Perinatal Research, Kolling Institute of Medical Research, University of Sydney, St. Leonards, Australia.
Med Care. 2012 Apr;50(4):e7-20. doi: 10.1097/MLR.0b013e31821d2b1d.
Administrative or population health datasets (PHDS) are increasingly being used for research related to maternal and infant health. However, the accuracy and completeness of the information in the PHDS is important to ensure validity of the results of this research.
To compile and review studies that validate the reporting of conditions and procedures related to pregnancy, childbirth, and newborns and provide a tool of reference for researchers.
A systematic search was conducted of Medline and EMBASE databases to find studies that validated routinely collected datasets containing diagnoses and procedures related to pregnancy, childbirth, and newborns. To be included datasets had to be validated against a gold standard, such as review of medical records, maternal interview or survey, specialized register, or laboratory data. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and/or κ statistic for each diagnosis or procedure code were calculated.
Forty-three validation studies were included. Under-enumeration was common, with the level of ascertainment increasing as time from diagnosis/procedure to birth decreased. Most conditions and procedures had high specificities indicating few false positives, and procedures were more accurately reported than diagnoses. Hospital discharge data were generally more accurate than birth data, however identifying cases from more than 1 dataset further increased ascertainment.
This comprehensive collection of validation studies summarizing the quality of perinatal population data will be an invaluable resource to all researchers working with PHDS.
行政或人口健康数据集（PHDS）越来越多地被用于与母婴健康相关的研究。然而，PHDS 中信息的准确性和完整性对于确保该研究结果的有效性非常重要。
编译和综述验证与妊娠、分娩和新生儿相关的情况和程序报告准确性的研究，并为研究人员提供参考工具。
系统检索 Medline 和 EMBASE 数据库，以查找验证常规收集的包含与妊娠、分娩和新生儿相关的诊断和程序的数据集的研究。要纳入的数据集必须与黄金标准（如病历审查、产妇访谈或调查、专门登记处或实验室数据）进行验证。为每个诊断或程序代码计算了灵敏度、特异性、阳性预测值（PPV）、阴性预测值（NPV）和/或κ统计量。
共纳入 43 项验证研究。普遍存在漏报情况，随着距诊断/程序至分娩的时间缩短，确定程度增加。大多数情况和程序具有较高的特异性，表明假阳性较少，并且程序的报告比诊断更准确。医院出院数据通常比分娩数据更准确，但从多个数据集识别病例可进一步提高确定程度。
本综述综合了验证围产期人群数据质量的研究，对于使用 PHDS 的所有研究人员来说，这将是一个非常有价值的资源。
