While genomic data is frequently collected under distinct research protocols and disparate clinical and research regimes, there is a benefit in streamlining sequencing strategies to create harmonized databases, particularly in the area of pediatric rare disease. Research hospitals seeking to implement unified genomics workflows for research and clinical practice face numerous challenges, as they need to address the unique requirements and goals of the distinct environments and many stakeholders, including clinicians, researchers and sequencing providers. Here, we present outcomes of the first phase of the Children’s Rare Disease Cohorts initiative (CRDC) that was completed at Boston Children’s Hospital (BCH). We have developed a broadly sharable database of 2441 exomes from 15 pediatric rare disease cohorts, with major contributions from early onset epilepsy and early onset inflammatory bowel disease. All sequencing data is integrated and combined with phenotypic and research data in a genomics learning system (GLS). Phenotypes were both manually annotated and pulled automatically from patient medical records. Deployment of a genomically-ordered relational database allowed us to provide a modular and robust platform for centralized storage and analysis of research and clinical data, currently totaling 8516 exomes and 112 genomes. The GLS integrates analytical systems, including machine learning algorithms for automated variant classification and prioritization, as well as phenotype extraction via natural language processing (NLP) of clinical notes. This GLS is extensible to additional analytic systems and growing research and clinical collections of genomic and other types of data. Yet, treatments for rare diseases are scarce due to the unique challenges of rare disease research3, including the small size of patient cohorts and the identification of precise genetic etiologies of disease. Of the demonstrated applications of genomics for patient care in rare disease cohorts8, most do not address the specific challenges of the pediatric population. Rare disease pediatric patients are few in number and tend to be geographically widely distributed9, and therefore combining small cohorts within an institution or with other institutions can be valuable15. Existing inter-institutional networks have increasingly focused on integration of genomic data, with some focused on pediatric populations17 and others focused on national enrollment13. Therefore it is increasingly important for individual institutions to develop approaches that are compatible with integration to these larger efforts.Ten million children are affected by a rare disease with a genetic etiology24 and researchers can identify rare variation leveraging extended pedigrees26. Whole genome sequencing (WGS) and whole exome sequencing (WES) have been shown to be more effective at diagnosing patients with suspected genetic diseases than other technologies30. Hospital-based sequencing analyses have been shown to be more effective than reference-lab-based analyses,27 because it often occurs at academic medical centers and in a research environment where analysts have more access to deep phenotypic information about patients, better ability to re-contact patients and their clinicians, and more domain-specific expertise. Many studies have also argued in support of the added utility of iterative re-analyses of sequencing data38 and highlighted the importance of these factors in28 pediatric and neonatal populations41. Institutional and national initiatives must consider additional questions around consenting mechanisms and language. Initiatives may opt to sequence patients using research sequencing or Clinical Laboratory Improvement Amendments (CLIA)-compliant standards. The computational burden of analyzing genomic data raises questions regarding how to develop infrastructure to analyze data.Institutional and national initiatives face common challenges in their decisions around patient selection, sequencing and analyses. These decisions shape gene discovery and genetic diagnosis outcomes. Family-based or trio-based sequencing has been shown to be substantially more effective in various patient populations than singleton sequencing,47. Data sharing issues are widely recognized and have prompted efforts such as Platform for Engaging Everyone Responsibly (PEER),48 National Institutes of Health’s Genomic Data Sharing Policy,49 and the Global Alliance for Genomics and Health (GA4GH) Data Use Ontology50 to find more elegant solutions. Adopting those recommendations across an institution (including modifying existing studies) would accelerate general readiness to engage in academic collaborations within the United States and internationally. During this adoption process, there is also additional opportunity to align consenting with the specific needs of clinical processes including access to identified data, clinical confirmation of findings, and access to data by patients.Of all of the challenges facing the community, perhaps none is as profound as that of consenting, particularly in the realm of data provenance and use. Within institutions, and even more so as data sources are combined across institutions, incompatibilities in research consenting practices limit the ability to combine data sources, especially given that once consent is obtained it cannot be readily modified. Many consent forms developed in the past and still used today do not support broad use of research data and instead restrict data use to disease-specific research, creating barriers to sharing data within and between institutions. Many studies have shown that most research participants are supportive of data sharing across the research community51. While valid privacy and regulatory policy considerations have historically necessitated the separation of data from the laboratory and the clinic, as well as data from different cohorts, these distinctions pose barriers to the utilization of multiple data sources to accelerate scientific discoveries and pediatric precision medicine. Whole exome and genome sequencing data are not yet readily incorporated into patient medical records53, and even the limited variant data contained in clinical reports from reference laboratories are often stored by Electronic Health Records (EHRs) in a manner that is not automatically searchable . Thus, though re-analyses, and in particular hospital-based re-analyses, of clinical sequencing data can lead to improved gene discovery and diagnosis rates, these re-analyses rarely happen in practice. These realities necessitate the creation of institutional data warehouses to store genomic and phenotypic information and provide unified infrastructure for the interrogation of full raw sequencing data, which is otherwise impossible or very time consuming for researchers and clinicians. Increasingly, research and clinical genomics overlap to provide patients with the latest discoveries, particularly in the translation of research findings to the clinic and in the treatment of raw sequencing data like other unprocessed measurements, reviewable by clinicians. As research and clinical sequencing require similar infrastructure to review genomic data, there is an opportunity to foster intra-institutional synergy through use of a unified platform that accommodates the unique regulatory requirements of each.Although hospital-based analyses frequently leverage genomic data together with phenotypic information collected through clinical practice and by the research laboratory, these data typically remain siloed in multiple locationsPatient enrollment in genomic research has been ongoing at BCH for close to three decades, yet data generated across research teams have historically not been available for collective analysis. Here, we describe the Children’s Rare Disease Cohorts (CRDC) initiative, a program at BCH integrating genomic, research, and clinical data to expedite pediatric precision medicine. Within its first year, the CRDC launched 15 studies of rare pediatric-onset Mendelian diseases, including epilepsy and inflammatory bowel disease (IBD). Together, these studies enrolled 2441 participants and collected genotypic data and phenotypic data from the medical record and research surveys. We developed a genomics learning system (GLS) for storage and use of the data. By developing institutional infrastructure, leveraging a uniform sequencing provider, and aligning IRB protocols with consistent and broad consenting principles, the de-identified genomic and phenotypic data of the CRDC was made available for collective analysis across the institution in the GLS. In addition to supporting the CRDC, the GLS was used to analyze clinical and research data of an additional set of 6075 exomes and 112 genomes. CRDC data in the GLS are accessible to translational researchers for gene discovery, to basic science researchers for functional studies of candidate genes, and to clinicians for accelerated hospital-based patient diagnoses.54.The mechanisms, processes and technologies implemented at BCH during the first phase of the CRDC were focused on enabling hospital-based analysis and re-analysis of genomic data to accelerate rates of discovery and patient diagnoses. This project involved a strategic, internally-funded investment in CLIA-compliant research sequencing of disease-specific cohorts. The project was informed by a series of faculty town hall meetings and the formation of a Genomics Blue Ribbon Committee who developed specific recommendations to the hospital leadership that resulted in institutional funding. Subsequently, a cohorts sub-committee was formed, which surveyed the BCH community in October 2017 requesting estimates regarding cohorts of pediatric patients that would benefit from sequencing. A total of 26,148 patients in 83 disease cohorts from 23 departments or divisions were estimated with an additional 4692 patients predicted to be diagnosed with those diseases at BCH in 2018. Two of these cohorts (epilepsy and IBD) anticipated high diagnosis rates (500 patients each) in 2018 and were selected by the cohort committee to pilot processes and technologies for cohort sequencing. Sample collection started in October 2018 and by January 2019, the cohort committee initiated a request for application (RFA) for additional cohorts. Applications from investigators across the institution included 36 investigators in 19 departments regarding 11,810 patients with 7987 that would be eligible for enrollment in 2019. An additional 13 pediatric-onset Mendelian disease cohorts were selected from the applications, expanding the engagement of the CRDC in phase I (October 2018-September 2019) to 15 cohorts. Implementation of phase I of CRDC was completed as a multidisciplinary effort across research teams, institutional leadership, the Research Computing (RC) group, which is embedded within the Information Services Department and affiliated with the Computational Health Informatics Program (CHIP), and members of the Manton Center for Orphan Disease Research55. This consenting framework was subsequently integrated into 15 IRB protocols and informed consent documents. The framework was designed to address four major aspects: (1) to empower the rights and interests of the research participants, (2) to support sample and data flow considerations, (3) to enable the use of data across the institution, and (4) to facilitate engagement with other academic networks and industry sponsors to accelerate discovery and therapeutics development . In these cases, research teams consented participants over the phone, utilizing mail, email or fax to send blank and signed consent forms.In a similar fashion to the CRDC recruitment process, the CRDC sample collection aligned IRB protocols and sample labeling systems. Sample collection is supported by BCH research teams Fig. . Samples57, which BCH uses widely as a research Electronic Data Capture (EDC) system. These registries were used to track phenotypic data derived from four sources: (1) manually from research notes (information directly added by the study team); (2) manually from clinical notes or unstructured EHR files; (3) self-reported by the participant to research staff, including via questionnaires; and (4) pulled automatically from the EHR  severity scores60, dates of specific symptoms and medical interventions, information on medications and patients’ reactions, structured fields automatically sourced from the EHR, and information on past surgeries and hospitalizations.Since analysis of genomic data benefits from access to phenotypic information, we phenotyped participants using automated and manual processes drawing from EHR and research data. Project-specific registries were developed in REDCapR files;  self-repres; and  pulled a61 to support rapid analysis of large cohorts. GORdb’s architecture, which has been previously applied to population scale projects in Iceland63 and Ireland64, readily extended to our current collection. Phenotypic information was derived from the EDC through a validated extract, transform, load (ETL) process  to extract HPO terms .The GLS provides access to three distinct types of data: (1) raw data in the precise format that was received from the sequencing provider, (2) harmonized data files that have been processed with a standard bioinformatics pipeline, and 3) integrated data stored in GORdb which includes genomic data, registry data from an EDC, and EHR data that incorporates clinical notes processed by CLiX Focus  were known disease-causing variants, two thirds (n = 171) were new variants in genes known to cause disease, and the remaining variants (n = 25) were phenotype expansions. Further, two in five had either a known drug/disease/gene interaction or an existing relevant clinical trial. The Emedgene-automated ACMG classification scored 20% of the variants as pathogenic or likely pathogenic (P/LP), and the remaining as variants of uncertain significance (VUS).The first batches of data in the GLS have recently been returned to researchers for further evaluation. Though this analysis will continue to expand, we have identified 253 variants of interest in 168 genes leveraging the variant annotation workflow described in Fig. 11, and a handful of new genotype-phenotype associations and many phenotype expansions have been preliminarily identified. Further, new collaborations and functional studies have been initiated. Beyond providing clinical confirmations to families, research teams also connected families with other services. For example, one family with two affected children were connected to a clinical trial at a nearby hospital for which they were eligible based on their genetic diagnosis and another could enable a patient with an atypical presentation to be seen at a specialized multidisciplinary clinic.Research teams’ analyses have led to the clinical confirmation of 43 variants in 32 patients that are new genetic diagnoses and confirmed etiological Mendelian associations  and complexities in consenting provenance. Additional benefits of the current multi-protocol approach include reliance on the participating study teams for enrollment, sample collection, education, and follow-up and that it allowed us to bootstrap the project without establishing a separate core. This current model will continue to be evaluated as the project continues, and we recognize that a centralized approach, such as the institution-wide Precision Link Biobank protocolNot surprisingly, given close interactions between BCH clinicians, researchers, and patients, the rates of enrollment were high in phase I. We believe that the use of buccal swabs and remote consenting helped greatly with enrolling more participants and sequencing more complete families. Even when patients visited a BCH clinic, many families had at least one family member who was remotely consented, as frequently only one parent attended the hospital visit. In order to further support the remote consenting process we introduced an electronic remote consenting system, which is similar to the electronic consenting system used at BCH locations and also utilizes REDCap. Notably, though most of the research teams still prefer in-person paper consenting to electronic consenting on tablets, they are enthusiastic about electronic remote consenting options. We will continue to grow these cohorts in phase II of the initiative; in cases where the BCH population is not significant, we may extend them with data from other pediatric institutions.74. CLIA sequencing laboratories are responsible for rigorous sample tracking processes and quality controls, therefore sequencing services provided under CLIA have been more expensive and more rarely used in research. Benefits of following CLIA workflows include cost avoidance of additional sample extraction, as well as added speed and simplicity when performing follow-up clinical testing. Moreover, research sequencing benefits when captured in a CLIA-compliant manner from improved pre-analytic handling of samples and quality control75. As the gap between CLIA and research sequencing costs is closing, the additional costs of CLIA sequencing have been endorsed by BCH clinicians and others76. We expect that CLIA sequencing providers will close the gap to adjust to increased research demand. More broadly, the distinction between research and clinical genomics is blurring; research findings are being integrated into clinical application when analyzed in the appropriate environment and data from clinical sequencing is being used for research. Increasingly, clinicians at BCH are reviewing clinical genomic data through the GLS. As these data are readily shared to researchers when consent is obtained, the GLS forms a basis for combined clinical-research workflows. With access to the GLS, clinicians may leverage shared modules for analysis, existing research data, and methodologies in their evaluations. Utilizing CLIA sequencing processes upfront enables rapid and seamless return of findings from researchers. We expect that researchers and institutions will find increasing relevance of CLIA processes, with potential long-term benefits including avoidance of additional sample collection and testing and suitability to creating merged genotypic, phenotypic and clinically sourced databases.The narrowing of the gap in pricing between CLIA-compliant and research sequencing creates unique opportunities for research hospitals. Here, we opted to sequence samples under CLIA guidelines. The Center for Medicare and Medicaid Services (CMS) regulates that all laboratory testing (except research) performed on humans in the United States occur in a CLIA-compliant manner77. Of the 253 variants of interest we have thus far identified, 14 were outside of exons. Access to the entire genome would enable the broad identification of variants in non-coding regions, as well as improved identification of copy-number variants (CNVs)79. A shift to WGS would necessitate the collection of whole blood to increase sensitivity, particularly when calling CNVs80. Utilizing blood collection would also enable the analysis of other biological molecules , which currently would only occur through the collection of a second research sample at the time of enrollment or via patient re-contact. CLIA WES costs less than research WGS, allowing us to maintain a rigorous CLIA process at scale. Moreover, data storage and transit costs are lower as the average size of a WES BAM file is many times smaller than that of a typical WGS BAM. These data management considerations also translate to compute costs as BAM files are processed to VCFs and integrated into GORdb. Most importantly, the approach to utilize buccal swabs in support of WES allowed us to dramatically scale up rates of sample collection, particularly in pediatric patients unable to donate blood and second parents that are not typically present at the hospital at the time of enrollment. In the CRDC’s second phase, we plan to pilot transitioning CRDC sequencing to WGS.WES is the most frequently used NGS modality in clinical application and is particularly effective at identifying single nucleotide variants and small indels in coding exons, as well as flanking intronic sequences involved in normal splicing81 and Genome Query Language,82 GORdb benefits from integration of back-end infrastructure, APIs, modules and GUI for the platform. This is integral for the project because WuXi NextCODE’s GORdb robustly handles large repositories with many thousands of samples, while remaining accessible to clinicians, researchers, and liberating bioinformaticians from the task of managing compute clusters directly. Because of the extensible nature of the system, additional modalities, such as methylation, immunoprecipitation, chromatin capture and microbiome analyses, can be readily incorporated and integrated together. Extending the other facets of the GLS to these additional modalities would leverage similar workflows as the existing WES/WGS infrastructure: raw data is processed into harmonized data that is uploaded to GORdb and other systems. These extensions would require deploying additional algorithms to the bioinformatics pipeline, and development, testing, validations and deployments of new GORdb queries, which would be used to integrate additional data with existing merged information in GORdb. WuXi NextCODE has already developed built-in modules for the analysis of CNVs and RNA-seq data. We have started using the CNV modules and incorporated 19 CNV profiles in the GLS. Further, the built-in GORdb API enables analyses that leverage the compendium of common bioinformatics tools, such as those available through Bioconductor83 or Bioconda84. Users can create and share custom modules within GORdb and less experienced researchers can leverage existing modules. Since de-identified genomic, research and clinical data is merged within GORdb and shared across BCH, the merged GORdb data lends itself to analyses aggregated across cohorts, such as reviewing if any patients in different cohorts cluster together or producing adequate statistical power for association studies. Unified analyses of standardized homogeneously collected genomic and phenotypic data from large cohorts of different rare or Mendelian diseases has been shown to be a powerful approach for genetic discovery85.The development of the GLS as a centralized institutional resource has enabled research teams to offload maintenance and information technology work, allowing them to focus on research. Research teams confirmed that the GLS functionality, interfaces, training and support met their needs. While other integrated genomic data systems are available, such as Hail86 persist, we conclude that in order to accelerate the pace of genomic research at pediatric research hospitals, a delicate interplay between academic and commercial entities will continue to be necessary.Carefully reviewed use of commercial vendors generally accelerates the use of cutting-edge technologies, mitigates costs, and allows research teams to remain focused around their leading expertise in rare diseases and discovery science. Some of the critical commercial contributions to the CRDC include CLIA-compliant research sequencing by GeneDx, secondary and tertiary analysis of datasets by WuXi NextCODE in an Amazon AWS Cloud environment, Emedgene variant prioritization with machine learning algorithms and AI knowledge graphs, and NLP of clinical notes by CLiX Focus. Systems such as TriNetX and i2b2/TranSMART, developed at Harvard in collaboration with BCH’s Computational Health Informatics Program, and other tools and utilities developed at BCH, extend and interconnect various CRDC platform components and will allow us to support potential data sharing with other institutions. Taken together, we find that a heterogeneous infrastructure combining systems and modules developed in-house with third-parties allowed us to rapidly meet requirements specific to BCH researchers or processes, while simultaneously retaining the ability to scale without needing to fundamentally reconfigure our architecture. The strategy we deployed to develop a GLS that integrated commercial technologies and in-house pipelines enabled us to rapidly deploy new systems while maintaining a lean in-house team of bioinformaticians. Licensing WuXi NextCODE, Emedgene and Clinithink enabled our GLS to have the scalability of GORdb, the AI of Emedgene and the NLP of CLiX Focus, each of which would have required large bioinformatics teams to have developed in-house and maintain, freeing CRDC bioinformaticians to focus on BCH context specific tasks. While challenges around data security and patients’ privacy3.While the cost of genomic sequencing continues to decrease, other expenses, including patient recruitment, data management and data analysis, remain largely unchanged. In order to accelerate genomic research, all research hospitals face challenges in identifying potential funding sources, third-party vendors, and commercial partners to advance the use of data for therapeutic development. The hospital decided to support the current project in order to stimulate the use of genomic data in research, the clinic and education. As an outcome, all CRDC datasets remain the property of BCH and third-party vendors can only access data in support of BCH-contracted work, as per the terms of the BCH Business Associate Agreement (BAA). Though the hospital has not yet decided to share the genomic data with other academic or commercial entities, all enrolled patients have consented to such data sharing, with the understanding that this would be reviewed by BCH’s Institutional Genomic Oversight Committee for use in the development of new therapeutics and to further our understanding of human disease. Moreover, legal documentation  required to share data would be secured and filed with the IRB. Perhaps most importantly, while the majority of technical and procedural processes have been streamlined, we are only now entering the phase of the project where the collected genomic data can be fully exploited and utilized by an extensive network of research laboratories at BCH, potentially in collaborations with scientists from other academic institutions. These partnerships have the potential to translate emerging findings, in line with recent projects from BCHIn this report, we describe how we scaled workflows and infrastructure for cohort sequencing at BCH to provide support for genomics research. We describe the data that was collected and how it was integrated into a GLS, combining commercial applications and custom development in a secure and transparent manner. We discussed the value of these data and the GLS to patients, researchers and clinicians. We have provided a model for institutional integration of data to support both discovery and clinical use. This work shows that there is value in aligning consent, developing a modular GLS and integrating clinical and research data sources and that being able to do this depends on the contributions of many stakeholders. We have demonstrated the viability of this system by rapidly enrolling, sequencing and reviewing data from over 2400 participants, and research is ongoing. The first phase of the project established the collection, integration, and sharing of research, clinical and genomic data within the GLS. In the next phase, the focus will shift to data analysis, integration with other institutions and integration of other modalities, including WGS. The approaches and lessons from our work serve as an example for developing institutional workflows for the integrated analysis of research, clinical, and genomic data. Adoption of comparable strategies by other institutions will further enhance research and clinician collaborations, provide greater power for genomic analyses and accelerate pediatric precision medicine.This work describes processes and results from the first year of Boston Children’s Hospital’s (BCH) Children’s Rare Disease Cohorts initiative. Phase I of this initiative began on October 1, 2018 and continued through September 30, 2019 at BCH, Boston, MA, USA. Phase II is ongoing and began on October 1, 2019. In Phase I, 2441 participants were enrolled. Genomic findings presented in this work strictly relate to these 2441 CRDC participants. This paper also describes our work in creating a common genomics learning system (GLS) for research and clinical genomics activity at BCH. The GLS contains a wide range of research and clinical genomic data. We integrated 6075 additional, non-CRDC exomes and 112 genomes during the first phase of the CRDC. These patients and family members were enrolled in studies that began prior to the CRDC initiative, and their data are not in general shareable. We do not present any results from analysis of these patients. The BCH IRB approved all research related to this study and written informed consent was obtained from all research participants.n = 146) or buccal swabs (n = 2093). The remaining participants had previous or upcoming clinical sequencing scheduled (n = 72) or did not complete the sample collection process (n = 130). Buccal swabs were collected either in clinics or at home. Patients who were consented remotely were typically mailed self-administered buccal swab kits with a return envelope included.Samples for the CRDC were collected as blood  had 99.4% of targets covered at 20× and an average depth of 114×. All sequencing data passed specific minimal quality control requirements, including pass-filtered sequencing yield of 4GB, thresholds for mapping percent to hg19 (>95%), target coverage at 10× , mean target coverage , duplicate read percentage , and read-quality metrics (80% Q30).87 was derived from a self-reported questionnaire in the IBD cohort, originated from research notes for the epilepsy cohort, and was pulled from the EHR for other cohorts. Fields such as name, date of birth (DOB), or gender were frequently manually curated from clinical notes, but in some cases, they were pulled automatically from the EHR. Others , family relationship information, and tracking information, including date consented, date and type of sample collected, sample collected location) originated from research records, many of which were added to REDCap databases during CRDC on-boarding  were sourced heterogeneously. Like the consenting and sample collection processes, patient phenotyping relied on research team expertise. Though timestamps for each event are stored in the EHR, the collection of temporal phenotype data in the research team’s registry is at the discretion of each research team and depends on disease specific characteristics.The de-identifiers were configured for each family and participant based on their relationship to the proband. The vast majority of fields were manually sourced from research notes, followed by manually sourced from clinical notes or the unstructured EHR. The least frequently used source of data was self-reported. For example, race61 in a research environment. Adapters were trimmed using Skewer v0.2.188, FASTQ analysis was performed by FastQC v0.11.789 and base quality was calculated using BBMap v37.9790. Read alignment, read depth calculation, realignment, recalibration, and variant calling were performed by Sentieon v201808.0391: BWA, HSMetricsAlgo, WGSMetricsAlgo, markduplication and Realigner, QualCal, Haplotyper and GVCFtyper91. Verifybamid 1.1.392 was used to check contamination and GATK 4.1.2.093 to count reads in bins. WuXi NextCODE GORpipe 4.3.061 converted other variant data to genomically ordered relational (GOR) format and annotated variants with VEP 96.294 and custom tools. Processed data (BAM/VCF files) were stored in AWS S3 buckets and are used to support other institutional databases such as i2b2/TranSMART70 and the Precision Link Biobank10. In the future, with an oversight from BCH and IRB, this access could be extended to other cross-institutional projects, such as Genomics Research and Innovation Network (GRIN)16  account and archived in a Simple Storage Service S3) bucket . Therefore, there are federal and institutional regulations that control access to this data. Each of the components of our workflow have features for access control that we were able to leverage in the GLS. PII is stored only in the EDC and EHR. Policies around data access in research team REDCap databases are institutional. Each REDCap project is tied to a specific IRB protocol, and access is only permitted to researchers on that protocol. Within a REDCap project, access can be further configured by using REDCap data access groups. Data in GORdb and the GLS analysis systems are all de-identified and built-in facilities are used for user authentication and data siloing. GORdb, Emedgene and Seqr are each able to employ project-based access control and user and administrative actions are logged in all systems. Data consistency across systems is maintained with regularly scheduled (daily or weekly) synchronization from our central REDCap database, managed by the Python web server, which runs a set of automated jobs. This REDcap acts as a source of truth for the entire GLS: data is pushed out from it to the downstream services. Data is fed into the central GLS REDCap from smaller cohort-specific or clinical REDCaps, but this data is validated and filtered before becoming integrated with the GLS.Harmonized genomic data is made available to systems via Identity and Access Management (IAM) policies in AWS, which control read-only access to the S3 buckets. Harmonized and raw data is also available to research teams via investigator-requested read-only access to cloud storage. Data standardization and quality controls are handled with a number of overlapping methods. Sample and subject metadata is managed through a central REDCap and a Python web server, which records and validates subject consent, sample collection, receipt of raw sequencing, and data harmonization. The web server that handles data transfer from EDC to GORdb performs basic validation and error-checking on raw participant data from the research lab. Further, this server unifies data formatting across all cohorts by enforcing a common data structure for all participant data , CLiX Focus’ CNLP would annotate focal seizures (HP:0007359), seizures (HP:0001250), and abnormality of the nervous system (HP:0000707)). Even though terms higher in the hierarchy are also annotated, more than half of the annotated HPO terms are terminal terms, and do not have a child term in the results.41, including validation regarding the use of hierarchical parents of extracted terms. We tuned the CLiX Focus system to BCH’s EHR construct and tested and validated the results utilizing a cohort of rare disease cases from The Manton Center for Orphan Disease Research. We applied the CLiX Focus NLP engine to individuals for whom detailed HPO annotation had already been conducted manually by trained researchers. For six test cases, the CLiX Focus output was compared with the manually extracted HPO terms and CLiX-derived HPO terms were scored as a true or false positive. We were able to leverage manually annotated HPO terms to determine a comparable recall as has been previously reported for CLiX Focus HPO terms (85%)41, but this comparison does not lend itself to meaningful precision or sensitivity calculations. This analysis was the basis for discussions on how to update the CLiX ENRICH rules and what types of notes should be included or excluded from the processing based on commonly identified problems. Some rule changes were necessitated by the specific structure of BCH clinical notes. Common reasons for false positives included imputation of specific eponymous diseases based on the names of patients or their health care providers, assignment of terms based on checklists of findings reported absent following a colon, and diagnoses associated with gene symbols contained in a differential list of diagnoses. Based on this analysis, the selection of input document types was also adjusted to retain intake and discharge notes while excluding in-patient status updates, pre-MRI screening questionnaires, nursing notes with checklists of potential findings, and other document types with repetitive nature and/or low information content  of their notes containing terms that mapped to seizures (HP:0001250). Before being loaded to GORdb, an additional frequency-based filter was leveraged to take advantage of the high number of notes per patient at BCH and to further enrich for phenotypes that had been observed by multiple clinicians. We applied heuristic filters to the HPO terms extracted by CLiX Focus and applied different filter cutoffs for patients with low (<20), moderate (20–50), or high (>50) HPO terms extracted (Supplementary Table Using the above filtering criteria, the average CRDC patient had 510.8 clinical notes in their medical records and CLiX Focus extracted 191.9 HPO terms on average, including parent terms. On average the top HPO term found in patient notes was found in 44.6% of notes. For example, a patient enrolled in the epilepsy cohort with 176 processed clinical notes was observed to have 50% (Further information on experimental design is available in the Supplementary InformationReporting Summary


The need for multisectoral approaches (MSAs) in prevention and control of vector-borne diseases (VBDs) has been identified. VBD programs often involve collaborations between health and nonhealth sectors; however, a systematic framework describing the process, requirements, challenges, and benefits of MSAs has been missing. A recent guidance document from UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases provides a framework to implement MSAs for prevention and control of VBDs. This article gives an introduction to the guidance document and describes the conceptual framework and coordination process. The next steps will be to test the framework through implementation research in specific VBD cases studies. The guidance document will thus be refined based on iterative and comprehensive monitoring and evaluation systems to assess the performance and impact of MSAs. The advocacy for MSA and necessary capacity building will be integrated into the testing of the framework. Despite the global effort to combat vector-borne diseases (VBDs), more than 80% of the world population is still exposed to at least 1 major VBD . The preGlobal Strategic Framework for Integrated Vector Management, which emphasized the need to collaborate with other sectors [Global Vector Control Response 2017–2030, “strengthen inter- and intra-sectoral action and collaboration” is recommended as 1 of the 4 pillars of action [Thirteenth General Program of Work 2019–2023, the need for strengthening MSAs is highlighted to address all the determinants of health and to achieve a paradigm shift [Multisectoral and intersectoral approaches are neither new nor reemerging trends in the field of public health. A simple search in PubMed of these 2 words in the titles and abstracts identified the first documented studies in the 1970s, notably on health self-reliance in Tanzania , the natf action , and in gm shift .Intersectoral Action for Health [Multisectoral Action Framework for Malaria [Global Strategic Framework on Integrated Vector Management [Action and Investment to Defeat Malaria 2016–2030 [The terms “multisectoral” and “intersectoral” have often been used interchangeably; however, semantically “multi-” focuses on “more than one, several, many,” and “inter-” refers to the interaction “between, among, amid” several actors . As suchr Health , 15, the Malaria , Global nagement , the Act016–2030 , and Uni016–2030 , an MSA 016–2030 : A recogAs mentioned above, the determinants of VBDs span across disciplines and can be grouped into 4 broad categories : (1) patMSA can strengthen health programs for VBDs prevention and control by achieving the following: (1) leveraging knowledge, expertise, and reach  of multiple sectors; (2) increasing synergy and coherence in each sector’s policies and activities with regard to VBDs; (3) strengthening capacity in all relevant sectors and empowering community; (4) fostering resource sharing, optimizing resource use, and maximizing impact; (5) tackling all relevant determinants of VBDs; and (6) facilitating scaling-up and increasing sustainability both institutionally and financially.TDR’s newly developed guidance document  aims to The conceptual framework called BET  is assembled by building blocks that together encompass all the essential elements of a successful MSA : (1) theEffective coordination is key to success in an MSA and thus one of the fundamental 3C pillars. A coordination pathway is proposed to be coupled with the BET framework. The pathway includes 6 interrelated steps that form an iterative cycle to effectively and systematically coordinate an MSA: (1) mandate the coordination committee; (2) conduct national or local multisectoral VBDs control needs and capacities assessment; (3) build partnership; (4) sensitize the public and build capacity in all partners; (5) manage collaboration; and (6) monitor and evaluate the impact.When countries plan to implement an MSA for the prevention and control of VBDs, it is importance to seek synergy with existing relevant programs and structures. Some multisectoral collaboration mechanisms may be available within the country for other health and nonhealth programs, such as the coordination mechanisms established for integrated vector management , multiseWhen planning an MSA, one must secure a sufficient share of government health spending and be aware of budgets and mechanisms for the funding flows of each stakeholder. Importantly, more and better should be done for less, and the efficiency and effectiveness of funds improved. Moreover, sustainable implementation of multisectoral interventions is reinforced by appropriate regulation and legislation.The main goals of MSAs for VBD control are to support countries to establish a robust mechanism that can facilitate the implementation of interventions and potentially incubate innovative strategies. However, the uptake and implementation of MSAs in full is often limited because of knowledge gaps in many areas, including the weak understanding on how to carry it out. Therefore, field testing and implementation research are required to systematically document evidence in those areas, including but not limited to the role and contribution of the different sectors and how they are working together in VBD prevention and control programs, the sectoral costs and return, and the contextual assessment of the best processes. In the same way, research is required to test the conceptual framework, the coordination pathway, and the sector-specific guidance to learn and identify the classical SWOT elements , as well as areas to improve. While the guidance document  providesThe specific objectives of the research projects for testing the MSA framework will include:Validating the guidance document  to identAssessing the operational feasibility and implementation barriers/constraints; the attitude/incentives of the health sector to work with nonhealth sectors and that of nonhealth sectors to participate in VBD programs;Identifying Specific, Measurable, Achievable, Relevant, Time-bound (SMART) indicators to measure the acceptability, efficiency, and cost-effectiveness of the programs and the overall MSA coordination system, as well as the impact of the MSA programs;Identifying the pathway/mechanism that best suits the local settings and context, including how to efficiently and sustainably engage with sectors and stakeholders for specific diseases; each sector’s roles and responsibilities; as well as resource mobilization mechanisms;Providing information to decision and policy makers.Indirectly, implementation research will also help to build the capacity of the health and nonhealth workforce in the implementation of MSA programs, including the capacity related to MSA coordination. The experimental studies will serve as real-life examples that, if successful, can potentially be scaled-up and expanded to other areas. Lessons will be learned for future work.Field testing of MSAs will provide an opportunity to strengthen the capacity of the different sectors involved in the implementation of multisectoral programs in coordination and complementarity. The capacity building will be achieved through: (1) extending understanding of VBDs determinants and their links with all relevant sectors in the local settings; (2) identifying local VBD prevention and control bottlenecks and the needs for reinforcement; (3) conducting high-quality interdisciplinary research on topics related to implementation of VBD prevention and control measures; (4) translating evidence into actions; (5) boosting willingness and capacity to collaborate, seeking synergy among sectors, and creating an enabling environment for partnership building; and (5) empowering and engaging communities.Monitoring and evaluation is discussed briefly in the guidance document . MonitorIn our continuous changing world, thanks to the intensive effort during the past several decades, the burden of some VBDs such as malaria has been alleviated in certain areas. Nevertheless, human health still faces increasing threats from other VBDs, such as dengue and other arboviral diseases. To properly fight these new threats, there is a need for comprehensive, robust, clearly mandated multisectoral mechanisms. A global call on MSAs with comprehensive guidance will create an important momentum in current and future VBD programs to break down the silos and empower all VBD-related and other health outcomes-related sectors and stakeholders to work together. The full deployment of robust MSAs for the prevention and control of VBDs will require a long-term and tremendous effort, but once we start to work together across different sectors, with our multidisciplinary knowledge base, concerted effort, and guiding tools available, we will be able to achieve our long-term goals.


Older adults tend to be religiously-affiliated to a greater extent than any other generational cohort . However, their level of engagement varies across cultural and national contexts. Complex life course trajectories related to spirituality and religion mean that these domains often interface with both challenges and a search for well-being. Individuals may align with spiritual and/or religious values to a greater or lesser extent at different periods in their lives becoming more spiritual/religious, less spiritual/religious, or differently so. These papers address diverse perspectives on spirituality, religion, and well-being using samples primarily from the UK, Europe, the US, and Canada. The first paper by Christina Victor sets context by comparing the role of religion, and spirituality in well-being across three separate older adult data sets, touching on loneliness and dementia. Holly Nelson-Becker discusses results from an online international survey of older persons who walked the ancient Camino de Santiago pilgrimage regarding their motivations and learning from the experience. Michael Thomas considers the complex role of spirituality and sexuality for older LGB couples who may choose to remain in or leave their religious faith as they integrate expanding views. Keith Anderson explores perceptions of belief changes among religious and spiritual older adults across the life course. Together, these papers will address benefit and harm from formal religious practice and the advancing roles of spirituality as well as nonspirituality (the “nones”) in global societies.


Sex and gender are widely recognised as two distinct, though related, constructs. While sex denotes an individual’s biological presentation as male, female, or intersex, gender represents the socially and culturally constructed behaviours, roles, rights, and characteristics associated with being male or female . The setIn addition to dictating behaviours and expectations for males and females, many prescribed gender norms engender inherent power imbalances between the two groups; in most societies, masculinity—and not femininity—is associated with higher status and value . Power rAs a result of the historical legacy of gender-based injustice, “the health consequences of gender inequality fall most heavily on women, especially poor women, but restrictive gender norms undermine the health and wellbeing of women, men, and gender minorities” . Gender Journal of Global Health builds on the evidence base linking gender norms and health, presenting findings to advance our thinking about these interconnected systems [This special collection in the  systems ,2,6,9,10Three of the articles in this special collection showcase potentially simple and effective ways to improve maternal, child, and reproductive health. Omidakhsh et al. draw on national-level data sets to demonstrate how changes in national child marriage laws improve attitudes about domestic violence and reduce IPV at scale. Using similar data sources, Ríos-Salas and Heymann show how paid parental leave policies increase women’s decision-making power and improve women’s reproductive health. Finally, Dehinga et al. analyse cross-sectional data from India to show how support by a husband or family has important implications for the success of local community health workers supporting maternal and child health. Together, these three articles help advance the evidence showing how gender progressive policies and support systems can lead to improved outcomes for maternal, child, and reproductive health.A second sub-set of articles presented in this special collection examines the ways in which attitudes and norms among relevant reference groups can influence adolescents’ behavior and the ways in which they internalise these behaviors. Shakya et al., for example, find through analysis of social network data from Honduras that a strong driver of adolescent childbirth is the frequency of adolescent childbirth both within the greater community and within an adolescent girl’s proximal social network; the important role of fathers’ presence is also highlighted. Mejía-Guevara et al. examine Demographic and Health Surveys data from nine countries in sub-Saharan Africa. They find that collective permissive attitudes of both adolescent and adult women towards premarital sex increases the use of, and demand for, contraception. In contrast, collective accepting attitudes towards wife-beating are negatively associated with the use of, and demand for, contraception. Stark et al. examine the impact of perpetrating IPV on the perpetrator’s mental health among adolescents and young adults in Nigeria. While males are more likely than females to perpetrate IPV, both male and female perpetrators report negative, but different, mental health outcomes, suggesting that socially determined gender norms may shape the ways in which distress from IPV perpetration is understood and expressed. Together, these articles not only identify ways in which gender norms may advance or impede progress in achieving the global health agenda, but also suggest specific groups that may be strategically targeted in order to promote and foster change among adolescents.In two final articles in this collection, the authors present innovative ways of looking at the relationships between gender norms and health and deriving insights for policy formulation. First, Cislaghi et al. offer a new approach for analysing causes of disability-adjusted life years to help policy makers distinguish drivers that may be rooted in biology as opposed to those that may be rooted in harmful gender systems. Such analyses call on expanding systems of social and gender justice alongside more traditional medical and public health approaches. Second, Shawar and Shiffman critically examine structural barriers affecting global health organisations in addressing gender inequality. Their thematic analysis identifies three major challenges currently impeding success: 1) a lack of cohesion among health champions, 2) differences concerning the nature of the problem and solutions, including whether reducing gender inequality or addressing harmful gender norms is the primary goal, and 3) disagreements among proponents on how to convey the problem. The authors conclude that the success of the global health agenda will depend in part on the ability of proponents to address these disagreements and develop strategies for negotiating complex organisational cultures and political environments.Taken together, the articles that make up the special collection advance our thinking around gender norms and health in important ways. First, the collection underscores how gender norms operate across the lifecycle, from infancy and early childhood into adolescence and throughout adulthood. It is never too early nor too late in the lifecycle to start thinking about how gender norms are affecting health. Second, the collection shows how norms operate at communal and relational levels all the way up to national policy frameworks. This finding supports our ability to make positive changes at multiple levels within a society to benefit population health. Finally, the collection shows how medicine and public health cannot focus on biological causes and mechanisms without also addressing social factors to achieve positive health outcomes.Women’s equality and empowerment is one of the 17 Sustainable Development Goals (SDGs), and addressing harmful gender norms for both males and females is integral to all dimensions of inclusive and sustainable development. In order to have truly sustainable development and health, the global community needs to create programmes and policies that seek to transform restrictive gender norms and promote equality. Despite the growing body of literature that illustrates the degree to which gender norms significantly impact all pathways to well-being, there is a need for greater coordination and collaboration of gendered intervention work across the development community. To accurately understand the multidimensional ways in which gendered systems influence the pathways to achieving the SDGs, there needs to be substantially greater investment in gender-focused research, policy development, and action.


In this study we modelled possible causes and consequences of student burnout and engagement on academic efficacy and dropout intention in university students. Further we asked, can student engagement protect against the effects of burnout? In total 4,061 university students from Portugal, Brazil, Mozambique, the United Kingdom, the United States of America, Finland, Serbia, and Macao SAR, Taiwan participated in this study. With the data collected we analyzed the influence of Social Support, Coping Strategies, and school/course related variables on student engagement and burnout using structural equation modeling. We also analyzed the effect of student engagement, student burnout, and their interaction, on Academic Performance and Dropout Intention. We found that both student engagement and burnout are good predictors of subjective academic performance and dropout intention. However, student burnout suppresses the effect of student engagement on these variables. This result has strong implications for practitioners and administrators. To prevent student dropout, it is not enough to promote student engagement—additionally, and importantly, levels of student burnout must be kept low. Other variables such as social support and coping strategies are also relevant predictors of student engagement and burnout and should be considered when implementing preventive actions, self-help and guided intervention programs for college students. The term ‘‘Burnout” was first used to describe a Syndrome of exhaustion observed among mental health professionals . DefinedSeveral situational and individual factors have been associated with the prevalence of the Burnout Syndrome in the workplace , 3. SituWith the publication of the MBI–General Survey (MBI-GS) it became possible to study the Burnout Syndrome outside of the human services –6. PsychIn the workplace, burnout is a serious condition that has been linked with multiple physical and psychological conditions such as heart disease , depressBurnout can lower academic engagement levels, such as class attendance, submission of schoolwork, and following teachers’ instructions , 26. In Coping strategies can be defined as efforts to avoid or decrease threats and reduce associated stress , 31. In It is important to note that academic demands are not the only stressor that students must deal with at the university level. Other challenges include peer pressure and competition, limited socioeconomic power, and distance from home and family, among others –39. One Finally, we focus on course expectations, students’ evaluation of teacher competence, and the need for medication as predictors of student engagement and burnout. Course expectations are a key motivational factor that promotes engagement  and theiIn this research, our first aim is to assess the possible causes and consequences of student engagement and burnout. We expect that social support, coping strategies, course expectations, teacher competence, and the need for medication will significantly predict student burnout (H1) and engagement (H2). We also expect student burnout (H3) and student engagement (H4) to be significant predictors of subjective academic performance, and dropout intention. Our second aim is to assess the moderation effect that student engagement has on the relationships between student burnout and each of the criterion variables. We expect that student engagement will act as a protective factor against the effects of student burnout (H5). The expected relationships between constructs can be found in the model path diagram in An online questionnaire was created using the Qualtrics platform. The order of appearance of the scales were randomized between participants and only fully completed questionnaires were considered for further analysis. At the end of the questionnaire, participants answered a series of sociodemographic and academic-related questions. The survey was designed to take 15 minutes to complete. The content, objectives, duration, risks, data policy, ethics approval, and contacts were provided at the start of the questionnaire. The study was properly validated by ISPA-Instituto Universitário Ethics Commission (Process: I/017/02/2019) and by the Northern Illinois University International Review Board . Informed consent was obtained from all individual participants in the study. All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional Ethics Research Committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.The minimum sample size required for confirmatory factor analysis for the USEI measurement model (the model with the larger number of parameters to estimate) was determined by Monte‐Carlo simulation as suggested by Brown  with criThe study sample was composed of 4,061 university students  from Portugal , Brazil (424), Mozambique (413), United Kingdom (314), United States of America (316), Finland (356), Serbia (409), Macao SAR and Taiwan (762). The typical participant was female (60%), pursuing a bachelor’s degree (74%) in human and social sciences (51%) in a public (88%) university (80%), living with their family (54%), which financed their studies (56%). See An online questionnaire was constructed containing the following psychological measures: the Maslach Burnout Inventory adapted to students, version MBI-Student Survey (MBI-SSi) , the SocThe Maslach Burnout Inventory—Student Survey with the efficacy dimension reversed (MBI-SSi)  was usedThe University Student Engagement Inventory (USEI)  was usedThe Satisfaction with Social Support (ESSS) , 44 was The BriefCOPE version of the COPE Inventory  was usedThe demographic variables assessed were gender, age, region, household, and financial support. The self-reported academic variables were the area of studies , type of degree , type of school (public/private university), course expectations, teacher competence, academic performance, need for medication, dropout intention, the total number of classes and number of failed classes.From the socio-demographic and academic-related variables, we selected course expectations, teacher competence and need for medication as predictor variables, academic performance, and dropout intention as criterion variables. Variables were measured using a 4 or 5-point Likert/rating scale. Course expectations were measured with the item "Concerning your initial expectations this degree is:" with answers ranging from "Much worse" to "Much better". Teacher competence was measured with the item "In general terms how do you classify your professors?" with answers ranging from "Very incompetent" to "Very competent". Subjective academic performance was assessed with the item "How do you classify your performance in this degree?" with answers ranging from "Bad" to "Excellent". Dropout intention was measured with the item "Have you thought of dropping out of college?" with answers ranging from "Never" to "Very frequently". Need for medication was measured with the item "Do you need, or have you needed any sort of medication because of your college studies?" with answers ranging from "Never" to "Always".A series of Confirmatory factor analysis (CFA) were conducted to evaluate the goodness-of-fit of the measuring models of Burnout, Engagement, Social Support, Positive Coping, and Negative Coping factors with the lavaan package  of the oAfter assuring good psychometric properties of the measurement model, four Structural Equation Models (SEM) were created with the lavaan package  for R. ABecause some items had unanswered categories, it was not possible to use WLSMV for categorical items to test the thresholds’ invariance of the models. However, when items have five or more points and a distribution that is not severely non-normal , Pearson correlations estimate the associations between variables reasonably well , 61. Thu2(df) (Chi-Square Statistic and degrees of freedom), CFI (Confirmatory Fit Index), TLI (Tucker-Lewis Index), RMSEA (Root Mean Square Error of Approximation) and SRMR . Because robust maximum likelihood estimation was used, the robust scaled versions of the indices were used. Model fit was considered adequate if CFI and TLI values were above .90 and RMSEA and SRMR values were below .06 and .08 respectively Acceptable reliability was assumed for Omega (ω) and Omega L2 larger or equal than .7  and b×Engagement + Burnout*Engagement + (…)] were evaluated. The estimated suppression effects (a − b) were tested with a Z-test for large samples. The suppression effect was significantly different from 0 when predicting students dropout intentions  and academic performance . The interaction effect of student burnout and student engagement also had a negative but relatively small impact on academic performance  suggesting that the effect of Burnout on self-reported academic performance was reduced when the Engagement is high (and vice-versa).The strongest burnout and engagement interaction effect was observed on the dropout intention . The negative sign of the interaction means that when student burnout increases, the effect of student engagement on dropout intention is attenuated (or vice-versa) and, in the presence of high burnout, the protective effect of engagement is suppressed when predicting dropout . To test this hypothesis and quantify the size of this suppression effect, the regressions weights associated with engagement from model in Using the Cheung and Rensvold (2002) ΔCFI criterion  and the 2) and model fit indices.Since only weak (metric) measurement invariance was found for the overall Burnout and Engagement interaction model per country/region, this interaction model was fitted to the samples from each of the countries/regions separately. Configural invariance (that is the same model configuration between countries/regions) was found previously see  suggesti2/df were indicative of very good fit). Variance of dropout intention explained by the model ranged from 22% (in Mozambique) to 48% (in Brazil). Explained variability for subjective academic performance ranged from 29% (in Finland) to 37% (in the UK).Overall model fit for individual countries/regions were acceptable  over the student burnout effect on intention to dropout. Student engagement strongest effects were observed on the self-related academic performance with average standardized effects between .2 and .4 for most participants.Negative coping strategies were the strongest and most statistically significant predictors of student burnout . For Student Engagement, no consistent predictors were found among countries/regions. Positive coping strategies were strongest predictors of Engagement in Portugal, the United Kingdom, Finland, and Mozambique with standardized effects around .5 to .7, followed closely by the fulfillment of course expectations with standardized effects of .2-.4 see . This vaThe academic and social demands of university life exert pressure on students’ cognitive and emotional resources, often leading to exhaustion. This exhaustion, combined with self-inefficacy, and cynicism towards studies form the Burnout Syndrome triad. Burnout has been identified as one of the leading causes of poor student performance and intention of dropout , 24. ConWe found that negative coping strategies  were the strongest predictors of student burnout among most countries/regions, a result that follow previous findings , 35. StuBecause the burnout syndrome has substantial overlap with depression symptoms , the neeSocial support was not a predictor of student engagement in most participants, with the UK being the only exception, a result that was not in line with previous findings . One posAfter performing the analysis of separate models of student burnout and student engagement, we tested the interaction effect of student engagement and burnout on dropout intention and self-reported academic performance. We found that student engagement and burnout show a significant interaction effect, albeit with small effect size, when explaining dropout intention but not academic performance. The data suggest that student engagement can attenuate the effect of burnout on dropout intention, validating our fifth hypothesis (H5). However, this effect was not consistent in all countries/regions. The constructs used in the engagement/burnout interaction model and their relationships were invariant across gender and different areas of this study. However, the model was not fully invariant across countries, partially validating our fourth hypothesis (H6). This result suggests a need for local specific analysis considering the cultural and societal contexts and the different cultures’ valuing of higher education.It is possible that the statistical significance of small observed effect of student burnout interaction with engagement on the criterion variables was due to the overall large sample size used in this study and its associated high level of statistical power. To the best of our knowledge, there are no other studies that have probed the interaction effect of student engagement and student burnout on these outcomes. Thus, further replication is necessary before a moderation effect of the two constructs can be introduced in models examining the effects of burnout and engagement on student’s academic life and well-being. Our models do show that in the presence of burnout, the effect of student engagement on dropout intentions and academic performance is attenuated. That is, engagement in one’s studies may not have expected effects on academic performance and degree completion if high levels of burnout related to studies develop. The first interpretation of this result is that when student engagement is associated with perfectionism, this can lead to burnout which in turn can affect academic performance, an interpretation supported by recent research . The secThe results of this study have strong implications for practitioners and administrators. Data gathered from a large sample of university students from eight countries and regions around the world show that, while promoting student engagement is important to prevent dropout, major efforts  must be pursued to prevent student burnout. Student engagement alone cannot prevent dropout intentions and promote good academic performance if student burnout is high. Our results suggest that culture features and school valuing may play a significant role in the comprehension of burnout syndrome and its relationship with other relevant variables. Therefore, it would be important to conceptualize future interventions taking into account specific cultural contexts.Because of the cross-sectional nature of the current study causation inferred from the data must be cautioned. Causation implies a correlation, but the reverse may not necessarily be true. It is important to avoid causal interpretations of the results, as that would require that longitudinal and experimental methods be used. Therefore, although causal relationships between these variables are implied by structural equation models, caution needs to be taken concerning the causal nature of their relationships. This is a strong limitation common to correlational studies.A second limitation of this study is the self-reported nature of the data collected, namely on the criterion variables, which may create a negativity bias. However, “in reference to variables such as burnout and engagement, self-reports may be the most accurate form of assessment, as the individual is the best person to report one’s own affective state.” . Also, given that data collection was carried out via an online survey, a self-selection bias also may be present. Unfortunately, most psychology studies will suffer from a self-selection bias as participants with certain characteristics may refuse to participate in such studies.Finally, a third limitation regards the poor fit of some measurement models in some of the countries/regions. Face value of higher education, multicultural, educational, and social diversity and norms in such diverse countries as Portugal, Finland, Serbia, Mozambique, The United States of America or Taiwan & Macao may hinder the generalizability of a full model of student burnout and engagement effects on college dropout and academic efficacy, even that a set of consistent predictors emerged from this study.Coping strategies, course expectations, need for medication and social support significantly predicted student burnout. Student engagement was significantly predicted by coping strategies and course expectations. Both student engagement and student burnout significantly predicted subjective academic performance, but only Burnout predicted dropout intentions. Negative coping was the strongest predictor of burnout which in turn was the strongest predictor of dropout intention. Student engagement was predicted by positive coping strategies, as well as course expectations, to a lesser extent by negative coping strategies, but not social support.Most surprisingly, burnout levels suppressed the effect of student engagement on dropout intention when an interaction between the two constructs is present. A negative and significant interaction of burnout and engagement on dropout intentions revealed that effect of burnout on dropout is attenuated by engagement. Given that this study solicited the participation of over four thousand participants from different countries/regions and areas of study, we believe that this is a noteworthy result that has important implications for educational psychologists, practitioners, and administrators.To prevent student dropout and promote academic performance it is not enough to promote student engagement—student burnout also must be kept as low as possible. Other variables such as coping strategies and social support also are relevant predictors of student engagement and burnout and should be considered when implementing preventive actions, self-help, and guided intervention programs for college students.S1 Appendix(DOCX)Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.