A comparison of content from across contemporary Australian population health surveys.

OBJECTIVE: Associations between place and population health are of interest to researchers and policymakers. The objective of this paper is to explore, summarise and compare content across contemporary Australian geo-referenced population health survey data sets. METHODS: A search for recent (2015 or later) population health surveys from within Australia containing geographic information from participants was conducted. Survey response frames were analysed and categorised based on demographic, risk factor and disease-related characteristics. Analysis using interactive Sankey diagrams shows the extent of content overlap and differences between population health surveys in Australia. RESULTS: Thirteen Australian geo-referenced population health survey data sets were identified. Information captured across surveys was inconsistent as was the spatial granularity of respondent information. Health and demographic features most frequently captured were symptoms, signs and clinical findings from the International Statistical Classification of Diseases and Related Health Problems version 11, employment, housing, income, self-rated health and risk factors, including alcohol consumption, diet, medical treatments, physical activity and weight-related questions. Sankey diagrams were deployed online for use by public health researchers. CONCLUSIONS: Identifying the relationship between place and health in Australia is made more difficult by inconsistencies in information collected across surveys deployed in different regions in Australia. IMPLICATIONS FOR PUBLIC HEALTH: Public health research investigating place and health involves a vast and inconsistent patchwork of information within and across states, which may impact broad-scale research questions. The tools developed here assist public health researchers to identify surveys suitable for their research queries related to place and health.

When tomorrow comes: A prospective risk assessment of a future artificial general intelligence-based uncrewed combat aerial vehicle system.

There are concerns that Artificial General Intelligence (AGI) could pose an existential threat to humanity; however, as AGI does not yet exist it is difficult to prospectively identify risks and develop requisite controls. We applied the Work Domain Analysis Broken Nodes (WDA-BN) and Event Analysis of Systemic Teamwork-Broken Links (EAST-BL) methods to identify potential risks in a future 'envisioned world' AGI-based uncrewed combat aerial vehicle system. The findings suggest five main categories of risk in this context: sub-optimal performance risks, goal alignment risks, super-intelligence risks, over-control risks, and enfeeblement risks. Two of these categories, goal alignment risks and super-intelligence risks, have not previously been encountered or dealt with in conventional safety management systems. Whereas most of the identified sub-optimal performance risks can be managed through existing defence design lifecycle processes, we propose that work is required to develop controls to manage the other risks identified. These include controls on AGI developers, controls within the AGI itself, and broader sociotechnical system controls.

Australians' perceptions of the potential effects of increased access to alcohol via autonomous delivery services: A multi-method study.

INTRODUCTION: Rapid technological change will affect how people access harmful products. For example, automated delivery services are forecast to provide fast and affordable product access options for those purchasing alcohol. Information about the potential impacts of such innovations on alcohol purchase and consumption behaviors is lacking. The aim of this study was to explore how consumers may respond to future scenarios where alcohol is available via a range of autonomous alcohol delivery options. METHODS: In a two-stage process, qualitative individual interviews (n = 100) and a quantitative online survey (n = 1078) were conducted with Australians aged 18+ years. Quotas were used to achieve national representation on key demographic variables. Participants were exposed to scenarios outlining how autonomous vehicles are likely to be used for alcohol deliveries in the future and asked to discuss their intentions to use such services and their perceptions of any impacts on alcohol consumption at a societal level. RESULTS: Automated alcohol deliveries were generally considered to be highly convenient and therefore likely to be popular. Around one-third (37%) of survey respondents reported an intention to use such automated alcohol delivery services once they are available and almost half (47%) expected overall levels of alcohol consumption to increase across the population. CONCLUSION: Given potential levels of uptake, careful consideration needs to be given to the development and implementation of appropriate policy and regulatory frameworks to minimize the increased risk of alcohol-related harm associated with enhanced alcohol availability resulting from the emerging availability of autonomous delivery services.

FEEDBACK trial - A randomised control trial to investigate the effect of personalised feedback and financial incentives on reducing the incidence of road crashes.

BACKGROUND: Road crashes continue to pose a significant threat to global health. Young drivers aged between 18 and 25 are over-represented in road injury and fatality statistics, especially the first six months after obtaining their license. This study is the first multi-centre two-arm parallel-group individually randomised controlled trial (the FEEDBACK Trial) that will examine whether the delivery of personalised driver feedback plus financial incentives is superior to no feedback and no financial incentives in reducing motor vehicle crashes among young drivers (18 to 20 years) during the first year of provisional licensing. METHODS: A total of 3,610 young drivers on their provisional licence (P1, the first-year provisional licensing) will participate in the trial over 28 weeks, including a 4-week baseline, 20-week intervention and 4-week post-intervention period. The primary outcome of the study will be police-reported crashes over the 20-week intervention period and the 4-week post-intervention period. Secondary outcomes include driving behaviours such as speeding and harsh braking that contribute to road crashes, which will be attained weekly from mobile telematics delivered to a smartphone app. DISCUSSION: Assuming a positive finding associated with personalised driver feedback and financial incentives in reducing road crashes among young drivers, the study will provide important evidence to support policymakers in introducing the intervention(s) as a key strategy to mitigate the risks associated with the burden of road injury among this vulnerable population. TRIAL REGISTRATION: Registered under the Australian New Zealand Clinical Trials Registry (ANZCTR) - ACTRN12623000387628p on April 17, 2023.

Discordant pathologic diagnoses of myelodysplastic neoplasms and their implications for registries and therapies.

Myelodysplastic neoplasms (MDS) are a collection of hematopoietic disorders with widely variable prognoses and treatment options. Accurate pathologic diagnoses present challenges because of interobserver variability in interpreting morphology and quantifying dysplasia. We compared local clinical site diagnoses with central, adjudicated review from 918 participants enrolled in the ongoing National Heart, Lung, and Blood Institute National MDS Natural History Study, a prospective observational cohort study of participants with suspected MDS or MDS/myeloproliferative neoplasms (MPNs). Locally, 264 (29%) were diagnosed as having MDS, 15 (2%) MDS/MPN overlap, 62 (7%) idiopathic cytopenia of undetermined significance (ICUS), 0 (0%) acute myeloid leukemia (AML) with <30% blasts, and 577 (63%) as other. Approximately one-third of cases were reclassified after central review, with 266 (29%) diagnosed as MDS, 45 (5%) MDS/MPN overlap, 49 (5%) ICUS, 15 (2%) AML with <30%, and 543 (59%) as other. Site miscoding errors accounted for more than half (53%) of the local misdiagnoses, leaving a true misdiagnosis rate of 15% overall, 21% for MDS. Therapies were reported in 37% of patients, including 43% of patients with MDS, 49% of patients with MDS/MPN, and 86% of patients with AML with <30% blasts. Treatment rates were lower (25%) in cases with true discordance in diagnosis compared with those for whom local and central diagnoses agreed (40%), and receipt of inappropriate therapy occurred in 7% of misdiagnosed cases. Discordant diagnoses were frequent, which has implications for the accuracy of study-related and national registries and can lead to inappropriate therapy. This trial was registered at www.clinicaltrials.gov as #NCT05074550.

The inexorable rise of automated food deliveries and potential anticipatory policy actions.

OBJECTIVE: Food deliveries to consumers using autonomous vehicles are forecast to become commonplace in Australia and globally. The aims of this study were to (i) explore the anticipated nature of AV food delivery services in Australia and (ii) identify available policy options to optimise positive outcomes and minimise any negative consequences for health and well-being. METHODS: In total, 36 interviews were conducted with 40 expert stakeholders across a range of relevant sectors including transport, urban planning, health and telecommunications. Interviewees discussed the ways in which automated food deliveries are likely to be implemented and the potential implications for lifestyles and health. RESULTS: The interviewees expected automated food deliveries to constitute an extension of current trends towards online food ordering and rapid home deliveries, with potential adverse implications for diet quality at the population level. CONCLUSIONS: Effective regulation of automated food and beverage delivery services will be needed to anticipate and address this emerging phenomenon. IMPLICATIONS FOR PUBLIC HEALTH: There is the opportunity to optimise the public health outcomes associated with automated food deliveries while minimising potential negative consequences through proactive anticipatory action. Delays could result in undesirable and irreversible changes to the food environment.

Health-related quality of life and vulnerability among people with myelodysplastic syndromes: a US national study.

Health-related quality of life (HRQoL) and vulnerability are variably affected in patients with myelodysplastic syndromes (MDS) and other cytopenic states; however, the heterogeneity of these diseases has limited our understanding of these domains. The National Heart, Lung, and Blood Institute-sponsored MDS Natural History Study is a prospective cohort enrolling patients undergoing workup for suspected MDS in the setting of cytopenias. Untreated patients undergo bone marrow assessment with central histopathology review for assignment as MDS, MDS/myeloproliferative neoplasm (MPN), idiopathic cytopenia of undetermined significance (ICUS), acute myeloid leukemia (AML) with <30% blasts, or "At-Risk." HRQoL data are collected at enrollment, including the MDS-specific Quality of Life in Myelodysplasia Scale (QUALMS). Vulnerability is assessed with the Vulnerable Elders Survey. Baseline HRQoL scores from 449 patients with MDS, MDS/MPN, AML <30%, ICUS or At-Risk were similar among diagnoses. In MDS, HRQoL was worse for vulnerable participants (eg, mean Patent-Reported Outcomes Management Information Systems [PROMIS] Fatigue of 56.0 vs 49.5; P < .001) and those with worse prognosis (eg, mean Euroqol-5 Dimension-5 Level [EQ-5D-5L] of 73.4, 72.7, and 64.1 for low, intermediate, and high-risk disease; P = .005). Among vulnerable MDS participants, most had difficulty with prolonged physical activity (88%), such as walking a quarter mile (74%). These data suggest that cytopenias leading to MDS evaluation are associated with similar HRQoL, regardless of eventual diagnosis, but with worse HRQoL among the vulnerable. Among those with MDS, lower-risk disease was associated with better HRQoL, but the relationship was lost among the vulnerable, showing for the first time that vulnerability trumps disease risk in affecting HRQoL. This study is registered at www.clinicaltrials.gov as NCT02775383.

Expert stakeholders' views on the potential nature and impacts of autonomous alcohol home delivery.

INTRODUCTION: The emergence of autonomous vehicles (AV) heralds new methods of alcohol delivery that are likely to have substantial implications for home drinking. The aim of this study was to explore the factors that have the potential to hasten or restrict widescale increases in alcohol availability resulting from AVs. METHODS: Thirty-six interviews were conducted with 44 stakeholders across diverse sectors. Interviewees discussed their perceptions of how AVs will evolve and the implications for alcohol access. The interview transcripts were analysed to develop a typology of factors that need to be addressed to minimise harms associated with greater home alcohol consumption due to AVs. RESULTS: The interviewees identified a range of demand-side (e.g., enhanced convenience for consumers) and supply-side (e.g., increased efficiency for providers) factors associated with AV home deliveries that could increase the prevalence and volume of home drinking. A third category of factors related to potential forms of regulation: suggested harm-minimisation options related to appropriate enhancements of licensing restrictions and responsible service requirements and the use of various fiscal levers to introduce price barriers. DISCUSSION AND CONCLUSIONS: The suggested strategies will require collaborative efforts across government and non-government organisations to address the potential issues arising from the advent of AV alcohol deliveries. Technological and policy solutions will be needed to ensure responsible service of alcohol. This study provides policy makers with insights into forms of intervention that may be required to minimise the harms associated with the use of AVs in alcohol home delivery systems.

The need for a complex systems approach in rural health research.

On a global scale, many major rural health issues have persisted for decades despite the introduction of new health interventions and public health policies. Although research efforts have generated valuable new knowledge about the aetiology of health, disease and health inequities in rural communities, rural health systems remain to be some of the most deprived and challenged in both the developing and developed world. While the reasons for this are many, a significant factor contributing to the current state of play is the pressing need for methodological innovation and relevant scientific approaches that have the capacity to support the translation of novel solutions into 'real world' rural contexts. Fortunately, complex systems approaches, which have seen an increase in popularity in the wider public health literature, could provide answers to some of the most resilient rural health problems in recent times. The purpose of this article is to promote the value and utility of a complex systems approach in rural health research. We explain the benefits of a complex systems approach and provide a background to the complexity sciences, including the main characteristics of complex systems. Two popular computational methods are described. The next step for rural health research involves exploring how a complex systems approach can help with the identification and evaluation of new and existing solutions to policy-resistant rural health issues. This includes generating awareness around the analytical trade-offs that occur between the use of traditional scientific methods and complex systems approaches.

A framework for considering the utility of models when facing tough decisions in public health: a guideline for policy-makers.

The COVID-19 pandemic has brought the combined disciplines of public health, infectious disease and policy modelling squarely into the spotlight. Never before have decisions regarding public health measures and their impacts been such a topic of international deliberation, from the level of individuals and communities through to global leaders. Nor have models-developed at rapid pace and often in the absence of complete information-ever been so central to the decision-making process. However, after nearly 3 years of experience with modelling, policy-makers need to be more confident about which models will be most helpful to support them when taking public health decisions, and modellers need to better understand the factors that will lead to successful model adoption and utilization. We present a three-stage framework for achieving these ends.

Development of a Computational Policy Model for Comparing the Effect of Compensation Scheme Policies on Recovery After Workplace Injury.

Introduction The direct comparison of real-world workers' compensation scheme management policies and their impact on aspects of scheme performance such as health and return to work outcomes, financial sustainability, and client experience metrics is made difficult through existing differences in scheme design that go beyond the factors of interest to the researcher or policymaker. Disentangling effects that are due purely to the result of policy and structural differences between schemes or jurisdictions to determine 'what works' can be difficult. Method We present a prototype policy exploration tool, 'WorkSim', built using an agent-based model and designed to enable workers' compensation system managers to directly compare the effect of simulated policies on the performance of workers compensation systems constructed using agreed and transparent principles. Results The utility of the model is demonstrated through and case-study comparison of overall scheme performance metrics across 6 simple policy scenarios. Discussion Policy simulation models of the nature described can be useful tools for managers of workplace compensation and rehabilitation schemes for trialing policy and management options ahead of their real-world implementation.

The impact of the COVID-19 pandemic on air pollution: A global assessment using machine learning techniques.

In response to the COVID-19 pandemic, most countries implemented public health ordinances that resulted in restricted mobility and a resultant change in air quality. This has provided an opportunity to quantify the extent to which carbon-based transport and industrial activity affect air quality. However, quantification of these complex effects has proven to be difficult, depending on the stringency of restrictions, country-specific emission source profiles, long-term trends and meteorological effects on atmospheric chemistry, emission levels and in-flow from nearby countries. In this study, confounding factors were disentangled for a direct comparison of pandemic-related reductions in absolute pollutions levels, globally. The non-linear relationships between atmospheric processes and daily ground-level NO 2 , PM10, PM2.5 and O 3 measurements were captured in city- and pollutant-specific XGBoost models for over 700 cities, adjusting for weather, seasonality and trends. City-level modelling allowed adaptation to the distinct topography, urban morphology, climate and atmospheric conditions for each city, individually, as the weather variables that were most predictive varied across cities. Pollution forecasts for 2020 in absence of a pandemic were generated based on weather and formed an ensemble for country-level pollution reductions. Findings were robust to modelling assumptions and consistent with various published case studies. NO 2 reduced most in China, Europe and India, following severe government restrictions as part of the initial lockdowns. Reductions were highly correlated with changes in mobility levels, especially trips to transit stations, workplaces, retail and recreation venues. Further, NO 2 did not fully revert to pre-pandemic levels in 2020. Ambient PM2.5 pollution, which has severe adverse health consequences, reduced most in China and India. Since positive health effects could be offset to some extent by prolonged exposure to indoor pollution, alternative transport initiatives could prove to be an important pathway towards better health outcomes in these countries. Increased O 3 levels during initial lockdowns have been documented widely. However, our analyses also found a subsequent reduction in O 3 for many countries below what was expected based on meteorological conditions during summer months (e.g., China, United Kingdom, France, Germany, Poland, Turkey). The effects in periods with high O 3 levels are especially important for the development of effective mitigation strategies to improve health outcomes.

Modelling SARS-CoV-2 disease progression in Australia and New Zealand: an account of an agent-based approach to support public health decision-making.

OBJECTIVE: In 2020, we developed a public health decision-support model for mitigating the spread of SARS-CoV-2 infections in Australia and New Zealand. Having demonstrated its capacity to describe disease progression patterns during both countries' first waves of infections, we describe its utilisation in Victoria in underpinning the State Government's then 'RoadMap to Reopening'. METHODS: Key aspects of population demographics, disease, spatial and behavioural dynamics, as well as the mechanism, timing, and effect of non-pharmaceutical public health policies responses on the transmission of SARS-CoV-2 in both countries were represented in an agent-based model. We considered scenarios related to the imposition and removal of non-pharmaceutical interventions on the estimated progression of SARS-CoV-2 infections. RESULTS: Wave 1 results suggested elimination of community transmission of SARS-CoV-2 was possible in both countries given sustained public adherence to social restrictions beyond 60 days' duration. However, under scenarios of decaying adherence to restrictions, a second wave of infections (Wave 2) was predicted in Australia. In Victoria's second wave, we estimated in early September 2020 that a rolling 14-day average of <5 new cases per day was achievable on or around 26 October. Victoria recorded a 14-day rolling average of 4.6 cases per day on 25 October. CONCLUSIONS: Elimination of SARS-CoV-2 transmission represented in faithfully constructed agent-based models can be replicated in the real world. IMPLICATIONS FOR PUBLIC HEALTH: Agent-based public health policy models can be helpful to support decision-making in novel and complex unfolding public health crises.

Association of Simulated COVID-19 Policy Responses for Social Restrictions and Lockdowns With Health-Adjusted Life-Years and Costs in Victoria, Australia.

Importance: Countries have varied enormously in how they have responded to the COVID-19 pandemic, ranging from elimination strategies (eg, Australia, New Zealand, Taiwan) to tight suppression (not aiming for elimination but rather to keep infection rates low [eg, South Korea]) to loose suppression (eg, Europe, United States) to virtually unmitigated (eg, Brazil, India). Weighing the best option, based on health and economic consequences due to lockdowns, is necessary. Objective: To determine the optimal policy response, using a net monetary benefit (NMB) approach, for policies ranging from aggressive elimination and moderate elimination to tight suppression (aiming for 1-5 cases per million per day) and loose suppression (5-25 cases per million per day). Design Setting and Participants: Using governmental data from the state of Victoria, Australia, and other collected data, 2 simulation models in series were conducted of all residents (population, 6.4 million) for SARS-CoV-2 infections for 1 year from September 1, 2020. An agent-based model (ABM) was used to estimate daily SARS-CoV-2 infection rates and time in 5 stages of social restrictions (stages 1, 1b, 2, 3, and 4) for 4 policy response settings (aggressive elimination, moderate elimination, tight suppression, and loose suppression), and a proportional multistate life table (PMSLT) model was used to estimate health-adjusted life-years (HALYs) associated with COVID-19 and costs (health systems and health system plus gross domestic product [GDP]). The ABM is a generic COVID-19 model of 2500 agents, or simulants, that was scaled up to the population of interest. Models were specified with data from 2019 (eg, epidemiological data in the PMSLT model) and 2020 (eg, epidemiological and cost consequences of COVID-19). The NMB of each policy option at varying willingness to pay (WTP) per HALY was calculated: NMB = HALYs × WTP - cost. The estimated most cost-effective (optimal) policy response was that with the highest NMB. Main Outcome and Measures: Estimated SARS-CoV-2 infection rates, time under 5 stages of restrictions, HALYs, health expenditure, and GDP losses. Results: In 100 runs of both the ABM and PMSLT models for each of the 4 policy responses, 31.0% of SARS-CoV-2 infections, 56.5% of hospitalizations, and 84.6% of deaths occurred among those 60 years and older. Aggressive elimination was associated with the highest percentage of days with the lowest level of restrictions (median, 31.7%; 90% simulation interval [SI], 6.6%-64.4%). However, days in hard lockdown were similar across all 4 strategies. The HALY losses (compared with a scenario without COVID-19) were similar for aggressive elimination (median, 286 HALYs; 90% SI, 219-389 HALYs) and moderate elimination (median, 314 HALYs; 90% SI, 228-413 HALYs), and nearly 8 and 40 times higher for tight suppression and loose suppression, respectively. The median GDP loss was least for moderate elimination (median, $41.7 billion; 90% SI, $29.0-$63.6 billion), but there was substantial overlap in simulation intervals between the 4 strategies. From a health system perspective, aggressive elimination was optimal in 64% of simulations above a WTP of $15 000 per HALY, followed by moderate elimination in 35% of simulations. Moderate elimination was optimal from a GDP perspective in half of the simulations, followed by aggressive elimination in a quarter. Conclusions and Relevance: In this simulation modeling economic evaluation of estimated SARS-CoV-infection rates, time under 5 stages of restrictions, HALYs, health expenditure, and GDP losses in Victoria, Australia, an elimination strategy was associated with the least health losses and usually the fewest GDP losses.

Safety and immunogenicity of a Zika purified inactivated virus vaccine given via standard, accelerated, or shortened schedules: a single-centre, double-blind, sequential-group, randomised, placebo-controlled, phase 1 trial.

BACKGROUND: The development of an effective vaccine against Zika virus remains a public health priority. A Zika purified inactivated virus (ZPIV) vaccine candidate has been shown to protect animals against Zika virus challenge and to be well tolerated and immunogenic in humans up to 8 weeks of follow-up. We aimed to assess the safety and immunogenicity of ZPIV in humans up to 52 weeks of follow-up when given via standard or accelerated vaccination schedules. METHODS: We did a single-centre, double-blind, randomised controlled, phase 1 trial in healthy adults aged 18-50 years with no known history of flavivirus vaccination or infection at Beth Israel Deaconess Medical Center in Boston, MA, USA. Participants were sequentially enrolled into one of three groups: ZPIV given at weeks 0 and 4 (standard regimen), weeks 0 and 2 (accelerated regimen), or week 0 alone (single-dose regimen). Within each group, participants were randomly assigned using a computer-generated randomisation schedule to receive an intramuscular injection of 5 µg ZPIV or saline placebo, in a ratio of 5:1. The sponsor, clinical staff, investigators, participants, and laboratory personnel were masked to treatment assignment. The primary endpoint was safety up to day 364 after final dose administration, and secondary endpoints were proportion of participants with positive humoral immune responses (50% microneutralisation titre [MN50] ≥100) and geometric mean MN50 at observed peak response (ie, the highest neutralising antibody level observed for an individual participant across all timepoints) and week 28. All participants who received at least one dose of ZPIV or placebo were included in the safety population; the analysis of immunogenicity at observed peak included all participants who received at least one dose of ZPIV or placebo and had any adverse events or immunogenicity data after dosing. The week 28 immunogenicity analysis population consisted of all participants who received ZPIV or placebo and had immunogenicity data available at week 28. This trial is registered with ClinicalTrials.gov, NCT02937233. FINDINGS: Between Dec 8, 2016, and May 17, 2017, 12 participants were enrolled into each group and then randomly assigned to vaccine (n=10) or placebo (n=2). There were no serious or grade 3 treatment-related adverse events. The most common reactions among the 30 participants who received the vaccine were injection-site pain (24 [80%]), fatigue (16 [53%]), and headache (14 [46%]). A positive response at observed peak titre was detected in all participants who received ZPIV via the standard regimen, in eight (80%) of ten participants who received ZPIV via the accelerated regimen, and in none of the ten participants who received ZPIV via the single-dose regimen. The geometric mean of all individual participants' observed peak values was 1153·9 (95% CI 455·2-2925·2) in the standard regimen group, 517·7 (142·9-1875·6) in the accelerated regimen group, and 6·3 (3·7-10·8) in the single-dose regimen group. At week 28, a positive response was observed in one (13%) of eight participants who received ZPIV via the standard regimen and in no participant who received ZPIV via the accelerated (n=7) or single-dose (n=10) regimens. The geomteric mean titre (GMT) at this timepoint was 13·9 (95% CI 3·5-55·1) in the standard regimen group and 6·9 (4·0-11·9) in the accelerated regimen group; antibody titres were undetectable at 28 weeks in participants who received ZPIV via the single-dose regimen. For all vaccine schedules, GMTs peaked 2 weeks after the final vaccination and declined to less than 100 by study week 16. There was no difference in observed peak GMTs between the standard 4-week and the accelerated 2-week boosting regimens (p=0·4494). INTERPRETATION: ZPIV was safe and well tolerated in humans up to 52 weeks of follow-up. ZPIV immunogenicity required two doses and was not durable. Additional studies of ZPIV to optimise dosing schedules are ongoing. FUNDING: The Henry M Jackson Foundation for the Advancement of Military Medicine.

The perils of perfect performance; considering the effects of introducing autonomous vehicles on rates of car vs cyclist conflict.

How humans will adapt and respond to the introduction of autonomous vehicles (AVs) is uncertain. This study used an agent-based model to explore how AVs, human-operated vehicles, and cyclists might interact based on the introduction of flawlessly performing AVs. Under two separate experimental conditions, results of experiment 1 showed that, despite no conflicts occurring between cyclists and AVs, modelled conflicts among human-operated cars and cyclists increased with the introduction of AVs due to cyclists' adjusted expectations of the behaviour and capability of human-operated and autonomous cars. Similarly, when human-operated cars were replaced with AVs over time in experiment 2, cyclist conflict rates did not follow a linear reduction consistent with the replacement rate but decreased more slowly in the early stages of replacement before 50% substitution. It is concluded that, although flawlessly performing AVs might reduce total conflicts, the introduction of AVs into a transport system where humans adjust to the behaviour and risk presented by AVs could create new sources of error that offset some of AVs assumed safety benefits. Practitioner summary: Ergonomics is an applied science that studies interactions between humans and other elements of a system, including non-human agents. Agent-Based Modelling (ABM) provides an approach for exploring dynamic and emergent interactions between agents. In this article, we demonstrate ABM through an analysis of how cyclists and pedestrians might interact with Autonomous Vehicles (AVs) in future road transport systems. Abbreviations: ABM: agent-based model; AV: autonomous vehicle; ODD; overview, design concepts and details; RW: rescorla-wagner.

Who is to blame for crashes involving autonomous vehicles? Exploring blame attribution across the road transport system.

The introduction of fully autonomous vehicles is approaching. This warrants a re-consideration of road crash liability, given drivers will have diminished control. This study, underpinned by attribution theory, investigated blame attribution to different road transport system actors following crashes involving manually driven, semi-autonomous and fully autonomous vehicles. It also examined whether outcome severity alters blame ratings. 396 participants attributed blame to five actors (vehicle driver/user, pedestrian, vehicle, manufacturer, government) in vehicle-pedestrian crash scenarios. Different and unique patterns of blame were found across actors, according to the three vehicle types. In crashes involving fully autonomous vehicles, vehicle users received low blame, while vehicle manufacturers and government were highly blamed. There was no difference in the level of blame attributed between high and low severity crashes regarding vehicle type. However, the government received more blame in high severity crashes. The findings have implications for policy and legislation surrounding crash liability. Practitioner summary: Public views relating to blame and liability in transport accidents is a vital consideration for the introduction of new technologies such as autonomous vehicles. This study demonstrates how a systems ergonomics framework can assist to identify the implications of changing public opinion on blame for future road transport systems. Abbreviation: ANOVA: analysis of variance; DAT: defensive attribution theory; IV: independent variable.