Public health priority setting on a national scale: The Scottish experience.

Objectives: Scotland has the lowest life expectancy in Western Europe and significant health inequalities. A national review of public health in 2015 found that there was a lack of coherent action across organisational boundaries, inhibiting progress. This paper describes a rapid (four-month) systematic approach to prioritisation of Scotland's public health challenges, which was evidence-based, transparent and made use of significant stakeholder engagement. Study design: Cross-sectional survey of stakeholders in deliberative meetings. Methods: An independent Expert Advisory Group (EAG) was formed to develop a typology of public health priorities, a long-list of potential priorities and ranking criteria. Deliberative stakeholder events were held at which the criteria were refined and priorities scored by participants from a wide range of stakeholder organisations. Results: The proposed typology identified three types of public health priorities: risk factors, social factors and system factors; medically defined disease entities were not used deliberately, to facilitate broad stakeholder participation. Fifteen criteria were identified to help identify priority issues, based on the scope of their burden, amenability to change, and multi-stakeholder preferences. Six public health priorities were selected by the EAG based on stakeholder scoring of a long-list against these criteria. Conclusion: Prioritisation is important in modern public health but it is challenging due to limited data availability, lack of agreed evidence on effectiveness and efficiency of interventions, and divergent stakeholder views. The Scottish experience nevertheless shows that useful public health priorities can be agreed upon by a wide range of stakeholders through a transparent, participatory and logical process.

Development of a critical appraisal tool for models predicting the impact of 'test, trace, and protect' programmes on COVID-19 transmission.

OBJECTIVES: To develop a critical appraisal tool for non-computational-specialist public health professionals to assess the quality and relevance of modelling studies about Test and Trace (and Protect - TTP) programmes' impact on COVID-19 transmission. STUDY DESIGN: Decision-making tool development. METHODS: Using Tugwell et al.'s 1985 Health Care Effectiveness equation as a conceptual framework, combined with a purposive search of the relevant early modeling literature, we developed six critical appraisal questions for the rapid assessment of modeling studies related to the evaluation of TTP programmes' effectiveness. RESULTS: By applying the critical appraisal tool to selected recent COVID-19 modeling studies, we demonstrate how models can be evaluated using the six questions to evaluate internal and external validity and relevance. CONCLUSIONS: These six critical appraisal questions are able to discriminate between modeling studies of higher and lower quality and relevance to evaluating TTP programmes' impact. However, these questions require independent validation in a larger and systematic sample of relevant modeling studies which have appeared in later stages of the pandemic.

Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signatures.

BACKGROUND: Tumor hypoxia increases resistance to radiotherapy and systemic therapy. Our aim was to develop and validate a disease-agnostic and disease-specific CT (+FDG-PET) based radiomics hypoxia classification signature. MATERIAL AND METHODS: A total of 808 patients with imaging data were included: N = 100 training/N = 183 external validation cases for a disease-agnostic CT hypoxia classification signature, N = 76 training/N = 39 validation cases for the H&N CT signature and N = 62 training/N = 36 validation cases for the Lung CT signature. The primary gross tumor volumes (GTV) were manually defined by experts on CT. In order to dichotomize between hypoxic/well-oxygenated tumors a threshold of 20% was used for the [18F]-HX4-derived hypoxic fractions (HF). A random forest (RF)-based machine-learning classifier/regressor was trained to classify patients as hypoxia-positive/ negative based on radiomic features. RESULTS: A 11 feature "disease-agnostic CT model" reached AUC's of respectively 0.78 (95% confidence interval [CI], 0.62-0.94), 0.82 (95% CI, 0.67-0.96) and 0.78 (95% CI, 0.67-0.89) in three external validation datasets. A "disease-agnostic FDG-PET model" reached an AUC of 0.73 (0.95% CI, 0.49-0.97) in validation by combining 5 features. The highest "lung-specific CT model" reached an AUC of 0.80 (0.95% CI, 0.65-0.95) in validation with 4 CT features, while the "H&N-specific CT model" reached an AUC of 0.84 (0.95% CI, 0.64-1.00) in validation with 15 CT features. A tumor volume-alone model was unable to significantly classify patients as hypoxia-positive/ negative. A significant survival split (P = 0.037) was found between CT-classified hypoxia strata in an external H&N cohort (n = 517), while 117 significant hypoxia gene-CT signature feature associations were found in an external lung cohort (n = 80). CONCLUSION: The disease-specific radiomics signatures perform better than the disease agnostic ones. By identifying hypoxic patients our signatures have the potential to enrich interventional hypoxia-targeting trials.

Distributed learning on 20 000+ lung cancer patients - The Personal Health Train.

BACKGROUND AND PURPOSE: Access to healthcare data is indispensable for scientific progress and innovation. Sharing healthcare data is time-consuming and notoriously difficult due to privacy and regulatory concerns. The Personal Health Train (PHT) provides a privacy-by-design infrastructure connecting FAIR (Findable, Accessible, Interoperable, Reusable) data sources and allows distributed data analysis and machine learning. Patient data never leaves a healthcare institute. MATERIALS AND METHODS: Lung cancer patient-specific databases (tumor staging and post-treatment survival information) of oncology departments were translated according to a FAIR data model and stored locally in a graph database. Software was installed locally to enable deployment of distributed machine learning algorithms via a central server. Algorithms (MATLAB, code and documentation publicly available) are patient privacy-preserving as only summary statistics and regression coefficients are exchanged with the central server. A logistic regression model to predict post-treatment two-year survival was trained and evaluated by receiver operating characteristic curves (ROC), root mean square prediction error (RMSE) and calibration plots. RESULTS: In 4 months, we connected databases with 23 203 patient cases across 8 healthcare institutes in 5 countries (Amsterdam, Cardiff, Maastricht, Manchester, Nijmegen, Rome, Rotterdam, Shanghai) using the PHT. Summary statistics were computed across databases. A distributed logistic regression model predicting post-treatment two-year survival was trained on 14 810 patients treated between 1978 and 2011 and validated on 8 393 patients treated between 2012 and 2015. CONCLUSION: The PHT infrastructure demonstrably overcomes patient privacy barriers to healthcare data sharing and enables fast data analyses across multiple institutes from different countries with different regulatory regimens. This infrastructure promotes global evidence-based medicine while prioritizing patient privacy.

Multifactorial risk factors for mortality after chemotherapy and radiotherapy for non-small cell lung cancer.

BACKGROUND AND PURPOSE: A higher radiation dose to the heart is known to be associated with increased mortality in non-small cell lung cancer (NSCLC) patients. It is however unknown what the contribution of the heart dose is when other risk factors for mortality are also accounted for. MATERIALS AND METHODS: We constructed and externally validated prediction models of mortality after definitive chemoradiotherapy for NSCLC. Models were developed in 145 stage I-IIIB NSCLC patients. Clinical (performance status, age, gross tumour volume (GTV) combining primary tumour and involved lymph nodes, current smoker) and dosimetric (mean lung (MLD) and heart (MHD) dose) variables were considered. Multivariable logistic regression models predicting 12 and 24 month mortality were built in 5-fold cross-validation. Discrimination and calibration was assessed in 3 external validation datasets containing 878 (via distributed learning), 127 and 96 NSCLC patients. RESULTS: The best discriminating prediction models combined GTV, smoker and/or MHD: bootstrapping AUC (95% CI) of 0.74 (0.66-0.78) and 0.69 (0.55-0.74) at 12 and 24 months. At external validation, the 24 month mortality GTV-smoker-MHD model robustly showed moderate discrimination (AUC = 0.61-0.64 before and 0.64-0.65 after model update) with limited 0.01-0.07 improvement over a GTV-only model, and calibration slope (0.64-0.65). This model can identify patients for whom a MHD reduction may be useful (e.g. PPV = 77%, NPV = 52% (60% cut-off)). CONCLUSIONS: Tumour volume is strongly related to mortality risk in the first 2 years after chemoradiotherapy for NSCLC. Modelling indicates that efforts to reduce cardiac dose may be relevant for small tumours and that smoking has an important negative association with survival.

Distributed radiomics as a signature validation study using the Personal Health Train infrastructure.

Prediction modelling with radiomics is a rapidly developing research topic that requires access to vast amounts of imaging data. Methods that work on decentralized data are urgently needed, because of concerns about patient privacy. Previously published computed tomography medical image sets with gross tumour volume (GTV) outlines for non-small cell lung cancer have been updated with extended follow-up. In a previous study, these were referred to as Lung1 (n = 421) and Lung2 (n = 221). The Lung1 dataset is made publicly accessible via The Cancer Imaging Archive (TCIA; https://www.cancerimagingarchive.net ). We performed a decentralized multi-centre study to develop a radiomic signature (hereafter "ZS2019") in one institution and validated the performance in an independent institution, without the need for data exchange and compared this to an analysis where all data was centralized. The performance of ZS2019 for 2-year overall survival validated in distributed radiomics was not statistically different from the centralized validation (AUC 0.61 vs 0.61; p = 0.52). Although slightly different in terms of data and methods, no statistically significant difference in performance was observed between the new signature and previous work (c-index 0.58 vs 0.65; p = 0.37). Our objective was not the development of a new signature with the best performance, but to suggest an approach for distributed radiomics. Therefore, we used a similar method as an earlier study. We foresee that the Lung1 dataset can be further re-used for testing radiomic models and investigating feature reproducibility.

Investigating the Performance of Bi-Static GPR Antennas for Near-Surface Object Detection.

Antennas are an important component in ground penetrating radar (GPR) systems. Although there has been much research reported on the design of individual antennas, there is less research reported on the design of the geometry of bi-static antennas. This paper considers the effects of key parameters in the setup of a GPR head consisting of a bi-static bow-tie pair to show the effect of these parameters on the GPR performance. The parameters investigated are the antenna separation, antenna height above the soil, and antenna input impedance. The investigation of the parameters was performed by simulation and measurements. It was found when the bi-static antennas were separated by 7 cm to 9 cm and were operated close to the soil (2 cm to 4 cm), the reflected signal from a near-surface object is relatively unaffected by height variation and object depth. An antenna input impedance of 250 Ω was chosen to feed the antennas to reduce the late-time ringing. Using these results, a new GPR system was designed and then evaluated at a test site near Benkovac, Croatia.

Supplementation of a Lactobacillus acidophilus fermentation product can attenuate the acute phase response following a lipopolysaccharide challenge in weaned pigs.

Lactobacillus acidophilus fermentation products have been used to improve the performance of nursery pigs. However, research on the influence of this supplement on health is lacking. This study was designed to determine if feeding a Lactobacillus acidophilus fermentation product to weaned pigs would reduce stress and acute phase responses (APR) following a lipopolysaccharide (LPS) challenge. Pigs (n=30; 6.4±0.1 kg) were individually housed in stainless steel pens with ad libitum access to feed and water. Pigs were weighed upon arrival, assigned to one of three groups (n=10/treatment), and fed for 18 days: (1) Control, fed a non-medicated starter diet; (2) Control diet with the inclusion of a Lactobacillus acidophilus fermentation product at 1 kg/metric ton (SGX1) and (3) Control diet with the inclusion of a Lactobacillus acidophilus fermentation product at 2 kg/metric ton (SGX2). On day 7 pigs were anesthetized for insertion of an i.p. temperature device, and similarly on day 14 for insertion of a jugular catheter. Pigs were challenged i.v. with LPS (25 µg/kg BW) on day 15. Blood samples were collected at 0.5 h (serum) and 1 h (complete blood cell counts) intervals from -2 to 8 h and at 24 h relative to LPS administration at 0 h. Pigs and feeders were weighed on days 7, 14 and 18. The supplemented pigs had increased BW and average daily gain before the challenge. In response to LPS, there was a greater increase in i.p. temperature in Control pigs compared with supplemented pigs. In addition, cortisol was reduced in SGX2 pigs while cortisol was elevated in SGX1 pigs at several time points post-challenge. White blood cells, neutrophils and lymphocytes were decreased in SGX1 and SGX2 compared with Control pigs. Furthermore, the pro-inflammatory cytokine response varied by treatment and dose of treatment. Specifically, serum TNF-α was greatest in SGX2, intermediate in Control, and least in SGX1 pigs, while the magnitude and temporal pattern of IFN-γ in SGX2 pigs was delayed and reduced. In contrast, IL-6 concentrations were reduced in both SGX treatment groups compared with Control pigs. These data demonstrate that different supplementation feed inclusion rates produced differential responses, and that feeding SynGenX to weaned pigs attenuated the APR to an LPS challenge.

Bridging the academic and practice/policy gap in public health: perspectives from Scotland and Canada.

This article presents a critical commentary of specific organizational models and practices for bridging 'the gap' between public health research and policy and practice. The authors draw on personal experiences of such models in addition to the wider knowledge translation and exchange literature to reflect on their strengths and weaknesses as implemented in Scotland and Canada since the early 1990s.

External validation of an NTCP model for acute esophageal toxicity in locally advanced NSCLC patients treated with intensity-modulated (chemo-)radiotherapy.

BACKGROUND AND PURPOSE: We externally validated a previously established multivariable normal-tissue complication probability (NTCP) model for Grade ≥2 acute esophageal toxicity (AET) after intensity-modulated (chemo-)radiotherapy or volumetric-modulated arc therapy for locally advanced non-small cell lung cancer. MATERIALS AND METHODS: A total of 603 patients from five cohorts (A-E) within four different Dutch institutes were included. Using the NTCP model, containing predictors concurrent chemoradiotherapy, mean esophageal dose, gender and clinical tumor stage, the risk of Grade ≥2 AET was estimated per patient and model discrimination and (re)calibration performance were evaluated. RESULTS: Four validation cohorts (A, B, D, E) experienced higher incidence of Grade ≥2 AET compared to the training cohort (49.3-70.2% vs 35.6%; borderline significant for one cohort, highly significant for three cohorts). Cohort C experienced lower Grade ≥2 AET incidence (21.7%, p < 0.001). For three cohorts (A-C), discriminative performance was similar to the training cohort (area under the curve (AUC) 0.81-0.89 vs 0.84). In the two remaining cohorts (D-E) the model showed poor discriminative power (AUC 0.64 and 0.63). Reasonable calibration performance was observed in two cohorts (A-B), and recalibration further improved performance in all three cohorts with good discrimination (A-C). Recalibration for the two poorly discriminating cohorts (D-E) did not improve performance. CONCLUSIONS: The NTCP model for AET prediction was successfully validated in three out of five patient cohorts (AUC ≥0.80). The model did not perform well in two cohorts, which included patients receiving substantially different treatment. Before applying the model in clinical practice, validation of discrimination and (re)calibration performance in a local cohort is recommended.

Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers.

PURPOSE: Machine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boosting) in terms of classification performance. The purpose of this study is to compare such classifiers specifically for (chemo)radiotherapy datasets and to estimate their average discriminative performance for radiation treatment outcome prediction. METHODS: We collected 12 datasets (3496 patients) from prior studies on post-(chemo)radiotherapy toxicity, survival, or tumor control with clinical, dosimetric, or blood biomarker features from multiple institutions and for different tumor sites, that is, (non-)small-cell lung cancer, head and neck cancer, and meningioma. Six common classification algorithms with built-in feature selection (decision tree, random forest, neural network, support vector machine, elastic net logistic regression, LogitBoost) were applied on each dataset using the popular open-source R package caret. The R code and documentation for the analysis are available online (https://github.com/timodeist/classifier_selection_code). All classifiers were run on each dataset in a 100-repeated nested fivefold cross-validation with hyperparameter tuning. Performance metrics (AUC, calibration slope and intercept, accuracy, Cohen's kappa, and Brier score) were computed. We ranked classifiers by AUC to determine which classifier is likely to also perform well in future studies. We simulated the benefit for potential investigators to select a certain classifier for a new dataset based on our study (pre-selection based on other datasets) or estimating the best classifier for a dataset (set-specific selection based on information from the new dataset) compared with uninformed classifier selection (random selection). RESULTS: Random forest (best in 6/12 datasets) and elastic net logistic regression (best in 4/12 datasets) showed the overall best discrimination, but there was no single best classifier across datasets. Both classifiers had a median AUC rank of 2. Preselection and set-specific selection yielded a significant average AUC improvement of 0.02 and 0.02 over random selection with an average AUC rank improvement of 0.42 and 0.66, respectively. CONCLUSION: Random forest and elastic net logistic regression yield higher discriminative performance in (chemo)radiotherapy outcome and toxicity prediction than other studied classifiers. Thus, one of these two classifiers should be the first choice for investigators when building classification models or to benchmark one's own modeling results against. Our results also show that an informed preselection of classifiers based on existing datasets can improve discrimination over random selection.

In vivo dosimetry in gynecological applications-A feasibility study.

PURPOSE: To investigate the feasibility of in vivo dosimetry using microMOSFET dosimeters in patients treated with brachytherapy using two types of gynecological applicators. METHODS AND MATERIALS: In this study, a microMOSFET was placed in an empty needle of an Utrecht Interstitial Fletcher applicator or MUPIT (Martinez Universal Perineal Interstitial Template) applicator for independent verification of treatment delivery. Measurements were performed in 10 patients, with one to three microMOSFETs per applicator and repeated for one to four fractions, resulting in 50 in vivo measurements. Phantom measurements were used to determine characteristics of the microMOSFETs. RESULTS: Phantom measurements showed a linear relationship between dose and microMOSFET threshold voltage, and a calibration coefficient (mV/cGy) was determined. Reproducibility of repeated 50 cGy irradiations was 2% (1 standard deviation). Distance and angle dependencies were measured and correction factors were determined. Subsequently, three microMOSFETs were placed in a phantom to measure a validation plan. The difference between predicted and measured dose was less than the measurement uncertainty (±9%, 2 standard deviations). In vivo measurements were corrected for distance and angle dependencies. Differences between predicted and measured dose in the patients were smaller than the measurement uncertainty for the majority of the measurements. CONCLUSIONS: In vivo dosimetry using microMOSFETs in MUPIT and Utrecht Interstitial Fletcher applicators has proved to be feasible. Reimaging should be performed after detection of differences larger than 10% between predicted and measured dose to verify the applicator configuration. Movement of the applicator relative to the target or organs at risk is undetectable with this method.

Inclusion of Incidental Radiation Dose to the Cardiac Atria and Ventricles Does Not Improve the Prediction of Radiation Pneumonitis in Advanced-Stage Non-Small Cell Lung Cancer Patients Treated With Intensity Modulated Radiation Therapy.

PURPOSE: To evaluate whether inclusion of incidental radiation dose to the cardiac atria and ventricles improves the prediction of grade ≥3 radiation pneumonitis (RP) in advanced-stage non-small cell lung cancer (AS-NSCLC) patients treated with intensity modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT). METHODS AND MATERIALS: Using a bootstrap modeling approach, clinical parameters and dose-volume histogram (DVH) parameters of lungs and heart (assessing atria and ventricles separately and combined) were evaluated for RP prediction in 188 AS-NSCLC patients. RESULTS: After a median follow-up of 18.4 months, 26 patients (13.8%) developed RP. Only the median mean lung dose (MLD) differed between groups (15.3 Gy vs 13.7 Gy for the RP and non-RP group, respectively; P=.004). The MLD showed the highest Spearman correlation coefficient (Rs) for RP (Rs = 0.21; P<.01). Most Rs of the lung DVH parameters exceeded those of the heart DVH parameters. After predictive modeling using a bootstrap procedure, the MLD was always included in the predictive model for grade ≥3 RP, whereas the heart DVH parameters were seldom included in the model. CONCLUSION: Incidental dose to the cardiac atria and ventricles did not improve RP risk prediction in our cohort of 188 AS-NSCLC patients treated with IMRT or VMAT.

Fermentation products as feed additives mitigate some ill-effects of heat stress in pigs.

Heat stress (HS) may result in economic losses to pig producers across the USA and worldwide. Despite significant advancements in management practices, HS continues to be a challenge. In this study, an in-feed antibiotic (carbadox, CBX) and antibiotic alternatives ( [XPC], and [SGX] fermentation products) were evaluated in a standard pig starter diet as mitigations against the negative effects of HS in pigs. A total of 100 gilts were obtained at weaning (6.87 ± 0.82 kg BW, 19.36 ± 0.72 d of age) and randomly assigned to dietary treatments (2 rooms/treatment, 2 pens/room, 6 to 7 pigs/pen). After 4 wk of dietary acclimation, half of the pigs in each dietary group (1 room/dietary treatment) were exposed to repeated heat stress conditions (RHS; daily cycles of 19 h at 25°C and 5 h at 40°C, repeated for 9 d), and the remaining pigs were housed at constant thermal neutral temperature (25°C, [NHS]). Pigs subjected to RHS had elevated skin surface temperature ( < 0.05; average 41.7°C) and respiration rate ( < 0.05; 199 breaths per minute (bpm) during HS, and overall reduced ( < 0.05) BW, ADG, ADFI, and G:F regardless of dietary treatment. Independent of diet, RHS pigs had significantly shorter ( < 0.05) jejunum villi on d 3 and d 9 compared to NHS pigs. Heat stress resulted in decreased villus height to crypt depth ratio (V:C) in pigs fed with control diet with no added feed additive (NON) and CBX diets at d 3, whereas the pigs fed diets containing XPC or SGX showed no decrease. Transcriptional expression of genes involved in cellular stress (, , , ), tight junction integrity (, , ), and immune response (, , and ) were measured in the ileum mucosa. Pigs in all dietary treatments subjected to RHS had significantly higher ( < 0.05) transcript levels of and , and an upward trend ( < 0.07) of mRNA expression. RHS pigs had higher ( < 0.05) transcript levels of and in NON diet, in XPC and CBX diets, and in SGX diet compared to the respective diet-matched pigs in the NHS conditions. Neither RHS nor diet affected peripheral natural killer () cell numbers or NK cell lytic activity. In conclusion, pigs subjected to RHS had decreased performance, and supplementation with fermentation products in the feed (XPC and SGX) protected pigs from injury to the jejunum mucosa.

Influence of dietary -arginine supplementation of sows during late pregnancy on piglet birth weight and sow and litter performance during lactation.

Two experiments were conducted to determine the effect of feeding supplemental -Arg during late pregnancy on piglet birth weight and preweaning performance. In Exp. 1, 97 gilts and sows were allotted (gestation d 93) to receive a control diet (CON; 19.8 g standardized ileal digestible [SID] Arg/d) or the CON + 1.0% -Arg (ARG; 46.6 g SID Arg/d). Gilts and sows were weighed on gestation d 93 and 110, 48 h after farrowing, and at weaning. Data, including number born alive, number weaned, individual birth and weaning weight, and placenta weight, were recorded. Blood samples were collected on d 93 and 110 and analyzed for plasma IGF-1, insulin, and blood urea nitrogen concentration. In a second experiment, 383 sows in a commercial research farm were allotted to receive CON or ARG. An -Arg premix was provided daily by top dress beginning at gestation d 81 (±0.1 d) and fed for an average of 35 d (±0.2 d). Sows received 2.73 kg feed/d with CON sows provided 17 g SID Arg/d and ARG sows receiving a total of 44 g SID Arg/d. Litter birth weight was recorded and average birth weight was computed. In a subset of 82 sows, individual birth weights were recorded. In Exp. 1, there was a tendency for greater late pregnancy maternal BW gain ( = 0.06) in ARG compared with CON. A tendency for a parity × treatment interaction was observed for late pregnancy BW gain, with first litter sows fed ARG gaining the most, gilts fed ARG intermediate, and all other treatments gaining the least ( = 0.10). No differences between treatment groups were observed for maternal plasma IGF-1, insulin, and urea nitrogen and in progeny performance to weaning ( > 0.28). In Exp. 2, piglet birth weight was more effectively tested because of the large number of multiparous sows involved. There was a tendency for individual birth weight to decline in ARG compared with CON ( < 0.08), but birth weight distribution between <0.80 and >2.8 kg was similar. No other differences were observed ( > 0.18). In conclusion, late pregnancy supplementation with -Arg had no effect on number of pigs born alive, piglet birth weight, or lactation performance.

Guideline for opioid therapy and chronic noncancer pain

Guideline for opioid therapy and chronic noncancer pain: the objective is to inform the prescribing of opioids for adults with chronic noncancer pain.

Young people's exposure to point-of-sale tobacco products and promotions.

OBJECTIVES: Point of sale (POS) displays are one of the most important forms of tobacco marketing still permitted in many countries. Reliable methods for measuring exposure to such displays are needed in order to assess their potential impact, particularly on smoking attitudes and uptake among young people. In this study we use a novel method for evaluating POS exposure based on young people's use of retail outlets and recall of tobacco displays and observational data on the characteristics of displays. STUDY DESIGN: Observational audit of retail outlets (n = 96) and school-based pupil survey (n = 1482) in four Scottish communities reflecting different levels of social deprivation and urbanisation, conducted in 2013 before legislation to remove POS displays was implemented in supermarkets. METHODS: Measures were taken of: visibility and placement of tobacco displays; internal and external advertising; display unit size, branding and design; visibility of pack warnings; proximity of tobacco products to products of potential interest to children and young people; pupils' self-reported frequency of visiting retail outlets; and pupils' recall of tobacco displays. Variation in POS exposure across social and demographic groups was assessed. RESULTS: Displays were highly visible within outlets and, in over half the stores, from the public footway outside. Tobacco products were displayed in close proximity to products of interest to children (e.g. confectionery, in 70% of stores). Eighty percent of pupils recalled seeing tobacco displays, with those from deprived areas more likely to recall displays in small shops. When confectioners, tobacconists and newsagents (CTNs) and grocery/convenience stores (two of the outlet types most often visited by young people) were examined separately, average tobacco display unit sizes were significantly larger in those outlets in more deprived areas. CONCLUSIONS: POS displays remain a key vector in most countries for advertising tobacco products, and it is important to develop robust measures of exposure. The data reported in this paper provide a baseline measure for evaluating the efficacy of legislation prohibiting such displays.

Effects of L-carnitine supplementation on quality characteristics of fresh pork bellies from pigs fed 3 levels of corn oil.

Crossbred pigs (n = 216) were used to test the effect of supplemental L-carnitine (CARN) on the fatty acid composition and quality characteristics of fresh pork bellies from pigs fed diets formulated with different inclusion levels of corn oil. Pigs were blocked by BW (43.6 ± 1.0 kg) and allotted randomly to pens of 6 pigs within blocks. Then, within blocks, pens were assigned randomly to 1 of 6 dietary treatments in a 2 × 3 factorial arrangement, with either 0 or 100 mg/kg of supplemental CARN and 3 dietary inclusion levels (0, 2, or 4%) of corn oil (CO). When the lightest block weighed 125.0 kg, all pigs were slaughtered, and left-side bellies were captured during carcass fabrication for quality data collection. Fresh pork bellies were evaluated for length, width, thickness, and firmness (bar-suspension and Instron-compression methods) before a 2.5-cm-wide strip of belly was removed and subsequently dissected into subcutaneous fat, primary lean (latissimus dorsi), secondary lean (cutaneous trunci), and intermuscular fat for fatty acid composition determination. Although belly length, width, and thickness of fresh pork bellies were not affected by CARN (P ≥ 0.128) or CO (P ≥ 0.073), belly firmness decreased linearly (P < 0.001) with increasing dietary CO, but there was no (P ≥ 0.137) effect of CARN on any belly firmness measure. Dietary CARN increased (P < 0.05) the proportion of total SFA in the intermuscular fat layer, increased (P < 0.05) the proportion of total MUFA in the primary and secondary lean layers, and decreased (P < 0.05) the proportion of total PUFA in the intermuscular fat and secondary lean layers of pork bellies. Moreover, the SFA and MUFA compositions decreased linearly (P < 0.001) with increasing dietary CO, and the rate of the decrease in SFA composition was greater (P < 0.001) in the fat layers than the lean layers. Conversely, the PUFA content increased linearly (P < 0.001) with increasing dietary CO, and the rate of the increase in PUFA was greater (P < 0.001) in the fat than the lean layers, and greater (P = 0.022) in the primary than secondary lean layer. Results from this study would indicate that differences in the amount and rate of fatty acid deposition associated with feeding increased amounts of CO, along with moisture differences among the belly layers, combine to negatively affect fresh pork belly firmness.