Doctor of Philosophy, The Ohio State University, 2024, Public Health

Background: Otitis media (OM), a prevalent middle ear inflammation, is a leading cause of medical consultations and antibiotic prescriptions among children globally, imposing significant financial burdens on healthcare systems. Untreated acute OM can progress to chronic suppurative OM, a primary cause of pediatric hearing loss and potential cognitive delays, affecting speech acquisition. Environmental factors such as air pollution and socioeconomic conditions contribute to OM's multifactorial etiology, yet research on these associations, especially in low- and middle-income countries (LMICs), remains limited. The impact of preventive measures such as pneumococcal conjugate vaccines (PCVs), known to reduce AOM incidence and subsequent hearing loss, on cognitive development requires further investigation. This dissertation addresses these gaps through an aggregated analysis integrating spatial data, examining OM prevalence, hearing loss, and cognitive development among children in Bohol, Philippines, while identifying demographic, environmental, and socioeconomic correlates. Methods: Utilizing data from a follow-up assessment of a randomized controlled trial (ISRCTN 62323832) evaluating an 11-valent PCV in Bohol, this dissertation integrated demographic, socioeconomic, and health data collected via parental questionnaires, ear assessments, and cognitive tests. This study redefined neighborhoods using a square grid methodology to standardize geographic units. Then I aggregated participant data at the neighborhood level and created maps to visualize the spatial distribution of each outcome. I assessed global spatial autocorrelation using Moran's I index, supplemented by Local Indicators of Spatial Association (LISA) maps to pinpoint local clusters. The three specific aims were: (1) to identify factors associated with OM prevalence using multivariable ordinary least squares (OLS) and spatial regression models; and to assess spatial heterogeneity in associations using ge (open full item for complete abstract)

Committee: Maria Gallo (Advisor); Bridget Freisthler (Committee Member); Jeffrey Wing (Committee Member); Sarah Anderson (Committee Member) Subjects: Epidemiology; Public Health

Doctor of Philosophy, University of Toledo, 2020, Spatially Integrated Social Science

Emission amounts of carbon dioxide (CO2) from different sources are important in the current climate change debate, and their measuring is also essential for the formulation and evaluation of public policies. However, emissions of CO2 are not usually observed and instead of estimated from the activity and repurposed data. In the cyclical biogeochemical process of carbon dioxide, the sectoral net emissions (i.e., Industrial, transportation, residential, commercial) are not identifiable and directly observable. In this respect, this study explored the direct statistical relationships among the spatial variability of observed atmospheric CO2 concentration, the reported industrial emissions, and other hypothesized factors. Methodologically, this study presents a statistical approach for understanding the effect magnitudes of the industrial emissions and other hypothesized factors on the atmospheric CO2 concentration using observed spatial data. Atmospheric CO2 concentration is a measurable and well-mixed media of all emissions. There are anthropogenic and natural sources of emissions from where CO2 emits to the atmosphere. The emitted CO2 from different sectors interacts with the sinks and finally settles as atmospheric CO2 concentration. Therefore, the magnitude and intensity of the sectoral net emissions and concentration of atmospheric CO2 vary over space. This study considers this resultant spatial variability of the atmospheric CO2 and emitted sectoral emissions over the space to determine the effects of emissions and other hypothesized factors on the atmospheric CO2 concentration. For this purpose, this study developed a framework for analyzing each identified anthropogenic factor that includes specific databases and statistical methods. This study developed a methodological approach to study CO2 emissions using observed hypothesized factors and the atmospheric phenomenon of column-averaged Carbon dioxide (XCO2). The study statistically established that indust (open full item for complete abstract)

Committee: Oleg Smirnov (Committee Chair); Kevin Czajkowski (Committee Member); Neil Reid (Committee Member); Defne Apul (Committee Member); Onur Sapci (Committee Member) Subjects: Environmental Studies; Geographic Information Science; Geography

Doctor of Philosophy, The Ohio State University, 2004, Evolution, Ecology, and Organismal Biology

We use a variety of methods to study population connectivity. In Chapter 1, we use stable isotope ratios in feathers to make Bayesian inferences about the migratory connectivity between breeding and wintering grounds of Henslow's sparrows. We use hydrogen and carbon stable isotope ratios (deltaH and deltaC). We compare the deltaH and deltaC of feathers from wintering sparrows to five breeding region deltaH and deltaC to estimate the probability that each individual wintering sparrow originated from each of the five regions. Breeding bird abundances are used as prior probabilities of breeding region origin. We conclude that there are no clear linkages between specific breeding regions and wintering sites. In Chapter 2, we use three methods to estimate dispersal in Henslow's sparrows. 1)deltaH in feathers are used to determine whether an individual breeding bird has a deltaH signature characteristic of the breeding site. 2) Song structure is used as the signature of an individual's previous breeding-ground origin. 3) Genetic markers are used to evaluate population structure. Genetic structure is evaluated using three estimates. Fst estimates and private alleles are used to calculate the number of migrants per generation (Nm) between sites. Private alleles are evaluated to determine if they are truly private. A Bayesian clustering method is used to infer the number of populations. All methods revealed high rates of dispersal. In Chapter 3, three methods for estimating dispersal are compared: deltaH in feathers, genetic population structure, and spatial autocorrelation (SAC). We compare the dispersal estimates of five migratory species. With the SAC analysis, we find no clear evidence for dispersal as a major synchronizing agent. However, new statistical methods may allow for the parsing out the effect of dispersal. One species had historically high dispersal (limited genetic structure) but currently low dispersal (high deltaH correlations). Another species had a deltaH (open full item for complete abstract)

Committee: Thomas Waite (Advisor) Subjects: Biology, Ecology