Health Information Solutions, Rocklin, CA, USA.
Division of Neonatology and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Division of Maternal-Fetal Medicine and Obstetrics, Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA.
Ann Epidemiol. 2023 Mar;79:10-18. doi: 10.1016/j.annepidem.2022.12.014. Epub 2023 Jan 2.
Rigorous perinatal epidemiologic research depends on population-based parental and neonatal sociodemographic and clinical data. Here we describe the creation of linked birth cohort files, an enriched data source that combines information from vital records with maternal delivery and infant hospital encounter records.
Probabilistic linkage techniques were used to link vital records (i.e., birth and fetal death certificates) from the California Department of Public Health with hospital inpatient, ambulatory surgery and emergency department encounter data for mothers and infants from the California Department of Health Care Access and Information.
From 2012 to 2018, 95% of live birth records were successfully linked to maternal and newborn hospital records while 85% of fetal death records were linked to a maternal delivery record. Overall, 93% of postnatal hospital encounters of infants (i.e., <1 year old) were matched to a linked record.
The linked birth cohort files is a rich resource opening many possibilities for understanding perinatal health outcomes and opportunities for linkage to longitudinal, social determinant, and environmental data. To optimally use this file for research, analysts should evaluate possible shortcomings or biases of the data sources being linked.
严格的围产流行病学研究取决于基于人群的父母和新生儿社会人口统计学和临床数据。在这里,我们描述了链接出生队列文件的创建,这是一个丰富的数据源,它将来自加利福尼亚州公共卫生部的生命记录信息与加利福尼亚州医疗保健获取和信息部的母亲和婴儿的医院住院、门诊手术和急诊部门就诊记录相结合。
使用概率链接技术将加利福尼亚州公共卫生部的生命记录(即出生和胎儿死亡证明)与医院住院、门诊手术和急诊部门就诊记录相链接,这些记录来自加利福尼亚州医疗保健获取和信息部的母亲和婴儿。
2012 年至 2018 年期间,95%的活产记录成功链接到母婴医院记录,而 85%的胎儿死亡记录链接到母亲分娩记录。总体而言,93%的婴儿(即<1 岁)的产后医院就诊记录与链接记录相匹配。
链接的出生队列文件是一个丰富的资源,为了解围产健康结局以及与纵向、社会决定因素和环境数据的链接提供了许多可能性。为了优化该文件在研究中的使用,分析人员应评估正在链接的数据源可能存在的缺点或偏差。