One-year clinical and economic consequences of oral atypical antipsychotics in the treatment of schizophrenia

OBJECTIVE

To assess the clinical and economic consequences of oral atypical antipsychotic treatment (aripiprazole, olanzapine, paliperidone ER, quetiapine, risperidone, and ziprasidone) in schizophrenia over one-year from a US healthcare system perspective.

METHODS

The decision model captured rates of discontinuation, symptom response, frequency and duration of relapse, adverse events (extrapyramidal symptoms and weight gain), resource utilization, and unit costs. Published randomized, double-blind, placebo-controlled clinical trial data were used to obtain response rates for comparators. Published clinical trial data from long-term effectiveness trials reflective of typical clinical settings were used for time on therapy, rates of discontinuation, likelihood of switching, relapse rates, and adverse event rates. Drug costs were based on Wholesale Acquisition Cost weighted by Wolters Kluwer Retail and First Databank Pricing drug utilization data. PharMetrics Patient-Centric database was utilized for length of stay, frequency of relapse, and unit cost of healthcare resource data. A clinical expert panel provided resource-use information not available in published literature or healthcare databases. To test the robustness of the findings, sensitivity analyses were performed using plausible ranges of key model input parameters.

RESULTS

The model estimated that, over 1 year, clinical outcomes of patients administered oral atypical antipsychotics would not vary considerably. This is partly due to differences 'washing out' because of frequent switching and discontinuation of medication. Economic outcomes did vary among pharmacotherapies: paliperidone ER was associated with cost savings in direct medical costs per patient per year compared to risperidone (cost savings using paliperidone ER vs. risperidone: $793), quetiapine ($1191), olanzapine ($1259), ziprasidone ($2159), and aripiprazole ($2204)). Limitations of this analysis include the absence of direct head-to-head long-term comparative data for antipsychotics. However, the results of the decision analysis held true when tested through a multitude of sensitivity analyses.

CONCLUSION

This modeling study showed that paliperidone ER had the most favorable clinical and economic outcomes compared to other oral atypical antipsychotics for patients with schizophrenia. The analysis supports the notion that frequent discontinuation of medication is a problem with all oral antipsychotic treatments for schizophrenia.

Local and distant residence times of contaminants in multi-compartment models. Part II: application to assessing environmental mobility and long-range atmospheric transport

In Part I, the concepts of inherent, local and distant residence times (DRTs) were reviewed as metrics of the extent to which chemical discharges or emissions in one region or box are transported to distant regions. In this second part, the concepts are applied to geographically relevant systems to illustrate their applicability to the assessment of chemicals for long-range transport potential (LRTP). It is shown that the relative ranking of chemicals as characterized by the DRT method is similar to that of the characteristic travel distance concept. A DRT source-receptor matrix is developed that can express the chemical-specific potential of source regions to contaminate a specific receptor region of concern such as the Arctic. The matrix can be modified to identify for a specific source region the likely destinations of emissions as well as to assess the relative vulnerability of regions in the global environment to contaminants of concern.

Learning-by-catching: uncertain invasive-species populations and the value of information

This paper develops a model of invasive species control when the species' population size is unknown. In the face of an uncertain population size, a resource manager's species-control efforts provide two potential benefits: (1) a direct benefit of possibly reducing the population of invasive species, and (2) an indirect benefit of information acquisition (due to learning about the population size, which reduces uncertainty). We provide a methodology that takes into account both of these benefits, and show how optimal management decisions are altered in the presence of the indirect benefit of learning. We then apply this methodology to the case of controlling the Brown Treesnake (Boiga irregularis) on the island of Saipan. We find that the indirect benefit--the value of information to reduce uncertainty--is likely to be quite large.

The solids-flux theory--confirmation and extension by using partial differential equations

The solids-flux theory has been used for half a century as a tool for estimating concentration and fluxes in the design and operation of secondary settling tanks during stationary conditions. The flux theory means that the conservation of mass is used in one dimension together with the batch-settling flux function according to the Kynch assumption. The flux theory results correspond to stationary solutions of a partial differential equation, a conservation law, with discontinuous coefficients modelling the continuous-sedimentation process in one dimension. The mathematical analysis of such an equation is intricate, partly since it cannot be interpreted in the classical sense. Recent results, however, make it possible to partly confirm and extend the previous flux theory statements, partly draw new conclusions also on the dynamic behaviour and the possibilities and limitations for control. We use here a single example of an ideal settling tank and a given batch-settling flux in a whole series of calculations. The mathematical results are adapted towards the application and many of them are conveniently presented in terms of operating charts.

Kernels for generalized multiple-instance learning

The multiple-instance learning (MIL) model has been successful in numerous application areas. Recently, a generalization of this model and an algorithm for it were introduced, showing significant advantages over the conventional MIL model on certain application areas. Unfortunately, that algorithm is not scalable to high dimensions. We adapt that algorithm to one using a support vector machine with our new kernel k\wedge. This reduces the time complexity from exponential in the dimension to polynomial. Computing our new kernel is equivalent to counting the number of boxes in a discrete, bounded space that contain at least one point from each of two multisets. We show that this problem is #P-complete, but then give a fully polynomial randomized approximation scheme (FPRAS) for it. We then extend k\wedge by enriching its representation into a new kernel kmin, and also consider a normalized version of k\wedge that we call k\wedge/\vee (which may or may not not be a kernel, but whose approximation yielded positive semidefinite Gram matrices in practice). We then empirically evaluate all three measures on data from content-based image retrieval, biological sequence analysis, and the musk data sets. We found that our kernels performed well on all data sets relative to algorithms in the conventional MIL model.

Simulation of atmospheric dispersion of radionuclides using an Eulerian-Lagrangian modelling system

In this paper we present an atmospheric dispersion scenario for a proposed nuclear power plant in Pakistan involving the hypothetical accidental release of radionuclides. For this, a concept involving a Lagrangian stochastic particle model (LSPM) coupled with an Eulerian regional atmospheric modelling system (RAMS) is used. The atmospheric turbulent dispersion of radionuclides (represented by non-buoyant particles/neutral traces) in the LSPM is modelled by applying non-homogeneous turbulence conditions. The mean wind velocities governed by the topography of the region and the surface fluxes of momentum and heat are calculated by the RAMS code. A moving least squares (MLS) technique is introduced to calculate the concentration of radionuclides at ground level. The numerically calculated vertical profiles of wind velocity and temperature are compared with observed data. The results obtained demonstrate that in regions of complex terrain it is not sufficient to model the atmospheric dispersion of particles using a straight-line Gaussian plume model, and that by utilising a Lagrangian stochastic particle model and regional atmospheric modelling system a much more realistic estimation of the dispersion in such a hypothetical scenario was ascertained. The particle dispersion results for a 12 h ground release show that a triangular area of about 400 km(2) situated in the north-west quadrant of release is under radiological threat. The particle distribution shows that the use of a Gaussian plume model (GPM) in such situations will yield quite misleading results.

Aversion to health inequalities and priority setting in health care

Traditionally aversion to health inequality is modelled through a concave utility function over health outcomes. Bleichrodt et al. [Bleichrodt, H., Diecidue E., Quiggin J., 2004. Equity weights in the allocation of health care: the rank-dependent QALY model. Journal of Health Economics 23, 157-171] have suggested a "dual" approach based on the introduction of explicit equity weights. The purpose of this paper is to analyze how priorities in health care are determined in the framework of these two models. It turns out that policy implications are highly sensitive to the choice of the model that will represent aversion to health inequality.

Central obesity as a precursor to the metabolic syndrome in the AusDiab study and Mauritius

Evidence from epidemiologic studies that central obesity precedes future metabolic change and does not occur concurrently with the appearance of the blood pressure, glucose, and lipid abnormalities that characterize the metabolic syndrome (MetS) has been lacking. Longitudinal surveys were conducted in Mauritius in 1987, 1992, and 1998, and in Australia in 2000 and 2005 (AusDiab). This analysis included men and women (aged > or = 25 years) in three cohorts: AusDiab 2000-2005 (n = 5,039), Mauritius 1987-1992 (n = 2,849), and Mauritius 1987-1998 (n = 1,999). MetS components included waist circumference, systolic blood pressure, fasting and 2-h postload plasma glucose, high-density lipoprotein (HDL) cholesterol, triglycerides, and homeostasis model assessment of insulin sensitivity (HOMA-S) (representing insulin sensitivity). Linear regression was used to determine which baseline components predicted deterioration in other MetS components over 5 years in AusDiab and 5 and 11 years in Mauritius, adjusted for age, sex, and ethnic group. Baseline waist circumference predicted deterioration (P < 0.01) in four of the other six MetS variables tested in AusDiab, five of six in Mauritius 1987-1992, and four of six in Mauritius 1987-1998. In contrast, an increase in waist circumference between baseline and follow-up was only predicted by insulin sensitivity (HOMA-S) at baseline, and only in one of the three cohorts. These results suggest that central obesity plays a central role in the development of the MetS and appears to precede the appearance of the other MetS components.

Gene-physical activity interactions in the etiology of obesity: behavioral considerations

Understanding how genes, environment, and personal motivation operate to influence physical activity will require (i) inclusion of properly validated measures of putative mediators (e.g., cultural values, efficacy and control beliefs, goals, intentions, enjoyment, and self-management skills) and moderators (e.g., age or maturation, personality, race/ethnicity, fitness, fatness, skill, and competing behaviors) of physical activity, (ii) a search for candidate genes involved with motivational systems of energy expenditure in addition to energy intake pathways, (iii) assessment of specific features physical activity exposure (i.e., type, intensity, timing, and context), (iv) manipulation of physical activity or prospective observation of change in physical activity at multiple times, rather than cross-sectional association and linkage studies, and (v) use of statistical procedures that permit multilevel modeling (i.e., personal and group-level variables) of direct, indirect (i.e., mediated), and moderated (i.e., interactions of mediators with external factors) relations with physical activity within theoretical gene-environment networks.

An epidemiological model for externally sourced vector-borne viruses applied to Bean yellow mosaic virus in lupin crops in a Mediterranean-type environment

A hybrid mechanistic/statistical model was developed to predict vector activity and epidemics of vector-borne viruses spreading from external virus sources to an adjacent crop. The pathosystem tested was Bean yellow mosaic virus (BYMV) spreading from annually self-regenerating, legume-based pastures to adjacent crops of narrow-leafed lupin (Lupinus angustifolius) in the winter-spring growing season in a region with a Mediterranean-type environment where the virus persists over summer within dormant seed of annual clovers. The model uses a combination of daily rainfall and mean temperature during late summer and early fall to drive aphid population increase, migration of aphids from pasture to lupin crops, and the spread of BYMV. The model predicted time of arrival of aphid vectors and resulting BYMV spread successfully for seven of eight datasets from 2 years of field observations at four sites representing different rainfall and geographic zones of the southwestern Australian grainbelt. Sensitivity analysis was performed to determine the relative importance of the main parameters that describe the pathosystem. The hybrid mechanistic/statistical approach used created a flexible analytical tool for vector-mediated plant pathosystems that made useful predictions even when field data were not available for some components of the system.

Population pharmacokinetics of ritonavir-boosted saquinavir regimens in HIV-infected individuals

OBJECTIVES

The aim of this study was to develop and validate a population pharmacokinetic model in order to describe ritonavir-boosted saquinavir concentrations dosed twice and once daily in human immunodeficiency virus (HIV)-infected patients from the UK, Uganda and Thailand and to identify factors that may influence saquinavir pharmacokinetics.

METHODS

Pharmacokinetic data from 10 clinical studies were combined. Non-linear mixed effects modelling (NONMEM version V) was applied to determine the saquinavir pharmacokinetic parameters, interindividual/interoccasion variability (IIV/IOV) and residual error. Various covariates potentially related to saquinavir pharmacokinetics were explored, and the final model was validated by means of 95% prediction interval and testing the predictive performance of the model with data not included in the model-building process.

RESULTS

Ninety-seven patients were included from the UK (n = 52), Uganda (n = 18) and Thailand (n = 27), contributing 347 saquinavir profiles (1-14 profiles per patient). A one-compartment model with zero-order absorption and lag-time best described the data with IIV/IOV on apparent oral clearance (CL/F) and volume of distribution (V/F) and with IIV on duration and absorption lag-time. The ritonavir area under the curve over the dosing interval was significantly associated with saquinavir CL/F and V/F. A typical patient from the UK had approximately 1.5- and 3-fold higher saquinavir CL/F compared with patients from Uganda (89.0 versus 49.8 L/h) and Thailand (89.0 versus 26.7 L/h), respectively.

CONCLUSIONS

A model to characterize ritonavir-boosted saquinavir pharmacokinetics in HIV-infected adults has been developed and validated. The model could be used for dosage adaptation following therapeutic drug monitoring and to assess patients' suitability for once-daily boosted saquinavir therapy.

Aggressive zero balance ultrafiltration on CPB in patients with renal failure may cause cerebral edema: a theoretical analysis

The objective of this study was to determine the brain volume changes that occur secondary to hemofiltration during cardiopulmonary bypass in patients with renal failure. We hypothesized that in patients with elevated urea levels, quick aggressive hemofiltration could be associated with cerebral edema. We constructed a simple two-compartment model similar to the urea kinetic model developed by Depner. Intracellular urea exit was assumed to be minimal based on known urea redistribution times. Calculations were based on a 70-kg patient, with an intracellular volume of 25 L, extracellular volume of 15 L, and a preoperative urea of 40 mmol/L filtered to a post-procedure urea of 6 mmol/L. Analysis showed that a standard size 1500-mL human brain filtered from a preoperative urea of 40 to 6 mmol/L over a short period will expand by 59 mL secondary to the osmotic disequilibrium secondary to hemofiltration (p < .05). The higher the preoperative urea, the larger the fluid shift. This figure does not include the cerebral edema component that is known to arise secondary to cardiopulmonary bypass. Significant cerebral edema theoretically occurs secondary to hemofiltration during cardiopulmonary bypass. More detailed mathematical urea kinetic analysis and clinical correlation are needed.

Numerical investigation of out-of-plane sound propagation in a shallow water experiment

In an experiment in the Florida Straits, broadband pulses were transmitted over a range of 10 km and received by a vertical hydrophone array. For pulses with center frequency below 400 Hz, the received signal consisted of a dominant arrival followed by a secondary one delayed by about 0.4 s. A hypothesis that the secondary arrival was caused by 3D out-of-plane propagation is investigated here numerically with a 3D parabolic equation model (3DWAPE) and a 3D ray model (MOC3D). Both models clearly predict a secondary arrival caused by 3D horizontal refraction from the sloping bottom in the shoreward direction.

Infrasound induced instability by modulation of condensation process in the atmosphere

A sound wave in supersaturated water vapor can modulate both the process of heat release caused by condensation, and subsequently, as a result, the resonance interaction of sound with the modulated heat release provides sound amplification. High-intensity atmospheric perturbations such as cyclones and thunderstorms generate infrasound, which is detectable at large distances from the source. The wave-condensation instability can lead to variation in the level of infrasound radiation by a developing cyclone, and this can be as a precursor of these intense atmospheric events.

Source localization from an elevated acoustic sensor array in a refractive atmosphere

Localization of sound sources on the ground from an acoustic sensor array elevated on a tethered aerostat is considered. To improve estimation of the source coordinates, one should take into account refraction of sound rays due to atmospheric stratification. Using a geometrical acoustics approximation for a stratified moving medium, formulas for the source coordinates are derived that account for sound refraction. The source coordinates are expressed in terms of the direction of sound propagation as measured by the sensor array, its coordinates, and the vertical profiles of temperature and wind velocity. Employing these formulas and typical temperature and wind velocity profiles in the atmosphere, it is shown numerically that sound refraction is important for accurate predictions of the source coordinates. Furthermore, it is shown that the effective sound speed approximation, which is widely used in atmospheric acoustics, fails to correctly predict the source coordinates if the grazing angle of sound propagation is relatively large.

Personal audio with multiple dark zones

This work considers providing personal audio for a listener by use of a local array of acoustic sources driven in such a way as to maintain the reproduced levels in one seat, in the so-called "bright zone," while minimizing the levels experienced by others sitting close by, in the so-called "dark zones." Multiple dark zones are considered both adjacent to and in front of the listener, initially using monopole simulations to investigate three different control strategies, and it is shown that a particular orientation of two sources is able to provide good control over the dark zones in these directions. It is shown that minimizing the sound power radiated by the local array while constraining the reproduced level in the bright zone is an effective method of achieving isolation when the dark zones are numerous. The findings of the investigations are used to design an optimal source arrangement, for which the isolation performance is improved by about 20 dB up to 1 kHz, which is verified with Boundary Element Method (BEM) models and predictions based on measured responses.

Effects of shear waves on boundary-coupled vector sensors

The impact of the acoustic properties of a solid medium on the response of an acoustic vector sensor placed at the boundary between water and the solid is examined. In particular, the components of the vector field both normal and tangential to the boundary are studied. It is shown that the component of the velocity tangential to the boundary is affected by the introduction of shear in the solid medium, which leads to amplitude and phase discontinuities across the boundary. Such discontinuities can lead to mismatches between the vector sensor components that interfere with common, coherent processing techniques. Implications for ocean bottom- and hull-mounted examples are provided.

Greenhouse gas mitigation-induced rough-interval programming for municipal solid waste management

A greenhouse gas (GHG) mitigation-induced rough-interval programming model is proposed in this study. Components of GHG emission and environmental pollution control are incorporated into the objective function and a series of relevant constraints. To explicitly examine more complexities existing in many parameters, rough intervals are also communicated into the modeling framework. The proposed model presents satisfactory capabilities in analyzing complicated interrelationships among municipal solid waste (MSW) management, climate-change impact, and environmental pollution control. It can also provide optimal allocation schemes and facilitate decision-makers regulating environmentally sustainable strategies. The developed model is then applied to a case study for demonstrating its applicability. Two representative scenarios (relatively representing two potential management policies that may be implemented in the future years) are considered. The results indicate that the developed model presents advantages in mitigating GHG emissions and the associated climate-change impact. The comparison between the GHG mitigation-induced model with and without rough-interval parameters is also investigated. Completely different solutions of the two models imply the significant impact of dual-uncertain information on the system, which can hardly be addressed through the existing optimization approaches.

Forecasting daily source air quality using multivariate statistical analysis and radial basis function networks

It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.

[Black soil organic matter content prediction based on reflectance simulation models]

The hyperspectral reflectance characteristics of black soil in Heilongjiang province were analyzed quantitatively, and then the main characteristic controlling points of reflectance were determined and used to build soil reflectance prediction models; the relationship between organic matter content and reflectance and the coefficients of simulating models were studied, Black soil organic matter content spectral prediction models were built, and the feasibility of hyperspectral reflectance simulatiib method was discussed. The results are as follows (1) Organic matter content is the determining factor of black soil reflectance characteristics in the range less than 1000 nm. When the content is low, the covering effect of organic matter on the black soil parent matrix reflectance characteristics is very weak, there are two absorption vales at 500 and 640 rim; when the content reaches a certain content (about 5%), the reflectance characteristics of black soil parent matrix are totally covered by organic matter, and there is only one large absorption vale in the region caused by organic matter. (2) The spectral characteristic controlling points of black soil hyperspectral reflectance in the range of 450-930 om are located at 450, 500, 590, 660 and 930 nrn, and divide the black soil reflectance into four parts. (3) Simulation models (linear, quadratic) rightly describe the characteristics of black soil hyperspectral reflectance, and the linear piecewise model shows a better performance. (4) The organic matter content prediction models with the coefficients of reflectance simulation models as independent variables are more precise than that based on soil reflectance and its derivate, which indicates that the characteristic controlling points for reflectance simulation models are selected reasonably and representatively, and the simulation models partly solve the data redundancy problem of soil hyperspectral reflectance, and improve the precision of black soil organic matter content prediction models with remote sensing methods. Reflectance simulating method can be used for data simplification and compression, data redundancy removal, organic matter and other soil pararneters remote sensing studies.

Algebraic reconstruction techniques for spectral reconstruction in diffuse optical tomography

Reconstruction in diffuse optical tomography (DOT) necessitates solving the diffusion equation, which is nonlinear with respect to the parameters that have to be reconstructed. Currently applied solving methods are based on the linearization of the equation. For spectral three-dimensional reconstruction, the emerging equation system is too large for direct inversion, but the application of iterative methods is feasible. Computational effort and speed of convergence of these iterative methods are crucial since they determine the computation time of the reconstruction. In this paper, the iterative methods algebraic reconstruction technique (ART) and conjugated gradients (CGs) as well as a new modified ART method are investigated for spectral DOT reconstruction. The aim of the modified ART scheme is to speed up the convergence by considering the specific conditions of spectral reconstruction. As a result, it converges much faster to favorable results than conventional ART and CG methods.

Sorption of polycyclic aromatic hydrocarbons (PAHs) to carbonaceous materials in a river floodplain soil

We report on sorption isotherm of phenanthrene (Phe) for river floodplain soil associated with carbonaceous materials, with particular attention being devoted to the natural loading of Phe. Our sorption experiments with original soil samples, size, and density sub-fractions showed that the light fraction had the highest sorption capacity comparable to low rank coals. In addition, the light fraction contributed most for the sorption of Phe in total soil samples. K(oc) values for all fractions were in the same range, thus indicating that coal and coal-derived particles in all samples are responsible for the enhanced sorption for Phe. Sorption was strongly nonlinear and the combined partitioning and pore-filling model gave a better fit than the Freundlich sorption model. In addition, the spiked PAHs did not show the same behavior as the naturally aged ones, therefore the accessibility of indigenous background organic contaminants was reduced when coal and coal-derived particles are associated with the soils.

A two-agent dynamic model with an invasive weed diffusion externality: an application to Yellow Starthistle (Centaurea solstitialis L.) in New Mexico

This paper presents a bioeconomic model for two agents, a cattle rancher and a non-rancher, with interdependencies between their individual effort of invasive weed management and profitability. Dynamic simulations allow us to find numerically the optimal effort of weed control over a 5-year time horizon under a variety of beginning infestation levels. In a base-case scenario without governmental cost-share of control costs, we find that efforts to control the weed are not profitable. The base-case scenarios also indicate that grazing contributes to giving the invasive weed a competitive edge. A second series of simulations include incentive payments for weed control which are set at the minimum level required to have a net positive impact on the rancher's profitability. From these simulations, we find that the level of infestation impacts the size of the incentive necessary to get the agent to control the weeds and that the incentive payments impact the level of effort of the rancher. In addition, results reveal that the higher incentive payments for lower levels of weed infestations reduce the total cumulative incentive payments over time. Efficient policies directed towards management of invasive weeds may need to be adjusted for each individual case.