Modelling pesticide degradation and leaching in conservation agriculture: Effect of no-till and mulching.

No-till and mulching are typical management operations in conservation agriculture (CA). To model pesticide degradation and leaching under a CA scenario, as compared to a conventional-tillage scenario (CT), the mulch module of the agro-hydrological model Daisy was extended. A Daisy soil column was parameterized with measurements of topsoil, mulch, and a realistic subsoil, and tested against published experimental data of pesticide fate in laboratory soil columns covered by mulch. Uncertainty and sensitivity analyses of the new Daisy version were conducted for a series of weather, soil, pesticide, and mulch parameters, using 4939 Monte Carlo simulations under each scenario. Results showed that there was no systematic difference in pesticide leaching from the topsoil (to the subsoil and directly to drains via drain-connected biopores) between CA and CT, but pesticide degradation and sorption were significantly different; degradation in the mulch and uppermost soil surface layer (0-3.5 cm) was larger in CA while degradation was larger in CT when considering the whole topsoil (0-30 cm). This difference for the whole topsoil could be explained by pesticide interception in CA in the part of the mulch not in direct contact with the soil where degradation is assumed not to occur. The sensitivity analysis highlighted non-influential parameters and seven parameters out of twenty-five to be better estimated to improve the accuracy of the predictions.

Practical guidance to evaluate in vitro dermal absorption studies for pesticide registration: An industry perspective.

While there are some regulatory assessment criteria available on how to generally evaluate dermal absorption (DA) studies for risk assessment purposes, practical guidance and examples are lacking. The current manuscript highlights the challenges in interpretating data from in vitro assays and proposes holistic data-based assessment strategies from an industry perspective. Inflexible decision criteria may be inadequate for real data and may lead to irrelevant DA estimates. We recommend the use of mean values for reasonably conservative DA estimates from in vitro studies. In cases where additional conservatism is needed, e.g., due to non-robust data and acute exposure scenarios, the upper 95% confidence interval of the mean may be appropriate. It is critical to review the data for potential outliers and we provide some example cases and strategies to identify aberrant responses. Some regional regulatory authorities require the evaluation of stratum corneum (SC) residue, but here, as a very simple pro-rata approach, we propose to review whether the predicted post 24-h absorption flux exceeds the predicted elimination flux by desquamation because otherwise it is not possible for the SC residue to contribute to systemic dose. Overall, the adjustment of DA estimates due to mass balance (normalization) is not recommended.

Using R in Regulatory Toxicology.

Statistical analyses are an essential part of regulatory toxicological evaluations. While projects would be ideally monitored by both toxicologists and statisticians, this is often not possible in practice. Hence, toxicologists should be trained in some common statistical approaches but also need a tool for statistical evaluations. Due to transparency needed in regulatory processes and standard tests that can be evaluated with template approaches, the freely available open-source statistical software R may be suitable. R is a well-established software in the statistical community. The principal input method is via software code, which is both benefit and weakness of the tool. It is increasingly used by regulating authorities globally and can be easily extended by software packages, e.g., for new statistical functions and features. This manuscript outlines how R can be used in regulatory toxicology, allowing toxicologists to perform all regulatory required data evaluations in a single software solution. Practical applications are shown in case studies on simulated and experimental data. The examples cover a) Dunnett testing of treatment groups against a common control and in relation to a biological relevance threshold, assessing the test's assumptions and plotting the results; b) dose-response analysis and benchmark dose derivation for chronic kidney inflammation as a function of Pyridine; and c) graphical/exploratory data analysis of previously published developmental neurotoxicity data for Chlorpyrifos.

Benchmark dose modelling in regulatory ecotoxicology, a potential tool in pest management.

For several authorities, benchmark dose (BMD) methodology has become the recommended approach by which to derive reference values for risk assessment. However, in practice, the BMD approach is not standard use in risk assessment for pesticides where the no observed adverse effect level, lowest observed adverse effect level and effective dose (ED50 or EDx ) prevail. Regression-based BMD and the benchmark dose lower confidence limit (BMDL) have several advantages, such as utilizing more information from the generated data and being less dependent on tested dose levels. However, the BMD approach requires some degree of expert knowledge for defining an appropriate risk level for estimating the BMD and using more sophisticated statistical methods to calculate BMD and BMDL. The BMD approach is one way to move away from p value-based binary decision-making towards putting the weight on effect sizes. We review the advantages and disadvantages of focusing on the BMD approach for risk assessment of pesticides. Further, we discuss potential applications in efficacy trials for pest management purposes. © 2021 Society of Chemical Industry.

Using historical control data in bioassays for regulatory toxicology.

Historical control data (HCD) consist of pooled control group responses from bioassays. These data must be collected and are often used or reported in regulatory toxicology studies for multiple purposes: as quality assurance for the test system, to help identify toxicological effects and their effect-size relevance and to address the statistical multiple comparison problem. The current manuscript reviews the various classical and potential new approaches for using HCD. Issues in current practice are identified and recommendations for improved use and discussion are provided. Furthermore, stakeholders are invited to discuss whether it is necessary to consider uncertainty when using HCD formally and statistically in toxicological discussions and whether binary inclusion/exclusion criteria for HCD should be revised to a tiered information contribution to assessments. Overall, the critical value of HCD in toxicological bioassays is highlighted when used in a weight-of-evidence assessment.

A Nonmechanistic Parametric Modeling Approach for Benchmark Dose Estimation of Event-Time Data.

We propose benchmark dose estimation for event-time data, using a two-step approach. This approach avoids estimation of complex models and has been previously shown to give robust results for summarizing relevant parameters for risk assessment. In the first step, the probability of the event of interest to occur (in a certain time interval) is described as a function of time, resulting in an event-time model; such a model is fitted allowing an individual curve for each dose, and relevant estimates are extracted. In the second step, a dose-response model is fitted to the estimates of t50 obtained from the event-time model in the first step. Given a predefined benchmark response, the benchmark dose is then estimated from the resulting model. This novel approach is demonstrated in two examples. Our application of the time-to-event model showed a gain in power compared to the traditional analysis of end-of-study summary data.

Expanding the toxicologist's statistical toolbox: Using effect size estimation and dose-response modelling for holistic assessments instead of generic testing.

It is tempting to base (eco-)toxicological assay evaluation solely on statistical significance tests. The approach is stringent, objective and facilitates binary decisions. However, tests according to null hypothesis statistical testing (NHST) are thought experiments that rely heavily on assumptions. The generic and unreflected application of statistical tests has been called "mindless" by Gigerenzer. While statistical tests have an appropriate application domain, the present work investigates how unreflected testing may affect toxicological assessments. Dunnett multiple-comparison and Williams trend testing and their compatibility intervals are compared with dose-response-modelling in case studies, where data do not follow textbook behavior, nor behave as expected from a toxicological point of view. In such cases, toxicological assessments based only on p-values may be biased and biological evaluations based on plausibility may be prioritized. If confidence in a negative assay outcome cannot be established, further data may be needed for a robust toxicological assessment.

bmd: an R package for benchmark dose estimation.

The benchmark dose (BMD) methodology is used to derive a hazard characterization measure for risk assessment in toxicology or ecotoxicology. The present paper's objective is to introduce the R extension package bmd, which facilitates the estimation of BMD and the benchmark dose lower limit for a wide range of dose-response models via the popular package drc. It allows using the most current statistical methods for BMD estimation, including model averaging. The package bmd can be used for BMD estimation for binomial, continuous, and count data in a simple set up or from complex hierarchical designs and is introduced using four examples. While there are other stand-alone software solutions available to estimate BMDs, the package bmd facilitates easy estimation within the established and flexible statistical environment R. It allows the rapid implementation of available, novel, and future statistical methods and the integration of other statistical analyses.

A Review of Recent Advances in Benchmark Dose Methodology.

In this review, recent methodological developments for the benchmark dose (BMD) methodology are summarized. Specifically, we introduce the advances for the main steps in BMD derivation: selecting the procedure for defining a BMD from a predefined benchmark response (BMR), setting a BMR, selecting a dose-response model, and estimating the corresponding BMD lower limit (BMDL). Although the last decade has shown major progress in the development of BMD methodology, there is still room for improvement. Remaining challenges are the implementation of new statistical methods in user-friendly software and the lack of consensus about how to derive the BMDL.

A comparison of approaches for simultaneous inference of fixed effects for multiple outcomes using linear mixed models.

Longitudinal studies with multiple outcomes often pose challenges for the statistical analysis. A joint model including all outcomes has the advantage of incorporating the simultaneous behavior but is often difficult to fit due to computational challenges. We consider 2 alternative approaches to quantify and assess the loss in efficiency as compared with joint modelling when evaluating fixed effects. The first approach is pairwise fitting of pseudolikelihood functions for pairs of outcomes. The second approach recovers correlations between parameter estimates across multiple marginal linear mixed models. The methods are evaluated in terms of a data example both from a study on the effects of milk protein on health in young adolescents and in an extensive simulation study. We find that the 2 alternatives give similar results in settings where an exchangeability condition is met, but otherwise, pairwise fitting shows a larger loss in efficiency than the marginal models approach. Using an alternative to the joint modelling strategy will lead to some but not necessarily a large loss of efficiency for small sample sizes.

Factors associated with non-participation in cervical cancer screening - A nationwide study of nearly half a million women in Denmark.

Cervical cancer occurs most often in under-screened women. In this nationwide register study, we described differences in sociodemographic characteristics between passive and active non-participants and examined socio-demographic characteristics, reproductive history, and mental and physical health as potential determinants for passive non-participation compared with participation in the Danish cervical cancer screening program. Screening history in women aged 23-49 years invited for cervical cancer screening in 2008-2009 was retrieved from the Danish Pathology Databank with information about dates of invitation and unsubscription. We identified participants (n = 402,984), active non-participants (n = 10,251) and passive non-participants (n = 63,435) within four years following baseline invitation and retrieved data about the study population from high-quality registries. We examined differences in socio-demographic characteristics of passive and active non-participants, and used multiple logistic regression analyses to identify potential determinants of passive non-participation. We found that active and passive non-participants differed in relation to socio-demography. When compared with screening participants, the odds of passive non-participation was increased in women who originated from less developed countries; were unmarried; had basic education or low income; had four or more children; smoked during pregnancy; had multiple induced abortions; or had a history of obesity, intoxicant abuse or schizophrenia or other psychoses. In conclusion, in this nationwide, prospective, population-based study, differences in socio-demographic characteristics between passive and active non-participants were found. Furthermore, sociodemography, reproductive history, and mental and physical health were determinants for passive non-participation. Addressing inequalities in screening attendance may help to further decrease the incidence of and mortality from cervical cancer.

Mediation analysis for logistic regression with interactions: Application of a surrogate marker in ophthalmology.

Mediation analysis is often based on fitting two models, one including and another excluding a potential mediator, and subsequently quantify the mediated effects by combining parameter estimates from these two models. Standard errors of such derived parameters may be approximated using the delta method. For a study evaluating a treatment effect on visual acuity, a binary outcome, we demonstrate how mediation analysis may conveniently be carried out by means of marginally fitted logistic regression models in combination with the delta method. Several metrics of mediation are estimated and results are compared to findings using existing methods.

Experimental design matters for statistical analysis: how to handle blocking.

BACKGROUND: Nowadays, evaluation of the effects of pesticides often relies on experimental designs that involve multiple concentrations of the pesticide of interest or multiple pesticides at specific comparable concentrations and, possibly, secondary factors of interest. Unfortunately, the experimental design is often more or less neglected when analysing data. Two data examples were analysed using different modelling strategies. First, in a randomized complete block design, mean heights of maize treated with a herbicide and one of several adjuvants were compared. Second, translocation of an insecticide applied to maize as a seed treatment was evaluated using incomplete data from an unbalanced design with several layers of hierarchical sampling. Extensive simulations were carried out to further substantiate the effects of different modelling strategies. RESULTS: It was shown that results from suboptimal approaches (two-sample t-tests and ordinary ANOVA assuming independent observations) may be both quantitatively and qualitatively different from the results obtained using an appropriate linear mixed model. The simulations demonstrated that the different approaches may lead to differences in coverage percentages of confidence intervals and type 1 error rates, confirming that misleading conclusions can easily happen when an inappropriate statistical approach is chosen. CONCLUSION: To ensure that experimental data are summarized appropriately, avoiding misleading conclusions, the experimental design should duly be reflected in the choice of statistical approaches and models. We recommend that author guidelines should explicitly point out that authors need to indicate how the statistical analysis reflects the experimental design. © 2017 Society of Chemical Industry.

Cigarette smoking is associated with adverse survival among women with ovarian cancer: Results from a pooled analysis of 19 studies.

Cigarette smoking is associated with an increased risk of developing mucinous ovarian tumors but whether it is associated with ovarian cancer survival overall or for the different histotypes is unestablished. Furthermore, it is unknown whether the association between cigarette smoking and survival differs according to strata of ovarian cancer stage at diagnosis. In a large pooled analysis, we evaluated the association between various measures of cigarette smoking and survival among women with epithelial ovarian cancer. We obtained data from 19 case-control studies in the Ovarian Cancer Association Consortium (OCAC), including 9,114 women diagnosed with ovarian cancer. Cox regression models were used to estimate adjusted study-specific hazard ratios (HRs), which were combined into pooled hazard ratios (pHR) with corresponding 95% confidence intervals (CIs) under random effects models. Overall, 5,149 (57%) women died during a median follow-up period of 7.0 years. Among women diagnosed with ovarian cancer, both current (pHR = 1.17, 95% CI: 1.08-1.28) and former smokers (pHR = 1.10, 95% CI: 1.02-1.18) had worse survival compared with never smoking women. In histotype-stratified analyses, associations were observed for mucinous (current smoking: pHR = 1.91, 95% CI: 1.01-3.65) and serous histotypes (current smoking: pHR = 1.11, 95% CI: 1.00-1.23; former smoking: pHR = 1.12, 95% CI: 1.04-1.20). Further, our results suggested that current smoking has a greater impact on survival among women with localized than disseminated disease. The identification of cigarette smoking as a modifiable factor associated with survival has potential clinical importance as a focus area to improve ovarian cancer prognosis.

The association between socioeconomic status and tumour stage at diagnosis of ovarian cancer: A pooled analysis of 18 case-control studies.

PURPOSE: Socioeconomic status (SES) is a known predictor of survival for several cancers and it has been suggested that SES differences affecting tumour stage at diagnosis may be the most important explanatory factor for this. However, only a limited number of studies have investigated SES differences in tumour stage at diagnosis of ovarian cancer. In a pooled analysis, we investigated whether SES as represented by level of education is predictive for advanced tumour stage at diagnosis of ovarian cancer, overall and by histotype. The effect of cigarette smoking and body mass index (BMI) on the association was also evaluated. METHODS: From 18 case-control studies, we obtained information on 10,601 women diagnosed with epithelial ovarian cancer. Study specific odds ratios (ORs) with corresponding 95% confidence intervals (CI) were obtained from logistic regression models and combined into a pooled odds ratio (pOR) using a random effects model. RESULTS: Overall, women who completed ≤high school had an increased risk of advanced tumour stage at diagnosis compared with women who completed >high school (pOR 1.15; 95% CI 1.03-1.28). The risk estimates for the different histotypes of ovarian cancer resembled that observed for ovarian cancers combined but did not reach statistical significance. Our results were unchanged when we included BMI and cigarette smoking. CONCLUSION: Lower level of education was associated with an increased risk of advanced tumour stage at diagnosis of ovarian cancer. The observed socioeconomic difference in stage at diagnosis of ovarian cancer calls for further studies on how to reduce this diagnostic delay.

Factors associated with type-specific persistence of high-risk human papillomavirus infection: A population-based study.

Persistent genital infection with high-risk (HR) human papillomavirus (HPV) is a prerequisite for cervical cancer development. The aim of this study was to identify factors associated with type-specific persistence of HR HPV infections. From a population-based cohort of 40,399 women participating in cervical cancer screening established during 2002-2005, we selected all HR HPV-positive women (N = 7,778). During follow-up (2005-2008), we collected cervical samples from these women and tested them for HPV DNA to determine type-specific HR HPV persistence in the interval 1-4.5 years after enrolment. Data on hospitalisations, prescriptions and socioeconomic factors were obtained from nationwide registers. Women with abnormal cytology at baseline or who had undergone conisation during follow-up were excluded. Factors associated with persistence were identified by logistic regression analysis. The overall rate of HR HPV persistence was 31.4%. The risk for persistence was significantly increased among women with a previous episode of genital warts (OR, 1.35; 95% CI, 1.04-1.74), current use of oral contraceptives (OR, 1.35; 95% CI, 1.13-1.63) or use of systemic glucocorticoids (OR, 2.04; 95% CI, 1.16-3.56). The number of pregnancies or births or use of a hormonal intrauterine device, hormonal therapy or nonsteroidal anti-inflammatory drugs was not associated with risk for HR HPV persistence. A history of genital warts and current use of oral contraceptives or systemic glucocorticoids increased the risk, potentially indicating a decreased immune response to HPV infection. These findings suggest that host immune response characteristics are important in HR HPV persistence and consequently in cervical cancer development.

Acute disruption of glucagon secretion or action does not improve glucose tolerance in an insulin-deficient mouse model of diabetes.

AIMS/HYPOTHESIS: Normal glucose metabolism depends on pancreatic secretion of insulin and glucagon. The bihormonal hypothesis states that while lack of insulin leads to glucose underutilisation, glucagon excess is the principal factor in diabetic glucose overproduction. A recent study reported that streptozotocin-treated glucagon receptor knockout mice have normal glucose tolerance. We investigated the impact of acute disruption of glucagon secretin or action in a mouse model of severe diabetes by three different approaches: (1) alpha cell elimination; (2) glucagon immunoneutralisation; and (3) glucagon receptor antagonism, in order to evaluate the effect of these on glucose tolerance. METHODS: Severe diabetes was induced in transgenic and wild-type mice by streptozotocin. Glucose metabolism was investigated using OGTT in transgenic mice with the human diphtheria toxin receptor expressed in proglucagon producing cells allowing for diphtheria toxin (DT)-induced alpha cell ablation and in mice treated with either a specific high affinity glucagon antibody or a specific glucagon receptor antagonist. RESULTS: Near-total alpha cell elimination was induced in transgenic mice upon DT administration and resulted in a massive decrease in pancreatic glucagon content. Oral glucose tolerance in diabetic mice was neither affected by glucagon immunoneutralisation, glucagon receptor antagonism, nor alpha cell removal, but did not deteriorate further compared with mice with intact alpha cell mass. CONCLUSIONS/INTERPRETATION: Disruption of glucagon action/secretion did not improve glucose tolerance in diabetic mice. Near-total alpha cell elimination may have prevented further deterioration. Our findings support insulin lack as the major factor underlying hyperglycaemia in beta cell-deficient diabetes.

GLP1- and GIP-producing cells rarely overlap and differ by bombesin receptor-2 expression and responsiveness.

The incretin hormones glucagon-like peptide-1 (GLP1) and glucose-dependent insulinotropic polypeptide (GIP) are secreted from intestinal endocrine cells, the so-called L- and K-cells. The cells are derived from a common precursor and are highly related, and co-expression of the two hormones in so-called L/K-cells has been reported. To investigate the relationship between the GLP1- and GIP-producing cells more closely, we generated a transgenic mouse model expressing a fluorescent marker in GIP-positive cells. In combination with a mouse strain with fluorescent GLP1 cells, we were able to estimate the overlap between the two cell types. Furthermore, we used primary cultured intestinal cells and isolated perfused mouse intestine to measure the secretion of GIP and GLP1 in response to different stimuli. Overlapping GLP1 and GIP cells were rare (∼5%). KCl, glucose and forskolin+IBMX increased the secretion of both GLP1 and GIP, whereas bombesin/neuromedin C only stimulated GLP1 secretion. Expression analysis showed high expression of the bombesin 2 receptor in GLP1 positive cells, but no expression in GIP-positive cells. These data indicate both expressional and functional differences between the GLP1-producing 'L-cell' and the GIP-producing 'K-cell'.

The impact of early growth patterns and infant feeding on body composition at 3 years of age.

Early excessive weight gain is positively associated with later obesity, and yet the effect of weight gain during specific periods and the impact of infant feeding practices are debated. The objective of the present study was to examine the impact of weight gain in periods of early childhood on body composition at 3 years, and whether infant feeding modified the relationship between early growth and body composition at 3 years. We studied 233 children from the prospective cohort study, SKOT (in Danish: Småbørns Kost og Trivsel). Birth weight z-scores (BWZ) and change in weight-for-age z-scores (WAZ) from 0 to 5, 5 to 9, 9 to 18 and 18 to 36 months were analysed for relations with body composition (anthropometry and bioelectrical impedance) at 3 years by multivariate regression analysis. BWZ and change in WAZ from 0 to 5 months were positively associated with BMI, fat mass index (FMI) and fat-free mass index (FFMI) at 3 years. Full breastfeeding for 6 months (compared to less than 1 month) eliminated the effect of early growth (P = 0.01). Full breastfeeding for 6 months (compared to less than 1 month) also eliminated the positive relation between BWZ and FMI (P = 0.009). No effect modification of infant feeding was found for FFMI. In conclusion, high birth weight and rapid growth from 0 to 5 months were associated with increased FMI and FFMI at 3 years. Longer duration of full breastfeeding reduced the effect of birth weight and early weight gain on fat mass.

Infant BMI peak, breastfeeding, and body composition at age 3 y.

BACKGROUND: With the increasing focus on obesity, growth patterns in infancy and early childhood have gained much attention. Although the adiposity rebound has been in focus because of a shown association with adult obesity, not much has been published about the infant peak in body mass index (BMI). OBJECTIVE: This study links age and BMI at infant peak to duration of breastfeeding and body composition at 3 y of age. DESIGN: Frequent weight and height measurements for 311 Danish children in the SKOT (Complementary and Young Child Feeding - Impact on Short and Long Term Development and Health; in Danish) cohort were used to estimate BMI growth curves for the age span from 14 d to 19 mo by using a nonlinear mixed-effects model. BMI growth velocity before peak and age and BMI at peak were derived from the subject-specific models. Information about pregnancy and breastfeeding was assessed from background questionnaires. Assessment of body composition at age 3 y was made based on bioelectrical impedance, weight, and height. RESULTS: A longer duration of exclusive breastfeeding was associated with an earlier peak in infant BMI (P = 0.0003) and a lower prepeak velocity (P < 0.0001). BMI level at peak and prepeak velocity was positively associated with fat and fat-free mass at age 3 y (all P < 0.0001), whereas a later age at peak was associated with a lower fat mass, fat mass index, and fat-free mass index at age 3 y (all P < 0.001). CONCLUSIONS: BMI peak characteristics are strongly associated with both duration of exclusive breastfeeding and body composition at 3 y of age. Thus, a better knowledge of characteristics and determinants of the early BMI peak is likely to improve our understanding of early development of obesity.