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Background: cystic fibrosis (CF) is a life-threatening genetic disease, affecting around 10 500 people in the UK. Precision medicines have been developed to treat specific CF-gene mutations. The newest, elexacaftor/tezacaftor/ivacaftor (ELEX/TEZ/IVA), has been found to be highly effective in randomised controlled trials (RCTs) and became available to a large proportion of UK CF patients in 2020. Understanding the potential health economic impacts of ELEX/TEZ/IVA is vital to planning service provision.Methods: we combined observational UK CF Registry data with RCT results to project the impact of ELEX/TEZ/IVA on total days of intravenous (IV) antibiotic treatment at a population level. Registry data from 2015 to 2017 were used to develop prediction models for IV days over a 1-year period using several predictors, and to estimate 1-year population total IV days based on standards of care pre-ELEX/TEZ/IVA. We considered two approaches to imposing the impact of ELEX/TEZ/IVA on projected outcomes using effect estimates from RCTs: approach 1 based on effect estimates on FEV1% and approach 2 based on effect estimates on exacerbation rate.Results: ELEX/TEZ/IVA is expected to result in significant reductions in population-level requirements for IV antibiotics of 16.1% (~17 800 days) using approach 1 and 43.6% (~39 500 days) using approach 2. The two approaches require different assumptions. Increased understanding of the mechanisms through which ELEX/TEZ/IVA acts on these outcomes would enable further refinements to our projections.Conclusions: this work contributes to increased understanding of the changing healthcare needs of people with CF and illustrates how Registry data can be used in combination with RCT evidence to estimate population-level treatment impacts.

The Centre of Excellence for Development Impact and Learning (CEDIL) has recently funded several studies that use machine learning methods to enhance the inferences made from impact evaluations. These studies focus on assessing the impact of complex development interventions, which can be expected to have impacts in different domains, possibly over an extended period of time. These studiestherefore involve study participants being followed up at multiple time-points after the intervention, and typically collect large numbers of variables at each follow-up. The hope is that machine learning approaches can uncover new insights into the variation in responses to interventions, and possible causal mechanisms, which in turn can highlight potential areas that policy and planning can focus on. This paper describes these studies using machine learning methods, gives an overview of the common aims and methodological approaches used in impact evaluations, and highlights some lessons and important caveats.

This work identifies factors that influence crash occurrence within a traffic analysis zone (TAZ) by accounting for location-specific effects and serial correlation in longitudinal crash data. This is accomplished by applying a random effect negative binomial (RENB) model. Unlike commonly used count models such as Poisson and negative binomial (NB), RENB accounts for heterogeneity and serial correlation in crash occurrence. An RENB was applied to 15 years of crash data in Arkansas with 1,817 TAZs. Four models were developed for total crashes and by severity (property damage only (PDO), injury, and fatal). RENB-estimated impacts were measured using the incidence rate ratio (IRR). The significant causal factors found to increase in observed crashes include: (i) average precipitation (a one-unit increase in average precipitation results in a 134% increase in total monthly crashes for a TAZ); (ii) average wind speed (16%); (iii) urban designation (7%); (iv) traffic volume (2%); and (v) total roadway mileage (1% for each functional class). Snow depth and days of sunshine were found to decrease the number of accidents by 15% and 2%, respectively. Employment and total population had no impact on crash occurrence. Goodness-of-fit comparisons show that RENB provides the best fit among Poisson and NB formulations. All four model diagnostics confirm the presence of over-dispersion and serial correlation indicating the necessity of RENB model estimation. The main contribution of this work is the identification of crash causal factors at the TAZ level for longitudinal data, which supports data-driven performance measurement requirements of recent federal legislation.

We consider mediated effects of an exposure, X on an outcome, Y, via a mediator, M, under no unmeasured confounding assumptions in the setting where models for the conditional expectation of the mediator and outcome are partially linear. We propose G-estimators for the direct and indirect effects and demonstrate consistent asymptotic normality for indirect effects when models for the conditional means of M, or X and Y are correctly specified, and for direct effects, when models for the conditional means of Y, or X and M are correct. This marks an improvement, in this particular setting, over previous ‘triple’ robust methods, which do not assume partially linear mean models. Testing of the no-mediation hypothesis is inherently problematic due to the composite nature of the test (either X has no effect on M or M no effect on Y), leading to low power when both effect sizes are small. We use generalized methods of moments (GMM) results to construct a new score testing framework, which includes as special cases the no-mediation and the no-direct-effect hypotheses. The proposed tests rely on an orthogonal estimation strategy for estimating nuisance parameters. Simulations show that the GMM-based tests perform better in terms of power and small sample performance compared with traditional tests in the partially linear setting, with drastic improvement under model misspecification. New methods are illustrated in a mediation analysis of data from the COPERS trial, a randomized trial investigating the effect of a non-pharmacological intervention of patients suffering from chronic pain. An accompanying R package implementing these methods can be found at github.com/ohines/plmed.

On March 11th 2020, the World Health Organization characterised COVID-19 as a pandemic. Responses to containing the spread of the virus have relied heavily on policies involving restricting contact between people. Evolving policies regarding shielding and individual choices about restricting social contact will rely heavily on perceived risk of poor outcomes from COVID-19. In order to make informed decisions, both individual and collective, good predictive models are required. For outcomes related to an infectious disease, the performance of any risk prediction model will depend heavily on the underlying prevalence of infection in the population of interest. Incorporating measures of how this changes over time may result in important improvements in prediction model performance. This protocol reports details of a planned study to explore the extent to which incorporating time-varying measures of infection burden over time improves the quality of risk prediction models for COVID-19 death in a large population of adult patients in England. To achieve this aim, we will compare the performance of different modelling approaches to risk prediction, including static cohort approaches typically used in chronic disease settings and landmarking approaches incorporating time-varying measures of infection prevalence and policy change, using COVID-19 related deaths data linked to longitudinal primary care electronic health records data within the OpenSAFELY secure analytics platform.

Adjustment for baseline covariates in randomized trials has been shown to lead to gains in power and can protect against chance imbalances in covariates. For continuous covariates, there is a risk that the the form of the relationship between the covariate and outcome is misspecified when taking an adjusted approach. Using a simulation study focusing on individually randomized trials with small sample sizes, we explore whether a range of adjustment methods are robust to misspecification, either in the covariate–outcome relationship or through an omitted covariate–treatment interaction. Specifically, we aim to identify potential settings where G-computation, inverse probability of treatment weighting (IPTW), augmented inverse probability of treatment weighting (AIPTW) and targeted maximum likelihood estimation (TMLE) offer improvement over the commonly used analysis of covariance (ANCOVA). Our simulations show that all adjustment methods are generally robust to model misspecification if adjusting for a few covariates, sample size is 100 or larger, and there are no covariate–treatment interactions. When there is a non-linear interaction of treatment with a skewed covariate and sample size is small, all adjustment methods can suffer from bias; however, methods that allow for interactions (such as G-computation with interaction and IPTW) show improved results compared to ANCOVA. When there are a high number of covariates to adjust for, ANCOVA retains good properties while other methods suffer from under- or over-coverage. An outstanding issue for G-computation, IPTW and AIPTW in small samples is that standard errors are underestimated; they should be used with caution without the availability of small-sample corrections, development of which is needed. These findings are relevant for covariate adjustment in interim analyses of larger trials.

The aim of this study was to identify dietary patterns (DPs) and analyze their association with cardiovascular risk factors including metabolic biomarkers and markers of inflammation and oxidative stress in a cross-sectional population-based study with 959 Brazilian adults from Viçosa.Food consumption was assessed by food frequency questionnaire, and DPs were identified by principal component analysis. A self-administered questionnaire was applied to assess sociodemographic and behavioral variables.Four distinct DPs were identified: Western, Snacks and Processed Food, Healthy, and Traditional Brazilian. Comparing the highest to the lowest tertile of each DP: Snacks and Processed Food DP was associated with a significantly higher diastolic blood pressure (β = 2.81; 95% confidence interval [CI], 0.48-5.14), waist circumference (β = 4.75; 95% CI, 2.77-6.73), body mass index (β = 1.65; 95% CI, 0.63-2.67), neck circumference (β = 0.74; 95% CI, 0.15-1.34), uric acid (β = 0.32; 95% CI, 0.13-0.51), and C-reactive protein (β = 0.31; 95% CI, 0.07-0.55). The Healthy DP was associated with lower Homeostatic Model Assessment of Insulin Resistance (HOMA-IR; β = -0.17; 95% CI, -0.34 to -0.008), lower tumor necrosis factor-α (odds ratio [OR] = 0.46; 95% CI, 0.26-0.84), lower interleukin (IL)-8 (OR = 0.50; 95% CI, 0.28-0.91), and lower catalase (OR = 0.36; 95% CI, 0.16-0.78). The Traditional Brazilian DP was associated with lower high-density lipoprotein (HDL) cholesterol (β = -5.04; 95% CI, -7.60 to -2.48), non-HDL cholesterol (β: -10.25; 95% CI, -19.07 to -1.43), and superoxide dismutase (OR = 0.52; 95% CI, 0.32-0.87), and higher uric acid (β = 0.24; 95% CI, 0.01-0.48), IL-12p70 (OR = 2.55; 95% CI, 1.23-5.32), IL-1β (OR = 2.22; 95% CI, 1.01-4.89), IL-10 (OR = 2.03; 95% CI, 1.05-3.93), and ferric reducing ability of plasma (OR = 2.23; 95% CI, 1.16-4.27).The Snacks and Processed Food DP was associated with increases in several risk factors for cardiovascular disease, and the Healthy DP with lower HOMA-IR scores, tumor necrosis factor-α, IL-8, and catalase. A diet based on rice and beans (Traditional Brazilian) may have a protective role against non-HDL cholesterol while presenting other risks related to inflammation and oxidative stress, as shown by a direct association with the interleukins IL-12p70, IL-1β, and IL-10 and an inverse association with superoxide dismutase.

Policymakers seeking to target health policies efficiently towards specific population groups need to know which individuals stand to benefit the most from each of these policies. While traditional approaches for subgroup analyses are constrained to only consider a small number of pre-defined subgroups, recently proposed causal machine learning (CML) approaches help explore treatment-effect heterogeneity in a more flexible yet principled way. Causal forests use a generalisation of the random forest algorithm to estimate heterogenous treatment effects both at the individual and the subgroup level. Our paper aims to explore this approach in the setting of health policy evaluation with strong observed confounding, applied specifically to the context of mothers’ health insurance enrolment in Indonesia. Comparing two health insurance schemes (subsidised and contributory) against no insurance, we find beneficial average impacts of enrolment in contributory health insurance on maternal health care utilisation and infant mortality, but no impact of subsidised health insurance. The causal forest algorithm identified significant heterogeneity in the impacts of contributory insurance, not just along socioeconomic variables that we pre-specified (indicating higher benefits for poorer, less educated, and rural women), but also according to some other characteristics not foreseen prior to the analysis, suggesting in particular important geographical impact heterogeneity. Our study demonstrates the power of CML approaches to uncover unexpected heterogeneity in policy impacts. The findings from our evaluation of past health insurance expansions can potentially guide the re-design of the eligibility criteria for subsidised health insurance in Indonesia.

Funder: Department of Health, Funder: Medical Research Council, BackgroundTargeted obesity prevention policies would benefit from the identification of population groups with the highest risk of weight gain. The relative importance of adult age, sex, ethnicity, geographical region, and degree of social deprivation on weight gain is not known. We aimed to identify high-risk groups for changes in weight and BMI using electronic health records (EHR).MethodsIn this longitudinal, population-based cohort study we used linked EHR data from 400 primary care practices (via the Clinical Practice Research Datalink) in England, accessed via the CALIBER programme. Eligible participants were aged 18-74 years, were registered at a general practice clinic, and had BMI and weight measurements recorded between Jan 1, 1998, and June 30, 2016, during the period when they had eligible linked data with at least 1 year of follow-up time. We calculated longitudinal changes in BMI over 1, 5, and 10 years, and investigated the absolute risk and odds ratios (ORs) of transitioning between BMI categories (underweight, normal weight, overweight, obesity class 1 and 2, and severe obesity [class 3]), as defined by WHO. The associations of demographic factors with BMI transitions were estimated by use of logistic regression analysis, adjusting for baseline BMI, family history of cardiovascular disease, use of diuretics, and prevalent chronic conditions.FindingsWe included 2 092 260 eligible individuals with more than 9 million BMI measurements in our study. Young adult age was the strongest risk factor for weight gain at 1, 5, and 10 years of follow-up. Compared with the oldest age group (65-74 years), adults in the youngest age group (18-24 years) had the highest OR (4·22 [95% CI 3·86-4·62]) and greatest absolute risk (37% vs 24%) of transitioning from normal weight to overweight or obesity at 10 years. Likewise, adults in the youngest age group with overweight or obesity at baseline were also at highest risk to transition to a higher BMI category; OR 4·60 (4·06-5·22) and absolute risk (42% vs 18%) of transitioning from overweight to class 1 and 2 obesity, and OR 5·87 (5·23-6·59) and absolute risk (22% vs 5%) of transitioning from class 1 and 2 obesity to class 3 obesity. Other demographic factors were consistently less strongly associated with these transitions; for example, the OR of transitioning from normal weight to overweight or obesity in people living in the most socially deprived versus least deprived areas was 1·23 (1·18-1·27), for men versus women was 1·12 (1·08-1·16), and for Black individuals versus White individuals was 1·13 (1·04-1·24). We provide an open access online risk calculator, and present high-resolution obesity risk charts over a 1-year, 5-year, and 10-year follow-up period.InterpretationA radical shift in policy is required to focus on individuals at the highest risk of weight gain (ie, young adults aged 18-24 years) for individual-level and population-level prevention of obesity and its long-term consequences for health and health care.FundingThe British Hearth Foundation, Health Data Research UK, the UK Medical Research Council, and the National Institute for Health Research.

Objectives To derive and validate risk prediction algorithms to estimate the risk of covid-19 related mortality and hospital admission in UK adults after one or two doses of covid-19 vaccination. Design Prospective, population based cohort study using the QResearch database linked to data on covid-19 vaccination, SARS-CoV-2 results, hospital admissions, systemic anticancer treatment, radiotherapy, and the national death and cancer registries. Settings Adults aged 19-100 years with one or two doses of covid-19 vaccination between 8 December 2020 and 15 June 2021. Main outcome measures Primary outcome was covid-19 related death. Secondary outcome was covid-19 related hospital admission. Outcomes were assessed from 14 days after each vaccination dose. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance was evaluated in a separate validation cohort of general practices. Results Of 6 952 440 vaccinated patients in the derivation cohort, 5 150 310 (74.1%) had two vaccine doses. Of 2031 covid-19 deaths and 1929 covid-19 hospital admissions, 81 deaths (4.0%) and 71 admissions (3.7%) occurred 14 days or more after the second vaccine dose. The risk algorithms included age, sex, ethnic origin, deprivation, body mass index, a range of comorbidities, and SARS-CoV-2 infection rate. Incidence of covid-19 mortality increased with age and deprivation, male sex, and Indian and Pakistani ethnic origin. Cause specific hazard ratios were highest for patients with Down’s syndrome (12.7-fold increase), kidney transplantation (8.1-fold), sickle cell disease (7.7-fold), care home residency (4.1-fold), chemotherapy (4.3-fold), HIV/AIDS (3.3-fold), liver cirrhosis (3.0-fold), neurological conditions (2.6-fold), recent bone marrow transplantation or a solid organ transplantation ever (2.5-fold), dementia (2.2-fold), and Parkinson’s disease (2.2-fold). Other conditions with increased risk (ranging from 1.2-fold to 2.0-fold increases) included chronic kidney disease, blood cancer, epilepsy, chronic obstructive pulmonary disease, coronary heart disease, stroke, atrial fibrillation, heart failure, thromboembolism, peripheral vascular disease, and type 2 diabetes. A similar pattern of associations was seen for covid-19 related hospital admissions. No evidence indicated that associations differed after the second dose, although absolute risks were reduced. The risk algorithm explained 74.1% (95% confidence interval 71.1% to 77.0%) of the variation in time to covid-19 death in the validation cohort. Discrimination was high, with a D statistic of 3.46 (95% confidence interval 3.19 to 3.73) and C statistic of 92.5. Performance was similar after each vaccine dose. In the top 5% of patients with the highest predicted covid-19 mortality risk, sensitivity for identifying covid-19 deaths within 70 days was 78.7%. Conclusion This population based risk algorithm performed well showing high levels of discrimination for identifying those patients at highest risk of covid-19 related death and hospital admission after vaccination.

Supplemental Digital Content is available in the text., Background: Cross-sectional measures of body mass index (BMI) are associated with cardiovascular disease (CVD) incidence, but less is known about whether weight change affects the risk of CVD. Methods: We estimated the effect of 2-y weight change interventions on 7-y risk of CVD (CVD death, myocardial infarction, stroke, hospitalization from coronary heart disease, and heart failure) by emulating hypothetical interventions using electronic health records. We identified 138,567 individuals with 45–69 years of age without chronic disease in England from 1998 to 2016. We performed pooled logistic regression, using inverse-probability weighting to adjust for baseline and time-varying confounders. We categorized each individual into a weight loss, maintenance, or gain group. Results: Among those of normal weight, both weight loss [risk difference (RD) vs. weight maintenance = 1.5% (0.3% to 3.0%)] and gain [RD = 1.3% (0.5% to 2.2%)] were associated with increased risk for CVD compared with weight maintenance. Among overweight individuals, we observed moderately higher risk of CVD in both the weight loss [RD = 0.7% (−0.2% to 1.7%)] and the weight gain group [RD = 0.7% (−0.1% to 1.7%)], compared with maintenance. In the obese, those losing weight showed lower risk of coronary heart disease [RD = −1.4% (−2.4% to −0.6%)] but not of stroke. When we assumed that chronic disease occurred 1–3 years before the recorded date, estimates for weight loss and gain were attenuated among overweight individuals; estimates for loss were lower among obese individuals. Conclusion: Among individuals with obesity, the weight-loss group had a lower risk of coronary heart disease but not of stroke. Weight gain was associated with increased risk of CVD across BMI groups. See video abstract at, http://links.lww.com/EDE/B838.

Nonadherence to assigned treatment is common in randomized controlled trials (RCTs). Recently, there has been increased interest in estimating causal effects of treatment received, for example, the so-called local average treatment effect (LATE). Instrumental variables (IV) methods can be used for identification, with estimation proceeding either via fully parametric mixture models or two-stage least squares (TSLS). TSLS is popular but can be problematic for binary outcomes where the estimand of interest is a causal odds ratio. Mixture models are rarely used in practice, perhaps because of their perceived complexity and need for specialist software. Here, we propose using multiple imputation (MI) to impute the latent compliance class appearing in the mixture models. Since such models include an interaction term between the latent compliance class and randomized treatment, we use "substantive model compatible" MI (SMC MIC), which can additionally handle missing data in outcomes and other variables in the model, before fitting the mixture models via maximum likelihood to the MI data sets and combining results via Rubin's rules. We use simulations to compare the performance of SMC MIC to existing approaches and also illustrate the methods by reanalyzing an RCT in UK primary health. We show that SMC MIC can be more efficient than full Bayesian estimation when auxiliary variables are incorporated, and is superior to two-stage methods, especially for binary outcomes.

Non-adherence to assigned treatment is a common issue in cluster randomised trials (CRTs). In these settings, the efficacy estimand may be also of interest. Many methodological contributions in recent years have advocated using instrumental variables to identify and estimate the local average treatment effect (LATE). However, the clustered nature of randomisation in CRTs adds to the complexity of such analyses. In this paper, we show that under certain assumptions, the LATE can be estimated via two-stage least squares (TSLS) using cluster-level summaries of outcomes and treatment received. Implementation needs to account for this, as well as the possible heteroscedasticity, to obtain valid inferences. We use simulations to assess the performance of TSLS of cluster-level summaries under cluster-level or individual-level non-adherence, with and without weighting and robust standard errors. We also explore the impact of adjusting for cluster-level covariates and of appropriate degrees of freedom correction for inference. We find that TSLS estimation using cluster-level summaries provides estimates with small to negligible bias and coverage close to nominal level, provided small sample degrees of freedom correction is used for inference, with appropriate use of robust standard errors. We illustrate the methods by re-analysing a CRT in UK primary health settings., 21 pages, 6 Figures

Increased demand for truck parking resulting from hours-of-service regulations and growing truck volumes, coupled with limited supply of parking facilities, is concerning for transportation agencies and industry stakeholders. To monitor truck parking congestion, the Arkansas Department of Transportation (ARDOT) conducts an annual observational survey of truck parking facilities. As a result of survey methodology, it cannot capture patterns of diurnal and seasonal use, arrival times, and duration. Truck Global Positioning System (GPS) data provide an apt alternative for monitoring parking facility utilization. The issue is that most truck GPS datasets represent a sample of the truck population and the representativeness of that sample may differ by application. Currently no method exists to accurately expand a GPS sample to reflect population-level truck parking facility utilization. This paper leverages the ARDOT study to estimate GPS “expansion factors” by parking facility type and defines two expansion factors: (1) the ratio of trucks parked derived from the GPS sample to those observed during the Overnight Study, and (2) the ratio of truck volume derived from the GPS sample to total truck volume measured on the nearest roadway. Varied expansion factors are found for public, private commercial (e.g., restaurant, retail store, etc.), and private truck stop facilities. Comparatively, the expansion factor based on roadway truck volumes was at least twice as high as that derived from the Overnight Study. Considering this, the method to determine expansion factors has significant implications on the estimated magnitudes of parking facility congestion, and thus will have consequences for investment prioritization.

Evaluation of treatment effects and more general estimands is typically achieved via parametric modeling, which is unsatisfactory since model misspecification is likely. Data-adaptive model building (e.g., statistical/machine learning) is commonly employed to reduce the risk of misspecification. Naïve use of such methods, however, delivers estimators whose bias may shrink too slowly with sample size for inferential methods to perform well, including those based on the bootstrap. Bias arises because standard data-adaptive methods are tuned toward minimal prediction error as opposed to, for example, minimal MSE in the estimator. This may cause excess variability that is difficult to acknowledge, due to the complexity of such strategies. Building on results from nonparametric statistics, targeted learning and debiased machine learning overcome these problems by constructing estimators using the estimand’s efficient influence function under the nonparametric model. These increasingly popular methodologies typically assume that the efficient influence function is given, or that the reader is familiar with its derivation. In this article, we focus on derivation of the efficient influence function and explain how it may be used to construct statistical/machine-learning-based estimators. We discuss the requisite conditions for these estimators to perform well and use diverse examples to convey the broad applicability of the theory.

Background\ud \ud Mortality rates in hospitalised patients with COVID-19 in the UK appeared to decline during the first wave of the pandemic. We aimed to quantify potential drivers of this change and identify groups of patients who remain at high risk of dying in hospital.\ud \ud \ud Methods\ud \ud In this multicentre prospective observational cohort study, the International Severe Acute Respiratory and Emerging Infections Consortium WHO Clinical Characterisation Protocol UK recruited a prospective cohort of patients with COVID-19 admitted to 247 acute hospitals in England, Scotland, and Wales during the first wave of the pandemic (between March 9 and Aug 2, 2020). We included all patients aged 18 years and older with clinical signs and symptoms of COVID-19 or confirmed COVID-19 (by RT-PCR test) from assumed community-acquired infection. We did a three-way decomposition mediation analysis using natural effects models to explore associations between week of admission and in-hospital mortality, adjusting for confounders (demographics, comorbidities, and severity of illness) and quantifying potential mediators (level of respiratory support and steroid treatment). The primary outcome was weekly in-hospital mortality at 28 days, defined as the proportion of patients who had died within 28 days of admission of all patients admitted in the observed week, and it was assessed in all patients with an outcome. This study is registered with the ISRCTN Registry, ISRCTN66726260.\ud \ud \ud Findings\ud \ud Between March 9, and Aug 2, 2020, we recruited 80 713 patients, of whom 63 972 were eligible and included in the study. Unadjusted weekly in-hospital mortality declined from 32·3% (95% CI 31·8–32·7) in March 9 to April 26, 2020, to 16·4% (15·0–17·8) in June 15 to Aug 2, 2020. Reductions in mortality were observed in all age groups, in all ethnic groups, for both sexes, and in patients with and without comorbidities. After adjustment, there was a 32% reduction in the risk of mortality per 7-week period (odds ratio [OR] 0·68 [95% CI 0·65–0·71]). The higher proportions of patients with severe disease and comorbidities earlier in the first wave (March and April) than in June and July accounted for 10·2% of this reduction. The use of respiratory support changed during the first wave, with gradually increased use of non-invasive ventilation over the first wave. Changes in respiratory support and use of steroids accounted for 22·2%, OR 0·95 (0·94–0·95) of the reduction in in-hospital mortality.\ud \ud \ud Interpretation\ud \ud The reduction in in-hospital mortality in patients with COVID-19 during the first wave in the UK was partly accounted for by changes in the case-mix and illness severity. A significant reduction in in-hospital mortality was associated with differences in respiratory support and critical care use, which could partly reflect accrual of clinical knowledge. The remaining improvement in in-hospital mortality is not explained by these factors, and could be associated with changes in community behaviour, inoculum dose, and hospital capacity strain.\ud \ud \ud Funding\ud \ud National Institute for Health Research and the Medical Research Council.

Background Physical activity (PA) may positively stimulate the brain, cognition and mental health during adolescence, a period of dynamic neurobiological development. High-intensity interval training (HIIT) or vigorous PA interventions are time-efficient, scalable and can be easily implemented in existing school curricula, yet their effects on cognitive, academic and mental health outcomes are unclear. The primary aim of the Fit to Study trial was to investigate whether a pragmatic and scalable HIIT-style VPA intervention delivered during school physical education (PE) could improve attainment in maths. The primary outcome has previously been reported and was null. Here, we report the effect of the intervention on prespecified secondary outcomes, including cardiorespiratory fitness, cognitive performance, and mental health in young adolescents. Methods The Fit to Study cluster randomised controlled trial included Year 8 pupils (n = 18,261, aged 12–13) from 104 secondary state schools in South/Mid-England. Schools were randomised into an intervention condition (n = 52), in which PE teachers delivered an additional 10 min of VPA per PE lesson for one academic year (2017–2018), or into a “PE as usual” control condition. Secondary outcomes included assessments of cardiorespiratory fitness (20-m shuttle run), cognitive performance (executive functions, relational memory and processing speed) and mental health (Strength and Difficulties Questionnaire and self-esteem measures). The primary intention-to-treat (ITT) analysis used linear models and structural equation models with cluster-robust standard errors to test for intervention effects. A complier-average causal effect (CACE) was estimated using a two-stage least squares procedure. Results The HIIT-style VPA intervention did not significantly improve cardiorespiratory fitness, cognitive performance (executive functions, relational memory or processed speed), or mental health (all p > 0.05). Subgroup analyses showed no significant moderation of intervention effects by sex, socioeconomic status or baseline fitness levels. Changes in cardiorespiratory fitness were not significantly related to changes in cognitive or mental health outcomes. The trial was marked by high drop-out and low intervention compliance. Findings from the CACE analysis were in line with those from the ITT analysis. Conclusion The one-academic year HIIT-style VPA intervention delivered during regular school PE did not significantly improve fitness, cognitive performance or mental health, but these findings should be interpreted with caution given low implementation fidelity and high drop-out. Well-controlled, large-scale, school-based trials that examine the effectiveness of HIIT-style interventions to enhance cognitive and mental health outcomes are warranted. Trial registration ISRCTN registry, 15,730,512. Trial protocol and analysis plan for primary outcome prospectively registered on 30th March 2017. ClinicalTrials.gov, NCT03286725. Secondary measures (focus of current manuscript) retrospectively registered on 18 September 2017.

Analysis code and data for Increased mortality in community-tested cases of SARS-CoV-2 lineage B.1.1.7 Nicholas G. Davies1†, Christopher I. Jarvis1, CMMID COVID-19 Working Group, W. John Edmunds1, Nicholas P. Jewell2,3, Karla Diaz-Ordaz2,3*, Ruth H. Keogh2,3* Article published in Nature(2021) 1. Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK. 2. Department of Medical Statistics, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK. 3. Centre for Statistical Methodology, London School of Hygiene and Tropical Medicine, London, UK. †Corresponding author. E-mail: nicholas.davies@lshtm.ac.uk *Equal contribution This repository is maintained athttps://github.com/nicholasdavies/cfrvoc. SARS-CoV-2 lineage B.1.1.7, a variant first detected in the UK in September 2020, has spread to multiple countries worldwide. Several studies have established that B.1.1.7 is more transmissible than preexisting variants, but have not identified whether it leads to any change in disease severity. Here we analyse a dataset linking 2,245,263 positive SARS-CoV-2 community tests and 17,452 COVID-19 deaths in England from 1 September 2020 to14 February 2021. For 1,146,534 (51%) of these tests, the presence or absence of B.1.1.7 can be identified because of mutations in this lineage preventing PCR amplification of the spike gene target (S gene target failure, SGTF). Based on 4,945 deaths with known SGTF status, we estimate that the hazard of death associated with SGTF is 55% (95% CI 39–72%) higher after adjustment for age, sex, ethnicity, deprivation, care home residence, local authority of residence and test date. This corresponds to the absolute risk of death for a 55–69-year-old male increasing from 0.6% to 0.9% (95% CI 0.8–1.0%) within 28 days after a positive test in the community. Correcting for misclassification of SGTF and missingness in SGTF status, we estimate a 61% (42–82%) higher hazard of death associated with B.1.1.7. Our analysis suggests that B.1.1.7 is not only more transmissible than preexisting SARS-CoV-2 variants, but may also cause more severe illness.

ObjectivesTo compare approaches for obtaining relative and absolute estimates of risk of 28-day COVID-19 mortality for adults in the general population of England in the context of changing levels of circulating infection.DesignThree designs were compared. (A) case-cohort which does not explicitly account for the time-changing prevalence of COVID-19 infection, (B) 28-day landmarking, a series of sequential overlapping sub-studies incorporating time-updating proxy measures of the prevalence of infection, and (C) daily landmarking. Regression models were fitted to predict 28-day COVID-19 mortality.SettingWorking on behalf of NHS England, we used clinical data from adult patients from all regions of England held in the TPP SystmOne electronic health record system, linked to Office for National Statistics (ONS) mortality data, using the OpenSAFELY platform.ParticipantsEligible participants were adults aged 18 or over, registered at a general practice using TPP software on 1st March 2020 with recorded sex, postcode and ethnicity. 11,972,947 individuals were included, and 7,999 participants experienced a COVID-19 related death. The study period lasted 100 days, ending 8th June 2020.PredictorsA range of demographic characteristics and comorbidities were used as potential predictors. Local infection prevalence was estimated with three proxies: modelled based on local prevalence and other key factors; rate of A&E COVID-19 related attendances; and rate of suspected COVID-19 cases in primary care.Main outcome measuresCOVID-19 related death.ResultsAll models discriminated well between patients who did and did not experience COVID-19 related death, with C-statistics ranging from 0.92-0.94. Accurate estimates of absolute risk required data on local infection prevalence, with modelled estimates providing the best performance.ConclusionsReliable estimates of absolute risk need to incorporate changing local prevalence of infection. Simple models can provide very good discrimination and may simplify implementation of risk prediction tools in practice.

Cardiovascular disease (CVD) risk-prediction models are used to identify high-risk individuals and guide statin initiation. However, these models are usually derived from individuals who might initiate statins during follow-up. We present a simple approach to address statin initiation to predict “statin-naive” CVD risk. We analyzed primary care data (2004–2017) from the UK Clinical Practice Research Datalink for 1,678,727 individuals (aged 40–85 years) without CVD or statin treatment history at study entry. We derived age- and sex-specific prediction models including conventional risk factors and a time-dependent effect of statin initiation constrained to 25% risk reduction (from trial results). We compared predictive performance and measures of public-health impact (e.g., number needed to screen to prevent 1 event) against models ignoring statin initiation. During a median follow-up of 8.9 years, 103,163 individuals developed CVD. In models accounting for (versus ignoring) statin initiation, 10-year CVD risk predictions were slightly higher; predictive performance was moderately improved. However, few individuals were reclassified to a high-risk threshold, resulting in negligible improvements in number needed to screen to prevent 1 event. In conclusion, incorporating statin effects from trial results into risk-prediction models enables statin-naive CVD risk estimation and provides moderate gains in predictive ability but had a limited impact on treatment decision-making under current guidelines in this population.

BackgroundAdult obesity prevention policies, which are largely untargeted, have met with limited success globally. Population groups with the highest risk of weight gain, if they could be reliably identified using readily available information, might benefit from targeted policy. The relative importance of age, sex, ethnicity, geographical region and social deprivation for weight gain is unknown.MethodsWe calculated longitudinal changes in BMI over one, five and ten years and investigated transition between BMI categories using 11,187,383 clinically recorded, repeated measures of BMI from population-based electronic health records of 2,328,477 adults in England (1998-2016). The influence of risk factors was tested using logistic regression.FindingsThe youngest adult age group (18-24 years) was more strongly associated with risk of weight gain than older age, male sex, socioeconomic deprivation, ethnicity or geographic region. Among the youngest adults, the top quartile gained 15.9kg in men and 12kg in women at 10 years. The odds of transitioning to a higher BMI category over 10 years were 4-6 times higher in the youngest (18-24 years) compared to oldest (65-74 years) individuals; odds ratio (95% confidence interval) 4.22 (3.85-4.62)) from normal weight to overweight or obesity, 4.60 (4.06-5.22) for overweight to obesity, and 5.87 (5.23-6.59) from obesity to severe obesity in multiple adjusted analyses. Among the youngest adults, socially deprived men were at greater risk of transitioning from normal weight to overweight (72%) and from overweight to obesity (68%) over 10 years. We provide an open access online risk calculator (https://pasea.shinyapps.io/bmi_shiny_app/) and present high resolution obesity risk charts over a 1-, 5- and 10-year follow-up.InterpretationA radical shift in policy is required to focus on those at highest risk of weight gain-young adults-for individual and population level prevention of obesity and its long-term consequences for health and health care.FundingBHF, HDR-UK, MRC

BackgroundMortality rates of UK patients hospitalised with COVID-19 appeared to fall during the first wave. We quantify potential drivers of this change and identify groups of patients who remain at high risk of dying in hospital.MethodsThe International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) WHO Clinical Characterisation Protocol UK recruited a prospective cohort admitted to 247 acute UK hospitals with COVID-19 in the first wave (March to August 2020). Outcome was hospital mortality within 28 days of admission. We performed a three-way decomposition mediation analysis using natural effects models to explore associations between week of admission and hospital mortality adjusting for confounders (demographics, comorbidity, illness severity) and quantifying potential mediators (respiratory support and steroids).FindingsUnadjusted hospital mortality fell from 32.3% (95%CI 31.8, 32.7) in March/April to 16.4% (95%CI 15.0, 17.8) in June/July 2020. Reductions were seen in all ages, ethnicities, both sexes, and in comorbid and non-comorbid patients. After adjustment, there was a 19% reduction in the odds of mortality per 4 week period (OR 0.81, 95%CI 0.79, 0.83). 15.2% of this reduction was explained by greater disease severity and comorbidity earlier in the epidemic. The use of respiratory support changed with greater use of non-invasive ventilation (NIV). 22.2% (OR 0.94, 95%CI 0.94, 0.96) of the reduction in mortality was mediated by changes in respiratory support.InterpretationThe fall in hospital mortality in COVID-19 patients during the first wave in the UK was partly accounted for by changes in case mix and illness severity. A significant reduction was associated with differences in respiratory support and critical care use, which may partly reflect improved clinical decision making. The remaining improvement in mortality is not explained by these factors, and may relate to community behaviour on inoculum dose and hospital capacity strain.FundingNIHR & MRCKey points / Research in ContextEvidence before this studyRisk factors for mortality in patients hospitalised with COVID-19 have been established. However there is little literature regarding how mortality is changing over time, and potential explanations for why this might be. Understanding changes in mortality rates over time will help policy makers identify evolving risk, strategies to manage this and broader decisions about public health interventions.Added value of this studyMortality in hospitalised patients at the beginning of the first wave was extremely high. Patients who were admitted to hospital in March and early April were significantly more unwell at presentation than patients who were admitted in later months. Mortality fell in all ages, ethnic groups, both sexes and in patients with and without comorbidity, over and above contributions from falling illness severity. After adjustment for these variables, a fifth of the fall in mortality was explained by changes in the use of respiratory support and steroid treatment, along with associated changes in clinical decision-making relating to supportive interventions. However, mortality was persistently high in patients who required invasive mechanical ventilation, and in those patients who received non-invasive ventilation outside of critical care.Implications of all the available evidenceThe observed reduction in hospital mortality was greater than expected based on the changes seen in both case mix and illness severity. Some of this fall can be explained by changes in respiratory care, including clinical learning. In addition, introduction of community policies including wearing of masks, social distancing, shielding of vulnerable patients and the UK lockdown potentially resulted in people being exposed to less virus.The decrease in mortality varied depending on the level of respiratory support received. Patients receiving invasive mechanical ventilation have persistently high mortality rates, albeit with a changing case-mix, and further research should target this group.Severe COVID-19 disease has primarily affected older people in the UK. Many of these people, but not all have significant frailty. It is essential to ensure that patients and their families remain at the centre of decision-making, and we continue with an individualised approach to their treatment and care.