Quality patient handoff is vital in patient care and attainable with structured handoff systems, such as the I-PASS mnemonic. This paper describes a continuous quality improvement study occurring after the implementation of the I-PASS handoff bundle. Our objectives were to (1) determine compliance with the inclusion of I-PASS elements during handoff and (2) determine whether the addition of CORES, an electronic tool that generates a patient list designed for use with I-PASS, would improve compliance and sustainability.We developed an aim statement: 90% of handoffs would include all 6 I-PASS elements within 6 months of the addition of CORES. Two plan–do–study–act (PDSA) cycles were conducted. In PDSA 1, we implemented CORES. In PDSA 2, we reeducated residents on I-PASS elements and the importance of a quality handoff. We used a checklist to evaluate the inclusion of I-PASS elements. Following PDSA 2, we administered a survey regarding CORES to involved residents.During PDSA 1, illness severity, diagnosis, patient summary, contingency planning, action list, and receiver synthesis were present in 13%, 62%, 52%, 87%, 42%, and 25% of handoffs, respectively. Overall compliance was 47%. During PDSA 2, illness severity remained stable at 13% whereas the remainder increased to 84%, 82%, 93%, 91%, and 37%. Overall compliance increased to 67%. Following PDSA 2, 100% of survey respondents reported improved handoff with CORES.In this study, we show that neither implementation of CORES nor resident reeducation resulted in the return to high postintervention compliance observed after implementation of the I-PASS handoff bundle. The I-PASS handoff bundle is at the forefront of this transition. This handoff bundle is based on the evidence-based I-PASS mnemonic, which represents the essential elements of a successful handoff process: I, illness severity; P, patient summary; A, action items; S, situational awareness and contingency planning; S, synthesis by receiver.2The importance of quality patient handoff in decreasing sentinel events and improving patient care2 The I-PASS mnemonic continues to prove its efficacy in producing a positive transformational change in handoff structure across several academic medical centers.4After developing the I-PASS mnemonic in 2012, Starmer et al applied the project to 9 residency programs in the United States and Canada with a reduction in the overall medical error rate by 23% and a decrease in preventable adverse events by 30%.6 including the absence of a structured electronic handoff tool to be used alongside the I-PASS mnemonic.8 In a 2016 study by Clarke et al, implementation of a structured electronic tool resulted in improved trainee handoff compliance from 73% to 96% and decreased communication error 50%, with improved efficiency and workflow.9Despite this positive change, limitations to I-PASS exist, the most evident of which is the maintenance of bundle compliance. Several studies documented potential barriers to achieving sustained practice change,10 This program integrates into the Cerner electronic medical record  and formulates patient lists centered around the I-PASS mnemonic. Specifically, CORES organizes each patient’s medical information into the 6 key elements of I-PASS. The program also auto-populates up-to-date information onto the patient list, including new laboratory and diagnostic imaging studies, negating the need for our residents to update these portions of the patient list manually. The situational awareness portion of the patient list requires manual updates by our residents but will auto-populate any previously input information that is not deleted.One such structured electronic tool is the CORES handoff program by TransformativeMed Inc. (Seattle).11 However, this improvement was not sustained. Compliance with all 6 elements had deteriorated during the next 18 months.Before this study, a resident-driven project was performed to determine whether a multidisciplinary quality improvement process could improve the inclusion of I-PASS elements in the resident handoff. After completion of 3 plan–do–study–act (PDSA) cycles, the aim to include all 6 I-PASS elements in at least 90% of handoffs was accomplished.This study is a continuous quality improvement project occurring after the initial successful implementation of the I-PASS handoff bundle. It is unique in that it continues to be resident driven. The primary question of our study was to determine compliance with the inclusion of I-PASS elements during handoff and whether the addition of CORES would improve compliance and sustainability. We created an aim statement for the process measure: 90% of handoffs would include all 6 I-PASS elements within 6 months of the addition of CORES.The setting is a 25-bed pediatric unit within a children’s hospital affiliated with an academic institution. The pediatric medical team typically consists of 1 attending physician, 2–3 senior residents, and 3–4 interns. One intern resident works at night and is directly supervised by 1 senior resident, who is also responsible for covering the pediatric intensive care unit. Interns with senior resident supervision lead the verbal handoff between day and night shifts.12 as a guide for successful project execution and sustainment of positive outcomes.A senior resident recognized the positive transformational change after the initial implementation of the I-PASS mnemonic and also recognized the potential for poor sustained compliance with the current written handoff process. Written handoff consisted of a patient list in the form of an excel spreadsheet. Residents updated the spreadsheet manually with new results and changes in treatment. This senior resident saw that resident satisfaction and workflow could be improved with the implementation and integration of CORES into the handoff. The senior resident was compelled to lead a quality improvement team to achieve these goals. In developing this project, the resident used the Kurt Lewin change theory, a 3-step model of “unfreeze, change, freeze,”Our team consisted of 1 attending pediatric hospitalist, 1 senior resident, and 1 medical student. We planned to evaluate the process measure of the inclusion of individual I-PASS elements in the handoff between residents during each PDSA cycle. We used this process measure to evaluate the outcome measure of compliance with the inclusion of all 6 key elements during handoff. This project was a quality improvement project and not human subject research. Therefore, the institutional review board did not require review and approval.11 We use the results of that study as the targeted goal for the present study, whereby illness severity, diagnosis, patient summary, contingency planning, action list, and receiver synthesis were present in 97%, 100%, 100%, 100%, 100%, and 97% of handoffs, respectively.11Implementation of the I-PASS handoff bundle is outlined in the previously published article “Quality Improvement Regarding Handoff” by Studeny et al.11 Therefore, we implemented the CORES handoff program. The information technology staff integrated this program into the Cerner electronic medical record. The program generated a patient list in which each of the 6 key elements was organized and presented for each patient to guide residents through the I-PASS handoff process.Residents expressed dissatisfaction with the patient list on surveys administered during the implementation of the I-PASS handoff bundle.Residents were involved in the creation of the patient list, including what information should auto-populate. Residents were given no formal training on the CORES handoff program upon completion, as all participated in the development of the program and were therefore familiar with it. An example of the CORES patient list is shown in Table This intervention involved the reeducation of the residents. This education was less involved than the education courses completed during the implementation of the I-PASS handoff bundle. Here, all residents were given a 1-hour lecture by the chief resident. During this lecture, the chief resident reminded the residents of the 6 key elements of a quality handoff and the importance of accurate communication during patient care. This PDSA cycle occurred 6 months following PDSA 1.Following PDSA 2, our attending pediatric hospitalist and medical student developed and administered a survey to all involved residents for the attainment of qualitative data. We surveyed 24 residents anonymously on the CORES handoff tool and the perceived difficulties in maintained compliance in the inclusion of I-PASS elements during patient handoff. Written comments were encouraged. This survey is shown in Table We collected the process data that included the individual 6 key elements of the I-PASS mnemonic using a checklist. On this checklist, the observer documented whether or not each patient handoff included the elements of illness severity, diagnosis, patient summary, contingency planning, action list, and receiver synthesis. We calculated the averaged percent inclusion of all 6 key elements to determine the outcome measure of overall compliance. The total time for handoff was documented and evaluated as a balancing measure. Verbal handoff occurred between recurring day and night shift teams throughout the week and between random resident coverage on weekends. Both morning and evening handoffs were observed on weekdays and weekends. On average, residents included 15 patients during each handoff.A medical student member of our quality improvement team completed the data collection. The student was trained in I-PASS along with residents during the implementation of the handoff bundle and acted as an independent observer during handoff. The team participating in handoff was not involved in data collection. The medical student acted as the sole observer in each PDSA cycle, including those throughout the implementation of the I-PASS handoff bundle, for consistency in data collection. The checklist used for data collection is shown in Table We conducted this study during late winter and early spring months to ensure that every intern resident had experience giving verbal handoff before data collection.During the study period, the medical student observed verbal handoffs on a total of 428 individual patients for the inclusion of the 6 elements, 222 after PDSA 1, and 206 after PDSA 2. During PDSA 1, element compliance of illness severity, diagnosis, patient summary, contingency planning, action list, and receiver synthesis was 13%, 62%, 52%, 87%, 42%, and 25%, respectively. Overall compliance was 47%. During PDSA 2, documentation of illness severity remained stable at 13%, whereas the other elements of diagnosis, patient summary, contingency planning, action list, and receiver synthesis increased to 84%, 82%, 93%, 91%, and 37%, respectively. Overall compliance increased to 67%. Handoff time per patient decreased from an average of 1.85 minutes in PDSA 1 to 1.01 minutes in PDSA 2. Full compliance data after each PDSA cycle is shown in Figure In the postintervention survey, 100% of surveyed residents reported that the implementation of CORES improved patient handoff between residents. Residents believed this improvement occurred because of the auto-population of patient information by the CORES handoff tool. Specifically, 1 resident stated, “CORES improved handoff and decreased error on documenting medications, labs, etc. because this information is auto-populated. It has streamlined the checkout process and cut down on time spent updating the list, which gives more time to education and patient care.” Many positives of CORES were identified, including handoff becoming more “efficient and streamlined” and that it “served as a reminder to talk about contingency plans and rank sickness of patients.” However, respondents also identified negatives. Residents reported that CORES made the handoff feel “redundant when known facts of long-term patient’s history were repeated” and reported information was “too much for straightforward patients, making it feel like an easy patient took too long to go through.” Another identified negative was that “residents would forget to manually add patients to the CORES list if they were admitted from another service, and the night resident would not receive handoff on these patients.” When asked to comment on the decline of inclusion of 6 key elements of I-PASS over time, residents attributed this decline to “infrequent reeducation,” “poor dissemination to younger classes,” and that I-PASS was “never really ingrained into our culture, making it easy to go back to our old handoff style.”With hopes of improving resident satisfaction and patient care, this project began with resident-driven integration of the CORES handoff program via a quality improvement project. In the interim 18-month time-lapse between the implementation of the I-PASS handoff bundle and integration of the CORES handoff tool, we report a significant decrease in the inclusion of the 6 key elements during handoff. The inclusion of the 6 key elements improved after completion of resident reeducation 6 months later. However, neither implementation of CORES nor resident reeducation was followed by a return to the high compliance observed after implementation of the original I-PASS handoff bundle.We note that compliance in every key element improved after resident reeducation except patient illness severity. We believe this may be due to excluding all phrases other than “stable, watcher, or unstable” when collecting data for that key element. For example, if a resident described a patient as “doing well” during his or her handoff, we did not equate that to “stable” nor document that the resident included patient severity in the handoff. This difference in resident style and verbiage may have been a barrier to improvement in the inclusion of that key element.Our results reflect that we continue to face the challenge of culture change after the implementation of the I-PASS handoff bundle. We cannot obtain culture change until we “freeze” our changes, as outlined in the Kurt Lewin change theory. We believe our interventions were not followed by a return to previously high postimplementation levels because our lack of reeducation never allowed for “freezing” after changes were made. This deficiency was made evident by resident responses in the administered survey. For example, 1 respondent stated, “We do not reevaluate I-PASS often enough to make it our culture. We discussed the process at the beginning of the residency, but the process was discussed minimally afterward. Perhaps readdressing I-PASS once every quarter would be beneficial to helping the residents implement I-PASS more often and efficiently. It can be discussed at our monthly resident forum or between lectures. Also, making sure the senior residents are trained effectively so that the process will be passed down to the interns may be beneficial.”Furthermore, our results indicate that although CORES improved workflow, it did not in itself produce improved handoffs. This result may be due to the overreliance on the computer program by our residents. Our residents may not focus on ensuring all key elements are included in their verbal handoff if each key element is present on the patient list. However, it is also possible that compliance would have been lower in the 18-month time-lapse if we had not implemented CORES. We did not evaluate these data, which is a limitation of the study.We conclude that education is the key to facing the challenge of culture change, which in turn will improve compliance with and sustainability of the I-PASS handoff. Therefore, we plan to reeducate our senior residents in the late spring or early summer months before incoming intern residents begin. This education will remind senior residents of the importance of quality handoff and prompt their use of I-PASS, so that quality verbal handoff may be modeled for the incoming intern residents. Second, we plan to educate incoming intern residents on the entire I-PASS handoff bundle during their orientation. By doing these educational interventions with both senior and intern residents, we hope I-PASS will become the culture of patient handoff and improve sustained compliance at our institution.In this study, we measured the inclusion of the key elements but did not evaluate the accuracy of spoken content and patient-related details during handoff. Therefore, our future goals are not only to maintain compliance with the I-PASS handoff but also to evaluate and ensure accurate handoffs. Once we achieve both compliance and accuracy, we plan to evaluate for relationships with adverse events and medication errors to determine any difference after implementation of the I-PASS handoff. Lessons learned here will guide our attainment of these future goals and our global aim to improve patient safety.The authors have no financial interest to declare in relation to the content of this article.


Upper limb functional deficits are common after stroke and result from motor weakness, ataxia, spasticity, spatial neglect, and poor stamina. Past studies employing a range of commercial gaming systems to deliver rehabilitation to stroke patients provided short-term efficacy but have not yet demonstrated whether or not those games are acceptable, that is, motivational, comfortable, and engaging, which are all necessary for potential adoption and use by patients.The goal of the study was to assess the acceptability of a smartphone-based augmented reality game as a means of delivering stroke rehabilitation for patients with upper limb motor function loss.Patients aged 50 to 70 years, all of whom experienced motor deficits after acute ischemic stroke, participated in 3 optional therapy sessions using augmented reality therapeutic gaming over the course of 1 week, targeting deficits in upper extremity strength and range of motion. After completion of the game, we administered a 16-item questionnaire to the patients to assess the game’s acceptability; 8 questions were answered by rating on a scale from 1 (very negative experience) to 5 (very positive experience); 8 questions were qualitative.P=.04; patient 2: P=.04; patient 4: P=.004; patient 5: P=.04).Patients (n=5) completed a total of 23 out of 45 scheduled augmented reality game sessions, with patient fatigue as the primary factor for uncompleted sessions. Each patient consented to 9 potential game sessions and completed a mean of 4.6 (SE 1.3) games. Of the 5 patients, 4 (80%) completed the questionnaire at the end of their final gaming session. Of note, patients were motivated to continue to the end of a given gaming session , to try other game-based therapies , to do another session , and to perform other daily rehabilitation exercises . In addition, participants gave mean scores of 4.00 (95% CI 2.87-5.13) for overall experience; 4.25 (95% CI 3.31-5.19) for comfort; 3.25 (95% CI 2.31-4.19) for finding the study fun, enjoyable, and engaging; and 3.50 (95% CI 2.52-4.48) for believing the technology could help them reach their rehabilitation goals. For each of the 4 patients, their reported scores were statistically significantly higher than those generated by a random sampling of values (patient 1: Based on the questionnaire scores, the patients with upper limb motor deficits following stroke who participated in our case study found our augmented reality game motivating, comfortable, engaging, and tolerable. Improvements in augmented reality technology motivated by this case study may one day allow patients to work with improved versions of this therapy independently in their own home. We therefore anticipate that smartphone-based augmented reality gaming systems may eventually provide useful postdischarge self-treatment as a supplement to professional therapy for patients with upper limb deficiencies from stroke. Stroke induces a variety of functional impairments, as well as pain and other ailments, depending on its type and location . Common Patients who engage in rigorous, time-intensive, and challenging therapeutic exercises after ischemic stroke tend to experience greater functional recovery, while if ignored or insufficiently treated, impairments may remain ,5. The dRehabilitation doctors and medical staff, therefore, face a significant problem: how can they provide high intensity therapy in large quantities for upper limb impairments with this seemingly intrinsic motivational deficit? Especially problematic are patient’s therapeutic needs after their discharge from the hospital—their therapeutic needs still exist, but medical staff have substantially reduced access to the patient to provide targeted care. Given the difficulty of this problem, an insufficient percentage of patients regain the full functional potential of their upper limb after ischemic stroke . This reUse of commercial augmented reality devices has found recent application in stroke rehabilitation using existing expensive commercial headsets ,6-17. HoSince patient motivation often drives a larger dosage of rehabilitation therapy, hence, improved clinical outcomes ,21, we hThis acceptability study was conducted at Harborview Medical Center in Seattle, Washington from November 2018 to March 2019. Inpatients who were recovering from an acute ischemic stroke participated and provided consent. These patients had impaired strength as determined by physical and occupational therapists. To be included in the study, they had to have at least antigravity strength in deltoid or biceps muscles as well as the ability to perform internal and external shoulder rotations. All patients in this study had a Medical Research Council manual muscle score of 3 or 4 in the affected limb.We designed and built an augmented reality game using Unity (Unity Technologies) that is deployable on any modern smartphone with a camera . The gamNotably, we used the TeamViewer (TeamViewer AG) app to project the screen view of the patient from the phone to a laptop, so we could see the patient’s view with, however, the complex landscape was also projected in the background, so we could check the viewer’s alignment with the landscape while they played .Set-up of the game, to ensure that system function was verified, occurred prior to patients using the system. Patients followed verbal directions and instructions from study staff on how to use the system, facilitated by demonstration of the game using the TeamViewer app. Examples of directions included how to start the game, the actions required to pick up the fish, and how to colocate the dolphin plus fish with the turtle for point accumulation. Some patients required physical assistance to adjust the view of the environment. Examples of physical assistance included moving the patient’s chair or wheelchair closer or farther away from the images recognized by the camera .In this study, we asked the patients to complete 3 30-minute multigaming sessions on separate days over the course of 1 week while the patients continued their standard therapy schedule. Sessions were conducted after all prescribed therapy so as not to interfere with the patient’s schedule. We gave patients the choice to start with their affected or unaffected hand each game while encouraging them to try their affected hand.Before gaming, we fitted the patient with a nitrile glove. Before and after every gaming session the headsets, smartphone, and TeamViewer laptop were wiped with hospital-grade bleach wipes and left to dry for at least 2 minutes. Each game lasted approximately 4 minutes and was always played in a seated position. The game displayed a score to the user in real time and at the end of the game. We recorded final scores for each user and game. We also recorded which hand the patient used for each game. After each game, we removed the headset and restarted the game on the mobile phone. During a 2-minute break after each game, we asked the user to describe their experience and took notes on their verbal commentary. Our goal was for each patient to play 9 games over the course of 1 week.To test our hypothesis, we provided an 8-question questionnaire that assayed the patient’s perceived engagement and the acceptability of their experience at the end of the third completed session. This questionnaire assessed the patient’s feelings regarding the gaming experience, their perceived acceptability of the experience in terms of its motivational qualities, their perception of comfortability, and their enjoyment of the game . We also asked an additional 8 questions about their previous experiences with videogames and the likelihood that such a system would be used by them in the future.We determined the statistical significance of the answers to the first 8 questions by comparing the numerical distribution of each patient’s answers against those of a random distribution of answers to the same questions .We recruited 5 patients in rehabilitation into the study. Patient 1 was a 58-year-old man with right thalamic intraparenchymal hemorrhage and presented with left-sided hemiparesis with major fatigue in the left arm post–daily therapy. Patient 2 was a 70-year-old man with bilateral right>left pontine ischemic stroke with visual impairment and double vision in the left eye. The patient had limited range of motion in his right arm and hand with function that increased sufficiently after admittance that he met our inclusion criteria. Patient 3 was a 67-year-old man with left middle cerebral artery ischemic stroke. His left arm was his nondominant hand and was self-reported to be functioning at “100%.” His affected right hand was his dominant hand and self-reported to be functioning “at 50%.” Patient 4 was a 50-year-old woman with right thalamic intraparenchymal hemorrhage. The patient’s left hand was affected. Patient 5 was a 65-year-old man with right anterior cerebral artery ischemic stroke. The patient typically wore glasses. He said it was “small double vision/blurriness in his right eye.” The mean age of the 5 patients was 62 years old. Of the 4 patients who completed our questionnaire, all 4 lacked experience with augmented reality while 3 out of 4 had no experience with videogames. Finally, 1 patient reported little experience with videogames (less than 3 times in 24 months).No adverse medical events occurred during our study. Patient 2, despite the relative severity of his reduced function, felt sufficiently motivated to try the game for each of the 3 sessions. Patient 3 was discharged early, and therefore, did not complete 6 of 45 possible sessions (13%) across all patients. A total of 3 of 45 sessions (7%) from 2 patients were incomplete due to their fatigue from daily rehabilitation sessions; 12 of 45 sessions (27%) from 3 patients were incomplete due to discontinuation of the study session. Finally, 1 of 45 sessions (2%) was incomplete due to malfunction of the gaming apparatus.Patients 1, 2, 4, and 5 completed the questionnaire that we gave at the end of their final session; patient 3 was discharged before completion of their participation in the study. P values for each patient are reported in P=.04; patient 2: P=.04; patient 4: P=.004; patient 5: P=.04) consistent with the interpretation that the patients found our augmented reality game acceptable.Here we report all of the patient comments offered to us. A common theme expressed by the patients was their desire to have better hand-held controllers for the game than the custom boxes, since the boxes made it difficult to navigate the dolphin to the target. Patient 3 did not complete the questionnaire due to early discharge. Some patients chose not to comment on all 3 open response questions.In our observational case study on the acceptability of a therapeutic smartphone-based augmented reality gaming for patients with upper limb motor impairment from acute ischemic stroke, 5 users with ages in the range of 50 to 70 years volunteered and completed a total of 23 of 45 possible gaming sessions; 4 patients who remained in the rehabilitation unit completed a questionnaire after completing their gaming sessions and before their discharge. The patients rated their motivation, comfort, the value of their experience, and desire to play another round of the game the highest. In contrast, the lowest rated aspects were enjoyability of the game itself and motivation to try other rehabilitation technologies. It is worth noting, however, that all scores were equal to or better than neutral scores. In this study, point accumulation was unrelated to acceptability. Nonetheless, even when a patient did not earn any points during a given game, they reported that our augmented reality game was generally comfortable and that they were motivated to try to play the game again.While 3 out of 4 of the patients engaged in all 3 of their gaming sessions, 1 patient left the study due to their early discharge, which reduced the total number of games by 13%. Enrolling more patients would address this issue.Another limitation was the quality of the game, affected in part by limitations imposed by our use of state-of-the-art but limited camera control software and our box-shaped controller, whose size—necessitated by the need to encompass the hand of the patient—made it difficult for users to select a moving target with their affected hand even with additional verbal cues. Compared to virtual reality, state-of-the-art augmented reality programs deployable on smartphones currently lack features and stability mainly due to outdated hardware specifications. This resulted in some frustration and lower participant satisfaction scores as reflected by questionnaire responses.Given our focus on assaying the patient’s experience of the augmented reality game, another limitation was that we did not include a comparison of the patient’s experience with standard therapy plus the game versus a separate group of patients who experienced only their standard therapy. Future work will include this comparison as part of an efficacy study, once we improve the mechanics of the game itself.The fatigue experienced by patients during the day of their sessions also impacted their experience with our technology. Recall that all test patients experienced our augmented reality technology after completing their regularly schedule therapy. While all patients reported fatigue, 2 out of 5 patients dropped out of at least 1 game session due to postrehabilitation fatigue. Also, most of our patients who participated in the study had little to no experience with augmented reality and videogames. They, therefore, could not compare our game with other such games. This minimal experience with augmented and virtual reality is typical of this demographic of adults 50 years and older , a fact With this case study, we report our initial findings regarding the acceptability of our augmented reality approach to acute stroke rehabilitation, in anticipation of future studies that would test for efficacy. This allows us to gauge the requirements for more formal, and eventually, long-term studies of augmented reality game rehabilitation in an older stroke population. The next logical step is to refine the augmented reality platform and therapeutic gaming software to make it more engaging and with more robust functionality. For example, body ownership studies suggest that the visual feedback a patient receives from viewing their own limb may be more beneficial while recovering from stroke than that of an unrelated virtual component ,23,24. WMembers of an older population recovering from acute stroke found smartphone-based augmented reality game targeting therapy of the upper limb acceptable. We also identified improvements to the experience that will inform the next study of this potential therapy. Of importance, such a gaming set-up could be used for home-based therapy due to its relatively low cost and ease of use. Therefore, this work informs the next formal study of this technology for upper limb motor rehabilitation. We anticipate eventual study of this technology in the home setting, after acute rehabilitation, which is of interest since patients spend most of their time at home performing rehabilitation exercises after a stroke. We anticipate that a sufficiently engaging smartphone-based game will lead to more use and greater therapeutic benefit experienced by the patient, as well as possibly improved clinical outcomes for patients.


Depressive symptoms show different trajectories throughout childhood and adolescence that may have different consequences for adult outcomes.To examine trajectories of childhood depressive symptoms and their association with education and employment outcomes in early adulthood.We estimated latent trajectory classes from participants with repeated measures of self-reported depressive symptoms between 11 and 24 years of age and examined their association with two distal outcomes: university degree and those not in employment, education or training at age 24.n = 9399) yielded five heterogenous trajectories of depressive symptoms. The largest group found (70.5% of participants) had a stable trajectory of low depressive symptoms (stable–low). The other four groups had symptom profiles that reached full-threshold levels at different developmental stages and for different durations. We identified the following groups: childhood–limited (5.1% of participants) with full-threshold symptoms at ages 11–13; childhood–persistent (3.5%) with full-threshold symptoms at ages 13–24; adolescent onset (9.4%) with full-threshold symptoms at ages 17–19; and early-adult onset (11.6%) with full-threshold symptoms at ages 22–24. Relative to the majority ‘stable–low’ group, the other four groups all exhibited higher risks of one or both adult outcomes.Our main analyses (Accurate identification of depressive symptom trajectories requires data spanning the period from early adolescence to early adulthood. Consideration of changes in, as well as levels of, depressive symptoms could improve the targeting of preventative interventions in early-to-mid adolescence. The general understanding is that symptoms are rare in childhood but begin to rise around age 12 and peak in late adolescence (age 16–19), before decreasing in young adulthood.4 Research has recently focused on heterogeneity in trajectories of depressive symptoms, on the basis that classification of distinct subgroups may inform prognosis and development of interventions.7 Studies tend to identify a large group with low and stable symptoms throughout adolescence and smaller groups with moderate, high, increasing or decreasing trajectories. However, attempts to find a consensus on a taxonomy of depressive trajectories are complicated by marked differences in periods over which trajectories are estimated. A recent systematic review of trajectory-based studies of adolescent depression found only 3 (out of 20) studies that used a follow-up period encompassing the 12–20 age range, with the majority covering different subsets of the years prior to age 20 only.5 A trajectory group that appears homogeneous over a restricted developmental stage may in fact contain heterogenous groups whose trajectories diverge before or after the observation period. The median age at onset of major depressive episodes is the early-to-mid 20s8 and studies ending before this age cannot identify potentially distinct trajectories associated with differences that emerged during that period.4Worldwide, depressive disorders are the single largest contributor to the health burden among 10- to 24-year-olds.7 and genetic risk.9 Much less attention has been paid to the later-life consequences of different trajectories,3 and most studies addressing this were focused on associations between trajectory and later psychiatric diagnoses.7 This study examines the consequences of adolescent depressive symptoms for education and employment-related outcomes in young adulthood. Specifically, for individuals aged 24, we examine whether they have a university degree and whether they are currently not in education, employment or training (NEET). The benefits of higher education qualifications to an individual, both financially and in terms of broader well-being, are well-documented, as are the social returns associated with lower public social welfare spending and higher tax revenue.10 Furthermore, young people who are NEET are regarded as a high-risk group for adverse health, personal and societal outcomes. Whereas degree attainment distinguishes around one-third of under-30s at the higher end of the socioeconomic spectrum,10 NEET status distinguishes just over one in ten 16- to 24-year-olds at the lower end of the socioeconomic spectrum.11 Understanding of the full burden of early mental health conditions requires knowledge of the impact not only on health but also on a range of social outcomes such as these, and can potentially have a major influence on economic calculations of the benefits of intervention programmes.12Several previous studies explored the risk factors associated with membership of different trajectory groups in depth, establishing that female gender is strongly associated with groups with higher symptoms, along with (less consistently) low socioeconomic status, stressful life events, substance use, conduct problems, lack of social/peer support, lack of a positive parental relationship, parental depressive symptoms14 The relationship with higher-education participation or completion is less clear with both significant15 and non-significant16 associations found. Adolescent depression has also been linked to poor young adult employment outcomes16 including NEET status.18 Mechanisms operating during school years discussed in this literature include impaired concentration, reduced motivation and disengagement;13 and truancy, long-term school absence, peer problems, disruptive classroom behaviour and substance use.19 Poor educational achievement and disengagement from school are themselves mediators for adult NEET status19 and continuation of mental disorders into young adulthood may also have current effects on ability to engage in employment or training, for example via reduced motivation or coping with stress during the job application process.18 However, it has also been argued that the associations with depression reflect confounding16 or bidirectionality, with evidence that academic problems20 and NEET status18 themselves have mental health consequences.A considerable body of research has explored the links between adolescent mental health conditions (including depression) and education and employment-related outcomes, but this literature typically considers mental health measures at just one or two time points. It is well established that adolescent emotional problems are negatively related to school grades/completion.21 It is clear that much remains to be learned about distinct trajectories of depressive symptoms that are (and are not) linked to poorer adult social outcomes and the timing at which different groups can be identified. The overarching aim of this study is to add to this small strand of literature and provide the first UK evidence on the educational and employment consequences associated with different trajectory profiles. Unlike most previous trajectory-based studies, we are able to estimate trajectories using nine measurements from pre-adolescence (age 11) to young adulthood (age 24) for a large population-based sample. These features of the data make it possible to identify heterogeneous trajectory groups that may be conflated in studies with shorter follow-up periods and/or smaller sample sizes. Previous work using the same data has analysed repeated measures of depressive symptoms9 or explored the relationship between one-off measures of depressive symptoms and educational outcomes/NEET status,14 but this is the first study using these data to test for the presence of heterogeneous trajectory groups over the full 13-year follow-up period and relate these trajectories to young adulthood outcomes. We also tested whether the associations between trajectory group and the distal outcomes are robust to controls for a set of baseline confounders suggested in the literature. Because of the fluctuating nature of depressive symptoms during adolescence, associations with educational/employment outcomes can be attenuated when symptoms are measured at only one time point. Using repeated measures may also aid early identification of at-risk individuals if distinct trajectories, as well as symptom severity, predict poor adult outcomes.6Few studies (and none from the UK) have directly explored the association between trajectories of depressive symptoms in adolescence and subsequent education/employment outcomes, with only two following individuals from adolescence into young adulthood.22 First, it may be that the effects of depressive symptoms are time-limited, so that trajectories characterised by high current levels of symptoms at the time of NEET status measurement (age 24) and university study (ages 17–22) are the ones most strongly associated with the outcomes (recency hypothesis). Second, it may be that depressive symptoms at a particular developmental stage have consistent consequences for adult outcomes, regardless of the trajectory, because they coincide with a key transition period for education and/or employment (sensitive period hypothesis). Third, it may be that chronicity of symptoms matters, so that risks are greatest for those on trajectories with long spells of symptoms at or above a clinical level (accumulation of risk model).Although scarcity of previous evidence hinders hypothesis formulation, insights from life-course epidemiological theory suggest three alternatives.24 Ethical approval for the study was obtained from the ALSPAC Ethics and Law committee and the local research ethics committees. Individuals consented to participate in the study on the clear understanding that all measures would be used for research purposes only and not to inform decisions about their health (see supplementary File 1 available at https://doi.org/10.1192/bjo.2019.90 for further details).The Avon Longitudinal Study of Parents and Children (ALSPAC) was set up by enrolling pregnant women resident in Avon, UK with expected dates of delivery from 1 April 1991 to 31 December 1992. A total of 14 901 were alive at 1 year of age, with 14 691 constituting the full sample size available for this study.25 is a 13-item self-reported questionnaire widely used for screening of depressive symptoms in adolescents. Individuals are asked to appraise each phrase as descriptive of their experiences ‘most of the time’, ‘sometimes’, or ‘not at all’ in the past 2 weeks. Total scores range between 0 and 26, with higher scores indicating more depressive symptoms. ALSPAC participants completed this questionnaire on nine occasions between the ages of approximately 11 and 24. Although there is no agreed cut-off, scores above 11 have been previously interpreted as indicating the presence of depression.14The SMFQ26 is a semi-structured interview examining mental health symptoms and their impact on children aged 11–16 years. It can be used to assign psychiatric diagnoses following DSM-IV criteria. Diagnosis of depression was assessed at the 15-year visit.The DAWBA27 is a structured instrument designed for use in general practice and community settings. Psychiatric diagnoses can be assigned following ICD-10 criteria. This includes depression, which was assessed at the 18-year visit.The CIS-RAs part of the data collection at 23 years, participants were asked if they had ever been diagnosed with depression by a clinician.Participants were asked whether they attempted suicide between 21 and 24 years.University degree. We used self-reported information collected at the 23- and 24-year waves to create a binary variable indicating whether the participant obtained a university degree by age 24.11At age 24 participants were asked to complete a checklist of whether they were currently engaged in 11 different education/employment activities (see supplementary Table 1). Participants were classified as NEET if they indicated they were not engaged in any of the following: full- or part-time paid work; government training scheme; full-time education; self-employment; other self-reported activity indicating a job or training . This definition is in line with the definition used by the UK Office for National Statistics.28 8 weeks after giving birth. This ten-item instrument is intended for pregnant women and new mothers. Total scores range between 0 and 30, with scores of 13 or above often interpreted as signifying depression.Mothers completed the Edinburgh Post-Natal Depression ScaleAt the child's 8-year clinic visit, mothers were asked whether they had finished compulsory school.29 Verbal and non-verbal scores were averaged for the analyses.Child IQ was also measured at the 8-year visit using the Third Edition of the Weschler Intelligence Scale for Children.Children were asked at age 11 whether they enjoyed school and whether they were afraid of school failure. Both variables were recoded into two categories .We used school records to identify students performing at the expected level or above in English, maths, and science at age 11 .30 using the SMFQ measures as manifest variables with the timing of measurements fixed to the sample mean age at each measurement occasion (see supplementary Table 2). We considered sample-size-adjusted Bayesian Information Criterion (ssaBIC),31 Lo-Mendell-Rubin adjusted likelihood ratio test (LRT),32 bootstrap LRT test,33 size of smallest class and substantial interpretability to decide between models with different numbers of classes. We validated the identified trajectories by using three-step bias-adjusted growth mixture models34 to estimate their association with clinically diagnosed depression at age 15 and 18, history of depression by age 23, and suicide attempts between 21 and 24 years. This approach recognises that latent class categories can only be assigned probabilistically to participants. Further details of the statistical analysis are provided in supplementary File 1.We conducted growth mixture modelling in Mplus v8,35 that utilises data from participants with partially, as well as fully, observed data. FIML produces unbiased estimates under the assumption that data are missing at random (MAR). Since MAR is unlikely to hold perfectly in this application, we assessed the sensitivity of our results to the exclusion of participants with several patterns of missing data. Our main first-stage latent class model utilised the maximum possible sample of respondents compatible with FIML estimation: those with at least one SMFQ measurement (n = 9399). We also performed analyses separately for boys and girls; restricting the sample to participants with either three or more, or all nine SMFQ measures; and excluding the cases of 183 individuals with unusual reporting patterns. In the second-stage models that use latent class membership to predict the distal outcomes, we compared results for participants where outcomes were observed and those where FIML was used to incorporate participants with missing outcome data. Mplus does not allow for FIML modelling of missing covariate data, hence the sample size for the adjusted models was reduced from n = 9399 to n = 4501.As shown below, there were large amounts of missing data in our analyses. Exclusion of individuals with missing data may result in unrepresentative samples, so we implement full-information-maximum-likelihood (FIML) estimationn = 14 691). Many variables are only partially observed for this sample, but it is possible to compare the full cohort with the analysis sample of those with at least one SMFQ measure . The analysis sample is similar in terms of maternal age, has a higher percentage of female participants, maternal educational attainment and children's academic attainment at age 11, and a lower rate of postnatal depression.The earliest SMFQ measure was taken at an average age of 10.6 years and was reported by 7364 children, whereas the most recent measure was obtained at an average age of 23.8 years and was completed by 3915 participants. A wide range of response patterns were observed, (see supplementary Tables 2–4). Correlations between SMFQ measures showed stronger associations for measurements taken at similar ages (see supplementary Table 5). Descriptive characteristics for a range of subsamples with different patterns of missing data are presented in n = 7084), and particularly when all nine measurements are required (n = 1031).Models that adjust for potential confounding from these covariates allow us to assess the sensitivity of results to these dimensions of sample composition. Our main analyses are conducted on the sample in the second column and the first-stage latent class model uses FIML to include participants with missing SMFQ scores. Supplementary Table 6 show the proportions of missing data for each trajectory group. Even if MAR is unlikely to hold perfectly, the descriptive statistics in the third and fourth columns of 11 The final column in n = 4051). Its composition is generally similar to the main analysis sample in the second column although proportions of maternal education, child academic attainment at 11 and degree attainment are higher and NEET status is less prevalent. The consequences of this final sample selection at the second stage can be assessed by comparing unadjusted results with those from the full-analysis sample.Statistics in the first two columns reveal a large amount of missing data on our NEET and degree outcome measures, which were measured at the end of the observation period. Among those with valid outcome data, 56.2% had a degree and 9.1% were NEET at age 24. These proportions indicate noticeably higher levels of advantage than 2016 national estimates, which put the per cent of the UK population under 30 with degrees at 34% and the per cent of 24-year-olds in England with NEET status at 14.0%.We fitted models using a quadratic polynomial for shapes and adding classes incrementally. The variance of the intercept term was fixed to zero because for models with more than two classes, the classes explained the variation in the intercept. Solutions with seven or more classes yielded very small class sizes and therefore we did not consider those further. Adding classes to the model generally resulted in a better model fit  and was supported by both tests up to five classes (see supplementary Table 7). The entropy of this model (0.736) suggests some degree of uncertainty in the classification.Adding a sixth class still improved model fit, however, the Lo-Mendell-Rubin test suggested that the six-class solution did not significantly improve fit over the five-class solution. Furthermore, although all trajectories in the five-class solution had a meaningful interpretation, this was less clear for the six-class solution, which split the category with the highest symptoms from the five-class model into two categories with different severity levels, one of them accounting for only 2.1% of the sample. Therefore, the remaining analyses of this paper focus on the five-class solution.Sensitivity analyses restricted to boys, girls, participants with at least three SMFQ measures, participants with all nine SMFQ measures, or excluding unusual reporting patterns further supported the five-class solution, although the distribution of participants across classes showed some variations .The third column for each outcome can be compared with the first column to isolate the impact of dropping participants with any missing data on covariates. In most cases the results are very similar, the evidence of a lower proportion of degree attainment in the adolescent onset compared with the stable–low group (odds ratio (OR)=0.54, 95% CI 0.41–0.71) becomes unclear with the restricted sample . Comparison of the third and fourth columns for each outcome provides evidence on the extent to which the unadjusted ORs reflect confounding from baseline characteristics. Estimates changed only marginally, suggesting that differences in the outcomes across groups are not driven by pre-existing differences in maternal characteristics, cognitive ability or educational experiences by age 11.7 low (here stable–low); high (childhood–persistent); increasing (early-adult onset); and decreasing (childhood–limited). Conversely, the adolescent onset group with symptoms that rise to a peak around age 17–19 before declining again is more unusual, although it has been found in at least one trajectory-based study2 and is consistent with studies examining aggregate patterns for the whole population.4 Notably, symptoms in the adolescent onset, childhood–persistent and early-adult onset groups all follow consistently increasing trajectories, albeit at different levels, between 11 and 19 years.In this study, we identified five groups of individuals with different trajectories of depressive symptoms between late childhood (approximately age 11) and early adulthood (approximately age 24) and explored the associations of each group with educational and employment outcomes. Four of these trajectories have been widely reported in previous studies,Most previous studies did not survey individuals into their 20s and, as a result, may conflate the adolescent onset group with one of the other increasing-trajectory groups. One contribution of this study, therefore, is to provide evidence of heterogeneity among those whose symptoms rise through to late adolescence and identify a sizable group (9.4% of the sample) whose symptoms remit in early adulthood, in contrast to other groups whose symptoms persist or worsen. The increasing symptoms trajectories found in many previous studies of adolescent depression may in fact consist of two different subgroups with quite distinct prognoses. Whether these differences are the result of different experiences in late adolescence  or different pre-existing characteristics is an intriguing question for future research.21 These results suggest that studies examining associations between depressive symptoms measured on a single occasion in adolescence and adult outcomes will tend to understate the later risks associated with depressive symptoms in young people. Such studies are also unable to examine hypotheses regarding the importance of symptom timing and chronicity.We then went on to examine the association of trajectory groups with two young adult social outcomes: attainment of a university degree and NEET status at 24 years. All four groups with a full-threshold period of depressive symptoms were at greater risk of at least one poor adult outcome compared with the stable–low group. This finding is in line with a previous study that found three trajectories between ages 16 and 30 and showed that those in the ‘moderate–decreasing’ and ‘high–stable’ groups were at progressively greater risk of poor educational attainment and low income at age 30 than individuals in a ‘low–decreasing’ group.We highlighted three hypotheses derived from life-course epidemiology  and found some support for each one. The distinction is somewhat blurred for the degree outcome, which is the culmination of a lengthy process that took place between 17 and 22 years for most participants. The recency hypothesis states that the outcome will be predicted most strongly by symptoms experienced just before or at the time of its measurement. This is supported by the fact that the three groups with full-threshold symptoms at some point between ages 17 and 22 all had lower odds of attaining a university degree than the stable–low group. Further, two of these groups – childhood–persistent and early-adult onset – had continued full-threshold levels of symptoms at ages 22–24 and higher odds of being NEET at age 24. Furthermore, consistently with the sensitive period hypothesis, all children with full-threshold levels of symptoms between 11 and 13 years were at higher risk of NEET status at 24, regardless of whether those symptoms tended to worsen over time (childhood–persistent) or improve (childhood–limited). Finally, the group with sustained clinical symptom levels throughout the longest period of time (childhood–persistent) showed the poorest outcomes in early adulthood, which supports the accumulation hypothesis.Our results suggest that full-threshold levels of symptoms at any age between 11 and 24 are a marker for poor adult outcomes in at least one domain. We have argued that, perhaps unsurprisingly, measurement of adolescent depressive symptoms on any single occasion will tend to yield false negatives. A valuable insight from our trajectory-based analysis, however, is that identification could be improved by drawing on information on whether an individual's mood has worsened over the previous 1–3 years, in addition to information on the level of current symptoms.36For instance, the early–adult onset group only exhibits full-threshold levels of symptoms after age 20 but can be distinguished from the stable–low group as early as 17. Moreover, increasing symptoms between 11 and 13 years characterise the adolescent onset and childhood–persistent groups, but the former only reaches full-threshold levels of symptoms until age 17. Hence an implication of our findings is that if practitioners were to collect and interpret retrospective information on symptom trajectories, as well as symptom levels, in early-to-mid adolescence more individuals could potentially be effectively targeted for preventative interventions. Many preventative interventions for internalising disorders in young people have been trialled, mostly delivered within the school setting and psychologically centred. The evidence suggests that, on average, these interventions are effective but with considerable heterogeneity in effects and larger effects for targeted interventions.16 The stability of our results when covariates are added to the models provides little support for this explanation perhaps because, as explained above, trajectory group differences are likely to be larger and more precisely estimated than single time point effects. However, there is also evidence that bivariate associations may be driven by co-occurring disorders such as anxiety, substance use, delinquency and psychotic experiences, although interpretations vary as to whether these are considered confounders18 or mediators.19 We did not examine this type of explanation in this study but we note that preventative mental health interventions often have a positive impact on a range of disorders36 and so may be effective even if depressive symptoms are not causal in a narrow sense. Regardless of the mechanism targeted by interventions, our finding of problematic trajectories characterised by symptoms that remain high or increase into early adulthood adds further support to calls for the elimination of discontinuities in provision of mental health services at age 18.37A number of studies have argued that bivariate relationships between depressive symptoms in young people and educational and employment outcomes reflect confounding by common factors such as childhood socioeconomic conditions and academic aptitude.This study is rare in the literature in drawing on a large population-based sample with high-frequency repeated measures of depressive symptoms spanning early adolescence to early adulthood and measures of key young adult outcomes of social importance. However, it has several limitations. First, there are large amounts of missing data on many of the analysis variables. We dealt with this via FIML methods and sensitivity analyses to explore the robustness of the results. However, we acknowledge that the necessary MAR assumption is unlikely to hold perfectly and there may be remaining biases in the results.Second, this is an observational study and therefore the associations that we found are prone to confounding. Adjustment for a set of baseline covariates commonly discussed in the literature had little impact on the results, but they do not encompass all potential sources of unobserved heterogeneity. Third, this study did not explore the role of any time-varying covariates. Future research should explore factors driving the trajectory shapes, including co-occurring mental disorders, treatment (or lack of it) for mental health disorders and a range of social processes.17 Here we simply use the UK government definition of NEET and note that parents/homemakers make up only a small fraction of our NEET group. Fifth, our findings are limited to our sample and might not generalise to other nations, ethnicities or cultures.Fourth, some research has found evidence of heterogeneity within the NEET group and argued that parents/homemakers should be grouped with those engaged in work or training.


What proportion of coronavirus disease 2019 (COVID-19) spread is associated with transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from persons with no symptoms?In this decision analytical model assessing multiple scenarios for the infectious period and the proportion of transmission from individuals who never have COVID-19 symptoms, transmission from asymptomatic individuals was estimated to account for more than half of all transmission.The findings of this study suggest that the identification and isolation of persons with symptomatic COVID-19 alone will not control the ongoing spread of SARS-CoV-2. This decision analytical model assesses the proportion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmissions in the community that likely occur from persons without symptoms. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiology of coronavirus disease 2019 (COVID-19), is readily transmitted person to person. Optimal control of COVID-19 depends on directing resources and health messaging to mitigation efforts that are most likely to prevent transmission, but the relative importance of such measures has been disputed.To assess the proportion of SARS-CoV-2 transmissions in the community that likely occur from persons without symptoms.This decision analytical model assessed the relative amount of transmission from presymptomatic, never symptomatic, and symptomatic individuals across a range of scenarios in which the proportion of transmission from people who never develop symptoms  and the infectious period were varied according to published best estimates. For all estimates, data from a meta-analysis was used to set the incubation period at a median of 5 days. The infectious period duration was maintained at 10 days, and peak infectiousness was varied between 3 and 7 days (−2 and +2 days relative to the median incubation period). The overall proportion of SARS-CoV-2 was varied between 0% and 70% to assess a wide range of possible proportions.Level of transmission of SARS-CoV-2 from presymptomatic, never symptomatic, and symptomatic individuals.The baseline assumptions for the model were that peak infectiousness occurred at the median of symptom onset and that 30% of individuals with infection never develop symptoms and are 75% as infectious as those who do develop symptoms. Combined, these baseline assumptions imply that persons with infection who never develop symptoms may account for approximately 24% of all transmission. In this base case, 59% of all transmission came from asymptomatic transmission, comprising 35% from presymptomatic individuals and 24% from individuals who never develop symptoms. Under a broad range of values for each of these assumptions, at least 50% of new SARS-CoV-2 infections was estimated to have originated from exposure to individuals with infection but without symptoms.In this decision analytical model of multiple scenarios of proportions of asymptomatic individuals with COVID-19 and infectious periods, transmission from asymptomatic individuals was estimated to account for more than half of all transmissions. In addition to identification and isolation of persons with symptomatic COVID-19, effective control of spread will require reducing the risk of transmission from people with infection who do not have symptoms. These findings suggest that measures such as wearing masks, hand hygiene, social distancing, and strategic testing of people who are not ill will be foundational to slowing the spread of COVID-19 until safe and effective vaccines are available and widely used. However, measures to reduce transmission from individuals who do not have COVID-19 symptoms have become controversial and politicized and have likely had negative effects on the economy and many societal activities. Optimal control of COVID-19 depends on directing resources and health messaging to mitigation efforts that are most likely to prevent transmission. The relative importance of mitigation measures that prevent transmission from persons without symptoms has been disputed. Determining the proportion of SARS-CoV-2 transmission that occurs from persons without symptoms is foundational to prioritizing control practices and policies.As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the novel coronavirus that causes coronavirus disease 2019 (COVID-19), began to spread globally, it became apparent that the virus, unlike the closely related SARS-CoV in the 2003 outbreak, could not be contained by symptom-based screening alone. Asymptomatic and clinically mild infections were uncommon during the 2003 SARS-CoV outbreak, and there were no reported instances of transmission from persons before the onset of symptoms.5 This finding meant that the epidemic was growing faster than would be expected if transmission were limited to the period of illness during which individuals were symptomatic. By the time a second generation of individuals was developing symptoms, a third generation was already being infected. Epidemiological data from early in the pandemic also suggested the possibility of presymptomatic transmission,7 and laboratory studies confirmed that levels of viral RNA in respiratory secretions were already high at the time of symptom onset.10Transmission by persons who are infected but do not have any symptoms can arise from 2 different infection states: presymptomatic individuals (who are infectious before developing symptoms) and individuals who never experience symptoms . Early modeling studies of COVID-19 case data found that the generation interval of SARS-CoV-2 was shorter than the serial interval, indicating that the average time between 1 person being infected and that person infecting someone else was shorter than the average time between 1 person developing symptoms and the person they infected developing symptoms.14 and laboratory and epidemiological evidence suggests that individuals who never develop symptoms may be as likely as individuals with symptoms to transmit SARS-CoV-2 to others.16Asymptomatic SARS-CoV-2 transmission also occurs because of individuals with infection who are never symptomatic . The proportion of individuals with infection who never have apparent symptoms is difficult to quantify because it requires intensive prospective clinical sampling and symptom screening from a representative sample of individuals with and without infection. Nonetheless, evidence from household contact studies indicates that asymptomatic or very mild symptomatic infections occur,The Centers for Disease Control and Prevention determined that this decision analytical study, which involved no enrollment of human subjects, did not require institutional review board approval. We used a simple model to assess the proportion of transmission from presymptomatic , never symptomatic, and symptomatic individuals across a range of scenarios in which we varied the timing of the infectious period to assess different contributions of presymptomatic transmission and the proportion of transmission from individuals who never develop symptoms .17 Therefore the daily (t) probability of symptom onset (sop) for individuals who develop symptoms was:20 We then assumed that infectiousness (I) over time can be approximated by a γ density function and that the average person is infectious for as long as approximately 10 days 11:For all estimates we used data from a meta-analysis of 8 studies from China to set the incubation period at a median of 5 days with 95% of symptomatic individuals developing symptoms by day 12.om onset .9,12,14,nsp) and the relative contribution of these infections to transmission (nsr). Estimates of nsp range from single digits to more than 50%, many with potential biases related to the study population  and the extent of long-term follow-up20 :ns, between 0% and 70% to assess a wide range of possible proportions. The daily proportion of transmission from individuals after symptom onset (sT) was therefore:psT) individuals, ie, those who develop symptoms but become infectious prior to symptom onset, is:Uncertainty also remains about the proportion of individuals with infection who are never symptomatic . No statistical testing was conducted, so no prespecified level of significance was set.Under baseline assumptions, approximately 59% of all transmission came from asymptomatic transmission: 35% from presymptomatic individuals and 24% from individuals who are never symptomatic . BecauseHolding the day of peak infectiousness constant at day 5 and decreasing the proportion of transmission from individuals who are never symptomatic to 10% with a relative infectiousness of 75% (baseline assumption), the proportion of all transmission from those who are never symptomatic decreased to 8%, presymptomatic transmission increased to 42%, and combined asymptomatic transmission was 50% of all transmission D. In conUncertainty remains regarding the magnitude of both presymptomatic and never symptomatic transmission. Therefore, we analyzed a wider range of each of these components, with peak infectiousness varying between 2 days before (more presymptomatic transmission) to 2 days after (less presymptomatic transmission) median symptom onset and with never symptomatic transmission ranging from 0% to 70% . Under t21 and reinforce the importance of asymptomatic transmission: across a range of plausible scenarios, at least 50% of transmission was estimated to have occurred from persons without symptoms. This overall proportion of transmission from presymptomatic and never symptomatic individuals is key to identifying mitigation measures that may be able to control SARS-CoV-2. For example, if the reproduction number (R) in a given setting is 2.0, then at least a 50% reduction in transmission is needed to drive the reproductive number below 1.0. Given that in some settings R is likely much greater than 2 and more than half of transmissions may come from individuals who are asymptomatic at the time of transmission, effective control must mitigate transmission risk from people without symptoms.The findings presented here complement an earlier assessmentThis study has limitations. First, we used a simplistic model to represent a complex phenomenon, ie, the average infectiousness of SARS-CoV-2 infections over time. We used this model deliberately to test assumptions about the timing of peak infectiousness and transmission among asymptomatic individuals so that we could vary only these 2 critical parameters and assess their relative effects. Therefore, these results lack quantitative precision, but they demonstrate the qualitative roles of these 2 components and show that across broad ranges of possible assumptions, the finding that asymptomatic transmission is a critical component of SARS-CoV-2 transmission dynamics remains constant.20 so in populations of older individuals, never asymptomatic transmission may be less important. However, specific age groups are rarely exclusively isolated from other age groups, so asymptomatic transmission risk is still important in those groups and even more so in younger age groups, in which transmission may be even more dominated by asymptomatic transmission.20As discussed here, the exact proportions of presymptomatic and never symptomatic transmission are not known. This also applies to the incubation period estimates, which are based on individual exposure and onset windows that are difficult to observe with precision and therefore include substantial uncertainty even when leveraging estimates across multiple studies. Moreover, they likely vary substantially in different populations. For example, older individuals are more likely than younger persons to experience symptoms,Real-world transmission dynamics are also not entirely dependent on the individual-level dynamics of infectiousness over time. Now that COVID-19 is widely recognized, individuals with COVID-19 symptoms are more likely to isolate themselves and further reduce the proportion of transmission from symptomatic individuals, shifting a greater proportion of transmission to those who do not have symptoms. In this sense, the estimates here represent the lower end of the proportion of asymptomatic transmission in the presence of interventions to reduce symptomatic transmission.21 These measures can also be supplemented by strategic testing of people who are not ill, such as those who have exposures to known cases  or are at high risk of exposing others . Multiple measures that effectively address transmission risk in the absence of symptoms are imperative to control SARS-CoV-2.Under a range of assumptions of presymptomatic transmission and transmission from individuals with infection who never develop symptoms, the model presented here estimated that more than half of transmission comes from asymptomatic individuals. In the absence of effective and widespread use of therapeutics or vaccines that can shorten or eliminate infectivity, successful control of SARS-CoV-2 cannot rely solely on identifying and isolating symptomatic cases; even if implemented effectively, this strategy would be insufficient. These findings suggest that effective control also requires reducing the risk of transmission from people with infection who do not have symptoms. Measures such as mask wearing and social distancing empower individuals to protect themselves and, if infected, to reduce risk to their communities.


Sickle cell disease (SCD) is a heterogeneous group of inherited hemoglobinopathies associated with mutations in the beta subunit of the hemoglobin protein. Several case reports and scientific reviews of the current literature have been published that indicate individuals having a single copy of the mutant sickle cell allele, known as sickle cell trait (SCT), can experience the same functional asplenia and increased risk of cerebrovascular accidents, kidney disease, cardiovascular effects, and veno-occlusive diseases as SCD patients when they are exposed to extreme conditions and stressful environments such as high-altitude, deep-sea diving, and intense physical activity. SCT also impacts the management of chronic illnesses such as diabetes mellitus. Here, we report a patient presenting for primary care follow-up after an SCT-related splenic infarction in order to emphasize the unique impact of SCT on long-term care and preventive medicine in the primary care setting. Sickle cell disease (SCD) is a heterogeneous group of inherited hemoglobinopathies associated with mutations in the beta subunit of the hemoglobin protein. It can present with a variety of complications that revolve around the general pathophysiology of vaso-occlusive pain crises. As it was first described as an autosomal recessive condition, it was initially hypothesized that disease states and complications were only seen in patients homozygous for the mutant allele or for those determined to be compound heterozygotes with mutant alleles associated with other hemoglobinopathies  -2. SinceSCT is the most common hemoglobinopathy in the United States, affecting one in 12 (8%-10%) of African American individuals . Given tA 63-year-old Hispanic male, with a past medical history of type 2 diabetes mellitus (T2DM), stage I nonalcoholic fatty liver disease, hyperlipidemia, and erectile dysfunction, presented to the primary care clinic for follow-up after hospital admission for splenic infarction. The patient has no previous history of trauma to the area, recent infections, or prior abdominal surgeries. The patient began to have left upper quadrant (LUQ) pain while on a trip to Peru  associated with shortness of breath and O2 saturation of 63%. The patient had never traveled to high altitudes before and had no recollection of any similar past events of shortness of breath. He was admitted to a local hospital and an abdominal ultrasound was performed, showing no remarkable findings that could explain LUQ pain and desaturation. The pain continued after he returned to the United States at sea level, causing him to go to the emergency room where computed tomography (CT) showed a 2 cm subcapsular splenic hematoma, grade 2. He underwent splenic artery embolization on hospital day 3 and was cleared for discharge on hospital day 5. The patient underwent evaluation for the etiology of the desaturation-related splenic hemorrhage while admitted. Echocardiogram, pulmonary function tests, and the four-extremity ultrasound were all within normal limits. Hematology was consulted to rule out infectious causes and malignant causes of splenic infarction. Blood was drawn for hemoglobin electrophoresis, and the patient was given a new diagnosis of SCT based on a hemoglobin S percentage of 42.6%. After an otherwise benign workup, the patient was diagnosed with splenic infarction secondary to splenic syndrome. While hospitalized, lab data indicated uncontrolled T2DM with glycated hemoglobin (HbA1c) of 8.2 mmol/mol. The patient was discharged with instructions to follow-up with gastroenterology for a repeat abdominal CT with contrast in four weeks and with his primary care physician (PCP) for diabetes management. His most recent HbA1c level recorded at his PCP office two months prior to hospitalization was also 8.2 mmol/mol. Prior to hospitalization, the patient was taking metformin 1000 mg PO twice daily, atorvastatin 40 mg po daily, and glipizide 5 mg PO daily. No other prescribed or over-the-counter medicines were reported by the patient. While hospitalized, 10 units of long-acting insulin glargine were added to his treatment regimen at bedtime. Full medicine reconciliation while admitted did not indicate the ingestion of any medications that could have contributed to the development of the splenic infarction.In this case, the patient had no known personal or familial history of SCT or SCD, thus the evaluation for the etiology of the splenic rupture was initially negative until SCD was considered. The Journal of Genetic Counseling stated in 2018 that only 16% of individuals of childbearing age know their SCT status, indicating that a large percentage of the SCT patient population is unaware of their diagnosis and its impact on their health and the health of their offspring . AlthougThe management of chronic illnesses and preventive health measures can be affected by an SCT diagnosis, and the patient may require closer monitoring by the primary care physician. Research indicates that SCT can worsen vascular dysfunction seen in patients with hyperlipidemia and T2DM . The forHbA1c concentration is the most commonly utilized method for measuring and monitoring blood glucose concentrations and diabetes management. Several studies, including a retrospective cohort study published in The Journal of the American Medical Association (JAMA) in 2017, provide evidence that HbA1c concentrations are an inadequate technique for monitoring blood glucose management in SCT and SCD patients with T2DM -13. In tMultiple hepatobiliary pathologies are associated with SCD, but no data indicate a correlation between SCT/SCD and fatty liver . This paSCT patients require close monitoring of renal function. The partial oxygen pressure of the renal medullary environment is below the threshold for sickling and can be associated with infarction and hematuria related to papillary necrosis . AfricanMeticulous eye examinations should be performed due to the increased risk of retinal changes and retinal artery/arteriole occlusion. Optometry examination guidelines have not yet been released. These patients may present on physical examination with vitreous hemorrhages, chorioretinitis, tortuous/dilated retinal veins, and retinal microaneurysms. Optometric exams should include a measurement of intraocular pressure and visual acuity as well as the evaluation of the anterior and posterior structures by fluorescein angiography .SCT patients also require additional lifestyle education to lower the risk of having a vaso-occlusive crisis. The National Athletic Trainers Association released a statement in 2002 promoting increased screening and offering measures to decrease the risk of SCT-related exertional collapse. SCT patients are encouraged to participate in year-round activities that allow for a constant, steady-state of exertion and should not participate in activities or performance tests such as mile run, serial sprints, or other activities that could cause a sudden increase in exertional status. SCT patients are also encouraged to limit exertion while ill, dehydrated, or experiencing extreme heat or high altitude. These recommendations were supported by a similar statement from the American Heart Association and American College of Cardiology in 2015 ,17. MultThis case contributes supporting data that SCT patients are at risk for vaso-occlusive crises in high physiologic stress environments and emphasizes the importance of considering SCT pathophysiology in the management of chronic illnesses in the primary care setting. Tight control of hyperlipidemia, coronary artery disease, hypertension, and T2DM is required to reduce the risk of SCT-related complications. Further studies and the development of T2DM screening and management guidelines are needed in order to optimize T2DM care in SCT patients. Preventive care and education must also be performed diligently in these patients, as they are at an increased risk for infections and adverse events in high-stress environments. Patients require close follow-up of vaccination status as well as periodic optometry visits. The development of guidelines needs to be addressed for some of the SCT/SCD-related complications such as retinal and renal pathologies. Extensive education on exercise safety and concerning symptoms of SCT-related complications and diseases is essential. A public health concern is also brought to light, as many patients with sickle cell-related disorders are living unaware of their diagnosis, placing them and their future generations at risk for SCT or SCD-related complications.