Biases arising from linked administrative data for epidemiological research: a conceptual framework from registration to analyses.
Biases arising from linked administrative data for epidemiological research: a conceptual framework from registration to analyses.
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流行病学研究的链接行政数据引起的偏见：从注册到分析的概念框架。
DOI：
10.1007/s10654-022-00934-w
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发表时间：
2022-12
影响因子：
13.6
通讯作者：
Katikireddi, Srinivasa Vittal
中科院分区：
文献类型：
--
作者：
Shaw, Richard J.;Harron, Katie L.;Pescarini, Julia M.;Pinto Junior, Elzo Pereira;Allik, Mirjam;Siroky, Andressa N.;Campbell, Desmond;Dundas, Ruth;Ichihara, Maria Yury;Leyland, Alastair H.;Barreto, Mauricio L.;Katikireddi, Srinivasa Vittal
关联的行政数据提供了丰富的信息来源，可用于描述疾病模式、了解其原因和评价干预措施。然而，行政数据的收集主要是出于业务原因，如为法律的目的记录人口动态事件，以及规划、提供和监测服务。生成和链接行政数据集的过程可能会产生偏差，而这些偏差往往没有被研究人员充分考虑。我们提供了一个描述这些偏见的框架，借鉴了我们使用1亿巴西队列（1亿队列）的经验，该队列包含2001年至2018年期间申请社会援助的家庭超过1.31亿人的记录。流行病学研究的数据集是通过将1亿人队列与死亡率信息系统和医院信息系统等与健康有关的数据库连接而获得的。使用该框架，我们展示了如何选择和错误分类的偏见可能会在三个不同的阶段引入：登记和记录人们的生活事件和使用的服务，跨行政数据库的链接，以及清洁和编码的变量从派生的数据集。最后，我们提出了八项建议，这些建议可能会减少分析行政来源数据时的偏见。
Linked administrative data offer a rich source of information that can be harnessed to describe patterns of disease, understand their causes and evaluate interventions. However, administrative data are primarily collected for operational reasons such as recording vital events for legal purposes, and planning, provision and monitoring of services. The processes involved in generating and linking administrative datasets may generate sources of bias that are often not adequately considered by researchers. We provide a framework describing these biases, drawing on our experiences of using the 100 Million Brazilian Cohort (100MCohort) which contains records of more than 131 million people whose families applied for social assistance between 2001 and 2018. Datasets for epidemiological research were derived by linking the 100MCohort to health-related databases such as the Mortality Information System and the Hospital Information System. Using the framework, we demonstrate how selection and misclassification biases may be introduced in three different stages: registering and recording of people’s life events and use of services, linkage across administrative databases, and cleaning and coding of variables from derived datasets. Finally, we suggest eight recommendations which may reduce biases when analysing data from administrative sources.
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影响因子：
3.5
作者：
Almeida, Daniela;Gorender, David;Barreto, Mauricio L.
通讯作者：
Barreto, Mauricio L.
影响因子：
3.4
作者：
Hagger-Johnson, Gareth;Harron, Katie;Goldstein, Harvey
通讯作者：
Goldstein, Harvey
影响因子：
3.3
作者：
França E;Ishitani LH;Teixeira R;Duncan BB;Marinho F;Naghavi M
通讯作者：
Naghavi M
影响因子：
5.4
作者：
Hernán, MA;Hernández-Díaz, S;Robins, JM
通讯作者：
Robins, JM
DOI：
10.1111/rssa.12315
发表时间：
2018-06-01
影响因子：
2
作者：
Hand, David J.
通讯作者：
Hand, David J.