Harnessing Big Data to Understand Self-Medication Trends in England
Consumer Data Research Centre
Promoting the use of consumer data and research to provide insight into societal and economic challenges (ESRC funded)
Self-treatment for minor aliments has increased due to the availability and growing awareness of medicine. This trend, where individuals diagnose and prescribe over-the-counter (OTC) medicines for their treatment, has significant implications for health policy. It can potentially alleviate the burden on the National Health Service (NHS), empowering patients and freeing up resources for more serious ailments.
However, understanding how self-medication behaviours vary among different population groups has been a challenge due to the lack of available data. A 2018 study leveraged data from the Consumer Data Research Centre (CDRC) to shed light on how individuals in England self-medicate.
High street retailer loyalty card data:
The study used transaction-level loyalty card data from a national high street retailer in England for the period 2012-2014. The data, accessed via the CDRC, included customer data, store location data, and transactions with loyalty card. This data was then processed using machine learning techniques to explore how 50 sociodemographic and health accessibility features were associated with the purchasing of each product group.
Key findings:
The findings revealed that pain relief was the most commonly purchased medicine. Interestingly, there was little difference in purchasing behaviours by sex, except for sun preps. The machine learning models showed that the socioeconomic status of areas, as well as air pollution, were important predictors of each medicine. Increased levels of deprivation correlated with decreased purchasing of OTC products, whilst increased air pollution had a positive correlation with purchasing OTC products, perhaps due to the increased risk of hayfever and susceptibility to coughs and colds.?
Implications for healthcare providers, policy makers, and health research:
Self-medication is a growing trend that has significant implications for healthcare providers and policymakers. Understanding the patterns of self-medication can help in resource allocation in the healthcare sector. By identifying the most commonly purchased medicines, healthcare providers can ensure adequate supply and distribution of these medicines.
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The findings can also help to guide public health policies. For instance, the association between socioeconomic status, air pollution, and self-medication suggests that public health interventions should consider these factors to promote safe and effective self-medication practices.
Lastly, the study demonstrates the potential of big data in health research. The use of CDRC data and loyalty card records provides a novel way to gain insights about self-medication at a national level. This approach can complement traditional studies and offer opportunities to improve public health decision-making.
Publication: Davies, A., Green, M. A., & Singleton, A. D. (2018). Using machine learning to investigate self-medication purchasing in England via high street retailer loyalty card data.?PloS one,?13(11), e0207523. DOI: https://doi.org/10.1371/journal.pone.0207523
CDRC Data: High Street Retailer – https://data.cdrc.ac.uk/dataset/high-street-retailer-retail-and-consumer-data-2012-2017-only?
Access to Health Assets and Hazards - https://data.cdrc.ac.uk/dataset/access-healthy-assets-hazards-ahah?
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