Lorgelly Paula K, Doble Brett, Knott Rachel J
Centre for Health Economics, 15 Innovation Walk, Monash University, Clayton, VIC, 3800, Australia.
Pharmacoeconomics. 2016 Feb;34(2):139-54. doi: 10.1007/s40273-015-0343-2.
There is a growing appetite for large complex databases that integrate a range of personal, socio-demographic, health, genetic and financial information on individuals. It has been argued that 'Big Data' will provide the necessary catalyst to advance both biomedical research and health economics and outcomes research. However, it is important that we do not succumb to being data rich but information poor. This paper discusses the benefits and challenges of building Big Data, analysing Big Data and making appropriate inferences in order to advance cancer care, using Cancer 2015 (a prospective, longitudinal, genomic cohort study in Victoria, Australia) as a case study. Cancer 2015 has been linked to State and Commonwealth reimbursement databases that have known limitations. This partly reflects the funding arrangements in Australia, a country with both public and private provision, including public funding of private healthcare, and partly the legislative frameworks that govern data linkage. Additionally, linkage is not without time delays and, as such, achieving a contemporaneous database is challenging. Despite these limitations, there is clear value in using linked data and creating Big Data. This paper describes the linked Cancer 2015 dataset, discusses estimation issues given the nature of the data and presents panel regression results that allow us to make possible inferences regarding which patient, disease, genomic and treatment characteristics explain variation in health expenditure.
对于整合个人一系列个人、社会人口统计学、健康、基因和财务信息的大型复杂数据库的需求日益增长。有人认为，“大数据”将为推进生物医学研究以及健康经济学和结果研究提供必要的催化剂。然而，重要的是我们不要陷入数据丰富但信息匮乏的境地。本文以《癌症2015》（澳大利亚维多利亚州一项前瞻性、纵向、基因组队列研究）为案例研究，讨论构建大数据、分析大数据并做出适当推断以推进癌症护理的益处和挑战。《癌症2015》已与已知存在局限性的州和联邦报销数据库相链接。这部分反映了澳大利亚的资金安排，该国既有公共医疗服务也有私人医疗服务，包括对私人医疗保健的公共资金投入，部分也反映了管理数据链接的立法框架。此外，链接并非没有时间延迟，因此，建立一个同期数据库具有挑战性。尽管存在这些局限性，但使用链接数据和创建大数据显然具有价值。本文描述了链接后的《癌症2015》数据集，讨论了鉴于数据性质的估计问题，并展示了面板回归结果，这些结果使我们能够就哪些患者、疾病、基因组和治疗特征可解释健康支出的变化做出可能的推断。
