Suchergebnisse

The disease burden of pathogens as estimated by QMRA (quantitative microbial risk assessment) and EA (epidemiological analysis) often differs considerably. This is an unsatisfactory situation for policymakers and scientists. We explored methods to obtain a unified estimate using campylobacteriosis in the Netherlands as an example, where previous work resulted in estimates of 4.9 million (QMRA) and 90,600 (EA) cases per year. Using the maximum likelihood approach and considering EA the gold standard, the QMRA model could produce the original EA estimate by adjusting mainly the dose‐infection relationship. Considering QMRA the gold standard, the EA model could produce the original QMRA estimate by adjusting mainly the probability that a gastroenteritis case is caused by Campylobacter. A joint analysis of QMRA and EA data and models assuming identical outcomes, using a frequentist or Bayesian approach (using vague priors), resulted in estimates of 102,000 or 123,000 campylobacteriosis cases per year, respectively. These were close to the original EA estimate, and this will be related to the dissimilarity in data availability. The Bayesian approach further showed that attenuating the condition of equal outcomes immediately resulted in very different estimates of the number of campylobacteriosis cases per year and that using more informative priors had little effect on the results. In conclusion, EA was dominant in estimating the burden of campylobacteriosis in the Netherlands. However, it must be noted that only statistical uncertainties were taken into account here. Taking all, usually difficult to quantify, uncertainties into account might lead to a different conclusion.

Epidemiology and quantitative microbiological risk assessment are disciplines in which the same public health measures are estimated, but results differ frequently. If large, these differences can confuse public health policymakers. This article aims to identify uncertainty sources that explain apparent differences in estimates for Campylobacter spp. incidence and attribution in the Netherlands, based on four previous studies (two for each discipline). An uncertainty typology was used to identify uncertainty sources and the NUSAP method was applied to characterize the uncertainty and its influence on estimates. Model outcomes were subsequently calculated for alternative scenarios that simulated very different but realistic alternatives in parameter estimates, modeling, data handling, or analysis to obtain impressions of the total uncertainty. For the epidemiological assessment, 32 uncertainty sources were identified and for QMRA 67. Definitions (e.g., of a case) and study boundaries (e.g., of the studied pathogen) were identified as important drivers for the differences between the estimates of the original studies. The range in alternatively calculated estimates usually overlapped between disciplines, showing that proper appreciation of uncertainty can explain apparent differences between the initial estimates from both disciplines. Uncertainty was not estimated in the original QMRA studies and underestimated in the epidemiological studies. We advise to give appropriate attention to uncertainty in QMRA and epidemiological studies, even if only qualitatively, so that scientists and policymakers can interpret reported outcomes more correctly. Ideally, both disciplines are joined by merging their strong respective properties, leading to unified public health measures.

Quantitative microbiological risk assessment was used to quantify the risk associated with the exposure to Legionella pneumophila in a whirlpool. Conceptually, air bubbles ascend to the surface, intercepting Legionella from the traversed water. At the surface the bubble bursts into dominantly noninhalable jet drops and inhalable film drops. Assuming that film drops carry half of the intercepted Legionella, a total of four (95% interval: 1–9) and 4.5×104 (4.4×104 – 4.7×104) cfu/min were estimated to be aerosolized for concentrations of 1 and 1,000 legionellas per liter, respectively. Using a dose‐response model for guinea pigs to represent humans, infection risks for active whirlpool use with 100 cfu/L water for 15 minutes were 0.29 (∼0.11–0.48) for susceptible males and 0.22 (∼0.06–0.42) for susceptible females. A L. pneumophila concentration of ≥1,000 cfu/L water was estimated to nearly always cause an infection (mean: 0.95; 95% interval: 0.9–∼1). Estimated infection risks were time‐dependent, ranging from 0.02 (0–0.11) for 1‐minute exposures to 0.93 (0.86–0.97) for 2‐hour exposures when the L. pneumophila concentration was 100 cfu/L water. Pool water in Dutch bathing establishments should contain <100 cfu Legionella/L water. This study suggests that stricter provisions might be required to assure adequate public health protection.

BACKGROUND: The Dutch government has set targets for reduction of antimicrobial usage in food animals, stipulating a 50% reduction in usage (on a weight basis) in 2013 as compared to 2009 and a 70% decrease in 2015. A monitoring program has been instituted to evaluate the impact on antimicrobial resistance (AMR). The Dutch Ministry of Public Health Welfare and Sports has expressed the need for a summary index to present the results of the monitoring data concisely to policy makers. METHODS: We use data on AMR in bacteria from randomly collected samples from broiler chickens, fattening pigs, veal calves and dairy cows. Escherichia coli was selected for resistance monitoring because they are intrinsically susceptible to the antibiotics included in the test panel (ciprofloxacin, cefotaxime, tetracycline and ampicillin) and they are present in all samples, which facilitates proper randomization and trend analysis. The AMR summary index was calculated for each animal species as a weighted average over the four antibiotics, taking into account their clinical relevance. Weights were obtained by conjoint analysis, a pairwise comparison study involving infectious diseases professionals with clinical and public health backgrounds, with data analysis by conditional logistic regression. The AMR summary index was then computed by Monte Carlo simulation, accounting for sampling and regression uncertainty. RESULTS: The highest weights (0.35) were given to ciprofloxacin and cefotaxime followed by ampicillin (0.23) and tetracycline (0.07). Throughout the years, the AMR index was highest in broiler chickens, followed by pigs and veal calves, while the lowest values were consistently recorded in dairy cows. In all animal species, the index in 2014 was significantly lower than in 2009. CONCLUSIONS: We demonstrate that high-dimensional data on surveillance of antimicrobial resistance can be summarized in an index for evaluating trends between and within food animal species by a process involving decision makers and scientists to ...

Sources for human hepatitis E virus (HEV) infections of genotype 3 are largely unknown. Pigs are potential animal reservoirs for HEV. Intervention at pig farms may be desired when pigs are confirmed as a source for human infections, requiring knowledge about transmission routes. These routes are currently understudied. The current study aims to quantify the likelihood of pig feces in causing new HEV infections in pigs due to oral ingestion. We estimated the daily infection risk for pigs by modeling the fate of HEV in the fecal–oral (F–O) pathway. Using parameter values deemed most plausible by the authors based on current knowledge the daily risk of infection was 0.85 (95% interval: 0.03–1). The associated expected number of new infections per day was ∼4 (2.5% limit 0.1, the 97% limit tending to infinity) compared to 0.7 observed in a transmission experiment with pigs, and the likelihood of feces causing the transmission approached 1. In alternative scenarios, F–O transmission of HEV was also very likely to cause new infections. By reducing the total value of all explanatory variables by 2 orders of magnitude, the expected numbers of newly infected pigs approached the observed number. The likelihood of F–O transmission decreased by decreasing parameter values, allowing for at most 94% of infections being caused by additional transmission routes. Nevertheless, in all scenarios F–O transmission was estimated to contribute to HEV transmission. Thus, despite the difficulty in infecting pigs with HEV via oral inoculation, the F–O route is likely to cause HEV transmission among pigs.

Climate change may impact waterborne and foodborne infectious disease, but to what extent is uncertain. Estimating climate‐change‐associated relative infection risks from exposure to viruses, bacteria, or parasites in water or food is critical for guiding adaptation measures. We present a computational tool for strategic decision making that describes the behavior of pathogens using location‐specific input data under current and projected climate conditions. Pathogen‐pathway combinations are available for exposure to norovirus, Campylobacter, Cryptosporidium, and noncholera Vibrio species via drinking water, bathing water, oysters, or chicken fillets. Infection risk outcomes generated by the tool under current climate conditions correspond with those published in the literature. The tool demonstrates that increasing temperatures lead to increasing risks for infection with Campylobacter from consuming raw/undercooked chicken fillet and for Vibrio from water exposure. Increasing frequencies of drought generally lead to an elevated infection risk of exposure to persistent pathogens such as norovirus and Cryptosporidium, but decreasing risk of exposure to rapidly inactivating pathogens, like Campylobacter. The opposite is the case with increasing annual precipitation; an upsurge of heavy rainfall events leads to more peaks in infection risks in all cases. The interdisciplinary tool presented here can be used to guide climate change adaptation strategies focused on infectious diseases.

Abstract

Objectives
Slaughterhouse staff is occupationally exposed to antimicrobial resistant bacteria. Studies reported high antimicrobial resistance gene (ARG) abundances in slaughter pigs. This cross-sectional study investigated occupational exposure to tetracycline (tetW) and macrolide (ermB) resistance genes and assessed determinants for faecal tetW and ermB carriage among pig slaughterhouse workers.


Methods
During 2015–2016, 483 faecal samples and personal questionnaires were collected from workers in a Dutch pig abattoir, together with 60 pig faecal samples. Human dermal and respiratory exposure was assessed by examining 198 carcass, 326 gloves, and 33 air samples along the line, next to 198 packed pork chops to indicate potential consumer exposure. Samples were analyzed by qPCR (tetW, ermB). A job exposure matrix was created by calculating the percentage of tetW and ermB positive carcasses or gloves for each job position. Multiple linear regression models were used to link exposure to tetW and ermB carriage.


Results
Workers are exposed to tetracycline and macrolide resistance genes along the slaughter line. Tetw and ermB gradients were found for carcasses, gloves, and air filters. One packed pork chop contained tetW, ermB was non-detectable. Human faecal tetW and ermB concentrations were lower than in pig faeces. Associations were found between occupational tetW exposure and human faecal tetW carriage, yet, not after model adjustments. Sampling round, nationality, and smoking were determinants for ARG carriage.


Conclusion
We demonstrated clear environmental tetracycline and macrolide resistance gene exposure gradients along the slaughter line. No robust link was found between ARG exposure and human faecal ARG carriage.