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ObjectiveTo evaluate the extent to which the inter-institutional, inter-disciplinary mobilisation of data and skills in the Farr Institute contributed to establishing the emerging field of data science for health in the UK.Design and outcome measuresWe evaluated evidence of six domains characterising a new field of science:defining central scientific challenges,demonstrating how the central challenges might be solved,creating novel interactions among groups of scientists,training new types of experts,re-organising universities,demonstrating impacts in society.We carried out citation, network and time trend analyses of publications, and a narrative review of infrastructure, methods and tools.SettingFour UK centres in London, North England, Scotland and Wales (23 university partners), 2013-2018.Results1. The Farr Institute helped define a central scientific challenge publishing a research corpus, demonstrating insights from electronic health record (EHR) and administrative data at each stage of the translational cycle in 593 papers with at least one Farr Institute author affiliation on PubMed. 2. The Farr Institute offered some demonstrations of how these scientific challenges might be solved: it established the first four ISO27001 certified trusted research environments in the UK, and approved more than 1000 research users, published on 102 unique EHR and administrative data sources, although there was no clear evidence of an increase in novel, sustained record linkages. The Farr Institute established open platforms for the EHR phenotyping algorithms and validations (>70 diseases, CALIBER). Sample sizes showed some evidence of increase but remained less than 10% of the UK population in primary care-hospital care linked studies. 3.The Farr Institute created novel interactions among researchers: the co-author publication network expanded from 944 unique co-authors (based on 67 publications in the first 30 months) to 3839 unique co-authors (545 papers in the final 30 months). 4. Training expanded substantially with 3 new masters courses, training >400 people at masters, short-course and leadership level and 48 PhD students. 5. Universities reorganised with 4/5 Centres established 27 new faculty (tenured) positions, 3 new university institutes. 6. Emerging evidence of impacts included: > 3200 citations for the 10 most cited papers and Farr research informed eight practice-changing clinical guidelines and policies relevant to the health of millions of UK citizens.ConclusionThe Farr Institute played a major role in establishing and growing the field of data science for health in the UK, with some initial evidence of benefits for health and healthcare. The Farr Institute has now expanded into Health Data Research (HDR) UK but key challenges remain including, how to network such activities internationally.

Background: We aimed to estimate the relative risk of mortality following a first positive SARS-CoV-2 test during the first, second, and third waves of the COVID-19pandemic in England by age, sex, and vaccination status, taking into account pre-existing health conditions and lifestyle factors. Methods: We conducted a retrospective cohort study of all individuals registered with the National Health Service (NHS) in England from 1 March 2020 to September 2022. Data for all individuals were obtained and linked including primary care records, hospital admission episodes, SARS-CoV-2 test results, vaccinations, and death registrations. We fitted Cox Proportional Hazards models with time dependent covariates for confirmed SARS-CoV-2 infection to model the risk of subsequent mortality. Results: The hazard ratio for death after testing positive for subsequent, compared with those not testing positive, amongst unvaccinated individuals, ranged from 11 to 89 by age and sex, in the first four weeks following a positive test in wave one and reduced to 14 to 50 in wave three. This hazard was further reduced amongst those who had three vaccines to between 1.4 and 7 in wave three. Conclusions: This study provides robust estimates of increased mortality risk among those who tested positive over the first three waves of the COVID-19 pandemic in England. The estimates show the impact of various factors affecting the risk of mortality from COVID-19. The results provide the first step towards estimating the magnitude and pattern of mortality displacement due to COVID-19, which is essential to understanding subsequent mortality rates in England. [ABSTRACT FROM AUTHOR]

Background: Surgical audit is key in upholding the standards of surgical care but may be inadequate in capturing morbidity experienced by patients being transferred across different health systems. The aim of this study was to assess the utility of an objective framework in the evaluation of clinical issues surrounding interhospital transfers (IHTs). Methods: A retrospective cohort study was conducted at a Victorian state bariatric hospital. Patients transferred with bariatric surgery related complications between 2014 and 2021 were included. Each case was reviewed by two surgeons using an objective framework developed via a modified Delphi‐process. Key issues and preventability surrounding each transfer were evaluated. Inter‐observer agreement was assessed using weighted Cohen's Kappa coefficient. Results: Seventy‐three patients were included. The most common indication for transfer was proximal staple line leak post sleeve gastrectomy (34/73, 46.6%). Length of stay was 38.3 ± 58.8 days. Cost of care amounted to AUD $110 666.18 per patient. Delay in transfer and complication recognition were present in 20% of cases (Cohen's Kappa 0.51;0.61). Human factors and patient related factors were the most common principal underlying causes (Cohen's Kappa 0.59). A third of the complications (n = 25/73, 34.2%), were potentially preventable (Cohen's Kappa 0.58) and more than half (39/73, 53.4%) did not have documented objective feedback to referring clinicians. Conclusion: IHTs associated with bariatric surgery complications have significant morbidity and costs. A structured framework in reviewing IHT can consistently identify potentially modifiable factors that improve clinical outcomes, and constructive feedback to the referring clinician should be actively facilitated and documented. [ABSTRACT FROM AUTHOR]

Author(s): Johan L. Vinther, Tim Cadman, Demetris Avraam, Claus T. Ekstrøm, Thorkild I. A. Sørensen, Ahmed Elhakeem, Ana C. Santos, Angela Pinot de Moira, Barbara Heude, Carmen Iñiguez, Costanza Pizzi, [...]

Background Preterm birth is the leading cause of perinatal morbidity and mortality and is associated with adverse developmental and long-term health outcomes, including several cardiometabolic risk factors and outcomes. However, evidence about the association of preterm birth with later body size derives mainly from studies using birth weight as a proxy of prematurity rather than an actual length of gestation. We investigated the association of gestational age (GA) at birth with body size from infancy through adolescence. Methods and findings We conducted a two-stage individual participant data (IPD) meta-analysis using data from 253,810 mother-child dyads from 16 general population-based cohort studies in Europe (Denmark, Finland, France, Italy, Norway, Portugal, Spain, the Netherlands, United Kingdom), North America (Canada), and Australasia (Australia) to estimate the association of GA with body mass index (BMI) and overweight (including obesity) adjusted for the following maternal characteristics as potential confounders: education, height, prepregnancy BMI, ethnic background, parity, smoking during pregnancy, age at child's birth, gestational diabetes and hypertension, and preeclampsia. Pregnancy and birth cohort studies from the LifeCycle and the EUCAN-Connect projects were invited and were eligible for inclusion if they had information on GA and minimum one measurement of BMI between infancy and adolescence. Using a federated analytical tool (DataSHIELD), we fitted linear and logistic regression models in each cohort separately with a complete-case approach and combined the regression estimates and standard errors through random-effects study-level meta-analysis providing an overall effect estimate at early infancy (>0.0 to 0.5 years), late infancy (>0.5 to 2.0 years), early childhood (>2.0 to 5.0 years), mid-childhood (>5.0 to 9.0 years), late childhood (>9.0 to 14.0 years), and adolescence (>14.0 to 19.0 years). GA was positively associated with BMI in the first decade of life, with the greatest increase in mean BMI z-score during early infancy (0.02, 95% confidence interval (CI): 0.00; 0.05, p < 0.05) per week of increase in GA, while in adolescence, preterm individuals reached similar levels of BMI (0.00, 95% CI: -0.01; 0.01, p 0.9) as term counterparts. The association between GA and overweight revealed a similar pattern of association with an increase in odds ratio (OR) of overweight from late infancy through mid-childhood (OR 1.01 to 1.02) per week increase in GA. By adolescence, however, GA was slightly negatively associated with the risk of overweight (OR 0.98 [95% CI: 0.97; 1.00], p 0.1) per week of increase in GA. Although based on only four cohorts (n = 32,089) that reached the age of adolescence, data suggest that individuals born very preterm may be at increased odds of overweight (OR 1.46 [95% CI: 1.03; 2.08], p < 0.05) compared with term counterparts. Findings were consistent across cohorts and sensitivity analyses despite considerable heterogeneity in cohort characteristics. However, residual confounding may be a limitation in this study, while findings may be less generalisable to settings in low- and middle-income countries. Conclusions This study based on data from infancy through adolescence from 16 cohort studies found that GA may be important for body size in infancy, but the strength of association attenuates consistently with age. By adolescence, preterm individuals have on average a similar mean BMI to peers born at term., Author(s): Johan L. Vinther 1,*, Tim Cadman 2, Demetris Avraam 3, Claus T. Ekstrøm 4, Thorkild I. A. Sørensen 1,5, Ahmed Elhakeem 2, Ana C. Santos 6,7, Angela Pinot de [...]

Many surveys target population subgroups that may not be readily identified in sampling frames. In the case study that motivated this study, the target population was households with children between the ages of 3 and 10 from two areas surrounding Cleveland, Ohio and Dallas, Texas. A standard approach is to sample households from these two areas and then screen for the presence of age-eligible children. Based on the estimated number of age-eligible households in these two areas, this approach would have required completing screening interviews with 5.4 to 5.7 households to find one eligible household. We developed a model-assisted sample design strategy to improve screening efficiency by attaching a measure of eligibility propensity to each household in the population. For this, we used a modeling and imputation strategy that combined information from several data sources: (1) the population of addresses for these two areas with demographic covariates from a commercial vendor, (2) external population data (from the American Community Survey and Census Planning Data) for these two areas, and (3) screening data from a large nationally representative survey. We first tested this sampling strategy in a pilot study and then implemented it in the main study. This strategy required 4.2 to 4.3 completed screeners to identify one eligible household. The proposed approach therefore improved the sampling efficiency by about 25% relative to the standard approach. [ABSTRACT FROM AUTHOR]