Insights into Antidepressant Prescribing Using Open Health Data

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The growth of big data is transforming many economic sectors, including the medical and healthcare sector. Despite this, research into the practical application of data analytics to the development of health policy is still limited. In this study we examine how data science and machine learning methods can be applied to a variety of open health datasets, including GP prescribing data, disease prevalence data and economic deprivation data. This paper discusses the context of mental health and antidepressant prescribing in Northern Ireland and highlights its importance as a public policy issue. A hypothesis is proposed, suggesting that the link between antidepressant usage and economic deprivation is mediated by depression prevalence. An analysis of various heterogeneous open datasets is used to test this hypothesis. A description of the methodology is provided, including the open health datasets under investigation and an explanation of the data processing pipeline. Correlations between key variables and several different clustering analyses are presented. Evidence is provided which suggests that the depression prevalence hypothesis is flawed. Clusters of GP practices based on prescribing behaviour and disease prevalence are described and key characteristics are identified and discussed. Possible policy implications are explored and opportunities for future research are identified.

Original languageEnglish
Pages (from-to)41-48
Number of pages8
JournalBig Data Research
Early online date2 Mar 2018
Publication statusPublished (in print/issue) - 31 Jul 2018


  • Health Policy
  • Data Analytics
  • Big Data
  • Prescribing
  • Prevalence
  • Machine learning
  • Deprivation.


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