An attempt has been made in this study to critically appraise, systematically review and gather together the results obtained in individual trials and examine the strength of evidence supporting the component for Education for a Partnership in Asthma Care of the National Asthma Education and Prevention Program (NAEPP) to test whether health outcomes are influenced by education and self-management programs.

Examines the relationship between parental education on asthma and the frequency of emergency room visits with their children for asthma incidences. The research was conducted at an acute care hospital in Southern California using a sample of 32 parents. Data was collected using a self-administered survey questionnaire employing a quantitative research design and chi-square test. Results indicate that although parents found education beneficial in controlling their child's asthma, there was no significant correlation between education and frequency of hospitalization and emergency room visits.

Background: The physiological and pathophysiological roles of secreted membrane-enclosed vesicles known as extracellular vesicles (EVs) have become increasingly recognized, making the EV field a quickly evolving area of research. EVs and their encapsulated molecular material including microRNAs are key mediators of intercellular communication, making EVs analogous to a message in a bottle. This discovery has fundamentally changed the study of gene regulation, and understanding the central role of EVs and their cargo in health and disease will generate new opportunities for basic biology, diagnostics, and disease treatment. EV release and the packaging of molecular material into EVs can be altered by stressors such as air pollution exposure. Exposure to air pollution is associated with significant morbidity among individuals with asthma, especially children who participate in more frequent outdoor activities and are more susceptible to exposure due to their narrower airways and higher breathing rate. Thus, sensitive biomarkers of air pollution exposure are needed to identify children at risk of worsened symptoms and asthma exacerbations. Given their role in cell-to-cell communication, EVs also represent a plausible molecular mechanism in the etiology of disorders such as aging-related cognitive decline. Individuals with mild cognitive impairment and experiencing increased rates of cognitive decline are more likely to develop Alzheimer’s disease and other dementias, signifying the importance of identifying and treating cognitive impairment early. More precise identification of the neurobiological processes of cognitive decline in aging populations may provide critical insights into the precursors of late-life dementias and identify health interventions that can delay cognitive impairment or therapeutic targets for treatment. This dissertation evaluates the utility of EV-encapsulated microRNAs (EV-miRNAs) as biomarkers of environmental exposure (i.e., air pollution) and assesses their role in disease risk (i.e., cognitive decline) in two separate studies. First, in Chapters 2-3, using a cohort of children with asthma in the greater Boston area, we describe saliva EVs isolated from these children using a high-throughput method and explore the potential of salivary EV-miRNAs as easy-to-measure biomarkers of exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O₃). Then, in Chapter 4, we evaluate the association between EV-miRNAs and cognitive function and rates of cognitive decline in a cohort of elderly men and discuss the utility of circulating EV-miRNAs as biomarkers of risk. Furthermore, we discuss the pathways that these EV-miRNAs target if they play a causal role in cognitive decline which could have implications for development of therapeutics. Methods: Drawing from the School Inner-City Asthma Study (SICAS), we isolated salivary EVs and EV-miRNA from children with asthma for analysis in relation to ambient exposure to PM₂.₅, NO₂, and O₃. In accordance with the recommended minimal experimental requirements for the definition of EVs, in Chapter 2 we employ multiple orthogonal methods to describe the EVs that were isolated from cell-free saliva using a high-throughput polymer-based reagent (ExoQuick-TC). In Chapter 3, we utilize EV-miRNA data generated via RNA sequencing and ambient air pollution data estimated using a validated spatiotemporal high-resolution model. We perform differential expression analyses to examine the effect of high exposure to PM₂.₅, NO₂, and O₃ on saliva EV-miRNA abundance. In Chapter 4, we leverage data from the Normative Aging Study, a longitudinal cohort of elderly men, to investigate whether circulating EV-miRNAs are associated with cognitive function and rates of cognitive decline. We used linear models to assess the relationship between plasma EV-miRNAs and cognitive function and linear mixed models to evaluate the relationship between plasma EV-miRNAs and rates of cognitive decline. We performed gene ontology functional enrichment and pathway enrichment analyses to identify the biological pathways that these EV-miRNAs would target if they play causal roles in cognitive decline. Results:In Chapter 2, we demonstrate that EVs can be isolated from human saliva using ExoQuick-TC. The saliva EVs isolated from ExoQuick (N=180) ranged in size but were mostly ~55 nm in diameter and expressed tetraspanins CD9 and CD63, canonical markers for EVs, but did not highly express the tetraspanin CD81. In Chapter 3, in a subset of the SICAS cohort (N=69), we show that relatively high (>19.37 parts per billion) short-term ambient NO2 exposure and relatively high (>38.38 parts per million) prior-day O3 exposure are associated with down-regulation of several saliva EV-miRNAs. We did not detect differential expression of any EV-miRNAs in relation to PM₂.₅ exposure over multiple time windows of exposure. Finally, in Chapter 4, multivariable linear and linear mixed models demonstrated a relationship between several plasma EV-miRNAs and global cognitive function and rates of global cognitive decline, measured by the Mini-Mental State Examination. Functional enrichment and pathway enrichment analyses revealed that the biological pathways targeted by these miRNAs are relevant in neurodegeneration, including pathways regulating synaptic function and plasticity and neuronal death. We found no association between EV-miRNAs and cognitive function or cognitive decline as assessed by cognitive tests measuring specific domains of cognitive function. Conclusion: This work demonstrates the opportunities that EV-miRNAs can create for advancing environmental health research. EV-miRNAs may serve as sensitive biomarkers of environmental exposures as well as biomarkers of risk and may play mechanistic roles in disease. We also make recommendations for integrating EV research into the field of environmental health. Future studies should continue to evaluate the potential of EV-miRNAs and seek to identify EV-miRNAs that can serve as mechanistic biomarkers between exposures and effect across all stages of life to (1) increase our understanding of the consequences of circulating miRNA changes and the contribution of the environment to heterogeneous disorders, (2) advance development of non-invasive diagnostics to allow for longitudinal monitoring, and (3) pave the way for new opportunities for disease prevention and treatment.

While a single residential move is a common experience for many families with children, residential moves that occur in higher frequency may serve as disruptive events in a child’s life. The present study draws upon data from the Fragile Families and Child Wellbeing Study of children from birth to 15 years of age to examine associations between residential moves and five measures of health and cognition: emergency room utilization, body mass index, incidence of asthma attack or asthma episode, repeated school grades, and scores on the Peabody Picture Vocabulary Test. Age-dependent, cumulative, and differential associations by sex and race are explored. Finally, the present study examines potential moderation of these associations by neighborhood context of a child’s city of birth. Cumulative analyses from the present study suggest that residential mobility is significantly associated with increased emergency room utilization over time, decreased body mass index over time, and a higher likelihood of a experiencing a repeated grade over time, and an increase in PPVT score over time. Age-dependent analyses of all children suggest that mobility in early childhood is significantly associated with emergency room usage and body mass index outcomes, while moves later in life are associated with increased body mass index and higher odds of repeating a school grade. The study further reveals significant sex and racial differences in both age-dependent and cumulative analyses. Evidence for age-dependent and cumulative associations between mobility and odds of an asthma attack emerged only when sex differences were examined. Several racial differences were observed in analyses. Notably, mobility was not a significant predictor of emergency room utilization for Black children at any time point examined nor in longitudinal analyses. Finally, evidence of consistent moderation effects by a child’s birth city neighborhood context was not found; however, significant moderation effects by neighborhood context were found for associations between mobility and emergency room utilization at age 1, BMI at age 3 and BMI at age 5. A central aim of the present study is to contribute to the growing body of empirical research about housing mobility and correlates to developmental outcomes for children. Results from the present study’s analysis can help inform housing-centered strategies to mitigate adverse outcomes for children from families experiencing housing hardship.

Many applied statistical applications face the potential problem of model contamination and measurement error. The form and degree of contamination as well as the measurement error are usually unknown and sample-specific, which brings additional challenges for researchers. In this thesis, we have proposed several Bayesian inference models to address these issues, with the application to one type of special data for allergen concentration measurement, which is called serial dilution data and is self-calibrated. In our first chapter, we address the problem of model contamination by using a multilevel model to simultaneously flag problematic observations and estimate unknown concentrations in serial dilution data, a problem where the current approach can lead to noisy estimates and difficulty in estimating very low or high concentrations. In our second chapter, we propose the Bayesian joint contamination model for modeling multiple measurement units at the same time while adjusting for differences between experiments using the idea of global calibration, and it could account for uncertainty in both predictors and response variables in Bayesian regression. We are able to get efficacy gain by analyzing multiple experiments together while maintaining robustness with the use of hierarchical models. In our third chapter, we develop a Bayesian two-step inference model to account for measurement uncertainty propagation in regression analysis when the joint inference model is infeasible. We aim to increase model inference reliability while providing flexibility to users by not restricting the type of inference model used in the first step. For each of the proposed methods, We also demonstrate how to integrate multiple model building blocks through the idea of Bayesian workflow. In extensive simulation studies, we show that our proposed methods outperform other commonly used approaches. For the data applications, we apply the proposed new methods to the New York City Neighborhood Asthma and Allergy Study (NYC NAAS) data to estimate indoor allergen concentrations more accurately as well as reveal the underlying associations between dust mite allergen concentrations and the exhaled nitric oxide (NO) measurement for asthmatic children. The methods and tools developed here have a wide range of applications and can be used to improve lab analyses, which are crucial for quantifying exposures to assess disease risk and evaluating interventions.

Measurement error is common in epidemiologic studies and can lead to biased statistical inference. It is well known, for example, that regression analyses involving measurement error in predictors often produce biased model coefficient estimates. The work in this dissertation adds to the existing vast literature on measurement error by proposing a missing data treatment of measurement error through general location models. The focus is on the case in which information about the measurement error model is not obtained from a subsample of the main study data but from separate, external information, namely the external calibration. Methods for handling measurement error in the setting of external calibration are in need with the increase in the availability of external data sources and the popularity of data integration in epidemiologic studies. General location models are well suited for the joint analysis of continuous and discrete variables. They offer direct relationships with the linear and logistic regression models and can be readily implemented using frequentist and Bayesian approaches. We use the general location models to correct for measurement error and misclassification in the context of three practical problems. The first problem concerns measurement error in a continuous variable from a dataset containing both continuous and categorical variables. In the second problem, measurement error in the continuous variable is further complicated by the limit of detection (LOD) of the measurement instrument, resulting in some measures of the error-prone continuous variable undetectable if they are below LOD. The third problem deals with misclassification in a binary treatment variable. We implement the proposed methods using Bayesian approaches for the first two problems and using the Expectation-maximization algorithm for the third problem. For the first problem we propose a Bayesian approach, based on the general location model, to correct measurement error of a continuous variable in a data set with both continuous and categorical variables. We consider the external calibration setting where in addition to the main study data of interest, calibration data are available and provide information on the measurement error but not on the error-free variables. The proposed method uses observed data from both the calibration and main study samples and incorporates relationships among all variables in measurement error adjustment, unlike existing methods that only use the calibration data for model estimation. We assume by strong nondifferential measurement error (sNDME) that the measurement error is independent of all the error-free variables given the true value of the error-prone variable. The sNDME assumption allows us to identify our model parameters. We show through simulations that the proposed method yields reduced bias, smaller mean squared error, and interval coverage closer to the nominal level compared to existing methods in regression settings. Furthermore, this improvement is pronounced with increased measurement error, higher correlation between covariates, and stronger covariate effects. We apply the new method to the New York City Neighborhood Asthma and Allergy Study to examine the association between indoor allergen concentrations and asthma morbidity among urban asthmatic children. The simultaneous occurrence of measurement error and LOD is common particularly in environmental exposures such as measurements of the indoor allergen concentrations mentioned in the first problem. Statistical analyses that do not address these two problems simultaneously could lead to wrong scientific conclusions. To address this second problem, we extend the Bayesian general location models for measurement error adjustment to handle both measurement error and values below LOD in a continuous environmental exposure in a regression setting with mixed continuous and discrete variables. We treat values below LOD as censored. Simulations show that our method yields smaller bias and root mean squared error and the posterior credible interval of our method has coverage closer to the nominal level compared to alternative methods, even when the proportion of data below LOD is moderate. We revisit data from the New York City Neighborhood Asthma and Allergy Study and quantify the effect of indoor allergen concentrations on childhood asthma when over 50% of the measured concentrations are below LOD. We finally look at the third problem of group mean comparison when treatment groups are misclassified. Our motivation comes from the Frequent User Services Engagement (FUSE) study. Researchers wish to compare quantitative health and social outcome measures for frequent jail-and-shelter users who were assigned housing and those who were not housed, and misclassification occurs as a result of noncompliance. The recommended intent-to-treat analysis which is based on initial group assignment is known to underestimate group mean differences. We use the general location model to estimate differences in group means after adjusting for misclassification in the binary grouping variable. Information on the misclassification is available through the sensitivity and specificity. We assume nondifferential misclassification so that misclassification does not depend on the outcome. We use the expectation-maximization algorithm to obtain estimates of the general location model parameters and the group means difference. Simulations show the bias reduction in the estimates of group means difference.

[Truncated abstract] Asthma genetic studies have identified many genes that contribute to the pathogenesis of asthma and related variables. Members of the secretoglobin family appear to play an important role in controlling airway inflammation but they have received relatively little attention in asthma genetic research. In this thesis, I have investigated the genes of two members of the secretoglobin family (16 kDa Clara cell secretory protein (CC16) and secretoglobin 3A2 (SCGB3A2)) that are expressed at high levels in the airways and are important anti-inflammatory agents. The overall aim of these studies was to investigate the genetic variability of the CC16 and SCGB3A2 genes and their influence on airway inflammatory disease. The main hypothesis was that genetic variability in the genes for CC16 and SCGB3A2 exert an influence on airway inflammatory disease. Three populations were investigated: (1) a paediatric case control population (n=99), (2) an unselected birth cohort followed longitudinally at ages 1 month (n=244), six (n=123) and 11 years (n=195) and (3) an unselected Aboriginal Australian population (n=251). The case-control population was screened for novel DNA sequence variants in the CC16 promoter and the SCGB3A2 5’UTR and exons. No novel sequence variants were identified in the CC16 promoter and two were identified in the SCGB3A2 5’UTR (G- 811A and G-205A). A single nucleotide polymorphism previously identified in the CC16 gene (A38G) and the two polymorphisms identified in the SCGB3A2 gene were genotyped in both unselected populations. Genotype/phenotype associations were identified with adjustment for potential confounders such as age, gender, height and maternal tobacco smoking, where appropriate. This was due to the contribution of these factors to the aetiology of asthma, atopy and related phenotypes. All three polymorphism frequencies were significantly different between these two ethnically diverse populations

Thesis (PhD)-- Stellenbosch University, 2013. / ENGLISH ABSTRACT: Although the effect of inhaled corticosteroids (ICS) on the hypothalamic- pituitary-adrenal axis (HPA) has been regarded as a “benign physiological response”, a survey published in 2002 suggested that adrenal crisis is more common in asthmatic children on ICS than previously thought. Relying on clinical features to detect chronic adrenal insufficiency secondary to corticosteroids may not be wise, as these are non-specific and can therefore easily be missed. Accurate biochemical assessment of the whole axis to detect subclinical HPA suppression (HPAS) is thus desirable. A review of the literature indicates that basal adrenal function tests, including plasma cortisol profiles, do not identify which children can appropriately respond to stress. There is no evidence to suggest that the degree of the physiological adjustment of the HPA to ICS and/or nasal steroids (by reducing basal cortisol production), predicts HPAS. Cortisol profiles should therefore only be used to demonstrate differences in systemic activity of various ICS and delivery devices. Only two tests, considered as gold standard adrenal function tests [the insulin tolerance test (ITT) and the metyrapone test] can assess the integrity of the whole axis. / AFRIKAANSE OPSOMMING: Die outeurs van ´n opname wat in 2002 gepubliseer is stel voor dat ´n bynierkrisis meer algemeen by asmatiese kinders, wat inhalasie kortikosteroïede ontvang, voorkom as wat voorheen gedink is. Dit is strydig met die gevestigde opvatting dat die effek van IKS op die hipotalamiese-hipofise-bynier-as (HHB) ’n “goedaardige fisiologiese reaksie” is. Die kliniese kenmerke van kroniese bynierontoereikendheid sekondêr tot die gebruik van kortikosteroïede (KS) is nie-spesifiek en gevolglik onbetroubaar. ´n Akkurate biochemiese toets van subkliniese HBB onderdrukking (HHBO) sou gevolglik waardevol wees. ´n Literatuur oorsig toon dat toetse van basale bynierfunksie, insluitend plasma kortisol (K) profiele, nie kinders uitken wat toepaslik op stres sal reageer nie. Daar is geen bewyse dat die graad van fisiologiese aanpassing van die HHB, soos aangedui deur laer K-vlakke, na die gebruik van IKS en/of nasale steroïede (NS), HHBO voorspel nie. Serum K profiele is dus slegs van waarde om die sistemiese aktiwiteit van verskillende IKS en toedieningsstelsels te ondersoek. Slegs twee toetse, naamlik die insulien toleransie toets (ITT) en die metyrapone -(MTP)-toets (wat beide as die goue standaard van bynier funksie beskou word), kan die integriteit van die hele as meet. / Stellenbosch University / Medical Research Council / SA Thoracic Society / Harry Crossley Foundation / Red Cross Children’s Hospital.

Thesis (Ph. D.)--University of Missouri--Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 111-120). Also available on the Internet.

[Truncated abstract] Asthma is a chronic and complex disorder and despite our increase in the understanding of the genetics, pathology and mechanisms underlying asthma a gold standard definition of asthma does not exist. A criterion for recognising and diagnosing asthma in epidemiological studies is crucial in order to determine risk factors for disease. Prospective longitudinal birth cohort studies have increased our understanding of the natural history and risk factors for asthma, yet we are still not able to accurately predict which children will go on to have asthma as adults. It is during the transition from childhood to adolescence where factors underlying asthma change and the prevalence of asthma shifts between the sexes. There are inconsistencies regarding risk factors for the development and persistence of disease during this transitional period. Risk factors predicting the development and persistence of asthma and intermediate phenotypes (BHR, airway inflammation and atopy) may be influenced by gender and risk factors predicting disease may differ between childhood and adolescence. Aims 1. To identify risk factors for Asthma, BHR and Atopy at 14yrs of age. 2. To determine risk factors for persistence of asthma between 6 and 14 years. 3. To examine the influence of gender on risk factors during adolescence. Method The West Australian Pregnancy Cohort is a longitudinal birth cohort. The cohort initially consisted of 2868 live births with follow-ups at 1, 2, 3, 6, 8, 10 and 14 years of V age. ... Strong associations were seen with BHR and new diagnosis of wheeze and asthma in VI teenagers. Interestingly having either a cat or dog inside was protective for persistence of disease; in particular stronger associations were seen in teenage girls not in boys. During this transitional period the risk factors for asthma and intermediate phenotypes differ between the sexes. Different mechanisms are likely to be involved in determining asthma in boys and girls during adolescence and shed new light on the recognised switch in the gender balance in asthma prevalence from the male predominance in childhood to the female predominance in adult life. Our understanding of the natural course of disease from the prenatal period to adulthood and the identification of the various asthma phenotypes has the potential to change prognosis and planning of therapeutic strategies. Identifying those at high risk for persistence of disease in the early stages of life will allow therapeutic interventions to be more appropriately targeted.