Institute of Health Informatics, University College London, London, UK
Health Data Research UK, London, UK.
BMJ Open. 2020 Feb 13;10(2):e034396. doi: 10.1136/bmjopen-2019-034396.
To demonstrate how data-driven variability methods can be used to identify changes in disease recording in two English electronic health records databases between 2001 and 2015.
Repeated cross-sectional analysis that applied data-driven temporal variability methods to assess month-by-month changes in routinely collected medical data. A measure of difference between months was calculated based on joint distributions of age, gender, socioeconomic status and recorded cardiovascular diseases. Distances between months were used to identify temporal trends in data recording.
400 English primary care practices from the Clinical Practice Research Datalink (CPRD GOLD) and 451 hospital providers from the Hospital Episode Statistics (HES).
The proportion of patients (CPRD GOLD) and hospital admissions (HES) with a recorded cardiovascular disease (CPRD GOLD: coronary heart disease, heart failure, peripheral arterial disease, stroke; HES: International Classification of Disease codes I20-I69/G45).
Both databases showed gradual changes in cardiovascular disease recording between 2001 and 2008. The recorded prevalence of included cardiovascular diseases in CPRD GOLD increased by 47%-62%, which partially reversed after 2008. For hospital records in HES, there was a relative decrease in angina pectoris (-34.4%) and unspecified stroke (-42.3%) over the same time period, with a concomitant increase in chronic coronary heart disease (+14.3%). Multiple abrupt changes in the use of myocardial infarction codes in hospital were found in March/April 2010, 2012 and 2014, possibly linked to updates of clinical coding guidelines.
Identified temporal variability could be related to potentially non-medical causes such as updated coding guidelines. These artificial changes may introduce temporal correlation among diagnoses inferred from routine data, violating the assumptions of frequently used statistical methods. Temporal variability measures provide an objective and robust technique to identify, and subsequently account for, those changes in electronic health records studies without any prior knowledge of the data collection process.
展示如何使用数据驱动的可变性方法来识别 2001 年至 2015 年间两个英国电子健康记录数据库中疾病记录的变化。
重复的横截面分析，应用数据驱动的时间可变性方法来评估常规收集的医疗数据逐月变化。基于年龄、性别、社会经济地位和记录的心血管疾病的联合分布，计算了月份之间的差异度量。月份之间的距离用于识别数据记录的时间趋势。
来自临床实践研究数据链接（CPRD GOLD）的 400 个英语初级保健实践和来自医院事件统计（HES）的 451 个医院提供者。
记录的心血管疾病患者比例（CPRD GOLD：冠心病、心力衰竭、外周动脉疾病、中风；HES：国际疾病分类代码 I20-I69/G45）（CPRD GOLD）和住院人数（HES）。
两个数据库都显示 2001 年至 2008 年间心血管疾病记录逐渐变化。CPRD GOLD 中包含的心血管疾病的记录患病率增加了 47%-62%，2008 年后部分逆转。同期，HES 中住院记录的心绞痛（-34.4%）和未特指的中风（-42.3%）相对减少，同时慢性冠心病（+14.3%）增加。2010 年 3/4 月、2012 年和 2014 年，医院心肌梗死编码的多次突然变化可能与临床编码指南的更新有关。
确定的时间可变性可能与更新的编码指南等潜在非医学原因有关。这些人为变化可能会在常规数据推断的诊断中引入时间相关性，从而违反了常用统计方法的假设。时间可变性测量提供了一种客观而强大的技术，可以在没有任何数据收集过程先验知识的情况下识别电子健康记录研究中的这些变化，并随后进行解释。
