OBJECTIVE To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of becoming infected with covid-19 or being admitted to hospital with the disease. DESIGNLiving systematic review and critical appraisal. DATA SOURCESPubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 7 April 2020.Cite this as: BMJ 2020;369:m1328 http://dx.

ObjectiveTo determine the effectiveness of a web-based self-management programme for people with type 2 diabetes in improving glycaemic control and reducing diabetes-related distress.Methods and designIndividually randomised two-arm controlled trial.Setting21 general practices in England.ParticipantsAdults aged 18 or over with a diagnosis of type 2 diabetes registered with participating general practices.Intervention and comparatorUsual care plus either Healthy Living for People with Diabetes (HeLP-Diabetes), an interactive, theoretically informed, web-based self-management programme or a simple, text-based website containing basic information only.Outcomes and data collectionJoint primary outcomes were glycated haemoglobin (HbA1c) and diabetes-related distress, measured by the Problem Areas in Diabetes (PAID) scale, collected at 3 and 12 months after randomisation, with 12 months the primary outcome point. Research nurses, blind to allocation collected clinical data; participants completed self-report questionnaires online.AnalysisThe analysis compared groups as randomised (intention to treat) using a linear mixed effects model, adjusted for baseline data with multiple imputation of missing values.ResultsOf the 374 participants randomised between September 2013 and December 2014, 185 were allocated to the intervention and 189 to the control. Final (12 month) follow-up data for HbA1c were available for 318 (85%) and for PAID 337 (90%) of participants. Of these, 291 (78%) and 321 (86%) responses were recorded within the predefined window of 10–14 months. Participants in the intervention group had lower HbA1c than those in the control (mean difference −0.24%; 95% CI −0.44 to −0.049; p=0.014). There was no significant overall difference between groups in the mean PAID score (p=0.21), but prespecified subgroup analysis of participants who had been more recently diagnosed with diabetes showed a beneficial impact of the intervention in this group (p = 0.004). There were no reported harms.ConclusionsAccess to HeLP-Diabetes improved glycaemic control over 12 months.Trial registration numberISRCTN02123133.

Objectives To develop and validate a prediction model for fat mass in children aged 4-15 years using routinely available risk factors of height, weight, and demographic information without the need for more complex forms of assessment. Design Individual participant data meta-analysis. Setting Four population based cross sectional studies and a fifth study for external validation, United Kingdom. Participants A pooled derivation dataset (four studies) of 2375 children and an external validation dataset of 176 children with complete data on anthropometric measurements and deuterium dilution assessments of fat mass. Main outcome measure Multivariable linear regression analysis, using backwards selection for inclusion of predictor variables and allowing non-linear relations, was used to develop a prediction model for fat-free mass (and subsequently fat mass by subtracting resulting estimates from weight) based on the four studies. Internal validation and then internal-external cross validation were used to examine overfitting and generalisability of the model’s predictive performance within the four development studies; external validation followed using the fifth dataset. Results Model derivation was based on a multi-ethnic population of 2375 children (47.8% boys, n=1136) aged 4-15 years. The final model containing predictor variables of height, weight, age, sex, and ethnicity had extremely high predictive ability (optimism adjusted R 2 : 94.8%, 95% confidence interval 94.4% to 95.2%) with excellent calibration of observed and predicted values. The internal validation showed minimal overfitting and good model generalisability, with excellent calibration and predictive performance. External validation in 176 children aged 11-12 years showed promising generalisability of the model (R 2 : 90.0%, 95% confidence interval 87.2% to 92.8%) with good calibration of observed and predicted fat mass (slope: 1.02, 95% confidence interval 0.97 to 1.07). The mean difference between observed and predicted fat mass was −1.29 kg (95% confidence interval −1.62 to −0.96 kg). Conclusion The developed model accurately predicted levels of fat mass in children aged 4-15 years. The prediction model is based on simple anthropometric measures without the need for more complex forms of assessment and could improve the accuracy of assessments for body fatness in children (compared with those provided by body mass index) for effective surveillance, prevention, and management of clinical and public health obesity.

Background/Objectives:Body mass index (BMI) (weight per height2) is the most widely used marker of childhood obesity and total body fatness (BF). However, its validity is limited, especially in children of South Asian and Black African origins. We aimed to quantify BMI adjustments needed for UK children of Black African and South Asian origins so that adjusted BMI related to BF in the same way as for White European children.Methods:We used data from four recent UK studies that made deuterium dilution BF measurements in UK children of White European, South Asian and Black African origins. A height-standardized fat mass index (FMI) was derived to represent BF. Linear regression models were then fitted, separately for boys and girls, to quantify ethnic differences in BMI–FMI relationships and to provide ethnic-specific BMI adjustments.Results:We restricted analyses to 4–12 year olds, to whom a single consistent FMI (fat mass per height5) could be applied. BMI consistently underestimated BF in South Asians, requiring positive BMI adjustments of +1.12 kg m−2 (95% confidence interval (CI): 0.83, 1.41 kg m−2; P<0.0001) for boys and +1.07 kg m−2 (95% CI: 0.74, 1.39 kg m−2; P<0.0001) for girls of all age groups and FMI levels. BMI overestimated BF in Black Africans, requiring negative BMI adjustments for Black African children. However, these were complex because there were statistically significant interactions between Black African ethnicity and FMI (P=0.004 boys; P=0.003 girls) and also between FMI and age group (P<0.0001 for boys and girls). BMI adjustments therefore varied by age group and FMI level (and indirectly BMI); the largest adjustments were in younger children with higher unadjusted BMI and the smallest in older children with lower unadjusted BMI.Conclusions:BMI underestimated BF in South Asians and overestimated BF in Black Africans. Ethnic-specific adjustments, increasing BMI in South Asians and reducing BMI in Black Africans, can improve the accuracy of BF assessment in these children.

Clinical prediction models provide individualized outcome predictions to inform patient counseling and clinical decision making. External validation is the process of examining a prediction model's performance in data independent to that used for model development. Current external validation studies often suffer from small sample sizes, and subsequently imprecise estimates of a model's predictive performance. To address this, we propose how to determine the minimum sample size needed for external validation of a clinical prediction model with a continuous outcome. Four criteria are proposed, that target precise estimates of (i) R 2 (the proportion of variance explained), (ii) calibration-in-the-large (agreement between predicted and observed outcome values on average), (iii) calibration slope (agreement between predicted and observed values across the range of predicted values), and (iv) the variance of observed outcome values. Closed-form sample size solutions are derived for each criterion, which require the user to specify anticipated values of the model's performance (in particular R 2) and the outcome variance in the external validation dataset. A sensible starting point is to base values on those for the model development study, as obtained from the publication or study authors. The largest sample size required to meet all four criteria is the recommended minimum sample size needed in the external validation dataset. The calculations can also be applied to estimate expected precision when an existing dataset with a fixed sample size is available, to help gauge if it is adequate. We illustrate the proposed methods on a case-study predicting fat-free mass in children.

BackgroundType 2 diabetes mellitus is one of the most common long-term conditions, and costs health services approximately 10% of their total budget. Active self-management by patients improves outcomes and reduces health service costs. While the existing evidence suggested that uptake of self-management education was low, the development of internet-based technology might improve the situation.ObjectiveTo establish the cost-effectiveness of a Web-based self-management program for people with type 2 diabetes (HeLP-Diabetes) compared to usual care.MethodsAn incremental cost-effectiveness analysis was conducted, from a National Health Service and personal and social services perspective, based on data collected from a multi-center, two-arm individually randomized controlled trial over 12 months. Adults aged 18 or over with a diagnosis of type 2 diabetes and registered with the 21 participating general practices (primary care) in England, UK, were approached. People who were unable to provide informed consent or to use the intervention, terminally ill, or currently participating in a trial of an alternative self-management intervention, were excluded. The participants were then randomized to either usual care plus HeLP-Diabetes, an interactive, theoretically-informed Web-based self-management program, or to usual care plus access to a comparator website containing basic information only. The participants’ intervention costs and wider health care resource use were collected as well as two health-related quality of life measures: the Problem Areas in Diabetes (PAID) Scale and EQ-5D-3L. EQ-5D-3L was then used to calculate quality-adjusted life years (QALYs). The primary analysis was based on intention-to-treat, using multiple imputation to handle the missing data.ResultsIn total, 374 participants were randomized, with 185 in the intervention group and 189 in the control group. The primary analysis showed incremental cost-effectiveness ratios of £58 (95% CI –411 to 587) per unit improvement on PAID scale and £5550 (95% CI –21,077 to 52,356) per QALY gained by HeLP-Diabetes, compared to the control. The complete case analysis showed less cost-effectiveness and higher uncertainty with incremental cost-effectiveness ratios of £116 (95% CI –1299 to 1690) per unit improvement on PAID scale and £18,500 (95% CI –203,949 to 190,267) per QALY. The cost-effectiveness acceptability curve showed an 87% probability of cost-effectiveness at £20,000 per QALY willingness-to-pay threshold. The one-way sensitivity analyses estimated 363 users would be needed to use the intervention for it to become less costly than usual care.ConclusionsFacilitated access to HeLP-Diabetes is cost-effective, compared to usual care, under the recommended threshold of £20,000 to £30,000 per QALY by National Institute of Health and Care Excellence.Trial RegistrationInternational Standard Randomized Controlled Trial Number (ISRCTN) 02123133; http://www.controlled-trials.com/ISRCTN02123133 (Archived by WebCite at http://www.webcitation.org/6zqjhmn00)

PurposeThe Examining Neighbourhood Activities in Built Living Environments in London (ENABLE London) project is a natural experiment which aims to establish whether physical activity and other health behaviours show sustained changes among individuals and families relocating to East Village (formerly the London 2012 Olympics Athletes' Village), when compared with a control population living outside East Village throughout.ParticipantsBetween January 2013 and December 2015, 1497 individuals from 1006 households were recruited and assessed (at baseline) (including 392 households seeking social housing, 421 seeking intermediate and 193 seeking market rent homes). The 2-year follow-up rate is 62% of households to date, of which 57% have moved to East Village.Findings to dateAssessments of physical activity (measured objectively using accelerometers) combined with Global Positioning System technology and Geographic Information System mapping of the local area are being used to characterise physical activity patterns and location among study participants and assess the attributes of the environments to which they are exposed. Assessments of body composition, based on weight, height and bioelectrical impedance, have been made and detailed participant questionnaires provide information on socioeconomic position, general health/health status, well-being, anxiety, depression, attitudes to leisure time activities and other personal, social and environmental influences on physical activity, including the use of recreational space and facilities in their residential neighbourhood.Future plansThe main analyses will examine the changes in physical activity, health and well-being observed in the East Village group compared with controls and the influence of specific elements of the built environment on observed changes. The ENABLE London project exploits a unique opportunity to evaluate a ‘natural experiment’, provided by the building and rapid occupation of East Village. Findings from the study will be generalisable to other urban residential housing developments, and will help inform future evidence-based urban planning.

BackgroundThe National Child Measurement Programme (NCMP) records weight and height and assesses overweight-obesity patterns in English children using body mass index (BMI), which tends to underestimate body fatness in South Asian children and overestimate body fatness in Black children of presumed African ethnicity. Using BMI adjustments to ensure that adjusted BMI was similarly related to body fatness in South Asian, Black and White children, we reassessed population overweight and obesity patterns in these ethnic groups in NCMP.MethodsAnalyses were based on 2012-2013 NCMP data in 582,899 children aged 4-5 years and 485,362 children aged 10-11 years. Standard centile-based approaches defined weight status in each age-group before and after applying BMI adjustments for English South Asian and Black children derived from previous studies using the deuterium dilution method.FindingsAmong White children, overweight-obesity prevalences (boys, girls) were 23% and 21% respectively in 4-5 year-olds and 33% and 30% respectively in 10-11 year-olds. Before adjustment, South Asian children had lower overweight-obesity prevalences at 4-5 years (19%, 19%) and slightly higher prevalences at 10-11 years (42%, 34%), while Black children had higher overweight-obesity prevalences both at 4-5 years (31%, 29%) and 10-11 years (42%, 45%). Following adjustment, overweight-obesity prevalences were markedly higher in South Asian children both at 4-5 years (39%, 35%) and at 10-11 years (52%, 44%), while Black children had lower prevalences at 4-5 years (11%, 12%); at 10-11 years, prevalences were slightly lower in boys (32%) but higher in girls (35%).InterpretationBMI adjustments revealed extremely high overweight-obesity prevalences among South Asian children in England, which were not apparent in unadjusted data. In contrast, after adjustment, Black children had lower overweight-obesity prevalences except among older girls.FundingBritish Heart Foundation, NIHR CLAHRC (South London), NIHR CLAHRC (North Thames).