H(1) = 7.91, p = .005) and related to the movements, speech and game scenarios of the robot. Parent ratings on the added value of the robot were mainly positive , while lower parent ratings were related to inflexibility of robot behaviour.The aim of this study is to present a robot-assisted therapy protocol for children with ASD based on the current state-of-the-art in both ASD intervention research and robotics research, and critically evaluate its adherence and acceptability based on child as well as parent ratings. The robot-assisted therapy was designed based on motivational components of Pivotal Response Treatment (PRT), a highly promising and feasible intervention focused at training “pivotal” (key) areas such as motivation for social interaction and self-initiations, with the goal of establishing collateral gains in untargeted areas of functioning and development, affected by autism spectrum disorders. Overall, children (3–8 y) could adhere to the robot-assisted therapy protocol (Mean percentage of treatment adherence 85.5%), showed positive affect ratings after therapy sessions (positive in 86.6% of sessions) and high robot likability scores (high in 79.4% of sessions). Positive likability ratings were mainly given by school-aged children ( Since demands from the social environment can be challenging and confusing for children with autism spectrum disorder (ASD)4 and their gains from high predictability, the use of technology within intervention such as robotics can be especially beneficial for them. Robots may be intrinsically appealing to children with ASD and contribute to their motivation for social interaction6 and the number of studies to the use of robots for children with ASD increased markedly during the last decade7.Digital technology can support interventions and care for children with psychiatric disorders12. Also, heterogeneity in findings is reported in studies examining the potential value of adding a robot to existing interventions for ASD. In a study examining a 8-week robot-assisted intervention based on the principles of applied behaviour analysis (ABA), Treatment and Education of Autistic and related Communication-Handicapped Children (TEACCH) and the Picture Exchange Communication System (PECS), no increases in joint attention were found13. Moreover, although children showed more social attention in the robot-assisted intervention condition, this attention was predominantly not spontaneous and reduced during the mid and late sessions13. Another study showed that spontaneous question asking increased during a 6-session Social Story intervention with a robot14. In contrast, other studies found no significant changes in verbal initiations, responses and play behaviour during a 5-week intervention with a robot based on Lego© therapy15 or a similar increase in self-initiated question asking during a 4-session ABA-based intervention with a robot compared to a human trainer condition in children with ASD16. Results of these studies differ highly, which may reflect the use of different sample sizes and research methodology17. Furthermore, little is known about how children with ASD and their parents rate the acceptability of using a robot within existing interventions, with only one study reporting on this15. Specifically, parents of children with ASD reported that their child enjoyed the robot-assisted intervention and that components of the intervention where rated as acceptable by children with ASD15, although only 3 children with ASD were included in the study. Currently, no studies are available that elaborate on reasons for low or high acceptability and likability ratings during a robot-assisted intervention for children with ASD. Additionally, while treatment adherence is regarded as a core underlying assumption for evidence-ba sed interventions18 it is remarkable that earlier studies to newly developed protocols for robot assistance within established interventions for ASD do not report on this.Despite the assumed benefits, thus far, pilot studies do not show unambiguously positive results of using robots in interventions that target core symptoms  of children with ASDwithin small study samples of children with ASD; in eye contact, imitation, and touch behaviour19 and on approach and avoidance behaviour20. Although investigation of subgroups based on individual characteristics such as age,  IQ, severity of ASD symptoms and psychiatric comorbidity is key for a better understanding of which children with ASD benefit from robot-assisted interventions21 this has not been done in earlier studies.Furthermore, it is unclear how the design of the robot-assisted intervention relates to treatment adherence and acceptability of both children with ASD and their parents. Specific features of robot-assisted interventions may only be appropriate for subgroups of children with ASD, since a high heterogeneity in responses towards a robot exists also 24, 2) using a robot within established treatment models for ASD25 and during multiple sessions17, 3) using a robot that is roughly the size of the child undergoing the therapy while balancing mechanical and human-like robotic features26, 4) providing input for robot behaviour by a human therapist25 while using movements and sounds within the robot’s behaviour for attracting the child’s attention and for use as positive reinforcements26, 5) incorporating child choice and a high level of adaptability to each specific child within the robot’s behaviour26, and 6) emphasizing, throughout the intervention, the possibility of generalization of skills learned during robot interaction to human interaction27. However, little is known about whether young children with ASD and their parents can adhere to a robot-assisted intervention protocol that incorporates these promising design features.In previous studies, specific design features are identified that may facilitate the appropriateness of robot-assisted interventions for children with ASD: 1) co-creation between researchers in psychopathology, neuroscience, robotics, and engineering in developing robot-assisted intervention protocols28. In our design we combine specific motivational components for children with ASD derived from both PRT and robotics research (see Methods). Incorporating these, we hypothesize that children with ASD will show high likability and affect scores towards the robot during a 20-session weekly intervention. Similarly, we anticipate that parental ratings on whether the robot has an added value to the PRT are high. Additionally, we aim to explore whether child and parent ratings of using a robot within PRT differ by individual characteristics of age, IQ, severity of ASD symptoms and psychiatric comorbidity. Lastly, reasons for child and parent ratings are explored to obtain a comprehensive view on which features of the robot-assisted intervention can be beneficial for children with ASD.Here, we aim to present a robot-assisted Pivotal Response Treatment (PRT) protocol while exploring its treatment adherence and acceptability based on both child and parent ratings for subgroups of children with ASD. As a highly promising and feasible intervention, PRT aims at establishing collateral gains in untargeted areas of functioning and development by training “pivotal” (key) areas such as motivation for social interaction and self-initiationshttps://www.trialregister.nl/trial/4487; NL4487/NTR4712). Inclusion criteria were: 1) a clinical diagnosis of ASD, 2) meeting criteria for ASD based on DSM-IV29, 3) aged 3–8 years, 4) a total intelligence quotient (TIQ) of above 70, 5) ability to speak with one-word sentences at minimum, and 6) at least one of the parents speaks Dutch to the child. Additionally, an exclusion criterion was having received PRT previously. Dosages of medication were stable at start of the intervention phase and maintained so as much as possible during the intervention. Of the 25 participants, 23 (92%) scored above the cut-off for ASD on the Autism Diagnostic Observation Schedule (ADOS-2)30 while two participants scored one point below the cut-off. These participants were included in the study as they had a very clear clinical ASD diagnosis, based on a thorough multidisciplinary and multi-informant psychiatric examination. For mothers and fathers of participants respectively, education level was low for 24% and 27%; average for 36% and 23%, and high for 40% and 50%.Twenty-five participants received robot-assisted PRT. This group was part of a larger randomised clinical trial to the effectiveness of PRT for children with ASD . Prior to the robot-assisted PRT, parents received psycho-education on ASD if not received in the past, either individually or group-based. Robot-assisted PRT consisted of 20 sessions, once a week, by a certified PRT-therapist trained up until level III. The study received ethical approval by the Dutch Research Ethical Committee  and all procedures were in accordance with the 1964 Helsinki declaration and its later amendments.Participants were recruited from clinical referrals to the outpatient departments of Karakter, an expert center for child and adolescent psychiatry in The Netherlands. Parents received verbal and extensive written information on the outline and aims of the study and both parents signed an informed consent form prior to inclusion. Eventual missing baseline measures were administered if these had not been administered as part of the diagnostic procedure  was involved to discuss and practice the use of the PRT techniques at school/daycare. The sessions were held weekly, with two parent-child sessions followed by one parent-only session throughout the intervention. Each PRT session had a duration of 45 minutes, except for one 90-min visit for PRT implementation within the class- or day-care room.The robot-assisted intervention was based on the treatment manual that was developed by the original PRT developersA NAO robot, controlled by the PRT therapist, was used in the first 15–20 minutes of each of the 14 parent-child sessions. Parents were seated close to the child and were asked to observe how the PRT techniques were used to improve their child’s communication. In this way, the therapist was able to model the PRT techniques for the parents by controlling the robot. Although the game scenarios were primarily designed for robot-child interaction, the parent was instructed to directly and naturally reward the child when he or she showed an appropriate initiation towards the parents. After the robot-assisted part of the session, parents practiced the PRT techniques during a game with their child and the therapist provided feedback during and after the session.32 were used in the development of game scenarios for robot-child interaction:The following PRT motivational techniquesNine different game scenarios for robot-child interaction were created that are developmentally appropriate for children aged 3–8 years. Based on parental information and child’s preferences during the therapy session(s), the type of game  was chosen. The kind of game  was chosen by the child during the interaction with the robot. Furthermore, a text-to-speech module could be activated when the child changed the subject of the robot-child conversation.Learning opportunities were included in the game scenarios for robot-child interaction by 1) placing the desired materials in a closed box before start of the game, 2) providing the child with only parts of the game materials at once, and 3) providing only parts of information about a game. Learning opportunities were provided if the child was interested in the robot and the game. If the child’s attention was drawn to another game or subject, the therapist used the text-to-speech module to respond accordingly.A game scenario was selected that included learning opportunities for both maintained (easy) and new (difficult) tasks for each child. Nine different therapeutic game scenarios were created, each with seven different levels of complexity. The level of prompting (i.e. the help that the child received for showing appropriate behaviour) could be adjusted throughout the game scenario and interspersed between easy (e.g. tell prompt) and difficult (e.g. wait prompt).In the pre-programmed game scenarios, direct and natural reinforcement was provided upon the child’s behaviour. For instance, when the child asked: “robot, can you open the box?” the therapist directly controlled the robot in opening the box. Also, when a child took an initiative that was not anticipated in the pre-programmed game scenario, the therapist used the text-to-speech module to provide a direct and natural reinforcement.When the child showed the target behaviour or an appropriate attempt, a direct and natural reinforcement was provided by the robot (i.e. the therapist controlling the programming environment). However, when the child did not initiate spontaneously or the attempt was deemed inappropriate, the the text-to-speech module was activated to prompt the child.Information on the type of robot and controlling of robot behavior and a detailed description on the development of game scenarios for robot-child interaction is described in the Supplementary Information.33 Wechsler Preschool and Primary Scale of Intelligence (WPPSI-III)34 or Mullen Scales of Early Learning (MSEL)35.Demographic information on participant’s age of inclusion, gender, and psychiatric comorbidity was extracted from case files. Estimating total intelligence quotient (TIQ) was based on either the Wechsler Intelligence Scale for Children (WISC-III)30, a semi-structured observation schedule measuring social communication and repetitive, restricted behaviour. An ADOS-2 severity score was calculated36. Scores 1–4, 5–7 and 8–10 represented low, moderate and high severity of ASD symptoms respectively.Severity of ASD symptoms was assessed by initial administration of the ADOS-2Treatment adherence was defined as the percentage of therapy sessions in which both the child and parents accepted the use of the NAO robot and in which the child showed sufficient motivation to complete the 15- to 20-min robot-child interaction. For participants that were unable to complete the protocol due to reasons that were unrelated to the use of the NAO robot (e.g. because of another indication for intervention), the percentage was calculated over the number of sessions that were received. Reasons for incomplete treatment adherence were documented.At the start and end of each robot-assisted session, the child was presented with a 5-point Visual Analogue Scale (VAS) measuring child affect . At the end of the session child likability of the robot  was measured. Additionally, when possible, the child was asked to indicate a reason for their affect and likability ratings. Reasons for child affect ratings were categorized into (1) related to the robot’s movements and/or speech, (2) related to the game play with the robot, (3) related to the therapy session, but not to the robot (e.g. game play with parents), (4) not related to therapy session, (5) no reason stated. Percentages were calculated for negative (score 1 or 2), neutral (score 3) and positive (score 4 and 5) affect ratings. Reasons for robot likability by the child were categorized into: (1) related to robot appearance, (2) related to robot’s movements and/or speech, (3) related to the game play with the robot, (4) not directly related to the robot, (5) no reason stated. Percentages were calculated for negative (score 1 or 2), neutral (score 3) and positive (score 4 and 5) robot likability ratings.37 after each robot-assisted parent-child session. The SRS has been developed for clinical use during different interventions and has been shown to have a high reliability (Cronbach’s α = .93) and moderate concurrent validity (range of r = .37-.63 with Working Alliance Inventory)38. On a VAS line ranging from 0 to 100, parents indicated 1) whether they felt heard, understood and respected (Relationship), 2) whether the session worked on what parents wanted to work on , 3) whether the therapist’s approach was a good fit (Approach or Method) and 4) overall, whether today’s session was right for the parent . For the purpose of this study, two VAS lines ranging from 0 to 100 were added to the SRS which were the main focus of this study: 1) whether the communication of the robot towards the child was clear (Robot Communication) and 2) whether the robot was an additional value to the current therapy session . Higher scores on the VAS reflected a more positive attitude of parents.Additional to the child ratings, parents were asked to complete the Session Rating Scale (SRS)All procedures performed in studies involving human participants were in accordance with ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.Informed consent was obtained from all individual participants included in the study.To explore whether child and parent ratings of using a robot within PRT differ by individual characteristics, subgroups of participants were made based on age (pre-school: 3–5 years versus school-aged: 6–8 years), gender , TIQ , severity of ASD symptoms (low versus moderate versus high severity) and psychiatric comorbidity (co-morbid diagnosis of attention-deficit hyperactivity disorder (ADHD) versus other co-morbid psychiatric disorder(s), versus no co-morbidity).Table Reasons for aborting the use of the robot in these participants were either that the participant rejected the game scenario’s with the robot when noticing that the therapist controlled robot’s behaviour (2 participants), or that the participant showed anxiety towards the robot during multiple sessions (1 participant).H(1) = 4.54, p = .033), but not between the low and moderate ASD severity group (H(1) = 2.74, p = .098), and between the low and high ASD severity group (H(1) = 0.56, p = .456). Furthermore, the preschool-aged group had lower mean robot likability scores compared with the school-aged group  on Relationship; 87.9 (SD = 8.3) on Goals and Topics; 88.3 (SD = 9.0) on Approach or Method; and 90.2 (SD = 7.6) on the Overall scale. Table Robot Communication and Robot Value were also high in each subgroup. Of Robot Communication ratings, 95.0% were above 50 (indicating “robot communicated clearly to the child”) and of Robot Value ratings, 94.3% were above 50 . There were no significant differences on parent ratings of Robot Communication between subgroups based on individual child characteristics. However, in the subgroup of females, parents showed significantly higher mean ratings of Robot Value compared with the subgroup of males  parents noticed that the child could not fully understand the robot speech (Robot Communication), 2) parents noticed that there was a delay in robot’s response towards the child (Robot Communication) and 3) parents noticed that the game scenarios were either too easy or too difficult .Exploring reasons for high ratings indicated that parents: 1) noticed that the child was enthusiastic and motivated for the game scenario with the robot, 2) noticed that the child initiated at home and at school by telling about the robot. Furthermore, exploration of outliers indicated that 9.5% of parent ratings were below 2 SDs of the mean on The majority of child affects ratings were positive, both before the session . Of the positive affect ratings after the session, 6.5% was related to the movements and/or speech of the robot and 9.0% was related to the game play with the robot. Few affect ratings after the session were either neutral (10.1%) or negative (3.4%). Similar to the reasons for positive affect scores, few reasons for neutral or negative affect scores were directly related to robot movements and/or speech  or the game play with the robot .Additionally, in 79.4% of the sessions, the child reported a positive robot likability score see Fig. . The majOur aim was to present an intervention protocol for children with ASD, who can highly benefit from the use of technology in their therapy, combining motivational components of both PRT and robotics. The current protocol is the first in designing specific game scenarios for a 20-week intervention based on motivational techniques of PRT, an established intervention for children with ASD. Moreover, with this protocol, we aimed to account for individual differences in target behaviour and interests among children with ASD by using nine different game scenarios with seven levels of complexity. Furthermore, as little is known about treatment adherence and acceptability of robot-assisted interventions for children with ASD, the current study adds to the state of the literature by exploring both child and parent ratings with reasons.15 in which children with ASD reported the parts of the therapy without the robot (but with a human trainer) as more enjoyable compared with the parts including a robot. These findings suggest that it is important to combine specific motivational features of robotics with those of already established treatment models for ASD, as has been recommended earlier25. Yet, within PRT as an established treatment for children with ASD, the robot can be added when this is desirable for following the child’s interests.Overall, children with ASD in our study could adhere to the treatment protocol, showed positive affect after the robot-assisted therapy sessions and showed high robot likability scores. Although affect ratings after the session where slightly lower in the high ASD severity group compared with the moderate ASD severity group, these ratings were also mainly positive. Most reasons for positive affect ratings were categorized as related to the second part of the therapy session (i.e. without the robot), in which the child could play a self-chosen game with the parent, while the parent implemented PRT techniques. This finding is in line with an earlier study20 found that vocalizations may increase more during an interactional task with a contingent robot (i.e. responding to the child’s behaviour) compared with a robot showing non-contingent reinforcement. However, not all robot likability ratings were positive and preschool-age children had lower likability ratings, which suggests that the game scenarios for robot-child interaction may fit less well with the interests of younger children with ASD.Within our sample, positive robot likability ratings as measures for robot acceptability were obtained in most therapy sessions, with reasons related to the robotic movements, speech, and game scenarios. This may indicate that the child’s motivation can be enhanced when the interaction with the robot is meaningful to the child by using game scenarios. Indeed, a previous study39. The use of a robot within PRT may facilitate initiations in children with ASD within the natural environment, which are pivotal skills for further social-communicative development28. However, whether use of a robot within therapy facilitates initiations in children with ASD beyond the effects of PRT, is yet unclear and currently under study.In line with child ratings, parent ratings of acceptability of the robot were mainly positive, since parents noticed that the child was enthusiastic and motivated during the sessions and showed initiations at home and at school by talking to classmates about the robot. Parents of both girls and boys showed high ratings of robot value, although parents of girls showed a slightly higher score. Involvement of parents in the robot-assisted intervention is very important for observing child’s behaviour outside the sessions and facilitating generalization of learned skills40. Although we attempted to design the game scenarios to assure direct and natural reinforcement, there was a delay of a few seconds between (1) the therapist initiating the reinforcement by clicking a button and (2) the robot providing the reinforcement. Also, when the child showed an appropriate spontaneous initiation during the robot-assisted game scenario , these could only be reinforced when specific movements were pre-programmed. Since direct reinforcement is a key PRT motivational technique, this issue limits the use of a robot in established interventions for children with ASD. Furthermore, lower parental ratings of robot value were mainly due to the game scenarios being either too easy or too difficult for the child. Although we aimed to tailor the robot-assisted scenarios more to the specific needs of each child compared to what has earlier been done26, nine different game scenarios with seven different levels of complexity was insufficient for the needs of each individual child. Children with ASD tended to reject the robot-assisted game scenarios when noticing that the robot could not perform autonomously or when the scenarios became too predictable. In contrast, the game scenarios may have been too challenging for other children with ASD and the therapist could not easily amend the difficulty during the robot-assisted session. This advocates increasing the flexibility of robot behaviour by developing a larger library of game scenarios with a high diversity in complexity, which can be easily upgraded or downgraded by the therapist during the therapy session.One main reason for lower parent’s ratings was a delay in robot’s responses. Indeed, some robotic responses may not be fast enough to assure contingent reinforcement41. In our study, about only half of the robot likability scores were directly related to the robot in the preschool age group, while this was over 66% of ratings by school-aged children. This may reflect a predictive lack of understanding of one’s own mental state or preferences in preschool-aged group, or an underdeveloped ability to verbally express reasons for ratings. Yet, child affect and likability scales that were used in this study were highly simplified and visual information was combined with verbal categorical response options, which is preferable when asking children about likability or emotional states42. Although many preschool-aged children are able to provide reliable self-reported ratings when providing an age-appropriate scale44, validated self-report scales that can be used to assess young children with ASD’s affect and likability ratings during therapy are currently lacking and should be developed in the future. Moreover, we did not investigate how affect and likability differed over the course of the intervention. Since child outcomes of robot-child interaction might be affected by the number of trials45, it would be interesting in future research to investigate whether affect and likability ratings differ related to the duration of robot-assisted interventions.Although it was meaningful to explore child affect ratings, we did not compare this with a ‘PRT-only’ group. This may provide better insight in whether the effect of motivational components of PRT on child affect can be significantly enhanced by inclusion of a robot. Furthermore, the number of participants in some subgroups within the exploratory analyses was low which limits conclusions that can be drawn from this data. Also, the reliability of child’s self-reported ratings may have been limited, since children with ASD show impairments in recognizing their own mental states and intentionsIn summary, we recommend that the following points should be taken into account when developing robot-assisted interventions for children with ASD: (1) combine the use of a robot with motivational components of established treatment models for ASD while emphasizing parent-child interaction in the sessions besides robot-child interaction, (2) use game scenarios that fit well with child’s interest in each session, especially for younger children with ASD, (3) assure that the child can be directly (without delay) and naturally reinforced by minimizing the robot’s response delay and the time the therapist needs to switch between a pre-programmed scenario and text-to-speech module, and (4) increase flexibility of the robot to match needs of each individual child by using a larger library of game scenarios, that can be easily amended by the therapist during the session. When incorporating these components, we are closer to effectively design robot-assisted interventions that might be of benefit to a larger group of children with ASD. This study contributes to the increased efforts to develop new technologies for use in mental health care interventions for children with psychiatric disorders.Supplementary InformationSupplementary Information2


Many patients in HIV care in Africa considered lost to follow up (LTFU) at one facility are reportedly accessing care in another. The success of these unofficial transfers as measured by time to re-entry at the new-facility, prevalence of treatment interruptions, speed of ART-initiation, and overall continuity of care is not well characterized but may reveal opportunities for improvement. We traced a random sample of LTFU HIV-infected patients in Zambia. Among those found alive and reported in care at a new-facility, we reviewed records at the receiving facility to verify transfer; and when verified, documented the transfer experience. We used Kaplan-Meier methods to examine incidence of ART-initiation after transfer to new clinic. We assessed demographic and clinical characteristics, official and cross-provincial transfer for associations with HIV treatment re-engagement using Poisson regression models and associations between official-transfer and same-day ART initiation at the new-facility. Among 350 LTFU-patients, 178 (51%) were successfully verified through chart review at the new-facility. 132 (74.2%) were female, 72 (40.4%) aged 25–35, and 51% were ever recorded as previously being on ART. 110 patients (61.8%) were registered under new ART-IDs and 97 (54.5%) received a new HIV test. 54% of those previously on ART-initiated on the same-day. Using the same ART-ID was associated with same-day initiation compared to those receiving a new ART-ID (p = 0.07). 80% (n = 91) of those ever on ART had evidence of medication initiation at new clinic. Among these, initiation reached 66% (95% CI: 56–75) by 30 days, 77.5% (95% CI: 68–86) by 90 days after new-facility presentation. Many patients use new identifiers at new facilities, indicative of inefficiencies. Re-entry into new facilities among the unofficial-transfer population is often delayed and timely treatment initiation is inconsistent, suggesting interruptions in treatment. Health systems innovations to ensure smooth and safe transfers are needed to maintain quality HIV care. In the current era of universal treatment for all persons infected with HIV, poor retention in care remains a substantial barrier to optimizing viral suppression . AlthougAs HIV care is life-long and mobility is a normal part of livelihood and social practices, improving our understanding of the experience and safety of transfers across facilities is imperative for informing how to optimize receipt of patients previously on treatment elsewhere . ExistinTo address these gaps, we undertook an assessment of patients who were LTFU according to EMR records at their original facility and reported having transferred to a new facility after being traced in the community in Zambia. We examined medical records at the receiving facility to verify patient-reported transfer and documented the transfer process including whether patients registered under new names or ART IDs, whether the receiving facility appropriately and rapidly staged and treated patients, and distances between original and new facility. We characterized the incidence of ART-initiation between facilities and assessed whether the rate of initiation was associated with transferring officially and/or across a province. Finally, we evaluated characteristics associated with same-day ART-initiation at the transfer/new clinic.Better Information for Health in Zambia cohort study [The target population for this study are adults ≥18 years) living with HIV infection in Zambia who had transferred to a new facility. Our study population consisted of patients who were considered LTFU from HIV care  at their original clinic based on EMR records, reported transferring to a new facility after being actively traced in the field, and whose transfer was then verified at the new facility as part of study activities for the 8 years lWe hypothesise that some patients considered LTFU in one clinic are in fact seeking care elsewhere. Among all patients who made at least one clinic visit between August 1, 2013 and July 31, 2015 to one of the study facilities, we actively traced in the community a random sample of patients considered LTFU as of July 31, 2015 and administered a LTFU questionnaire that included questions regarding whether they had transferred to a new facility, to which facility they had transferred, the date of their first visit to the new facility, and their reasons for transfer. Of note, patients who had a transfer to another facility with official documentation that was documented in the EMR were not considered LTFU and were not included in our population. We defined patients as “Previously on ART” if they had ever previously been initiated on ART at their original clinic as per national ART guidelines. “Not on ART” or “Never yet initiated on ART” was used to define those who had tested positive for HIV at their original clinic but had never initiated ART based on the CD4 count threshold as per national ART guidelines at the time . Per ourgadm.org University of California, Davis U.S.A.) administrative boundary layers v2.8 (accessed November 2019), and used the Vincenty formula to measure the distance in kilometres between clinic locations [We summarized patient and transfer characteristics using counts and proportions for categorical variables, and medians and interquartile ranges (IQRs) for continuous data. To estimate the transfer distances, we geocoded clinic locations using latitude and longitude measure, mapped transfers using QGIS 2.1.8 with standardized world geodetic system 1984  using GADM , we used the mean time to next pharmacy pickup at their original clinic to impute their next scheduled pickup. We fit a modified Poisson regression with robust variances to assess the relationship between having no treatment interruption, defined as transferring to the new facility prior to being 14 days late to their previously scheduled pharmacy pickup, with sociodemographic, clinical, and transfer characteristics. Covariates included in the multivariable model were selected based on founders  [2, 4, 71, we usea priori hypotheses of causal relationships.To assess re-engagement of patients after transferring to the new facility, we estimated the cumulative incidence of ART-initiation at the new clinic using the Kaplan-Meier method among patients who had previously been initiated on ART. Time zero was the date of the first visit at the new clinic and patients were censored at the date of ART start (based on pharmacy records) or administratively censored at the time of record review at the new facility . We then fit a modified Poisson regression to assess the relationship between being initiated on ART at the first visit after transfer with sociodemographic and transfer characteristics based on Ethical approval was obtained from the University of Zambia Biomedical Research Ethics Committee (UNZABREC), University of Alabama at Birmingham (UAB) institutional review boards (IRB) and Ministry of Health (MOH) prior to initiating study procedures. All participants provided written informed consent which was recorded on the consent form. All IRBs approved this consent procedure.As described in the parent study, at the 32 selected sites, 104,966 patients made any visit between August 1, 2013 and July 31, 2015 and 17,602 (17%) were LTFU [Among the total verified transfer population, the median gap between last scheduled pharmacy pick up visit/ last scheduled clinic visit at the original facility and first clinic visit at the new facility was 188 days (IQR: 44–446) . 46 out Among patients previously initiated on ART, only 49 (53.8%) and 66 (72.5%) out of 91 had transferred to the new facility by 6 months and 12 months after their last scheduled appointment at their original facility, indicating that a majority of patients experienced a prolonged gap in medications and were out of care, meaning they had not yet presented to the new clinic (e.g. 42 (46.2%) and 25 (27.5%) out of 91 at 6 months and 12 months) despite eventually transferring to a new facility . For patIn multivariate Poisson regression, we observed those that had official transfer paperwork had a lower risk  of experiencing a medication gap of ≥14 days  ..Based on Kaplan-Meier estimates, 61.3% (95% CI 51.4–71.3%), 77.5% (68.3%-85.6%), and 79.9% (70.9–87.5%) of patients who had previously been on ART had been started on ART at the new clinic by 14, 90, and 180 days after new clinic presentation. Only 55.6% (95% CI 45.7–66.0) were initiated on ART on the day of transfer while 20.1% (95% CI) had not yet been initiated on ART by 180 days after transfer despite previously having been on ART .In multivariate Poisson regression to assess same-day ART-initiation, using the same ART ID from the previous clinic  was associated with starting ART on the same day among patients who had previously been on ART .We found evidence of engagement in care at the receiving clinic for patients originally classified as LTFU, with over half of transfers we attempted to verify contacted in person and verified as receiving care at an alternate facility. Our findings revealed evidence suggestive of potential poor transfer management at original and receiving sites, substantial gaps between treatment visits, and rates to ART initiation which varied by important transfer characteristics. In this study, patients who transferred from their original clinic were either “official transfers”, in which a letter from original to receiving clinic was provided, or unofficial transfers/ “silent transfers”, whereby no formal transfer documentation process was followed . Using tOur transfer verification process demonstrated that- while patients transferring officially experienced shorter average gaps in treatment between sites- most patients still experience a prolonged treatment lapse, a similar finding to Hickey et al. . Other iOur verification assessment revealed other important administrative and clinical inefficiencies. For example, among the patients we were able to verify at transfer sites, we found that just over half received a new HIV test and just under two thirds received a new ART ID. In the often overcrowded and overburdened HIV care clinics both in Zambia and in SSA at large, time spent with providers is perhaps one the most precious of the limited resources . ParticuWe found evidence for factors associated with ART-initiation even after successful re-engagement with care: among patients who ever initiated, some failed to begin ART even up to 180 days since new clinic presentation. Distance of transfer, especially among those transferring across provinces, could be a potential factor inhibiting swift ART initiation through sub-optimal patient confirmation/identification processes at the facility-level. Nevertheless, it was encouraging to find that in our study population, over 55% of patients who transferred initiated ART on the same day they presented at the receiving clinic. Our mixed-effects Poisson regression model revealed that among patients previously on ART once at the new site, using the same ART I.D from the previous clinic had 1.8 times the odds of same-day initiation compared to those without an ART I.D. Based on our knowledge of patient transfers in Zambia, some of this could be due to the current process where patients sometimes carry a box of their drugs when they report to the new facility or having some form of paperwork therefore making their transfer seamless. The fact that these patients were recorded as LTFU at their original facility shows that smooth management of a formal transfer process is driven by patients. In some facilities, it has been reported that patients sometimes collect drugs under false names hence it’s common for some providers to treat patients as new HIV patients prior to ART start and this is sometimes based on the behaviour/ stigma in their clinic catchment population . TherefoWe were unable to verify just under 50% of transfers perhaps because patients may have registered to their new clinic under a falsified name or acted as a new patient and subsequently registered under a new ART ID, perhaps to avoid being reprimanded by clinic staff, or due to a desire to maintain a level of anonymity , 24, 25.With the adoption of universal test and treat , it is pGiven that we found that patients originally considered LTFU in the original facility had transferred to a new facility and were accessing care, platforms that are able to uniquely identify patients, allowing for better coordination and easy transition of patients from one facility to another, as well as policies/procedures that are more flexible to patient clinic transfer are lacking and/or require much improvement in the current context of HIV care in Zambia. Further research that explores whether patients we were unable to verify at a new facility may have indeed returned to care at original facility is required.We speculate that improved facilitation of patient transfers will be imperative for preserving the positive impact of ART for PLHIV and for the larger population as a whole. A nationally comprehensive database and unique identifiers linking patient records over multiple years can provide a more complete dataset than those available to researchers and planners. For example, use of unique identifiers would avoid double counting the number of patients who have been tested/ treated and improve follow-up with patients referred to other services such as ART or even truly LTFU patients. Until now, and largely due to the success of ART programs, insufficient attention has been given to the establishment of necessary linkages and integration of HIV care within the broader health system; however, our findings indicate that this is an important area of focus as we move towards reaching ‘90-90-90’ and ending the HIV epidemic. Strategies that encourage patients to be more involved in discussions with health care workers about their care may reduce unnecessary transfers due to poor staff attitude which leads to disengagement and transfer to other facilities , 24, 29.In this study, we were limited to only one verification visit of transfers at the receiving facility, hence further follow up with unverified transfers to understand why our verification was unsuccessful would be necessary in order to ascertain if there were any reasons for disengagement. It is possible that the unverified population may not have given us accurate information on the facility they transferred to perhaps due to their experience of being reprimanded for delayed return to care . AdditioThird, our relatively small sample size precluded from further stratification to assess for less prominent associations. Finally, it may be that the unverified patients never transferred to a new facility but reported to the tracer to have done so due to social desirability bias. While our failure to confirm patient transfer at the receiving site could have been due to any one or combination of these reasons, our inability to verify a large proportion (~ 50%) of these patients at all, even after undertaking exhaustive tracing efforts, highlights an important lesson. Our study described transfers among those who accessed care, but the self-reported transfers of those who did not access care is not represented; thus, the transfers within our study population—whether provincial or cross provincial is likely higher than we found through our tracing activities. Lastly, our sample size is not representative of all transfers but only those that were not in EMR at original facility.S1 FigDirect acyclic graph to identify confounders of ART initiation after presentation at a new site.(TIF)Click here for additional data file.


Synthetic cannabinoids (SCs) are full agonists of both cannabinoid receptors. Conventional magnetic resonance imaging (MRI) findings of SC users are mainly defined as diffusion restriction and T2/FLAIR hyperintensity. Diffusion tensor imaging (DTI) studies examining SC users have shown contradictory results. The aim of this study was to define white matter (WM) changes of SC users using DTI.The study included 22 patients with a history of using SC for 5–37 months, and 22 healthy, age and sex-matched control subjects. A total of 41 diffusion gradient directions were used in the acquisition of diffusion imaging data. Fractional anisotropy (FA) and apparent diffusion coefficients (ADC) values were obtained. ROIs were placed on WM areas of normal appearance. In the SC users, significantly lower FA values were determined in the left temporal lobe  and right hippocampus . The ADC values of the hippocampus and temporal lobe were significantly higher than those of the control group on both the left and right sides.The SC use causes WM microstructural changes, especially in the hippocampus and temporal lobes. DTI is a useful tool to reveal WM changes in SC addicts and can be used earlier than conventional MRI. Synthetic cannabinoids (SCs) are full agonists of both cannabinoid receptors (CB 1 and CB 2). There are various names to define SCs, such as Bonzai, Jamaika, Aroma, K2, or Kronic. SCs are preferred to original cannabinoid products because they are cheaper, more readily available, and more difficult to detect in drug tests .The harmful effects of SCs are similar to those of natural cannabis. As SCs contain various compounds, they can result in greater toxicity than natural cannabis. The most frequent adverse effect to the central nervous system (CNS) is embolic ischemic stroke, similar to that induced by natural cannabis [3].Conventional magnetic resonance imaging (MRI) findings of SC users are mainly defined as diffusion restriction and T2/FLAIR hyperintensity [4]. Diffusion tensor imaging (DTI) is an MRI technique that is used to assess white matter (WM) microstructure. DTI is an effective tool to assess WM injury by means of quantitatively evaluating fractional anisotropy of brain-water diffusion and the myelin sheath of brain WM fiber bundles. The SC use has been known to reduce the integrity of WM [5]. Fractional anisotropy (FA) is a parameter of DTIthat can be defined as an index for the amount of diffusion asymmetry within a voxel. FA indirectly represents the WM microstructure, and axonal damage or demyelization is associated with reduced FA values [6].DTI studies examining SC users have shown contradictory results. Some studies have indicated that FA values in SC users increased , whereas others have reported decreased FA values . In addition, these studies have only focused on natural cannabis use. The aim of the current study was to define WM changes in the CNS of SC users in comparison with normal controls using DTI.The current study included a total of 22 patients with a history of SC use for 5–37 months and 22 healthy, age and sex-matched control subjects. Any patients with a diagnosis of alcoholism or any addiction other than SC, neurological or psychiatric disorders other than SC addiction, severe hepatic, renal or endocrine disease, and those with a contraindication for MRI examination were excluded from the study. The SC users included in the study were also tested using the Montreal cognitive assessment (MoCA) test [11] to define cognitive functions and any possible relationship between cognitive impairments and DTI findings. MRI examinations were performed with a 1.5 Tesla MRI system . The diffusion imaging data were acquired from 41 diffusion gradient directions  and T2 and FLAIR reference images were also obtained . The DTI protocol involved structural 3D-T1, T2-weighted images.FA maps were created on a Siemens, Leonardo workstation using the Neuro3D application, and FA values were then measured on the FA maps. ADC values were obtained using the same software. FA and apparent diffusion coefficients (ADC) values were acquired using approximately 30 mm2 regions of interest (ROI). ROIs were placed on WM areas of normal appearance if any WM lesions were present on T2 or FLAIR images. All measurements were performed by the same researcher (SA). Three ROIs were placed into the examined location, and the mean values of 3 measurements were recorded as the final data. Measurements were performed from temporal lobes, the hippocampus, and the brainstem since SCs have been known to affect the limbic system and, in addition, previously performed DTI studies generally chose the same locations (5). The brainstem was also preferred in the investigation of life-threatening effects. Measurement levels can be seen in Figure 1.Approval for the study was granted by the Institutional Ethics Review Board, and informed consent was obtained from each participant.Data obtained in the study were analyzed statistically using SPSS statistics 21.0 . Normal distribution was tested with the Kolmogorov-Smirnov test. Continuous parameters were stated as mean ± standard deviation (SD), and numerical variables without normal distribution were shown as median (minimum-maximum) values. Normally distributed variables were compared between the patients and control groups with the Student’s t-test, whereas parameters with abnormal distribution were compared via the Mann-Whitney U test between the 2 groups. The correlations between age, sex, duration of usage, and FA values were evaluated with the Pearson and Spearman correlation tests.A two-tailed value of P < 0.05 was considered statistically significant.All of the participants were male, and all were currently using SC. Both the SC users and control subjects smoked at least one pack of cigarettes a day. The mean age of the SC users and control group were 23.3 ± 5.6 years and 22.7±4.8 years, respectively. Median duration of SC use was 13 (5–37) months, and median daily SC use was 4 (1–12) times per day. The median MoCA test result of the SC users was 27; only 3 SC users had an abnormal test result . Conventional MRIs of the patients were mostly normal. Subcentimeter WM lesions were detected in only 4 patients. The lesions were located at the temporal (3 patients) and parietal (1 patient) lobes.Significantly lower FA values were obtained from the left temporal lobe  and right hippocampus  in the SC group than from the healthy control group . The FA values obtained from the brainstem were similar in the SC users and the control group (435.6 ± 36.5 vs. 442.1 ± 62.2) (Table 1) .The ADC values acquired from the brainstem did not differ between the SC users and the control group (703.3 ± 145.2 vs. 752.7 ± 24.2). In the SC group, the ADC values of the hippocampus and temporal lobe were significantly higher than those of the control group for both the left and right sides  (Table 2). No significant correlation was determined between the ADC and FA values and age, duration of usage, and amount of daily use. No significant correlation was detected between the MoCA results and FA and ADC values. The number of patients with an abnormal MoCA result (3 patients) was insufficient for statistical analysis to be applied. The main aim of the current study was to define WM changes resulting from SC use. The results showed that SC addiction causes WM structural changes, and these changes can be revealed with DTI. Cannabinoids are widely used throughout the world. The main psychoactive compound of cannabinoids is tetrahydrocannabinol, which can activate cannabinoid receptors in the brain. Cannabinoid receptors are mostly located in basal ganglia, the cerebellum, and limbic cortices. SCs are cannabinoid-like substances that are widely used because of their cheap costs and easy availability [12]. In Turkey, the most popular SC is Bonzai, and its chemical compound mainly consists of JWH-081 [13]. There is limited information in the literature about SC toxicity on the CNS, and the studies thathave been published are generally case reports. The toxic findings defined in the literature are related to the combination of direct neurotoxicity and cerebral hypoxia, as in natural cannabinoids. Therefore, MRI findings generally present ischemic findings . In a case report about JWH-081 toxicity, it was stated that JWH-081 might cause myelin toxicity and leukoencephalopathy, although the report only contained conventional MRI data [4]. It has been emphasized in the literature that myelin-related genes, myelin basic proteins, and myelin proteolipid proteins are altered following prolonged exposure to cannabis [16]. Chronic SC exposure has also been shown to cause dose-dependent downregulation of CB1 receptors in an animal model (rodents), especially in the hypothalamus and hippocampus. This downregulation might cause apoptosis of oligodendrocytes, the myelinating cells in the CNS, and thereby deplete myelinization . Consistent with this information, the current study results demonstrated that the WM of SC users is changed, especially in the hippocampus and temporal lobe since the FA values obtained from these areas were lower than those of the control group. In a similar study [5], it was shown that SC use decreases FA values obtained from the left temporal lobe, similar to the current study results. However, unlike the current study’s results, the previous study found that SC use had an effect on the left hippocampus. This difference could be the result of the duration of SC use. In the abovementioned study [5], all of the participants had been using SC for 30 ± 14.4 months ; our population had a median duration of use of 15 months. Therefore, there is a need for further prospective studies to be able to clarify the order and process of influence on specific CNS regions. It is also highly possible that both sides of the brain can be similarly affectedwhen the above-mentioned pathogenesis  is considered. The results of the current study showed that FA values obtained from the left temporal lobes of SC users were significantly lower than those of the healthy control group. These findings are consistent with the literature. Recently performed studies have similarly stated that FA values are reduced in the temporal lobes of cannabis users  and in the left temporal lobe of SC users [5]. No significant FA and ADC value changes in the brainstem were determined between SC users and the control group, and these results were consistent with the literature. It has been stated in previous studies that CB1 receptors are expressed at a low level in the brainstem [21], so the effect of SC on the brainstem is weak. The ADC values were also significantly higher in both temporal lobes and the hippocampus of SC users in the current study. The ADC values are also related with WM degeneration, and this was similar to the FA values. It has been stated that in normal aging or in degenerative processes, ADC generally increases and FA decreases . The current study results of FA and ADC are both consistent with the literature and support the effectiveness of DTI in the detection of degenerative WM changes in SC users. No significant correlation was determined between FA values and duration of use and amount of daily use, contrary to expectations. To the best of our knowledge, there is no study in the literature that has examined these relationships. One possible explanation of these results could be that downregulation of CB1 receptors occurs earlier than after 5 months of SC use and is not dose-dependent. However, it is also possible that the current study population was not large enough to detect such a correlation. Further prospective studies are needed to clarify these possible relationships. Some limitations of the study must be emphasized. The sample size was relatively small as it is difficult to find patients addicted to only SC and who are also willing to participate in a study of this kind. The participants in this study were using Bonzai, a popular SC in Turkey, the content of which is known to be mainly JWH-018. However, it was impossible to detect the exact content and ratio of contents of the Bonzai used by the participants. The study population comprised of males only, so the results cannot be generalized to the female population. Multiple addictions and psychiatric problems, together with addiction, are also common problems. Addicts using SC only were selected from their psychiatric reports and statements. However, it was not possible to create a fully homogenous study group. Since all of the measurements were only performed by the same researcher one time, we cannot offer inter/intraobserver variability data. In conclusion, SC use causes WM microstructural changes, especially in the hippocampus and temporal lobes. These changes can be detected via FA and ADC values using DTI. Therefore, DTI is a more useful tool than conventional MRI in the early detection ofWM changes in SC addicts.No funding was received for this study.All authors declare that they have no conflict of interest. All procedures performed in the following studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and within the standards set in the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.Institutional review board approval was obtained, and the need for informed consent was waived for the current retrospective study.


P value = 2.2 × 10−4, and raw P value = 3.1 × 10−9). Although short resting time had no association with the poor trajectory, it had a significant interaction with short walking time , which was further strengthened by the other two components . Likewise, components that were not associated with a poor trajectory in bivariate analysis were included in every observed significant risk combination due to their interactions with other components. Comprehensive combination detection by MP-LAMP is essential for explaining multifactorial psychiatric symptoms by revealing the hidden combinations of risk factors.The nature of the recovery process of posttraumatic stress disorder (PTSD) symptoms is multifactorial. The Massive Parallel Limitless-Arity Multiple-testing Procedure (MP-LAMP), which was developed to detect significant combinational risk factors comprehensively, was utilized to reveal hidden combinational risk factors to explain the long-term trajectory of the PTSD symptoms. In 624 population-based subjects severely affected by the Great East Japan Earthquake, 61 potential risk factors encompassing sociodemographics, lifestyle, and traumatic experiences were analyzed by MP-LAMP regarding combinational associations with the trajectory of PTSD symptoms, as evaluated by the Impact of Event Scale-Revised score after eight years adjusted by the baseline score. The comprehensive combinational analysis detected 56 significant combinational risk factors, including 15 independent variables, although the conventional bivariate analysis between single risk factors and the trajectory detected no significant risk factors. The strongest association was observed with the combination of short resting time, short walking time, unemployment, and evacuation without preparation (adjusted  In a population-based longitudinal study, Welch et al.2 identified six clusters of PTSD symptom trajectories after the disaster: low-stable (48.9%), moderate-stable (28.3%), moderate-increasing (8.2%), high-stable (6.0%), high-decreasing (6.6%), and very high-stable (2.0%).The symptoms of posttraumatic stress disorder (PTSD) after the disasters could take multiple trajectories4. For example, Kessler et al.3 reported that middle age and low income were slightly associated with the trends in PTSD symptoms in a longitudinal surveillance study after a hurricane, but these risks explained only 2.1% of the variance in the trajectories of PTSD symptoms. Both Kessler et al.3 and Adams and Boscarino4 reported that the degree of exposure to stressful events was a significant predictor only for the onset of PTSD and not for the trends in PTSD after a disaster.Although factors that modulate the prognosis of PTSD symptoms after disaster have been investigated, the effect size of each single factor seems to be too weak to explain the variety of trajectories observed in clinical practice9. Satisfaction with social support has a significantly larger positive effect on the prognosis of PTSD symptoms in females than in males5. Excessive alcohol intake can have a large impact on the exacerbation of PTSD symptoms in males6. Loss of family members or lack of family support influences the prognosis of PTSD more in younger subjects than in older subjects8. Drožđek et al. considers combinations of risk factors and shows the hidden long-term impacts of exposure to war and violence9.Considering the multifactorial nature of the condition, the most straightforward approach for obtaining useful information with sufficient effect sizes regarding the prognosis of PTSD would be effective accumulation of risk factors by considering interaction among the factors. Several studies have demonstrated interactions among risk and protective factors for the prognosis of PTSD symptomsIn previous studies to elucidate combinational risk factors by focusing on interactions, the major limitation was that candidate risk factors were selected based on their association with the target symptom. However, a factor showing no association with the target symptom in bivariate analysis could plausibly contribute to reliable combinational risk predictors by strong interaction with other factors; such a risk factor that is apparent only in combinational analysis can be referred to as a “hidden risk component”. Therefore, although several risk predictors for PTSD prognosis have already been suggested by previous bivariate association studies, a comprehensive combination detection study based on a number of potential risk factors, without selection by other statistics, would be useful to detect reliable combinational risk predictors.30 (> 109) possible combinations. Therefore, if all of these combinations were tested, the computational cost would be so high as to render the calculation impractical, and the raw P value would need to be no greater than 4.6 × 10−11 for “significance” at the α = 0.05 level after a Bonferroni correction.Despites the potential usefulness of comprehensive combination detection studies in detecting hidden risk components, such studies have been infeasible due to high computational costs and excessively severe multiple-testing correction. For example, if 30 potential risk factors were tested for combinational risks, there would be 211. LAMP is a novel algorithm that renders comprehensive detection of significant combinations feasible by reducing computational costs and preventing excessively severe multiple-testing correction by avoiding unnecessary significance tests of potential risk combinations that (1) cannot be significant or (2) are completely dependent on each other15. First, if the number of subjects with a potential risk combination is sufficiently small, the association between the combination and the target variable  can never be significant, regardless of the values of the target variables  and avoids unnecessary duplicate tests. Through the abovementioned two procedures, the LAMP algorithm makes comprehensive significant combination detection feasible under the condition that the familywise error rate is controlled rigorously under the threshold. MP-LAMP is a software tool to accelerate LAMP calculations and render it feasible in large datasets by utilizing parallel calculations.The Massive Parallel Limitless-Arity Multiple-testing Procedure (MP-LAMP) was developed to explore significant combinational risk factors among a large number of independent variables4. Then, people who have severe PTSD just after the disaster easily obtain access to specialized treatments. In contrast, the long-term prognosis of PTSD is weakly explained by the symptoms just after the disaster17, and appropriate support is possibly not provided to people who suffer from delayed PTSD symptoms. In this case, the prediction of PTSD prognosis based on various risk factors would be useful to provide adequate support to high-risk populations. Nevertheless, the long-term prognosis of PTSD after a natural disaster has rarely been surveyed, and there is little evidence we can consult17.The current study targets a relatively long-term prognosis of PTSD symptoms because of clinical importance. According to previous studies, the short-term prognosis of PTSD was largely explained by the severity of PTSD symptoms just after the disaster19. We utilized datasets including 624 subjects who completed the surveys in 2011, 2012 and 2018. To investigate the risk factors that modulate the prognosis of PTSD symptoms, we used IES-R scores in the 8th year adjusted for those in the 1st year, referred to hereinafter as “PTSD trajectory scores”, as the target variables, following the methods of previous studies20. The PTSD trajectory score represents the change in PTSD symptoms that is not explained by the baseline PTSD symptoms. This derived measure is beneficial in the search for useful risk factors that can be used in conjunction with baseline symptomatology to predict the prognosis of PTSD symptoms. We utilized MP-LAMP to explore combinational explanatory factors for PTSD trajectory scores based on information about stressors , sociodemographics, lifestyle, and clinical information collected just after the disaster. The results of MP-LAMP regarding combinational risk factors were compared with those of the conventional association tests for individual risk factors, referred to hereinafter as “bivariate analyses”.In the current study, we applied MP-LAMP to identify combinational risk factors that modulate the prognosis of residents severely affected by the Great East Japan Earthquake regarding PTSD symptoms, as measured by Impact of Event Scale-Revised (IES-R) scores. We conducted annual surveys to evaluate the mental health condition of all residents whose houses were located in the town of Shichigahama and had been destroyed or severely damaged by the catastrophe19. The first survey was conducted in November 2011 following the Great East Japan Earthquake and Tsunami of March 11th, 2011. Annual surveys were conducted thereafter, and the latest survey before the current analysis was conducted in October 2018. This study is based on the questionnaire collected on the first, second, and eighth  survey. In the study population of 2,478 Japanese subjects who were at least 18 years old and whose houses were totally collapsed or severely damaged, 1,791 subjects participated in the first year survey and returned the questionnaire after giving written informed consent. Among those subjects, 1,173 participated in the second survey, and 636 participated in the first, second, and eighth surveys. Then, the subjects who omitted > 20% of items on the IES-R items or potential risk factors were excluded based on previous studies23, and those who omitted > 50% of items on the questionnaire were also excluded based on the literature reviewed26.This study is based on a health survey administered as part of a project called the Shichigahama Health Promotion ProjectBecause the purpose of the current study is to elucidate risk predictors available just after the disaster for the prognosis of PTSD, the data utilized as potential risk factors were mostly based on the questionnaire collected in the first year. The data from the first survey included sociodemographic characteristics , lifestyle , clinical information , the Kessler Psychological Distress scale (K6), the Athens insomnia scale (AIS), and the Lubben Social Network Scale-6 (LSNS-6). In addition, the data related to experiences of the earthquake and tsunami  and changes in income or work volume were collected in the second year survey. The abovementioned 61 variables were utilized as potential risk factors for the prognosis of PTSD in the following analyses.28. To evaluate the long-term change in PTSD symptoms that was not explained by the baseline PTSD symptoms, we utilized the eighth-year IES-R adjusted by the first year IES-R as a target variable in the following analysis; we refer to this measure as the “PTSD trajectory score” throughout the manuscript.The IES-R score was used as an indicator of PTSD symptoms. The respondents were asked about their PTSD symptoms over the previous week based on 22 questions, to which they responded by selecting “extremely” (4 points), “quite a bit” (3 points), “moderately” (2 points), “a little bit” (1 points), or “not at all” (0 points). The total scores ranged from 0 to 88. IES-R scores correlate well with the criteria for PTSD in the Diagnostic and Statistical Manual of Mental Disorders (DSM), and IES-R is one of the most commonly used metrics of PTSD symptomatology29 in R because the LAMP analyses require datasets without missing data For comparison with the results of the combinational analysis, conventional association analysis for the same response and independent variables was also performed. We implemented linear regression adjusted by age and sex to evaluate the association between adjusted IES-R and each independent variable, a procedure referred to as “bivariate analysis” throughout the manuscript in contrast to the combinational analysis by MP-LAMP. Multiple-testing correction was performed using the Bonferroni method to control the familywise error rate.37. P < 0.05 was considered to indicate statistical significance.The Mann–Whitney U test was implemented to evaluate the association between the potential risk combinations and the PTSD trajectory score. In addition to the MP-LAMP software, R was utilized in statistical analysesAll protocols for the studies were approved by the Ethics Committee of Tohoku University. Written informed consent was obtained from all subjects. This study was carried out according to the principles expressed in the Declaration of Helsinki.Of the variance of IES-R scores in the 8th year, only 23.5% was explained by the baseline IES-R, and the remaining explanatory factors were explored using the PTSD trajectory score as a target variable in the following analyses.The demographic characteristics and trauma exposure of the subjects are summarized in Table P values of the representative significant combinations shown by MP-LAMP and the components of the significant combinations are illustrated in Fig. The 61 abovementioned potential risk factors were subjected to comprehensive combinational risk detection analysis by MP-LAMP and bivariate analysis. Although bivariate analyses detected no significant predictors of PTSD trajectory scores, combinational association analyses by MP-LAMP detected 56 significant combinations, in which 15 independent variables were used at least once each as components. The P value > 0.05) in bivariate analyses. The average (SD) numbers of interactions with P < 0.05 and P < 0.01 by analysis of variance among the components of the significant risk combinations were 4.9 (2.9) and 2.5 (1.3), respectively, which were substantially higher than the 95% confidence intervals of 1.2–1.9 and 0.3–0.7 calculated from randomly selected combinations consisting of the equivalent number of components . The effect size of this combination and its components on the IES-R scores are illustrated in Fig. The combination that was most strongly associated with the PTSD trajectory score was unemployment, walking less than 30 min/day, short resting time (sitting or napping for less than 3 h/day), and evacuation without preparation , it had a significant interaction with short walking time , which was further strengthened by the other two components . To illustrate this significant interaction, Fig. Although short resting time was not significantly associated with the PTSD trajectory score in bivariate analyses  the combinational risk approach increased the power and detected novel significant risk factors and that (2) the significant combinations detected by the comprehensive combination approach included interactions among the components.P value > 0.05); these 10 variables could be referred to as “hidden risk components”. Based on this finding, in the search for risk factors to increase predictive performance, the conventional approach of combining the previously reported risk factors would be useless for identifying the most reliable predictor combinations including hidden risk components; only a comprehensive combination detection approach considering all possible interactions among the variables, regardless of whether each variable would be counted as a risk factor based on bivariate analyses, could detect hidden risk components.Although bivariate analyses detected no significant risk factors, the combinational approach detected 56 combinational risk factors consisting of 15 independent variables, demonstrating that the combinational approach substantially increased the power to detect risk factors associated with PTSD trajectory scores. The remarkable point was not merely that the detection power was increased by the combinational analyses but that the risk factors newly detected in combinational analyses were completely different from the ones detected by loosening significance levels in the bivariate analyses. Among 15 independent variables included at least once in the significant combinations, there were 10 variables that had no association with the target variable in bivariate analyses  but also interactions among three or more components (51%). Most of the previous studies investigating interactions among risk factors for PTSD symptoms analyzed only the interactions between pairs of components among several risk factors4. However, the results from the current risk combination analysis presented another view about the relationship between the traumatic experience and the prognosis of PTSD. In the current study, most of the significant risk combinations include the risk factors of a traumatic experience , working status , and lifestyle factors . The distribution of PTSD trajectory scores in the set of subjects selected by combinational or single risk factors is shown in Supplementary Fig. The risk combination approach can provide useful interpretation for clinical practice based on the relationship between risk factors. Previous studies using conventional bivariate analysis stated that the degree of traumatic experience influenced only PTSD symptoms just after the disaster but did not influence PTSD symptom prognosisP value = 3.1 × 10−4) but did not influence the PTSD trajectory score  in bivariate analyses. However, the gender factor had a significant interaction with decreased income , physical condition (not good)  and older age , and was included in some of the significant risk combinations for PTSD trajectory scores. Based on these findings, the factor of gender alone cannot be considered to influence the trajectory of recovery from PTSD symptoms; however, the risks factors of income, physical condition, and age can influence recovery from PTSD symptoms more severely in females than in males.In the current analyses, female gender was associated with elevated baseline PTSD symptoms  was 2.1% in a 2-year longitudinal surveillance study after a disaster3. The current study’s approach to creating risk combinations was shown to be useful to combine the effects of single risk factors.The variance explained by the risk factors was calculated to compare the results with those of the previous studies , life events , and distress scale . Although there are no risk combination studies, there are a couple of studies using bivariate analysis to search for risk factors for PTSD prognosis after the disaster. Kessler et al. performed a 2-year longitudinal study after Hurricane Katrina that suggested that PTSD prognosis was influenced by the risk factors of age and working condition3. Adams et al. performed a 2-year longitudinal study after the World Trade Center Disaster, which suggested that the change in PTSD symptoms was influenced by negative life events, Latino ethnicity, and reduced self-esteem4. Considering the similarity between the results of the current study and those of previous studies, the current results could be applied to PTSD prognosis after various types of disasters. In contrast, the risk factors for PTSD prognosis from the other types of trauma  should be explored in future studies based on an appropriate study population.The components of the significant risk combinations in the current study did not conflict with the previous PTSD prognosis study after the disaster4, the relationship between the risk factors and the predicted prognosis would be more complicated. Future studies that utilize information about new exposure after the disaster and detailed trajectory of PTSD symptoms would support our further understanding of the long-term prognosis of PTSD.The current study discussed the long-term prognosis of PTSD symptoms based on information from mainly two time points . Compared with previous studies on the short-term prognosis of PTSD symptomsP values for each combination against the outcomes, the risk factors identified by LAMP should be confirmed using ordinary statistical methods. In the current study, the validity of statistical methods was confirmed by checking the interaction, the distribution, and the variance explained by significant risk combinations as well as bivariate analysis for each risk component.Although the LAMP minimizes false negatives by calibrating the Bonferroni factor, maintains statistical power under multiple comparisons and provides the significant 39. On the other hand, we achieved high levels of significance when we applied the combinational analysis, which suggests that the results in the present study are reliable. Second, the current MP-LAMP source code does not implement the function to adjust covariates. Therefore, we additionally performed the analysis using the target variable adjusted for potential confounding factors . This is a common problem for PTSD prognosis studies after natural disasters because a limited number of people are exposed to the disasterA comprehensive approach using MP-LAMP to detect significant combinations increased the power of the analysis and revealed significant risk combinations for high PTSD trajectory scores. Considering that hidden risk components were included in all of the detected significant risk combinations, a comprehensive combinational approach will be essential for detecting reliable risk combinations strongly associated with psychiatric conditions.Supplementary Information.


This work describes the lightning nitric oxide (LNO) production schemes in the Community Multiscale Air Quality (CMAQ) model. We first document the existing LNO production scheme and vertical distribution algorithm. We then describe updates that were made to the scheme originally based on monthly National Lightning Detection Network (mNLDN) observations. The updated scheme uses hourly NLDN (hNLDN) observations. These NLDN-based schemes are good for retrospective model applications when historical lightning data are available. For applications when observed data are not available , we have developed a scheme that is based on linear and log-linear parameters derived from regression of multiyear historical NLDN (pNLDN) observations and meteorological model simulations. Preliminary assessment for total column LNO production reveals that the mNLDN scheme overestimates LNO by over 40% during summer months compared with the updated hNLDN scheme that reflects the observed lightning activity more faithfully in time and space. The pNLDN performance varies with year, but it generally produced LNO columns that are comparable to hNLDN and mNLDN, and in most cases it outperformed mNLDN. Thus, when no observed lightning data are available, pNLDN can provide reasonable estimates of LNO emissions over time and space for this important natural NO source that influences air quality regulations. NOx produced by lightning flashes is referred to as lightning NOx (LNOx) in the literature. As one of the major natural sources of NO, LNO is mainly produced in the middle and upper troposphere. It plays an essential role in regulating ozone (O3) mixing ratios and influences the oxidizing capacity of the troposphere  is produced by the intense heating of air molecules during a lightning discharge and subsequent rapid cooling of the hot lightning channel . Since Nposphere . Despiteposphere . Most esx budget . HoweverTo simulate the amount of LNO production in space and time in a chemical transport model (CTM), it is important to know the following: (1) where and when lightning flashes occur, (2) the amount of LNO produced per flash and (3) how LNO is vertically distributed. Historically, the lightning flash rates are derived with the aid of parameterizations in CTMs . VariousZ + 1, where Z is the climatological IC / CG ratio from An LNO production module, based on the lightning flash rate and LNO parameterizations of In this paper, we present the updates and development of the LNO module that was released in CMAQ version 5.2 in June 2017 and present a preliminary assessment of the spatial and temporal distribution of LNO columns in the existing (mNLDN), updated (hNLDN), and newly developed (pNLDN) schemes. In a follow-on paper, a comprehensive evaluation of model performance with the various schemes will be presented.22.1The observed lightning activity data were obtained from the National Lightning Detection Network (NLDN) . The raw2.2The meteorological fields used in developing the LNO schemes are provided by WRF model simulations . The WRF2 by reacting with O3 and other species in the atmosphere. Under most circumstances, NO and NO2 coexist in chemical and/or photochemical equilibrium, so lightning-produced nitrogen oxides are generally referred to as LNOx. But only NO is involved in the actual implementation of the schemes in CMAQ. We, hereafter, refer to all the schemes as LNO schemes. All the LNO schemes include three steps: (1) derive or use observed lightning flashes at a grid cell, (2) translate the lightning flashes into total column lightning NO at the grid cell, and (3) distribute the total column NO among model layers based on vertical distribution algorithms. After the lightning NO is injected into the vertical layers, it is then combined with (added to) the existing NO from other emissions (both anthropogenic and biogenic sources). From there, it undergoes the same chemical and/or photochemical and physical processes as any other species do.NO is the direct product of lightning flashes, and after release a large portion of it can be quickly turned into NO33.1−1 of convective precipitation (CP) corresponds to 147 lightning flashes for a 36km× 36 km horizontal grid cell . A preliminary analysis indicated that this scheme produced unrealistically excessive LNO during summer months (not shown). This option was removed from CMAQ in version 5.2.Beginning with CMAQv5.0, the LNO module contains two options for in-line (based on model simulated parameters at the run time) LNO production. The first option is an over-simplified parameterization that assumes that 1 mm hThe second option in CMAQv5.0 was developed by CG ratio  onto theThis value is capped at 50 to avoid placing excessive amounts of lightning-NO emissions in model grid cells with much less CP than observed in an attempt to match observed monthly flash rates. Finally, the moles of NO produced per hour and grid cell are calculated in the lightning module in CMAQ as follows:3.2p) is calculated as follows:σ is the sigma value of the layer, psfc is the surface pressure and ptop is the pressure at the top of the model domain.The moles of LNO are then distributed vertically using the two-peak algorithm described in p), the standardized Gaussian parameter (x) is calculated as follows:At each pressure level (p) is calculated by the following distribution function:x ≥ 0, and produces −1.0 otherwise.Then the fraction of the column emissions at the pressure . F1 and F2 are scaling factors that control the relative contributions to W from the top and the bottom distributions, respectively. Ideally, W would match the vertical profile presented in W at all the layers is equal to 1. In the current CMAQ configuration, F1 = 1 and F2 = 0.2.At each model layer, the weighted contribution isL) is the LNO at layer L, W(L) is the weight at layer L as calculated by Finally, the LNO at each layer is3.3As described above, the LNO production scheme, mNLDN, calculates CLNO using scaled values of the convective precipitation. To simplify the procedure to generate LNO, in CMAQv5.2 we used the gridded hourly NLDN (hNLDN) flash data in the lightning module, which reduces NLDNCG flashes are generated using a Fortran program adapted from NLDN_2D by reading in the raw NLDN CG flashes. Ocean_factor and ICCG are the same as in 3 production. However, when the lightning assimilation technique . Again, the data points over the two regions , the Kain–Fritsch (KF) convective scheme  was used5Statistically, the relationship between CP rate and NLDN lightning flash rate over large regions suggests similar yields within each region. But considerable scatter still exists within each region, and the overall statistics may be dictated by certain large values. As an estimate, the most direct approach would be to use regression equations to determine LNO from CP for western US grid cells and regression equations for eastern US grid cells as shown in 5.1A robust parameterization scheme should be relatively insensitive to the training time period. In order to test this, the lightning yield (slope of the linear and log-linear regression) was re-calculated using data from 2002 to 2012 (P02-12), 2002 to 2014 but excluding 2011 and 2013 (P02-14sb2), and 2009 to 2014 (P09-14). The results are shown in To test the sensitivity of LNO to the parameters derived from different time periods, 5.2As discussed earlier, the log-linear regression between NLDN lightning flashes and CP produced better correlation coefficients than the simple linear regression. We also noticed, however, that if the log-scale parameters are applied to all the data, too much LNO is produced relative to the hNLDN scheme, especially during winter months when both lightning activity and convective precipitation occur less frequently. This high bias exists because the log scale tends to inflate contributions from small values when linear regression is performed after the log transformation. To test the impact of log scale on the production of LNO, we choose the summer months (JJA) in 2011 and specify a series of cutoff values for CP (cm) that is linear regression parameters are applied if CP is smaller than a specific cutoff value, and log-linear regression parameters are applied otherwise. 6As a preliminary assessment of these LNO production schemes, we only investigate the distribution of column LNO in time and space; a more detailed evaluation of the impact of these schemes on air quality will be presented in a subsequent study.The spatial distributions of monthly total column LNO produced by each of the three schemes over the contiguous United States for July 2011 and July 2013 are presented in 7In this study, we described the LNO production schemes in the CMAQ model’s lightning module and updated the existing monthly NLDN observation-based scheme with the current understanding and resources. For retrospective model applications, the hourly NLDN observation-based scheme, hNLDN, is expected to provide the highest-fidelity spatial–temporal LNO. If observations are not available, such as in air quality forecasts and future climate studies, the linear and log-linear regression parameter-based scheme, pNLDN, provides a spatial–temporal estimate of LNO. Note that even though the pNLDN scheme can provide LNO estimates for past or future climate studies, the spatial dependency of the relationship presented here may not hold under changing climate conditions.Large uncertainties are still associated with each of these schemes resulting from the various assumptions common to all the LNO production schemes, e.g., the uniform NO production rate per flash, the IC / CG ratios, the difference of LNO production rates over land and ocean, and uniform vertical profiles in time and space. The regression parameter-based scheme suffers additional uncertainties resulting from the way the parameters are derived. First, the CP values were only produced by the KF convective scheme in this regression analysis. If other convective schemes are used in the upstream meteorological model, the regression relationship will differ. Spatially this scheme is only applicable to the area over which the regression analysis was performed . In addition, the parameters may need to be reproduced when the model resolution or version is changed or when updated observational data become available.F1 and F2) associated with the vertical LNO distribution algorithm in Lightning and LNO will remain an active research area in atmospheric sciences for the foreseeable future. For example, lightning data from Geostationary Lightning Mapper (GLM) instruments on the Geostationary Operational Environment Satellite (GOES) 16 and 17  are now