Predicting hematologic toxicity in patients undergoing radioimmunotherapy with 90Y-ibritumomab tiuxetan or 131I-tositumomab.

This study aimed at identifying clinical factors for predicting hematologic toxicity after radioimmunotherapy with (90)Y-ibritumomab tiuxetan or (131)I-tositumomab in clinical practice. Hematologic data were available from 14 non-Hodgkin lymphoma patients treated with (90)Y-ibritumomab tiuxetan and 18 who received (131)I-tositumomab. The percentage baseline at nadir and 4 wk post nadir and the time to nadir were selected as the toxicity indicators for both platelets and neutrophils. Multiple linear regression analysis was performed to identify significant predictors (P < 0.05) of each indicator. For both platelets and neutrophils, pooled and separate analyses of (90)Y-ibritumomab tiuxetan and (131)I-tositumomab data yielded the time elapsed since the last chemotherapy as the only significant predictor of the percentage baseline at nadir. The extent of bone marrow involvement was not a significant factor in this study, possibly because of the short time elapsed since the last chemotherapy of the 7 patients with bone marrow involvement. Because both treatments were designed to deliver a comparable bone marrow dose, this factor also was not significant. None of the 14 factors considered was predictive of the time to nadir. The R(2) value for the model predicting percentage baseline at nadir was 0.60 for platelets and 0.40 for neutrophils. This model predicted the platelet and neutrophil toxicity grade to within ±1 for 28 and 30 of the 32 patients, respectively. For the 7 patients predicted with grade I thrombocytopenia, 6 of whom had actual grade I-II, dosing might be increased to improve treatment efficacy. The elapsed time since the last chemotherapy can be used to predict hematologic toxicity and customize the current dosing method in radioimmunotherapy.

Surfactant protein-D and exposure to bioaerosols in wastewater and garbage workers.

PURPOSE: Bioaerosols and their constituents, such as endotoxins, are capable of causing an inflammatory reaction at the level of the lung-blood barrier, which becomes more permeable. Thus, it was hypothesized that occupational exposure to bioaerosols can increase leakage of surfactant protein-D (SP-D), a lung-specific protein, into the bloodstream. METHODS: SP-D was determined by ELISA in 316 wastewater workers, 67 garbage collectors, and 395 control subjects. Exposure was assessed with four interview-based indicators and by preliminary endotoxin measurements using the Limulus amoebocyte lysate assay. Influence of exposure on serum SP-D was assessed by multiple linear regression considering smoking, glomerular function, lung diseases, obesity, and other confounders. RESULTS: Overall, mean exposure levels to endotoxins were below 100 EU/m(3). However, special tasks of wastewater workers caused higher endotoxin exposure. SP-D concentration was slightly increased in this occupational group and associated with the occurrence of splashes and contact to raw sewage. No effect was found in garbage collectors. Smoking increased serum SP-D. No clinically relevant correlation between spirometry results and SP-D concentrations appeared. CONCLUSIONS: These results support the hypothesis that inhalation of bioaerosols, even at low concentrations, has a subclinical effect on the lung-blood barrier, the permeability of which increases without associated spirometric changes.

Model Switching and Model Averaging in Time- Varying Parameter Regression Models

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.

Modelling dependence in a ratemaking procedure with multivariate Poisson regression models

When actuaries face with the problem of pricing an insurance contract that contains different types of coverage, such as a motor insurance or homeowner's insurance policy, they usually assume that types of claim are independent. However, this assumption may not be realistic: several studies have shown that there is a positive correlation between types of claim. Here we introduce different regression models in order to relax the independence assumption, including zero-inflated models to account for excess of zeros and overdispersion. These models have been largely ignored to multivariate Poisson date, mainly because of their computational di±culties. Bayesian inference based on MCMC helps to solve this problem (and also lets us derive, for several quantities of interest, posterior summaries to account for uncertainty). Finally, these models are applied to an automobile insurance claims database with three different types of claims. We analyse the consequences for pure and loaded premiums when the independence assumption is relaxed by using different multivariate Poisson regression models and their zero-inflated versions.

Mixture of bivariate Poisson regression models with an application to insurance

In a recent paper Bermúdez [2009] used bivariate Poisson regression models for ratemaking in car insurance, and included zero-inflated models to account for the excess of zeros and the overdispersion in the data set. In the present paper, we revisit this model in order to consider alternatives. We propose a 2-finite mixture of bivariate Poisson regression models to demonstrate that the overdispersion in the data requires more structure if it is to be taken into account, and that a simple zero-inflated bivariate Poisson model does not suffice. At the same time, we show that a finite mixture of bivariate Poisson regression models embraces zero-inflated bivariate Poisson regression models as a special case. Additionally, we describe a model in which the mixing proportions are dependent on covariates when modelling the way in which each individual belongs to a separate cluster. Finally, an EM algorithm is provided in order to ensure the models’ ease-of-fit. These models are applied to the same automobile insurance claims data set as used in Bermúdez [2009] and it is shown that the modelling of the data set can be improved considerably.

Effect of antenatal corticosteroids on fetal growth and gestational age at birth.

OBJECTIVE: To estimate the effect of multiple courses of antenatal corticosteroids on neonatal size, controlling for gestational age at birth and other confounders, and to determine whether there was a dose-response relationship between number of courses of antenatal corticosteroids and neonatal size. METHODS: This is a secondary analysis of the Multiple Courses of Antenatal Corticosteroids for Preterm Birth Study, a double-blind randomized controlled trial of single compared with multiple courses of antenatal corticosteroids in women at risk for preterm birth and in which fetuses administered multiple courses of antenatal corticosteroids weighed less, were shorter, and had smaller head circumferences at birth. All women (n=1,858) and children (n=2,304) enrolled in the Multiple Courses of Antenatal Corticosteroids for Preterm Birth Study were included in the current analysis. Multiple linear regression analyses were undertaken. RESULTS: Compared with placebo, neonates in the antenatal corticosteroids group were born earlier (estimated difference and confidence interval [CI]: -0.428 weeks, CI -0.10264 to -0.75336; P=.01). Controlling for gestational age at birth and confounding factors, multiple courses of antenatal corticosteroids were associated with a decrease in birth weight (-33.50 g, CI -66.27120 to -0.72880; P=.045), length (-0.339 cm, CI -0.6212 to -0.05676]; P=.019), and head circumference (-0.296 cm, -0.45672 to -0.13528; P<.001). For each additional course of antenatal corticosteroids, there was a trend toward an incremental decrease in birth weight, length, and head circumference. CONCLUSION: Fetuses exposed to multiple courses of antenatal corticosteroids were smaller at birth. The reduction in size was partially attributed to being born at an earlier gestational age but also was attributed to decreased fetal growth. Finally, a dose-response relationship exists between the number of corticosteroid courses and a decrease in fetal growth. The long-term effect of these findings is unknown. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, www.clinicaltrials.gov, NCT00187382. LEVEL OF EVIDENCE: II.

Validation of Logistic Regression Models for Landslide Susceptibility Maps

A wide range of numerical models and tools have been developed over the last decades to support the decision making process in environmental applications, ranging from physical models to a variety of statistically-based methods. In this study, a landslide susceptibility map of a part of Three Gorges Reservoir region of China was produced, employing binary logistic regression analyses. The available information includes the digital elevation model of the region, geological map and different GIS layers including land cover data obtained from satellite imagery. The landslides were observed and documented during the field studies. The validation analysis is exploited to investigate the quality of mapping.

Diagnostic accuracy of the Eurotest for dementia: a naturalistic, multicenter phase II study

BACKGROUND Available screening tests for dementia are of limited usefulness because they are influenced by the patient's culture and educational level. The Eurotest, an instrument based on the knowledge and handling of money, was designed to overcome these limitations. The objective of this study was to evaluate the diagnostic accuracy of the Eurotest in identifying dementia in customary clinical practice. METHODS A cross-sectional, multi-center, naturalistic phase II study was conducted. The Eurotest was administered to consecutive patients, older than 60 years, in general neurology clinics. The patients' condition was classified as dementia or no dementia according to DSM-IV diagnostic criteria. We calculated sensitivity (Sn), specificity (Sp) and area under the ROC curves (aROC) with 95% confidence intervals. The influence of social and educational factors on scores was evaluated with multiple linear regression analysis, and the influence of these factors on diagnostic accuracy was evaluated with logistic regression. RESULTS Sixteen neurologists recruited a total of 516 participants: 101 with dementia, 380 without dementia, and 35 who were excluded. Of the 481 participants who took the Eurotest, 38.7% were totally or functionally illiterate and 45.5% had received no formal education. Mean time needed to administer the test was 8.2+/-2.0 minutes. The best cut-off point was 20/21, with Sn = 0.91 (0.84-0.96), Sp = 0.82 (0.77-0.85), and aROC = 0.93 (0.91-0.95). Neither the scores on the Eurotest nor its diagnostic accuracy were influenced by social or educational factors. CONCLUSION This naturalistic and pragmatic study shows that the Eurotest is a rapid, simple and useful screening instrument, which is free from educational influences, and has appropriate internal and external validity.

High Prevalence of Systemic Autoimmune Diseases in Patients with Meniere's Disease

BACKGROUND. Autoimmunity appears to be associated with the pathophysiology of Meniere's disease (MD), an inner ear disorder characterized by episodes of vertigo associated with hearing loss and tinnitus. However, the prevalence of autoimmune diseases (AD) in patients with MD has not been studied in individuals with uni or bilateral sensorineural hearing loss (SNHL). METHODS AND FINDINGS. We estimated the prevalence of AD in 690 outpatients with MD with uni or bilateral SNHL from otoneurology clinics at six tertiary referral hospitals by using clinica criteria and an immune panel (lymphocyte populations, antinuclear antibodies, C3, C4 and proinflammatory cytokines TNFα, INFγ). The observed prevalence of rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) and ankylosing spondylitis (AS) was higher than expected for the general population (1.39 for RA, 0.87 for SLE and 0.70 for AS, respectively). Systemic AD were more frequently observed in patients with MD and diagnostic criteria for migraine than cases with MD and tension-type headache (p = 0.007). There were clinical differences between patients with uni or bilateral SNHL, but no differences were found in the immune profile. Multiple linear regression showed that changes in lymphocytes subpopulations were associated with hearing loss and persistence of vertigo, suggesting a role for the immune response in MD. CONCLUSIONS. Despite some limitations, MD displays an elevated prevalence of systemic AD such as RA, SLE and AS. This finding, which suggests an autoimmune background in a subset of patients with MD, has important implications for the treatment of MD.

A geoprocessing approach for studying and controlling schistosomiasis in the state of Minas Gerais, Brazil

Geographical information systems (GIS) are tools that have been recently tested for improving our understanding of the spatial distribution of disease. The objective of this paper was to further develop the GIS technology to model and control schistosomiasis using environmental, social, biological and remote-sensing variables. A final regression model (R² = 0.39) was established, after a variable selection phase, with a set of spatial variables including the presence or absence of Biomphalaria glabrata, winter enhanced vegetation index, summer minimum temperature and percentage of houses with water coming from a spring or well. A regional model was also developed by splitting the state of Minas Gerais (MG) into four regions and establishing a linear regression model for each of the four regions: 1 (R² = 0.97), 2 (R² = 0.60), 3 (R² = 0.63) and 4 (R² = 0.76). Based on these models, a schistosomiasis risk map was built for MG. In this paper, geostatistics was also used to make inferences about the presence of Biomphalaria spp. The result was a map of species and risk areas. The obtained risk map permits the association of uncertainties, which can be used to qualify the inferences and it can be thought of as an auxiliary tool for public health strategies.

Solving non-linear stochastic models by parameterizing expectations: An application to asset pricing with production

A new algorithm called the parameterized expectations approach(PEA) for solving dynamic stochastic models under rational expectationsis developed and its advantages and disadvantages are discussed. Thisalgorithm can, in principle, approximate the true equilibrium arbitrarilywell. Also, this algorithm works from the Euler equations, so that theequilibrium does not have to be cast in the form of a planner's problem.Monte--Carlo integration and the absence of grids on the state variables,cause the computation costs not to go up exponentially when the numberof state variables or the exogenous shocks in the economy increase. \\As an application we analyze an asset pricing model with endogenousproduction. We analyze its implications for time dependence of volatilityof stock returns and the term structure of interest rates. We argue thatthis model can generate hump--shaped term structures.

A test of the predictive validity of non-linear QALY models using time trade-off utilities

This paper presents a test of the predictive validity of various classes ofQALY models (i.e., linear, power and exponential models). We first estimatedTTO utilities for 43 EQ-5D chronic health states and next these states wereembedded in health profiles. The chronic TTO utilities were then used topredict the responses to TTO questions with health profiles. We find that thepower QALY model clearly outperforms linear and exponential QALY models.Optimal power coefficient is 0.65. Our results suggest that TTO-based QALYcalculations may be biased. This bias can be avoided using a power QALY model.

Clinical factors associated with trabecular bone score.

Dual-energy X-ray absorptiometry (DXA) measurement of bone mineral density (BMD) is the reference standard for diagnosing osteoporosis but does not directly reflect deterioration in bone microarchitecture. The trabecular bone score (TBS), a novel grey-level texture measurement that can be extracted from DXA images, predicts osteoporotic fractures independent of BMD. Our aim was to identify clinical factors that are associated with baseline lumbar spine TBS. In total, 29,407 women ≥50yr at the time of baseline hip and spine DXA were identified from a database containing all clinical results for the Province of Manitoba, Canada. Lumbar spine TBS was derived for each spine DXA examination blinded to clinical parameters and outcomes. Multiple linear regression and logistic regression (lowest vs highest tertile) was used to define the sensitivity of TBS to other risk factors associated with osteoporosis. Only a small component of the TBS measurement (7-11%) could be explained from BMD measurements. In multiple linear regression and logistic regression models, reduced lumbar spine TBS was associated with recent glucocorticoid use, prior major fracture, rheumatoid arthritis, chronic obstructive pulmonary disease, high alcohol intake, and higher body mass index. In contrast, recent osteoporosis therapy was associated with a significantly lower likelihood for reduced TBS. Similar findings were seen after adjustment for lumbar spine or femoral neck BMD. In conclusion, lumbar spine TBS is strongly associated with many of the risk factors that are predictive of osteoporotic fractures. Further work is needed to determine whether lumbar spine TBS can replace some of the clinical risk factors currently used in fracture risk assessment.

Adoption of the Healthy Heart Kit by Alberta family physicians

OBJECTIVE: The Healthy Heart Kit (HHK) is a risk management and patient education kit for the prevention of cardiovascular disease (CVD) and the promotion of CV health. There are currently no published data examining predictors of HHK use by physicians. The main objective of this study was to examine the association between physicians' characteristics (socio-demographic, cognitive, and behavioural) and the use of the HHK. METHODS: All registered family physicians in Alberta (n=3068) were invited to participate in the "Healthy Heart Kit" Study. Consenting physicians (n=153) received the Kit and were requested to use it for two months. At the end of this period, a questionnaire collected data on the frequency of Kit use by physicians, as well as socio-demographic, cognitive, and behavioural variables pertaining to the physicians. RESULTS: The questionnaire was returned by 115 physicians (follow-up rate = 75%). On a scale ranging from 0 to 100, the mean score of Kit use was 61 [SD=26]. A multiple linear regression showed that "agreement with the Kit" and the degree of "confidence in using the Kit" was strongly associated with Kit use, explaining 46% of the variability for Kit use. Time since graduation was inversely associated with Kit use, and a trend was observed for smaller practices to be associated with lower use. CONCLUSION: Given these findings, future research and practice should explore innovative strategies to gain initial agreement among physicians to employ such clinical tools. Participation of older physicians and solo-practitioners in this process should be emphasized.

Genome-wide profiling of blood pressure in adults and children.

Hypertension is an important determinant of cardiovascular morbidity and mortality and has a substantial heritability, which is likely of polygenic origin. The aim of this study was to assess to what extent multiple common genetic variants contribute to blood pressure regulation in both adults and children and to assess overlap in variants between different age groups, using genome-wide profiling. Single nucleotide polymorphism sets were defined based on a meta-analysis of genome-wide association studies on systolic blood pressure and diastolic blood pressure performed by the Cohort for Heart and Aging Research in Genome Epidemiology (n=29 136), using different P value thresholds for selecting single nucleotide polymorphisms. Subsequently, genetic risk scores for systolic blood pressure and diastolic blood pressure were calculated in an independent adult population (n=2072) and a child population (n=1034). The explained variance of the genetic risk scores was evaluated using linear regression models, including sex, age, and body mass index. Genetic risk scores, including also many nongenome-wide significant single nucleotide polymorphisms, explained more of the variance than scores based only on very significant single nucleotide polymorphisms in adults and children. Genetic risk scores significantly explained ≤1.2% (P=9.6*10(-8)) of the variance in adult systolic blood pressure and 0.8% (P=0.004) in children. For diastolic blood pressure, the variance explained was similar in adults and children (1.7% [P=8.9*10(-10)] and 1.4% [P=3.3*10(-5)], respectively). These findings suggest the presence of many genetic loci with small effects on blood pressure regulation both in adults and children, indicating also a (partly) common polygenic regulation of blood pressure throughout different periods of life.