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Objective: To determine the level of agreement of disease flare severity (distinguishing severe, moderate, and mild flare and persistent disease activity) in a large paper-patient exercise involving 988 individual cases of systemic lupus erythematosus.Methods: A total of 988 individual lupus case histories were assessed by 3 individual physicians. Complete agreement about the degree of flare (or persistent disease activity) was obtained in 451 cases (46%), and these provided the reference standard for the second part of the study. This component used 3 flare activity instruments (the British Isles Lupus Assessment Group [BILAG] 2004, Safety of Estrogens in Lupus Erythematosus National Assessment [SELENA] flare index [SFI] and the revised SELENA flare index [rSFI]). The 451 patient case histories were distributed to 18 pairs of physicians, carefully randomized in a manner designed to ensure a fair case mix and equal distribution of flare according to severity.Results: The 3-physician assessment of flare matched the level of flare using the 3 indices, with 67% for BILAG 2004, 72% for SFI, and 70% for rSFI. The corresponding weighted kappa coefficients for each instrument were 0.82, 0.59, and 0.74, respectively. We undertook a detailed analysis of the discrepant cases and several factors emerged, including a tendency to score moderate flares as severe and persistent activity as flare, especially when the SFI and rSFI instruments were used. Overscoring was also driven by scoring treatment change as flare, even if there were no new or worsening clinical features.Conclusion: Given the complexity of assessing lupus flare, we were encouraged by the overall results reported. However, the problem of capturing lupus flare accurately is not completely solved. [ABSTRACT FROM AUTHOR]

Objectives: A new patient-reported outcome (PRO) instrument to measure fatigue symptoms and impacts in relapsing multiple sclerosis (RMS) was developed in a qualitative stage, followed by psychometric validation and migration from paper to an electronic format.Methods: Adult patients with relapsing-remitting multiple sclerosis (RRMS) were interviewed to elicit fatigue-related symptoms and impacts. A draft questionnaire was debriefed in cognitive interviews with further RRMS patients, and revised. Content confirmation interviews were conducted with patients with progressive-relapsing multiple sclerosis (PRMS) and relapsing secondary-progressive multiple sclerosis (RSPMS). Psychometric analyses used data from adult patients with different RMS subtypes and matched non-RMS controls in a multicenter, observational study. After item reduction, the final instrument was migrated to a smartphone (eDiary) and usability was confirmed in interviews with additional adult RMS patients.Results: The qualitative stage included 37 RRMS, 5 PRMS, and 5 RSPMS patients. Saturation of concepts was reached during concept elicitation. Cognitive interviews confirmed that participants understood the instructions, items, and response options of the instrument-named FSIQ-RMS-as intended. Psychometric validation included 164 RMS and 74 control patients. Internal consistency and test-retest reliability were demonstrated. The symptoms domain discriminated along the RMS symptom-severity continuum and between patients and controls. Patients were able to attribute fatigue-related symptoms to RMS. Usability and conceptual equivalence of the eDiary were confirmed (n = 10 participants).Conclusions: With 7 symptom items and 13 impact items (in 3 impacts subdomains: physical, cognitive and emotional, and coping) after item reduction, the FSIQ-RMS is a comprehensive, valid, and reliable measure of fatigue-related symptoms and impacts in RMS patients. [ABSTRACT FROM AUTHOR]

Aim: To assess the reliability and predictive validity of the developmental and socio-emotional scales of the Standardized Infant NeuroDevelopmental Assessment (SINDA).Method: To assess reliability, two sets of three assessors forming eight assessor-pairs independently rated the developmental and socio-emotional scales of 60 infants. To evaluate predictive validity, 223 infants (gestational age 30wks [range 23-41wks]; 117 males, 106 females) attending a non-academic outpatient clinic were assessed by different assessors with SINDA's neurological, developmental, and socio-emotional scales. Atypical neurodevelopmental outcome at a corrected age of 24 months or older implied a Bayley Mental or Psychomotor Developmental Index score of less than 70 or neurological disorder (including cerebral palsy). Behavioural and emotional disorders were classified according to the International Classification of Diseases, 10th Revision. Predictive values were calculated from SINDA (2-12mo corrected age, median 7mo) and typical versus atypical outcome, and for intellectual disability only (Mental Developmental Index <70).Results: Assessors highly agreed on the developmental and socio-emotional assessments (developmental scores: Spearman's rank correlation coefficient ρ=0.972; single socio-emotional behaviour items: Cohen's κ=0.783-0.896). At 24 months or older, 65 children had atypical outcome. Atypical neurological scores predicted atypical outcome (sensitivity 83%, specificity 96%); atypical developmental scores predicted intellectual disability (sensitivity 77%, specificity 92%). Atypical emotionality and atypical self-regulation were associated with behavioural and emotional disorders.Interpretation: SINDA's three scales are reliable, and have a satisfactory predictive validity for atypical developmental outcome at 24 months or older in a non-academic outpatient setting. SINDA's developmental scale has promising predictive validity for intellectual disability. SINDA's socio-emotional scale is a tool for caregiver counselling.What This Paper Adds: Standardized Infant NeuroDevelopmental Assessment (SINDA)'s developmental and socio-emotional scales have excellent interrater reliability. Replication of the satisfactory validity of SINDA's neurological scale for atypical outcome. [ABSTRACT FROM AUTHOR]

Objectives: This paper is aimed to explore the value of double source CT angiography (DS-CTA) for diagnosing in-stent restenosis in lower limb artery.Methods: From January 2016 to October 2018, all patients with stent in lower limb artery in our hospital were investigated by both DS-CTA and digital subtraction angiography. We measured the minimum lumen diameter and the diameter of the proximal normal vessels under each stent placement. The in-stent restenosis is defined as restenosis when the lumen area decreased by more than 50%. Digital subtraction angiography was performed within 1 week after DS-CT scan. Relationship between DS-CTA and digital subtraction angiography for diagnosing in-stent restenosis in lower limb artery was analyzed. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of DS-CTA for diagnosis of in-stent restenosis were analyzed with digital subtraction angiography as the reference standard. A total of 68 stents were placed in 51 patients. Among these patients, 27 cases were diagnosed as in-stent restenosis, presenting as endovascular contrast agent bias or crescent filling defect with the lumen area reducing over 50%, 6 cases of which had no significant in-stent restenosis by digital subtraction angiography analysis. Furthermore, 12 cases were occlusion, in which there was no high density contrast agent in stents; the remaining 41 stents were unobstructed and the contrast agent was filled well, 8 cases of which had significant in-stent restenosis by digital subtraction angiography analysis. In addition, four stents were deformed or distorted. Statistical analysis demonstrated the concentrations of DS-CTA and digital subtraction angiography in diagnosing in-stent restenosis for lower limb artery were closely related, and the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of DS-CTA were 72.4%, 84.6%, 77.8%, 80.5%, and 79.4%, respectively.Conclusion: DS-CTA has a potential reliability for diagnosis of in-stent restenosis in lower limb artery, which may be further improved to be used for clinical interventional treatment of vascular diseases. [ABSTRACT FROM AUTHOR]

Background: Free and Cued Selective Reminding Test (FCSRT) is a reliable cognitive marker for Alzheimer's disease (AD), and the identification of neuropsychological tests sensitive to the early signs of AD pathology is crucial both in research and clinical practice.Objective: The study aimed to ascertain the ability of FCSRT in predicting the amyloid load as determined from amyloid PET imaging (Amy-PET) in patients with cognitive disorders.Methods: For our purpose, 79 patients (71 MCI, 8 mild dementia) underwent a complete workup for dementia, including the FCSRT assessment and a [18F]florbetaben PET scan. FCSRT subitem scores were used as predictors in different binomial regression models.Results: Immediate free recall and delayed free recall were the best predictors overall in the whole sample; whereas in patients <76 years, all models further improved with immediate total recall (ITR) and Index of Sensitivity of Cueing (ISC) resulting the most accurate in anticipating Amy-PET results, with a likelihood of being Amy-PET positive greater than 85% for ITR and ISC scores of less than 25 and 0.5, respectively.Conclusion: FCSRT proved itself to be a valid tool in dementia diagnosis, also being able to correlate with amyloid pathology. The possibility to predict Amy-PET results through a simple and reliable neuropsychological test might be helpful for clinicians in the dementia field, adding value to a paper and pencil tool compared to most costly biomarkers. [ABSTRACT FROM AUTHOR]

For patients with major burn injuries, an accurate burn size estimation is essential to plan appropriate treatment and minimize medical and surgical complications. However, current clinical methods for burn size estimation lack accuracy and reliability. To overcome these limitations, this paper proposes a 3D-based approach-with personalized 3D models from a limited set of anthropometric measurements-to accurately assess the percent TBSA affected by burns. First, a reliability and feasibility study of the anthropometric measuring process was performed to identify clinically relevant measurements. Second, a large representative stratified random sample was generated to output several anthropometric features required for predictive modeling. Machine-learning algorithms assessed the importance and the subsets of anthropometric measurements for predicting the BSA according to specific patient morphological features. Then, the accuracy of both the morphology and BSA of 3D models built from a limited set of measurements was evaluated using error metrics and maximum distances 3D color maps. Results highlighted the height and circumferences of the bust, neck, hips, and waist as the best predictors for BSA. 3D models built from three to four anthropometric measurements showed good accuracy and were geometrically close to gold standard 3D scans. Outcomes of this study aim to decrease medical and surgical complications by decreasing errors in percent TBSA assessments and, therefore, improving patient outcomes by personalizing care. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the psychometric properties of the Child and Adolescent Intellectual Disability Screening Questionnaire (CAIDS-Q) in paediatric neurodevelopment clinics.Method: Participants were 181 children (aged 6-18y) attending paediatric services in Scotland, divided into three age groups according to previous CAIDS-Q standardization cut-off scores. Fifty-four children (37 males, 17 females; mean age 117mo [SD 29.9mo]) met the criteria for intellectual disability and 127 did not (88 males, 39 females; mean age 120.1mo [SD 32.7mo]). A number of psychometric properties of the CAIDS-Q were evaluated, including test-retest and interrater reliability, convergent validity, sensitivity, specificity, and positive and negative predictive values based on existing cut-off scores.Results: Significant positive relationships were found for all three age groups between CAIDS-Q scores and measures of intellectual and adaptive functioning. Test-retest reliability ranged from 'moderate' to 'almost perfect', whereas interrater reliability ranged from 'fair' to 'almost perfect'. Sensitivity and positive predictive value were 100% for all groups and specificity was between 83% and 94%, depending on age. Negative predictive values ranged from 75% to 91%.Interpretation: The CAIDS-Q appears to show psychometric properties that support its use as a screen for intellectual disability in paediatric neurodevelopmental settings.What This Paper Adds: The Child and Adolescent Intellectual Disability Screening Questionnaire showed good psychometric properties. It identified all participating children who met the criteria for intellectual disability. Between 83% and 94% of children without intellectual disability were also correctly identified. [ABSTRACT FROM AUTHOR]

The present paper has three objectives. First, methods for comparing alternative tests for the purpose of replacement of one test with a second presumably superior test are described. Second, problems in the interpretation of the relevance of different diagnostic thresholds (thresholds of positivity) that define who is and who is not a disordered gambler are examined and a potential solution offered in the form of a common quantitative measure of the risk of being a disordered gambler. Third, alternative methodologies are described as potential solutions to the lack of a gold or reference standard in the evaluation of new tests. [ABSTRACT FROM AUTHOR]

The brain connections in the different regions demonstrate the characteristics of brain activities. In addition, in various conditions and with neuropsychological disorders, the brain has special patterns in different regions. This paper presents a model to show and compare the connection patterns in different brain regions of children with autism (53 boys and 36 girls) and control children (61 boys and 33 girls). The model is designed by cellular neural networks and it uses the proper features of electroencephalography. The results show that there are significant differences and abnormalities in the left hemisphere, (p < 0.05) at the electrodes AF3, F3, P7, T7, and O1 in the children with autism compared with the control group. Also, the evaluation of the obtained connections values between brain regions demonstrated that there are more abnormalities in the connectivity of frontal and parietal lobes and the relations of the neighboring regions in children with autism. It is observed that the proposed model is able to distinguish the autistic children from the control subjects with an accuracy rate of 95.1% based on the obtained values of CNN using the SVM method. [ABSTRACT FROM AUTHOR]

Diagnostic information regarding the health status of the corneal endothelium may be obtained by analyzing the size and the shape of the endothelial cells in specular microscopy images. Prior to the analysis, the endothelial cells need to be extracted from the image. Up to today, this has been performed manually or semi-automatically. Several approaches to automatic segmentation of endothelial cells exist; however, none of them is perfect. Therefore this paper proposes to perform cell segmentation using a U-Net-based convolutional neural network. Particularly, the network is trained to discriminate pixels located at the borders between cells. The edge probability map outputted by the network is next binarized and skeletonized in order to obtain one-pixel wide edges. The proposed solution was tested on a dataset consisting of 30 corneal endothelial images presenting cells of different sizes, achieving an AUROC level of 0.92. The resulting DICE is on average equal to 0.86, which is a good result, regarding the thickness of the compared edges. The corresponding mean absolute percentage error of cell number is at the level of 4.5% which confirms the high accuracy of the proposed approach. The resulting cell edges are well aligned to the ground truths and require a limited number of manual corrections. This also results in accurate values of the cell morphometric parameters. The corresponding errors range from 5.2% for endothelial cell density, through 6.2% for cell hexagonality to 11.93% for the coefficient of variation of the cell size. [ABSTRACT FROM AUTHOR]

Lung sounds convey relevant information related to pulmonary disorders, and to evaluate patients with pulmonary conditions, the physician or the doctor uses the traditional auscultation technique. However, this technique suffers from limitations. For example, if the physician is not well trained, this may lead to a wrong diagnosis. Moreover, lung sounds are non-stationary, complicating the tasks of analysis, recognition, and distinction. This is why developing automatic recognition systems can help to deal with these limitations. In this paper, we compare three machine learning approaches for lung sounds classification. The first two approaches are based on the extraction of a set of handcrafted features trained by three different classifiers (support vector machines, k-nearest neighbor, and Gaussian mixture models) while the third approach is based on the design of convolutional neural networks (CNN). In the first approach, we extracted the 12 MFCC coefficients from the audio files then calculated six MFCCs statistics. We also experimented normalization using zero mean and unity variance to enhance accuracy. In the second approach, the local binary pattern (LBP) features are extracted from the visual representation of the audio files (spectrograms). The features are normalized using whitening. The dataset used in this work consists of seven classes (normal, coarse crackle, fine crackle, monophonic wheeze, polyphonic wheeze, squawk, and stridor). We have also experimentally tested dataset augmentation techniques on the spectrograms to enhance the ultimate accuracy of the CNN. The results show that CNN outperformed the handcrafted feature based classifiers. [ABSTRACT FROM AUTHOR]

Background: Multistep immunoassay kits for the diagnosis of rupture of membranes are relatively complex and are not designed to be used by pregnant women themselves. These kits require procedural steps of specimen extraction and preparation. We evaluated the ability of a sanitary pad containing a qualitative immunoassay for alpha-fetoprotein (AFP) to serve as a one-step self-test to detect amniotic fluid leakage.Technique: Four sets of pads were evaluated. The pads in the study set were worn by 288 pregnant women with confirmed rupture of membranes. Three controls were evaluated: 1) pads worn by 93 pregnant women with intact membranes, 2) additional pads instilled with urine specimens obtained from the 381 women described previously (study set plus control set 1), and 3) pads instilled with semen collected from 40 men.Experience: All 288 pads that absorbed amniotic fluid had positive results. Approximately half of the pads absorbed with normal vaginal discharge had a sufficient amount to yield valid results, which were all negative. All 381 pads with instilled urine and all 40 pads with instilled semen had negative results.Conclusion: An immunoassay for AFP, embedded in a pad, appears to be a feasible and reproducible self-test for the detection of rupture of membranes. [ABSTRACT FROM AUTHOR]

Background: The outcomes of psychological therapies for anxiety and depression vary across individuals and symptom domains. Being able to predict treatment response from readily available patient data at presentation has potentially important benefits in aiding decisions about the most suitable interventions for a patient. This paper presents a method of identifying subgroups of patients using latent profile analysis, and comparing response to psychological treatments between these profiles.Methods: All outpatients taken into treatment at two psychological treatment services in London, UK and who provided basic demographic information and standardized symptom measures were included in the analysis (n=16636).Results: Latent Profile Analysis was performed on intake data to identify statistically different groups of patients, which were then examined in longitudinal analyses to determine their capacity to predict treatment outcomes. Comparison between profiles showed considerable variation in recovery (74-15%), deterioration rates (5-20%), and levels of attrition (17-40%). Further variation in outcomes was found within the profiles when different intensities of psychological intervention were delivered.Limitations: Latent profiles were identified using data from two services, so generalisability to other services should be considered. Routinely collected patient data was included, additional patient information may further enhance utility of the profiles.Conclusions: These results suggest that intake data can be used to reliably classify patients into profiles that are predictive of outcome to different intensities of psychological treatment in routine care. Algorithms based on these kinds of data could be used to optimize decision-making and aid the appropriate matching of patients to treatment. [ABSTRACT FROM AUTHOR]

Background: This paper explores the importance of electronic medical records (EMR) for predicting 30-day all-cause non-elective readmission risk of patients and presents a comparison of prediction performance of commonly used methods.Methods: The data are extracted from eight Advocate Health Care hospitals. Index admissions are excluded from the cohort if they are observation, inpatient admissions for psychiatry, skilled nursing, hospice, rehabilitation, maternal and newborn visits, or if the patient expires during the index admission. Data are randomly and repeatedly divided into fitting and validating sets for cross validations. Approaches including LACE, STEPWISE logistic, LASSO logistic, and AdaBoost, are compared with sample sizes varying from 2,500 to 80,000.Results: Our results confirm that LACE has moderate discrimination power with the area under receiver operating characteristic curve (AUC) around 0.65-0.66, which can be improved to 0.73-0.74 when additional variables from EMR are considered. These variables include Inpatient in the last six months, Number of emergency room visits or inpatients in the last year, Braden score, Polypharmacy, Employment status, Discharge disposition, Albumin level, and medical condition variables such as Leukemia, Malignancy, Renal failure with hemodialysis, History of alcohol substance abuse, Dementia and Trauma. When sample size is small (≤5000), LASSO is the best; when sample size is large (≥20,000), the predictive performance is similar. The STEPWISE method has a slightly lower AUC (0.734) comparing to LASSO (0.737) and AdaBoost (0.737). More than one half of the selected predictors can be false positives when using a single method and a single division of fitting/validating data.Conclusions: True predictors can be identified by repeatedly dividing data into fitting/validating subsets and referring the final model based on summarizing results. LASSO is a better alternative to the STEPWISE logistic regression, especially when sample size is not large. The evidence for adequate sample size can be explored by fitting models on gradually reduced samples. Our model comparison strategy is not only good for 30-day all-cause non-elective readmission risk predictions, but also applicable to other types of predictive models in clinical studies. [ABSTRACT FROM AUTHOR]

Objective: This paper aims at developing an automated gastroscopic video summarization algorithm to assist clinicians to more effectively go through the abnormal contents of the video.Methods and Materials: To select the most representative frames from the original video sequence, we formulate the problem of gastroscopic video summarization as a dictionary selection issue. Different from the traditional dictionary selection methods, which take into account only the number and reconstruction ability of selected key frames, our model introduces the similar-inhibition constraint to reinforce the diversity of selected key frames. We calculate the attention cost by merging both gaze and content change into a prior cue to help select the frames with more high-level semantic information. Moreover, we adopt an image quality evaluation process to eliminate the interference of the poor quality images and a segmentation process to reduce the computational complexity.Results: For experiments, we build a new gastroscopic video dataset captured from 30 volunteers with more than 400k images and compare our method with the state-of-the-arts using the content consistency, index consistency and content-index consistency with the ground truth. Compared with all competitors, our method obtains the best results in 23 of 30 videos evaluated based on content consistency, 24 of 30 videos evaluated based on index consistency and all videos evaluated based on content-index consistency.Conclusions: For gastroscopic video summarization, we propose an automated annotation method via similar-inhibition dictionary selection. Our model can achieve better performance compared with other state-of-the-art models and supplies more suitable key frames for diagnosis. The developed algorithm can be automatically adapted to various real applications, such as the training of young clinicians, computer-aided diagnosis or medical report generation. [ABSTRACT FROM AUTHOR]

With rapid development in the technology of measuring disease characteristics at molecular or genetic level, it is possible to collect a large amount of data on various potential predictors of the clinical outcome of interest in medical research. It is often of interest to effectively use the information on a large number of predictors to make prediction of the interested outcome. Various statistical tools were developed to overcome the difficulties caused by the high-dimensionality of the covariate space in the setting of a linear regression model. This paper focuses on the situation, where the interested outcomes are subjected to right censoring. We implemented the extended partial least squares method along with other commonly used approaches for analyzing the high-dimensional covariates to the ACTG333 data set. Especially, we compared the prediction performance of different approaches with extensive cross-validation studies. The results show that the Buckley-James based partial least squares, stepwise subset model selection and principal components regression have similar promising predictive power and the partial least square method has several advantages in terms of interpretability and numerical computation. [ABSTRACT FROM AUTHOR]

Fluid-solid interaction is a primary feature of cardiovascular flows. There is increasing interest in the numerical solution of these systems as the extensive computational resource required for such studies becomes available. One form of coupling is an external weak coupling of separate solid and fluid mechanics codes. Information about the stress tensor and displacement vector at the wetted boundary is passed between the codes, and an iterative scheme is employed to move towards convergence of these parameters at each time step. This approach has the attraction that separate codes with the most extensive functionality for each of the separate phases can be selected, which might be important in the context of the complex rheology and contact mechanics that often feature in cardiovascular systems. Penrose and Staples describe a weak coupling of CFX for computational fluid mechanics to ANSYS for solid mechanics, based on a simple Jacobi iteration scheme. It is important to validate the coupled numerical solutions. An extensive analytical study of flow in elastic-walled tubes was carried out by Womersley in the late 1950s. This paper describes the performance of the coupling software for the straight elastic-walled tube, and compares the results with Womersley's analytical solutions. It also presents preliminary results demonstrating the application of the coupled software in the context of a stented vessel. [ABSTRACT FROM AUTHOR]

Part 2 of this paper presents an analysis of variance (ANOVA) for investigating the precedence of the various parameters, and the effects of varying these parameters, in assessment of burn injury resulting from the exposure of skin surface to heat sources. A one-dimensional model based on the finite difference method (FDM), as implemented in a spreadsheet software application, is applied to the assessment of burn injury. Henriques' theory of skin burns is used for determining the spatial and temporal extent of tissue damage. The ranks of the effects of various factors were obtained. It was found that the highest ranked factor is the initial tissue temperature followed by the thermal conductivity of the epidermal layer. The effect of blood perfusion rate is ranked much below the combinations of other factors. The results from the present numerical experiment agree well with the results obtained by Palla. Sensitivity analysis of the critical exposure levels was also carried out and results are discussed. In this study, the effects of the various parameters on injury threshold were investigated. Again, the results indicate that the four parameters: thermal conductivity of the epidermis and dermis, convective heat transfer coefficient and initial tissue temperature, have a pronounced influence on assessing the burn injury threshold. It was also found that fat thermal conductivity and blood perfusion rate have no obvious effect on injury threshold. A two-dimensional analysis was further conducted to determine the sensitivity of the predicted injury to the values of frequency factor, P, and apparent activation energy, deltaE, used in the models. Part 1 of this study details the development of the computer models based on the one- and two-dimensional bioheat equations. [ABSTRACT FROM AUTHOR]

Several authors have suggested that dementia screening tests may be biased against low levels of education, whereas others find that a low level of education is a genuine risk factor for dementia. The present paper attempts to reconcile these conflicting views by examining item bias and test bias indices of the mini mental state examination (MMSE). Psychometric calculations and receiver operating characteristics (ROC) analyses of sensitivity and specificity as performed by earlier studies were replicated and extended from the database of the Amsterdam Study of the Elderly. This is a population survey on cognitive decline and dementia (age range 65-84). Subjects with a low level of education (primary school) were compared with better educated subjects (at least some secondary education). Cases were matched by age and sex. The results indicate that the MMSE is not educationally biased as far as item characteristics, reliability, and construct validity are concerned. Yet its predictive validity as a screening test for dementia is educationally biased. This bias will effectively be eliminated with a two point higher cut off score for the subjects whose education extends beyond primary school. Even after such score correction, a low level of education probably remains a genuine risk factor for dementia. [ABSTRACT FROM AUTHOR]

The Epidemiological Catchment Area (ECA) program is a major step in psychiatric epidemiology because it uses a structured interview: the Diagnostic Interview Schedule (DIS). This gives psychiatric diagnoses in different diagnostic systems and can be administered by lay interviewers. However many surveys on health-related topics cannot give the time needed to administer the DIS and train the personnel to use such a complex schedule as the DIS. The DISSA is a self-administered abridged form of the DIS and represents an effort to produce a short and simple instrument for three types of diagnoses: major depressive disorder, all anxiety disorders (generalized anxiety, phobias, panic) and alcohol disorders. In this paper we describe the DISSA and give preliminary evidence about its efficiency. This will be done by comparing DISSA results with clinicians judgments, the DIS and a checklist derived from HSC. The DISSA functions in a manner similar to its parent instrument but achieves this in a shorter and less costly mode. Actually the approach by episode used for assessing depression seems to work better than the DIS. Comparisons with the checklist show a clear superiority of the DISSA for depression, alcohol and anxiety disorders but not for generalized anxiety. [ABSTRACT FROM AUTHOR]

The structural response of a large artery is characteristically complex and includes the highly non-linear, history-dependent response of a nonhomogeneous anisotropic structure undergoing finite deformations. The mechanics of the arterial wall has been studied for nearly two centuries. The goals of such research range from the desire to have a basic knowledge and understanding of the mechanics and physiology of this complex structure to the need for data and methods for the design of arterial prostheses. In this paper, the models for arterial wall behaviour are critically reviewed. Firstly, the structure and general characteristics of the arterial wall are discussed. This is followed by a comprehensive review of the constitutive laws. Finally, structural analyses of the arterial wall by mathematical and numerical methods are discussed. Predictions using the authors' preferred models give focus to important issues, and in Part 2 the review and predictions are extended to the fluid solid coupled situation. [ABSTRACT FROM AUTHOR]

Current international attention to Health in all policies (HiAP) has its origins in a more sophisticated understanding of the impact of public policies on health, and a recognition that policies across government have an impact on the social and environmental determinants of health and related inequalities in health. As an emerging field, there has been limited attention focused on comprehensive approaches to the evaluation of HiAP to date, and the research focus around HiAP has mainly examined the processes of cross-sectoral policy development, rather than their health-related impacts or outcomes. The purpose of this paper is to explore issues in assessing the implementation of HiAP and describe an expanded evaluation framework for assessing the potential intermediate and end-point effects of HiAP actions, using a planning logic model for ‘complex programs’. This meets the needs of public sector policy-makers who express an interest in understanding the relationship between HiAP and health-related and social outcomes. The paper proposes applying a contribution analysis method to estimate and model the anticipated impacts of HiAP policies on intermediate and longer term outcomes, in advance of empirical studies of these outcomes, and as an innovative input into HiAP and evaluation planning. A broader long-term evaluation framework will enhance the political saliency of HiAP initiatives, especially from governments considering HiAP approaches in financially constrained environments. [ABSTRACT FROM PUBLISHER]