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

评估和解决 COVID-19 限制对电子健康记录的影响，以估计因果影响

• 批准号: MR/Z503769/1
• 负责人: Ian Douglas
• 金额: $ 32.13万
• 依托单位: London School of Hygiene & Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FZ503769%2F1
• 关键词: Evaluating; addressing; impact; COVID; 19

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.

该提案旨在提高我们使用在 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。而电子健康记录 (EHR) 越来越多地用于流行病学研究以估计因果影响在药物暴露和临床结果之间，COVID-19 大流行造成的干扰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实施封锁，导致寻求医疗服务的行为发生变化，从而导致 EHR 中的处方模式、临床观察和测量记录以及疾病诊断发生变化。具体来说，诊断记录的变化可能意味着诊断延迟或丢失通常会记录的诊断。它可能会导致研究人群识别和结果确定中的测量误差，从而损害研究结果的有效性。因此，这项拟议的工作将使用代表不同临床背景的两个案例研究作为说明来识别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）量化与短程氟喹诺酮类抗生素相关的已知肌腱断裂副作用。我们将使用来自英国临床实践研究数据链 Aurum 的数据，该数据链与医院病例统计和国家统计办公室相关。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。通过对大流行前、期间和后三个时期进行分类，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。在每个案例研究中，我们将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根据随机对照试验或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，以评估分层周期中的治疗效果。我们将开发和评估方法，通过利用和扩展稳健的方法来尝试纠正由于大流行限制而导致的测量错误。其中包括使用模拟外推法和应用定量偏差分析。然后，我们将根据研究结果推荐一种最佳方法，以使用 EHR 处理与流行病相关的测量错误。这项拟议的工作非常可行，因为数据是定期收集的，并且易于分析。它将告知测量误差将如何影响不同设置下因果效应的估计。我们的研究结果将为使用电子病历的研究人员提供建议，以设计未来的研究，其中包括跨越大流行期间的数据/后续行动。所开发的方法将使未来的因果流行病学问题得到尽可能有力的回答，并将有利于政策制定者、临床医生、患者、护理人员为医疗保健决策提供信息。