Evaluating and addressing the impact of COVID-19 restrictions on electronic health records in estimating causal effects

该提案旨在提高我们使用COVID-19大流行期间收集的常规健康数据来回答有关药物有效性和安全性的重要临床问题的能力。虽然电子健康记录（EHRs）越来越多地用于流行病学研究，以估计药物暴露与临床结果之间的因果关系，但COVID-19大流行造成的中断对我们成功开展这项研究的能力产生了尚未量化的影响。值得注意的是，COVID-19大流行导致包括英国在内的许多国家封锁，导致人们在寻求医疗服务方面的行为发生变化，从而改变了处方模式、临床观察和测量记录以及电子病历中的疾病诊断。具体来说，诊断记录的变化可能意味着诊断的延迟或通常会记录的诊断缺失。这可能导致在确定研究人群和确定结果时出现测量误差，损害研究结果的有效性。因此，本文提出的工作将通过两个代表不同临床背景的案例研究来确定和量化潜在的测量误差：1)调查口服抗凝剂长期常规治疗的风险和益处；2)量化短期氟喹诺酮类抗生素引起肌腱断裂的已知副作用。我们将使用与医院事件统计和国家统计办公室相关联的英国临床实践研究数据链Aurum的数据。这个世界著名的初级保健数据库拥有约19.8%的英国人口样本的综合医疗记录，在年龄、性别和种族方面具有广泛的代表性。通过对流行病前、期间和后三个时期进行分类，我们将通过描述用于确定研究人群和结果的疾病诊断的绝对比率来确定可能的测量误差。我们将在每个案例研究中仅使用大流行前数据与结合大流行前、大流行期间和大流行后数据的治疗效果进行比较。使用初级保健数据的案例研究的结果将首先通过随机对照试验或采用荟萃分析的系统评价进行验证。我们将使用周期-治疗相互作用来量化测量误差，以评估分层时期的治疗效果。我们将开发和评估各种方法，试图通过开发和扩展强大的方法来纠正由于大流行限制而造成的测量误差。这些方法包括使用模拟外推法和应用定量偏差分析。然后，我们将根据调查结果推荐一种最佳方法，使用电子病历处理与大流行相关的测量误差。这项建议的工作是高度可行的，因为数据是常规收集的，并且易于分析。它将告知测量误差将如何影响在不同情况下因果效应的估计。我们的发现将为使用电子病历的研究人员提供建议，以设计包括大流行期间的数据/随访在内的未来研究。所开发的方法将允许尽可能有力地回答未来的因果流行病学问题，并将有利于决策者、临床医生、患者和护理人员为卫生保健决策提供信息。

This proposal aims to improve our ability to use routine health data collected during the COVID-19 pandemic to answer important clinical questions about the effectiveness and safety of medications.While electronic health records (EHRs) are increasingly used for epidemiological research to estimate the causal effects between drug exposures and clinical outcomes, disruptions arising from the COVID-19 pandemic have had an as yet unquantified effect on our ability to successfully conduct this research. Notably, the COVID-19 pandemic led to lockdowns in many countries including the UK, resulting in behavioural changes in seeking healthcare services and thus prescribing patterns, recordings of clinical observations and measurements, and disease diagnoses in the EHRs. Specifically, the change in diagnostic recording could imply delays in diagnoses or missing diagnoses that would normally have been recorded. It could lead to measurement errors in the identification of study populations and ascertainment of outcomes, compromising the validity of study findings.This proposed work will therefore identify and quantify potential measurement errors using two case-studies representing diverse clinical contexts as illustrations: 1) investigating the risks and benefits of long-term routine therapy with oral anticoagulants; and 2) quantifying the known side effect of tendon rupture associated with short course fluoroquinolone antibiotics. We will use data from the UK Clinical Practice Research Datalink Aurum linked with Hospital Episode Statistics and Office for National Statistics. This world-renowned primary care database has comprehensive medical records for a sample of ~19.8% of the UK population that is broadly representative in terms of age, sex and ethnicity. By categorising three periods which are pre-, during and post-pandemic periods, we will identify possible measurement errors by describing absolute rates of disease diagnoses for the identification of both study populations and outcomes. We will compare treatment effects using pre-pandemic data only with that combining pre-pandemic, during and post-pandemic data in each case study. The findings of the case studies using primary care data will first be validated against randomised controlled trials or a systematic review with meta-analysis. We will quantify the measurement errors using a period-treatment interaction to evaluate treatment effects in stratified periods. We will develop and evaluate approaches that attempt to correct for measurement errors due to pandemic restrictions, by exploiting and extending robust methods. These include using a simulation-extrapolation method and applying quantitative bias analysis. We will then recommend an optimal methodological approach to handle pandemic-related measurement errors using EHRs based on the findings.This proposed work is highly feasible as the data is routinely collected and readily available for analysis. It will inform how measurement errors will impact the estimation of causal effect in different settings. Our findings will lead to recommendations for researchers using EHRs to design future studies that include data/follow-up spanning the pandemic period. The methods developed will allow future causal epidemiological questions to be answered as robustly as possible, and will benefit policymakers, clinicians, patients, carers to inform healthcare decision-making.