Urban planning capabilities for bushfire: treatment categories and scenario testing

The challenges facing settlements relating to bushfire require integrated approaches that manage risks across a wide range of factors. This paper sets out a framework demonstrating how urban planning, when coupled with appropriate decision support and future scenario testing, can reduce risks relating to bushfire while considering future growth. Examples of how planning can modify aspects of risk in association with scenario testing are included. Five main categories of risk reduction treatments are shown. The paper contributes to risk reduction by providing practical mechanisms for risk avoidance and treatment via urban and land-use planning systems combined with forward scenario testing to guide existing settlements and future growth

Review article: Natural hazard risk assessments at the global scale

Since 1990, natural hazards have led to over 1.6 million fatalities globally, and economic losses are estimated at an average of around $260–310 billion per year. The scientific and policy community recognise the need to reduce these risks. As a result, the last decade has seen a rapid development of global models for assessing risk from natural hazards at the global scale. In this paper, we review the scientific literature on natural hazard risk assessments at the global scale, and specifically examine whether and how they have examined future projections of hazard, exposure, and/or vulnerability. In doing so, we examine similarities and differences between the approaches taken across the different hazards, and identify potential ways in which different hazard communities can learn from each other. For example, we show that global risk studies focusing on hydrological, climatological, and meteorological hazards, have included future projections and disaster risk reduction measures (in the case of floods), whilst these are missing in global studies related to geological hazards. The methods used for projecting future exposure in the former could be applied to the geological studies. On the other hand, studies of earthquake and tsunami risk are now using stochastic modelling approaches to allow for a fully probabilistic assessment of risk, which could benefit the modelling of risk from other hazards. Finally, we discuss opportunities for learning from methods and approaches being developed and applied to assess natural hazard risks at more continental or regional scales. Through this paper, we hope to encourage dialogue on knowledge sharing between scientists and communities working on different hazards and at different spatial scales

Natural hazard risk assessments at the global scale

Since 1990, natural hazards have led to over 1.6 million fatalities globally, and economic losses are estimated at an average of around USD 260–310 billion per year. The scientific and policy communities recognise the need to reduce these risks. As a result, the last decade has seen a rapid development of global models for assessing risk from natural hazards at the global scale. In this paper, we review the scientific literature on natural hazard risk assessments at the global scale, and we specifically examine whether and how they have examined future projections of hazard, exposure, and/or vulnerability. In doing so, we examine similarities and differences between the approaches taken across the different hazards, and we identify potential ways in which different hazard communities can learn from each other. For example, there are a number of global risk studies focusing on hydrological, climatological, and meteorological hazards that have included future projections and disaster risk reduction measures (in the case of floods), whereas fewer exist in the peer-reviewed literature for global studies related to geological hazards. On the other hand, studies of earthquake and tsunami risk are now using stochastic modelling approaches to allow for a fully probabilistic assessment of risk, which could benefit the modelling of risk from other hazards. Finally, we discuss opportunities for learning from methods and approaches being developed and applied to assess natural hazard risks at more continental or regional scales. Through this paper, we hope to encourage further dialogue on knowledge sharing between disciplines and communities working on different hazards and risk and at different spatial scales

Machine learning for classification of hypertension subtypes using multi-omics: a multi-centre, retrospective, data-driven study

Background: Arterial hypertension is a major cardiovascular risk factor. Identification of secondary hypertension in its various forms is key to preventing and targeting treatment of cardiovascular complications. Simplified diagnostic tests are urgently required to distinguish primary and secondary hypertension to address the current underdiagnosis of the latter. Methods: This study uses Machine Learning (ML) to classify subtypes of endocrine hypertension (EHT) in a large cohort of hypertensive patients using multidimensional omics analysis of plasma and urine samples. We measured 409 multi-omics (MOmics) features including plasma miRNAs (PmiRNA: 173), plasma catechol O-methylated metabolites (PMetas: 4), plasma steroids (PSteroids: 16), urinary steroid metabolites (USteroids: 27), and plasma small metabolites (PSmallMB: 189) in primary hypertension (PHT) patients, EHT patients with either primary aldosteronism (PA), pheochromocytoma/functional paraganglioma (PPGL) or Cushing syndrome (CS) and normotensive volunteers (NV). Biomarker discovery involved selection of disease combination, outlier handling, feature reduction, 8 ML classifiers, class balancing and consideration of different age- and sex-based scenarios. Classifications were evaluated using balanced accuracy, sensitivity, specificity, AUC, F1, and Kappa score. Findings: Complete clinical and biological datasets were generated from 307 subjects (PA=113, PPGL=88, CS=41 and PHT=112). The random forest classifier provided ∼92% balanced accuracy (∼11% improvement on the best mono-omics classifier), with 96% specificity and 0.95 AUC to distinguish one of the four conditions in multi-class ALL-ALL comparisons (PPGL vs PA vs CS vs PHT) on an unseen test set, using 57 MOmics features. For discrimination of EHT (PA + PPGL + CS) vs PHT, the simple logistic classifier achieved 0.96 AUC with 90% sensitivity, and ∼86% specificity, using 37 MOmics features. One PmiRNA (hsa-miR-15a-5p) and two PSmallMB (C9 and PC ae C38:1) features were found to be most discriminating for all disease combinations. Overall, the MOmics-based classifiers were able to provide better classification performance in comparison to mono-omics classifiers. Interpretation: We have developed a ML pipeline to distinguish different EHT subtypes from PHT using multi-omics data. This innovative approach to stratification is an advancement towards the development of a diagnostic tool for EHT patients, significantly increasing testing throughput and accelerating administration of appropriate treatment. Funding: European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 633983, Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to Z.E. and F.B.), and Deutsche Forschungsgemeinschaft (CRC/Transregio 205/1)

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Foresight for risk – using scenarios for strategic risk assessment and management of emergent disaster risk

Disaster impacts around the world are increasing with 2011 and 2017 the largest on record in terms of total losses from disasters in recorded history (USD 444billion and USD 341billion, respectively). The reasons for the increase in losses are multiple. Climate change is increasing the likelihood and intensity of several natural hazard types, and as the world’s population and economy grow, and humans increasingly develop in areas exposed to natural hazard (e.g. along rivers, and coastal areas), the values exposed are also rapidly increasing. These multiple factors contribute to the complex nature of disaster risk, which is considered to be the combination of natural hazard intensity and extent, exposure (assets, people, other values), and vulnerabilities of the exposed values to the characteristics of the hazards. This can be considered the risk triangle – hazard, exposure and vulnerability – and each of these factors change into the future impacted by a range of drivers; population and economic change, technology, urbanisation rates, political actions etc. To reduce the impacts of disasters, risk management and reduction activities are designed and implemented, and are typically underpinned by risk assessments. Risk assessments use qualitative and/or quantitative approaches to attempt to characterise the likelihood and impact of disaster types for a region or organisation. Currently, risk assessments do not capture future changes across all dimensions of risk in a manner that provides insight into the strategic threats and opportunities of emergent disaster risks. Therefore, there is a need for approaches to consider realistic degrees of complexity within the disaster risk system and account for the uncertainty in emergent risk. By capturing this within disaster risk assessments, treatment options can be developed and tested that strategically manage these risks over time. This research has developed these approaches and provides three key contributions through the use of foresight, primarily scenarios within disaster risk assessment processes, to support effective policy and investment decision making to reduce future impacts. The thesis is organised around three publications, all contributing to the development of a generic framework which integrates foresight into disaster risk management and specific approaches to develop and use scenarios for strategic risk assessment and management of emergent disaster risk. The first paper (Chapter 2) proposes and demonstrates this generic framework for the incorporation of the principles of foresight into risk assessment and management processes. The second paper (Chapter 3) focuses on the design of scenarios to support policy making for disaster risk reduction through several improvements to the methodological approach for constructing relevant and challenging scenarios using an “outcomes of interest” framing. The third paper (Chapter 4) outlines and applies an approach for the use of exploratory scenarios within quantitative disaster risk assessment through the development of alternative pathways of disaster risk using scenarios and integrated risk models.Thesis (Ph.D.) -- University of Adelaide, School of Civil, Environmental and Mining Engineering, 201

WA_NDRP_DSS_ProjectOverview_20170822

Project overview presentation given to Western Australia NDRP funded project kick-off meeting. <div>Perth, 22/08/2017</div

Informal caregivers in early psychosis:evaluation of need for psychosocial intervention and unresolved grief

Aim: Relatives of service users involved with Early Intervention in Psychosis services often experience substantial distress and need associated with their role as caregivers. This study adapted versions of the Relatives Cardinal Needs Schedule and the Texas Inventory of Grief and tested their utility for use among relatives of service users experiencing a first episode of psychosis. Methods: Staff, service users and relatives were consulted and a pilot took place which facilitated the creation of the Relatives' Urgent Needs Schedule - Early Intervention version (RUNS-EI) and the Texas Inventory of Grief Early Intervention version (TIG-EI). Thirty service user-caregiver dyads were recruited for the evaluation of reliability and validity. Results: The level of 'urgent need' identified by the RUNS-EI demonstrated good concurrent validity with measures of service user social and global functioning as well as measures assessing relatives' distress, expressed emotion and grief. The measure demonstrated acceptable interrater and test-retest reliability. The profile of need is reported. The TIG-EI demonstrated 'excellent' internal consistency. It also demonstrated good concurrent validity with increased TIG-EI scores correlated with reduced service user social and global functioning as well as increased scores on measures assessing relatives' distress, expressed emotion and caregiving needs. Conclusions: Results appear to support these assessments' utility as measures of need for psychosocial intervention and grief among relatives supporting service users experiencing a first episode of psychosis