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的能力，以回答有关药物有效性和安全性的重要临床问题。虽然电子健康记录（EHR）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，COVID-19疫情对我们成功进行此项研究的能力产生了尚未量化的影响。值得注意的是，COVID-19疫情导致包括英国在内的多个国家实施封城，导致寻求医疗服务的行为发生变化，从而导致处方模式、临床观察和测量记录以及电子健康记录中的疾病诊断发生变化。具体而言，诊断记录的变化可能意味着诊断延迟或通常记录的诊断缺失。因此，本研究将以两个代表不同临床背景的病例研究为例，识别和量化潜在的测量误差：1）研究长期口服抗凝剂常规治疗的风险和益处;和2）量化与短期氟喹诺酮类抗生素相关的肌腱断裂的已知副作用。我们将使用来自英国临床实践研究Datalink Aurum的数据，该数据与医院事件统计和国家统计办公室相关联。这个世界知名的初级保健数据库拥有约19.8%的英国人口样本的全面医疗记录，在年龄，性别和种族方面具有广泛代表性。通过将大流行前、大流行期间和大流行后三个时期分类，我们将通过描述疾病诊断的绝对率来识别可能的测量误差，以识别研究人群和结果。我们将在每个病例研究中比较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。使用初级保健数据的病例研究的结果将首先通过随机对照试验或荟萃分析的系统性综述进行验证。我们将使用阶段-治疗相互作用量化测量误差，以评估分层阶段的治疗效果。我们将开发和评估试图通过利用和扩展稳健的方法来纠正由于大流行限制而导致的测量误差的方法。这些措施包括使用模拟外推法和应用定量偏差分析。然后，我们将建议一个最佳的方法学方法来处理与流行病相关的测量误差使用EHR的基础上的发现。这项拟议的工作是非常可行的，因为数据是定期收集和随时可供分析。它将告知如何测量误差将影响因果效应的估计在不同的设置。我们的研究结果将为研究人员使用EHR设计未来的研究提供建议，这些研究包括跨越大流行时期的数据/随访。开发的方法将使未来的因果流行病学问题得到尽可能强有力的回答，并将有利于政策制定者，临床医生，患者，护理人员为医疗决策提供信息。

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.