A systematic review identifying common data items in neonatal trials and assessing their completeness in routinely recorded United Kingdom national neonatal data

Background We aimed to test whether a common set of key data items reported across high impact neonatal clinical trials could be identified, and to quantify their completeness in routinely recorded United Kingdom neonatal data held in the National Neonatal Research Database (NNRD). Methods We systematically reviewed neonatal clinical trials published in four high impact medical journals over 10 years (2006-2015) and extracted baseline characteristics, stratification items, and potential confounders used to adjust primary outcomes. Completeness was examined using data held in the NNRD for identified data items, for infants admitted to neonatal units in 2015. The NNRD is a repository of routinely recorded data extracted from neonatal Electronic Patient Records (EPR) of all admissions to National Health Service (NHS) Neonatal Units in England, Wales and Scotland. We defined missing data as an empty field or an implausible value. We reported common data items as frequencies and percentages alongside percentages of completeness. Results We identified 44 studies involving 32,095 infants and 126 data items. Fourteen data items were reported by more than 20% of studies (table 2). Gestational age (95%), sex (93%) and birth weight (91%) were the most common baseline data items. The completeness of data in the NNRD was high for these data with greater than 90% completeness found for 9 of the 14 most common items. Conclusion High impact neonatal clinical trials share common data items. In the United Kingdom, these items can be obtained at a high level of completeness from routinely recorded data held in the NNRD. The feasibility and efficiency using routinely recorded EPR data, such as that held in the NNRD, for clinical trials, rather than collecting these items anew, should be examined

Adjusting for verification bias in diagnostic accuracy measures when comparing multiple screening 2 tests - an application to the IP1-PROSTAGRAM study

Introduction Novel screening tests used to detect a target condition are compared against either a reference standard or other existing screening methods. However, as it is not always possible to apply the reference standard on the whole population under study, verification bias is introduced. Statistical methods exist to adjust estimates to account for this bias. We extend common methods to adjust for verification bias when multiple tests are compared to a reference standard using data from a prospective double blind screening study for prostate cancer. Methods Begg and Greenes method and multiple imputation are extended to include the results of multiple screening tests which determine condition verification status. These two methods are compared to the complete case analysis using the IP1-PROSTAGRAM study data. IP1-PROSTAGRAM used a paired84 cohort double-blind design to evaluate the use of imaging as alternative tests to screen for prostate 85 cancer, compared to a blood test called prostate specific antigen (PSA). Participants with positive imaging (index) and/or PSA (control) underwent a prostate biopsy (reference). Results When comparing complete case results to Begg and Greenes and methods of multiple imputation there is a statistically significant increase in the specificity estimates for all screening tests. Sensitivity estimates remained similar across the methods, with completely overlapping 95% confidence intervals. Negative predictive value (NPV) estimates were higher when adjusting for verification bias, compared to complete case analysis, even though the 95% confidence intervals overlap. Positive predictive value (PPV) estimates were similar across all methods. Conclusion Statistical methods are required to adjust for verification bias in accuracy estimates of screening tests. Expanding Begg and Greenes method to include multiple screening tests can be computationally intensive, hence multiple imputation is recommended, especially as it can be modified for low prevalence of the target condition

Access to weight reduction interventions for overweight and obese patients in UK primary care: Population-based cohort study

Objectives To investigate access to weight management interventions for overweight and obese patients in primary care. Setting UK primary care electronic health records. Participants A cohort of 91 413 overweight and obese patients aged 30–100 years was sampled from the Clinical Practice Research Datalink (CPRD). Patients with body mass index (BMI) values ≥25 kg/m2 recorded between 2005 and 2012 were included. BMI values were categorised using WHO criteria. Interventions Interventions for body weight management, including advice, referrals and prescription of antiobesity drugs, were evaluated. Primary and secondary outcome measures The rate of body weight management interventions and time to intervention were the main outcomes. Results Data were analysed for 91 413 patients, mean age 56 years, including 55 094 (60%) overweight and 36 319 (40%) obese, including 4099 (5%) with morbid obesity. During the study period, 90% of overweight patients had no weight management intervention recorded. Intervention was more frequent among obese patients, but 59% of patients with morbid obesity had no intervention recorded. Rates of intervention increased with BMI category. In morbid obesity, rates of intervention per 1000 patient years were: advice, 60.2 (95% CI 51.8 to 70.4); referral, 75.7 (95% CI 69.5 to 82.6) and antiobesity drugs 89.9 (95% CI 85.0 to 95.2). Weight management interventions were more often accessed by women, older patients, those with comorbidity and those in deprivation. Follow-up of body weight subsequent to interventions was infrequent. Conclusions Limited evidence of weight management interventions in primary care electronic health records may result from poor recording of advice given, but may indicate a lack of patient access to appropriate body weight management interventions in primary care

Severity of obesity and management of hypertension, hypercholesterolaemia and smoking in primary care: population-based cohort study

Obesity and obesity-associated cardiovascular risk are increasing worldwide. This study aimed to determine how different levels of obesity are associated with the management of smoking, hypertension and hypercholesterolaemia in primary care. We conducted a cohort study of adults aged 30–100 years in England, sampled from the primary care electronic health records in the Clinical Practice Research Datalink. Prevalence, treatment and control were estimated for each risk factor by body mass index (BMI) category. Adjusted odds ratios (AOR) were estimated, allowing for age, gender, comorbidity and socioeconomic status, with normal weight as reference category. Data were analysed for 247 653 patients including 153 308 (62%) with BMI recorded, of whom 46 149 (30%) were obese. Participants were classified into simple (29 257), severe (11 059) and morbid obesity (5833) categories. Smoking declined with the increasing BMI category, but smoking cessation treatment increased. Age-standardised hypertension prevalence was twice as high in morbid obesity (men 78.6%; women 66.0%) compared with normal weight (men 37.3%; women 29.4%). Hypertension treatment was more frequent (AOR 1.75, 1.59–1.92) but hypertension control less frequent (AOR 0.63, 0.59–0.69) in morbid obesity, with similar findings for severe obesity. Hypercholesterolaemia was more frequent in morbid obesity (men 48.2%; women 36.3%) than normal weight (men 25.0%; women 20.0%). Lipid lowering therapy was more frequent in morbid obesity (AOR 1.83, 1.61–2.07) as was cholesterol control (AOR 1.19, 1.06–1.34). Increasing obesity category is associated with elevated risks from hypertension and hypercholesterolaemia. Inadequate hypertension control in obesity emerges as an important target for future interventions

Screening for type 2 diabetes is feasible, acceptable, but associated with increased short-term anxiety: A randomised controlled trial in British general practice

Background To assess the feasibility and uptake of a diabetes screening programme; to examine the effects of invitation to diabetes screening on anxiety, self-rated health and illness perceptions. Methods Randomised controlled trial in two general practices in Cambridgeshire. Individuals aged 40–69 without known diabetes were identified as being at high risk of having undiagnosed type 2 diabetes using patient records and a validated risk score (n = 1,280). 355 individuals were randomised in a 2 to 1 ratio into non-invited (n = 238) and invited (n = 116) groups. A stepwise screening programme confirmed the presence or absence of diabetes. Six weeks after the last contact (either test or invitation), a questionnaire was sent to all participants, including non-attenders and those who were not originally invited. Outcome measures included attendance, anxiety (short-form Spielberger State Anxiety Inventory-STAI), self-rated health and diabetes illness perceptions. Results 95 people (82% of those invited) attended for the initial capillary blood test. Six individuals were diagnosed with diabetes. Invited participants were more anxious than those not invited (37.6 vs. 34.1 STAI, p-value = 0.015), and those diagnosed with diabetes were considerably more anxious than those classified free of diabetes (46.7 vs. 37.0 STAI, p-value = 0.031). Non-attenders had a higher mean treatment control sub-scale (3.87 vs. 3.56, p-value = 0.016) and a lower mean emotional representation sub-scale (1.81 vs. 2.68, p-value = 0.001) than attenders. No differences in the other five illness perception sub-scales or self-rated health were found. Conclusion Screening for type 2 diabetes in primary care is feasible but may be associated with higher levels of short-term anxiety among invited compared with non-invited participants. Trial registration ISRCTN9917549

Mortality of Care Home Residents and Community-Dwelling Controls During the COVID-19 Pandemic in 2020: Matched Cohort Study

Objective:This study aimed to estimate and compare mortality of care home residents, and matched community-dwelling controls, during the COVID-19 pandemic from primary care electronic health records in England. Design: Matched cohort study. Setting and Participants: Family practices in England in the Clinical Practice Research Datalink Aurum database. There were 83,627 care home residents in 2020, with 26,923 deaths; 80,730 (97%) were matched on age, sex, and family practice with 300,445 community-dwelling adults. Methods: All-cause mortality was evaluated and adjusted rate ratios by negative binomial regression were adjusted for age, sex, number of long-term conditions, frailty category, region, calendar month or week, and clustering by family practice. Results:Underlying mortality of care home residents was higher than community controls (adjusted rate ratio 5.59, 95% confidence interval 5.23‒5.99, P < .001). During April 2020, there was a net increase in mortality of care home residents over that of controls. The mortality rate of care home residents was 27.2 deaths per 1000 patients per week, compared with 2.31 per 1000 for controls. Excess deaths for care home residents, above that predicted from pre-pandemic years, peaked between April 13 and 19 (men, 27.7, 95% confidence interval 25.1‒30.3; women, 17.4, 15.9‒18.8 per 1000 per week). Compared with care home residents, long-term conditions and frailty were differentially associated with greater mortality in community-dwelling controls. Conclusions and Implications: Individual-patient data from primary care electronic health records may be used to estimate mortality in care home residents. Mortality is substantially higher than for community-dwelling comparators and showed a disproportionate increase in the first wave of the COVID-19 pandemic. Care home residents require particular protection during periods of high infectious disease transmission

Improving the interpretation of afternoon cortisol levels and SSTs to prevent misdiagnosis of Adrenal Insufficiency

Introduction Adrenal Insufficiency (AI), especially iatrogenic-AI, is a treatable cause of mortality. The difficulty in obtaining 9am cortisol levels means samples are taken at suboptimal times, including a substantial proportion in the afternoon. Low afternoon cortisol levels often provoke short Synacthen Tests (SSTs). It is important that this does not lead to patients misdiagnosed with AI, exposing them to the excess mortality and morbidity of inappropriate steroid replacement therapy. Methods This retrospective study collected 60,178 cortisol results. Medical records, including subsequent SSTs of initial cortisol results measured after midday were reviewed. Results ROC analysis (AUC- 0.89) on 6531 suitable cortisol values showed that a limit of 95% on the Abbott analyser platform. Conclusion An afternoon cortisol >234nmol/L excludes AI on Abbott analyser platforms. In patients who have an afternoon cortisol <234nmol/L, including both a 30-minute and a 60-minute SST cortisol values prevents unnecessary glucocorticoid replacement therapy in 22.3% of individuals in this study. The Abbott analyser SST cortisol cut-offs used to define AI should be 366.5nmol/L and 418.5nmol/L at 30- and 60-minutes respectively. All patients remained well subsequently with at least one year longitudinal follow up

Nutritional labelling for healthier food or non-alcoholic drink purchasing and consumption.

BACKGROUND: Nutritional labelling is advocated as a means to promote healthier food purchasing and consumption, including lower energy intake. Internationally, many different nutritional labelling schemes have been introduced. There is no consensus on whether such labelling is effective in promoting healthier behaviour. OBJECTIVES: To assess the impact of nutritional labelling for food and non-alcoholic drinks on purchasing and consumption of healthier items. Our secondary objective was to explore possible effect moderators of nutritional labelling on purchasing and consumption. SEARCH METHODS: We searched 13 electronic databases including CENTRAL, MEDLINE and Embase to 26 April 2017. We also handsearched references and citations and sought unpublished studies through websites and trials registries. SELECTION CRITERIA: Eligible studies: were randomised or quasi-randomised controlled trials (RCTs/Q-RCTs), controlled before-and-after studies, or interrupted time series (ITS) studies; compared a labelled product (with information on nutrients or energy) with the same product without a nutritional label; assessed objectively measured purchasing or consumption of foods or non-alcoholic drinks in real-world or laboratory settings. DATA COLLECTION AND ANALYSIS: Two authors independently selected studies for inclusion and extracted study data. We applied the Cochrane 'Risk of bias' tool and GRADE to assess the quality of evidence. We pooled studies that evaluated similar interventions and outcomes using a random-effects meta-analysis, and we synthesised data from other studies in a narrative summary. MAIN RESULTS: We included 28 studies, comprising 17 RCTs, 5 Q-RCTs and 6 ITS studies. Most (21/28) took place in the USA, and 19 took place in university settings, 14 of which mainly involved university students or staff. Most (20/28) studies assessed the impact of labelling on menus or menu boards, or nutritional labelling placed on, or adjacent to, a range of foods or drinks from which participants could choose. Eight studies provided participants with only one labelled food or drink option (in which labelling was present on a container or packaging, adjacent to the food or on a display board) and measured the amount consumed. The most frequently assessed labelling type was energy (i.e. calorie) information (12/28).Eleven studies assessed the impact of nutritional labelling on purchasing food or drink options in real-world settings, including purchases from vending machines (one cluster-RCT), grocery stores (one ITS), or restaurants, cafeterias or coffee shops (three RCTs, one Q-RCT and five ITS). Findings on vending machines and grocery stores were not interpretable, and were rated as very low quality. A meta-analysis of the three RCTs, all of which assessed energy labelling on menus in restaurants, demonstrated a statistically significant reduction of 47 kcal in energy purchased (MD -46.72 kcal, 95% CI -78.35, -15.10, N = 1877). Assuming an average meal of 600 kcal, energy labelling on menus would reduce energy purchased per meal by 7.8% (95% CI 2.5% to 13.1%). The quality of the evidence for these three studies was rated as low, so our confidence in the effect estimate is limited and may change with further studies. Of the remaining six studies, only two (both ITS studies involving energy labels on menus or menus boards in a coffee shop or cafeteria) were at low risk of bias, and their results support the meta-analysis. The results of the other four studies which were conducted in a restaurant, cafeterias (2 studies) or a coffee shop, were not clearly reported and were at high risk of bias.Seventeen studies assessed the impact of nutritional labels on consumption in artificial settings or scenarios (henceforth referred to as laboratory studies or settings). Of these, eight (all RCTs) assessed the effect of labels on menus or placed on a range of food options. A meta-analysis of these studies did not conclusively demonstrate a reduction in energy consumed during a meal (MD -50 kcal, 95% CI -104.41, 3.88, N = 1705). We rated the quality of the evidence as low, so our confidence in the effect estimate is limited and may change with further studies.Six laboratory studies (four RCTs and two Q-RCTs) assessed the impact of labelling a single food or drink option (such as chocolate, pasta or soft drinks) on energy consumed during a snack or meal. A meta-analysis of these studies did not demonstrate a statistically significant difference in energy (kcal) consumed (SMD 0.05, 95% CI -0.17 to 0.27, N = 732). However, the confidence intervals were wide, suggesting uncertainty in the true effect size. We rated the quality of the evidence as low, so our confidence in the effect estimate is limited and may change with further studies.There was no evidence that nutritional labelling had the unintended harm of increasing energy purchased or consumed. Indirect evidence came from five laboratory studies that involved mislabelling single nutrient content (i.e. placing low energy or low fat labels on high-energy foods) during a snack or meal. A meta-analysis of these studies did not demonstrate a statistically significant increase in energy (kcal) consumed (SMD 0.19, 95% CI -0.14to 0.51, N = 718). The effect was small and the confidence intervals wide, suggesting uncertainty in the true effect size. We rated the quality of the evidence from these studies as very low, providing very little confidence in the effect estimate. AUTHORS' CONCLUSIONS: Findings from a small body of low-quality evidence suggest that nutritional labelling comprising energy information on menus may reduce energy purchased in restaurants. The evidence assessing the impact on consumption of energy information on menus or on a range of food options in laboratory settings suggests a similar effect to that observed for purchasing, although the evidence is less definite and also of low quality.Accordingly, and in the absence of observed harms, we tentatively suggest that nutritional labelling on menus in restaurants could be used as part of a wider set of measures to tackle obesity. Additional high-quality research in real-world settings is needed to enable more certain conclusions.Further high-quality research is also needed to address the dearth of evidence from grocery stores and vending machines and to assess potential moderators of the intervention effect, including socioeconomic status

Changes in diet, cardiovascular risk factors and modelled cardiovascular risk following diagnosis of diabetes: 1-year results from the ADDITION-Cambridge trial cohort.

AIMS: To describe change in self-reported diet and plasma vitamin C, and to examine associations between change in diet and cardiovascular disease risk factors and modelled 10-year cardiovascular disease risk in the year following diagnosis of Type 2 diabetes. METHODS: Eight hundred and sixty-seven individuals with screen-detected diabetes underwent assessment of self-reported diet, plasma vitamin C, cardiovascular disease risk factors and modelled cardiovascular disease risk at baseline and 1 year (n = 736) in the ADDITION-Cambridge trial. Multivariable linear regression was used to quantify the association between change in diet and cardiovascular disease risk at 1 year, adjusting for change in physical activity and cardio-protective medication. RESULTS: Participants reported significant reductions in energy, fat and sodium intake, and increases in fruit, vegetable and fibre intake over 1 year. The reduction in energy was equivalent to an average-sized chocolate bar; the increase in fruit was equal to one plum per day. There was a small increase in plasma vitamin C levels. Increases in fruit intake and plasma vitamin C were associated with small reductions in anthropometric and metabolic risk factors. Increased vegetable intake was associated with an increase in BMI and waist circumference. Reductions in fat, energy and sodium intake were associated with reduction in HbA1c , waist circumference and total cholesterol/modelled cardiovascular disease risk, respectively. CONCLUSIONS: Improvements in dietary behaviour in this screen-detected population were associated with small reductions in cardiovascular disease risk, independently of change in cardio-protective medication and physical activity. Dietary change may have a role to play in the reduction of cardiovascular disease risk following diagnosis of diabetes