Probabilistic risk assessment of cotton pyrethroids: III. A spatial analysis of the Mississippi, USA, cotton landscape

Estimates of potential aquatic exposure concentrations arising from the use of pyrethroid insecticides on cotton produced using conventional procedures outlined by the U.S. Environmental Protection Agency's Office of Pesticide Programs Environmental Fate and Effects Division seem unrealistically high. Accordingly, the assumptions inherent in the pesticide exposure assessment modeling scenarios were examined using remote sensing of a significant Mississippi, USA, cotton-producing county. Image processing techniques and a geographic information system were used to investigate the number and size of the water bodies in the county and their proximity to cotton. Variables critical to aquatic exposure modeling were measured for approximately 600 static water bodies in the study area. Quantitative information on the relative spatial orientation of cotton and water, regional soil texture and slope, and the detailed nature of the composition of physical buffers between agricultural fields and water bodies was also obtained. Results showed that remote sensing and geographic information systems can be used cost effectively to characterize the agricultural landscape and provide verifiable data to refine conservative model assumptions. For example, 68% of all ponds in the region have no cotton within 360 m and 92% of the ponds have no cotton within 60 m. Only 2% of ponds have cotton present in all directions around the ponds and within 120 m. These are significant modifications to conventional pesticide risk assessment exposure modeling assumptions and exemplify the importance of using landscape-level risk assessments to better describe the Mississippi cotton agricultural landscape. Incorporating spatially characterized landscape information into pesticide aquatic exposure scenarios is likely to have greater impact on the model output than many other refinements.     

Spatiotemporal incidence rate data analysis by nonparametric regression

To monitor the incidence rates of cancers, AIDS, cardiovascular diseases, and other chronic or infectious diseases, some global, national, and regional reporting systems have been built to collect/provide population‐based data about the disease incidence. Such databases usually report daily, monthly, or yearly disease incidence numbers at the city, county, state, or country level, and the disease incidence numbers collected at different places and different times are often correlated, with the ones closer in place or time being more correlated. The correlation reflects the impact of various confounding risk factors, such as weather, demographic factors, lifestyles, and other cultural and environmental factors. Because such impact is complicated and challenging to describe, the spatiotemporal (ST) correlation in the observed disease incidence data has complicated ST structure as well. Furthermore, the ST correlation is hidden in the observed data and cannot be observed directly. In the literature, there has been some discussion about ST data modeling. But, the existing methods either impose various restrictive assumptions on the ST correlation that are hard to justify, or ignore partially or entirely the ST correlation. This paper aims to develop a flexible and effective method for ST disease incidence data modeling, using nonparametric local smoothing methods. This method can properly accommodate the ST data correlation. Theoretical justifications and numerical studies show that it works well in practice.     

Distribution and variance/covariance structure of pesticide environmental fate data

Hydrophobicity, persistence, and volatility data for individual pesticides are widely used in risk assessment and transport modeling, so it is important to understand their distribution, variation, and covariation. Correlations (normalized covariance) among properties across a range of multiple pesticides are also important for understanding fundamental relationships among the properties. For the present study, multiple determinations of 11 physicochemical properties of 262 individual pesticides were compiled, primarily from registrant submissions. A Z-score normality analysis indicates that, barring specific data to the contrary, log normality is a reasonable assumption for three properties commonly treated as random variables in modeling: Organic carbon-normalized soil sorption coefficient, aerobic soil metabolism half-life, and field dissipation half-life. Various percentiles for coefficients of variation of the variables are provided, allowing probabilistic modelers to choose realistic population parameters for sampling distributions. A second data set consisting of median values of individual properties for each pesticide was used to investigate the covariance structure of eight of the most important fate properties across 172 pesticides using correlation analysis and exploratory common factor analysis. That analysis demonstrated the use of common factor analysis for reducing the dimensionality of multicollinear environmental fate data, yielding three new orthogonal variables containing most of the information in the original data, and provided insight into the fundamental data structure.     

Dynamic Mortality Modeling: Incorporating Predictions of Future General Population Mortality Into Cost-Effectiveness Analysis

ObjectivesHealth economic models commonly apply observed general population mortality rates to simulate future deaths in a cohort. This is potentially problematic, because mortality statistics are records of the past, not predictions for the future. We propose a new dynamic general population mortality modeling approach, which enables analysts to implement predictions of future changes in mortality rates. The potential implications of moving from a conventional static approach to a dynamic approach are illustrated using a case study.MethodsThe model utilized in National Institute for Health and Care Excellence appraisal TA559, axicabtagene ciloleucel axi for diffuse large B-cell lymphoma, was replicated. National mortality projections were taken from the UK Office for National Statistics. Mortality rates by age and sex were updated each modeled year with the first modeled year using 2022 rates, the second modeled year 2023 and so on. A total of 4 different assumptions were made around age distribution: fixed mean age, lognormal, normal, and gamma. The dynamic model outcomes were compared with those from a conventional static approach.ResultsIncluding dynamic calculations increased the undiscounted life-years attributed to general population mortality by 2.4 to 3.3 years. This led to an increase in discounted incremental life-years within the case study of 0.38 to 0.45 years (8.1%-8.9%), and a commensurate impact on the economically justifiable price of £14 456 to £17 097.ConclusionsThe application of a dynamic approach is technically simple and has the potential to meaningfully affect estimates of cost-effectiveness analysis. Therefore, we call on health economists and health technology assessment bodies to move toward use of dynamic mortality modeling in future.     

The spatial extent of contaminants and the landscape scale: an analysis of the wildlife, conservation biology, and population modeling literature

Many contaminant releases to the terrestrial environment are of small areal extent. Thus, rather than evaluating the ecological impact on species in the immediate vicinity of the release, it may be more ecologically meaningful to determine if population impacts occur at the landscape level. In order to do this, the cumulative impact of all releases in the landscape under consideration must be evaluated. If the release sites are viewed as localized areas that are no longer available for use by ecological receptors (i.e., no longer part of the habitat), this can be thought of as a form of habitat fragmentation. Habitat fragmentation is typically viewed as the loss of large areas of habitat within a landscape, leaving small isolated patches of intact habitat within a hostile matrix. Small-scale contaminant releases, on the other hand, result in small uninhabitable areas within a primarily intact habitat. With this consideration in mind, we analyzed the wildlife and conservation biology literature to determine if information on habitat size requirements such as home-range or critical patch size could inform us about the potential for impact at the landscape level from release sites based on the size of the release alone. We determined that evaluating the impact of release size had to be conducted within a contextual basis (considering the existing state of the landscape). Therefore, we also reviewed the population modeling literature to determine if models could be developed to further evaluate the impact of the spatial extent of chemical releases on the landscape. We identified individual-based models linked to geographic information systems to have the greatest potential in investigating the role of release size with respect to population impacts at the landscape level.     

Assessment of demographic and sociohygienic monitoring

Experience in solving specific problems in sociohygienic monitoring shows that among risk factors, health losses are most commonly considered from the man-caused environmental load on the population, by employing statistical modeling methods and reports on monitoring socioeconomic risk factors are evidently insufficient. A cause-effect relation in the habitat-population health system should be revealed, by taking into account the combined multienvironmental influence of man-caused (chemical, physical) and social factors. For solution of the problems put in work, multivariate analysis was used to reveal the common mechanisms of an association of socioeconomic conditions, national composition, morbidity and disability rates, migration processes, and demographic structure with birth and mortality rates in villagers in the context of economic areas and municipal entities.     

A strategy for comparing the contributions of environmental chemicals and other risk factors to neurodevelopment of children

Background: The impact of environmental chemicals on children’s neurodevelopment is sometimes dismissed as unimportant because the magnitude of the impairments are considered to be clinically insignificant. Such a judgment reflects a failure to distinguish between individual and population risk. The population impact of a risk factor depends on both its effect size and its distribution (or incidence/prevalence).Objective: The objective was to develop a strategy for taking into account the distribution (or incidence/prevalence) of a risk factor, as well as its effect size, in order to estimate its population impact on neurodevelopment of children.Methods: The total numbers of Full-Scale IQ points lost among U.S. children 0–5 years of age were estimated for chemicals (methylmercury, organophosphate pesticides, lead) and a variety of medical conditions and events (e.g., preterm birth, traumatic brain injury, brain tumors, congenital heart disease).Discussion: Although the data required for the analysis were available for only three environmental chemicals (methylmercury, organophosphate pesticides, lead), the results suggest that their contributions to neurodevelopmental morbidity are substantial, exceeding those of many nonchemical risk factors.Conclusion: A method for comparing the relative contributions of different risk factors provides a rational basis for establishing priorities for reducing neurodevelopmental morbidity in children.     

Uncertainty in environmental health impact assessment: Quantitative methods and perspectives

Environmental health impact assessment models are subjected to great uncertainty due to the complex associations between environmental exposures and health. Quantifying the impact of uncertainty is important if the models are used to support health policy decisions. We conducted a systematic review to identify and appraise current methods used to quantify the uncertainty in environmental health impact assessment. In the 19 studies meeting the inclusion criteria, several methods were identified. These were grouped into random sampling methods, second-order probability methods, Bayesian methods, fuzzy sets, and deterministic sensitivity analysis methods. All 19 studies addressed the uncertainty in the parameter values but only 5 of the studies also addressed theuncertainty in the structure of the models. None of the articles reviewed considered conceptual sources of uncertainty associated with the framing assumptions or the conceptualisation of the model. Future research should attempt to broaden the way uncertainty is taken into account in environmental health impact assessments.     

Time and space issues in ecotoxicology: population models,landscape pattern analysis,and long-range environmental chemistry

Advances in ecotoxicology addressing problems of time and spatial scales are presented and interpreted in the frame of concepts on population/community dynamics and landscape pattern analysis. Example deterministic/probabilistic modeling experiments are used to illustrate key concepts. Space and time scales analyzed are single and multigenerations of local populations, metapopulations, community, and ecosystem/landscape. Most population models used in recent ecotoxicology studies are deterministic and do not include a formal treatment of spatial processes, like migration or local random extinction. Some metapopulation models have been applied with success. Upscaling of ecotoxicological results at the community level is less developed, probably because of the inherent complexity of indirect and direct coactions among organisms. Community and ecosystem toxicity end points that could find a broad use in regulatory applications have not yet been identified. Some practical issues like the estimation of the potential for the natural attenuation of toxicity and the transport of contaminants along food chains must be addressed at these scales/levels of biological complexity. The estimation of ecotoxicological effects has been increasingly evolving to integrate modeling and monitoring contaminant transport and fate, landscape pattern analysis, and spatially explicit population dynamics (including direct and indirect communal interactions).     

Dealing with uncertainties in environmental burden of disease assessment

Disability Adjusted Life Years (DALYs) combine the number of people affected by disease or mortality in a population and the duration and severity of their condition into one number. The environmental burden of disease is the number of DALYs that can be attributed to environmental factors. Environmental burden of disease estimates enable policy makers to evaluate, compare and prioritize dissimilar environmental health problems or interventions. These estimates often have various uncertainties and assumptions which are not always made explicit. Besides statistical uncertainty in input data and parameters – which is commonly addressed – a variety of other types of uncertainties may substantially influence the results of the assessment. We have reviewed how different types of uncertainties affect environmental burden of disease assessments, and we give suggestions as to how researchers could address these uncertainties. We propose the use of an uncertainty typology to identify and characterize uncertainties. Finally, we argue that uncertainties need to be identified, assessed, reported and interpreted in order for assessment results to adequately support decision making.     

环境农药暴露和人群肺癌发病、死亡的相关分析

目的：研究环境中农药暴露与农业人群肺癌发病率和死亡率的相关关系。方法：利用生态比较研究方法，收集沈阳市郊区县农药接触人群1993－1998年各年的农药使用种类、使用量和农作物播种种类、播种面积等资料，同时收集1998－2000年相应地区的肺癌发病和死亡资料。应用SPSS 10．0统计软件进行等级相关分析。结果：总的肺癌发病率与农药使用密度无显性相关。年龄别分析表明，男性40－49岁及女性60－69岁年龄组的发病率与农药使用密度有显性相关，P＜0．05。1998年男性及男、女合计的肺癌死亡率与1993年农药使用密度有显性相关，相关系数均为0．886，P＜0．05。按年龄别、性别分析显示，40－49岁年龄组的男性、女性和男、女合计的肺癌死亡率与农药使用密度有显性相关，相关系数分别为0．886，0．886，0．943。结论：农业人群肺癌的发病率和死亡率与环境中农药暴露存在一定的相关。     

Using physiologically-based pharmacokinetic models to incorporate chemical and non-chemical stressors into cumulative risk assessment: a case study of pesticide exposures

Cumulative risk assessment has been proposed as an approach to evaluate the health risks associated with simultaneous exposure to multiple chemical and non-chemical stressors. Physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models can allow for the inclusion and evaluation of multiple stressors, including non-chemical stressors, but studies have not leveraged PBPK/PD models to jointly consider these disparate exposures in a cumulative risk context. In this study, we focused on exposures to organophosphate (OP) pesticides for children in urban low-income environments, where these children would be simultaneously exposed to other pesticides (including pyrethroids) and non-chemical stressors that may modify the effects of these exposures (including diet). We developed a methodological framework to evaluate chemical and non-chemical stressor impacts on OPs, utilizing an existing PBPK/PD model for chlorpyrifos. We evaluated population-specific stressors that would influence OP doses or acetylcholinesterase (AChE) inhibition, the relevant PD outcome. We incorporated the impact of simultaneous exposure to pyrethroids and dietary factors on OP dose through the compartments of metabolism and PD outcome within the PBPK model, and simulated combinations of stressors across multiple exposure ranges and potential body weights. Our analyses demonstrated that both chemical and non-chemical stressors can influence the health implications of OP exposures, with up to 5-fold variability in AChE inhibition across combinations of stressor values for a given OP dose. We demonstrate an approach for modeling OP risks in the presence of other population-specific environmental stressors, providing insight about co-exposures and variability factors that most impact OP health risks and contribute to children's cumulative health risk from pesticides. More generally, this framework can be used to inform cumulative risk assessment for any compound impacted by chemical and non-chemical stressors through metabolism or PD outcomes.     

Quantitative health impact assessment: current practice and future directions

STUDY OBJECTIVE: To assess what methods are used in quantitative health impact assessment (HIA), and to identify areas for future research and development. DESIGN: HIA reports were assessed for (1) methods used to quantify effects of policy on determinants of health (exposure impact assessment) and (2) methods used to quantify health outcomes resulting from changes in exposure to determinants (outcome assessment). MAIN RESULTS: Of 98 prospective HIA studies, 17 reported quantitative estimates of change in exposure to determinants, and 16 gave quantified health outcomes. Eleven (categories of) determinants were quantified up to the level of health outcomes. Methods for exposure impact assessment were: estimation on the basis of routine data and measurements, and various kinds of modelling of traffic related and environmental factors, supplemented with experts' estimates and author's assumptions. Some studies used estimates from other documents pertaining to the policy. For the calculation of health outcomes, variants of epidemiological and toxicological risk assessment were used, in some cases in mathematical models. CONCLUSIONS: Quantification is comparatively rare in HIA. Methods are available in the areas of environmental health and, to a lesser extent, traffic accidents, infectious diseases, and behavioural factors. The methods are diverse and their reliability and validity are uncertain. Research and development in the following areas could benefit quantitative HIA: methods to quantify the effect of socioeconomic and behavioural determinants; user friendly simulation models; the use of summary measures of public health, expert opinion and scenario building; and empirical research into validity and reliability.     

Methodologic issues in epidemiologic risk assessment.

This paper reviews methodologic issues pertinent to the application of epidemiology in risk assessment and discusses concerns in the presentation of results from such an activity. Assessment of the health risks associated with occupational and environmental exposures involves four phases: hazard identification, i.e., the detection of the potential for agents to cause adverse health effects in exposed populations; exposure assessment, i.e., the quantification of exposures and the estimation of the characteristics and sizes of the exposed populations; dose-response assessment, i.e., the modeling for risk realization; and risk characterization, i.e., the evaluation of the impact of a change in exposure levels on public health effects. The risk-assessment process involves limitations of exposure data, many assumptions, and subjective choices that need to be considered when using this approach to provide guidance for health policy or action. In view of these uncertainties, we suggest that the provision of estimates of individual risk and disease burden in a population must be accompanied by the corresponding estimates of precision; risks should be presented in a sufficiently disaggregated form so that population heterogeneities are not lost in the data aggregation; and different scenarios and risk models should be applied. The methods are illustrated by an assessment on the health impacts of exposure to silica.     

Comparative analysis of remotely-sensed data products via ecological niche modeling of avian influenza case occurrences in Middle Eastern poultry

Background

Ecological niche modeling integrates known sites of occurrence of species or phenomena with data on environmental variation across landscapes to infer environmental spaces potentially inhabited (i.e., the ecological niche) to generate predictive maps of potential distributions in geographic space. Key inputs to this process include raster data layers characterizing spatial variation in environmental parameters, such as vegetation indices from remotely sensed satellite imagery. The extent to which ecological niche models reflect real-world distributions depends on a number of factors, but an obvious concern is the quality and content of the environmental data layers.

Methods

We assessed ecological niche model predictions of H5N1 avian flu presence quantitatively within and among four geographic regions, based on models incorporating two means of summarizing three vegetation indices derived from the MODIS satellite. We evaluated our models for predictive ability using partial ROC analysis and GLM ANOVA to compare performance among indices and regions.

Results

We found correlations between vegetation indices to be high, such that they contain information that overlaps broadly. Neither the type of vegetation index used nor method of summary affected model performance significantly. However, the degree to which model predictions had to be transferred (i.e., projected onto landscapes and conditions not represented on the landscape of training) impacted predictive strength greatly (within-region model predictions far out-performed models projected among regions).

Conclusion

Our results provide the first quantitative tests of most appropriate uses of different remotely sensed data sets in ecological niche modeling applications. While our testing did not result in a decisive "best" index product or means of summarizing indices, it emphasizes the need for careful evaluation of products used in modeling (e.g. matching temporal dimensions and spatial resolution) for optimum performance, instead of simple reliance on large numbers of data layers.

     

An overview of human exposure modeling activities at the USEPA's National Exposure Research Laboratory

The computational modeling of human exposure to environmental pollutants is one of the primary activities of the US Environmental Protection Agency (USEPA)s National Exposure Research Laboratory (NERL). Assessment of human exposures is a critical part of the overall risk assessment paradigm. In exposure assessment, we analyze the source-to-dose sequence of processes, in which pollutants are released from sources into the environment, where they may move through multiple environmental media, and to human receptors via multiple pathways. Exposure occurs at the environment-human interface, where pollutants are contacted in the course of human activities. Exposure may result in a dose, by which chemicals enter the body through multiple portals of entry, primarily inhalation, ingestion, and dermal absorption. Within the body, absorbed pollutants are distributed to, metabolized within, and eliminated from various organs and tissues, where they may cause toxicologic responses or adverse health effects. The NERL's modeling efforts are directed at improving our understanding of this sequence of processes, by characterizing the various factors influencing exposures and dose, and their associated variabilities and uncertainties. Modeling at the NERL is one of three essential programmatic elements, along with measurements and methods development. These are pursued interactively to advance our understanding of exposure-related processes. Exposure models are developed and run using the best currently available measurement data to simulate and predict population exposure and dose distributions, and to identify the most important factors and their variabilities and uncertainties. This knowledge is then used to guide the development of improved methods and measurements needed to obtain better data to improve the assessment and reduce critical uncertainties. These models and measurement results are tools that can be used in risk assessments and in risk management decisions in order to reduce harmful exposures. Current areas of the NERL's exposure modeling emphasis include: Pollutant concentrations in ambient (outdoor) air using the Third Generation Air Quality Modeling System's Community Multiscale Air Quality model (Models-3/CMAQ); Air flow and pollutant concentrations at local and microenvironmental scales using computational fluid dynamics (CFD); Human inhalation exposure to airborne particulate matter, air toxics, and multipathway exposure to pesticides, using the Stochastic Human Exposure and Dose Simulation (SHEDS) model; Human and ecological exposure and risk assessments of hazardous waste sites using Framework for Risk Analysis in Multimedia Environmental Systems--Multimedia, Multipathway, Multireceptor Risk Assessment (FRAMES-3MRA), one of many software programs available from the NERL's Center for Exposure Assessment Modeling (CEAM); Physiologically based pharmacokinetic (PBPK) modeling of pesticides and volatile organic compounds (VOCs) in the Exposure-Related Dose-Estimating Model (ERDEM). A brief historical overview of the NERL's evolution of human exposure models is presented, with examples of the present state-of-the-science represented by SHEDS and FRAMES-3MRA.     

Cancers in Australia in 2010 attributable to modifiable factors: introduction and overview

Objective: To describe the approach underpinning a national project to estimate the numbers and proportions of cancers occurring in Australia in 2010 that are attributable to modifiable causal factors. Methods: We estimated the population attributable fraction (PAF) (or prevented fraction) of cancers associated with exposure to causal (or preventive) factors using standard formulae. Where possible, we also estimated the potential impact on cancer incidence resulting from changes in prevalence of exposure. Analyses were restricted to factors declared causal by international agencies: tobacco smoke; alcohol; solar radiation; infectious agents; obesity; insufficient physical activity; insufficient intakes of fruits, vegetables and fibre; red and processed meat; menopausal hormone therapy (MHT); oral contraceptive pill (OCP); and insufficient breast feeding. Separately, we estimated numbers of cancers prevented by: aspirin; sunscreen; MHT; and OCP use. We discuss assumptions pertaining to latent periods between exposure and cancer onset, choices of prevalence data and risk estimates, and approaches to sensitivity analyses. Results: Numbers and population attributable fractions of cancer are presented in accompanying papers. Conclusions: This is the first systematic assessment of population attributable fractions of cancer in Australia.     

Creating a dementia-friendly environment through the use of outdoor natural landscape design intervention in long-term care facilities: A narrative review

There is an increasing volume of literature on the positive effects of outdoor natural landscapes on health and well-being. However, to date, there is a paucity of research on the effect of outdoor natural landscapes designed for people with dementia living in long-term care (LTC) facilities, in particular, those which have incorporated the characteristics of a dementia-friendly environment (DFE). This narrative literature review synthesizes current knowledge on the effect of outdoor natural landscape design, which is aligned with the characteristics of a DFE, to improve agitation, apathy and engagement of people with dementia living in LTC facilities. The reviewed studies predominantly support the positive effects of outdoor natural landscapes on agitation, apathy and engagement of people with dementia. However, there are concerns about the methodological approaches, principles incorporated in the applied outdoor natural landscapes' designs, and the environmental assessment. Further rigorous research is required to understand the impact of the outdoor natural landscapes, with the application of DFE characteristics in the design, on agitation, apathy and engagement of people with dementia living in LTC facilities.     

Adverse outcome pathways and ecological risk assessment: bridging to population-level effects

Maintaining the viability of populations of plants and animals is a key focus for environmental regulation. Population-level responses integrate the cumulative effects of chemical stressors on individuals as those individuals interact with and are affected by their conspecifics, competitors, predators, prey, habitat, and other biotic and abiotic factors. Models of population-level effects of contaminants can integrate information from lower levels of biological organization and feed that information into higher-level community and ecosystem models. As individual-level endpoints are used to predict population responses, this requires that biological responses at lower levels of organization be translated into a form that is usable by the population modeler. In the current study, we describe how mechanistic data, as captured in adverse outcome pathways (AOPs), can be translated into modeling focused on population-level risk assessments. First, we describe the regulatory context surrounding population modeling, risk assessment and the emerging role of AOPs. Then we present a succinct overview of different approaches to population modeling and discuss the types of data needed for these models. We describe how different key biological processes measured at the level of the individual serve as the linkage, or bridge, between AOPs and predictions of population status, including consideration of community-level interactions and genetic adaptation. Several case examples illustrate the potential for use of AOPs in population modeling and predictive ecotoxicology. Finally, we make recommendations for focusing toxicity studies to produce the quantitative data needed to define AOPs and to facilitate their incorporation into population modeling.