910 resultados para Negative Binomial Regression Model (NBRM)
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This study aims to assess the association between schistosomiasis and hookworm infection with hemoglobin levels of schoolchildren in northern Mozambique. Through a cross-sectional survey, 1,015 children from five to 12 years old in the provinces of Nampula, Cabo Delgado and Niassa were studied. Hookworm infection and urinary schistosomiasis were diagnosed, through Ritchie and filtration methods, with a prevalence of 31.3% and 59.1%, respectively. Hemoglobin levels were obtained with a portable photometer (Hemocue®). The average hemoglobin concentration was 10.8 ± 1.42 g/dL, and 62.1% of the children presented levels below 11.5 g/dL, of which 11.8% of the total number of children had hemoglobin levels below 9 g/dL. A multiple linear regression analysis demonstrated negative interactions between hemoglobin levels and ancylostomiasis, this being restricted to the province of Cabo Delgado (β = -0.55; p < 0.001) where an independent interaction between hemoglobin levels and urinary schistosomiasis was also observed (β = -0.35; p = 0.016). The logistical regression model indicated that hookworm infection represents a predictor of mild (OR = 1.87; 95% CI = 1.17-3.00) and moderate/severe anemia (OR = 2.71; 95% CI = 1.50 - 4.89). We concluded that, in the province of Cabo Delgado, hookworm and Schistosoma haematobium infections negatively influence hemoglobin levels in schoolchildren. Periodical deworming should be considered in the region. Health education and improvements in sanitary infrastructure could achieve long-term and sustainable reductions in soil-transmitted helminthiases and schistosomiasis prevalence rates.
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Objectives: To characterize the epidemiology and risk factors for acute kidney injury (AKI) after pediatric cardiac surgery in our center, to determine its association with poor short-term outcomes, and to develop a logistic regression model that will predict the risk of AKI for the study population. Methods: This single-center, retrospective study included consecutive pediatric patients with congenital heart disease who underwent cardiac surgery between January 2010 and December 2012. Exclusion criteria were a history of renal disease, dialysis or renal transplantation. Results: Of the 325 patients included, median age three years (1 day---18 years), AKI occurred in 40 (12.3%) on the first postoperative day. Overall mortality was 13 (4%), nine of whom were in the AKI group. AKI was significantly associated with length of intensive care unit stay, length of mechanical ventilation and in-hospital death (p<0.01). Patients’ age and postoperative serum creatinine, blood urea nitrogen and lactate levels were included in the logistic regression model as predictor variables. The model accurately predicted AKI in this population, with a maximum combined sensitivity of 82.1% and specificity of 75.4%. Conclusions: AKI is common and is associated with poor short-term outcomes in this setting. Younger age and higher postoperative serum creatinine, blood urea nitrogen and lactate levels were powerful predictors of renal injury in this population. The proposed model could be a useful tool for risk stratification of these patients.
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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics
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RESUMO - Caracterização do problema: O sistema de saúde português atingiu um patamar de ineficiência tal que urge ser reestruturado de forma a torná-lo sustentável. De forma a atingir este nível de sustentabilidade, uma série de soluções podem ser consideradas das quais destacamos a integração de cuidados. Este conceito exige que os diferentes níveis de saúde sigam um único caminho, trabalhando de forma coordenada e contínua. A integração de cuidados pode ser implementada através de várias tipologias entre as quais se destaca a integração clínica que por sua vez é composta pela continuidade de cuidados. Assim, ao medir a continuidade de cuidados, quantifica-se de certa forma a integração de cuidados. Objetivos: Avaliar o impacto da continuidade de cuidados nos custos. Metodologia: Os dados foram analisados através de estatísticas descritivas para verificar o seu grau de normalidade. Posteriormente foram aplicados testes t-student para analisar a existência de diferenças estatisticamente significativas entre as médias das diferentes variáveis. Foi então estudado o grau de associação entre variáveis através da correlação de spearman. Por fim, foi utilizado o modelo de regressão log-linear para verificar a existência de uma relação entre as várias naturezas de custos e os índices de continuidade. Com base neste modelo foram simulados dois cenários para estimar o impacto da maximização da continuidade de cuidados nas várias naturezas de custos. Conclusões: No geral, verifica-se uma relação muito ligeira entre a continuidade de cuidados e os custos. Mais especificamente, uma relação mais duradoura entre o médico e o doente resulta numa poupança de custos, independentemente da tipologia. Analisando a densidade da relação, observa-se uma relação positiva entre a mesma e os custos totais e o custo com Meios Complementares de Diagnóstico e Terapêutica (MCDT). Contudo verifica-se uma relação médico-doente negativa entre a densidade e os custos com medicamentos e com pessoal. Ao analisar o impacto da continuidade de cuidados nos custos, conclui-se que apenas a duração da relação médico-doente tem um impacto negativo em todas as categorias de custos, exceto o custo com medicamentos. A densidade de cuidados tem um impacto negativo apenas no custo com pessoal, influenciando positivamente as outras categorias de custos. Extrapolando para o nível nacional se o nível de densidade de uma relação fosse maximizado, existiria uma poupança de 0,18 euros, por ano, em custos com pessoal.
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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics
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This project focuses on the study of different explanatory models for the behavior of CDS security, such as Fixed-Effect Model, GLS Random-Effect Model, Pooled OLS and Quantile Regression Model. After determining the best fitness model, trading strategies with long and short positions in CDS have been developed. Due to some specifications of CDS, I conclude that the quantile regression is the most efficient model to estimate the data. The P&L and Sharpe Ratio of the strategy are analyzed using a backtesting analogy, where I conclude that, mainly for non-financial companies, the model allows traders to take advantage of and profit from arbitrages.
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Introduction The control of bacillary dysentery (BD) remains a big challenge for China. Methods Negative binomial multivariable regression was used to study relationships between meteorological variables and the occurrence of BD during the period of 2006-2012. Results Each 1°C rise of temperature corresponded to an increase of 3.60% (95%CI, 3.03% to 4.18%) in the monthly number of BD cases, whereas a 1 hPa rise in atmospheric pressure corresponded to a decrease in the number of BD cases by 2.85% (95%CI = 3.34% to 2.37% decrease). Conclusions Temperature and atmospheric pressure may be considered as predictors for the occurrence of BD in Guangzhou.
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Academics are often ranked on citation counts’, which is considered an adequate proxy for author's quality and reputation. This paper seeks to find what is behind a cited academic / a cited article. We constructed a rich dataset from Portuguese affiliated economists and use zero inflated negative binomial model. This procedure is appropriate for count outcomes, correcting for overdispersion and excess zeros. We also use a fixed effect poisson model to accomodate authors' unobserved heterogeneity. We analyze results in detail comparing with existing literature and making some theoretical considerations around.
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INTRODUCTION: Strongyloides stercoralis is a soil-transmitted helminth that produces an infection that can persist for decades. The relationships between certain clinical conditions and strongyloidiasis remains controversial. This study aims to identify the clinical conditions associated with intestinal strongyloidiasis at a reference center for infectious diseases in Rio de Janeiro, Brazil. METHODS: The clinical conditions that were assessed included HIV/AIDS, HTLV infection, cardiovascular diseases, diabetes, obstructive respiratory diseases, viral hepatitis, tuberculosis, cancer, chronic renal disease, nutritional/metabolic disorders, psychiatric conditions, rheumatic diseases and dermatologic diseases. We compared 167 S. stercoralis-positive and 133 S. stercoralis-negative patients. RESULTS: After controlling for sex (male/female OR = 2.29; 95% (CI): (1.42 - 3.70), rheumatic diseases remained significantly associated with intestinal strongyloidiasis (OR: 4.96; 95% CI: 1.34-18.37) in a multiple logistic regression model. With respect to leukocyte counts, patients with strongyloidiasis presented with significantly higher relative eosinophil (10.32% ± 7.2 vs. 4.23% ± 2.92) and monocyte (8.49% ± 7.25 vs. 5.39% ± 4.31) counts and lower segmented neutrophil (52.85% ± 15.31 vs. 61.32% ± 11.4) and lymphocyte counts (28.11% ± 9.72 vs. 30.90% ± 9.51) than S. stercoralis-negative patients. CONCLUSIONS: Strongyloidiasis should be routinely investigated in hospitalized patients with complex conditions facilitate the treatment of patients who will undergo immunosuppressive therapy. Diagnoses should be determined through the use of appropriate parasitological methods, such as the Baermann-Moraes technique.
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This paper aims at developing a collision prediction model for three-leg junctions located in national roads (NR) in Northern Portugal. The focus is to identify factors that contribute for collision type crashes in those locations, mainly factors related to road geometric consistency, since literature is scarce on those, and to research the impact of three modeling methods: generalized estimating equations, random-effects negative binomial models and random-parameters negative binomial models, on the factors of those models. The database used included data published between 2008 and 2010 of 177 three-leg junctions. It was split in three groups of contributing factors which were tested sequentially for each of the adopted models: at first only traffic, then, traffic and the geometric characteristics of the junctions within their area of influence; and, lastly, factors which show the difference between the geometric characteristics of the segments boarding the junctionsâ area of influence and the segment included in that area were added. The choice of the best modeling technique was supported by the result of a cross validation made to ascertain the best model for the three sets of researched contributing factors. The models fitted with random-parameters negative binomial models had the best performance in the process. In the best models obtained for every modeling technique, the characteristics of the road environment, including proxy measures for the geometric consistency, along with traffic volume, contribute significantly to the number of collisions. Both the variables concerning junctions and the various national highway segments in their area of influence, as well as variations from those characteristics concerning roadway segments which border the already mentioned area of influence have proven their relevance and, therefore, there is a rightful need to incorporate the effect of geometric consistency in the three-leg junctions safety studies.
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Dissertação de mestrado em Estatística
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Background:Previous reports have inferred a linear relationship between LDL-C and changes in coronary plaque volume (CPV) measured by intravascular ultrasound. However, these publications included a small number of studies and did not explore other lipid markers.Objective:To assess the association between changes in lipid markers and regression of CPV using published data.Methods:We collected data from the control, placebo and intervention arms in studies that compared the effect of lipidlowering treatments on CPV, and from the placebo and control arms in studies that tested drugs that did not affect lipids. Baseline and final measurements of plaque volume, expressed in mm3, were extracted and the percentage changes after the interventions were calculated. Performing three linear regression analyses, we assessed the relationship between percentage and absolute changes in lipid markers and percentage variations in CPV.Results:Twenty-seven studies were selected. Correlations between percentage changes in LDL-C, non-HDL-C, and apolipoprotein B (ApoB) and percentage changes in CPV were moderate (r = 0.48, r = 0.47, and r = 0.44, respectively). Correlations between absolute differences in LDL-C, non‑HDL-C, and ApoB with percentage differences in CPV were stronger (r = 0.57, r = 0.52, and r = 0.79). The linear regression model showed a statistically significant association between a reduction in lipid markers and regression of plaque volume.Conclusion:A significant association between changes in different atherogenic particles and regression of CPV was observed. The absolute reduction in ApoB showed the strongest correlation with coronary plaque regression.
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The Republic of Haiti is the prime international remittances recipient country in the Latin American and Caribbean (LAC) region relative to its gross domestic product (GDP). The downside of this observation may be that this country is also the first exporter of skilled workers in the world by population size. The present research uses a zero-altered negative binomial (with logit inflation) to model households' international migration decision process, and endogenous regressors' Amemiya Generalized Least Squares method (instrumental variable Tobit, IV-Tobit) to account for selectivity and endogeneity issues in assessing the impact of remittances on labor market outcomes. Results are in line with what has been found so far in this literature in terms of a decline of labor supply in the presence of remittances. However, the impact of international remittances does not seem to be important in determining recipient households' labor participation behavior, particularly for women.
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In automobile insurance, it is useful to achieve a priori ratemaking by resorting to gene- ralized linear models, and here the Poisson regression model constitutes the most widely accepted basis. However, insurance companies distinguish between claims with or without bodily injuries, or claims with full or partial liability of the insured driver. This paper exa- mines an a priori ratemaking procedure when including two di®erent types of claim. When assuming independence between claim types, the premium can be obtained by summing the premiums for each type of guarantee and is dependent on the rating factors chosen. If the independence assumption is relaxed, then it is unclear as to how the tari® system might be a®ected. In order to answer this question, bivariate Poisson regression models, suitable for paired count data exhibiting correlation, are introduced. It is shown that the usual independence assumption is unrealistic here. These models are applied to an automobile insurance claims database containing 80,994 contracts belonging to a Spanish insurance company. Finally, the consequences for pure and loaded premiums when the independence assumption is relaxed by using a bivariate Poisson regression model are analysed.
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Excessive exposure to solar ultraviolet (UV) is the main cause of skin cancer. Specific prevention should be further developed to target overexposed or highly vulnerable populations. A better characterisation of anatomical UV exposure patterns is however needed for specific prevention. To develop a regression model for predicting the UV exposure ratio (ER, ratio between the anatomical dose and the corresponding ground level dose) for each body site without requiring individual measurements. A 3D numeric model (SimUVEx) was used to compute ER for various body sites and postures. A multiple fractional polynomial regression analysis was performed to identify predictors of ER. The regression model used simulation data and its performance was tested on an independent data set. Two input variables were sufficient to explain ER: the cosine of the maximal daily solar zenith angle and the fraction of the sky visible from the body site. The regression model was in good agreement with the simulated data ER (R(2)=0.988). Relative errors up to +20% and -10% were found in daily doses predictions, whereas an average relative error of only 2.4% (-0.03% to 5.4%) was found in yearly dose predictions. The regression model predicts accurately ER and UV doses on the basis of readily available data such as global UV erythemal irradiance measured at ground surface stations or inferred from satellite information. It renders the development of exposure data on a wide temporal and geographical scale possible and opens broad perspectives for epidemiological studies and skin cancer prevention.