Health and nutrition claims for infant formula: international cross sectional survey

Objectives To review available health and nutrition claims for infant formula products in multiple countries and to evaluate the validity of the evidence used for substantiation of claims. Design International cross sectional survey. Setting Public facing and healthcare professional facing company owned or company managed formula industry websites providing information about products marketed for healthy infants delivered at full term in 15 countries: Australia, Canada, Germany, India, Italy, Japan, Nigeria, Norway, Pakistan, Russia, Saudi Arabia, South Africa, Spain, the United Kingdom, and the United States in 2020-22. Main outcome measures Number and type of claims made for each product and ingredient. References cited were reviewed and risk of bias was assessed for registered clinical trials using the Cochrane risk of bias tool, and for systematic reviews using the Risk Of Bias in Systematic reviews tool. Results 757 infant formula products were identified, each with a median of two claims (range from 1 (Australia) to 4 (US)), and 31 types of claims across all products. Of 608 products with ≥1 claims, the most common claim types were “helps/supports development of brain and/or eyes and/or nervous system” (323 (53%) products, 13 ingredients), “strengthens/supports a healthy immune system” (239 (39%) products, 12 ingredients), and “helps/supports growth and development” (224 (37%) products, 20 ingredients). 41 groups of ingredients were associated with ≥1claims, but many claims were made without reference to a specific ingredient (307 (50%) products). The most common groups of ingredients cited in claims were long chain polyunsaturated fatty acids (278 (46%) products, 9 different claims); prebiotics, probiotics, or synbiotics (225 (37%) products, 19 claims); and hydrolysed protein (120 (20%) products, 9 claims). 161/608 (26%) products with ≥1 claims provided a scientific reference to support the claim—266 unique references were cited for 24 different claim types for 161 products. The reference types most frequently cited were clinical trials (50%, 134/266) and reviews (20%, 52/266). 28% (38/134) of referenced clinical trials were registered, 14% (19/134) prospectively. 58 claims referred to 32 registered clinical trials, of which 51 claims (27 trials) related to a randomised comparison. 46 of 51 claims (90%) referenced registered clinical trial outcomes at high risk of bias, and all cited systematic reviews and pooled analyses, carried a high risk of bias. Conclusions Most infant formula products had at least one health and nutrition claim. Multiple ingredients were claimed to achieve similar health or nutrition effects, multiple claims were made for the same ingredient type, most products did not provide scientific references to support claims, and referenced claims were not supported by robust clinical trial evidence

Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients

© The Author(s) 2019. Published by Springer Nature on behalf of Cancer Research UK.BACKGROUND: An accurate and simple risk prediction model that would facilitate earlier detection of pancreatic adenocarcinoma (PDAC) is not available at present. In this study, we compare different algorithms of risk prediction in order to select the best one for constructing a biomarker-based risk score, PancRISK. METHODS: Three hundred and seventy-nine patients with available measurements of three urine biomarkers, (LYVE1, REG1B and TFF1) using retrospectively collected samples, as well as creatinine and age, were randomly split into training and validation sets, following stratification into cases (PDAC) and controls (healthy patients). Several machine learning algorithms were used, and their performance characteristics were compared. The latter included AUC (area under ROC curve) and sensitivity at clinically relevant specificity. RESULTS: None of the algorithms significantly outperformed all others. A logistic regression model, the easiest to interpret, was incorporated into a PancRISK score and subsequently evaluated on the whole data set. The PancRISK performance could be even further improved when CA19-9, commonly used PDAC biomarker, is added to the model. CONCLUSION: PancRISK score enables easy interpretation of the biomarker panel data and is currently being tested to confirm that it can be used for stratification of patients at risk of developing pancreatic cancer completely non-invasively, using urine samples.Peer reviewe

Clinical characteristics, activity levels and mental health problems in children with long coronavirus disease: a survey of 510 children

Background: Whether long coronavirus disease pertains to children as well is not yet clear. Methods: The authors performed a survey in children suffering from persistent symptoms since initial infection. A total of 510 children infected between January 2020 and January 2021 were included. Results: Symptoms such as fatigue, headache, muscle and joint pain, rashes and heart palpitations and issues such as lack of concentration and short-term memory problems were particularly frequent and confirm previous observations, suggesting that they may characterize this condition. Conclusion: A better comprehension of long coronavirus disease is urgently needed

Formula milk supplementation on the postnatal ward: a cross-sectional analytical study

Breastfeeding rates are low in the UK, where approximately one quarter of infants receive a breastmilk substitute (BMS) in the first week of life. We investigated the reasons for early BMS use in two large maternity units in the UK, in order to understand the reasons for the high rate of early BMS use in this setting. Data were collected through infant feeding records, as well as maternal and midwife surveys in 2016. During 2016, 28% of infants received a BMS supplement prior to discharge from the hospital maternity units with only 10% supplementation being clinically indicated. There was wide variation in BMS initiation rates between different midwives, which was associated with ward environment and midwife educational level. Specific management factors associated with non-clinically indicated initiation of BMS were the absence of skin-to-skin contact within an hour of delivery (p = 0.01), and no attendance at an antenatal breastfeeding discussion (p = 0.01). These findings suggest that risk of initiating a BMS during postnatal hospital stay is largely modifiable. Concordance with UNICEF Baby Friendly 10 steps, attention to specific features of the postnatal ward working environment, and the targeting of midwives and mothers with poor educational status may all lead to improved exclusive breastfeeding rates at hospital discharge

Venn diagrams and probability in clinical research

Statistics is the science dealing with the collecting, summarizing and interpreting of associations in research data, and has a leading role in medical research. This article is an introductory publication in a series devoted to biomedical statistics. The aim of this article is to acquaint the readers with the basic concepts of Venn diagrams, probability and set theory, which are required to further understand descriptive and inferential statistics. First, we discuss the applications of Venn diagrams in current clinical research. Then we discuss the definitions of sample space, events, basic set operations (union and intersection) and their implementation in the classical approach to probability theory. All examples are introduced with Venn diagrams to illustrate the cases

Basic principles of descriptive statistics in medical research

Descriptive statistics provides tools to explore, summarize and illustrate the research data. In this tutorial we discuss two main types of data – qualitative and quantitative variables, and the most common approaches to characterize data distribution numerically and graphically. This article presents two important sets of parameters – measures of the central tendency (mean, median and mode) and variation (standard deviation, quantiles) and suggests the most suitable conditions for their application. We explain the difference between the general population and random samples, that are usually analyzed in studies. The parameters which characterize the sample (for example, measures of the central tendency) are point estimates, that can differ from the respective parameters of the general population. We introduce the concept of confidence interval – the range of values, which likely includes the true value of the parameter for the general population. All concepts and definitions are illustrated with examples, which simulate the research data