Developing models for the data-based mechanistic approach to systems analysis:Increasing objectivity and reducing assumptions

Stochastic State-Space Time-Varying Random Walk models have been developed, allowing the existing Stochastic State Space models to operate directly on irregularly sampled time-series. These TVRW models have been successfully applied to two different classes of models benefiting each class in different ways. The first class of models - State Dependent Parameter (SDP) models and used to investigate the dominant dynamic modes of nonlinear dynamic systems and the non-linearities in these models affected by arbitrary State Variables. In SDP locally linearised models it is assumed that the parameters that describe system’s behaviour changes are dependent upon some aspect of the system (it’s ‘state’). Each parameter can be dependent on one or more states. To estimate the parameters that are changing at a rate related to that of it’s states, the estimation procedure is conducted in the state-space along the potentially multivariate trajectory of the states which drive the parameters. The introduction of the newly developed TVRW models significantly improves parameter estimation, particularly in data rich neighbourhoods of the state-space when the parameter is dependent on more than one state, and the ends of the data-series when the parameter is dependent on one state with few data points. The second class of models are known as Dynamic Harmonic Regression (DHR) models and are used to identify the dominant cycles and trends of time-series. DHR models the assumption is that a signal (such as a time-series) can be broken down into four (unobserved) components occupying different parts of the spectrum: trend, seasonal cycle, other cycles, and a high frequency irregular component. DHR is confined to uniformly sampled time-series. The introduction of the TVRW models allows DHR to operate on irregularly sampled time-series, with the added benefit of forecasting origin no longer being confined to starting at the end of the time-series but can now begin at any point in the future. Additionally, the forecasting sampling rate is no longer limited to the sampling rate of the time-series. Importantly, both classes of model were designed to follow the Data-Based Mechanistic (DBM) approach to modelling environmental systems, where the model structure and parameters are to be determined by the data (Data-Based) and then the subsequent models are to be validated based on their physical interpretation (Mechanistic). The aim is to remove the researcher’s preconceptions from model development in order to eliminate any bias, and then use the researcher’s knowledge to validate the models presented to them. Both classes of model lacked model structure identification procedures and so model structure was determined by the researcher, against the DBM approach. Two different model structure identification procedures, one for SDP and the other for DHR, were developed to bring both classes of models back within the DBM framework. These developments have been presented and tested here on both simulated data and real environmental data, demonstrating their importance, benefits and role in environmental modelling and exploratory data analysis

Psychometric Properties of a New Scale for Measuring Anxiety in People with a Learning Disability: The Glasgow Anxiety Scale for People with a Learning Disability (GAS-LD) and Research Portfolio

There has been relatively limited research on the mental health of people with a learning disability, in spite of the high prevalence of disorder in this population. Anxiety disorders are among the most common psychological difficulties, and comprise a considerable proportion of research effort in the general adult field. However there is a dearth of research involving people with a learning disability. Consequently models of anxiety are based on the general adult population and there has been little consideration of the way in which anxiety in people with a learning disability should be conceptualised. One reason for this, may be the difficulty in developing relevant assessment tools due to communication problems and lack of procedural standardisation in a relatively heterogeneous population. However the recent development of a DSM-IV based psychiatric interview represents substantial progress in this area. In this review the need for the development of self-report measures of anxiety is considered in some depth. Such measures are widely available in adult mental health and are useful for symptom screening, outcome measurement and as an aid to diagnosis. The development of a reliable and valid scale for use with people with a learning disability is long overdue

Dynamic harmonic regression and irregular sampling; avoiding pre-processing and minimising modelling assumptions

Many environmental time-series measurements are characterised by irregular sampling. A significant improvement of the Dynamic Harmonic Regression (DHR) modelling technique to accommodate irregular sampled time-series, without the need for data pre-processing, has been developed. Taylor's series is used to obtain the time-step state increments, modifying the transition equation matrices. This allows the user to avoid artefacts arising and insertion of assumptions from interpolation and regularisation of the data to a regular time-base and makes DHR more consistent with the Data-Based Mechanistic approach to modelling environmental systems. The new technique implemented as a Matlab package has been tested on demanding simulated data-sets and demonstrated on various environmental time-series data with significantly varying sampling times. The results have been compared with standard DHR, where possible, and the method reduces analysis time and produces unambiguous results (by removing the need for pre-processing – always based on assumptions) based only on the observed environmental data

Extended State Dependent Parameter modelling with a Data-Based Mechanistic approach to nonlinear model structure identification

Abstract A unified approach to Multiple and single State Dependent Parameter modelling, termed Extended State Dependent Parameters (ESDP) modelling, of nonlinear dynamic systems described by time-varying dynamic models applied to ARX or transfer-function model structures. Crucially, the approach proposes an effective model structure identification method using a novel Information Criterion (IC) taking into account model complexity in terms of the number of states involved. In ESDP, model structure involves not only the model orders, but also selection of the states driving the parameters, which effectively prevents the use of most current IC. This leads to a powerful methodology for investigating nonlinear systems building on the Data-Based Mechanistic (DBM) philosophy of Young and expanding the applications of the existing DBM methods. The methodologies presented are tested and demonstrated on both simulated data and on high frequency hydrological observations, showing how structure identification leads to discovery of dynamic relationships between system variables

Polycyclic Aromatic Hydrocarbons not declining in Arctic air despite global emission reduction

Two decades of atmospheric measurements of polycyclic aromatic hydrocarbons (PAHs) were conducted at three Arctic sites, i.e., Alert, Canada; Zeppelin, Svalbard; and Pallas, Finland. PAH concentrations decrease with increasing latitude in the order of Pallas>Zeppelin>Alert. Forest fire was identified as an important contributing source. Three representative PAHs, phenanthrene (PHE), pyrene (PYR), and benzo(a)pyrene (BaP) were selected for the assessment of their long-term trends. Significant decline of these PAHs was not observed contradicting the expected decline due to PAH emission reductions. A global 3-D transport model was employed to simulate the concentrations of these three PAHs at the three sites. The model predicted that warming in the Arctic would cause the air concentrations of PHE and PYR to increase in the Arctic atmosphere, while that of BaP, which tends to be particle-bound, is less affected by temperature. The expected decline due to the reduction of global PAH emissions is offset by the increment of volatilization caused by warming. This work shows that this phenomenon may affect the environmental occurrence of other anthropogenic substances, such as, the more volatile flame retardants and pesticides

International Consortium for Health Outcomes Measurement (ICHOM): Standardized Patient-Centered Outcomes Measurement Set for Heart Failure Patients

Whereas multiple national, international, and trial registries for heart failure have been created, international standards for clinical assessment and outcome measurement do not currently exist. The working group's objective was to facilitate international comparison in heart failure care, using standardized parameters and meaningful patient-centered outcomes for research and quality of care assessments. The International Consortium for Health Outcomes Measurement recruited an international working group of clinical heart failure experts, researchers, and patient representatives to define a standard set of outcomes and risk-adjustment variables. This was designed to document, compare, and ultimately improve patient care outcomes in the heart failure population, with a focus on global feasibility and relevance. The working group employed a Delphi process, patient focus groups, online patient surveys, and multiple systematic publications searches. The process occurred over 10 months, employing 7 international teleconferences. A 17-item set has been established, addressing selected functional, psychosocial, burden of care, and survival outcome domains. These measures were designed to include all patients with heart failure, whether entered at first presentation or subsequent decompensation, excluding cardiogenic shock. Sources include clinician report, administrative data, and validated patient-reported outcome measurement tools: the Kansas City Cardiomyopathy Questionnaire; the Patient Health Questionnaire-2; and the Patient-Reported Outcomes Measurement Information System. Recommended data included those to support risk adjustment and benchmarking across providers and regions. The International Consortium for Health Outcomes Measurement developed a dataset designed to capture, compare, and improve care for heart failure, with feasibility and relevance for patients and clinicians worldwide

Effectiveness of cognitive-behaviour therapy for hoarding disorder in people with mild intellectual disabilities

Evaluations of cognitive behavioural interventions for hoarding for those with intellectual disabilities (ID) have not been previously attempted. This investigation therefore examined the acceptability and effectiveness of cognitive-behavioural therapy (CBT) in a sample of N = 14 adults with mild ID. All participants had hoarding as their primary problem and received twelve individual CBT sessions, all conducted via domiciliary visits. The primary outcome measure was an environmental measure (Clutter Image Rating Scale), which was scored at baseline, end of treatment and at six-month follow-up. Acceptability of CBT was measured via the treatment refusal and dropout rate. Secondary self-report outcomes included measures of hoarding, depression and anxiety. Results demonstrate that hoarding significantly reduced following treatment on both self-report and environmental assessment. No participants refused or dropped out of treatment and that there was no evidence of relapse over the follow-up period. No adverse treatment incidences were reported. This open trial suggests that CBT may be a safe and effective intervention for hoarding difficulties in people with ID, but that the evidence base in this population needs urgent and detailed attention