Skatova Anya
Digital Footprints Lab & Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.
NPJ Digit Med. 2024 Sep 30;7(1):264. doi: 10.1038/s41746-024-01231-4.
Novel sources of population data, especially administrative and medical records, as well as the digital footprints generated through interactions with online services, present a considerable opportunity for advancing health research and policymaking. An illustrative example is shopping history records that can illuminate aspects of population health by scrutinizing extensive sets of everyday choices made in the real world. However, like any dataset, these sources possess specific limitations, including sampling biases, validity issues, and measurement errors. To enhance the applicability and potential of shopping data in health research, we advocate for the integration of individual-level shopping data with external datasets containing rich repositories of longitudinal population cohort studies. This strategic approach holds the promise of devising innovative methodologies to address inherent data limitations and biases. By meticulously documenting biases, establishing validated associations, and discerning patterns within these amalgamated records, researchers can extrapolate their findings to encompass population-wide datasets derived from national supermarket chain. The validation and linkage of population health data with real-world choices pertaining to food, beverages, and over-the-counter medications, such as pain relief, present a significant opportunity to comprehend the impact of these choices and behavioural patterns associated with them on public health.
新型人口数据来源，尤其是行政和医疗记录，以及通过与在线服务交互产生的数字足迹，为推进健康研究和政策制定提供了相当大的机遇。一个典型的例子是购物历史记录，通过仔细研究现实世界中大量的日常选择，可以揭示人口健康的各个方面。然而，与任何数据集一样，这些来源存在特定的局限性，包括抽样偏差、有效性问题和测量误差。为了提高购物数据在健康研究中的适用性和潜力，我们主张将个体层面的购物数据与包含丰富纵向人口队列研究库的外部数据集相结合。这种战略方法有望设计出创新方法来解决固有的数据局限性和偏差。通过精心记录偏差、建立经过验证的关联以及在这些合并记录中识别模式，研究人员可以将他们的发现外推到涵盖来自全国连锁超市的全人口数据集。将人口健康数据与与食品、饮料和非处方药（如止痛药物）相关的现实世界选择进行验证和关联，为理解这些选择及其相关行为模式对公众健康的影响提供了重要机会。
