44 research outputs found

    COVID-19 prevalence and mortality in patients with cancer and the effect of primary tumour subtype and patient demographics: a prospective cohort study

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    BACKGROUND: Patients with cancer are purported to have poor COVID-19 outcomes. However, cancer is a heterogeneous group of diseases, encompassing a spectrum of tumour subtypes. The aim of this study was to investigate COVID-19 risk according to tumour subtype and patient demographics in patients with cancer in the UK. METHODS: We compared adult patients with cancer enrolled in the UK Coronavirus Cancer Monitoring Project (UKCCMP) cohort between March 18 and May 8, 2020, with a parallel non-COVID-19 UK cancer control population from the UK Office for National Statistics (2017 data). The primary outcome of the study was the effect of primary tumour subtype, age, and sex and on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prevalence and the case-fatality rate during hospital admission. We analysed the effect of tumour subtype and patient demographics (age and sex) on prevalence and mortality from COVID-19 using univariable and multivariable models. FINDINGS: 319 (30·6%) of 1044 patients in the UKCCMP cohort died, 295 (92·5%) of whom had a cause of death recorded as due to COVID-19. The all-cause case-fatality rate in patients with cancer after SARS-CoV-2 infection was significantly associated with increasing age, rising from 0·10 in patients aged 40-49 years to 0·48 in those aged 80 years and older. Patients with haematological malignancies (leukaemia, lymphoma, and myeloma) had a more severe COVID-19 trajectory compared with patients with solid organ tumours (odds ratio [OR] 1·57, 95% CI 1·15-2·15; p<0·0043). Compared with the rest of the UKCCMP cohort, patients with leukaemia showed a significantly increased case-fatality rate (2·25, 1·13-4·57; p=0·023). After correction for age and sex, patients with haematological malignancies who had recent chemotherapy had an increased risk of death during COVID-19-associated hospital admission (OR 2·09, 95% CI 1·09-4·08; p=0·028). INTERPRETATION: Patients with cancer with different tumour types have differing susceptibility to SARS-CoV-2 infection and COVID-19 phenotypes. We generated individualised risk tables for patients with cancer, considering age, sex, and tumour subtype. Our results could be useful to assist physicians in informed risk-benefit discussions to explain COVID-19 risk and enable an evidenced-based approach to national social isolation policies. FUNDING: University of Birmingham and University of Oxford

    Modelling the Health Impact of an English Sugary Drinks Duty at National and Local Levels

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    Increasing evidence associates excess refined sugar intakes with obesity, Type 2 diabetes and heart disease. Worryingly, the estimated volume of sugary drinks purchased in the UK has more than doubled between 1975 and 2007, from 510ml to 1140ml per person per week. We aimed to estimate the potential impact of a duty on sugar sweetened beverages (SSBs) at a local level in England, hypothesising that a duty could reduce obesity and related diseases. Methods and Findings We modelled the potential impact of a 20% sugary drinks duty on local authorities in England between 2010 and 2030. We synthesised data obtained from the British National Diet and Nutrition Survey (NDNS), drinks manufacturers, Office for National Statistics, and from previous studies. This produced a modelled population of 41 million adults in 326 lower tier local authorities in England. This analysis suggests that a 20% SSB duty could result in approximately 2,400 fewer diabetes cases, 1,700 fewer stroke and coronary heart disease cases, 400 fewer cancer cases, and gain some 41,000 Quality Adjusted Life Years (QALYs) per year across England. The duty might have the biggest impact in urban areas with young populations. Conclusions This study adds to the growing body of evidence suggesting health benefits for a duty on sugary drinks. It might also usefully provide results at an area level to inform local price interventions in England

    The individual environment, not the family is the most important influence on preferences for common non-alcoholic beverages in adolescence

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    Beverage preferences are an important driver of consumption, and strong liking for beverages high in energy (e.g. sugar-sweetened beverages [SSBs]) and dislike for beverages low in energy (e.g. non-nutritive sweetened beverages [NNSBs]) are potentially modifiable risk factors contributing to variation in intake. Twin studies have established that both genes and environment play important roles in shaping food preferences; but the aetiology of variation in non-alcoholic beverage preferences is unknown. 2865 adolescent twins (18–19-years old) from the Twins Early Development Study were used to quantify genetic and environmental influence on variation in liking for seven non-alcoholic beverages: SSBs; NNSBs; fruit cordials, orange juice, milk, coffee, and tea. Maximum Likelihood Structural Equation Modelling established that beverage preferences have a moderate to low genetic basis; from 18% (95% CI: 10%, 25%) for orange juice to 42% (36%, 43%) for fruit cordials. Aspects of the environment that are not shared by twin pairs explained all remaining variance in drink preferences. The sizeable unique environmental influence on beverage preferences highlights the potential for environmental modification. Policies and guidelines to change preferences for unhealthy beverages may therefore be best directed at the wider environment

    Four Regional Marine Biodiversity Studies: Approaches and Contributions to Ecosystem-Based Management

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    We compare objectives and approaches of four regional studies of marine biodiversity: Gulf of Maine Area Census of Marine Life, Baltic Sea History of Marine Animal Populations, Great Barrier Reef Seabed Biodiversity Project, and Gulf of Mexico Biodiversity Project. Each program was designed as an "ecosystem" scale but was created independently and executed differently. Each lasted 8 to 10 years, including several years to refine program objectives, raise funding, and develop research networks. All resulted in improved baseline data and in new, or revised, data systems. Each contributed to the creation or evolution of interdisciplinary teams, and to regional, national, or international science-management linkages. To date, there have been differing extents of delivery and use of scientific information to and by management, with greatest integration by the program designed around specific management questions. We evaluate each research program's relative emphasis on three principal elements of biodiversity organization: composition, structure, and function. This approach is used to analyze existing ecosystem-wide biodiversity knowledge and to assess what is known and where gaps exist. In all four of these systems and studies, there is a relative paucity of investigation on functional elements of biodiversity, when compared with compositional and structural elements. This is symptomatic of the current state of the science. Substantial investment in understanding one or more biodiversity element(s) will allow issues to be addressed in a timely and more integrative fashion. Evaluating research needs and possible approaches across specific elements of biodiversity organization can facilitate planning of future studies and lead to more effective communication between scientists, managers, and stakeholders. Building a general approach that captures how various studies have focused on different biodiversity elements can also contribute to meta-analyses of worldwide experience in scientific research to support ecosystem-based management

    BDA Update

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    Estimating the cost-effectiveness of salt reformulation and increasing access to leisure centres in England, with PRIMEtime CE model validation using the AdViSHE tool

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    Background: PRIMEtime CE is a multistate life table model that can directly compare the cost effectiveness of public health interventions affecting diet and physical activity levels, helping to inform decisions about how to spend finite resources. This paper estimates the costs and health outcomes in England of two scenarios: reformulating salt and expanding subsidised access to leisure centres. The results are used to help validate PRIMEtime CE, following the steps outlined in the Assessment of the Validation Status of Health-Economic decision models (AdViSHE) tool. Methods: The PRIMEtime CE model estimates the difference in quality adjusted life years (QALYs) and difference in NHS and social care costs of modelled interventions compared with doing nothing. The salt reformulation scenario models how salt consumption would change if food producers met the 2017 UK Food Standards Agency salt reformulation targets. The leisure centre scenario models change in physical activity levels if the Birmingham Be Active scheme (where swimming pools and gym access is free to residents during defined periods) was rolled out across England. The AdViSHE tool was developed by health economic modellers and divides model validation into five parts: validation of the conceptual model, input data validation, validation of computerised model, operational validation, and other validation techniques. PRIMEtime CE is discussed in relation to each part. Results: Salt reformulation was dominant compared with doing nothing, and had a 10-year return on investment of £1.44 (£0.50 to £2.94) for every £1 spent. By contrast, over 10 years the Be Active expansion would cost £727,000 (£514,000 to £1,064,000) per QALY. PRIMEtime CE has good face validity of its conceptual model and has robust input data. Cross-validation produces mixed results and shows the impact of model scope, input parameters, and model structure on cost-per-QALY estimates. Conclusions: This paper illustrates how PRIMEtime CE can be used to compare the cost-effectiveness of two different public health measures affecting diet and physical activity levels. The AdViSHE tool helps to validate PRIMEtime CE, identifies some of the key drivers of model estimates, and highlights the challenges of externally validating public health economic models against independent data. </p
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