This study investigated the profile of language abilities in a sample of high‐achieving English speaking adults with developmental disorders. Ninety‐seven adult participants were recruited: 49 with a dyslexia diagnosis (dyslexic group), 16 with a diagnosis of a different developmental disorder including dyspraxia, autism and SpLD  and 32 with no diagnosis (non‐disordered group). Dyslexic and non‐dyslexic developmental disorder groups demonstrated similar impairments across measures of word reading, working memory, processing speed and oral language. Dyslexic participants showed the usual pattern of impaired phonological skills but spared non‐verbal intelligence and vocabulary. There were also some suggestions of impaired structural oral language skills in this group. A data‐driven clustering analysis found that diagnosis was not a reliable predictor of similarity between cases, with diagnostic categories split between data‐driven clusters. Overall, the findings indicate that high‐achieving adults with developmental disorders do demonstrate impairments that are likely to affect success in higher education, but that support needs should be assessed on a case‐by‐case basis, rather than according to diagnostic label. Disorders that come under this term include developmental dyslexia, developmental language disorder , dyspraxia , autism and attention deficit hyperactivity disorder (ADHD).Although such disorders are first recognized early in development and can in many cases improve with age, they often have persistent effects into adulthood. Large amounts of research have gone into characterizing the profiles of impairment in children with developmental disorders, but few studies have followed cases through to adulthood. This limits our understanding of how developmental disorders may continue to have detrimental effects in adults, even in those who appear to be “high achieving.” Furthermore, it is unclear to what extent different diagnostic labels, applied in the context of measures taken in childhood, have lasting validity in adulthood.The current study aimed to provide some insights into the nature of developmental disorders in adulthood, with a particular focus on language abilities. Two key questions were considered: (1) Do high achieving adults with diagnoses of developmental disorders demonstrate persisting deficits on measures of language and memory? and (2) Do diagnostic categories in adulthood correspond to discrete patterns of impairment?1.1Understanding the persisting areas of difficulty shown by adults with developmental disorders is relevant for their support needs in higher education. More and more adults with developmental disorders are entering higher education; in particular, there have been large increases in the number of dyslexic students entering University . According to self‐reports of handedness, the developmental disorder group consisted of 56 right handers, 8 left handers and 1 ambidextrous; and the non‐disordered group consisted of 30 right handers and 2 left handers. Exclusion criteria for both groups were significant hearing loss, history of neurological disease, head injury or epilepsy. All participants were native speakers of English and had normal or corrected to normal vision. Inclusion in the developmental disorder group did not rely on the application of psychometric criteria from collected data, but simply on self‐report from the participant of a formal diagnosis of a recognized developmental disorder  or specific learning difficulties (SpLD). Most participants were current students enrolled on a university or college of higher education course (82 participants); all participants had been through higher education. This was therefore a high achieving sample. Recruitment was predominantly via disability services at local institutes of higher education for the developmental disorder group, and via an online participant recruitment system for the non‐disordered group.Participants were 97 adults , 65 of whom had a prior diagnosis of a developmental disorder  and 32 of whom had no such diagnosis (non‐disordered group). The two groups did not significantly differ in age . Individuals in this latter category did not reach criteria for any specific diagnosis such as dyslexia, but had been diagnosed by their university disability advisory service as having specific processing difficulties relative to other aspects of cognition, such as slower audio‐visual processing.According to self‐report questionnaire data from the 65 individuals in the developmental disorder group, 50.8% of this sample had received special help or learning support while at school or university. Only 9.2% of the developmental disorder group reported having received speech and language therapy. A family history of similar problems was reported in just over half this group, with 52.3% reporting they had a family member with a history of speech, language, reading or communication disorder. The majority of the participants received their diagnosis while at school (64.6%), with the rest being diagnosed at university (except for one participant diagnosed through work).Ethical approval for this study was granted by the Research Ethics Committee at the University of Oxford . The experiment was undertaken with the understanding and written consent of each participant.2.2Data was collected as part of a larger study of language lateralization  electronic data capture tools . The following sections will describe statistical analyses and report results for each of the three study aims in turn.All analyses were conducted using R software , and participants with other diagnoses . This latter group was mainly composed of individuals with a dyspraxia diagnosis (n = 10), plus four individuals with autism and two with SpLD. Due to the limitations on statistical power imposed by the relatively small sample size in this non‐dyslexic developmental disorder group, mean scores across measures in the three groups are simply presented and discussed qualitatively, without quantitative statistical analyses. In addition, standardized scores were used to compare the performance of each group to that of the normative samples provided by the tests. This was done to demonstrate the extent to which the scores of the developmental disorder groups were in the impaired range relative to general population norms. The percentage of participants scoring below a specified threshold on each measure was calculated for dyslexic, non‐dyslexic developmental disorder and non‐disordered groups separately. A summary of the scores, normative samples and cut‐offs used for each measure is given in Table The first aim was to investigate the extent and nature of language impairments present in the developmental disorder group relative to the non‐disordered group. To consider differences between broad diagnostic categories, the developmental disorder group was further decomposed into participants with a dyslexia diagnosis . This step's purpose was solely to establish effect sizes for comparison with those reported in previous literature, rather than to determine statistical significance of differences.To investigate differences between the dyslexic and non‐disordered groups more specifically, we used a two‐step approach to determine which indicators showed a statistically significant difference. Firstly, we obtained effect sizes (Cohen's d) for differences between dyslexic and non‐disordered participants for each measure, using either parametric t‐tests or non‐parametric Mann–Whitney‐U tests, depending on the normality of each measure within each group. In the non‐parametric case, Cohen's d was calculated from the Q2, follows a chi‐squared distribution and thus can be compared with the chi‐squared critical value to test for significance. This non‐parametric multivariate test was run using the R package MNM , phonological short term memory/working memory (digit span), speed of processing (rapid naming), structural oral language  and sentence assembly (TOAL). In contrast, it was predicted that the two groups would not differ on measures of vocabulary (WASI), comprehension (ERRNI comprehension) and non‐verbal ability (Cattell).4.2Q2 = 36.29, df = 12, p < .001. Bootstrapped 95% confidence intervals revealed significant differences between groups on TOWRE word and non‐word reading, YAA‐R object rapid naming, digit span, NEPSY oromotor sequences and non‐word repetition. No significant differences were found for Cattell non‐verbal ability, WASI vocabulary, ERRNI comprehension, ERRNI MLU, TOAL sentence assembly, CC‐SR language structure, CC‐SR pragmatic skills or CC‐SR social engagement. A plot of these bootstrapped 95% confidence intervals can be found in the R markdown document for these analyses which is available on OSF (https://osf.io/bwsha/); significant differences were indicated by confidence intervals that did not overlap with zero.Standardized scores for dyslexic and non‐disordered groups across the 14 measures are given in Figure 55.1z‐scores based on the mean and standard deviation of the whole sample for this analysis.The aim of this analysis was to derive data‐driven sub‐groups from the language measures, and to examine whether these corresponded to diagnostic labels. To obtain data‐driven sub‐groups, an unsupervised machine learning (UML) approach was used on all 14 language measures. Interpretation of clusters can become difficult when including too many input variables for clustering, known as “the curse of dimensionality” . Firstly, a scree plot was constructed by iteratively fitting the k‐means clustering process over different values of k and calculating the average within‐cluster sum of squares, which were then plotted against each other. Typically, the optimal value of k is chosen by identifying the “elbow” within the scree plot. Secondly, we constructed a silhouette score plot. This provides a “cohesion” score (how similar an observation is to its own cluster). Clusters with higher cohesion scores are interpreted as showing a better overall fit. Finally, the gap statistic was calculated which compared the total intra‐cluster variation for different values of k against their expected values under a null distribution . The Dunn index is another measure of the internal clustering validation. It takes into account both the distance from points to their cluster centroid, and the distance between clusters. A larger Dunn index is optimal. The Rand index is a measure of the similarity of two data clusterings, and was used here to determine the level of agreement between groupings based on pre‐known diagnosis categories and groupings in the clustering solution yielded by the UML analysis. This index can thus be used to test how well the data‐driven groupings match up to the diagnostic labels. This index has a range from −1 (no agreement) to +1 (perfect agreement).Cluster models were compared using three fit indices: compactness, the Dunn index and the Rand Index. The 5.2k‐cluster solutions  to see which provided the best fit to the data. Figure k‐means model (plotting the first two discriminant functions).Firstly, we considered how many clusters should be included in the clustering model. The scree plot for the data Figure  was founk) produces clusters that are relatively distinct, with centroids that are well spaced from each other  indicated that the six cluster solution was the best among the four models at .319 . The Rand index also indicated that the six cluster solution provided a cluster allocation that was most similar to the diagnostic labels. It must be highlighted however that all solutions provided a poor correspondence to the diagnostic labels (no model exceeded .071 which is relatively weak evidence of any correspondence).Although there was no clear “winning” model that provided a consistently optimal fit to the data across these measures, to illustrate in more detail one possible cluster solution we now present the two‐cluster model. This model provided the best fit according to the Silhouette score plot. The make‐up of the two clusters derived by this approach in terms of diagnoses of the grouped cases is given in Figure z‐scores in the two clusters on the 14 language measures used in the clustering analysis are given in Table Mean standardized Overall, the results from this clustering analysis suggest that scores on the 14 language measures included in the current assessment battery did not show a cluster structure that corresponded well to the diagnosis categories present in the sample (indicated by the low Rand indices). Furthermore, none of the models provided a particularly good fit to the data (indicated by the low compactness statistics), suggesting that this data did not show a clearly defined cluster structure.k (plotting the first two discriminant functions). It can be seen that these cluster solutions are very similar to those seen for the full sample with k values of 2 and 3  would reveal a cluster structure showing a more well‐defined cluster structure and better agreement with groupings based on diagnostic labels. Therefore, we re‐ran this clustering analysis excluding cases with autism and SpLD. Figure 66.1This study aimed to examine the profile of impairment in a sample of high achieving adults with a history of developmental disorders. We showed that these adults were impaired relative to non‐disordered participants across a range of tests of language, memory and processing skills. When considering specifically those participants with a diagnosis of dyslexia, impairments were found on measures of single word reading, phonological skills, processing speed, working memory and structural oral language skills. Spared functioning was seen for measures of non‐verbal intelligence and vocabulary, in line with previous research , a prior diagnosis of a developmental disorder was associated with poorer language and memory skills relative to a comparable sample of university educated adults without such disorders. For both dyslexic and non‐dyslexic developmental disorder groups, similar levels of impairment were found for measures of word reading, working memory and processing speed (rapid naming). In particular, several individuals in the non‐dyslexic developmental disorder group scored below norm cut‐offs for rapid naming, with a very similar mean reading rate to dyslexics. This is consistent with a slower speed of processing in this group, which may underlie their poorer performance for word reading. Furthermore, this group demonstrated signs of poorer oral language skills compared to both non‐disordered cases and dyslexics on measures that relate to everyday use of language . The heterogeneous nature of this group makes it difficult to draw conclusions specific to particular diagnoses; however, given the high number of dyspraxic individuals in this group, this provides some insight into the types of difficulties that may be associated with this disorder in adulthood.Some caution needs to be exercised however when considering normative cut‐offs for a few of the measures, given some instances of high percentages of non‐disordered participants scoring below cut‐offs; notably on the TOAL sentence assembly task and CC‐SR language structure measure. This highlights the dangers of relying on test norms to identify impairment, especially for tests that were normed on populations that are very different in age, background or geographical location. For a more realistic estimate of impairment profile, we need well‐matched groups of non‐disordered and disordered participants from the same population, as was done in the current study (the majority of participants across these two groups were students of a similar age from the same university).Bearing that caveat in mind, comparisons with test standardization norms indicated that a substantial proportion of both developmental disorder groups demonstrated skills within the normal range when compared to standardized test norms based on more representative population samples. This confirms that the current sample was indeed a highly functioning sample compared to the general population, and was not severely impaired in terms of everyday functioning. Nevertheless, relative to the well‐matched non‐disordered group, these adults with developmental disorders still show persisting areas of weakness. This indicates that success in entering higher education is not always associated with complete resolution of difficulties experienced in childhood. These individuals thus likely still require additional support in higher education in order to attain a level of academic success that is on par with that of their unaffected peers.6.3Analyses comparing individuals with a diagnosis of dyslexia to non‐disordered cases yielded results that were in good agreement with previous literature. As expected, impairments were demonstrated on measures of phonological skills (non‐word reading and non‐word repetition), rapid naming and working memory. Interestingly, impairments were also demonstrated on measures of structural oral language skills; moderate effect sizes based on univariate tests were found for ERRNI mean length of utterance and the NEPSY oromotor sequences sub‐test (the latter reaching significance in the multivariate test). Lower language structure composite scores on the CC‐SR were also apparent in the dyslexic group, reflected in the moderate percentage (36%) of individuals scoring below norm levels.The NEPSY oromotor sequences test is defined by the NEPSY manual as assessing “oromotor coordination,” and so can be characterized as a test of articulatory oral language skills. However, it is important to note that this test does also place additional demands on other processes, such as phonological short‐term memory, cognitive monitoring and attention. It is therefore possible that poorer performance on this test could reflect impairments in one or several of these other processes, apart from problems with articulatory oral motor skills.Impairments in structural language skills have however been reported in some previous studies of adults with dyslexia. A recent study by Wiseheart and Altmann  using a Conversely, our study found that other aspects of oral language skills were spared in our sample of adults with dyslexia. Consistent with previous research, vocabulary skills were found to be unaffected  that provided an optimal fit to the data. This suggests that, based on scores from the current broad battery of language measures, this sample did not show a clearly defined cluster structure. For illustrative purposes, we presented a two‐cluster model to demonstrate how the data‐driven clustering would divide up the sample. Interestingly, this split the sample into a poorly performing group and a typically performing group, with different diagnoses scattered across the two clusters. Thus, in this cluster solution the difference between clusters was simply one of severity, rather than test profile. This has similarities with a clustering analysis presented by Dyck et al.  from other cases may be due to the absence of certain measures more specifically relevant to dyslexia. In particular, measures of phonological awareness, spelling and writing could have increased the sensitivity of the clustering analysis for distinguishing cases of dyslexia from the other categories. Their omission reflects the fact that this test battery was not designed to be specifically tailored to dyslexia, but rather to address a broad range of language functions that may be differentially affected in different disorders. Selection was thus constrained by the need to compile a comprehensive battery that would not be too lengthy to administer.A further limitation of the study is the small number of participants in diagnosis categories other than dyslexia, with only 10 cases of pure dyspraxia, 4 of autism and 2 of SpLD. This limited our comparisons of the non‐dyslexic developmental disorder group with the other groups to be descriptive rather than statistical. The sample was recruited predominantly through university disability advisory services with the aim of collecting a sample with a broad range of language, literacy and communication skills rather than with regard to specific diagnoses. The over‐representation of dyslexia in the resulting sample was thus unexpected and not reflective of skewed recruitment sources.Lastly, it should be acknowledged that this sample of adults with developmental disorders is likely to be a highly skewed sample. Recruitment was predominantly via the Disability Services at the University of Oxford, resulting in a sample with a large proportion of high achieving adults with developmental disorders. The findings with this sample may therefore not be representative of samples from other institutes of higher education. It should be further pointed out that adults with a history of learning disabilities who reach higher education are in general likely to be a highly selective sample compared to the general population with learning difficulties. For example, a recent study by Chatzitheochari and Platt  reported7The findings presented in this paper provide insight into the profile of impairments demonstrated by high functioning adults with a history of developmental disorders. This adds to the growing body of evidence suggesting that although these individuals can compensate for their difficulties to a point that enables them to achieve academic success in higher education, they still experience areas of weakness that are likely to put them at a disadvantage during their studies. Difficulties with reading familiar and unfamiliar words as well as limitations in working memory in students with dyslexia would be expected to both hinder learning from written material (inherent to most courses of study) and result in poorer performance under timed exam conditions. This suggests that the common practise of allowing dyslexic participants extra time in exams is justified. In addition to these well‐established difficulties, impairments in structural oral language skills in adults with dyslexia and other related disorders as demonstrated here deserve more attention in future research. The preliminary evidence presented in the current data‐driven clustering analysis with this relatively small sample suggests that the type of support given to an adult student with a developmental disorder should be based on their individual needs, rather than their diagnostic label.Data S1 Supporting informationClick here for additional data file.


The 2020 Atlantic hurricane season was extremely active and included, as of early November, six hurricanes that made landfall in the United States during the global coronavirus disease 2019 (COVID‐19) pandemic. Such an event would necessitate a large‐scale evacuation, with implications for the trajectory of the pandemic. Here we model how a hypothetical hurricane evacuation from four counties in southeast Florida would affect COVID‐19 case levels. We find that hurricane evacuation increases the total number of COVID‐19 cases in both origin and destination locations; however, if transmission rates in destination counties can be kept from rising during evacuation, excess evacuation‐induced case numbers can be minimized by directing evacuees to counties experiencing lower COVID‐19 transmission rates. Ultimately, the number of excess COVID‐19 cases produced by the evacuation depends on the ability of destination counties to meet evacuee needs while minimizing virus exposure through public health directives. These results are relevant to disease transmission during evacuations stemming from additional climate‐related hazards such as wildfires and floods. Hurricane evacuation increases the total number of COVID‐19 cases in both the origin and destination counties of evacueesWe can minimize excess COVID‐19 cases by directing evacuees to counties with low COVID‐19 transmission ratesMinimizing evacuation‐related COVID‐19 cases will also require keeping virus transmission rates low in evacuees' destination counties Mathematically, the transmission dynamics in the origin and destination are depicted by a susceptible‐exposed‐infected‐recovered (SEIR) model. We simulate the disease transmission as a stochastic Markov process using the following equations:During evacuation, we assume a fraction , In model simulations, we fixed the following parameters in Equations The objective of this two‐county modeling exercise was to explore model parameter space and determine which combinations of parameters would produce the most favorable outcomes. We varied six parameters to generate a large number of parameter combinations for use in model simulation: 2.3Following the two‐county model analysis, we conducted in‐depth COVID‐19 simulations using a nation‐wide metapopulation SEIR model representing all 3,142 U.S. counties   to inform declines of intercounty movement on a county‐by‐county basis. We generated these estimates using data from SafeGraph  than the baseline scenario if evacuees are directed to counties with lower (higher) transmission rates. However, as the transmission rate in the destination counties increases, the differences between the low and high scenarios become less pronounced. This result indicates that the benefits of a directed evacuation would be amplified by more stringent control efforts in destination counties.4.3The model simulations for our hypothetical evacuation scenarios indicate that a strategic evacuation plan could reduce excess COVID‐19 infections. However, these scenarios neither accounted for the accommodation capacities of destination counties nor provided a framework for optimizing evacuation plans.To address these issues, we developed a greedy search optimization algorithm aimed at minimizing total excess COVID‐19 cases by strategically assigning evacuees to optimal destination counties. As indicated by the two‐county model simulations, evacuating individuals to destinations with low ily; and  we presc section .We repeated this evacuation optimization with three different settings: no increase, 10% increase, and 20% increase of transmission rates in destination counties, again to reflect differences in control efforts and accommodation type. We assumed that 10% of evacuees will maintain their original destination and are thus not redirected and that each destination has a capacity of 120% of the evacuees accepted in the baseline scenario. We then generated the optimized evacuation plan for each setting. During the optimization process, assigning more evacuees to low‐o Figure . The opto Figure . In geneo Figure . The redo Figure .5The results of this study have far‐reaching consequences not only for hurricane evacuation this season but also for long‐term U.S. hurricane preparedness and evacuation planning.Research suggests that people rely on past experiences when choosing their evacuation routes and destinations (Mesa‐Arango et al., This research shows that the magnitude of the impact of evacuation on COVID‐19 caseloads is highly dependent on conditions in destination counties. The degree to which counties are prepared to host, isolate, and meet the needs of evacuees while also minimizing virus exposure through public health directives such as social distancing and mask wearing will be a key determinant of the impact of evacuation on COVID‐19 case numbers. Preparedness within destination counties is particularly important because, as this analysis shows, destination counties will bear the brunt of the excess COVID‐19 cases that result from an evacuation event. Destination counties must be aware of the influx of evacuees should it occur and must be allocated the financial and human resources needed to ensure the safety of both their residents and the evacuees they are sheltering. Assurances that officials have the necessary resources and procedures in place to keep both the evacuees and the local population safe during the evacuation period could increase the willingness of destination counties with low transmission rates to take in evacuees from origin counties with higher transmission rates.The U.S. response to the COVID‐19 pandemic has varied widely from state to state and from county to county. As a result of policy, communication, and ideological differences, compliance with mask wearing, for example, has varied substantially even within a given state (Katz et al., Centuries of systemic racism in the United States have left Black, Native American, Latinx, and other non‐White people with both higher exposure to and fewer resources to cope with environmental or health‐related stressors compared with White populations (Bell & Ebisu, Due to systemic health inequities, including higher exposure to air pollution (Bell & Ebisu, This study has used idealized scenarios to model hurricane evacuation patterns. These scenarios cannot fully capture many household‐level choices that could alter levels of social contact—and therefore potential COVID‐19 exposure—during the evacuation period. For example, previous studies of hurricane evacuations along the U.S. Southeast and Gulf Coasts show that evacuees strongly and consistently prefer to stay with friends and family over going to hotels/motels or public shelters (Bian et al., As a result of the ongoing economic and physical toll of the pandemic, household‐level decision making regarding evacuation may differ from that of past years. There are many sociodemographic factors associated with decision making around evacuation including experience with past hurricanes, length of residence, home ownership, age, income, race, employment status, level of social connectivity, social cues, perceived levels of self‐efficacy and risk, and storm conditions (Collins et al., The movement of people in and out of hurricane‐affected counties does not simply cease after all evacuees have returned to their homes. For instance, communities affected by hurricanes often experience an influx of workers who assist with rebuilding and recovery efforts Fussell, , which cCritically, hurricane evacuation is intended to save lives and prevent serious injuries to residents of hurricane‐prone regions. While this study evaluates excess COVID‐19 cases resulting from evacuation, it does not evaluate non‐COVID‐19 related risks to human health and lives in the event that people choose to remain in their homes despite receiving evacuation orders—risks that could increase if people are afraid to evacuate out of concern for contracting COVID‐19. Nor does it address evacuations of hospitals, nursing homes, prisons, or other facilities. It will be critical for emergency managers to factor in these—and other—complexities when developing plans (Wong & Shaheen, Finally, the results presented here are based on scenarios that, while plausible, are strictly hypothetical. While the overall notion that distributing evacuees to destination counties with low transmission rates minimizes excess cases should theoretically apply to geographies outside Florida or the United States, additional model simulations of such scenarios should be generated. Moreover, while this study models evacuation specifically from a hurricane, the same notion should apply to evacuations from other geophysical hazards such as wildfires and floods, though evacuations from such hazards tend involve fewer people than hurricane evacuations.The data presented here show that while a large‐scale hurricane evacuation would increase the total number of COVID‐19 cases in the United States, directing evacuees to plausible destination counties with low COVID‐19 transmission rates would minimize the excess cases induced by the evacuation event. These results have far‐reaching implications for immediate emergency management and communications practices, as well as long‐term disaster preparedness.Faced with the prospect of tens of thousands of additional cases arising from a hurricane evacuation, states and counties at both ends of evacuation routes must be allocated the necessary financial and human resources required to meet evacuees' needs while also ensuring community safety and health through measures intended to reduce COVID‐19 transmission rates. Further, resource distribution must prioritize the nation's most vulnerable groups.J. S. and Columbia University disclose partial ownership of SK Analytics. J. S. also discloses consulting for Merck and BNI.Supporting Information S1Click here for additional data file.


Integr Environ Assess Manag 2020;16:472–480. © 2020 Syngenta Crop Protection AG. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC)The European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation requires that quantitative environmental risk assessment is carried out for hazardous substances used as coformulants in plant protection products (PPPs), if registered above 10 t/y. The European Crop Protection Association (ECPA) has developed generic exposure scenarios and specific environmental release categories (SpERCs) to support these risk assessments. The SpERCs offer refinements to the default release factors defined in environmental release categories (ERCs) and are intended to be used with nested multimedia mass balance models as part of the assessment of regional predicted environmental concentrations. Based on the application method of PPPs, 2 scenarios were defined for which SpERCs were developed: 1) spraying of PPPs and 2) direct application of granular products or treated seeds to soil. The SpERC for spray applications includes release factors to air and soil that depend on the vapor pressure of the coformulant. Calculations are presented to support the subSpERCs describing the transition from nonvolatile to volatile behavior. The most recent version of the spray application SpERC defines a release factor for surface water and more conservative release factors to soil compared with previous versions. Use of the ECPA SpERCs allows the coformulant emissions from PPPs to be fully accounted for in the regional‐scale environmental risk assessment for a given substance, along with all other sources of emissions. Qualitative and quantitative justification for the ECPA‐derived SpERCs is presented and serves as the background documentation to the online European Chemicals Agency (ECHA) SpERC factsheets. The approach developed here whereby regional‐scale SpERCs are used in combination with a customized local‐scale exposure model is potentially applicable for other sectors that are required to conduct exposure assessments outside the scope of the standard environmental REACH models.  Specific environmental release categories (SpERCs) have been developed to support the environmental risk assessment of coformulants used in plant protection products under the European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation.The SpERCs were developed for 2 scenarios: spraying of plant protection products and direct application of granular products or treated seeds to soil.Use of these SpERCs allows the coformulant emissions from plant protection products to be fully accounted for in the regional‐scale exposure assessment, along with all other sources of emissions. European Regulation (EC) Nr 1907/2006  EC .The environmental risk assessments performed for REACH typically utilize a local‐scale model and nested multimedia mass balance models for the regional and continental scales ECB , referreUse descriptors  have been developed by the European Chemicals Agency (ECHA) for grouping activities from the environmental perspective ECHA . Each calocal. The approach developed here whereby regional‐scale SpERCs are used in combination with a customized local‐scale exposure model is potentially applicable for other sectors required to conduct environmental exposure assessments outside the local‐scale scope of the EU TGD models.In response to the ERC release factor conservatism, several industry sector organizations have published specific environmental release categories (SpERCs), taking into account the operational conditions and risk management measures relevant for use of substances in those sectors , and aims to serve as the more detailed background documentation required for the summary SpERC factsheets hosted by ECHA in the Use Maps Library ECHA .Specific environmental release categories can be justified in different ways  use of substances as coformulants in PPPs  is often used as a trigger value, where the concern is the potential for significant atmospheric concentrations over a short duration (minutes to hours) . This USES module was an extension to the standard EU TGD, and was used for environmental assessments by the Dutch competent authority under previous national legislation for PPP registrations. The Fair values in USES 4.0 were derived from the averaged 24‐h emission strength, based on a 1 kg/ha application of a substance, assuming 90% of the emission occurred in the first day. Evaporation of volatile components, and coevaporation of less volatile components, from the very high surface area of a spray mist is rapid  4.0  and VP is vapor pressure (Pa). The pooled data set spanned a vapor pressure range of 0.0001 to >30 000 Pa, and was based on evaporation of pure substances and mixtures from plant, soil, and laboratory studies at ambient relative humidity (30%–50%). The vapor pressure range is particularly relevant for coformulants, whose vapor pressure ranges are often higher than active substances.A relation derived by van Wesenbeeck et al.  from a d2), giving a final data set of 166 values.A data set was constructed to investigate the real‐world vapor pressure distribution of coformulants from 3 sources: 42 predominantly hazardous coformulants identified in an unpublished survey as being on the Irish market; a 180‐coformulant subset of the coformulants reported as being used in formulations approved for the German market; and an individual company data set of 96 substances. From this list, vapor pressure values were assembled from the ECHA REACH registration dissemination pages. Substances with no data, implausible values, or polymers were discarded, and based on chemistry 18 inorganic substances were assigned to the lowest vapor pressure band  ditch and stream scenarios, which are most relevant for regional‐scale exposure, assume that each 1‐ha agricultural field is adjacent to a 1‐m wide, 100‐m long water body FOCUS . These ssurface water release factors. Only direct emission that may result from spray drift is considered to be relevant to the ECPA SpERC release factor describing PPP‐related emission to surface water.The EU TGD explicitly models runoff from soil to surface water at the regional and continental scale. Because emission to soil must occur first, and runoff from treated areas is dictated by a heavy rain event and is thus temporally separated from the initial spray emission, runoff should not be considered to contribute to the FThe realistic worst‐case spray drift, expressed as a percentage of the application rate, was assumed to be 15.7%, compared with the 2.8% expected for the majority of arable crops FOCUS . This reA literature search was carried out for mass‐based quantification of spray drift losses to surface water in catchments to check the plausibility of the derived release factor for surface water.The coformulant maximum annual application rate  can be considered an operational condition in REACH terminology ECHA . Risk maThe distribution of the vapor pressures of the investigated coformulants is shown in Figure air, proposed in USES 4.0, were adopted as the starting point for developing vapor pressure dependent subSpERCs because they had preexisting regulatory acceptance . The vapor pressure range encompassed by the USES 4.0 variable release factors is that expected to span the transition from nonvolatile to volatile behavior of substances in the environment  , but unlike spraying this would be considered the normal scenario. As a result, full emission to the soil compartment was assumed, including for substances with vapor pressure ≥0.01 Pa, should they be used (Fsurface water = 0), following the FOCUS guidelines  are not available. The emission scenario document for plastic additives suggests that for powders of particle size >40 µm, 0.01% could be expected to remain in packaging OECD . Based oThe latest ECPA SpERCs for application of granules and spray application of PPP formulations were designated 8d.1.v4 and 8d.2.v4, respectively, following the standardized naming nomenclature, derived from ERC 8d ECHA . Both arair is usually not available) and only indirectly influences the PECregional in the other compartments via redeposition, mass balance and conservatism were maintained by reducing Fair rather than Fsoil. Despite the conceptual improvement, because of the conservative approach used to model runoff in the EU TGD, only very small changes to the calculated PECregional have been seen in practice, compared to the preceding versions that assume no direct emission to surface water.The first versions of the ECPA SpERC set the release factor to surface water to 0, on the basis that PPP approvals under EU Regulation (EC) Nr 1107/2009 include specific labeling instructions designed to prevent emission to waste and surface water, and intentional release would be noncompliant with that legislation   and therefore needs to be carried out with a suitable alternative, for example, the ECPA LET model. Depending on the EU TGD implementation, the local release factor should be manually set to 0 , or the calculated PEClocal simply discarded if this is not possible. The latest version of Chesar (version 3) automatically disables the calculation of PEClocal when “agricultural soil” is set  or with an equivalent  or smaller release factor . Where there is uncertainty associated with the type of indoor application, the guidance on risk assessment for PPPs in the EU generally assumes that the risk assessment for field uses represents the worst case EFSA . The chaThe 2 ECPA SpERCs do not currently differentiate professional and consumer uses of PPPs. In terms of tonnage, it was anticipated that the consumer and/or amenity use made up only a small portion of the total use of PPPs. An analysis of the usage of pesticides in the United Kingdom (UK) in the year 2012, for example, showed that amenity use accounted for less than 4% of the total pesticide use in the UK Goulds . TherefoThe ECPA SpERCs do not assume the use of any specific RMMs. Risk management measures such as buffer strips, although possible to specify as a justification to further reduce spray drift (and hence the surface‐water release factor), would inevitably conflict with the use instructions of existing authorized products, which did not specify these for certain formulations or crops. Considering the extremely conservative assumptions made in deriving the release factors to surface water, such additional RMMs would not be meaningful. Product labeling is assumed to be followed for waste disposal, and any intentional PPP release to surface water or a sewage treatment plant is clear product misuse.In general the ECPA SpERCs have traded uncertainty for conservatism by selecting the worst‐case values. This offers the possibility of further refinement for substance‐specific risk assessments, provided appropriate justification can be given.air could be refined on the basis of vapor pressure, particularly for substances that fall in the transition from nonvolatile to volatile. Similarly, Fsoil could benefit from further information on crop interception. In both cases it would, however, be expected that the need for refinements to the risk assessment would be driven by the LET and PEClocal rather than by PECregional.On a case‐by‐case basis, Fsurface water at the regional scale suggested little benefit was to be gained from further refining surface‐water release factors, in terms of obtaining less conservative estimates for PECregional. It must be emphasized that the values for Fsurface water presented here (Table For the ECPA SpERCs, further refinements could be envisaged to justify a smaller release factor to surface water on the basis of land‐use patterns through spatial modeling, for example, taking into account crop type, associated drift rates, and proximity to surface‐water bodies. Different release factors for regional and local scales could be conceived reflecting regional‐scale crop diversity and local‐scale homogeneity. However, empirical testing of the sensitivity of the EU TGD model to Fre Table  are a fiThe ECPA has developed 2 sector‐specific SpERCs to aid in the regional‐scale risk assessment of substances used as coformulants in PPPs. One SpERC covers formulations that are applied in granular form or treated seeds. The second covers formulations that are applied as sprays and takes account of the vapor pressure of the substance. Both SpERCs provide release factors that reflect the typical application methods of PPPs and the substance properties that are used in these formulations. Calculations have supported the spray application subSpERC transition from nonvolatile to volatile behavior, based on vapor pressure. Use of the ECPA SpERCs allows the contributions from coformulant emissions from PPPs to be fully accounted for in the REACH regional‐scale risk assessment of a given substance, and the regional background from other nonPPP uses to be accounted for in a local‐scale assessment of the use as a coformulant. The use of SpERCs limited to the calculation of regional‐scale PECs with EU TGD–based models, in combination with a customized local‐scale exposure model, is a potentially useful approach for other sectors looking to address scenarios that lie outside the scope of the standard EU TGD.The ECPA SpERCs were developed by an expert group formed from the member companies of the European Crop Protection Association and tasked with developing a methodology for assessing coformulants under REACH. Cambridge Environmental Assessments received direct financial support for the development of the LET model. All other authors participated in the expert group during the normal course of their employment. The ECPA member companies are commercial entities which produce and market products that are subject to regulation by REACH. The authors have responsibility for the writing and contents of the manuscript, and the views expressed in this article are those of the authors and do not necessarily represent the views or policies of the ECPA, or their respective employers.Table S1a. USES 4.0 pesticide module release factors to airTable S1b. ECPA SpERC Version 2Table S1c. ECPA SpERC Version 3.This article contains online‐only Supplemental Data.Supporting informationClick here for additional data file.


Radiotherapy has been optimized over the last decades not only through technological advances, but also through the translation of biological knowledge into clinical treatment schedules. Optimization of fractionation schedules and/or the introduction of simultaneous combined systemic treatment have significantly improved tumour cure rates in several cancer types. With modern techniques, we are currently able to measure factors of radiation resistance or radiation sensitivity in patient tumours; the definition of new biomarkers is expected to further enable personalized treatments. In this Review article, we overview important translation paths and summarize the quality requirements for preclinical and translational studies that will help to avoid bias in trial results. Translation of preclinical radiobiological research into clinical radiotherapy has largely improved treatment outcomes. Indicatively, based on the results of current biological research, fractionation schedules have been optimized, and combined treatments have been developed. In this Review, we discuss translation paths that have substantially improved radiotherapy and summarize what quality requirements within preclinical and translational studies will ensure the development of unbiased clinical trials. CD44cluster of differentiation 44 (cell surface molecule)CXCR‐4C‐X‐C motif chemokine receptor 4DAHANCADanish Head and Neck Cancer GroupDKTKGerman Cancer ConsortiumEGFRepidermal growth factor receptorFAZA[18F]‐fluoroazomycin arabinoside (hypoxia PET tracer)FMISO[18F]‐fluoromisonidazole (hypoxia PET tracer)HNSCChead and neck squamous cell carcinomaHPVhuman papillomavirusPETpositron emission tomography11Application of radiotherapy not at once, but in several sessions over usually several weeks., including overall treatment time, dose per treatment fraction, and the interval between fractions, are considered in the design of fractionation treatment schedules,. With the advent of molecular techniques and methods to identify molecular markers, molecular or cell‐based treatment options evolved, and radiobiological and translational radiooncological research developed into new areas allowing for better characterization of tumours and the development of personalized treatments, not only through the modification of radiation parameters, but also through combined treatment approaches.Radiotherapy is, together with surgery and systemic treatments, one of the three main treatment options in oncology. Together with surgery, radiotherapy is one of the two treatment options that are able to cure solid tumours. The curative effect of radiotherapy depends on the inactivation of all cancer stem cells, that is, of those tumour cells that have an unlimited potential of cell division and thus can repopulate a tumour if surviving treatment. The potential of this subgroup of tumour cells has been described already many decades ago .The infection with oncogenic viruses has been identified to be involved in the development of various cancers. For head and neck squamous cell carcinomas (HNSCC), it has been shown that the infection with the human papillomavirus (HPV) is another risk factor for the development of the disease in addition to the well‐accepted risk factors smoking and alcohol consumption. Moreover, it has been shown in a number of preclinical and clinical studies that tumours that are driven by HPV are more radiosensitive than HPV‐negative tumours, which is associated with impaired DNA repair (reviewed in Lassen ). This s2.2et al., et al., et al., In vivo analyses have shown that pretherapeutic tumour hypoxia significantly impacts local tumour control after radiotherapy  or [18F]‐fluoroazomycin arabinoside (FAZA) as specific tracers for hypoxia imaging in positron emission tomography (PET) can be used in combination with computed tomography in patients with head and neck cancer or other tumour entities. In a prospective study on patients with locally advanced squamous cell carcinoma of the head and neck, a strong association of FMISO‐measured hypoxia after the first two weeks of treatment with local progression‐free survival after radiochemotherapy was demonstrated .In addition to the improvement of biological imaging, molecular investigations of tumour specimens for the assessment of hypoxia‐associated or hypoxia‐induced gene expression changes are being extensively carried out. Initial data suggest a predictive role of such hypoxia‐induced gene signatures for the outcome of patients with HNSCC and other tumours  (CD44) mRNA and CD44 protein expression, as putative stem cell markers on tumour material from patients with early laryngeal carcinoma, the intertumoural heterogeneity of the stem cell density as an important factor for local tumour control after radiotherapy could be shown for the first time in a clinical dataset  is crucially involved in this process. Overexpression of the EGFR is frequently identified in a variety of tumour entities, and several strategies have been developed to target EGFR in various tumours, including EGFR‐specific monoclonal antibodies (such as cetuximab) and tyrosine kinase inhibitors . Some clinical data have shown promising results for radiotherapy combined with EGFR‐targeting treatments: combined treatment of patients with locally advanced HNSCC with cetuximab plus radiotherapy significantly improved overall survival at 5 years, as compared to radiotherapy alone . If successful, this approach can provide a basis for reducing the currently high severe late toxicity rate in this group of patients while keeping cure rates constant.Setting up on the optimization of fractionation and combined treatments, individualization of radiotherapy is another important strategy to improve treatment outcome. An example is the evaluation of biomarkers for personalized radio(chemo)therapy in head and neck cancer by the ROG of the DKTK. Within this multi‐institutional retrospective–prospective study, a retrospective cohort of patients with primary or postoperative radiochemotherapy was set up. The multi‐institutional approach allowed very strong inclusion criteria, leading to a very high homogeneity of the patient parameters in the cohort. After centralized processing of tumour material, hypothesis‐based evaluation of multiple candidate biomarkers was performed by the participating centres. HPV positivity was confirmed as a strong biomarker for both primary and postoperative radiochemotherapy . This assay is time‐, labour‐ and cost‐intensive, but excludes many combined treatments by proving nonsuperiority over standard treatment despite promising tumour growth delay data  is available with recommendations for content that should be provided to insure unbiased information of the reader  .Publish negative data.et al., Consider ARRIVE guidelines for publication , Merck KGaA  and Medipan GmbH (2014–2018). She is involved in an ongoing publicly funded  project with the companies Medipan, Attomol GmbH, GA Generic Assays GmbH, Gesellschaft für medizinische und wissenschaftliche genetische Analysen, Lipotype GmbH and PolyAn GmbH (2019–2021). For the present manuscript, Dr. Krause confirms that none of the above‐mentioned funding sources were involved. Annett Linge is involved in an ongoing publicly funded  project with the companies Medipan, Attomol GmbH, GA Generic Assays GmbH, Gesellschaft für medizinische und wissenschaftliche genetische Analysen, Lipotype GmbH and PolyAn GmbH (2019–2021). For the present manuscript, Dr. Linge confirms that this above‐mentioned funding source was not involved. Jan Alsner is listed as co‐inventor on a patent for a method of determining clinically relevant hypoxia in cancer (WO/2012/146259) owned by Aarhus University, Aarhus, Denmark. The other authors confirm no conflict of interest.


To describe the effect that validation of venous thromboembolism (VTE) coded entries in the health improvement network (THIN) has on incidence rates of VTE among a cohort of rivaroxaban/warfarin users.Among 36 701 individuals with a first prescription for rivaroxaban/warfarin between 2012 and 2015, we performed a two‐step VTE case identification process followed by a two‐step case validation process involving manual review of patient records. A valid case required a coded entry for VTE at some point after their first rivaroxaban/warfarin prescription with evidence of referral/hospitalization either as a coded entry or entered as free text. Positive predictive values (PPVs) with 95% confidence intervals (CIs) were calculated using validated cases as the gold standard. Incidence rates were calculated per 1000 person‐years with 95% CIs.We identified 2166 patients with a coded entry of VTE after their initial rivaroxaban/warfarin prescription; incidence rate of 45.31 per 1000 person‐years (95% CI: 43.49‐47.22). After manual review of patient records including the free text, there were 712 incident VTE cases; incidence rate of 14.90 per 1000 person‐years (95% CI: 13.85‐16.02). The PPV for coded entries of VTE alone was 32.9%, and the PPV for coded entries of VTE with a coded entry of referral/hospitalization was 39.8%; this increased to 69.6% after manual review of coded clinical entries in patient records.Among rivaroxaban/warfarin users in THIN, valid VTE case identification requires manual review of patient records including the free text to prevent outcome misclassification and substantial overestimation of VTE incidence rates. Use of only coded clinical information in THIN database is insufficient to accurately identify incident cases of major venous thromboembolism (VTE).Manual review of patient records substantially increases the validity of VTE cases identified through algorithmic searches for coded diagnoses.The free text comments in THIN commonly provide clinical information important for valid case identification.A manual review process including scrutiny of the free text comments is a valid method to identify cases of VTE and avoid misclassification, especially to reduce false positives.Without manual review of free‐text comments in THIN, incidence rates of VTE will be substantially overestimated.1Rivaroxaban is one of several non‐vitamin K antagonist oral anticoagulants (NOACs) licensed in the United Kingdom for the treatment of venous thromboembolism (VTE), prophylaxis of VTE after knee/hip surgery, and prophylaxis of recurrent VTE.Databases of electronic health records (EHRs) are other appropriate sources to efficiently conduct rivaroxaban effectiveness studies. One such database—The Health Improvement Network (THIN)—has been used extensively for pharmacoepidemiological research. It holds the pseudo‐anonymized primary care EHRs of approximately 6% of the UK population,It is recommended that outcome identification using primary care databases such as THIN involves supportive evidence to validate the recorded diagnosis and avoid misclassification.22.1Details of the study cohort including the age and sex distribution have been published previously.2.2Our operational definition of the first VTE event recorded during follow‐up was a VTE event that led to a referral either to a specialist or to hospitalization, or was recorded in the primary care record as the cause of death. We did not restrict to cases with evidence of hospital admission because a previous VTE validation study in the Clinical Practice Research Datalink , found that approximately 20% of VTE cases confirmed by the PCP via paper questionnaires did not have a database entry indicative of hospital admission for the event.As shown in Figure 2.3In Step 3, we manually reviewed the coded entries in the primary care record of potential VTE cases identified in the previous step to confirm the diagnosis and that the referral/hospitalization was related to the VTE event. For potential cases retained after this step 3, we then requested and accessed free‐text comments in the patients' primary care record for further manual review and final confirmation of the VTE event. To undertake this review efficiently we only accessed free text comments entered in the 15 days either side of the event, as well as all those specifically attached to an entry of DVT/PE or to any entry of hospitalization or referral in the 16 to 180 days after the event. These comments often contain information on referrals and details from hospital discharge letters describing the clinical evaluation and tests performed , as well as information from death certificates. A first manual review of these free‐text comments was performed independently by one researcher (AR) to confirm VTE case status and to establish whether the text referred to a current event or to a previous event. Subsequently, potential cases were grouped according to three main characteristics: (a) the interval between the date of first rivaroxaban/warfarin prescription and the date of the recorded VTE, (b) the indication for the anticoagulation (VTE or other), and (c) whether it was a recorded hospitalization or a referral to a specialist that led to the case being confirmed. For cases where the status was not clear after this review, a second independent review was performed by another researcher (LAGR) and consensus on case status was reached through discussion.2.4For cases identified in each step, we calculated the incidence rate of VTE per 1000 person‐years with 95% confidence interval (CI) as the number of first VTE cases identified during follow‐up divided by the corresponding observed person‐years. The incidence rate of final confirmed hospitalized/referred VTE cases was also calculated according to referral/hospitalization case status. We calculated the positive predictive value (PPV) of each validation step, as the ratio of the number of participants with a confirmed diagnosis after final confirmation (step 4) to number of patients with a VTE diagnosis identified after each step, and expressed this as a percentage with 95% CIs. As a post‐hoc analysis, stratified analyses were performed calculating the confirmation rate according to age at the start of follow‐up , sex, indication for the first rivaroxaban/warfarin prescription, VTE diagnosis , time between the first rivaroxaban/warfarin prescription and VTE event during follow‐up, history of VTE , hospitalization/death or referral related to the VTE diagnosis, and specific VTE Read code entry.33.1During the automatic computer algorithm searches in steps 1 and 2, a total of 2166 patients were identified with a Read code for VTE, and 1790 (82.6%) of these had a Read code for a referral to a specialist and/or a hospitalization during the specified time period and were consequently deemed to be potential cases. A total of 1023 (57.2%) of these patients had the referral/hospitalization confirmed as being related to the VTE event after the first manual review validation process in step 3 (before accessing free text comments). Of these 1023 patients, 301 had the hospitalization confirmed as related to the VTE event, 708 had no confirmed hospitalization but were confirmed as having a referral to a specialist, and 14 patients had DVT recorded on the date of death. As shown in Figure 3.2Incidence rates of VTE and the PPV of cases identified at each step of the case identification process, using cases confirmed from the final step (step 4) as the gold standard, are shown in Table In stratified analyses, variation was seen in the confirmation rate across patient characteristics Table . Of all 4We have shown that the PPV of coded VTE entries alone among new users of warfarin/rivaroxaban in THIN primary care database was 32.9%. This increased only slightly to 39.8% when we required cases to also have a coded entry for a referral/hospitalization recorded close in time to the event. However, the PPV increased substantially to almost 70% when the patients' primary care records were manually reviewed enabling scrutiny of other coded entries relating to secondary care referrals, hospital discharge letters, and information originating from death certificates. Furthermore, 30% of cases retained after manual review of the patient's primary care record were not subsequently confirmed as true new cases following scrutiny of the free text comments, showing that this information is required to obtain cases with the highest level of validity. The incidence rate of VTE among our rivaroxaban/warfarin study cohort was overestimated three‐fold when using coded VTE entries alone compared with when using cases confirmed after manual review of the primary care record including the free‐text comments.Almost half of confirmed VTE cases in our study had no evidence of hospitalization for their event, indicating that there is a high level of outpatient management VTE in UK clinical practice. In the study by Lawrenson et al,We have previously shown the benefit of accessing and reviewing the data in free‐text comments to validate cases of major gastrointestinal and urogenital bleeding events among our cohort of anticoagulant users, with incidence rates overestimated more than two‐fold when this process was not undertaken.Manual review of patient records including the free‐text comments is costly and labor intensive, and requires the reviewer to have good medical acumen, yet we have demonstrated its importance in the study of VTE. Our findings are of increasing relevance because of the large number of post‐marketing VTE effectiveness studies currently being carried out among users of NOACs in healthcare databases. Furthermore, databases such as THIN are increasingly being used to generate such real‐world evidence for use in regulatory decision‐making and validation exercises will increase confidence in the use of observational data. If researchers do not have access to the free‐text comments in primary care databases such as THIN in studies of VTE, an acknowledgement of the level of misclassification and resulting bias should be made and taken into consideration when interpreting the results. Additionally, owners of databases such as THIN should be encouraged to make anonymized free‐text comments available for research purposes.The study protocol was approved by an Independent Scientific Research Committee for THIN (reference THIN14‐018). No individual patient consent was required because the study used de‐identified data provided by patients as a part of their routine primary care.LAGR and AR work for CEIFE, which has received research funding from Bayer AG. LAGR has also received honoraria for serving on advisory boards for Bayer AG. GB is a full‐time employee of Bayer AB. PV is a full‐time employee of Bayer AG.Table S1. Read codes for VTE.Click here for additional data file.


Habitat loss leading to smaller patch sizes and decreasing connectivity is a major threat to global biodiversity. While some species vanish immediately after a change in habitat conditions, others show delayed extinction, that is, an extinction debt. In case of an extinction debt, the current species richness is higher than expected under present habitat conditions.We investigated wetlands of the canton of Zürich in the lowlands of Eastern Switzerland where a wetland loss of 90% over the last 150 years occurred. We related current species richness to current and past patch area and connectivity . We compared current with predicted species richness in wetlands with a substantial loss in patch area based on the species‐area relationship of wetlands without substantial loss in patch area and studied relationships between the richness of different species groups and current and historical area and connectivity of wetland patches.We found evidence of a possible extinction debt for long‐lived wetland specialist vascular plants: in wetlands, which substantially lost patch area, current species richness of long‐lived specialist vascular plants was higher than would have been expected based on current patch area. Additionally and besides current wetland area, historical area also explained current species richness of these species in a substantial and significant way. No evidence for an extinction debt in bryophytes was found.The possible unpaid extinction debt in the wetlands of the canton of Zürich is an appeal to nature conservation, which has the possibility to prevent likely future extinctions of species through specific conservation measures. In particular, a further reduction in wetlands must be prevented and restoration measures must be taken to increase the number of wetlands. We found several lines of evidence for a possible unpaid extinction debt for vascular plants in the wetlands of the canton of Zurich, that is, historic wetland area is still reflected in today's wetland specialist species richness but not in the species richness of generalist species or bryophytes. Specialist species which are in the focus of nature conservation management may thus vanish from wetlands in the future. Increasing the area of wetlands should be a main focus of the nature conservation agencies to prevent future extinctions. The canton of Zürich has a very high population density  at elevations between 350 and 950 m a.s.l.  all vascular plant species; (2) wetlands specialists among vascular plants; (3) generalists, which were all nonspecialist vascular plant species; (4) short‐lived vascular plant specialists; (5) long‐lived vascular plant specialists; (6) short‐lived vascular plant generalists; (7) long‐lived vascular plant generalists; (8) bryophyte species. Specialist vascular plant species included all characteristic species listed in Appendix of the wetland inventory of Switzerland BUWAL, . In the 2.5r = .83). The area of focal wetland patches (patch area henceforth) was only moderately correlated with buffer area in all time periods (r between .20 and .45).To check for collinearity among connectivity variables, we correlated them in a pairwise way using Pearson correlation coefficients for 1850, 1900, 1950, and 2000, respectively, in R. Many connectivity variables were highly correlated. We selected wetland area within a buffer of 2 km with starting point at the center of a focal wetland patch (buffer area henceforth) for our models, because it was highly correlated with all other connectivity variables in the different time periods  for the species richness of specialists, the species richness of short‐lived specialists and the species richness of long‐lived specialists in all four time periods (Table SD 7.04%) on average. The species richness‐area relationship in these patches thus remained relatively stable, that is, species richness is consistent with an equilibrium conclusion. We therefore used the regression of current species richness on current patch area (both variables log‐transformed) in the constant‐area or equilibrium wetlands to predict species richness in the area‐reduced wetlands  was significantly different between the two wetland types  vascular plant species and of 27.2 (±1.3 SE) bryophyte species was found per wetland. Generalist vascular plant species were slightly more frequent (37.7 ± 1.7 SE) than specialist vascular plant species (32.5 ± 1.4 SE).In total, we found 567 species in the 55 wetlands studied in the canton of Zürich Table . An averR2‐values of multiple regression models with the independent variables patch area and buffer area for specialists were higher in all time periods than those of other species groups. Models explained up to 39% of the variation for current short‐ and long‐lived specialist species richness . In contrast, species richness of generalists was not affected by current patch area Tables  and A3. R2‐values of single regressions of species richness against patch area were much lower for generalists than for specialists . This means that the mean slope of the regressions of specialists on patch area of the area‐reduced wetlands was shallower than the slopes of the constant‐area wetlands. For long‐ and short‐lived generalists, the difference between regression slopes (constant‐area wetland minus area‐reduced wetland) was not significant .Current species richness of all specialists and of long‐lived specialists in wetlands, which strongly decreased in patch area, were significantly more often above the regression line of the species‐area relationship based on constant‐area wetlands than below it Table . This me4Our study provided several lines of evidence consistent with a possible extinction debt in the wetlands of the canton of Zürich although current habitat area and connectivity explained species richness best: (1) current species richness of wetlands with a substantial loss in area was significantly higher than expected for long‐lived specialists; (2) regression slopes for long‐ and short‐lived specialist species richness were shallower in area‐reduced wetlands than in constant‐area wetlands; (3) historical patch area and to a lesser degree also historical connectivity measured by buffer area explained significant amounts of variation in current species richness especially for long‐lived specialist species, despite low correlations between patch area and buffer area among the different time periods studied. Therefore, we assumed an unpaid extinction debt especially for long‐lived specialist vascular plant species and to a weaker degree also for short‐lived vascular specialists of wetlands. Without the implementation of adequate conservation measures, future extinctions among these species in individual wetlands are expected to occur .The connectivity variable buffer area in 2000 showed a weaker relationship to current species richness than patch area in most species groups. However, current richness of long‐lived species and buffer area in 2000 were strongly related which points to the importance of current connectivity for these species. Functional connectivity is only strongly decreased if suitable area in a landscape is reduced to 10%–20% of its original area Fahrig, . In factR2 value was low. The positive effect of current patch area and the lack of effects of historical patch area lead to the conclusion that there was no extinction debt for bryophytes. This is supported by the lack of systematic positive deviations of the species richness of area‐reduced wetlands from that of constant‐area wetlands in bryophytes. Local extinctions of bryophytes may thus have already occurred. Fast extinctions of bryophyte species in habitat remnants have been shown for epiphytes  that vanished from the wetlands of Zürich in the early 20th century (Hofmann et al., Bryophyte species richness was not affected by an extinction debt in our study: historical patch area was never significantly related to current bryophyte species richness. Current patch area, however, affected bryophyte species richness positively, although the Evidence for an extinction debt means that many species are not yet lost from unfavorable habitat patches, but still occur in habitat patches in which they should not actually occur due to current environmental conditions and habitat area. From our results, it can be implied that long‐lived specialist vascular plant species and, to a lesser degree, short‐lived specialists have yet only partly responded to the severe habitat loss of the wetlands of the canton of Zürich. This delayed response makes it possible to prevent or at least to reduce future extinctions of these species through specific conservation measures (Kuussaari et al., 5We show that there is evidence to suggest an unpaid extinction debt in the wetlands of the canton of Zürich, especially for long‐lived specialist species and somewhat weaker for short‐lived specialists. This is evidenced by our findings that, on the one hand, the expected species richness of long‐lived specialist species in wetlands with a substantial loss in area was lower than their actually observed species richness and, on the other hand, that historical wetland area—besides current wetland area—explained a substantial and significant part of the current species richness of long‐lived wetland specialist plant species. There is still time to take conservation measures to prevent future extinction in the wetlands of canton of Zürich and elsewhere (Kuussaari et al., None declared.A. J., A. B., and R. H. designed the study. M. P. and A. B. did the fieldwork. U. G. did the historical reconstruction of the wetlands of the canton of Zürich and calculated patch areas and connectivity measurements. A. J. and A. B. did all the analyses and wrote the manuscript, and all authors commented on it and approved the final version.


The U.S. Ending the HIV Epidemic (EHE) Initiative was launched nationally in February 2019. With a target of ending the HIV epidemic by 2030, EHE initially scales up effective HIV prevention and care in 57 localities that comprise the greatest proportion of annual HIV diagnoses in the United States (US). However, the EHE effort has been eclipsed by another infectious disease 11 months into the Initiative’s implementation. SARS‐COV‐2, a novel coronavirus, has infected more than eight million Americans and at least 223 000 (as of 23 October 2020) have succumbed to the disease. This commentary explores the social conditions that place communities of colour at greater risk for COVID‐19 and HIV, and assesses challenges to EHE in a post‐COVID‐19 universe.One of the many common threads between HIV and COVID‐19 is the disproportionate impact of each disease among communities of colour. A recent report by the National Academy of Sciences surmised that as much as 70% of health outcomes are due to health access, socio‐economic factors and environmental conditions. Social determinants of health associated with greater HIV burden in Black and Brown communities have re‐emerged in epidemiological studies of disproportionate COVID‐19 cases, hospitalizations and deaths in communities of colour. Using data from the scientific literature, this commentary makes direct comparisons between HIV and COVID‐19 racial disparities across the social determinants of health. Furthermore, I examine three sets of challenges facing EHE: (1) Challenges that hamper both the EHE and COVID‐19 response  (2) Challenges posed by COVID‐19 ; and (3) Challenges unrelated to COVID‐19 .Racism and discrimination place communities of colour at greater risk for COVID‐19 as well as HIV. Achieving and sustaining an end to the U.S. HIV epidemic will require structural change to eliminate conditions that give rise to and maintain disparities. As striThis commentary fleshes out data presented during the opening plenary of the 2020 International AIDS Conference where similarities between COVID‐19 and HIV racial disparities in the US were investigated . I also 22.1The COVID‐19 pandemic is actually a syndemic , 13. A sA spate of high‐profile police killings of Black Americans in tandem with the ongoing disproportionate impact of COVID‐19 by race and ethnicity catapulted structural racism into the popular lexicon. Black Lives Matter protests galvanized nationwide during the summer of 2020 in the wake of the murder of George Floyd . Mr. FloSyndemics and structural racism may also explain why infectious disease burden remains concentrated in communities of colour despite greater reported preventative behaviours. For instance Black Americans remain more likely to acquire HIV despite greater HIV testing and fewer risk behaviours than White Americans , 19. TheA recent National Academy of Sciences report suggests that as much as 70% of health outcomes stem from socio‐economic factors, physical environment, as well as health care . Reviews2.1.1Neither wealth nor income is concentrated in communities of colour in the US , and havRacial and ethnic health disparities persist even among groups with high incomes. Gopal, et al (2013) found higher all‐cause HIV mortality rates among Blacks with higher incomes compared to their White counterparts . Similar2.1.2Both substandard and lack of housing are associated with a panoply of health conditions , and comHousing conditions also create an environment that facilitates COVID‐19 transmission. Denser living quarters, specifically the number of residents per room, is associated with greater COVID‐19 diagnoses in epidemiological studies of Black  and LatiWhere people live also determines health. The federal government’s promotion of racial segregation via redlining practices in the 1930s have had profound health effects that remain to this day . Cities Meaningfully addressing racial segregation could improve health equity. An economic analysis reported that “a one standard deviation decrease in segregation would eliminate one‐third of the Black‐White differences” .2.1.3The United States has the highest rate of incarceration globally . HarsherAlthough the probability of transmission is lower than in the general population, HIV transmission has been documented in carceral settings ; and an Each of the top 10 COVID‐19 infection outbreaks nationwide have links to correctional facilities, and as many as 87 of the top 100 outbreaks are linked to detention centres . JuvenilImmigration detention centres may also create environments that facilitate COVID‐19. As early as May, 2020, at least 5000 detainees nationwide had already tested positive for COVID‐19 . Althoug2.1.4President Obama’s Affordable Care Act (ACA) increased healthcare access for an additional 20 million Americans, including people living with HIV , 60. A cThe health benefits of Medicaid expansion are clear. It is associated with greater uptake of pre‐exposure prophylaxis (PrEP) , higher 2.2The implementation of the EHE was already a challenge prior to COVID‐19 , but the2.2.1An estimated eight million Americans (disproportionately Black and Brown) have slipped into poverty since May 2020 . People The influence of the social determinants loom large among those affected by HIV or COVID‐19. A CDC study reported that HIV mortality was lower for people living with HIV in states with greater healthcare coverage, anti‐discrimination laws, and viral suppression among those enrolled in the Ryan White programme . LikewisSuitable EHE and COVID‐19 responses must be coordinated, government‐wide and part of a comprehensive plan with short‐ and long‐term targets that include the social determinants of health.Any new health innovations introduced during the EHE effort will magnify health disparities. The rollout of antiretroviral therapy and later PrEP were both marred by unequal access to these innovations by communities of colour , 75. Lik2.2.2A major impediment to the successful implementation and sustainability of EHE is funding. Nearly $35 million was allocated to launch EHE in 2019, and $266 million was appropriated in 2020 with additional funding allocated through the Coronavirus Aids, Relief and Economic Security (CARES) Act. Although it is tacitly understood that EHE funding must increase in successive years to reach the Initiative’s 2030 targets, funding has fallen short. As of the writing of this Commentary, the House of Representatives only appropriated an additional $87 million in funding for EHE—far short of the $716 million FY’21 funds proposed by the Administration .Funding to fight COVID‐19 will likely affect funding for other initiatives like the EHE. The Congressional Budget Office estimates that it will cost the U.S. $9 trillion dollars over the next 10 years to address the COVID‐19 crisis . Larry SThe COVID‐19 pandemic is delaying HIV research activities and affecting HIV care delivery. Clinical trials and lab research have yet to return to pre‐pandemic operations . Moreovebefore the COVID‐19 pandemic found that four of six US cities will achieve HIV epidemic control by 2040 at the earliest [Ongoing COVID‐19 disruptions will extend the time horizon for ending HIV far beyond EHE’s 2030 target. A modelling analysis performed earliest . Even mo2.2.3Unanticipated health challenges may present additional hurdles to implementing EHE. For instance, Black Americans are less likely to be prescribed opioids by providers . This paChanges in the composition of the Supreme Court may imperil the Affordable Care Act and, in turn, EHE . Expansi3How wonderful it is that nobody need wait a single moment before starting to improve the world. ‐Anne Frankprior to the COVID‐19 pandemic [Racial and ethnic disparities in COVID‐19 are part of a predictable pattern of disease burden in the US that stem from structural racism. Although expansion of ACA and innovative models of health equity can reduce observed disparities, they may not be sufficient to end them , 91. A spandemic .For EHE to be successful and sustainable in racial and ethnic communities at greater risk for HIV, we must address the structural issues at the root of HIV and other health disparities. This requires a Marshall Plan that tackles inequities across health and other sectors, and creates economic, housing, educational opportunities for Black and Brown communities. It also requires re‐prioritization of the US budget to place a premium on primary as well as secondary prevention investments in communities of colour. Last, it requires meaningful representation in policy‐making bodies nationally and locally. An analysis found that various health policies consistently disadvantage communities of colour  likely dNone declared.GM devised and wrote the manuscript based upon his opening plenary for AIDS 2020.


Laughter is a multifaceted signal, which can convey social acceptance facilitating social bonding as well as social rejection inflicting social pain. In the current study, we addressed the neural correlates of social intent attribution to auditory or visual laughter within an fMRI study to identify brain areas showing linear increases of activation with social intent ratings. Negative social intent attributions were associated with activation increases within the medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC). Interestingly, negative social intent attributions of auditory laughter were represented more rostral than visual laughter within this area. Our findings corroborate the role of the mPFC/ACC as key node for processing “social pain” with distinct modality‐specific subregions. Other brain areas that showed an increase of activation included bilateral inferior frontal gyrus and right superior/middle temporal gyrus (STG/MTG) for visually presented laughter and bilateral STG for auditory presented laughter with no overlap across modalities. Similarly, positive social intent attributions were linked to hemodynamic responses within the right inferior parietal lobe and right middle frontal gyrus, but there was no overlap of activity for visual and auditory laughter. Our findings demonstrate that social intent attribution to auditory and visual laughter is located in neighboring, but spatially distinct neural structures. This is impressively documented by the fact that the probability of its occurrence is 30 times higher in the presence of others as compared to situations without other humans being present Provine, . CharlesIn the current study, we presented visual or acoustic stimuli of friendly, tickling, and taunting laughter and instructed the participants to rate the perceived social intent on a four‐point scale during functional magnetic resonance imaging (fMRI). A previous study using audiovisual laughter as stimuli demonstrated that neural responses within the right superior temporal cortex increase with perceived negative social intent in children and adolescents , social intents are often expressed in a more implicit and subtle manner by nonverbal signals. It has long been recognized that laughter represents a nonverbal signal that is well suited to express messages ranging from sexual solicitation to aversion Grammer, . To inve22.1SD and range. Fifty two healthy subjects  participated in the study. Participants were recruited via public announcements and screened beforehand for any current and/or history of neurological and/or psychopathological impairments by trained psychologists using the SCID I and SCID II interviews  and quality using Adobe Premiere Pro CS3 software including editing of videos with respect to the alignment of the vertical facial symmetry axis and the size of the portrayed faces. Normalization of sound intensity to a mean of 70 dB was achieved by using PRAAT, version 5.1.07 Boersma, . From thThe social intention of these stimuli was rated by 14 healthy subjects  in a prestudy to evaluate the impact of the communicational channel on the perceived intention. To this end, participants were asked to judge to which extent the laughter sequences express a positive emotional attitude toward the receiver signaling the intention to further positive social interaction and bonding  or a negative emotional attitude toward the receiver signaling the intention to reduce positive social interaction and bonding . Thus, they were instructed to rate the social intent of the expressed laughter on a four‐point scale  as strong/slight positive social intent  or strong/slight negative social intent . We chose the words “Anlachen” versus “Auslachen” instead of “Soziale Akzeptanz”  versus “Soziale Ablehnung”  for the employed scale as the first are more frequently used in daily language whereas the latter represent technical terms that are more appropriate for academic use.p = .20, two‐tailed). Stimuli presented visually (V) as mute video clip were rated more frequently as representing positive social intent  than A or AV stimuli (both paired T(19) > 4.1, p < .001, two‐tailed).Percentages of positive social intent (strong or slight) and negative social intent (strong or slight) ratings were similar for stimuli presented as auditory (A) sound clips  and audiovisual (AV) movie clips  with no significant difference between modalities (paired T(19) = 1.34, 2.3http://www.neurobs.com/). The acquisition of behavioral data was achieved using an MRI‐compatible response system for four fingers .In the fMRI experiment, we employed only A and V stimuli of the prestudy, but not the AV stimuli from which both classes of stimuli were generated to avoid crossmodal effects that might be induced by presentation of bimodal stimuli preceding unimodal stimuli as well as order effects that would result if AV stimuli were always presented following the unimodal stimuli. About 60 V and 60 A stimuli were presented using an event‐related design with an inter stimulus interval (ISI) ranging from 9 to 12 s  during two consecutive fMRI sessions (30 V and 30 A stimuli per session). In addition, six null events with a duration of 10.5 s were randomly inserted . The study participants were not informed that the auditory and visual stimuli were created from the same stimulus set and it was emphasized that there are no “correct” or “incorrect” responses, but that their individual perception of the laughter stimuli is important. They were instructed to rate the social intent of the expressed laughter on the same four‐point scale used in the prestudy (see above). Visual stimuli and the rating scale were presented via back‐projection onto a screen approximately 2 m behind the participants' head and viewed over a mirror mounted on the head coil. Auditory stimuli were presented via MRI‐compatible audio headphones. Stimulus presentation and recording of behavioral responses were carried out using the software Presentation  and functional images  were collected with a 3 T scanner  using a 20 channel head coil. Time series consisted of 478 images for each of the two sessions. For image distortion correction, a field map  = 5.19 ms, TE(2) = 7.65 ms, voxel size: 3 × 3 × 3 mm3) was obtained.High resolution structural T1‐weighted images , we additionally compared the data based on non‐parametrical testing (Wilcoxon test).Only behavioral responses occurring within 5 s after stimulus onset were included in the analysis. The percentage for each of the four response options was calculated separately for visual and auditory trials and statistically compared using paired two‐sided 2.63) based on the unified segmentation approach integrated in SPM . Average responses (mean ± SE) for each of the four response options within brain regions showing significant effects were calculated to visualize the effect of perceived social intent of laughter on corresponding brain activation.Images were analyzed with statistical parametric mapping software . Preprocessing comprised slice time correction, realignment, unwarping to correct for field distortions and to remove residual movement‐related variance due to interactions between motion and field distortions  and non‐parametric  tests. The distribution of the four different response options is presented in Figure Participants responded to 96.3% ± 1.0% of the presented trials. Although the visual and auditory stimuli were created from the same laughter sequences, higher percentages of positive social intent attributions  were found for laughter presented in the visual than auditory modality which is in agreement with the evaluations of the stimuli in the prestudy. The differences between auditory and visual trials were significant for all four response options as indicated by parametric  > 2.45; all 3.2p < .05, FWE corrected) are shown in red/yellow if no significant differences across modalities were found and in blue/light blue if the regression slopes determined for the two modalities were significantly different  for visually and auditory presented laughter  within the framework of an event‐related fMRI study capturing linear relationships between brain activation and individual ratings. Importantly, our study design allowed us to examine such effects separately for auditory and visual laughter.Behavioral ratings revealed that participants judged visual stimuli with a significantly higher probability as socially accepting  than auditory stimuli. These findings obtained during fMRI were in agreement with results obtained in a prestudy outside the scanner. Furthermore, we also included AV stimuli in the prestudy serving as a reference of social intent attribution when the full information in voice and face is available. Social intent attribution of auditory and AV stimuli were highly similar suggesting that purely visual laughter stimuli elicit a positivity bias . While this bias resulted in more positive social intent attributions  in the visual than in the auditory modality, the overall distribution of responses across the four response options was sufficiently even enough to enable investigation of parametric relationships between brain activation and individual ratings. As explained above, we relied on a scale for rating of the social intent expressed by laughter  instead of the more technical terms social acceptance versus social rejection. This represents a limitation regarding the interpretation of our results within the framework of the previous literature on social acceptance/rejection, but we feel that both terms can be used interchangeably at the semantic level regarding the denomination of social intent of laughter and the neurobiological results of our study revealing similar brain activations as in previous studies on social rejection/acceptance support this view (see below).So far, the vast majority of fMRI studies conducted to study the neural correlates of social rejection are based on the Cyberball paradigm  individuals, while each laughter stimulus expressed feedback from exactly one individual. A particular sensitivity of the PCC/PC region to rejection from social groups could explain why no significant activation was found in our study or experiments relying on the Chatroom paradigm  consistently resulted in stronger activation than slightly positive (+) which was always the response option yielding the lowest activation level. A possible explanation for this response pattern might be that slightly positive stimuli grab the lowest level of attention as they are more often encountered in real life social situations and thus perceived as a kind of “social default.” While we cannot make strong inferences on this issue as direct comparisons between the beta estimates of the four response options failed to reach significance, this observation might still serve as a starting point for future studies to inform hypotheses on such nonlinear relationships between brain activation and rating of socially relevant signals.So far, there is much more knowledge on the neural correlates of social rejection than social acceptance, which is most probably due to the fact that social rejection has a stronger reference to symptoms experienced in clinical populations. In social anxiety, fears concerning negative evaluation and social rejection represent even the core symptom of the disorder (Trower & Gilbert, In conclusion, the current study allowed us to disambiguate brain structures for processing perceived positive versus negative social intent expressed by laughter. Negative social intent attribution recruited the mPFC/ACC for both visual and auditory laughter, which nicely dovetails with results from neuroimaging studies relying on the Cyberball paradigm to induce feelings of social rejection. Interestingly, there was little overlap in activation between sensory modalities with auditory laughter being represented more rostral than visual laughter within the mPFC/ACC. Similarly, perceived positive social intent expressed by laughter recruited the right IPL and right MFG with no overlap across modalities. These findings argue for distinct rather than common processing of social signals conveyed by visual and acoustic laughter stimuli.The authors declare no potential conflict of interest.