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在Covid-19-19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（EHRS）越来越多地用于流行病学研究，以估算药物外交作用和临床效果的因素，因此在临床上产生了成功的效果，并在临床上取得了成功，并在临床上取得了成功的效果。这项研究。值得注意的是，共同199导致包括英国在内的许多国家的封锁，导致了寻求医疗服务的行为变化，从而规定了EHR中的临床观察和测量结果记录，临床观察和测量结果以及疾病诊断。具体而言，诊断记录的变化可能意味着通常会记录的诊断或缺失诊断。这可能导致研究人群的识别和确定结果的确定误差，损害研究结果的有效性。因此，这项拟议的工作将使用两种代表不同的临床环境的病例研究来识别和量化潜在的测量误差，这些临床环境代表了插图：1）研究长期常规治疗与抗抗癌抗体的长期常规治疗的风险和益处； 2）量化与短期氟喹诺酮抗生素相关的肌腱破裂的已知副作用。我们将使用英国临床实践研究数据链接Aurum的数据，该数据链接与医院发作统计和国家统计局有关。这个世界知名的初级保健数据库拥有约19.8％英国人口样本的全面医疗记录，这些样本在年龄，性别和种族方面是广泛代表的。通过对三个时期的三个时期进行分类，我们将通过描述鉴定研究人群和结果的绝对疾病诊断率来确定可能的测量误差。在每个案例研究中，我们将仅使用流行前数据与大流行前，流行后数据相结合的治疗效果。使用初级保健数据的案例研究结果将首先针对随机对照试验或进行荟萃分析的系统审查验证。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。我们将开发和评估试图通过利用和扩展强大方法来纠正由于流行限制而纠正测量错误的方法。这些包括使用模拟驱除方法和应用定量偏置分析。然后，我们将建议一种最佳的方法论方法，使用基于发现的EHR来处理与大流行有关的测量误差。这项提出的工作是非常可行的，因为数据是常规收集并易于分析。它将告知测量错误将如何影响不同环境中因果效应的估计。我们的发现将为使用EHRS设计未来研究的研究人员提出建议，其中包括大流行时期的数据/随访。开发的方法将使未来的因果流行病学问题尽可能牢固地回答，并使政策制定者，临床医生，患者，照顾者受益，以告知医疗保健决策。