Association between tooth loss and obesity in Brazilian adults: a population-based study

OBJECTIVE: To examine the association between tooth loss and general and central obesity among adults. METHODS: Population-based cross-sectional study with 1,720 adults aged 20 to 59 years from Florianópolis, Southern Brazil. Home interviews were performed and anthropometric measures were taken. Information on sociodemographic data, self-reported diabetes, self-reported number of teeth, central obesity (waist circumference [WC] > 88 cm in women and > 102 cm in men) and general obesity (body mass index [BMI] ≥ 30 kg/m²) was collected. We used multivariable Poisson regression models to assess the association between general and central obesity and tooth loss after controlling for confounders. We also performed simple and multiple linear regressions by using BMI and WC as continuous variables. Interaction between age and tooth loss was also assessed. RESULTS: The mean BMI was 25.9 kg/m² (95%CI 25.6;26.2) in men and 25.4 kg/m2 (95%CI 25.0;25.7) in women. The mean WC was 79.3 cm (95%CI 78.4;80.1) in men and 88.4 cm (95%CI 87.6;89.2) in women. A positive association was found between the presence of less than 10 teeth in at least one arch and increased mean BMI and WC after adjusting for education level, self-reported diabetes, gender and monthly per capita income. However, this association was lost when the variable age was included in the model. The prevalence of general obesity was 50% higher in those with less than 10 teeth in at least one arch when compared with those with 10 or more teeth in both arches after adjusting for education level, self-reported diabetes and monthly per capita family income. However, the statistical significance was lost after controlling for age. CONCLUSIONS: Obesity was associated with number of teeth, though it depended on the participants' age groups.

Weather parameters and nosocomial bloodstream infection: a case-referent study

OBJECTIVE To evaluate if temperature and humidity influenced the etiology of bloodstream infections in a hospital from 2005 to 2010.METHODS The study had a case-referent design. Individual cases of bloodstream infections caused by specific groups or pathogens were compared with several references. In the first analysis, average temperature and humidity values for the seven days preceding collection of blood cultures were compared with an overall “seven-days moving average” for the study period. The second analysis included only patients with bloodstream infections. Several logistic regression models were used to compare different pathogens and groups with respect to the immediate weather parameters, adjusting for demographics, time, and unit of admission.RESULTS Higher temperatures and humidity were related to the recovery of bacteria as a whole (versus fungi) and of gram-negative bacilli. In the multivariable models, temperature was positively associated with the recovery of gram-negative bacilli (OR = 1.14; 95%CI 1.10;1.19) or Acinetobacter baumannii (OR = 1.26; 95%CI 1.16;1.37), even after adjustment for demographic and admission data. An inverse association was identified for humidity.CONCLUSIONS The study documented the impact of temperature and humidity on the incidence and etiology of bloodstream infections. The results correspond with those from ecological studies, indicating a higher incidence of gram-negative bacilli during warm seasons. These findings should guide policies directed at preventing and controlling healthcare-associated infections.

Gender differences in the association between tooth loss and obesity among older adults in Brazil

OBJECTIVE To analyze if differences according to gender exists in the association between tooth loss and obesity among older adults.METHODS We analyzed data on 1,704 older adults (60 years and over) from the baseline of a prospective cohort study conducted in Florianopolis, SC, Southern Brazil. Multivariable logistic regression models were used to assess the association between tooth loss and general and central obesity after adjustment for confounders (age, gender, skin color, educational attainment, income, smoking, physical activity, use of dentures, hypertension, and diabetes). Linear regressions were also assessed with body mass index and waist circumference as continuous outcomes. Interaction between gender and tooth loss was further assessed.RESULTS Overall mean body mass index was 28.0 kg/m2. Mean waist circumference was 96.8 cm for males and 92.6 cm for females. Increasing tooth loss was positively associated with increased body mass index and waist circumference after adjustment for confounders. Edentates had 1.4 (95%CI 1.1;1.9) times higher odds of being centrally obese than individuals with a higher number of teeth; however, the association lost significance after adjustment for confounders. In comparison with edentate males, edentate females presented a twofold higher adjusted prevalence of general and central obesity. In the joint effects model, edentate females had a 3.8 (95%CI 2.2;6.6) times higher odds to be centrally obese in comparison with males with more than 10 teeth present in both the arches. Similarly, females with less than 10 teeth in at least one arch had a 2.7 (95%CI 1.6;4.4) times higher odds ratio of having central obesity in comparison with males with more than 10 teeth present in both the arches.CONCLUSIONS Central obesity was more prevalent than general obesity among the older adults. We did not observe any association between general obesity and tooth loss. The association between central obesity and tooth loss depends on gender – females with tooth loss had greater probability of being obese.

Health self-assessment by hemodialysis patients in the Brazilian Unified Health System

ABSTRACT OBJECTIVE To examine whether the level of complexity of the services structure and sociodemographic and clinical characteristics of patients in hemodialysis are associated with the prevalence of poor health self-assessment. METHODS In this cross-sectional study, we evaluated 1,621 patients with chronic terminal kidney disease on hemodialysis accompanied in 81 dialysis services in the Brazilian Unified Health System in 2007. Sampling was performed by conglomerate in two stages and a structured questionnaire was applied to participants. Multilevel multiple logistic regression was used for data analysis. RESULTS The prevalence of poor health self-assessment was of 54.5%, and in multivariable analysis it was associated with the following variables: increasing age (OR = 1.02; 95%CI 1.01–1.02), separated or divorced marital status (OR = 0.62; 95%CI 0.34–0.88), having 12 years or more of study (OR = 0.51; 95%CI 0.37–0.71), spending more than 60 minutes in commuting between home and the dialysis service (OR = 1.80; 95%CI 1.29–2.51), having three or more self-referred diseases (OR = 2.20; 95%CI 1.33–3.62), and reporting some (OR = 2.17; 95%CI 1.66–2.84) or a lot of (OR = 2.74; 95%CI 2.04–3.68) trouble falling asleep. Individuals in treatment in dialysis services with the highest level of complexity in the structure presented less chance of performing a self-assessment of their health as bad (OR = 0.59; 95%CI 0.42–0.84). CONCLUSIONS We showed poor health self-assessment is associated with age, years of formal education, marital status, home commuting time to the dialysis service, number of self-referred diseases, report of trouble sleeping, and also with the level of complexity of the structure of health services. Acknowledging these factors can contribute to the development of strategies to improve the health of patients in hemodialysis in the Brazilian Unified Health System.

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

Predicting Xerostomia induced by IMRT treatments: A logistic regression approach

Radiotherapy is one of the main treatments used against cancer. Radiotherapy uses radiation to destroy cancerous cells trying, at the same time, to minimize the damages in healthy tissues. The planning of a radiotherapy treatment is patient dependent, resulting in a lengthy trial and error procedure until a treatment complying as most as possible with the medical prescription is found. Intensity Modulated Radiation Therapy (IMRT) is one technique of radiation treatment that allows the achievement of a high degree of conformity between the area to be treated and the dose absorbed by healthy tissues. Nevertheless, it is still not possible to eliminate completely the potential treatments’ side-effects. In this retrospective study we use the clinical data from patients with head-and-neck cancer treated at the Portuguese Institute of Oncology of Coimbra and explore the possibility of classifying new and untreated patients according to the probability of xerostomia 12 months after the beginning of IMRT treatments by using a logistic regression approach. The results obtained show that the classifier presents a high discriminative ability in predicting the binary response “at risk for xerostomia at 12 months”

Double coverage and demand for health care: Evidence from quantile regression

An individual experiences double coverage when he bene ts from more than one health insurance plan at the same time. This paper examines the impact of such supplementary insurance on the demand for health care services. Its novelty is that within the context of count data modelling and without imposing restrictive parametric assumptions, the analysis is carried out for di¤erent points of the conditional distribution, not only for its mean location. Results indicate that moral hazard is present across the whole outcome distribution for both public and private second layers of health insurance coverage but with greater magnitude in the latter group. By looking at di¤erent points we unveil that stronger double coverage e¤ects are smaller for high levels of usage. We use data for Portugal, taking advantage of particular features of the public and private protection schemes on top of the statutory National Health Service. By exploring the last Portuguese Health Survey, we were able to evaluate their impacts on the consumption of doctor visi

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Induced Labor– Is There an Increased Risk of Perinatal Infection?

Objectives: To assess induced labor-associated perinatal infection risk at Hospital D.Estefânia from January to June of 2010 at Hospital de D. Estefânia’s delivery rooms, reviewing the indications for inducing labor as well as the techniques used. Material and Methods: Performing an historical prospective study searching the clinical processes as well as the mother and newborn’s computer database from January to June of 2010. An exposed and an unexposed group were created; the first group comprises pregnant women and their newborns whose labor was induced. The unexposed group is constituted by newborns and pregnant women whose labor was spontaneous. Labor induction was performed using intra-vaginal prostaglandins in women who didn’t start it spontaneously; perinatal infection was defined either clinically or using blood tests. The gestational age was ≥ 37 weeks for both groups. 19 variables were studied for both groups. Results: A total of 190 mother-newborn pairs were included: 55 in the exposed group and 135 in the unexposed group. 3 cases of perinatal infection were reported, two in the exposed group and one in the unexposed group. Preliminary data resulted in a perinatal infection rate of 3.6% in the exposed group and 0.7% in the unexposed group; preliminary data suggest that the risk of perinatal infection may be increased in up to 5-fold when labor is inducted. Conclusions: A larger series of patients and a multivariable analysis using logistic regression are both necessary in order to perform a more thorough assessment of labor induction’s role in perinatal infection risk. One must also try to distinguish labor inducing- and clinical practicesrelated factors.

Analyzing Multiple Outcomes: Is it Really Worth the use of Multivariate Linear Regression?

In health related research it is common to have multiple outcomes of interest in a single study. These outcomes are often analysed separately, ignoring the correlation between them. One would expect that a multivariate approach would be a more efficient alternative to individual analyses of each outcome. Surprisingly, this is not always the case. In this article we discuss different settings of linear models and compare the multivariate and univariate approaches. We show that for linear regression models, the estimates of the regression parameters associated with covariates that are shared across the outcomes are the same for the multivariate and univariate models while for outcome-specific covariates the multivariate model performs better in terms of efficiency.

ASSOCIATION BETWEEN SMOKING, CRACK COCAINE ABUSE AND THE DISCONTINUATION OF COMBINATION ANTIRETROVIRAL THERAPY IN RECIFE, PERNAMBUCO, BRAZIL

Despite the effectiveness of combination antiretroviral therapy in the treatment of people living with HIV/AIDS (PLWHA), nonadherence to medication has become a major threat to its effectiveness. This study aimed to estimate the prevalence of self-reported irregular use of antiretroviral therapy and the factors associated with such an irregularity in PLWHA. A cross-sectional study of PLWHA who attended two referral centers in the city of Recife, in Northeastern Brazil, between June 2007 and October 2009 was carried out. The study analyzed socioeconomic factors, social service support and personal habits associated with nonadherence to antiretroviral therapy, adjusted by multivariable logistic regression analysis. The prevalence of PLWHA who reported irregular use of combination antiretroviral therapy (cART) was 25.7%. In the final multivariate model, the irregular use of cART was associated with the following variables: being aged less than 40 years (OR = 1.66, 95%-CI: 1.29-2.13), current smokers (OR = 1.76, 95%-CI: 1.31-2.37) or former smokers (OR = 1.43, 95%-CI: 1.05-1.95), and crack cocaine users (OR = 2.79, 95%-CI: 1.24-6.32). Special measures should be directed towards each of the following groups: individuals aged less than 40 years, smokers, former smokers and crack cocaine users. Measures for giving up smoking and crack cocaine should be incorporated into HIV-control programs in order to promote greater adherence to antiretroviral drugs and thus improve the quality of life and prolong life expectancy.

Análise espacial dos resultados eleitorais em Portugal – Aplicação do Geographically Weighted Regression

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação.

Análise espacial dos resultados eleitorais em Portugal – Aplicação do Geographically Weighted Regression

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação.

Adequate Seal and No Endoleak on the First Postoperative Computed Tomography Angiography as Criteria for No Additional Imaging Up to 5 Years after Endovascular Aneurysm Repair

OBJECTIVE: Intensive image surveillance after endovascular aneurysm repair is generally recommended due to continued risk of complications. However, patients at lower risk may not benefit from this strategy. We evaluated the predictive value of the first postoperative computed tomography angiography (CTA) characteristics for aneurysm-related adverse events as a means of patient selection for risk-adapted surveillance. METHODS: All patients treated with the Low-Permeability Excluder Endoprosthesis (W. L. Gore & Assoc, Flagstaff, Ariz) at a tertiary institution from 2004 to 2011 were included. First postoperative CTAs were analyzed for the presence of endoleaks, endograft kinking, distance from the lowermost renal artery to the start of the endograft, and for proximal and distal sealing length using center lumen line reconstructions. The primary end point was freedom from aneurysm-related adverse events. Multivariable Cox regression was used to test postoperative CTA characteristics as independent risk factors, which were subsequently used as selection criteria for low-risk and high-risk groups. Estimates for freedom from adverse events were obtained using Kaplan-Meier survival curves. RESULTS: Included were 131 patients. The median follow-up was 4.1 years (interquartile range, 2.1-6.1). During this period, 30 patients (23%) sustained aneurysm-related adverse events. Seal length <10 mm and presence of endoleak were significant risk factors for this end point. Patients were subsequently categorized as low-risk (proximal and distal seal length ≥10 mm and no endoleak, n = 62) or high-risk (seal length <10 mm or presence of endoleak, or both; n = 69). During follow-up, four low-risk patients (3%) and 26 high-risk patients (19%) sustained events (P < .001). Four secondary interventions were required in three low-risk patients, and 31 secondary interventions in 23 high-risk patients. Sac growth was observed in two low-risk patients and in 15 high-risk patients. The 5-year estimates for freedom from aneurysm-related adverse events were 98% for the low-risk group and 52% for the high-risk group. For each diagnosis, 81.7 image examinations were necessary in the low-risk group and 8.2 in the high-risk group. CONCLUSIONS: Our results suggest that the first postoperative CTA provides important information for risk stratification after endovascular aneurysm repair when the Excluder endoprosthesis is used. In patients with adequate seal and no endoleaks, the risk of aneurysm-related adverse events was significantly reduced, resulting in a large number of unnecessary image examinations. Adjusting the imaging protocol beyond 30 days and up to 5 years, based on individual patients' risk, may result in a more efficient and rational postoperative surveillance.