This thesis looks at the problem of developmental respiration in Sarcophaga crassipalpis Macquart from the biological and instrumental points of view and adapts mathematical and statistical tools in order to analyze the data gathered. The biological motivation and current state of research is given as well as instrumental considerations and problems in the measurement of carbon dioxide production. A wide set of mathematical and statistical tools are used to analyze the time series produced in the laboratory. The objective is to assemble a methodology for the production and analysis of data that can be used in further developmental respiration research.

Longitudinal data arise when individuals are measured several times during an ob- servation period and thus the data for each individual are not independent. There are several ways of analyzing longitudinal data when different treatments are com- pared. Multilevel models are used to analyze data that are clustered in some way. In this work, multilevel models are used to analyze longitudinal data from a case study. Results from other more commonly used methods are compared to multilevel models. Also, comparison in output between two software, SAS and R, is done. Finally a method consisting of fitting individual models for each individual and then doing ANOVA type analysis on the estimated parameters of the individual models is proposed and its power for different sample sizes and effect sizes is studied by simulation.

Tornado intensity and impacts vary drastically across space, thus spatial and statistical analyses were used to identify patterns of tornado severity in the Southeastern United States and to assess the vulnerability and estimated recurrence of tornadic activity. Records from the Storm Prediction Center's tornado database (1950-2014) were used to estimate kernel density to identify areas of high and low tornado frequency at both the regional- and state-scales. Return periods (2-year, 5-year, 10-year, 25-year, 50-year, and 100-year) were calculated at both scales as well using a composite score that included EF-scale magnitude, injury counts, and fatality counts. Results showed that the highest density of tornadoes occur in Alabama, Mississippi, and Arkansas, while the highest return period intensities occur in Alabama and Mississippi. Scaledependent analysis revealed finer details of density and intensity for each state. Better communication of high hazard areas and integration into existing mitigation plans is suggested.

The response of a karst aquifer to storm events is often faster and more severe than that of a non-karst aquifer. This distinction is often problematic for planners and municipalities, because karst flooding does not typically occur along perennial water courses; thus, traditional flood management strategies are usually ineffective. The City of Bowling Green (CoBG), Kentucky is a representative example of an area plagued by karst flooding. The CoBG, is an urban karst area (UKA), that uses Class V Injection Wells to lessen the severity of flooding. The overall effectiveness, siting, and flooding impact of Injection Wells in UKA’s is lacking; their influence on groundwater is evident from decades of recurring problems in the form of flooding and groundwater contamination. This research examined Class V Injection Wells in the CoBG to determine how Injection Well siting, design, and performance influence urban karst hydrology. The study used high-resolution monitoring, as well as hydrologic modeling, to evaluate Injection Well and spring responses during storm and baseflow conditions. In evaluating the properties of the karst aquifer and the influences from the surrounding environment, a relationship was established between precipitation events, the drainage capacity of the Injection Wells, and the underlying karst system. Ultimately, the results from this research could be used to make sound data-driven policy recommendations and to inform stormwater management in UKAs.

The United States is in the midst of an opioid epidemic. In addition to other system level interventions, almost all states have responded to the crisis by implementing prescription drug monitoring programs (PDMPs). PDMPs are state-level interventions that track the dispensing of Controlled Substances. Data generated at the time of medication dispensing is uploaded to a central data server that may be used to assist in identifying drug diversion, medication misuse, or potentially aberrant prescribing practices. Prior studies assessing the impact of PDMPs on trends in opioid-related morbidity have often failed to take into account the wide heterogeneity of program features and how the effectiveness of these features may be mitigated by insurance status. Previous research has also failed to differentiate the effects of these programs on prescription vs. illicit opioid-related morbidity. The studies in this dissertation attempt to address these gaps using epidemiological techniques to examine the associations between specific PDMP features and trends in prescription and illicit opioid-related poisonings in populations of different insurance beneficiaries. Results of these studies demonstrate that implementation of specific PDMP features is significantly associated with differential trends in prescription and illicit-opioid related poisonings and that the effectiveness of these features vary depending on the insurance status of the population studied. These results suggest that PDMPs offer a valuable tool in addressing the United States’ opioid epidemic, and may be used as empirical evidence to support PDMP best practices in the future.

Increased opioid analgesic (OA) prescribing has been associated with increased risk of prescription opioid diversion, misuse, and abuse. States established prescription drug monitoring programs (PDMPs) to collect and analyze electronic records for dispensed controlled substances to reduce prescription drug abuse and diversion. PDMP data can be used by prescribers for tracking patient’s history of controlled substance prescribing to inform clinical decisions. The studies in this dissertation are focused on the less utilized potential of the PDMP data to enhance public health surveillance to monitor OA prescribing and co-prescribing and association with opioid overdose mortality and morbidity. Longitudinal analysis of OA prescribing and evaluation of the effect of recent policies and opioid prescribing guidelines require consensus measures for OA utilization and computational tools for uniform operationalization by researchers and agencies. Statistical macros and computational tools for OA utilization measures were developed and tested with Kentucky PDMP data. A set of covariate measures using mortality and morbidity surveillance data were also developed as proxy measures for prevalence of painful conditions justifying OA utilization, and availability of heroin and medication treatment for opioid use disorder. A series of epidemiological studies used the developed OA measures as outcomes, and adjusted for time-varying socio-demographic and health care utilization covariates in population-averaged statistical models to assess longitudinal trend and pattern changes in OA utilization in Kentucky in recent years. The first study, “Trends and Patterns of OA Prescribing: Regional and Rural-Urban Variations in Kentucky from 2012 to 2015,” shows significant downward trends in rates of residents with OA prescriptions. Despite the significant decline over time, and after accounting for prevalence of injuries and cancer, the rate of dispensed OA prescriptions among residents in Kentucky Appalachian counties remained significantly higher than the rest of the state. The second study, “Population-Level Measures for High-Risk OA Prescribing: Longitudinal Trends and Relationships with Pain-Associated Conditions,” shows significant reduction in high-risk OA prescribing (e.g., high daily dosage, long-term use, concurrent prescriptions for OA and benzodiazepines) from 2012 to 2016, significantly positive associations between high-risk OA prescribing and cancer mortality rates with no substantial change in the association magnitude over time, and declining strengths of positive associations between high-risk OA prescribing and acute traumatic injuries or chronic non-cancer pain over the study period. The third study, “A Reciprocal Association between Longitudinal Trends of Buprenorphine/Naloxone Prescribing and High-Dose OA Prescribing,” indicates a significant reciprocal relationship between high-dose OA prescribing and buprenorphine/ naloxone prescribing, and a clinically meaningful effect of buprenorphine/naloxone prescribing on reducing OA utilization. The results from the studies advanced the understanding of the epidemiology of opioid use and misuse in Kentucky, and identified actionable risk and protective factors that can inform policy, education, and drug overdose prevention interventions. The developed operational definition inventory and computational tools could stimulate further research in Kentucky and comparative studies in other states.

In longitudinal studies, outcomes are measured repeatedly over time, but in reality clinical studies are full of missing data points of monotone and non-monotone nature. Often this missingness is related to the unobserved data so that it is non-ignorable. In such context, pattern-mixture model (PMM) is one popular tool to analyze the joint distribution of outcome and missingness patterns. Then the unobserved outcomes are imputed using the distribution of observed outcomes, conditioned on missing patterns. However, the existing methods suffer from model identification issues if data is sparse in specific missing patterns, which is very likely to happen with a small sample size or a large number of repetitions. We extend the existing methods using latent class analysis (LCA) and a shared-parameter PMM. The LCA groups patterns of missingness with similar features and the shared-parameter PMM allows a subset of parameters to be different among latent classes when fitting a model, thus restoring model identifiability. A novel imputation method is also developed using the distribution of observed data conditioned on latent classes. We develop this model for continuous response data and extend it to handle ordinal rating scale data. Our model performs better than existing methods for data with small sample size. The method is applied to two datasets from a phase II clinical trial that studies the quality of life for patients with prostate cancer receiving radiation therapy, and another to study the relationship between the perceived neighborhood condition in adolescence and the drinking habit in adulthood.

Signal detection from Adverse Event Reports (AERs) is important for identifying and analysing drug safety concern after a drug has been released into the market. A safety signal is defined as a possible causal relation between an adverse event and a drug. There are a number of safety signal detection algorithms available for detecting drug safety concern. They compare the ratio of observed count to expected count to find instances of disproportionate reportings of an event for a drug or combination of events for a drug. In this thesis, we present an algorithm to mine the AERs to identify drugs which show sudden and large changes in patterns of reporting of adverse events. Unlike other algorithms, the proposed algorithm creates time series for each drug and use it to identify start of a potential safety problem. A novel vectorized timeseries utilizing multiple attributes has been proposed here. First a time series with a small time period was created; then to remove local variations of the number of reports in a time period, a time-window based averaging was done. This method helped to keep a relatively long time-series, but eliminated local variations. The steps in the algorithm include partitioning the counts on attribute values, creating a vector out of the partitioned counts for each time period, use of a sliding time window, normalizing the vectors and computing vector differences to find the changes in reporting over time. Weights have been assigned to attributes to highlight changes in the more significant attributes. The algorithm was tested with Adverse Event Reporting System (AERS) datasets from Food and Drug Administation (FDA). From AERS datasets the proposed algorithm identified five drugs that may have safety concern. After searching literature and the Internet it was found that the five drugs the algorithm identified, two were recalled, one was suspended, one had to undergo label change and the other one has a lawsuit pending against it.

Orientador: Jurandir Zullo Junior / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola / Made available in DSpace on 2018-08-15T01:18:51Z (GMT). No. of bitstreams: 1 Nascimento_CristinaRodrigues_D.pdf: 5361579 bytes, checksum: 0b5533ef40b677f1518712497bdd6258 (MD5) Previous issue date: 2010 / Resumo: O Brasil é líder mundial na fabricação, exportação de açúcar e na produção de álcool. O estado de São Paulo responde por 60% da produção de açúcar e 61% de todo o álcool produzido no país. Em função da alta relevância da produção, é importante que se tenham estimativas e levantamentos seguros das áreas cultivadas com a cultura. O avanço das diferentes técnicas de sensoriamento remoto tem permitido utilizar imagens de satélites para monitorar e auxiliar a estimativa dessas áreas. São inúmeras as opções, entre elas as imagens do sensor AVHRR/NOAA. Aliando a necessidade de obter estimativas mais precisas das safras de cana-de-açúcar, com o potencial de adquirir informações agrícolas da cultura através do NDVI, o presente trabalho explorou a análise de séries temporais das imagens NDVI/AVHRR, na identificação das áreas com cana-de-açúcar no Estado de São Paulo. A partir da identificação operacional, foram selecionados municípios com áreas expressivas a fim de testar a viabilidade do uso de um modelo fenológico-espectral, no fornecimento de informações objetivas que possam auxiliar os sistemas de previsão de safras. Os resultados apontam que as áreas com cana-de-açúcar foram bem modeladas, a partir da análise harmônica, nas cinco safras analisadas, permitindo sua diferenciação entre outras culturas, nas composições RGB utilizadas, em função dos respectivos ciclos vegetativos. A partir da classificação supervisionada, utilizando o algoritmo máxima verossimilhança das imagens amplitude, termo aditivo e fase foi gerado um mapa representando a distribuição espacial das áreas com a cultura no estado nos anos-safras 03/04, 04/05, 05/06, 06/07 e 07/08. Nestes mapas a classe cana-de-açúcar foi representada em função da área ocupada, sendo este parâmetro avaliado a partir de duas metodologias distintas: Expansão direta e Matriz de erro. Os resultados obtidos em cada ano-safra foram comparados com o dado considerado referência, fornecidos pelo projeto CANASAT/INPE. Verificou-se, que os menores erros relativos, em torno de 10% para a safra 07/08, foram encontrados a partir da estimativa baseado na Matriz de erro. Quanto ao modelo fenológico-espectral, a utilização de imagens do AVHRR/NOAA-17 apresentou resultados bastante satisfatórios, possibilitando um aumento da objetividade dos métodos de acompanhamento e previsão de safras. Ao ser aplicado como modelo de crescimento, este apresentou resultados favoráveis para apoio ao acompanhamento e previsão de safra da cultura. A utilização de valores de NDVI do meio do ciclo da cultura gerou melhores resultados quando aplicado nas fórmulas de Massa Seca de Colmos e nas fórmulas de MSC Máxima e Proporcional. O modelo possibilitou um monitoramento mais frequente das condições de campo e a obtenção de resultados de uma maneira mais rápida e objetiva. A equação de regressão, considerando o dia após o corte em torno de 122 a 132 dias, foi a que apresentou resultado melhor na estimativa da produtividade. Devido ao baixo custo de aquisição das imagens, à longevidade do sistema, à abrangência espacial das imagens e à possibilidade de geração de índices a partir de suas bandas espectrais, a utilização de metodologias que envolvam a aplicação da série temporal dessas imagens, é uma ferramenta útil em sistemas operacionais de acompanhamento e previsão de safras. / Abstract: Brazil is the first world producer of sugar cane. The State of Sao Paulo is the first national producer of sugar cane, contributing with more than 60% of national production. Due to the high relevance of production, it is important to have insurance estimates and surveys of areas cultivated with the crop. The progress of the different remote sensing techniques has allowed to use satellite images to monitor and assist the estimation of these areas. There are numerous options, including images from the AVHRR/NOAA. Combining the need to obtain more precise estimates of the yields of cane sugar, with the potential to acquire agricultural information culture through the NDVI, this study explored the time series analysis of NDVI/AVHRR images and the identification of areas with sugar cane in the State of Sao Paulo. After identifying operational, were selected municipalities with significant areas in order to test the feasibility of using a model phenological-spectral, in providing objective information that can help systems crop forecast. The results indicate that areas with sugar cane are well modeled, from the harmonic analysis, the five crops examined, allowing them to differentiate them from other cultures, the RGB compositions used according to their growing seasons. From the supervised classification using the maximum likelihood algorithm of image amplitude, phase and aditive term was generated a map representing the spatial distribution of culture in the state in the crop-season 03/04, 04/05, 05/06, 06/07 and 07/08. These maps the class sugar cane was represented as a function of the area occupied, and this parameter assessed using two different methodologies: direct expansion and error matrix. The results obtained in each crop-season were compared with a figure regarded as reference, provided by the project CANASAT/INPE. It was found that the smaller relative errors, around 10% for the season 07/08, were found from the estimate based on the error matrix. The phenological-spectral model, the use of time series AVHRR/NOAA-17 showed satisfactory results, allowing an increase in the objectivity of the methods of monitoring and forecasting of seasons. When applied as a model of growth, it showed favorable results to support the monitoring and prediction of crop culture. The use of NDVI values of the middle of the cycle gave better results when applied to formulas of culm dry mass and follow-on MSC Maximum and Proportional. The model allowed a more frequent monitoring of field conditions and obtain results in a more rapid and objective. The regression equation, considering the day after the cut at around 122 to 132 days, showed the best result in the estimation of productivity. Due to the low cost of acquisition of images, the longevity of the system, the spatial extent of images and the possibility of generating indexes based on their spectral bands, using methodologies that involve the application of time series of images, is a tool useful in operating systems for monitoring and forecasting crop. / Doutorado / Planejamento e Desenvolvimento Rural Sustentável / Doutor em Engenharia Agrícola

Smoking prevalence among adolescents in the Middle East remains high while rates of smoking have been declining among adolescents elsewhere. The aims of this research were to (1) describe patterns of cigarette and waterpipe (WP) smoking, (2) identify determinants of WP smoking initiation, and (3) identify determinants of cigarette smoking initiation in a cohort of Jordanian school children. Among this cohort of school children in Irbid, Jordan, (age ≈ 12.6 at baseline) the first aim (N=1,781) described time trends in smoking behavior, age at initiation, and changes in frequency of smoking from 2008-2011 (grades 7 – 10). The second aim (N=1,243) identified determinants of WP initiation among WP-naïve students; and the third aim (N=1,454) identified determinants of cigarette smoking initiation among cigarette naïve participants. Determinants of initiation were assessed with generalized mixed models. All analyses were stratified by gender. Baseline prevalence of current smoking (cigarettes or WP) for boys and girls was 22.9% and 8.7% respectively. Prevalence of ever- and current- any smoking, cigarette smoking, WP smoking, and dual cigarette/WP smoking was higher in boys than girls each year (p These studies reveal intensive smoking patterns at early ages among Jordanian youth in Irbid, characterized by a predominance of WP smoking. WP may be a vehicle for tobacco dependence and subsequent cigarette uptake. The sizeable incidence of WP and cigarette initiation among students of both sexes points to a need for culturally relevant smoking prevention interventions. Gender-specific factors, refusal skills, and smoking cessation of both WP and cigarettes for youth and their parents/teachers would be important components of such initiatives.