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  • The use of commercial food purchase data for public health nutrition research: A systematic review

    24 January 2019

    © 2019 Bandy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background Traditional methods of dietary assessment have their limitations and commercial sources of food sales and purchase data are increasingly suggested as an additional source to measuring diet at the population level. However, the potential uses of food sales data are less well understood. The aim of this review is to establish how sales data on food and soft drink products from third-party companies have been used in public health nutrition research. Methods A search of five electronic databases was conducted in February-March 2018 for studies published in peer-reviewed journals that had used food sales or purchase data from a commercial company to analyse trends and patterns in food purchases or in the nutritional composition of foods. Study quality was evaluated using the National Institutes of Health (NIH) Quality Assessment Tool for Cohort and Cross-Sectional Studies. Results Of 2919 papers identified in the search, 68 were included. The selected studies used sales or purchase data from four companies: Euromonitor, GfK, Kantar and Nielsen. Sales and purchase data have been used to evaluate interventions, including the impact of the saturated fat tax in Denmark, the soft drink and junk food taxes in Mexico and supplemental nutrition programmes in the USA. They have also been used to identify trends in the nutrient composition of foods over time and patterns in food purchasing, including socio-demo-graphic variations in purchasing. Conclusion Food sales and purchase data are a valuable tool for public health nutrition researchers and their use has increased markedly in the last four years, despite the cost of access, the lack of transparency on data-collection methods and restrictions on publication. The availability of product and brand-level sales data means they are particularly useful for assessing how changes by individual food companies can impact on diet and public health.

  • Novel genetically-modified chimpanzee adenovirus and MVA-vectored respiratory syncytial virus vaccine safely boosts humoral and cellular immunity in healthy older adults.

    18 February 2019

    OBJECTIVES: Respiratory syncytial virus (RSV) causes respiratory infection across the world, with infants and the elderly at particular risk of developing severe disease and death. The replication-defective chimpanzee adenovirus (PanAd3-RSV) and modified vaccinia virus Ankara (MVA-RSV) vaccines were shown to be safe and immunogenic in young healthy adults. Here we report an extension to this first-in-man vaccine trial to include healthy older adults aged 60-75 years. METHODS: We evaluated the safety and immunogenicity of a single dose of MVA-RSV given by intra-muscular (IM) injection (n=6), two doses of IM PanAd3-RSV given 4-weeks apart (n=6), IM PanAd3-RSV prime and IM MVA-RSV boost 8-weeks later (n=6), intra-nasal (IN) spray of PanAd3-RSV prime and IM MVA-RSV boost 8-weeks later (n=6), or no vaccine (n=6). Safety measures included all adverse events within one week of vaccination and blood monitoring. Immunogenicity measures included serum antibody responses (RSV- and PanAd3-neutralising antibody titres measured by plaque-reduction neutralisation and SEAP assays respectively), peripheral B-cell immune responses (frequencies of F-specific IgG and IgA antibody secreting cells and memory B-cells by ex vivo and cultured dual-colour ELISpot assays respectively), and peripheral RSV-specific T-cell immune responses (frequencies of IFNγ-producing T-cells by ex vivo ELISpot and CD4+/CD8+/Tfh-like cell frequencies by ICS/FACS assay). RESULTS: The vaccines were safe and well tolerated. Compared with each individual baseline immunity the mean fold-changes in serum RSV-neutralising antibody, appearance and magnitude of F-specific IgG and IgA ASCs and expansion of CD4+/CD8+ IFNγ-producing T-cells in peripheral circulation were comparable to the results seen from younger healthy adults who received the same vaccine combination and dose. There were little/no IgA memory B-cell responses in younger and older adults. Expansion of IFNγ-producing T-cells was most marked in older adults following IM prime, with balanced CD4+ and CD8+ T cell responses. The RSV-specific immune responses to vaccination did not appear to be attenuated in the presence of PanAd3 (vector) neutralising antibody. CONCLUSIONS: PanAd3-RSV and MVA-RSV was safe and immunogenic in older adults and the parallel induction of RSV-specific humoral and cellular immunity merits further assessment in providing protection from severe disease.

  • A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models.

    18 February 2019

    OBJECTIVE: To compare performance of logistic regression (LR) with machine learning (ML) for clinical prediction modeling. STUDY DESIGN AND SETTING: We conducted a Medline literature search (1/2016 to 8/2017), and extracted comparisons between LR and ML models for binary outcomes. RESULTS: We included 71 out of 927 studies. The median sample size was 1250 (range 72-3,994,872), with 19 predictors considered (range 5-563) and 8 events per predictor (range 0.3-6,697). The most common ML methods were classification trees (30 studies), random forests (28), artificial neural networks (26), and support vector machines (24). Sixty-four (90%) studies used the area under the receiver operating characteristic curve (AUC) to assess discrimination. Calibration was not addressed in 56 (79%) studies. We identified 282 comparisons between a LR and ML model (AUC range, 0.52-0.99). For 145 comparisons at low risk of bias, the difference in logit(AUC) between LR and ML was 0.00 (95% confidence interval, -0.18 to 0.18). For 137 comparisons at high risk of bias, logit(AUC) was 0.34 (0.20 to 0.47) higher for ML. CONCLUSIONS: We found no evidence of superior performance of ML over LR for clinical prediction modeling, but improvements in methodology and reporting are needed for studies that compare modeling algorithms.

  • Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore.

    18 February 2019

    OBJECTIVE: To develop and validate a new diabetes risk algorithm (the QDScore) for estimating 10 year risk of acquiring diagnosed type 2 diabetes over a 10 year time period in an ethnically and socioeconomically diverse population. DESIGN: Prospective open cohort study using routinely collected data from 355 general practices in England and Wales to develop the score and from 176 separate practices to validate the score. PARTICIPANTS: 2 540 753 patients aged 25-79 in the derivation cohort, who contributed 16 436 135 person years of observation and of whom 78 081 had an incident diagnosis of type 2 diabetes; 1 232 832 patients (7 643 037 person years) in the validation cohort, with 37 535 incident cases of type 2 diabetes. OUTCOME MEASURES: A Cox proportional hazards model was used to estimate effects of risk factors in the derivation cohort and to derive a risk equation in men and women. The predictive variables examined and included in the final model were self assigned ethnicity, age, sex, body mass index, smoking status, family history of diabetes, Townsend deprivation score, treated hypertension, cardiovascular disease, and current use of corticosteroids; the outcome of interest was incident diabetes recorded in general practice records. Measures of calibration and discrimination were calculated in the validation cohort. RESULTS: A fourfold to fivefold variation in risk of type 2 diabetes existed between different ethnic groups. Compared with the white reference group, the adjusted hazard ratio was 4.07 (95% confidence interval 3.24 to 5.11) for Bangladeshi women, 4.53 (3.67 to 5.59) for Bangladeshi men, 2.15 (1.84 to 2.52) for Pakistani women, and 2.54 (2.20 to 2.93) for Pakistani men. Pakistani and Bangladeshi men had significantly higher hazard ratios than Indian men. Black African men and Chinese women had an increased risk compared with the corresponding white reference group. In the validation dataset, the model explained 51.53% (95% confidence interval 50.90 to 52.16) of the variation in women and 48.16% (47.52 to 48.80) of that in men. The risk score showed good discrimination, with a D statistic of 2.11 (95% confidence interval 2.08 to 2.14) in women and 1.97 (1.95 to 2.00) in men. The model was well calibrated. CONCLUSIONS: The QDScore is the first risk prediction algorithm to estimate the 10 year risk of diabetes on the basis of a prospective cohort study and including both social deprivation and ethnicity. The algorithm does not need laboratory tests and can be used in clinical settings and also by the public through a simple web calculator (www.qdscore.org).