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.

Trabecular Bone Score Helps Classifying Women At Riskof Fracture Prospectively In The Ofely Study

Objectives: Trabecular Bone Score (TBS, Med-Imaps, France) is an index of bone microarchitecture calculated from antero-posterior spine DXA scan and reported to be associated with fracture in prior case-control studies and in a large prospective study with the Prodigy DXA device. Our aim was to assess the ability of TBS to predict incident fracture and improve the classification of fracture prospectively in the OFELY study.Materials/Methods: TBS was assessed in 564 postmenopausal women (66±8 years old) from the OFELY cohort, who had a spine DXA scan (QDR 4500A, Hologic, USA) between year 2000 and 2001. During a mean follow up of 7.8±1.3 years, 94 women sustained a fragility fracture.Results: At the time of baseline DXA scan, women with incident fracture were significantly older (70±9 vs. 65± 8 years), had a lower spine BMD (T-score: −1.9±1.2 vs. −1.3±1.3, p<0.001) and spine TBS (−3.1%, p<0.001) than women without incident fracture. After adjustment for age, BMI and the presence of prevalent fracture, the magnitude of fracture prediction was similar for spine BMD (OR=1.42 [1.11;1.82] per SD decrease [95% CI]) and TBS (OR=1.34 [1.04;1.74]) but the combination of TBS and spine BMD did not improve fracture prediction. Spine BMD and TBS were both correlated with age (respectively r=−0.17 and −0.49, p<0.001) and correlated together with 39% of TBS explained by spine BMD (r=0.63, p<0.001). When using the WHO classification, 38% of the fractures occurred in osteoporotic (fracture rate=29%), 47% in osteopenic (fracture rate=16%) and 15% in women with T-score >−1 (fracture rate=9%). By classifying our population in tertiles of TBS, we found that 47% of the fractures occurred in the lowest tertile of TBS (fracture rate=23%) and 39% of the fracture that occurred in osteopenic women were in the lowest tertile of TBS.Conclusions: Spine BMD and TBS predicted fractures equally well. The addition of TBS to spine BMD added only limited information on fracture risk prediction in our cohort when considering the all range of BMD. Nevertheless combining the osteopenic T-score and the lowest TBS helped defining a subset of osteopenic women at higher risk of fracture.Disclosure of Interest: None declared.

Industrial Location and Space: New Insights

This paper tries to resolve some of the main shortcomings in the empirical literature of location decisions for new plants, i.e. spatial effects and overdispersion. Spatial effects are omnipresent, being a source of overdispersion in the data as well as a factor shaping the functional relationship between the variables that explain a firm’s location decisions. Using Count Data models, empirical researchers have dealt with overdispersion and excess zeros by developments of the Poisson regression model. This study aims to take this a step further, by adopting Bayesian methods and models in order to tackle the excess of zeros, spatial and non-spatial overdispersion and spatial dependence simultaneously. Data for Catalonia is used and location determinants are analysed to that end. The results show that spatial effects are determinant. Additionally, overdispersion is descomposed into an unstructured iid effect and a spatially structured effect. Keywords: Bayesian Analysis, Spatial Models, Firm Location. JEL Classification: C11, C21, R30.

Modelos de viento aplicados a la mejora en la predicción de incendios forestales

La predicción de incendios forestales es uno de los grandes retos de la comunidad científica debido al impacto medioambiental, humano y económico que tienen en la sociedad. El comportamiento de este fenómeno es difícil de modelar debido a la gran cantidad de variables que intervienen y la dificultad que implica su correcta medición. Los simuladores de fuego son herramientas muy útiles pero, actualmente, los resultados que obtenemos tienen un alto grado de imprecisión. Desde nuestro grupo se ha trabajado en la predicción en dos etapas, donde antes de realizar cualquier predicción, se incorpora una etapa de ajuste de los parámetros de entrada para obtener mejores predicciones. Pese a la mejora que supone este nuevo paradigma de predicción, las simulaciones sobre incendios reales tienen un alto grado de error por el efecto de las condiciones meteorológicas que, usualmente, varían de manera notable durante el transcurso de la simulación. Uno de los factores más determinantes en el comportamiento de un incendio, junto con las características del terreno, es el viento. Los modelos de predicción son extremadamente sensibles al cambio en los componentes de dirección y velocidad del viento por lo que cualquier mejora que podamos introducir para mejorar la calidad de estas componentes influye directamente en la calidad de la predicción. Nuestro sistema de predicción utiliza la dirección y velocidad del viento de forma global en todo el terreno, y lo que proponemos con este trabajo es introducir un modelo de vientos que nos permita generar vientos locales en todas las celdas en las que se divide el terreno. Este viento local dependerá del viento general y de las características del terreno de dichas celdas. Consideramos que la utilización de un viento general no es suficiente para realizar una buena predicción del comportamiento de un incendio y hemos comprobado que la inclusión de un simulador de campo de vientos en nuestro sistema puede llegar a mejorar nuestras predicciones considerablemente. Los resultados obtenidos en los experimentos sintéticos que hemos realizado nos hacen ser optimistas, puesto que consideramos que la inclusión de componentes de viento locales permitirá mejorar nuestras predicciones en incendios reales.

Effect of Temperature on the Development and Survival of the Argentine Ant, Linepithema humile

The influence of temperature on the developmental times and survival of insects can largely determine their distribution. For invasive species, like the Argentine ant, Linepithema humile Mayr (Hymenoptera: Formicidae), these data are essential for predicting their potential range based on mechanistic models. In the case of this species, such data are too scarce and incomplete to make accurate predictions based on its physiological needs. This research provides comprehensive new data about brood survival and developmental times at a wide range of temperatures under laboratory conditions. Temperature affected both the complete brood development from egg to adult worker and each of the immature stages separately. The higher the temperature, the shorter the development times. Brood survival from egg to adult was low, with the maximum survival rate being only 16% at 26º C. Temperature also affected survival of each of the immature stages differently: eggs were negatively affected by high temperatures, while larvae were negatively affected by low temperatures, and the survival of pupae was apparentlyindependent of environmental temperature. At 32º C no eggs survived, while at 18º C less than 2% of the eggs hatched into larva. The data from the present study are essential for developing prediction models about the distribution range of this tramp species based on its physiological needs in relation to temperature

Relevant gender differences in epidemiological profile, exposure to first antiretroviral regimen and survival in the Spanish AIDS Research Network Cohort.

BACKGROUND The possible differences in the disease spectrum and prognosis of HIV infection in women and men is a major point of concern. Women are under-represented in randomized clinical trials and in some cohorts. Discordant results have often been obtained depending on the setting. METHODS We assessed gender differences in clinical and epidemiological features, antiretroviral treatment (ART) exposure and survival in two multicentre cohorts of HIV-positive subjects in Spain: CoRIS-MD and CoRIS. Competing risk regression models were used to assess gender effect on time to start ART and time to first ART change, and a Cox regression model to estimate gender effect on time to death. RESULTS Between January 1996 and December 2008, 1,953 women and 6,072 men naive to ART at study entry were included. The trend analysis over time showed the percentage of women in the younger (<20 years) and older (>50 years) strata increased significantly (P<0.001) from 0.5% and 1.8% in 1996 to 4.9% and 4.2% in 2008, respectively. By competing risk analysis women started ART earlier than men (adjusted subhazard ratio [ASHR] 1.21, 95% CI 1.11, 1.31) in CoRIS cohort, while in CoRIS-MD none of these differences were observed. In both cohorts women showed a shorter time to the first ART change (ASHR 1.10, 95% CI 1.01, 1.19). Pregnancy and patient's/physician's decisions as reasons for changing were more frequent in women than in men in CoRIS. In the Cox regression model, gender was not associated with differences in survival. CONCLUSIONS In two large cohorts in Spain, we observed relevant gender differences in epidemiological characteristics and antiretroviral exposure outcomes, while survival differences were not attributable to gender.

Inferring ultraviolet anatomical exposure patterns while distinguishing the relative contribution of radiation components

Exposure to solar ultraviolet (UV) radiation is the main causative factor for skin cancer. UV exposure depends on environmental and individual factors, but individual exposure data remain scarce. While ground UV irradiance is monitored via different techniques, it is difficult to translate such observations into human UV exposure or dose because of confounding factors. A multi-disciplinary collaboration developed a model predicting the dose and distribution of UV exposure on the basis of ground irradiation and morphological data. Standard 3D computer graphics techniques were adapted to develop a simulation tool that estimates solar exposure of a virtual manikin depicted as a triangle mesh surface. The amount of solar energy received by various body locations is computed for direct, diffuse and reflected radiation separately. Dosimetric measurements obtained in field conditions were used to assess the model performance. The model predicted exposure to solar UV adequately with a symmetric mean absolute percentage error of 13% and half of the predictions within 17% range of the measurements. Using this tool, solar UV exposure patterns were investigated with respect to the relative contribution of the direct, diffuse and reflected radiation. Exposure doses for various body parts and exposure scenarios of a standing individual were assessed using erythemally-weighted UV ground irradiance data measured in 2009 at Payerne, Switzerland as input. For most anatomical sites, mean daily doses were high (typically 6.2-14.6 Standard Erythemal Dose, SED) and exceeded recommended exposure values. Direct exposure was important during specific periods (e. g. midday during summer), but contributed moderately to the annual dose, ranging from 15 to 24% for vertical and horizontal body parts, respectively. Diffuse irradiation explained about 80% of the cumulative annual exposure dose.

Effect of Temperature on the Development and Survival of the Argentine Ant, Linepithema humile

The influence of temperature on the developmental times and survival of insects can largely determine their distribution. For invasive species, like the Argentine ant, Linepithema humile Mayr (Hymenoptera: Formicidae), these data are essential for predicting their potential range based on mechanistic models. In the case of this species, such data are too scarce and incomplete to make accurate predictions based on its physiological needs. This research provides comprehensive new data about brood survival and developmental times at a wide range of temperatures under laboratory conditions. Temperature affected both the complete brood development from egg to adult worker and each of the immature stages separately. The higher the temperature, the shorter the development times. Brood survival from egg to adult was low, with the maximum survival rate being only 16% at 26° C. Temperature also affected survival of each of the immature stages differently: eggs were negatively affected by high temperatures, while larvae were negatively affected by low temperatures, and the survival of pupae was apparently independent of environmental temperature. At 32° C no eggs survived, while at 18° C less than 2% of the eggs hatched into larva. The data from the present study are essential for developing prediction models about the distribution range of this tramp species based on its physiological needs in relation to temperature

Population PK-guided simulation study in children exposed to drugs in human milk

Risks of significant infant drug exposure through human milk arepoorly defined due to lack of large-scale PK data. We propose to useBayesian approach based on population PK (popPK)-guided modelingand simulation for risk prediction. As a proof-of-principle study, weexploited fluoxetine milk concentration data from 25 women. popPKparameters including milk-to-plasma ratio (MP ratio) were estimatedfrom the best model. The dose of fluoxetine the breastfed infant wouldreceive through mother's milk, and infant plasma concentrations wereestimated from 1000 simulated mother-infant pairs, using randomassignment of feeding times and milk volume. A conservative estimateof CYP2D6 activity of 20% of the allometrically-adjusted adult valuewas assumed. Derived model parameters, including MP ratio were consistentwith those reported in the literature. Visual predictive check andother model diagnostics showed no signs of model misspecifications.The model simulation predicted that infant exposure levels to fluoxetinevia mother's milk were below 10% of weight-adjusted maternal therapeuticdoses in >99% of simulated infants. Predicted median ratio ofinfant-mother serum levels at steady state was 0.093 (range 0.033-0.31),consistent with literature reported values (mean=0.07; range 0-0.59).Predicted incidence of relatively high infant-mother ratio (>0.2) ofsteady-state serum fluoxetine concentrations was <1.3%. Overall, ourpredictions are consistent with clinical observations. Our approach maybe valid for other drugs, allowing in silico prediction of infant drugexposure risks through human milk. We will discuss application of thisapproach to another drug used in lactating women.

Influence of IFNL3/4 Polymorphisms on the Incidence of Cytomegalovirus Infection After Solid-Organ Transplantation.

BACKGROUND: Polymorphisms in IFNL3 and IFNL4, the genes encoding interferon λ3 and interferon λ4, respectively, have been associated with reduced hepatitis C virus clearance. We explored the role of such polymorphisms on the incidence of cytomegalovirus (CMV) infection in solid-organ transplant recipients. METHODS: White patients participating in the Swiss Transplant Cohort Study in 2008-2011 were included. A novel functional TT/-G polymorphism (rs368234815) in the CpG region upstream of IFNL3 was investigated. RESULTS: A total of 840 solid-organ transplant recipients at risk for CMV infection were included, among whom 373 (44%) received antiviral prophylaxis. The 12-month cumulative incidence of CMV replication and disease were 0.44 and 0.08 cases, respectively. Patient homozygous for the minor rs368234815 allele (-G/-G) tended to have a higher cumulative incidence of CMV replication (subdistribution hazard ratio [SHR], 1.30 [95% confidence interval {CI}, .97-1.74]; P = .07), compared with other patients (TT/TT or TT/-G). The association was significant among patients followed by a preemptive approach (SHR, 1.46 [95% CI, 1.01-2.12]; P = .047), especially in patients receiving an organ from a seropositive donor (SHR, 1.92 [95% CI, 1.30-2.85]; P = .001), but not among those who received antiviral prophylaxis (SHR, 1.13 [95% CI, .70-1.83]; P = .6). These associations remained significant in multivariate competing risk regression models. CONCLUSIONS: Polymorphisms in the IFNL3/4 region influence susceptibility to CMV replication in solid-organ transplant recipients, particularly in patients not receiving antiviral prophylaxis.

Predicting Bacteremia in Children With Cancer and Fever in Chemotherapy-induced Neutropenia: Results of the Prospective Multicenter SPOG 2003 FN Study.

STUDY AIM:: To develop a score predicting the risk of bacteremia in cancer patients with fever and neutropenia (FN), and to evaluate its performance. METHODS:: Pediatric patients with cancer presenting with FN induced by nonmyeloablative chemotherapy were observed in a prospective multicenter study. A score predicting the risk of bacteremia was developed from a multivariate mixed logistic regression model. Its cross-validated predictive performance was compared with that of published risk prediction rules. RESULTS:: Bacteremia was reported in 67 (16%) of 423 FN episodes. In 34 episodes (8%), bacteremia became known only after reassessment after 8 to 24 hours of inpatient management. Predicting bacteremia at reassessment was better than prediction at presentation with FN. A differential leukocyte count did not increase the predictive performance. The reassessment score predicting future bacteremia in 390 episodes without known bacteremia used the following 4 variables: hemoglobin ≥90 g/L at presentation (weight 3), platelet count <50 G/L (3), shaking chills (5), and other need for inpatient treatment or observation according to the treating physician (3). Applying a threshold ≥3, the score-simplified into a low-risk checklist-predicted bacteremia with 100% sensitivity, with 54 episodes (13%) classified as low-risk, and a specificity of 15%. CONCLUSIONS:: This reassessment score, simplified into a low-risk checklist of 4 routinely accessible characteristics, identifies pediatric patients with FN at risk for bacteremia. It has the potential to contribute to the reduction of use of antimicrobials in, and to shorten the length of hospital stays of pediatric patients with cancer and FN.

Making Predictions with Textual Contents

Forecasting real-world quantities with basis on information from textual descriptions has recently attracted significant interest as a research problem, although previous studies have focused on applications involving only the English language. This document presents an experimental study on the subject of making predictions with textual contents written in Portuguese, using documents from three distinct domains. I specifically report on experiments using different types of regression models, using state-of-the-art feature weighting schemes, and using features derived from cluster-based word representations. Through controlled experiments, I have shown that prediction models using the textual information achieve better results than simple baselines such as taking the average value over the training data, and that richer document representations (i.e., using Brown clusters and the Delta- TF-IDF feature weighting scheme) result in slight performance improvements.

Importancia de la información contable para el análisis y predicción de la viabilidad de las explotaciones agrícolas

Spanish and Western agriculture show a continuous decrease in the numberof farms. One of the main factors for this trend is the economicnon-viability of many of the existing farms. In addition, interrelationshipof agriculture with other industries is growing. Thus, policymakers, banks,creditors and other stakeholders are interested in predicting farm viability.The aim of this paper is to provide empirical evidence that the use ofaccounting-based information could significantly improve understandingand prediction of various degrees of farm viability. Two multinomial logitmodels were applied to a sample of farms of Catalonia, Spain. One modelincluded non-accounting-based variables, while the other also consideredaccounting-based variables. It was found that accounting added significantinformation to predict various degrees of farm viability. This findingreveals, both the need of encouraging the little existing use of accountingby farms and to develop appropriate accounting standards for agriculture.

Estimates of patient costs related with population morbidity: Can indirect costs affect the results?

A number of health economics works require patient cost estimates as a basic information input.However the accuracy of cost estimates remains in general unspecified. We propose to investigate howthe allocation of indirect costs or overheads can affect the estimation of patient costs in order to allow forimprovements in the analysis of patient costs estimates. Instead of focusing on the costing method, thispaper proposes to highlight changes in variance explained observed when a methodology is chosen. Wecompare three overhead allocation methods for a specific Spanish population adjusted using the ClinicalRisk Groups (CRG), and we obtain different series of full-cost group estimates. As a result, there aresignificant gains in the proportion of the variance explained, depending upon the methodology used.Furthermore, we find that the global amount of variation explained by risk adjustment models dependsmainly on direct costs and is independent of the level of aggregation used in the classification system.