Prevalence of asthma and risk factors associated: population based study in Sao Paulo, Southeastern Brazil, 2008-2009

OBJECTIVE: To assess the prevalence of asthma and risk factors associated in children and adolescents. METHODS: Population-based cross-sectional study with 1,185 female and male children and adolescents carried out in the city of Sao Paulo, Southeastern Brazil, from 2008 to 2009. Data were collected through home interviews. Respondents were selected from two-stage (census tract, household) cluster random sampling stratified by gender and age. Multiple Poisson regression was used in the adjusted analysis between the outcome and socioeconomic, demographic, lifestyle and health condition variables. RESULTS: Of all respondents, 9.1% (95%CI 7.0; 11.7) reported asthma. After adjustment, the following variables were found independently associated with asthma: age (0 to 4 years vs. 15 to 19) (PR 3.18, 95%CI 1.20;8.42); age (5 to 9 years vs. 15 to 19) (PR 6.37, 95%CI 2.64;15.39); age (10 to 14 years vs. 15 to 19) (PR 4.51,95%CI 1.95;10.40); allergy (yes vs. no) (PR 2.22, 95%CI 1.24;4.00); rhinitis (yes vs. no) (PR 2.13, 95%CI 1.22;3.73); health conditions in the 15 days preceding the interview (yes vs. no) (PR 1.96, 95%CI 1.23;3.11); number of rooms in the household (1 to 3 vs. 4 and more) (PR 1.67, 95%CI 1.05;2.66); and skin color (black and mixed vs. white) (PR 2.00, 95%CI 1.14;3.49). CONCLUSIONS: This study showed the importance of factors associated with asthma including rhinitis and allergy; age between 5 to 9 years old; black and mixed skin color; and household with few rooms. Frequent health problems are seen as a common consequence of asthma.

Application of discriminant analysis-based model for prediction of risk of low back disorders due to workplace design in industrial jobs

The occupational exposure limits of different risk factors for development of low back disorders (LBDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors for LBDs interact in causing injury, since the nature and mechanism of these disorders are relatively unknown phenomena. Industrial ergonomists' role becomes further complicated because the potential risk factors that may contribute towards the onset of LBDs interact in a complex manner, which makes it difficult to discriminate in detail among the jobs that place workers at high or low risk of LBDs. The purpose of this paper was to develop a comparative study between predictions based on the neural network-based model proposed by Zurada, Karwowski & Marras (1997) and a linear discriminant analysis model, for making predictions about industrial jobs according to their potential risk of low back disorders due to workplace design. The results obtained through applying the discriminant analysis-based model proved that it is as effective as the neural network-based model. Moreover, the discriminant analysis-based model proved to be more advantageous regarding cost and time savings for future data gathering.

Work Precariousness and Risk of Accidents in Civil Construction: A study based on the Collective Work Analysis

This paper aims to describe the construction workers' activities, as well as their perceptions about risks and workload. The study, based on the Collective Work Analysis, is part of a broader public policies project for the improvement of SIVAT (Surveillance System of Work Accidents) - in the city of Piracicaba (Southeastern Brazil). Civil construction was prioritized given the epidemiological magnitude of the occurrence of work accidents and the limited efficacy of traditional surveillance initiatives in this sector due to informal employment practices, outsourcing, high staff turnover, etc. The workers have a high level of awareness concerning the risk of accidents, but they believe that the main preventive measures hinder or even make it impossible for them to carry out the tasks. Our findings question the efficacy of traditional training for adherence to safety practices, thus highlighting the need for a transformative pedagogy for preventive practices and the health promotion of workers.

Autoantibodies and High-Risk HLA Susceptibility Markers in First-Degree Relatives of Brazilian Patients with Type 1 Diabetes Mellitus: A Progression to Disease Based Study

The objective of this study was to determine the frequencies of autoantibodies to heterogeneous islet-cell cytoplasmic antigens (ICA), glutamic acid decarboxylase(65) (GAD(65)A), insulinoma-associated antigen-2 (IA-2A) and insulin (IAA)-and human leukocyte antigen (HLA) class II markers (HLA-DR and -DQ) in first degree relatives of heterogeneous Brazilian patients with type I diabetes(T1DM). A major focus of this study was to determine the influence of age, gender, proband characteristics and ancestry on the prevalence of autoantibodies and HLA-DR and -DQ alleles on disease progression and genetic predisposition to T1DM among the first-degree relatives. IAA, ICA, GAD(65)A, IA-2A and HLA- class II alleles were determined in 546 first-degree-relatives, 244 siblings, 55 offspring and 233 parents of 178 Brazilian patients with T1DM. Overall, 8.9% of the relatives were positive for one or more autoantibodies. IAA was the only antibody detected in parents. GAD(65) was the most prevalent antibody in offspring and siblings as compared to parents and it was the sole antibody detected in offspring. Five siblings were positive for the IA-2 antibody. A significant number (62.1%) of siblings had 1 or 2 high risk HLA haplotypes. During a 4-year follow-up study, 5 siblings (expressing HLA-DR3 or -DR4 alleles) and 1 offspring positive for GAD(65)A progressed to diabetes. The data indicated that the GAD(65) and IA-2 antibodies were the strongest predictors of T1DM in our study population. The high risk HLA haplotypes alone were not predictive of progression to overt diabetes.

Gender-based violence and socioeconomic inequalities: Does living in more deprived neighbourhoods increase women's risk of intimate partner violence?

This study investigates the influence of neighbourhood socioeconomic conditions on women's likelihood of experiencing intimate partner violence (IPV) in Sao Paulo, Brazil. Data from 940 women who were interviewed as part of the WHO multi-country study on women's health and domestic violence against women, and census data for Sao Paulo City, were analyzed using multilevel regression techniques. A neighbourhood socioeconomic-level scale was created, and proxies for the socioeconomic positions of the couple were included. Other individual level variables included factors related to partner's behaviour and women's experiences and attitudes. Women's risk of IPV did not vary across neighbourhoods in Sao Paulo nor was it influenced by her individual socioeconomic characteristics. However, women in the middle range of the socioeconomic scale were significantly more likely to report having experienced violence by a partner. Partner behaviours such as excessive alcohol use, controlling behaviour and multiple sexual partnerships were important predictors of IPV. A women's likelihood of IPV also increased if either her mother had experienced IPV or if she used alcohol excessively. These findings suggest that although the characteristics of people living in deprived neighbourhoods may influence the probability that a woman will experience IPV, higher-order contextual dynamics do not seem to affect this risk. While poverty reduction will improve the lives of individuals in many ways, strategies to reduce IPV should prioritize shifting norms that reinforce certain negative male behaviours. (C) 2012 Elsevier Ltd. All rights reserved.

Goal-directed fluid management based on pulse pressure variation monitoring during high-risk surgery: a pilot randomized controlled trial

Abstract Introduction Several studies have shown that maximizing stroke volume (or increasing it until a plateau is reached) by volume loading during high-risk surgery may improve post-operative outcome. This goal could be achieved simply by minimizing the variation in arterial pulse pressure (ΔPP) induced by mechanical ventilation. We tested this hypothesis in a prospective, randomized, single-centre study. The primary endpoint was the length of postoperative stay in hospital. Methods Thirty-three patients undergoing high-risk surgery were randomized either to a control group (group C, n = 16) or to an intervention group (group I, n = 17). In group I, ΔPP was continuously monitored during surgery by a multiparameter bedside monitor and minimized to 10% or less by volume loading. Results Both groups were comparable in terms of demographic data, American Society of Anesthesiology score, type, and duration of surgery. During surgery, group I received more fluid than group C (4,618 ± 1,557 versus 1,694 ± 705 ml (mean ± SD), P < 0.0001), and ΔPP decreased from 22 ± 75 to 9 ± 1% (P < 0.05) in group I. The median duration of postoperative stay in hospital (7 versus 17 days, P < 0.01) was lower in group I than in group C. The number of postoperative complications per patient (1.4 ± 2.1 versus 3.9 ± 2.8, P < 0.05), as well as the median duration of mechanical ventilation (1 versus 5 days, P < 0.05) and stay in the intensive care unit (3 versus 9 days, P < 0.01) was also lower in group I. Conclusion Monitoring and minimizing ΔPP by volume loading during high-risk surgery improves postoperative outcome and decreases the length of stay in hospital. Trial registration NCT00479011

Complex organochlorine pesticide mixtures as determinant factor for breast cancer risk: a population-based case-control study in the Canary Islands (Spain)

[EN] Background: All the relevant risk factors contributing to breast cancer etiology are not fully known. Exposure to organochlorine pesticides has been linked to an increased incidence of the disease, although not all data have been consistent. Most published studies evaluated the exposure to organochlorines individually, ignoring the potential effects exerted by the mixtures of chemicals. Methods: This population-based study was designed to evaluate the profile of mixtures of organochlorines detected in 103 healthy women and 121 women diagnosed with breast cancer from Gran Canaria Island, and the relation between the exposure to these compounds and breast cancer risk.Results: The most prevalent mixture of organochlorines among healthy women was the combination of lindane and endrin, and this mixture was not detected in any affected women. Breast cancer patients presented more frequently a combination of aldrin, dichlorodiphenyldichloroethylene (DDE) and dichlorodiphenyldichloroethane (DDD), and this mixture was not found in any healthy woman. After adjusting for covariables, the risk of breast cancer was moderately associated with DDD (OR = 1.008, confidence interval 95% 1.001-1.015, p = 0.024).Conclusions: This study indicates that healthy women show a very different profile of organochlorine pesticide mixtures than breast cancer patients, suggesting that organochlorine pesticide mixtures could play a relevant role in breast cancer risk.

Quantifying and modeling financial market fluctuations

Die Entstehung eines Marktpreises für einen Vermögenswert kann als Superposition der einzelnen Aktionen der Marktteilnehmer aufgefasst werden, die damit kumulativ Angebot und Nachfrage erzeugen. Dies ist in der statistischen Physik mit der Entstehung makroskopischer Eigenschaften vergleichbar, die von mikroskopischen Wechselwirkungen zwischen den beteiligten Systemkomponenten hervorgerufen werden. Die Verteilung der Preisänderungen an Finanzmärkten unterscheidet sich deutlich von einer Gaußverteilung. Dies führt zu empirischen Besonderheiten des Preisprozesses, zu denen neben dem Skalierungsverhalten nicht-triviale Korrelationsfunktionen und zeitlich gehäufte Volatilität zählen. In der vorliegenden Arbeit liegt der Fokus auf der Analyse von Finanzmarktzeitreihen und den darin enthaltenen Korrelationen. Es wird ein neues Verfahren zur Quantifizierung von Muster-basierten komplexen Korrelationen einer Zeitreihe entwickelt. Mit dieser Methodik werden signifikante Anzeichen dafür gefunden, dass sich typische Verhaltensmuster von Finanzmarktteilnehmern auf kurzen Zeitskalen manifestieren, dass also die Reaktion auf einen gegebenen Preisverlauf nicht rein zufällig ist, sondern vielmehr ähnliche Preisverläufe auch ähnliche Reaktionen hervorrufen. Ausgehend von der Untersuchung der komplexen Korrelationen in Finanzmarktzeitreihen wird die Frage behandelt, welche Eigenschaften sich beim Wechsel von einem positiven Trend zu einem negativen Trend verändern. Eine empirische Quantifizierung mittels Reskalierung liefert das Resultat, dass unabhängig von der betrachteten Zeitskala neue Preisextrema mit einem Anstieg des Transaktionsvolumens und einer Reduktion der Zeitintervalle zwischen Transaktionen einhergehen. Diese Abhängigkeiten weisen Charakteristika auf, die man auch in anderen komplexen Systemen in der Natur und speziell in physikalischen Systemen vorfindet. Über 9 Größenordnungen in der Zeit sind diese Eigenschaften auch unabhängig vom analysierten Markt - Trends, die nur für Sekunden bestehen, zeigen die gleiche Charakteristik wie Trends auf Zeitskalen von Monaten. Dies eröffnet die Möglichkeit, mehr über Finanzmarktblasen und deren Zusammenbrüche zu lernen, da Trends auf kleinen Zeitskalen viel häufiger auftreten. Zusätzlich wird eine Monte Carlo-basierte Simulation des Finanzmarktes analysiert und erweitert, um die empirischen Eigenschaften zu reproduzieren und Einblicke in deren Ursachen zu erhalten, die zum einen in der Finanzmarktmikrostruktur und andererseits in der Risikoaversion der Handelsteilnehmer zu suchen sind. Für die rechenzeitintensiven Verfahren kann mittels Parallelisierung auf einer Graphikkartenarchitektur eine deutliche Rechenzeitreduktion erreicht werden. Um das weite Spektrum an Einsatzbereichen von Graphikkarten zu aufzuzeigen, wird auch ein Standardmodell der statistischen Physik - das Ising-Modell - auf die Graphikkarte mit signifikanten Laufzeitvorteilen portiert. Teilresultate der Arbeit sind publiziert in [PGPS07, PPS08, Pre11, PVPS09b, PVPS09a, PS09, PS10a, SBF+10, BVP10, Pre10, PS10b, PSS10, SBF+11, PB10].

Automatic risk evaluation in elderly patients based on Autonomic Nervous System assessment

Dysfunction of Autonomic Nervous System (ANS) is a typical feature of chronic heart failure and other cardiovascular disease. As a simple non-invasive technology, heart rate variability (HRV) analysis provides reliable information on autonomic modulation of heart rate. The aim of this thesis was to research and develop automatic methods based on ANS assessment for evaluation of risk in cardiac patients. Several features selection and machine learning algorithms have been combined to achieve the goals. Automatic assessment of disease severity in Congestive Heart Failure (CHF) patients: a completely automatic method, based on long-term HRV was proposed in order to automatically assess the severity of CHF, achieving a sensitivity rate of 93% and a specificity rate of 64% in discriminating severe versus mild patients. Automatic identification of hypertensive patients at high risk of vascular events: a completely automatic system was proposed in order to identify hypertensive patients at higher risk to develop vascular events in the 12 months following the electrocardiographic recordings, achieving a sensitivity rate of 71% and a specificity rate of 86% in identifying high-risk subjects among hypertensive patients. Automatic identification of hypertensive patients with history of fall: it was explored whether an automatic identification of fallers among hypertensive patients based on HRV was feasible. The results obtained in this thesis could have implications both in clinical practice and in clinical research. The system has been designed and developed in order to be clinically feasible. Moreover, since 5-minute ECG recording is inexpensive, easy to assess, and non-invasive, future research will focus on the clinical applicability of the system as a screening tool in non-specialized ambulatories, in order to identify high-risk patients to be shortlisted for more complex investigations.

Family characteristics as risk factors for childhood acute lymphoblastic leukemia: a population-based case-control study

To date, few risk factors for childhood acute lymphoblastic leukemia (ALL) have been confirmed and the scientific literature is full of controversial "evidence." We examined if family characteristics, particularly maternal and paternal age and number of older siblings, were risk factors for childhood acute lymphoblastic leukemia (ALL).

Efficacy, tolerability and risk factors for virological failure of darunavir-based therapy for treatment-experienced HIV-infected patients: the Swiss HIV Cohort Study

Darunavir was designed for activity against HIV resistant to other protease inhibitors (PIs). We assessed the efficacy, tolerability and risk factors for virological failure of darunavir for treatment-experienced patients seen in clinical practice.

A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context

Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.

Corpus-Based Speech Enhancement With Uncertainty Modeling and Cepstral Smoothing

We present a new approach for corpus-based speech enhancement that significantly improves over a method published by Xiao and Nickel in 2010. Corpus-based enhancement systems do not merely filter an incoming noisy signal, but resynthesize its speech content via an inventory of pre-recorded clean signals. The goal of the procedure is to perceptually improve the sound of speech signals in background noise. The proposed new method modifies Xiao's method in four significant ways. Firstly, it employs a Gaussian mixture model (GMM) instead of a vector quantizer in the phoneme recognition front-end. Secondly, the state decoding of the recognition stage is supported with an uncertainty modeling technique. With the GMM and the uncertainty modeling it is possible to eliminate the need for noise dependent system training. Thirdly, the post-processing of the original method via sinusoidal modeling is replaced with a powerful cepstral smoothing operation. And lastly, due to the improvements of these modifications, it is possible to extend the operational bandwidth of the procedure from 4 kHz to 8 kHz. The performance of the proposed method was evaluated across different noise types and different signal-to-noise ratios. The new method was able to significantly outperform traditional methods, including the one by Xiao and Nickel, in terms of PESQ scores and other objective quality measures. Results of subjective CMOS tests over a smaller set of test samples support our claims.