Background Around one-third of older adults aged 65 years or older who live in the community fall each year. Interventions to prevent falls can be designed to target the whole community, rather than selected individuals. These population-level interventions may be facilitated by different healthcare, social care, and community-level agencies. They aim to tackle the determinants that lead to risk of falling in older people, and include components such as community-wide polices for vitamin D supplementation for older adults, reducing fall hazards in the community or people's homes, or providing public health information or implementation of public health programmes that reduce fall risk (e.g. low-cost or free gym membership for older adults to encourage increased physical activity). Objectives To review and synthesise the current evidence on the effects of population-based interventions for preventing falls and fall-related injuries in older people. We defined population-based interventions as community-wide initiatives to change the underlying societal, cultural, or environmental conditions increasing the risk of falling. Search methods We searched CENTRAL, MEDLINE, Embase, three other databases, and two trials registers in December 2020, and conducted a top-up search of CENTRAL, MEDLINE, and Embase in January 2023. Selection criteria We included randomised controlled trials (RCTs), cluster RCTs, trials with stepped-wedge designs, and controlled non-randomised studies evaluating population-level interventions for preventing falls and fall-related injuries in adults = 60 years of age. Population-based interventions target entire communities. We excluded studies only targeting people at high risk of falling or with specific comorbidities, or residents living in institutionalised settings. Data collection and analysis We used standard methodological procedures expected by Cochrane, and used GRADE to assess the certainty of the evidence. We prioritised seven outcomes: rate of falls, number of fallers, number of people experiencing one or more fall-related injuries, number of people experiencing one or more fall-related fracture, number of people requiring hospital admission for one or more falls, adverse events, and economic analysis of interventions. Other outcomes of interest were: number of people experiencing one or more falls requiring medical attention, health-related quality of life, fall-related mortality, and concerns about falling. Main results We included nine studies: two cluster RCTs and seven non-randomised trials (of which five were controlled before-and-after studies (CBAs), and two were controlled interrupted time series (CITS)). The numbers of older adults in intervention and control regions ranged from 1200 to 137,000 older residents in seven studies. The other two studies reported only total population size rather than numbers of older adults (67,300 and 172,500 residents). Most studies used hospital record systems to collect outcome data, but three only used questionnaire data in a random sample of residents; one study used both methods of data collection. The studies lasted between 14 months and eight years. We used Prevention of Falls Network Europe (ProFaNE) taxonomy to classify the types of interventions. All studies evaluated multicomponent falls prevention interventions. One study (n = 4542) also included a medication and nutrition intervention. We did not pool data owing to lack of consistency in study designs. Medication or nutrition Older people in the intervention area were offered free-of-charge daily supplements of calcium carbonate and vitamin D3. Although female residents exposed to this falls prevention programme had fewer fall-related hospital admissions (with no evidence of a difference for male residents) compared to a control area, we were unsure of this finding because the certainty of evidence was very low. This cluster RCT included high and unclear risks of bias in several domains, and we could not determine levels of imprecision in the effect estimate reported by study authors. Because this evidence is of very low certainty, we have not included quantitative results here. This study reported none of our other review outcomes. Multicomponent interventions Types of interventions included components of exercise, environment modification (home; community; public spaces), staff training, and knowledge and education. Studies included some or all of these components in their programme design. The effectiveness of multicomponent falls prevention interventions for all reported outcomes is uncertain. The two cluster RCTs included high or unclear risk of bias, and we had no reasons to upgrade the certainty of evidence from the non-randomised trial designs (which started as low-certainty evidence). We also noted possible imprecision in some effect estimates and inconsistent findings between studies. Given the very low-certainty evidence for all outcomes, we have not reported quantitative findings here. One cluster RCT reported lower rates of falls in the intervention area than the control area, with fewer people in the intervention area having one or more falls and fall-related injuries, but with little or no difference in the number of people having one or more fall-related fractures. In another cluster RCT (a multi-arm study), study authors reported no evidence of a difference in the number of female or male residents with falls leading to hospital admission after either a multicomponent intervention ("environmental and health programme") or a combination of this programme and the calcium and vitamin D-3 programme (above). One CBA reported no difference in rate of falls between intervention and control group areas, and another CBA reported no difference in rate of falls inside or outside the home. Two CBAs found no evidence of a difference in the number of fallers, and another CBA found no evidence of a difference in fall-related injuries. One CITS found no evidence of a difference in the number of people having one or more fall-related fractures. No studies reported adverse events. Authors' conclusions Given the very low-certainty evidence, we are unsure whether population-based multicomponent or nutrition and medication interventions are effective at reducing falls and fall-related injuries in older adults. Methodologically robust cluster RCTs with sufficiently large communities and numbers of clusters are needed. Establishing a rate of sampling for population-based studies would help in determining the size of communities to include. Interventions should be described in detail to allow investigation of effectiveness of individual components of multicomponent interventions; using the ProFaNE taxonomy for this would improve consistency between studies.

The sudden onset of the COVID-19 global health crisis and associated economic and social fall-out has highlighted the importance of speed in modeling emergency scenarios so that robust, reliable evidence can be placed in policy and decision-makers’ hands as swiftly as possible. For computational social scientists who are building complex policy models but who lack ready access to high-performance computing facilities, such time-pressure can hinder effective engagement with end-users. Popular and accessible agent-based modeling platforms in computational social science such as NetLogo can make models fast to develop, but slow to run when exploring broad parameter spaces on individual workstations. However, while deployment on high-performance computing (HPC) clusters can achieve marked performance improvements, transferring models from workstations to HPC clusters can also be a technically challenging and time-consuming task for social scientists or those from non computer science-related backgrounds. In this paper we present a set of generic templates that can be used and adapted by NetLogo users who have access to HPC clusters but require additional support for deploying their models on such infrastructure. We show how model run-time speed improvements of between 200× and 400× over desktop machines are possible using (1) a benchmark ‘wolf-sheep predation’ model in addition to (2) an example drawn from our own applied policy modeling work surrounding COVID-19 management settings for Government in Australia. We describe how a focus on improving model speed is a non-trivial concern for model developers in the social sciences and discuss its practical importance for improved policy and decision-making in the real world. We provide all associated documentation in a linked git repository.

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.

While the burden of injury is large, injury does not feature on most people’s list of global grand challenges—climate change, equity, ageing, racism, human rights, poverty, COVID-19. Each person’s ‘grand challenge list’ is different. What is yours? Does injury feature? As an injury prevention researcher, how do I make sense of a list of global grand challenges that does not include injury? As a practitioner, how can I achieve influence in a world that does not recognise injury as an important problem? There has been a tendency to explain the importance of injury prevention by highlighting the burden of injury then fragment the overall problem into its components (road safety, falls, drowning, etc) before developing countermeasures to address the burden. Rather …

I have not met an executive yet, who thinks they are poor decision-makers. I have not met many scientists who do not believe that data speak for themselves. From an executive’s perspective, poor decisions are simply the consequence of flawed (or insufficient) data, and from the scientist’s perspective, poor decisions are what happens when decision-makers do not listen to scientists. To address the problem, executives seek to obtain more and better data, and scientists advocate more loudly for data they produce. But there is more to it than that. Decision-making is an active process somewhere between data inputs and public health outcome. There is a science that supports our understanding and optimal application of the decision-making process—decision science. When it comes down to it, public health and thus …

The sudden onset of the COVID-19 global health crisis and as-sociated economic and social fall-out has highlighted the im-portance of speed in modeling emergency scenarios so that ro-bust, reliable evidence can be placed in policy and decision-makers’ hands as swiftly as possible. For computational social scientists who are building complex policy models but who lack ready access to high-performance computing facilities, such time-pressure can hinder effective engagement. Popular and ac-cessible agent-based modeling platforms such as NetLogo can be fast to develop, but slow to run when exploring broad param-eter spaces on individual workstations. However, while deploy-ment on high-performance computing (HPC) clusters can achieve marked performance improvements, transferring models from workstations to HPC clusters can also be a technically challenging and time-consuming task. In this paper we present a set of generic templates that can be used and adapted by NetLogo users who have access to HPC clusters but require ad-ditional support for deploying their models on such infrastruc-ture. We show that model run-time speed improvements of be-tween 200x and 400x over desktop machines are possible using 1) a benchmark ‘wolf-sheep predation’ model in addition to 2) an example drawn from our own work modeling the spread of COVID-19 in Victoria, Australia. We describe how a focus on improving model speed is non-trivial for model development and discuss its practical importance for improved policy and de-cision-making in the real world. We provide all associated doc-umentation in a linked git repository.

A safe systems approach has been acknowledged as the underlying philosophy of contemporary road safety strategies. Despite this, systemic applications in road transport evaluation and design remain sparse. This paper explores the value of using Ergonomics design and evaluation methods such as Cognitive Work Analysis in conjunction with road transport theories such as the field of safe travel to provide easily interpretable analyses of road designs. The goal is that this would provide a facilitation platform for communication between Ergonomics analysts and road transport designers, aiming to facilitate systemic applications in road transport. The application of Cognitive Work Analysis and the field of safe travel theory in the evaluation of a new intersection design concept demonstrated that this proves a promising cross method collaboration. Cognitive Work Analysis provided the analytical detail of road user behavior possible as a result of the interaction between the intersection design, road users, vehicles and the environment. Subsequently, the field of safe travel theory provided a visual means to communicate these findings directly related to the intersection designs. The application furthermore provided additional insights into the constraints acting upon the field of safe travel and the paths that road users can possibly take within this field.

The field of injury prevention has long struggled with the ‘splitting versus clumping’ dilemma. The most obvious example of this is our highlighting the burden of the injury as a whole (which is ever so much more a compelling figure than the burden of any of its components), and then addressing that burden by focusing, necessarily, on its component parts. A close look at the cause, and body part-injured codes in the International Classification of Diseases (ICD)1 shows the extent to which efforts to reduce the burden of injury can be fragmented. However, the ICD classification goes only part way towards mapping the full range of specifics that fall within our territory. Information about individual injuries can be aggregated at the community, national or global levels but the specific aspects of each injury occurrence needs somehow to be captured. A range of scientific methods are used to do …

At a personal and public level, the world is experiencing true devastation. In homes, hospitals, streets and societies, loss is profound. Public health, and what it means in terms of individual and collective responsibility, is forefront in public and political discourse. Expertise (and science) is being recognised as friend not foe, and the technical tools used by public health scientists and practitioners are standard fare in the lay press. So, what happens next? It is hard to know, and many of us are needing to focus so much on survival that we don’t have the luxury to think about it. However, many of us are hoping that soon things will return to ‘normal’ and that we can again get about our business as usual. The business world has been quick to point out that whatever we get back to, it will not be business as usual as we currently know it.1 People will adapt practices to mitigate day-to-day threats of endemic disease, and the structural changes brought about as an acute phase response will become system-level enablers with long-term benefit for national and global economies …

One of the privileges of being the editor of Injury Prevention is the opportunity to read 600 or so prepublication reports of the latest research in the injury prevention field—each year, year in year out. After some years of doing this, I have developed a sense that the field of modern injury prevention has matured. Rather than trace our development, what I will do here is use the manuscripts published in this issue to show the extent to which the field is now comfortable in its own skin. There are 14 manuscripts published in this issue. Collectively, they reveal injury prevention to be a profession no longer questioning its identity, just confidently going about important programmes of work in concert with the world of which it is part. The manuscripts in this issue have not been especially selected and collated. They are simply …

The complexity and scale of problems being tackled by ergonomists is growing. Work and societal systems are becoming increasingly reliant on technology, and the technologies themselves are becoming...

Background: For countries with strong border control and an epidemic that is not yet advanced, there is an opportunity to eliminate SARS-CoV-2 that is causing the COVID-19 pandemic. We show how public health policies and their effects can be modelled to estimate the probability of elimination of SARS-COV-2 under i) strict physical distancing policies implemented in New Zealand and Australia on the 26th and 28th March, respectively, continuing until such time that elimination is achieved; and ii) the same policy, but with physical distancing decaying over 60 days to 26th and 28th of May. Methods: We developed an agent-based SEIR model that simulated key aspects of both country's populations, disease, spatial and behavioural dynamics, as well as the mechanism and effect of public health policy responses on the transmission of SARS-CoV-2. Findings: Under maintained strong physical distancing, we estimated a median elimination date of July 12th for Australia (95% SI: June 5th to August 28th) and June 3rd for New Zealand (95% SI May 4th to June 27th). A 90% probability of elimination is achievable in Australia on August 17th (95% SI: August 8th – August 30th) and on June 14th (95% SI: June 17th – June 29th) in New Zealand. However, under our scenario of decaying adherence to physical distancing from implementation to 60 days post restrictions, a rebound in SARS-CoV-2 infections (i.e., a second wave) was likely in Australia and possibly in New Zealand. At 100 days, the probability of elimination was reduced to 10% in Australia and 68% in New Zealand. Interpretation: The findings suggest that with the effective implementation and maintenance of public health policies limiting physical interaction, it was possible to estimate a pattern the elimination of SARS-CoV-2 transmission in both countries. It seems highly likely NZ will have successfully eliminated as of 8 June, but not in Australia; the latter is consistent with our lower probabilities that modelled less stringent restrictions applied in Australia.

The contemporary public health model for injury and violence prevention is a four-step process, which has been difficult to fully actualize in real-world contexts. This difficulty results from challenges in bridging science to practice and developing and applying population-level approaches. Prevention programmes and policies are embedded within and impacted by a range of system-level factors, which must be considered and actively managed when addressing complex public health challenges involving multiple sectors and stakeholders. To address these concerns, a systemic approach to population-level injury and violence prevention is being developed and explored by the Division of Analysis, Research, and Practice Integration in the National Center for Injury Prevention and Control at the Centers for Disease Control and Prevention. This article makes the case for and provides a high-level overview of this systemic approach, its various components, and how it is being applied in one governmental unit.

How much data are enough? How accurate do they have to be before they are useful? Do data have to be collected from us for them to be relevant to me? Well, it depends, on a lot of things, but mostly on the questions we are hoping the data will help us address. And even when we have enough data, and they are sufficiently accurate, and they are relevant to us, they are still not much help unless they are the right data and we know how to use them. Before unpacking these issues, let us first anchor our discussion in the roots of the discipline that underpinned the Journal’s establishment: the epidemiological approach to injury prevention. Descriptive epidemiology is the science that grounds public health. As practitioners we cannot address what cannot ‘see’. Descriptive epidemiology is the means by which we can elucidate the nature and extent of a problem and describe its distribution by time, person, place, severity, activity, location and …

In its modern form, injury prevention is about 60 years old.1 2 For all of this time injury prevention has been a leader in the field of academic public health. Injury prevention has led the way in multidisciplinary and transdisciplinary thinking, has been an early adopter of technologies supporting the data revolution and has pioneered the development of implementation science to achieve population-level improvements. In the 1960s, Haddon was explicitly linking the principles of engineering to those of epidemiology, in defining kinetic energy as the causal agent of injury (which he recommended be managed using a host-agent-environment/primary-secondary-tertiary prevention model).3 In the 1980s, authors in the USA were describing trauma deaths as a system performance issue,4 and in the 1990s the Swedish Government was articulating a systems solution to the escalating problem of road crash injury.5 With its …

Editorials throughout 2018 made explicit the journal’s editorial direction. In particular, we drew readers’ attention to the process of manuscript selection1 and the purpose selection was aiming to achieve.2 Rather than being a passive filtering process, editorial selection aims to encourage changes within the research world. At the year’s end, the journal was publishing ‘manuscripts of the highest quality that provided information of high relevance to prevention practice’,3 and hopefully changing the type and quality of research undertaken. Now, 18 months into the current editorial team’s lead-in period, let us take a look at the manuscripts published in this issue. Is the journal publishing the science that supports population-level changes …

One of the defining characteristics of a profession is that its members take responsibility for enabling, educating and training emerging professionals in their field. This education role goes hand-in-hand with a profession’s responsibility to monitor members’ adherence to ethical, technical and professional standards. In the field of injury prevention, there are many who have incorporated this service role into their professional DNA. We can all identify mentors whose passion for educating future injury prevention researchers and practitioners seems to have grown as their experience and opportunity to contribute does too. Yet surprisingly, there are very few opportunities for injury education enthusiasts to share their teaching methodologies and approaches for other educators to learn from. There are …

In this ecological study, we attempt to quantify the extent to which differences in homicide and suicide death rates between three countries, and among states/provinces within those countries, may be explained by differences in their social, economic, and structural characteristics. We examine the relationship between state/province level measures of societal risk factors and state/province level rates of violent death (homicide and suicide) across Australia, Canada, and the United States. Census and mortality data from each of these three countries were used. Rates of societal level characteristics were assessed and included residential instability, self-employment, income inequality, gender economic inequity, economic stress, alcohol outlet density, and employment opportunities). Residential instability, self-employment, and income inequality were associated with rates of both homicide and suicide and gender economic inequity was associated with rates of suicide only. This study opens lines of inquiry around what contributes to the overall burden of violence-related injuries in societies and provides preliminary findings on potential societal characteristics that are associated with differences in injury and violence rates across populations.

Results from randomised controlled trials make an important contribution to improvements in injury-related health. However, publication of findings from randomised controlled trials remains a rarity in the injury prevention literature. Increasing the quantity of randomised controlled trials research need not reduce other types of injury prevention research activity. Highlighting the importance of publishing trials does not devalue the contribution of other designs. The paucity of trial evidence in injury prevention poses a risk to the advancement of the field. We need more randomised controlled trials in injury prevention. In this issue of the journal, we publish five study protocols and the results of one completed study that illustrate how injury prevention researchers are using trial methodology to address important research questions. The first protocol describes a randomised controlled trial designed to quantify the effect of a health education intervention on the safe-sleep knowledge, attitudes and practices of primary care givers of infant children. The second protocol is a cluster randomised controlled trial involving female soccer (football) players 14–18 years of age that aims to quantify the effect of a soccer-specific ankle brace on the incidence of acute lateral sprain of the …