Financial Risk Analysis of Portuguese Textile and Tourism Companies

Textile and tourism sectors are two important industries in the Portuguese economy. However, its high exposure to both internal and international economic volatility make the companies operating in these economic sectors particularly vulnerable to economic crises, such as the ones which have been impacting Portugal and the European Union. The objective of this paper is to evaluate and understand the impact of size and age on the financial health of textile and tourism companies, measured by economic indices. An empirical based model is proposed. Its implications are derived and tested on a sample of 4061 Portuguese companies from textile and tourism sectors, during the period 2005-2009. The findings suggest that age has a major impact on the risk of failure, rather than size. Whereas the effect of age is generally positive regarding the financial health of the company, the effect of size is less clear and ultimately depends on the age of the company.

The impact of size and age of the company on assessing financial risk.

Textiles and tourism sectors are two important sectors in Portuguese economy. Its high exposure to both internal and international economy volatility transform the companies operating in these economic sectors especially vulnerable to recent economic crises in Portugal and European Union. The objective of this paper is to evaluate and understand the impact of size and age on the financial health of textile and tourism companies, measured by economic indices. An empirical based model is proposed. Its implications are derived and tested on a sample of 4061 Portuguese companies from textile and tourism sector, during the period 2005-2009. The results conclude that age has a stronger impact on the risk of failure than size. Whereas the effect of age is generally positive regarding the financial health of the company, the effect of size is less clear and depends on the age of the company.

Microscopic analysis of "prato", "mussarela" and "mineiro" cheese sold in street markets of the City of S. Paulo, Southeastern Brazil

INTRODUCTION: Cheese should be produced from ingredients of good quality and processed under hygienic conditions. Further, cheese should be transported, stored and sold in an appropriate manner in order to avoid, among other things, the incorporation of extraneous materials (filth) of biological origin or otherwise, in contravention of the relevant food legislation. The aim of the study was to evaluate the hygienic conditions of "prato", "mussarela", and "mineiro" cheeses sold at the street food markets in the city of S. Paulo, Brazil. MATERIALS AND METHOD: Forty-seven samples of each of the three types of cheese were collected during the period from March, 1993 to February, 1994. The Latin square was used as a statistical model for sampling and random selection of the street markets from which to collect the cheese samples. The samples were analysed for the presence of extraneous matters outside for which purpose the samples were washed and filtered and inside, for which the methodology of enzymathic digestion of the sample with pancreatine, followed by filtering,was used. RESULTS AND CONCLUSION: Of the 141 samples analysed, 75.9% exhibited at least one sort of extraneous matters. For the "prato" and "mussarela" cheeses, the high number of contaminated samples was due mainly to extraneous matters present inside the cheese, whereas in the "mineiro" cheese, besides the internal filth, 100% of the samples had external filth.

Sucesso terapêutico no controlo da tuberculose em Portugal : determinantes do resultado do tratamento e a sua capacidade preditiva

RESUMO - Enquadramento/objectivos: Apesar do elevado nível de comprometimento em estratégias eficazes para o controlo da tuberculose, em todo o mundo, esta constitui ainda um sério problema de Saúde Pública, com uma estimativa global de 9,4milhões de casos novos em 2008 e 1,8milhões de mortes/ano. O reduzido conhecimento das barreiras e facilitadores para o sucesso terapêutico constitui um importante obstáculo na procura de soluções eficazes de melhoramento da qualidade dos programas de controlo da tuberculose. Este estudo procura contribuir para a identificação atempada de doentes com perfis preditivos de insucesso terapêutico, através da identificação inicial de potenciais determinantes do resultado, com base num modelo epidemiológico e estatístico. Métodos: Foi desenvolvido um estudo de caso-controlo para a população de casos notificados ao Programa Nacional de Controlo da Tuberculose (n=24491), entre 2000-2007. Os factores preditivos de insucesso terapêutico foram identificados na análise bivariada e multivariada, com um nível de significância de 5%; a regressão logística foi utilizada para estimar a odds ratio de insucesso terapêutico, em comparação com o sucesso terapêutico, para diversos factores identificados na literatura, e para os quais os dados se encontravam disponíveis. Resultados: A dependência alcoólica (OR=2,889), o país de origem (OR=3,910), a situação sem-abrigo (OR=3.919), a co-infecção pelo VIH (OR=5,173), a interrupção (OR=60.615) ou falha terapêutica no tratamento anterior (OR=67.345) e a duração do tratamento inferior a 165 dias (OR=1930,133) foram identificados como factores preditivos de insucesso terapêutico. A duração do tratamento inferior a 165 dias provou ser o mais importante determinante do resultado terapêutico. Conclusões: Os resultados sugerem que um doente imigrante, em situação de sem-abrigo, dependente alcoólico, com tratamentos anteriores para a tuberculose e co-infectado pelo VIH apresenta uma elevada probabilidade de insucesso terapêutico. Assim, deverão ser definidas estratégias específicas, centradas no doente por forma a impedir este resultado. A base de dados (SVIG-TB), provou ser uma ferramenta de qualidade para a investigação sobre diversos aspectos do controlo da tuberculose. ------------------------------- ABSTRACT - Background/Objective: Despite the high commitment in good strategies for tuberculosis control worldwide, this is still a serious Public Health problem, with global estimates of 9,4million new cases in 2008 and 1,8million deaths/year. The poor understanding of the barriers and facilitators to treatment success is a major obstacle to find effective solutions to improve the quality of tuberculosis programs. This study tries to contribute to the timely identification of patients with predictive profiles of unsuccessful treatment outcomes, through the initial identification of characteristics probably affecting treatment outcome, found on the basis of an epidemiological and statistical model. Methods: A case-control study was conducted for the population of cases notified to the National Program for Tuberculosis Control (n=24 491), between 2000-2007. Predictive factors for unsuccessful outcome were assessed in a bivariate and multivariate analysis, using a significance level of 5%; a logistic regression was used to estimate the odds-ratio of unsuccessful, as compared to successful outcome, for several factors identified in the literature and to which data was available. Results: Alcohol abuse (OR=2,889), patient´s foreign origin (OR=3,910), homelessness (OR=3,919), HIV co-infection (OR=5,173), interruption (OR=60,615) or unsuccessful outcome in the previous treatment (OR=67,345) and treatment duration below 165 days (OR=1930,133) were identified as predictive of unsuccessful outcomes. A low treatment duration proved to be the most powerful factor affecting treatment outcome. Conclusions: Results suggest that a foreign-born patient, alcohol abuser, who has had a previous treatment for tuberculosis and is co-infected with HIV is very likely to have an unsuccessful outcome. Therefore, specific, patient-centered strategies should be taken to prevent an unsuccessful outcome. The database (SVIG-TB), has proved to be a quality tool on research of various aspects of tuberculosis control.

Joint modelling of longitudinal and survival data on breast cancer

Tese de Doutoramento em Ciências (Especialidade em Matemática)

Эмпирическая радиолокационная модель суперячейкового конвективного облака

სტატიაში გაანალიზებულია საქართველოს მთიანი რეგიონის სუპერუჯრედიან კონვექციურ ღრუბლებზე მრავალწლიანი რადიოლოკაციური დაკვირვებების მონაცემები.

Modeling usage of medical care services: the medical expenditure panel survey data, 1996-2000

We explore the determinants of usage of six different types of health care services, using the Medical Expenditure Panel Survey data, years 1996-2000. We apply a number of models for univariate count data, including semiparametric, semi-nonparametric and finite mixture models. We find that the complexity of the model that is required to fit the data well depends upon the way in which the data is pooled across sexes and over time, and upon the characteristics of the usage measure. Pooling across time and sexes is almost always favored, but when more heterogeneous data is pooled it is often the case that a more complex statistical model is required.

Predicting smoking cessation and its relapse in HIV-infected patients: the Swiss HIV Cohort Study.

OBJECTIVES: The aim of the study was to assess whether prospective follow-up data within the Swiss HIV Cohort Study can be used to predict patients who stop smoking; or among smokers who stop, those who start smoking again. METHODS: We built prediction models first using clinical reasoning ('clinical models') and then by selecting from numerous candidate predictors using advanced statistical methods ('statistical models'). Our clinical models were based on literature that suggests that motivation drives smoking cessation, while dependence drives relapse in those attempting to stop. Our statistical models were based on automatic variable selection using additive logistic regression with component-wise gradient boosting. RESULTS: Of 4833 smokers, 26% stopped smoking, at least temporarily; because among those who stopped, 48% started smoking again. The predictive performance of our clinical and statistical models was modest. A basic clinical model for cessation, with patients classified into three motivational groups, was nearly as discriminatory as a constrained statistical model with just the most important predictors (the ratio of nonsmoking visits to total visits, alcohol or drug dependence, psychiatric comorbidities, recent hospitalization and age). A basic clinical model for relapse, based on the maximum number of cigarettes per day prior to stopping, was not as discriminatory as a constrained statistical model with just the ratio of nonsmoking visits to total visits. CONCLUSIONS: Predicting smoking cessation and relapse is difficult, so that simple models are nearly as discriminatory as complex ones. Patients with a history of attempting to stop and those known to have stopped recently are the best candidates for an intervention.

Volumetric quantification of atherosclerotic plaque in CT considering partial volume effect.

Coronary artery calcification (CAC) is quantified based on a computed tomography (CT) scan image. A calcified region is identified. Modified expectation maximization (MEM) of a statistical model for the calcified and background material is used to estimate the partial calcium content of the voxels. The algorithm limits the region over which MEM is performed. By using MEM, the statistical properties of the model are iteratively updated based on the calculated resultant calcium distribution from the previous iteration. The estimated statistical properties are used to generate a map of the partial calcium content in the calcified region. The volume of calcium in the calcified region is determined based on the map. The experimental results on a cardiac phantom, scanned 90 times using 15 different protocols, demonstrate that the proposed method is less sensitive to partial volume effect and noise, with average error of 9.5% (standard deviation (SD) of 5-7mm(3)) compared with 67% (SD of 3-20mm(3)) for conventional techniques. The high reproducibility of the proposed method for 35 patients, scanned twice using the same protocol at a minimum interval of 10 min, shows that the method provides 2-3 times lower interscan variation than conventional techniques.

Prevalence of chronic conditions – Stroke

IPH has estimated and forecast clinical diagnosis rates of stroke among adults for the years 2010, 2015 and 2020. In the Republic of Ireland, the data are based on the Survey of Lifestyle, Attitudes and Nutrition (SLÁN) 2007. The data describe the number of adults who report that they have experienced doctor-diagnosed stroke in the previous 12 months. Data are available by age and sex for each Local Health Office of the Health Service Executive (HSE) in the Republic of Ireland. In Northern Ireland, the data are based on the Health and Social Wellbeing Survey 2005/06. The data describe the number of adults who report that they have experienced doctor-diagnosed stroke at any time in the past. Data are available by age and sex for each Local Government District in Northern Ireland. Clinical diagnosis rates in the Republic of Ireland relate to the previous 12 months and are not directly comparable with clinical diagnosis rates in Northern Ireland which relate to anytime in the past. The IPH estimated prevalence per cents may be marginally different to estimated prevalence per cents taken directly from the reference study. There are two reasons for this: 1) The IPH prevalence estimates relate to 2010 while the reference studies relate to earlier years (Northern Ireland Health and Social Wellbeing Survey 2005/06, Survey of Lifestyle, Attitudes and Nutrition 2007, Understanding Society 2009). Although we assume that the risk of the condition in the risk groups do not change over time, the distribution of the number of people in the risk groups in the population changes over time (eg the population ages).  This new distribution of the risk groups in the population means that the risk of the condition is weighted differently to the reference study and this results in a different overall prevalence estimate. 2) The IPH prevalence estimates are based on a statistical model of the reference study. The model includes a number of explanatory variables to predict the risk of the condition. Therefore the model does not include records from the reference study that are missing data on these explanatory variables. A prevalence estimate for a condition taken directly from the reference study would include these records.

Prevalence of chronic conditions – Diabetes

IPH has estimated and forecast clinical diagnosis rates of diabetes among adults for the years 2010, 2015 and 2020. In the Republic of Ireland, the data are based on the Survey of Lifestyle, Attitudes and Nutrition (SLÁN) 2007. The data describe the number of people who report that they have experienced doctor-diagnosed diabetes in the previous 12 months (annual clinical diagnosis).  Data are available by age and sex for each Local Health Office of the Health Service Executive (HSE) in the Republic of Ireland. Note that an adjustment was made for diabetes medication use recorded in the SLÁN physical examination sub-group of 45+ year olds. In Northern Ireland, the data is based on the Health and Social Wellbeing Survey 2005/06 . The data describe the number of people who report that they have experienced doctor-diagnosed diabetes at any time in the past (lifetime clinical diagnosis). Data are available by age and sex for each Local Government District in Northern Ireland.Clinical diagnosis rates in the Republic of Ireland relate to the previous 12 months and are not directly comparable with clinical diagnosis rates in Northern Ireland which relate to anytime in the past. Differences between IPH estimates and reference study estimates: The IPH estimated prevalence per cents may be marginally different to estimated prevalence per cents taken directly from the reference study. There are two reasons for this: 1) The IPH prevalence estimates relate to 2010 while the reference studies relate to earlier years (Northern Ireland Health and Social Wellbeing Survey 2005/06, Survey of Lifestyle, Attitudes and Nutrition 2007, Understanding Society 2009). Although we assume that the risk of the condition in the risk groups do not change over time, the distribution of the number of people in the risk groups in the population changes over time (eg the population ages).  This new distribution of the risk groups in the population means that the risk of the condition is weighted differently to the reference study and this results in a different overall prevalence estimate. 2) The IPH prevalence estimates are based on a statistical model of the reference study. The model includes a number of explanatory variables to predict the risk of the condition. Therefore the model does not include records from the reference study that are missing data on these explanatory variables. A prevalence estimate for a condition taken directly from the reference study would include these records.  

Prevalence of chronic conditions – Hypertension

IPH has estimated and forecast clinical diagnosis rates of hypertension among adults for the years 2010, 2015 and 2020. In the Republic of Ireland, the data are based on the Survey of Lifestyle, Attitudes and Nutrition (SLÁN) 2007. The data describe the number of people who report that they have experienced doctor-diagnosed hypertension in the previous 12 months (annual clinical diagnosis). Data are available by age and sex for each Local Health Office of the Health Service Executive (HSE) in the Republic of Ireland. In Northern Ireland, the data is based on the Health and Social Wellbeing Survey 2005/06. The data describe the number of people who report that they have experienced doctor/nurse-diagnosed hypertension at any time in the past (lifetime clinical diagnosis). Data are available by age and sex for each Local Government District in Northern Ireland. Clinical diagnosis rates in the Republic of Ireland relate to the previous 12 months and are not directly comparable with clinical diagnosis rates in Northern Ireland which relate to anytime in the past.   The IPH estimated prevalence per cents may be marginally different to estimated prevalence per cents taken directly from the reference study. There are two reasons for this: 1) The IPH prevalence estimates relate to 2010 while the reference studies relate to earlier years (Northern Ireland Health and Social Wellbeing Survey 2005/06, Survey of Lifestyle, Attitudes and Nutrition 2007, Understanding Society 2009). Although we assume that the risk of the condition in the risk groups do not change over time, the distribution of the number of people in the risk groups in the population changes over time (eg the population ages).  This new distribution of the risk groups in the population means that the risk of the condition is weighted differently to the reference study and this results in a different overall prevalence estimate. 2) The IPH prevalence estimates are based on a statistical model of the reference study. The model includes a number of explanatory variables to predict the risk of the condition. Therefore the model does not include records from the reference study that are missing data on these explanatory variables. A prevalence estimate for a condition taken directly from the reference study would include these records.

Prevalence of chronic conditions – Coronary Heart Disease

IPH has estimated and forecast clinical diagnosis rates of CHD (heart attack and/or angina) among adults for the years 2010, 2015 and 2020. In the Republic of Ireland, the data are based on the Survey of Lifestyle, Attitudes and Nutrition (SLÁN) 2007 . The data describe the number of people who report that they have experienced doctor-diagnosed heart attack and/or angina in the previous 12 months (annual clinical diagnosis). Data is available by age and sex for each Local Health Office of the Health Service Executive (HSE) in the Republic of Ireland. In Northern Ireland, the data are based on the Health and Social Wellbeing Survey 2005/06 . The data describe the number of people who report that they have experienced doctor-diagnosed heart attack and/or angina at any time in the past (lifetime clinical diagnosis). Data are available by age and sex for each Local Government District in Northern Ireland. Clinical diagnosis rates in the Republic of Ireland relate to the previous 12 months and are not directly comparable with clinical diagnosis rates in Northern Ireland which relate to anytime in the past. The IPH estimated prevalence per cents may be marginally different to estimated prevalence per cents taken directly from the reference study. There are two reasons for this: 1) The IPH prevalence estimates relate to 2010 while the reference studies relate to earlier years (Northern Ireland Health and Social Wellbeing Survey 2005/06, Survey of Lifestyle, Attitudes and Nutrition 2007, Understanding Society 2009). Although we assume that the risk of the condition in the risk groups do not change over time, the distribution of the number of people in the risk groups in the population changes over time (eg the population ages).  This new distribution of the risk groups in the population means that the risk of the condition is weighted differently to the reference study and this results in a different overall prevalence estimate. 2) The IPH prevalence estimates are based on a statistical model of the reference study. The model includes a number of explanatory variables to predict the risk of the condition. Therefore the model does not include records from the reference study that are missing data on these explanatory variables. A prevalence estimate for a condition taken directly from the reference study would include these records.

Prevalence of chronic conditions – Chronic Airflow Obstruction

IPH has estimated and forecast clinical diagnosis rates of CAO among adults for the years 2010, 2015 and 2020. In the Republic of Ireland, the data are based on the Survey of Lifestyle, Attitudes and Nutrition (SLÁN) 2007. The data describe the number of people who report that they have experienced doctor-diagnosed chronic bronchitis, chronic obstructive lung (pulmonary) disease, or emphysema in the previous 12 months (annual clinical diagnosis). Data is available by age and sex for each Local Health Office of the Health Service Executive (HSE) in the Republic of Ireland. In Northern Ireland, the data are based on the Health and Social Wellbeing Survey 2005/06. The data describe the number of people who report that they have experienced doctor-diagnosed COPD or chronic obstructive pulmonary disease eg chronic bronchitis / emphysema or both disorders at any time in the past (lifetime clinical diagnosis). Data are available by age and sex for each Local Government District in Northern Ireland. Clinical diagnosis rates in the Republic of Ireland relate to the previous 12 months and are not directly comparable with clinical diagnosis rates in Northern Ireland which relate to anytime in the past.   The IPH estimated prevalence per cents may be marginally different to estimated prevalence per cents taken directly from the reference study. There are two reasons for this: 1) The IPH prevalence estimates relate to 2010 while the reference studies relate to earlier years (Northern Ireland Health and Social Wellbeing Survey 2005/06, Survey of Lifestyle, Attitudes and Nutrition 2007, Understanding Society 2009). Although we assume that the risk of the condition in the risk groups do not change over time, the distribution of the number of people in the risk groups in the population changes over time (eg the population ages).  This new distribution of the risk groups in the population means that the risk of the condition is weighted differently to the reference study and this results in a different overall prevalence estimate. 2) The IPH prevalence estimates are based on a statistical model of the reference study. The model includes a number of explanatory variables to predict the risk of the condition. Therefore the model does not include records from the reference study that are missing data on these explanatory variables. A prevalence estimate for a condition taken directly from the reference study would include these records.

Prevalence of chronic conditions – Musculoskeletal Conditions

IPH has estimated and forecast the number of adults with MSCs for the years 2010, 2015 and 2020. In the Republic of Ireland, the data are based on the Survey of Lifestyle, Attitudes and Nutrition (SLÁN) 2007 . The data describe the number of people who report that they have experienced doctor-diagnosed MSC in the previous 12 months:     Lower back pain or any other chronic back condition     Rheumatoid arthritis (inflammation of the joints)     Osteoarthritis (arthrosis, joint degradation) Data are  available by age and sex for each Local Health Office of the Health Service Executive (HSE) in the Republic of Ireland. In Northern Ireland, the data are based on the Health and Social Wellbeing Survey 2005/06 and Understanding Society 2009. The data describe the number of adults who:     Have ever consulted a doctor about back pain     Are currently receiving treatment for musculoskeletal problems (such as arthritis, rheumatism)     Have ever been told by a doctor or other health professional that they had have arthritis? Data are available by age and sex for each Local Government District in Northern Ireland. There are significant differences between the definitions used in RoI and NI and North-South comparisons are not valid. The RoI measures relate to specific MSCs in the previous 12 months that had been diagnosed by a doctor. The NI measures relate to doctor-consultations at any time in the past, doctor-diagnosis at any time in the past and current treatment. The IPH estimated prevalence per cents may be marginally different to estimated prevalence per cents taken directly from the reference study. There are two reasons for this: 1) The IPH prevalence estimates relate to 2010 while the reference studies relate to earlier years (Northern Ireland Health and Social Wellbeing Survey 2005/06, Survey of Lifestyle, Attitudes and Nutrition 2007, Understanding Society 2009). Although we assume that the risk of the condition in the risk groups do not change over time, the distribution of the number of people in the risk groups in the population changes over time (eg the population ages).  This new distribution of the risk groups in the population means that the risk of the condition is weighted differently to the reference study and this results in a different overall prevalence estimate. 2) The IPH prevalence estimates are based on a statistical model of the reference study. The model includes a number of explanatory variables to predict the risk of the condition. Therefore the model does not include records from the reference study that are missing data on these explanatory variables. A prevalence estimate for a condition taken directly from the reference study would include these records.