Transitioning to routine breast cancer risk assessment and management in primary care: what can we learn from cardiovascular disease?

To capitalise on advances in breast cancer prevention, all women would need to have their breast cancer risk formally assessed. With ~85% of Australians attending primary care clinics at least once a year, primary care is an opportune location for formal breast cancer risk assessment and management. This study assessed the current practice and needs of primary care clinicians regarding assessment and management of breast cancer risk. Two facilitated focus group discussions were held with 17 primary care clinicians (12 GPs and 5 practice nurses (PNs)) as part of a larger needs assessment. Primary care clinicians viewed assessment and management of cardiovascular risk as an intrinsic, expected part of their role, often triggered by practice software prompts and facilitated by use of an online tool. Conversely, assessment of breast cancer risk was not routine and was generally patient- (not clinician-) initiated, and risk management (apart from routine screening) was considered outside the primary care domain. Clinicians suggested that routine assessment and management of breast cancer risk might be achieved if it were widely endorsed as within the remit of primary care and supported by an online risk-assessment and decision aid tool that was integrated into primary care software. This study identified several key issues that would need to be addressed to facilitate the transition to routine assessment and management of breast cancer risk in primary care, based largely on the model used for cardiovascular disease.

iPrevent®: a tailored, web-based, decision support tool for breast cancer risk assessment and management

We aimed to develop a user-centered, web-based, decision support tool for breast cancer risk assessment and personalized risk management. Using a novel model choice algorithm, iPrevent(®) selects one of two validated breast cancer risk estimation models (IBIS or BOADICEA), based on risk factor data entered by the user. Resulting risk estimates are presented in simple language and graphic formats for easy comprehension. iPrevent(®) then presents risk-adapted, evidence-based, guideline-endorsed management options. Development was an iterative process with regular feedback from multidisciplinary experts and consumers. To verify iPrevent(®), risk factor data for 127 cases derived from the Australian Breast Cancer Family Study were entered into iPrevent(®), IBIS (v7.02), and BOADICEA (v3.0). Consistency of the model chosen by iPrevent(®) (i.e., IBIS or BOADICEA) with the programmed iPrevent(®) model choice algorithm was assessed. Estimated breast cancer risks from iPrevent(®) were compared with those attained directly from the chosen risk assessment model (IBIS or BOADICEA). Risk management interventions displayed by iPrevent(®) were assessed for appropriateness. Risk estimation model choice was 100 % consistent with the programmed iPrevent(®) logic. Discrepant 10-year and residual lifetime risk estimates of >1 % were found for 1 and 4 cases, respectively, none was clinically significant (maximal variation 1.4 %). Risk management interventions suggested by iPrevent(®) were 100 % appropriate. iPrevent(®) successfully integrates the IBIS and BOADICEA risk assessment models into a decision support tool that provides evidence-based, risk-adapted risk management advice. This may help to facilitate precision breast cancer prevention discussions between women and their healthcare providers.

Assessing and managing breast cancer risk: Clinicians' current practice and future needs

Decision support tools for the assessment and management of breast cancer risk may improve uptake of prevention strategies. End-user input in the design of such tools is critical to increase clinical use. Before developing such a computerized tool, we examined clinicians' practice and future needs. Twelve breast surgeons, 12 primary care physicians and 5 practice nurses participated in 4 focus groups. These were recorded, coded, and analyzed to identify key themes. Participants identified difficulties assessing risk, including a lack of available tools to standardize practice. Most expressed confidence identifying women at potentially high risk, but not moderate risk. Participants felt a tool could especially reassure young women at average risk. Desirable features included: evidence-based, accessible (e.g. web-based), and displaying absolute (not relative) risks in multiple formats. The potential to create anxiety was a concern. Development of future tools should address these issues to optimize translation of knowledge into clinical practice.

Quantitative microbial risk assessment modelling for the use of reclaimed water in irrigated horticulture

Reuse of treated sewage effluent for the irrigation of horticultural crops is being propounded and practiced as a means of alleviating pressure on freshwater resources. Concerns have been raised. however, as to the risk to human health, primarily disease, associated with this practice. Quantitative Microbial Risk Assessment (QMRA) is a useful tool for estimating this risk. We describe how QMRA works and the current state of knowledge of the components of QMRA models for the horticultural reuse scenario.

Arsenic, mode of action at biologically plausible low doses : what are the implications for low dose cancer risk?

Arsenic is an established human carcinogen. However, there has been much controversy about the shape of the arsenic response curve, particularly at low doses. This controversy has been exacerbated by the fact that the  mechanism(s) of arsenic carcinogenesis are still unclear and because there are few satisfactory animal models for arsenic-induced carcinogenesis. Recent epidemiological studies have shown that the relative risk for cancer among populations exposed to ≤60 ppb As in their drinking water is often lower than the risk for the unexposed control population. We have found that treatment of human keratinocyte and fibroblast cells with 0.1 to 1 μM arsenite (As^III) also produces a low dose protective effect against oxidative stress and DNA damage. This response includes increased transcription, protein levels and enzyme activity of several base excision repair genes, including DNA polymerase β and DNA ligase I. At higher concentrations (> 10 μM), As induces down-regulation of DNA repair, oxidative DNA damage and apoptosis. This low dose adaptive (protective) response by a toxic agent is known as hormesis and is characteristic of many agents that induce oxidative stress. A mechanistic model for arsenic carcinogenesis based on these data would predict that the low dose risk for carcinogenesis should be sub-linear. The threshold dose where toxicity outweighs protection is hard to predict based on in vitro dose response data, but might be estimated if one could determine the form (metabolite) and concentration of arsenic responsible for changes in gene regulation in the target tissues.

Risk assessment in child protection: problem rather than solution?

Risk assessment in child protection services has been promoted as the most reliable way to ensure that maltreatment to children is prevented and has become central to practice with children and families. However, recent research in Australia has suggested that children are being left in unsafe situations, leading to further maltreatment, by the very agencies responsible for their protection. The present article explores the reasons why child protection has become central to child protection practice and presents a wide ranging critical appraisal of risk assessment and its application. It is argued that risk assessment is a flawed process and, as a central tenet of practice, is implicated in any problems that children's protective services face. Consequently, any future reconfiguration of services for children in need of protection needs to include a re-evaluation of the efficacy of risk  assessment.

Quantitative microbial risk assessment models for consumption of raw vegetables irrigated with reclaimed water

Quantitative microbial risk assessment models for estimating the annual risk of enteric virus infection associated with consuming raw vegetables that have been overhead irrigated with nondisinfected secondary treated reclaimed water were constructed. We ran models for several different scenarios of crop type, viral concentration in effluent, and time since last irrigation event. The mean annual risk of infection was always less for cucumber than for broccoli, cabbage, or lettuce. Across the various crops, effluent qualities, and viral decay rates considered, the annual risk of infection ranged from 10–3 to 10–1 when reclaimed-water irrigation ceased 1 day before harvest and from 10–9 to 10–3 when it ceased 2 weeks before harvest. Two previously published decay coefficients were used to describe the die-off of viruses in the environment. For all combinations of crop type and effluent quality, application of the more aggressive decay coefficient led to annual risks of infection that satisfied the commonly propounded benchmark of ≤10–4, i.e., one infection or less per 10,000 people per year, providing that 14 days had elapsed since irrigation with reclaimed water. Conversely, this benchmark was not attained for any combination of crop and water quality when this withholding period was 1 day. The lower decay rate conferred markedly less protection, with broccoli and cucumber being the only crops satisfying the 10–4 standard for all water qualities after a 14-day withholding period. Sensitivity analyses on the models revealed that in nearly all cases, variation in the amount of produce consumed had the most significant effect on the total uncertainty surrounding the estimate of annual infection risk. The models presented cover what would generally be considered to be worst-case scenarios: overhead irrigation and consumption of vegetables raw. Practices such as subsurface, furrow, or drip irrigation and postharvest washing/disinfection and food preparation could substantially lower risks and need to be considered in future models, particularly for developed nations where these extra risk reduction measures are more common.

A retail supply chain risk assessment conceptual model

Supply chains are increasingly relying on information and communications technologies and in particular electronic commerce to facilitate transactions between supply chain partners. The adoption of these enabling technologies brings several enhancements to the conduct of business including gains in efficiency. However there are also drawbacks inherent in these technologies that include threats that are imposed on businesses that use them. This paper presents a study on retail supply chains and the risks and vulnerabilities that cooperating supply chain partners are exposed to when adopting these technologies. In particular, the paper discusses the various threats and vulnerabilities of retail supply and presents a conceptual model of such risks.

The mental health Risk Assessment and Management Process (RAMP) for schools: II. Process evaluation

The Risk Assessment and Management Process (RAMP) is a whole-school process for the assessment and management of student’s mental health and wellbeing in primary and secondary schools. A process evaluation revealed that RAMP was implemented as intended across six primary and three secondary schools in Melbourne, Australia. Using the RAMP risk and protective factors monitoring form and screening processes, each school identified ‘at-risk’ students who had not previously been identified or received assistance from welfare staff at the school. School staff and mental health workers from local agencies reported improvements in their knowledge of risk and protective factors, and their ability to identify at-risk students following RAMP. They also reported satisfaction in outcomes for at-risk students managed within the school using RAMP. All the primary schools and one of the
secondary schools continued to use some RAMP processes in their school up to 6 months after the initial implementation of the program.

Deterministic versus stochastic quantitative microbial Risk Assessment models for wastewater irrigation of food crops

Reuse of wastewater to irrigate food crops is being practiced in many parts of the world and is becoming more commonplace as the competition for, and stresses on, freshwater resources intensify. But there are risks associated with wastewater irrigation, including the possibility of transmission of pathogens causing infectious disease, to both workers in the field and to consumers buying and eating produce irrigated with wastewater. To manage these risks appropriately we need objective and quantitative estimates of them. This is typically achieved through one of two modelling approaches: deterministic or stochastic. Each parameter in a deterministic model is represented by a single value, whereas in stochastic models probability functions are used. Stochastic models are theoretically superior because they account for variability and uncertainty, but they are computationally demanding and not readily accessible to water resource and public health managers. We constructed models to estimate risk of enteric virus infection arising from the consumption of wastewater-irrigated horticultural crops (broccoli, cucumber and lettuce), and compared the resultant levels of risk between the deterministic and stochastic approaches. Several scenarios were tested for each crop, accounting for different concentrations of enteric viruses and different lengths of environmental exposure (i.e. the time between the last irrigation event and harvest, when the viruses are liable to decay or inactivation). In most situations modelled the two approaches yielded similar estimates of risk (within 1 order-of-magnitude). The two methods diverged most markedly, up to around 2 orders-of-magnitude, when there was large uncertainty associated with the estimate of virus concentration and the exposure period was short (1 day). Therefore, in some circumstances deterministic modelling may offer water resource managers a pragmatic alternative to stochastic modelling, but its usefulness as a surrogate will depend upon the level of uncertainty in the model parameters.

Child protection, risk assessment and blame ideology

In this article we use qualitative data drawn from a sample of child protection cases 10 demonstrate holV the process of al1ributing blame to parents and carers for child maltreatment is a sign!ficanr influence 011 decisionmaking,
sometimes to the detriment of assessing the flltllre safety of children. We foctls on two cases which both demonstrate how the process of apportioning blame can lead to decisions which might not be considered 10 be in the best interests of the children concerned. We conceptualise blame as an 'ideology' with its roots in the discourse of the 'risk society', pelpetuated and sustained by the technology of risk assessment. The concept of blame ideology is offered as an addition to theOlY which seeks 10 explain the influences on decision making in child protection practice.

The effects of time pressure and experience on nurses' risk assessment decisions : a signal detection analysis

Background: Time pressure and, occasionally, suboptimal assessment decisions are features of nursing in acute care.

Objectives: To explore the effect of generic and specialist clinical experience on the ability to detect the need to take action in acute care and the impact of time pressure on nurses' decision-making performance.

Methods: Experienced acute care registered nurses (n = 241) were presented with 50 vignettes of real clinical risk assessments. Each vignette contained seven information cues. In response to these vignettes, nurses had to decide whether to intervene or not. The 26 vignettes were time limited and mixed randomly into the 50 cases. Signal detection analysis was used to establish nurses' performance, personal decision thresholds ([beta]), and their abilities (d') to distinguish a signal of clinical risk from the clinical noise of noncontributory information.

Results: Nurses had significantly lower d' and were significantly less likely to indicate intervening under time pressure. For ability-but not threshold-there was a significant interaction of time pressure and years of experience in acute care. With no time pressure, d' increased in line with years of experience. Under time pressure, there was no effect.

Discussion: Time pressure reduced nurses' ability to detect the need and the tendency to report intervening. Thus, there were more failures to report appropriate intervention under time pressure, and the positive effects of clinical experience were negated under time pressure. More and larger scale research on the effect on clinical outcomes of time pressured nursing choices is required.