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Objective:Outdoor advertisements for food and drink products form a large part of the food environment and they disproportionately promote unhealthy products. However, less is known about the social patterning of such advertisements. The main aim of this study was to explore the socio-economic patterning of food and drink advertising at bus stops in Scotland’s capital city, Edinburgh.Design:Bus stop advertisements were audited to identify food/drink adverts and classify them by food/drink category (i.e. ‘advert category’). This data were then linked to area-based deprivation and proximity measures. Neighbourhood deprivation was measured using the bus stop x/y co-ordinates, which were converted to postcodes to identify the matching 2012 deprivation level via the Scottish Index of Multiple Deprivation. Distance to schools and leisure centres were also collected using location data. Generalised estimating equations and linear regression analyses were used to assess associations between the promotion of advert categories and deprivation and proximity to schools/leisure centres, respectively.Setting:Edinburgh city, United Kingdom.Results:561 food/drink advertisements were identified across 349 bus stops, with 8 advertisement categories noted and included in the final analysis, including alcohol, fast food outlets and confectionary. The majority of adverts were for ‘unhealthy’ food and drink categories, however there was no evidence for any socio-economic patterning of these advertisements. There was no evidence of a relationship between advertisements and proximity to schools and leisure centres.Conclusions:While there is no evidence for food and drink advertising being patterned by neighbourhood deprivation, the scale of unhealthy advertising is an area for policy evaluations and interventions on the control of such outdoor advertising.

Additional file 1.

BACKGROUND: National health surveys linked to vital statistics and health care information provide a growing source of individual-level population health data. Pooling linked surveys across jurisdictions would create comprehensive datasets that are larger than most existing cohort studies, and that have a unique international and population perspective. This paper's objectives are to examine the feasibility of pooling linked population health surveys from three countries, facilitate the examination of health behaviours, and present useful information to assist in the planning of international population health surveillance and research studies.DATA AND METHODS: The design, methodologies and content of the Canadian Community Health Survey (2003 to 2008), the United States National Health Interview Survey (2000, 2005) and the Scottish Health Survey (SHeS) (2003, 2008 to 2010) were examined for comparability and consistency. The feasibility of creating common variables for measuring smoking, alcohol consumption, physical activity and diet was assessed. Sample size and estimated mortality events were collected.RESULTS: The surveys have comparable purposes, designs, sampling and administration methodologies, target populations, exclusions, and content. Similar health behaviour questions allow for comparable variables to be created across the surveys. However, the SHeS uses a more detailed risk factor evaluation for alcohol consumption and diet data. Therefore, comparisons of alcohol consumption and diet data between the SHeS and the other two surveys should be performed with caution. Pooling these linked surveys would create a dataset with over 350,000 participants, 28,424 deaths and over 2.4 million person-years of follow-up.DISCUSSION: Pooling linked national population health surveys could improve population health research and surveillance. Innovative methodologies must be used to account for survey dissimilarities, and further discussion is needed on how to best access and analyze data across jurisdictions.

Le présent document a pour objectif d'examiner la faisabilité du regroupement de données d'enquêtes sur la santé de la population couplées provenant de trois pays, de faciliter l'examen des comportements influant sur la santé et de présenter des renseignements utiles pour la planification de la surveillance internationale de la santé de la population et les travaux de recherche connexes.

Executive Summary The Scottish Ambulance Service (SAS) responds to around 1.8 million calls per year, including responses to 700,000 emergency and unscheduled incidents. Of these responses, over 500,000 are received through the 999 call service. SAS transfers around 90,000 patients between hospitals each year and responds to over 150,000 urgent requests for admission, transfer and discharge from GPs and hospitals (SAS, 2015). In 2017 SAS began to implement a new clinical response model (NCRM). The aims of the NCRM are to: - Save more lives by more accurately identifying patients with immediately life-threatening conditions, such as cardiac arrest; - Safely and more effectively send a matched resource first time to all patients based on their clinical need. The University of Stirling, commissioned to carry out an independent evaluation of the NCRM using data provided by SAS and NHS Information Services Division (ISD), considered the following questions: 1. Are patients with Immediately Life Threatening (ILT) conditions more quickly and accurately identified? 2. Are more lives saved as a consequence of the best available resources being dispatched to the patient? 3. Are improved clinical outcomes achieved if the matched resources are sent first time for patients with non-ILT conditions? Methods A quantitative analysis was conducted comparing SAS data on response to 999 calls from a pre-NCRM implementation time-period (January 2016) and a post-implementation time-period (January 2017 and January 2018). NHS ISD linked additional data from the Unscheduled Care Data Mart (UCDM) to the SAS data. UCDM contains emergency department data (ED) and data from the National Records of Scotland (NRS) for mortality data. Data were examined for the purple code (the highest risk category of call to the 999 service) and within the purple category, those patients in cardiac arrest. The same analyses were conducted for the remaining colour codes and a selection of clinical categories within these colour codes: breathing difficulties (red), stroke (amber) and falls (yellow). Key Findings Interpreting this data It should be noted that data is taken from only three (and in some cases two) time points and only from the month of January. While this does allow some relevant comparisons between the years, the findings cannot be generalised to the whole year and the whole time-period in question (January 2016 – January 2018). In addition, call volume was approximately 9% higher in 2018 compared to 2017 and 2016 (which were similar) with over 4,000 more calls in January 2018. Further analysis of the data using data from each month, as well as individual-level data (rather than it being aggregated), would allow much more robust and relevant evidence of change and the impact on the service and patients. 1. Are patients with Immediately Life Threatening (ILT) conditions more quickly and accurately identified? Patients with ILT conditions (purple calls) would appear to be more accurately identified post-NCRM with a noticeable increase in patients coded with ILT conditions by 2018. The time to respond to ILT conditions was slightly longer (but not statistically significant). Speed Resource allocation was used as an indicator of speed of identification. We found that resource allocation (and in turn response times) did not differ significantly between January 2016 (pre-NCRM) and January 2017 (post-NCRM introduction) for ILT (purple) calls. However, there was a longer time to allocate resources (i.e. identify) purple calls in 2018 compared to 2016 and this was statistically significant. For all other colour codes, 2017 and 2018 resource allocation were also significantly slower than 2016 (except amber 2017 calls) as expected with a priority-based system. Call handlers were provided with further training and development in the process of triage over the course of 2016 onwards, with the aim of more accurately allocating patients into the most appropriate category, and therefore it was to be expected that time to allocate resources and identification into the correct category would take longer. Accuracy Comparing 2016 (pre-NCRM) and 2017 (post-NCRM introduction) outcomes data, we found that sensitivity (correctly identifying a purple, ILT condition) was higher in 2017 compared to 2016, but specificity (correctly identifying a non-ILT condition) was lower in 2017. Overall accuracy (the likelihood of being correctly identified as either ILT or non-ILT) was not different between the two-time points. Similar results were also seen for the cardiac arrest cases within the purple calls. 2. Are more lives saved as a consequence of the best available resources being dispatched to the patient? Survival for purple-coded patients is markedly lower with respect to all other causes (as one would expect) and reflects that purple-coded calls/conditions are a unique category (in terms of risk of death) and represent the majority of incidents where patients face an immediate threat to life (ILT). The risk of death across the other colour codes is small in comparison and therefore differences of survival seem to exist only for the purple-coded patients. The cardiac arrest rate within the purple coded is around 53%. Survival analysis for all patients within the purple code and specifically for those affected by cardiac arrest are considered next. There seems to be a considerable (~20%) increase in survival for all purple-coded patients comparing January 2016 to January 2017, which is constant over time from time 0 (confirmed dead when the ambulance arrives at the scene) to 30 days post-call. When comparing January 2016 to January 2018 for the same group, survival also increased (~10%). The number of lives saved, 30 days post-call, in patients with ILT conditions in January 2016 (pre-NCRM) was 32 (14.2% of purple calls), and in post-NCRM in January 2017 was 134 (28.6% of purple calls) and in January 2018 was 182 (26.6% of purple calls). Although the numbers of patients with ILT conditions has increased, the data from the specificity and sensitivity analysis (Table 14) shows that there is no difference in false positive rates between the years. This suggests that the acuity of these patients remains very high and that the increase in volume represents patients correctly identified with the highest requirement for immediate response. Therefore, the increase in survival probability with those with ILT conditions is not likely to be caused by artificial inflation caused by conservative allocation of patients with ILT conditions to the purple code but rather by appropriate allocation and intervention(s) to those patients at risk from death due to ILT conditions. In terms of the 2018 survival probability being lower than in 2017, it is possible that the higher call-load in 2018 has limited the impact previously seen in 2017. Continued monitoring of these data is needed to identify how mortality has been impacted by the NCRM over the longer-term. 3. Are improved clinical outcomes achieved if the matched resources are sent first time for patients with non-ILT conditions? Overall survival for all non-ILT codes (Red, Amber, Yellow and Green) was similar, as noted above (where purple calls carry much higher risk of death). For these codes there was also no clear difference in survival in 2017 versus 2016 or 2018 versus 2016. Breathing difficulty (a sub-set of the red calls) seems to have worsened between 2016 and 2017, with 451 patients having a decrease in survival from 3% to 6%, with the gap widening as time passes. However, by 2018, survival was at 2016 levels despite the number of incidents (n=2044) back to the levels seen in 2016 (n=2018). No differences between years seem to be present for stroke or falls. Data on further clinical outcomes were not available within this dataset to analyse in any further detail. Conclusions By January 2018 the number of incidents (n=52,871) had increased by 9% when compared to January 2016 (n=48,544), amounting to over 4000 more incidents in 2018 than seen in 2016 or 2017. During this time of high demand in 2017 and particularly 2018, the NCRM does accurately identify patients who have the greatest need for services from SAS. The NCRM’s identification and triage of patients into triage categories, although taking time for the call handler and dispatching system, can get the ambulance and its crew to patients with the greatest need and this has improved the survival of those with immediate life-threatening conditions. Those with lower acuity needs are responded to but in a longer time period as expected when using a priority-based system (but with no apparent impact on survival). These conclusions are reached in the context of analysing aggregated data over three fairly short time-periods and further research over a longer time frame, with longitudinal data on individual cases, would further improve the evidence base for the NCRM.

BACKGROUND: Evaluation of the potential effectiveness of a programme's objectives (health or otherwise) is important in demonstrating how programmes work. However, evaluations are expensive and can focus on unrealistic outcomes not grounded in strong theory, especially where there is pressure to show effectiveness. The aim of this research was to demonstrate that the evaluability assessment (a cost-effective pre-evaluation tool that primarily gives quick, constructive feedback) can be used to help develop programme and outcome objectives to improve programmes while they run and to assist in producing more effective evaluations. This was done using the example of a community development programme aiming to improve health and reduce health inequalities in its target population.METHODS: The setting was Glasgow, Scotland, UK and focused on the Health Issues in the Community programme. Data were collected from documents and nine individual stakeholder interviews. Thematic analysis and a realist approach were used to analyse both datasets and, in conjunction with a workshop with stakeholders, produce a logic model of the programme theory and related evaluation options to explore further.RESULTS: Five main themes emerged from the analysis: History; Framework; Structure and Delivery of the Course; Theory of Action; and Barriers to Delivery and Successful Outcomes. These themes aided in drafting the logic model which revealed they key programme activities (e.g. facilitating group learning) and 23 potential outcomes. The majority of these outcomes (16) were deemed to be short-term outcomes (more easily measured within the timeframe of an individual being involved in the programme) e.g. increased self-esteem or awareness of individual/community health. The remaining 6 outcomes were deemed longer-term and included outcomes such as increased social capital and individual mental health and wellbeing.CONCLUSIONS: We have shown that the evaluability assessment tool can be applied to the evaluation of community health programmes, providing short- and long-term outcomes that could be evaluated to demonstrate effectiveness and avoid unnecessary or poorly designed full-scale evaluations. This type of pre-evaluation method is already a useful resource for national policy evaluations, but could be a valuable evaluation tool for other regional or community health programmes.

Background:\ud Insomnia symptoms are associated with type 2 diabetes incidence but are also associated with a range of potential time-varying covariates which may confound and/or mediate associations. We aimed to assess whether cumulative exposure to insomnia symptoms has a causal effect on type 2 diabetes incidence.\ud \ud Methods:\ud A prospective cohort study in the West of Scotland, following respondents for 20 years from age 36. 996 respondents were free of diabetes at baseline and had valid data from up to four follow-up visits. Type 2 diabetes was assessed at the final visit by self-report, taking diabetic medication, or blood-test (HbA1c ≥ 6.5% or 48 mmol/mol). Effects of cumulative insomnia exposure on type 2 diabetes incidence were estimated with traditional regression and marginal structural models, adjusting for time-dependent confounding (smoking, diet, physical inactivity, obesity, heavy drinking, psychiatric distress) as well as for gender and baseline occupational class.\ud \ud Results:\ud Traditional regression yielded an odds ratio (OR) of 1.34 (95% CI: 1.06-1.70) for type 2 diabetes incidence for each additional survey wave in which insomnia was reported. Marginal structural models adjusted for prior covariates (assuming concurrently measured covariates were potential mediators), reduced this OR to 1.20 (95% CI: 0.98-1.46), and when concurrent covariates were also included (viewing them as potential confounders) this dropped further to 1.08 (95% CI: 0.85-1.37).\ud \ud Conclusions:\ud The association between cumulative experience of insomnia and type 2 diabetes incidence appeared confounded. Evidence for a residual causal effect depended on assumptions as to whether concurrently measured covariates were confounders or mediators.

Calculation of weights. Gives further technical details of the specific models used to calculate analysis weights. (DOCX 17Â kb)

Covariate balance. Includes 5 supplementary figures showing how wave-specific weights achieve covariate balance on prior measures of covariates. (DOCX 164Â kb)

Allostatic Load clinical cut-offs. (DOCX 31Â kb)

Background At 77 years for males and 81 years for females, Scotland has the lowest life expectancy in Western Europe. The ‘excess mortality’ (higher mortality over and above that which is explained by higher levels of deprivation) exists in all geographical areas and at all levels of deprivation, but is most pronounced in the most deprived areas, particularly in Glasgow and the west of Scotland. Recent work has focused on comparing the cities of Glasgow, Liverpool and Manchester, where deprivation levels have been identified as being similar, to try and better understand the causes of this excess mortality. Despite these similarities in socioeconomic deprivation, Glasgow has consistently shown excess mortality compared to its English neighbours. A number of hypotheses have been proposed to help explain this excess mortality, ranging from differences in poor health behaviours (such as alcohol consumption, smoking, low physical activity and poor diet) to lower social capital and even climate. Although many of these factors have been empirically explored, much of the excess mortality remains unexplained. Differences in historical and contemporary employment conditions (for example, types of occupations, occupational health and safety, occupational precariousness, trade union membership, unemployment etc.) remain as possible, yet under-researched, explanations for this excess mortality. Aims and objectives The overall aim of this project is to assess whether—and to what extent— aspects of the labour market and employment differ between Scottish and English populations, specifically between those living in Glasgow compared to residents of Liverpool and Manchester. Objectives include: • Develop plausible hypotheses about how employment may contribute to Glasgow and Scotland’s excess mortality • Bring together knowledge on employment and work characteristics to understand which aspects have differed over the last 60 years between the three cities of Glasgow, Liverpool and Manchester • Assess the feasibility of conducting quantitative analyses to test the hypotheses linking employment and health inequalities Method A. Plausible Theories Linking Employment & Excess Mortality We developed hypotheses as to how the nature of employment may contribute to Scotland and Glasgow’s excess mortality based on reviewing relevant articles and reports and distilling the key possible employment characteristics important for health. B. Literature Review A rapid review of the literature on differences in employment conditions and peoples’ experiences of the labour market in Glasgow, Liverpool and Manchester was carried out. We used four approaches to identify relevant articles. First, an electronic search was conducted, described in detail below. Second, the reference sections of articles deemed suitable for review were scrutinised. Third, each team member checked his or her own files for relevant material. Finally, experts in the field were contacted. This multidimensional approach was used to help identify articles not readily identified through traditional database searching alone. Search terms included a number of words/phrases relevant to the theories identified in section A and relevant place terms for Glasgow/Clydeside, Liverpool/Merseyside and Manchester. After applying relevant inclusion and exclusion to filter the number of studies down to those most relevant to the research question, we then extracted information on study participants (location, sample size, age range, sex, study design), employment characteristics, data type (e.g. quantitative versus qualitative) and main results from all relevant fulltexts. C. Identify and Appraise Datasets for Further Analyses We aimed to identify and appraise existing datasets that could be used to explore similarities and differences in factors related to the labour market between the three cities/regions. The same employment search terms identified for the literature review were used to search the UK Data Service to aid identification of datasets that may allow further analysis of Scotland and Glasgow’s excess mortality by employment characteristics. In addition, the research team used their knowledge and checked their own files for relevant datasets. Information on study type (sample, time dimensions and method of data collection), locations, year(s) covered, demographics (population type, representativeness, ages, sexes included and sample size), relevant employment data and relevant health data (morbidity, symptoms, general health and wellbeing, medications, biomarkers and administratively linked health records) were extracted from the Data Service online data catalogues, data documentation and through searching the data files. Datasets were then assessed on their strengths and limitations according to nine criteria across study quality and relevance to the research topic. Summary of main or key results A. Plausible Theories Linking Employment & Excess Mortality Seven characteristics were identified: overall employment levels; job types; precarious employment; pay; physical work environment; psychosocial work environment; and employee representation. B. Literature Review The literature review focused on identifying literature that had included any of these characteristics and the three cities. Fifty-one records were identified, with the focus for most being on employment status, job type and pay, with only 11 looking at other employment characteristics. Overall, there was little evidence that Glasgow suffered worse employment features compared to Liverpool and Manchester across multiple datasets and evidence sources for these employment characteristics, although there were notable gaps for precariousness, the physical and psychosocial work environments and employee representation. Across employment levels, job types and pay, data was typically limited to the last 20-30 years with single measurements multiple years apart, but even where data stretched back further, the similarities across the three cities/regions were notable. There remains a lack of research investigating precariousness, the physical and psychosocial work environments and employee representation as possible links between employment and excess mortality in Glasgow. C. Identify and Appraise Datasets for Further Analyses There were a number of datasets identified that could be available to investigate this issue further (28 identified here), although none focus on the three cities specifically, or even Liverpool and Manchester alone (there is one Glasgowbased study) to allow cross-cohort comparisons. However, many datasets do have UK data with local authority region identifiers, but have not been used to compare regions within the literature identified in this report given the need for specific requests for regional data and decreasing sample sizes at the lower city level. All the datasets contained data on employment status and/or job types, but there was much more limited and less consistent data for the other measures, as seen with the literature review. Some basic data descriptives were extracted from freely available data looking at employment status (time-series aggregate-level data) and pay (cross-sectional health survey data, 2003), but again this data is limited in being relatively modern and/or only available at one time-point. However, as seen with the literature review, few differences between the three cities were identified for employment levels, job types and pay. Conclusions This comprehensive and novel review has highlighted that while there is currently insufficient evidence to link employment as a major explanatory factor for the excess in mortality in Glasgow, there are gaps in the research that could be explored further. Previous research and hypotheses have perhaps failed to fully acknowledge the complex and nuanced nature of the term ‘employment’, meaning that important features such as precariousness and the psychosocial work environment have been neglected in terms of data collection and research. However, emerging data in the UK does hold the potential to explore this topic in more detail, but it may require many years to fully link employment exposures to health outcomes.

Background Improving mental health and diet are important components of the public health agenda in the UK. Community organisations have an important role to play in promoting mental health and wellbeing, and a number of community-based food projects are specifically designed to address this issue. However, impact assessment for such projects is difficult, and in-depth, high-quality evaluation data linking project activities to health outcomes is lacking. Aims and objectives This study aimed to summarise findings from Scottish evaluations of community food work on mental health and wellbeing. Method The project was undertaken in three different phases that had different methods. Phase 1 was a prospective evaluation of one community-based food project’s work, called Eat Well – Keep Active, run by the CHANGES Community Health Project. All participants who took part in the course were asked to complete a paper survey at the beginning of the course (baseline) and at six weeks. Various types of statistical analyses were then used to assess change in mental wellbeing and in eating habits. Phase 2 entailed qualitative interviews with key staff from seven community-based food projects and two Community Food and Health (Scotland) staff. The interview transcripts were qualitatively analysed to draw out key themes around how and why community food projects and their evaluations work. In Phase 3, a meta-synthesis of self-evaluations from eight community food projects was undertaken. Information from each of the reports was extracted to compare and summarise the project and participant characteristics, evaluation tools used, and outcomes measured by each project. Summary of main results Nearly all of the community-based food projects reported improvements in participants’ mental health and wellbeing. This was measured through a wide range of indicators and by a variety of tools, most qualitative in nature. Interviews with project staff suggested that the mechanism by which mental health and wellbeing benefit as a result of these projects may be related to social connectedness and support, rather than purely dietary (biochemical) mechanisms. The interviews also revealed that because outcome evaluation is not often built into the projects offered by mental health charities, securing the time, expertise, and funding for evaluation is a major barrier. Conclusions Community-based food programs offer participants the opportunity to develop skills, confidence, and social connections, in addition to learning about and making nutritious food, but current standard evaluation tools may fail to capture many important aspects of change in the lives and behaviours of community-based food project participants with mental health problems. This work highlights a key opportunity for community-academic partnerships, through which rigorous alternatives to quantitative questionnaires can be developed for use in community-based mental health work.

BACKGROUND: It is widely believed that females have longer telomeres than males, although results from studies have been contradictory.METHODS: We carried out a systematic review and meta-analyses to test the hypothesis that in humans, females have longer telomeres than males and that this association becomes stronger with increasing age. Searches were conducted in EMBASE and MEDLINE (by November 2009) and additional datasets were obtained from study investigators. Eligible observational studies measured telomeres for both females and males of any age, had a minimum sample size of 100 and included participants not part of a diseased group. We calculated summary estimates using random-effects meta-analyses. Heterogeneity between studies was investigated using sub-group analysis and meta-regression.RESULTS: Meta-analyses from 36 cohorts (36,230 participants) showed that on average females had longer telomeres than males (standardised difference in telomere length between females and males 0.090, 95% CI 0.015, 0.166; age-adjusted). There was little evidence that these associations varied by age group (p=1.00) or cell type (p=0.29). However, the size of this difference did vary by measurement methods, with only Southern blot but neither real-time PCR nor Flow-FISH showing a significant difference. This difference was not associated with random measurement error.CONCLUSIONS: Telomere length is longer in females than males, although this difference was not universally found in studies that did not use Southern blot methods. Further research on explanations for the methodological differences is required. Background: It is widely believed that females have longer telomeres than males, although results from studies have been contradictory. Methods: We carried out a systematic review and meta-analyses to test the hypothesis that in humans, females have longer telomeres than males and that this association becomes stronger with increasing age. Searches were conducted in EMBASE and MEDLINE (by November 2009) and additional datasets were obtained from study investigators. Eligible observational studies measured telomeres for both females and males of any age, had a minimum sample size of 100 and included participants not part of a diseased group. We calculated summary estimates using random-effects meta-analyses. Heterogeneity between studies was investigated using sub-group analysis and meta-regression. Results: Meta-analyses from 36 cohorts (36,230 participants) showed that on average females had longer telomeres than males (standardised difference in telomere length between females and males 0.090, 95% CI 0.015, 0.166; age-adjusted). There was little evidence that these associations varied by age group (p = 1.00) or cell type (p = 0.29). However, the size of this difference did vary by measurement methods, with only Southern blot but neither real-time PCR nor Flow-FISH showing a significant difference. This difference was not associated with random measurement error. Conclusions: Telomere length is longer in females than males, although this difference was not universally found in studies that did not use Southern blot methods. Further research on explanations for the methodological differences is required.

Award-fee arrangements are a valuable contractual approach for the Department of Defense (DoD) when used properly. Award-fee contracts provide incentives to motivate contractor performance in areas critical to program success that are susceptible to judgmental and qualitative measurement and evaluation. The award-fee process allows the DoD to evaluate a contractors performance, appropriately recognize accomplishments, and provide a reward. The DoD has the flexibility to develop criteria to evaluate both the performance levels and the conditions under which these levels were achieved during the evaluation period. An award-fee arrangement rewards satisfactory or better performance, provides incentives for the contractor to improve performance, and records the assessment of the contractors performance. While it is DoD policy to use objective criteria whenever possible to measure performance, there are times when only an incentive based on subjective criteria will achieve the desired objectives. However, subjective evaluations may raise concerns that some award fees are paid but not earned. Lack of DoD acquisition workforce training and experience in utilizing award fee type contracts, coupled with subjective evaluations, may contribute toward award fees being paid even when the contractor does not achieve the desired objectives for which incentives were offered. http://archive.org/details/problemswithward1094538960 Civilian, United States Army Approved for public release; distribution is unlimited.