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.

大数据的增长正在改变许多经济领域，包括医疗和保健领域。尽管如此，对数据分析在卫生政策制定中的实际应用的研究仍然有限。在这项研究中，我们研究了如何将数据科学和机器学习方法应用于各种开放式健康数据集，包括全科医生处方数据，疾病患病率数据和经济剥夺数据。本文讨论了北爱尔兰心理健康和抗抑郁药处方的背景，并强调了其作为公共政策问题的重要性。提出了一个假设，表明抗抑郁药的使用与经济剥夺之间的联系是由抑郁症患病率介导的。对各种异构开放数据集的分析用于检验该假设。提供了该方法的描述，包括正在调查的开放健康数据集以及对数据处理管道的说明。提出了关键变量与几种不同聚类分析之间的相关性。提供的证据表明抑郁症患病率的假设是有缺陷的。描述了基于处方行为和疾病患病率的全科医生实践，并确定和讨论了关键特征。探索了可能的政策含义，并确定了未来研究的机会。提供的证据表明抑郁症患病率的假设是有缺陷的。描述了基于处方行为和疾病患病率的全科医生实践，并确定和讨论了关键特征。探索了可能的政策含义，并确定了未来研究的机会。提供的证据表明抑郁症患病率的假设是有缺陷的。描述了基于处方行为和疾病患病率的全科医生实践，并确定和讨论了关键特征。探索了可能的政策含义，并确定了未来研究的机会。