Targeting tumor heterogeneity: multiplex-detection-based multiple instance learning for whole slide image classification.

MOTIVATION: Multiple instance learning (MIL) is a powerful technique to classify whole slide images (WSIs) for diagnostic pathology. The key challenge of MIL on WSI classification is to discover the critical instances that trigger the bag label. However, tumor heterogeneity significantly hinders the algorithm's performance. RESULTS: Here, we propose a novel multiplex-detection-based multiple instance learning (MDMIL) which targets tumor heterogeneity by multiplex detection strategy and feature constraints among samples. Specifically, the internal query generated after the probability distribution analysis and the variational query optimized throughout the training process are utilized to detect potential instances in the form of internal and external assistance, respectively. The multiplex detection strategy significantly improves the instance-mining capacity of the deep neural network. Meanwhile, a memory-based contrastive loss is proposed to reach consistency on various phenotypes in the feature space. The novel network and loss function jointly achieve high robustness towards tumor heterogeneity. We conduct experiments on three computational pathology datasets, e.g. CAMELYON16, TCGA-NSCLC, and TCGA-RCC. Benchmarking experiments on the three datasets illustrate that our proposed MDMIL approach achieves superior performance over several existing state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: MDMIL is available for academic purposes at https://github.com/ZacharyWang-007/MDMIL.

iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets.

The rapid accumulation of molecular data motivates development of innovative approaches to computationally characterize sequences, structures and functions of biological and chemical molecules in an efficient, accessible and accurate manner. Notwithstanding several computational tools that characterize protein or nucleic acids data, there are no one-stop computational toolkits that comprehensively characterize a wide range of biomolecules. We address this vital need by developing a holistic platform that generates features from sequence and structural data for a diverse collection of molecule types. Our freely available and easy-to-use iFeatureOmega platform generates, analyzes and visualizes 189 representations for biological sequences, structures and ligands. To the best of our knowledge, iFeatureOmega provides the largest scope when directly compared to the current solutions, in terms of the number of feature extraction and analysis approaches and coverage of different molecules. We release three versions of iFeatureOmega including a webserver, command line interface and graphical interface to satisfy needs of experienced bioinformaticians and less computer-savvy biologists and biochemists. With the assistance of iFeatureOmega, users can encode their molecular data into representations that facilitate construction of predictive models and analytical studies. We highlight benefits of iFeatureOmega based on three research applications, demonstrating how it can be used to accelerate and streamline research in bioinformatics, computational biology, and cheminformatics areas. The iFeatureOmega webserver is freely available at http://ifeatureomega.erc.monash.edu and the standalone versions can be downloaded from https://github.com/Superzchen/iFeatureOmega-GUI/ and https://github.com/Superzchen/iFeatureOmega-CLI/.

Should the Use of Adaptive Machine Learning Systems in Medicine be Classified as Research?

A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called "update problem," which concerns how regulators should approach systems whose performance and parameters continue to change even after they have received regulatory approval. In this paper, we draw attention to a prior ethical question: whether the continuous learning that will occur in such systems after their initial deployment should be classified, and regulated, as medical research? We argue that there is a strong prima facie case that the use of continuous learning in medical ML systems should be categorized, and regulated, as research and that individuals whose treatment involves such systems should be treated as research subjects.

Evaluating the Potential and Pitfalls of AI-Powered Conversational Agents as Humanlike Virtual Health Carers in the Remote Management of Noncommunicable Diseases: Scoping Review.

BACKGROUND: The rising prevalence of noncommunicable diseases (NCDs) worldwide and the high recent mortality rates (74.4%) associated with them, especially in low- and middle-income countries, is causing a substantial global burden of disease, necessitating innovative and sustainable long-term care solutions. OBJECTIVE: This scoping review aims to investigate the impact of artificial intelligence (AI)-based conversational agents (CAs)-including chatbots, voicebots, and anthropomorphic digital avatars-as human-like health caregivers in the remote management of NCDs as well as identify critical areas for future research and provide insights into how these technologies might be used effectively in health care to personalize NCD management strategies. METHODS: A broad literature search was conducted in July 2023 in 6 electronic databases-Ovid MEDLINE, Embase, PsycINFO, PubMed, CINAHL, and Web of Science-using the search terms "conversational agents," "artificial intelligence," and "noncommunicable diseases," including their associated synonyms. We also manually searched gray literature using sources such as ProQuest Central, ResearchGate, ACM Digital Library, and Google Scholar. We included empirical studies published in English from January 2010 to July 2023 focusing solely on health care-oriented applications of CAs used for remote management of NCDs. The narrative synthesis approach was used to collate and summarize the relevant information extracted from the included studies. RESULTS: The literature search yielded a total of 43 studies that matched the inclusion criteria. Our review unveiled four significant findings: (1) higher user acceptance and compliance with anthropomorphic and avatar-based CAs for remote care; (2) an existing gap in the development of personalized, empathetic, and contextually aware CAs for effective emotional and social interaction with users, along with limited consideration of ethical concerns such as data privacy and patient safety; (3) inadequate evidence of the efficacy of CAs in NCD self-management despite a moderate to high level of optimism among health care professionals regarding CAs' potential in remote health care; and (4) CAs primarily being used for supporting nonpharmacological interventions such as behavioral or lifestyle modifications and patient education for the self-management of NCDs. CONCLUSIONS: This review makes a unique contribution to the field by not only providing a quantifiable impact analysis but also identifying the areas requiring imminent scholarly attention for the ethical, empathetic, and efficacious implementation of AI in NCD care. This serves as an academic cornerstone for future research in AI-assisted health care for NCD management. TRIAL REGISTRATION: Open Science Framework; https://doi.org/10.17605/OSF.IO/GU5PX.

2023 Cardiac Society of Australia and New Zealand Expert Position Statement on Catheter and Surgical Ablation for Atrial Fibrillation.

Catheter ablation for atrial fibrillation (AF) has increased exponentially in many developed countries, including Australia and New Zealand. This Expert Position Statement on Catheter and Surgical Ablation for Atrial Fibrillation from the Cardiac Society of Australia and New Zealand (CSANZ) recognises healthcare factors, expertise and expenditure relevant to the Australian and New Zealand healthcare environments including considerations of potential implications for First Nations Peoples. The statement is cognisant of international advice but tailored to local conditions and populations, and is intended to be used by electrophysiologists, cardiologists and general physicians across all disciplines caring for patients with AF. They are also intended to provide guidance to healthcare facilities seeking to establish or maintain catheter ablation for AF.

Body mass index and type 2 diabetes in Thai adults: defining risk thresholds and population impacts.

BACKGROUND: Body mass index (BMI) cut-off values (>25 and >30) that predict diabetes risk have been well validated in Caucasian populations but less so in Asian populations. We aimed to determine the BMI threshold associated with increased type 2 diabetes (T2DM) risk and to calculate the proportion of T2DM cases attributable to overweight and obesity in the Thai population. METHODS: Participants were those from the Thai Cohort Study who were diabetes-free in 2005 and were followed-up in 2009 and 2013 (n = 39,021). We used multivariable logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the BMI-T2DM association. We modelled non-linear associations using restricted cubic splines. We estimated population attributable fractions (PAF) and the number of T2DM incident cases attributed to overweight and obesity. We also calculated the impact of reducing the prevalence of overweight and obesity on T2DM incidence in the Thai population. RESULTS: Non-linear modelling indicated that the points of inflection where the BMI-T2DM association became statistically significant compared to a reference of 20.00 kg/m2 were 21.60 (OR = 1.27, 95% CI 1.00-1.61) and 20.03 (OR = 1.02, 95% CI 1.02-1.03) for men and women, respectively. Approximately two-thirds of T2DM cases in Thai adults could be attributed to overweight and obesity. Annually, if prevalent obesity was 5% lower, ~13,000 cases of T2DM might be prevented in the Thai population. CONCLUSIONS: A BMI cut-point of 22 kg/m2, one point lower than the current 23 kg/m2, would be justified for defining T2DM risk in Thai adults. Lowering obesity prevalence would greatly reduce T2DM incidence.

Low back pain and limitations of daily living in Asia: longitudinal findings in the Thai cohort study.

BACKGROUND: Low back pain (LBP) is a major cause of disability throughout the world. However, longitudinal evidence to relate low back pain and functional limitations is mostly confined to Western countries. In this study, we investigate the associations between low back pain and functional limitations in a prospective cohort of Thai adults. METHODS: We analysed information from the Thai Cohort Study of adult Open University adults which included 42,785 participants in both 2009 and 2013, with the majority aged 30 to 65 years and residing nationwide. We used multivariate logistic regression to explore the longitudinal associations between LBP in 2009 and 2013 ('never': no LBP in 2009 or 2013; 'reverting': LBP in 2009 but not in 2013; 'incident': no LBP in 2009 but LBP in 2013; and 'chronic': reporting LBP at both time points) and the outcome of functional limitations relating to Activities of Daily Living (ADL) in 2013. RESULTS: Low back pain was common with 30% of cohort members reporting low back pain in both 2009 and 2013 ('chronic LBP'). The 'chronic LBP' group was more likely than the 'never' back pain group to report functional limitations in 2013: adjusted odds ratios 1.60 [95% Confidence Interval: 1.38-1.85] for difficulties getting dressed; 1.98 [1.71-2.30] for walking; 2.02 [1.71-2.39] for climbing stairs; and 3.80 [3.38-4.27] for bending/kneeling. Those with 'incident LBP' or 'reverting LBP' both had increased odds of functional limitations in 2013 but the odds were not generally as high. CONCLUSIONS: Our nationwide data from Thailand suggests that LBP is a frequent public health problem among economically productive age groups with adverse effects on the activities of daily living. This study adds to the limited longitudinal evidence on the substantial impact of low back pain in Southeast Asia.

Impact of High-Sensitivity Troponin I Testing with Sex-Specific Cutoffs on the Diagnosis of Acute Myocardial Infarction.

BACKGROUND: High-sensitivity cardiac troponin I (hs-cTnI) assays show sex-dependent differences in the 99th percentile of healthy populations, with concentrations in women approximately 50% lower. The adoption of sex-specific cutoffs seems appropriate, although it is not yet clear what effect these will have on acute myocardial infarction (AMI) diagnosis and management. METHODS: We conducted a retrospective pre- and postchangeover analysis of troponin I testing in the 6 months before and after moving from the contemporary Abbott Architect TnI assay (cTnI) to hs-cTnI at 2 tertiary centers in Australia and New Zealand. The cTnI cutoff was 30 ng/L for both sexes, whereas a female-specific cutoff of 16 ng/L was adopted upon changeover to hsTnI. RESULTS: Changeover from the cTnI assay to the hs-cTnI assay increased the number of female patients with increased troponin I concentrations at both sites (from 29.7% to 34.9% and from 22.4% to 30.8%; P < 0.001). There was no statistically significant change in the number of men with increased concentrations in the same time period (P = 0.09). The increased percentage of women with increased troponin I was not associated with an increase in the number of women with AMI diagnoses at either center. Angiographic data available from 1 center showed no change in the percentage of angiograms performed in women. CONCLUSIONS: Although increasing the proportion of women with increased troponin I, adopting sex-specific cutoffs with the hs-cTnI assay did not lead to an increase in AMI diagnoses in females, or in the number of women undergoing angiography.

A health advocacy intervention for adolescents with intellectual disability: a cluster randomized controlled trial.

AIM: Adolescents with intellectual disability experience poorer heath than their peers in the general population, partially due to communication barriers and knowledge gaps in their health history. This study aimed to test a health intervention package against usual care for a range of health promotion and disease detection outcomes. METHOD: A parallel-group cluster randomized controlled trial was conducted with Australian adolescents with intellectual disability living in the community. Randomization occurred at school level. The intervention package consisted of classroom-based health education, a hand-held personalized health record, and a health check. Evidence of health promotion, disease prevention, and case-finding activities were extracted from general practitioners' records for 12 months post-intervention. RESULTS: Clinical data was available for 435 of 592 (73.5%) participants from 85 schools. Adolescents allocated to receive the health intervention were more likely to have their vision (odds ratio [OR] 3.3; 95% confidence interval [CI] 1.8-6.1) and hearing (OR 2.7; 95% CI 1.0-7.3) tested, their blood pressure checked (OR 2.4; 95% CI 1.6-3.7), and weight recorded (OR 4.8; 95% CI 3.1-7.6). There was no difference between health intervention and usual care for identification of new diseases. INTERPRETATION: The school-based intervention package increased healthcare activity in adolescents with intellectual disability living in the community.

Self-reported health and subsequent mortality: an analysis of 767 deaths from a large Thai cohort study.

BACKGROUND: Few studies have examined the link between self-reported health (SRH) and subsequent mortality in developing countries, and very few considered mortality effects of changes in SRH. We examined the relationship between SRH and subsequent all cause or cause-specific mortality in Thailand. We also noted if mortality varied after people changed their SRH. METHODS: We used longitudinal data including SRH from a nationwide Thai Cohort Study (baseline 2005-follow-up 2009) and linked to official death records (2005-2012). Cox regression examined the association between SRH in 2005 and subsequent all-cause mortality or cause-specific mortality, with results given as confounder-adjusted hazard ratios (HR). We further assessed association between changes in SRH during 2005-2009 and mortality from 2009 to 2012. RESULTS: Poor SRH at baseline independently relates strongly with subsequent cardiovascular disease (CVD) mortality (HR = 2.8, CI: 1.3-5.9) and "other" causes of death (HR = 1.9, CI: 1.1-3.3) but moderately with cancer mortality (HR = 1.4, CI: 0.7-3.0). SRH did not exhibit a relationship with injury mortality (HR = 1.0, CI: 0.5-2.1). Worsening SRH from 2005 to 2009 associated with increased mortality in 2009-2012 for females but not for males. CONCLUSIONS: In Thailand, SRH is a good predictor of population mortality due to internal causes (e.g. CVD). SRH is holistic, simple to measure and low cost; when repeated it measures dynamic health status. In many developing countries chronic diseases are emerging and morbidity information is limited. SRH could help monitor such transitions in burdens and trends of population health.

Pericardial Effusion Detection on Post-Mortem Computed Tomography Images Using Convolutional Neural Networks.

Pericardial effusion can be a sign of significant underlying diease and, in some cases, may lead to death. Post-mortem computed tomography (PMCT) is a well-established tool to assist death investigation processes in the forensic setting. In practice, the scarcity of well-trained radiologists is a challenge in processing raw whole-body PMCT images for pericardial effusion detection. In this work, we propose a Pericardial Effusion Automatic Detection (PEAD) framework to automatically process raw whole-body PMCT images to filter out the irrelevant images with heart organ absent and focus on pericardial effusion detection. In PEAD, the standard convolutional neural network architectures of VGG and ResNet are carefully modified to fit the specific characteristics of PMCT images. The experimental results prove the effectiveness of the proposed framework and modified models. The modified VGG and ResNet models achieved superior detection accuracy than the standard architecture with reduced processing speed.

Inflammation risk before cardiac surgery and the treatment effect of intraoperative dexamethasone.

Patients who exhibit high systemic inflammation after cardiac surgery may benefit most from pre-emptive anti-inflammatory treatments. In this secondary analysis (n = 813) of the randomised, double-blind Intraoperative High-Dose Dexamethasone for Cardiac Surgery trial, we set out to develop an inflammation risk prediction model and assess whether patients at higher risk benefit from a single intraoperative dose of dexamethasone (1 mg/kg). Inflammation risk before surgery was quantified from a linear regression model developed in the placebo arm, relating preoperatively available covariates to peak postoperative C-reactive protein. The primary endpoint was the interaction between inflammation risk and the peak postoperative C-reactive protein reduction associated with dexamethasone treatment. The impact of dexamethasone on the main clinical outcome (a composite of death, myocardial infarction, stroke, renal failure, or respiratory failure within 30 days) was also explored in relation to inflammation risk. Preoperatively available covariates explained a minority of peak postoperative C-reactive protein variation and were not suitable for clinical application (R2 = 0.058, P = 0.012); C-reactive protein before surgery (excluded above 10 mg/L) was the most predictive covariate (P < 0.001). The anti-inflammatory effect of dexamethasone increased as the inflammation risk increased (-0.689 mg/L per unit predicted peak C-reactive protein, P = 0.002 for interaction). No treatment-effect heterogeneity was detected for the main clinical outcome (P = 0.167 for interaction). Overall, risk predictions from a model of inflammation after cardiac surgery were associated with the degree of peak postoperative C-reactive protein reduction derived from dexamethasone treatment. Future work should explore the impact of this phenomenon on clinical outcomes in larger surgical populations.

Dual-stream multi-dependency graph neural network enables precise cancer survival analysis.

Histopathology image-based survival prediction aims to provide a precise assessment of cancer prognosis and can inform personalized treatment decision-making in order to improve patient outcomes. However, existing methods cannot automatically model the complex correlations between numerous morphologically diverse patches in each whole slide image (WSI), thereby preventing them from achieving a more profound understanding and inference of the patient status. To address this, here we propose a novel deep learning framework, termed dual-stream multi-dependency graph neural network (DM-GNN), to enable precise cancer patient survival analysis. Specifically, DM-GNN is structured with the feature updating and global analysis branches to better model each WSI as two graphs based on morphological affinity and global co-activating dependencies. As these two dependencies depict each WSI from distinct but complementary perspectives, the two designed branches of DM-GNN can jointly achieve the multi-view modeling of complex correlations between the patches. Moreover, DM-GNN is also capable of boosting the utilization of dependency information during graph construction by introducing the affinity-guided attention recalibration module as the readout function. This novel module offers increased robustness against feature perturbation, thereby ensuring more reliable and stable predictions. Extensive benchmarking experiments on five TCGA datasets demonstrate that DM-GNN outperforms other state-of-the-art methods and offers interpretable prediction insights based on the morphological depiction of high-attention patches. Overall, DM-GNN represents a powerful and auxiliary tool for personalized cancer prognosis from histopathology images and has great potential to assist clinicians in making personalized treatment decisions and improving patient outcomes.

Body mass index, physical activity, and fracture among young adults: longitudinal results from the Thai cohort study.

BACKGROUND: We investigated risk factors for fracture among young adults, particularly body mass index (BMI) and physical activity, which although associated with fracture in older populations have rarely been investigated in younger people. METHODS: In 2009, 4 years after initial recruitment, 58 204 Thais aged 19 to 49 years were asked to self-report fractures incident in the preceding 4 years. Conditional logistic regression was used to calculate odds ratios (ORs) and 95% CIs for associations of fracture incidence with baseline BMI and physical activity. RESULTS: Very obese women had a 70% increase in fracture risk (OR = 1.73, 95% CI 1.21-2.46) as compared with women with a normal BMI. Fracture risk increased by 15% with every 5-kg/m(2) increase in BMI. The effects were strongest for fractures of the lower limbs. Frequent purposeful physical activity was also associated with increased fracture risk among women (OR = 1.52, 95% CI 1.12-2.06 for 15 episodes/week vs none). Neither BMI nor physical activity was associated with fracture among men, although fracture risk decreased by 4% with every additional 2 hours of average sitting time per day (OR = 0.96, 95% CI 0.93-0.99). CONCLUSIONS: The increase in obesity prevalence will likely increase fracture burden among young women but not young men. While active lifestyles have health benefits, our results highlight the importance of promoting injury prevention practices in conjunction with physical activity recommendations, particularly among women.

A digital health ecosystem ontology from the perspective of Australian consumers: a mixed-method literature analysis.

This study presents an ontology that scopes the digital health ecosystem from a consumer-centered perspective. We used a mixed-method analysis on a set of papers collected for a comprehensive review to identify common themes, components, and patterns that repeatedly emerge within Australian-based digital health studies. Three major and four child themes were identified as the foundational aspects of the proposed ontology. The child themes have more precise concept definitions, inherited and distinguishing attributes. Out of 179 recognized concepts, 33 were related to the Healthcare theme; 23 concepts formed a cluster of employed devices under the Technology theme; 40 concepts were associated with Use and Usability factors. 60 other concepts formed the cluster of the consumer-user theme. The theme of Digital Health was seen as being connected to 2 independent clusters. The main cluster embodied 21 extracted concepts, semantically related to "data, information, and knowledge," whilst the second cluster embodied concepts related to "healthcare." Different stakeholders can utilize this ontology to define their landscape of digitally enabled healthcare. The novelty of this work resides in capturing a consumer-centered perspective and the method we used in deriving the ontology - formalizing the results of a systematic review based on data-driven analysis methods.

Lessons Learned from Natural Disasters around Digital Health Technologies and Delivering Quality Healthcare.

As climate change drives increased intensity, duration and severity of weather-related events that can lead to natural disasters and mass casualties, innovative approaches are needed to develop climate-resilient healthcare systems that can deliver safe, quality healthcare under non-optimal conditions, especially in remote or underserved areas. Digital health technologies are touted as a potential contributor to healthcare climate change adaptation and mitigation, through improved access to healthcare, reduced inefficiencies, reduced costs, and increased portability of patient information. Under normal operating conditions, these systems are employed to deliver personalised healthcare and better patient and consumer involvement in their health and well-being. During the COVID-19 pandemic, digital health technologies were rapidly implemented on a mass scale in many settings to deliver healthcare in compliance with public health interventions, including lockdowns. However, the resilience and effectiveness of digital health technologies in the face of the increasing frequency and severity of natural disasters remain to be determined. In this review, using the mixed-methods review methodology, we seek to map what is known about digital health resilience in the context of natural disasters using case studies to demonstrate what works and what does not and to propose future directions to build climate-resilient digital health interventions.

Surformer: An interpretable pattern-perceptive survival transformer for cancer survival prediction from histopathology whole slide images.

BACKGROUND AND OBJECTIVE: High-resolution histopathology whole slide images (WSIs) contain abundant valuable information for cancer prognosis. However, most computational pathology methods for survival prediction have weak interpretability and cannot explain the decision-making processes reasonably. To address this issue, we propose a highly interpretable neural network termed pattern-perceptive survival transformer (Surformer) for cancer survival prediction from WSIs. METHODS: Notably, Surformer can quantify specific histological patterns through bag-level labels without any patch/cell-level auxiliary information. Specifically, the proposed ratio-reserved cross-attention module (RRCA) generates global and local features with the learnable prototypes (pglobal, plocals) as detectors and quantifies the patches correlative to each plocal in the form of ratio factors (rfs). Afterward, multi-head self&cross-attention modules proceed with the computation for feature enhancement against noise. Eventually, the designed disentangling loss function guides multiple local features to focus on distinct patterns, thereby assisting rfs from RRCA in achieving more explicit histological feature quantification. RESULTS: Extensive experiments on five TCGA datasets illustrate that Surformer outperforms existing state-of-the-art methods. In addition, we highlight its interpretation by visualizing rfs distribution across high-risk and low-risk cohorts and retrieving and analyzing critical histological patterns contributing to the survival prediction. CONCLUSIONS: Surformer is expected to be exploited as a useful tool for performing histopathology image data-driven analysis and gaining new insights for interpreting the associations between such images and patient survival states.

Genetics and epigenetics in perioperative medicine.

PURPOSE OF REVIEW: To summarize is to review recent progress in 'genomic' science and how this may be applied to the perioperative environment. Although investigations that relate genetic variation to perioperative outcomes continue, it is increasingly apparent that epigenetic mechanisms may contribute to much of the observed variation in complex outcomes not otherwise explained by differences in genetic sequence. RECENT FINDINGS: Examples of recent findings relating to the role of epigenetic modifications in complex disease and outcomes are derived from research into type 1 diabetes, pain, and the hypoxic response. These studies provide models for future cohort study design, potential perioperative drug targets, and hypothesis development. Genetic and epigenetic factors combine to alter both gene expression and drug responses at both pharmacokinetic and pharmacodynamic levels. These factors impact on the efficacy and safety of multiple drug classes used in perioperative medicine. SUMMARY: Enhancing our understanding of the way in which patients as genomic organisms interact with the perioperative environment requires a more sophisticated appreciation of the factors governing gene expression than has been the case to date. Epigenetic mechanisms are sure to play a pivotal role in what is essentially an acquired phenotype.

Ask: a health advocacy program for adolescents with an intellectual disability: a cluster randomised controlled trial.

BACKGROUND: Adolescents with intellectual disability often have poor health and healthcare. This is partly as a consequence of poor communication and recall difficulties, and the possible loss of specialised paediatric services. METHODS/DESIGN: A cluster randomised trial was conducted with adolescents with intellectual disability to investigate a health intervention package to enhance interactions among adolescents with intellectual disability, their parents/carers, and general practitioners (GPs). The trial took place in Queensland, Australia, between February 2007 and September 2010. The intervention package was designed to improve communication with health professionals and families' organisation of health information, and to increase clinical activities beneficial to improved health outcomes. It consisted of the Comprehensive Health Assessment Program (CHAP), a one-off health check, and the Ask Health Diary, designed for on-going use. Participants were drawn from Special Education Schools and Special Education Units. The education component of the intervention was delivered as part of the school curriculum. Educators were surveyed at baseline and followed-up four months later. Carers were surveyed at baseline and after 26 months. Evidence of health promotion, disease prevention and case-finding activities were extracted from GPs clinical records. Qualitative interviews of educators occurred after completion of the educational component of the intervention and with adolescents and carers after the CHAP. DISCUSSION: Adolescents with intellectual disability have difficulty obtaining many health services and often find it difficult to become empowered to improve and protect their health. The health intervention package proposed may aid them by augmenting communication, improving documentation of health encounters, and improving access to, and quality of, GP care. Recruitment strategies to consider for future studies in this population include ensuring potential participants can identify themselves with the individuals used in promotional study material, making direct contact with their families at the start of the study, and closely monitoring the implementation of the educational intervention. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Identifier: NCT00519311.

Mortality from diabetic renal disease: a hidden epidemic.

BACKGROUND: Population-level mortality indicators can be useful outcome measures of diabetes care. Death registration systems serve as the main source of data for such measures. However, standard mortality indicators based on underlying causes do not adequately reflect the burden from diabetic renal disease. METHODS: This article presents findings from analysis of multiple causes of death available from death registration data for Australia and USA. Both countries use an automated system that applies prescribed rules to select and code the underlying cause for each registered death. Deaths with diabetes as underlying cause were grouped according to their diabetic complications as defined by the International Classification of Diseases. Age-standardized mortality rates were calculated for the underlying cause rubric 'diabetes with renal complications'. These were contrasted with rates calculated using additional deaths where diabetes was the underlying cause and renal failure was listed as a consequence. RESULTS: These analyses identified that current automated programmes code three-fourths of all diabetes deaths to 'diabetes without complications', despite additional factors being listed. Estimated multiple cause death rates from diabetic renal disease are four to nine times higher than underlying cause rates for 'diabetes with renal complications' in both countries; and show a rising trend in contrast to the latter. CONCLUSION: These findings indicate that routine underlying cause statistics for USA and Australia grossly under estimate mortality from diabetic renal disease. Clear guidelines on the certification, coding and statistical presentation of diabetes mortality are needed for epidemiology and health policy.