Models of screening

This article describes models for disease screening and how they have developed in recent years. The discussion focuses on screening for cancer, because most of the methodological advances in screening design and evaluation have concerned cancer screening. The first part of the article describes the characteristics of these models and illustrates them with a discussion of a simple screening model. The second part describes the development, strengths, and weaknesses of the two main types of screening model—analytic and simulation models—with a particular focus on microsimulation models. The third part discusses model fitting and validation, and the final part briefly describes models for diseases other than cancer—in particular, models for screening for infectious diseases—and discusses the current state and possible future directions for models of disease screening.

Disability support services 2005–06 - national data on services provided under the Commonwealth State/Territory Disability Agreement

This report presents information on disability services collected from over 9,000 service outlets throughout Australia, which are funded under an agreement between the Australian and state/territory governments. These services aim to improve the quality of life of people with disability by providing support and assistance across a range of life activities. The report profiles the people with disability who use the services, the types of services they use and the supports they need (including information on their informal carers). Most information presented in this report is derived from the 2005–06 Commonwealth State/Territory Disability Agreement National Minimum Data Set (CSTDA NMDS) collection.

Statistical models for cancer screening

This paper reviews the application of statistical models to planning and evaluating cancer screening programmes. Models used to analyse screening strategies can be classified as either surface models, which consider only those events which can be directly observed such as disease incidence, prevalence or mortality, or deep models, which incorporate hypotheses about the disease process that generates the observed events. This paper focuses on the latter type. These can be further classified as analytic models, which use a model of the disease to derive direct estimates of characteristics of the screening procedure and its consequent benefits, and simulation models, which use the disease model to simulate the course of the disease in a hypothetical population with and without screening and derive measures of the benefit of screening from the simulation outcomes. The main approaches to each type of model are described and an overview given of their historical development and strengths and weaknesses. A brief review of fitting and validating such models is given and finally a discussion of the current state of, and likely future trends in, cancer screening models is presented.

Identification of nonlinear fMRI models using Auxiliary Particle Filter and kernel smoothing method

Hemodynamic models have a high potential in application to understanding the functional differences of the brain. However, full system identification with respect to model fitting to actual functional magnetic resonance imaging (fMRI) data is practically difficult and is still an active area of research. We present a simulation based Bayesian approach for nonlinear model based analysis of the fMRI data. The idea is to do a joint state and parameter estimation within a general filtering framework. One advantage of using Bayesian methods is that they provide a complete description of the posterior distribution, not just a single point estimate. We use an Auxiliary Particle Filter adjoined with a kernel smoothing approach to address this joint estimation problem.

Overcoming data constraints to create meaningful ecological models

Many techniques used to model ecosystems cannot be meaningfully applied to large-scale ecological problems due to data constraints. Disparate collection methods, data types and incomplete data sets, or limited theoretical understanding mean that a wide range of modelling techniques used to model physical processes or for problems specific to species or populations cannot be used at an ecosystem scale. In developing an ecological response model for the Coorong, a South Australian hypersaline estuary, we combined several flexible modelling approaches in a statistical framework to develop an approach we call ‘ecosystem states’. This model uses simulated hydrodynamic conditions as input to predict one of a suite of states per space and time, allowing prediction of likely ecological conditions under a variety of scenarios. Each ecosystem state has defined sets of biota and physico-chemical parameters. The existing model is limited in that its predictions have yet to be tested and, as yet, no spatial or temporal connectivity has been incorporated into simulated time series of ecosystem states. This approach can be used in a wide range of ecosystems, where enough data are available to model ecosystem states. We are in the process of applying the technique to a nearby lake system. This has been more difficult than for the Coorong as there is little overlap in the spatial and temporal coverage of biological data sets for that region. The approach is robust to low-quality biological data and missing environmental data, so should suit situations where community or management monitoring programs have occurred through time.

Spike sorting using hidden markov models

In this paper, hidden Markov models (HMM) is studied for spike sorting. We notice that HMM state sequences have capability to represent spikes precisely and concisely. We build a HMM for spikes, where HMM states respect spike significant shape variations. Four shape variations are introduced: silence, going up, going down and peak. They constitute every spike with an underlying probabilistic dependence that is modelled by HMM. Based on this representation, spikes sorting becomes a classification problem of compact HMM state sequences. In addition, we enhance the method by defining HMM on extracted Cepstrum features, which improves the accuracy of spike sorting. Simulation results demonstrate the effectiveness of the proposed method as well as the efficiency.

Measurement of stable changes of self-management skills after rehabilitation: a latent state-trait analysis of the Health Education Impact Questionnaire (heiQ™)

To assess stable effects of self-management programs, measurement instruments should primarily capture the attributes of interest, for example, the self-management skills of the measured persons. However, measurements of psychological constructs are always influenced by both aspects of the situation (states) and aspects of the person (traits). This study tests whether the Health Education Impact Questionnaire (heiQ™), an instrument assessing a wide range of proximal outcomes of self-management programs, is primarily influenced by person factors instead of situational factors. Furthermore, measurement invariance over time, changes in traits and predictors of change for each heiQ™ scale were examined.

Partial state estimation for linear systems with output and input time delays.

This paper deals with the problem of partial state observer design for linear systems that are subject to time delays in the measured output as well as the control input. By choosing a set of appropriate augmented Lyapunov-Krasovskii functionals with a triple-integral term and using the information of both the delayed output and input, a novel approach to design a minimal-order observer is proposed to guarantee that the observer error is ε-convergent with an exponential rate. Existence conditions of such an observer are derived in terms of matrix inequalities for the cases with time delays in both the output and input and with output delay only. Constructive design algorithms are introduced. Numerical examples are provided to illustrate the design procedure, practicality and effectiveness of the proposed observer.

Estimating cumulative exposure of wildlife to non-lethal disturbance using spatially explicit capture-recapture models

Impact assessments often focus on short-term behavioral responses of animals to human disturbance. However, the cumulative effects caused by repeated behavioral disruptions are of management concern because these effects have the potential to influence individuals' survival and reproduction. We need to estimate individual exposure rates to disturbance to determine cumulative effects. We present a new approach to estimate the spatial exposure of minke whales to whalewatching boats in Faxaflõi Bay, Iceland. We used recent advances in spatially explicit capture-recapture modeling to estimate the probability that whales would encounter a disturbance (i.e., whalewatching boat). We obtained spatially explicit individual encounter histories of individually identifiable animals using photo-identification. We divided the study area into 1-km2 grid cells and considered each cell a spatially distinct sampling unit. We used capture history of individuals to model and estimate spatial encounter probabilities of individual minke whales across the study area, accounting for heterogeneity in sampling effort. We inferred the exposure of individual minke whales to whalewatching vessels throughout the feeding season by estimating individual whale encounters with vessels using the whale encounter probabilities and spatially explicit whalewatching intensity in the same area, obtained from recorded whalewatching vessel tracks. We then estimated the cumulative time whales spent with whalewatching boats to assess the biological significance of whalewatching disturbances. The estimated exposure levels to boats varied considerably between individuals because of both temporal and spatial variations in the activity centers of whales and the whalewatching intensity in the area. However, although some whales were repeatedly exposed to whalewatching boats throughout the feeding season, the estimated cumulative time they spent with boats was very low. Although whalewatching boat interactions caused feeding disruptions for the whales, the estimated low cumulative exposure indicated that the whalewatching industry in its current state likely is not having any long-term negative effects on vital rates.

Implementing novel models of posttreatment care for cancer survivors: Enablers, challenges and recommendations

AIM: The American Society of Clinical Oncology and US Institute of Medicine emphasize the need to trial novel models of posttreatment care, and disseminate findings. In 2011, the Victorian State Government (Australia) established the Victorian Cancer Survivorship Program (VCSP), funding six 2-year demonstration projects, targeting end of initial cancer treatment. Projects considered various models, enrolling people of differing cancer types, age and residential areas. We sought to determine common enablers of success, as well as challenges/barriers. METHODS: Throughout the duration of the projects, a formal "community of practice" met regularly to share experiences. Projects provided regular formal progress reports. An analysis framework was developed to synthesize key themes and identify critical enablers and challenges. Two external reviewers examined final project reports. Discussion with project teams clarified content. RESULTS: Survivors reported interventions to be acceptable, appropriate and effective. Strong clinical leadership was identified as a critical success factor. Workforce education was recognized as important. Partnerships with consumers, primary care and community organizations; risk stratified pathways with rapid re-access to specialist care; and early preparation for survivorship, self-management and shared care models supported positive project outcomes. Tailoring care to individual needs and predicted risks was supported. Challenges included: lack of valid assessment and prediction tools; limited evidence to support novel care models; workforce redesign; and effective engagement with community-based care and issues around survivorship terminology. CONCLUSION: The VCSP project outcomes have added to growing evidence around posttreatment care. Future projects should consider the identified enablers and challenges when designing and implementing survivorship care.

Using occupancy as a state variable for monitoring the critically endangered Alaotran gentle lemur Hapalemur alaotrensis

Although monitoring is an essential tool for biodiversity conservation, monitoring programmes are often poorly designed and thus unlikely to produce results that are meaningful for management. Monitoring is especially challenging when dealing with rare and elusive species in areas where conservation resources are particularly limited. In such cases, monitoring techniques aimed at estimating occupancy represent an attractive alternative to traditional methods concerned with estimating population size, as the collection of detection/non-detection data is in general less costly and easier to implement. In this study, we evaluated the use of occupancy as a state variable for the monitoring of the Alaotran gentle lemur Hapalemur alaotrensis, a Critically Endangered primate exclusively inhabiting the dense marshes around Lake Alaotra in Madagascar. We used a likelihood-based modelling approach that explicitly accounts for detectability. This showed that the probability of detection of H. alaotrensis was extremely low and depended on site characteristics that can vary in space and time, confirming the need to account for imperfect detection when monitoring this species. We used our models to explore factors affecting the probability of occupancy and detection to identify management implications, and also developed recommendations for the ongoing monitoring of this species. The method applied in this study provides an efficient tool for the monitoring of an elusive species and has the potential to provide a flexible sampling framework for local community based monitoring initiatives.

State-owned enterprises, competition and product quality

We consider a general oligopoly model with consumer surplus moderated quantity competition among state-owned enterprises (SOEs), where the SOEs employ workers who are members of the state-owned worker union and produce differentiated products. We show that increasing the number of SOEs would lead to an outcome in which these enterprises choose a lower level of product quality and this, in turn, results in welfare losses for the society, depending on the degree of substitutability. Our findings are consistent with the evidence from China and uncovers important linkages that exist between worker union, product quality and competition, and that have mostly been ignored in the industrial organisation, trade and development literature.

Stabilizing l1-norm prediction models by supervised feature grouping

Emerging Electronic Medical Records (EMRs) have reformed the modern healthcare. These records have great potential to be used for building clinical prediction models. However, a problem in using them is their high dimensionality. Since a lot of information may not be relevant for prediction, the underlying complexity of the prediction models may not be high. A popular way to deal with this problem is to employ feature selection. Lasso and l1-norm based feature selection methods have shown promising results. But, in presence of correlated features, these methods select features that change considerably with small changes in data. This prevents clinicians to obtain a stable feature set, which is crucial for clinical decision making. Grouping correlated variables together can improve the stability of feature selection, however, such grouping is usually not known and needs to be estimated for optimal performance. Addressing this problem, we propose a new model that can simultaneously learn the grouping of correlated features and perform stable feature selection. We formulate the model as a constrained optimization problem and provide an efficient solution with guaranteed convergence. Our experiments with both synthetic and real-world datasets show that the proposed model is significantly more stable than Lasso and many existing state-of-the-art shrinkage and classification methods. We further show that in terms of prediction performance, the proposed method consistently outperforms Lasso and other baselines. Our model can be used for selecting stable risk factors for a variety of healthcare problems, so it can assist clinicians toward accurate decision making.