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Background: Epidemiological studies show a dose–response association between cannabis use and the risk of psychosis. This review aimed to determine whether there are identifiable risk-thresholds between the frequency of cannabis use and psychosis development. Methods: Systematic search of Embase, MEDLINE, PsycINFO, CINAHL, and Web of Science for relevant studies (1 January 2010–26 April 2021). Case–control or cohort studies that investigated the relationship between cannabis use and the risk of psychosis development that reported effect estimates [odds ratios (OR), hazard ratios (HR), risk ratios (RR)] or the raw data to calculate them, with information on the frequency of cannabis consumption were included. Effect estimates were extracted from individual studies and converted to RR. Two-stage dose–response multivariable meta-analytic models were utilized and sensitivity analyses conducted. The Newcastle Ottawa Scale was used to assess the risk of bias of included studies. Results: Ten original (three cohorts, seven case–control) studies were included, including 7390 participants with an age range of 12–65 years. Random-effect model meta-analyses showed a significant log-linear dose–response association between cannabis use frequency and psychosis development. A restricted cubic-splines model provided the best fit for the data, with the risk of psychosis significantly increasing for weekly or more frequent cannabis use [RR = 1.01, 95% confidence interval (CI) 0.93–1.11 yearly; RR = 1.10, 95% CI 0.97–1.25 monthly; RR = 1.35, 95% CI 1.19–1.52 weekly; RR = 1.76, 95% CI 1.47–2.12 daily] Conclusion: Individuals using cannabis frequently are at increased risk of psychosis, with no significant risk associated with less frequent use. Public health prevention messages should convey these risk-thresholds, which should be refined through further work. [ABSTRACT FROM AUTHOR]

Abstract Microplastic particles (MPs) are ubiquitous across a wide range of aquatic habitats but determining an appropriate level of risk management is hindered by a poor understanding of environmental risk. Here, we introduce a risk management framework for aquatic ecosystems that identifies four critical management thresholds, ranging from low regulatory concern to the highest level of concern where pollution control measures could be introduced to mitigate environmental emissions. The four thresholds were derived using a species sensitivity distribution (SSD) approach and the best available data from the peer-reviewed literature. This included a total of 290 data points extracted from 21 peer-reviewed microplastic toxicity studies meeting a minimal set of pre-defined quality criteria. The meta-analysis resulted in the development of critical thresholds for two effects mechanisms: food dilution with thresholds ranging from ~ 0.5 to 35 particles/L, and tissue translocation with thresholds ranging from ~ 60 to 4100 particles/L. This project was completed within an expert working group, which assigned high confidence to the management framework and associated analytical approach for developing thresholds, and very low to high confidence in the numerical thresholds. Consequently, several research recommendations are presented, which would strengthen confidence in quantifying threshold values for use in risk assessment and management. These recommendations include a need for high quality toxicity tests, and for an improved understanding of the mechanisms of action to better establish links to ecologically relevant adverse effects.

BackgroundIt is widely recognized that glycated hemoglobin (HbA1c) and systolic blood pressure (SBP) are two key risk factors for albuminuria and renal function impairment in patients with type 2 diabetes mellitus (T2DM). Our study aimed to identify the specific numerical relationship of albumin/creatinine ratio (ACR) with HbA1c and SBP among a large population of adults with T2DM.MethodA total of 8,626 patients with T2DM were included in the data analysis from the National Health and Nutrition Examination Surveys (NHANES) (1999-2018). The multiple linear regressions were used to examine the associations of ACR with HbA1c and SBP. Generalized additive models with smooth functions were performed to identify the non-linear relations between variables and interactions were also tested.ResultsSignificantly threshold effects were observed between ACR and HbA1c or SBP after multivariable adjustment, with the risk threshold values HbA1c = 6.4% and SBP = 127 mmHg, respectively. Once above thresholds were exceeded, the lnACR increased dramatically with higher levels of HbA1c (β = 0.23, 95 CI%:0.14, 0.32, P < 0.001) and SBP (β = 0.03, 95 CI%:0.03, 0.04, P < 0.001). Subgroup analysis showed high protein diet was related to higher ACR. In addition, a higher risk of ACR progression was observed in central obesity participants with HbA1C ≥ 6.4% or hyperuricemia participants with SBP ≥ 127 mmHg among patients withT2DM.ConclusionWe identified thresholds of HbA1c and SBP to stratify patients with T2DM through rapid albuminuria progression. These might provide a clinical reference value for preventing and controlling diabetes kidney disease.

When research poses risks to non‐participant bystanders, it is not always practicable to obtain their consent. One approach to assessing how much research risk may be imposed on nonconsenting bystanders is to examine analogous circumstances, including risk thresholds deemed acceptable for nonconsenting research participants and for nonconsensual risks imposed outside the research setting. For nonconsenting participants, US research regulations typically limit risks to those deemed to be "minimal." Outside the research context, US tort law tolerates a more flexible "reasonable" risk threshold. This article advances a preliminary case that nonconsenting participants and nonconsenting bystanders exposed to similar research risks may be entitled to the same level of protection, but that risks generated by research may not be special in kind. Thus, limiting research risks to those that are "reasonable," rather than demanding that they be held to the "minimal" standard, may be the best approach for both nonconsenting participants and nonconsenting bystanders. Further work is needed to establish whether the descriptive standards used to support the analogies relied on here are normatively justifiable, as well as the extent to which the minimal risk standard and the reasonable risk standard would lead to meaningfully different outcomes in practice. [ABSTRACT FROM AUTHOR]

Abstract Background Larval indices such as Premise Index (PI), Breteau Index (BI) and Container Index (CI) are widely used to interpret the density of dengue vectors in surveillance programmes. These indices may be useful for forecasting disease outbreaks in an area. However, use of the values of these indices as alarm signals is rarely considered in control programmes. Therefore, the current study aims to propose threshold values for vector indices based on an empirical modeling approach for the Kandy District of Sri Lanka. Methods Monthly vector indices, viz PI, BI and CI, for Aedes aegypti and Aedes albopictus, of four selected dengue high risk Medical Officer of Health (MOH) areas in the Kandy District from January 2010 to August 2017, were used in the study. Gumbel frequency analysis was used to calculate the exceedance probability of quantitative values for each individual larval index within the relevant MOH area, individually and to set up the threshold values for the entomological management of dengue vectors. Results Among the study MOH areas, Akurana indicated a relatively high density of both Ae. aegypti and Ae. albopictus, while Gangawata Korale MOH area had the lowest. Based on Ae. aegypti, threshold values were defined for Kandy as low risk (BIagp > 1.77), risk (BIagp > 3.23), moderate risk (BIagp > 4.47) and high risk (BIagp > 6.23). In addition, PI > 6.75 was defined as low risk, while PI > 9.43 and PI>12.82 were defined as moderate and high risk, respectively as an average. Conclusions Threshold values recommended for Ae. aegypti (primary vector for dengue) along with cut-off values for PI (for Ae. aegypti and Ae. albopictus), could be suggested as indicators for decision making in vector control efforts. This may also facilitate the rational use of financial allocations, technical and human resources for vector control approaches in Sri Lanka in a fruitful manner.

Classification of a continuous risk score into risk levels is common. However, while the absolute risk score is essential, it is arguably unethical to label anyone at ‘high, moderate or low risk’ of a serious event, simply because management based on a single criterion (e.g. avoiding the target condition) has been determined to be effective or cost-effective at a population level. Legally, mono-criterial risk labeling can inhibit the obtaining of a fully-informed, preference-based consent, since multiple considerations (various benefits and harms) matter to most individuals, not only the single criterion that is the basis of the provided risk category. These ethical and legal challenges can be met by preference-sensitive multi-criteria decision support tools. In this future vision paper, we demonstrate, at a conceptual proof-of-method level, how such decision support can and should be developed without reference to risk-level classifications. The statin decision is used as illustration, without any empirical claims. [ABSTRACT FROM AUTHOR]

Background: Larval indices such as Premise Index (PI), Breteau Index (BI) and Container Index (CI) are widely used to interpret the density of dengue vectors in surveillance programmes. These indices may be useful for forecasting disease outbreaks in an area. However, use of the values of these indices as alarm signals is rarely considered in control programmes. Therefore, the current study aims to propose threshold values for vector indices based on an empirical modeling approach for the Kandy District of Sri Lanka. Methods: Monthly vector indices, viz PI, BI and CI, for Aedes aegypti and Aedes albopictus, of four selected dengue high risk Medical Officer of Health (MOH) areas in the Kandy District from January 2010 to August 2017, were used in the study. Gumbel frequency analysis was used to calculate the exceedance probability of quantitative values for each individual larval index within the relevant MOH area, individually and to set up the threshold values for the entomological management of dengue vectors. Results: Among the study MOH areas, Akurana indicated a relatively high density of both Ae. aegypti and Ae. albopictus, while Gangawata Korale MOH area had the lowest. Based on Ae. aegypti, threshold values were defined for Kandy as low risk (BIagp > 1.77), risk (BIagp > 3.23), moderate risk (BIagp > 4.47) and high risk (BIagp > 6.23). In addition, PI > 6.75 was defined as low risk, while PI > 9.43 and PI>12.82 were defined as moderate and high risk, respectively as an average. Conclusions: Threshold values recommended for Ae. aegypti (primary vector for dengue) along with cut-off values for PI (for Ae. aegypti and Ae. albopictus), could be suggested as indicators for decision making in vector control efforts. This may also facilitate the rational use of financial allocations, technical and human resources for vector control approaches in Sri Lanka in a fruitful manner. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to achieve the identification of optimal hedging rules for operating reservoir systems, seeking to mitigate the drought impacts. The heuristic Particle Swarm Optimization (PSO) method is adopted as the optimization solver. This procedure establishes a two-phase method that combines PSO with the simulation of the water system, representing a system of reservoirs that are jointly operated to satisfy a set of demands with different priorities. The hedging rules are based on monthly storage levels that trigger restrictions on the demands. As model parameters, monthly rule activation thresholds and rationing factors were used for each type of demand. The optimization procedure minimizes an objective function that penalizes large deficits and assigns different weights to different demand types. Since the whole problem is quite complex, its dimensionality is reduced through: i) a set of candidate monthly activation thresholds are selected a priori associated to given risk conditions; and ii) the rationing factors are defined for every demand of each threshold throughout all months. In addition, an effort is made to avoid the trap in local optimums, whilst several other comments considering the application of the PSO method in the examined applications are provided. The procedure has been successfully applied to four water resource systems in Spain. From the application it can be seen that the deficits of the water supply demand are nearly removed, thanks to the larger weight given to the deficits of this demand type. The irrigation deficits are also reduced, since we lead to a sequence of smaller shortages than only one potential catastrophic shortage. [ABSTRACT FROM AUTHOR]

This study focused on the determination of the cyanobacteria development in terms of the probabilistic recognition of the bloom intensity level in the ecologically relevant state of lakes. Assuming the possibility of using once-a-year sampling frequency, the ecological status or potential was assessed based on the modified Polish phytoplankton-based method (PMPLMOD) in 23 Masurian lakes. The summer cyanobacteria biomass reached 52.3 mg l-1. The biomass did not exceed the WHO-defined low risk threshold of 2 mg l-1, and was usually observed in lakes with at least good ecological status/potential. This threshold may be sufficient to propose it as a good/moderate cyanobacterial bloom intensity threshold. The average values of 3.3, 9.9 and 22.4 mg l-1 were recorded in the lakes with a moderate, poor and bad ecological status/ potential, respectively. The WHO-defined moderate risk threshold of 10 mg l-1 was exceeded only in lakes with a poor or bad ecological status/potential. The structure of cyanobacteria assemblages changed along with the deterioration of the ecological status/ potential and a decrease in Chroococcales biomass and an increase in Oscillatoriales and Nostocales biomass. Nevertheless, Planktolyngbya limnetica, Pseudanabaena limnetica and Aphanizomenon gracile had a significant presence in the total cyanobacterial biomass in all surveyed lakes. [ABSTRACT FROM AUTHOR]

The problem of dam safety is one of the most important research topics of water conservancy projects, and many researchers pay much attention to study the risk of earth dam overtopping. This paper synthesizes in the definition of risk the probabilities of dam failure and the corresponding losses, including the probability estimation, losses evaluation and criteria exploring risk approaches. Then, a comprehensive risk assessment system of dam flood overtopping is established, which is widely applicable. Gate failure, randomness of flood, initial water level and time-varying effects are incorporated in the failure probability model. Many complex factors are simplified in losses estimation. In addition, thresholds of various types of losses are proposed and are adapted to the national conditions. The methodology is applied to the Lianghekou hydropower station in China to illustrate the assessment process of flood overtopping risk and to evaluate its safe loophole with a view to the failure of spillway gates. Monte Carlo simulation and JC method programs are adopted to solve the model based on MATLAB tools and DELPHI. The results show that the losses pose significant impact on the risk assessment and should be considered in the assessment of risk. Probability calculation and loss estimation could be well combined with standards, providing a basis for risk management and decision-making. [ABSTRACT FROM AUTHOR]

Background: It is widely recognized that glycated hemoglobin (HbA1c) and systolic blood pressure (SBP) are two key risk factors for albuminuria and renal function impairment in patients with type 2 diabetes mellitus (T2DM). Our study aimed to identify the specific numerical relationship of albumin/creatinine ratio (ACR) with HbA1c and SBP among a large population of adults with T2DM., Method: A total of 8,626 patients with T2DM were included in the data analysis from the National Health and Nutrition Examination Surveys (NHANES) (1999-2018). The multiple linear regressions were used to examine the associations of ACR with HbA1c and SBP. Generalized additive models with smooth functions were performed to identify the non-linear relations between variables and interactions were also tested., Results: Significantly threshold effects were observed between ACR and HbA1c or SBP after multivariable adjustment, with the risk threshold values HbA1c = 6.4% and SBP = 127 mmHg, respectively. Once above thresholds were exceeded, the lnACR increased dramatically with higher levels of HbA1c (β = 0.23, 95 CI%:0.14, 0.32, P < 0.001) and SBP (β = 0.03, 95 CI%:0.03, 0.04, P < 0.001). Subgroup analysis showed high protein diet was related to higher ACR. In addition, a higher risk of ACR progression was observed in central obesity participants with HbA1C ≥ 6.4% or hyperuricemia participants with SBP ≥ 127 mmHg among patients withT2DM., Conclusion: We identified thresholds of HbA1c and SBP to stratify patients with T2DM through rapid albuminuria progression. These might provide a clinical reference value for preventing and controlling diabetes kidney disease., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Xu, Xue, Chen, Han, Cai, Tian, Jin and Lu.)

An alternative procedure for drought risk assessment and for the mitigation of drought risk is proposed in the paper. An analysis of the relationship between failure of water supply systems and reservoir volumes for the urban area of Firenze in central Tuscany, in central Italy, is performed. Long term simulations are carried out using the software package WEAP. A simplified model of the water resources system is built to assess the threshold values and the management rules. The probability to have definite degree of shortage in the water supply system is evaluated in function of the volume stored in the reservoir at the beginning of the month with Monte Carlo simulations. The reservoir levels and volumes are simulated using time series of the period 1970-2005. Four scenarios (i.e. normal, pre-alert, alert and emergency) associated with different levels of severity of drought are defined. Threshold values are identified considering the probability to assure a given fraction of the demand in a certain time horizon, and are calibrated with an optimization method, which try to minimize the water shortages, especially the heavier. The critical situations are assessed month by month in order to evaluate optimal management rules during the year and avoid conditions of total water shortage. [ABSTRACT FROM AUTHOR]

Many efforts to prevent alcohol-related harm are aimed at reducing risk drinking. This article outlines the many conceptual and methodological challenges to defining risk drinking. It summarizes recent evidence regarding associations of various aspects of alcohol consumption with chronic and acute alcohol-related harms, including mortality, morbidity, injury, and alcohol use disorders, and summarizes the study designs most appropriate to defining risk thresholds for these types of harm. In addition, it presents an international overview of low-risk drinking guidelines from more than 20 countries, illustrating the wide range of interpretations of the scientific evidence related to risk drinking. This article also explores the impact of drink size on defining risk drinking and describes variation in what is considered to be a standard drink across populations. Actual and standard drink sizes differ in the United States, and this discrepancy affects definitions of risk drinking and prevention efforts. [ABSTRACT FROM AUTHOR]

This paper presents a probabilistic modeling framework for component failures in electric power grids. Failure probabilities for grid components are often estimated using parametric models informed by past observations of failure. This work formulates a Bayesian hierarchical framework designed to integrate data and domain expertise to understand the failure properties of a regional power system, where variability in the expected performance of individual components gives rise to failure processes that are heterogeneous and uncertain. We use Bayesian methods to fit failure models to failure data generated in simulation. We test our algorithm by evaluating differences between the data-generating model, our Bayesian hierarchical model, and maximum likelihood parameter estimates. We evaluate how well each model can approximate the failure properties of individual components, and of the system overall. Finally, we define an upgrade policy for achieving targeted reductions in risk exposure, and compare the magnitude of upgrades recommended by each model. [ABSTRACT FROM AUTHOR]

Eutrophication and climate change scenarios engender the need to develop good predictive models for harmful cyanobacterial blooms (CyanoHABs). Nevertheless, modeling cyanobacterial biomass is a challenging task due to strongly skewed distributions that include many absences as well as extreme values (dense blooms). Most modeling approaches alter the natural distribution of the data by splitting them into zeros (absences) and positive values, assuming that different processes underlie these two components. Our objectives were (1) to develop a probabilistic model relating cyanobacterial biovolume to environmental variables in the Río de la Plata Estuary (35°S, 56°W, n = 205 observations) considering all biovolume values (zeros and positive biomass) as part of the same process; and (2) to use the model to predict cyanobacterial biovolume under different risk level scenarios using water temperature and conductivity as explanatory variables. We developed a compound Poisson-Gamma (CPG) regression model, an approach that has not previously been used for modeling phytoplankton biovolume, within a Bayesian hierarchical framework. Posterior predictive checks showed that the fitted model had a good overall fit to the observed cyanobacterial biovolume and to more specific features of the data, such as the proportion of samples crossing three threshold risk levels (0.2, 1 and 2 mm³ L−1) at different water temperatures and conductivities. The CPG model highlights the strong control of cyanobacterial biovolume by nonlinear and interactive effects of water temperature and conductivity. The highest probability of crossing the three biovolume levels occurred at 22.2 °C and at the lowest observed conductivity (∼0.1 mS cm−1). Cross-validation of the fitted model using out-of-sample observations (n = 72) showed the model's potential to be used in situ , as it enabled prediction of cyanobacterial biomass based on two readily measured variables (temperature and conductivity), making it an interesting tool for early alert systems and management strategies. Furthermore, this novel application demonstrates the potential of the Bayesian CPG approach for predicting cyanobacterial dynamics in response to environmental change. Image 1 • Cyanobacterial biovolume was modeled using a Bayesian compound Poisson-Gamma approach. • The model uses two readily measured predictors: water temperature and conductivity. • Cyanobacterial biovolume in out-of-sample observations was well predicted. • Conductivity and temperature had an interactive effect on cyanobacterial biovolume. • The model has a great potential for water management in coastal ecosystems. [ABSTRACT FROM AUTHOR]