Common Risk Assessment Research Articles

Development and validation of a prediction model of deep venous thrombosis for patients with acute poisoning following hemoperfusion: a retrospective analysis.

ObjectiveTo develop and confirm an individualized predictive model to ascertain the probability of deep venous thrombosis in patients with acute poisoning after undergoing hemoperfusion.MethodsThree hundred eleven patients with acute poisoning who were admitted to a hospital in China between October 2017 and February 2019 were included in the development group. Eighty patients with acute poisoning who were admitted between February and May 2019 were included in the validation group. The independent risk factors for deep venous thrombosis were examined. An individualized predictive model was developed using regression coefficients.ResultsThe number of catheter indwelling days, having a catheter while being transported, elevated serum homocysteine concentrations, and dyslipidemia were independent risk factors for deep venous thrombosis following hemoperfusion in patients with acute poisoning. The areas under the receiver operating characteristic curve of the development and validation groups were 0.713 and 0.702, respectively, which suggested that the prediction model had good discrimination capacity. The calibration belts of the two groups were ideal.ConclusionsOur prediction model has a moderate predictive effect for the occurrence of deep venous thrombosis in patients with acute poisoning. In clinical practice, this model could be combined with a common thrombosis risk assessment model.

Scientific Research Review on Dependability of Complex Automotive Systems Developed towards Autonomous Driving

This brief scientific research review is related to EFOP project titled Dynamics and Control of Autonomous Vehicles meeting the Synergy Demands of Automated Transport Systems (EFOP-3.6.2-16-2017-00016), in which the following scientific consortium is taking part: Széchenyi István University, John von Neumann University, University of Dunaújváros and Óbudai University. The goal of the project is to improve mathematical techniques and processes that assist the increasingly complex design of technical structures and constituents implemented in self-driven automobiles and transport systems, focusing on safety and risk factor maintenance. This means examining the reliability of vehicle sensor networks and developing common risk assessment methods.

Collaboration Between Child Protection and Domestic and Family Violence: A Case File Review

ABSTRACT Collaboration across child protection and domestic and family violence sectors has been identified as central to responding to complexities experienced by families within these practice fields. However, there is little research that explores how collaboration happens and what outcomes are produced. This paper reports on a study that reviewed 21 case files between 2015 and 2019 to identify the indicators of collaboration when domestic and family violence are present along with child protection concerns, and what activities and processes enable collaborative practice at these intersections. The study found that collaboration is visible in case files through activities such as information sharing, referral, planning, and case conferencing. However, analysis of case notes and discussions with practitioners revealed that these indicators do not necessarily lead to collaboration consistently. The implication for practice is that effective collaboration is fundamentally relational. It is through relationship building between agencies, practitioners, and families that collaboration is enacted. IMPLICATIONS Collaboration between child protection and domestic and family violence practitioners can be identified through indictors such as information sharing, referral, shared case planning, and shared case conferencing. The democratising of practice can be seen most clearly at case conferences where information is shared for decision making to support victims of domestic and family violence. Collaboration becomes compromised when information is shared without purposeful and meaningful linkages between agencies, or without the goals of responsive service pathways for women and children and a common risk assessment of the perpetrator.

Dispersal potential rather than risk assessment scores predict the spread rate of non‐native pines across New Zealand

Abstract Pine (genus Pinus) species are planted extensively for forestry purposes in areas where they are non‐native, with the result that biological invasions by many of these species are of considerable concern in many regions. Owing to the economic importance of these species, management approaches must focus on reducing the risk that they will spread from plantings, which requires access to accurate and rigorous risk analysis protocols. However, while a variety of tools are available that accurately predict which species are likely to naturalise, their performance in forecasting the subsequent rates of spread of species once established remain unproven. We compared 10 measures of risk that could be used to forecast the likelihood of pine spread, including established risk assessment tools as well as dispersal‐related plant traits: expert assessment of spreading vigour, the Z‐score, the Australian weed risk assessment protocol (WRA), seed terminal velocity, tree height, modelled dispersal potential, and planting effort. We examined the extent to which these measures were consistent in their estimate of risk and assessed their ability to accurately predict the spread rates of 10 non‐native naturalised pine species within New Zealand. Of the measures of risk assessed here, only the models of dispersal potential were able to significantly predict the species' spread rates within New Zealand. In addition, seed terminal velocity was significantly correlated with expert assessment of the species spreading vigour, indicating these two measures capture similar aspects of risk. Modelled long‐distance dispersal from forests had the greatest explanatory power for the species spread rates in New Zealand. Synthesis and applications. Common weed risk assessment tools and expert knowledge were unable to predict the spread rates of non‐native naturalised pine species in New Zealand, and should not be used to provide an index of spread risk. Instead, we recommend a move towards the use of dispersal models when assessing the spread risk of these species, even at national scales. Current practices relying on expert assessment are likely to underestimate the spread rate of species currently considered ‘low risk’, suggesting that these tools may be inadequate for predicting spread of these species.

Detecting Impending Stroke From Cognitive Traits Evident in Internet Searches: Analysis of Archival Data.

BackgroundCerebrovascular disease is a leading cause of mortality and disability. Common risk assessment tools for stroke are based on the Framingham equation, which relies on traditional cardiovascular risk factors to predict an acute event in the near decade. However, no tools are currently available to predict a near/impending stroke, which might alert patients at risk to seek immediate preventive action (eg, anticoagulants for atrial fibrillation, control of hypertension).ObjectiveHere, we propose that an algorithm based on internet search queries can identify people at increased risk for a near stroke event.MethodsWe analyzed queries submitted to the Bing search engine by 285 people who self-identified as having undergone a stroke event and 1195 controls with regard to attributes previously shown to reflect cognitive function. Controls included random people 60 years and above, or those of similar age who queried for one of nine control conditions.ResultsThe model performed well against all comparator groups with an area under the receiver operating characteristic curve of 0.985 or higher and a true positive rate (at a 1% false-positive rate) above 80% for separating patients from each of the controls. The predictive power rose as the stroke date approached and if data were acquired beginning 120 days prior to the event. Good prediction accuracy was obtained for a prospective cohort of users collected 1 year later. The most predictive attributes of the model were associated with cognitive function, including the use of common queries, repetition of queries, appearance of spelling mistakes, and number of queries per session.ConclusionsThe proposed algorithm offers a screening test for a near stroke event. After clinical validation, this algorithm may enable the administration of rapid preventive intervention. Moreover, it could be applied inexpensively, continuously, and on a large scale with the aim of reducing stroke events.

Improving dynamic stroke risk prediction in non-anticoagulated patients with and without atrial fibrillation: comparing common clinical risk scores and machine learning algorithms.

AimsDiversified cardiovascular/non-cardiovascular multi-morbid risk and efficient machine learning algorithms may facilitate improvements in stroke risk prediction, especially in newly diagnosed non-anticoagulated atrial fibrillation (AF) patients where initial decision-making on stroke prevention is needed. Therefore the aims of this article are to study common clinical risk assessment for stroke risk prediction in AF/non-AF cohorts together with cardiovascular/ non-cardiovascular multi-morbid conditions; to improve stroke risk prediction using machine learning approaches; and to compare the improved clinical prediction rules for multi-morbid conditions using machine learning algorithmsMethods and resultsWe used cohort data from two health plans with 6 457 412 males/females contributing 14,188,679 person-years of data. The model inputs consisted of a diversified list of comorbidities/demographic/ temporal exposure variables, with the outcome capturing stroke event incidences. Machine learning algorithms used two parametric and two nonparametric techniques. The best prediction model was derived on the basis of non-linear formulations using machine learning criteria, with the highest c-index was obtained for logistic regression [0.892; 95% confidence interval (CI) 0.886–0.898] with consistency on external validation (0.891; 95% CI 0.882–0.9). These were significantly higher than those based on the conventional stroke risk scores (CHADS2: 0.7488, 95% CI 0.746–0.7516; CHA2DS2-VASc: 0.7801, 95% CI 0.7772–0.7831) and multi-morbid index (0.8508, 95% CI 0.8483–0.8532). The machine learning algorithm had good internal and external calibration and net benefit values.ConclusionIn this large cohort of newly diagnosed non-anticoagulated AF/non-AF patients, large improvements in stroke risk prediction can be shown with cardiovascular/non-cardiovascular multi-morbid index and a machine learning approach accounting for dynamic changes in risk factors.

Risk assessment and management of hepatocellular carcinoma occurrence in patients with chronic hepatitis B in the era of antiviral therapy

Chronic hepatitis B (CHB) virus infection remains the leading cause of hepatocellular carcinoma (HCC) in China. Oral antiviral drugs have been shown to significantly reduce the HCC risk in CHB patients, but it cannot completely eliminate the HCC occurrence. Therefore, accurate HCC risk assessment and standardized screening in CHB patients are essential for early diagnosis and improved prognosis. In recent years, medical researchers have established a variety of HCC risk prediction models for CHB patients, providing clinicians with a good assessment method. However, due to the differences in the baseline characteristics of each model when deriving and verifying the population, the applicable population and prediction effectiveness are still different, and it is difficult to achieve a "one-size-fits all" . To this end, this paper reviews the common HCC risk assessment model for CHB patients during antiviral therapy, and introduces the monitoring and screening strategies of domestic and foreign guidelines for high-risk HCC patients in order to help clinicians better manage such patients.

Progress of researches on the application of risk assessment models in assessment of the schistosomiasis transmission risk

With the deepening of the schistosomiasis research, risk assessment models have been widely used in schistosomiasis research and control. This paper reviews the theoretical basis and applications of common schistosomiasis risk assessment models and the Bayesian model, so as to provide insights into national schistosomiasis elimination program in China.

Developing Credit Risk Assessment Methods to Make loss Provisions for Potential loans

According to Bank of Russia Regulation No. 590-P dated June 28, 2017, Russian banks assess credit risk and make loss provisions for potential loans. Since 01.01.2018, credit institutions have been required to create loss provisions for expected losses in accordance with international standards (IFRS 9). This novation seems relevant due to the lack of a common risk assessment method and the importance of cost optimization on loan provisions. The aim of the study is to improve the credit risk assessment method for making loss provisions for potential loans. The author used the methods of system and logical analysis and synthesis, techniques of high financial calculations, the balance method, the method of financial ratios. When estimating the probability of borrowers’ default, potential credit losses and loan provisions, the author applied actuarial, market, statistical and econometric methods. Based on a Russian bank’s sample data for 2012-2019, the author developed a regression model that establishes the relationship between financial ratios and the default of corporate borrowers — agricultural producers, and checked the significance of the model’s financial ratios. The author divided the borrowers into rating groups by score. The probability of default is the ratio of the number of defaults to the number of borrowers by group. The average default loss for each group depends on the collection / debt ratio in the bank under review. The score of a borrower brings them into a certain rating group, helps calculate the probability of a default and losses in case of default. The calculated expected losses may be of further use when determining loss provisions for potential loans. The author concludes that this method allows assessing risks and making a decision on lending to borrowers — agricultural producers. The expected credit loss approach will allow for more reasonable provisioning, which corresponds to other authors’ findings. Applying this method in a particular bank requires considering the specifics of the composition and structure of the loan portfolio. It is necessary to analyze the impact of the expected credit loss method on the profitability of banks.

Significance of Multiple Bioactivation Pathways for Meclofenamate as Revealed through Modeling and Reaction Kinetics.

Meclofenamate is a nonsteroidal anti-inflammatory drug used in the treatment of mild-to-moderate pain yet poses a rare risk of hepatotoxicity through an unknown mechanism. Nonsteroidal anti-inflammatory drug (NSAID) bioactivation is a common molecular initiating event for hepatotoxicity. Thus, we hypothesized a similar mechanism for meclofenamate and leveraged computational and experimental approaches to identify and characterize its bioactivation. Analyses employing our XenoNet model indicated possible pathways to meclofenamate bioactivation into 19 reactive metabolites subsequently trapped into glutathione adducts. We describe the first reported bioactivation kinetics for meclofenamate and relative importance of those pathways using human liver microsomes. The findings validated only four of the many bioactivation pathways predicted by modeling. For experimental studies, dansyl glutathione was a critical trap for reactive quinone metabolites and provided a way to characterize adduct structures by mass spectrometry and quantitate yields during reactions. Of the four quinone adducts, we were able to characterize structures for three of them. Based on kinetics, the most efficient bioactivation pathway led to the monohydroxy para-quinone-imine followed by the dechloro-ortho-quinone-imine. Two very inefficient pathways led to the dihydroxy ortho-quinone and a likely multiply adducted quinone. When taken together, bioactivation pathways for meclofenamate accounted for approximately 13% of total metabolism. In sum, XenoNet facilitated prediction of reactive metabolite structures, whereas quantitative experimental studies provided a tractable approach to validate actual bioactivation pathways for meclofenamate. Our results provide a foundation for assessing reactive metabolite load more accurately for future comparative studies with other NSAIDs and drugs in general.Significance StatementMeclofenamate bioactivation may initiate hepatotoxicity, yet common risk assessment approaches are often cumbersome and inefficient and yield qualitative insights that do not scale relative bioactivation risks. We developed and applied innovative computational modeling and quantitative kinetics to identify and validate meclofenamate bioactivation pathways and relevance as a function of time and concentration. This strategy yielded novel insights on meclofenamate bioactivation and provides a tractable approach to more accurately and efficiently assess other drug bioactivations and correlate risks to toxicological outcomes.

Deep Learning With Electronic Health Records for Short-Term Fracture Risk Identification: Crystal Bone Algorithm Development and Validation.

BackgroundFractures as a result of osteoporosis and low bone mass are common and give rise to significant clinical, personal, and economic burden. Even after a fracture occurs, high fracture risk remains widely underdiagnosed and undertreated. Common fracture risk assessment tools utilize a subset of clinical risk factors for prediction, and often require manual data entry. Furthermore, these tools predict risk over the long term and do not explicitly provide short-term risk estimates necessary to identify patients likely to experience a fracture in the next 1-2 years.ObjectiveThe goal of this study was to develop and evaluate an algorithm for the identification of patients at risk of fracture in a subsequent 1- to 2-year period. In order to address the aforementioned limitations of current prediction tools, this approach focused on a short-term timeframe, automated data entry, and the use of longitudinal data to inform the predictions.MethodsUsing retrospective electronic health record data from over 1,000,000 patients, we developed Crystal Bone, an algorithm that applies machine learning techniques from natural language processing to the temporal nature of patient histories to generate short-term fracture risk predictions. Similar to how language models predict the next word in a given sentence or the topic of a document, Crystal Bone predicts whether a patient’s future trajectory might contain a fracture event, or whether the signature of the patient’s journey is similar to that of a typical future fracture patient. A holdout set with 192,590 patients was used to validate accuracy. Experimental baseline models and human-level performance were used for comparison.ResultsThe model accurately predicted 1- to 2-year fracture risk for patients aged over 50 years (area under the receiver operating characteristics curve [AUROC] 0.81). These algorithms outperformed the experimental baselines (AUROC 0.67) and showed meaningful improvements when compared to retrospective approximation of human-level performance by correctly identifying 9649 of 13,765 (70%) at-risk patients who did not receive any preventative bone-health-related medical interventions from their physicians.ConclusionsThese findings indicate that it is possible to use a patient’s unique medical history as it changes over time to predict the risk of short-term fracture. Validating and applying such a tool within the health care system could enable automated and widespread prediction of this risk and may help with identification of patients at very high risk of fracture.

Development of a Novel Computational Model for Evaluating Fall Risk in Patient Room Design.

This study proposes a computational model to evaluate patient room design layout and features that contribute to patient stability and mitigate the risk of fall. While common fall risk assessment tools in nursing have an acceptable level of sensitivity and specificity, they focus on intrinsic factors and medications, making risk assessment limited in terms of how the physical environment contributes to fall risk. We use literature to inform a computational model (algorithm) to define the relationship between these factors and the risk of fall. We use a trajectory optimization approach for patient motion prediction. Based on available data, the algorithm includes static factors of lighting, flooring, supportive objects, and bathroom doors and dynamic factors of patient movement. This preliminary model was tested using four room designs as examples of typical room configurations. Results show the capabilities of the proposed model to identify the risk associated with different room layouts and features. This innovative approach to room design evaluation and resulting estimation of patient fall risk show promise as a proactive evidence-based tool to evaluate the relationship of potential fall risk and room design. The development of the model highlights the challenge of heterogeneity in factors and reporting found in the studies of patient falls, which hinder our understanding of the role of the built environment in mitigating risk. A more comprehensive investigation comparing the model with actual patient falls data is needed to further refine model development.

20.C. Round table: Harnessing the digital transformation of disease prevention: a focus on m-health and genomics

Abstract Digital transformation in the health sector is one of the key drivers of innovation that can contribute to the health systems goals of being more effective, resilient, and sustainable. In December 2019, EUPHA published a conceptual framework on the potential value of digitalization for public health. In this framework the two digital health technologies that can impact disease prevention and health promotion are m-health and genomics, which are also two key facilitators of the broader asset of technologies that allow for a greater personalization of health and care. Digital transformation, however, is not only a technical process but an organizational and cultural one. In view of such complexity, there is a growing need for robust evidences to support digital solutions and the related organizational models that can positively contribute to such health system goals. In this context it is worth mentioning the latest report on the Digital Transformation of Health Services from the Expert Panel on Effective Investing in Health, which outlined some positive impacts in terms of effectiveness and cost-effectiveness of m-health interventions. In this field of genomics, the use of polygenic scores (PRS) for common disease risk assessment are becoming an important area of development that warrants close attention from public health professionals as it brings the potential for improving the effectiveness and efficiency of current models of health promotion and disease prevention. This WS aims to convene a discussion between different stakeholders including academia, industry, international organizations and policy makers to discuss the current evidence and implications that such new technologies have for public health practitioners, and suggests next steps to support the adoption of technology that currently demonstrate their value for population health. Key messages Although digital technologies brings the potential to improve adherence to health promotion and prevention at population level, the real value needs to be addressed from well conducted large studies. PRS might increase the effectiveness and reducing the costs of available preventive interventions: we need large studies to address the clinical utility for implementation in healthcare.

OR13-07 Crystal Bone: Personalized, Short-Term Fracture Risk Prediction with Natural Language Processing Methods

Fragility fractures due to osteoporosis are common and are associated with significant clinical, personal, and economic burden. Even after a fragility fracture, osteoporosis remains widely underdiagnosed and undertreated. Common fracture risk assessment tools, such as FRAX1 and Garvan,2 confer risk over the long term but do not provide short-term risk estimates necessary to identify very high-risk patients likely to fracture in the next 1–2 years. Furthermore, these tools utilize cross-sectional data representing a subset of all available clinical risk factors for risk prediction. Thus, these methods are generalized across patient populations and may not fully utilize patient histories commonly found in electronic health records (EHRs) that contain temporal information for thousands of unique features. The Optum® de-identified EHR dataset (2007–2018) provides an opportunity to use historical medical data to generate short-term, personalized fracture risk predictions for individual patients. We used the Optum® dataset to develop Crystal Bone, a method that applies machine learning techniques commonly used in natural language processing to the temporal nature of patient histories in order to predict fracture risk over a 1- to 2-year timeframe. Specifically, we repurposed deep-learning models typically applied to language-based prediction tasks in which the goal is to learn the meanings of words and sentences to classify them. Crystal Bone uses context-based embedding techniques to learn an equivalent “semantic” meaning of various medical events. Similar to how language models predict the next word in a given sentence or the topic of an overall document, Crystal Bone can predict that a patient’s future trajectory may contain a fracture or that the “signature” of the patient’s overall journey is similar to that of a typical fracture patient. We applied Crystal Bone to two datasets, one enriched for fracture patients and one representative of a typical hospital system. In both datasets, when predicting likelihood of fracture in the next 1–2 years, Crystal Bone had an area under the receiver operating characteristic (AUROC) score ranging from 72% to 83% on a test (hold-out) dataset. These results suggest performance similar to that of FRAX and Garvan, which have 10-year fracture risk prediction AUROC scores of 64.4% +/- 3.7%.3 Our results suggest that it is possible to use each patient’s unique medical history as it changes over time to predict patients at risk for fracture in 1–2 years. Furthermore, it is theoretically possible to integrate a model like Crystal Bone directly into an EHR system, enabling “hands-off” fracture risk prediction, which could lead to improved identification of patients at very high risk for fracture. 1Kanis JA, Osteoporos Int. 2012;23:2239–56. 2Rubin KH, J Bone Miner Res. 2013;28:1701–17. 3Leslie WD, Osteoporos Int. 2014;25:1–21.

Blast Risk Assessment of Wood Residential Buildings: West Fertilizer Plant Explosion Case

AbstractTo predict the hazard-induced risks of buildings and infrastructures and assess the losses caused by hazards, the fragility curve method is a common quantitative risk assessment procedure f...

A commentary on some epidemiology data for chlorpyrifos

Extensive animal and human studies on chlorpyrifos (CPF) point to changes in a blood enzyme as its first biological effect, and governments and health groups around the world have used this effect in the determination of its safe dose. Preventing this first biological effect, referred to in risk assessment parlance as the critical effect, is part and parcel of chemical regulation in general and of CFP specifically. Rauh et al. (2011), one of the published studies from the Columbia Center for Children's Environmental Health (CCCEH), reported evidence of deficits in Working Memory Index and Full-Scale IQ in children at 7 years old as a function of prenatal CPF exposures that are much lower than levels causing cholinesterase inhibition. Since the raw data on which Rauh et. al. (2011) publicly-funded (in part) findings were based have not been made available despite repeated requests, we show extracted data in Fig. 1A and 1E of Rauh et al. (2011), and plotted these extracted data as response versus log dose, a common risk assessment approach. Surprisingly, a significant portion of the data stated to be available in Rauh et al. (2011) were not found in these published figures, perhaps due to data point overlay. However, the reported associations of chlorpyrifos levels with Working Memory and Full Scale IQ were also not replicated in our analysis due perhaps to this missing data. Multiple requests were made to Rauh et al. (2011) for access to data from this, in part, publicly funded study, so that confirmation could be attempted. This general lack of data and inconsistency with cholinergic responses in other researches raises concerns about the lack of data transparency.

Review of risk assessment methods for infectious diseases

At present, the global epidemic situation of infectious diseases is becoming more and more serious and complex, which brings difficulties and challenges to the prevention and control of infectious diseases and poses a serious threat to public health and safety. The premise and basis of the prevention and control of infectious diseases is to carry out scientific risk assessment. Firstly, the common risk assessment methods were introduced, and the limitations of these one-time static assessment methods were analysed. Then the dynamic risk assessment methods for the transmission of infectious diseases were introduced, and expressing the transmission of infectious diseases as scenarios was proposed. Scenarios were deduced to predict the future development trends of infectious diseases. It is of great positive significance to evaluate the risks of infectious diseases according to different development trends, and realize the transition from traditional “prediction-response” to “scenario-response” for improving the emergency management capability of the transmission of infectious diseases.

Quantitative differences between common occupational health risk assessment models.

ObjectivesMethodological studies on occupational health risk assessment (OHRA) models are rarely reported. This study aimed to explore the quantitative differences between common OHRA models.MethodsThe risk ratios (RRs) in five typical industries (leather, wooden furniture manufacturing, printing and dyeing, printing, and garment manufacturing) were investigated using six OHRA models, namely the models from the US Environmental Protection Agency (EPA), Singapore, the Control of Substances Hazardous to Health (COSHH), Australia, Romania, and International Council on Mining and Metals (ICMM). The consistency, correlation, and reliability were evaluated for quantitative differences between the models.ResultsThe order of the RRs obtained from the EPA, Singaporean, and COSHH models in the five industries was consistent with the order of the inherent risk levels in those industries. The EPA and Singaporean models could effectively distinguish the inherent risk levels of risk factors like xylene and ethyl acetate. The order of RR between the six models was: RR EPA > RR COSHH > RR Singaporean > RR Australian > RR Romanian and RR ICMM (P < .05). The EPA model had the weakest correlations with other models. The Singaporean model had positive correlations in RRs with the other models (P＜0.01).ConclusionsThe EPA and Singaporean models exhibited good reliability since they could distinguish the inherent risk of the industry or risk factor and tended to get higher risk levels. The EPA model was independent and the Singaporean model had a good correlation with other models. More studies on OHRA methodology are needed.

DERMAL ABSORPTION OF DIQUAT AND POTENTIAL OCCUPATIONAL RISK

The aim: The toxicological-hygienic assessment of dermal absorption of diquat in terms of potential risk of its bioavailability in professional use. Materials and methods: The object of the study was cutaneous exposure of diquat, determined in toxicological experiments of different duration (data of scientific literature) and at the stage of state testing of pesticide preparations based on diquat dibromide (data of a full-scale hygiene experiment, prognostic model of risk assessment), the technical concentrate of diquat dibromide (active substance content not less than 377 g / kg) contains relevant supplements, the content of which is regulated by the Food and Agriculture Organization. Results and conclusions: Due to the high risk of the diquat adverse effects affecting the personnel, general public and environment, the European Union has introduced administrative decisions to forbid plant protection products containing the diquat. Fulfillment of the conditions of the Association Agreement between Ukraine and the European Union indicates the need to develop common regulations and risk assessment methods aimed at ensuring high level of protection of human health and the environment.

Adherence to Current Lipid Guidelines by Physicians in Kuwait

Background: Alarmingly high rates of dyslipidemia have been found in the Gulf region in patients presenting with acute coronary syndrome, with the highest being found in Kuwait (37%). Therefore, it is of utmost importance to treat dyslipidemia promptly and effectively. In an effort to understand the practices of local physicians, the use of evidence-based medicine, and adherence to lipid treatment guidelines, the objective of this study was to survey and assess the current standards of care. Methods: A survey questionnaire, designed to assess physicians’ attitudes and practice towards lipid guidelines, was completed by 279 participants and returned between October 2015 and June 2016. Statistical analysis was done using SPSS. Results: Over 90% of physicians claimed to use lipid guidelines, with the majority rating themselves as knowledgeable. Younger physicians were found to be less knowledgeable and consequently used guidelines less frequently. The most important factor influencing clinical decision-making was the availability of clinical guidelines. The majority (72.4%) of physicians identified time limitation as a key barrier. The most commonly selected lipid guideline in daily practice was a guideline on the treatment of blood cholesterol published by the American College of Cardiology/American Heart Association in 2013. The most common risk assessment tool used was the Framingham risk score. Conclusions: Multiple interventions to improve guideline adherence are proposed in this study. We have taken into account the barriers to adherence, i.e., attitude, behavior, and (most importantly) knowledge, all 3 of which were reaffirmed in our investigation in Kuwait.