Necessary for long-term survival, quiescent chromatin is compact, hypoacetylated, and transcriptionally inactive. How transcription activates upon cell-cycle re-entry is undefined. Here we report robust, widespread transcription within the first minutes of quiescence exit. During quiescence, the chromatin-remodeling enzyme RSC was already bound to the genes induced upon quiescence exit. RSC depletion caused severe quiescence exit defects: a global decrease in RNA polymerase II (Pol II) loading, Pol II accumulation at transcription start sites, initiation from ectopic upstream loci, and aberrant antisense transcription. These phenomena were due to a combination of highly robust Pol II transcription and severe chromatin defects in the promoter regions and gene bodies. Together, these results uncovered multiple mechanisms by which RSC facilitates initiation and maintenance of large-scale, rapid gene expression despite a globally repressive chromatin state.Quiescence is a reversible G The quiescent state provides advantages to organisms: quiescence allows cells to remain dormant for long periods of time to survive harsh conditions or to prevent over-proliferation  cultures . Log phaferation . Notwithce entry . These ece entry . TogetheSaccharomyces cerevisiae  by the first 10 min of exit, despite the highly repressive chromatin architecture present in quiescence. We found that this hypertranscription  event isTo determine the earliest time at which transcription reactivates during quiescence exit, we fed purified quiescent cells YPD medium and took time points to determine the kinetics of Pol II C-terminal domain (CTD) phosphorylation by western blot analysis . UnexpecHighlighting the high level of transcription occurring in the first 10 min of quiescence exit, we observed a drop-off in Pol II occupancy levels around the first G2/M phase (240 min) . Indeed,Given the exceptionally high transcriptional response during the first 10 min of quiescence exit, we wondered whether chromatin changes reflected hypertranscription. To this end, we performed ChIP-seq analysis of H3 to measure nucleosome occupancy levels genome wide over time. Global H3 patterns during the early exit time points, especially at the 5 min time point, were more similar to that of the quiescent state than to the 240 min time point . HoweverWe next tested if a burst of histone acetylation occurred during these early exit time points to help overcome the repressive quiescent chromatin environment. To test this, we performed ChIP-seq analysis of H4ac using an antibody that recognizes penta-acetylated H4. Similar to nucleosome occupancy and positions, a modest increase in histone H4 acetylation occurred, but the levels did not reflect that of log cells . This sudst1∆), quiescent yeast cells exhibited defects in cell-cycle re-entry, where cells lacking TFIIS stall at the first G1 during exit, which is not the case during the mitotic cell cycle  exhibited increased nascent sense-strand RNA signals starting upstream of their normal TSSs, demonstrating widespread defects in TSS selection. Canonical NDRs at these sites were severely reduced in the absence of RSC . ExaminaGiven the robust transcriptional response during the early minutes of quiescence exit , we examIn this report, we have shown that there is a rapid and robust transcriptional response during the very early minutes of quiescence exit . This redst1∆. High numbers of stalled Pol II are present in cycling cells .Q exit experiments were performed as follows: Q cells were harvested and added to YPD to 1 OD660 into the appropriate YPD volume for a given experiment. Cells were grown for 16 hr and monitored for glucose exhaustion using glucose strips. Six hours after glucose exhaustion, 1 mg/mL of indole-3-acetic acid (IAA)  was added, in powder form, to the culture. IAA remained in the culture for 7 days before harvesting Q cells. Q cells were purified as described above, and depletion efficiency was determined by western blot analysis . Cells were incubated with 4tU for 5 min before pelleting  and flash frozen in liquid nitrogen. For the 10 min time points, 50 OD units of quiescent cells were released into YPD for 5 min before an additional 5 min incubation with 4tU at a final concentration of 5 mM. All time points were labeled with 4tU for a total of 5 min before pelleting and freezing. Total RNA was isolated using Ambion’s RiboPure Yeast Kit . S. cerevisiae cells were lysed in the presence of Kluvomyces lactis (K. lactis) cells in a 100:1 mixture. RNA was treated with DNAseI according to the TURBO DNase kit . Forty microgram RNA was then biotinylated with MTSEA biotin-XX (diluted in 20% DMF) at a final concentration of 16.4 µM in 20 mM HEPES pH 7.4 and 1 mM EDTA at room temperature for 30 min. Unreacted MTS-biotin was removed from samples by phenol:chloroform:isoamyl-alcohol extraction and resuspended in 100 µL nuclease-free water. Strepavidin beads (Invitrogen 65001) were washed with high-salt wash buffer  and blocked for 1 hr in high-salt wash buffer containing 40 ng/µL glycogen. Forty microliters of streptavidin beads were added to the RNA samples and incubated for 15 min at room temperature. Beads were washed three times in 1 mL high-salt wash buffer and eluted for 15 min at room temperature in 50 µL streptavidin elution buffer . The resulting RNA was then purified and concentrated using the Qiagen miRNeasy kit (#217084). Libraries were prepared from 5 ng of RNA using the Ovation SoLo kit . Libraries were subjected to 50 bp paired-end sequencing on an Illumina HiSeq 2500 at the Fred Hutchinson Cancer Research Center genomics facility.Generally, nascent RNA-seq experiments were performed as described in K. lactis (Ensembl ASM251v1) genomes and filtered reads using SAMtools as described above for ChIP-seq analysis. Reads were normalized to the spike-in control and RPKM. Differential expression analysis was performed using DESeq2  feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Acceptance summary:Saccharomyces cerevisiae yeast cells emerge from quiescence, noting an unexpected, intense burst of expression from much of the genome. They focus primarily on the role of the nucleosome remodeler RSC in managing chromatin architecture over promoters during quiescence and as cells re-enter the cell cycle using a broad range of genome-wide measurements that strongly support the conclusions. This important process of cell cycle re-entry from the quiescent state is understudied but impacts a range of phenomena from development and carcinogenesis in multicellular organisms to adaptation of microorganisms to environmental cues, so the results will be of interest to a broad audience.Cucinotta et al. describe the transcription that occurs within minutes of nutrient repletion as Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Tim Formosa as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Jessica Tyler as the Senior Editor.Thank you for submitting your article "RSC primes the quiescent genome for hypertranscription upon cell cycle re-entry" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:Note that while the reviewers felt strongly that more integration of, and cross-correlation among the datasets would improve the impact of this work, the suggestions for ways to do that listed below are individually optional. The long list of recommendations may look daunting, but the authors are encouraged to view them as potential options, not as a list that has to be completed.1. Integration and correlation of different datasets should be enhanced. For example, a heatmap of the MNase-seq, TFIIB-seq, and RSC ChIP-seq data using the gene clusters in Figure 1C could be used to correlate induction of transcription with RSC occupancy, favorable NDR width, and binding of TFIIB. Another example would be to examine whether genes with transcription defects due to RSC depletion show greater changes in promoter nucleosome occupancy when compared to unaffected genes. Some attempt at this was made in Figures 6 and 7, but more analysis of this type would provide better support for the key conclusions.2. Improvements should be made to the bioinformatic and statistical handling of the data. The existing bioinformatic tools employed are appropriate, but they are not consistently applied for different experiments. How specific gene groups were chosen or clustered should be more carefully explained, and, if appropriate, cut-offs listed. Similar information should be included to support statements in the text . Some of the data is "normalized" but it isn't always clear what it is normalized to (spike-ins? Input?). Statistical analyses should be used to back up key results.3. Authors should acknowledge the impact of the delayed exit from quiescence following RSC depletion on their results. Cells that were able to exit are likely those with subtle differences in chromatin packing over a handful of genes required for exit. Cells that do not exit are left with quiescence signatures, which could be due to the lack of transcription as opposed to lack of RSC.4. The authors Pol II occupancy data via ChIP-seq with and without RSC depletion. Could these locations be compared with the transcription changes shown in Figure 1 to demonstrate which groups of genes are altered?5. In Figure 1, it is interesting that some of the clusters show sustained expression (Cluster 1), but some have hyper-transcription that is then reduced to log levels (Clusters 2 and 3). Is there a difference between these and RSC occupancy; or specific changes to one of these clusters when RSC is depleted? Furthermore, what genes make up these clusters?6. In Figure 3a, the authors show a decrease in fragile nucleosome occupancy through low MNase upon exit from quiescence. Is the effect more pronounced in a subset of genes such as just quartile 1, with the widest NDR? Similarly, does an analysis of MNase data at only those locations with the highest RSC occupancy produce a different outcome?7. Do the authors see decreased H3 occupancy (from ChIP-seq data) at the genes where the fragile nucleosome decreases (via MNase-seq data)?8. In Figure 4, the authors find that Pol II occupancy never reaches the 10-minute hyper-transcription enrichment when RSC is depleted. Do the cells ever reach this peak, or are they unable to obtain this hyper-transcription in the absence of RSC?9. Figure 5A shows that RSC occupancy changes to decrease over NDRs and increase over gene bodies. The authors then follow with inhibiting Pol II, and show an accumulation of Pol II at NDRs and decreasing over gene bodies. Do these data correlate? For example, does an ROC curve show correlation? Or if you compare the datasets do they show that the largest changes in Pol II distribution occur at the locations where RSC redistributes?10. Does the increased aberrant transcription shown in Figure 7 correlate with the level of RSC occupancy or the change in occupancy of Pol II?11. While Figure 8 accurately summarizes the key results of this study, more molecular insight either here or in the discussion would be helpful. One model is that RSC has a preference for "easy to remodel" nucleosomes, such as fragile nucleosomes. In quiescence, due to lack of ATP, RSC might be less able to evict these nucleosomes and becomes stuck at NDRs. Following the eviction of fragile nucleosomes at cells exit quiescence, RSC might move onto other "easy to remodel" nucleosomes, such as those partially disrupted by Pol II. This, or some mechanism preferred by the authors, might be discussed to help the reader.Reviewer #1 (Recommendations for the authors):1) In the abstract, the authors state that ~60% of the genome is transcribed during the first 10 minutes after exit, but the data in figure 1 state that ~20% of genes are transcribed above the cutoff. It would be useful to reconcile this apparent discrepancy, and also to state whether the percentage of transcription is based on gene number or the total fraction of the genome.2) On line 156, the authors note a result in "Fig. 2-supplement 1B" but there is no figure that matches this description or seems to be related to the statement being supported.3) On line 169, the authors state that "the MNase-sensitivity of the +1 and +2 nucleosomes increased at the 10-minute time point" but this conclusion is not evident in Fig 3. The effect on the left side of the NDR in 3A-3B is clear, but the reader will need more guidance with seeing the change that supports the statement above.4) The flow cytometry result in Fig 4A +RSC seems like it should be similar to the results in Fig1-Sup-D and 2F, but instead it looks like a fully occupied G2 delay/arrest. Can the authors explain the difference?5) In Fig 5C, the authors describe the rise in Pol II ChIP signal at +1 Kb as a termination delay. It seems more appropriate to perform gene-averaging (TSS to TTS) or to align all termination sites instead of looking at 1 Kb past the TSS, which is early in some genes and late in others.6) The basis for placing genes in Class I and II in Fig 7 is not clear. The text says that Class I has high antisense and "generally reduced sense" transcripts. This seems mostly true, although about 20% of the Sense levels are up significantly. Class II, however, is described as having "modest changes in both sense and antisense transcript levels," but sense ranges from significantly up to significantly down, and antisense from down to strongly down. The authors should describe the rationale for defining these classes more clearly; are they based initially on levels of antisense transcripts? Without a clearer understanding of how these classes are defined, it is difficult to determine whether or not the conclusions drawn are supported.7) On line 327, it is not clear what an "incredibly strong" transcriptional response would be, so another adjective might be more appropriate.Reviewer #2 (Recommendations for the authors):I find the data very compelling and complete in the description of RSC regulating exist from quiescence. As mentioned in the public review, some aspects of data comparisons could be made. Some examples are listed below. While some may not be necessary, some of these may help to strengthen conclusions:1) The authors Pol II occupancy data via ChIP-seq with and without RSC depletion. Could these locations be compared with the transcription changes shown in Figure 1 to demonstrate which groups of genes are altered?2) In Figure 1, it is interesting that some of the clusters show sustained expression (Cluster 1), but some have hypertranscription that is then reduced to log levels (Clusters 2 and 3). Is there a difference between these and RSC occupancy; or specific changes to one of these clusters when RSC is depleted? Furthermore, what genes make up these clusters?3) In Figure 3a, the authors show a decrease in fragile nucleosome occupancy through low MNase upon exit from Q. This is a metaplot of all genes, and I am curious if the authors see more pronounced results if they show just quartile 1, with the widest NDR. Also, if the authors analyze the MNase data and show only locations that have RSC occupancy.4) Do the authors see decreased H3 occupancy (from ChIP-seq data) at the genes where the fragile nucleosome decreases (via MNase-seq data)?5) In Figure 4, the authors find that Pol II occupancy never reaches the 10-minute hypertranscription enrichment when RSC is depleted. Curiously, do the cells ever reach this peak, or are they unable to obtain this hypertranscription in the absence of RSC?6) Figure 5A shows that RSC occupancy changes to decrease over NDR and increase over gene bodies. The authors then follow with inhibiting Pol II, and show an accumulation of Pol II at NDRs and decreasing over gene bodies. Do these data correlate? For example, does an ROC curve show correlation? Or if you compare the datasets do they show that at the locations where RSC redistributes, there is the largest change in Pol II distribution?7) The locations that have increased aberrant transcription, as shown in Figure 7, are these genes that have RSC occupancy? If you compare these locations with Pol II altered occupancy, is there a correlation?Reviewer #3 (Recommendations for the authors):1. It is surprising that little attempt was made to integrate the results from the various analyses. For example, a heatmap of the MNase-seq, TFIIB-seq, and RSC ChIP-seq data using the gene clusters in Figure 1C would show that gene induction following Q exit is linked to RSC occupancy, favorable NDR width, and binding of TFIIB. Another example would be to examine whether genes with transcription defects due to RSC depletion show greater changes in promoter nucleosome occupancy when compared to unaffected genes. Some attempt at this was made in Figures 6 and 7, but I thought that more connections would better support the key results of this study.2. Improvements should be made to the bioinformatic and statistical handling of the data. The existing bioinformatic tools employed are fine, but they are not consistently applied for different experiments. How specific gene groups were chosen or clustered should be more carefully explained, and, if appropriate, cut-offs listed. Similar information should be included to support statements in the text . Some of the data is "normalized" but it isn't always clear what it is normalized to (spike-ins? Input?). Statistical analyses should be used to back up key results.3. Authors should acknowledge the impact of the delayed Q-exit following RSC depletion on their results. Cells that were able to exit are likely those with subtle differences in chromatin packing over a handful of genes required for Q exit (down the road identifying these might be possible using SGA?). Cells that do not exit are left with Q signatures, which could be due to the lack of transcription upon Q exit as opposed to lack of RSC.4. While Figure 8 accurately summarizes the key results of this study, more molecular insight either here or in the discussion would be helpful. One model is that RSC has a preference for "easy to remodel" nucleosomes, such as fragile nucleosomes. In Q, due to lack of ATP, RSC cannot evict these nucleosomes and becomes stuck at NDRs. Following the eviction of fragile nucleosomes at Q-exit, RSC moves onto other "easy to remodel" nucleosomes, such as those partially disrupted by PolII. Essential revisions:Note that while the reviewers felt strongly that more integration of, and cross-correlation among the datasets would improve the impact of this work, the suggestions for ways to do that listed below are individually optional. The long list of recommendations may look daunting, but the authors are encouraged to view them as potential options, not as a list that has to be completed.1. Integration and correlation of different datasets should be enhanced. For example, a heatmap of the MNase-seq, TFIIB-seq, and RSC ChIP-seq data using the gene clusters in Figure 1C could be used to correlate induction of transcription with RSC occupancy, favorable NDR width, and binding of TFIIB. Another example would be to examine whether genes with transcription defects due to RSC depletion show greater changes in promoter nucleosome occupancy when compared to unaffected genes. Some attempt at this was made in Figures 6 and 7, but more analysis of this type would provide better support for the key conclusions.We are very grateful for this suggestion and have added several panels addressing these questions to a new figure: Figure 4—supplement 1. In summary, we found the following: genes with widest NDRs (clusters 1 and 2) have high levels of TFIIB  and Pol II during early exit. While these two clusters were impacted by RSC depletion, some level of Pol II occupancy remained . We believe redundant factors may enable transcription to occur at these genes (ex: other remodelers or transcription factors). Furthermore, these genes (clusters 1 and 2) also had the widest NDRs and lowest H3 occupancy regardless of RSC, although we did see H3 occupancy rise in the absence of RSC . RSC occupancy levels did not correlate with transcription levels or NDR width , with cluster 1 transcription and NDR width deviating from RSC occupancy the most. We again believe this could be due to redundant factors at loci in cluster 1.2. Improvements should be made to the bioinformatic and statistical handling of the data. The existing bioinformatic tools employed are appropriate, but they are not consistently applied for different experiments. How specific gene groups were chosen or clustered should be more carefully explained, and, if appropriate, cut-offs listed. Similar information should be included to support statements in the text . Some of the data is "normalized" but it isn't always clear what it is normalized to (spike-ins? Input?). Statistical analyses should be used to back up key results.We thank the reviewers for this feedback and have updated the text to better describe how the analyses were completed, for example page 26, line 683. In addition, we added a section for analysis for each experiment type in the methods section.3. Authors should acknowledge the impact of the delayed exit from quiescence following RSC depletion on their results. Cells that were able to exit are likely those with subtle differences in chromatin packing over a handful of genes required for exit. Cells that do not exit are left with quiescence signatures, which could be due to the lack of transcription as opposed to lack of RSC.We agree this should be acknowledged and we have updated the text  to discuss this possibility.4. The authors Pol II occupancy data via ChIP-seq with and without RSC depletion. Could these locations be compared with the transcription changes shown in Figure 1 to demonstrate which groups of genes are altered?We have added this analysis using the same gene ordering as shown in Figure 1 and generated a new supplemental figure  to illustrate this comparison. These results are now discussed in page 12, lines 262-268.5. In Figure 1, it is interesting that some of the clusters show sustained expression (Cluster 1), but some have hyper-transcription that is then reduced to log levels (Clusters 2 and 3). Is there a difference between these and RSC occupancy; or specific changes to one of these clusters when RSC is depleted? Furthermore, what genes make up these clusters?We agree that the differences in gene expression varied over time across the clusters were interesting. We have added a heatmap of RSC occupancy in Q cells and during early exit at these loci , and discuss in page 12, lines 261-270. We note differences in the RSC profile: specifically striking to us was how much RSC remains in the NDR vs the gene body. Genes showing more RSC movement into the gene body (clusters 1 and 2) also had the highest binding of Pol II in exit. All genes had reduced Pol II occupancy however clusters 1 and 2 still had detectable levels of Pol II occupancy. We included a new figure showing the top five GO terms for each cluster , and discuss on page 7, lines 129-131. Cluster 1 had the highest enrichment of GO terms and contained genes involved in rRNA processing and translation.6. In Figure 3a, the authors show a decrease in fragile nucleosome occupancy through low MNase upon exit from quiescence. Is the effect more pronounced in a subset of genes such as just quartile 1, with the widest NDR?We see a moderate effect of Q-exit on MNase-digestion ability after the data are subset into quartiles .Similarly, does an analysis of MNase data at only those locations with the highest RSC occupancy produce a different outcome?We thank the reviewers for this question and have added this analysis to the paper. We see a difference in MNase-digestion levels comparing high RSC levels to the lowest RSC levels .7. Do the authors see decreased H3 occupancy (from ChIP-seq data) at the genes where the fragile nucleosome decreases (via MNase-seq data)?Yes. We see decreased H3 occupancy at these genes when cells exit. We show these metaplots in a new figure .8. In Figure 4, the authors find that Pol II occupancy never reaches the 10-minute hyper-transcription enrichment when RSC is depleted. Do the cells ever reach this peak, or are they unable to obtain this hyper-transcription in the absence of RSC?We have performed ChIP-seq analysis to the 30-minute time point and did not observe transcription levels reaching this peak. We have updated the text to emphasize that the level could potentially reach this later on .9. Figure 5A shows that RSC occupancy changes to decrease over NDRs and increase over gene bodies. The authors then follow with inhibiting Pol II, and show an accumulation of Pol II at NDRs and decreasing over gene bodies. Do these data correlate? For example, does an ROC curve show correlation? Or if you compare the datasets do they show that the largest changes in Pol II distribution occur at the locations where RSC redistributes?We have added a figure to reflect the clusters shown in Figure 1C . We believe that this illustrates the difference of RSC localization with and without phenanthroline, as there are further differences at different clusters. The most striking changes are in clusters 1 and 2, where Pol II is normally highly bound. At these sites, RSC movement is dramatically sequestered to the NDR when phenanthroline is added, supporting our conclusion that RSC co-transcriptionally moves into gene bodies. This result is now discussed in page 13, lines 294-297.10. Does the increased aberrant transcription shown in Figure 7 correlate with the level of RSC occupancy or the change in occupancy of Pol II?We thank the reviewers for this question. While it would have been very interesting if so, we did not observe any correlation with the levels of RSC occupancy or Pol II occupancy. We believe that increased aberrant transcription occurs when an unaffected or “wide-enough” NDR is present and increased transcription is thus an indirect effect of RSC depletion rather than direct action of RSC at these loci .11. While Figure 8 accurately summarizes the key results of this study, more molecular insight either here or in the discussion would be helpful. One model is that RSC has a preference for "easy to remodel" nucleosomes, such as fragile nucleosomes. In quiescence, due to lack of ATP, RSC might be less able to evict these nucleosomes and becomes stuck at NDRs. Following the eviction of fragile nucleosomes at cells exit quiescence, RSC might move onto other "easy to remodel" nucleosomes, such as those partially disrupted by Pol II. This, or some mechanism preferred by the authors, might be discussed to help the reader.We are grateful to the reviewers for these suggestions and are in favor of this as a potential model. We have added these ideas to the discussion on page 19 line 479-491.


Additional interventions are needed for survivors of psychological trauma because of several barriers to and limitations of existing treatment options . Case studies are an important step in exploring the development of novel interventions, allowing detailed examination of individual responses to treatment over time. Here, we present a case study that aims to test a novel intervention designed to disrupt memory reconsolidation, taking a single-symptom approach by focusing on intrusive memories of a traumatic event.This study aims to examine a novel brief cognitive intervention to reduce the number of intrusive memories of trauma in an Icelandic setting and to extend previous studies by examining long-term effects for up to 3 months. The intervention was guided by a clinical psychologist and comprised a brief memory reminder, followed by Tetris gameplay with mental rotation, targeting one memory at a time in each session.repetitions of an AB design . We examined changes in both the total number of intrusions  and individually for each memory. In addition, we explored whether having fewer intrusive memories would have an impact on functioning, posttraumatic stress, and depression or anxiety symptoms.This was a single case study in Iceland with a woman in her 50s  with subthreshold posttraumatic stress disorder and a diagnosis of obsessive-compulsive disorder and social anxiety disorder. The participant had four different intrusive memories from a traumatic event that happened in her childhood. The primary outcome was the change in the number of intrusive memories from baseline to intervention phase and to follow-ups. The number of intrusions was monitored in a daily diary for 4 weeks preintervention, 8 weeks during the intervention, and 1 week at 1-month and 3-month follow-ups. Intrusions were targeted one by one over six intervention sessions, creating four The total number of intrusions per week was 12.6 at baseline, 6.1 at the intervention phase (52% reduction from baseline), 3.0 at the 1-month follow-up (76% reduction), and 1.0 at the 3-month follow-up (92% reduction). Reductions in the symptoms of posttraumatic stress and depression were observed postintervention. Sleep, concentration, stress, and functioning improved. The participant considered the gameplay intervention acceptable and helpful in that she found that the memories disappeared while she was playing.This guided brief cognitive intervention reduced the number of intrusive memories over the intervention phase and follow-ups. The brief memory reminder was well tolerated, removing the need to discuss trauma in detail. The next steps require an extension to more cases and exploring remote delivery of the intervention. Psychological trauma  is experienced by most people at some point during their lifetime ,2. Many Current evidence-based treatments for PTSD include individualized trauma-focused cognitive behavioral therapy interventions and eye movement desensitization and reprocessing ,8. HowevAnother barrier to treatment is the lack of service provision . IcelandDiagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5]) are repeated and unwanted memories of scenes from a traumatic event, and they are predominantly visual  of ≥33) or not likely PTSD , using clinical interviews. When taking part in the substudy, two semistructured interviews were administered . When taking part in the substudy, women were screened for the presence of intrusive memories of trauma. The screening included a short description of the symptom, followed by questions about the presence of the symptom to assess their eligibility for this study. A total of 4 women from the substudy who provided consent to be contacted regarding additional research were assessed for inclusion in this case study. A total of 3 women did not meet the inclusion criteria  cohort study were screened for eligibility. The SAGA cohort study is a population-based longitudinal cohort study of Icelandic women who completed an extensive questionnaire on trauma history and mental health . The substudy  involves comparing two samples of women from the SAGA cohort study with either likely PTSD  having experienced criterion A trauma as defined by DSM-5 ; (2) havMeasures section). The participant also met criteria for social anxiety disorder and obsessive-compulsive disorder according to the MINI diagnostic interview (see the Measures section). The participant received psychological treatment in the past for problems related to work but had never received trauma-related psychological treatment. She reported not taking any psychotropic medication in the 3 months before taking part.The participant reported clinically significant past-month PTSD symptoms from physical violence experienced in childhood, with a total symptom severity score of 22 of 80 on the CAPS and missing one symptom in the E cluster to meet full diagnostic criteria . This assessment took place 2 months before participation in this study as part of the substudy of the SAGA cohort using the CAPS  and intervention (B) phases varied across each of the four intrusive memories, depending on when each memory was targeted. The baseline phases for each individual memory are used as control periods to compare their numbers before and after being targeted by the intervention.This single case study took a specific single-symptom probe approach, whereby each of the four intrusive memories was targeted one at a time in different sessions . CriticaThe number of each intrusive memory was monitored in a daily diary for 4 weeks preintervention, over 8 weeks of the intervention, and then for 1 week at the 1-month and 3-month follow-ups, that is, the participant monitored the occurrence of her intrusive memories in a daily diary before each intervention session to establish a baseline level of intrusion. This baseline phase was intended to be 1 week; however, the diary was kept for 4 weeks, as the participant was not able to meet with the researcher when planned. The intervention phase lasted 8 weeks rather than 6 weeks, as planned for the same reason. However, the participant did monitor the frequency of her intrusive memories in these extended periods, and we included all the data in the analyses. The daily diary was kept again for 1 week at the 1-month and 3-month follow-ups.The participant’s four different intrusive memories were targeted one by one over six intervention sessions guided by a clinical psychologist who specialized in trauma-focused cognitive behavioral therapy. The design thus involved four repetitions of an AB design. In addition to the six guided sessions, the participant could also self-administer the intervention at any time after the first session if she so chose for memories already targeted in the session. The primary outcome was the change in the number of intrusive memories from baseline to the intervention phase and to long-term follow-ups (1 and 3 months). The participant also completed self-report measures for PTSD, depression and anxiety symptoms, and functional impairment at baseline, the last intervention session, and the 1-month and 3-month follow-ups.To promote adequate intervention delivery and protocol adherence, the researcher delivering the intervention  received training and clinical supervision from experienced researchers or clinical psychologists who had expertise in delivering the novel intervention (EAH and MK). Training included two in vivo workshops for 3 days and then approximately 6 months later for 2 days. Workshops covered theoretical and practical aspects of intervention delivery and included role-plays with trainers until adequate performance was reached. Training also included how to explain and capture the primary outcome measure (intrusive memory diary). During data collection, the researcher received continued supervision, adherence checks, and support regarding any adaptations necessary from a clinical supervisor via telephone after sessions with the participant and weekly supervision meetings. The researcher also participated in remote group training meetings twice a month with other researchers using the intervention.hotspots sheet that was clearly visible to the participant. The participant did not talk about the trauma with the researcher or about the intrusive memories in detail. The participant labeled each of her intrusive memories with a symbol  and was instructed on how to monitor the daily frequency of them in a diary (primary outcome measure). When indicating experiencing a memory, the participant noted the symbol corresponding to that specific memory in a specific time frame of that day. Each diary included 7 days and four periods each day (see the Measures section).In the first session, the participant answered baseline questionnaires (relating to secondary outcomes), and the researcher explained what intrusive memories are . The participant identified her different intrusive memories by briefly describing them to the researcher using only a few words to indicate their visual content; the researcher wrote the description on a marathon mode and ghost piece off, on a 10.1-inch screen. Between sessions, the participant was invited to self-administer the intervention using a Tetris app ) × 100=52% reduction in the intervention phase compared with baseline.A) and intervention (B) phase per memory, depending on which session it was targeted. The percentage reduction in each intrusion after being targeted was calculated as 1−(mean number per week during intervention phase/mean number per week during baseline) × 100. Percentage reductions were then calculated in the same way for the 1- and 3-month follow-ups compared with baseline.Next, we examined the data per intrusive memory. Here, each intrusion acts as its own control, that is, the specific baseline phase for each individual memory is used as a control period to compare its number before and after being targeted by the intervention. There is a different baseline . The participant provided written and informed consent. All sessions followed a written protocol. No adverse events were reported by the participant.This single case study was not preregistered but precedes and is similar to the design and procedures of a case series (n=5)that we later registered on ClinicalTrials.gov (NCT04209283) on December 4, 2019. All anonymized summary-level data are reportedin this manuscript.Study materials may be made available upon reasonable request with an appropriate materials transfer agreement with University of Iceland. It should be noted that the delivery of this intervention requires extensive training and supervision (see the Procedure: Training section).The participant had four different intrusive memories that were all predominantly visual and tracked each intrusion over time. All her intrusive memories were from a single traumatic event that took place roughly four decades before participation. All four intrusive memories were targeted with the intervention at different time points during the intervention phase .One memory (memory A) was targeted three times (reported as the most distressing and frequent by the participant), and the other memories were targeted once. The participant readily understood the instructions given and successfully completed the intervention sessions and procedures. Intrusive memory diary data were missing for days 8-12 and 21-22 during the baseline phase and for days 40-41, day 49, days 57-63, and day 71 during the intervention phase; the diary was fully completed at follow-ups. Most missing diary data were because of extra days passing in between sessions, that is, when the participant had completed their current diary (covering a period of only 1 week) but had not received their next diary. No attempt was made to retrieve data for the missing days. In total, the diary was completed successfully for 82% (81/99 days) of the study period.Across the 4-week baseline period, the total number of intrusions was relatively stable and approximately 12.6 per week . This number reduced to 6.1 per week across the 8-week intervention phase (52% reduction from baseline), to 3.0 per week at 1-month follow-up (76% reduction), and to 1.0 per week at the 3-month follow-up , and the frequency reduction remained stable at follow-up. Less changes in frequency were visible for memory D throughout the intervention phase (targeted in intervention session 6), whereas there was a reduction in frequency at the 3-month follow-up.The participant rated whether she would recommend the intervention to a friend as 10/10 (meaning she would certainly recommend it). She also rated whether she considered gameplay to be an acceptable way to reduce intrusive memories as 10/10 (very acceptable). When asked how she felt about playing Tetris after she had an intrusive memory, she reported the intervention to be “very good,” and when asked if she found the intervention helpful, she said, “Yes, I forgot time and place and the memory went away immediately.”Initial high levels of PTSD symptoms (a PCL-5 score of 51) were reduced by over half at postintervention, and the reduction was clearly clinically significant at the 3-month follow-up, with a score of only 6 . Depressconcentration reduced from 5 at baseline to 1 at the 3-month follow-up, and estimated duration of concentration disruption per intrusion reduced from 4 (30-60 minutes) at baseline to 1 (1-5 minutes) at follow-up. The impact intrusions had on sleep reduced from 5 at baseline to 0 at the 3-month follow-up. The impact intrusions had on stress reduced from 5 at baseline to 1 at the 3-month follow-up. The impact intrusive memories had on the participant´s ability to function in her daily life reduced from 5 at baseline to 1 postintervention and was 0 at the 3-month follow-up.At baseline, the participant responded to an open question on how her intrusive memories had affected her ability to function in daily life: “I don’t sleep very well, and that leads to fatigue which interferes with my daily functioning.” In the last intervention session, she said, “It took some energy to try not to think about them, but they bother me very little anymore,” and at the 1-month follow-up she reported, “I can´t concentrate when I have an intrusive memory, but the memories don’t really bother me anymore even though I have been in quarantine. Usually when I am not busy that has meant more memories.” She also said, “I have not needed to play Tetris, but it’s nice to know that I can if I have an intrusive memory.” At the 3-month follow-up, she responded, “They have not been bothering me in the past weeks. It is a little uncomfortable that they may come, but they bother me very little.”long-term effects at 3 months postintervention and, in this case, at least suggests that symptoms may continue to improve in the long term rather than rebound. This is perhaps because of the fact that the intervention is simple to use independently once it has been learned so that the participant can self-administer booster doses if needed.In this single case study, we investigated the effects of a brief visuospatial intervention designed to disrupt memory reconsolidation, thereby reducing the number of intrusive memories of trauma. Different intrusive memories were targeted one by one over six sessions, guided by a clinical psychologist. The intervention stemmed from earlier laboratory studies ,47 as wememory A) was still present, occurring only once during the past week. This quantitative reduction was mirrored in the participant’s qualitative feedback, with her noting that the intrusive memories bothered her very little at this time.The specific symptom probe design allowed us to zoom in on the effect of each intervention session on each of the participant’s four intrusive memories. All four memories reduced after being targeted, with reductions ranging between 26% and 86% (from baseline to intervention phase). By the 3-month follow-up, only the most distressing intrusive memory (Symptoms of PTSD (subthreshold for this participant) were reduced postintervention, and the same pattern was observed for symptoms of both depression and anxiety. This change was similar to the results reported by Kessler et al . InteresThe intrusive memories affected the participant’s general functioning at baseline, for example, it affected her sleep, leading to fatigue, which affected her daily functioning. After the intervention, her functioning improved as the intrusions no longer interfered with her day-to-day life at the 3-month follow-up. Her concentration improved considerably from baseline to postintervention and further at follow-up. The participant, in effect, gained back hours during which her concentration was not disrupted by intrusive memories. Both sleep and stress improved postintervention and continued to improve at follow-up.Importantly, the participant found the gameplay to be a very acceptable way to reduce the frequency of intrusive memories, similar to the Holmes et al  study wiThis intervention approach , intended not to treat the whole of PTSD but rather a single symptom, is unlike existing treatment options and potentially removes some common barriers to them. For example, barriers include a sparse number of qualified psychological therapists in Iceland  as well as the high cost of treatment and high dropout rates, stigma, and patients' reluctance to talk about the traumatic experience -12.This intervention removes patients' need to talk about and describe the trauma in detail, is low cost, and because of its simplicity, it may be delivered by nonexperts after training. It is important to explore further how this intervention approach can address other common barriers in existing treatments. Future research should explore remote delivery of the intervention  instead of in-person meetings . This wononclinicians, and to further tailor the intervention to this setting based on feedback from target users.Overall, the results of this single case study indicate that the intervention is promising, showing initial signs of effectiveness in reducing the frequency of intrusive memories of trauma that had occurred 4 decades ago and improving mental health and functioning in an Icelandic setting at least for the first participant. The intervention was well tolerated and acceptable, and the effects of the intervention may even continue after the intervention phase. The next step will be to examine whether such effects extend to other participants  and to explore remote delivery of the intervention, to explore whether it is possible to deliver by


This is a prospective population screening study for autism in toddlers aged 18–30 months old using the Quantitative Checklist for Autism in Toddlers (Q-CHAT), with follow-up at age 4.Observational study.Luton, Bedfordshire and Cambridgeshire in the UK.13 070 toddlers registered on the Child Health Surveillance Database between March 2008 and April 2009, with follow-up at age 4; 3770 (29%) were screened for autism at 18–30 months using the Q-CHAT and the Childhood Autism Spectrum Test (CAST) at follow-up at age 4.A stratified sample across the Q-CHAT score distribution was invited for diagnostic assessment (phase 1). The 4-year follow-up included the CAST and the Checklist for Referral (CFR). All with CAST ≥15, phase 1 diagnostic assessment or with developmental concerns on the CFR were invited for diagnostic assessment (phase 2). Standardised diagnostic assessment at both time-points was conducted to establish the test accuracy of the Q-CHAT.Consensus diagnostic outcome at phase 1 and phase 2.At phase 1, 3770 Q-CHATs were returned (29% response) and 121 undertook diagnostic assessment, of whom 11 met the criteria for autism. All 11 screened positive on the Q-CHAT. The positive predictive value (PPV) at a cut-point of 39 was 17% (95% CI 8% to 31%). At phase 2, 2005 of 3472 CASTs and CFRs were returned (58% response). 159 underwent diagnostic assessment, including 82 assessed in phase 1. All children meeting the criteria for autism identified via the Q-CHAT at phase 1 also met the criteria at phase 2. The PPV was 28% (95% CI 15% to 46%) after phase 1 and phase 2.The Q-CHAT can be used at 18–30 months to identify autism and enable accelerated referral for diagnostic assessment. The low PPV suggests that for every true positive there would, however, be ~4–5 false positives. At follow-up, new cases were identified, illustrating the need for continued surveillance and rescreening at multiple time-points using developmentally sensitive instruments. Not all children who later receive a diagnosis of autism are detectable during the toddler period. In the UK, a systematic population screening programme for autism is not recommended to facilitate early detection. This is because general population screening tests that have been evaluated using systematic follow-up have not proved accurate.It is possible to detect autism at 18–30 months.It is not possible to identify every child at a very young age who will later be diagnosed with autism.The Quantitative Checklist for Autism in Toddlers can be used to screen toddlers at 18–30 months.Autism affects approximately 1%–2% of the population.Lengthy delays between the first concerns and an eventual diagnosis are often experienced.The CHATThe M-CHATet alThe Quantitative Checklist for Autism in Toddlers (Q-CHAT)The objective of this study is to report a population screening study of nearly 4000 toddlers at 18–30 months using the Q-CHAT, with diagnostic assessments on a subsample of responders and subsequent follow-up at 4 years. The Child Health Surveillance Database was used to identify the population eligible to screen with the Q-CHAT. The study is reported in two phases, as undertaken. The aims of phase 1 were (1) to determine the test accuracy of the Q-CHAT in the toddler period and (2) to determine an optimal screening cut-point that could be used to select toddlers for a diagnostic assessment. The aims of phase 2 were (1) to rescreen the population at a minimum age of 4 using the Childhood Autism Spectrum Test (CAST)The first and senior authors (CA and SB-C) gave talks to parent support groups and clinicians about this research during all phases of the research and received feedback through questions and discussion. The format of the Q-CHAT itself was co-designed with a parent support group.All caregivers of infants between the ages of 18 and 30 months who were registered on the Child Health Surveillance Database at the three primary care trusts (PCT) on the date of mailing were invited to complete the Q-CHAT between March 2008 and April 2009. Questionnaires were sent in three batches to manage capacity of the team. Screening was conducted via a postal questionnaire, distributed by the PCT. Luton questionnaires were mailed in March 2008, Bedfordshire questionnaires were mailed in May 2008, and Cambridgeshire questionnaires were mailed in April 2009. Questionnaires were posted twice, 2 weeks apart, to maximise response. Returned questionnaires were scored by the research team and the sampling strategy, as detailed in the next section, was applied to establish the diagnostic assessment sample. Research diagnostic assessments were arranged as soon as possible after completion of the Q-CHAT. All members of the diagnostic assessment team remained blind to the child’s Q-CHAT score. The Q-CHAT was completed a second time prior to any of the diagnostic assessment battery in order to later examine the test–retest reliability of the Q-CHAT in a sample enriched with high scorers, which will be reported separately. Diagnostic outcome was defined by the researchers reaching a consensus diagnosis using all diagnostic assessment data and researcher judgement.The strategy for selecting participants for follow-up diagnostic assessment was weighted towards those with higher scores. The rationale for choosing 44 as the cut-point for inviting participants for follow-up diagnostic assessment at phase 1 was based on anticipated estimates of the prevalence of autism (approximately 1%) balanced with the knowledge that over 70% of children who already had a diagnosis on the autism spectrum scored 44 and above in our initial study.The final sampling groups were determined according to the maximum score, taking into account the movement across score groups from observed score to maximum score. For example, if a child’s observed score was 40 and there were two missing Q-CHAT items, 8 was added to the observed score to give a score of 48, which would therefore be that child’s sampling score. Sampling group allocation and randomisation took place prior to establishing whether or not the family had consented for further contact.www.random.org). One per cent of the children who had a maximum score ≤37 were selected for diagnostic assessment to ensure that the diagnostic assessment team were blind to whether there were likely to be concerns about the child at the time of diagnostic assessment.When the chance of being selected was less than 100%, selection for diagnostic assessment was undertaken using a random number generator (All caregivers who consented to further contact following the phase 1 screening (n=3472) were invited to participate in phase 2 at a minimum age of 48 months, between January and July 2011. Caregivers were sent the CAST, a 37-item tool to detect autism in children among those aged 4–11 years, in a non-clinical setting.All children who scored ≥15 on the CAST In both phase 1 and phase 2, diagnostic outcome was defined by the researchers reaching a consensus diagnosis using all diagnostic assessment data and researcher judgement as possible autism or autism spectrum . For each item, the response representing the most ‘autistic’ symptomatology scores 4 points and the least ‘autistic’ response scores 0. On approximately half the items  the ‘autistic’ response is the positive one. On the other half  the ‘autistic’ response is the negative one. Item 4 is concerned with speech and includes a sixth option, ‘my child does not speak’, also scoring 4 points. The total Q-CHAT score is obtained by summing the score on each item, giving a maximum of 100. Each of the 25 questions is accompanied by a colour illustration to attempt to increase both response and comprehensibility of the items.Data collected about the child included age, sex, birth order, ethnicity and multiple birth status. Data collected about the caregivers included socioeconomic status (SES) and parental educational attainment. SES was derived at person level rather than household level, using the five class system of the National Statistics Socio-economic Classification.The Autism Diagnostic Observation Schedule-Generic (ADOS-G)The Autism Diagnostic Interview-Revised (ADI-R)The Mullen Scales of Early Learning (MSEL)The Vineland Adaptive Behavior Scale (VABS)Due to the stratified sample all analyses were adjusted for the probability of being selected into the diagnostic assessment subsample at each phase via inverse probability weighting. This takes account of the differences in sampling proportions and the selective participation in the diagnostic assessment across the sampling groups. A sensitivity analysis was undertaken that also included the non-response rates at each health trust within the weights. A receiver operating characteristic (ROC) area under the curve analysis assessed the discriminant power of the Q-CHAT and CAST and CFR in distinguishing autism cases from non-autism cases  and from autism and all other atypical development from typically developing children, across all score values. CIs were obtained from robust SEs. All analyses were undertaken in Stata V.14.There were 3770 Q-CHAT questionnaires returned: 436 (14% response) from Luton, 1256 (25% response) from Bedfordshire and 2078 (42% response) from Cambridgeshire. Of these 223 were selected for diagnostic assessment, 32 did not give consent to be contacted and therefore 191 were invited and 121 completed the diagnostic assessments. See 10.1136/bmjpo-2020-000700.supp1Supplementary dataFollowing diagnostic assessment, 11 children were defined as possible autism, 16 as atypical  and 94 as typical. At a cut-point of ≥39, the PPV for autism was 17% (95% CI 8% to 31%) .From phase 1 caregivers, 3472 consenting caregivers were sent the phase 2 study materials, and 2005 returned both the CFR and the CAST (58% response), a further 6 returned only the CAST, and 5 returned only the CFR. Forty-two children who had a diagnostic assessment in phase 1 did not return the CAST or the CFR.In phase 2, 172 caregivers reported developmental concerns on the CFR and 43 children scored 15 and above on the CAST (not in phase 1 diagnostic assessment sample) and were selected for diagnostic assessment. Eighty-one children who had been assessed in phase 1 completed the diagnostic assessment . In total, 158 children completed the diagnostic assessment at phase 2, 23 with CFR concerns and CAST score ≥15, 63 with CFR concerns and CAST scores ≤15, 6 with CAST scores ≥15 with no CFR concerns, 56 who scored ≤15 on the CAST and who did not report any developmental concerns on the CFR but who were assessed in phase 1, and 10 children who were assessed in phase 1 but who did not return the CFR or CAST. Eighty-one children were assessed at both phase 1 and phase 2. Of the nine children whose diagnostic outcome at phase 1 was possible autism and who had a diagnostic assessment in phase 2, all were still classified with autism. A further six children were now classified with autism . Seventy-seven children were assessed for the first time at phase 2, having completed the Q-CHAT at phase 1. Some of these children were invited for diagnostic assessment due to indicating on the CFR that they had received a clinical diagnosis of autism and others because of their CAST score (15 or above). Of these 29 were classified as autism, 24 as atypical development and 24 as typically developing, giving a prevalence of autism of 1.57% (95% CI 0.90% to 2.74%). Using the CAST alone as the screen at phase 2, 1994 children had complete data. At a cut-point of ≥15, the sensitivity was 56% (95% CI 37% to 74%), specificity was 100% (95% CI 99% to 100%) and PPV was 81% (95% CI 59% to 92%) . The areAt a cut-point of ≥39, the sensitivity of the Q-CHAT in predicting phase 2 outcome  was 44% (95% CI 26% to 64%), specificity was 98% (95% CI 97% to 99%) and PPV was 28% (95% CI 15% to 46%) . The areOptimising the Q-CHAT cut-point for phase 2 outcome found two additional cut-points: Q-CHAT 31+ maximised sensitivity and specificity at the cost of false positives and using Q-CHAT 38+ marginally improved sensitivity with little additional benefit. Other sensitivity analyses at phase 1 and phase 2 are shown in The first two aims of the study were to determine the optimal screening cut-point on the Q-CHAT and to determine the test accuracy. At phase 1 outcome, all children who were classified as possible autism failed the Q-CHAT at a cut-point of 39, demonstrating that early detection and diagnosis of autism is possible. The PPV of the Q-CHAT for autism was 17%. Misinterpretation of behaviours that are not well established as well as developmental immaturity may contribute to low PPV. An aim of phase 2 was to rescreen the population at a minimum age of 4 using the CAST in order to identify children with autism who were not identified at phase 1  and the CFR to seek information on the child’s history of developmental concerns and diagnoses. The results indicated that the Q-CHAT did not identify all children who were later diagnosed with autism by age 4. We therefore were able to confirm the outcome of those who were identified by the Q-CHAT at 18–30 months and those who were not. A further aim in phase 2 was to confirm the discriminant power of the Q-CHAT and CAST and CFR in distinguishing autism cases from non-autism cases. At a cut-point of ≥39, the sensitivity of the Q-CHAT in predicting autism was 44% (95% CI 26% to 64%), specificity was 98% (95% CI 97% to 99%) and PPV was 28% (95% CI 15% to 46%). This demonstrates that the Q-CHAT alone cannot be used to identify all children with autism. This might reflect the Q-CHAT properties, but equally it likely reflects a subgroup of children with autism who do not show symptoms of sufficient ‘severity’ until later in childhood, and/or that symptoms of autism change with age, and so require developmentally sensitive instruments at different ages. The Diagnostic and Statistical Manual of Mental Disorders-5’s caveat that symptoms might not fully manifest until social demands exceed capacities may apply here. The CAST picked up many of the cases of autism that were missed by the Q-CHAT, demonstrating the need for repeat screening rather than relying on a single instrument at one time-point. However, the sensitivity of the CAST at a cut-point of 15 was only moderate (56%), compared with our earlier study showing a sensitivity of 100%.A strength of this study was the prospective, population-based design which allowed us to assess the Q-CHAT accurately at two time-points. Our approach to missing data and subsequent sampling strategy was conservative and ensured maximum capture of potential cases in the diagnostic assessment phase, maximising sensitivity. However, studies such as the current one are challenged by modest participation rates, selection bias and attrition over time. Despite adopting strategies to attempt to increase response, overall it was low, but not unusual for an unsolicited postal survey. Whether or not those who responded initially to the Q-CHAT (which determined the population for the rest of the study) were biased towards those with concerns (either justified or the ‘worried well’) or in the opposite direction remains largely unknown. A second limitation is that in phase 1, only 1% of children who scored ≤37 were sampled. This proportion was chosen for pragmatic reasons in relation to feasibility . None of the sampled children with scores ≤37 met our diagnostic assessment case definition. It is important to underline that these results are based purely on outcome at phase 1 and do not reflect the overall test accuracy statistics from phase 2.Few studies attempt population screening partly because they are very time-consuming and expensive. Younger toddlers have mild or transient developmental delays that resolve, resulting in low PPV, as was found in another study.Total Q-CHAT score was used to determine which sampling band each child fell into. A sum  score based on equal weighting of all items may not be the most appropriate method by which to quantify an individual’s autistic traits. Other work by our group has shown that there are at least two items on the Q-CHAT whereby the prevalence of endorsement of the trait in the autistic direction is lower for toddlers with a diagnosis of autism, compared with population controls (items 8 and 18).Overall, this study demonstrates that (1) the Q-CHAT identified every child who received an autism diagnosis at phase 1 outcome and therefore it is possible to detect autism at 18–30 months; and (2) it is not possible to identify every child at a very young age who will later be diagnosed with autism. The Q-CHAT can be used to screen toddlers at 18–30 months for autism to enable accelerated referral into the diagnostic pathway. However, the PPV is low, thus potentially generating a high number of children who may not have autism, but who may still require a developmental assessment to determine whether they warrant a referral for intervention and support. Outcome data from phase 2 revealed many children with autism that the Q-CHAT did not identify. The autism spectrum is broad and not every child will be detectable as a young toddler. Therefore, when looking at the Q-CHAT longitudinally, it is evident that screening at a single time-point results in less than optimal test properties. In a future study, we will evaluate whether the missed cases reflect a subgroup of children with autism whose symptoms are not severe enough to warrant a diagnosis during toddlerhood and that the symptoms of autism change with age. We therefore recommend continued surveillance and rescreening at multiple time-points using developmentally sensitive instruments in order to identify a higher proportion of cases for maximal public health impact, which is in line with the recommendation by the American Academy of Pediatrics.


While the relation between care involvement of informal caregivers and caregiver burden is well-known, the additional psychosocial burden related to care involvement during the COVID-19 pandemic has not yet been investigated.A total of 1000 informal caregivers, recruited offline, participated in a cross-sectional online survey from April 21 to May 2, 2020. Questionnaires were used to assess COVID-19-specific changes in the care situation, negative feelings in the care situation, problems with implementation of COVID-19 measures, concerns/excessive demands, loss of support, change in informal caregivers’ own involvement in care and problems with provision, comprehension & practicability of COVID-19 information, and to relate these issues to five indicators of care involvement . Binomial and multiple regression analyses were applied.Across indicators of care involvement, 25.5–39.7% reported that the care situation rather or greatly worsened during the COVID-19 pandemic, especially for those caring for someone with dementia or those usually relying on professional help. In a multiple regression model, the mean number of involvement indicators met was associated with age , excessive demands , problems with implementation of COVID-19 measures , an increase in caregiving by the informal caregivers themselves  as well as with no change in the amount of caregiving  and loss of support . No significant associations with the mean number of involvement indicators met were found for gender, educational level, change in the care situation, negative feelings, and provision, comprehension & practicability of COVID-19 information.Those caregivers who perceived extensive care burden were those who suffered most during the pandemic, calling for structural support by the healthcare system now and in the future.This article does not report the results of a health care intervention on human participants. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing the 2019 novel coronavirus disease (COVID-19) pandemic and the mechanisms determining the cause of COVID-19 are under medical research worldwide  but infoThe aim of the present study was to investigate the relation of COVID-19-related burden and support factors during the coronavirus pandemic with the care involvement of informal caregivers. According to the model of caregiving and stress process , we assuhttps://www.forsa.de/1/). The study sample was recruited offline and the questionnaire was provided online. Informed consent was obtained from all study participants and people were able to participate from April 21 to May 2, 2020. Contact precautions due to SARS-CoV-2 still applied during that period. For this ad-hoc survey, we followed the Declaration of Helsinki. According to statutes of the ethics committee of the Medical Faculty Charité – Universitätsmedizin Berlin, secondary data analyses do not require ethical approval according to national guidelines .We performed an ad-hoc survey with cross-sectional secondary data analysis (data that was collected by someone else for another primary purpose ) based oThe sample consisting of 1000 informal caregivers was recruited offline from a panel of about 80,000 German-speaking people ranging in age from 40 to 85 years. The sample was weighted based on the German Ageing Survey 2014, a representative survey of 40 to 85-year-olds living in private households in Germany , 24. ThoThe scales used are based on or informed by established scales and were adapted to the COVID-19 situation by an interdisciplinary team consisting of psychologists, sociologists, a political scientist, a medical doctor, and a health economist who are professionals regarding burdens and challenges of informal caregivers. This is common practice in ad-hoc survey research .The current context is the COVID-19 pandemic in spring 2020. Gender, age, and education level are background or contextual variables according to Pearlin and colleagues  and willFive involvement indicators were: main caregiver (yes [indicator met] or no by self-definition), expenditure of time (high [indicator met] = daily for 3 to 6 h or longer or low = once to multiple times a week for a minimum of an hour), level of care , dementia in care recipient (yes [indicator met]/no), professional help (day care facility and / or ambulatory care = yes [indicator met]/no). Subsequently, involvement indicators were added up to determine the mean number. The number of involvement indicators ranges from 0 to 5 with higher results indicating a higher involvement in care. In order to determine the level of care  on the basis of the German long-term care insurance guidelines according to the German Social Security Code XI, items in six modules contribute to the final score: 1) mobility = 10%; 2) either cognitive and communicative abilities; 3) behavior and psychiatric problem = 15%; 4) self-care = 40%; 5) dealing with requirements because of illness or therapy = 20%; and 6) organization of everyday life and social contacts = 15%), which are weighted differently. The score ranges from 0 to 100, with higher scores indicating worse condition. An applicant receives level of care 1 if the total score is > 12.5, indicating minor impairments of autonomy or of skills; level 2 means considerable impairments, level 3 means serious, level 4 means severe, and level 5 means most severe impairments of autonomy or of skills with 90 to 100 overall points .To evaluate the change in the care situation during the coronavirus pandemic we asked informal caregivers how their personal care situation developed overall due to the coronavirus situation in the last 4 to 8 weeks on a 3-point Likert scale .Informal caregivers were asked to state to what extent the following statements apply to them and their care situation on a 4-point Likert scale with response categories ‘applies completely, applies somewhat, applies slightly, does not apply at all’. Items were 1) I’m worried about not being able to cope with domestic nursing care in future; 2) My financial situation has deteriorated due to the combination of care work and the coronavirus situation; 3) I have more work because local services and support structures have been cancelled; 4) The current care situation is excessively stressful for me and 5) The current caregiving situation is excessively stressful for the person I care for. The score ranges from 5 to 20 with higher results indicating more concerns/excessive demands due to COVID-19.Informal caregivers were asked whether they are able to implement the following recommended COVID-19 measures into domestic nursing care; two answer categories were possible: (1) very well/fairly well and (2) not very well/not at all well. 1) Informing yourself about the latest official hints and recommendations on how to behave in the coronavirus situation; 2) Avoiding direct contact to other people outside your own household as far as possible; 3) Avoiding physical contact with the care receiver as far as possible ; 4) Washing your hands thoroughly with soap before and after direct contact with the care receiver; 5) Not touching your own face; 6) Explaining the coronavirus situation to the care receiver and/or reassuring them; 7) Wearing a face mask when close in contact to the care receiver, e.g. for personal hygiene; 8) Shifting contact to the care receiver increasingly to phone calls or video calls. The score ranges from 8 to 16 with higher results indicating more problems with implementation of COVID-19 measures.Informal caregivers were asked to state whether in each of the following cases a coronavirus-related change occurred for their care recipient on a 3-point scale: no change (0), decreased (1), has stopped completely (2). The items covered support by 1) other family members or friends; 2) neighbors; 3) ambulatory care service; 4) day care; 5) a 24 h-care worker from Germany; 6) a 24 h-care worker from abroad; 7) other health services ; 8) family physician, and 9) other helpers . The scoInformal caregivers were asked whether they agree with the following statements (strongly agree/somewhat agree/somewhat disagree/strongly disagree). The items were 1) I feel that I have access to good public information about coronavirus in Germany; 2) I feel that I can understand the public information about coronavirus and 3) I feel that I can implement the public recommendations on coronavirus effectively in the care situation. The score ranges from 3 to 6 with higher results indicating more problems with provision, comprehension & practicability of COVID-19 information.Informal caregivers were asked to state how the current situation affects their feelings  in relation to the care situation . Items were adapted from the Sense of Competence Questionnaire (SCQ)  which meInformal caregivers where asked whether caregiving by themselves 1) has stopped completely; 2) decreased; 3) hardly changed or 4) increased due to the COVID-19 pandemic.2 was used to assess the level of explained variance for the logistic regression models and adjusted R2 for the multiple regression model. Analyses were performed using IBM SPSS Statistics for Windows, version 25.0 . P values <.05 were considered to indicate statistical significance.Characteristics of informal caregivers were presented as mean and standard deviation (SD) for the continuous variable ‘number of involvement indicators met’ and as percentage for categorical variables. We conducted chi-square tests of homogeneity for categorical variables and Welch and t-tests for scales to evaluate differences in the COVID-19 specific constructs between the informal caregivers who met involvement indicators and those who did not. Furthermore, we used binomial logistic regression and multiple regression analysis to assess associations between the COVID-19 specific constructs and each of the dichotomous involvement indicators and the mean number of involvement indicators met. Gender, age and educational level were used as sociodemographic covariates. Nagelkerke RN = 1000 informal caregivers participated in the survey with a mean (±SD) age of 60 years (±9.3). The proportion of female informal caregivers was 54.7% in the total sample and the majority of informal caregivers had a medium (48.6%) or high-level  education (background and context of the stress process). Further variables characterizing the study population are household size (including caregiver), where 51.7% of the informal caregivers stated that they live with another person. 46.9% of the care recipients in this sample live alone. Moreover, 56.1% of informal caregivers were employed and most of them earned between 2500 and 4000 € per month. 55.1% of the total study population lived in a town with a population of 5000 to less than 100,000 people. The highest mean number of involvement indicators was met by caregivers aged between 70 and 79 (M = 2.6). Characteristics of informal caregivers according to involvement indicators are shown in Table In total, We further investigated COVID-19-specific constructs across five care involvement indicators (see Table 2 = 14.80%). There were no significant associations between gender, change in care situation, negative feelings or problems with provision, comprehension and practicability of COVID-19 measures with one of the involvement indicators.Results indicate that being the main caregiver was significantly associated to the age of the caregivers . Further, it was significantly associated to an increase in caregiving duty by the informal caregivers themselves  as well as to no change in how much they care they provided during COVID-19 . A high expenditure of time spent on informal caregiving was also significantly related to the age of the caregivers , to an increase in caregiving by the informal caregivers themselves , and to no change in how much care they provided during COVID-19 . A high level of care was significantly related to concerns/excessive demands , problems with implementation of COVID-19 measures , and loss of support  during COVID-19. Taking care of someone with dementia was significantly related to concerns/excessive demands  and problems with implementation of COVID-19 measures . Lastly, having no professional help for caregiving was significantly related to loss of support  = 4.96, p < .001.Finally, we conducted a multiple regression analysis with the number of involvement indicators met, regressed on the caregiver’s characteristics and the COVIC-19-specific assessments. We found age , concerns/excessive demands , problems with implementation of COVID-19 measures , an increase in caregiving by the informal caregivers themselves , no change in how much care they provided , and loss of support  during COVID-19 to be significantly related to the mean number of involvement indicators met  a worsening in the care situation during the pandemic, 2) more negative feelings, 3) more concerns/excessive demands, 4) problems with the implementation of COVID-19 measures 5) problems with provision, comprehension and practicability of COVID-19 information 6) an increase in caregiving by the informal caregivers themselves and 7) loss of support even after controlling for age, gender, and education level. As outlined by Pearlin and colleagues  the careMore than half of the present study population of caregivers are women with an average age of about sixty years and medium education level, which is much in accordance with the official data from the German national nursing care statistics . The curOur analyses show that main caregivers were more than twice as likely to either increase caregiving activities or not change anything during the COVID-19 pandemic in spring 2020. An increase in caregiving by the informal caregiver might be caused by loss of support services or retreat of the individual network. This is even clearer in the subgroup of those already spending a large amount of time  on caring activities; their effort either increased with about five-fold likelihood or did not change. Thus, it should not be taken for granted that main caregivers or those already spending a large amount of time on caregiving activities take over even more caregiving tasks. The risk for burn-out or depression might increase immediately, and support structures should especially focus on the mental wellbeing of caregivers, rather than providing financial help.Findings further indicate that with every year of age, a caregiver has a slightly increased risk of spending more time on caregiving. However, our data do not tell us if this is caused by the caregivers’ aging progress or the care receivers’ worsening health condition or both. Moreover, results underline informal caregivers’ additional burden during the pandemic in spring 2020 for those caregivers caring for someone with a high level of care or with dementia. They have a slightly increased risk for concerns, excessive demands, and problems with implementation of COVID-19 measures, indicating remarkable challenges and the need for reliable support structures to be provided by the German health care system. Those caregivers without professional help had a lower risk for loss of support in general, likely because they do the main portion of the daily care work already without any help. Even that group of informal caregivers might need professional support during the second wave of the pandemic and beyond. Finally, concerning the number of care involvement indicators, within the multivariate analysis we found the caregiver’s age to be substantially associated with a higher number of care involvement indicators. Further, substantial associations were found for more concerns/excessive demands, problems with the implementation of COVID-19 measures, loss of support, almost no change in ‘caregiving by myself’ and an increase in that as well. Therefore, it is an essential task for health professionals to mitigate these factors during the second wave of the COVID-19 pandemic and beyond through a better approach to available information on the implementation of COVID-19 measures. Moreover, staying in close contact via telephone or video conferences with friends and relatives might be important to discuss concerns and reduce different facets of lost support. A daily structure including caregiving activities and time outs for physical and mental recreation is even more important during the COVID-19 pandemic than before.To our knowledge, this was the first ad-hoc survey about the burden faced by and resources of informal caregivers due to the COVID-19 pandemic in spring 2020 in Germany. The ad-hoc survey allowed us to obtain standardized answers from 1000 informal caregivers aged 40 to 85 years immediately during contact restrictions. Additionally, our results are valuable for the current upcoming challenges for informal caregivers during the so-called second wave of the COVID-19 pandemic and beyond. However, the study’s limitations need to be addressed. Although the genuine population was recruited offline, our current survey comprised a weighted ad-hoc online survey that may limit generalizability to the entire population of informal caregivers in Germany. Thus, informal caregivers with limited access to internet or web-enabled devices might not have taken part in this survey. Moreover, the COVID-19 pandemic has shown regional variations, but our data do not differentiate for regions or hotspots. Finally, the unexpected event of the COVID-19 pandemic followed by our ad-hoc survey did not allow us to use validated assessment instruments focusing on the additional burden of caregiving. However, it is common practice in ad-hoc surveys to adapt existing validated instruments . As a liOur findings shed light on the additional burden experienced by informal caregivers due to the COVID-19 pandemic in spring 2020 in Germany. Overall, the situation deteriorated for both the main informal caregiver and the care receiver. As informal caregivers are a main pillar of many care systems around the world, who have continued to be involved rather than relinquishing responsibilities during the COVID-19 pandemic, the daily service they provide for care recipients and for society is essential and must be supported by an incoming support structure, in order to maintain the caregivers’ health and safeguard ambulatory informal care during future waves of the COVID-19 pandemic or other major crises. Our findings yield the recommendation that all institutions with a mandate to support informal caregivers  should be supported with protective clothing, regular testing for SARS-CoV-2-RNA, and the time for appropriate hygiene measures to be able to protect others and themselves and to maintain their work throughout the COVID-19 pandemic. Special attention should be paid to informal caregivers of persons with dementia and those without professional help, inter alia to avoid an increase in elder abuse or neglect that might be negative side effects of the COVID-19 pandemic. Thus, the focus should be on supporting the mental wellbeing and social participation of the caregivers along with measures that protect the caregivers and their relatives from COVID-19. In conclusion, support structures for informal caregivers should be kept running during crises now and in the future.