Fitzpatrick Tiffany, Wilton Andrew S, Guttmann Astrid
Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
ICES, Toronto, Canada.
J Obstet Gynaecol. 2021 Feb;41(2):207-211. doi: 10.1080/01443615.2020.1726304. Epub 2020 Jun 26.
Gestational age is often incompletely recorded in administrative records, despite being critical to paediatric and maternal health research. Several algorithms exist to estimate gestational age using administrative databases; however, many have not been validated or use complicated methods that are not readily adaptable. We developed a simple algorithm to estimate common gestational age categories from routine administrative data. We leveraged a population-based registry of all hospital births occurring in Ontario, Canada over 2002-2016 including 1.8 million birth records. In this sample, this simple algorithm had excellent performance compared to a verified measure of gestational age; 87.61% agreement (95% CI: 87.49, 87.74). The accuracy of the algorithm exceeded 98% for all of the gestational age categories. Agreement notably increased over time and was greatest among singleton births and infants born at 2500-2999 g. This study provides a straight-forward algorithm for accurately estimating common gestational age categories that is easily adaptable for use in other countries.Impact Statement Gestational age is often incompletely or inaccurately recorded in administrative health databases, despite being critical to the study of many paediatric and maternal health outcomes. Consequently, researchers must rely on various methods to estimate gestational age, many of these methods are either overly simple (i.e. assuming a uniform duration) or analytically complicated and difficult to adapt for new populations (e.g. regression-based approaches). This study, based on a population-based registry of all 1.8 million births occurring in Ontario, Canada 2003-2016, found that a simple, sex-specific algorithm using three commonly recorded birth record characteristics performs almost perfectly compared to a clinical estimate recorded near birth. This study suggests that a straight-forward, sex-specific algorithm based on routinely collected birth record data is able to accurately estimate common gestational age categories (i.e. extreme preterm, <28 weeks; very preterm, 28-32 weeks; moderate-to-late preterm, 33-26 weeks; and term, 37 weeks of completed gestational age). This work will be of greatest interest to perinatal researchers using routinely collected health administrative data.
尽管胎龄对于儿科和孕产妇健康研究至关重要，但在行政记录中其记录往往并不完整。目前有几种算法可利用行政数据库来估算胎龄；然而，许多算法尚未经过验证，或者使用的复杂方法不易应用。我们开发了一种简单算法，可从常规行政数据中估算常见的胎龄类别。我们利用了加拿大安大略省2002年至2016年期间所有医院分娩的基于人群的登记册，其中包括180万条出生记录。在这个样本中，与经过验证的胎龄测量方法相比，这种简单算法表现出色；一致性为87.61%（95%置信区间：87.49，87.74）。该算法在所有胎龄类别中的准确率均超过98%。随着时间推移，一致性显著提高，在单胎分娩和出生体重为2500 - 2999克的婴儿中一致性最高。本研究提供了一种简单的算法，可准确估算常见的胎龄类别，且易于在其他国家应用。影响声明 尽管胎龄对于许多儿科和孕产妇健康结局的研究至关重要，但在行政健康数据库中其记录往往不完整或不准确。因此，研究人员必须依靠各种方法来估算胎龄，其中许多方法要么过于简单（即假设孕期时长一致），要么分析复杂且难以应用于新人群（例如基于回归的方法）。这项基于加拿大安大略省2003年至2016年期间所有180万例分娩的人群登记册的研究发现，一种使用三个常见记录的出生记录特征的简单、针对性别的算法与出生时记录的临床估算相比，表现几乎完美。这项研究表明，基于常规收集的出生记录数据的简单、针对性别的算法能够准确估算常见的胎龄类别（即极早产儿，<28周；极早产儿，28 - 32周；中晚期早产儿，33 - 36周；足月儿，孕龄满37周）。这项工作对于使用常规收集的健康行政数据的围产期研究人员来说将最具吸引力。
