In this paper we investigate how well residents of the Andaman coast in Phang Nga province, Thailand, are prepared for earthquakes and tsunami. It is hypothesized that formal education can promote disaster preparedness because education enhances individual cognitive and learning skills, as well as access to information. A survey was conducted of 557 households in the areas that received tsunami warnings following the Indian Ocean earthquakes on 11 April 2012. Interviews were carried out during the period of numerous aftershocks, which put residents in the region on high alert. The respondents were asked what emergency preparedness measures they had taken following the 11 April earthquakes. Using the partial proportional odds model, the paper investigates determinants of personal disaster preparedness measured as the number of preparedness actions taken. Controlling for village effects, we find that formal education, measured at the individual, household, and community levels, has a positive relationship with taking preparedness measures. For the survey group without past disaster experience, the education level of household members is positively related to disaster preparedness. The findings also show that disaster-related training is most effective for individuals with high educational attainment. Furthermore, living in a community with a higher proportion of women who have at least a secondary education increases the likelihood of disaster preparedness. In conclusion, we found that formal education can increase disaster preparedness and reduce vulnerability to natural hazards.

Background: Investigators are often interested in using population-level health administrative data in inflammatory bowel disease (IBD) patients to study disease outcomes, risk factors and treatment effects to enhance knowledge, shape clinical practice and influence health care policy. A major limitation of using health administrative data for these purposes is the lack of detailed clinical data to adjust for the confounding effects of differential disease severity on observed associations. Methods to account for disease severity using administrative variables would offer a major advance to population-level studies in IBD patients. Thus, in this study we aimed to use a cohort of IBD patients from The Ottawa Hospital (TOH) to validate a model that was originally developed in Manitoba for estimating clinical disease course in IBD patients through healthcare utilization measures. Objectives: The objectives of this thesis are: 1) To identify and characterize a reference cohort of IBD patients in the ambulatory clinics of four gastroenterologists from TOH on clinical disease course in the preceding year (reference cohort), based on a Manitoba definition of clinical disease course; 2) To fit a partial proportional odds (PPO) model for predicting IBD course, derived using Manitoba health administrative data, to the reference cohort of IBD patients using Ontario health administrative data; 3) To derive new PPO models of IBD disease course for the reference cohort using Ontario administrative variables and compare model performance; and 4) To apply the models to the Ontario Crohn’s and Colitis cohort (OCCC) to estimate IBD course in Ontario, and compare the distribution to that of the Manitoba IBD population.Methods: We first identified a reference cohort of IBD patients in Ontario from the outpatient clinics at TOH during fiscal year 2015. Through chart review, we classified these patients into one of four clinical disease categories (remission, mild, moderate, or severe) using the Manitoba definition. We linked these patients to Ontario health administrative datasets. Given slight differences in data structure and coding between Manitoba and Ontario, we were unable to directly test the Manitoba model and instead fit a PPO model to the Ontario cohort using analogous administrative variables to those used in the final Manitoba model (“adapted model”). We subsequently derived new PPO models using unique Ontario administrative variables under three strategies: 1) Stepwise variable selection (“stepwise model”); 2) Forced fitting of all variables (“all-variables model”); and 3) Using a two-step modelling algorithm that considered IBD-related hospitalizations separate from other administrative variables (“two-step model”). We then compared model performance from the four strategies. Finally, we applied the models to the Ontario IBD population from 2004 to 2016 and compared model estimates to those from Manitoba. Results: We identified 963 patients with IBD from TOH outpatient clinics, of which 52.3% (n=504) were males, 64.6% (n=622) had Crohn's Disease, and 89.2% (n=859) resided in an urban setting. Based on the Manitoba definition, 64.9% of patients within our reference cohort were classified as remission, while 11.4%, 14.1%, and 9.6% were classified as mild, moderate, and severe disease course, respectively. The adapted model (c-statistic 0.77, goodness-fit p-value 0.28) performed comparably to the other models: the stepwise model (c-statistic 0.77, goodness-fit p-value 0.50), the all-variables model (c-statistic 0.77, goodness-fit p-value 0.53), and the two-step model (c-statistic 0.78, goodness-fit p-value 0.75). The adapted model also resulted in overall similar estimates with regards to the disease course distribution among the Ontario IBD population. However, on closer inspection, our two-step model, in which individuals who had been hospitalized for an IBD-related indication within the past year were assumed to have severe disease, performed better with respect to accurately classifying individuals with moderate or severe disease, without sacrificing discriminative ability. Based on the two-step model, from 2004 to 2016, 89.2-91.2% of the Ontario IBD population was in remission, 0% had mild disease, 2.4-3.2% had moderate disease, and 5.9-8.4% had severe disease. Distribution of disease course among IBD patients in Ontario differed considerably than that in Manitoba. Conclusion: In the absence of clinical information within health administrative data, we present and compare four different models that can be used to partially account for the confounding effect of disease course among IBD patients in future population-based studies using Ontario health administrative data. Given that our models did not perform as originally expected, especially with regards to accurately identifying individuals with more active disease states, we advise researchers to use these models at their own discretion.

Made available in DSpace on 2015-03-26T13:32:17Z (GMT). No. of bitstreams: 1 texto completo.pdf: 764829 bytes, checksum: 8dbd03463c4800428f75900ca1340eb0 (MD5) Previous issue date: 2012-07-23 / The use of logistic regression modeling as a tool for modeling statistical probability of an event as a function of one or more independents variables, has grown among researchers in several areas, including Phytopathology. At about the dichotomous logistic regression in which the dependent variable is the type binary or dummy, is the extensive number of studies in the literature that discuss the modeling assumptions and the interpretation of the analyzes, as well as alternatives for implementation in statistical packages. However, when the variable response requires the use three or more categories, the number of publications is scarce. This is not only due to the scarcity of relevant publications on the subject, but also the inherent difficulty of coverage on the subject. In this paper we address the applicability of the model polytomous ordinal logistic regression, as well as differences between the proportional odds models, nonproportional and partial proportional odds. For this, we analyzed data from an experiment in which we evaluated the potential antagonistic fungus Clonostachys rosea in biocontrol of the disease called "gray mold", caused by Botrytis cinerea in strawberry and tomato. The partial proportional odds models and nonproportional were adjusted and compared, since the proportionality test score accused rejection of the proportional odds assumption. The estimates of the model coefficients as well as the odds ratios were interpreted in practical terms for Phytopathology. The polytomous ordinal logistic regression is introduced as an important statistical tool for predicting values, showing the potential of C. rosea in becoming a commercial product to be developed and used in the biological control of the disease, because the application of C. rosea was as or more effective than the use of fungicides in the control of gray mold. / O uso da regressão logística como uma ferramenta estatística para modelar a probabilidade de um evento em função de uma ou mais variáveis explicativas, tem crescido entre pesquisadores em várias áreas, inclusive na Fitopatologia. À respeito da regressão logística dicotômica, na qual a variável resposta é do tipo binária ou dummy, é extenso o número de trabalhos na literatura que abordam a modelagem, as pressuposições e a interpretação das análises, bem como alternativas de implementação em pacotes estatísticos. No entanto, quando a variável resposta requer que se utilize três ou mais categorias, o número de publicações é escasso. Isso devido não somente à escassez de publicações relevantes sobre o assunto, mas também à inerente dificuldade de abrangência sobre o tema. No presente trabalho aborda-se a aplicabilidade do modelo de regressão logística politômica ordinal, bem como as diferenças entre os modelos de chances proporcionais, chances proporcionais parciais e chances não proporcionais. Para isso, foram analisados dados de um experimento em que se avaliou o potencial do fungo antagonista Clonostachys rosea no biocontrole da doença denominada mofo cinzento , causada por Botrytis cinerea em morangueiro e tomateiro. Os modelos de chances proporcionais parciais e não proporcionais foram ajustados e comparados, uma vez que o teste score de proporcionalidade acusou rejeição da pressuposição de chances proporcionais. As estimativas dos coeficientes dos modelos bem como das razões de chances foram interpretadas em termos práticos para a Fitopatologia. A regressão logística politômica ordinal se apresentou como uma importante ferramenta estatística para predição de valores, mostrando o potencial do C. rosea em se tornar um produto comercial a ser desenvolvido e usado no controle biológico da doença, pois a aplicação de C. rosea foi tão ou mais eficiente do que a utilização de fungicidas no controle do mofo cinzento.