Using machine learning to investigate self- medication purchasing in England via high street retailer loyalty card data

Davies, Alec ORCID: 0000-0002-4538-1375, Green, Mark A ORCID: 0000-0002-0942-6628 and Singleton, Alex D (2018) Using machine learning to investigate self- medication purchasing in England via high street retailer loyalty card data. PLOS ONE, 13 (11). e0207523-.

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Abstract

The availability alongside growing awareness of medicine has led to increased self-treatment of minor ailments. Self-medication is where one 'self' diagnoses and prescribes over the counter medicines for treatment. The self-care movement has important policy implications, perceived to relieve the National Health Service (NHS) burden, increasing patient subsistence and freeing resources for more serious ailments. However, there has been little research exploring how self-medication behaviours vary between population groups due to a lack of available data. The aim of our study is to evaluate how high street retailer loyalty card data can help inform our understanding of how individuals self-medicate in England. Transaction level loyalty card data was acquired from a national high street retailer for England for 2012-2014. We calculated the proportion of loyalty card customers (n ~ 10 million) within Lower Super Output Areas who purchased the following medicines: 'coughs and colds', 'Hayfever', 'pain relief' and 'sun preps'. Machine learning was used to explore how 50 sociodemographic and health accessibility features were associated towards explaining purchasing of each product group. Random Forests are used as a baseline and Gradient Boosting as our final model. Our results showed that pain relief was the most common medicine purchased. There was little difference in purchasing behaviours by sex other than for sun preps. The gradient boosting models demonstrated that socioeconomic status of areas, as well as air pollution, were important predictors of each medicine. Our study adds to the self-medication literature through demonstrating the usefulness of loyalty card records for producing insights about how self-medication varies at the national level. Big data offer novel insights that add to and address issues that traditional studies are unable to consider. New forms of data through data linkage may offer opportunities to improve current public health decision making surrounding at risk population groups within self-medication behaviours.

Item Type:	Article
Uncontrolled Keywords:	Humans, Self Medication, Models, Economic, Databases, Factual, England, Female, Male, Nonprescription Drugs, Machine Learning
Depositing User:	Symplectic Admin
Date Deposited:	20 Nov 2018 09:07
Last Modified:	19 Jan 2023 01:12
DOI:	10.1371/journal.pone.0207523
Open Access URL:	https://doi.org/10.1371/journal.pone.0207523
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3028915