David Vose

A Linear Model for Managing the Risk of Antimicrobial Resistance Originating in Food Animals

A linear population risk model used by the U.S. Food and Drug Administration (FDA) Center for Veterinary Medicine (CVM) estimates the risk of human cases of campylobacteriosis caused by fluoroquinolone-resistant Campylobacter. Among the cases of campylobacteriosis attributed to domestically produced chicken, the fluoroquinolone resistance is assumed to result from the use of fluoroquinolones in poultry in the United States. Properties of the linear population risk model are contrasted with those of a farm-to-fork model commonly used for microbial risk assessments. The utility of the linear population model for the purpose for which it was used by CVM is discussed.

Framework for Microbial Food‐Safety Risk Assessments Amenable to Bayesian Modeling

Regulatory agencies often perform microbial risk assessments to evaluate the change in the number of human illnesses as the result of a new policy that reduces the level of contamination in the food supply. These agencies generally have regulatory authority over the production and retail sectors of the farm-to-table continuum. Any predicted change in contamination that results from new policy that regulates production practices occurs many steps prior to consumption of the product. This study proposes a framework for conducting microbial food-safety risk assessments; this framework can be used to quantitatively assess the annual effects of national regulatory policies. Advantages of the framework are that estimates of human illnesses are consistent with national disease surveillance data (which are usually summarized on an annual basis) and some of the modeling steps that occur between production and consumption can be collapsed or eliminated. The framework leads to probabilistic models that include uncertainty and variability in critical input parameters; these models can be solved using a number of different Bayesian methods. The Bayesian synthesis method performs well for this application and generates posterior distributions of parameters that are relevant to assessing the effect of implementing a new policy. An example, based on Campylobacter and chicken, estimates the annual number of illnesses avoided by a hypothetical policy; this output could be used to assess the economic benefits of a new policy. Empirical validation of the policy effect is also examined by estimating the annual change in the numbers of illnesses observed via disease surveillance systems.

NUSAP Method for Evaluating the Data Quality in a Quantitative Microbial Risk Assessment Model for Salmonella in the Pork Production Chain

The numeral unit spread assessment pedigree (NUSAP) system was implemented to evaluate the quality of input parameters in a quantitative microbial risk assessment (QMRA) model for Salmonella spp. in minced pork meat. The input parameters were grouped according to four successive exposure pathways: (1) primary production (2) transport, holding, and slaughterhouse, (3) postprocessing, distribution, and storage, and (4) preparation and consumption. An inventory of 101 potential input parameters was used for building the QMRA model. The characteristics of each parameter were defined using a standardized procedure to assess (1) the source of information, (2) the sampling methodology and sample size, and (3) the distributional properties of the estimate. Each parameter was scored by a panel of experts using a pedigree matrix containing four criteria (proxy, empirical basis, method, and validation) to assess the quality, and this was graphically represented by means of kite diagrams. The parameters obtained significantly lower scores for the validation criterion as compared with the other criteria. Overall strengths of parameters related to the primary production module were significantly stronger compared to the other modules (the transport, holding, and slaughterhouse module, the processing, distribution, and storage module, and the preparation and consumption module). The pedigree assessment contributed to select 20 parameters, which were subsequently introduced in the QMRA model. The NUSAP methodology and kite diagrams are objective tools to discuss and visualize the quality of the parameters in a structured way. These two tools can be used in the selection procedure of input parameters for a QMRA, and can lead to a more transparent quality assurance in the QMRA.

Indicators for Tracking European Vulnerabilities to the Risks of Infectious Disease Transmission due to Climate Change

A wide range of infectious diseases may change their geographic range, seasonality and incidence due to climate change, but there is limited research exploring health vulnerabilities to climate change. In order to address this gap, pan-European vulnerability indices were developed for 2035 and 2055, based upon the definition vulnerability = impact/adaptive capacity. Future impacts were projected based upon changes in temperature and precipitation patterns, whilst adaptive capacity was developed from the results of a previous pan-European study. The results were plotted via ArcGISTM to EU regional (NUTS2) levels for 2035 and 2055 and ranked according to quintiles. The models demonstrate regional variations with respect to projected climate-related infectious disease challenges that they will face, and with respect to projected vulnerabilities after accounting for regional adaptive capacities. Regions with higher adaptive capacities, such as in Scandinavia and central Europe, will likely be better able to offset any climate change impacts and are thus generally less vulnerable than areas with lower adaptive capacities. The indices developed here provide public health planners with information to guide prioritisation of activities aimed at strengthening regional preparedness for the health impacts of climate change. There are, however, many limitations and uncertainties when modeling health vulnerabilities. To further advance the field, the importance of variables such as coping capacity and governance should be better accounted for, and there is the need to systematically collect and analyse the interlinkages between the numerous and ever-expanding environmental, socioeconomic, demographic and epidemiologic datasets so as to promote the public health capacity to detect, forecast, and prepare for the health threats due to climate change.

Vulnerabilities to the risks of changes in infectious disease transmission caused by climate change: a modelling study

Abstract Background The geographic range, seasonality, and incidence of many infectious diseases might change with climate change. Yet, most models of these effects have not been able to account for the exacerbating or mitigating effects of socioeconomic variables. Vulnerability is understood to be the propensity to be adversely affected by a given cause. We assessed vulnerabilities and how these affect the relations between infectious disease transmission and climate change. Methods We developed vulnerability indices at the subnational level for 2035, and 2055. We assessed various datasets for their inclusion in a conceptual framework of vulnerability (consisting of impact and adaptive capacity). We plotted the projected change in absolute precipitation and temperature for European regions and used it as a proxy for change in temperature-sensitive infectious disease incidence. We used climate change projections of European-wide monthly means for daily temperature (T min and T max in °C) and daily precipitation (in mm) for 2035 and 2055, to map possible changes in infectious disease risk. These projections were based on a multimodel ensemble with three emission scenarios (A1b, A2, B1) and four earth system models (ECHAM5, MIROC3, CNRM, CSIRO3) and downscaled to 1 km resolution. We used a composite adaptive capacity indicator to assess vulnerability on the basis of regional adaptive capacity index developed by the ESPON project. Final vulnerability indices combined the impact and adaptive capacity indices, plotted via ArcGIS to EU regions for 2035 and 2055 and ranked into quintiles. We assessed the key factors driving the impact and vulnerability indices with a Spearman rank correlation test. Findings Many areas in southeast England, Norway, Denmark, Sweden, and southern Germany had lower rankings for vulnerabilities than for projected impacts, showing a protective effect of strong adaptive capacity. Conversely, regions in Romania, Bulgaria, Greece, and southern Italy were in higher vulnerability index quintiles than impact index quintiles. Precipitation had more of an effect than temperature in driving the impact indices, but this was regionally variable. Interpretation Some European regions probably require additional investment to develop strategies to adapt to climate change for public health. Investing generally in factors that strengthen adaptive capacity could lower vulnerabilities. Presently, modelling vulnerabilities to climate change has many uncertainties and limitations. Systematic collection and analysis is needed of the links between environmental, socioeconomic, demographic, and epidemiologic variables to better understand the risk to health presented by climate change. Funding European Centre for Disease Prevention and Control.