daniel S., author; Wilson, Ander, advisor; Sharp, Julia, committee member; Keller, Josh, committee member; Neophytou, Andreas, committee memberIn many applications there is interest in regressing an outcome on exposures observed over a previous time window. This frequently arises in environmental epidemiology where either a health outcome on one day is regressed on environmental exposures (e.g. temperature or air pollution) observed on that day and several proceeding days or when a birth or children's health outcome is regressed on exposures observed daily or weekly throughout pregnancy. The distributed lag model (DLM) is a statistical method commonly implemented to estimate an exposure-time-response function by regressing the outcome on repeated measures of a single exposure over a preceding time period, for example, mean exposure during each week of pregnancy. Inferential goals include estimating the exposure-time-response function and identifying critical windows during which exposures can alter a health endpoint. In this dissertation, we develop novel formulations of Bayesian additive regression trees that allow for estimating a DLM. First, we propose treed distributed lag nonlinear models to estimate the association between weekly maternal exposure to air pollution and a birth outcome when the exposure-response relation is nonlinear. We introduce a regression tree-based model that accommodates a multivariate predictor along with parametric control for fixed effects. Second, we propose a tree-based method for estimating the association between repeated measures of a mixture of multiple pollutants and a health outcome. The proposed approach introduces regression tree pairs, which allow for estimation of marginal effects of exposures along with structured interactions that account for the temporal ordering of the exposure data. Finally, we present a framework to estimate a heterogeneous DLM in the presence of a potentially high dimensional set of modifying variables. We present simulation studies to validate the models. We apply these methods to estimate the association between ambient pollution exposures and birth weight for a Colorado, USA birth cohort.Item Open Access 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 memberExposure 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.Item Open Access Reference values of the distal sensory median and ulnar nerves among newly hired workers(Colorado State University. Libraries, 2021) Hischke, Molly, author; Rosecrance, John, advisor; Neophytou, Andreas, committee member; Anderson, Brooke, committee member; Gerr, Fredric, committee member; Reiser, Raoul F., II, committee memberCarpal tunnel syndrome (CTS) is the most common entrapment neuropathy in the upper extremity and more common among workers in industrial occupations than in the general population (Atroshi et al., 1999; Mattioli et al., 2009; Palmer, Harris, & Coggon, 2007). Because of the high prevalence of CTS in certain industries, some employers have implemented post-offer pre-placement screening programs using nerve conduction studies (NCS) to identify those at higher risk of developing CTS. NCS are commonly used to identify the median neuropathy characteristic of CTS by assessing the nerve conduction speed of the median nerve. There have been a number of retrospective and prospective cohort studies that have examined the relationship between NCS indicating median neuropathy among workers and the subsequent development of CTS (Werner et al., 2001; Franzblau et al., 2004; Gell et al., 2005; Silverstein et al., 2010; Dale et al., 2014). These studies have indicated that workers with NCS indicating median neuropathy across the carpal tunnel who were initially asymptomatic for CTS, eventually developed CTS at a statistically significant greater rate than workers with normal nerve studies. Some employers have used NCS to identify workers at higher risk of developing CTS and placing them into low hand-intensive work tasks to reduce the high prevalence of work-related CTS. To identify workers at higher risk, their NCS results are often compared to population-based reference values. However, many of these published reference values are limited by their small samples sizes and unsuitable statistical methodologies (Dillingham et al., 2016). Further, some researchers have questioned whether population-based reference values are representative of working populations, especially those in industries with a high prevalence of abnormal NCS (Dale, Gardner, Buckner-petty, Strickland, & Evanoff, 2016; Salerno et al., 1998). The purpose of this dissertation research was to (1) establish reference values for NCS outcomes of the distal upper extremity from a large sample (N=17,630) of newly hired manufacturing workers using novel statistical methods more appropriate for nerve conduction data, (2) investigate comorbid conditions associated with nerve conduction outcomes, and (3) determine the sensitivity and specificity of CTS symptoms for identifying workers with median mononeuropathy.