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Transaction data for population health

人口健康交易数据

基本信息

批准号：
MR/T043520/1
负责人：
Anya Skatova
金额：
$ 141.97万
依托单位：
University of Bristol
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FT043520%2F1
关键词：
Transaction data population health

项目摘要

Digital technology opens up a new era in the understanding of human behaviour and lifestyle choices, with people's daily activities and habits leaving 'footprints' in their digital records. For example, when we buy goods in supermarkets and use loyalty cards to obtain benefits (e.g., future discounts), the supermarket records our purchases and creates a representation of our habits and preferences. Until now the use of 'digital footprint' data has mostly been limited to private companies. Companies have been using aggregates of these data to track sales of their products, to understand the factors that impact sales levels, and to target marketing and promotions. Changes in Data Protection law in the UK, i.e. General Data Protection Regulation, mean the public can now access and donate their data for academic research. Shopping history data, recorded through loyalty cards by retailers, are an extremely useful source of information for population health research as it can provide granular, objective data on real world choices and behaviours (e.g. painkillers, food) and other behaviours (e.g., pain and weight, wellbeing management). This information is often hard to obtain in the health research domain. Links between lifestyle choices and health outcomes are commonly studied through self-report questionnaires that ask people to remember their everyday choices and behaviours, which can bias results: responses about behaviours do not always reflect the reality of what people actually do. If and when shopping history data are used in a privacy preserving and ethical manner, these data can be utilised for public good, benefiting health research (e.g., helping to understand how everyday behaviours and lifestyle choices impact health and social outcomes). For example, what are the exact levels of alcohol consumption that lead to irreversible health damage for unborn babies accounting for moderating factors (e.g., age, gender, genetic makeup, etc.)? Under which conditions do different types of ready meals contribute to obesity? Do chemicals in household products lead to higher risks of cancer and other adverse health outcomes in children? The Transaction Data for Population Health research programme utilises commercially collected datasets for privacy-preserving, ethical research to benefit the public good. This program questions whether shopping history data can be used in a positive way to support health research and the development of new interventions. The fellowship will establish the feasibility of novel ways of assessing both health outcomes and associated lifestyle choices through objective measures of real world behaviours reflected in retail shopping history data recorded through loyalty cards. At the same time it will build a framework that can be used by future researchers. My research programme in Yrs 1-4 will unfold in three stages. First, it will use commercially collected datasets to identify and study reproductive health outcomes through patterns in the shopping data. Second, it will validate patterns in the data which are associated with health outcomes using established Longitudinal Population Studies such as the Avon Longitudinal Study of Parents And Children (aka Children of the 90s). Third, I will use the linked datasets to research questions of population health importance in the domain of reproductive health, such as what are the true rates of miscarriages, how do women manage postpartum health and wellbeing, whether breastfeeding is better in the long run for children's mental health, and others. This will be done through studies with Children of the 90s participants and the general public helping to validate the results. The impact of the project will realised in Yrs 5-7 and include a conceptual change in techniques for studying population health, making it possible to identify lifestyle causes of diseases, assess the impact of national policies, and provide recommendations for health interventions.

数字技术开启了理解人类行为和生活方式选择的新纪元，人们的日常活动和习惯在他们的数字记录中留下了“足迹”。例如，当我们在超市购买商品并使用会员卡获得好处(例如，未来的折扣)时，超市会记录我们的购买并创建我们的习惯和偏好的表示。到目前为止，“数字足迹”数据的使用大多仅限于私营公司。公司一直在使用这些数据的汇总来跟踪其产品的销售情况，了解影响销售水平的因素，并针对营销和促销活动进行定位。英国数据保护法的变化，即一般数据保护条例，意味着公众现在可以访问和捐赠他们的数据用于学术研究。零售商通过会员卡记录的购物历史数据是人口健康研究的一个极其有用的信息来源，因为它可以提供关于现实世界选择和行为(例如止痛药、食物)和其他行为(例如疼痛和体重、福祉管理)的细粒度、客观的数据。在卫生研究领域，这种信息往往很难获得。生活方式选择和健康结果之间的联系通常是通过自我报告问卷来研究的，这些问卷要求人们记住他们每天的选择和行为，这可能会影响结果：对行为的反应并不总是反映人们实际做了什么。如果购物历史数据以保护隐私和合乎道德的方式使用，这些数据可以用于公益，有利于健康研究(例如，帮助了解日常行为和生活方式选择如何影响健康和社会结果)。例如，考虑到缓和因素(如年龄、性别、基因构成等)，酒精摄入量对未出生婴儿造成不可逆转的健康损害的确切水平是什么？在什么情况下，不同类型的即食餐会导致肥胖？家用产品中的化学物质是否会导致儿童罹患癌症和其他不良健康后果的风险增加？人口健康交易数据研究计划利用商业收集的数据集进行隐私保护和伦理研究，以造福于公共利益。该项目质疑购物历史数据是否能以积极的方式用于支持健康研究和新干预措施的开发。该奖学金将确立评估健康结果和相关生活方式选择的新方法的可行性，方法是客观衡量通过会员卡记录的零售购物历史数据中反映的现实世界行为。同时，它将建立一个可供未来研究人员使用的框架。我在1-4年的研究计划将分三个阶段展开。首先，它将使用商业收集的数据集，通过购物数据中的模式来识别和研究生殖健康结果。其次，它将使用既定的纵向人口研究，如雅芳父母和儿童纵向研究(又名90年代儿童)，验证数据中与健康结果相关的模式。第三，我将使用关联的数据集来研究生殖健康领域中人口健康重要性的问题，例如真实的流产率是多少，女性如何管理产后健康和福祉，从长远来看，母乳喂养是否对儿童的心理健康更好，等等。这将通过对90后参与者的儿童和普通公众的研究来完成，以帮助验证结果。该项目的影响将在5-7年实现，包括研究人口健康的技术的概念变化，从而有可能确定疾病的生活方式原因，评估国家政策的影响，并为卫生干预提供建议。