A system safety approach to assessing risks in the sepsis treatment process

In healthcare, most accidents occur as a result of inadequate interactions between system components rather than component failures. In such cases traditional risk analysis methods are of limited use for analysing system safety, so methods such as Systems Theoretic Process Analysis (STPA) and the Functional Resonance Analysis Method (FRAM) have been developed. This study uses STPA to assess risks in the sepsis treatment process, discusses the potential value STPA adds and compares the results of STPA with the results of another study that used FRAM. The findings indicate that STPA and FRAM have different strengths which reflect the different scientific approaches behind these two methods. FRAM facilitates an in-depth understanding of a system, while STPA allows for more comprehensive risk analysis by identifying more risks, scenarios and safety recommendations. Nevertheless, it is reasonable to say that not only does STPA provide more comprehensive risk analysis; its terminology and philosophy are also closer to the current safety management applications employed in complex systems.

Analytical study to find the impacts of using a mobile phone on driver's inattentions while driving - A case study of Lahore

The road traffic injuries are one of the leading cause of death in children and young adults according to the World Health Organization (WHO). The risk of a crash increases approximately four times for drivers using mobile phones during driving. This study investigates the importance of different factors affecting the driver's choice to use mobile phones for conversation during driving in Lahore, Pakistan. A questionnaire survey was conducted to check the tendency of mobile phone usage during driving from different locations of the city. Participants were asked to indicate frequency, risk, importance and emotionality of ten different conversations. A Structural Equation Model (SEM), similar to a previous study, conducted in Beijing, was developed for the frequency of calling and texting during driving with perceived risk of calling and texting, perceived importance and emotionality as predictors. The frequency of different conversations shows that perceived importance of the call mainly influences the driver's choice to make a call during driving in Lahore. The result of the model show that perceived risk has a significant negative effect on driver's decision to call or text in Lahore, similarly to Beijing. The results also indicates that drivers prefer calling on mobile phones in comparison to texting during driving in Lahore.

Beyond COVID-19 deaths during the COVID-19 pandemic in the United States

COVID-19 has disrupted society and health care systems, creating a fertile environment for deaths beyond the virus. The year 2020 will prove to be the most deadly year on record in the United States. Direct deaths due to COVID-19 have been well documented and reported. Older people (those over 65) have been hardest hit, with over 80% of the COVID-19 deaths in this age group. What has been less clear is the impact on those under 65 years old, particularly those under 44 years old. This study considers both COVID-19 deaths and non-COVID-19 deaths during a 39 weeks period beginning 1 March in both 2020 and averaged over the five years from 2015 to 2019. Across 22 age and gender cohorts, death risks are compared using odds ratios. The results indicate that younger people (those under 15 years old) have experienced the same or a reduction in death risk between 2020 and the average from 2015 to 2019, suggesting that societal changes were protective for some of them. With all COVID-19 deaths removed from the 2020 death counts, 15-64 year olds experienced increased death risk between 2020 and the 2015 to 2019 average. For example, 15-44 year old males experienced a significant increase in their death risk, even though the absolute number of COVID-19 deaths for this cohort is small. The key take away from this study is that COVID-19 resulted in a large number of additional deaths in 2020 compared to the average from 2015 to 2019, both directly from the virus and indirectly due to societal responses to the virus.

Methodology for risk assessment of COVID-19 pandemic propagation

This paper proposes a methodology to perform risk analysis of the virus spread. It is based on the coupling between CFD modelling of bioaerosol dispersion to the calculation of probability of contact events. CFD model of near-field sneeze droplets dispersion is developed to build the SARS-CoV-2 effect zones and to adequately capture the safe distance. The most shared classification of droplets size distribution of sneezes was used.

Droplets were modeled through additive heating/evaporation/boiling laws and their impact on the continuous phase was examined. Larger droplets move behind the droplet nuclei front and exhibit greater vertical drop due to the effect of gravity. CFD simulations provided the iso-risk curves extension (i.e., the maximum distance as well as the angle) enclosed by the incident outcome effect zone. To calculate the risk indexes, a fault tree was developed and the probability of transmission assuming as of the top event “COVID-19 infection” was calculated starting from the virus spread curve, as main base case. Four phases of virus spread evolution were identified: initiation, propagation, generalised propagation and termination. For each phase, the maximum allowable close contact was computed, being fixed the values of the acceptable risk index. In particular, it was found that during the propagation case, the maximum allowable close contacts is two, suggesting that at this point lockdown should be activated. The here developed methodology could drive policy containment design to curb spread COVID-19 infection.

A review about methods for supporting failure risks analysis in eco-assessment

This paper critically reviewed 106 scientific papers proposing methods to enrich eco-assessment with failure determination and risk assessment. The provided research perspective is new and significantly different from the reviews in the literature which are mostly limited to analyse the environmental impacts of uncertainties and off-design functioning rather than the failures. The analysis, based on the contributions of the literature over more than 20 years, was carried out manually and allowed to identify and classify the application fields, the types of identifiable failures and the approaches used for their determination, for the analysis of their risk of occurrence and for their eco-assessment. The different classifications have also been intersected with each other and all the proposed approaches have been discussed in detail, highlighting the advantages and disadvantages in relation to eco-assessment. From the study emerged a growing and heterogeneous interest on the subject by the scientific community, and a certain independence of the analysed methods with respect to traditional approaches of both failure risk analysis and eco-assessment. Great attention of the methods about product functioning has been highlighted, in addition to the use of tests, simulations, FMEA (failure mode and effect analysis)-based approaches and knowledge databases to determine the failures, while statistical methods are preferred to support risks analysis and LCA (life cycle assessment) for environmental impact calculation. If, in the coming years, this argument also spreads in industry, the results provided by this review could be exploited as a first framework for practitioners.

Vulnerability mapping and risk analysis of sand and dust storms in Ahvaz, IRAN

In this work, a sand and dust storm vulnerability mapping (SDS-VM) approach is developed to model the vulnerability of urban blocks to SDS using GIS spatial analysis and a range of geographical data. The SDS-VM was carried out in Ahvaz, IRAN, representing one of the most dust-polluted cities in West Asia. Here, vulnerability is defined as a function of three components: exposure, sensitivity, and adaptive capacity of the people in the city blocks to sand and dust storms. These components were formulated into measurable indicators (i.e. GIS layers) including: PM_2.5, wind speed, distance from dust emission sources, demographic statistics (age, gender, family size, education level), number of building floors, building age, land surface temperature (LST), land use, percentage of literate population, distance from health services, distance from city facilities (city center, shopping centers), distance from infrastructure (public transportation, main roads and highways), distance from parks and green spaces, and green area per capita. The components and the indicators were weighted using analytical hierarchy process (AHP). Different levels of risks for the components and the indicators were defined using ordered weighted averaging (OWA). Urban SDS vulnerability maps at different risk levels were generated through spatial multi-criteria data analysis procedure. Vulnerability maps, with different risk levels, were validated against field-collected data of 781 patients hospitalized for dust-related diseases (i.e. respiratory, cardiovascular, and skin). Results showed that (i) SDS vulnerability map, obtained from the developed methodology, gives an overall accuracy of 79%; (ii); regions 1 and 5 of Ahvaz are recognized with the highest and lowest vulnerabilities to SDS, respectively; and (iii) ORness equal to 0 (very low risk) is the optimum SDS-VM risk level for decision-making to mitigate the harmful impacts of SDS in the deposition areas of Ahvaz city.

Association between extreme ambient temperatures and general indistinct and work-related road crashes. A nationwide study in Italy

Despite the relevance of road crashes and their impact on social and health care costs, the effects of extreme temperatures on road crashes risk have been scarcely investigated, particularly for those occurring in occupational activities. A nationwide epidemiological study was carried out to estimate the risk of general indistinct and work-related road crashes related with extreme temperatures and to identify crash and occupation parameters mostly involved. Data about road crashes, resulting in death or injury, occurring during years 2013-2015 in Italy, were collected from the National Institute of Statistics, for general indistinct road crashes, and from the compensation claim applications registered by the national workers' compensation authority, for work-related ones. Time series of hourly temperature were derived from the results provided by the meteorological model WRF applied at a national domain with 5 km resolution. To consider the different spatial-temporal characteristics of the two road crashes archives, the association with extreme temperatures was estimated by means of a case-crossover time-stratified approach using conditional logistic regression analysis, and a time-series analysis, using over-dispersed Poisson generalized linear regression model, for general indistinct and work-related datasets respectively. The analyses were controlled for other covariates and confounding variables (including precipitation). Non-linearity and lag effects were considered by using a distributed lag non-linear model. Relative risks were calculated for increment from 75th to 99th percentiles (hot) and from 25 to first percentile (cold) of temperature. Results for general indistinct crashes show a positive association with hot temperature (RR = 1.12, 95 % CI: 1.09-1.16) and a negative one for cold (RR = 0.93, 95 % CI: 0.91-0.96), while for work-related crashes a positive association was found for both hot and cold (RR = 1.06 (95 % CI: 1.01-1.11) and RR = 1.10 (95 % CI: 1.05-1.16). The use of motorcycles, the location of accident (urban vs out of town), presence of crossroads, as well as occupational factors like the use of a vehicle on duty were all found to produce higher risks of road crashes during extreme temperatures. Mitigation and prevention measures are needed to limit social and health care costs.

Pandemic risk management using engineering safety principles

The containment of infectious diseases is challenging due to complex transmutation in the biological system, intricate global interactions, intense mobility, and multiple transmission modes. An emergent disease has the potential to turn into a pandemic impacting millions of people with loss of life, mental health, and severe economic impairment. Multifarious approaches to risk management have been explored for combating an epidemic spread. This work presents the implementation of engineering safety principles to pandemic risk management. We have assessed the pandemic risk using Paté-Cornell's six levels of uncertainty. The susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD), an advanced mechanistic model, along with the Monte Carlo simulation, has been used to estimate the fatality risk. The risk minimization strategies have been categorized into hierarchical safety measures. We have developed an event tree model of pandemic risk management for distinct risk-reducing strategies realized due to natural evolution, government interventions, societal responses, and individual practices. The roles of distinct interventions have also been investigated for an infected individual's survivability with the existing healthcare facilities. We have studied the Corona Virus Disease of 2019 (COVID-19) for pandemic risk management using the proposed framework. The results highlight effectiveness of the proposed strategies in containing a pandemic.

Risk analysis of gas leakage in gas pressure reduction station and its consequences: A case study for Zahedan

Industrial accidents have increased the importance of dealing with the risks of toxic exposure, fire and explosion. Despite the measures taken in the chemical industry to prevent accidents, the accidents occur often due to human error or process faults during repairs. Although several studies have been conducted on the accidents in the process industry, no research has modeled the risks caused by the leakage of toxic substances in the gas pressure reduction station. The consequences of gas leak and fire in Zahedan's gas pressure reduction station were investigated in Iran. This research aims to determine the safe range of the station and observe the safety measures required for the gas pressure reduction station in Zahedan. For modelling gas leak and fire, the ALOHA software was used to display the threat zone. In this research, with respect to the environmental data, the desired scenario was modeled. The results, based on two scenarios of gas leak and fire in both hot and cold seasons, indicate that the gas leak scenario in hot seasons and the fire scenario in cold seasons influence a larger region.

Hyperglycemia as a risk factor in pancreatic cancer: A nested case-control study using prediagnostic blood glucose levels

OBJECTIVE

To determine the risk association between fasting glucose levels and pancreatic cancer using systematically collected prediagnostic blood glucose samples.

METHODS

Prospective nested case-control study of participants from the Northern Sweden Health and Disease Study, including 182 cases that developed pancreatic cancer and four matched controls per case. Blood glucose levels collected up to 24 years before pancreatic cancer diagnosis were analyzed. The association between fasting glucose levels and pancreatic cancer risk was determined using unconditional and conditional logistic regression models. The association between fasting glucose and the time to pancreatic cancer diagnosis, tumor stage and survival was determined using likelihood-ratio test, t-test and log rank test.

RESULTS

The unadjusted risk of developing pancreatic cancer increased with increasing fasting glucose levels (OR 1.30, 95% CI 1.05-1.60, P = .015). Impaired fasting glucose (≥6.1 mmol/L) was associated with an adjusted risk of 1.77 for developing pancreatic cancer (95% CI 1.05-2.99, P = .032). In subgroup analysis, fasting glucose levels were associated with an increased risk in never-smokers (OR 4.02, 95% CI 1.26-12.77, P = .018) and non-diabetics (OR 3.08, 95% CI 1.08-8.79, P = .035) (non-significant for interaction). The ratio between fasting glucose and BMI was higher among future pancreatic cancer patients and an increased ratio was associated with elevated risk of pancreatic cancer (OR 1.66, 95% CI 1.04-2.66, P = .034). Fasting glucose levels were not associated with TNM stage at diagnosis or survival.

CONCLUSIONS

High fasting glucose is associated with an increased risk of being diagnosed with pancreatic cancer.

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centred on applying ML for dvsetecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modelling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centred in aims, such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g. blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.

Arsenic toxicokinetic modeling and risk analysis: Progress, needs and applications

Arsenic (As) poses unique challenges in PBTK model development and risk analysis applications. Arsenic metabolism is complex, adequate information to attribute specific metabolites to particular adverse effects in humans is sparse, and measurement of relevant metabolites in biological media can be difficult. Multiple As PBTK models have been published and used or adapted for use in various exposure and risk analysis applications. These applications illustrate the broad utility of PBTK models for exposure and dose-response analysis, particularly for arsenic where multi-pathway, multi-route exposures and multiple toxic effects are of concern. Arsenic PBTK models have been used together with exposure reconstruction and dose-response functions to estimate risk of specific adverse health effects due to drinking water exposure and consumption of specific foodstuffs (e.g. rice, seafood), as well as to derive safe exposure levels and develop consumption advisories. Future refinements to arsenic PBTK models can enhance the confidence in such analyses. Improved estimates for methylation biotransformation parameters based on in vitro to in vivo extrapolation (IVIVE) methods and estimation of interindividual variability in key model parameters for specific toxicologically relevant metabolites are two important areas for consideration.

Occurrence and removal of drugs and endocrine disruptors in the Bolonha Water Treatment Plant in Belém/PA (Brazil)

This study aimed to investigate the occurrence of drugs and endocrine disrupters in water supplies and in water for human consumption. Twelve sampling campaigns were carried out during the rainy and dry season at four sampling points in the Bolonha Complex, in the city of Belém, northern region of Brazil: Bolonha reservoir (catchment) and Water Treatment Plant (WTP) Bolonha (filtered water chamber, treated water tank, and washing water from the filters). The determination of the compounds was performed by solid phase extraction followed by gas and liquid chromatography coupled to mass spectrometry. The results confirmed the anthropic influence that the reservoir and WTP-Bolonha have been suffering, as consequence of the discharge of domestic sewage in natura. Among 25 microcontaminants analyzed, 12 were quantified in raw water and 10 in treated water. The antiallergic Loratadine (LRT) was the contaminant that occurred most frequently in all sample points, having been poorly removed (median 12%) in the conventional treatment used. Losartana (LST), 4-octylphenol (4-OP), and Bisphenol A (BPA) also occurred very frequently in raw water with concentrations ranging from 3.7 to 194 ng L^-1. Although such contaminants occurred in treated water in concentrations varying from 4.0 to 135 ng L^-1, the estimated margin of exposure ranged from 55 to 3333 times which indicates low risk of human exposure to such contaminants through ingestion of treated water.

Developing a clinical decision support system based on the fuzzy logic and decision tree to predict colorectal cancer

Background: Colorectal Cancer (CRC) is the most prevalent digestive system- related cancer and has become one of the deadliest diseases worldwide. Given the poor prognosis of CRC, it is of great importance to make a more accurate prediction of this disease. Early CRC detection using computational technologies can significantly improve the overall survival possibility of patients. Hence this study was aimed to develop a fuzzy logic-based clinical decision support system (FL-based CDSS) for the detection of CRC patients. Methods: This study was conducted in 2020 using the data related to CRC and non-CRC patients, which included the 1162 cases in the Masoud internal clinic, Tehran, Iran. The chi-square method was used to determine the most important risk factors in predicting CRC. Furthermore, the C4.5 decision tree was used to extract the rules. Finally, the FL-based CDSS was designed in a MATLAB environment and its performance was evaluated by a confusion matrix. Results: Eleven features were selected as the most important factors. After fuzzification of the qualitative variables and evaluation of the decision support system (DSS) using the confusion matrix, the accuracy, specificity, and sensitivity of the system was yielded 0.96, 0.97, and 0.96, respectively. Conclusion: We concluded that developing the CDSS in this field can provide an earlier diagnosis of CRC, leading to a timely treatment, which could decrease the CRC mortality rate in the community.

Spatial distribution of trace elements (As, Cd, Ni, Pb) from PM10 aerosols and human health impact assessment in an Eastern European country, Romania

In the present study, the concentrations of trace elements in PM₁₀ were determined and analyzed at 115 monitoring stations in Romania throughout the period 2009-2018. The spatiotemporal distribution of trace element concentrations of PM₁₀, the source apportionment and health impact assessment, was carried out. The results showed a very high multi-annual mean concentration for PM₁₀ and trace elements as well. The multiannual average concentration of PM₁₀ was higher by 29.75% than the World Health Organization recommendation. All studied air pollutants showed a decreasing trend during the studied years, showing with 17.84%, 50.21%, 43.36%, 11.27%, and 72.09% lower values for PM₁₀, As-, Cd-, Ni-, and Pb-, respectively, due to environmental regulations. To assess the human health impact, the hazard quotient (HQ) and cancer risk (CR) were calculated using the health risk model developed by the US Environmental Protection Agency (EPA). The Cd and Ni might present a non-carcinogenic risk to both adults and children; however, the hazard quotient values are higher than the safe limit, with 9.53 and 1.93, respectively. In addition, our study results revealed that the inhalation of As, Cd and the dermal absorption of all studied trace elements were considered as the most important risk factors for developing cancer, especially in case of adults.

Risk factors for fecal carriage of extended-spectrum beta-lactamase-producing and carbapenem-resistant Escherichia coli and Klebsiella pneumoniae strains among patients at hospital admission

AIM

Extended-spectrum beta-lactamase (ESBL)-producing and carbapenem-resistant (CR) Enterobacteriaceae are substantial problems in hospital-acquired infections worldwide. We analyzed the risk factors for fecal carriage of ESBL-positive and/or CR E. coli and K. pneumoniae (EcKp) strains in a hospital in Turkey, an endemic country for both resistances.

MATERIALS AND METHODS

A prospective cross-sectional study including the rectal swab samples of 168 patients, obtained at the day of admission, was conducted. ESBL-producing and CR EcKp were investigated with phenotypic tests and PCR, and the clonal relatedness of isolates was studied. Risk analysis was performed with logistic regression method.

RESULTS

A total of 67 (39.8%) and 21 (12.5%) patient samples tested positive for ESBL-producing and CR EcKp, respectively. CTX-M (n = 27) and OXA-48 (n = 12) were the dominant ESBL and carbapenemase types, and 4.5%-10.7% of the isolates were clonally-related. Among 15 potential risk factors studied, longer lengths of hospital stay and antimicrobial use, and receiving total parenteral nutrition in the last 6 months were determined as independent risk factors for fecal carriage of ESBL-producing and/or CR EcKp, while prior antimicrobial treatment was only a risk factor for ESBL producers.

CONCLUSION

Certain conditions in patients' medical backgrounds may be associated with increased likelihood of resistant bacterial colonization. Notably, questioning these situations at admission can help to identify potential carriers and proactively administer appropriate infection control measures.

Novel framework of GIS based automated monitoring process on environmental biodegradability and risk analysis using Internet of Things

A proper method on real-time monitoring of organic biomass degradation and its evaluation for safeguarding the ecosystem is the need of the hour. The work process designed in this study is to demarcate the anaerobic digestion potential using kinetic modelling and web GIS application methods. Wastewater source that causes pollution are identified through satellite maps such as solid earth, drain system, surface of earth structure, land filling and land use. The grabbed data are utilized for identifying the concentration of sludge availability. Based on literature resource multi influencing factor techniques are introduced along with overlay method to differentiate digestion potential of sludge source. This study optimizes the biodegradation potential of domestic sewage at different sludge concentrations in a pilot model operated with the samples identified through topographical drainage survey. The materialization of devices is using the Internet of Things (IoTs), that is pragmatic to be the promising tendency. Kinetic study, methanogenic assay test are performed with three different cation binding agents to find its solubilization potential and methane evolution, which is further subjected to digestion potential in anaerobic conditions for possible application in the field of environmental science. Risk analysis reveals that land filling method will have highest impact on maintaining sustainable environment. The results outcome on natural biodegradation may be used for individual house hold wastewater management for the locality.

Air pollution diffusion simulation and seasonal spatial risk analysis for industrial areas

The petrochemical industry produces many air pollutants during production, such as airborne particulate matters (PM₁₀ and PM_2.5), sulfur oxides, nitrogen oxides, volatile organic compounds, carbon oxides, etc. Petrochemical industrial accidents are more likely to cause major air pollution hazards in a short period. Therefore this study simulated diffusion and performed air pollution spatial risk analysis for potential air pollutants generated by the petrochemical industry using meteorological observation data from 2017 to 2019. The study targets were No. 6 Naphtha Cracker Complex Petrochemical Industrial Park (6NCC) of Formosa Petrochemical Corporation and Taichung Thermal Power Plant (TTPP) in central Taiwan. We used the industrial source complex model short term (ISCST3) air simulation model developed by the US Environmental Protection Agency to simulate pollutant diffusion under different weather conditions and seasons. Air pollution spatial risk was investigated for neighboring hospitals and schools for pollutant emission and diffusion to provide feedback to petrochemical related industry's risk management. Emission areas (6NCC and TTPP) were all in the southwest since the main air pollution accumulation and diffusion is to the northeast during monsoon season (October through March). Air pollution April through September each year is more evenly distributed, with pollutant concentrations low in all directions, approximately half the concentration in winter. Simulated air pollutant concentrations often overlapped with high risk population clusters (schools and hospitals). 6NCC posed little impact on nearby schools throughout the year; whereas TTPP posed relatively low risks to nearby schools and hospitals in summer, with slightly higher risk for Shenren Elementary School in Shengang township, Changhua County in winter. Overall 6NCC posed higher risk for Mailiao and Taixi townships in Yunlin County; whereas the TTPP posed higher risk on Longjing District of Taichung City, Shengang and Xianxi townships in Changhua County, particularly during winter. The results of this study will help the petrochemical industry and public health authority to wider manage air pollution risks.

Microbial shelf life of coconut water subjected to various inoculation levels of Listeria monocytogenes and storage conditions

The study determined the growth kinetic parameters of a cocktail of Listeria monocytogenes 1/2c and 4b strains in coconut water (pH 4.76, 5.0°Brix, 0.09% malic acid, a_w 0.998) subjected to low (~2.0 log CFU/mL) and high (~4.0 log CFU/mL) contamination levels, and exposed to different storage temperatures (4 °C, 17 °C, 30 °C, and 35 °C). The pathogen proliferated in all tested conditions except in that with low contamination stored at 4 °C. Despite not growing at 4 °C, the pathogen was detectable throughout the storage period, which lasted for almost 400 h. In conditions where the pathogens proliferated, growth lag (t_lag) ranged from 0.0 to 68.3 h. The growth rates (K_G) ranged from 0.05 to 0.48 log CFU/h, while the final populations ranged from 6.3 to 8.7 log CFU/mL. Both storage temperature and contamination level significantly (P < 0.05) affected the growth parameters. Sanitary risk times (SRT) were determined with the microbiological shelf life (SL) of coconut water. In some of the conditions tested, SRT took place before SL (SRT < SL), emphasizing the importance of having good hygienic and manufacturing practices in place for such a vulnerable commodity.

[Quality guidelines for radiopharmacy: Development of a risk-assessment tool]

OBJECTIVES

The medical management of patients, which involves securing the drug circuit, is a major public health objective. As part of quality management, a number of risk assessment and risk management tools in care units are validated and available. However, medication management in radiopharmacy departments represents a complex and specific process. The aim of the "Quality guidelines for radiopharmacy" working group of the French society of radiopharmacy (SoFRa) was to develop a risk-assessment tool that is a priori adapted to radiopharmacy activity.

METHODS

A qualitative risk matrix was developed, based on available analysis tools and current regulations concerning radiopharmacy practice. The tool was then programmed to obtain a summary and scoring for each risk category, as well as a quantitative analysis of the risks identified in radiopharmacy.

RESULTS

Our tool contains 262 issues. The qualitative study integrates the risks related to the circuit of radiopharmaceuticals, but also risks related to personnel. The quantitative study makes it possible to carry out an automated analysis of the actions to carry out in priority to improve the practices.

CONCLUSIONS

This work led to the development of a self-assessment tool for the a priori analysis of risks that are adapted to the practice of radiopharmacy. It allows easy analysis of the entire circuit of radiopharmaceuticals from a single tool and meet the expectations of health authorities. This common and validated tool is available to the pharmaceutical community.

Decline of Listeria monocytogenes on fresh apples during long-term, low-temperature simulated international sea-freight transport

Listeria monocytogenes has caused outbreaks of foodborne illness from apples in the USA, and is also a major issue for regulatory compliance worldwide. Due to apple's significance as an important export product from New Zealand, we aimed to determine the effect of long-term, low-temperature sea-freight from New Zealand to the USA (July) and Europe (March-April), two key New Zealand markets, on the survival and/or growth of L. monocytogenes on fresh apples. Temperature and humidity values were recorded during a shipment to each market (USA and Europe), then the observed variations around the 0.5 °C target temperature were simulated in laboratory trials using open ('Scired') and closed ('Royal Gala' for the USA and 'Cripps Pink' for Europe) calyx cultivars of apples inoculated with a cocktail of 10⁷-10⁸ cells of seven strains of L. monocytogenes. Samples were analysed for L. monocytogenes quantification at various intervals during the simulation and on each occasion, an extra set was analysed after a subsequent 8 days at 20 °C. When both the sea-freight simulations concluded, L. monocytogenes showed 5 log reductions on the equatorial surface of skin of apples, but only about 2.5 log reduction for USA and about 3.3 log reduction for Europe in the calyx. Cultivar type had no significant effect on the survival of L. monocytogenes for both sea-freight simulations, either in the calyx or on the skin (P > 0.05). Most of the reduction in the culturable cells on the skin occurred during the initial 2 weeks of the long-term storage simulations. There was also no significant difference in the reduction of L. monocytogenes at 0.5 or 20 °C. No correlation was observed between firmness or total soluble solids and survival of L. monocytogenes. Because the inoculated bacterial log reduction was lower in the calyx than on the skin, it is speculated that the risk of causing illness is higher if contaminated apple cores are eaten. The result suggested that the international sea-freight transportation does not result in the growth of L. monocytogenes irrespective of time and temperature. The results of this study provide useful insights into the survival of L. monocytogenes on different apple cultivars that can be used to develop effective risk mitigation strategies for fresh apples during long-term, low-temperature international sea-freight transportation.

Bayesian Networks in Environmental Risk Assessment: A Review

Human activities both depend upon and have consequences on the environment. Environmental risk assessment (ERA) is a process of estimating the probability and consequences of the adverse effects of human activities and other stressors on the environment. Bayesian networks (BNs) can synthesize different types of knowledge and explicitly account for the probabilities of different scenarios, therefore offering a useful tool for ERA. Their use in formal ERA practice has not been evaluated, however, despite their increasing popularity in environmental modeling. This paper reviews the use of BNs in ERA based on peer-reviewed publications. Following a systematic mapping protocol, we identified studies in which BNs have been used in an environmental risk context and evaluated the scope, technical aspects, and use of the models and their results. The review shows that BNs have been applied in ERA, particularly in recent years, and that there is room to develop both the model implementation and participatory modeling practices. Based on this review and the authors' experience, we outline general guidelines and development ideas for using BNs in ERA. Integr Environ Assess Manag 2021;17:62-78. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

The Data Behind Risk Analysis of Campylobacter Jejuni and Campylobacter Coli Infections

Campylobacter jejuni and Campylobacter coli are major causes of food-borne enteritis in humans. Poultry meat is known to be responsible for a large proportion of cases of human campylobacteriosis. However, other food-borne, environmental and animal sources are frequently associated with the disease in humans as well. Human campylobacteriosis causes gastroenteritis that in most cases is self-limiting. Nevertheless, the burden of the disease is relatively large compared with other food-borne diseases, which is mostly due to rare but long-lasting symptoms related to immunological sequelae. In order to pave the way to improved surveillance and control of human campylobacteriosis, we review here the data that is typically used for risk analysis to quantify the risk and disease burden, identify specific surveillance strategies and assist in choosing the most effective control strategies. Such data are mostly collected from the literature, and their nature is discussed here, for each of the three processes that are essential for a complete risk analysis procedure: risk assessment, risk management and risk communication. Of these, the first, risk assessment, is most dependent on data, and this process is subdivided into the steps of hazard identification, hazard characterization, exposure assessment and risk characterization. For each of these steps of risk assessment, information from published material that is typically collected will be summarized here. In addition, surveillance data are highly valuable for risk assessments. Different surveillance systems are employed in different countries, which can make international comparison of data challenging. Risk analysis typically results in targeted control strategies, and these again differ between countries. The applied control strategies are as yet not sufficient to eradicate human campylobacteriosis. The surveillance tools of Campylobacter in humans and exposure sources in place in different countries are briefly reviewed to better understand the Campylobacter dynamics and guide control strategies. Finally, the available control measures on different risk factors and exposure sources are presented.