BACKGROUND Ontario's birth Registry (BORN) was established in 2009 to collect, interpret, and share critical data about pregnancy, birth and the early childhood period to facilitate and improve the provision of healthcare. Since the use of routinely-collected health data has been prioritized internationally by governments and funding agencies to improve patient care, support health system planning, and facilitate epidemiological surveillance and research, high quality data is essential. The purpose of this study was to verify the accuracy of a selection of data elements that are entered in the Registry. METHODS Data quality was assessed by comparing data re-abstracted from patient records to data entered into the Ontario birth Registry. A purposive sample of 10 hospitals representative of hospitals in Ontario based on level of care, birth volume and geography was selected and a random sample of 100 linked mother and newborn charts were audited for each site. Data for 29 data elements were compared to the corresponding data entered in the Ontario birth Registry using percent agreement, kappa statistics for categorical data elements and intra-class correlation coefficients (ICCs) for continuous data elements. RESULTS Agreement ranged from 56.9 to 99.8%, but 76% of the data elements (22 of 29) had greater than 90% agreement. There was almost perfect (kappa 0.81-0.99) or substantial (kappa 0.61-0.80) agreement for 12 of the categorical elements. Six elements showed fair-to-moderate agreement (kappa <0.60). We found moderate-to-excellent agreement for four continuous data elements (ICC >0.50). CONCLUSION Overall, the data elements we evaluated in the birth Registry were found to have good agreement with data from the patients' charts. Data elements that showed moderate kappa or low ICC require further investigation.

中文翻译：

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安大略省出生登记处的数据准确性：图表摘要研究。

背景技术安大略省的出生登记处（BORN）成立于2009年，旨在收集，解释和共享有关怀孕，出生和幼儿期的关键数据，以促进和改善医疗保健的提供。由于各国政府和资助机构已优先使用常规收集的健康数据，以改善患者护理，支持卫生系统规划并促进流行病学监测和研究，因此高质量的数据至关重要。这项研究的目的是验证选择在注册表中输入的数据元素的准确性。方法通过比较从患者记录中提取的数据与输入安大略省出生登记处的数据来评估数据质量。根据护理水平，对安大略省10家医院的代表进行有针对性的抽样调查，选择出生量和地理位置，并对每个地点的100个链接的母亲和新生儿图表进行随机抽样审核。使用百分比一致性，分类数据元素的kappa统计量和连续数据元素的类内相关系数（ICC），将29个数据元素的数据与在安大略省出生登记系统中输入的相应数据进行了比较。结果一致性从56.9到99.8％不等，但是76％的数据元素（29个中的22个）具有大于90％的一致性。其中的12个分类元素几乎达成了完美（kappa 0.81-0.99）或实质性（kappa 0.61-0.80）协议。六个要素显示出公平至中度的一致性（kappa <0.60）。我们发现四个连续数据元素（ICC> 0.50）的一致性为中到优。结论总体而言，我们在出生登记处评估的数据元素与患者图表中的数据具有很好的一致性。显示中度κ或低ICC的数据元素需要进一步研究。