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Supplemental material, sj-pdf-3-jrs-10.1177_01410768231182389 for COVID-19 risk mitigation in reopening mass cultural events: population-based observational study for the UK Events Research Programme in Liverpool City Region by Girvan Burnside, Christopher P Cheyne, Gary Leeming, Michael Humann, Alistair Darby, Mark A Green, Alexander Crozier, Simon Maskell, Kay O’Halloran, Elena Musi, Elinor Carmi, Naila Khan, Debra Fisher, Rhiannon Corcoran, Jake Dunning, W John Edmunds, Kukatharmini Tharmaratnam, David M Hughes, Liora Malki-Epshtein, Malcolm Cook, Ben M Roberts, Eileen Gallagher, Kate Howell, Meera Chand, Robin Kemp, Matthew Boulter, Tom Fowler, Malcolm G Semple, Emer Coffey, Matt Ashton, The COVID-19 Genomics UK (COG-UK) Consortium, Marta García-Fiñana and Iain E Buchan in Journal of the Royal Society of Medicine

Supplemental material, sj-pdf-3-jrs-10.1177_01410768231182389 for COVID-19 risk mitigation in reopening mass cultural events: population-based observational study for the UK Events Research Programme in Liverpool City Region by Girvan Burnside, Christopher P Cheyne, Gary Leeming, Michael Humann, Alistair Darby, Mark A Green, Alexander Crozier, Simon Maskell, Kay O’Halloran, Elena Musi, Elinor Carmi, Naila Khan, Debra Fisher, Rhiannon Corcoran, Jake Dunning, W John Edmunds, Kukatharmini Tharmaratnam, David M Hughes, Liora Malki-Epshtein, Malcolm Cook, Ben M Roberts, Eileen Gallagher, Kate Howell, Meera Chand, Robin Kemp, Matthew Boulter, Tom Fowler, Malcolm G Semple, Emer Coffey, Matt Ashton, The COVID-19 Genomics UK (COG-UK) Consortium, Marta García-Fiñana and Iain E Buchan in Journal of the Royal Society of Medicine

Supplemental material, sj-pdf-2-jrs-10.1177_01410768231182389 for COVID-19 risk mitigation in reopening mass cultural events: population-based observational study for the UK Events Research Programme in Liverpool City Region by Girvan Burnside, Christopher P Cheyne, Gary Leeming, Michael Humann, Alistair Darby, Mark A Green, Alexander Crozier, Simon Maskell, Kay O’Halloran, Elena Musi, Elinor Carmi, Naila Khan, Debra Fisher, Rhiannon Corcoran, Jake Dunning, W John Edmunds, Kukatharmini Tharmaratnam, David M Hughes, Liora Malki-Epshtein, Malcolm Cook, Ben M Roberts, Eileen Gallagher, Kate Howell, Meera Chand, Robin Kemp, Matthew Boulter, Tom Fowler, Malcolm G Semple, Emer Coffey, Matt Ashton, The COVID-19 Genomics UK (COG-UK) Consortium, Marta García-Fiñana and Iain E Buchan in Journal of the Royal Society of Medicine

Supplemental material, sj-pdf-4-jrs-10.1177_01410768231182389 for COVID-19 risk mitigation in reopening mass cultural events: population-based observational study for the UK Events Research Programme in Liverpool City Region by Girvan Burnside, Christopher P Cheyne, Gary Leeming, Michael Humann, Alistair Darby, Mark A Green, Alexander Crozier, Simon Maskell, Kay O’Halloran, Elena Musi, Elinor Carmi, Naila Khan, Debra Fisher, Rhiannon Corcoran, Jake Dunning, W John Edmunds, Kukatharmini Tharmaratnam, David M Hughes, Liora Malki-Epshtein, Malcolm Cook, Ben M Roberts, Eileen Gallagher, Kate Howell, Meera Chand, Robin Kemp, Matthew Boulter, Tom Fowler, Malcolm G Semple, Emer Coffey, Matt Ashton, The COVID-19 Genomics UK (COG-UK) Consortium, Marta García-Fiñana and Iain E Buchan in Journal of the Royal Society of Medicine

Supplemental material, sj-pdf-6-jrs-10.1177_01410768231182389 for COVID-19 risk mitigation in reopening mass cultural events: population-based observational study for the UK Events Research Programme in Liverpool City Region by Girvan Burnside, Christopher P Cheyne, Gary Leeming, Michael Humann, Alistair Darby, Mark A Green, Alexander Crozier, Simon Maskell, Kay O’Halloran, Elena Musi, Elinor Carmi, Naila Khan, Debra Fisher, Rhiannon Corcoran, Jake Dunning, W John Edmunds, Kukatharmini Tharmaratnam, David M Hughes, Liora Malki-Epshtein, Malcolm Cook, Ben M Roberts, Eileen Gallagher, Kate Howell, Meera Chand, Robin Kemp, Matthew Boulter, Tom Fowler, Malcolm G Semple, Emer Coffey, Matt Ashton, The COVID-19 Genomics UK (COG-UK) ConsortiumMarta García-Fiñana and Iain E Buchan in Journal of the Royal Society of Medicine

Supplemental material, sj-pdf-5-jrs-10.1177_01410768231182389 for COVID-19 risk mitigation in reopening mass cultural events: population-based observational study for the UK Events Research Programme in Liverpool City Region by Girvan Burnside, Christopher P Cheyne, Gary Leeming, Michael Humann, Alistair Darby, Mark A Green, Alexander Crozier, Simon Maskell, Kay O’Halloran, Elena Musi, Elinor Carmi, Naila Khan, Debra Fisher, Rhiannon Corcoran, Jake Dunning, W John Edmunds, Kukatharmini Tharmaratnam, David M Hughes, Liora Malki-Epshtein, Malcolm Cook, Ben M Roberts, Eileen Gallagher, Kate Howell, Meera Chand, Robin Kemp, Matthew Boulter, Tom Fowler, Malcolm G Semple, Emer Coffey, Matt Ashton, The COVID-19 Genomics UK (COG-UK) Consortium, Marta García-Fiñana and Iain E Buchan in Journal of the Royal Society of Medicine

Supplemental material, sj-pdf-2-jrs-10.1177_01410768231182389 for COVID-19 risk mitigation in reopening mass cultural events: population-based observational study for the UK Events Research Programme in Liverpool City Region by Girvan Burnside, Christopher P Cheyne, Gary Leeming, Michael Humann, Alistair Darby, Mark A Green, Alexander Crozier, Simon Maskell, Kay O’Halloran, Elena Musi, Elinor Carmi, Naila Khan, Debra Fisher, Rhiannon Corcoran, Jake Dunning, W John Edmunds, Kukatharmini Tharmaratnam, David M Hughes, Liora Malki-Epshtein, Malcolm Cook, Ben M Roberts, Eileen Gallagher, Kate Howell, Meera Chand, Robin Kemp, Matthew Boulter, Tom Fowler, Malcolm G Semple, Emer Coffey, Matt Ashton, The COVID-19 Genomics UK (COG-UK) Consortium, Marta García-Fiñana and Iain E Buchan in Journal of the Royal Society of Medicine

Supplemental material, sj-pdf-5-jrs-10.1177_01410768231182389 for COVID-19 risk mitigation in reopening mass cultural events: population-based observational study for the UK Events Research Programme in Liverpool City Region by Girvan Burnside, Christopher P Cheyne, Gary Leeming, Michael Humann, Alistair Darby, Mark A Green, Alexander Crozier, Simon Maskell, Kay O’Halloran, Elena Musi, Elinor Carmi, Naila Khan, Debra Fisher, Rhiannon Corcoran, Jake Dunning, W John Edmunds, Kukatharmini Tharmaratnam, David M Hughes, Liora Malki-Epshtein, Malcolm Cook, Ben M Roberts, Eileen Gallagher, Kate Howell, Meera Chand, Robin Kemp, Matthew Boulter, Tom Fowler, Malcolm G Semple, Emer Coffey, Matt Ashton, The COVID-19 Genomics UK (COG-UK) Consortium, Marta García-Fiñana and Iain E Buchan in Journal of the Royal Society of Medicine

Supplemental material, sj-pdf-1-jrs-10.1177_01410768231182389 for COVID-19 risk mitigation in reopening mass cultural events: population-based observational study for the UK Events Research Programme in Liverpool City Region by Girvan Burnside, Christopher P Cheyne, Gary Leeming, Michael Humann, Alistair Darby, Mark A Green, Alexander Crozier, Simon Maskell, Kay O’Halloran, Elena Musi, Elinor Carmi, Naila Khan, Debra Fisher, Rhiannon Corcoran, Jake Dunning, W John Edmunds, Kukatharmini Tharmaratnam, David M Hughes, Liora Malki-Epshtein, Malcolm Cook, Ben M Roberts, Eileen Gallagher, Kate Howell, Meera Chand, Robin Kemp, Matthew Boulter, Tom Fowler, Malcolm G Semple, Emer Coffey, Matt Ashton, The COVID-19 Genomics UK (COG-UK) Consortium, Marta García-Fiñana and Iain E Buchan in Journal of the Royal Society of Medicine

Supplemental material, sj-pdf-4-jrs-10.1177_01410768231182389 for COVID-19 risk mitigation in reopening mass cultural events: population-based observational study for the UK Events Research Programme in Liverpool City Region by Girvan Burnside, Christopher P Cheyne, Gary Leeming, Michael Humann, Alistair Darby, Mark A Green, Alexander Crozier, Simon Maskell, Kay O’Halloran, Elena Musi, Elinor Carmi, Naila Khan, Debra Fisher, Rhiannon Corcoran, Jake Dunning, W John Edmunds, Kukatharmini Tharmaratnam, David M Hughes, Liora Malki-Epshtein, Malcolm Cook, Ben M Roberts, Eileen Gallagher, Kate Howell, Meera Chand, Robin Kemp, Matthew Boulter, Tom Fowler, Malcolm G Semple, Emer Coffey, Matt Ashton, The COVID-19 Genomics UK (COG-UK) Consortium, Marta García-Fiñana and Iain E Buchan in Journal of the Royal Society of Medicine

Supplemental material, sj-pdf-6-jrs-10.1177_01410768231182389 for COVID-19 risk mitigation in reopening mass cultural events: population-based observational study for the UK Events Research Programme in Liverpool City Region by Girvan Burnside, Christopher P Cheyne, Gary Leeming, Michael Humann, Alistair Darby, Mark A Green, Alexander Crozier, Simon Maskell, Kay O’Halloran, Elena Musi, Elinor Carmi, Naila Khan, Debra Fisher, Rhiannon Corcoran, Jake Dunning, W John Edmunds, Kukatharmini Tharmaratnam, David M Hughes, Liora Malki-Epshtein, Malcolm Cook, Ben M Roberts, Eileen Gallagher, Kate Howell, Meera Chand, Robin Kemp, Matthew Boulter, Tom Fowler, Malcolm G Semple, Emer Coffey, Matt Ashton, The COVID-19 Genomics UK (COG-UK) ConsortiumMarta García-Fiñana and Iain E Buchan in Journal of the Royal Society of Medicine

According to the Adult Psychiatric Morbidity Survey (APMS) 2014, one in six (17%) people in England met the criteria for a common mental disorder (CMD), with most mental disorders frequently present in those with poor physical health (McManus et al, 2016). Data from the same survey using the Alcohol Use Disorders Identification Test (AUDIT) showed that nearly 20% of adults drank at a hazardous level that is potentially harmful to health (AUDIT scores 8+) (Drummond et al, 2016). Alcohol has been found to cause numerous physical health conditions (Wholly Alcohol Attributable Conditions, WAACs; Lopez et al, 2014) and to contribute to the increased risk of a large number of chronic and acute diseases (Partially Alcohol Attributable Conditions, PAACs; PHE, 2020). It has been estimated that one in five patients in UK hospitals use alcohol in a harmful way and one in 10 are dependent on alcohol (Roberts et al, 2019). Alcohol use disorders have been linked with a range of mental health difficulties (PHE, 2016b) with 86% of people using alcohol treatment services having a co-occurring mental health difficulty (PHE, 2017). Multimorbidity (MM) is typically defined as the presence of two or more conditions, and research in this area has investigated how different conditions can occur together. For example, Weich et al (2011) used latent class analysis to analyse the APMS 2007 to describe the extent and nature of psychiatric comorbidity. The results revealed four latent classes of MM with the most common conditions of anxiety and depression dominating three of the multimorbid classes (Co-thymia, Addictions, Highly co-morbid) accounting for 18.4% of the participants. Another study with veterans discovered that most individuals with a mental health problem also had multiple medical and psychiatric diagnoses (Bhalla, Robert, & Rosenheck, 2018). Those with more than one mental disorder are different with more complex healthcare needs and thus treatment need to be tailored accordingly. Treating the conditions in isolation is likely to be ineffective, impractical, or even risky (e.g. medical risk can interact with psychiatric treatment) (Bhalla et al, 2018). Existing research focusing on multimorbidity has typically examined which factors are associated with the number of health problems rather than defining clusters of disease. This provides less specific advice for clinicians to act on, but has identified the important role of mental health in predicting multimorbidity (Read et al., 2017). A recent study examining specific clusters of multimorbidity found that a cluster of mental disorders and substance/alcohol use disorders was associated with the greatest risk of premature mortality (Zhu et al, 2020). There is a recognised dearth of research in understanding the complexity of the links between mental health and alcohol use and how this determines an individual’s future physical health. This project will use the APMS 2014 dataset to identify the multimorbidity clusters of physical non-communicable diseases (NCDs) with common and severe mental disorders (CMDs and SMIs). We know that mental health and behavioural issues are associated with the development of numerous socioeconomically patterned physical health conditions (Kivimäki et al, 2020). Our work will allow us to better understand how the health inequalities relating to alcohol attributable disease are greater for individuals with, compared to without, a mental disorder.

PURPOSE: To prospectively validate two risk scores to predict mortality (4C Mortality) and in-hospital deterioration (4C Deterioration) among adults hospitalised with COVID-19. METHODS: Prospective observational cohort study of adults (age ≥18 years) with confirmed or highly suspected COVID-19 recruited into the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) WHO Clinical Characterisation Protocol UK (CCP-UK) study in 306 hospitals across England, Scotland and Wales. Patients were recruited between 27 August 2020 and 17 February 2021, with at least 4 weeks follow-up before final data extraction. The main outcome measures were discrimination and calibration of models for in-hospital deterioration (defined as any requirement of ventilatory support or critical care, or death) and mortality, incorporating predefined subgroups. RESULTS: 76 588 participants were included, of whom 27 352 (37.4%) deteriorated and 12 581 (17.4%) died. Both the 4C Mortality (0.78 (0.77 to 0.78)) and 4C Deterioration scores (pooled C-statistic 0.76 (95% CI 0.75 to 0.77)) demonstrated consistent discrimination across all nine National Health Service regions, with similar performance metrics to the original validation cohorts. Calibration remained stable (4C Mortality: pooled slope 1.09, pooled calibration-in-the-large 0.12; 4C Deterioration: 1.00, -0.04), with no need for temporal recalibration during the second UK pandemic wave of hospital admissions. CONCLUSION: Both 4C risk stratification models demonstrate consistent performance to predict clinical deterioration and mortality in a large prospective second wave validation cohort of UK patients. Despite recent advances in the treatment and management of adults hospitalised with COVID-19, both scores can continue to inform clinical decision making. TRIAL REGISTRATION NUMBER: ISRCTN66726260.

Psychosis is a major mental illness with first onset in young adults. The prognosis is poor in around half of the people affected, and difficult to predict. The few tools available to predict prognosis have major weaknesses which limit their use in clinical practice. We aimed to develop and validate a risk prediction model of symptom nonremission in first-episode psychosis. Our development cohort consisted of 1027 patients with first-episode psychosis recruited between 2005 and 2010 from 14 early intervention services across the National Health Service in England. Our validation cohort consisted of 399 patients with first-episode psychosis recruited between 2006 and 2009 from a further 11 English early intervention services. The one-year nonremission rate was 52% and 54% in the development and validation cohorts, respectively. Multivariable logistic regression was used to develop a risk prediction model for nonremission, which was externally validated. The prediction model showed good discrimination C-statistic of 0.73 (0.71, 0.75) and adequate calibration with intercept alpha of 0.12 (0.02, 0.22) and slope beta of 0.98 (0.85, 1.11). Our model improved the net-benefit by 15% at a risk threshold of 50% compared to the strategy of treating all, equivalent to 15 more detected nonremitted first-episode psychosis individuals per 100 without incorrectly classifying remitted cases. Once prospectively validated, our first episode psychosis prediction model could help identify patients at increased risk of nonremission at initial clinical contact.

Additional file 1. Population demographics, MCMC output (parameter estimates, trace plots and posterior distributions) for all models and log likelihood profile for infectiveness.

Additional file 1: Additional results and sensitivity analyses that supplement the core paper and are referred to in text.

Supplemental Material for The DynAIRx Project Protocol: Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity by Lauren E Walker, Aseel S Abuzour, Danushka Bollegala, Andrew Clegg, Mark Gabbay, Alan Griffiths, Cecil Kullu, Gary Leeming, Frances S Mair, Simon Maskell, Samuel Relton, Roy A Ruddle, Eduard Shantsila, Matthew Sperrin, Tjeerd Van Staa, Alan Woodall and Iain Buchan in Journal of Multimorbidity and Comorbidity

BackgroundMen and older women have been shown to be at higher risk of adverse COVID-19 outcomes. Animal model studies of SARS-CoV and MERS suggest that the age and sex difference in COVID-19 symptom severity may be due to a protective effect of the female sex hormone estrogen. Females have shown an ability to mount a stronger immune response to a variety of viral infections because of more robust humoral and cellular immune responses.ObjectivesWe sought to determine whether COVID-19 positivity increases in women entering menopause. We also aimed to identify whether premenopausal women taking exogenous hormones in the form of the combined oral contraceptive pill (COCP) and post-menopausal women taking hormone replacement therapy (HRT) have lower predicted rates of COVID-19, using our published symptom-based model.DesignThe COVID Symptom Study developed by King’s College London and Zoe Global Limited was launched in the UK on 24th March 2020. It captured self-reported information related to COVID-19 symptoms. Data used for this study included records collected between 7th May - 15th June 2020.Main outcome measuresWe investigated links between COVID-19 rates and 1) menopausal status, 2) COCP use and 3) HRT use, using symptom-based predicted COVID-19, tested COVID-19, and disease severity based on requirement for hospital attendance or respiratory support.ParticipantsFemale users of the COVID Symptom Tracker Application in the UK, including 152,637 women for menopause status, 295,689 for COCP use, and 151,193 for HRT use. Analyses were adjusted for age, smoking and BMI.ResultsPost-menopausal women aged 40-60 years had a higher rate of predicted COVID (P=0.003) and a corresponding range of symptoms, with consistent, but not significant trends observed for tested COVID-19 and disease severity. Women aged 18-45 years taking COCP had a significantly lower predicted COVID-19 (P=8.03E-05), with a reduction in hospital attendance (P=0.023). Post-menopausal women using HRT or hormonal therapies did not exhibit consistent associations, including increased rates of predicted COVID-19 (P=2.22E-05) for HRT users alone.ConclusionsOur findings support a protective effect of estrogen on COVID-19, based on positive association between predicted COVID-19 and menopausal status, and a negative association with COCP use. HRT use was positively associated with COVID-19 symptoms; however, the results should be considered with caution due to lack of data on HRT type, route of administration, duration of treatment, and potential comorbidities.Trial registrationThe App Ethics has been approved by KCL ethics Committee REMAS ID 18210, review reference LRs-19/20-18210

AimsPsychosis is a major mental illness with first onset in young adults. The prognosis is poor in around half of the people affected, and difficult to predict. The few tools available to predict prognosis have major weaknesses which limit their use in clinical practice. We aimed to develop and validate a risk prediction model of symptom non-remission in first-episode psychosis.MethodOur development cohort consisted of 1027 patients with first-episode psychosis recruited between 2005 to 2010 from 14 early intervention services across the National Health Service in England. Our validation cohort consisted of 399 patients with first-episode psychosis recruited between 2006 to 2009 from a further 11 English early intervention services. The one-year non-remission rate was 52% and 54% in the development and validation cohorts, respectively. Multivariable logistic regression was used to develop a risk prediction model for non-remission, which was externally validated.ResultThe prediction model showed good discrimination (C-statistic of 0.74 (0.72, 0.76) and adequate calibration with intercept alpha of 0.13 (0.03, 0.23) and slope beta of 0.99 (0.87, 1.12). Our model improved the net-benefit by 16% at a risk threshold of 50%, equivalent to 16 more detected non-remitted first-episode psychosis individuals per 100 without incorrectly classifying remitted cases.ConclusionOnce prospectively validated, our first episode psychosis prediction model could help identify patients at increased risk of non-remission at initial clinical contact.

We are facing a major challenge in bridging the gap between identifying subtypes of asthma to understand causal mechanisms and translating this knowledge into personalized prevention and management strategies. In recent years, “big data” has been sold as a panacea for generating hypotheses and driving new frontiers of health care; the idea that the data must and will speak for themselves is fast becoming a new dogma. One of the dangers of ready accessibility of health care data and computational tools for data analysis is that the process of data mining can become uncoupled from the scientific process of clinical interpretation, understanding the provenance of the data, and external validation. Although advances in computational methods can be valuable for using unexpected structure in data to generate hypotheses, there remains a need for testing hypotheses and interpreting results with scientific rigor. We argue for combining data- and hypothesis-driven methods in a careful synergy, and the importance of carefully characterized birth and patient cohorts with genetic, phenotypic, biological, and molecular data in this process cannot be overemphasized. The main challenge on the road ahead is to harness bigger health care data in ways that produce meaningful clinical interpretation and to translate this into better diagnoses and properly personalized prevention and treatment plans. There is a pressing need for cross-disciplinary research with an integrative approach to data science, whereby basic scientists, clinicians, data analysts, and epidemiologists work together to understand the heterogeneity of asthma.

Definition of conditions being present; flow chart describing study cohort selection; additional figures S2-S18. (DOCX 4106 kb)

Figures S1-S9 that show details of the three Presentations, highlighting the three areas of interest under study; Table S1 that reports a contingency table for the number of patients who underestimated and overestimated the need for action; Tables S2 and S3 that show the characteristics of patients who underestimated and overestimated the need for actions and those who did not; Figure S10 that shows participantâ s dwell time on the different areas of interest across the different presentations and clinical scenarios, stratified by patients who underestimated the need for action and those who did not. (DOCX 2455Â kb)

ackground Social, economic and health disparities between northern and southern England have persisted despite Government policies to reduce them. We examine long-term trends in premature mortality in northern and southern England across age groups, and whether mortality patterns changed after the 2008–2009 Great Recession. Methods Population-wide longitudinal (1965–2015) study of mortality in England's five northernmost versus four southernmost Government Office Regions – halves of overall population. Main outcome measure: directly age-sex adjusted mortality rates; northern excess mortality (percentage excess northern vs southern deaths, age-sex adjusted). Results From 1965 to 2010, premature mortality (deaths per 10 000 aged

Number of animals stratified by species, British country and Index of Multiple Deprivation (IMD). Species assessed include dogs, cats and other species. IMD category 1 indicates the least deprived areas and category 5 the most deprived. The percentage that each species made up within each IMD category in each country is shown in brackets. (DOCX 13Â kb)

Details of the Partial Least Squared regression methodology, additional details of the simulation design, additional tables and the simulation R code. (DOCX 49Â kb)

Results of the mixed effects logistic regression models, assessing the association between a range of an animal’s characteristics and the Index of Multiple Deprivation (IMD). Shown are odds ratios of fixed effects IMD in England, Wales and Scotland from the final mixed effects logistic regression models of; the probability of animals being a dog in the veterinary-visiting population; the probability of animals being a cat in the veterinary-visiting population; and of the probability of dogs and cats being purebred in the veterinary-visiting dog and cat population, respectively. Three asterisks (***), two asterisks (**) and one asterisk (*) indicate p

Demographics of the SAVSNET veterinary-visiting population of dogs, cats and rabbits by each region considered in the study. (DOCX 18Â kb)

Results of the mixed effects logistic regression models, assessing the association between a range of an animal’s key performance indicators and the Index of Multiple Deprivation (IMD). Shown are odds ratios of fixed effects IMD in England, Wales and Scotland from the final mixed effects logistic regression models of; the probability of dogs and cats being neutered by sex; the probability of dogs and cats being insured; and of the probability of dogs and cats being microchipped. Asterisk (*) indicates p

Supplementary file for â Outcome-sensitive Multiple Imputation: a Simulation Studyâ . Additional results for the covariate and the interaction term for the main analyses, but also all results from sensitivity analyses. (PDF 480Â kb)

We develop a simulation tool to support policy-decisions about healthcare for chronic diseases in defined populations. Incident disease-cases are generated in-silico from an age-sex characterised general population using standard epidemiological approaches. A novel disease-treatment model then simulates continuous life courses for each patient using discrete event simulation. Ideally, the discrete event simulation model would be inferred from complete longitudinal healthcare data via a likelihood or Bayesian approach. Such data is seldom available for relevant populations, therefore an innovative approach to evidence synthesis is required. We propose a novel entropy-based approach to fit survival densities. This method provides a fully flexible way to incorporate the available information, which can be derived from arbitrary sources. Discrete event simulation then takes place on the fitted model using a competing hazards framework. The output is then used to help evaluate the potential impacts of policy options for a given population., Comment: 24 pages, 13 figures, 11 tables

Full details about included CKD prediction models and sensitivity analyses results. (DOCX 189 kb)

Full details about included CKD prediction models and sensitivity analyses results. (DOCX 189 kb)

Next-generation sequencing (NGS) technologies have superseded traditional Sanger sequencing approach in many experimental settings, given their tremendous yield and affordable cost. Nowadays it is possible to sequence any microbial organism or meta-genomic sample within hours, and to obtain a whole human genome in weeks. Nonetheless, NGS technologies are error-prone. Correcting errors is a challenge due to multiple factors, including the data sizes, the machine-specific and non-at-random characteristics of errors, and the error distributions. Errors in NGS experiments can hamper the subsequent data analysis and inference. This work proposes an error correction method based on the de Bruijn graph that permits its execution on Gigabyte-sized data sets using normal desktop/laptop computers, ideal for genome sizes in the Megabase range, e.g. bacteria. The implementation makes extensive use of hashing techniques, and implements an A∗ algorithm for optimal error correction, minimizing the distance between an erroneous read and its possible replacement with the Needleman-Wunsch score. Our approach outperforms other popular methods both in terms of random access memory usage and computing times.

BACKGROUND: Serum total cholesterol is one of the major targets for cardiovascular disease prevention. Statins are effective for cholesterol control in individual patients. At the population level, however, their contribution to total cholesterol decline remains unclear. The aim of this study was to quantify the contribution of statins to the observed fall in population mean cholesterol levels in England over the past two decades, and explore any differences between socioeconomic groups. METHODS AND FINDINGS: This is a modelling study based on data from the Health Survey for England. We analysed changes in observed mean total cholesterol levels in the adult England population between 1991-92 (baseline) and 2011-12. We then compared the observed changes with a counterfactual 'no statins' scenario, where the impact of statins on population total cholesterol was estimated and removed. We estimated uncertainty intervals (UI) using Monte Carlo simulation, where confidence intervals (CI) were impractical. In 2011-12, 13.2% (95% CI: 12.5-14.0%) of the English adult population used statins at least once per week, compared with 1991-92 when the proportion was just 0.5% (95% CI: 0.3-1.0%). Between 1991-92 and 2011-12, mean total cholesterol declined from 5.86 mmol/L (95% CI: 5.82-5.90) to 5.17 mmol/L (95% CI: 5.14-5.20). For 2011-12, mean total cholesterol was lower in more deprived groups. In our 'no statins' scenario we predicted a mean total cholesterol of 5.36 mmol/L (95% CI: 5.33-5.40) for 2011-12. Statins were responsible for approximately 33.7% (95% UI: 28.9-38.8%) of the total cholesterol reduction since 1991-92. The statin contribution to cholesterol reduction was greater among the more deprived groups of women, while showing little socio-economic gradient among men. CONCLUSIONS: Our model suggests that statins explained around a third of the substantial falls in total cholesterol observed in England since 1991. Approximately two thirds of the cholesterol decrease can reasonably be attributed non-pharmacological determinants.