Rapid benefit-risk assessments: no escape from expert judgments in risk management

Antibiotics in agriculture and the risk to human health: how worried should we be?

The use of antibiotics in agriculture is routinely described as a major contributor to the clinical problem of resistant disease in human medicine. While a link is plausible, there are no data conclusively showing the magnitude of the threat emerging from agriculture. Here, we define the potential mechanisms by which agricultural antibiotic use could lead to human disease and use case studies to critically assess the potential risk from each. The three mechanisms considered are as follows 1: direct infection with resistant bacteria from an animal source, 2: breaches in the species barrier followed by sustained transmission in humans of resistant strains arising in livestock, and 3: transfer of resistance genes from agriculture into human pathogens. Of these, mechanism 1 is the most readily estimated, while significant is small in comparison with the overall burden of resistant disease. Several cases of mechanism 2 are known, and we discuss the likely livestock origins of resistant clones of Staphylococcus aureus and Enterococcus faecium, but while it is easy to show relatedness the direction of transmission is hard to assess in robust fashion. More difficult yet to study is the contribution of mechanism 3, which may be the most important of all.

Improving the decision-making process for nonprescription drugs: a framework for benefit-risk assessment

Nonprescription drugs pose unique challenges to regulators. The fact that the barriers to access are lower for nonprescription drugs as compared with prescription drugs may permit additional consumers to obtain effective drugs. However, the use of these drugs by consumers in the absence of supervision by a health-care professional may result in unacceptable rates of misuse and suboptimal clinical outcomes. A value-tree method is proposed that defines important benefit and risk domains relevant to nonprescription drugs. This value tree can be used to comprehensively identify product-specific attributes in each domain and can also support formal benefit-risk assessment using a variety of tools. This is illustrated here, using a modification of the International Risk Governance Council (IRGC) framework, a flexible tool previously applied in a number of fields, which systematizes an approach to issue review, early alignment of stakeholders, evaluation, and risk mitigation/management. The proposed approach has the potential to provide structured, transparent tools for regulatory decision making for nonprescription drugs.

A model for probabilistic health impact assessment of exposure to food chemicals

A statistical model is presented extending the integrated probabilistic risk assessment (IPRA) model of van der Voet and Slob [van der Voet, H., Slob, W., 2007. Integration of probabilistic exposure assessment and probabilistic hazard characterisation. Risk Analysis, 27, 351-371]. The aim is to characterise the health impact due to one or more chemicals present in food causing one or more health effects. For chemicals with hardly any measurable safety problems we propose health impact characterisation by margins of exposure. In this probabilistic model not one margin of exposure is calculated, but rather a distribution of individual margins of exposure (IMoE) which allows quantifying the health impact for small parts of the population. A simple bar chart is proposed to represent the IMoE distribution and a lower bound (IMoEL) quantifies uncertainties in this distribution. It is described how IMoE distributions can be combined for dose-additive compounds and for different health effects. Health impact assessment critically depends on a subjective valuation of the health impact of a given health effect, and possibilities to implement this health impact valuation step are discussed. Examples show the possibilities of health impact characterisation and of integrating IMoE distributions. The paper also includes new proposals for modelling variable and uncertain factors describing food processing effects and intraspecies variation in sensitivity.

Antimicrobial resistance in beef and dairy cattle production

Observational studies of cattle production systems usually find that cattle from conventional dairies harbor a higher prevalence of antimicrobial resistant (AMR) enteric bacteria compared to organic dairies or beef-cow operations; given that dairies usually use more antimicrobials, this result is not unexpected. Experimental studies have usually verified that application of antimicrobials leads to at least a transient expansion of AMR bacterial populations in treated cattle. Nevertheless, on dairy farms the majority of antibiotics are used to treat mastitis and yet AMR remains relatively low in mastitis pathogens. Other studies have shown no correlation between antimicrobial use and prevalence of AMR bacteria including documented cases where the prevalence of AMR bacteria is non-responsive to antimicrobial applications or remains relatively high in the absence of antimicrobial use or any other obvious selective pressures. Thus, there are multi-factorial events and pressures that influence AMR bacterial populations in cattle production systems. We introduce a heuristic model that illustrates how repeated antimicrobial selection pressure can increase the probability of genetic linkage between AMR genes and niche- or growth-specific fitness traits. This linkage allows persistence of AMR bacteria at the herd level because subpopulations of AMR bacteria are able to reside long-term within the host animals even in the absence of antimicrobial selection pressure. This model highlights the need for multiple approaches to manage herd health so that the total amount of antimicrobials is limited in a manner that meets animal welfare and public health needs while reducing costs for producers and consumers over the long-term.

Expert judgment-based risk assessment using statistical scenario analysis: a case study-running the bulls in Pamplona (Spain)

In this article, we present a methodology to assess the risk incurred by a participant in an activity involving danger of injury. The lack of high-quality historical data for the case considered prevented us from constructing a sufficiently detailed statistical model. It was therefore decided to generate a risk assessment model based on expert judgment. The methodology is illustrated in a real case context: the assessment of risk to participants in a San Fermin bull-run in Pamplona (Spain). The members of the panel of "experts on the bull-run" represented very different perspectives on the phenomenon: runners, surgeons and other health care personnel, journalists, civil defense workers, security staff, organizers, herdsmen, authors of books on the bull-run, etc. We consulted 55 experts. Our methodology includes the design of a survey instrument to elicit the experts' views and the statistical and mathematical procedures used to aggregate their subjective opinions.