Publications by authors named "Iain Buchan"

193 Publications

Missing data was handled inconsistently in UK prediction models: a review of method used.

J Clin Epidemiol 2021 Sep 11. Epub 2021 Sep 11.

Centre for Health Informatics, Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.

Objective: No clear guidance exists on handling missing data at each stage of developing, validating and implementing a clinical prediction model (CPM). We aimed to review the approaches to handling missing data that underly the CPMs currently recommended for use in UK healthcare.

Study Design And Setting: A descriptive cross-sectional meta-epidemiological study aiming to identify CPMs recommended by the National Institute for Health and Care Excellence (NICE), which summarized how missing data is handled across their pipelines.

Results: 23 CPMs were included through 'sampling strategy'. Six missing data strategies were identified: complete case analysis (CCA), multiple imputation, imputation of mean values, k-nearest neighbours imputation, using an additional category for missingness, considering missing values as risk-factor-absent. 52% of the development articles and 48% of the validation articles did not report how missing data were handled. CCA was the most common approach used for development (40%) and validation (44%). At implementation, 57% of the CPMs required complete data entry, whilst 43% allowed missing values. 3 CPMs had consistent paths in their pipelines.

Conclusion: A broad variety of methods for handling missing data underly the CPMs currently recommended for use in UK healthcare. Missing data handling strategies were generally inconsistent. Better quality assurance of CPMs needs greater clarity and consistency in handling of missing data.
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http://dx.doi.org/10.1016/j.jclinepi.2021.09.008DOI Listing
September 2021

Estrogen and COVID-19 symptoms: Associations in women from the COVID Symptom Study.

PLoS One 2021 10;16(9):e0257051. Epub 2021 Sep 10.

Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom.

It has been widely observed that adult men of all ages are at higher risk of developing serious complications from COVID-19 when compared with women. This study aimed to investigate the association of COVID-19 positivity and severity with estrogen exposure in women, in a population based matched cohort study of female users of the COVID Symptom Study application in the UK. Analyses included 152,637 women for menopausal status, 295,689 women for exogenous estrogen intake in the form of the combined oral contraceptive pill (COCP), and 151,193 menopausal women for hormone replacement therapy (HRT). Data were collected using the COVID Symptom Study in May-June 2020. Analyses investigated associations between predicted or tested COVID-19 status and menopausal status, COCP use, and HRT use, adjusting for age, smoking and BMI, with follow-up age sensitivity analysis, and validation in a subset of participants from the TwinsUK cohort. Menopausal women had higher rates of predicted COVID-19 (P = 0.003). COCP-users had lower rates of predicted COVID-19 (P = 8.03E-05), with reduction in hospital attendance (P = 0.023). 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. The findings support a protective effect of estrogen exposure on COVID-19, based on positive association between predicted COVID-19 with menopausal status, and negative association with COCP use. HRT use was positively associated with COVID-19, but the results should be considered with caution due to lack of data on HRT type, route of administration, duration of treatment, and potential unaccounted for confounders and comorbidities.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0257051PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8432854PMC
September 2021

Tobacco Control Policy Simulation Models: Protocol for a Systematic Methodological Review.

JMIR Res Protoc 2021 Jul 26;10(7):e26854. Epub 2021 Jul 26.

Department of Public Health, Policy and Systems, University of Liverpool, Liverpool, United Kingdom.

Background: Tobacco control models are mathematical models predicting tobacco-related outcomes in defined populations. The policy simulation model is considered as a subcategory of tobacco control models simulating the potential outcomes of tobacco control policy options. However, we could not identify any existing tool specifically designed to assess the quality of tobacco control models.

Objective: The aims of this systematic methodology review are to: (1) identify best modeling practices, (2) highlight common pitfalls, and (3) develop recommendations to assess the quality of tobacco control policy simulation models. Crucially, these recommendations can empower model users to assess the quality of current and future modeling studies, potentially leading to better tobacco policy decision-making for the public. This protocol describes the planned systematic review stages, paper inclusion and exclusion criteria, data extraction, and analysis.

Methods: Two reviewers searched five databases (Embase, EconLit, PsycINFO, PubMed, and CINAHL Plus) to identify eligible studies published between July 2013 and August 2019. We included papers projecting tobacco-related outcomes with a focus on tobacco control policies in any population and setting. Eligible papers were independently screened by two reviewers. The data extraction form was designed and piloted to extract model structure, data sources, transparency, validation, and other qualities. We will use a narrative synthesis to present the results by summarizing model trends, analyzing model approaches, and reporting data input and result quality. We will propose recommendations to assess the quality of tobacco control policy simulation models using the findings from this review and related literature.

Results: Data collection is in progress. Results are expected to be completed and submitted for publication by April 2021.

Conclusions: This systematic methodological review will summarize the best practices and pitfalls existing among tobacco control policy simulation models and present a recommendation list of a high-quality tobacco control simulation model. A more standardized and quality-assured tobacco control policy simulation model will benefit modelers, policymakers, and the public on both model building and decision making.

Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42020178146; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020178146.

International Registered Report Identifier (irrid): DERR1-10.2196/26854.
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http://dx.doi.org/10.2196/26854DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367099PMC
July 2021

Enhanced lateral flow testing strategies in care homes are associated with poor adherence and were insufficient to prevent COVID-19 outbreaks: Results from a mixed methods implementation study.

Age Ageing 2021 Jul 16. Epub 2021 Jul 16.

Public Health Department, Liverpool City Council, Liverpool, Cunard Building, Water Street, Liverpool, L3 1DS.

Introduction: Care homes have been severely affected by the SARS-CoV-2 pandemic. Rapid antigen testing could identify most SARS-CoV-2 infected staff and visitors before they enter homes. We explored implementation of staff and visitor testing protocols using lateral flow devices (LFDs).

Methods: An evaluation of a SARS-CoV-2 LFD based testing protocol in 11 care homes in Liverpool, UK, including staff and visitor testing, plus a qualitative exploratory study in 9 of these homes. The proportion of pilot homes with outbreaks, and outbreak size, were compared to non-pilot homes in Liverpool. Adherence to testing protocols was evaluated. Fifteen staff were interviewed, and transcript data were thematically coded using an iterative analysis to identify and categorize factors influencing testing implementation.

Results: 1638 LFD rapid tests were performed on 407 staff. Protocol adherence was poor with 8.6% of staff achieving >75% protocol adherence, and 25.3% achieving $\ge$50%. Six care homes had outbreaks during the study. Compared to non-pilot care homes, there was no evidence of significant difference in the proportion of homes with outbreaks, or the size of outbreaks. Qualitative data showed difficulty implementing testing strategies due to excessive work burden. Factors influencing adherence related to test integration and procedural factors, socio-economic factors, cognitive overload, and the emotional value of testing.

Conclusion: Implementation of staff and visitor care home LFD testing protocols was poorly adhered to and consequently did not reduce the number or scale of COVID-19 outbreaks. More focus is needed on the contextual and behavioural factors that influence protocol adherence.
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http://dx.doi.org/10.1093/ageing/afab162DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8406873PMC
July 2021

Performance of the Innova SARS-CoV-2 antigen rapid lateral flow test in the Liverpool asymptomatic testing pilot: population based cohort study.

BMJ 2021 07 6;374:n1637. Epub 2021 Jul 6.

Institute of Population Health Sciences, University of Liverpool, Liverpool, UK.

Objective: To assess the performance of the SARS-CoV-2 antigen rapid lateral flow test (LFT) versus polymerase chain reaction testing in the asymptomatic general population attending testing centres.

Design: Observational cohort study.

Setting: Community LFT pilot at covid-19 testing sites in Liverpool, UK.

Participants: 5869 asymptomatic adults (≥18 years) voluntarily attending one of 48 testing sites during 6-29 November 2020.

Interventions: Participants were tested using both an Innova LFT and a quantitative reverse-transcriptase polymerase chain reaction (RT-qPCR) test based on supervised self-administered swabbing at testing sites.

Main Outcome Measures: Sensitivity, specificity, and predictive values of LFT compared with RT-qPCR in an epidemic steady state of covid-19 among adults with no classic symptoms of the disease.

Results: Of 5869 test results, 22 (0.4%) LFT results and 343 (5.8%) RT-qPCR results were void (that is, when the control line fails to appear within 30 minutes). Excluding the void results, the LFT versus RT-qPCR showed a sensitivity of 40.0% (95% confidence interval 28.5% to 52.4%; 28/70), specificity of 99.9% (99.8% to 99.99%; 5431/5434), positive predictive value of 90.3% (74.2% to 98.0%; 28/31), and negative predictive value of 99.2% (99.0% to 99.4%; 5431/5473). When the void samples were assumed to be negative, a sensitivity was observed for LFT of 37.8% (26.8% to 49.9%; 28/74), specificity of 99.6% (99.4% to 99.8%; 5431/5452), positive predictive value of 84.8% (68.1% to 94.9%; 28/33), and negative predictive value of 93.4% (92.7% to 94.0%; 5431/5814). The sensitivity in participants with an RT-qPCR cycle threshold (Ct) of <18.3 (approximate viral loads >10 RNA copies/mL) was 90.9% (58.7% to 99.8%; 10/11), a Ct of <24.4 (>10 RNA copies/mL) was 69.4% (51.9% to 83.7%; 25/36), and a Ct of >24.4 (<10 RNA copies/mL) was 9.7% (1.9% to 23.7%; 3/34). LFT is likely to detect at least three fifths and at most 998 in every 1000 people with a positive RT-qPCR test result with high viral load.

Conclusions: The Innova LFT can be useful for identifying infections among adults who report no symptoms of covid-19, particularly those with high viral load who are more likely to infect others. The number of asymptomatic adults with lower Ct (indicating higher viral load) missed by LFT, although small, should be considered when using single LFT in high consequence settings. Clear and accurate communication with the public about how to interpret test results is important, given the chance of missing some cases, even at high viral loads. Further research is needed to understand how infectiousness is reflected in the viral antigen shedding detected by LFT versus the viral loads approximated by RT-qPCR.
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http://dx.doi.org/10.1136/bmj.n1637DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8259455PMC
July 2021

Could expanding the covid-19 case definition improve the UK's pandemic response?

BMJ 2021 06 30;374:n1625. Epub 2021 Jun 30.

NIHR Health Protection Research Unit in Emerging and Zoonotic Infections and Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK.

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http://dx.doi.org/10.1136/bmj.n1625DOI Listing
June 2021

SARS-CoV-2 antigen testing: weighing the false positives against the costs of failing to control transmission.

Lancet Respir Med 2021 07 14;9(7):685-687. Epub 2021 Jun 14.

Department of Clinical Sciences, and Department of International Public Health, Liverpool School of Tropical Medicine, Liverpool, UK; Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK; WHO Collaborating Centre in Tuberculosis and Social Medicine, Department of Global Public Health, Karolinska Institutet, Solna, Sweden.

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http://dx.doi.org/10.1016/S2213-2600(21)00234-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8203180PMC
July 2021

Evaluating social and spatial inequalities of large scale rapid lateral flow SARS-CoV-2 antigen testing in COVID-19 management: An observational study of Liverpool, UK (November 2020 to January 2021).

Lancet Reg Health Eur 2021 Jul 12;6:100107. Epub 2021 May 12.

Chair in Public Health and Clinical Informatics, Department of Public Health and Policy, University of Liverpool, Liverpool, UK.

Background: Large-scale asymptomatic testing of communities in Liverpool (UK) for SARS-CoV-2 was used as a public health tool for containing COVID-19. The aim of the study is to explore social and spatial inequalities in uptake and case-detection of rapid lateral flow SARS-CoV-2 antigen tests (LFTs) offered to people without symptoms of COVID-19.

Methods: Linked pseudonymised records for asymptomatic residents in Liverpool who received a LFT for COVID-19 between 6th November 2020 to 31st January 2021 were accessed using the Combined Intelligence for Population Health Action resource. Bayesian Hierarchical Poisson Besag, York, and Mollié models were used to estimate ecological associations for uptake and positivity of testing.

Findings: 214 525 residents (43%) received a LFT identifying 5192 individuals as positive cases of COVID-19 (1.3% of tests were positive). Uptake was highest in November when there was military assistance. High uptake was observed again in the week preceding Christmas and was sustained into a national lockdown. Overall uptake were lower among males (e.g. 40% uptake over the whole period), Black Asian and other Minority Ethnic groups (e.g. 27% uptake for 'Mixed' ethnicity) and in the most deprived areas (e.g. 32% uptake in most deprived areas). These population groups were also more likely to have received positive tests for COVID-19. Models demonstrated that uptake and repeat testing were lower in areas of higher deprivation, areas located further from test sites and areas containing populations less confident in the using Internet technologies. Positive tests were spatially clustered in deprived areas.

Interpretation: Large-scale voluntary asymptomatic community testing saw social, ethnic, digital and spatial inequalities in uptake. COVID-19 testing and support to isolate need to be more accessible to the vulnerable communities most impacted by the pandemic, including non-digital means of access.

Funding: Department of Health and Social Care (UK) and Economic and Social Research Council.
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http://dx.doi.org/10.1016/j.lanepe.2021.100107DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8114854PMC
July 2021

COVID-19 point-of-care testing in care homes: what are the lessons for policy and practice?

Age Ageing 2021 09;50(5):1442-1444

School of Medicine, University of Nottingham, Nottingham, UK.

COVID-19 has devastated care homes. Point-of-care tests (POCTs), mainly using lateral flow devices (LFDs), have been deployed hurriedly without much consideration of their usability or impact on care workflow. Even after the pandemic, POCTs, particularly multiplex tests, may be an important control against spread of SARS-CoV-2 and other respiratory infections in care homes by enabling identification of cases. They should not, however, replace other infection control measures such as barrier methods and quarantine. Adherence to LFDs as implemented among care home staff is suboptimal. Other tests-such as point-of-care polymerase chain reaction and automated antigen tests-would also need to be accommodated into care home workflows to improve adherence. The up-front costs of POCTs are straightforward but additional costs, including staffing preparation and reporting processes and the impacts of false positive and negative tests on absence rates and infection days, are more complex and as yet unquantified. A detailed appraisal is needed as the future of testing in care homes is considered.
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http://dx.doi.org/10.1093/ageing/afab101DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8194678PMC
September 2021

Rapid antigen testing in COVID-19 responses.

Science 2021 05;372(6542):571-572

Institute of Population Health, University of Liverpool, Waterhouse Building, Liverpool L69 3BX, UK.

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http://dx.doi.org/10.1126/science.abi6680DOI Listing
May 2021

Assessing the severity of cardiovascular disease in 213 088 patients with coronary heart disease: a retrospective cohort study.

Open Heart 2021 04;8(1)

NIHR School for Primary Care Research, Centre for Primary Care and Health Services Research, Manchester Academic Health Science Centre (MAHSC), The University of Manchester, Manchester, UK.

Objective: Most current cardiovascular disease (CVD) risk stratification tools are for people without CVD, but very few are for prevalent CVD. In this study, we developed and validated a CVD severity score in people with coronary heart disease (CHD) and evaluated the association between severity and adverse outcomes.

Methods: Primary and secondary care data for 213 088 people with CHD in 398 practices in England between 2007 and 2017 were used. The cohort was randomly divided into training and validation datasets (80%/20%) for the severity model. Using 20 clinical severity indicators (each assigned a weight=1), baseline and longitudinal CVD severity scores were calculated as the sum of indicators. Adjusted Cox and competing-risk regression models were used to estimate risks for all-cause and cause-specific hospitalisation and mortality.

Results: Mean age was 64.5±12.7 years, 46% women, 16% from deprived areas, baseline severity score 1.5±1.2, with higher scores indicating a higher burden of disease. In the training dataset, 138 510 (81%) patients were hospitalised at least once, and 39 944 (23%) patients died. Each 1-unit increase in baseline severity was associated with 41% (95% CI 37% to 45%, area under the receiver operating characteristics (AUROC) curve=0.79) risk for 1 year for all-cause mortality; 59% (95% CI 52% to 67%, AUROC=0.80) for cardiovascular (CV)/diabetes mortality; 27% (95% CI 26% to 28%) for any-cause hospitalisation and 37% (95% CI 36% to 38%) for CV/diabetes hospitalisation. Findings were consistent in the validation dataset.

Conclusions: Higher CVD severity score is associated with higher risks for any-cause and cause-specific hospital admissions and mortality in people with CHD. Our reproducible score based on routinely collected data can help practitioners better prioritise management of people with CHD in primary care.
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http://dx.doi.org/10.1136/openhrt-2020-001498DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8061853PMC
April 2021

Prospective observational study of SARS-CoV-2 infection, transmission and immunity in a cohort of households in Liverpool City Region, UK (COVID-LIV): a study protocol.

BMJ Open 2021 03 17;11(3):e048317. Epub 2021 Mar 17.

Department of Clinical Infection Microbiology and Immunology, Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool, UK

Introduction: The emergence and rapid spread of COVID-19 have caused widespread and catastrophic public health and economic impact, requiring governments to restrict societal activity to reduce the spread of the disease. The role of household transmission in the population spread of SARS-CoV-2, and of host immunity in limiting transmission, is poorly understood. This paper describes a protocol for a prospective observational study of a cohort of households in Liverpool City Region, UK, which addresses the transmission of SARS-CoV-2 between household members and how immunological response to the infection changes over time.

Methods And Analysis: Households in the Liverpool City Region, in which members have not previously tested positive for SARS-CoV-2 with a nucleic acid amplification test, are followed up for an initial period of 12 weeks. Participants are asked to provide weekly self-throat and nasal swabs and record their activity and presence of symptoms. Incidence of infection and household secondary attack rates of COVID-19 are measured. Transmission of SARS-CoV-2 will be investigated against a range of demographic and behavioural variables. Blood and faecal samples are collected at several time points to evaluate immune responses to SARS-CoV-2 infection and prevalence and risk factors for faecal shedding of SARS-CoV-2, respectively.

Ethics And Dissemination: The study has received approval from the National Health Service Research Ethics Committee; REC Reference: 20/HRA/2297, IRAS Number: 283 464. Results will be disseminated through scientific conferences and peer-reviewed open access publications. A report of the findings will also be shared with participants. The study will quantify the scale and determinants of household transmission of SARS-CoV-2. Additionally, immunological responses before and during the different stages of infection will be analysed, adding to the understanding of the range of immunological response by infection severity.
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http://dx.doi.org/10.1136/bmjopen-2020-048317DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7977072PMC
March 2021

Authors' reply to Sabour and Ghajari "Clinical prediction models to predict the risk of multiple binary outcomes: Methodological issues".

Stat Med 2021 03;40(7):1861-1862

Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Staffordshire, UK.

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http://dx.doi.org/10.1002/sim.8872DOI Listing
March 2021

Clarifying the evidence on SARS-CoV-2 antigen rapid tests in public health responses to COVID-19.

Lancet 2021 04 17;397(10283):1425-1427. Epub 2021 Feb 17.

Institute of Population Health, University of Liverpool, Liverpool L36 3GF, UK. Electronic address:

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http://dx.doi.org/10.1016/S0140-6736(21)00425-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8049601PMC
April 2021

How does vulnerability to COVID-19 vary between communities in England? Developing a Small Area Vulnerability Index (SAVI).

J Epidemiol Community Health 2021 08 4;75(8):729-734. Epub 2021 Feb 4.

Department of Public Health, Policy and Systems, University of Liverpool, Liverpool, UK.

Background: During the initial wave of the COVID-19 epidemic in England, several population characteristics were associated with increased risk of mortality-including, age, ethnicity, income deprivation, care home residence and housing conditions. In order to target control measures and plan for future waves of the epidemic, public health agencies need to understand how these vulnerabilities are distributed across and clustered within communities.

Methods: We performed a cross-sectional ecological analysis across 6789 small areas in England. We assessed the association between COVID-19 mortality in each area and five vulnerability measures relating to ethnicity, poverty, prevalence of long-term health conditions, living in care homes and living in overcrowded housing. Estimates from multivariable Poisson regression models were used to derive a Small Area Vulnerability Index.

Results: Four vulnerability measures were independently associated with age-adjusted COVID-19 mortality. Each SD increase in the proportion of the population (1) living in care homes, (2) admitted to hospital in the past 5 years for a long-term health condition, (3) from an ethnic minority background and (4) living in overcrowded housing was associated with a 28%, 19% 8% and 11% increase in age-adjusted COVID-19 mortality rate, respectively.

Conclusion: Vulnerability to COVID-19 was noticeably higher in the North West, West Midlands and North East regions, with high levels of vulnerability clustered in some communities. Our analysis indicates the communities who will be most at risk from a second wave of the pandemic.
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http://dx.doi.org/10.1136/jech-2020-215227DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7868127PMC
August 2021

Put to the test: use of rapid testing technologies for covid-19.

BMJ 2021 02 3;372:n208. Epub 2021 Feb 3.

Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK.

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http://dx.doi.org/10.1136/bmj.n208DOI Listing
February 2021

Development and validation of the ISARIC 4C Deterioration model for adults hospitalised with COVID-19: a prospective cohort study.

Lancet Respir Med 2021 04 11;9(4):349-359. Epub 2021 Jan 11.

School of Informatics, University of Edinburgh, Edinburgh, UK.

Background: Prognostic models to predict the risk of clinical deterioration in acute COVID-19 cases are urgently required to inform clinical management decisions.

Methods: We developed and validated a multivariable logistic regression model for in-hospital clinical deterioration (defined as any requirement of ventilatory support or critical care, or death) among consecutively hospitalised adults with highly suspected or confirmed COVID-19 who were prospectively recruited to the International Severe Acute Respiratory and Emerging Infections Consortium Coronavirus Clinical Characterisation Consortium (ISARIC4C) study across 260 hospitals in England, Scotland, and Wales. Candidate predictors that were specified a priori were considered for inclusion in the model on the basis of previous prognostic scores and emerging literature describing routinely measured biomarkers associated with COVID-19 prognosis. We used internal-external cross-validation to evaluate discrimination, calibration, and clinical utility across eight National Health Service (NHS) regions in the development cohort. We further validated the final model in held-out data from an additional NHS region (London).

Findings: 74 944 participants (recruited between Feb 6 and Aug 26, 2020) were included, of whom 31 924 (43·2%) of 73 948 with available outcomes met the composite clinical deterioration outcome. In internal-external cross-validation in the development cohort of 66 705 participants, the selected model (comprising 11 predictors routinely measured at the point of hospital admission) showed consistent discrimination, calibration, and clinical utility across all eight NHS regions. In held-out data from London (n=8239), the model showed a similarly consistent performance (C-statistic 0·77 [95% CI 0·76 to 0·78]; calibration-in-the-large 0·00 [-0·05 to 0·05]); calibration slope 0·96 [0·91 to 1·01]), and greater net benefit than any other reproducible prognostic model.

Interpretation: The 4C Deterioration model has strong potential for clinical utility and generalisability to predict clinical deterioration and inform decision making among adults hospitalised with COVID-19.

Funding: National Institute for Health Research (NIHR), UK Medical Research Council, Wellcome Trust, Department for International Development, Bill & Melinda Gates Foundation, EU Platform for European Preparedness Against (Re-)emerging Epidemics, NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool, NIHR HPRU in Respiratory Infections at Imperial College London.
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http://dx.doi.org/10.1016/S2213-2600(20)30559-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832571PMC
April 2021

Risk Factor Control and Cardiovascular Event Risk in People With Type 2 Diabetes in Primary and Secondary Prevention Settings.

Circulation 2020 Nov 16;142(20):1925-1936. Epub 2020 Nov 16.

Division of Diabetes, Endocrinology and Gastroenterology, School of Medical Sciences, University of Manchester, United Kingdom (A.K.W., M.K.R.).

Background: To examine the association between the degree of risk factor control and cardiovascular disease (CVD) risk in type 2 diabetes and to assess if the presence of cardio-renal disease modifies these relationships.

Methods: A retrospective cohort study using data from English practices from CPRD GOLD (Clinical Practice Research Datalink) and the SCI-Diabetes dataset (Scottish Care Information-Diabetes), with linkage to hospital and mortality data. We identified 101 749 with type 2 diabetes (T2D) in CPRD matched with 378 938 controls without diabetes and 330 892 with type 2 diabetes in SCI-Diabetes between 2006 and 2015. The main exposure was number of optimized risk factors: nonsmoker, total cholesterol ≤4 mmol/L, triglycerides ≤1.7 mmol/L, glycated haemoglobin (HbA1c) ≤53 mmol/mol (≤7.0%), systolic blood pressure <140mm Hg, or <130 mm Hg if high risk. Cox models were used to assess cardiovascular risk associated with levels of risk factor control.

Results: In CPRD, the mean baseline age in T2D was 63 years and 28% had cardio-renal disease (SCI-Diabetes: 62 years; 35% cardio-renal disease). Over 3 years follow-up (SCI-Diabetes: 6 years), CVD events occurred among 27 900 (27%) CPRD-T2D, 101 362 (31%) SCI-Diabetes-T2D, and 75 520 (19%) CPRD-controls. In CPRD, compared with controls, T2D participants with optimal risk factor control (all risk factors controlled) had a higher risk of CVD events (adjusted hazard ratio, 1.21; 95% confidence interval, 1.12-1.29). In T2D participants from CPRD and SCI-Diabetes, pooled hazard ratios for CVD associated with 5 risk factors being elevated versus optimal risk factor control were 1.09 (95% confidence interval, 1.01-1.17) in people with cardio-renal disease but 1.96 (95% confidence interval, 1.82-2.12) in people without cardio-renal disease. People without cardio-renal disease were younger and more likely to have likely to have suboptimal risk factor control but had fewer prescriptions for risk factor modifying medications than those with cardio-renal disease.

Conclusions: Optimally managed people with T2D have a 21% higher CVD risk when compared with controls. People with T2D without cardio-renal disease would be predicted to benefit greatly from CVD risk factor intervention.
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http://dx.doi.org/10.1161/CIRCULATIONAHA.120.046783DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7664968PMC
November 2020

Clinical prediction models to predict the risk of multiple binary outcomes: a comparison of approaches.

Stat Med 2021 01 26;40(2):498-517. Epub 2020 Oct 26.

Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Staffordshire, UK.

Clinical prediction models (CPMs) can predict clinically relevant outcomes or events. Typically, prognostic CPMs are derived to predict the risk of a single future outcome. However, there are many medical applications where two or more outcomes are of interest, meaning this should be more widely reflected in CPMs so they can accurately estimate the joint risk of multiple outcomes simultaneously. A potentially naïve approach to multi-outcome risk prediction is to derive a CPM for each outcome separately, then multiply the predicted risks. This approach is only valid if the outcomes are conditionally independent given the covariates, and it fails to exploit the potential relationships between the outcomes. This paper outlines several approaches that could be used to develop CPMs for multiple binary outcomes. We consider four methods, ranging in complexity and conditional independence assumptions: namely, probabilistic classifier chain, multinomial logistic regression, multivariate logistic regression, and a Bayesian probit model. These are compared with methods that rely on conditional independence: separate univariate CPMs and stacked regression. Employing a simulation study and real-world example, we illustrate that CPMs for joint risk prediction of multiple outcomes should only be derived using methods that model the residual correlation between outcomes. In such a situation, our results suggest that probabilistic classification chains, multinomial logistic regression or the Bayesian probit model are all appropriate choices. We call into question the development of CPMs for each outcome in isolation when multiple correlated or structurally related outcomes are of interest and recommend more multivariate approaches to risk prediction.
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http://dx.doi.org/10.1002/sim.8787DOI Listing
January 2021

Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score.

BMJ 2020 09 9;370:m3339. Epub 2020 Sep 9.

NIHR Health Protection Research Unit, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK.

Objective: To develop and validate a pragmatic risk score to predict mortality in patients admitted to hospital with coronavirus disease 2019 (covid-19).

Design: Prospective observational cohort study.

Setting: International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study (performed by the ISARIC Coronavirus Clinical Characterisation Consortium-ISARIC-4C) in 260 hospitals across England, Scotland, and Wales. Model training was performed on a cohort of patients recruited between 6 February and 20 May 2020, with validation conducted on a second cohort of patients recruited after model development between 21 May and 29 June 2020 PARTICIPANTS: Adults (age ≥18 years) admitted to hospital with covid-19 at least four weeks before final data extraction.

Main Outcome Measure: In-hospital mortality.

Results: 35 463 patients were included in the derivation dataset (mortality rate 32.2%) and 22 361 in the validation dataset (mortality rate 30.1%). The final 4C Mortality Score included eight variables readily available at initial hospital assessment: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea level, and C reactive protein (score range 0-21 points). The 4C Score showed high discrimination for mortality (derivation cohort: area under the receiver operating characteristic curve 0.79, 95% confidence interval 0.78 to 0.79; validation cohort: 0.77, 0.76 to 0.77) with excellent calibration (validation: calibration-in-the-large=0, slope=1.0). Patients with a score of at least 15 (n=4158, 19%) had a 62% mortality (positive predictive value 62%) compared with 1% mortality for those with a score of 3 or less (n=1650, 7%; negative predictive value 99%). Discriminatory performance was higher than 15 pre-existing risk stratification scores (area under the receiver operating characteristic curve range 0.61-0.76), with scores developed in other covid-19 cohorts often performing poorly (range 0.63-0.73).

Conclusions: An easy-to-use risk stratification score has been developed and validated based on commonly available parameters at hospital presentation. The 4C Mortality Score outperformed existing scores, showed utility to directly inform clinical decision making, and can be used to stratify patients admitted to hospital with covid-19 into different management groups. The score should be further validated to determine its applicability in other populations.

Study Registration: ISRCTN66726260.
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http://dx.doi.org/10.1136/bmj.m3339DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7116472PMC
September 2020

Pride and prejudice - What can we learn from peer review?

Med Teach 2020 09 6;42(9):1012-1018. Epub 2020 Jul 6.

Centre for Health Informatics, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.

Peer review is a powerful tool that steers the education and practice of medical researchers but may allow biased critique by anonymous reviewers. We explored factors unrelated to research quality that may influence peer review reports, and assessed the possibility that sub-types of reviewers exist. Our findings could potentially improve the peer review process. We evaluated the harshness, constructiveness and positiveness in 596 reviews from journals with open peer review, plus 46 reviews from colleagues' anonymously reviewed manuscripts. We considered possible influencing factors, such as number of authors and seasonal trends, on the content of the review. Finally, using machine-learning we identified latent types of reviewer with differing characteristics. Reviews provided during a northern-hemisphere winter were significantly harsher, suggesting a seasonal effect on language. Reviews for articles in journals with an open peer review policy were significantly less harsh than those with an anonymous review process. Further, we identified three types of reviewers: nurturing, begrudged, and blasé. Nurturing reviews were in a minority and our findings suggest that more widespread open peer reviewing could improve the educational value of peer review, increase the constructive criticism that encourages researchers, and reduce pride and prejudice in editorial processes.
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http://dx.doi.org/10.1080/0142159X.2020.1774527DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7497287PMC
September 2020

Effect of delaying treatment of first-episode psychosis on symptoms and social outcomes: a longitudinal analysis and modelling study.

Lancet Psychiatry 2020 07;7(7):602-610

Department of Mental Health & Wellbeing, University of Warwick, Warwick, UK.

Background: Delayed treatment for first episodes of psychosis predicts worse outcomes. We hypothesised that delaying treatment makes all symptoms more refractory, with harm worsening first quickly, then more slowly. We also hypothesised that although delay impairs treatment response, worse symptoms hasten treatment, which at presentation mitigates the detrimental effect of treatment delay on symptoms.

Methods: In this longitudinal analysis and modelling study, we included two longitudinal cohorts of patients with first-episode psychosis presenting to English early intervention services from defined catchments: NEDEN (recruiting 1003 patients aged 14-35 years from 14 services between Aug 1, 2005, and April 1, 2009) and Outlook (recruiting 399 patients aged 16-35 years from 11 services between April 1, 2006, and Feb 28, 2009). Patients were assessed at baseline, 6 months, and 12 months with the Positive and Negative Symptom Scale (PANSS), Calgary Depression Scale for Schizophrenia, Mania Rating Scale, Insight Scale, and Social and Occupational Functioning Assessment Scale. Regression was used to compare different models of the relationship between duration of untreated psychosis (DUP) and total symptoms at 6 months. Growth curve models of symptom subscales tested predictions arising from our hypotheses.

Findings: We included 948 patients from the NEDEN study and 332 patients from the Outlook study who completed baseline assessments and were prescribed dopamine antagonist antipsychotics. For both cohorts, the best-fitting models were logarithmic, describing a curvilinear relationship of DUP to symptom severity: longer DUP predicted reduced treatment response, but response worsened more slowly as DUP lengthened. Increasing DUP by ten times predicted reduced improvement in total symptoms (ie, PANSS total) by 7·339 (95% CI 5·762 to 8·916; p<0·0001) in NEDEN data and 3·846 (1·689 to 6·003; p=0·0005) in Outlook data. This was true of treatment response for all symptom types. Nevertheless, longer DUP was not associated with worse presentation for any symptoms except depression in NEDEN (coefficients 0·099 [95% CI 0·033 to 0·164]; p=0·0028 in NEDEN and 0·007 [-0·081 to 0·095]; p=0·88 in Outlook).

Interpretation: Long DUP was associated with reduced treatment response across subscales, consistent with a harmful process upstream of individual symptoms' mechanisms; response appeared to worsen quickly at first, then more slowly. These associations underscore the importance of rapid access to a comprehensive range of treatments, especially in the first weeks after psychosis onset.

Funding: UK Department of Health, National Institute of Health Research, and Medical Research Council.
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http://dx.doi.org/10.1016/S2215-0366(20)30147-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7606908PMC
July 2020

Age-, sex- and ethnicity-related differences in body weight, blood pressure, HbA and lipid levels at the diagnosis of type 2 diabetes relative to people without diabetes.

Diabetologia 2020 08 21;63(8):1542-1553. Epub 2020 May 21.

Institute of Cardiovascular & Medical Sciences, BHF Glasgow Cardiovascular Research Centre, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK.

Aims/hypothesis: The aim of this work was to determine how weight patterns together with blood glucose, BP and lipids vary at diagnosis of diabetes by age, sex and ethnicity.

Methods: Using the UK Clinical Practice Research Datalink, we identified people with type 2 diabetes (n = 187,601) diagnosed in 1998-2015 and compared their weights, HbA, BP and lipid levels at diagnosis with age-matched people without diabetes (n = 906,182), by sex and ethnic group.

Results: Younger age at diagnosis was associated with greater adjusted mean difference (95% CI) in weight between those with vs without type 2 diabetes: 18.7 (18.3, 19.1) kg at age 20-39 years and 5.3 (5.0, 5.5) kg at age ≥ 80 years. Weight differentials were maximal in white women, and were around double in white people compared with South Asian and black people. Despite lower absolute values, BP differences were also greater at younger age of diabetes onset: 7 (6, 7) mmHg at age 20-39 years vs -0.5 (-0.9, -0.2) at age ≥ 80 years. BP differences were greatest in white people, and especially in women. Triacylglycerol level differences were greatest in younger men. Finally, HbA levels were also higher with younger onset diabetes, particularly in black people.

Conclusions/interpretation: At diagnosis of type 2 diabetes, when compared with people without diabetes, weight and BP differentials were greater in younger vs older people, in women vs men and in white vs South Asian and black people. These differences were observed even though South Asian and black people tend to develop diabetes a decade earlier with either similar or greater dysglycaemia. These striking patterns may have implications for management and prevention. Graphical abstract.
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http://dx.doi.org/10.1007/s00125-020-05169-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351865PMC
August 2020

Development and validation of the DIabetes Severity SCOre (DISSCO) in 139 626 individuals with type 2 diabetes: a retrospective cohort study.

BMJ Open Diabetes Res Care 2020 05;8(1)

NIHR School for Primary Care Research, Centre for Primary Care and Health Services Research, Manchester Academic Health Science Centre (MAHSC), The University of Manchester, Manchester, UK.

Objective: Clinically applicable diabetes severity measures are lacking, with no previous studies comparing their predictive value with glycated hemoglobin (HbA). We developed and validated a type 2 diabetes severity score (the DIabetes Severity SCOre, DISSCO) and evaluated its association with risks of hospitalization and mortality, assessing its additional risk information to sociodemographic factors and HbA.

Research Design And Methods: We used UK primary and secondary care data for 139 626 individuals with type 2 diabetes between 2007 and 2017, aged ≥35 years, and registered in general practices in England. The study cohort was randomly divided into a training cohort (n=111 748, 80%) to develop the severity tool and a validation cohort (n=27 878). We developed baseline and longitudinal severity scores using 34 diabetes-related domains. Cox regression models (adjusted for age, gender, ethnicity, deprivation, and HbA) were used for primary (all-cause mortality) and secondary (hospitalization due to any cause, diabetes, hypoglycemia, or cardiovascular disease or procedures) outcomes. Likelihood ratio (LR) tests were fitted to assess the significance of adding DISSCO to the sociodemographics and HbA models.

Results: A total of 139 626 patients registered in 400 general practices, aged 63±12 years were included, 45% of whom were women, 83% were White, and 18% were from deprived areas. The mean baseline severity score was 1.3±2.0. Overall, 27 362 (20%) people died and 99 951 (72%) had ≥1 hospitalization. In the training cohort, a one-unit increase in baseline DISSCO was associated with higher hazard of mortality (HR: 1.14, 95% CI 1.13 to 1.15, area under the receiver operating characteristics curve (AUROC)=0.76) and cardiovascular hospitalization (HR: 1.45, 95% CI 1.43 to 1.46, AUROC=0.73). The LR tests showed that adding DISSCO to sociodemographic variables significantly improved the predictive value of survival models, outperforming the added value of HbA for all outcomes. Findings were consistent in the validation cohort.

Conclusions: Higher levels of DISSCO are associated with higher risks for hospital admissions and mortality. The new severity score had higher predictive value than the proxy used in clinical practice, HbA. This reproducible algorithm can help practitioners stratify clinical care of patients with type 2 diabetes.
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http://dx.doi.org/10.1136/bmjdrc-2019-000962DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7228474PMC
May 2020

Correction to: The comorbidity burden of type 2 diabetes mellitus: patterns, clusters and predictions from a large English primary care cohort.

BMC Med 2020 Jan 25;18(1):22. Epub 2020 Jan 25.

NIHR School for Primary Care Research, Centre for Primary Care and Health Services Research, Manchester Academic Health Science Centre (MAHSC), University of Manchester, 5th floor Williamson Building, Manchester, M13 9PL, UK.

The original article [1] contains an omitted grant acknowledgement and affiliation as relates to the contribution of co-author, Rafael Perera-Salazar. As such, the following two amendments should apply to the original article.
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http://dx.doi.org/10.1186/s12916-020-1492-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6982380PMC
January 2020

Statistical thinking, machine learning.

J Clin Epidemiol 2019 12 16;116:136-137. Epub 2019 Aug 16.

Department of Epidemiology, University of Florida, Gainesville, FL, USA. Electronic address:

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http://dx.doi.org/10.1016/j.jclinepi.2019.08.003DOI Listing
December 2019

The comorbidity burden of type 2 diabetes mellitus: patterns, clusters and predictions from a large English primary care cohort.

BMC Med 2019 07 25;17(1):145. Epub 2019 Jul 25.

NIHR School for Primary Care Research, Centre for Primary Care and Health Services Research, Manchester Academic Health Science Centre (MAHSC), University of Manchester, 5th floor Williamson Building, Manchester, M13 9PL, UK.

Background: The presence of additional chronic conditions has a significant impact on the treatment and management of type 2 diabetes (T2DM). Little is known about the patterns of comorbidities in this population. The aims of this study are to quantify comorbidity patterns in people with T2DM, to estimate the prevalence of six chronic conditions in 2027 and to identify clusters of similar conditions.

Methods: We used the Clinical Practice Research Datalink (CPRD) linked with the Index of Multiple Deprivation (IMD) data to identify patients diagnosed with T2DM between 2007 and 2017. 102,394 people met the study inclusion criteria. We calculated the crude and age-standardised prevalence of 18 chronic conditions present at and after the T2DM diagnosis. We analysed longitudinally the 6 most common conditions and forecasted their prevalence in 2027 using linear regression. We used agglomerative hierarchical clustering to identify comorbidity clusters. These analyses were repeated on subgroups stratified by gender and deprivation.

Results: More people living in the most deprived areas had ≥ 1 comorbidities present at the time of diagnosis (72% of females; 64% of males) compared to the most affluent areas (67% of females; 59% of males). Depression prevalence increased in all strata and was more common in the most deprived areas. Depression was predicted to affect 33% of females and 15% of males diagnosed with T2DM in 2027. Moderate clustering tendencies were observed, with concordant conditions grouped together and some variations between groups of different demographics.

Conclusions: Comorbidities are common in this population, and high between-patient variability in comorbidity patterns emphasises the need for patient-centred healthcare. Mental health is a growing concern, and there is a need for interventions that target both physical and mental health in this population.
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http://dx.doi.org/10.1186/s12916-019-1373-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6659216PMC
July 2019
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