Reconstructing annual inflows to the headwater catchments of the Murray River, Australia, using the Pacific Decadal Oscillation

The Pacific Decadal Oscillation (PDO) is a major forcing of inter-decadal to quasi-centennial variability of the hydroclimatology of the Pacific Basin. Its effects are most pronounced in the extra-tropical regions, while it modulates the El Nino Southern Oscillation (ENSO), the largest forcing of global inter-annual climate variability. PalaeoPDO indices are now available for at least the past 500 years. Here we show that the \u3e500 year PDO index of Shen et al. (2006) is highly correlated with inflows to the headwaters of Australia\u27s longest river system, the Murray-Darling. We then use the PDO to reconstruct annual inflows to the Murray River back to A.D. 1474. These show penta-decadal and quasi-centennial cycles of low inflows and a possible 500 year cycle of much greater inflow variability. Superimposed on this is the likely influence of recent anthropogenic global warming. We believe this may explain the exceptionally low inflows of the past decade, the lowest of the previous 529 years

Maintaining face-to-face contact during the COVID-19 pandemic:a longitudinal qualitative investigation in UK primary care

Background: In March 2020, the COVID-19 pandemic required a rapid reconfiguration of UK general practice to minimise face-to-face contact with patients to reduce infection risk. However, some face-to-face contact remained necessary and practices needed to ensure such contact could continue safely. Aim: To examine how practices determined when face-to-face contact was necessary and how face-to-face consultations were reconfigured to reduce COVID-19 infection risk. Design & setting: Qualitative interview study in general practices in Bristol, North Somerset, and South Gloucestershire. Method: Longitudinal semi-structured interviews with clinical and managerial practice staff were undertaken at four timepoints between May and July 2020. Results: Practices worked flexibly within general national guidance to determine when face-to-face contact with patients was necessary, influenced by knowledge of the patient, experience, and practice resilience. For example, practices prioritised patients according to clinical need using face-to-face contact to resolve clinician uncertainty or provide adequate reassurance to patients. To make face-to-face contact as safe as possible and keep patients separated, practices introduced a heterogeneous range of measures that exploited features of their indoor and outdoor spaces, and altered their appointment processes. As national restrictions eased in June and July, the number and proportion of patients seen face to face generally increased. However, the reconfiguration of buildings and processes reduced the available capacity and put increased pressure on practices. Conclusion: Practices responded rapidly and creatively to the initial lockdown restrictions. The variety of ways practices organised face-to-face contact to minimise infection highlights the need for flexibility in guidance

Optimising data curation pipelines for population-level analytics in secure data environments: Findings from a phenome-wide analysis in the NHS England Secure Data Environment

Objective Secure data environments (SDE) ensure safe access to large population-level sensitive data. However, computational capacity is limited in these environments, which leads to challenges in the analysis of large population data within the constraints of a complex cloud architecture leveraging multiple software ecosystems. Here we present an efficient pipeline to conduct phenome-wide analyses using electronic health records (EHR) in the NHS England SDE. Methods We accessed deidentified linked EHR from NHS SDE for around 50 million people in England. The exposure is SARS-CoV-2 infection, with outcomes being a phenome-wide atlas of all diseases recorded in EHR data. For computational efficiency, we created three cohorts tables using PySpark within the Databricks environment and a sampling algorithm with inverse probability weights which adds a flag to the dataset to mark the inclusion of a row in the sample of a specific outcome. We will conduct survival analysis using Cox models in RStudio on the samples while adjusting for potential confounders in the main datasets and 15 subgroups. Results Sampling with inverse probability weighting produced datasets that are statistically equivalent to the original population data. In terms of computational efficiency, the time needed to sample and read the data for modeling one outcome is 2.3 min compared to ~45 min when trying to read the entire dataset, which could fail due to the 4GB memory limits of in Rstudio within the SDE. This is particularly important in our study since we will be running at least 13,296 models for main and subgroup analysis in the three cohorts. By adding a flag to each data row to indicate its inclusion in a sample, the sampling strategy significantly reduced the storage space required for the outcome table of each sample. Conclusion Preparing datasets in Databricks and applying sampling can increase the efficiency of big data analysis pipelines within SDE, save storage space, and help in avoiding memory overload caused by using complete datasets for statistical analysis

Risk factors for ultrasound-diagnosed endometritis and its impact on fertility in Scottish dairy cattle herds

Background: The aim of this study was to investigate the risk factors for and the impact of ultrasound-diagnosed endometritis (UDE) on lactating dairy cows’ reproductive performance. Methods: Data were analysed from 1123 Holstein and Holstein-Friesian cows from two Scottish dairy farms. A reproductive ultrasound examination was conducted on two occasions, at 43 ± 3 and 50 ± 3 days in milk (DIM), to screen for hyperechoic fluid in the uterus. Statistical analyses were performed using multivariable logistic regression modelling and Cox proportional hazards models. Results: The overall incidence of UDE was 8.8% (99/1123). Risk factors for UDE included calving during autumn/winter seasons, increased parity and the presence of two or more diseases in the first 50 ± 3 days postpartum. The presence of UDE was associated with a reduced odds of pregnancy after all artificial inseminations up to 150 DIM. Limitations: The retrospective design of this study led to some inherent limitations with the quality and quantity of data collected. Conclusions: The findings of this study indicate which risk factors should be monitored in postpartum dairy cows to limit the impact of UDE on future reproductive performance

A multi-stakeholder approach to the co-production of the research agenda for medicines optimisation

© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence (http://creativecommons.org/licenses/by/4.0/), and indicate if changes were made.BACKGROUND: Up to 50% of medicines are not used as intended, resulting in poor health and economic outcomes. Medicines optimisation is 'a person-centred approach to safe and effective medicines use, to ensure people obtain the best possible outcomes from their medicines'. The purpose of this exercise was to co-produce a prioritised research agenda for medicines optimisation using a multi-stakeholder (patient, researcher, public and health professionals) approach. METHODS: A three-stage, multiple method process was used including: generation of preliminary research questions (Stage 1) using a modified Nominal Group Technique; electronic consultation and ranking with a wider multi-stakeholder group (Stage 2); a face-to-face, one-day consensus meeting involving representatives from all stakeholder groups (Stage 3). RESULTS: In total, 92 research questions were identified during Stages 1 and 2 and ranked in order of priority during stage 3. Questions were categorised into four areas: 'Patient Concerns' [e.g. is there a shared decision (with patients) about using each medicine?], 'Polypharmacy' [e.g. how to design health services to cope with the challenge of multiple medicines use?], 'Non-Medical Prescribing' [e.g. how can the contribution of non-medical prescribers be optimised in primary care?], and 'Deprescribing' [e.g. what support is needed by prescribers to deprescribe?]. A significant number of the 92 questions were generated by Patient and Public Involvement representatives, which demonstrates the importance of including this stakeholder group when identifying research priorities. CONCLUSIONS: A wide range of research questions was generated reflecting concerns which affect patients, practitioners, the health service, as well the ethical and philosophical aspects of the prescribing and deprescribing of medicines. These questions should be used to set future research agendas and funding commissions.Peer reviewedFinal Published versio

UK research data resources based on primary care electronic health records: review and summary for potential users

BACKGROUND: The range and scope of electronic health record (EHR) data assets in the UK has recently increased, which has been mainly in response to the COVID-19 pandemic. Summarising and comparing the large primary care resources will help researchers to choose the data resources most suited to their needs. AIM: To describe the current landscape of UK EHR databases and considerations of access and use of these resources relevant to researchers. DESIGN & SETTING: Narrative review of EHR databases in the UK. METHOD: Information was collected from the Health Data Research Innovation Gateway, publicly available websites and other published data, and from key informants. The eligibility criteria were population-based open-access databases sampling EHRs across the whole population of one or more countries in the UK. Published database characteristics were extracted and summarised, and these were corroborated with resource providers. Results were synthesised narratively. RESULTS: Nine large national primary care EHR data resources were identified and summarised. These resources are enhanced by linkage to other administrative data to a varying extent. Resources are mainly intended to support observational research, although some can support experimental studies. There is considerable overlap of populations covered. While all resources are accessible to bona fide researchers, access mechanisms, costs, timescales, and other considerations vary across databases. CONCLUSION: Researchers are currently able to access primary care EHR data from several sources. Choice of data resource is likely to be driven by project needs and access considerations. The landscape of data resources based on primary care EHRs in the UK continues to evolve

Breast MRI segmentation for density estimation:Do different methods give the same results and how much do differences matter?

PURPOSE: To compare two methods of automatic breast segmentation with each other and with manual segmentation in a large subject cohort. To discuss the factors involved in selecting the most appropriate algorithm for automatic segmentation and, in particular, to investigate the appropriateness of overlap measures (e.g., Dice and Jaccard coefficients) as the primary determinant in algorithm selection. METHODS: Two methods of breast segmentation were applied to the task of calculating MRI breast density in 200 subjects drawn from the Avon Longitudinal Study of Parents and Children, a large cohort study with an MRI component. A semiautomated, bias-corrected, fuzzy C-means (BC-FCM) method was combined with morphological operations to segment the overall breast volume from in-phase Dixon images. The method makes use of novel, problem-specific insights. The resulting segmentation mask was then applied to the corresponding Dixon water and fat images, which were combined to give Dixon MRI density values. Contemporaneously acquired T1 - and T2 -weighted image datasets were analyzed using a novel and fully automated algorithm involving image filtering, landmark identification, and explicit location of the pectoral muscle boundary. Within the region found, fat-water discrimination was performed using an Expectation Maximization-Markov Random Field technique, yielding a second independent estimate of MRI density. RESULTS: Images are presented for two individual women, demonstrating how the difficulty of the problem is highly subject-specific. Dice and Jaccard coefficients comparing the semiautomated BC-FCM method, operating on Dixon source data, with expert manual segmentation are presented. The corresponding results for the method based on T1 - and T2 -weighted data are slightly lower in the individual cases shown, but scatter plots and interclass correlations for the cohort as a whole show that both methods do an excellent job in segmenting and classifying breast tissue. CONCLUSIONS: Epidemiological results demonstrate that both methods of automated segmentation are suitable for the chosen application and that it is important to consider a range of factors when choosing a segmentation algorithm, rather than focus narrowly on a single metric such as the Dice coefficient