Early-life infection depletes preleukemic cells in a mouse model of hyperdiploid B-cell acute lymphoblastic leukemia.

ABSTRACT: Epidemiological studies report opposing influences of infection on childhood B-cell acute lymphoblastic leukemia (B-ALL). Although infections in the first year of life appear to exert the largest impact on leukemia risk, the effect of early pathogen exposure on the fetal preleukemia cells (PLC) that lead to B-ALL has yet to be reported. Using cytomegalovirus (CMV) infection as a model early-life infection, we show that virus exposure within 1 week of birth induces profound depletion of transplanted E2A-PBX1 and hyperdiploid B-ALL cells in wild-type recipients and in situ-generated PLC in Eµ-ret mice. The age-dependent depletion of PLC results from an elevated STAT4-mediated cytokine response in neonates, with high levels of interleukin (IL)-12p40-driven interferon (IFN)-γ production inducing PLC death. Similar PLC depletion can be achieved in adult mice by impairing viral clearance. These findings provide mechanistic support for potential inhibitory effects of early-life infection on B-ALL progression and could inform novel therapeutic or preventive strategies.

Serum Cytokine Panels in Pediatric Clinical Practice.

BACKGROUND: Cytokines are soluble signaling proteins that regulate inflammation and coordinate immune responses. Serum cytokine assays are increasingly used in medical practice, yet our understanding of cytokines as biomarkers for disease remains limited. OBJECTIVES: We aimed to analyze real-world single center usage of a multiplexed cytokine panel, correlate its results with diagnosis and severity, and explore its utility in pediatric practice. METHODS: A multiplexed cytokine panel, able to return same-day results, was implemented in April 2020 at our institution and its performance was validated for clinical use. Coded patient data were collected using a REDCap database, and correlations between cytokine levels and outcomes of interest were analyzed retrospectively. RESULTS: Cytokine levels correlate with acuity of care, with patients admitted to the pediatric intensive care unit (PICU) having the highest cytokine values. Patients with familial HLH (fHLH) showed prominent peaks in IFNγ, IL-10, and TNF, while patients with sepsis exhibited high IL-6 and IL-8 with relatively modest IFNγ, and cytokine release syndrome (CRS) post-CAR T cell therapy often demonstrated pan-panel positivity at peak levels, with a similar pattern as that of fHLH. A ratio of [IFNγ]+[IL-10]/[IL-6]+[IL-8] levels was able to distinguish fHLH and CRS from severe sepsis. CONCLUSIONS: Cytokine levels correlate with severity of illness and can help differentiate between syndromes that present similarly, including fHLH and CRS compared to sepsis. Cytokine panels can be used as biomarkers to inform diagnosis and management decisions, but significant work remains to dissect complex clinical patterns of disease.

Association of the social disorganization index with time to first septic shock event in children with acute myeloid leukemia.

BACKGROUND: Pediatric acute myeloid leukemia (AML) chemotherapy increases the risk of life-threatening complications, including septic shock (SS). An area-based measure of social determinants of health, the social disorganization index (SDI), was hypothesized to be associated with SS and SS-associated death (SS-death). METHODS: Children treated for de novo AML on two Children's Oncology Group trials at institutions contributing to the Pediatric Health Information System (PHIS) database were included. The SDI was calculated via residential zip code data from the US Census Bureau. SS was identified via PHIS resource utilization codes. SS-death was defined as death within 2 weeks of an antecedent SS event. Patients were followed from 7 days after the start of chemotherapy until the first of end of front-line therapy, death, relapse, or removal from study. Multivariable-adjusted Cox regressions estimated hazard ratios (HRs) comparing time to first SS by SDI group. RESULTS: The assembled cohort included 700 patients, with 207 (29.6%) sustaining at least one SS event. There were 233 (33%) in the SDI-5 group (highest disorganization). Adjusted time to incident SS did not statistically significantly differ by SDI (reference, SDI-1; SDI-2: HR, 0.84 [95% confidence interval (CI), 0.51-1.41]; SDI-3: HR, 0.70 [95% CI, 0.42-1.16]; SDI-4: HR, 0.97 [95% CI, 0.61-1.53]; SDI-5: HR, 0.72 [95% CI, 0.45-1.14]). Nine patients (4.4%) with SS experienced SS-death; seven of these patients (78%) were in SDI-4 or SDI-5. CONCLUSIONS: In a large, nationally representative cohort of trial-enrolled pediatric patients with AML, there was no significant association between the SDI and time to SS.

Susceptibility to BK polyomavirus-associated hemorrhagic cystitis in children undergoing allogeneic transplant.

Not available.

Dispersal-Limited Symbionts Exhibit Unexpectedly Wide Variation in Host Specificity.

A fundamental aspect of symbiotic relationships is host specificity, ranging from extreme specialists associated with only a single host species to generalists associated with many different species. Although symbionts with limited dispersal capabilities are expected to be host specialists, some are able to associate with multiple hosts. Understanding the micro- and macro-evolutionary causes of variations in host specificity is often hindered by sampling biases and the limited power of traditional evolutionary markers. Here, we studied feather mites to address the barriers associated with estimates of host specificity for dispersal-limited symbionts. We sampled feather mites (Proctophyllodidae) from a nearly comprehensive set of North American breeding warblers (Parulidae) to study mite phylogenetic relationships and host-symbiont codiversification. We used pooled-sequencing (Pool-Seq) and short-read Illumina technology to interpret results derived from a traditional barcoding gene (cytochrome c oxidase subunit 1) versus 11 protein-coding mitochondrial genes using concatenated and multispecies coalescent approaches. Despite the statistically significant congruence between mite and host phylogenies, mite-host specificity varies widely, and host switching is common regardless of the genetic marker resolution (i.e., barcode vs. multilocus). However, the multilocus approach was more effective than the single barcode in detecting the presence of a heterogeneous Pool-Seq sample. These results suggest that presumed symbiont dispersal capabilities are not always strong indicators of host specificity or of historical host-symbiont coevolutionary events. A comprehensive sampling at fine phylogenetic scales may help to better elucidate the microevolutionary filters that impact macroevolutionary processes regulating symbioses, particularly for dispersal-limited symbionts. [Codiversification; cophylogenetics; feather mites; host switching; pooled sequencing; species delineation; symbiosis, warblers.].

Empirically tuned theory reveals why symbiont abundance 'mite' vary across hosts.

Research Highlight: del Mar Labrador, M., Serrano, D., Doña, J., Aguilera, E., Arroyo, J. L., Atiénzar, F., Barba, E., Bermejo, A., Blanco, G., Borràs, A., Calleja, J. A., Cantó, J. L., Cortés, V., de la Puente, J., de Palacio, D., Fernández-González, S., Figuerola, J., Frías, Ó., Fuertes-Marcos, B. Garamszegi, L. Z., Gordo, Ó., Gurpegui, M., Kovács, I., Martínez, J. L., Meléndez, L., Mestre, A., Møller, A. P., Monrós, J. S., Moreno-Opo, R., Navarro, C., Pap, P. L., Pérez-Tris, J., Piculo, R., Ponce, C., Proctor, H., Rodríguez, R., Sallent, Á., Senar, J., Tella, J. L., Vágási, C. I., Vögeli, M., & Jovani, R. (2023). Host space, not energy or symbiont size, constrains feather mite abundance across passerine bird species. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.14032. Symbionts represent crucial links between species in ecosystems. Consequently, understanding their patterns of abundance is a major goal in the study of symbioses. However, multiple biotic and abiotic factors may regulate symbionts, and disentangling the mechanisms that drive variation in their abundance across host species is challenging. One promising strategy to approach this challenge is to incorporate biologically relevant data into theoretical models. In a recent study, Labrador et al. (2023) used this strategy to investigate the poorly understood symbiosis between feather mites and their avian hosts. They integrate a remarkable amount of empirical data with models based on the metabolic theory of ecology to determine what factors limit feather mite abundance across European passerines. Their quantitative analyses indicate that the number of feather barbs limits mite abundance across host species, suggesting that mite populations are spatially, but not energetically, constrained. These findings not only reveal mechanisms that may drive the variation in feather mite abundances across hosts, but also advance our understanding of the ecology of interspecific interactions more generally.

Absolute neutrophil count clinical trial eligibility criteria for pediatric oncology phase I and phase I/II trials by sponsorship.

Normal absolute neutrophil count (ANC) variations, as seen with Duffy-null associated neutrophil count (DANC), are not accounted for in trial eligibility, which may contribute to racial enrollment disparities. We describe ANC eligibility for pediatric oncology phase I/II clinical trials according to primary sponsorship from 2010 to 2023 using ClinicalTrials.gov. Out of 438 trials, 20% were industry-sponsored. Total 17% of trials required ANC ≥1500 cells/µL for enrollment; however, industry-sponsored trials were significantly more likely to require ANC ≥1500 cells/µL than non-industry-sponsored trials (odds ratio 2.53, 95% confidence interval: 1.39-4.62; p < .001). These data suggest laboratory exclusion criteria are one possible mechanism for pediatric clinical trial enrollment disparities.

Applying machine learning to identify pediatric patients with newly diagnosed acute lymphoblastic leukemia using administrative data.

Case identification in administrative databases is challenging as diagnosis codes alone are not adequate for case ascertainment. We utilized machine learning (ML) to efficiently identify pediatric patients with newly diagnosed acute lymphoblastic leukemia. We tested nine ML models and validated the best model internally and externally. The optimal model had 97% positive predictive value (PPV) and 99% sensitivity in internal validation; 94% PPV and 82% sensitivity in external validation. Our ML model identified a large cohort of 21,044 patients, demonstrating an efficient approach for cohort assembly and enhancing the usability of administrative data.

Multilevel challenges to equitable inclusion of children in trials when parents use languages other than English: A qualitative report from Children's Oncology Group's Diversity and Health Disparities Committee Language Equity Working Group.

BACKGROUND: Increasing representation in clinical trials is a priority for the National Cancer Institute and Children's Oncology Group (COG). Our survey of COG-affiliated institutions revealed that many sites have insufficient processes and resources to enroll children whose parents use languages other than English (LOE). We describe reported barriers and facilitators to enrolling children in clinical trials when parents use LOE and propose opportunities for improvement. PROCEDURES: We sent a 20-item survey to COG-affiliated institutions. Five items allowed respondents to expand on replies to questions about (a) local institutional review board (IRB) requirements regarding translation of consent documents, (b) contributors to provider discomfort consenting parents who use LOE, (c) available language services and resources, and (d) barriers to enrolling children whose parents use LOE or offer ideas about approaches to improvements. Two pairs of researchers independently coded free-text responses and compared results for concordance. RESULTS: A total of 139 (N = 230; 60%) institutions returned the survey. Respondents were mainly physician principal investigators (n = 79/139; 57%) at the United States sites (n = 118/139; 85%) serving less than 100 newly diagnosed children per year (n = 99/139, 71%). They described challenges at multiple levels. Proposed approaches to improvements included centralized provision of translated materials and video educational materials in various languages, and collaborating with IRBs on regulatory processes that protect families and facilitate equitable clinical trial access. CONCLUSIONS: Clinical trial consortia, such as COG, face challenges in enrolling representative samples. Further research is required to design and implement multilevel interventions to ensure equitable access for all, regardless of language used, and mitigate disparate research participation.

PFAS ghosts: how to identify, evaluate, and exorcise new and existing analytical interference.

With increasing public awareness of PFAS, and their presence in biological and environmental media across the globe, comes a matching increase in the number of PFAS monitoring studies. As more matrices and sample cohorts are examined, there are more opportunities for matrix interferents to appear as PFAS where there are none (i.e., "seeing ghosts"), impacting subsequent reports. Addressing these ghosts is vital for the research community, as proper analytical measurements are necessary for decision-makers to understand the presence, levels, and potential risks associated with PFAS and protect human and environmental health. To date, PFAS interference has been identified in several matrices (e.g., food, shellfish, blood, tissue); however, additional unidentified interferents are likely to be observed as PFAS research continues to expand. Therefore, the aim of this commentary is several fold: (1) to create and support a publicly available dataset of all currently known PFAS analytical interferents, (2) to allow for the expansion of that dataset as more sources of interference are identified, and (3) to advise the wider scientific community on how to both identify and eliminate current or new analytical interference in PFAS analyses.

Approach for defining human adenovirus infection and disease for central review adjudication in clinical studies.

BACKGROUND: Pediatric allogeneic hematopoietic cell transplant (allo-HCT) recipients are at risk for morbidity and mortality from human adenovirus (HAdV). HAdV can be detected in an asymptomatic state, referred to as infection or with signs or symptoms of illness, referred to as disease. Standardized case definitions are needed to distinguish infection from disease and allow for consistent reporting in both observational cohort studies and therapeutic clinical trials. METHODS: A working group of experts in virology, transplant infectious disease, and HCT was assembled to develop HAdV infection and disease definitions with the degree of certainty (i.e., possible, probable, and proven). Definitions were further refined through an iterative process and independently applied by two central review committees (CRCs) to 20 pediatric allo-HCT recipients with at least one HAdV-positive PCR. RESULTS: Initial HAdV infection and disease definitions were developed and updated through an iterative process after reviewing clinical and virological details for 81 subjects with at least one positive HAdV PCR detected in a clinical specimen. Independent application of final definitions to 20 HAdV positive allo-HCT recipients by two CRCs yielded similar number of HAdV infection or disease events but with variation of degree of certainty for some events. CONCLUSIONS: Application of definitions by a CRC for a study of HAdV infection and disease is feasible and can provide consistency in the assignment of outcomes. Definitions need further refinement to improve reproducibility and to provide guidance on determining clinical improvement or worsening after initial diagnosis of HAdV infection or disease.

BK Polyomavirus Diversity After Hematopoietic Stem Cell Transplantation.

BK polyomavirus (BKPyV) infection is common after hematopoietic stem cell transplantation (HSCT) and is associated with the development of hemorrhagic cystitis (HC). The role that BKPyV plays in the pathogenesis of HC is not well characterized. We investigated the impact of BKPyV diversity on the development of HC using a previously established cohort of pediatric HSCT patients. There were 147 urine samples with quantifiable BKPyV at month 1 after HSCT; 137 (93.2%) were amplified using our in-house polymerase chain reaction approach and sent for next-generation sequencing. Subtype Ia was most frequent (61.3%), followed by subtype Ib1 (31.4%). The median viral load of subtype Ia samples was higher than for subtype Ib1 at month 1. Across the protein coding regions, APOBEC-induced mutations and signature patterns associated with HC were identified. This is the largest sequencing study of a single cohort of HSCT patients, providing a vast resource of sequence data for future analyses.

Novel insights into symbiont population structure: Globe-trotting avian feather mites contradict the specialist-generalist variation hypothesis.

Researchers often examine symbiont host specificity as a species-level pattern, but it can also be key to understanding processes occurring at the population level, which are not as well understood. The specialist-generalist variation hypothesis (SGVH) attempts to explain how host specificity influences population-level processes, stating that single-host symbionts (specialists) exhibit stronger population genetic structure than multi-host symbionts (generalists) because of fewer opportunities for dispersal and more restricted gene flow between populations. However, this hypothesis has not been tested in systems with highly mobile hosts, in which population connectivity may vary temporally and spatially. To address this gap, we tested the SGVH on proctophyllodid feather mites found on migratory warblers (family Parulidae) with contrasting host specificities, Amerodectes protonotaria (a host specialist of Protonotaria citrea) and A. ischyros (a host generalist of 17 parulid species). We used a pooled-sequencing approach and a novel workflow to analyse genetic variants obtained from whole genome data. Both mite species exhibited fairly weak population structure overall, and contrary to predictions of the SGVH, the generalist was more strongly structured than the specialist. These results may suggest that specialists disperse more freely among conspecifics, whereas generalists sort according to geography. Furthermore, our results may reflect an unexpected period for mite transmission - during the nonbreeding season of migratory hosts - as mite population structure more closely reflects the distributions of hosts during the nonbreeding season. Our findings alter our current understanding of feather mite biology and highlight the potential for studies to explore factors driving symbiont diversification at multiple evolutionary scales.

Leveraging machine learning to identify acute myeloid leukemia patients and their chemotherapy regimens in an administrative database.

BACKGROUND: Administrative datasets are useful for identifying rare disease cohorts such as pediatric acute myeloid leukemia (AML). Previously, cohorts were assembled using labor-intensive, manual reviews of patients' longitudinal chemotherapy data. METHODS: We utilized a two-step machine learning (ML) method to (i) identify pediatric patients with newly diagnosed AML, and (ii) among the identified AML patients, their chemotherapy courses, in an administrative/billing database. Using 2558 patients previously manually reviewed, multiple ML algorithms were derived from 75% of the study sample, and the selected model was tested in the remaining hold-out sample. The selected model was also applied to assemble a new pediatric AML cohort and further assessed in an external validation, using a standalone cohort established by manual chart abstraction. RESULTS: For patient identification, the selected Support Vector Machine model yielded a sensitivity of 0.97 and a positive predictive value (PPV) of 0.97 in the hold-out test sample. For course-specific chemotherapy regimen and start date identification, the selected Random Forest model yielded overall PPV greater than or equal to 0.88 and sensitivity greater than or equal to 0.86 across all courses in the test sample. When applied to new cohort assembly, ML identified 3016 AML patients with 10,588 treatment courses. In the external validation subset, PPV was greater than or equal to 0.75 and sensitivity was greater than or equal to 0.82 for patient identification, and PPV was greater than or equal to 0.93 and sensitivity was greater than or equal to 0.94 for regimen identifications. CONCLUSION: A carefully designed ML model can accurately identify pediatric AML patients and their chemotherapy courses from administrative databases. This approach may be generalizable to other diseases and databases.

Absolute lymphocyte count recovery following initial acute myelogenous leukemia therapy: Implications for adoptive cell therapy.

BACKGROUND: An adequate absolute lymphocyte count (ALC) is an essential first step in autologous chimeric antigen receptor (CAR) T-cell manufacturing. For patients with acute myelogenous leukemia (AML), the intensity of chemotherapy received may affect adequate ALC recovery required for CAR T-cell production. We sought to analyze ALC following each course of upfront therapy as one metric for CAR T-cell manufacturing feasibility in children and young adults with AML. PROCEDURE: ALC data were collected from an observational study of patients with newly diagnosed AML between the ages of 1 month and 21 years who received treatment between the years of 2006 and 2018 at one of three hospitals in the Leukemia Electronic Abstraction of Records Network (LEARN) consortium. RESULTS: Among 193 patients with sufficient ALC data for analysis, the median ALC following induction 1 was 1715 cells/µl (interquartile range: 1166-2388), with successive decreases in ALC with each subsequent course. Similarly, the proportion of patients achieving an ALC >400 cells/µl decreased following each course, ranging from 98.4% (190/193) after course 1 to 66.7% (22/33) for patients who received a fifth course of therapy. CONCLUSIONS: There is a successive decline of ALC recovery with subsequent courses of chemotherapy. Despite this decline, ALC values are likely sufficient to consider apheresis prior to the initiation of each course of upfront therapy for the majority of newly diagnosed pediatric AML patients, thereby providing a window of opportunity for T-cell collection for those patients identified at high risk of relapse or with refractory disease.

Target and Suspect Screening Integrated with Machine Learning to Discover Per- and Polyfluoroalkyl Substance Source Fingerprints.

This study elucidates per- and polyfluoroalkyl substance (PFAS) fingerprints for specific PFAS source types. Ninety-two samples were collected from aqueous film-forming foam impacted groundwater (AFFF-GW), landfill leachate, biosolids leachate, municipal wastewater treatment plant effluent (WWTP), and wastewater effluent from the pulp and paper and power generation industries. High-resolution mass spectrometry operated with electrospray ionization in negative mode was used to quantify up to 50 target PFASs and screen and semi-quantify up to 2,266 suspect PFASs in each sample. Machine learning classifiers were used to identify PFASs that were diagnostic of each source type. Four C5-C7 perfluoroalkyl acids and one suspect PFAS (trihydrogen-substituted fluoroethernonanoic acid) were diagnostic of AFFF-GW. Two target PFASs (5:3 and 6:2 fluorotelomer carboxylic acids) and two suspect PFASs (4:2 fluorotelomer-thia-acetic acid and N-methylperfluoropropane sulfonamido acetic acid) were diagnostic of landfill leachate. Biosolids leachates were best classified along with landfill leachates and N-methyl and N-ethyl perfluorooctane sulfonamido acetic acid assisted in that classification. WWTP, pulp and paper, and power generation samples contained few target PFASs, but fipronil (a fluorinated insecticide) was diagnostic of WWTP samples. Our results provide PFAS fingerprints for known sources and identify target and suspect PFASs that can be used for source allocation.

Outcomes of children treated for multiple Epstein-Barr virus-associated post-transplant tumors.

BACKGROUND: After solid organ transplantation, children are at risk for Epstein-Barr virus-associated post-transplant lymphoproliferative disorder and smooth muscle tumors. Little is known about the clinical course, Epstein-Barr viral load variations, and optimal treatment for such patients. We set forth to understand the course of repeated episodes of post-transplant lymphoproliferative disorder and smooth muscle tumors. METHODS: We performed a retrospective chart review of patients up to 21 years old with solid organ transplantation and post-transplant lymphoproliferative disorder at the Children's Hospital of Philadelphia from January 2003 through June 30, 2020. RESULTS: Six patients had multiple episodes of Epstein-Barr virus-associated post-transplant lymphoproliferative disorder and smooth muscle tumors. When the second episode was discovered, only one patient was symptomatic. Histology differed from diagnosis in four patients. Treatment included viral-specific T-lymphocytes (2), rituximab (3), reduction in immunosuppression alone (1). Five patients had complete response, and one had stable disease, but three patients developed a subsequent tumor. Two patients developed Epstein-Barr virus-associated smooth muscle tumors. Of these six patients, four are alive. The deaths were not related to their tumors. CONCLUSIONS: Despite a complete response to initial therapy, children are at risk for repeated episodes of Epstein-Barr virus-associated post-transplant lymphoproliferative disorder and smooth muscle tumors. Histology and location were not typically consistent with initial diagnosis, suggesting these are second primaries rather than recurrences. Disease may be managed with individualized treatment plans but EBV-specific T cells need further study in such tumors.

Interventions to prevent or cease electronic cigarette use in children and adolescents.

BACKGROUND: The prevalence of e-cigarette use has increased globally amongst children and adolescents in recent years. In response to the increasing prevalence and emerging evidence about the potential harms of e-cigarettes in children and adolescents, leading public health organisations have called for approaches to address increasing e-cigarette use. Whilst evaluations of approaches to reduce uptake and use regularly appear in the literature, the collective long-term benefit of these is currently unclear. OBJECTIVES: The co-primary objectives of the review were to: (1) evaluate the effectiveness of interventions to prevent e-cigarette use in children and adolescents (aged 19 years and younger) with no prior use, relative to no intervention, waitlist control, usual practice, or an alternative intervention; and (2) evaluate the effectiveness of interventions to cease e-cigarette use in children and adolescents (aged 19 years and younger) reporting current use, relative to no intervention, waitlist control, usual practice, or an alternative intervention. Secondary objectives were to: (1) examine the effect of such interventions on child and adolescent use of other tobacco products (e.g. cigarettes, cigars types, and chewing tobacco); and (2) describe the unintended adverse effects of the intervention on individuals (e.g. physical or mental health of individuals), or on organisations (e.g. intervention displacement of key curricula or learning opportunities for school students) where such interventions are being implemented. SEARCH METHODS: We searched CENTRAL, Ovid MEDLINE, Ovid Embase, Ovid PsycINFO, EBSCO CINAHL, and Clarivate Web of Science Core Collection from inception to 1 May 2023. Additionally, we searched two trial registry platforms (WHO International Clinical Trials Registry Platform; US National Institutes of Health Ongoing Trials Register ClinicalTrials.gov), Google Scholar, and the reference lists of relevant systematic reviews. We contacted corresponding authors of articles identified as ongoing studies. SELECTION CRITERIA: We included randomised controlled trials (RCTs), including cluster-RCTs, factorial RCTs, and stepped-wedge RCTs. To be eligible, the primary targets of the interventions must have been children and adolescents aged 19 years or younger. Interventions could have been conducted in any setting, including community, school, health services, or the home, and must have sought to influence children or adolescent (or both) e-cigarette use directly. Studies with a comparator of no intervention (i.e. control), waitlist control, usual practice, or an alternative intervention not targeting e-cigarette use were eligible. We included measures to assess the effectiveness of interventions to: prevent child and adolescent e-cigarette use (including measures of e-cigarette use amongst those who were never-users); and cease e-cigarette use (including measures of e-cigarette use amongst children and adolescents who were e-cigarette current-users). Measures of e-cigarette use included current-use (defined as use in the past 30 days) and ever-use (defined as any lifetime use). DATA COLLECTION AND ANALYSIS: Two review authors independently screened the titles and abstracts of references, with any discrepancies resolved through consensus. Pairs of review authors independently assessed the full-text articles for inclusion in the review. We planned for two review authors to independently extract information from the included studies and assess risk of bias using the Cochrane RoB 2 tool. We planned to conduct multiple meta-analyses using a random-effects model to align with the co-primary objectives of the review. First, we planned to pool interventions to prevent child and adolescent e-cigarette use and conduct two analyses using the outcome measures of 'ever-use' and 'current-use'. Second, we planned to pool interventions to cease child and adolescent e-cigarette use and conduct one analysis using the outcome measure of 'current-use'. Where data were unsuitable for pooling in meta-analyses, we planned to conduct a narrative synthesis using vote-counting approaches and to follow the Cochrane Handbook for Systematic Reviews of Interventions and the Synthesis Without Meta-analysis (SWiM) guidelines. MAIN RESULTS: The search of electronic databases identified 7141 citations, with a further 287 records identified from the search of trial registries and Google Scholar. Of the 110 studies (116 records) evaluated in full text, we considered 88 to be ineligible for inclusion for the following reasons: inappropriate outcome (27 studies); intervention (12 studies); study design (31 studies); and participants (18 studies). The remaining 22 studies (28 records) were identified as ongoing studies that may be eligible for inclusion in a future review update. We identified no studies with published data that were eligible for inclusion in the review. AUTHORS' CONCLUSIONS: We identified no RCTs that met the inclusion criteria for the review, and as such, there is no evidence available from RCTs to assess the potential impact of interventions targeting children and adolescent e-cigarette use, tobacco use, or any unintended adverse effects. Evidence from studies employing other trial designs (e.g. non-randomised) may exist; however, such studies were not eligible for inclusion in the review. Evidence from studies using non-randomised designs should be examined to guide actions to prevent or cease e-cigarette use. This is a living systematic review. We search for new evidence every month and update the review when we identify relevant new evidence. Please refer to the Cochrane Database of Systematic Reviews for the current status of this review.

Healthy eating interventions delivered in early childhood education and care settings for improving the diet of children aged six months to six years.

BACKGROUND: Dietary intake during early childhood can have implications on child health and developmental trajectories. Early childhood education and care (ECEC) services are recommended settings to deliver healthy eating interventions as they provide access to many children during this important period. Healthy eating interventions delivered in ECEC settings can include strategies targeting the curriculum (e.g. nutrition education), ethos and environment (e.g. menu modification) and partnerships (e.g. workshops for families). Despite guidelines supporting the delivery of healthy eating interventions in this setting, little is known about their impact on child health. OBJECTIVES: To assess the effectiveness of healthy eating interventions delivered in ECEC settings for improving dietary intake in children aged six months to six years, relative to usual care, no intervention or an alternative, non-dietary intervention. Secondary objectives were to assess the impact of ECEC-based healthy eating interventions on physical outcomes (e.g. child body mass index (BMI), weight, waist circumference), language and cognitive outcomes, social/emotional and quality-of-life outcomes. We also report on cost and adverse consequences of ECEC-based healthy eating interventions. SEARCH METHODS: We searched eight electronic databases including CENTRAL, MEDLINE, Embase, CINAHL, PsycINFO, ERIC, Scopus and SportDiscus on 24 February 2022. We searched reference lists of included studies, reference lists of relevant systematic reviews, the World Health Organization International Clinical Trials Registry Platform, ClinicalTrials.gov and Google Scholar, and contacted authors of relevant papers. SELECTION CRITERIA: We included randomised controlled trials (RCTs), including cluster-RCTs, stepped-wedge RCTs, factorial RCTs, multiple baseline RCTs and randomised cross-over trials, of healthy eating interventions targeting children aged six months to six years that were conducted within the ECEC setting. ECEC settings included preschools, nurseries, kindergartens, long day care and family day care. To be included, studies had to include at least one intervention component targeting child diet within the ECEC setting and measure child dietary or physical outcomes, or both. DATA COLLECTION AND ANALYSIS: Pairs of review authors independently screened titles and abstracts and extracted study data. We assessed risk of bias for all studies against 12 criteria within RoB 1, which allows for consideration of how selection, performance, attrition, publication and reporting biases impact outcomes. We resolved discrepancies via consensus or by consulting a third review author. Where we identified studies with suitable data and homogeneity, we performed meta-analyses using a random-effects model; otherwise, we described findings using vote-counting approaches and via harvest plots. For measures with similar metrics, we calculated mean differences (MDs) for continuous outcomes and risk ratios (RRs) for dichotomous outcomes. We calculated standardised mean differences (SMDs) for primary and secondary outcomes where studies used different measures. We applied GRADE to assess certainty of evidence for dietary, cost and adverse outcomes. MAIN RESULTS: We included 52 studies that investigated 58 interventions (described across 96 articles). All studies were cluster-RCTs. Twenty-nine studies were large (≥ 400 participants) and 23 were small (< 400 participants). Of the 58 interventions, 43 targeted curriculum, 56 targeted ethos and environment, and 50 targeted partnerships. Thirty-eight interventions incorporated all three components. For the primary outcomes (dietary outcomes), we assessed 19 studies as overall high risk of bias, with performance and detection bias being most commonly judged as high risk of bias. ECEC-based healthy eating interventions versus usual practice or no intervention may have a positive effect on child diet quality (SMD 0.34, 95% confidence interval (CI) 0.04 to 0.65; P = 0.03, I2 = 91%; 6 studies, 1973 children) but the evidence is very uncertain. There is moderate-certainty evidence that ECEC-based healthy eating interventions likely increase children's consumption of fruit (SMD 0.11, 95% CI 0.04 to 0.18; P < 0.01, I2 = 0%; 11 studies, 2901 children). The evidence is very uncertain about the effect of ECEC-based healthy eating interventions on children's consumption of vegetables (SMD 0.12, 95% CI -0.01 to 0.25; P =0.08, I2 = 70%; 13 studies, 3335 children). There is moderate-certainty evidence that ECEC-based healthy eating interventions likely result in little to no difference in children's consumption of non-core (i.e. less healthy/discretionary) foods (SMD -0.05, 95% CI -0.17 to 0.08; P = 0.48, I2 = 16%; 7 studies, 1369 children) or consumption of sugar-sweetened beverages (SMD -0.10, 95% CI -0.34 to 0.14; P = 0.41, I2 = 45%; 3 studies, 522 children). Thirty-six studies measured BMI, BMI z-score, weight, overweight and obesity, or waist circumference, or a combination of some or all of these. ECEC-based healthy eating interventions may result in little to no difference in child BMI (MD -0.08, 95% CI -0.23 to 0.07; P = 0.30, I2 = 65%; 15 studies, 3932 children) or in child BMI z-score (MD -0.03, 95% CI -0.09 to 0.03; P = 0.36, I2 = 0%; 17 studies; 4766 children). ECEC-based healthy eating interventions may decrease child weight (MD -0.23, 95% CI -0.49 to 0.03; P = 0.09, I2 = 0%; 9 studies, 2071 children) and risk of overweight and obesity (RR 0.81, 95% CI 0.65 to 1.01; P = 0.07, I2 = 0%; 5 studies, 1070 children). ECEC-based healthy eating interventions may be cost-effective but the evidence is very uncertain (6 studies). ECEC-based healthy eating interventions may have little to no effect on adverse consequences but the evidence is very uncertain (3 studies). Few studies measured language and cognitive skills (n = 2), social/emotional outcomes (n = 2) and quality of life (n = 3). AUTHORS' CONCLUSIONS: ECEC-based healthy eating interventions may improve child diet quality slightly, but the evidence is very uncertain, and likely increase child fruit consumption slightly. There is uncertainty about the effect of ECEC-based healthy eating interventions on vegetable consumption. ECEC-based healthy eating interventions may result in little to no difference in child consumption of non-core foods and sugar-sweetened beverages. Healthy eating interventions could have favourable effects on child weight and risk of overweight and obesity, although there was little to no difference in BMI and BMI z-scores. Future studies exploring the impact of specific intervention components, and describing cost-effectiveness and adverse outcomes are needed to better understand how to maximise the impact of ECEC-based healthy eating interventions.

Healthy eating interventions delivered in early childhood education and care settings for improving the diet of children aged six months to six years.

BACKGROUND: Dietary intake during early childhood can have implications on child health and developmental trajectories. Early childhood education and care (ECEC) services are recommended settings to deliver healthy eating interventions as they provide access to many children during this important period. Healthy eating interventions delivered in ECEC settings can include strategies targeting the curriculum (e.g. nutrition education), ethos and environment (e.g. menu modification) and partnerships (e.g. workshops for families). Despite guidelines supporting the delivery of healthy eating interventions in this setting, little is known about their impact on child health. OBJECTIVES: To assess the effectiveness of healthy eating interventions delivered in ECEC settings for improving dietary intake in children aged six months to six years, relative to usual care, no intervention or an alternative, non-dietary intervention. Secondary objectives were to assess the impact of ECEC-based healthy eating interventions on physical outcomes (e.g. child body mass index (BMI), weight, waist circumference), language and cognitive outcomes, social/emotional and quality-of-life outcomes. We also report on cost and adverse consequences of ECEC-based healthy eating interventions. SEARCH METHODS: We searched eight electronic databases including CENTRAL, MEDLINE, Embase, CINAHL, PsycINFO, ERIC, Scopus and SportDiscus on 24 February 2022. We searched reference lists of included studies, reference lists of relevant systematic reviews, the World Health Organization International Clinical Trials Registry Platform, ClinicalTrials.gov and Google Scholar, and contacted authors of relevant papers. SELECTION CRITERIA: We included randomised controlled trials (RCTs), including cluster-RCTs, stepped-wedge RCTs, factorial RCTs, multiple baseline RCTs and randomised cross-over trials, of healthy eating interventions targeting children aged six months to six years that were conducted within the ECEC setting. ECEC settings included preschools, nurseries, kindergartens, long day care and family day care. To be included, studies had to include at least one intervention component targeting child diet within the ECEC setting and measure child dietary or physical outcomes, or both. DATA COLLECTION AND ANALYSIS: Pairs of review authors independently screened titles and abstracts and extracted study data. We assessed risk of bias for all studies against 12 criteria within RoB 1, which allows for consideration of how selection, performance, attrition, publication and reporting biases impact outcomes. We resolved discrepancies via consensus or by consulting a third review author. Where we identified studies with suitable data and homogeneity, we performed meta-analyses using a random-effects model; otherwise, we described findings using vote-counting approaches and via harvest plots. For measures with similar metrics, we calculated mean differences (MDs) for continuous outcomes and risk ratios (RRs) for dichotomous outcomes. We calculated standardised mean differences (SMDs) for primary and secondary outcomes where studies used different measures. We applied GRADE to assess certainty of evidence for dietary, cost and adverse outcomes.  MAIN RESULTS: We included 52 studies that investigated 58 interventions (described across 96 articles). All studies were cluster-RCTs. Twenty-nine studies were large (≥ 400 participants) and 23 were small (< 400 participants). Of the 58 interventions, 43 targeted curriculum, 56 targeted ethos and environment, and 50 targeted partnerships. Thirty-eight interventions incorporated all three components. For the primary outcomes (dietary outcomes), we assessed 19 studies as overall high risk of bias, with performance and detection bias being most commonly judged as high risk of bias. ECEC-based healthy eating interventions versus usual practice or no intervention may have a positive effect on child diet quality (SMD 0.34, 95% confidence interval (CI) 0.04 to 0.65; P = 0.03, I2 = 91%; 6 studies, 1973 children) but the evidence is very uncertain. There is moderate-certainty evidence that ECEC-based healthy eating interventions likely increase children's consumption of fruit (SMD 0.11, 95% CI 0.04 to 0.18; P < 0.01, I2 = 0%; 11 studies, 2901 children). The evidence is very uncertain about the effect of ECEC-based healthy eating interventions on children's consumption of vegetables (SMD 0.12, 95% CI -0.01 to 0.25; P =0.08, I2 = 70%; 13 studies, 3335 children). There is moderate-certainty evidence that ECEC-based healthy eating interventions likely result in little to no difference in children's consumption of non-core (i.e. less healthy/discretionary) foods (SMD -0.05, 95% CI -0.17 to 0.08; P = 0.48, I2 = 16%; 7 studies, 1369 children) or consumption of sugar-sweetened beverages (SMD -0.10, 95% CI -0.34 to 0.14; P = 0.41, I2 = 45%; 3 studies, 522 children). Thirty-six studies measured BMI, BMI z-score, weight, overweight and obesity, or waist circumference, or a combination of some or all of these. ECEC-based healthy eating interventions may result in little to no difference in child BMI (MD -0.08, 95% CI -0.23 to 0.07; P = 0.30, I2 = 65%; 15 studies, 3932 children) or in child BMI z-score (MD -0.03, 95% CI -0.09 to 0.03; P = 0.36, I2 = 0%; 17 studies; 4766 children). ECEC-based healthy eating interventions may decrease child weight (MD -0.23, 95% CI -0.49 to 0.03; P = 0.09, I2 = 0%; 9 studies, 2071 children) and risk of overweight and obesity (RR 0.81, 95% CI 0.65 to 1.01; P = 0.07, I2 = 0%; 5 studies, 1070 children). ECEC-based healthy eating interventions may be cost-effective but the evidence is very uncertain (6 studies). ECEC-based healthy eating interventions may have little to no effect on adverse consequences but the evidence is very uncertain (3 studies). Few studies measured language and cognitive skills (n = 2), social/emotional outcomes (n = 2) and quality of life (n = 3). AUTHORS' CONCLUSIONS: ECEC-based healthy eating interventions may improve child diet quality slightly, but the evidence is very uncertain, and likely increase child fruit consumption slightly. There is uncertainty about the effect of ECEC-based healthy eating interventions on vegetable consumption. ECEC-based healthy eating interventions may result in little to no difference in child consumption of non-core foods and sugar-sweetened beverages. Healthy eating interventions could have favourable effects on child weight and risk of overweight and obesity, although there was little to no difference in BMI and BMI z-scores. Future studies exploring the impact of specific intervention components, and describing cost-effectiveness and adverse outcomes are needed to better understand how to maximise the impact of ECEC-based healthy eating interventions.