Ethnic inequities in 6-8 week baby check coverage in England 2006-2021: a cohort study using the Clinical Practice Research Datalink.

BACKGROUND: Inequities in the coverage of 6-8 week maternal checks, health visitor reviews and infant vaccinations have been reported in England. Ethnic inequities in 6-8 week baby checks have not been studied nationally. AIM: To examine the effect of maternal ethnicity on 6-8 week baby check coverage in England 2006-2021. DESIGN AND SETTING: Cohort study using electronic health records. METHODS: We calculated baby check coverage in 16 ethnic groups, by year and region, and risk ratios using modified Poisson regression. We calculated coverage and timing of baby checks in relation to maternal checks and infant vaccinations by ethnic group. RESULTS: Ethnic inequities in 6-8 week baby check coverage in England varied by year and region. Coverage increased 2006-07 to 2015-16, then stabilised to 80-90% for most groups. Coverage was lowest for Bangladeshi and Pakistani groups 2006-07 to 2011-12. In the West Midlands, coverage was lowest at 59% for four groups: Bangladeshi, Caribbean, African, and Any other Black, African or Caribbean background. In the North West, coverage was lowest for Bangladeshi (65%) and Pakistani (69%) groups. These patterns remained after adjusting for other factors, and persisted over time. Coverage was highest in those whose mothers received a maternal check and those who received at least one dose of 8 week infant vaccinations. CONCLUSIONS: Coordinated action at the level of integrated commissioning boards, primary care networks and GP practices is required to better understand the reasons behind these inequities and redress the persistent disparities in 6-8 week baby check coverage.

A framework for longitudinal latent factor modelling of treatment response in clinical trials with applications to Psoriatic Arthritis and Rheumatoid Arthritis.

OBJECTIVE: Clinical trials involve the collection of a wealth of data, comprising multiple diverse measurements performed at baseline and follow-up visits over the course of a trial. The most common primary analysis is restricted to a single, potentially composite endpoint at one time point. While such an analytical focus promotes simple and replicable conclusions, it does not necessarily fully capture the multi-faceted effects of a drug in a complex disease setting. Therefore, to complement existing approaches, we set out here to design a longitudinal multivariate analytical framework that accepts as input an entire clinical trial database, comprising all measurements, patients, and time points across multiple trials. METHODS: Our framework composes probabilistic principal component analysis with a longitudinal linear mixed effects model, thereby enabling clinical interpretation of multivariate results, while handling data missing at random, and incorporating covariates and covariance structure in a computationally efficient and principled way. RESULTS: We illustrate our approach by applying it to four phase III clinical trials of secukinumab in Psoriatic Arthritis (PsA) and Rheumatoid Arthritis (RA). We identify three clinically plausible latent factors that collectively explain 74.5% of empirical variation in the longitudinal patient database. We estimate longitudinal trajectories of these factors, thereby enabling joint characterisation of disease progression and drug effect. We perform benchmarking experiments demonstrating our method's competitive performance at estimating average treatment effects compared to existing statistical and machine learning methods, and showing that our modular approach leads to relatively computationally efficient model fitting. CONCLUSION: Our multivariate longitudinal framework has the potential to illuminate the properties of existing composite endpoint methods, and to enable the development of novel clinical endpoints that provide enhanced and complementary perspectives on treatment response.

Ethnic inequities in routine childhood vaccinations in England 2006-2021: an observational cohort study using electronic health records.

Background: Population groups that are underserved by England's childhood vaccination programme must be identified to address the country's declining vaccination coverage. We examined routine childhood vaccination coverage in England by maternal ethnicity between 2006 and 2021. Methods: We created first, second and fifth birthday cohorts using mother-child linked electronic health records from the Clinical Practice Research Datalink (CPRD) Aurum. After validation against the UK Health Security Agency (UKHSA) and National Health Service England (NHSE) annual statistical reports, we described vaccination coverage for each vaccine by ethnicity and year. We used modified Poisson regression to analyse the effect of ethnicity on receiving the primary and full course of each vaccine. Findings: Up to 1,170,804 children born after 1 April 2006 were included in the first birthday cohort, reducing to 645,492 by the fifth birthday. Children were followed up until 31 March 2021 at the latest. Children born to mothers in 9 minority ethnic groups and those of unknown ethnicity had lower vaccination coverage (61.3-97.5%) than the White British group (79.9-97.8%) for all vaccines. Indian, Pakistani, Bangladeshi, Chinese, Any other Asian background, and White and Asian ethnic groups had similar vaccination coverage to the White British group (above 90% for most vaccines in most years). Inequities particularly affected the Caribbean group (e.g. 61% coverage for the 6/5/4-in-1 full course in 2020-21 by children's fifth birthday; RR 0.66, 95% CI 0.6-0.74 compared with the White British group) and Any other Black, African and Caribbean background (e.g. coverage 68% for the MMR primary course in 2020-21; RR 0.71, 95% CI 0.64-0.78). These inequities widened over the study period. For example, the absolute difference in coverage between the Caribbean and White British groups for the full course of MMR increased from 12% in 2011-12 to 22% in 2019-20. These inequities remained even after accounting for sociodemographic, maternal and birth related factors, and also widened from primary course to full course. Interpretation: Our findings suggest that urgent policy action is needed to address the ethnic inequities throughout England's routine childhood vaccination programme, which have been worsening over time. Funding: University of Oxford Clarendon Fund, St Cross College and Nuffield Department of Population Health.

Response to anti-IL17 therapy in inflammatory disease is not strongly impacted by genetic background.

Response to the anti-IL17 monoclonal antibody secukinumab is heterogeneous, and not all participants respond to treatment. Understanding whether this heterogeneity is driven by genetic variation is a key aim of pharmacogenetics and could influence precision medicine approaches in inflammatory diseases. Using changes in disease activity scores across 5,218 genotyped individuals from 19 clinical trials across four indications (psoriatic arthritis, psoriasis, ankylosing spondylitis, and rheumatoid arthritis), we tested whether genetics predicted response to secukinumab. We did not find any evidence of association between treatment response and common variants, imputed HLA alleles, polygenic risk scores of disease susceptibility, or cross-disease components of shared genetic risk. This suggests that anti-IL17 therapy is equally effective regardless of an individual's genetic background, a finding that has important implications for future genetic studies of biological therapy response in inflammatory diseases.

Spatial and temporal intra-tumoral heterogeneity in advanced HGSOC: Implications for surgical and clinical outcomes.

Limited evidence exists on the impact of spatial and temporal heterogeneity of high-grade serous ovarian cancer (HGSOC) on tumor evolution, clinical outcomes, and surgical operability. We perform systematic multi-site tumor mapping at presentation and matched relapse from 49 high-tumor-burden patients, operated up front. From SNP array-derived copy-number data, we categorize dendrograms representing tumor clonal evolution as sympodial or dichotomous, noting most chemo-resistant patients favor simpler sympodial evolution. Three distinct tumor evolutionary patterns from primary to relapse are identified, demonstrating recurrent disease may emerge from pre-existing or newly detected clones. Crucially, we identify spatial heterogeneity for clinically actionable homologous recombination deficiency scores and for poor prognosis biomarkers CCNE1 and MYC. Copy-number signature, phenotypic, proteomic, and proliferative-index heterogeneity further highlight HGSOC complexity. This study explores HGSOC evolution and dissemination across space and time, its impact on optimal surgical cytoreductive effort and clinical outcomes, and its consequences for clinical decision-making.

Advancing data science in drug development through an innovative computational framework for data sharing and statistical analysis.

BACKGROUND: Novartis and the University of Oxford's Big Data Institute (BDI) have established a research alliance with the aim to improve health care and drug development by making it more efficient and targeted. Using a combination of the latest statistical machine learning technology with an innovative IT platform developed to manage large volumes of anonymised data from numerous data sources and types we plan to identify novel patterns with clinical relevance which cannot be detected by humans alone to identify phenotypes and early predictors of patient disease activity and progression. METHOD: The collaboration focuses on highly complex autoimmune diseases and develops a computational framework to assemble a research-ready dataset across numerous modalities. For the Multiple Sclerosis (MS) project, the collaboration has anonymised and integrated phase II to phase IV clinical and imaging trial data from ≈35,000 patients across all clinical phenotypes and collected in more than 2200 centres worldwide. For the "IL-17" project, the collaboration has anonymised and integrated clinical and imaging data from over 30 phase II and III Cosentyx clinical trials including more than 15,000 patients, suffering from four autoimmune disorders (Psoriasis, Axial Spondyloarthritis, Psoriatic arthritis (PsA) and Rheumatoid arthritis (RA)). RESULTS: A fundamental component of successful data analysis and the collaborative development of novel machine learning methods on these rich data sets has been the construction of a research informatics framework that can capture the data at regular intervals where images could be anonymised and integrated with the de-identified clinical data, quality controlled and compiled into a research-ready relational database which would then be available to multi-disciplinary analysts. The collaborative development from a group of software developers, data wranglers, statisticians, clinicians, and domain scientists across both organisations has been key. This framework is innovative, as it facilitates collaborative data management and makes a complicated clinical trial data set from a pharmaceutical company available to academic researchers who become associated with the project. CONCLUSIONS: An informatics framework has been developed to capture clinical trial data into a pipeline of anonymisation, quality control, data exploration, and subsequent integration into a database. Establishing this framework has been integral to the development of analytical tools.

Targeting the PI3K/AKT/mTOR pathway in epithelial ovarian cancer, therapeutic treatment options for platinum-resistant ovarian cancer.

The survival rates for women with ovarian cancer have shown scant improvement in recent years, with a 5-year survival rate of less than 40% for women diagnosed with advanced ovarian cancer. High-grade serous ovarian cancer (HGSOC) is the most lethal subtype where the majority of women develop recurrent disease and chemotherapy resistance, despite over 70%-80% of patients initially responding to platinum-based chemotherapy. The phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling pathway regulates many vital processes such as cell growth, survival and metabolism. However, this pathway is frequently dysregulated in cancers including different subtypes of ovarian cancer, through amplification or somatic mutations of phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), amplification of AKT isoforms, or deletion or inactivation of PTEN. Further evidence indicates a role for the PI3K/AKT/mTOR pathway in the development of chemotherapy resistance in ovarian cancer. Thus, targeting key nodes of the PI3K/AKT/mTOR pathway is a potential therapeutic prospect. In this review, we outline dysregulation of PI3K signaling in ovarian cancer, with a particular emphasis on HGSOC and platinum-resistant disease. We review pre-clinical evidence for inhibitors of the main components of the PI3K pathway and highlight past, current and upcoming trials in ovarian cancers for different inhibitors of the pathway. Whilst no inhibitors of the PI3K/AKT/mTOR pathway have thus far advanced to the clinic for the treatment of ovarian cancer, several promising compounds which have the potential to restore platinum sensitivity and improve clinical outcomes for patients are under evaluation and in various phases of clinical trials.