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Updated: Nov 26, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
Associations between socioeconomic deprivation and pharmaceutical prescribing in primary care in England
Jessica Mooney1, Roger Yau1, Haseeb Moiz1
1Warwick Medical School, University of Warwick, Coventry, UK.
Background:
Socioeconomic deprivation is associated with health inequality. Previous studies have described associations between primary care prescribing rates and deprivation for individual drugs or drug classes. We explore the correlation between socioeconomic deprivation and the rate of prescribing of individual pharmaceutical drugs, and drug classes, in primary care in England, to identify prescribing inequalities that would require further investigation.
Methods:
In this cross-sectional study, national primary care prescribing data, by primary care practice, were retrieved for the calendar year 2019 in England. Socioeconomic deprivation was quantified using the Index of Multiple Deprivation (IMD) score. Correlations were calculated using Spearman's rank correlation coefficient (ρ), adjusting for practice list size and demographics, with a Bonferroni-corrected p value threshold of 5×10-5.
Results:
We included 1.05 billion prescription items dispensed from 6896 England practices. 142/206 (69%) drug classes and 505/774 (65%) drugs were significantly correlated with IMD score (p<5×10-5). Of the 774 included drugs, 31 (4%) were moderately positively associated with IMD score (ρ>0.4). Only one was moderately negatively correlated with IMD score (ρ<-0.4), suggesting higher prescribing rates in more affluent areas. The drug classes most strongly associated with IMD score included opioid and non-opioid analgesics, antipsychotics and reflux medications. Drug classes most strongly associated with affluence included epinephrine, combined oral contraceptives and hormone replacement therapy.
Conclusion:
We identify novel associations of prescribing with deprivation. Further work is required to identify the underlying reasons for these associations so that appropriate interventions can be formulated to address drivers of inequality.
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