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PubMed1990

Developing the credibility of continuing education.

The paper begins by acknowledging both the need for nursing to be a research based profession, and the reasons for the well-documented theory-practice gap. It goes on to suggest that what is not so well-documented is an examination of the reasons for the commitment gap between basic and continuing education. The relevance of continuing education to clinical staff will be discussed in relation to the change in the content of nursing theory--from the unwieldy 'certainty-based' medical model, to a more flexible, thus perhaps 'uncertainty-based' medical model. The paper will explore the author's belief that clinical staff need to feel a sense of security in their knowledge base, which therefore leads them at times to reject nursing theory. The paper proposes that a legal framework for nurses practice gives the clinical staff the security in their practice to view the relevance of new nursing theory.

Communication↗
PubMed2004

Bayesian phylogenetic model selection using reversible jump Markov chain Monte Carlo.

A common problem in molecular phylogenetics is choosing a model of DNA substitution that does a good job of explaining the DNA sequence alignment without introducing superfluous parameters. A number of methods have been used to choose among a small set of candidate substitution models, such as the likelihood ratio test, the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and Bayes factors. Current implementations of any of these criteria suffer from the limitation that only a small set of models are examined, or that the test does not allow easy comparison of non-nested models. In this article, we expand the pool of candidate substitution models to include all possible time-reversible models. This set includes seven models that have already been described. We show how Bayes factors can be calculated for these models using reversible jump Markov chain Monte Carlo, and apply the method to 16 DNA sequence alignments. For each data set, we compare the model with the best Bayes factor to the best models chosen using AIC and BIC. We find that the best model under any of these criteria is not necessarily the most complicated one; models with an intermediate number of substitution types typically do best. Moreover, almost all of the models that are chosen as best do not constrain a transition rate to be the same as a transversion rate, suggesting that it is the transition/transversion rate bias that plays the largest role in determining which models are selected. Importantly, the reversible jump Markov chain Monte Carlo algorithm described here allows estimation of phylogeny (and other phylogenetic model parameters) to be performed while accounting for uncertainty in the model of DNA substitution.

Algorithms↗
PubMed2003

Biologically motivated computational modeling of formaldehyde carcinogenicity in the F344 rat.

Formaldehyde inhalation at 6 ppm and above causes nasal squamous cell carcinoma (SCC) in F344 rats. The human health implications of this effect are of significant interest since human exposure to environmental formaldehyde is widespread, though at lower concentrations than those that cause cancer in rats. In this article, which is part of a larger effort to predict the human cancer risks of inhaled formaldehyde, we describe biologically motivated quantitative modeling of the exposure-tumor response continuum in the rat. An anatomically realistic, three-dimensional fluid dynamics model of the F344 rat nasal airways was used to predict site-specific flux of formaldehyde from inhaled air into tissue, since both SCC and preneoplastic lesions develop in a characteristic site-specific pattern. Flux into tissue was used as a dose metric for two modes of action, direct mutagenicity and cytolethality-regenerative cellular proliferation (CRCP), which in turn were linked to key parameters of a two-stage clonal growth model. The direct mutagenicity mode of action was represented by a low dose linear dose-response model of DNA-protein cross-link (DPX) formation. An empirical J-shaped dose-response model and a threshold model fit to the empirical data were used for CRCP. In the clonal growth model, the probability of mutation per cell generation was a function of the tissue concentration of DPX while the rate of cell division was calculated from the CRCP data. Maximum likelihood methods were used to estimate parameter values. Survivor (a nontumor outcome) and tumor data for controls from the National Toxicology Program database and from two formaldehyde inhalation bioassays were used for likelihood calculations. The J-shaped dose-response for CRCP provided a better description of the SCC data than did the threshold model. Sensitivity analyses indicated that the rodent tumor response is due to the CRCP mode of action, with the directly mutagenic pathway having little, if any, influence. When evaluated in light of modeling and database uncertainties, particularly the specification of the clonal growth model and the dose-response data for CRCP, this work provides suggestive though not definitive evidence for a J-shaped dose-response for formaldehyde-mediated nasal SCC in the F344 rat.

Administration, Inhalation↗
PubMed1997

An introduction to the use of physiologically based pharmacokinetic models in risk assessment.

Many extrapolation issues surface in quantitative risk assessments. The extrapolation from high-dose animal studies to low-dose human exposures is of particular concern. Physiologically based pharmacokinetic (PBPK) models are often proposed as tools to mitigate the problems of extrapolation. These models provide a representation of the disposition, metabolism, and excretion of xenobiotics that are believed to possess the potential of inducing adverse human health responses. Given a model of xenobiotic disposition that is applicable for multiple species and appropriate for nonlinearity of the xenobiotic biotransformation process, better extrapolation may be possible. Unfortunately, the true structure of these models (e.g. number of compartments, type of metabolism, etc.) is seldom known, and attributes of these models (tissue volumes, partition coefficients, etc.) are often experimentally determined and often only central measures of these quantities are reported. We describe the use of PBPK models in risk assessment, the structural and parameter uncertainty in these models, and provide a simple illustration of how these characteristics can be incorporated in a statistical analysis of PBPK models. Additional complexity in the analysis of variability in the models is also outlined. This discussion is illustrated using data from methylene chloride.

Animals↗
PubMed2004

Propagation of uncertainty in hourly utility NOx emissions through a photochemical grid air quality model: a case study for the Charlotte, NC, modeling domain.

One of the major hypothesized sources of uncertainties in air quality model inputs is the emission inventory. A probabilistic hourly NOx emission inventory for 32 units of nine coal-fired power plants in the Charlotte domain for the year 1995 was propagated through the Multiscale Air Quality Simulation Platform (MAQSIP). The inventory was developed using time series techniques. Time series for a 4-d episode were simulated and propagated through the air quality model 50 times in order to represent the ranges of uncertainty in hourly emissions and predicted ozone levels. Intra-unit autocorrelation in emissions and inter-unit dependence were accounted for. The range of uncertainty in predicted ozone was greater when inter-unit dependence was included as compared to when units were treated as statistically independent. Uncertainties in maximum ozone hourly or 8-h concentrations at a specific location could be attributed to a specific power plant based upon regression analysis. Out of 3969 grid cells in the modeling domain, there were 43 and 1654 grid cells with a probability greater than 0.9 of exceeding a 1-h 120 ppb standard and an 8-h 80 ppb standard, respectively. The time series of predicted ozone values had similar autocorrelation as compared to monitored data. The implications of these results for air quality management are addressed.

Air Pollutants↗
PubMedDate not supplied

Uncertainty in exposure estimates made by modeling versus monitoring.

To conduct an initial exposure assessment for an airborne toxicant, industrial hygienists usually prefer air monitoring to mathematical modeling, even if only one exposure value is to be measured. This article argues that mathematical modeling may provide a more accurate (less uncertain) exposure estimate than monitoring if only a few air samples are to be collected, if anticipated exposure variability is high, and if information on exposure determinants is not too uncertain. To explore this idea, a hypothetical "true" distribution of 8-hour time-weighted average airborne exposure values, C, is posited based on an NF exposure model. The C distribution is approximately lognormal. Estimation of the mean value, microC (the long-term average exposure level), is considered. Based on simple random sampling of workdays and use of the sample mean C to estimate microC, accuracy (uncertainty) in the estimate is measured by the mean square error, MSE(C). In the alternative, a modeling estimate can be made using estimates of the mean chemical emission rate microG, the mean room dilution supply air rate microQ, and the mean dilution ventilation rate in the NF of the source mu beta. By positing uniform distributions for the estimates microG, microQ, and mu beta, an equation for the modeling mean square error MSE(microC) is presented. It is shown that for a sample size of three or fewer workdays, mathematical modeling rather than air monitoring should provide a more accurate estimate of microC if the anticipated geometric standard deviation for the C distribution exceeds 2.3.

Air Pollution, Indoor↗
PubMed2005

Bayesian calibration of process-based forest models: bridging the gap between models and data.

Process-based forest models generally have many parameters, multiple outputs of interest and a small underlying empirical database. These characteristics hamper parameterization. Bayesian calibration offers a solution to the calibration problem because it applies to models of any type or size. It provides parameter estimates, with measures of uncertainty and correlation among the parameters. The procedure begins by quantifying the uncertainty about parameter values in the form of a prior probability distribution. Then data on the output variables are used to update the parameter distribution by means of Bayes' Theorem. This yields a posterior calibrated distribution for the parameters, which can be summarized in the form of a mean vector and variance matrix. The predictive uncertainty of the model can be quantified by running it with different parameter settings, sampled from the posterior distribution. In a further step, one may evaluate the posterior probability of the model itself (rather than that of the parameters) and compare that against the probability of other models, to aid in model selection or improvement. Bayesian calibration of process-based models cannot be performed analytically, so the posterior parameter distribution must be approximated in the form of a representative sample of parameter values. This can be achieved by means of Markov Chain Monte Carlo simulation, which is suitable for process-based models because of its simplicity and because it does not require advance knowledge of the shape of the posterior distribution. Despite the suitability of Bayesian calibration, the technique has rarely been used in forestry research. We introduce the method, using the example of a typical forest model. Further, we show that reductions in parameter uncertainty, and thus in output uncertainty, can be effected by increasing the variety of data, increasing the accuracy of measurements and increasing the length of time series.

Algorithms↗
PubMedDate not supplied

Uncertainty in gynecological cancer: a test of the mediating functions of mastery and coping.

The purpose of this study was to test a portion of the uncertainty in illness model to determine whether mastery would be strengthened or weakened under conditions of uncertainty, and to test the ability of mastery and coping to function as mediators in the model. Mastery was proposed to mediate the relationship between uncertainty and the appraisal of danger and opportunity. Coping was proposed to mediate the relationships between danger or opportunity and emotional distress. Data were collected on a sample of 131 women receiving treatment for gynecological cancer. Findings support mastery as weakened under conditions of uncertainty and functioning as a situationally specific personality factor. Mastery was a significant mediator of the relationship between uncertainty and the appraisal of danger and opportunity; although the mediation effect for the relationship between uncertainty and danger was stronger. While two coping strategies were found to function as significant mediators between danger or opportunity and emotional distress, the mediation effect was very small.

Adaptation, Psychological↗
PubMed1987

Sensitivity and uncertainty studies of the CRAC2 computer code.

We have studied the sensitivity of health impacts from nuclear reactor accidents, as predicted by the CRAC2 computer code, to the following sources of uncertainty: (1) the model for plume rise, (2) the model for wet deposition, (3) the meteorological bin-sampling procedure for selecting weather sequences with rain, (4) the dose conversion factors for inhalation as affected by uncertainties in the particle size of the carrier aerosol and the clearance rates of radionuclides from the respiratory tract, (5) the weathering half-time for external ground-surface exposure, and (6) the transfer coefficients for terrestrial foodchain pathways. Predicted health impacts usually showed little sensitivity to use of an alternative plume-rise model or a modified rain-bin structure in bin-sampling. Health impacts often were quite sensitive to use of an alternative wet-deposition model in single-trial runs with rain during plume passage, but were less sensitive to the model in bin-sampling runs. Uncertainties in the inhalation dose conversion factors had important effects on early injuries in single-trial runs. Latent cancer fatalities were moderately sensitive to uncertainties in the weathering half-time for ground-surface exposure, but showed little sensitivity to the transfer coefficients for terrestrial foodchain pathways. Sensitivities of CRAC2 predictions to uncertainties in the models and parameters also depended on the magnitude of the source term, and some of the effects on early health effects were comparable to those that were due only to selection of different sets of weather sequences in bin-sampling.

Accidents↗
PubMed2006

Spatial epidemiology of human schistosomiasis in Africa: risk models, transmission dynamics and control.

This paper reviews recent studies on the spatial epidemiology of human schistosomiasis in Africa. The integrated use of geographical information systems, remote sensing and geostatistics has provided new insights into the ecology and epidemiology of schistosomiasis at a variety of spatial scales. Because large-scale patterns of transmission are influenced by climatic conditions, an increasing number of studies have used remotely sensed environmental data to predict spatial distributions, most recently using Bayesian methods of inference. Such data-driven approaches allow for a more rational implementation of intervention strategies across the continent. It is suggested that improved incorporation of transmission dynamics into spatial models and assessment of uncertainties inherent in data and modelling approaches represent important future research directions.

Africa↗
PubMed2006

Estimating benzene exposure at a solvent parts washer.

A mathematical model is described for estimating benzene exposure at a parts washer using petroleum distillates solvent containing benzene. The basic assumptions are that the benzene mass emission rate exponentially decreases over time, and that the air above the parts washer basin to which a worker is exposed is part of a well-mixed air zone termed the near field (relative to the source location). Two previously conducted simulations of the parts washer process are described. A single 1-hour time-weighted average (TWA) benzene concentration was measured during Simulation #1, and two 4-hour TWA benzene concentrations were measured during Simulation #2. The initial benzene concentrations in the solvents were known, and the exponential loss rate constants were estimated from subsequent determinations of the benzene concentrations. Values for the interzonal airflow rate were estimated based on the conceptual geometry of the near field zone and sparse information on air speed near the parts washers. Minimum values for the room supply/exhaust air rate were estimated based on the room volumes and ventilation conditions. The modeled benzene concentrations were within a multiplicative range of one-half to twofold the measured concentrations. Uncertainty in a model estimate was quantified by Monte Carlo analysis; the distributions of model estimates exhibited coefficients of variation of approximately 40%. Issues related to uncertainty in exposure estimates made by mathematical modeling are discussed.

Air Pollutants, Occupational↗
PubMed2005

Finding more meaning: the antecedents of uncertainty revisited.

AIMS AND OBJECTIVES: The objective of this study was to explore the extent to which several antecedents explained the uncertainty of men who were undergoing the watchful waiting management option for prostate cancer. BACKGROUND: Uncertainty plays a significant role in explaining various outcomes in the chronically ill. However, little is known about the factors that accompany and modify uncertainty. For uncertainty to predict the outcomes of individuals coping with illness, it is imperative to understand its accompanying antecedents. DESIGN: The study used a correlational single group non-experimental design. METHODS: The convenience sample completed a one-time mailed questionnaire aimed at measuring the antecedents of uncertainty. RESULTS: A total of 19 participants completed questionnaires. The results revealed significant relationships between level of education and length of time with illness and uncertainty. Stepwise multiple regression indicated that education explained 52% of the variance in uncertainty. CONCLUSIONS: This study strengthens the relationship between both education and length of time with illness, and uncertainty. It further supports the Uncertainty in Illness Model and enhances the understanding of the factors that influence uncertainty in the sample. RELEVANCE TO CLINICAL PRACTICE: This study assisted in the identification of factors that influence uncertainty in men undergoing the watchful waiting management option for prostate cancer. Future study should examine the role of antecedents in explaining uncertainty in additional populations and using alternative measures, when necessary.

Aged↗
PubMed2004

Colorectal cancer screening in Australia: an economic evaluation of a potential biennial screening program using faecal occult blood tests.

OBJECTIVE: To evaluate whether the introduction of a national, co-ordinated screening program using the faecal occult blood test represents 'value-for-money' from the perspective of the Australian Government as third-party funder. METHODS: The annual equivalent costs and consequences of a biennial screening program in 'steady-state' operation were estimated for the Australian population using 1996 as the reference year. Disability-adjusted life years (DALYs) and the years of life lost (YLLs) averted, and the health service costs were modelled, based on the epidemiology and the costs of colorectal cancer in Australia together with the mortality reduction achieved in randomised controlled trials. Uncertainty in the model was examined using Monte Carlo simulation methods. RESULTS: We estimate a minimum or 'base program' of screening those aged 55 to 69 years could avert 250 deaths per annum (95% uncertainty interval 99-400), at a gross cost of dollarsA55 million (95% UI dollarsA46 million to dollarsA96 million) and a gross incremental cost-effectiveness ratio of dollarsA17,000/DALY (95% UI dollarsA13,000/DALY to dollarsA52,000/DALY). Extending the program to include 70 to 74-year-olds is a more effective option (cheaper and higher health gain) than including the 50 to 54-year-olds. CONCLUSIONS: The findings of this study support the case for a national program directed at the 55 to 69-year-old age group with extension to 70 to 74-year-olds if there are sufficient resources. The pilot tests recently announced in Australia provide an important opportunity to consider the age range for screening and the sources of uncertainty, identified in the modelled evaluation, to assist decisions on implementing a full national program.

Australia↗
PubMed1996

Incorporating Monte Carlo simulation into physiologically based pharmacokinetic models using advanced continuous simulation language (ACSL): a computational method.

Biologically based models with physiological parameters are becoming more popular as a tool to estimate target tissue doses from chemical exposures. However, the majority of current physiologically based pharmacokinetic (PBPK) models do not take into account the uncertainty and/or variability within the various model parameters. Consideration of uncertainty is important to evaluate the predictive ability and complexity of a model as well as identification of parameters which contribute disproportionately to variability in model output. In order to estimate the uncertainty in PBPK model output, a versatile and simple computational method is presented which can be readily incorporated into the majority of PBPK models without extensive additions to model computer code. In this paper, a separate computer program for Monte Carlo simulation is furnished that randomly samples values for model parameters and writes them into a run-time language (command file) format which can then be utilized to execute individual PBPK models. Modifications to the PBPK model allow the desired output to be written to a data file for statistical analysis. The method presented in this paper is applied to a simple PBPK model for benzene disposition.

Algorithms↗
PubMed2000

Comparison of different computer models of the neural control system of the lower urinary tract.

This paper presents a series of five models that were formulated for describing the neural control of the lower urinary tract in humans. A parsimonious formulation of the effect of the sympathetic system, the pre-optic area, and urethral afferents on the simulated behavior are included. In spite of the relative simplicity of the five models studied, behavior that resembles normal lower urinary tract behavior as seen during an urodynamic investigation could be simulated. The models were tested by studying their response to disturbances of the afferent signal from the bladder. It was found that the inhibiting reflex that results from including the sympathetic system or the pre-optic area (PrOA) only counteracts the disturbance in the storage phase. Once micturition has started, these inhibiting reflexes are suppressed. A detrusor contraction that does not result in complete micturition similar to an unstable detrusor contraction could be simulated in a model including urethral afferents. Owing to the number of uncertainties in these models, so far no unambiguous explanation of normal and pathological lower urinary tract behavior can be given. However, these models can be used as an additional tool in studies of the mechanisms of the involved neural control.

Computer Simulation↗
PubMed2005

Handling of contamination variability in exposure assessment: a case study with ochratoxin A.

The contamination of foods dedicated to human consumption varies over space and time. In exposure assessment, this is usually addressed through probabilistic modelling. The present work explores how the variability and uncertainty of exposures estimated at the population level are affected by: (a) the (non-)parametric nature of input contamination distributions; (b) the time-window used to sample contamination values within those distributions. Focusing on exposure of the French population to food mycotoxin ochratoxin A, we implement a range of second-order Monte-Carlo simulations that allow distinguishing variability of exposures from uncertainty of distributional parameters estimates. A simulation runs 10,000 iterations. Overall estimates of parameters are given by the median across iterations and 95%CI by 2.5th and 97.5th percentiles. Our results show that: (a) parametric (log-normal) input distributions may lead to over-estimation of variability and greater uncertainty as compared to non-parametric ones (P97.5 [95%CI] of 7.1 [6.6;7.7] for Parametric-Occasion, 4.6 [4.3;5.0] for Non-Parametric-Occasion), and that (b) the 'Occasion' time-window combines better estimate of variability and lower uncertainty when exposure modelling is applied to populations living in developed countries with complex agri-food systems (P97.5 [95%CI]: 7.3 [6.2;8.9] for Non-Parametric-Week, 4.6 [4.3;5.0] for Non-Parametric-Occasion). A deterministic approach is nevertheless preferred to probabilistic modelling every time input data quality is questionable.

Adolescent↗
PubMed2002

Modelling the 137Cs ingestion dose from consumption of marine fish in Hong Kong.

This paper presents a compartmental model for estimating the ingestion dose, due to 137Cs, arising from the consumption of marine fish in Hong Kong. 137Cs is one of the more important radionuclides released in routine liquid effluents discharged from the Guangdong Nuclear Power Station at Daya Bay, which began commercial operation in 1994. In the model, three sea/ocean compartments are considered. Assuming the discharge of this radionuclide is maintained at a constant rate, the model shows that the concentration of 137Cs in the water and in the marine fish in the three sea compartments would become steady after 5 years. The predicted annual dose to an average local individual in Hong Kong, for a release rate of 10 GBq.y(-1), is 3.2 x 10(-5) microSv, which is dominated by the contribution from fish cultured in Hong Kong waters. The cumulative collective dose to the local population of 6 million, at 50 years of discharge, amounts to 9.0 x 10(-3) man.Sv. The annual dose to members of the critical group of local fish farmers does not exceed 3.0 x 10(-3) microSv. All these doses are small compared to the dose of around 1.2 microSv.y(-1) arising from ingestion of naturally occurring radionuclides found in marine fish. Sensitivity of model parameters and uncertainties of prediction are also studied. Difficulties encountered in model validation are discussed. Despite such difficulties. limited field data that are available show that the predicted results are generally within one order of magnitude with measurements.

Animals↗