Quality of life of family caregivers of children and young adults with Down syndrome: A systematic review and meta-analysis.

Introduction: The aims of this systematic review and meta-analysis are to synthesise quality of life (QOL) of family caregivers of children and young adults with Down syndrome (DS) and determine factors affecting their QOL. Method: This review was conducted as per Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Key search terms were "quality of life", "down syndrome" and "trisomy 21". Meta-analysis using random effect model was conducted where feasible. All studies underwent qualitative synthesis. The study protocol was registered with PROSPERO (CRD42023413532). Results: Eighteen studies with 1956 caregivers were included. Of the 10 studies utilising the World Health Organization Quality of Life Instrument-Brief Version, 5 were included in the meta-analysis. Psychosocial domain had the highest score with mean (95% confidence interval [CI]) of 63.18 (39.10-87.25). Scores were poorer in physical, environmental and social domains: 59.36 (28.24-90.48), 59.82 (19.57-100.07) and 59.83 (44.24-75.41), respectively. Studies were heterogenous with I2 values ranging from 99-100% (P<0.01). The remaining 8 studies used 6 other instruments. Qualitative synthesis revealed that caregivers' QOL was adversely affected by child-related factors, such as level of functional independence, developmental delay, presence of multiple comorbidities, impaired activities of daily living and poor sleep quality. Environmental factors that adversely affected caregivers' QOL included number of children, housing and support from the family. Personal factors that affected caregivers' QOL included age, being a single mother, low education and low income. Conclusion: QOL of caregivers of children with DS was lower than population reference data. Understand-ing the factors that influence family caregivers' QOL is an essential step towards improving the QOL of caregivers and their children with DS.

Quality of life of children and young adults with Down syndrome from caregivers' perspective: A systematic review and meta-analysis.

Introduction: Down syndrome (DS) negatively impacts the well-being of affected individuals. This study aimed to summarise the evidence on quality of life (QOL) of children and young adults with DS using quantitative measures from caregivers' perspective and identify factors that affected their QOL. Method: Database search was conducted on PubMed, Embase, Web of Science and CINAHL on 24 April 2024. Meta-analysis using random effects model was conducted where feasible. All studies underwent qualitative synthesis. The study protocol was registered with PROSPERO (CRD42023413532). Results: Seventeen studies involving 3038 children with DS using various QOL measures were included: Pediatric Quality of Life Inventory (PedsQL) (8 studies), KIDSCREEN (4 studies), KidsLife (2 studies), The Netherlands Organization for Applied Scientific Research Academic Medical Center Children's QOL (2 studies) and Personal Outcome Scale (1 study). Meta-analysis on PedsQL studies compared scores between children with DS and typically developing (TD) children. Total scale score was lower in children with DS (mean 70.28, 95% confidence interval [CI] 64.31-76.24) compared to TD children (mean 88.17, 95% CI 80.50-95.83). All subdomains of PedsQL were also lower in children with DS. Within the domain of psychosocial health, children with DS had statistically significant lower social functioning (standardised mean difference -1.40, 95% CI -2.27 to -0.53) and school functioning (standardised mean difference -1.09, 95% CI -1.55 to -0.62) scores, but similar emotional functioning scores. Qualitative synthesis revealed poorer subdomain QOL compared to TD children, especially in social functioning and cognitive functioning. QOL worsened during adolescent years. Family variables (parental education and occupation) did not affect parental perception of children's QOL. Children with DS who had higher intelligent quotient had better QOL. Conclusion: Children with DS have lower caregiver-reported QOL than TD children, especially in social functioning and school functioning subdomains.

Comparison of Industry-Sponsored Trials (IST) and Investigator-Initiated Trials (IIT) in Advanced Genitourinary Cancers in the United States, Canada, United Kingdom and France.

BACKGROUND: Clinical trials are categorized as industry sponsored trials (ISTs) or investigator-initiated trials (IITs) based on the source of funding and sponsor of the trial. ISTs are usually run by pharmaceutical companies, and are primarily aimed at developing new drugs that ultimately gain regulatory approval. IITs are developed by academic investigators or cooperative groups, often sparked by a clinical need. Both are vital in advancing the field of oncology. To date, little has been published about current trends in ISTs or IITs in genitourinary (GU) oncology. The aim of this study was to assess growth trends of GU oncology ISTs and IITs in 4 countries with similar healthcare infrastructures. METHODS: We searched ClinicalTrials.gov for bladder, kidney, and prostate cancer trials conducted in the United States (US), Canada, France, and United Kingdom (UK) from January 2007 to December 2021. Trials were determined to be ISTs or IITs based on their funding source and sponsor. Trials were characterized based on type, purpose, phase, participants, masking, assignment, and allocation. RESULTS: Overall, 5,834 GU trials were identified, with a balanced distribution of ISTs (n = 3064, n = 52.5%) and IITs (n = 2770, 47.4%). By country, the US conducted the most GU trials (n = 3814) followed by Canada (n = 709), France (n = 677), and the UK (n = 634). Most ISTs were phase 3 trials with over 500 participants while most IITs were open-label phase 2 studies with only 20-49 participants. From 2017 onwards, there was a shift towards more ISTs, most noticeably in Canada and the UK. The COVID-19 pandemic did not have a major impact on the growth of ISTs and IITs. CONCLUSION: The gap between ISTs and IITs continues to widen, likely driven by resource and funding challenges faced by investigators. Barriers to completing IITs need to be better understood to promote IIT development and maintain their academically driven intentions.

Artificial intelligence for geoscience: Progress, challenges, and perspectives.

This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth's complexities and uncertainties pose challenges in optimization and real-world applicability. In contrast, contemporary data-driven models, particularly those utilizing machine learning (ML) and deep learning (DL), leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge. ML techniques have shown promise in addressing Earth science-related questions. Nevertheless, challenges such as data scarcity, computational demands, data privacy concerns, and the "black-box" nature of AI models hinder their seamless integration into geoscience. The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm. These models, which incorporate domain knowledge to guide AI methodologies, demonstrate enhanced efficiency and performance with reduced training data requirements. This review provides a comprehensive overview of geoscientific research paradigms, emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience. It examines major methodologies, showcases advances in large-scale models, and discusses the challenges and prospects that will shape the future landscape of AI in geoscience. The paper outlines a dynamic field ripe with possibilities, poised to unlock new understandings of Earth's complexities and further advance geoscience exploration.

Nonthermal Plasma Activation of Adsorbates: The Case of CO on Pt.

Nonthermal plasmas provide a unique approach to electrically driven heterogeneous catalytic processes. Despite much interest from the community, fundamental activation pathways in these processes remain poorly understood. Here, we investigate how exposure to a nonthermal plasma sustained in an argon nonreactive atmosphere affects the desorption of carbon monoxide (CO) from platinum nanoparticles. Temperature-programmed desorption measurements indicate that the plasma reduces the effective binding energy (BE) of CO to Pt surfaces by as much as ∼0.3 eV, with the reduction in the BE scaling linearly with the plasma density. We find that the effective CO BE is most strongly reduced for under-coordinated sites (steps and edges) compared to well-coordinated sites (terraces). Density functional theory calculations suggest that this is due to plasma-induced charging and electric fields at the catalyst surface, which preferentially affect under-coordinated sites. This study provides direct experimental evidence of plasma-induced nonthermal activation of the adsorbate-catalyst couple.

Binding Energies and Optical Properties of Power-Exponential and Modified Gaussian Quantum Dots.

We examine the optical and electronic properties of a GaAs spherical quantum dot with a hydrogenic impurity in its center. We study two different confining potentials: (1) a modified Gaussian potential and (2) a power-exponential potential. Using the finite difference method, we solve the radial Schrodinger equation for the 1s and 1p energy levels and their probability densities and subsequently compute the optical absorption coefficient (OAC) for each confining potential using Fermi's golden rule. We discuss the role of different physical quantities influencing the behavior of the OAC, such as the structural parameters of each potential, the dipole matrix elements, and their energy separation. Our results show that modification of the structural physical parameters of each potential can enable new optoelectronic devices that can leverage inter-sub-band optical transitions.

Implementation factors of non-communicable disease policies and programmes for children and youth in low-income and middle-income countries: a systematic review.

INTRODUCTION: Despite declared life-course principles in non-communicable disease (NCD) prevention and management, worldwide focus has been on older rather than younger populations. However, the burden from childhood NCDs has mounted; particularly in low-income and middle-income countries (LMICs). There is limited knowledge regarding the implementation of paediatric NCD policies and programmes in LMICs, despite their disproportionate burden of morbidity and mortality. We aimed to understand the barriers to and facilitators of paediatric NCD policy and programme implementation in LMICs. METHODS: We systematically searched medical databases, Web of Science and WHOLIS for studies on paediatric NCD policy and programme implementation in LMICs. Screening and quality assessment were performed independently by researchers, using consensus to resolve differences. Data extraction was conducted within the WHO health system building-blocks framework. Narrative thematic synthesis was conducted. RESULTS: 93 studies (1992-2020) were included, spanning 86 LMICs. Most were of moderate or high quality. 78% reported on paediatric NCDs outside the four major NCD categories contributing to the adult burden. Across the framework, more barriers than facilitators were identified. The most prevalently reported factors were related to health service delivery, with system fragmentation impeding the continuity of age-specific NCD care. A significant facilitator was intersectoral collaborations between health and education actors to deliver care in trusted community settings. Non-health factors were also important to paediatric NCD policies and programmes, such as community stakeholders, sociocultural support to caregivers and school disruptions. CONCLUSIONS: Multiple barriers prevent the optimal implementation of paediatric NCD policies and programmes in LMIC health systems. The low sociopolitical visibility of paediatric NCDs limits their prioritisation, resulting in fragmented service delivery and constraining the integration of programmes across key sectors impacting children, including health, education and social services. Implementation research is needed to understand specific contextual solutions to improve access to paediatric NCD services in diverse LMIC settings.

Emerging contaminants: A One Health perspective.

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary health. Despite global efforts to mitigate legacy pollutants, the continuous introduction of new substances remains a major threat to both people and the planet. In response, global initiatives are focusing on risk assessment and regulation of emerging contaminants, as demonstrated by the ongoing efforts to establish the UN's Intergovernmental Science-Policy Panel on Chemicals, Waste, and Pollution Prevention. This review identifies the sources and impacts of emerging contaminants on planetary health, emphasizing the importance of adopting a One Health approach. Strategies for monitoring and addressing these pollutants are discussed, underscoring the need for robust and socially equitable environmental policies at both regional and international levels. Urgent actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

Harnessing a Dielectric/Plasma Photonic Crystal as an Optical Microwave Filter: Role of Defect Layers and External Magnetic Fields.

We investigate the transmittance spectrum of a multichannel filter composed of dielectric (A) and plasma (P) materials in the microwave region within the transfer matrix formalism. Two configurations of the proposed filter are studied under the influence of an applied magnetic field: (1) a periodic structure containing (A/P)N unit cells surrounded by air and (2) the introduction of a second dielectric material (D) acting as a defect layer to produce an (AP)N/2/D/(AP)N/2 structure. Our findings reveal that in the periodic case, the number of resonant states of the transmittance increases with number N; however, the observed blue and red shifts depend on the intensity and orientation of the applied magnetic field. We present contour plots of the transmission coefficients that show the effect of the incident angle on the shifts of the photonic band gaps. Furthermore, we find that the introduction of a defect layer generates additional resonant states and merges the central resonant peak into a miniband of resonances. Moreover, we show that the number of resonant peaks and their locations can be modulated by increasing the unit cell number, N, as well as increasing the width of the inserted defect layer. Our proposed structures enable the design of novel photonic filters using magnetized plasma materials operating in the microwave region.

Silicone cryogel skeletons enhance the survival and mechanical integrity of hydrogel-encapsulated cell therapies.

The transplantation of engineered cells that secrete therapeutic proteins presents a promising method for addressing a range of chronic diseases. However, hydrogels used to encase and protect non-autologous cells from immune rejection often suffer from poor mechanical properties, insufficient oxygenation, and fibrotic encapsulation. Here, we introduce a composite encapsulation system comprising an oxygen-permeable silicone cryogel skeleton, a hydrogel matrix, and a fibrosis-resistant polymer coating. Cryogel skeletons enhance the fracture toughness of conventional alginate hydrogels by 23-fold and oxygen diffusion by 2.8-fold, effectively mitigating both implant fracture and hypoxia of encapsulated cells. Composite implants containing xenogeneic cells engineered to secrete erythropoietin significantly outperform unsupported alginate implants in therapeutic delivery over 8 weeks in immunocompetent mice. By improving mechanical resiliency and sustaining denser cell populations, silicone cryogel skeletons enable more durable and miniaturized therapeutic implants.

Triboelectric Energy Harvesting from Highly Conjugated Fused Aromatic Ladder Structure Under Extreme Environmental Conditions.

Practical application of triboelectric nanogenerators (TENGs) has been challenging, particularly, under harsh environmental conditions. This work proposes a novel 3D-fused aromatic ladder (FAL) structure as a tribo-positive material for TENGs, to address these challenges. The 3D-FAL offers a unique materials engineering platform for tailored properties, such as high specific surface area and porosity, good thermal and mechanical stability, and tunable electronic properties. The fabricated 3D-FAL-based TENG reaches a maximum peak power density of 451.2 µW cm-2 at 5 Hz frequency. More importantly, the 3D-FAL-based TENG maintains stable output performance under harsh operating environments, over wide temperature (-45-100 °C) and humidity ranges (8.3-96.7% RH), representing the development of novel FAL for sustainable energy generation under challenging environmental conditions. Furthermore, the 3D-FAL-based TENG proves to be a promising device for a speed monitoring system engaging reconstruction in virtual reality in a snowy environment.

A predicted-loss based active learning approach for robust cancer pathology image analysis in the workplace.

BACKGROUND: Convolutional neural network-based image processing research is actively being conducted for pathology image analysis. As a convolutional neural network model requires a large amount of image data for training, active learning (AL) has been developed to produce efficient learning with a small amount of training data. However, existing studies have not specifically considered the characteristics of pathological data collected from the workplace. For various reasons, noisy patches can be selected instead of clean patches during AL, thereby reducing its efficiency. This study proposes an effective AL method for cancer pathology that works robustly on noisy datasets. METHODS: Our proposed method to develop a robust AL approach for noisy histopathology datasets consists of the following three steps: 1) training a loss prediction module, 2) collecting predicted loss values, and 3) sampling data for labeling. This proposed method calculates the amount of information in unlabeled data as predicted loss values and removes noisy data based on predicted loss values to reduce the rate at which noisy data are selected from the unlabeled dataset. We identified a suitable threshold for optimizing the efficiency of AL through sensitivity analysis. RESULTS: We compared the results obtained with the identified threshold with those of existing representative AL methods. In the final iteration, the proposed method achieved a performance of 91.7% on the noisy dataset and 92.4% on the clean dataset, resulting in a performance reduction of less than 1%. Concomitantly, the noise selection ratio averaged only 2.93% on each iteration. CONCLUSIONS: The proposed AL method showed robust performance on datasets containing noisy data by avoiding data selection in predictive loss intervals where noisy data are likely to be distributed. The proposed method contributes to medical image analysis by screening data and producing a robust and effective classification model tailored for cancer pathology image processing in the workplace.

Beyond Conventional Density Functional Theory: Advanced Quantum Dynamical Methods for Understanding Degradation of Per- and Polyfluoroalkyl Substances.

Computational chemistry methods, such as density functional theory (DFT), have now become more common in environmental research, particularly for simulating the degradation of per- and polyfluoroalkyl substances (PFAS). However, the vast majority of PFAS computational studies have focused on conventional DFT approaches that only probe static, time-independent properties of PFAS near stationary points on the potential energy surface. To demonstrate the rich mechanistic information that can be obtained from time-dependent quantum dynamics calculations, we highlight recent studies using these advanced techniques for probing PFAS systems. We briefly discuss recent applications ranging from ab initio molecular dynamics to DFT-based metadynamics and real-time time-dependent DFT for probing PFAS degradation in various reactive environments. These quantum dynamical approaches provide critical mechanistic information that cannot be gleaned from conventional DFT calculations. We conclude with a perspective of promising research directions and recommend that these advanced quantum dynamics simulations be more widely used by the environmental research community to directly probe PFAS degradation dynamics and other environmental processes.

Velocity-Gauge Real-Time Time-Dependent Density Functional Tight-Binding for Large-Scale Condensed Matter Systems.

We present a new velocity-gauge real-time, time-dependent density functional tight-binding (VG-rtTDDFTB) implementation in the open-source DFTB+ software package (https://dftbplus.org) for probing electronic excitations in large, condensed matter systems. Our VG-rtTDDFTB approach enables real-time electron dynamics simulations of large, periodic, condensed matter systems containing thousands of atoms with a favorable computational scaling as a function of system size. We provide computational details and benchmark calculations to demonstrate its accuracy and computational parallelizability on a variety of large material systems. As a representative example, we calculate laser-induced electron dynamics in a 512-atom amorphous silicon supercell to highlight the large periodic systems that can be examined with our implementation. Taken together, our VG-rtTDDFTB approach enables new electron dynamics simulations of complex systems that require large periodic supercells, such as crystal defects, complex surfaces, nanowires, and amorphous materials.

Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per- and Polyfluoroalkyl Substances (PFASs).

Many per- and polyfluoroalkyl substances (PFASs) pose significant health hazards due to their bioactive and persistent bioaccumulative properties. However, assessing the bioactivities of PFASs is both time-consuming and costly due to the sheer number and expense of in vivo and in vitro biological experiments. To this end, we harnessed new unsupervised/semi-supervised machine learning models to automatically predict bioactivities of PFASs in various human biological targets, including enzymes, genes, proteins, and cell lines. Our semi-supervised metric learning models were used to predict the bioactivity of PFASs found in the recent Organisation of Economic Co-operation and Development (OECD) report list, which contains 4730 PFASs used in a broad range of industries and consumers. Our work provides the first semi-supervised machine learning study of structure-activity relationships for predicting possible bioactivities in a variety of PFAS species.

FLUID-GPT (Fast Learning to Understand and Investigate Dynamics with a Generative Pre-Trained Transformer): Efficient Predictions of Particle Trajectories and Erosion.

The deleterious impact of erosion due to high-velocity particle impingement adversely affects a variety of engineering and industrial systems, resulting in irreversible mechanical wear of materials/components. Brute force computational fluid dynamics (CFD) calculations are commonly used to predict surface erosion by directly solving the Navier-Stokes equations for fluid and particle dynamics; however, these numerical approaches often require significant computational resources. In contrast, recent data-driven approaches using machine learning (ML) have shown immense promise for more efficient and accurate predictions to sidestep computationally demanding CFD calculations. To this end, we have developed FLUID-GPT (Fast Learning to Understand and Investigate Dynamics with a Generative Pre-Trained Transformer), a new hybrid ML architecture for accurately predicting particle trajectories and erosion on an industrial-scale steam header geometry. Our FLUID-GPT approach utilizes a Generative Pre-Trained Transformer 2 (GPT-2) with a convolutional neural network (CNN) for the first time to predict surface erosion using only information from five initial conditions: particle size, main-inlet speed, main-inlet pressure, subinlet speed, and subinlet pressure. Compared to the bidirectional long- and short-term memory (BiLSTM) ML techniques used in previous work, our FLUID-GPT model is much more accurate (a 54% decrease in the mean squared error) and efficient (70% less training time). Our work demonstrates that FLUID-GPT is an accurate and efficient ML approach for predicting time-series trajectories and their subsequent spatial erosion patterns in these complex dynamic systems.

Hexaazatriphenylene-Based Two-Dimensional Conductive Covalent Organic Framework with Anisotropic Charge Transfer.

The development of covalent organic frameworks (COFs) with efficient charge transport is of immense interest for applications in optoelectronic devices. To enhance COF charge transport properties, electroactive building blocks and dopants can be used to induce extended conduction channels. However, understanding their intricate interplay remains challenging. We designed and synthesized a tailor-made COF structure with electroactive hexaazatriphenylene (HAT) core units and planar dioxin (D) linkages, denoted as HD-COF. With the support of theoretical calculations, we found that the HAT units in the HD-COF induce strong, eclipsed π-π stacking. The unique stacking of HAT units and the weak in-plane conjugation of dioxin linkages leads to efficient anisotropic charge transport. We fabricated HD-COF films to minimize the grain boundary effect of bulk COFs, which resulted in enhanced conductivity. As a result, the HD-COF films showed an electrical conductivity as high as 1.25 S cm-1 after doping with tris(4-bromophenyl)ammoniumyl hexachloroantimonate.

Electron/Hole Mobilities of Periodic DNA and Nucleobase Structures from Large-Scale DFT Calculations.

Electron/hole transfer mechanisms in DNA and polynucleotide structures continue to garner considerable interest as emerging charge-transport systems and molecular electronics. To shed mechanistic insight into these electronic properties, we carried out large-scale density functional theory (DFT) calculations (up to 650 atoms) to systematically analyze the structural and electron/hole transport properties of fully periodic single- and double-stranded DNA. We examined the performance of various exchange-correlation functionals (LDA, BLYP, B3LYP, and B3LYP-D) and found that single-stranded thymine (T) and cytosine (C) are predominantly hole conductors, whereas single-stranded adenine (A) and guanine (G) are better electron conductors. For double-stranded DNA structures, the periodic A-T and G-C electronic band structures undergo a significant renormalization, which causes hole transport to only occur on the A and G nucleobases. Our calculations (1) provide new benchmarks for periodic nucleobase structures using dispersion-corrected hybrid functionals with large basis sets and (2) highlight the importance of dispersion effects for obtaining accurate geometries and electron/hole mobilities in these extended systems.

Degradation of Perfluorooctanoic Acid on Aluminum Oxide Surfaces: New Mechanisms from Ab Initio Molecular Dynamics Simulations.

Perfluorooctanoic acid (PFOA) is a part of a large group of anthropogenic, persistent, and bioaccumulative contaminants known as per- and polyfluoroalkyl substances (PFAS) that can be harmful to human health. In this work, we present the first ab initio molecular dynamics (AIMD) study of temperature-dependent degradation dynamics of PFOA on (100) and (110) surfaces of γ-Al2O3. Our results show that PFOA degradation does not occur on the pristine (100) surface, even when carried out at high temperatures. However, introducing an oxygen vacancy on the (100) surface facilitates an ultrafast (<100 fs) defluorination of C-F bonds in PFOA. We also examined degradation dynamics on the (110) surface and found that PFOA interacts strongly with Al(III) centers on the surface of γ-Al2O3, resulting in a stepwise breaking of C-F, C-C, and C-COO bonds. Most importantly, at the end of the degradation process, strong Al-F bonds are formed on the mineralized γ-Al2O3 surface, which prevents further dissociation of fluorine into the surrounding environment. Taken together, our AIMD simulations provide critical reaction mechanisms at a quantum level of detail and highlight the importance of temperature effects, defects, and surface facets for PFOA degradation on reactive surfaces, which have not been systematically explored or analyzed.

Access to essential cancer medicines for children: a comparative mixed-methods analysis of availability, price, and health-system determinants in east Africa.

BACKGROUND: Access to essential childhood cancer medicines is a core determinant of childhood cancer outcomes. Available evidence, although scarce, suggests that access to these medicines is highly variable across countries, particularly in low-income and middle-income countries, where the burden of childhood cancer is greatest. To support evidence-informed national and regional policies for improved childhood cancer outcomes, we aimed to analyse access to essential childhood cancer medicines in four east African countries-Kenya, Rwanda, Tanzania, and Uganda-by determining the availability and price of these medicines and the health system determinants of access. METHODS: In this comparative analysis, we used prospective mixed-method analyses to track and analyse the availability and price of essential childhood cancer medicines, investigate contextual determinants of access to childhood cancer medicines within and across included countries, and assess the potential effects of medicine stockouts on treatment. Eight tertiary care hospitals were included, seven were public sites (Kenyatta National Hospital [KNH; Nairobi, Kenya], Jaramogi Oginga Odinga Referral and Teaching Hospital [JOORTH; Kisumu, Kenya], Moi University Teaching and Referral Hospital [MTRH; Eldoret, Kenya], Bugando Medical Centre [BMC; Mwanza, Tanzania], Muhimbili National Hospital [MNH; Dar es Salaam, Tanzania], Butaro Cancer Centre of Excellence [BCCE; Butaro Sector, Rwanda], and Uganda Cancer Institute [UCI; Kampala, Uganda]) and one was a private site (Aga Khan University Hospital [AKU; Nairobi, Kenya]). We catalogued prices and stockouts for 37 essential drugs from each of the eight study siteson the basis of 52 weeks of prospective data that was collected across sites from May 1, 2020, to Jan 31, 2022. We analysed determinants of medicine access using thematic analysis of academic literature, policy documents, and semi-structured interviews from a purposive sample of health system stakeholders. FINDINGS: Recurrent stockouts of a wide range of cytotoxic and supportive care medicines were observed across sites, with highest mean unavailability in Kenya (JOORTH; 48·5%), Rwanda (BCCE; 39·0%), and Tanzania (BMC; 32·2%). Drugs that had frequent stockouts across at least four sites included methotrexate, bleomycin, etoposide, ifosfamide, oral morphine, and allopurinol. Average median price ratio of medicines at each site was within WHO's internationally accepted threshold for efficient procurement (median price ratio ≤1·5). The effect of stockouts on treatment was noted across most sites, with the greatest potential for treatment interruptions in patients with Hodgkin lymphoma, retinoblastoma, and acute lymphocytic leukaemia. Policy prioritisation of childhood cancers, health financing and coverage, medicine procurement and supply chain management, and health system infrastructure emerged as four prominent determinants of access when the stratified purposive sample of key informants (n=64) across all four countries (Kenya n=19, Rwanda n=15, Tanzania n=13, and Uganda n=17) was interviewed. INTERPRETATION: Access to childhood cancer medicines across east Africa is marked by gaps in availability that have implications for effective treatment delivery for a range of childhood cancers. Our findings provide detailed evidence of barriers to access to childhood cancer medicine at multiple points in the pharmaceutical value chain. These data could inform national and regional policy makers to optimise cancer medicine availability and affordability as part of efforts to improve childhood cancer outcomes specific regions and internationally. FUNDING: American Childhood Cancer Organization, Childhood Cancer International, and the Friends of Cancer Patients Ameera Fund.